In this study, we evaluate the ability of the Weather Research and Forecasting model to simulate surface energy fluxes in the southeast Pacific stratocumulus region. A total of 18 simulations is performed for the period of October to November 2008, with various combinations of boundary layer, microphysics, and cumulus schemes. Simulated surface energy fluxes are compared to those measured during VOCALS-REx. Using a process-based model evaluation, errors in surface fluxes are attributed to errors in cloud properties. Net surface flux errors are mostly traceable to errors in cloud liquid water path (LWP_cld), which produce biases in downward shortwave radiation. Two mechanisms controlling LWP_cld are diagnosed. One involves microphysics schemes, which control LWP_cld through the production of raindrops. The second mechanism involves boundary layer and cumulus schemes, which control moisture available for cloud by regulating boundary layer height. In this study, we demonstrate that when parameterizations are appropriately chosen, the stratocumulus deck and the related surface energy fluxes are reasonably well represented. In the most realistic experiments, the net surface flux is underestimated by about 10 W m⁻². This remaining low bias is due to a systematic overestimation of the total surface cooling due to sensible and latent heat fluxes in our simulations. There does not appear to be a single physical reason for this bias. Finally, our results also suggest that inaccurate representation of boundary layer height is an important factor limiting further gains in model realism.

In this study we developed and examined a hybrid modeling approach integrating physically-based equations and statistical downscaling to estimate fine-scale daily-mean surface turbulent fluxes (i.e., sensible and latent heat fluxes) for a region of southern California that is extensively covered by varied vegetation types over a complex terrain. The selection of model predictors is guided by physical parameterizations of surface flux used in land surface models and analysis showing net shortwave radiation that is a major source of variability in the surface energy budget. Through a structure of multivariable regression processes with an application of near-surface wind estimates from a previous study, we successfully reproduce dynamically-downscaled 3 km resolution surface flux data. The overall error in our estimates is less than 20 % for both sensible and latent heat fluxes, while slightly larger errors are seen in high-altitude regions. The major sources of error in estimates include the limited information provided in coarse reanalysis data, the accuracy of near-surface wind estimates, and an ignorance of the nonlinear diurnal cycle of surface fluxes when using daily-mean data. However, with reasonable and acceptable errors, this hybrid modeling approach provides promising, fine-scale products of surface fluxes that are much more accurate than reanalysis data, without performing intensive dynamical simulations.

How tropical low clouds change with climate remains the dominant source of uncertainty in global warming projections. An analysis of an ensemble of CMIP5 climate models reveals that a significant part of the spread in the models’ climate sensitivity can be accounted by differences in the climatological shallowness of tropical low clouds in weak-subsidence regimes: models with shallower low clouds in weak-subsidence regimes tend to have a higher climate sensitivity than models with deeper low clouds. The dynamical mechanisms responsible for the model differences are analyzed. Competing effects of parameterized boundary-layer turbulence and shallow convection are found to be essential. Boundary-layer turbulence and shallow convection are typically represented by distinct parameterization schemes in current models—parameterization schemes that often produce opposing effects on low clouds. Convective drying of the boundary layer tends to deepen low clouds and reduce the cloud fraction at the lowest levels; turbulent moistening tends to make low clouds more shallow but affects the low-cloud fraction less. The relative importance different models assign to these opposing mechanisms contributes to the spread of the climatological shallowness of low clouds and thus to the spread of low-cloud changes under global warming.

Spatial and temporal variability of nearshore winds in eastern boundary current systems is affected by orography, coastline shape, and air-sea interaction. These lead to a weakening of the wind close to the coast: the so-called wind drop-off. In this study, regional atmospheric simulations over the US West Coast are used to demonstrate monthly characteristics of the wind drop-off and assess the mechanisms controlling it. Using a long-term simulation, we show the wind drop-off has spatial and seasonal variability in both its offshore extent and intensity. The offshore extent varies from around 10 to 80 km from the coast and the wind reduction from 10 to 80 %. We show that when the mountain orography is combined with the coastline shape of a cape, it has the biggest influence on wind drop-off. The primary associated processes are the orographically-induced vortex stretching and the surface drag related to turbulent momentum flux divergence that has an enhanced drag coefficient over land. Orographically-induced tilting/twisting can also be locally significant in the vicinity of capes. The land-sea drag difference acts as a barrier to encroachment of the wind onto the land through turbulent momentum flux divergence. It turns the wind parallel to the shore and slightly reduces it close to the coast. Another minor factor is the sharp coastal sea surface temperature front associated with upwelling. This can weaken the surface wind in the coastal strip by shallowing the marine boundary layer and decoupling it from the overlying troposphere.

A physically-based statistical modeling approach to downscale coarse resolution reanalysis near-surface winds over a region of complex terrain is developed and tested in this study. Our approach is guided by physical variables and meteorological relationships that are important for determining near-surface wind flow. Preliminary fine scale winds are estimated by correcting the course-to-fine grid resolution mismatch in roughness length. Guided by the physics shaping near-surface winds, we then formulate a multivariable linear regression model which uses near-surface micrometeorological variables and the preliminary estimates as predictors to calculate the final wind products. The coarse-to-fine grid resolution ratio is approximately 10–1 for our study region of southern California. A validated 3-km resolution dynamically-downscaled wind dataset is used to train and validate our method. Winds from our statistical modeling approach accurately reproduce the dynamically-downscaled near-surface wind field with wind speed magnitude and wind direction errors of <1.5 ms⁻¹ and 30°, respectively. This approach can greatly accelerate the production of near-surface wind fields that are much more accurate than reanalysis data, while limiting the amount of computational and time intensive dynamical downscaling. Future studies will evaluate the ability of this approach to downscale other reanalysis data and climate model outputs with varying coarse-to-fine grid resolutions and domains of interest.

We examine the tropical inversion strength, measured by the estimated inversion strength (EIS), and its response to climate change in 18 models associated with phase 5 of the coupled model intercomparison project (CMIP5). While CMIP5 models generally capture the geographic distribution of observed EIS, they systematically underestimate it off the west coasts of continents, due to a warm bias in sea surface temperature. The negative EIS bias may contribute to the low bias in tropical low-cloud cover in the same models. Idealized perturbation experiments reveal that anthropogenic forcing leads directly to EIS increases, independent of “temperature-mediated” EIS increases associated with long-term oceanic warming. This fast EIS response to anthropogenic forcing is strongly impacted by nearly instantaneous continental warming. The temperature-mediated EIS change has contributions from both uniform and non-uniform oceanic warming. The substantial EIS increases in uniform oceanic warming simulations are due to warming with height exceeding the moist adiabatic lapse rate in tropical warm pools. EIS also increases in fully-coupled ocean–atmosphere simulations where CO2CO2 concentration is instantaneously quadrupled, due to both fast and temperature-mediated changes. The temperature-mediated EIS change varies with tropical warming in a nonlinear fashion: The EIS change per degree tropical warming is much larger in the early stage of the simulations than in the late stage, due to delayed warming in the eastern parts of the subtropical oceans. Given the importance of EIS in regulating tropical low-cloud cover, this suggests that the tropical low-cloud feedback may also be nonlinear.

Wildland fires in Southern California can be divided into two categories: fall fires, which are typically driven by strong offshore Santa Ana winds, and summer fires, which occur with comparatively weak onshore winds and hot and dry weather. Both types of fire contribute significantly to annual burned area and economic loss. An improved understanding of the relationship between Southern California's meteorology and fire is needed to improve predictions of how fire will change in the future and to anticipate management needs. We used output from a regional climate model constrained by reanalysis observations to identify Santa Ana events and partition fires into those occurring during periods with and without Santa Ana conditions during 1959–2009. We then developed separate empirical regression models for Santa Ana and non‐Santa Ana fires to quantify the effects of meteorology on fire number and size. These models explained approximately 58% of the seasonal and interannual variation in the number of Santa Ana fires and 36% of the variation in non‐Santa Ana fires. The number of Santa Ana fires increased during years when relative humidity during Santa Ana events and fall precipitation were below average, indicating that fuel moisture is a key controller of ignition. Relative humidity strongly affected Santa Ana fire size. Cumulative precipitation during the previous three winters was significantly correlated with the number of non‐Santa Ana fires, presumably through increased fine fuel density and connectivity between infrastructure and nearby vegetation. Both relative humidity and the preceding wet season precipitation influenced non‐Santa Ana fire size. Regression models driven by meteorology explained 57% of the temporal variation in Santa Ana burned area and 22% of the variation in non‐Santa Ana burned area. The area burned by non‐Santa Ana fires has increased steadily by 1.7% year⁻¹ since 1959 (p < 0.006); the occurrence of extremely large Santa Ana fires has increased abruptly since 2003. Our results underscore the need to separately consider the fuel and meteorological controls on Santa Ana and non‐Santa Ana fires when projecting climate change impacts on regional fire.

Using output from a high‐resolution meteorological simulation, we evaluate the sensitivity of southern California wind energy generation to variations in key characteristics of current wind turbines. These characteristics include hub height, rotor diameter and rated power, and depend on turbine make and model. They shape the turbine's power curve and thus have large implications for the energy generation capacity of wind farms. For each characteristic, we find complex and substantial geographical variations in the sensitivity of energy generation. However, the sensitivity associated with each characteristic can be predicted by a single corresponding climate statistic, greatly simplifying understanding of the relationship between climate and turbine optimization for energy production. In the case of the sensitivity to rotor diameter, the change in energy output per unit change in rotor diameter at any location is directly proportional to the weighted average wind speed between the cut‐in speed and the rated speed. The sensitivity to rated power variations is likewise captured by the percent of the wind speed distribution between the turbines rated and cut‐out speeds. Finally, the sensitivity to hub height is proportional to lower atmospheric wind shear. Using a wind turbine component cost model, we also evaluate energy output increase per dollar investment in each turbine characteristic. We find that rotor diameter increases typically provide a much larger wind energy boost per dollar invested, although there are some zones where investment in the other two characteristics is competitive. Our study underscores the need for joint analysis of regional climate, turbine engineering and economic modeling to optimize wind energy production.

Changes in wintertime 10 m winds due to the El Niño-Southern Oscillation are examined using a 6 km resolution climate simulation of Southern California covering the period from 1959 through 2001. Wind speed statistics based on regional averages reveal a general signal of increased mean wind speeds and wind speed variability during El Niño across the region. An opposite and nearly as strong signal of decreased wind speed variability during La Niña is also found. These signals are generally more significant than the better-known signals in precipitation. In spite of these regional-scale generalizations, there are significant sub-regional mesoscale structures in the wind speed impacts. In some cases, impacts on mean winds and wind variability at the sub-regional scale are opposite to those of the region as a whole. All of these signals can be interpreted in terms of shifts in occurrences of the region’s main wind regimes due to the El Niño phenomenon. The results of this study can be used to understand how interannual wind speed variations in regions of Southern California are influenced by the El Niño phenomenon.

The performance of five boundary layer parameterizations in the Weather Research and Forecasting Model is examined for marine boundary layer cloud regions running in single-column mode. Most parameterizations show a poor agreement of the vertical boundary layer structure when compared with large-eddy simulation models. These comparisons against large-eddy simulation show that a parameterization based on the eddy-diffusivity/mass-flux approach provides a better performance. The results also illustrate the key role of boundary layer parameterizations in model performance.

We examine the influence of the South-American land-mass and its mountains on the significant cyclic diurnal and semidiurnal components of the average circulation in the adjacent area of the southeastern tropical Pacific (SEP). Our approach is based on a number of numerical simulations with the regional atmospheric model weather research and forecasting forced by the National Centers for Environmental Prediction’s final analysis operational analysis data. In the control simulation the model domain covers the SEP and a large part of South America. In several sensitivity experiments the domain is reduced to progressively exclude continental areas. We find that the mean diurnal cycle is sensitive to model domain in ways that reveal the existence of different contributions originating from the Chilean and Peruvian land-masses. The experiments suggest that diurnal variations in circulations and thermal structures over the SEP (mainly forced by local insolation) are influenced by convection over the Peruvian sector of the Andes cordillera, while the mostly dry mountain-breeze circulations force an additional component that results in semi-diurnal variations near the coast. A series of numerical tests, however, reveal sensitivity of the simulations to the choice of vertical grid, limiting the possibility of solid quantitative statements on the amplitudes and phases of the diurnal and semidiurnal components across the domain.

Changes in wintertime 10 m winds due to the El Niño-Southern Oscillation are examined using a 6 km resolution climate simulation of Southern California covering the period from 1959 through 2001. Wind speed statistics based on regional averages reveal a general signal of increased mean wind speeds and wind speed variability during El Niño across the region. An opposite and nearly as strong signal of decreased wind speed variability during La Niña is also found. These signals are generally more significant than the better-known signals in precipitation. In spite of these regional-scale generalizations, there are significant sub-regional mesoscale structures in the wind speed impacts. In some cases, impacts on mean winds and wind variability at the sub-regional scale are opposite to those of the region as a whole. All of these signals can be interpreted in terms of shifts in occurrences of the region’s main wind regimes due to the El Niño phenomenon. The results of this study can be used to understand how interannual wind speed variations in regions of Southern California are influenced by the El Niño phenomenon.

This study used numerical experiments to investigate two important concerns in simulating the cold season snowpack: the impact of the alterations of snow albedo due to anthropogenic aerosol deposition on snowpack and the treatment of snow physics using a multi-layer snow model. The snow albedo component considered qualitatively future changes in anthropogenic emissions and the subsequent increase or decrease of black carbon deposition on the Sierra Nevada snowpack by altering the prescribed snow albedo values. The alterations in the snow albedo primarily affect the snowpack via surface energy budget with little impact on precipitation. It was found that a decrease in snow albedo (by as little as 5–10% of the reference values) due to an increase in local emissions enhances snowmelt and runoff (by as much as 30–50%) in the early part of a cold season, resulting in reduced snowmelt-driven runoff (by as much as 30–50%) in the later part of the cold season, with the greatest impacts at higher elevations. An increase in snow albedo associated with reduced anthropogenic emissions results in the opposite effects. Thus, the most notable impact of the decrease in snow albedo is to enhance early-season snowmelt and to reduce late-season snowmelt, resulting in an adverse impact on warm season water resources in California. The timing of the sensitivity of snow water equivalent (SWE), snowmelt, and runoff vary systematically according to terrain elevation; as terrain elevation increases, the peak response of these fields occurs later in the cold season. The response of SWE and surface energy budget to the alterations in snow albedo found in this study shows that the effects of snow albedo on snowpack are further enhanced via local snow-albedo feedback. Results from this experiment suggest that a reduction in local emissions, which would increase snow albedo, could alleviate the early snowmelt and reduced runoff in late winter and early spring caused by global climate change, at least partially. The most serious uncertainties associated with this part of the study are a quantification of the relationship between the amount of black carbon deposition and snow albedo—a subject of future study. The comparison of the spring snowpack simulated with a single- and multi-layer snow model during the spring of 1998 shows that a more realistic treatment of snow physics in a multi-layer snow model could improve snowpack simulations, especially during spring when snow ablation is significant, or in conjunction with climate change projections.

The frequency and character of Southern California’s Santa Ana wind events are investigated within a 12-km-resolution downscaling of late-20th and mid-21st century time periods of the National Center for Atmospheric Research Community Climate System Model global climate change scenario run. The number of Santa Ana days per winter season is approximately 20% fewer in the mid 21st century compared to the late 20th century. Since the only systematic and sustained difference between these two periods is the level of anthropogenic forcing, this effect is anthropogenic in origin. In both time periods, Santa Ana winds are partly katabatically-driven by a temperature difference between the cold wintertime air pooling in the desert against coastal mountains and the adjacent warm air over the ocean. However, this katabatic mechanism is significantly weaker during the mid 21st century time period. This occurs because of the well-documented differential warming associated with transient climate change, with more warming in the desert interior than over the ocean. Thus the mechanism responsible for the decrease in Santa Ana frequency originates from a well-known aspect of the climate response to increasing greenhouse gases, but cannot be understood or simulated without mesoscale atmospheric dynamics. In addition to the change in Santa Ana frequency, we investigate changes during Santa Anas in two other meteorological variables known to be relevant to fire weather conditions—relative humidity and temperature. We find a decrease in the relative humidity and an increase in temperature. Both these changes would favor fire. A fire behavior model accounting for changes in wind, temperature, and relative humidity simultaneously is necessary to draw firm conclusions about future fire risk and growth associated with Santa Ana events. While our results are somewhat limited by a relatively small sample size, they illustrate an observed and explainable regional change in climate due to plausible mesoscale processes.

In this study, we examine marine low cloud cover variability in the Southeast Pacific and its association with lower-tropospheric stability (LTS) across a spectrum of timescales. On both daily and interannual timescales, LTS and low cloud amount are very well correlated in austral summer (DJF). Meanwhile in winter (JJA), when ambient LTS increases, the LTS–low cloud relationship substantially weakens. The DJF LTS–low cloud relationship also weakens in years with unusually large ambient LTS values. These are generally strong El Niño years, in which DJF LTS values are comparable to those typically found in JJA. Thus the LTS–low cloud relationship is strongly modulated by the seasonal cycle and the ENSO phenomenon. We also investigate the origin of LTS anomalies closely associated with low cloud variability during austral summer. We find that the ocean and atmosphere are independently involved in generating anomalies in LTS and hence variability in the Southeast Pacific low cloud deck. This highlights the importance of the physical (as opposed to chemical) component of the climate system in generating internal variability in low cloud cover. It also illustrates the coupled nature of the climate system in this region, and raises the possibility of cloud feedbacks related to LTS. We conclude by addressing the implications of the LTS–low cloud relationship in the Southeast Pacific for low cloud feedbacks in anthropogenic climate change.

The depth and timing of snowpack in the Sierra Nevada Mountains are of fundamental importance to California water resource availability, and recent studies indicate a shift toward earlier snowmelt consistent with projected impacts of anthropogenic climate change. In order for future studies to assess snowpack variability on seasonal to centennial time scales, physically based models of snowpack evolution at high spatial resolution must be improved. Here we evaluate modeled snowpack accuracy for the central Sierra Nevada in the Weather Research and Forecasting regional climate model coupled to the Noah land surface model. A simulation with nested domains at 27, 9, and 3 km grid spacings is presented for November 2001 to July 2002. Model outputs are compared with daily snowpack observations at 41 locations, air temperature at 31 locations, and precipitation at 10 locations. Comparison of snowpack at different resolutions suggests that 27 km simulations substantially underestimate snowpack, while 9 and 3 km simulations are closer to observations. Regional snowpack accumulation is accurately simulated at these high resolutions, but model snowmelt occurs an average of 22–25 days early. Some error can be traced to differences in elevation and observation scale between point‐based measurements and model grid cells, but these factors cannot explain the persistent bias toward early snowmelt. A high correlation between snowmelt and error in modeled surface air temperature is found, with melt coinciding systematically with excessively cold air temperatures. One possible source of bias is an imbalance in turbulent heat fluxes, erroneously warming the snowpack while cooling the surface atmosphere.

In the middle of March 2002 a synoptic upwelling event occurred in the Southern California Bight; it was marked by a precipitous cooling of at least 4°C within 10–20 km of the coast. By the end of the month the preevent temperatures had slowly recovered. The Regional Oceanic Model System (ROMS) is used to simulate the event with an atmospheric downscaling reanalysis for surface wind and buoyancy flux forcing. Lateral boundary conditions of temperature, salinity, velocity, and sea level are taken from a global oceanic product. Barotropic tidal fields from a global barotropic model are imposed along the open boundaries. The simulation reproduces well the upwelling process compared with observed data. The sensitivity of the simulation is examined to wind resolution, heat flux, and tidal forcing. The oceanic response to the different wind resolutions converges at the level of the 6 km resolution, which is the finest scale present in the terrain elevation data set used in the atmospheric downscaling. The combination of an analytical diurnal cycle in the solar radiation and the empirical coupling with the instantaneous ROMS sea surface temperature produces a similar oceanic response to the downscaled heat flux. Tidal effects are significant in the upwelling evolution due to the increase in wind energy input through a quasi‐resonant alignment of the wind and surface current, probably by chance.

