Improving climate model skill over High Mountain Asia by adapting snow cover parameterization to complex-topography areas
International audience Abstract. This study investigates the impact of topography on five snow cover fraction (SCF) parameterizations developed for global climate models (GCMs), including two novel ones. The parameterization skill is first assessed with the High Mountain Asia Snow Reanalysis (HMASR)...
Published in: | The Cryosphere |
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Main Authors: | , , , , |
Other Authors: | , , , , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
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HAL CCSD
2023
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Online Access: | https://hal.science/hal-04321343 https://hal.science/hal-04321343/document https://hal.science/hal-04321343/file/tc-17-5095-2023.pdf https://doi.org/10.5194/tc-17-5095-2023 |
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Université Grenoble Alpes: HAL |
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[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment |
spellingShingle |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment Lalande, Mickaël Ménégoz, Martin Krinner, Gerhard Ottle, Catherine Cheruy, Frédérique Improving climate model skill over High Mountain Asia by adapting snow cover parameterization to complex-topography areas |
topic_facet |
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment |
description |
International audience Abstract. This study investigates the impact of topography on five snow cover fraction (SCF) parameterizations developed for global climate models (GCMs), including two novel ones. The parameterization skill is first assessed with the High Mountain Asia Snow Reanalysis (HMASR), and three of them are implemented in the ORCHIDEE land surface model (LSM) and tested in global land–atmosphere coupled simulations. HMASR includes snow depth (SD) uncertainties, which may be due to the elevation differences between in situ stations and HMASR grid cells. Nevertheless, the SCF–SD relationship varies greatly between mountainous and flat areas in HMASR, especially during the snow-melting period. The new parameterizations that include a dependency on the subgrid topography allow a significant SCF bias reduction, reaching 5 % to 10 % on average in the global simulations over mountainous areas, which in turn leads to a reduction of the surface cold bias from −1.8 ∘C to about −1 ∘C in High Mountain Asia (HMA). Furthermore, the seasonal hysteresis between SCF and SD found in HMASR is better captured in the parameterizations that split the accumulation and the depletion curves or that include a dependency on the snow density. The deep-learning SCF parameterization is promising but exhibits more resolution-dependent and region-dependent features. Persistent snow cover biases remain in global land–atmosphere experiments. This suggests that other model biases may be intertwined with the snow biases and points out the need to continue improving snow models and their calibration. Increasing the model resolution does not consistently reduce the simulated SCF biases, although biases get narrower around mountain areas. This study highlights the complexity of calibrating SCF parameterizations since they affect various land–atmosphere feedbacks. In summary, this research spots the importance of considering topography in SCF parameterizations and the challenges in accurately representing snow cover in mountainous ... |
author2 |
Institut des Géosciences de l’Environnement (IGE) Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ) Université Grenoble Alpes (UGA) Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette (LSCE) Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)) Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA) Modélisation des Surfaces et Interfaces Continentales (MOSAIC) Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)) Laboratoire de Météorologie Dynamique (UMR 8539) (LMD) Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris École normale supérieure - Paris (ENS-PSL) Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-École normale supérieure - Paris (ENS-PSL) Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL) |
format |
Article in Journal/Newspaper |
author |
Lalande, Mickaël Ménégoz, Martin Krinner, Gerhard Ottle, Catherine Cheruy, Frédérique |
author_facet |
Lalande, Mickaël Ménégoz, Martin Krinner, Gerhard Ottle, Catherine Cheruy, Frédérique |
author_sort |
Lalande, Mickaël |
title |
Improving climate model skill over High Mountain Asia by adapting snow cover parameterization to complex-topography areas |
title_short |
Improving climate model skill over High Mountain Asia by adapting snow cover parameterization to complex-topography areas |
title_full |
Improving climate model skill over High Mountain Asia by adapting snow cover parameterization to complex-topography areas |
title_fullStr |
Improving climate model skill over High Mountain Asia by adapting snow cover parameterization to complex-topography areas |
title_full_unstemmed |
Improving climate model skill over High Mountain Asia by adapting snow cover parameterization to complex-topography areas |
