Improving climate model skill over High Mountain Asia by adapting snow cover parameterization to complex-topography areas
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 implement...
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ftdoajarticles:oai:doaj.org/article:5ff271001791492f86c29fe0758621dc 2024-01-07T09:47:02+01:00 Improving climate model skill over High Mountain Asia by adapting snow cover parameterization to complex-topography areas M. Lalande M. Ménégoz G. Krinner C. Ottlé F. Cheruy 2023-12-01T00:00:00Z https://doi.org/10.5194/tc-17-5095-2023 https://doaj.org/article/5ff271001791492f86c29fe0758621dc EN eng Copernicus Publications https://tc.copernicus.org/articles/17/5095/2023/tc-17-5095-2023.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-17-5095-2023 1994-0416 1994-0424 https://doaj.org/article/5ff271001791492f86c29fe0758621dc The Cryosphere, Vol 17, Pp 5095-5130 (2023) Environmental sciences GE1-350 Geology QE1-996.5 article 2023 ftdoajarticles https://doi.org/10.5194/tc-17-5095-2023 2023-12-10T01:41:41Z 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 regions. It calls for further ... Article in Journal/Newspaper The Cryosphere Directory of Open Access Journals: DOAJ Articles The Cryosphere 17 12 5095 5130 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
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Environmental sciences GE1-350 Geology QE1-996.5 M. Lalande M. Ménégoz G. Krinner C. Ottlé F. Cheruy Improving climate model skill over High Mountain Asia by adapting snow cover parameterization to complex-topography areas |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
description |
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 regions. It calls for further ... |
format |
Article in Journal/Newspaper |
author |
M. Lalande M. Ménégoz G. Krinner C. Ottlé F. Cheruy |
author_facet |
M. Lalande M. Ménégoz G. Krinner C. Ottlé F. Cheruy |
author_sort |
M. Lalande |
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 |
Copernicus Publications |
publishDate |
2023 |
url |
https://doi.org/10.5194/tc-17-5095-2023 https://doaj.org/article/5ff271001791492f86c29fe0758621dc |
genre |
The Cryosphere |
genre_facet |
The Cryosphere |
op_source |
The Cryosphere, Vol 17, Pp 5095-5130 (2023) |
op_relation |
https://tc.copernicus.org/articles/17/5095/2023/tc-17-5095-2023.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-17-5095-2023 1994-0416 1994-0424 https://doaj.org/article/5ff271001791492f86c29fe0758621dc |
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 |
5130 |
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1787428990005280768 |