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 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.

scholarly journals Estimating Snow Cover Duration from Ground Temperature

2015 ◽  
Vol 54 (5) ◽  
pp. 959-965 ◽  
Author(s):  
Irene E. Teubner ◽  
Leopold Haimberger ◽  
Michael Hantel
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Rank Correlation ◽  
Correlation Coefficients ◽  
Ground Temperature ◽  
Large Dataset ◽  
Diurnal Range ◽  
Snow Water ◽  
Snow Cover Duration ◽  
Primary Variable

AbstractSnow cover duration is commonly derived from snow depth, snow water equivalent, or satellite data. Snow cover duration has more recently also been inferred from ground temperature data. In this study, a probabilistic snow cover duration (SCD) model is introduced that estimates the conditional probability for snow cover given the daily mean and the diurnal range of ground temperature. For the application of the SCD model, 87 Austrian sites in the Alpine region are investigated in the period of 2000 to 2011. The daily range of ground temperature is identified to represent the primary variable in determining the snow cover duration. In the case of a large dataset, however, the inclusion of the daily mean ground temperature as the second given parameter improves results. Rank correlation coefficients of predicted versus observed snow cover duration are typically between 0.8 and 0.9.

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 Evaluation of Remotely Sensed Snow Water Equivalent and Snow Cover Extent over the Contiguous United States

2018 ◽  
Vol 19 (11) ◽  
pp. 1777-1791 ◽  
Author(s):  
Nicholas Dawson ◽  
Patrick Broxton ◽  
Xubin Zeng
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Snow Cover Extent ◽  
Snow Water ◽  
The Mean

Abstract Global snow water equivalent (SWE) products derived at least in part from satellite remote sensing are widely used in weather, climate, and hydrometeorological studies. Here we evaluate three such products using our recently developed daily 4-km SWE dataset available from October 1981 to September 2017 over the conterminous United States. This SWE dataset is based on gridded precipitation and temperature data and thousands of in situ measurements of SWE and snow depth. It has a 0.98 correlation and 30% relative mean absolute deviation with Airborne Snow Observatory data and effectively bridges the gap between small-scale lidar surveys and large-scale remotely sensed data. We find that SWE products using remote sensing data have large differences (e.g., the mean absolute difference from our SWE data ranges from 45.8% to 59.3% of the mean SWE in our data), especially in forested areas (where this percentage increases up to 73.5%). Furthermore, they consistently underestimate average maximum SWE values and produce worse SWE (including spurious jumps) during snowmelt. Three additional higher-resolution satellite snow cover extent (SCE) products are used to compare the SCE values derived from these SWE products. There is an overall close agreement between these satellite SCE products and SCE generated from our SWE data, providing confidence in our consistent SWE, snow depth, and SCE products based on gridded climate and station data. This agreement is also stronger than that between satellite SCE and those derived from the three satellite SWE products, further confirming the deficiencies of the SWE products that utilize remote sensing data.

scholarly journals Evaluation of the Snow Simulations from the Community Land Model, Version 4 (CLM4)

2015 ◽  
Vol 17 (1) ◽  
pp. 153-170 ◽  
Author(s):  
Ally M. Toure ◽  
Matthew Rodell ◽  
Zong-Liang Yang ◽  
Hiroko Beaudoing ◽  
Edward Kim ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Average Bias ◽  
Community Land Model ◽  
Model Version ◽  
Snow Cover Extent ◽  
Moderate Resolution ◽  
Snow Water ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract This paper evaluates the simulation of snow by the Community Land Model, version 4 (CLM4), the land model component of the Community Earth System Model, version 1.0.4 (CESM1.0.4). CLM4 was run in an offline mode forced with the corrected land-only replay of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-Land) and the output was evaluated for the period from January 2001 to January 2011 over the Northern Hemisphere poleward of 30°N. Simulated snow-cover fraction (SCF), snow depth, and snow water equivalent (SWE) were compared against a set of observations including the Moderate Resolution Imaging Spectroradiometer (MODIS) SCF, the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover, the Canadian Meteorological Centre (CMC) daily snow analysis products, snow depth from the National Weather Service Cooperative Observer (COOP) program, and Snowpack Telemetry (SNOTEL) SWE observations. CLM4 SCF was converted into snow-cover extent (SCE) to compare with MODIS SCE. It showed good agreement, with a correlation coefficient of 0.91 and an average bias of −1.54 × 102 km2. Overall, CLM4 agreed well with IMS snow cover, with the percentage of correctly modeled snow–no snow being 94%. CLM4 snow depth and SWE agreed reasonably well with the CMC product, with the average bias (RMSE) of snow depth and SWE being 0.044 m (0.19 m) and −0.010 m (0.04 m), respectively. CLM4 underestimated SNOTEL SWE and COOP snow depth. This study demonstrates the need to improve the CLM4 snow estimates and constitutes a benchmark against which improvement of the model through data assimilation can be measured.