title_sort |
improving climate model skill over high mountain asia by adapting snow cover parameterization to complex-topography areas |
publisher |
HAL CCSD |
publishDate |
2023 |
url |
https://hal.science/hal-04321343 https://hal.science/hal-04321343/document https://hal.science/hal-04321343/file/tc-17-5095-2023.pdf https://doi.org/10.5194/tc-17-5095-2023 |
genre |
The Cryosphere |
genre_facet |
The Cryosphere |
op_source |
ISSN: 1994-0424 EISSN: 1994-0416 The Cryosphere https://hal.science/hal-04321343 The Cryosphere, 2023, 17 (12), pp.5095-5130. ⟨10.5194/tc-17-5095-2023⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.5194/tc-17-5095-2023 hal-04321343 https://hal.science/hal-04321343 https://hal.science/hal-04321343/document https://hal.science/hal-04321343/file/tc-17-5095-2023.pdf doi:10.5194/tc-17-5095-2023 WOS: 001168786600001 |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.5194/tc-17-5095-2023 |
container_title |
The Cryosphere |
container_volume |
17 |
container_issue |
12 |
container_start_page |
5095 |
op_container_end_page |
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1802650656251576320 |
spelling |
ftunigrenoble:oai:HAL:hal-04321343v1 2024-06-23T07:57:10+00:00 Improving climate model skill over High Mountain Asia by adapting snow cover parameterization to complex-topography areas Lalande, Mickaël Ménégoz, Martin Krinner, Gerhard Ottle, Catherine Cheruy, Frédérique Institut des Géosciences de l’Environnement (IGE) Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ) Université Grenoble Alpes (UGA) Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette (LSCE) Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)) Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA) Modélisation des Surfaces et Interfaces Continentales (MOSAIC) Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)) Laboratoire de Météorologie Dynamique (UMR 8539) (LMD) Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris École normale supérieure - Paris (ENS-PSL) Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-École normale supérieure - Paris (ENS-PSL) Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL) 2023-12-04 https://hal.science/hal-04321343 https://hal.science/hal-04321343/document https://hal.science/hal-04321343/file/tc-17-5095-2023.pdf https://doi.org/10.5194/tc-17-5095-2023 en eng HAL CCSD Copernicus info:eu-repo/semantics/altIdentifier/doi/10.5194/tc-17-5095-2023 hal-04321343 https://hal.science/hal-04321343 https://hal.science/hal-04321343/document https://hal.science/hal-04321343/file/tc-17-5095-2023.pdf doi:10.5194/tc-17-5095-2023 WOS: 001168786600001 info:eu-repo/semantics/OpenAccess ISSN: 1994-0424 EISSN: 1994-0416 The Cryosphere https://hal.science/hal-04321343 The Cryosphere, 2023, 17 (12), pp.5095-5130. ⟨10.5194/tc-17-5095-2023⟩ [SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment info:eu-repo/semantics/article Journal articles 2023 ftunigrenoble https://doi.org/10.5194/tc-17-5095-2023 2024-06-04T00:01:42Z International audience Abstract. This study investigates the impact of topography on five snow cover fraction (SCF) parameterizations developed for global climate models (GCMs), including two novel ones. The parameterization skill is first assessed with the High Mountain Asia Snow Reanalysis (HMASR), and three of them are implemented in the ORCHIDEE land surface model (LSM) and tested in global land–atmosphere coupled simulations. HMASR includes snow depth (SD) uncertainties, which may be due to the elevation differences between in situ stations and HMASR grid cells. Nevertheless, the SCF–SD relationship varies greatly between mountainous and flat areas in HMASR, especially during the snow-melting period. The new parameterizations that include a dependency on the subgrid topography allow a significant SCF bias reduction, reaching 5 % to 10 % on average in the global simulations over mountainous areas, which in turn leads to a reduction of the surface cold bias from −1.8 ∘C to about −1 ∘C in High Mountain Asia (HMA). Furthermore, the seasonal hysteresis between SCF and SD found in HMASR is better captured in the parameterizations that split the accumulation and the depletion curves or that include a dependency on the snow density. The deep-learning SCF parameterization is promising but exhibits more resolution-dependent and region-dependent features. Persistent snow cover biases remain in global land–atmosphere experiments. This suggests that other model biases may be intertwined with the snow biases and points out the need to continue improving snow models and their calibration. Increasing the model resolution does not consistently reduce the simulated SCF biases, although biases get narrower around mountain areas. This study highlights the complexity of calibrating SCF parameterizations since they affect various land–atmosphere feedbacks. In summary, this research spots the importance of considering topography in SCF parameterizations and the challenges in accurately representing snow cover in mountainous ... Article in Journal/Newspaper The Cryosphere Université Grenoble Alpes: HAL The Cryosphere 17 12 5095 5130 |