Estimating surface melt in Antarctica from 1979 to 2022, using a statistically parameterized positive degree-day model
Surface melt is one of the primary drivers of ice shelf collapse in Antarctica. Surface melting is expected to increase in the future as the global climate continues to warm, because there is a statistically significant positive relationship between air temperature and melt. Enhanced surface melt wi...
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ftcopernicus:oai:publications.copernicus.org:tcd106809 2023-05-15T13:38:41+02:00 Estimating surface melt in Antarctica from 1979 to 2022, using a statistically parameterized positive degree-day model Zheng, Yaowen Golledge, Nicholas R. Gossart, Alexandra Picard, Ghislain Leduc-Leballeur, Marion 2022-10-19 application/pdf https://doi.org/10.5194/tc-2022-192 https://tc.copernicus.org/preprints/tc-2022-192/ eng eng doi:10.5194/tc-2022-192 https://tc.copernicus.org/preprints/tc-2022-192/ eISSN: 1994-0424 Text 2022 ftcopernicus https://doi.org/10.5194/tc-2022-192 2022-10-24T16:22:42Z Surface melt is one of the primary drivers of ice shelf collapse in Antarctica. Surface melting is expected to increase in the future as the global climate continues to warm, because there is a statistically significant positive relationship between air temperature and melt. Enhanced surface melt will negatively impact the mass balance of the Antarctic Ice Sheet (AIS) and, through dynamic feedbacks, induce changes in global mean sea level (GMSL). However, current understanding of surface melt in Antarctica remains limited in past, present or future contexts. Continental-scale spaceborne observations of surface melt are limited to the satellite era (1979–present), meaning that current estimates of Antarctic surface melt are typically derived from surface energy balance (SEB) or positive degree-day (PDD) models. SEB models require diverse and detailed input data that are not always available and require considerable computational resources. The PDD model, by comparison, has fewer input and computational requirements and is therefor suited for exploring surface melt scenarios in the past and future. The use of PDD schemes for Antarctic melt has been less extensively explored than their application to surface melting of the Greenland Ice Sheet, particularly in terms of a spatially-varying parameterization. Here, we construct a PDD model, force it only with 2-m air temperature reanalysis data, and parameterize it by minimizing the error with respect to satellite observations and SEB model outputs over the period 1979 to 2022. We compare the spatial and temporal variability of surface melt from our PDD model over the last 43 years with that of satellite observations and SEB simulations. We find that the PDD model can generally capture the same spatial and temporal surface melt patterns. Although there were at most four years over/under- estimation on ice shelf regions in the epoch, these discrepancies reduce when considering the whole AIS. With the limitations discussed, we suggest that an appropriately parameterized ... Text Antarc* Antarctic Antarctica Greenland Ice Sheet Ice Shelf Copernicus Publications: E-Journals Antarctic Greenland The Antarctic |
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Copernicus Publications: E-Journals |
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English |
description |
Surface melt is one of the primary drivers of ice shelf collapse in Antarctica. Surface melting is expected to increase in the future as the global climate continues to warm, because there is a statistically significant positive relationship between air temperature and melt. Enhanced surface melt will negatively impact the mass balance of the Antarctic Ice Sheet (AIS) and, through dynamic feedbacks, induce changes in global mean sea level (GMSL). However, current understanding of surface melt in Antarctica remains limited in past, present or future contexts. Continental-scale spaceborne observations of surface melt are limited to the satellite era (1979–present), meaning that current estimates of Antarctic surface melt are typically derived from surface energy balance (SEB) or positive degree-day (PDD) models. SEB models require diverse and detailed input data that are not always available and require considerable computational resources. The PDD model, by comparison, has fewer input and computational requirements and is therefor suited for exploring surface melt scenarios in the past and future. The use of PDD schemes for Antarctic melt has been less extensively explored than their application to surface melting of the Greenland Ice Sheet, particularly in terms of a spatially-varying parameterization. Here, we construct a PDD model, force it only with 2-m air temperature reanalysis data, and parameterize it by minimizing the error with respect to satellite observations and SEB model outputs over the period 1979 to 2022. We compare the spatial and temporal variability of surface melt from our PDD model over the last 43 years with that of satellite observations and SEB simulations. We find that the PDD model can generally capture the same spatial and temporal surface melt patterns. Although there were at most four years over/under- estimation on ice shelf regions in the epoch, these discrepancies reduce when considering the whole AIS. With the limitations discussed, we suggest that an appropriately parameterized ... |
format |
Text |
author |
Zheng, Yaowen Golledge, Nicholas R. Gossart, Alexandra Picard, Ghislain Leduc-Leballeur, Marion |
spellingShingle |
Zheng, Yaowen Golledge, Nicholas R. Gossart, Alexandra Picard, Ghislain Leduc-Leballeur, Marion Estimating surface melt in Antarctica from 1979 to 2022, using a statistically parameterized positive degree-day model |
author_facet |
Zheng, Yaowen Golledge, Nicholas R. Gossart, Alexandra Picard, Ghislain Leduc-Leballeur, Marion |
author_sort |
Zheng, Yaowen |
title |
Estimating surface melt in Antarctica from 1979 to 2022, using a statistically parameterized positive degree-day model |
title_short |
Estimating surface melt in Antarctica from 1979 to 2022, using a statistically parameterized positive degree-day model |
title_full |
Estimating surface melt in Antarctica from 1979 to 2022, using a statistically parameterized positive degree-day model |
title_fullStr |
Estimating surface melt in Antarctica from 1979 to 2022, using a statistically parameterized positive degree-day model |
title_full_unstemmed |
Estimating surface melt in Antarctica from 1979 to 2022, using a statistically parameterized positive degree-day model |
title_sort |
estimating surface melt in antarctica from 1979 to 2022, using a statistically parameterized positive degree-day model |
publishDate |
2022 |
url |
https://doi.org/10.5194/tc-2022-192 https://tc.copernicus.org/preprints/tc-2022-192/ |
geographic |
Antarctic Greenland The Antarctic |
geographic_facet |
Antarctic Greenland The Antarctic |
genre |
Antarc* Antarctic Antarctica Greenland Ice Sheet Ice Shelf |
genre_facet |
Antarc* Antarctic Antarctica Greenland Ice Sheet Ice Shelf |
op_source |
eISSN: 1994-0424 |
op_relation |
doi:10.5194/tc-2022-192 https://tc.copernicus.org/preprints/tc-2022-192/ |
op_doi |
https://doi.org/10.5194/tc-2022-192 |
_version_ |
1766109625055182848 |