An analysis of present and future seasonal Northern Hemisphere land snow cover simulated by CMIP5 coupled climate models

Brutel‐Vuilmet, Claire; Ménégoz, Martin; Krinner, Gerhard

doi:10.5194/tc-7-67-2013

Cited by 141 publications

(137 citation statements)

References 44 publications

(47 reference statements)

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“…Recently, Brutel-Vuilmet et al (2013) found that, while there is still substantial intermodel dispersion among the CMIP5 models, on average the springtime snowmelt is slightly delayed in northern Eurasia. Taken at face value, the default version of ECHAM5 agrees with this result for the eastern parts of northern Eurasia, while in the west, snow vanishes too early (Figs.…”

Section: Discussionmentioning

confidence: 99%

“…These include several intercomparisons of snow conditions simulated by atmospheric and fully coupled general circulation models (GCMs) with observational data (Foster et al, 1996;Frei and Robinson, 1998;Frei et al, 2003Frei et al, , 2005Roesch, 2006;Derksen and Brown, 2012;Brutel-Vuilmet et al, 2013). Most recently, Brutel-Vuilmet et al (2013) evaluated the snow cover simulated by models participating in Phase 5 of the Coupled Model Intercomparison Project (CMIP5).…”

Section: P Räisänen Et Al: Snow-off Timing In Echam5mentioning

confidence: 99%

“…Derksen and Brown (2012) further demonstrated, for a subset of eight CMIP5 models, that the models failed to capture the rapid decline in Northern Hemisphere late spring (May-June) snow cover observed in 2008-2012. Regarding the reasons for biases in modelled snow conditions, the intercomparison studies have, in general, not been very conclusive. Most attention has been paid to biases in simulated air temperature (Foster et al, 1996;Räisänen, 2008) and total precipitation or snowfall (Foster et al, 1996;Roesch, 2006;Brutel-Vuilmet et al, 2013). Frei et al (2005) further suggested that the exclusion of subgrid-scale treatments for terrain and land cover contributed to overestimated ablation rate of snow in spring over North America in AMIP2 models.…”

Section: P Räisänen Et Al: Snow-off Timing In Echam5mentioning

confidence: 99%

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Evaluation of North Eurasian snow-off dates in the ECHAM5.4 atmospheric general circulation model

et al. 2014

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Abstract. The timing of springtime end of snowmelt (snowoff date) in northern Eurasia in version 5.4 of the ECHAM5 atmospheric general circulation model (GCM) is evaluated through comparison with a snow-off date data set based on space-borne microwave radiometer measurements and with Russian snow course data. ECHAM5 reproduces well the observed gross geographical pattern of snow-off dates, with earliest snow-off (in March) in the Baltic region and latest snow-off (in June) in the Taymyr Peninsula and in northeastern parts of the Russian Far East. The primary biases are (1) a delayed snow-off in southeastern Siberia (associated with too low springtime temperature and too high surface albedo, in part due to insufficient shielding by canopy); and (2) an early bias in the western and northern parts of northern Eurasia. Several sensitivity experiments were conducted, where biases in simulated atmospheric circulation were corrected through nudging and/or the treatment of surface albedo was modified. While this alleviated some of the model biases in snow-off dates, 2 m temperature and surface albedo, especially the early bias in snow-off in the western parts of northern Eurasia proved very robust and was actually larger in the nudged runs.A key issue underlying the snow-off biases in ECHAM5 is that snowmelt occurs at too low temperatures. Very likely, this is related to the treatment of the surface energy budget. On one hand, the surface temperature T s is not computed separately for the snow-covered and snow-free parts of the grid cells, which prevents T s from rising above 0 • C before all snow has vanished. Consequently, too much of the surface net radiation is consumed in melting snow and too little in heating the air. On the other hand, ECHAM5 does not include a canopy layer. Thus, while the albedo reduction due to canopy is accounted for, the shielding of snow on ground by the overlying canopy is not considered, which leaves too much solar radiation available for melting snow.

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Section: Discussionmentioning

confidence: 99%

Section: P Räisänen Et Al: Snow-off Timing In Echam5mentioning

confidence: 99%

Section: P Räisänen Et Al: Snow-off Timing In Echam5mentioning

confidence: 99%

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Evaluation of North Eurasian snow-off dates in the ECHAM5.4 atmospheric general circulation model

et al. 2014

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“…A standard target for snow process analysis in climate models is snow-cover extent trends, which are strongly temperature 25 controlled (e.g. Brutel-Vuilmet et al, 2013;Mudryk et al, 2017). Assessing the ability of models to capture these trends needs to account for natural variability, forced variability, observational uncertainty, and intermodel differences.…”

Section: Canesm2 Climatology and Trendsmentioning

confidence: 99%

Assessment of Snow, Sea Ice, and Related Climate Processes in Canada's Earth-System Model and Climate Prediction System

Kushner

Mudryk

Merryfield

et al. 2017

Preprint

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Abstract. This study assesses the ability of the Canadian Seasonal to Interannual Prediction System (CanSIPS) and the Canadian Earth-system Model 2 (CanESM2) to predict and simulate snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth-System Models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5) archive, and initial condition ensembles of CanESM2 and other 25 models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as coldregion temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow cover over the Canadian land mass, reflecting a broader Northern Hemisphere positive bias. It also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational 30 uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea-ice trends there. The strengths and weaknesses of the modeling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their use in operational seasonal prediction and for generating large ensembles of multidecadal simulations.Improvements in climate prediction systems like CanSIPS rely not just on simulation quality but also on using novel 35The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-157 Manuscript under review for journal The Cryosphere Discussion started: 5 September 2017 c Author(s) 2017. CC BY 4.0 License. 2 observational constraints and the ready transfer of research to an operational setting. Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea-ice thickness initialization using statistical predictors available in real time.

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“…Brutel-Vuilmet et al (2013) have compared the 20th century seasonal northern hemisphere land snow cover as simulated by available CMIP5 model output to observations, and the results show that the models reproduce the observed snow cover extent very well on average but the significant trend toward a reduced spring snow cover extent over the 1979-2005 period is underestimated. Zhu and Dong (2013) have pointed out that the CMIP5 coupled models can catch the main distribution characteristics of the observed northern hemisphere March-April snow cover but overestimate the mean snow cover in complex terrain area such as Qinghai-Tibet Plateau.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Simulations of Snow Depth in the Qinghai-Tibetan Plateau Using CMIP5 Multi-Models

Wei

Dong

2015

Arctic, Antarctic, and Alpine Research

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An analysis of present and future seasonal Northern Hemisphere land snow cover simulated by CMIP5 coupled climate models

Cited by 141 publications

References 44 publications

Evaluation of North Eurasian snow-off dates in the ECHAM5.4 atmospheric general circulation model

Evaluation of North Eurasian snow-off dates in the ECHAM5.4 atmospheric general circulation model

Assessment of Snow, Sea Ice, and Related Climate Processes in Canada's Earth-System Model and Climate Prediction System

Assessment of Simulations of Snow Depth in the Qinghai-Tibetan Plateau Using CMIP5 Multi-Models

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