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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.