ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks

Krinner, Gerhard; Derksen, Chris; Essery, Richard; Flanner, M.; Hagemann, Stefan; Clark, Martyn P.; Hall, Alex; Rott, Helmut; Brutel‐Vuilmet, Claire; Kim, Hyungjun; Ménard, Cécile B.; Mudryk, Lawrence; Thackeray, Chad W.; Wang, Libo; Arduini, Gabriele; Balsamo, Gianpaolo; Bartlett, Paul; Boike, Julia; Boone, Aaron; Chéruy, F.; Colin, Jeanne; Cuntz, Matthias; Dai, Yongjiu; Decharme, Bertrand; Derry, Jeff; Ducharne, Agnès; Dutra, Emanuel; Fang, Xing; Fierz, Charles; Ghattas, Josephine; Gusev, Yeugeniy M.; Haverd, Vanessa; Kontu, Anna; Lafaysse, Matthieu; Law, R. M.; Lawrence, David M.; Li, Weiping; Marke, Thomas; Marks, Danny; Ménégoz, Martin; Насонова, О. Н.; Nitta, Tomoko; Niwano, Michio; Pomeroy, John W.; Raleigh, M. S.; Schaedler, G.; Семенов, В. А.; Smirnova, Tanya; Stacke, Tobias; Strasser, Ulrich; Svenson, Sean; Turkov, Dmitry; Wang, Tao; Wever, Nander; Yuan, Hua; Zhou, Wenyan; Zhu, Dan

doi:10.5194/gmd-11-5027-2018

Cited by 168 publications

(153 citation statements)

References 124 publications

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“…Our results suggest that ignoring a source of uncertainty might not just affect sensitivity index values relative to each other (as shown in Figures 10 and 12) but can even change which variable is found to be the most influential on model output performance (Figure 11). These findings highlight the difficulty of extrapolating results from SA of a single model to other snow models and have direct implications for future model intercomparison studies as in the past these tended to focus primarily on model structures (e.g., Krinner et al, 2018;Lafaysse et al, 2017). The results show a considerable interannual variability of the interaction effect, indicating for years with high interactions that an improvement of knowledge (i.e., reduction of uncertainty) of one factor alone might not improve model results (Baroni & Tarantola, 2014).…”

Section: Interaction Effectsmentioning

confidence: 98%

Uncertainties in Snowpack Simulations—Assessing the Impact of Model Structure, Parameter Choice, and Forcing Data Error on Point‐Scale Energy Balance Snow Model Performance

Günther

Marke

Essery

et al. 2019

Water Resources Research

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104

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In this study, we assess the impact of forcing data errors, model structure, and parameter choices on 1‐D snow simulations simultaneously within a global variance‐based sensitivity analysis framework. This approach allows inclusion of interaction effects, drawing a more representative picture of the resulting sensitivities. We utilize all combinations of a multiphysics snowpack model to mirror the influence of model structure. Uncertainty ranges of model parameters and input data are extracted from the literature. We evaluate a suite of 230,000 model realizations at the snow monitoring station Kühtai (Tyrol, Austria, 1,920 m above sea level) against snow water equivalent observations. The results show throughout the course of 25 winter seasons (1991–2015) and different model performance criteria a large influence of forcing data uncertainty and its interactions on the model performance. Mean interannual total sensitivity indices are in the general order of parameter choice < model structure < forcing error, with precipitation, air temperature, and the radiative forcings controlling the variance during the accumulation period and air temperature and longwave irradiance controlling the variance during the ablation period, respectively. Model skill is highly sensitive to the snowpack liquid water transport scheme throughout the whole winter period and to albedo representation during the ablation period. We found a sufficiently long evaluation period (>10 years) is required for robust averaging. A considerable interaction effect was revealed, indicating that an improvement in the knowledge (i.e., reduction of uncertainty) of one factor alone might not necessarily improve model results.

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Section: Interaction Effectsmentioning

confidence: 98%

Uncertainties in Snowpack Simulations—Assessing the Impact of Model Structure, Parameter Choice, and Forcing Data Error on Point‐Scale Energy Balance Snow Model Performance

Günther

Marke

Essery

et al. 2019

Water Resources Research

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104

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“…See Appendix for the definition of these quantities. NRMSE enables the performance (skill) of different models or experiments over different sites to be compared and quantified (Krinner et al, ).…”

Section: Evaluation Of the Offline Simulations At The Esm‐snowmip Sitesmentioning

confidence: 99%

“…This paper describes the implementation of a new multilayer snow scheme in the ECMWF Integrated Forecasting System (IFS) and its evaluation. First, the evaluation of the new scheme is performed using the in situ observations of the Earth System Model Snow Intercomparison Project (ESM‐SnowMIP; Krinner et al, ; Ménard et al, ). This data set includes meteorological, snow, and soil observations from 10 reference sites covering a wide range of climates, with at least 7 years of data for each site (some sites have more than 15 years of data).…”

