Snow cover duration trends observed at sites  and predicted by multiple models

Essery, Richard; Kim, Hyungjun; Wang, Libo; Bartlett, Paul; Boone, Aaron; Brutel‐Vuilmet, Claire; Burke, Eleanor J.; Cuntz, Matthias; Decharme, Bertrand; Dutra, Emanuel; Fang, Xing; Gusev, Yeugeniy M.; Hagemann, Stefan; Haverd, Vanessa; Kontu, Anna; Krinner, Gerhard; Lafaysse, Matthieu; Lejeune, Yves; Marke, Thomas; Marks, Danny; Marty, Christoph; Ménard, Cécile B.; Насонова, О. Н.; Nitta, Tomoko; Pomeroy, John W.; Schädler, Gerd; Семенов, В. А.; Smirnova, Tatiana G.; Swenson, Sean; Turkov, Dmitry; Wever, Nander; Yuan, Hua

doi:10.5194/tc-14-4687-2020

Cited by 23 publications

(8 citation statements)

References 27 publications

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“…Unfortunately, few if any comparable data sets are currently available, since most reanalyses have coarser resolution than ERA5-Land and/or have artificial sources or sinks of snow due to the assimilation of snow observations (as, for example, in the parent ERA5 reanalysis). Regarding the simulation of the snow-on-ground fraction, offline comparison of land surface models represents one way forward (Essery et al, 2020). The big picture, in which interannual SWE variability is dominated by variations in winter precipitation in colder areas and by variations in the snow-on-ground and snowfall fractions in milder areas is, however, consistent with simple physical reasoning.…”

Section: Discussionmentioning

confidence: 86%

“…A caveat in any model-based analysis is that climate changes in the real world may or may not follow the model projections. Interestingly, despite a decrease in winter mean and maximum snow depth in large parts of Europe since the 1950s (Fontrodona Bach et al, 2018), Skaugen et al (2012) found generally positive trends in the winter maximum SWE above 850 m altitude in southern Norway in the period 1931-2009. On a larger scale, Zhong et al (2018) analysed observations of winter maximum snow depth in the former Soviet Union, Mongolia and China, finding an average positive trend of 0.6 cm per decade from 1966 through 2012.…”

Section: Further Discussion Of Swe Changes: Future Projections Versus Interannual Variability and Observed Trendsmentioning

confidence: 91%

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Snow conditions in northern Europe: the dynamics of interannual variability versus projected long-term change

Räisänen

2021

The Cryosphere

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Abstract. Simulations by the EURO-CORDEX (European branch of the Coordinated Regional Climate Downscaling Experiment) regional climate models indicate a widespread future decrease in snow water equivalent (SWE) in northern Europe. This concurs with the negative interannual correlation between SWE and winter temperature in the southern parts of the domain but not with the positive correlation observed further north and over the Scandinavian mountains. To better understand these similarities and differences, interannual variations and projected future changes in SWE are attributed to anomalies or changes in three factors: total precipitation, the snowfall fraction of precipitation and the fraction of accumulated snowfall that remains on the ground (the snow-on-ground fraction). In areas with relatively mild winter climate, the latter two terms govern both the long-term change and interannual variability, resulting in less snow with higher temperatures. In colder areas, however, interannual SWE variability is dominated by variations in total precipitation. Since total precipitation is positively correlated with temperature, more snow tends to accumulate in milder winters. Still, even in these areas, SWE is projected to decrease in the future due to the reduced snowfall and snow-on-ground fractions in response to higher temperatures. Although winter total precipitation is projected to increase, its increase is smaller than would be expected from the interannual covariation of temperature and precipitation and is therefore insufficient to compensate the lower snowfall and snow-on-ground fractions. Furthermore, interannual SWE variability in northern Europe in the simulated warmer future climate is increasingly governed by variations in the snowfall and snow-on-ground fractions and less by variations in total precipitation.

