Matthieu Lafaysse


2023

DOI bib
Snow Level From Post‐Processing of Atmospheric Model Improves Snowfall Estimate and Snowpack Prediction in Mountains
Vincent Vionnet, M. Verville, Vincent Fortin, Melinda M. Brugman, Maria Abrahamowicz, François Lemay, Julie M. Thériault, Matthieu Lafaysse, Jason A. Milbrandt, Vincent Vionnet, M. Verville, Vincent Fortin, Melinda M. Brugman, Maria Abrahamowicz, François Lemay, Julie M. Thériault, Matthieu Lafaysse, Jason A. Milbrandt, Vincent Vionnet, M. Verville, Vincent Fortin, Melinda M. Brugman, Maria Abrahamowicz, François Lemay, Julie M. Thériault, Matthieu Lafaysse, Jason A. Milbrandt
Water Resources Research, Volume 58, Issue 12

In mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation-phase partitioning methods (PPMs) that use near-surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric-based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5-km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric-based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post-processing algorithm determining the snow level and the associated precipitation phase. Two ground-based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet-bulb temperature. Compared to the upper benchmark, the snow-level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post-processed snow level. The limitations to drive snowpack models at slope scale are also discussed.

DOI bib
Snow Level From Post‐Processing of Atmospheric Model Improves Snowfall Estimate and Snowpack Prediction in Mountains
Vincent Vionnet, M. Verville, Vincent Fortin, Melinda M. Brugman, Maria Abrahamowicz, François Lemay, Julie M. Thériault, Matthieu Lafaysse, Jason A. Milbrandt, Vincent Vionnet, M. Verville, Vincent Fortin, Melinda M. Brugman, Maria Abrahamowicz, François Lemay, Julie M. Thériault, Matthieu Lafaysse, Jason A. Milbrandt, Vincent Vionnet, M. Verville, Vincent Fortin, Melinda M. Brugman, Maria Abrahamowicz, François Lemay, Julie M. Thériault, Matthieu Lafaysse, Jason A. Milbrandt
Water Resources Research, Volume 58, Issue 12

In mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation-phase partitioning methods (PPMs) that use near-surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric-based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5-km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric-based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post-processing algorithm determining the snow level and the associated precipitation phase. Two ground-based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet-bulb temperature. Compared to the upper benchmark, the snow-level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post-processed snow level. The limitations to drive snowpack models at slope scale are also discussed.

DOI bib
Snow Level From Post‐Processing of Atmospheric Model Improves Snowfall Estimate and Snowpack Prediction in Mountains
Vincent Vionnet, M. Verville, Vincent Fortin, Melinda M. Brugman, Maria Abrahamowicz, François Lemay, Julie M. Thériault, Matthieu Lafaysse, Jason A. Milbrandt, Vincent Vionnet, M. Verville, Vincent Fortin, Melinda M. Brugman, Maria Abrahamowicz, François Lemay, Julie M. Thériault, Matthieu Lafaysse, Jason A. Milbrandt, Vincent Vionnet, M. Verville, Vincent Fortin, Melinda M. Brugman, Maria Abrahamowicz, François Lemay, Julie M. Thériault, Matthieu Lafaysse, Jason A. Milbrandt
Water Resources Research, Volume 58, Issue 12

In mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation-phase partitioning methods (PPMs) that use near-surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric-based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5-km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric-based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post-processing algorithm determining the snow level and the associated precipitation phase. Two ground-based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet-bulb temperature. Compared to the upper benchmark, the snow-level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post-processed snow level. The limitations to drive snowpack models at slope scale are also discussed.

2022

DOI bib
Snow Level From Post‐Processing of Atmospheric Model Improves Snowfall Estimate and Snowpack Prediction in Mountains
Vincent Vionnet, M. Verville, Vincent Fortin, Melinda M. Brugman, Maria Abrahamowicz, François Lemay, Julie M. Thériault, Matthieu Lafaysse, Jason A. Milbrandt, Vincent Vionnet, M. Verville, Vincent Fortin, Melinda M. Brugman, Maria Abrahamowicz, François Lemay, Julie M. Thériault, Matthieu Lafaysse, Jason A. Milbrandt, Vincent Vionnet, M. Verville, Vincent Fortin, Melinda M. Brugman, Maria Abrahamowicz, François Lemay, Julie M. Thériault, Matthieu Lafaysse, Jason A. Milbrandt
Water Resources Research, Volume 58, Issue 12

In mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation-phase partitioning methods (PPMs) that use near-surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric-based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5-km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric-based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post-processing algorithm determining the snow level and the associated precipitation phase. Two ground-based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet-bulb temperature. Compared to the upper benchmark, the snow-level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post-processed snow level. The limitations to drive snowpack models at slope scale are also discussed.