scholarly journals The Response of Areal Snow Cover to Climate Change in a Snowmelt—Runoff Model

1997 ◽  
Vol 25 ◽  
pp. 232-236 ◽  
Author(s):  
A. Rango
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Major Influence ◽  
Snowmelt Runoff ◽  
Snow Cover Extent ◽  
Physically Based ◽  
Depletion Rate ◽  
Snow Water ◽  
Runoff Model

The cryosphere is represented in some hydrological models by the arcal extent of snow cover, a variable that has been operationally available in recent years through remote sensing. In particular, the snowmelt runoff model (SRM) requires the remotely sensed snow-cover extent as a major input variable. The SRM is well-suited for simulating the hydrological response of a basin to hypothetical climate change because it is a non-calibrated model. In order to run the SRM in a climate-change mode, the response of the areal snow cover to a change in climate is critical, and must be calculated as a function of elevation, precipitation, temperature, and snow-water equivalent. For the snowmelt-runoff season, the effect of climate change on conditions in the winter months has a major influence. In a warmer climate, winter may experience more rain vs snow events, and more periods of winter snowmelt that reduce the snow water equivalent present in the basin at the beginning of spring snow melt. As a result, the spring snowmelt runoff under conditions of climate warming will be affected not only by different temperatures and precipitation, but also by a different snow cover with a changed depletion rate. A new radiation-based version of the SRM is under development that will also take changes in cloudiness and humidity into account, making climate-change studies of the cryosphere even more physically based.

scholarly journals Investigating the Variability of High-Elevation Seasonal Orographic Snowfall Enhancement and Its Drivers across Sierra Nevada, California

2018 ◽  
Vol 19 (1) ◽  
pp. 47-67 ◽  
Author(s):  
Laurie S. Huning ◽  
Steven A. Margulis
Keyword(s):  
Sierra Nevada ◽  
Snow Water Equivalent ◽  
High Elevation ◽  
Mountain Range ◽  
Distributed Data ◽  
Mountainous Regions ◽  
Average Maximum ◽  
Spatially Distributed ◽  
Orographic Enhancement ◽  
Snow Water

Abstract While orographically driven snowfall is known to be important in mountainous regions, a complete understanding of orographic enhancement from the basin to the mountain range scale is often inhibited by limited distributed data and spatial and/or temporal resolutions. A novel, 90-m spatially distributed snow water equivalent (SWE) reanalysis was used to overcome these limitations. Leveraging this SWE information, the interannual variability of orographic gradients in cumulative snowfall (CS) was investigated over 14 windward (western) basins in the Sierra Nevada in California from water years 1985 to 2015. Previous studies have not provided a detailed multidecadal climatology of orographic CS gradients or compared wet-year and dry-year orographic CS patterns, distributions, and gradients across an entire mountain range. The magnitude of seasonal CS gradients range from over 15 cm SWE per 100-m elevation to under 1 cm per 100 m with a 31-yr average of 6.1 cm per 100 m below ~2500 m in the western basins. The 31-yr average CS gradients generally decrease in higher elevation zones across the western basins and become negative at the highest elevations. On average, integrated vapor transport and zonal winds at 700 hPa are larger during wet years, leading to higher orographically driven CS gradients across the Sierra Nevada than in dry years. Below ~2500 m, wet years yield greater enhancement (relative to dry years) by factors of approximately 2 and 3 in the northwestern and southwestern basins, respectively. Overall, the western Sierra Nevada experiences about twice as much orographic enhancement during wet years as in dry years below the elevation corresponding to the 31-yr average maximum CS.

