A multiphysical ensemble system of numerical snow modelling

Lafaysse, Matthieu; Cluzet, Bertrand; Dumont, Marie; Lejeune, Yves; Vionnet, Vincent; Morin, Samuel

doi:10.5194/tc-11-1173-2017

Cited by 99 publications

(158 citation statements)

References 73 publications

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“…These results endorse the idea that snowpack ensemble simulations are necessary to mitigate error compensations, as recently developed for Crocus with ESCROC (Ensemble System Crocus; Lafaysse et al, 2017).…”

supporting

confidence: 57%

“…The parametrisation of the albedo evolu-485 tion also needs to be questioned: Lafaysse et al (2017) underlined a positive bias of Crocussimulated albedo at Col de Porte (Fig. 1a), which they partly attributed to the parametrisation of albedo decrease in the visible range as a function of the age of the snow layer and the altitude of the site.…”

mentioning

confidence: 99%

“…They should tackle factors such as the turbulent fluxes simulated by 550 AROME-Crocus and the albedo parametrisation in Crocus. Ensemble snowpack modelling would also enable to account for simulation errors (Lafaysse et al, 2017). Apart from the AROME-Crocus modelling context, in the light of the quality assessment performed in Sect.…”

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confidence: 99%

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Satellite products of incoming solar and longwave radiations used for snowpack modelling in mountainous terrain

Quéno¹,

Karbou²,

Vionnet³

et al. 2017

Preprint

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Abstract. In mountainous terrain, the snowpack is strongly affected by incoming shortwave and longwave radiations. In this study, a thorough evaluation of the incoming solar and longwave radiation products (DSSF and DSLF) derived from the Meteosat Second Generation satellite was undertaken in the French Alps and the Pyrenees. The satellite products were compared with forecast fields from the meteorological model AROME and with analyses fields from the SAFRAN system. 5A new satellite-derived product (DSLFnew) was developed by combining satellite observations and AROME forecasts. An evaluation against in situ measurements showed lower errors for DSSF than AROME and SAFRAN in terms of solar irradiances. For longwave irradiances, contrasted results falling in the range of uncertainty of sensors did not enable us to select the best product. Spatial comparisons of the different datasets over the Alpine and Pyrenean domains highlighted a better 10 representation of the spatial variability of solar fluxes by DSSF and AROME than SAFRAN. We also showed that the altitudinal gradient of longwave irradiance is too strong for DSLFnew and too weak for SAFRAN. These datasets were then used as radiative forcing together with AROME near-surface forecasts to drive distributed snowpack simulations by the model Crocus in the French Alps and the Pyrenees. An evaluation against in-situ snow depth measurements showed higher bi-15 ases when using satellite-derived products, despite their quality. This effect is attributed to some error compensations in the atmospheric forcing and the snowpack model. However, satellite-derived radiation products are judged beneficial for snowpack modelling in mountains, when the error compensations are solved.

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supporting

confidence: 57%

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confidence: 99%

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Satellite products of incoming solar and longwave radiations used for snowpack modelling in mountainous terrain

Quéno¹,

Karbou²,

Vionnet³

et al. 2017

Preprint

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“…In our survey, the few land surfaceatmosphere integrated process-oriented models like PIHM and Parflow-CLM were exceptional in the level of integration and 25 application of observation network data. Along with a few other examples (e.g., Boone et al, 2009;Lafaysse et al, 2017), land surface models are rarely used to model processes with an integrated approach embracing biotic and abiotic variables using LTER/CZO data. A stronger communication between land surface modellers and LTER/CZO communities would enhance the integration of in-situ observations in models, but is challenged by the continental-to-global focus of land surface models versus the site-to-region focus of LTER/CZO observatories.…”

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confidence: 99%

Integration of terrestrial observational networks: opportunity for advancing Earth system dynamics modelling

