A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR
The Greenland Ice Sheet (GrIS) has been contributing directly to sea level rise, and this contribution is projected to accelerate over the next decades. A crucial tool for studying the evolution of surface mass loss (e.g., surface mass balance, SMB) consists of regional climate models (RCMs), which...
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ftcopernicus:oai:publications.copernicus.org:tc110800 2024-09-15T18:09:17+00:00 A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR Tedesco, Marco Colosio, Paolo Fettweis, Xavier Cervone, Guido 2023-11-30 application/pdf https://doi.org/10.5194/tc-17-5061-2023 https://tc.copernicus.org/articles/17/5061/2023/ eng eng doi:10.5194/tc-17-5061-2023 https://tc.copernicus.org/articles/17/5061/2023/ eISSN: 1994-0424 Text 2023 ftcopernicus https://doi.org/10.5194/tc-17-5061-2023 2024-08-28T05:24:15Z The Greenland Ice Sheet (GrIS) has been contributing directly to sea level rise, and this contribution is projected to accelerate over the next decades. A crucial tool for studying the evolution of surface mass loss (e.g., surface mass balance, SMB) consists of regional climate models (RCMs), which can provide current estimates and future projections of sea level rise associated with such losses. However, one of the main limitations of RCMs is the relatively coarse horizontal spatial resolution at which outputs are currently generated. Here, we report results concerning the statistical downscaling of the SMB modeled by the Modèle Atmosphérique Régional (MAR) RCM from the original spatial resolution of 6 km to 100 m building on the relationship between elevation and mass losses in Greenland. To this goal, we developed a geospatial framework that allows the parallelization of the downscaling process, a crucial aspect to increase the computational efficiency of the algorithm. Using the results obtained in the case of the SMB, surface and air temperature are assessed through the comparison of the modeled outputs with in situ and satellite measurement. The downscaled products show a considerable improvement in the case of the downscaled product with respect to the original coarse output, with the coefficient of determination ( R 2 ) increasing from 0.868 for the original MAR output to 0.935 for the SMB downscaled product. Moreover, the value of the slope and intercept of the linear regression fitting modeled and measured SMB values shifts from 0.865 for the original MAR to 1.015 for the downscaled product in the case of the slope and from the value −235 mm w.e. yr −1 (original) to −57 mm w.e. yr −1 (downscaled) in the case of the intercept, considerably improving upon results previously published in the literature. Text Greenland Ice Sheet Copernicus Publications: E-Journals The Cryosphere 17 12 5061 5074 |
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Open Polar |
collection |
Copernicus Publications: E-Journals |
op_collection_id |
ftcopernicus |
language |
English |
description |
The Greenland Ice Sheet (GrIS) has been contributing directly to sea level rise, and this contribution is projected to accelerate over the next decades. A crucial tool for studying the evolution of surface mass loss (e.g., surface mass balance, SMB) consists of regional climate models (RCMs), which can provide current estimates and future projections of sea level rise associated with such losses. However, one of the main limitations of RCMs is the relatively coarse horizontal spatial resolution at which outputs are currently generated. Here, we report results concerning the statistical downscaling of the SMB modeled by the Modèle Atmosphérique Régional (MAR) RCM from the original spatial resolution of 6 km to 100 m building on the relationship between elevation and mass losses in Greenland. To this goal, we developed a geospatial framework that allows the parallelization of the downscaling process, a crucial aspect to increase the computational efficiency of the algorithm. Using the results obtained in the case of the SMB, surface and air temperature are assessed through the comparison of the modeled outputs with in situ and satellite measurement. The downscaled products show a considerable improvement in the case of the downscaled product with respect to the original coarse output, with the coefficient of determination ( R 2 ) increasing from 0.868 for the original MAR output to 0.935 for the SMB downscaled product. Moreover, the value of the slope and intercept of the linear regression fitting modeled and measured SMB values shifts from 0.865 for the original MAR to 1.015 for the downscaled product in the case of the slope and from the value −235 mm w.e. yr −1 (original) to −57 mm w.e. yr −1 (downscaled) in the case of the intercept, considerably improving upon results previously published in the literature. |
format |
Text |
author |
Tedesco, Marco Colosio, Paolo Fettweis, Xavier Cervone, Guido |
spellingShingle |
Tedesco, Marco Colosio, Paolo Fettweis, Xavier Cervone, Guido A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR |
author_facet |
Tedesco, Marco Colosio, Paolo Fettweis, Xavier Cervone, Guido |
author_sort |
Tedesco, Marco |
title |
A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR |
title_short |
A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR |
title_full |
A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR |
title_fullStr |
A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR |
title_full_unstemmed |
A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR |
title_sort |
computationally efficient statistically downscaled 100 m resolution greenland product from the regional climate model mar |
publishDate |
2023 |
url |
https://doi.org/10.5194/tc-17-5061-2023 https://tc.copernicus.org/articles/17/5061/2023/ |
genre |
Greenland Ice Sheet |
genre_facet |
Greenland Ice Sheet |
op_source |
eISSN: 1994-0424 |
op_relation |
doi:10.5194/tc-17-5061-2023 https://tc.copernicus.org/articles/17/5061/2023/ |
op_doi |
https://doi.org/10.5194/tc-17-5061-2023 |
container_title |
The Cryosphere |
container_volume |
17 |
container_issue |
12 |
container_start_page |
5061 |
op_container_end_page |
5074 |
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1810446762360438784 |