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Section: Discussionmentioning

confidence: 86%

Section: Further Discussion Of Swe Changes: Future Projections Versus Interannual Variability and Observed Trendsmentioning

confidence: 91%

Snow conditions in northern Europe: the dynamics of interannual variability versus projected long-term change

Räisänen

2021

The Cryosphere

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“…SWE (snow water equivalent) is the amount of water contained in the snowpack (in units of kg m −2 ) or, equivalently, the height of the water layer (in units of mm) that would result from melting the whole snowpack instantaneously (Fierz et al, 2009). Recent studies show negative trends in global SWE (Bormann et al, 2018;Derksen and Brown, 2012;Essery et al, 2020;Hernández-Henríquez et al, 2015;Mortimer et al, 2020;Mudryk et al, 2017), but significant spatial variability exists: North America shows clear negative trends in observed SWE, while negative trends are less pronounced in Eurasia (Kunkel et al, 2016;Pulliainen et al, 2020). At mid-latitudes, SWE is more sensitive to warming than at high latitudes (Brown and Mote, 2009).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Northern Hemisphere snow water equivalent in CMIP6 models during 1982–2014

et al. 2022

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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. Previously, substantial uncertainties have been reported in NH snow water equivalent (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 NH SWE estimates between CMIP6 (Coupled Model Intercomparison Project Phase 6) models and observation-based SWE reference data north of 40∘ N for the period 1982–2014 and analyzed with a regression approach whether model biases in temperature (T) and precipitation (P) could explain the model biases in SWE. We analyzed separately SWE in winter and SWE change rate in spring. For SWE reference data, we used bias-corrected SnowCCI data for non-mountainous regions and the mean of Brown, MERRA-2 and Crocus v7 data for the mountainous regions. The SnowCCI SWE data are based on satellite passive microwave radiometer data and in situ snow depth 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. 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 cannot 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.

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“…Hence, the same valid dates are considered for the model. This approach has been used in a wide range of climates (Richer et al, 2013;Moore et al, 2015;Saavedra et al, 2017) to identify transitions between rainfall and snowmelt peak streamflow source regimes (Kampf and Lefsky, 2016) and for predicting water yield (Saavedra et al, 2017;Hammond et al, 2018). Two SP indices were extracted considering both S2 snow maps and model simulation.…”

Section: Discussionmentioning

confidence: 99%

“…A detailed description of the functionality and parameters of the snowmaking and grooming modules, which are used in all of the snowpack models, is shown in the study by Hanzer et al (2020) and the applied configuration for each ski resort in Table 1. The three snowpack models, AMUNDSEN, Crocus, and SNOWPACK-Alpine3D, are well established and have been widely applied in numerous studies throughout the past decades (Essery et al, 2020;Krinner et al, 2018). All three models require spatial input data for the snow management simulations consisting of a digital elevation model (DEM) covering the study sites, the locations of the ski slopes, and the locations and types of the snow guns (snow lances or snow fans -corresponding to different production rates for given ambient conditions as defined in Table 5 in Hanzer et al, 2020).…”

Section: Snowpack Modelsmentioning

confidence: 99%

Evaluating a prediction system for snow management

Ebner¹,

Koch

Premier

et al. 2021

The Cryosphere

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Abstract. The evaluation of snowpack models capable of accounting for snow management in ski resorts is a major step towards acceptance of such models in supporting the daily decision-making process of snow production managers. In the framework of the EU Horizon 2020 (H2020) project PROSNOW, a service to enable real-time optimization of grooming and snow-making in ski resorts was developed. We applied snow management strategies integrated in the snowpack simulations of AMUNDSEN, Crocus, and SNOWPACK–Alpine3D for nine PROSNOW ski resorts located in the European Alps. We assessed the performance of the snow simulations for five winter seasons (2015–2020) using both ground-based data (GNSS-measured snow depth) and spaceborne snow maps (Copernicus Sentinel-2). Particular attention has been devoted to characterizing the spatial performance of the simulated piste snow management at a resolution of 10 m. The simulated results showed a high overall accuracy of more than 80 % for snow-covered areas compared to the Sentinel-2 data. Moreover, the correlation to the ground observation data was high. Potential sources for local differences in the snow depth between the simulations and the measurements are mainly the impact of snow redistribution by skiers; compensation of uneven terrain when grooming; or spontaneous local adaptions of the snow management, which were not reflected in the simulations. Subdividing each individual ski resort into differently sized ski resort reference units (SRUs) based on topography showed a slight decrease in mean deviation. Although this work shows plausible and robust results on the ski slope scale by all three snowpack models, the accuracy of the results is mainly dependent on the detailed representation of the real-world snow management practices in the models. As snow management assessment and prediction systems get integrated into the workflow of resort managers, the formulation of snow management can be refined in the future.

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Snow cover duration trends observed at sites and predicted by multiple models

Cited by 23 publications

References 27 publications

Snow conditions in northern Europe: the dynamics of interannual variability versus projected long-term change

Snow conditions in northern Europe: the dynamics of interannual variability versus projected long-term change

Evaluation of Northern Hemisphere snow water equivalent in CMIP6 models during 1982–2014

Evaluating a prediction system for snow management

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