On the basis of 3‐D Monte Carlo photon tracing simulations, we have developed a parameterization of solar fluxes over mountain surfaces by means of the multiple linear regression analysis associated with topographic information, including elevation, solar incident angle, sky view factor, and terrain configuration factor. For clear skies without aerosols and clouds, the regression equation for the direct flux can explain more than 98% of the variation in which the solar incident angle is the dominant factor, except when the Sun is very low or at zenith. About 60% of the variation in the diffuse flux is predicted by the regression equation in which the mean elevation, sky view factor, and solar incident angle are key factors. The terrain‐reflected fluxes, proportional to the surface albedo, are well correlated with the terrain configuration factor with more than 80% of the variation that can be explained. The coupled fluxes involve intricate interactions, and the regression analysis is less satisfactory in cases of low albedo values. However, over high‐albedo surfaces, the terrain configuration factor becomes most dominant, leading to a significant improvement in regression performance. In these analyses, a surface albedo invariant with wavelength has been used. Using a region over the Sierra Nevada as a testbed, the preceding regression parameterizations have been specifically developed so that the fluxes evaluated from the 3‐D Monte Carlo model over intense topography can be used as a perturbation term to correct those computed from the plane‐parallel counterpart, commonly used in regional climate models and GCMs.

Low-level temperature inversions are a common feature of the wintertime troposphere in the Arctic and Antarctic. Inversion strength plays an important role in regulating atmospheric processes including air pollution, ozone destruction, cloud formation, and negative longwave feedback mechanisms that shape polar climate response to anthropogenic forcing. The Atmospheric Infrared Sounder (AIRS) instrument provides reliable measures of spatial patterns in mean wintertime inversion strength when compared with available radiosonde observations and reanalysis products. Here, we examine the influence of sea ice concentration on inversion strength in the Arctic and Antarctic. Correlation of inversion strength with mean annual sea ice concentration, likely a surrogate for the effective thermal conductivity of the wintertime ice pack, yields strong, linear relationships in the Arctic (r = 0.88) and Antarctic (r = 0.86). We find a substantially greater (stronger) linear relationship between sea ice concentration and surface air temperature than with temperature at 850 hPa, lending credence to the idea that sea ice controls inversion strength through modulation of surface heat fluxes. As such, declines in sea ice in either hemisphere may imply weaker mean inversions in the future. Comparison of mean inversion strength in AIRS and global climate models (GCMs) suggests that many GCMs poorly characterize mean inversion strength at high latitudes.

The Antarctic Peninsula has warmed significantly since the 1950s. This pronounced and isolated warming trend is collectively captured by 29 twentieth-century climate hindcasts participating in the version 3 Coupled Model Intercomparison Project. To understand the factors driving warming trends in the hindcasts, we examine trends in Peninsula region’s atmospheric heat budget in every simulation. We find that atmospheric latent heat release increases in nearly all hindcasts. These increases are generally anthropogenic in origin, and account for about 60% of the ensemble-mean warming trend in the Peninsula. They are driven primarily by well-understood features of the anthropogenic intensification of global hydrological cycle. As sea surface temperature increases, moisture contained in atmospheric flows increases. When such flows are forced to ascend the Peninsula’s topography, enhanced local latent heat release results. The mechanism driving the warming of the Antarctic Peninsula is therefore clear in the models. Evidence for a similar mechanism operating in the real world is seen in the increasing snow accumulation rates inferred from ice cores drilled in the Peninsula. However, the relative importance of this mechanism and other processes previously identified as potentially causing the observed warming, such as the recent sea ice retreat in the Bellingshausen Sea, is difficult to assess. Thus the relevance of the simulated warming mechanism to the observed warming is unclear, in spite of its robustness in the models.

Observational studies have shown that mesoscale variations in sea surface temperature may induce mesoscale variations in wind. In eastern subtropical upwelling regions such as the California coast, this mechanism could be of great importance for the mean state and variability of the climate system. In coastal regions orography also creates mesoscale variations in wind, and the orographic effect may extend more than 100 km offshore. The respective roles of SST/wind links and coastal orography in shaping mesoscale wind variations in nearshore regions is not clear. We address this question in the context of the California Upwelling System, using a high-resolution regional numerical modeling system coupling the WRF atmospheric model to the ROMS oceanic model, as well as additional uncoupled experiments to quantify and separate the effects of SST/wind links and coastal orography on mesoscale wind variations. After taking into account potential biases in the representation of the strength of SST/wind links by the model, our results suggest that the magnitude of mesoscale wind variations arising from the orographic effects is roughly twice that of wind variations associated with mesoscale SST anomalies. This indicates that even in this region where coastal orography is complex and leaves a strong imprint on coastal winds, the role of SST/winds links in shaping coastal circulation and climate cannot be neglected.

A study of the California Sierra Nevada snowpack has been conducted using snow station observations and reanalysis surface temperature data. Monthly snow water equivalent (SWE) measurements were combined from two datasets to provide sufficient data from 1930 to 2008. The monthly snapshots are used to calculate peak snow mass timing for each snow season. Since 1930, there has been an overall trend toward earlier snow mass peak timing by 0.6 days per decade. The trend toward earlier timing also occurs at nearly all individual stations. Even stations showing an increase in 1 April SWE exhibit the trend toward earlier timing, indicating that enhanced melting is occurring at nearly all stations. Analysis of individual years and stations reveals that warm daily maximum temperatures averaged over March and April are associated with earlier snow mass peak timing for all spatial and temporal scales included in the dataset. The influence is particularly pronounced for low accumulation years indicating the potential importance of albedo feedback for the melting of shallow snow. The robustness of the early spring temperature influence on peak timing suggests the trend toward earlier peak timing is attributable to the simultaneous warming trend (0.1°C decade⁻¹ since 1930, with an acceleration in warming in later time periods). Given future scenarios of warming in California, one can expect acceleration in the trend toward earlier peak timing; this will reduce the warm season storage capacity of the California snowpack.

Fire plays a crucial role in many ecosystems, and a better understanding of different controls on fire activity is needed. Here we analyze spatial variation in fire danger during episodic wind events in coastal southern California, a densely populated Mediterranean‐climate region. By reconstructing almost a decade of fire weather patterns through detailed simulations of Santa Ana winds, we produced the first high‐resolution map of where these hot, dry winds are consistently most severe and which areas are relatively sheltered. We also analyzed over half a century of mapped fire history in chaparral ecosystems of the region, finding that our models successfully predict where the largest wildfires are most likely to occur. There is a surprising lack of information about extreme wind patterns worldwide, and more quantitative analyses of their spatial variation will be important for effective fire management and sustainable long‐term urban development on fire‐prone landscapes.

The atmospheric conditions that lead to strong offshore surface winds in Southern California, commonly referred to as Santa Ana winds, are investigated using the North American Regional Reanalysis and a 12-year, 6-km resolution regional climate simulation of Southern California. We first construct an index to characterize Santa Ana events based on offshore wind strength. This index is then used to identify the average synoptic conditions associated with Santa Ana events—a high pressure anomaly over the Great Basin. This pressure anomaly causes offshore geostrophic winds roughly perpendicular to the region’s mountain ranges, which in turn cause surface flow as the offshore momentum is transferred to the surface. We find, however, that there are large variations in the synoptic conditions during Santa Ana conditions, and that there are many days with strong offshore flow and weak synoptic forcing. This is due to local thermodynamic forcing that also causes strong offshore surface flow: a large temperature gradient between the cold desert surface and the warm ocean air at the same altitude creates an offshore pressure gradient at that altitude, in turn causing katabatic-like offshore flow in a thin layer near the surface. We quantify the contribution of “synoptic” and “local thermodynamic” mechanisms using a bivariate linear regression model, and find that, unless synoptic conditions force strongly onshore winds, the local thermodynamic forcing is the primary control on Santa Ana variability.

Biologically produced dimethylsulfide (DMS) is an important source of sulfur to the marine atmosphere that may affect cloud formation and properties. DMS is involved in a complex set of biochemical transformations and ecological exchanges so its global distribution is influenced by numerous factors, including oxidative stress from UV radiation. We re‐examine correlations between global surface DMS concentrations and mixed layer solar radiation dose (SRD), and find that SRD accounts for only a very small fraction (14%) of total variance in DMS measurements when using minimal aggregation methods. Moreover this relationship arises in part from the fact that when mixed layers deepen, both SRD and DMS decrease. When we control for this confounding effect, the correlation between DMS and SRD is reduced even further. These results indicate that factors other than solar irradiance play a leading role in determining global DMS emissions.

Using a 6-km-resolution regional climate simulation of Southern California, the effect of orographic blocking on the precipitation climatology is examined. To diagnose whether blocking occurs, precipitating hours are categorized by a bulk Froude number. The precipitation distribution becomes much more spatially homogeneous as the Froude number decreases, and an inspection of winds confirms that this results from the increasing prevalence of orographic blocking. Low Froude (Froude approximately less than 1), blocked cases account for a large fraction of climatological precipitation, particularly at the coastline where more than half is attributable to blocked cases. Thus, the climatological precipitation–slope relationship seen in observations and in the simulation is a hybrid of blocked and unblocked cases.

Simulated precipitation distributions are compared to those predicted by a simple linear model that includes only rainfall arising from direct forced topographic ascent. The agreement is nearly perfect for high Froude (Froude substantially larger than 1) cases but degrades dramatically as the index decreases; as blocking becomes more prevalent, the precipitation–slope relationship becomes continuously weaker than that predicted by the linear model. Because of its high fidelity during unblocked cases, it is surmised that blocking effects are the primary limitation preventing the linear model from accurately representing precipitation climatology and that the representation would be significantly improved during low Froude hours by the addition of a term to reduce the effective slope of the topography. These results suggest orographic blocking may substantially affect climatological precipitation distributions in similarly configured coastal areas.

We developed a 3D Monte Carlo photon tracing program for the transfer of radiation in inhomogeneous and irregular terrain to calculate broadband solar and thermal infrared fluxes. We selected an area of 100 × 100 km² in the Tibetan Plateau centered at Lhasa city and used the albedo and surface temperature from MODIS/Terra for this study. We showed that anomalies of surface solar fluxes with reference to a flat surface can be as large as 600 W/m², depending on time of day, mountain configuration, and albedo. Surface temperature is the dominating factor in determining anomalies of the surface infrared flux distribution relative to a flat surface with values as high as 70 W/m² at cold mountain surfaces. The average surface solar flux over regional domains of 100 × 100 km² and 50 × 50 km² comprising intense topography can deviate from the smoothed surface conventionally assumed in climate models and GCMs by 10–50 W/m².

We examine the climatological diurnal cycle of surface air temperature in a 6 km resolution atmospheric simulation of Southern California from 1995 to the present. We find its amplitude and phase both have significant geographical structure. This is most likely due to diurnally-varying flows back and forth across the coastline and elevation isolines resulting from the large daily warming and cooling over land. Because the region’s atmosphere is generally stably stratified, these flow patterns result in air of lower (higher) potential temperature being advected upslope (downslope) during daytime (nighttime). This suppresses the temperature diurnal cycle amplitude at mountaintops where diurnal flows converge (diverge) during the day (night). The nighttime land breeze also advects air of higher potential temperature downslope toward the coast. This raises minimum temperatures in land areas adjacent to the coast in a manner analogous to the daytime suppression of maximum temperature by the cool sea breeze in these same areas. Because stratification is greater in the coastal zone than in the desert interior, these thermal effects of the diurnal winds are not uniform, generating spatial structures in the phase and shape of the temperature diurnal cycle as well as its amplitude. We confirm that the simulated characteristics of the temperature diurnal cycle as well as those of the associated diurnal winds are also found in a network of 30 observation stations in the region. This gives confidence in the simulation’s realism and our study’s findings. Diurnal flows are probably mainly responsible for the geographical structures in the temperature diurnal cycle in other regions of significant topography and surface heterogeneity, their importance depending partly on the degree of atmospheric stratification.

Atmospheric rivers (ARs) are characterized by intense moisture transport, which, on landfall, produce precipitation which can be both beneficial and destructive. ARs in California, for example, are known to have ended drought conditions but also to have caused substantial socio-economic damage from landslides and flooding linked to extreme precipitation. Understanding how AR characteristics will respond to a warming climate is, therefore, vital to the resilience of communities affected by them, such as the western USA, Europe, East Asia and South Africa. In this Review, we use a theoretical framework to synthesize understanding of the dynamic and thermodynamic responses of ARs to anthropogenic warming and connect them to observed and projected changes and impacts revealed by observations and complex models. Evidence suggests that increased atmospheric moisture (governed by Clausius–Clapeyron scaling) will enhance the intensity of AR-related precipitation — and related hydrological extremes — but with changes that are ultimately linked to topographic barriers. However, due to their dependency on both weather and climate-scale processes, which themselves are often poorly constrained, projections are uncertain. To build confidence and improve resilience, future work must focus efforts on characterizing the multiscale development of ARs and in obtaining observations from understudied regions, including the West Pacific, South Pacific and South Atlantic.

We compare historical and end‐of‐century temperature and precipitation patterns over California from one dynamically downscaled simulation using the Weather Research and Forecast (WRF) model and two simulations statistically downscaled using Localized Constructed Analogs (LOCA). We uniquely separate causes of differences between dynamically and statistically based future climate projections into differences in historical climate (gridded observations versus regional climate model output) and differences in how these downscaling techniques explicitly handle future climate changes (numerical modeling versus analogs). In these methods, solutions between different downscaling techniques differ more in the future compared to the historical period. Changes projected by LOCA are insensitive to the choice of driving data. Only through dynamical downscaling can we simulate physically consistent regional springtime warming patterns across the Sierra Nevada, while the statistical simulations inherit an unphysical signal from their parent Global Climate Model (GCM) or gridded data. The results of our study clarify why these different techniques produce different outcomes and may also provide guidance on which downscaled products to use for certain impact analyses in California and perhaps other Mediterranean regimes.

A highly uncertain aspect of anthropogenic climate change is the rate at which the global hydrologic cycle intensifies. The future change in global‐mean precipitation per degree warming, or hydrologic sensitivity, exhibits a threefold spread (1–3%/K) in current global climate models. In this study, we find that the intermodel spread in this value is associated with a significant portion of variability in future projections of extreme precipitation in the tropics, extending also into subtropical atmospheric river corridors. Additionally, there is a very tight intermodel relationship between changes in extreme and nonextreme precipitation, whereby models compensate for increasing extreme precipitation events by decreasing weak‐moderate events. Another factor linked to changes in precipitation extremes is model resolution, with higher resolution models showing a larger increase in heavy extremes. These results highlight ways various aspects of hydrologic cycle intensification are linked in models and shed new light on the task of constraining precipitation extremes.

Differences among climate models in equilibrium climate sensitivity (ECS; the equilibrium surface temperature response to a doubling of atmospheric CO₂) remain a significant barrier to the accurate assessment of societally important impacts of climate change. Relationships between ECS and observable metrics of the current climate in model ensembles, so-called emergent constraints, have been used to constrain ECS. Here a statistical method (including a backward selection process) is employed to achieve a better statistical understanding of the connections between four recently proposed emergent constraint metrics and individual feedbacks influencing ECS. The relationship between each metric and ECS is largely attributable to a statistical connection with shortwave low cloud feedback, the leading cause of intermodel ECS spread. This result bolsters confidence in some of the metrics, which had assumed such a connection in the first place. Additional analysis is conducted with a few thousand artificial metrics that are randomly generated but are well correlated with ECS. The relationships between the contrived metrics and ECS can also be linked statistically to shortwave cloud feedback. Thus, any proposed or forthcoming ECS constraint based on the current generation of climate models should be viewed as a potential constraint on shortwave cloud feedback, and physical links with that feedback should be investigated to verify that the constraint is real. In addition, any proposed ECS constraint should not be taken at face value since other factors influencing ECS besides shortwave cloud feedback could be systematically biased in the models.

In the current generation of climate models, the projected increase in global precipitation over the 21st century ranges from 2% to 10% under a high‐emission scenario. Some of this uncertainty can be traced to the rapid response to carbon dioxide (CO₂) forcing. We analyze an ensemble of simulations to better understand model spread in this rapid response. A substantial amount is linked to how the land surface partitions a change in latent versus sensible heat flux in response to the CO₂‐induced radiative perturbation; a larger increase in sensible heat results in a larger decrease in global precipitation. Model differences in the land surface response appear to be strongly related to the vegetation response to increased CO₂, specifically, the closure of leaf stomata. Future research should thus focus on evaluation of the vegetation physiological response, including stomatal conductance parameterizations, for the purpose of constraining the fast response of Earth's hydrologic cycle to CO₂ forcing.

Intensification of the hydrologic cycle is a key dimension of climate change, with substantial impacts on human and natural systems^1,2. A basic measure of hydrologic cycle intensification is the increase in global-mean precipitation per unit surface warming, which varies by a factor of three in current-generation climate models (about 1–3 per cent per kelvin)^3,4,5. Part of the uncertainty may originate from atmosphere–radiation interactions. As the climate warms, increases in shortwave absorption from atmospheric moistening will suppress the precipitation increase. This occurs through a reduction of the latent heating increase required to maintain a balanced atmospheric energy budget^6,7. Using an ensemble of climate models, here we show that such models tend to underestimate the sensitivity of solar absorption to variations in atmospheric water vapour, leading to an underestimation in the shortwave absorption increase and an overestimation in the precipitation increase. This sensitivity also varies considerably among models due to differences in radiative transfer parameterizations, explaining a substantial portion of model spread in the precipitation response. Consequently, attaining accurate shortwave absorption responses through improvements to the radiative transfer schemes could reduce the spread in the predicted global precipitation increase per degree warming for the end of the twenty-first century by about 35 per cent, and reduce the estimated ensemble-mean increase in this quantity by almost 40 per cent.

In 36 climate change simulations associated with phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5), changes in marine low cloud cover (LCC) exhibit a large spread, and may be either positive or negative. Here we develop a heuristic model to understand the source of the spread. The model’s premise is that simulated LCC changes can be interpreted as a linear combination of contributions from factors shaping the clouds’ large-scale environment. We focus primarily on two factors—the strength of the inversion capping the atmospheric boundary layer (measured by the estimated inversion strength, EIS) and sea surface temperature (SST). For a given global model, the respective contributions of EIS and SST are computed. This is done by multiplying (1) the current-climate’s sensitivity of LCC to EIS or SST variations, by (2) the climate-change signal in EIS or SST. The remaining LCC changes are then attributed to changes in greenhouse gas and aerosol concentrations, and other environmental factors. The heuristic model is remarkably skillful. Its SST term dominates, accounting for nearly two-thirds of the intermodel variance of LCC changes in CMIP3 models, and about half in CMIP5 models. Of the two factors governing the SST term (the SST increase and the sensitivity of LCC to SST perturbations), the SST sensitivity drives the spread in the SST term and hence the spread in the overall LCC changes. This sensitivity varies a great deal from model to model and is strongly linked to the types of cloud and boundary layer parameterizations used in the models. EIS and SST sensitivities are also estimated using observational cloud and meteorological data. The observed sensitivities are generally consistent with the majority of models as well as expectations from prior research. Based on the observed sensitivities and the relative magnitudes of simulated EIS and SST changes (which we argue are also physically reasonable), the heuristic model predicts LCC will decrease over the 21st-century. However, to place a strong constraint, for example on the magnitude of the LCC decrease, will require longer observational records and a careful assessment of other environmental factors producing LCC changes. Meanwhile, addressing biases in simulated EIS and SST sensitivities will clearly be an important step towards reducing intermodel spread in simulated LCC changes.

Processes in the climate system that can either amplify or dampen the climate response to an external perturbation are referred to as climate feedbacks. Climate sensitivity estimates depend critically on radiative feedbacks associated with water vapor, lapse rate, clouds, snow, and sea ice, and global estimates of these feedbacks differ among general circulation models. By reviewing recent observational, numerical, and theoretical studies, this paper shows that there has been progress since the Third Assessment Report of the Intergovernmental Panel on Climate Change in (i) the understanding of the physical mechanisms involved in these feedbacks, (ii) the interpretation of intermodel differences in global estimates of these feedbacks, and (iii) the development of methodologies of evaluation of these feedbacks (or of some components) using observations. This suggests that continuing developments in climate feedback research will progressively help make it possible to constrain the GCMs’ range of climate feedbacks and climate sensitivity through an ensemble of diagnostics based on physical understanding and observations.

Climatological planetary albedo obtained from the International Satellite Cloud Climatology Project (ISCCP) D-series flux dataset is broken down into contributions from the surface and atmosphere in cryosphere regions. The atmosphere accounts for much more of climatological planetary albedo (≥75%) than the surface at all times of the year. The insignificance of the surface contribution over highly reflective cryosphere regions is attributed mostly to the damping effect of the atmosphere. The overlying atmosphere attenuates the surface’s contribution to climatological planetary albedo by reducing the number of solar photons initially reaching the surface and the number of photons initially reflected by the surface that actually reach the top of the atmosphere.