DOI bib
Snow Level From Post‐Processing of Atmospheric Model Improves Snowfall Estimate and Snowpack Prediction in Mountains
Vincent Vionnet, M. Verville, Vincent Fortin, Melinda M. Brugman, Maria Abrahamowicz, François Lemay, Julie M. Thériault, Matthieu Lafaysse, Jason A. Milbrandt, Vincent Vionnet, M. Verville, Vincent Fortin, Melinda M. Brugman, Maria Abrahamowicz, François Lemay, Julie M. Thériault, Matthieu Lafaysse, Jason A. Milbrandt, Vincent Vionnet, M. Verville, Vincent Fortin, Melinda M. Brugman, Maria Abrahamowicz, François Lemay, Julie M. Thériault, Matthieu Lafaysse, Jason A. Milbrandt
Water Resources Research, Volume 58, Issue 12

In mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation-phase partitioning methods (PPMs) that use near-surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric-based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5-km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric-based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post-processing algorithm determining the snow level and the associated precipitation phase. Two ground-based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet-bulb temperature. Compared to the upper benchmark, the snow-level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post-processed snow level. The limitations to drive snowpack models at slope scale are also discussed.

DOI bib
Snow Level From Post‐Processing of Atmospheric Model Improves Snowfall Estimate and Snowpack Prediction in Mountains
Vincent Vionnet, M. Verville, Vincent Fortin, Melinda M. Brugman, Maria Abrahamowicz, François Lemay, Julie M. Thériault, Matthieu Lafaysse, Jason A. Milbrandt, Vincent Vionnet, M. Verville, Vincent Fortin, Melinda M. Brugman, Maria Abrahamowicz, François Lemay, Julie M. Thériault, Matthieu Lafaysse, Jason A. Milbrandt, Vincent Vionnet, M. Verville, Vincent Fortin, Melinda M. Brugman, Maria Abrahamowicz, François Lemay, Julie M. Thériault, Matthieu Lafaysse, Jason A. Milbrandt
Water Resources Research, Volume 58, Issue 12

In mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation-phase partitioning methods (PPMs) that use near-surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric-based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5-km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric-based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post-processing algorithm determining the snow level and the associated precipitation phase. Two ground-based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet-bulb temperature. Compared to the upper benchmark, the snow-level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post-processed snow level. The limitations to drive snowpack models at slope scale are also discussed.

2021

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Scientific and Human Errors in a Snow Model Intercomparison
Cécile B. Ménard, Richard Essery, Gerhard Krinner, Gabriele Arduini, Paul Bartlett, Aaron Boone, Claire Brutel‐Vuilmet, Eleanor J. Burke, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Emanuel Dutra, Xing Fang, Charles Fierz, Yeugeniy M. Gusev, Stefan Hagemann, Vanessa Haverd, Hyungjun Kim, Matthieu Lafaysse, Thomas Marke, О. Н. Насонова, Tomoko Nitta, Michio Niwano, John W. Pomeroy, Gerd Schädler, В. А. Семенов, Tatiana G. Smirnova, Ulrich Strasser, Sean Swenson, Dmitry Turkov, Nander Wever, Hua Yuan
Bulletin of the American Meteorological Society, Volume 102, Issue 1

Abstract Twenty-seven models participated in the Earth System Model–Snow Model Intercomparison Project (ESM-SnowMIP), the most data-rich MIP dedicated to snow modeling. Our findings do not support the hypothesis advanced by previous snow MIPs: evaluating models against more variables and providing evaluation datasets extended temporally and spatially does not facilitate identification of key new processes requiring improvement to model snow mass and energy budgets, even at point scales. In fact, the same modeling issues identified by previous snow MIPs arose: albedo is a major source of uncertainty, surface exchange parameterizations are problematic, and individual model performance is inconsistent. This lack of progress is attributed partly to the large number of human errors that led to anomalous model behavior and to numerous resubmissions. It is unclear how widespread such errors are in our field and others; dedicated time and resources will be needed to tackle this issue to prevent highly sophisticated models and their research outputs from being vulnerable because of avoidable human mistakes. The design of and the data available to successive snow MIPs were also questioned. Evaluation of models against bulk snow properties was found to be sufficient for some but inappropriate for more complex snow models whose skills at simulating internal snow properties remained untested. Discussions between the authors of this paper on the purpose of MIPs revealed varied, and sometimes contradictory, motivations behind their participation. These findings started a collaborative effort to adapt future snow MIPs to respond to the diverse needs of the community.

2020

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

Abstract. Thirty-year simulations of seasonal snow cover in 22 physically based models driven with bias-corrected meteorological reanalyses are examined at four sites with long records of snow observations. Annual snow cover durations differ widely between models but interannual variations are strongly correlated because of the common driving data. No significant trends are observed in starting dates for seasonal snow cover, but there are significant trends towards snow cover ending earlier at two of the sites in observations and most of the models. A simplified model with just two parameters controlling solar radiation and sensible heat contributions to snowmelt spans the ranges of snow cover durations and trends. This model predicts that sites where snow persists beyond annual peaks in solar radiation and air temperature will experience rapid decreases in snow cover duration with warming as snow begins to melt earlier and at times of year with more energy available for melting.

