The Response of Northern Hemisphere Snow Cover to a Changing Climate*

2009 ◽  
Vol 22 (8) ◽  
pp. 2124-2145 ◽  
Author(s):  
Ross D. Brown ◽  
Philip W. Mote
Keyword(s):  
Sensitivity Analysis ◽  
Snow Cover ◽  
Northern Hemisphere ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Coupled Model ◽  
Sensitivity Study ◽  
Snow Accumulation ◽  
Snow Cover Duration

Abstract A snowpack model sensitivity study, observed changes of snow cover in the NOAA satellite dataset, and snow cover simulations from the Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset are used to provide new insights into the climate response of Northern Hemisphere (NH) snow cover. Under conditions of warming and increasing precipitation that characterizes both observed and projected climate change over much of the NH land area with seasonal snow cover, the sensitivity analysis indicated snow cover duration (SCD) was the snow cover variable exhibiting the strongest climate sensitivity, with sensitivity varying with climate regime and elevation. The highest snow cover–climate sensitivity was found in maritime climates with extensive winter snowfall—for example, the coastal mountains of western North America (NA). Analysis of trends in snow cover duration during the 1966–2007 period of NOAA data showed the largest decreases were concentrated in a zone where seasonal mean air temperatures were in the range of −5° to +5°C that extended around the midlatitudinal coastal margins of the continents. These findings were echoed by the climate models that showed earlier and more widespread decreases in SCD than annual maximum snow water equivalent (SWEmax), with the zone of earliest significant decrease located over the maritime margins of NA and western Europe. The lowest SCD–climate sensitivity was observed in continental interior climates with relatively cold and dry winters, where precipitation plays a greater role in snow cover variability. The sensitivity analysis suggested a potentially complex elevation response of SCD and SWEmax to increasing temperature and precipitation in mountain regions as a result of nonlinear interactions between the duration of the snow season and snow accumulation rates.

scholarly journals Evaluation of Northern Hemisphere snow water equivalent in CMIP6 models with satellite-based SnowCCI data during 1982–2014

2021 ◽  
Author(s):  
Kerttu Kouki ◽  
Petri Räisänen ◽  
Kari Luojus ◽  
Anna Luomaranta ◽  
Aku Riihelä
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Global Climate ◽  
Dominant Factor ◽  
Large Variability ◽  
Cold Temperatures ◽  
Continental Scale ◽  
Snow Water

Abstract. Seasonal snow cover of the Northern Hemisphere (NH) is a major factor in the global climate system, which makes snow cover an important variable in climate models. Monitoring snow water equivalent (SWE) at continental scale is only possible from satellites, yet substantial uncertainties have been reported in NH SWE estimates. A recent bias-correction method significantly reduces the uncertainty of NH SWE estimation, which enables a more reliable analysis of the climate models' ability to describe the snow cover. We have intercompared the CMIP6 (Coupled Model Intercomparison Project Phase 6) and satellite-based NH SWE estimates north of 40° N for the period 1982–2014, and analyzed with a regression approach whether temperature (T) and precipitation (P) could explain the differences in SWE. We analyzed separately SWE in winter and SWE change rate in spring. The SnowCCI SWE data are based on satellite passive microwave radiometer data and in situ data. The analysis shows that CMIP6 models tend to overestimate SWE, however, large variability exists between models. In winter, P is the dominant factor causing SWE discrepancies especially in the northern and coastal regions. This is in line with the expectation that even too cold temperatures cannot cause too high SWE without precipitation. T contributes to SWE biases mainly in regions, where T is close to 0 °C in winter. In spring, the importance of T in explaining the snowmelt rate discrepancies increases. This is to be expected, because the increase in T is the main factor that causes snow to melt as spring progresses. Furthermore, it is obvious from the results that biases in T or P can not explain all model biases either in SWE in winter or in the snowmelt rate in spring. Other factors, such as deficiencies in model parameterizations and possibly biases in the observational datasets, also contribute to SWE discrepancies. In particular, linear regression suggests that when the biases in T and P are eliminated, the models generally overestimate the snowmelt rate in spring.

scholarly journals Detection and Attribution of Observed Changes in Northern Hemisphere Spring Snow Cover

2013 ◽  
Vol 26 (18) ◽  
pp. 6904-6914 ◽  
Author(s):  
David E. Rupp ◽  
Philip W. Mote ◽  
Nathaniel L. Bindoff ◽  
Peter A. Stott ◽  
David A. Robinson
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Internal Variability ◽  
Global Climate Models ◽  
Anthropogenic Forcing ◽  
Detection And Attribution ◽  
Snow Cover Extent