The ISCCP datasets were also used to determine the relative contributions of the surface and atmosphere to seasonal and interannual planetary albedo variability in cryosphere regions. Even damped by the atmosphere to the same degree as in the climatological case, the surface contribution dominates the variability in planetary albedo on seasonal and interannual time scales. The surface accounts for about 75% of the change in climatological planetary albedo from one season to another with similar zenith angle and more than 50% of its interannual variability at nearly all times of the year, especially during seasons with extensive snow and sea ice extent. The dominance of the surface in planetary albedo variability is because surface albedo variability associated with snow and ice fluctuations is significantly larger than atmospheric albedo variability due to cloud fluctuations. The large effect of snow and ice variations on planetary albedo variability suggests that if cloud fields do not change much in a future warmer climate, a retreat of snow cover or sea ice would lead to a significant increase in net incoming solar radiation, resulting in an enhancement of high-latitude climate sensitivity.

Milankovitch proposed that variations in the earth’s orbit cause climate variability through a local thermodynamic response to changes in insolation. This hypothesis is tested by examining variability in an atmospheric general circulation model coupled to an ocean mixed layer model subjected to the orbital forcing of the past 165 000 yr. During Northern Hemisphere summer, the model’s response conforms to Milankovitch’s hypothesis, with high (low) insolation generating warm (cold) temperatures throughout the hemisphere. However, during Northern Hemisphere winter, the climate variations stemming from orbital forcing cannot be solely understood as a local thermodynamic response to radiation anomalies. Instead, orbital forcing perturbs the atmospheric circulation in a pattern bearing a striking resemblance to the northern annular mode, the primary mode of simulated and observed unforced atmospheric variability. The hypothesized reason for this similarity is that the circulation response to orbital forcing reflects the same dynamics generating unforced variability. These circulation anomalies are in turn responsible for significant fluctuations in other climate variables: Most of the simulated orbital signatures in wintertime surface air temperature over midlatitude continents are directly traceable not to local radiative forcing, but to orbital excitation of the northern annular mode. This has paleoclimate implications: during the point of the model integration corresponding to the last interglacial (Eemian) period, the orbital excitation of this mode generates a 1°–2°C warm surface air temperature anomaly over Europe, providing an explanation for the warm anomaly of comparable magnitude implied by the paleoclimate proxy record. The results imply that interpretations of the paleoclimate record must account for changes in surface temperature driven not only by changes in insolation, but also by perturbations in atmospheric dynamics.

Past research on the climate response to orbital forcing has emphasized the glacial-interglacial variations in global ice volume, global-mean temperature, and the global hydrologic cycle. This emphasis may be inappropriate in the tropics, where the response to precessional forcing is likely to be somewhat independent of the glacial-interglacial variations, particularly in variables relating to the hydrologic cycle. To illustrate this point, we use an atmospheric general circulation model coupled to a slab ocean model, performing experiments that quantify the tropical climate’s response to (1) opposite phases of precessional forcing, and (2) Last Glacial Maximum boundary conditions. While the glacially-forced tropical temperature changes are typically more than an order of magnitude larger than those arising from precessional forcing, the hydrologic signals stemming from the two forcings are comparable in magnitude. The mechanisms behind these signals are investigated and shown to be quite distinct for the precessional and glacial forcing. Because of strong dynamical linkages in the tropics, the model results illustrate the impossibility of predicting the local hydrologic response to external forcing without understanding the response at much larger spatial scales. Examples from the paleoclimate record are presented as additional evidence for the importance of precessional signals in past variations of the tropical climate.

A coarse resolution coupled ocean–atmosphere simulation in which surface albedo feedback is suppressed by prescribing surface albedo, is compared to one where snow and sea ice anomalies are allowed to affect surface albedo. Canonical CO₂-doubling experiments were performed with both models to assess the impact of this feedback on equilibrium response to external forcing. It accounts for about half the high-latitude response to the forcing. Both models were also run for 1000 yr without forcing to assess the impact of surface albedo feedback on internal variability. Surprisingly little internal variability can be attributed to this feedback, except in the Northern Hemisphere continents during spring and in the sea ice zone of the Southern Hemisphere year-round. At these locations and during these seasons, it accounts for, at most, 20% of the variability. The main reason for this relatively weak signal is that horizontal damping processes dilute the impact of surface albedo feedback.

When snow albedo feedback in Northern Hemisphere continents is isolated from horizontal damping processes, it has a similar strength in the CO₂-doubling and internal variability contexts; a given temperature anomaly in these regions is associated with approximately the same change in snow depth and surface albedo whether it was externally forced or internally generated. This suggests that the presence of internal variability in the observed record is not a barrier to extracting information about snow albedo feedback's contribution to equilibrium climate sensitivity. This is demonstrated in principle in a “scenario run,” where estimates of past, present, and future changes in greenhouse gases and sulfate aerosols are imposed on the model with surface albedo feedback. This simulation contains a mix of internal variations and externally forced anomalies similar to the observed record. The snow albedo feedback to the scenario run's climate anomalies agrees very well with the snow albedo feedback in the CO₂-doubling context. Moreover, the portion of the scenario run corresponding to the present-day satellite record is long enough to capture this feedback, suggesting this record could be used to estimate snow albedo feedback's contribution to equilibrium climate sensitivity.

Temperature reconstructions from the North Atlantic region indicate frequent abrupt and severe climate fluctuations during the last glacial and Holocene periods. The driving forces for these events are unclear and coupled atmosphere–ocean models of global circulation have only simulated such events by inserting large amounts of fresh water into the northern North Atlantic Ocean. Here we report a drastic cooling event in a 15,000-yr simulation of global circulation with present-day climate conditions without the use of such external forcing. In our simulation, the annual average surface temperature near southern Greenland spontaneously fell 6–10 standard deviations below its mean value for a period of 30–40 yr. The event was triggered by a persistent northwesterly wind that transported large amounts of buoyant cold and fresh water into the northern North Atlantic Ocean. Oceanic convection shut down in response to this flow, concentrating the entire cooling of the northern North Atlantic by the colder atmosphere in the uppermost ocean layer. Given the similarity between our simulation and observed records of rapid cooling events, our results indicate that internal atmospheric variability alone could have generated the extreme climate disruptions in this region.

The response to warming of tropical low-level clouds including both marine stratocumulus and trade cumulus is a major source of uncertainty in projections of future climate. Climate model simulations of the response vary widely, reflecting the difficulty the models have in simulating these clouds. These inadequacies have led to alternative approaches to predict low-cloud feedbacks. Here, we review an observational approach that relies on the assumption that observed relationships between low clouds and the “cloud-controlling factors” of the large-scale environment are invariant across time-scales. With this assumption, and given predictions of how the cloud-controlling factors change with climate warming, one can predict low-cloud feedbacks without using any model simulation of low clouds. We discuss both fundamental and implementation issues with this approach and suggest steps that could reduce uncertainty in the predicted low-cloud feedback. Recent studies using this approach predict that the tropical low-cloud feedback is positive mainly due to the observation that reflection of solar radiation by low clouds decreases as temperature increases, holding all other cloud-controlling factors fixed. The positive feedback from temperature is partially offset by a negative feedback from the tendency for the inversion strength to increase in a warming world, with other cloud-controlling factors playing a smaller role. A consensus estimate from these studies for the contribution of tropical low clouds to the global mean cloud feedback is 0.25 ± 0.18 W m⁻² K⁻¹ (90% confidence interval), suggesting it is very unlikely that tropical low clouds reduce total global cloud feedback. Because the prediction of positive tropical low-cloud feedback with this approach is consistent with independent evidence from low-cloud feedback studies using high-resolution cloud models, progress is being made in reducing this key climate uncertainty.

In this study, we evaluate the ability of the Weather Research and Forecasting model to simulate surface energy fluxes in the southeast Pacific stratocumulus region. A total of 18 simulations is performed for the period of October to November 2008, with various combinations of boundary layer, microphysics, and cumulus schemes. Simulated surface energy fluxes are compared to those measured during VOCALS-REx. Using a process-based model evaluation, errors in surface fluxes are attributed to errors in cloud properties. Net surface flux errors are mostly traceable to errors in cloud liquid water path (LWP_cld), which produce biases in downward shortwave radiation. Two mechanisms controlling LWP_cld are diagnosed. One involves microphysics schemes, which control LWP_cld through the production of raindrops. The second mechanism involves boundary layer and cumulus schemes, which control moisture available for cloud by regulating boundary layer height. In this study, we demonstrate that when parameterizations are appropriately chosen, the stratocumulus deck and the related surface energy fluxes are reasonably well represented. In the most realistic experiments, the net surface flux is underestimated by about 10 W m⁻². This remaining low bias is due to a systematic overestimation of the total surface cooling due to sensible and latent heat fluxes in our simulations. There does not appear to be a single physical reason for this bias. Finally, our results also suggest that inaccurate representation of boundary layer height is an important factor limiting further gains in model realism.

How tropical low clouds change with climate remains the dominant source of uncertainty in global warming projections. An analysis of an ensemble of CMIP5 climate models reveals that a significant part of the spread in the models’ climate sensitivity can be accounted by differences in the climatological shallowness of tropical low clouds in weak-subsidence regimes: models with shallower low clouds in weak-subsidence regimes tend to have a higher climate sensitivity than models with deeper low clouds. The dynamical mechanisms responsible for the model differences are analyzed. Competing effects of parameterized boundary-layer turbulence and shallow convection are found to be essential. Boundary-layer turbulence and shallow convection are typically represented by distinct parameterization schemes in current models—parameterization schemes that often produce opposing effects on low clouds. Convective drying of the boundary layer tends to deepen low clouds and reduce the cloud fraction at the lowest levels; turbulent moistening tends to make low clouds more shallow but affects the low-cloud fraction less. The relative importance different models assign to these opposing mechanisms contributes to the spread of the climatological shallowness of low clouds and thus to the spread of low-cloud changes under global warming.

Emergent constraints are physically explainable empirical relationships between characteristics of the current climate and long-term climate prediction that emerge in collections of climate model simulations. With the prospect of constraining long-term climate prediction, scientists have recently uncovered several emergent constraints related to long-term cloud feedbacks. We review these proposed emergent constraints, many of which involve the behavior of low-level clouds, and discuss criteria to assess their credibility. With further research, some of the cases we review may eventually become confirmed emergent constraints, provided they are accompanied by credible physical explanations. Because confirmed emergent constraints identify a source of model error that projects onto climate predictions, they deserve extra attention from those developing climate models and climate observations. While a systematic bias cannot be ruled out, it is noteworthy that the promising emergent constraints suggest larger cloud feedback and hence climate sensitivity.

Differences in simulations of tropical marine low‐cloud cover (LCC) feedback are sources of significant spread in temperature responses of climate models to anthropogenic forcing. Here we show that in models the feedback is mainly driven by three large‐scale changes—a strengthening tropical inversion, increasing surface latent heat flux, and an increasing vertical moisture gradient. Variations in the LCC response to these changes alone account for most of the spread in model‐projected 21st century LCC changes. A methodology is devised to constrain the LCC response observationally using sea surface temperature (SST) as a surrogate for the latent heat flux and moisture gradient. In models where the current climate's LCC sensitivities to inversion strength and SST variations are consistent with observed, LCC decreases systematically, which would increase absorption of solar radiation. These results support a positive LCC feedback. Correcting biases in the sensitivities will be an important step toward more credible simulation of cloud feedbacks.

In 36 climate change simulations associated with phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5), changes in marine low cloud cover (LCC) exhibit a large spread, and may be either positive or negative. Here we develop a heuristic model to understand the source of the spread. The model’s premise is that simulated LCC changes can be interpreted as a linear combination of contributions from factors shaping the clouds’ large-scale environment. We focus primarily on two factors—the strength of the inversion capping the atmospheric boundary layer (measured by the estimated inversion strength, EIS) and sea surface temperature (SST). For a given global model, the respective contributions of EIS and SST are computed. This is done by multiplying (1) the current-climate’s sensitivity of LCC to EIS or SST variations, by (2) the climate-change signal in EIS or SST. The remaining LCC changes are then attributed to changes in greenhouse gas and aerosol concentrations, and other environmental factors. The heuristic model is remarkably skillful. Its SST term dominates, accounting for nearly two-thirds of the intermodel variance of LCC changes in CMIP3 models, and about half in CMIP5 models. Of the two factors governing the SST term (the SST increase and the sensitivity of LCC to SST perturbations), the SST sensitivity drives the spread in the SST term and hence the spread in the overall LCC changes. This sensitivity varies a great deal from model to model and is strongly linked to the types of cloud and boundary layer parameterizations used in the models. EIS and SST sensitivities are also estimated using observational cloud and meteorological data. The observed sensitivities are generally consistent with the majority of models as well as expectations from prior research. Based on the observed sensitivities and the relative magnitudes of simulated EIS and SST changes (which we argue are also physically reasonable), the heuristic model predicts LCC will decrease over the 21st-century. However, to place a strong constraint, for example on the magnitude of the LCC decrease, will require longer observational records and a careful assessment of other environmental factors producing LCC changes. Meanwhile, addressing biases in simulated EIS and SST sensitivities will clearly be an important step towards reducing intermodel spread in simulated LCC changes.

The performance of five boundary layer parameterizations in the Weather Research and Forecasting Model is examined for marine boundary layer cloud regions running in single-column mode. Most parameterizations show a poor agreement of the vertical boundary layer structure when compared with large-eddy simulation models. These comparisons against large-eddy simulation show that a parameterization based on the eddy-diffusivity/mass-flux approach provides a better performance. The results also illustrate the key role of boundary layer parameterizations in model performance.

In this study, we examine marine low cloud cover variability in the Southeast Pacific and its association with lower-tropospheric stability (LTS) across a spectrum of timescales. On both daily and interannual timescales, LTS and low cloud amount are very well correlated in austral summer (DJF). Meanwhile in winter (JJA), when ambient LTS increases, the LTS–low cloud relationship substantially weakens. The DJF LTS–low cloud relationship also weakens in years with unusually large ambient LTS values. These are generally strong El Niño years, in which DJF LTS values are comparable to those typically found in JJA. Thus the LTS–low cloud relationship is strongly modulated by the seasonal cycle and the ENSO phenomenon. We also investigate the origin of LTS anomalies closely associated with low cloud variability during austral summer. We find that the ocean and atmosphere are independently involved in generating anomalies in LTS and hence variability in the Southeast Pacific low cloud deck. This highlights the importance of the physical (as opposed to chemical) component of the climate system in generating internal variability in low cloud cover. It also illustrates the coupled nature of the climate system in this region, and raises the possibility of cloud feedbacks related to LTS. We conclude by addressing the implications of the LTS–low cloud relationship in the Southeast Pacific for low cloud feedbacks in anthropogenic climate change.

Biologically produced dimethylsulfide (DMS) is an important source of sulfur to the marine atmosphere that may affect cloud formation and properties. DMS is involved in a complex set of biochemical transformations and ecological exchanges so its global distribution is influenced by numerous factors, including oxidative stress from UV radiation. We re‐examine correlations between global surface DMS concentrations and mixed layer solar radiation dose (SRD), and find that SRD accounts for only a very small fraction (14%) of total variance in DMS measurements when using minimal aggregation methods. Moreover this relationship arises in part from the fact that when mixed layers deepen, both SRD and DMS decrease. When we control for this confounding effect, the correlation between DMS and SRD is reduced even further. These results indicate that factors other than solar irradiance play a leading role in determining global DMS emissions.

The area burned by Southern California wildfires has increased in recent decades, with implications for human health, infrastructure, and ecosystem management. Meteorology and fuel structure are universally recognized controllers of wildfire, but their relative importance, and hence the efficacy of abatement and suppression efforts, remains controversial. Southern California's wildfires can be partitioned by meteorology: fires typically occur either during Santa Ana winds (SA fires) in October through April, or warm and dry periods in June through September (non-SA fires). Previous work has not quantitatively distinguished between these fire regimes when assessing economic impacts or climate change influence. Here we separate five decades of fire perimeters into those coinciding with and without SA winds. The two fire types contributed almost equally to burned area, yet SA fires were responsible for 80% of cumulative 1990–2009 economic losses ($3.1 Billion). The damage disparity was driven by fire characteristics: SA fires spread three times faster, occurred closer to urban areas, and burned into areas with greater housing values. Non-SA fires were comparatively more sensitive to age-dependent fuels, often occurred in higher elevation forests, lasted for extended periods, and accounted for 70% of total suppression costs. An improved distinction of fire type has implications for future projections and management. The area burned in non-SA fires is projected to increase 77% (±43%) by the mid-21st century with warmer and drier summers, and the SA area burned is projected to increase 64% (±76%), underscoring the need to evaluate the allocation and effectiveness of suppression investments.

Wildland fires in Southern California can be divided into two categories: fall fires, which are typically driven by strong offshore Santa Ana winds, and summer fires, which occur with comparatively weak onshore winds and hot and dry weather. Both types of fire contribute significantly to annual burned area and economic loss. An improved understanding of the relationship between Southern California's meteorology and fire is needed to improve predictions of how fire will change in the future and to anticipate management needs. We used output from a regional climate model constrained by reanalysis observations to identify Santa Ana events and partition fires into those occurring during periods with and without Santa Ana conditions during 1959–2009. We then developed separate empirical regression models for Santa Ana and non‐Santa Ana fires to quantify the effects of meteorology on fire number and size. These models explained approximately 58% of the seasonal and interannual variation in the number of Santa Ana fires and 36% of the variation in non‐Santa Ana fires. The number of Santa Ana fires increased during years when relative humidity during Santa Ana events and fall precipitation were below average, indicating that fuel moisture is a key controller of ignition. Relative humidity strongly affected Santa Ana fire size. Cumulative precipitation during the previous three winters was significantly correlated with the number of non‐Santa Ana fires, presumably through increased fine fuel density and connectivity between infrastructure and nearby vegetation. Both relative humidity and the preceding wet season precipitation influenced non‐Santa Ana fire size. Regression models driven by meteorology explained 57% of the temporal variation in Santa Ana burned area and 22% of the variation in non‐Santa Ana burned area. The area burned by non‐Santa Ana fires has increased steadily by 1.7% year⁻¹ since 1959 (p < 0.006); the occurrence of extremely large Santa Ana fires has increased abruptly since 2003. Our results underscore the need to separately consider the fuel and meteorological controls on Santa Ana and non‐Santa Ana fires when projecting climate change impacts on regional fire.

Fire plays a crucial role in many ecosystems, and a better understanding of different controls on fire activity is needed. Here we analyze spatial variation in fire danger during episodic wind events in coastal southern California, a densely populated Mediterranean‐climate region. By reconstructing almost a decade of fire weather patterns through detailed simulations of Santa Ana winds, we produced the first high‐resolution map of where these hot, dry winds are consistently most severe and which areas are relatively sheltered. We also analyzed over half a century of mapped fire history in chaparral ecosystems of the region, finding that our models successfully predict where the largest wildfires are most likely to occur. There is a surprising lack of information about extreme wind patterns worldwide, and more quantitative analyses of their spatial variation will be important for effective fire management and sustainable long‐term urban development on fire‐prone landscapes.

This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes, including snow schemes that are included in Earth system models, in a wide variety of settings against local and global observations. The project aims to identify crucial processes and characteristics that need to be improved in snow models in the context of local- and global-scale modelling. A further objective of ESM-SnowMIP is to better quantify snow-related feedbacks in the Earth system. Although it is not part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), ESM-SnowMIP is tightly linked to the CMIP6-endorsed Land Surface, Snow and Soil Moisture Model Intercomparison (LS3MIP).

Emergent constraints use relationships between future and current climate states to constrain projections of climate response. Here we introduce a statistical, hierarchical emergent constraint (HEC) framework in order to link future and current climates with observations. Under Gaussian assumptions, the mean and variance of the future state are shown analytically to be a function of the signal‐to‐noise ratio between current climate uncertainty and observation error and the correlation between future and current climate states. We apply the HEC to the climate change, snow‐albedo feedback, which is related to the seasonal cycle in the Northern Hemisphere. We obtain a snow‐albedo feedback prediction interval of (−1.25,−0.58)%/K. The critical dependence on signal‐to‐noise ratio and correlation shows that neglecting these terms can lead to bias and underestimated uncertainty in constrained projections. The flexibility of using HEC under general assumptions throughout the Earth system is discussed.

