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.

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 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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.