Baatz

Sullivan

et al. 2017

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Abstract. Advancing our understanding of Earth System Dynamics (ESD) depends on the development of models and other analytical tools that integrate physical, biological and chemical data. This ambition of increased understanding and model development of ESD based on integrated site observations was at the origin of the creation of the networks of Long Term Ecological Research (LTER), Critical Zone Observatories (CZO), and others. We organized a survey to identify pressing gaps in data availability from these networks, in particular for the future development and evaluation of models that 5 represent ESD processes. With the survey results, we look for gaps between data collection and ESD model development, to draw perspectives for the improvement both in data collection and model integration.From this overview of model applications gathered in the context of LTER-and CZO research, we identified three challenges: 1) Improving integration of observational data in Earth system modelling, 2) developing coupled Earth system models, and 3) identifying complementarity and ways to integrate the existing networks. These challenges lead to 10 perspectives and recommendations for strategic integration of observational networks and links to the ESD modelling community. We propose the further integration of the observational networks, either by 1) making the existing sitedetermined networks also functional topological networks with organising spread and coverage, and/or 2) thematically and geographically restructuring or co-locating the existing networks, and further formalizing these recommendations among these communities. Such integration will enable cross-site comparison and synthesis studies, which will offer significant 15 insights and extraction of organizing principles, classifications, and general rules of coupling processes and environmental conditions. IntroductionIn light of accelerating global change (e.g. IPCC, 2014;Camill, 2010), the scientific and societal imperatives are paramount 20 to improve our understanding of Earth System Dynamics (ESD), which comprise complex interactions among rock, soil, water, air, and living organisms that regulate the natural habitat and determine the availability of life-sustaining resources for human well-being (MEA, 2005). Understanding and modelling of Earth system processes and interactions among Earth system compartments can be enhanced by accessing a wider range of both observational and experimental data (Banwart et al., 2012;Aronova et al., 2010;Reid et al., 2010). For this purpose, observational networks have been developed the last 25 decennia aiming at a temporal and multidisciplinary coverage of continental and global scale ecosystem observations. The Critical Zone Observatory network (Brantley et al. 2015), the International Long Term Ecosystem Research network (Mirtl et al., 2017), the National Ecological Observatory Network of the US , Schimel et al. 2001, the Chinese Ecosystem Research Network (Fu et al., 2010) and the Australian Terrestrial Ecosystem Research N...

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“…Underestimated melting was also found by Quéno et al (2016Quéno et al ( , 2017 and Vionnet et al (2016), and complementary studies are needed to investigate the cause of this issue. Lafaysse et al (2017) developed an ensemble snowpack model using SURFEX/Crocus called ESCROC (Ensemble System 10 Crocus) to address modeling errors. They found that by using optimal members they were able to more than half of simulation errors, and those ensembles have a significantly better predictive power than the classical deterministic approach.…”

mentioning

confidence: 99%

Forcing the SURFEX/Crocus snow model with combined hourly meteorological forecasts and gridded observations in southern Norway

Luijting¹,

Vikhamar-Schuler²,

Aspelien³

et al. 2017

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Abstract. In Norway, thirty percent of the annual precipitation falls as snow. Knowledge of the snow reservoir is therefore important for energy production and water resource management. The land surface model SURFEX with the detailed snowpack scheme Crocus (SURFEX/Crocus) has been run with a grid spacing of approximately 1 km over an area in southern Norway for two years (01 September 2014 -31 August 2016), using two different forcing data sets: 1) hourly meteorological forecasts from the operational weather forecast model AROME MetCoOp (2.5 km grid spacing), and 2) gridded hourly observations of 5 temperature and precipitation (1 km grid spacing) in combination with the meteorological forecasts from AROME MetCoOp.We present an evaluation of the modeled snow depth and snow cover, as compared to point observations of snow depth and to MODIS satellite images of the snow-covered area. The evaluation focuses on snow accumulation and snow melt. The results are promising. Both experiments are capable of simulating the snow pack over the two winter seasons, but there is an overestimation of snow depth when using only meteorological forecasts from AROME MetCoOp, although the snow-10 covered area throughout the melt season is better represented by this experiment. The errors, when using AROME MetCoOp as forcing, accumulate over the snow season, showing that assimilation of snow depth observations into SURFEX/Crocus might be necessary when using only meteorological forecasts as forcing. When using gridded observations, the simulation of snow depth is significantly improved, which shows that using a combination of gridded observations and meteorological forecasts to force a snowpack model is very useful and can give better results than only using meteorological forecasts. There is 15 however an underestimation of snow ablation in both experiments. This is mainly due to the absence of wind-induced erosion of snow in the SURFEX/Crocus model, underestimated snow melt and biases in the forcing data.

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A multiphysical ensemble system of numerical snow modelling

Cited by 99 publications

References 73 publications

Satellite products of incoming solar and longwave radiations used for snowpack modelling in mountainous terrain

Satellite products of incoming solar and longwave radiations used for snowpack modelling in mountainous terrain

Integration of terrestrial observational networks: opportunity for advancing Earth system dynamics modelling

Forcing the SURFEX/Crocus snow model with combined hourly meteorological forecasts and gridded observations in southern Norway

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