Toward Understanding Direct Absorption and Grain Size Feedbacks by Dust Radiative Forcing in Snow With Coupled Snow Physical and Radiative Transfer Modeling

Skiles, S. McKenzie; Painter, T. H.

doi:10.1029/2018wr024573

Cited by 46 publications

(57 citation statements)

References 62 publications

(96 reference statements)

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“…The most detailed snow models are also able to compute reflectances from the snowpack properties, through the use of a detailed radiative transfer (Libois et al, 2015;Skiles and Painter, 2019) and the explicit evolution of SSA (Carmagnola et al, 2013) and LAP (Tuzet et al, 2017). Such radiative transfer models play the role of observation operators, computing observation-like variables from the model state variables.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…Indeed, LAPs enhance solar energy absorption in the visible (direct impact), triggering changes in snow properties that further decrease albedo (indirect impacts; e.g. Hansen and Nazarenko, 2004;Painter et al, 2007;Skiles and Painter, 2019). As a consequence, LAPs have a strong effect on snowpack evolution and melt at global 5 and local scales.…”

Section: Introductionmentioning

confidence: 99%

“…Modelling uncertainties have recently been shown to affect the conclusions drawn on the impacts of LAPs using a model-based approach. Indeed, Skiles and Painter (2019) demonstrated that the estimated shortening of the The present study aims to answer two scientific questions: 1) What are the concentrations of BC and dust near the surface of an Alpine snowpack and how do they evolve over two snow seasons?…”

mentioning

confidence: 99%

“…These developments make it possible to simulate the indirect impact of LAPs. Skiles and Painter (2019) similarly coupled the snowpack evolution model SNOWPACK (Lehning et al, 2002) and the radiative transfer model SNICAR (SNow, Ice and Aerosol Radiation model; (Flanner and Zender, 2005)). Tuzet et al (2017) and Skiles and Painter (2019) estimated that the indirect RF is an efficient mechanism of the RF of LAPs, accounting for 15 to 20 % of the total RF of LAPs.…”

mentioning

confidence: 99%

“…Skiles and Painter (2019) similarly coupled the snowpack evolution model SNOWPACK (Lehning et al, 2002) and the radiative transfer model SNICAR (SNow, Ice and Aerosol Radiation model; (Flanner and Zender, 2005)). Tuzet et al (2017) and Skiles and Painter (2019) estimated that the indirect RF is an efficient mechanism of the RF of LAPs, accounting for 15 to 20 % of the total RF of LAPs. However, detailed snowpack models are 10 affected by many uncertainties coming either from the uncertainties in the atmospheric forcing or from intrinsic uncertainties in the representation of snow physics (modelling uncertainties; e.g.…”

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confidence: 99%

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Quantification of the radiative impact of light-absorbing particles during two contrasted snow seasons at Col du Lautaret (2058 m a.s.l., French Alps)

Tuzet¹,

Dumont²,

Picard³

et al. 2020

Preprint

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Abstract. The presence of light-absorbing particles (LAPs) in snow leads to a decrease in shortwave albedo, affecting the surface energy budget. Precisely quantifying the impacts of LAPs on snowpack evolution is crucial to characterise the spatio-temporal variability of snowmelt and assess snow albedo feedbacks in detail. However, the understanding of the impacts of LAPs is hampered by the lack of dedicated datasets, as well as the scarcity of models able to represent the interactions between LAPs and snow metamorphism. The present study aims to address both these limitations by introducing a survey of LAP concentrations over two snow seasons in the French Alps, as well as an estimation of their impacts based on the Crocus snowpack model that represents the complex interplays between LAP dynamics and snow metamorphism. First, we present a unique dataset collected at the Col du Lautaret (2058 m a.s.l; French Alps) for the two snow seasons 2016–2017 and 2017–2018. This dataset consists of spectral albedo measurements (manual and automated), vertical profiles of snow specific surface area (SSA), density, and concentrations of refractive Black Carbon (rBC), Elemental Carbon (EC) and mineral dust. Spectral albedo data are processed to estimate near-surface SSA and LAP absorption-equivalent concentrations near the surface of the snowpack. These estimates are then compared to chemical measurements of dust and BC concentrations, as well as to SSA measurements acquired by near-infrared reflectometry. Our dataset highlights large discrepancies between the two measurement techniques of BC concentrations, with EC concentrations being one order of magnitude higher than rBC measurements. In view of LAP absorption inferred from albedo measurements, the mass absorption efficiency (MAE) of BC used in our study (11.25 g m−2 at 550 nm) is more appropriate for EC measurements than for rBC ones. Second, we present ensemble snowpack simulations of ESCROC – the multi-physics version of the detailed snowpack model Crocus – forced with in-situ meteorological data as well as dust and BC deposition fluxes from the ALADIN-Climate atmospheric model. The results of these simulations are compared to the near-surface properties estimated from automatic albedo measurements, showing that the temporal variations of near-surface LAP concentration and SSA are correctly reproduced. The impact of dust and BC on our simulations is estimated by comparing this ensemble to a similar ensemble that does not account for LAPs. The seasonal radiative forcing of LAPs is 1.33 times higher for the 2017–2018 snow season than for the 2016–2017 one, highlighting a strong variability between these two seasons. However, the shortening of the snow season caused by LAPs are similar with 10 ± 5 and 11 ± 1 days for the first and the second snow seasons respectively. This counter-intuitive result is attributed to two small snowfalls at the end of the first season and highlights the importance to account for meteorological conditions to correctly predict the impact of LAPs. The strong variability of season shortening caused by LAPs in the multi-physics ensemble for the first season also points out the sensitivity of model-based estimations of LAP impact to modelling uncertainties of other processes. Finally, the indirect impact of LAPs (i.e. the enhancement of energy absorption due to acceleration of the metamorphism by LAPs) is negligible for the two years considered here, contrary to what was found in previous studies for other sites. This finding is mainly attributed to the meteorological conditions of the two studied snow seasons.

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Midwinter dry spells amplify post-fire snowpack decline

Hatchett

Koshkin

Guirguis

et al. 2022

Preprint

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Toward Understanding Direct Absorption and Grain Size Feedbacks by Dust Radiative Forcing in Snow With Coupled Snow Physical and Radiative Transfer Modeling

Cited by 46 publications

References 62 publications

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

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

Quantification of the radiative impact of light-absorbing particles during two contrasted snow seasons at Col du Lautaret (2058 m a.s.l., French Alps)

Midwinter dry spells amplify post-fire snowpack decline

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