A highly uncertain aspect of anthropogenic climate change is the rate at which the global hydrologic cycle intensifies. The future change in global‐mean precipitation per degree warming, or hydrologic sensitivity, exhibits a threefold spread (1–3%/K) in current global climate models. In this study, we find that the intermodel spread in this value is associated with a significant portion of variability in future projections of extreme precipitation in the tropics, extending also into subtropical atmospheric river corridors. Additionally, there is a very tight intermodel relationship between changes in extreme and nonextreme precipitation, whereby models compensate for increasing extreme precipitation events by decreasing weak‐moderate events. Another factor linked to changes in precipitation extremes is model resolution, with higher resolution models showing a larger increase in heavy extremes. These results highlight ways various aspects of hydrologic cycle intensification are linked in models and shed new light on the task of constraining precipitation extremes.

Snow albedo feedback (SAF) behaves similarly in the current and future climate contexts; thus, constraining the large intermodel variance in SAF will likely reduce uncertainty in climate projections. To better understand this intermodel spread, structural and parametric biases contributing to SAF variability are investigated. We find that structurally varying snowpack, vegetation, and albedo parameterizations drive most of the spread, while differences arising from model parameters are generally smaller. Models with the largest SAF biases exhibit clear structural or parametric errors. Additionally, despite widespread intermodel similarities, model interdependency has little impact on the strength of the relationship between SAF in the current and future climate contexts. Furthermore, many models now feature a more realistic SAF than in the prior generation, but shortcomings from two models limit the reduction in ensemble spread. Lastly, preliminary signs from ongoing model development are positive and suggest a likely reduction in SAF spread among upcoming models.

Biases in climate model simulations introduce biases in subsequent impact simulations. Therefore, bias correction methods are operationally used to post-process regional climate projections. However, many problems have been identified, and some researchers question the very basis of the approach. Here we demonstrate that a typical cross-validation is unable to identify improper use of bias correction. Several examples show the limited ability of bias correction to correct and to downscale variability, and demonstrate that bias correction can cause implausible climate change signals. Bias correction cannot overcome major model errors, and naive application might result in ill-informed adaptation decisions. We conclude with a list of recommendations and suggestions for future research to reduce, post-process, and cope with climate model biases.

The response to warming of tropical low-level clouds including both marine stratocumulus and trade cumulus is a major source of uncertainty in projections of future climate. Climate model simulations of the response vary widely, reflecting the difficulty the models have in simulating these clouds. These inadequacies have led to alternative approaches to predict low-cloud feedbacks. Here, we review an observational approach that relies on the assumption that observed relationships between low clouds and the “cloud-controlling factors” of the large-scale environment are invariant across time-scales. With this assumption, and given predictions of how the cloud-controlling factors change with climate warming, one can predict low-cloud feedbacks without using any model simulation of low clouds. We discuss both fundamental and implementation issues with this approach and suggest steps that could reduce uncertainty in the predicted low-cloud feedback. Recent studies using this approach predict that the tropical low-cloud feedback is positive mainly due to the observation that reflection of solar radiation by low clouds decreases as temperature increases, holding all other cloud-controlling factors fixed. The positive feedback from temperature is partially offset by a negative feedback from the tendency for the inversion strength to increase in a warming world, with other cloud-controlling factors playing a smaller role. A consensus estimate from these studies for the contribution of tropical low clouds to the global mean cloud feedback is 0.25 ± 0.18 W m⁻² K⁻¹ (90% confidence interval), suggesting it is very unlikely that tropical low clouds reduce total global cloud feedback. Because the prediction of positive tropical low-cloud feedback with this approach is consistent with independent evidence from low-cloud feedback studies using high-resolution cloud models, progress is being made in reducing this key climate uncertainty.

In the current generation of climate models, the projected increase in global precipitation over the 21st century ranges from 2% to 10% under a high‐emission scenario. Some of this uncertainty can be traced to the rapid response to carbon dioxide (CO₂) forcing. We analyze an ensemble of simulations to better understand model spread in this rapid response. A substantial amount is linked to how the land surface partitions a change in latent versus sensible heat flux in response to the CO₂‐induced radiative perturbation; a larger increase in sensible heat results in a larger decrease in global precipitation. Model differences in the land surface response appear to be strongly related to the vegetation response to increased CO₂, specifically, the closure of leaf stomata. Future research should thus focus on evaluation of the vegetation physiological response, including stomatal conductance parameterizations, for the purpose of constraining the fast response of Earth's hydrologic cycle to CO₂ forcing.

How tropical low clouds change with climate remains the dominant source of uncertainty in global warming projections. An analysis of an ensemble of CMIP5 climate models reveals that a significant part of the spread in the models’ climate sensitivity can be accounted by differences in the climatological shallowness of tropical low clouds in weak-subsidence regimes: models with shallower low clouds in weak-subsidence regimes tend to have a higher climate sensitivity than models with deeper low clouds. The dynamical mechanisms responsible for the model differences are analyzed. Competing effects of parameterized boundary-layer turbulence and shallow convection are found to be essential. Boundary-layer turbulence and shallow convection are typically represented by distinct parameterization schemes in current models—parameterization schemes that often produce opposing effects on low clouds. Convective drying of the boundary layer tends to deepen low clouds and reduce the cloud fraction at the lowest levels; turbulent moistening tends to make low clouds more shallow but affects the low-cloud fraction less. The relative importance different models assign to these opposing mechanisms contributes to the spread of the climatological shallowness of low clouds and thus to the spread of low-cloud changes under global warming.

Emergent constraints are physically explainable empirical relationships between characteristics of the current climate and long-term climate prediction that emerge in collections of climate model simulations. With the prospect of constraining long-term climate prediction, scientists have recently uncovered several emergent constraints related to long-term cloud feedbacks. We review these proposed emergent constraints, many of which involve the behavior of low-level clouds, and discuss criteria to assess their credibility. With further research, some of the cases we review may eventually become confirmed emergent constraints, provided they are accompanied by credible physical explanations. Because confirmed emergent constraints identify a source of model error that projects onto climate predictions, they deserve extra attention from those developing climate models and climate observations. While a systematic bias cannot be ruled out, it is noteworthy that the promising emergent constraints suggest larger cloud feedback and hence climate sensitivity.

Intensification of the hydrologic cycle is a key dimension of climate change, with substantial impacts on human and natural systems^1,2. A basic measure of hydrologic cycle intensification is the increase in global-mean precipitation per unit surface warming, which varies by a factor of three in current-generation climate models (about 1–3 per cent per kelvin)^3,4,5. Part of the uncertainty may originate from atmosphere–radiation interactions. As the climate warms, increases in shortwave absorption from atmospheric moistening will suppress the precipitation increase. This occurs through a reduction of the latent heating increase required to maintain a balanced atmospheric energy budget^6,7. Using an ensemble of climate models, here we show that such models tend to underestimate the sensitivity of solar absorption to variations in atmospheric water vapour, leading to an underestimation in the shortwave absorption increase and an overestimation in the precipitation increase. This sensitivity also varies considerably among models due to differences in radiative transfer parameterizations, explaining a substantial portion of model spread in the precipitation response. Consequently, attaining accurate shortwave absorption responses through improvements to the radiative transfer schemes could reduce the spread in the predicted global precipitation increase per degree warming for the end of the twenty-first century by about 35 per cent, and reduce the estimated ensemble-mean increase in this quantity by almost 40 per cent.

We examine the tropical inversion strength, measured by the estimated inversion strength (EIS), and its response to climate change in 18 models associated with phase 5 of the coupled model intercomparison project (CMIP5). While CMIP5 models generally capture the geographic distribution of observed EIS, they systematically underestimate it off the west coasts of continents, due to a warm bias in sea surface temperature. The negative EIS bias may contribute to the low bias in tropical low-cloud cover in the same models. Idealized perturbation experiments reveal that anthropogenic forcing leads directly to EIS increases, independent of “temperature-mediated” EIS increases associated with long-term oceanic warming. This fast EIS response to anthropogenic forcing is strongly impacted by nearly instantaneous continental warming. The temperature-mediated EIS change has contributions from both uniform and non-uniform oceanic warming. The substantial EIS increases in uniform oceanic warming simulations are due to warming with height exceeding the moist adiabatic lapse rate in tropical warm pools. EIS also increases in fully-coupled ocean–atmosphere simulations where CO2CO2 concentration is instantaneously quadrupled, due to both fast and temperature-mediated changes. The temperature-mediated EIS change varies with tropical warming in a nonlinear fashion: The EIS change per degree tropical warming is much larger in the early stage of the simulations than in the late stage, due to delayed warming in the eastern parts of the subtropical oceans. Given the importance of EIS in regulating tropical low-cloud cover, this suggests that the tropical low-cloud feedback may also be nonlinear.

In 36 climate change simulations associated with phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5), changes in marine low cloud cover (LCC) exhibit a large spread, and may be either positive or negative. Here we develop a heuristic model to understand the source of the spread. The model’s premise is that simulated LCC changes can be interpreted as a linear combination of contributions from factors shaping the clouds’ large-scale environment. We focus primarily on two factors—the strength of the inversion capping the atmospheric boundary layer (measured by the estimated inversion strength, EIS) and sea surface temperature (SST). For a given global model, the respective contributions of EIS and SST are computed. This is done by multiplying (1) the current-climate’s sensitivity of LCC to EIS or SST variations, by (2) the climate-change signal in EIS or SST. The remaining LCC changes are then attributed to changes in greenhouse gas and aerosol concentrations, and other environmental factors. The heuristic model is remarkably skillful. Its SST term dominates, accounting for nearly two-thirds of the intermodel variance of LCC changes in CMIP3 models, and about half in CMIP5 models. Of the two factors governing the SST term (the SST increase and the sensitivity of LCC to SST perturbations), the SST sensitivity drives the spread in the SST term and hence the spread in the overall LCC changes. This sensitivity varies a great deal from model to model and is strongly linked to the types of cloud and boundary layer parameterizations used in the models. EIS and SST sensitivities are also estimated using observational cloud and meteorological data. The observed sensitivities are generally consistent with the majority of models as well as expectations from prior research. Based on the observed sensitivities and the relative magnitudes of simulated EIS and SST changes (which we argue are also physically reasonable), the heuristic model predicts LCC will decrease over the 21st-century. However, to place a strong constraint, for example on the magnitude of the LCC decrease, will require longer observational records and a careful assessment of other environmental factors producing LCC changes. Meanwhile, addressing biases in simulated EIS and SST sensitivities will clearly be an important step towards reducing intermodel spread in simulated LCC changes.

Snow-albedo feedback (SAF) is examined in 25 climate change simulations participating in the Coupled Model Intercomparison Project version 5 (CMIP5). SAF behavior is compared to the feedback’s behavior in the previous (CMIP3) generation of global models. SAF strength exhibits a fivefold spread across CMIP5 models, ranging from 0.03 to 0.16 W m⁻² K⁻¹ (ensemble-mean = 0.08 W m⁻² K⁻¹). This accounts for much of the spread in 21st century warming of Northern Hemisphere land masses, and is very similar to the spread found in CMIP3 models. As with the CMIP3 models, there is a high degree of correspondence between the magnitudes of seasonal cycle and climate change versions of the feedback. Here we also show that their geographical footprint is similar. The ensemble-mean SAF strength is close to an observed estimate of the real climate’s seasonal cycle feedback strength. SAF strength is strongly correlated with the climatological surface albedo when the ground is covered by snow. The inter-model variation in this quantity is surprisingly large, ranging from 0.39 to 0.75. Models with large surface albedo when these regions are snow-covered will also have a large surface albedo contrast between snow-covered and snow-free regions, and therefore a correspondingly large SAF. Widely-varying treatments of vegetation masking of snow-covered surfaces are probably responsible for the spread in surface albedo where snow occurs, and the persistent spread in SAF in global climate models.

Projections of possible precipitation change in California under global warming have been subject to considerable uncertainty because California lies between the region anticipated to undergo increases in precipitation at mid-to-high latitudes and regions of anticipated decrease in the subtropics. Evaluation of the large-scale model experiments for phase 5 of the Coupled Model Intercomparison Project (CMIP5) suggests a greater degree of agreement on the sign of the winter (December–February) precipitation change than in the previous such intercomparison, indicating a greater portion of California falling within the increased precipitation zone. While the resolution of global models should not be relied on for accurate depiction of topographic rainfall distribution within California, the precipitation changes depend substantially on large-scale shifts in the storm tracks arriving at the coast. Significant precipitation increases in the region arriving at the California coast are associated with an eastward extension of the region of strong Pacific jet stream, which appears to be a robust feature of the large-scale simulated changes. This suggests that effects of this jet extension in steering storm tracks toward the California coast constitute an important factor that should be assessed for impacts on incoming storm properties for high-resolution regional model assessments.

Pithan and Mauritsen argue that the 2009 results of Boé et al. are not consistent with current understanding of the lapse-rate feedback in the Arctic. They also argue that these results arise to an important extent from self-correlation issues. In this response, the authors argue that their results are not inconsistent with current understanding of lapse-rate feedback and demonstrate that the conclusions remain unchanged when all possibilities of self-correlation are excluded.

The Antarctic Peninsula has warmed significantly since the 1950s. This pronounced and isolated warming trend is collectively captured by 29 twentieth-century climate hindcasts participating in the version 3 Coupled Model Intercomparison Project. To understand the factors driving warming trends in the hindcasts, we examine trends in Peninsula region’s atmospheric heat budget in every simulation. We find that atmospheric latent heat release increases in nearly all hindcasts. These increases are generally anthropogenic in origin, and account for about 60% of the ensemble-mean warming trend in the Peninsula. They are driven primarily by well-understood features of the anthropogenic intensification of global hydrological cycle. As sea surface temperature increases, moisture contained in atmospheric flows increases. When such flows are forced to ascend the Peninsula’s topography, enhanced local latent heat release results. The mechanism driving the warming of the Antarctic Peninsula is therefore clear in the models. Evidence for a similar mechanism operating in the real world is seen in the increasing snow accumulation rates inferred from ice cores drilled in the Peninsula. However, the relative importance of this mechanism and other processes previously identified as potentially causing the observed warming, such as the recent sea ice retreat in the Bellingshausen Sea, is difficult to assess. Thus the relevance of the simulated warming mechanism to the observed warming is unclear, in spite of its robustness in the models.

We show that intermodel variations in the anthropogenically-forced evolution of September sea ice extent (SSIE) in the Arctic stem mainly from two factors: the baseline climatological sea ice thickness (SIT) distribution, and the local climate feedback parameter. The roles of these two factors evolve over the course of the twenty-first century. The SIT distribution is the most important factor in current trends and those of coming decades, accounting for roughly half the intermodel variations in SSIE trends. Then, its role progressively decreases, so that around the middle of the twenty-first century the local climate feedback parameter becomes the dominant factor. Through this analysis, we identify the investments in improved simulation of Arctic climate necessary to reduce uncertainties both in projections of sea ice loss over the coming decades and in the ultimate fate of the ice pack.

Observation based estimates of controls on snow albedo feedback (SAF) are needed to constrain the snow and albedo parameterizations in general circulation model (GCM) projections of air temperature over the Northern Hemisphere (NH) landmass. The total April‐May NH SAF, corresponding to the sum of the effect of temperature on surface albedo over snow covered surfaces (‘metamorphism’) and over surfaces transitioning from snow covered to snow free conditions (‘snow cover’), is derived with daily NH snow cover and surface albedo products using Advanced Very High Resolution Radiometer Polar Pathfinder satellite data and surface air temperature from ERA40 reanalysis data between 1982–1999. Without using snow cover information, the estimated total SAF, for land surfaces north of 30°N, of −0.93 ± 0.06%K⁻¹ was not significantly different (95% confidence) from estimates based on International Satellite Cloud Climatology Project surface albedo data. The SAF, constrained to only snow covered areas, grew to −1.06 ± 0.08%K⁻¹ with similar magnitudes for the ‘snow cover’ and ‘metamorphosis’ components. The SAF pattern was significantly correlated with the ‘snow cover’ component pattern over both North America and Eurasia but only over Eurasia for the ‘metamorphosis’ component. However, in contrast to GCM model based diagnoses of SAF, the control on the ‘snow cover’ component related to the albedo contrast of snow covered and snow free surfaces was not strongly correlated to the total SAF.

Two main mechanisms can potentially explain the spread in the magnitude of global warming simulated by climate models: deep ocean heat uptake and climate feedbacks. Here, we show that deep oceanic heat uptake is a major source of spread in simulations of 21st century climate change. Models with deeper baseline polar mixed layers are associated with larger deep ocean warming and smaller global surface warming. Based on this result, we set forth an observational constraint on polar vertical oceanic mixing. This constraint suggests that many models may overestimate the efficiency of polar oceanic mixing and therefore may underestimate future surface warming. Thus to reduce climate change uncertainties at time‐scales relevant for policy‐making, improved understanding and modelling of oceanic mixing at high latitudes is crucial.

The Arctic climate is changing rapidly¹. From 1979 to 2006, September sea-ice extent decreased by almost 25% or about 100,000 km² per year (ref. 2). In September 2007, Arctic sea-ice extent reached its lowest level since satellite observations began³and in September 2008, sea-ice cover was still low. This development has raised concerns that the Arctic Ocean could be ice-free in late summer in only a few decades, with important economic and geopolitical implications. Unfortunately, most current climate models underestimate significantly the observed trend in Arctic sea-ice decline⁴, leading to doubts regarding their projections for the timing of ice-free conditions. Here we analyse the simulated trends in past sea-ice cover in 18 state-of-art-climate models and find a direct relationship between the simulated evolution of September sea-ice cover over the twenty-first century and the magnitude of past trends in sea-ice cover. Using this relationship together with observed trends, we project the evolution of September sea-ice cover over the twenty-first century. We find that under a scenario with medium future greenhouse-gas emissions, the Arctic Ocean will probably be ice-free in September before the end of the twenty-first century.

Climate change is expected to cause a reduction in the spatial extent of snow cover on land. Recent work suggests that this will exert a local influence on the atmosphere and the hydrology of snow‐margin areas through the snow‐albedo feedback (SAF) mechanism. A significant fraction of variability among IPCC AR4 general circulation model (GCM) predictions for future summertime climate change over these areas is related to the models' representation of springtime SAF. In this study, we demonstrate a nonlocal influence of SAF on the summertime circulation in the extratropical Northern Hemisphere. Increased land surface warming in models with stronger SAF is associated with large‐scale sea‐level pressure anomalies over the northern oceans and a poleward intensified subtropical jet. We find that up to 25–30% and, on average, 5–10% of the inter‐model spread in projections of the circulation response to climate change is linearly related to SAF strength.

The large spread of the response to anthropogenic forcing simulated by state-of-the-art climate models in the Arctic is investigated. A feedback analysis framework specific to the Arctic is developed to address this issue. The feedback analysis shows that a large part of the spread of Arctic climate change is explained by the longwave feedback parameter. The large spread of the negative longwave feedback parameter is in turn mainly due to variations in temperature feedback. The vertical temperature structure of the atmosphere in the Arctic, characterized by a surface inversion during wintertime, exerts a strong control on the temperature feedback and consequently on simulated Arctic climate change. Most current climate models likely overestimate the climatological strength of the inversion, leading to excessive negative longwave feedback. The authors conclude that the models’ near-equilibrium response to anthropogenic forcing is generally too small.

Across vast, agriculturally intensive regions of the United States, the spread in predictions of summer temperature and soil moisture under global warming is curiously elevated in current climate models. Some models show modest warming of 2–3C° and little drying or slight moistening by the 22nd century, while at the other extreme are simulations with warming as large as 7–8C° and 20–40% reductions in soil moisture. We show this region of large spread arises from differences in simulations of snow albedo feedback. During winter and early spring, models with strong snow albedo feedback exhibit large reductions in snowpack and hence water storage. This water deficit persists in summer soil moisture, with reduced evapotranspiration yielding warmer temperatures. Comparison of simulated feedback strength to observations of the feedback from the current climate's seasonal cycle suggests the inter‐model differences are excessive. At the same time, the multi‐model mean feedback strength agrees reasonably well with the observed value. We estimate that if the next generation of models were brought into line with observations of snow albedo feedback, the unusually wide divergence in simulations of summer warming and drying over the US would shrink by roughly one third to one half.

