The S2M meteorological and snow cover reanalysis over the French mountainous areas: description and evaluation (1958–2021)

Vernay, Matthieu; Lafaysse, Matthieu; Monteiro, Diego; Hagenmuller, Pascal; Nheili, Rafife; Samacoïts, Raphaëlle; Verfaillie, Déborah; Morin, Samuel

doi:10.5194/essd-14-1707-2022

Cited by 70 publications

(70 citation statements)

References 75 publications

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“…Simulations explicitly accounting for dust and BC deposition fluxes are compared to a pure snow simulation, excluding any LAP. Simulations are driven by the hourly meteorological reanalysis S2M 28 and hourly deposition fluxes of BC and dust from the regional climate model CNRM-ALADIN63 driven by reanalysis data, so as to follow the unfolding of observed meteorological conditions 29 . To quantify the impact of BC and dust on snow cover evolution we used the snow melt-out date (SMOD), defined as the last date of the annual longest period with at least 30 cm of snow.…”

Section: Resultsmentioning

confidence: 99%

Black carbon and dust alter the response of mountain snow cover under climate change

et al. 2022

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By darkening the snow surface, mineral dust and black carbon (BC) deposition enhances snowmelt and triggers numerous feedbacks. Assessments of their long-term impact at the regional scale are still largely missing despite the environmental and socio-economic implications of snow cover changes. Here we show, using numerical simulations, that dust and BC deposition advanced snowmelt by 17 ± 6 days on average in the French Alps and the Pyrenees over the 1979–2018 period. BC and dust also advanced by 10-15 days the peak melt water runoff, a substantial effect on the timing of water resources availability. We also demonstrate that the decrease in BC deposition since the 1980s moderates the impact of current warming on snow cover decline. Hence, accounting for changes in light-absorbing particles deposition is required to improve the accuracy of snow cover reanalyses and climate projections, that are crucial for better understanding the past and future evolution of mountain social-ecological systems.

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Section: Resultsmentioning

confidence: 99%

Black carbon and dust alter the response of mountain snow cover under climate change

et al. 2022

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“…Both percentiles are good indicators of the SLE location in winter and summer, respectively, and a higher variance can be interpreted as a higher recession of the SLE in summer and more frequent snow precipitation in lower elevations in winter. The positive shift of the 80 th percentile can very well be explained since increased warming and less snow cover have been reported for the high altitudes of the Western Alps in summer as an effect of the snow albedo feedback [63][64][65]. Also, increasing mean precipitation and precipitation extremes have been projected for the Alps which would explain a downwards shift of the 20 th percentile [29].…”

Section: Past and Future Sle Dynamics In The Alpsmentioning

confidence: 76%

Towards forecasting future Snow Cover Dynamics in the European Alps – The Potential of Long Optical Remote-Sensing Time Series

Koehler¹,

Bauer²,

Dietz³

et al. 2022

Preprint

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Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we therefore explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985-2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5-8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash-Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolut Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a Combined forecast based on the best performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022-2029. In the majority of the catchments the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach.

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“…Data availability. The S2M data used in this work provided by Météo-France CNRS, CNRM Centre d'Etudes de la Neige, through AERIS, are available at https://doi.org/10.25326/37#v2020.2 (Vernay et al, 2022b). All other data used in this work are available upon request to the corresponding author for non-commercial research use.…”

Section: Discussionmentioning

confidence: 99%

Formation of glacier tables caused by differential ice melting: field observation and modelling

Hénot

Langlois²,

Vessaire³

et al. 2022

The Cryosphere

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Abstract. Glacier tables are structures frequently encountered on temperate glaciers. They consist of a rock supported by a narrow ice foot which forms through differential melting of the ice. In this article, we investigate their formation by following their dynamics on the Mer de Glace (the Alps, France). We report field measurements of four specific glacier tables over the course of several days, as well as snapshot measurements of a field of 80 tables performed on a given day. We develop a simple model accounting for the various mechanisms of the heat transfer on the glacier using local meteorological data, which displays a quantitative agreement with the field measurements. We show that the formation of glacier tables is controlled by the global heat flux received by the rocks, which causes the ice underneath to melt at a rate proportional to the one of the surrounding ice. Under large rocks the ice ablation rate is reduced compared to bare ice, leading to the formation of glacier tables. This thermal insulation effect is due to the warmer surface temperature of rocks compared to the ice, which affects the net long-wave and turbulent fluxes. While the short-wave radiation, which is the main source of heat, is slightly more absorbed by the rocks than the ice, it plays an indirect role in the insulation by inducing a thermal gradient across the rocks which warms them. Under a critical size, however, rocks can enhance ice melting and consequently sink into the ice surface. This happens when the insulation effect is too weak to compensate for a geometrical amplification effect: the external heat fluxes are received on a larger surface than the contact area with the ice. We identified the main parameters controlling the ability of a rock to form a glacier table: the rock thickness, its aspect ratio, and the ratio between the averaged turbulent and short-wave heat fluxes.

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The S2M meteorological and snow cover reanalysis over the French mountainous areas: description and evaluation (1958–2021)

Cited by 70 publications

References 75 publications

Black carbon and dust alter the response of mountain snow cover under climate change

Black carbon and dust alter the response of mountain snow cover under climate change

Towards forecasting future Snow Cover Dynamics in the European Alps – The Potential of Long Optical Remote-Sensing Time Series

Formation of glacier tables caused by differential ice melting: field observation and modelling

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