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Long‐term trends (1958–2017) in snow cover duration and depth in the Pyrenees
J. I. López‐Moreno, Jean Michel Soubeyroux, Simon Gascoin, Esteban Alonso‐González, Nuria Durán-Gómez, Matthieu Lafaysse, Matthieu Vernay, Carlo Maria Carmagnola, Samuel Morin
International Journal of Climatology, Volume 40, Issue 14

This study investigated the temporal variability and changes in snow cover duration and the average snow depth from December to April in the Pyrenees at 1,500 and 2,100 m a.s.l. for the period 1958–2017. This is the first such analysis for the entire mountain range using SAFRAN‐Crocus simulations run for this specific purpose. The SAFRAN‐Crocus simulations were evaluated for the period 1980–2016 using 28 in situ snow depth data time series, and for the period 2000–2017 using MODIS observations of the snow cover duration. Following confirmation that the simulated snow series satisfactorily reproduced the observed evolution of the snowpack, the Mann–Kendall test showed that snow cover duration and average depth decreased during the full study period, but this was only statistically significant at 2,100 m a.s.l. The temporal evolution in the snow series indicated marked differences among massifs, elevations, and snow variables. In general, the most western massifs of the French Pyrenees underwent a greater decrease in the snowpack, while in some eastern massifs the snowpack did not decrease, and in some cases increased at 1,500 m a.s.l. The results suggest that the trends were consistent over time, as they were little affected by the start and end year of the study period, except if trends are computed only starting after 1980, when no significant trends were apparent. Most of the observed negative trends were not correlated with changes in the atmospheric circulation patterns during the snow season. This suggests that the continuous warming in the Pyrenees since the beginning of the industrial period, and particularly the sharp increase since 1955, is a major driver explaining the snow cover decline in the Pyrenees.

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Towards the assimilation of satellite reflectance into semi-distributed ensemble snowpack simulations
Bertrand Cluzet, Jesús Revuelto, Matthieu Lafaysse, François Tuzet, Emmanuel Cosme, Ghislain Picard, Laurent Arnaud, Marie Dumont
Cold Regions Science and Technology, Volume 170

Abstract 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 of Meteo-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 1000 km2 each), an altitudinal range (by step of 300 m) and aspect (by step of 45°). 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 R2 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 Meteo-France snowpack modelling system.

DOI bib
Snow cover duration trends observed at sites and predicted by multiple models
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel‐Vuilmet, Eleanor J. Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy M. Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cécile B. Ménard, О. Н. Насонова, Tomoko Nitta, John W. Pomeroy, Gerd Schädler, В. А. Семенов, Tatiana G. Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, Hua Yuan
The Cryosphere, Volume 14, Issue 12

Abstract. The 30-year simulations of seasonal snow cover in 22 physically based models driven with bias-corrected meteorological reanalyses are examined at four sites with long records of snow observations. Annual snow cover durations differ widely between models, but interannual variations are strongly correlated because of the common driving data. No significant trends are observed in starting dates for seasonal snow cover, but there are significant trends towards snow cover ending earlier at two of the sites in observations and most of the models. A simplified model with just two parameters controlling solar radiation and sensible heat contributions to snowmelt spans the ranges of snow cover durations and trends. This model predicts that sites where snow persists beyond annual peaks in solar radiation and air temperature will experience rapid decreases in snow cover duration with warming as snow begins to melt earlier and at times of year with more energy available for melting.

2018

DOI bib
ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks
Gerhard Krinner, Chris Derksen, Richard Essery, M. Flanner, Stefan Hagemann, Martyn P. Clark, Alex Hall, Helmut Rott, Claire Brutel‐Vuilmet, Hyungjun Kim, Cécile B. Ménard, Lawrence Mudryk, Chad W. Thackeray, Libo Wang, Gabriele Arduini, Gianpaolo Balsamo, Paul Bartlett, Julia Boike, Aaron Boone, F. Chéruy, Jeanne Colin, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Jeff Derry, Agnès Ducharne, Emanuel Dutra, Xing Fang, Charles Fierz, Josephine Ghattas, Yeugeniy M. Gusev, Vanessa Haverd, Anna Kontu, Matthieu Lafaysse, R. M. Law, David M. Lawrence, Weiping Li, Thomas Marke, Danny Marks, Martin Ménégoz, О. Н. Насонова, Tomoko Nitta, Michio Niwano, John W. Pomeroy, M. S. Raleigh, Gerd Schaedler, В. А. Семенов, Tanya Smirnova, Tobias Stacke, Ulrich Strasser, Sean Svenson, Dmitry Turkov, Tao Wang, Nander Wever, Hua Yuan, Wenyan Zhou, Dan Zhu
Geoscientific Model Development, Volume 11, Issue 12

Abstract. This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes, including snow schemes that are included in Earth system models, in a wide variety of settings against local and global observations. The project aims to identify crucial processes and characteristics that need to be improved in snow models in the context of local- and global-scale modelling. A further objective of ESM-SnowMIP is to better quantify snow-related feedbacks in the Earth system. Although it is not part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), ESM-SnowMIP is tightly linked to the CMIP6-endorsed Land Surface, Snow and Soil Moisture Model Intercomparison (LS3MIP).