We developed a 3D Monte Carlo photon tracing program for the transfer of radiation in inhomogeneous and irregular terrain to calculate broadband solar and thermal infrared fluxes. We selected an area of 100 × 100 km² in the Tibetan Plateau centered at Lhasa city and used the albedo and surface temperature from MODIS/Terra for this study. We showed that anomalies of surface solar fluxes with reference to a flat surface can be as large as 600 W/m², depending on time of day, mountain configuration, and albedo. Surface temperature is the dominating factor in determining anomalies of the surface infrared flux distribution relative to a flat surface with values as high as 70 W/m² at cold mountain surfaces. The average surface solar flux over regional domains of 100 × 100 km² and 50 × 50 km² comprising intense topography can deviate from the smoothed surface conventionally assumed in climate models and GCMs by 10–50 W/m².

The strength of snow-albedo feedback (SAF) in transient climate change simulations of the Fourth Assessment of the Intergovernmental Panel on Climate Change is generally determined by the surface-albedo decrease associated with a loss of snow cover rather than the reduction in snow albedo due to snow metamorphosis in a warming climate. The large intermodel spread in SAF strength is likewise attributable mostly to the snow cover component. The spread in the strength of this component is in turn mostly attributable to a correspondingly large spread in mean effective snow albedo. Models with large effective snow albedos have a large surface-albedo contrast between snow-covered and snow-free regions and exhibit a correspondingly large surface-albedo decrease when snow cover decreases. Models without explicit treatment of the vegetation canopy in their surface-albedo calculations typically have high effective snow albedos and strong SAF, often stronger than observed. In models with explicit canopy treatment, completely snow-covered surfaces typically have lower albedos and the simulations have weaker SAF, generally weaker than observed. The authors speculate that in these models either snow albedos or canopy albedos when snow is present are too low, or vegetation shields snow-covered surfaces excessively. Detailed observations of surface albedo in a representative sampling of snow-covered surfaces would therefore be extremely useful in constraining these parameterizations and reducing SAF spread in the next generation of models.

In this paper, the two factors controlling Northern Hemisphere springtime snow albedo feedback in transient climate change are isolated and quantified based on scenario runs of 17 climate models used in the Intergovernmental Panel on Climate Change Fourth Assessment Report. The first factor is the dependence of planetary albedo on surface albedo, representing the atmosphere's attenuation effect on surface albedo anomalies. It is potentially a major source of divergence in simulations of snow albedo feedback because of large differences in simulated cloud fields in Northern Hemisphere land areas. To calculate the dependence, an analytical model governing planetary albedo was developed. Detailed validations of the analytical model for two of the simulations are shown, version 3 of the Community Climate System Model (CCSM3) and the Geophysical Fluid Dynamics Laboratory global coupled Climate Model 2.0 (CM2.0), demonstrating that it facilitates a highly accurate calculation of the dependence of planetary albedo on surface albedo given readily available simulation output. In all simulations it is found that surface albedo anomalies are attenuated by approximately half in Northern Hemisphere land areas as they are transformed into planetary albedo anomalies. The intermodel standard deviation in the dependence of planetary albedo on surface albedo is surprisingly small, less than 10% of the mean. Moreover, when an observational estimate of this factor is calculated by applying the same method to the satellite-based International Satellite Cloud Climatology Project (ISCCP) data, it is found that most simulations agree with ISCCP values to within about 10%, despite further disagreements between observed and simulated cloud fields. This suggests that even large relative errors in simulated cloud fields do not result in significant error in this factor, enhancing confidence in climate models. The second factor, related exclusively to surface processes, is the change in surface albedo associated with an anthropogenically induced temperature change in Northern Hemisphere land areas. It exhibits much more intermodel variability. The standard deviation is about ⅓ of the mean, with the largest value being approximately 3 times larger than the smallest. Therefore this factor is unquestionably the main source of the large divergence in simulations of snow albedo feedback. To reduce the divergence, attention should be focused on differing parameterizations of snow processes, rather than intermodel variations in the attenuation effect of the atmosphere on surface albedo anomalies.

Processes in the climate system that can either amplify or dampen the climate response to an external perturbation are referred to as climate feedbacks. Climate sensitivity estimates depend critically on radiative feedbacks associated with water vapor, lapse rate, clouds, snow, and sea ice, and global estimates of these feedbacks differ among general circulation models. By reviewing recent observational, numerical, and theoretical studies, this paper shows that there has been progress since the Third Assessment Report of the Intergovernmental Panel on Climate Change in (i) the understanding of the physical mechanisms involved in these feedbacks, (ii) the interpretation of intermodel differences in global estimates of these feedbacks, and (iii) the development of methodologies of evaluation of these feedbacks (or of some components) using observations. This suggests that continuing developments in climate feedback research will progressively help make it possible to constrain the GCMs’ range of climate feedbacks and climate sensitivity through an ensemble of diagnostics based on physical understanding and observations.

We developed a three‐dimensional radiative transfer model simulating solar fluxes over mountain surfaces precisely given distributions of atmospheric scatterers and absorbers. The model quantifies direct, diffuse, terrain‐reflected, and coupling (i.e., photons reflected and scattered more than once) fluxes. We applied it to a midlatitude mountainous surface to study these components' diurnal, seasonal, and geographical variability under clear skies. Domain‐averaged direct and diffuse fluxes together comprise over 96% of the flux year‐round, with diffuse fluxes' relative importance varying inversely with that of direct radiation. Direct fluxes generally account for at least 80% of the total. However, the domain‐averaged diffuse flux proportion increases to nearly 40% at high zenith angles, and approaches 100% when neighboring slopes obscure the surface from the Sun. Terrain‐reflected and coupling components each account for less than 1% throughout much of the year. However, together they comprise ∼3% when surface albedo increases during winter and are similarly nonnegligible in deep valleys all year. We also studied controls on geographical variations in flux components: The sky view factor, a conventional predictor of diffuse fluxes, is surprisingly weakly correlated with them, posing a parameterization challenge. Terrain‐reflected and coupling fluxes may be easier to parameterize given topography. Finally, we assessed shortwave errors in General Circulation Models with smoothed topography by comparing results with the mountainous surface to identical calculations for a flat surface with the same mean elevation. The differences range from 5 to 20 W/m² and arise because the atmosphere absorbs a different amount of sunshine when underlying topography is smoothed.

Differences in simulations of climate feedbacks are sources of significant divergence in climate models' temperature response to anthropogenic forcing. Snow albedo feedback is particularly critical for climate change prediction in heavily‐populated northern hemisphere land masses. Here we show its strength in current models exhibits a factor‐of‐three spread. These large intermodel variations in feedback strength in climate change are nearly perfectly correlated with comparably large intermodel variations in feedback strength in the context of the seasonal cycle. Moreover, the feedback strength in the real seasonal cycle can be measured and compared to simulated values. These mostly fall outside the range of the observed estimate, suggesting many models have an unrealistic snow albedo feedback in the seasonal cycle context. Because of the tight correlation between simulated feedback strength in the seasonal cycle and climate change, eliminating the model errors in the seasonal cycle will lead directly to a reduction in the spread of feedback strength in climate change. Though this comparison to observations may put the models in an unduly harsh light because of uncertainties in the observed estimate that are difficult to quantify, our results map out a clear strategy for targeted observation of the seasonal cycle to reduce divergence in simulations of climate sensitivity.

This study uses dynamical and statistical methods to understand end‐of‐century mean changes to Sierra Nevada snowpack. Dynamical results reveal mid‐elevation watersheds experience considerably more rain than snow during winter, leading to substantial snowpack declines by spring. Despite some high‐elevation watersheds receiving slightly more snow in January and February, the warming signal still dominates across the wet‐season and leads to notable declines by springtime. A statistical model is created to mimic dynamical results for April 1 snowpack, allowing for an efficient downscaling of all available General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5. For all GCMs and emissions scenarios, dramatic April 1 snowpack loss occurs at elevations below 2500 meters, despite increased precipitation in many GCMs. Only 36% (±12%) of historical April 1 total snow water equivalent volume remains at the century's end under a “business‐as‐usual” emissions scenario, with 70% (±12%) remaining under a realistic “mitigation” scenario.

Using hybrid dynamical–statistical downscaling, 3-km-resolution end-of-twenty-first-century runoff timing changes over California’s Sierra Nevada for all available global climate models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are projected. All four representative concentration pathways (RCPs) adopted by the Intergovernmental Panel on Climate Change’s Fifth Assessment Report are examined. These multimodel, multiscenario projections allow for quantification of ensemble-mean runoff timing changes and an associated range of possible outcomes due to both intermodel variability and choice of forcing scenario. Under a “business as usual” forcing scenario (RCP8.5), warming leads to a shift toward much earlier snowmelt-driven surface runoff in 2091–2100 compared to 1991–2000, with advances of as much as 80 days projected in the 35-model ensemble mean. For a realistic “mitigation” scenario (RCP4.5), the ensemble-mean change is smaller but still large (up to 30 days). For all plausible forcing scenarios and all GCMs, the simulated changes are statistically significant, so that a detectable change in runoff timing is inevitable. Even for the mitigation scenario, the ensemble-mean change is approximately equivalent to one standard deviation of the natural variability at most elevations. Thus, even when greenhouse gas emissions are curtailed, the runoff change is climatically significant. For the business-as-usual scenario, the ensemble-mean change is approximately two standard deviations of the natural variability at most elevations, portending a truly dramatic change in surface hydrology by the century’s end if greenhouse gas emissions continue unabated.

California’s Sierra Nevada is a high-elevation mountain range with significant seasonal snow cover. Under anthropogenic climate change, amplification of the warming is expected to occur at elevations near snow margins due to snow albedo feedback. However, climate change projections for the Sierra Nevada made by global climate models (GCMs) and statistical downscaling methods miss this key process. Dynamical downscaling simulates the additional warming due to snow albedo feedback. Ideally, dynamical downscaling would be applied to a large ensemble of 30 or more GCMs to project ensemble-mean outcomes and intermodel spread, but this is far too computationally expensive. To approximate the results that would occur if the entire GCM ensemble were dynamically downscaled, a hybrid dynamical–statistical downscaling approach is used. First, dynamical downscaling is used to reconstruct the historical climate of the 1981–2000 period and then to project the future climate of the 2081–2100 period based on climate changes from five GCMs. Next, a statistical model is built to emulate the dynamically downscaled warming and snow cover changes for any GCM. This statistical model is used to produce warming and snow cover loss projections for all available CMIP5 GCMs. These projections incorporate snow albedo feedback, so they capture the local warming enhancement (up to 3°C) from snow cover loss that other statistical methods miss. Capturing these details may be important for accurately projecting impacts on surface hydrology, water resources, and ecosystems.

Future snowfall and snowpack changes over the mountains of Southern California are projected using a new hybrid dynamical–statistical framework. Output from all general circulation models (GCMs) in phase 5 of the Coupled Model Intercomparison Project archive is downscaled to 2-km resolution over the region. Variables pertaining to snow are analyzed for the middle (2041–60) and end (2081–2100) of the twenty-first century under two representative concentration pathway (RCP) scenarios: RCP8.5 (business as usual) and RCP2.6 (mitigation). These four sets of projections are compared with a baseline reconstruction of climate from 1981 to 2000. For both future time slices and scenarios, ensemble-mean total winter snowfall loss is widespread. By the mid-twenty-first century under RCP8.5, ensemble-mean winter snowfall is about 70% of baseline, whereas the corresponding value for RCP2.6 is somewhat higher (about 80% of baseline). By the end of the century, however, the two scenarios diverge significantly. Under RCP8.5, snowfall sees a dramatic further decline; 2081–2100 totals are only about half of baseline totals. Under RCP2.6, only a negligible further reduction from midcentury snowfall totals is seen. Because of the spread in the GCM climate projections, these figures are all associated with large intermodel uncertainty. Snowpack on the ground, as represented by 1 April snow water equivalent is also assessed. Because of enhanced snowmelt, the loss seen in snowpack is generally 50% greater than that seen in winter snowfall. By midcentury under RCP8.5, warming-accelerated spring snowmelt leads to snow-free dates that are about 1–3 weeks earlier than in the baseline period.

In this study we developed and examined a hybrid modeling approach integrating physically-based equations and statistical downscaling to estimate fine-scale daily-mean surface turbulent fluxes (i.e., sensible and latent heat fluxes) for a region of southern California that is extensively covered by varied vegetation types over a complex terrain. The selection of model predictors is guided by physical parameterizations of surface flux used in land surface models and analysis showing net shortwave radiation that is a major source of variability in the surface energy budget. Through a structure of multivariable regression processes with an application of near-surface wind estimates from a previous study, we successfully reproduce dynamically-downscaled 3 km resolution surface flux data. The overall error in our estimates is less than 20 % for both sensible and latent heat fluxes, while slightly larger errors are seen in high-altitude regions. The major sources of error in estimates include the limited information provided in coarse reanalysis data, the accuracy of near-surface wind estimates, and an ignorance of the nonlinear diurnal cycle of surface fluxes when using daily-mean data. However, with reasonable and acceptable errors, this hybrid modeling approach provides promising, fine-scale products of surface fluxes that are much more accurate than reanalysis data, without performing intensive dynamical simulations.

Using the hybrid downscaling technique developed in part I of this study, temperature changes relative to a baseline period (1981–2000) in the greater Los Angeles region are downscaled for two future time slices: midcentury (2041–60) and end of century (2081–2100). Two representative concentration pathways (RCPs) are considered, corresponding to greenhouse gas emission reductions over coming decades (RCP2.6) and to continued twenty-first-century emissions increases (RCP8.5). All available global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are downscaled to provide likelihood and uncertainty estimates. By the end of century under RCP8.5, a distinctly new regional climate state emerges: average temperatures will almost certainly be outside the interannual variability range seen in the baseline. Except for the highest elevations and a narrow swath very near the coast, land locations will likely see 60–90 additional extremely hot days per year, effectively adding a new season of extreme heat. In mountainous areas, a majority of the many baseline days with freezing nighttime temperatures will most likely not occur. According to a similarity metric that measures daily temperature variability and the climate change signal, the RCP8.5 end-of-century climate will most likely be only about 50% similar to the baseline. For midcentury under RCP2.6 and RCP8.5 and end of century under RCP2.6, these same measures also indicate a detectable though less significant climatic shift. Therefore, while measures reducing global emissions would not prevent climate change at this regional scale in the coming decades, their impact would be dramatic by the end of the twenty-first century.

In this study (Part I), the mid-twenty-first-century surface air temperature increase in the entire CMIP5 ensemble is downscaled to very high resolution (2 km) over the Los Angeles region, using a new hybrid dynamical–statistical technique. This technique combines the ability of dynamical downscaling to capture finescale dynamics with the computational savings of a statistical model to downscale multiple GCMs. First, dynamical downscaling is applied to five GCMs. Guided by an understanding of the underlying local dynamics, a simple statistical model is built relating the GCM input and the dynamically downscaled output. This statistical model is used to approximate the warming patterns of the remaining GCMs, as if they had been dynamically downscaled. The full 32-member ensemble allows for robust estimates of the most likely warming and uncertainty resulting from intermodel differences. The warming averaged over the region has an ensemble mean of 2.3°C, with a 95% confidence interval ranging from 1.0° to 3.6°C. Inland and high elevation areas warm more than coastal areas year round, and by as much as 60% in the summer months. A comparison to other common statistical downscaling techniques shows that the hybrid method produces similar regional-mean warming outcomes but demonstrates considerable improvement in capturing the spatial details. Additionally, this hybrid technique incorporates an understanding of the physical mechanisms shaping the region’s warming patterns, enhancing the credibility of the final results.

A new hybrid statistical–dynamical downscaling technique is described to project mid- and end-of-twenty-first-century local precipitation changes associated with 36 global climate models (GCMs) in phase 5 of the Coupled Model Intercomparison Project archive over the greater Los Angeles region. Land-averaged precipitation changes, ensemble-mean changes, and the spread of those changes for both time slices are presented. It is demonstrated that the results are similar to what would be produced if expensive dynamical downscaling techniques were instead applied to all GCMs. Changes in land-averaged ensemble-mean precipitation are near zero for both time slices, reflecting the region’s typical position in the models at the node of oppositely signed large-scale precipitation changes. For both time slices, the intermodel spread of changes is only about 0.2–0.4 times as large as natural interannual variability in the baseline period. A caveat to these conclusions is that interannual variability in the tropical Pacific is generally regarded as a weakness of the GCMs. As a result, there is some chance the GCM responses in the tropical Pacific to a changing climate and associated impacts on Southern California precipitation are not credible. It is subjectively judged that this GCM weakness increases the uncertainty of regional precipitation change, perhaps by as much as 25%. Thus, it cannot be excluded that the possibility that significant regional adaptation challenges related to either a precipitation increase or decrease would arise. However, the most likely downscaled outcome is a small change in local mean precipitation compared to natural variability, with large uncertainty on the sign of the change.

A physically-based statistical modeling approach to downscale coarse resolution reanalysis near-surface winds over a region of complex terrain is developed and tested in this study. Our approach is guided by physical variables and meteorological relationships that are important for determining near-surface wind flow. Preliminary fine scale winds are estimated by correcting the course-to-fine grid resolution mismatch in roughness length. Guided by the physics shaping near-surface winds, we then formulate a multivariable linear regression model which uses near-surface micrometeorological variables and the preliminary estimates as predictors to calculate the final wind products. The coarse-to-fine grid resolution ratio is approximately 10–1 for our study region of southern California. A validated 3-km resolution dynamically-downscaled wind dataset is used to train and validate our method. Winds from our statistical modeling approach accurately reproduce the dynamically-downscaled near-surface wind field with wind speed magnitude and wind direction errors of <1.5 ms⁻¹ and 30°, respectively. This approach can greatly accelerate the production of near-surface wind fields that are much more accurate than reanalysis data, while limiting the amount of computational and time intensive dynamical downscaling. Future studies will evaluate the ability of this approach to downscale other reanalysis data and climate model outputs with varying coarse-to-fine grid resolutions and domains of interest.

The area burned by Southern California wildfires has increased in recent decades, with implications for human health, infrastructure, and ecosystem management. Meteorology and fuel structure are universally recognized controllers of wildfire, but their relative importance, and hence the efficacy of abatement and suppression efforts, remains controversial. Southern California's wildfires can be partitioned by meteorology: fires typically occur either during Santa Ana winds (SA fires) in October through April, or warm and dry periods in June through September (non-SA fires). Previous work has not quantitatively distinguished between these fire regimes when assessing economic impacts or climate change influence. Here we separate five decades of fire perimeters into those coinciding with and without SA winds. The two fire types contributed almost equally to burned area, yet SA fires were responsible for 80% of cumulative 1990–2009 economic losses ($3.1 Billion). The damage disparity was driven by fire characteristics: SA fires spread three times faster, occurred closer to urban areas, and burned into areas with greater housing values. Non-SA fires were comparatively more sensitive to age-dependent fuels, often occurred in higher elevation forests, lasted for extended periods, and accounted for 70% of total suppression costs. An improved distinction of fire type has implications for future projections and management. The area burned in non-SA fires is projected to increase 77% (±43%) by the mid-21st century with warmer and drier summers, and the SA area burned is projected to increase 64% (±76%), underscoring the need to evaluate the allocation and effectiveness of suppression investments.

Wildland fires in Southern California can be divided into two categories: fall fires, which are typically driven by strong offshore Santa Ana winds, and summer fires, which occur with comparatively weak onshore winds and hot and dry weather. Both types of fire contribute significantly to annual burned area and economic loss. An improved understanding of the relationship between Southern California's meteorology and fire is needed to improve predictions of how fire will change in the future and to anticipate management needs. We used output from a regional climate model constrained by reanalysis observations to identify Santa Ana events and partition fires into those occurring during periods with and without Santa Ana conditions during 1959–2009. We then developed separate empirical regression models for Santa Ana and non‐Santa Ana fires to quantify the effects of meteorology on fire number and size. These models explained approximately 58% of the seasonal and interannual variation in the number of Santa Ana fires and 36% of the variation in non‐Santa Ana fires. The number of Santa Ana fires increased during years when relative humidity during Santa Ana events and fall precipitation were below average, indicating that fuel moisture is a key controller of ignition. Relative humidity strongly affected Santa Ana fire size. Cumulative precipitation during the previous three winters was significantly correlated with the number of non‐Santa Ana fires, presumably through increased fine fuel density and connectivity between infrastructure and nearby vegetation. Both relative humidity and the preceding wet season precipitation influenced non‐Santa Ana fire size. Regression models driven by meteorology explained 57% of the temporal variation in Santa Ana burned area and 22% of the variation in non‐Santa Ana burned area. The area burned by non‐Santa Ana fires has increased steadily by 1.7% year⁻¹ since 1959 (p < 0.006); the occurrence of extremely large Santa Ana fires has increased abruptly since 2003. Our results underscore the need to separately consider the fuel and meteorological controls on Santa Ana and non‐Santa Ana fires when projecting climate change impacts on regional fire.