Abstract Significant declines in spring Northern Hemisphere (NH) snow cover extent (SCE) have been observed over the last five decades. As one step toward understanding the causes of this decline, an optimal fingerprinting technique is used to look for consistency in the temporal pattern of spring NH SCE between observations and simulations from 15 global climate models (GCMs) that form part of phase 5 of the Coupled Model Intercomparison Project. The authors examined simulations from 15 GCMs that included both natural and anthropogenic forcing and simulations from 7 GCMs that included only natural forcing. The decline in observed NH SCE could be largely explained by the combined natural and anthropogenic forcing but not by natural forcing alone. However, the 15 GCMs, taken as a whole, underpredicted the combined forcing response by a factor of 2. How much of this underprediction was due to underrepresentation of the sensitivity to external forcing of the GCMs or to their underrepresentation of internal variability has yet to be determined.

scholarly journals Evaluation of snow cover and snow water equivalent in the continental Arctic in CMIP5 models

2020 ◽  
Vol 55 (11-12) ◽  
pp. 2993-3016
Author(s):  
María Santolaria-Otín ◽  
Olga Zolina
Keyword(s):  
Snow Cover ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Spatial And Temporal Patterns ◽  
Multiple Data ◽  
Snow Cover Extent ◽  
Snow Water ◽  
Intercomparison Project

Abstract Spatial and temporal patterns of snow cover extent (SCE) and snow water equivalent (SWE) over the terrestrial Arctic are analyzed based on multiple observational datasets and an ensemble of CMIP5 models during 1979–2005. For evaluation of historical simulations of the Coupled Model Intercomparison Project (CMIP5) ensemble, we used two reanalysis products, one satellite-observed product and an ensemble of different datasets. The CMIP5 models tend to significantly underestimate the observed SCE in spring but are in better agreement with observations in autumn; overall, the observed annual SCE cycle is well captured by the CMIP5 ensemble. In contrast, for SWE, the annual cycle is significantly biased, especially over North America, where some models retain snow even in summer, in disagreement with observations. The snow margin position (SMP) in the CMIP5 historical simulations is in better agreement with observations in spring than in autumn, when close agreement across the CMIP5 models is only found in central Siberia. Historical experiments from most CMIP5 models show negative pan-Arctic trends in SCE and SWE. These trends are, however, considerably weaker (and less statistically significant) than those reported from observations. Most CMIP5 models can more accurately capture the trend pattern of SCE than that of SWE, which shows quantitative and qualitative differences with the observed trends over Eurasia. Our results demonstrate the importance of using multiple data sources for the evaluation of snow characteristics in climate models. Further developments should focus on the improvement of both dataset quality and snow representation in climate models, especially ESM-SnowMIP.

Modeled Climate Responses to Realistic Extremes of Northern Hemisphere Spring and Summer Snow Anomalies

2020 ◽  
Vol 33 (22) ◽  
pp. 9905-9927
Author(s):  
Shizuo Liu ◽  
Qigang Wu ◽  
Lin Wang ◽  
Steven R. Schroeder ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Snow Cover ◽  
Northern Hemisphere ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Surface Air Temperature ◽  
Early Summer ◽  
The Arctic ◽  
Snow Cover Extent ◽  
Climate Responses

AbstractNorthern Hemisphere (NH) snow cover extent (SCE) has diminished in spring and early summer since the 1960s. Historical simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) estimated about half as much NH SCE reduction as observed, and thus underestimated the associated climate responses. This study investigates atmospheric responses to realistic decreasing snow anomalies using multiple ensemble transient integrations of climate models forced by observed light and heavy NH snow cover years, specifically satellite-based observations of NH SCE and snow water equivalent from March to August in 1990 (light snow) and 1985 (heavy snow), as a proxy for the trend. The primary atmospheric responses to March–August NH snow reduction are decreased soil moisture, increased surface air temperature, general tropospheric warming in the extratropics and the Arctic, increased geopotential heights, and weakening of the midlatitude jet stream and eddy kinetic energy. The localized response is maintained by persistent increased diabatic heating due to reduced snow anomalies and resulting soil moisture drying, and the remote atmospheric response results partly from horizontal propagation of stationary Rossby wave energy and also from a transient eddy feedback mechanism. In summer, atmospheric responses are significant in both the Arctic and the tropics and are mostly induced by contemporaneous snow forcing, but also by the summer soil moisture dry anomaly associated with early snow melting.

scholarly journals Northern Hemisphere Snow-Cover Trends (1967–2018): A Comparison between Climate Models and Observations

Geosciences ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 135 ◽  
Author(s):  
◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Climate Models ◽  
Climate Model ◽  
Coupled Model ◽  
Poor Performance ◽  
Four Seasons ◽  
Intercomparison Project ◽  
Autumn And Winter