Five decades of warming: impacts on snow cover in Norway

2017 ◽  
Vol 49 (3) ◽  
pp. 670-688 ◽  
Author(s):  
Jonathan Rizzi ◽  
Irene Brox Nilsen ◽  
James Howard Stagge ◽  
Kjersti Gisnås ◽  
Lena M. Tallaksen
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Land Area ◽  
Air Temperatures ◽  
Northern Latitudes ◽  
Snow Cover Extent ◽  
The World ◽  
Snow Water ◽  
Seasonal Analysis ◽  
High Elevations

Abstract Northern latitudes are experiencing faster warming than other regions in the world, which is partly explained by the snow albedo feedback. In Norway, mean temperatures have been increasing since the 1990s, with 2014 being the warmest year on record, 2.2 °C above normal (1961–1990). At the same time, a concurrent reduction in the land area covered by snow has been reported. In this study, we present a detailed spatial and temporal (monthly and seasonal) analysis of trends and changes in snow indices based on a high resolution (1 km) gridded hydro-meteorological dataset for Norway (seNorge). During the period 1961–2010, snow cover extent (SCE) was found to decrease, notably at the end of the snow season, with a corresponding decrease in snow water equivalent except at high elevations. SCE for all Norway decreased by more than 20,000 km2 (6% of the land area) between the periods 1961–1990 and 1981–2010, mainly north of 63° N. Overall, air temperature increased in all seasons, with the highest increase in spring (particularly in April) and winter. Mean monthly air temperatures were significantly correlated with the monthly SCE, suggesting a positive land–atmosphere feedback enhancing warming in winter and spring.

scholarly journals A regional climate model hindcast for Siberia – assessing the added value of snow water equivalent using ESA GlobSnow and reanalyses

2012 ◽  
Vol 6 (6) ◽  
pp. 4637-4671
Author(s):  
K. Klehmet ◽  
B. Geyer ◽  
B. Rockel
Keyword(s):  
Regional Climate Model ◽  
Snow Cover ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Regional Climate ◽  
Snow Accumulation ◽  
Added Value ◽  
Snow Cover Extent ◽  
Snow Water

Abstract. This study analyzes the added value of a regional climate model hindcast of CCLM compared to global reanalyses in providing a reconstruction of recent past snow water equivalent (SWE) for Siberia. Consistent regional climate data in time and space is necessary due to lack of station data in that region. We focus on SWE since it represents an important snow cover parameter in a region where snow has the potential to feed back to the climate of the whole Northern Hemisphere. The simulation was performed in a 50 km grid spacing for the period 1948 to 2010 using NCEP Reanalysis 1 as boundary forcing. Daily observational reference data for the period of 1987–2010 was obtained by the satellite derived SWE product of ESA DUE GlobSnow that enables a large scale assessment. The analyses includes comparisons of the distribution of snow cover extent, example time series of monthly SWE for January and April, regional characteristics of long-term monthly mean, standard deviation and temporal correlation averaged over subregions. SWE of CCLM is compared against the SWE information of NCEP-R1 itself and three more reanalyses (NCEP-R2, NCEP-CFSR, ERA-Interim). We demonstrate a significant added value of the CCLM hindcast during snow accumulation period shown for January for many subregions compared to SWE of NCEP-R1. NCEP-R1 mostly underestimates SWE during whole snow season. CCLM overestimates SWE compared to the satellite-derived product during April – a month representing the beginning of snow melt in southern regions. We illustrate that SWE of the regional hindcast is more consistent in time than ERA-Interim and NCEP-R2 and thus add realistic detail.

scholarly journals Seasonal and Ephemeral Snowpacks of the Conterminous United States

2020 ◽  
Author(s):  
Benjamin Hatchett
Keyword(s):  
United States ◽  
New England ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Atlantic Coast ◽  
Horizontal Resolution ◽  
Upper Midwest ◽  
Long Duration ◽  
Lower Elevation ◽  
Snow Water

Snowpack seasonality in the conterminous United States (U.S.) is explored using a daily,14 km horizontal resolution gridded snow water equivalent and snow depth reanalysis product. I2calculated seasonal snowpacks using two established methods: (1) the classic Sturm approach that3requires 60 days of snow cover with a peak depth >50 cm and (2) the snow seasonality metric (SSM)4that only requires 60 days of continuous snow cover. The latter approach yields continuous values5from -1 to +1, where -1 (+1) indicates an ephemeral (seasonal) snowpack. Both approaches identify6seasonal snowpacks in western mountains and the northernmost central and eastern U.S. By relaxing7the depth constraint and providing continuous values, the SSM identifies greater areas of seasonal8snowpacks compared to the Sturm method, particularly in the upper Midwest, New England, and the9Intermountain West. Ephemeral snowpacks are identified throughout lower elevation regions of the10western U.S. and across a broad swath centered near 35°N spanning the lee of the Rocky Mountains11to the Atlantic coast. Because it lacks a depth constraint, the SSM approach is sensitive to interannual12variability, indicating it may inform the location of shallow but long-duration snowpacks at risk13of transitioning to becoming ephemeral with climatic change. A case study in Oregon during an14extreme snow drought year highlights seasonal to ephemeral snowpack transitions.

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