In this study, uncoupled and coupled ocean–atmosphere simulations are carried out for the California Upwelling System to assess the dynamic ocean–atmosphere interactions, namely, the ocean surface current feedback to the atmosphere. The authors show the current feedback, by modulating the energy transfer from the atmosphere to the ocean, controls the oceanic eddy kinetic energy (EKE). For the first time, it is demonstrated that the current feedback has an effect on the surface stress and a counteracting effect on the wind itself. The current feedback acts as an oceanic eddy killer, reducing by half the surface EKE, and by 27% the depth-integrated EKE. On one hand, it reduces the coastal generation of eddies by weakening the surface stress and hence the nearshore supply of positive wind work (i.e., the work done by the wind on the ocean). On the other hand, by inducing a surface stress curl opposite to the current vorticity, it deflects energy from the geostrophic current into the atmosphere and dampens eddies. The wind response counteracts the surface stress response. It partly reenergizes the ocean in the coastal region and decreases the offshore return of energy to the atmosphere. Eddy statistics confirm the current feedback dampens the eddies and reduces their lifetime, improving the realism of the simulation. Finally, the authors propose an additional energy element in the Lorenz diagram of energy conversion: namely, the current-induced transfer of energy from the ocean to the atmosphere at the eddy scale.

Spatial and temporal variability of nearshore winds in eastern boundary current systems is affected by orography, coastline shape, and air-sea interaction. These lead to a weakening of the wind close to the coast: the so-called wind drop-off. In this study, regional atmospheric simulations over the US West Coast are used to demonstrate monthly characteristics of the wind drop-off and assess the mechanisms controlling it. Using a long-term simulation, we show the wind drop-off has spatial and seasonal variability in both its offshore extent and intensity. The offshore extent varies from around 10 to 80 km from the coast and the wind reduction from 10 to 80 %. We show that when the mountain orography is combined with the coastline shape of a cape, it has the biggest influence on wind drop-off. The primary associated processes are the orographically-induced vortex stretching and the surface drag related to turbulent momentum flux divergence that has an enhanced drag coefficient over land. Orographically-induced tilting/twisting can also be locally significant in the vicinity of capes. The land-sea drag difference acts as a barrier to encroachment of the wind onto the land through turbulent momentum flux divergence. It turns the wind parallel to the shore and slightly reduces it close to the coast. Another minor factor is the sharp coastal sea surface temperature front associated with upwelling. This can weaken the surface wind in the coastal strip by shallowing the marine boundary layer and decoupling it from the overlying troposphere.

In the middle of March 2002 a synoptic upwelling event occurred in the Southern California Bight; it was marked by a precipitous cooling of at least 4°C within 10–20 km of the coast. By the end of the month the preevent temperatures had slowly recovered. The Regional Oceanic Model System (ROMS) is used to simulate the event with an atmospheric downscaling reanalysis for surface wind and buoyancy flux forcing. Lateral boundary conditions of temperature, salinity, velocity, and sea level are taken from a global oceanic product. Barotropic tidal fields from a global barotropic model are imposed along the open boundaries. The simulation reproduces well the upwelling process compared with observed data. The sensitivity of the simulation is examined to wind resolution, heat flux, and tidal forcing. The oceanic response to the different wind resolutions converges at the level of the 6 km resolution, which is the finest scale present in the terrain elevation data set used in the atmospheric downscaling. The combination of an analytical diurnal cycle in the solar radiation and the empirical coupling with the instantaneous ROMS sea surface temperature produces a similar oceanic response to the downscaled heat flux. Tidal effects are significant in the upwelling evolution due to the increase in wind energy input through a quasi‐resonant alignment of the wind and surface current, probably by chance.

Low-level temperature inversions are a common feature of the wintertime troposphere in the Arctic and Antarctic. Inversion strength plays an important role in regulating atmospheric processes including air pollution, ozone destruction, cloud formation, and negative longwave feedback mechanisms that shape polar climate response to anthropogenic forcing. The Atmospheric Infrared Sounder (AIRS) instrument provides reliable measures of spatial patterns in mean wintertime inversion strength when compared with available radiosonde observations and reanalysis products. Here, we examine the influence of sea ice concentration on inversion strength in the Arctic and Antarctic. Correlation of inversion strength with mean annual sea ice concentration, likely a surrogate for the effective thermal conductivity of the wintertime ice pack, yields strong, linear relationships in the Arctic (r = 0.88) and Antarctic (r = 0.86). We find a substantially greater (stronger) linear relationship between sea ice concentration and surface air temperature than with temperature at 850 hPa, lending credence to the idea that sea ice controls inversion strength through modulation of surface heat fluxes. As such, declines in sea ice in either hemisphere may imply weaker mean inversions in the future. Comparison of mean inversion strength in AIRS and global climate models (GCMs) suggests that many GCMs poorly characterize mean inversion strength at high latitudes.

Observational studies have shown that mesoscale variations in sea surface temperature may induce mesoscale variations in wind. In eastern subtropical upwelling regions such as the California coast, this mechanism could be of great importance for the mean state and variability of the climate system. In coastal regions orography also creates mesoscale variations in wind, and the orographic effect may extend more than 100 km offshore. The respective roles of SST/wind links and coastal orography in shaping mesoscale wind variations in nearshore regions is not clear. We address this question in the context of the California Upwelling System, using a high-resolution regional numerical modeling system coupling the WRF atmospheric model to the ROMS oceanic model, as well as additional uncoupled experiments to quantify and separate the effects of SST/wind links and coastal orography on mesoscale wind variations. After taking into account potential biases in the representation of the strength of SST/wind links by the model, our results suggest that the magnitude of mesoscale wind variations arising from the orographic effects is roughly twice that of wind variations associated with mesoscale SST anomalies. This indicates that even in this region where coastal orography is complex and leaves a strong imprint on coastal winds, the role of SST/winds links in shaping coastal circulation and climate cannot be neglected.

Two main mechanisms can potentially explain the spread in the magnitude of global warming simulated by climate models: deep ocean heat uptake and climate feedbacks. Here, we show that deep oceanic heat uptake is a major source of spread in simulations of 21st century climate change. Models with deeper baseline polar mixed layers are associated with larger deep ocean warming and smaller global surface warming. Based on this result, we set forth an observational constraint on polar vertical oceanic mixing. This constraint suggests that many models may overestimate the efficiency of polar oceanic mixing and therefore may underestimate future surface warming. Thus to reduce climate change uncertainties at time‐scales relevant for policy‐making, improved understanding and modelling of oceanic mixing at high latitudes is crucial.

Biologically produced dimethylsulfide (DMS) is an important source of sulfur to the marine atmosphere that may affect cloud formation and properties. DMS is involved in a complex set of biochemical transformations and ecological exchanges so its global distribution is influenced by numerous factors, including oxidative stress from UV radiation. We re‐examine correlations between global surface DMS concentrations and mixed layer solar radiation dose (SRD), and find that SRD accounts for only a very small fraction (14%) of total variance in DMS measurements when using minimal aggregation methods. Moreover this relationship arises in part from the fact that when mixed layers deepen, both SRD and DMS decrease. When we control for this confounding effect, the correlation between DMS and SRD is reduced even further. These results indicate that factors other than solar irradiance play a leading role in determining global DMS emissions.

The depth of the planetary boundary layer (PBL) is a climatologically important quantity that has received little attention on regional to global scales. Here a 10-yr climatology of PBL depth from the University of California, Los Angeles (UCLA) atmospheric GCM is analyzed using the PBL mass budget. Based on the dominant physical processes, several PBL regimes are identified. These regimes tend to exhibit large-scale geographic organization. Locally generated buoyancy fluxes and static stability control PBL depth nearly everywhere, though convective mass flux has a large influence at tropical marine locations. Virtually all geographical variability in PBL depth can be linearly related to these quantities. While dry convective boundary layers dominate over land, stratocumulus-topped boundary layers are most common over ocean. This division of regimes leads to a dramatic land–sea contrast in PBL depth. Diurnal effects keep mean PBL depth over land shallow despite large daytime surface fluxes. The contrast arises because the large daily exchange of heat and mass between the PBL and free atmosphere over land is not present over the ocean, where mixing is accomplished by turbulent entrainment. Consistent treatment of remnant air from the deep, daytime PBL is necessary for proper representation of this diurnal behavior over land. Many locations exhibit seasonal shifts in PBL regime related to changes in PBL clouds. These shifts are controlled by seasonal variations in buoyancy flux and static stability.

Mediterranean climate regimes are particularly susceptible to rapid shifts between drought and flood—of which, California’s rapid transition from record multi-year dryness between 2012 and 2016 to extreme wetness during the 2016–2017 winter provides a dramatic example. Projected future changes in such dry-to-wet events, however, remain inadequately quantified, which we investigate here using the Community Earth System Model Large Ensemble of climate model simulations. Anthropogenic forcing is found to yield large twenty-first-century increases in the frequency of wet extremes, including a more than threefold increase in sub-seasonal events comparable to California’s ‘Great Flood of 1862’. Smaller but statistically robust increases in dry extremes are also apparent. As a consequence, a 25% to 100% increase in extreme dry-to-wet precipitation events is projected, despite only modest changes in mean precipitation. Such hydrological cycle intensification would seriously challenge California’s existing water storage, conveyance and flood control infrastructure.

In the current generation of climate models, the projected increase in global precipitation over the 21st century ranges from 2% to 10% under a high‐emission scenario. Some of this uncertainty can be traced to the rapid response to carbon dioxide (CO₂) forcing. We analyze an ensemble of simulations to better understand model spread in this rapid response. A substantial amount is linked to how the land surface partitions a change in latent versus sensible heat flux in response to the CO₂‐induced radiative perturbation; a larger increase in sensible heat results in a larger decrease in global precipitation. Model differences in the land surface response appear to be strongly related to the vegetation response to increased CO₂, specifically, the closure of leaf stomata. Future research should thus focus on evaluation of the vegetation physiological response, including stomatal conductance parameterizations, for the purpose of constraining the fast response of Earth's hydrologic cycle to CO₂ forcing.

Intensification of the hydrologic cycle is a key dimension of climate change, with substantial impacts on human and natural systems^1,2. A basic measure of hydrologic cycle intensification is the increase in global-mean precipitation per unit surface warming, which varies by a factor of three in current-generation climate models (about 1–3 per cent per kelvin)^3,4,5. Part of the uncertainty may originate from atmosphere–radiation interactions. As the climate warms, increases in shortwave absorption from atmospheric moistening will suppress the precipitation increase. This occurs through a reduction of the latent heating increase required to maintain a balanced atmospheric energy budget^6,7. Using an ensemble of climate models, here we show that such models tend to underestimate the sensitivity of solar absorption to variations in atmospheric water vapour, leading to an underestimation in the shortwave absorption increase and an overestimation in the precipitation increase. This sensitivity also varies considerably among models due to differences in radiative transfer parameterizations, explaining a substantial portion of model spread in the precipitation response. Consequently, attaining accurate shortwave absorption responses through improvements to the radiative transfer schemes could reduce the spread in the predicted global precipitation increase per degree warming for the end of the twenty-first century by about 35 per cent, and reduce the estimated ensemble-mean increase in this quantity by almost 40 per cent.

Changes to mean and extreme wet season precipitation over California on interannual time scales are analyzed using twenty-first-century precipitation data from 34 global climate models. Models disagree on the sign of projected changes in mean precipitation, although in most models the change is very small compared to historical and simulated levels of interannual variability. For the 2020/21–2059/60 period, there is no projected increase in the frequency of extremely dry wet seasons in the ensemble mean. Wet extremes are found to increase to around 2 times the historical frequency, which is statistically significant at the 95% level. Stronger signals emerge in the 2060/61–2099/2100 period. Across all models, extremely dry wet seasons are roughly 1.5 to 2 times more common, and wet extremes generally triple in their historical frequency (statistically significant). Large increases in precipitation variability in most models account for the modest increases to dry extremes. Increases in the frequency of wet extremes can be ascribed to equal contributions from increased variability and increases to the mean. These increases in the frequency of interannual precipitation extremes will create severe water management problems in a region where coping with large interannual variability in precipitation is already a challenge. Evidence from models and observations is examined to understand the causes of the low precipitation associated with the 2013/14 drought in California. These lines of evidence all strongly indicate that the low 2013/14 wet season precipitation total can be very likely attributed to natural variability, in spite of the projected future changes in extremes.

A new hybrid statistical–dynamical downscaling technique is described to project mid- and end-of-twenty-first-century local precipitation changes associated with 36 global climate models (GCMs) in phase 5 of the Coupled Model Intercomparison Project archive over the greater Los Angeles region. Land-averaged precipitation changes, ensemble-mean changes, and the spread of those changes for both time slices are presented. It is demonstrated that the results are similar to what would be produced if expensive dynamical downscaling techniques were instead applied to all GCMs. Changes in land-averaged ensemble-mean precipitation are near zero for both time slices, reflecting the region’s typical position in the models at the node of oppositely signed large-scale precipitation changes. For both time slices, the intermodel spread of changes is only about 0.2–0.4 times as large as natural interannual variability in the baseline period. A caveat to these conclusions is that interannual variability in the tropical Pacific is generally regarded as a weakness of the GCMs. As a result, there is some chance the GCM responses in the tropical Pacific to a changing climate and associated impacts on Southern California precipitation are not credible. It is subjectively judged that this GCM weakness increases the uncertainty of regional precipitation change, perhaps by as much as 25%. Thus, it cannot be excluded that the possibility that significant regional adaptation challenges related to either a precipitation increase or decrease would arise. However, the most likely downscaled outcome is a small change in local mean precipitation compared to natural variability, with large uncertainty on the sign of the change.

Projections of possible precipitation change in California under global warming have been subject to considerable uncertainty because California lies between the region anticipated to undergo increases in precipitation at mid-to-high latitudes and regions of anticipated decrease in the subtropics. Evaluation of the large-scale model experiments for phase 5 of the Coupled Model Intercomparison Project (CMIP5) suggests a greater degree of agreement on the sign of the winter (December–February) precipitation change than in the previous such intercomparison, indicating a greater portion of California falling within the increased precipitation zone. While the resolution of global models should not be relied on for accurate depiction of topographic rainfall distribution within California, the precipitation changes depend substantially on large-scale shifts in the storm tracks arriving at the coast. Significant precipitation increases in the region arriving at the California coast are associated with an eastward extension of the region of strong Pacific jet stream, which appears to be a robust feature of the large-scale simulated changes. This suggests that effects of this jet extension in steering storm tracks toward the California coast constitute an important factor that should be assessed for impacts on incoming storm properties for high-resolution regional model assessments.

Using a 6-km-resolution regional climate simulation of Southern California, the effect of orographic blocking on the precipitation climatology is examined. To diagnose whether blocking occurs, precipitating hours are categorized by a bulk Froude number. The precipitation distribution becomes much more spatially homogeneous as the Froude number decreases, and an inspection of winds confirms that this results from the increasing prevalence of orographic blocking. Low Froude (Froude approximately less than 1), blocked cases account for a large fraction of climatological precipitation, particularly at the coastline where more than half is attributable to blocked cases. Thus, the climatological precipitation–slope relationship seen in observations and in the simulation is a hybrid of blocked and unblocked cases.

Simulated precipitation distributions are compared to those predicted by a simple linear model that includes only rainfall arising from direct forced topographic ascent. The agreement is nearly perfect for high Froude (Froude substantially larger than 1) cases but degrades dramatically as the index decreases; as blocking becomes more prevalent, the precipitation–slope relationship becomes continuously weaker than that predicted by the linear model. Because of its high fidelity during unblocked cases, it is surmised that blocking effects are the primary limitation preventing the linear model from accurately representing precipitation climatology and that the representation would be significantly improved during low Froude hours by the addition of a term to reduce the effective slope of the topography. These results suggest orographic blocking may substantially affect climatological precipitation distributions in similarly configured coastal areas.

Across vast, agriculturally intensive regions of the United States, the spread in predictions of summer temperature and soil moisture under global warming is curiously elevated in current climate models. Some models show modest warming of 2–3C° and little drying or slight moistening by the 22nd century, while at the other extreme are simulations with warming as large as 7–8C° and 20–40% reductions in soil moisture. We show this region of large spread arises from differences in simulations of snow albedo feedback. During winter and early spring, models with strong snow albedo feedback exhibit large reductions in snowpack and hence water storage. This water deficit persists in summer soil moisture, with reduced evapotranspiration yielding warmer temperatures. Comparison of simulated feedback strength to observations of the feedback from the current climate's seasonal cycle suggests the inter‐model differences are excessive. At the same time, the multi‐model mean feedback strength agrees reasonably well with the observed value. We estimate that if the next generation of models were brought into line with observations of snow albedo feedback, the unusually wide divergence in simulations of summer warming and drying over the US would shrink by roughly one third to one half.

Atmospheric rivers (ARs) are responsible for a majority of extreme precipitation and flood events along the U.S. West Coast. To better understand the present‐day characteristics of AR‐related precipitation extremes, a selection of nine most intense historical AR events during 1980–2017 is simulated using a dynamical downscaling modeling framework based on the Weather Research and Forecasting Model. We find that the chosen framework and Weather Research and Forecasting Model configuration reproduces both large‐scale atmospheric features—including parent synoptic‐scale cyclones—as well as the filamentary corridors of integrated vapor transport associated with the ARs themselves. The accuracy of simulated extreme precipitation maxima, relative to in situ and interpolated gridded observations, improves notably with increasing model resolution, with improvements as large as 40–60% for fine scale (3 km) relative to coarse‐scale (27 km) simulations. A separate set of simulations using smoothed topography suggests that much of these gains stem from the improved representation of complex terrain. Additionally, using the 12 December 1995 storm in Northern California as an example, we demonstrate that only the highest‐resolution simulations resolve important fine‐scale features—such as localized orographically forced vertical motion and powerful near hurricane‐force boundary layer winds. Given the demonstrated ability of a targeted dynamical downscaling framework to capture both local extreme precipitation and key fine‐scale characteristics of the most intense ARs in the historical record, we argue that such a configuration may be highly conducive to understanding AR‐related extremes and associated changes in a warming climate.

Atmospheric rivers (ARs) are characterized by intense moisture transport, which, on landfall, produce precipitation which can be both beneficial and destructive. ARs in California, for example, are known to have ended drought conditions but also to have caused substantial socio-economic damage from landslides and flooding linked to extreme precipitation. Understanding how AR characteristics will respond to a warming climate is, therefore, vital to the resilience of communities affected by them, such as the western USA, Europe, East Asia and South Africa. In this Review, we use a theoretical framework to synthesize understanding of the dynamic and thermodynamic responses of ARs to anthropogenic warming and connect them to observed and projected changes and impacts revealed by observations and complex models. Evidence suggests that increased atmospheric moisture (governed by Clausius–Clapeyron scaling) will enhance the intensity of AR-related precipitation — and related hydrological extremes — but with changes that are ultimately linked to topographic barriers. However, due to their dependency on both weather and climate-scale processes, which themselves are often poorly constrained, projections are uncertain. To build confidence and improve resilience, future work must focus efforts on characterizing the multiscale development of ARs and in obtaining observations from understudied regions, including the West Pacific, South Pacific and South Atlantic.

Precipitation extremes will likely intensify under climate change. However, much uncertainty surrounds intensification of high-magnitude events that are often inadequately resolved by global climate models. In this analysis, we develop a framework involving targeted dynamical downscaling of historical and future extreme precipitation events produced by a large ensemble of a global climate model. This framework is applied to extreme “atmospheric river” storms in California. We find a substantial (10 to 40%) increase in total accumulated precipitation, with the largest relative increases in valleys and mountain lee-side areas. We also report even higher and more spatially uniform increases in hourly maximum precipitation intensity, which exceed Clausius-Clapeyron expectations. Up to 85% of this increase arises from thermodynamically driven increases in water vapor, with a smaller contribution by increased zonal wind strength. These findings imply substantial challenges for water and flood management in California, given future increases in intense atmospheric river-induced precipitation extremes.

