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Doctoral thesis . 2020
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Doctoral thesis . 2020
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Arctic snowpack characterization, climate monitoring and microwaves remote sensing

Authors: Vargel, Céline;

Arctic snowpack characterization, climate monitoring and microwaves remote sensing

Abstract

Les régions de hautes latitudes nord se réchauffent de façon plus intense que sur le reste du globe. Ce phénomène, appelé amplification arctique, est dû en partie à la diminution de l'étendue de glace de mer et de la couverture de neige. Par ses changements de pouvoirs réfléchissant et isolant, la neige, présente 9 mois de l'année, pourrait avoir un effet important sur l'augmentation des températures du sol. Le dégel du pergélisol à travers le carbone ainsi libéré serait susceptible d'avoir un impact important sur le climat futur de l'Arctique. Ce projet de recherche a pour objectif d'améliorer le suivi du couvert nival arctique et des températures du sol. À l'heure actuelle, les modèles détaillés d'évolution du manteau neigeux tels que le modèle Crocus ne parviennent pas à reproduire la physique particulière de la neige arctique ce qui conduit à des incertitudes importantes dans la modélisation des températures du sol. De nouvelles paramétrisations physiques ont été implémentées au sein du modèle Crocus pour améliorer la stratification verticale du manteau neigeux en introduisant les effets de la végétation (neige moins dense en profondeur) et les effets du vent (neige plus dense en surface), ainsi que pour modifier la conductivité thermique de la neige. Ces nouvelles paramétrisations permettent une meilleure représentation des températures du sol sous la neige, validée avec un large jeu de données en Alaska, dans l'Arctique canadien et en Sibérie. Les simulations ainsi réalisées à l'aide du modèle Crocus modifié, piloté par la réanalyse météorologique ERA-Interim sur les 39 dernières années (1979-2018), à l'échelle panarctique, montrent une augmentation significative de la densité de la neige au printemps ainsi que de l'humidité de la neige principalement au printemps et en automne, accompagnée d'une diminution significative de la durée d'enneigement. Ces effets cumulés à l'augmentation des températures de l'air entraînent une augmentation des températures du sol allant jusqu'à +0.89 K par décade pour le mois de juin. De façon à pouvoir améliorer le suivi de l'évolution spatiale et temporelle du couvert nival, l'utilisation de données d'observations satellitaires micro-onde est proposée. À partir de l'analyse d'un jeu de données unique de mesures radiométriques en surface associées à la caractérisation in-situ du manteau neigeux (119 snowpits avec des observations simultanées) en zone arctique et subarctique, une paramétrisation optimale du modèle de transfert radiatif SMRT a été définie. En utilisant une longueur de corrélation exponentielle ajustée comme paramètre de microstructure de la neige dans le modèle électromagnétique Improved Born Approximation (IBA), l'étude montre, par rapport aux autres configurations de modèles testées, de meilleurs résultats avec une erreur moyenne (RMSE) inférieure à 30% des observations pour la neige subarctique et 24% pour la neige arctique. Couplées à Crocus, les températures de brillance simulées sur l'ensemble de l'Arctique sont significativement meilleures avec Crocus modifié qu'avec Crocus standard (38 K d'amélioration de l'erreur en moyenne). Ces résultats ouvrent la voie à l'utilisation de l'assimilation des observations micro-onde satellitaires dans le modèle Crocus à grande échelle afin d'améliorer les simulations de densité de la neige arctique, paramètre clef du manteau neigeux influant sur l'évolution des températures du sol sous la neige.

Northern high-latitude regions are warming more intensely than the rest of the world. This phenomenon, called Arctic amplification, is due in part to the decrease in sea ice extent and snow cover. Snow, which is present 9 months of the year, could have a significant effect on the increase in land surface temperatures by changing its reflective and insulating properties. Thawing of permafrost which could release important amount of soil carbone into the atmosphere could have a significant positive feedback on the future climate of the Arctic. The objective of this research project is to improve the monitoring of Arctic snow cover and ground temperatures. Detailed models of snow cover evolution such as the Crocus multi-layered model are unable to reproduce the particular physics of Arctic snow, which leads to significant uncertainties in the modeling of ground temperatures. New physical parameterizations have been implemented within the Crocus model to improve the vertical stratification of the snowpack by introducing vegetation effects (less dense snow at the bottom) and wind effects (denser snow at the surface), as well as to modify the thermal conductivity of snow. These new parameterizations allow a better representation of ground temperatures under the snowpack, validated with a large dataset in Alaska, Canadian Arctic and Siberia. The simulations thus carried out using the modified Crocus model, driven by the ERA-Interim meteorological reanalysis over the last 39 years (1979-2018), at the pan-Arctic scale, show a significant increase in snow density in spring as well as in snow moisture, mainly in spring and fall, accompanied by a significant decrease in the duration of the snow cover. These effects, combined with the increase in air temperature, lead to an increase in ground temperature of up to +0.89 K per decade for the month of June. In order to improve monitoring the spatial and temporal evolution of the snow cover, the use of microwave satellite observation data is proposed. Based on the analysis of a unique dataset of surface radiometric measurements, associated with the in-situ characterization of the snowpit (119 snowpits with simultaneous observations) in the Arctic and sub-Arctic zones, an optimal parameterization of the SMRT model has been defined. The results show that using a fitted exponential correlation length as a snow microstructure parameter in the Improved Born Approximation (IBA) electromagnetic model gives the best results compared to the other model configurations tested, with a mean error (RMSE) of less than 30% of the observations for subarctic snow and 24% for Arctic snow. Coupled with Crocus, the simulated brightness temperatures over the entire Arctic are significantly better with modified Crocus than with standard Crocus (38 K improvement in mean bias). These results pave the way for using the assimilation of satellite microwave observations into the Crocus model to improve simulations of Arctic snow density, a key snowpack parameter influencing the evolution of ground temperatures under the snow.

Country
France
Keywords

Changement climatique, Arctique, Transfert radiatif, [SHS.LANGUE] Humanities and Social Sciences/Linguistics, Snowpack, Microwave remote sensing, Arctic, Manteau neigeux, Modélisation, Climate change, Radiative transfer, Modelisation, Télédétection micro-Onde, [SHS.LANGUE]Humanities and Social Sciences/Linguistics

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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