Observed changes in Northern Hemisphere snow cover from satellite records were compared to those predicted by all available Coupled Model Intercomparison Project Phase 5 (“CMIP5”) climate models over the duration of the satellite’s records, i.e., 1967–2018. A total of 196 climate model runs were analyzed (taken from 24 climate models). Separate analyses were conducted for the annual averages and for each of the seasons (winter, spring, summer, and autumn/fall). A longer record (1922–2018) for the spring season which combines ground-based measurements with satellite measurements was also compared to the model outputs. The climate models were found to poorly explain the observed trends. While the models suggest snow cover should have steadily decreased for all four seasons, only spring and summer exhibited a long-term decrease, and the pattern of the observed decreases for these seasons was quite different from the modelled predictions. Moreover, the observed trends for autumn and winter suggest a long-term increase, although these trends were not statistically significant. Possible explanations for the poor performance of the climate models are discussed.

scholarly journals The sensitivity of modeled snow accumulation and melt to precipitation phase methods across a climatic gradient

2019 ◽  
Author(s):  
Keith S. Jennings ◽  
Noah P. Molotch
Keyword(s):  
Snow Cover ◽  
Sierra Nevada ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Phase Method ◽  
Annual Peak ◽  
Method Selection ◽  
Snow Cover Duration ◽  
Precipitation Phase

Abstract. A critical component of hydrologic modeling in cold and temperate regions is partitioning precipitation into snow and rain, yet little is known about how uncertainty in precipitation phase propagates into variability in simulated snow accumulation and melt. Given the wide variety of methods for distinguishing between snow and rain, it is imperative to evaluate the sensitivity of snowpack model output to precipitation phase determination methods, especially considering the potential of snow-to-rain shifts associated with climate warming to fundamentally change the hydrology of snow-dominated areas. To address these needs we quantified the sensitivity of modeled snow accumulation and melt to rain-snow partitioning at research sites in the western United States. Simulations using the physics-based SNOWPACK model and 12 different precipitation phase methods indicated maritime sites were the most sensitive to method selection. Relative differences between the minimum and maximum annual snowfall fractions predicted by the different methods sometimes exceeded 100 % at elevations less than 2000 m in the Oregon Cascades and California’s Sierra Nevada mountains. This led to ranges in annual peak snow water equivalent (SWE) typically greater than 200 mm, exceeding 400 mm in certain years. At the warmer sites, ranges in snowmelt timing predicted by the different methods were generally larger than 2 weeks, while ranges in snow cover duration approached 1 month and greater. Conversely, the three coldest sites in this work were relatively insensitive to the choice of a precipitation phase method with average ranges in annual snowfall fraction, peak SWE, snowmelt timing, and snow cover duration less than 18 %, 62 mm, 10 d, and 15 d, respectively. Overall, sites with a greater proportion of precipitation falling at air temperatures between 0 °C and 4 °C exhibited the greatest sensitivity to method selection. These findings have large implications for modeled snowpack water storage and land surface albedo at the warmer fringes of the seasonal snow zone.

scholarly journals Projected 21st century changes in snow water equivalent over Northern Hemisphere landmasses from the CMIP5 model ensemble

2015 ◽  
Vol 9 (5) ◽  
pp. 1943-1953 ◽  
Author(s):  
H. X. Shi ◽  
C. H. Wang
Keyword(s):  
Northern Hemisphere ◽  
21St Century ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Global Climate ◽  
Coupled Model ◽  
Global Climate Models ◽  
The Tibetan Plateau ◽  
Model Ensemble ◽  
Snow Water

Abstract. Changes in snow water equivalent (SWE) over Northern Hemisphere (NH) landmasses are investigated for the early (2016–2035), middle (2046–2065) and late (2080–2099) 21st century using a multi-model ensemble from 20 global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The multi-model ensemble was found to provide a realistic estimate of observed NH mean winter SWE compared to the GlobSnow product. The multi-model ensemble projects significant decreases in SWE over the 21st century for most regions of the NH for representative concentration pathways (RCPs) 2.6, 4.5 and 8.5. This decrease is particularly evident over the Tibetan Plateau and North America. The only region with projected increases is eastern Siberia. Projected reductions in mean annual SWE exhibit a latitudinal gradient with the largest relative changes over lower latitudes. SWE is projected to undergo the largest decreases in the spring period where it is most strongly negatively correlated with air temperature. The reduction in snowfall amount from warming is shown to be the main contributor to projected changes in SWE during September to May over the NH.

scholarly journals Linear trends of the length of snow-cover season in the Northern Hemisphere as observed by the satellites in the period 1972–2000