This study investigates temperature impacts to snowpack and runoff‐driven flood risk over the Sierra Nevada during the extremely wet year of 2016–2017, which followed the extraordinary California drought of 2011–2015. By perturbing near‐surface temperatures from a 9‐km dynamically downscaled simulation, a series of offline land surface model experiments explore how Sierra Nevada hydrology has already been impacted by historical anthropogenic warming and how these impacts evolve under future warming scenarios. Results show that historical warming reduced 2016–2017 Sierra Nevada snow water equivalent by 20% while increasing early‐season runoff by 30%. An additional one third to two thirds loss of snowpack is projected by the end of the century, depending on the emission scenario, with middle elevations experiencing the most significant declines. Notably, the number of days in the future with runoff exceeding 20 mm nearly doubles under a mitigation emission scenarios and triples under a business‐as‐usual scenario. A smaller snow‐to‐rain ratio, as opposed to increased snowmelt, is found to be the primary mechanism of temperature impacts to Sierra snowpack and runoff. These findings are consequential to the prevalence of early‐season floods in the Sierra Nevada. In the Feather River Watershed, historical warming increased runoff by over one third during the period of heaviest precipitation in February 2017. This suggests that historical anthropogenic warming may have exacerbated runoff conditions underlying the Oroville Dam spillway overflow that occurred in this month. As warming continues in the future, the potential for runoff‐based flood risk may rise even higher.

Using hybrid dynamical–statistical downscaling, 3-km-resolution end-of-twenty-first-century runoff timing changes over California’s Sierra Nevada for all available global climate models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are projected. All four representative concentration pathways (RCPs) adopted by the Intergovernmental Panel on Climate Change’s Fifth Assessment Report are examined. These multimodel, multiscenario projections allow for quantification of ensemble-mean runoff timing changes and an associated range of possible outcomes due to both intermodel variability and choice of forcing scenario. Under a “business as usual” forcing scenario (RCP8.5), warming leads to a shift toward much earlier snowmelt-driven surface runoff in 2091–2100 compared to 1991–2000, with advances of as much as 80 days projected in the 35-model ensemble mean. For a realistic “mitigation” scenario (RCP4.5), the ensemble-mean change is smaller but still large (up to 30 days). For all plausible forcing scenarios and all GCMs, the simulated changes are statistically significant, so that a detectable change in runoff timing is inevitable. Even for the mitigation scenario, the ensemble-mean change is approximately equivalent to one standard deviation of the natural variability at most elevations. Thus, even when greenhouse gas emissions are curtailed, the runoff change is climatically significant. For the business-as-usual scenario, the ensemble-mean change is approximately two standard deviations of the natural variability at most elevations, portending a truly dramatic change in surface hydrology by the century’s end if greenhouse gas emissions continue unabated.

Low-level temperature inversions are a common feature of the wintertime troposphere in the Arctic and Antarctic. Inversion strength plays an important role in regulating atmospheric processes including air pollution, ozone destruction, cloud formation, and negative longwave feedback mechanisms that shape polar climate response to anthropogenic forcing. The Atmospheric Infrared Sounder (AIRS) instrument provides reliable measures of spatial patterns in mean wintertime inversion strength when compared with available radiosonde observations and reanalysis products. Here, we examine the influence of sea ice concentration on inversion strength in the Arctic and Antarctic. Correlation of inversion strength with mean annual sea ice concentration, likely a surrogate for the effective thermal conductivity of the wintertime ice pack, yields strong, linear relationships in the Arctic (r = 0.88) and Antarctic (r = 0.86). We find a substantially greater (stronger) linear relationship between sea ice concentration and surface air temperature than with temperature at 850 hPa, lending credence to the idea that sea ice controls inversion strength through modulation of surface heat fluxes. As such, declines in sea ice in either hemisphere may imply weaker mean inversions in the future. Comparison of mean inversion strength in AIRS and global climate models (GCMs) suggests that many GCMs poorly characterize mean inversion strength at high latitudes.

We show that intermodel variations in the anthropogenically-forced evolution of September sea ice extent (SSIE) in the Arctic stem mainly from two factors: the baseline climatological sea ice thickness (SIT) distribution, and the local climate feedback parameter. The roles of these two factors evolve over the course of the twenty-first century. The SIT distribution is the most important factor in current trends and those of coming decades, accounting for roughly half the intermodel variations in SSIE trends. Then, its role progressively decreases, so that around the middle of the twenty-first century the local climate feedback parameter becomes the dominant factor. Through this analysis, we identify the investments in improved simulation of Arctic climate necessary to reduce uncertainties both in projections of sea ice loss over the coming decades and in the ultimate fate of the ice pack.

The Arctic climate is changing rapidly¹. From 1979 to 2006, September sea-ice extent decreased by almost 25% or about 100,000 km² per year (ref. 2). In September 2007, Arctic sea-ice extent reached its lowest level since satellite observations began³and in September 2008, sea-ice cover was still low. This development has raised concerns that the Arctic Ocean could be ice-free in late summer in only a few decades, with important economic and geopolitical implications. Unfortunately, most current climate models underestimate significantly the observed trend in Arctic sea-ice decline⁴, leading to doubts regarding their projections for the timing of ice-free conditions. Here we analyse the simulated trends in past sea-ice cover in 18 state-of-art-climate models and find a direct relationship between the simulated evolution of September sea-ice cover over the twenty-first century and the magnitude of past trends in sea-ice cover. Using this relationship together with observed trends, we project the evolution of September sea-ice cover over the twenty-first century. We find that under a scenario with medium future greenhouse-gas emissions, the Arctic Ocean will probably be ice-free in September before the end of the twenty-first century.

Arctic sea ice has decreased substantially over recent decades, a trend projected to continue. Shrinking ice reduces surface albedo, leading to greater surface solar absorption, thus amplifying warming and driving further melt. This sea-ice albedo feedback (SIAF) is a key driver of Arctic climate change and an important uncertainty source in climate model projections. Using an ensemble of models, we demonstrate an emergent relationship between future SIAF and an observable version of SIAF in the current climate’s seasonal cycle. This relationship is robust in constraining SIAF over the coming decades (Pearson’s r = 0.76), and then it degrades. The degradation occurs because some models begin producing ice-free conditions, signalling a transition to a new ice regime. The relationship is strengthened when models with unrealistically thin historical ice are excluded. Because of this tight relationship, reducing model errors in the current climate’s seasonal SIAF and ice thickness can narrow SIAF spread under climate change.

This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes, including snow schemes that are included in Earth system models, in a wide variety of settings against local and global observations. The project aims to identify crucial processes and characteristics that need to be improved in snow models in the context of local- and global-scale modelling. A further objective of ESM-SnowMIP is to better quantify snow-related feedbacks in the Earth system. Although it is not part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), ESM-SnowMIP is tightly linked to the CMIP6-endorsed Land Surface, Snow and Soil Moisture Model Intercomparison (LS3MIP).

Emergent constraints use relationships between future and current climate states to constrain projections of climate response. Here we introduce a statistical, hierarchical emergent constraint (HEC) framework in order to link future and current climates with observations. Under Gaussian assumptions, the mean and variance of the future state are shown analytically to be a function of the signal‐to‐noise ratio between current climate uncertainty and observation error and the correlation between future and current climate states. We apply the HEC to the climate change, snow‐albedo feedback, which is related to the seasonal cycle in the Northern Hemisphere. We obtain a snow‐albedo feedback prediction interval of (−1.25,−0.58)%/K. The critical dependence on signal‐to‐noise ratio and correlation shows that neglecting these terms can lead to bias and underestimated uncertainty in constrained projections. The flexibility of using HEC under general assumptions throughout the Earth system is discussed.

This study uses dynamical and statistical methods to understand end‐of‐century mean changes to Sierra Nevada snowpack. Dynamical results reveal mid‐elevation watersheds experience considerably more rain than snow during winter, leading to substantial snowpack declines by spring. Despite some high‐elevation watersheds receiving slightly more snow in January and February, the warming signal still dominates across the wet‐season and leads to notable declines by springtime. A statistical model is created to mimic dynamical results for April 1 snowpack, allowing for an efficient downscaling of all available General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5. For all GCMs and emissions scenarios, dramatic April 1 snowpack loss occurs at elevations below 2500 meters, despite increased precipitation in many GCMs. Only 36% (±12%) of historical April 1 total snow water equivalent volume remains at the century's end under a “business‐as‐usual” emissions scenario, with 70% (±12%) remaining under a realistic “mitigation” scenario.

Snow albedo feedback (SAF) behaves similarly in the current and future climate contexts; thus, constraining the large intermodel variance in SAF will likely reduce uncertainty in climate projections. To better understand this intermodel spread, structural and parametric biases contributing to SAF variability are investigated. We find that structurally varying snowpack, vegetation, and albedo parameterizations drive most of the spread, while differences arising from model parameters are generally smaller. Models with the largest SAF biases exhibit clear structural or parametric errors. Additionally, despite widespread intermodel similarities, model interdependency has little impact on the strength of the relationship between SAF in the current and future climate contexts. Furthermore, many models now feature a more realistic SAF than in the prior generation, but shortcomings from two models limit the reduction in ensemble spread. Lastly, preliminary signs from ongoing model development are positive and suggest a likely reduction in SAF spread among upcoming models.

This study investigates temperature impacts to snowpack and runoff‐driven flood risk over the Sierra Nevada during the extremely wet year of 2016–2017, which followed the extraordinary California drought of 2011–2015. By perturbing near‐surface temperatures from a 9‐km dynamically downscaled simulation, a series of offline land surface model experiments explore how Sierra Nevada hydrology has already been impacted by historical anthropogenic warming and how these impacts evolve under future warming scenarios. Results show that historical warming reduced 2016–2017 Sierra Nevada snow water equivalent by 20% while increasing early‐season runoff by 30%. An additional one third to two thirds loss of snowpack is projected by the end of the century, depending on the emission scenario, with middle elevations experiencing the most significant declines. Notably, the number of days in the future with runoff exceeding 20 mm nearly doubles under a mitigation emission scenarios and triples under a business‐as‐usual scenario. A smaller snow‐to‐rain ratio, as opposed to increased snowmelt, is found to be the primary mechanism of temperature impacts to Sierra snowpack and runoff. These findings are consequential to the prevalence of early‐season floods in the Sierra Nevada. In the Feather River Watershed, historical warming increased runoff by over one third during the period of heaviest precipitation in February 2017. This suggests that historical anthropogenic warming may have exacerbated runoff conditions underlying the Oroville Dam spillway overflow that occurred in this month. As warming continues in the future, the potential for runoff‐based flood risk may rise even higher.

Sierra Nevada climate and snowpack is simulated during the period of extreme drought from 2011 to 2015 and compared to an identical simulation except for the removal of the twentieth century anthropogenic warming. Anthropogenic warming reduced average snowpack levels by 25%, with middle‐to‐low elevations experiencing reductions between 26 and 43%. In terms of event frequency, return periods associated with anomalies in 4 year 1 April snow water equivalent are estimated to have doubled, and possibly quadrupled, due to past warming. We also estimate effects of future anthropogenic warmth on snowpack during a drought similar to that of 2011–2015. Further snowpack declines of 60–85% are expected, depending on emissions scenario. The return periods associated with future snowpack levels are estimated to range from millennia to much longer. Therefore, past human emissions of greenhouse gases are already negatively impacting statewide water resources during drought, and much more severe impacts are likely to be inevitable.

California’s Sierra Nevada is a high-elevation mountain range with significant seasonal snow cover. Under anthropogenic climate change, amplification of the warming is expected to occur at elevations near snow margins due to snow albedo feedback. However, climate change projections for the Sierra Nevada made by global climate models (GCMs) and statistical downscaling methods miss this key process. Dynamical downscaling simulates the additional warming due to snow albedo feedback. Ideally, dynamical downscaling would be applied to a large ensemble of 30 or more GCMs to project ensemble-mean outcomes and intermodel spread, but this is far too computationally expensive. To approximate the results that would occur if the entire GCM ensemble were dynamically downscaled, a hybrid dynamical–statistical downscaling approach is used. First, dynamical downscaling is used to reconstruct the historical climate of the 1981–2000 period and then to project the future climate of the 2081–2100 period based on climate changes from five GCMs. Next, a statistical model is built to emulate the dynamically downscaled warming and snow cover changes for any GCM. This statistical model is used to produce warming and snow cover loss projections for all available CMIP5 GCMs. These projections incorporate snow albedo feedback, so they capture the local warming enhancement (up to 3°C) from snow cover loss that other statistical methods miss. Capturing these details may be important for accurately projecting impacts on surface hydrology, water resources, and ecosystems.

Future snowfall and snowpack changes over the mountains of Southern California are projected using a new hybrid dynamical–statistical framework. Output from all general circulation models (GCMs) in phase 5 of the Coupled Model Intercomparison Project archive is downscaled to 2-km resolution over the region. Variables pertaining to snow are analyzed for the middle (2041–60) and end (2081–2100) of the twenty-first century under two representative concentration pathway (RCP) scenarios: RCP8.5 (business as usual) and RCP2.6 (mitigation). These four sets of projections are compared with a baseline reconstruction of climate from 1981 to 2000. For both future time slices and scenarios, ensemble-mean total winter snowfall loss is widespread. By the mid-twenty-first century under RCP8.5, ensemble-mean winter snowfall is about 70% of baseline, whereas the corresponding value for RCP2.6 is somewhat higher (about 80% of baseline). By the end of the century, however, the two scenarios diverge significantly. Under RCP8.5, snowfall sees a dramatic further decline; 2081–2100 totals are only about half of baseline totals. Under RCP2.6, only a negligible further reduction from midcentury snowfall totals is seen. Because of the spread in the GCM climate projections, these figures are all associated with large intermodel uncertainty. Snowpack on the ground, as represented by 1 April snow water equivalent is also assessed. Because of enhanced snowmelt, the loss seen in snowpack is generally 50% greater than that seen in winter snowfall. By midcentury under RCP8.5, warming-accelerated spring snowmelt leads to snow-free dates that are about 1–3 weeks earlier than in the baseline period.

Snow-albedo feedback (SAF) is examined in 25 climate change simulations participating in the Coupled Model Intercomparison Project version 5 (CMIP5). SAF behavior is compared to the feedback’s behavior in the previous (CMIP3) generation of global models. SAF strength exhibits a fivefold spread across CMIP5 models, ranging from 0.03 to 0.16 W m⁻² K⁻¹ (ensemble-mean = 0.08 W m⁻² K⁻¹). This accounts for much of the spread in 21st century warming of Northern Hemisphere land masses, and is very similar to the spread found in CMIP3 models. As with the CMIP3 models, there is a high degree of correspondence between the magnitudes of seasonal cycle and climate change versions of the feedback. Here we also show that their geographical footprint is similar. The ensemble-mean SAF strength is close to an observed estimate of the real climate’s seasonal cycle feedback strength. SAF strength is strongly correlated with the climatological surface albedo when the ground is covered by snow. The inter-model variation in this quantity is surprisingly large, ranging from 0.39 to 0.75. Models with large surface albedo when these regions are snow-covered will also have a large surface albedo contrast between snow-covered and snow-free regions, and therefore a correspondingly large SAF. Widely-varying treatments of vegetation masking of snow-covered surfaces are probably responsible for the spread in surface albedo where snow occurs, and the persistent spread in SAF in global climate models.

Changes in wintertime 10 m winds due to the El Niño-Southern Oscillation are examined using a 6 km resolution climate simulation of Southern California covering the period from 1959 through 2001. Wind speed statistics based on regional averages reveal a general signal of increased mean wind speeds and wind speed variability during El Niño across the region. An opposite and nearly as strong signal of decreased wind speed variability during La Niña is also found. These signals are generally more significant than the better-known signals in precipitation. In spite of these regional-scale generalizations, there are significant sub-regional mesoscale structures in the wind speed impacts. In some cases, impacts on mean winds and wind variability at the sub-regional scale are opposite to those of the region as a whole. All of these signals can be interpreted in terms of shifts in occurrences of the region’s main wind regimes due to the El Niño phenomenon. The results of this study can be used to understand how interannual wind speed variations in regions of Southern California are influenced by the El Niño phenomenon.

This study used numerical experiments to investigate two important concerns in simulating the cold season snowpack: the impact of the alterations of snow albedo due to anthropogenic aerosol deposition on snowpack and the treatment of snow physics using a multi-layer snow model. The snow albedo component considered qualitatively future changes in anthropogenic emissions and the subsequent increase or decrease of black carbon deposition on the Sierra Nevada snowpack by altering the prescribed snow albedo values. The alterations in the snow albedo primarily affect the snowpack via surface energy budget with little impact on precipitation. It was found that a decrease in snow albedo (by as little as 5–10% of the reference values) due to an increase in local emissions enhances snowmelt and runoff (by as much as 30–50%) in the early part of a cold season, resulting in reduced snowmelt-driven runoff (by as much as 30–50%) in the later part of the cold season, with the greatest impacts at higher elevations. An increase in snow albedo associated with reduced anthropogenic emissions results in the opposite effects. Thus, the most notable impact of the decrease in snow albedo is to enhance early-season snowmelt and to reduce late-season snowmelt, resulting in an adverse impact on warm season water resources in California. The timing of the sensitivity of snow water equivalent (SWE), snowmelt, and runoff vary systematically according to terrain elevation; as terrain elevation increases, the peak response of these fields occurs later in the cold season. The response of SWE and surface energy budget to the alterations in snow albedo found in this study shows that the effects of snow albedo on snowpack are further enhanced via local snow-albedo feedback. Results from this experiment suggest that a reduction in local emissions, which would increase snow albedo, could alleviate the early snowmelt and reduced runoff in late winter and early spring caused by global climate change, at least partially. The most serious uncertainties associated with this part of the study are a quantification of the relationship between the amount of black carbon deposition and snow albedo—a subject of future study. The comparison of the spring snowpack simulated with a single- and multi-layer snow model during the spring of 1998 shows that a more realistic treatment of snow physics in a multi-layer snow model could improve snowpack simulations, especially during spring when snow ablation is significant, or in conjunction with climate change projections.

The depth and timing of snowpack in the Sierra Nevada Mountains are of fundamental importance to California water resource availability, and recent studies indicate a shift toward earlier snowmelt consistent with projected impacts of anthropogenic climate change. In order for future studies to assess snowpack variability on seasonal to centennial time scales, physically based models of snowpack evolution at high spatial resolution must be improved. Here we evaluate modeled snowpack accuracy for the central Sierra Nevada in the Weather Research and Forecasting regional climate model coupled to the Noah land surface model. A simulation with nested domains at 27, 9, and 3 km grid spacings is presented for November 2001 to July 2002. Model outputs are compared with daily snowpack observations at 41 locations, air temperature at 31 locations, and precipitation at 10 locations. Comparison of snowpack at different resolutions suggests that 27 km simulations substantially underestimate snowpack, while 9 and 3 km simulations are closer to observations. Regional snowpack accumulation is accurately simulated at these high resolutions, but model snowmelt occurs an average of 22–25 days early. Some error can be traced to differences in elevation and observation scale between point‐based measurements and model grid cells, but these factors cannot explain the persistent bias toward early snowmelt. A high correlation between snowmelt and error in modeled surface air temperature is found, with melt coinciding systematically with excessively cold air temperatures. One possible source of bias is an imbalance in turbulent heat fluxes, erroneously warming the snowpack while cooling the surface atmosphere.

A study of the California Sierra Nevada snowpack has been conducted using snow station observations and reanalysis surface temperature data. Monthly snow water equivalent (SWE) measurements were combined from two datasets to provide sufficient data from 1930 to 2008. The monthly snapshots are used to calculate peak snow mass timing for each snow season. Since 1930, there has been an overall trend toward earlier snow mass peak timing by 0.6 days per decade. The trend toward earlier timing also occurs at nearly all individual stations. Even stations showing an increase in 1 April SWE exhibit the trend toward earlier timing, indicating that enhanced melting is occurring at nearly all stations. Analysis of individual years and stations reveals that warm daily maximum temperatures averaged over March and April are associated with earlier snow mass peak timing for all spatial and temporal scales included in the dataset. The influence is particularly pronounced for low accumulation years indicating the potential importance of albedo feedback for the melting of shallow snow. The robustness of the early spring temperature influence on peak timing suggests the trend toward earlier peak timing is attributable to the simultaneous warming trend (0.1°C decade⁻¹ since 1930, with an acceleration in warming in later time periods). Given future scenarios of warming in California, one can expect acceleration in the trend toward earlier peak timing; this will reduce the warm season storage capacity of the California snowpack.