Section: Introductionmentioning

confidence: 99%

Impact of a Multi‐Layer Snow Scheme on Near‐Surface Weather Forecasts

Arduini

Balsamo

Dutra

et al. 2019

J Adv Model Earth Syst

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Snow cover properties have a large impact on the partitioning of surface energy fluxes and thereby on near‐surface weather parameters. Snow schemes of intermediate complexity have been widely used for hydrological and climate studies, whereas their impact on typical weather forecast time scales has received less attention. A new multilayer snow scheme is implemented in the European Centre for Medium‐range Weather Forecasts Integrated Forecasting System and its impact on snow and 2‐m temperature forecasts is evaluated. The new snow scheme is evaluated offline at well‐instrumented field sites and compared to the current single‐layer scheme. The new scheme largely improves the representation of snow depth for most of the sites considered, reducing the root‐mean‐square error averaged over all sites by more than 30%. The improvements are due to a better description of snow density in thick and cold snowpacks, but also due to an improved representation of sporadic melting episodes because of the inclusion of a thin top snow layer with a low thermal inertia. The evaluation of coupled 10‐day weather forecasts shows an improved representation of snow depth at all lead times, demonstrating a positive impact at the global scale. Regarding the impact on weather parameters, the multilayer snow scheme improves the simulated minimum 2‐m temperature, by decreasing the positive bias and improving the amplitude of the diurnal cycle over snow‐covered regions. However, the increased variability of the 2‐m temperature can have a detrimental impact in regions characterized by preexisting errors in the daily mean temperature, associated with errors in cloud processes or surface albedo.

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“…Moreover, site-specific calibrations are expected to be suboptimal when applied over a wide diversity of sites (Krinner et al, 2018).…”

Section: Ensemble Modellingmentioning

confidence: 99%

Towards the assimilation of satellite reflectance into semi-distributed ensemble snowpack simulations

Cluzet

Revuelto

Lafaysse

et al. 2020

Cold Regions Science and Technology

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Uncertainties of snowpack models and of their meteorological forcings limit their use by avalanche hazard forecasters, or for glaciological and hydrological studies. The spatialized simulations currently available for avalanche hazard forecasting are only assimilating sparse meteorological observations. As suggested by recent studies, their forecasting skills could be significantly improved by assimilating satellite data such as snow reflectances from satellites in the visible and the near-infrared spectra. Indeed, these data can help constrain the microstructural properties of surface snow and light absorbing impurities content, which in turn affect the surface energy and mass budgets. This paper investigates the prerequisites of satellite data assimilation into a detailed snowpack model. An ensemble version ofMétéo-France operational snowpack forecasting system (named S2M) was built for this study. This operational system runs on topographic classes instead of grid points, so-called 'semi-distributed' approach. Each class corresponds to one of the 23 mountain massifs of the French Alps (about 1000km 2 each), an altitudinal range (by step of 300m) and aspect (by step of 45 o ). We assess the feasability of satellite data assimilation in such a semi-distributed geometry. Ensemble simulations are compared with satellite observations from MODIS and Sentinel-2, and with in-situ reflectance observations. The study focuses on the 2013-2014 and 2016-2017 winters in the Grandes-Rousses massif. Substantial Pearson R 2 correlations (0.75-0.90) of MODIS observations with simulations are found over the domain. This suggests that assimilating it could have an impact on the spatialized snowpack forecasting system. However, observations contain significant biases (0.1-0.2 in reflectance) which prevent their direct assimilation. MODIS spectral band ratios seem to be much less biased. This may open the way to an operational assimilation of MODIS reflectances into the Météo-France snowpack modelling system. Highlights -Ensemble simulations of the snowpack are compared with satellite reflectances -Spatial aggregation into the semi-distributed geometry filters the observation noises -Satellite reflectances carry useful information worth to assimilate -MODIS reflectances can not be directly assimilated because they are biased -Ratios of MODIS reflectances show no evidence of bias and could be assimilated

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ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks

Cited by 168 publications

References 124 publications

Uncertainties in Snowpack Simulations—Assessing the Impact of Model Structure, Parameter Choice, and Forcing Data Error on Point‐Scale Energy Balance Snow Model Performance

Uncertainties in Snowpack Simulations—Assessing the Impact of Model Structure, Parameter Choice, and Forcing Data Error on Point‐Scale Energy Balance Snow Model Performance

Impact of a Multi‐Layer Snow Scheme on Near‐Surface Weather Forecasts

Towards the assimilation of satellite reflectance into semi-distributed ensemble snowpack simulations

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