2004 ◽  
Vol 38 ◽  
pp. 229-237 ◽  
Author(s):  
Kunio Rikiishi ◽  
Eisuke Hashiya ◽  
Masafumi Imai
Keyword(s):  
Global Warming ◽  
Snow Cover ◽  
Northern Hemisphere ◽  
Rocky Mountains ◽  
Data Center ◽  
High Elevation ◽  
Snow Accumulation ◽  
Snow Cover Area ◽  
Sea Ice Extent ◽  
Snow Cover Duration

AbstractThe dataset of Northern Hemisphere EASE-Grid weekly snow cover and sea-ice extent (U.S. National Snow and Ice Data Center) for the period September 1972–August 2000 is analyzed to examine the possible influence of recent global warming on the seasonal change of snow cover in the Northern Hemisphere. It is found that the total snow-cover area in the 1980s and 1990s is diminished by 36106 km2, and the length of snow-cover season is reduced by 2 –3 weeks, as compared with the 1970s. In general, the contribution from earlier snowmelt is greater than that from delayed snow accumulation. In addition, the maximum snow-cover area during January–February has gradually decreased by about 36106 km2 within the two decades. Geographically, the rate of decrease of snow-cover duration is 50.1 week per year (wpy) in the high-latitude regions such as the Siberian Plains and Northwest Territories of Canada and 40.2 wpy in the high-elevation regions such as the Scandinavian Peninsula, Tibetan Plateau and Rocky Mountains. The earlier snowmelt in the high-elevation regions suggests that the snowfall amounts there are decreasing owing to global warming.

Can the latest generation of regional climate models reproduce European snow conditions and how do biases translate into uncertainties of snow cover projections?

2020 ◽  
Author(s):  
Katharina Bülow ◽  
Sven Kotlarski ◽  
Christian Steger ◽  
Claas Teichmann
Keyword(s):  
Snow Cover ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Information Source ◽  
High Elevation ◽  
Horizontal Resolution ◽  
Mountainous Regions ◽  
Snow Cover Extent ◽  
Snow Water ◽  
Snow Cover Duration

<p>Snow cover is a crucial part of the climate system due to its distinctive alteration of surface reflectance (snow-albedo-feedback) and its influence on further physical surface properties (e.g. heat conduction and water storage). These effects are particularly relevant in alpine areas and high latitude regions, where snow coverage prevails for a significant part of the season. In addition, various human activities rely on snow cover duration and/or snow amounts, such as winter tourism, agriculture and hydropower production.</p><p>The EURO-CORDEX project provides an RCM ensemble with a horizontal resolution of ~50 and ~12 km for both present-day and future climates assuming different emission scenarios. These simulations present a potentially valuable information source for the future snow cover evolution. Prerequisite, however, is the ability of RCMs to reproduce historical snow cover conditions. These issues are addressed in the present work on a European scale. A horizontal resolution of ~12 km allows for an improved representation of topography and is thus particularly interesting for snow cover studies, as snow in alpine regions strongly correlates with elevation. We therefore only consider the high-resolution EURO-CORDEX RCMs and, for the climate projection part, simulations for RCP2.6, RCP4.5 and RCP8.5.</p><p>To assess the RCMs’ ability of reproducing current snow cover conditions in Europe, we evaluate simulated snow water equivalent and snow cover duration/extent by comparison against different reanalysis data (e.g. ERA5, UERRA MESCAN-SURFEX) and snow products derived from remote sensing. Regarding the spatial domain, we consider entire Europe with a focus on four mountainous regions (Alps, Norway, Pyrenees and Carpathians). The evaluation reveals that, on an European scale, mean yearly snow cover duration is well captured by the ensemble mean of the models. However, the majority of the RCMs underestimates snow cover extent throughout the season. This bias is more pronounced in the reanalysis (ERA-Interim) driven set of simulations than in the GCM-driven runs. In regions with complex topography, winter snow water equivalent is distinctively overestimated in some simulations - whereas certain grid cells reveal glaciation (i.e. year-round snow coverage). A comparison with E-OBS data indicates that biases in snow cover duration and amount are, besides arising from inaccurate snow schemes, linked to mismatches in simulated air temperature and precipitation patterns. Scenarios for the 21st century show a distinctive reduction in snow cover duration for low-elevation regions, whereas the magnitude of this decrease depends, amongst other factors, on the climate scenario. Projected decreases in the snow cover are less pronounced for medium to high-elevation regions.</p>

scholarly journals Nimbus-7 SMMR Derived Global Snow Cover Parameters

1987 ◽  
Vol 9 ◽  
pp. 39-44 ◽  
Author(s):  
A.T.C. Chang ◽  
J.L. Foster ◽  
D.K. Hall
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Snow Water Equivalent ◽  
Theoretical Calculations ◽  
Winter Season ◽  
Western China ◽  
High Plains ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Snow covers about 40 million km2of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

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