A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR
<p>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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-11-01
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| Series: | The Cryosphere |
| Online Access: | https://tc.copernicus.org/articles/17/5061/2023/tc-17-5061-2023.pdf |
| _version_ | 1797447663983001600 |
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| author | M. Tedesco M. Tedesco P. Colosio X. Fettweis G. Cervone |
| author_facet | M. Tedesco M. Tedesco P. Colosio X. Fettweis G. Cervone |
| author_sort | M. Tedesco |
| collection | DOAJ |
| description | <p>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 (<span class="inline-formula"><i>R</i><sup>2</sup>)</span> 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 <span class="inline-formula">−235</span> mm w.e. yr<span class="inline-formula"><sup>−1</sup></span> (original) to <span class="inline-formula">−57</span> mm w.e. yr<span class="inline-formula"><sup>−1</sup></span> (downscaled) in the case of the intercept, considerably improving upon results previously published in the literature.</p> |
| first_indexed | 2024-03-09T13:59:10Z |
| format | Article |
| id | doaj.art-9c0f9f3251ca4febbd9b6e648df5657d |
| institution | Directory Open Access Journal |
| issn | 1994-0416 1994-0424 |
| language | English |
| last_indexed | 2024-03-09T13:59:10Z |
| publishDate | 2023-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The Cryosphere |
| spelling | doaj.art-9c0f9f3251ca4febbd9b6e648df5657d2023-11-30T11:27:26ZengCopernicus PublicationsThe Cryosphere1994-04161994-04242023-11-01175061507410.5194/tc-17-5061-2023A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MARM. Tedesco0M. Tedesco1P. Colosio2X. Fettweis3G. Cervone4Lamont-Doherty Earth Observatory, Columbia University, New York, NY 10964, USANASA GISS, New York, NY 10025, USADepartment of Civil, Environmental, Architectural Engineering and Mathematics, University of Brescia, Brescia, 25123, ItalyDepartment of Geography, SPHERES research unit, University of Liège, Liège, 4000, BelgiumInstitute for Computational and Data Sciences and Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, PA 16801, USA<p>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 (<span class="inline-formula"><i>R</i><sup>2</sup>)</span> 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 <span class="inline-formula">−235</span> mm w.e. yr<span class="inline-formula"><sup>−1</sup></span> (original) to <span class="inline-formula">−57</span> mm w.e. yr<span class="inline-formula"><sup>−1</sup></span> (downscaled) in the case of the intercept, considerably improving upon results previously published in the literature.</p>https://tc.copernicus.org/articles/17/5061/2023/tc-17-5061-2023.pdf |
| spellingShingle | M. Tedesco M. Tedesco P. Colosio X. Fettweis G. Cervone A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR The Cryosphere |
| title | 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_short | A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR |
| title_sort | computationally efficient statistically downscaled 100 thinsp m resolution greenland product from the regional climate model mar |
| url | https://tc.copernicus.org/articles/17/5061/2023/tc-17-5061-2023.pdf |
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