Observation based estimates of controls on snow albedo feedback (SAF) are needed to constrain the snow and albedo parameterizations in general circulation model (GCM) projections of air temperature over the Northern Hemisphere (NH) landmass. The total April‐May NH SAF, corresponding to the sum of the effect of temperature on surface albedo over snow covered surfaces (‘metamorphism’) and over surfaces transitioning from snow covered to snow free conditions (‘snow cover’), is derived with daily NH snow cover and surface albedo products using Advanced Very High Resolution Radiometer Polar Pathfinder satellite data and surface air temperature from ERA40 reanalysis data between 1982–1999. Without using snow cover information, the estimated total SAF, for land surfaces north of 30°N, of −0.93 ± 0.06%K⁻¹ was not significantly different (95% confidence) from estimates based on International Satellite Cloud Climatology Project surface albedo data. The SAF, constrained to only snow covered areas, grew to −1.06 ± 0.08%K⁻¹ with similar magnitudes for the ‘snow cover’ and ‘metamorphosis’ components. The SAF pattern was significantly correlated with the ‘snow cover’ component pattern over both North America and Eurasia but only over Eurasia for the ‘metamorphosis’ component. However, in contrast to GCM model based diagnoses of SAF, the control on the ‘snow cover’ component related to the albedo contrast of snow covered and snow free surfaces was not strongly correlated to the total SAF.

Climate change is expected to cause a reduction in the spatial extent of snow cover on land. Recent work suggests that this will exert a local influence on the atmosphere and the hydrology of snow‐margin areas through the snow‐albedo feedback (SAF) mechanism. A significant fraction of variability among IPCC AR4 general circulation model (GCM) predictions for future summertime climate change over these areas is related to the models' representation of springtime SAF. In this study, we demonstrate a nonlocal influence of SAF on the summertime circulation in the extratropical Northern Hemisphere. Increased land surface warming in models with stronger SAF is associated with large‐scale sea‐level pressure anomalies over the northern oceans and a poleward intensified subtropical jet. We find that up to 25–30% and, on average, 5–10% of the inter‐model spread in projections of the circulation response to climate change is linearly related to SAF strength.

The strength of snow-albedo feedback (SAF) in transient climate change simulations of the Fourth Assessment of the Intergovernmental Panel on Climate Change is generally determined by the surface-albedo decrease associated with a loss of snow cover rather than the reduction in snow albedo due to snow metamorphosis in a warming climate. The large intermodel spread in SAF strength is likewise attributable mostly to the snow cover component. The spread in the strength of this component is in turn mostly attributable to a correspondingly large spread in mean effective snow albedo. Models with large effective snow albedos have a large surface-albedo contrast between snow-covered and snow-free regions and exhibit a correspondingly large surface-albedo decrease when snow cover decreases. Models without explicit treatment of the vegetation canopy in their surface-albedo calculations typically have high effective snow albedos and strong SAF, often stronger than observed. In models with explicit canopy treatment, completely snow-covered surfaces typically have lower albedos and the simulations have weaker SAF, generally weaker than observed. The authors speculate that in these models either snow albedos or canopy albedos when snow is present are too low, or vegetation shields snow-covered surfaces excessively. Detailed observations of surface albedo in a representative sampling of snow-covered surfaces would therefore be extremely useful in constraining these parameterizations and reducing SAF spread in the next generation of models.

In this paper, the two factors controlling Northern Hemisphere springtime snow albedo feedback in transient climate change are isolated and quantified based on scenario runs of 17 climate models used in the Intergovernmental Panel on Climate Change Fourth Assessment Report. The first factor is the dependence of planetary albedo on surface albedo, representing the atmosphere's attenuation effect on surface albedo anomalies. It is potentially a major source of divergence in simulations of snow albedo feedback because of large differences in simulated cloud fields in Northern Hemisphere land areas. To calculate the dependence, an analytical model governing planetary albedo was developed. Detailed validations of the analytical model for two of the simulations are shown, version 3 of the Community Climate System Model (CCSM3) and the Geophysical Fluid Dynamics Laboratory global coupled Climate Model 2.0 (CM2.0), demonstrating that it facilitates a highly accurate calculation of the dependence of planetary albedo on surface albedo given readily available simulation output. In all simulations it is found that surface albedo anomalies are attenuated by approximately half in Northern Hemisphere land areas as they are transformed into planetary albedo anomalies. The intermodel standard deviation in the dependence of planetary albedo on surface albedo is surprisingly small, less than 10% of the mean. Moreover, when an observational estimate of this factor is calculated by applying the same method to the satellite-based International Satellite Cloud Climatology Project (ISCCP) data, it is found that most simulations agree with ISCCP values to within about 10%, despite further disagreements between observed and simulated cloud fields. This suggests that even large relative errors in simulated cloud fields do not result in significant error in this factor, enhancing confidence in climate models. The second factor, related exclusively to surface processes, is the change in surface albedo associated with an anthropogenically induced temperature change in Northern Hemisphere land areas. It exhibits much more intermodel variability. The standard deviation is about ⅓ of the mean, with the largest value being approximately 3 times larger than the smallest. Therefore this factor is unquestionably the main source of the large divergence in simulations of snow albedo feedback. To reduce the divergence, attention should be focused on differing parameterizations of snow processes, rather than intermodel variations in the attenuation effect of the atmosphere on surface albedo anomalies.

Purpose of Review

Highlight significant developments that have recently been made to enhance our understanding of how snow responds to climate forcing and the role that snow plays in the climate system.

Recent Findings

Widespread snow loss has occurred in recent decades, with the largest decreases in spring. These changes are primarily driven by temperature and precipitation, but changes in vegetation, light-absorbing impurities, and sea ice also contribute to variability. Changes in snow cover can also affect climate through the snow albedo feedback (SAF). Recently, considerable progress has been made in better understanding the processes contributing to SAF. We also highlight advances in knowledge of how snow variability is linked to large-scale atmospheric changes. Lastly, large-scale snow losses are expected to continue under climate change in all but the coldest climates. These projected changes to snow raise considerable concerns over future freshwater availability in snow-dominated watersheds.

Summary

The results discussed here demonstrate the widespread implications that changes to snow have on the climate system and anthropogenic activity at large.

California’s Sierra Nevada is a high-elevation mountain range with significant seasonal snow cover. Under anthropogenic climate change, amplification of the warming is expected to occur at elevations near snow margins due to snow albedo feedback. However, climate change projections for the Sierra Nevada made by global climate models (GCMs) and statistical downscaling methods miss this key process. Dynamical downscaling simulates the additional warming due to snow albedo feedback. Ideally, dynamical downscaling would be applied to a large ensemble of 30 or more GCMs to project ensemble-mean outcomes and intermodel spread, but this is far too computationally expensive. To approximate the results that would occur if the entire GCM ensemble were dynamically downscaled, a hybrid dynamical–statistical downscaling approach is used. First, dynamical downscaling is used to reconstruct the historical climate of the 1981–2000 period and then to project the future climate of the 2081–2100 period based on climate changes from five GCMs. Next, a statistical model is built to emulate the dynamically downscaled warming and snow cover changes for any GCM. This statistical model is used to produce warming and snow cover loss projections for all available CMIP5 GCMs. These projections incorporate snow albedo feedback, so they capture the local warming enhancement (up to 3°C) from snow cover loss that other statistical methods miss. Capturing these details may be important for accurately projecting impacts on surface hydrology, water resources, and ecosystems.

The climate warming effects of accelerated urbanization along with projected global climate change raise an urgent need for sustainable mitigation and adaptation strategies to cool urban climates. Our modeling results show that historical urbanization in the Los Angeles and San Diego metropolitan areas has increased daytime urban air temperature by 1.3 °C, in part due to a weakening of the onshore sea breeze circulation. We find that metropolis-wide adoption of cool roofs can meaningfully offset this daytime warming, reducing temperatures by 0.9 °C relative to a case without cool roofs. Residential cool roofs were responsible for 67% of the cooling. Nocturnal temperature increases of 3.1 °C from urbanization were larger than daytime warming, while nocturnal temperature reductions from cool roofs of 0.5 °C were weaker than corresponding daytime reductions. We further show that cool roof deployment could partially counter the local impacts of global climate change in the Los Angeles metropolitan area. Assuming a scenario in which there are dramatic decreases in greenhouse gas emissions in the 21st century (RCP2.6), mid- and end-of-century temperature increases from global change relative to current climate are similarly reduced by cool roofs from 1.4 °C to 0.6 °C. Assuming a scenario with continued emissions increases throughout the century (RCP8.5), mid-century warming is significantly reduced by cool roofs from 2.0 °C to 1.0 °C. The end-century warming, however, is significantly offset only in small localized areas containing mostly industrial/commercial buildings where cool roofs with the highest albedo are adopted. We conclude that metropolis-wide adoption of cool roofs can play an important role in mitigating the urban heat island effect, and offsetting near-term local warming from global climate change. Global-scale reductions in greenhouse gas emissions are the only way of avoiding long-term warming, however. We further suggest that both climate mitigation and adaptation can be pursued simultaneously using 'cool photovoltaics'.

Using the hybrid downscaling technique developed in part I of this study, temperature changes relative to a baseline period (1981–2000) in the greater Los Angeles region are downscaled for two future time slices: midcentury (2041–60) and end of century (2081–2100). Two representative concentration pathways (RCPs) are considered, corresponding to greenhouse gas emission reductions over coming decades (RCP2.6) and to continued twenty-first-century emissions increases (RCP8.5). All available global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are downscaled to provide likelihood and uncertainty estimates. By the end of century under RCP8.5, a distinctly new regional climate state emerges: average temperatures will almost certainly be outside the interannual variability range seen in the baseline. Except for the highest elevations and a narrow swath very near the coast, land locations will likely see 60–90 additional extremely hot days per year, effectively adding a new season of extreme heat. In mountainous areas, a majority of the many baseline days with freezing nighttime temperatures will most likely not occur. According to a similarity metric that measures daily temperature variability and the climate change signal, the RCP8.5 end-of-century climate will most likely be only about 50% similar to the baseline. For midcentury under RCP2.6 and RCP8.5 and end of century under RCP2.6, these same measures also indicate a detectable though less significant climatic shift. Therefore, while measures reducing global emissions would not prevent climate change at this regional scale in the coming decades, their impact would be dramatic by the end of the twenty-first century.

Across vast, agriculturally intensive regions of the United States, the spread in predictions of summer temperature and soil moisture under global warming is curiously elevated in current climate models. Some models show modest warming of 2–3C° and little drying or slight moistening by the 22nd century, while at the other extreme are simulations with warming as large as 7–8C° and 20–40% reductions in soil moisture. We show this region of large spread arises from differences in simulations of snow albedo feedback. During winter and early spring, models with strong snow albedo feedback exhibit large reductions in snowpack and hence water storage. This water deficit persists in summer soil moisture, with reduced evapotranspiration yielding warmer temperatures. Comparison of simulated feedback strength to observations of the feedback from the current climate's seasonal cycle suggests the inter‐model differences are excessive. At the same time, the multi‐model mean feedback strength agrees reasonably well with the observed value. We estimate that if the next generation of models were brought into line with observations of snow albedo feedback, the unusually wide divergence in simulations of summer warming and drying over the US would shrink by roughly one third to one half.

We examine the climatological diurnal cycle of surface air temperature in a 6 km resolution atmospheric simulation of Southern California from 1995 to the present. We find its amplitude and phase both have significant geographical structure. This is most likely due to diurnally-varying flows back and forth across the coastline and elevation isolines resulting from the large daily warming and cooling over land. Because the region’s atmosphere is generally stably stratified, these flow patterns result in air of lower (higher) potential temperature being advected upslope (downslope) during daytime (nighttime). This suppresses the temperature diurnal cycle amplitude at mountaintops where diurnal flows converge (diverge) during the day (night). The nighttime land breeze also advects air of higher potential temperature downslope toward the coast. This raises minimum temperatures in land areas adjacent to the coast in a manner analogous to the daytime suppression of maximum temperature by the cool sea breeze in these same areas. Because stratification is greater in the coastal zone than in the desert interior, these thermal effects of the diurnal winds are not uniform, generating spatial structures in the phase and shape of the temperature diurnal cycle as well as its amplitude. We confirm that the simulated characteristics of the temperature diurnal cycle as well as those of the associated diurnal winds are also found in a network of 30 observation stations in the region. This gives confidence in the simulation’s realism and our study’s findings. Diurnal flows are probably mainly responsible for the geographical structures in the temperature diurnal cycle in other regions of significant topography and surface heterogeneity, their importance depending partly on the degree of atmospheric stratification.

A physically-based statistical modeling approach to downscale coarse resolution reanalysis near-surface winds over a region of complex terrain is developed and tested in this study. Our approach is guided by physical variables and meteorological relationships that are important for determining near-surface wind flow. Preliminary fine scale winds are estimated by correcting the course-to-fine grid resolution mismatch in roughness length. Guided by the physics shaping near-surface winds, we then formulate a multivariable linear regression model which uses near-surface micrometeorological variables and the preliminary estimates as predictors to calculate the final wind products. The coarse-to-fine grid resolution ratio is approximately 10–1 for our study region of southern California. A validated 3-km resolution dynamically-downscaled wind dataset is used to train and validate our method. Winds from our statistical modeling approach accurately reproduce the dynamically-downscaled near-surface wind field with wind speed magnitude and wind direction errors of <1.5 ms⁻¹ and 30°, respectively. This approach can greatly accelerate the production of near-surface wind fields that are much more accurate than reanalysis data, while limiting the amount of computational and time intensive dynamical downscaling. Future studies will evaluate the ability of this approach to downscale other reanalysis data and climate model outputs with varying coarse-to-fine grid resolutions and domains of interest.

Using output from a high‐resolution meteorological simulation, we evaluate the sensitivity of southern California wind energy generation to variations in key characteristics of current wind turbines. These characteristics include hub height, rotor diameter and rated power, and depend on turbine make and model. They shape the turbine's power curve and thus have large implications for the energy generation capacity of wind farms. For each characteristic, we find complex and substantial geographical variations in the sensitivity of energy generation. However, the sensitivity associated with each characteristic can be predicted by a single corresponding climate statistic, greatly simplifying understanding of the relationship between climate and turbine optimization for energy production. In the case of the sensitivity to rotor diameter, the change in energy output per unit change in rotor diameter at any location is directly proportional to the weighted average wind speed between the cut‐in speed and the rated speed. The sensitivity to rated power variations is likewise captured by the percent of the wind speed distribution between the turbines rated and cut‐out speeds. Finally, the sensitivity to hub height is proportional to lower atmospheric wind shear. Using a wind turbine component cost model, we also evaluate energy output increase per dollar investment in each turbine characteristic. We find that rotor diameter increases typically provide a much larger wind energy boost per dollar invested, although there are some zones where investment in the other two characteristics is competitive. Our study underscores the need for joint analysis of regional climate, turbine engineering and economic modeling to optimize wind energy production.

Changes in wintertime 10 m winds due to the El Niño-Southern Oscillation are examined using a 6 km resolution climate simulation of Southern California covering the period from 1959 through 2001. Wind speed statistics based on regional averages reveal a general signal of increased mean wind speeds and wind speed variability during El Niño across the region. An opposite and nearly as strong signal of decreased wind speed variability during La Niña is also found. These signals are generally more significant than the better-known signals in precipitation. In spite of these regional-scale generalizations, there are significant sub-regional mesoscale structures in the wind speed impacts. In some cases, impacts on mean winds and wind variability at the sub-regional scale are opposite to those of the region as a whole. All of these signals can be interpreted in terms of shifts in occurrences of the region’s main wind regimes due to the El Niño phenomenon. The results of this study can be used to understand how interannual wind speed variations in regions of Southern California are influenced by the El Niño phenomenon.

The frequency and character of Southern California’s Santa Ana wind events are investigated within a 12-km-resolution downscaling of late-20th and mid-21st century time periods of the National Center for Atmospheric Research Community Climate System Model global climate change scenario run. The number of Santa Ana days per winter season is approximately 20% fewer in the mid 21st century compared to the late 20th century. Since the only systematic and sustained difference between these two periods is the level of anthropogenic forcing, this effect is anthropogenic in origin. In both time periods, Santa Ana winds are partly katabatically-driven by a temperature difference between the cold wintertime air pooling in the desert against coastal mountains and the adjacent warm air over the ocean. However, this katabatic mechanism is significantly weaker during the mid 21st century time period. This occurs because of the well-documented differential warming associated with transient climate change, with more warming in the desert interior than over the ocean. Thus the mechanism responsible for the decrease in Santa Ana frequency originates from a well-known aspect of the climate response to increasing greenhouse gases, but cannot be understood or simulated without mesoscale atmospheric dynamics. In addition to the change in Santa Ana frequency, we investigate changes during Santa Anas in two other meteorological variables known to be relevant to fire weather conditions—relative humidity and temperature. We find a decrease in the relative humidity and an increase in temperature. Both these changes would favor fire. A fire behavior model accounting for changes in wind, temperature, and relative humidity simultaneously is necessary to draw firm conclusions about future fire risk and growth associated with Santa Ana events. While our results are somewhat limited by a relatively small sample size, they illustrate an observed and explainable regional change in climate due to plausible mesoscale processes.

Observational studies have shown that mesoscale variations in sea surface temperature may induce mesoscale variations in wind. In eastern subtropical upwelling regions such as the California coast, this mechanism could be of great importance for the mean state and variability of the climate system. In coastal regions orography also creates mesoscale variations in wind, and the orographic effect may extend more than 100 km offshore. The respective roles of SST/wind links and coastal orography in shaping mesoscale wind variations in nearshore regions is not clear. We address this question in the context of the California Upwelling System, using a high-resolution regional numerical modeling system coupling the WRF atmospheric model to the ROMS oceanic model, as well as additional uncoupled experiments to quantify and separate the effects of SST/wind links and coastal orography on mesoscale wind variations. After taking into account potential biases in the representation of the strength of SST/wind links by the model, our results suggest that the magnitude of mesoscale wind variations arising from the orographic effects is roughly twice that of wind variations associated with mesoscale SST anomalies. This indicates that even in this region where coastal orography is complex and leaves a strong imprint on coastal winds, the role of SST/winds links in shaping coastal circulation and climate cannot be neglected.

Fire plays a crucial role in many ecosystems, and a better understanding of different controls on fire activity is needed. Here we analyze spatial variation in fire danger during episodic wind events in coastal southern California, a densely populated Mediterranean‐climate region. By reconstructing almost a decade of fire weather patterns through detailed simulations of Santa Ana winds, we produced the first high‐resolution map of where these hot, dry winds are consistently most severe and which areas are relatively sheltered. We also analyzed over half a century of mapped fire history in chaparral ecosystems of the region, finding that our models successfully predict where the largest wildfires are most likely to occur. There is a surprising lack of information about extreme wind patterns worldwide, and more quantitative analyses of their spatial variation will be important for effective fire management and sustainable long‐term urban development on fire‐prone landscapes.

The atmospheric conditions that lead to strong offshore surface winds in Southern California, commonly referred to as Santa Ana winds, are investigated using the North American Regional Reanalysis and a 12-year, 6-km resolution regional climate simulation of Southern California. We first construct an index to characterize Santa Ana events based on offshore wind strength. This index is then used to identify the average synoptic conditions associated with Santa Ana events—a high pressure anomaly over the Great Basin. This pressure anomaly causes offshore geostrophic winds roughly perpendicular to the region’s mountain ranges, which in turn cause surface flow as the offshore momentum is transferred to the surface. We find, however, that there are large variations in the synoptic conditions during Santa Ana conditions, and that there are many days with strong offshore flow and weak synoptic forcing. This is due to local thermodynamic forcing that also causes strong offshore surface flow: a large temperature gradient between the cold desert surface and the warm ocean air at the same altitude creates an offshore pressure gradient at that altitude, in turn causing katabatic-like offshore flow in a thin layer near the surface. We quantify the contribution of “synoptic” and “local thermodynamic” mechanisms using a bivariate linear regression model, and find that, unless synoptic conditions force strongly onshore winds, the local thermodynamic forcing is the primary control on Santa Ana variability.