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A Comparison of Antarctic Ice Sheet Surface Mass Balance from Atmospheric Climate Models and In Situ Observations
In this study, 3265 multiyear averaged in situ observations and 29 observational records at annual time scale are used to examine the performance of recent reanalysis and regional atmospheric climate model products [ERA-Interim, JRA-55, MERRA, the Polar version of MM5 (PMM5), RACMO2.1, and RACMO2.3]...
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Published in: | Journal of climate 2016-07, Vol.29 (14), p.5317-5337 |
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creator | Wang, Yetang Ding, Minghu van Wessem, J. M. Schlosser, E. Altnau, S. van den Broeke, Michiel R. Lenaerts, Jan T. M. Thomas, Elizabeth R. Isaksson, Elisabeth Wang, Jianhui Sun, Weijun |
description | In this study, 3265 multiyear averaged in situ observations and 29 observational records at annual time scale are used to examine the performance of recent reanalysis and regional atmospheric climate model products [ERA-Interim, JRA-55, MERRA, the Polar version of MM5 (PMM5), RACMO2.1, and RACMO2.3] for their spatial and interannual variability of Antarctic surface mass balance (SMB), respectively. Simulated precipitation seasonality is also evaluated using three in situ observations and model intercomparison. All products qualitatively capture the macroscale spatial variability of observed SMB, but it is not possible to rank their relative performance because of the sparse observations at coastal regions with an elevation range from 200 to 1000 m. In terms of the absolute amount of observed snow accumulation in interior Antarctica, RACMO2.3 fits best, while the other models either underestimate (JRA-55, MERRA, ERA-Interim, and RACMO2.1) or overestimate (PMM5) the accumulation. Despite underestimated precipitation by the three reanalyses and RACMO2.1, this feature is clearly improved in JRA-55. However, because of changes in the observing system, especially the dramatically increased satellite observations for data assimilation, JRA-55 presents a marked jump in snow accumulation around 1979 and a large increase after the late 1990s. Although precipitation seasonality over the whole ice sheet is common for all products, ERA-Interim provides an unrealistic estimate of precipitation seasonality on the East Antarctic plateau, with high precipitation strongly peaking in summer. ERA-Interim shows a significant correlation with interannual variability of observed snow accumulation measurements at 28 of 29 locations, whereas fewer than 20 site observations significantly correlate with simulations by the other models. This suggests that ERA-Interim exhibits the highest performance of interannual variability in the observed precipitation. |
doi_str_mv | 10.1175/jcli-d-15-0642.1 |
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M. ; Schlosser, E. ; Altnau, S. ; van den Broeke, Michiel R. ; Lenaerts, Jan T. M. ; Thomas, Elizabeth R. ; Isaksson, Elisabeth ; Wang, Jianhui ; Sun, Weijun</creator><creatorcontrib>Wang, Yetang ; Ding, Minghu ; van Wessem, J. M. ; Schlosser, E. ; Altnau, S. ; van den Broeke, Michiel R. ; Lenaerts, Jan T. M. ; Thomas, Elizabeth R. ; Isaksson, Elisabeth ; Wang, Jianhui ; Sun, Weijun</creatorcontrib><description>In this study, 3265 multiyear averaged in situ observations and 29 observational records at annual time scale are used to examine the performance of recent reanalysis and regional atmospheric climate model products [ERA-Interim, JRA-55, MERRA, the Polar version of MM5 (PMM5), RACMO2.1, and RACMO2.3] for their spatial and interannual variability of Antarctic surface mass balance (SMB), respectively. Simulated precipitation seasonality is also evaluated using three in situ observations and model intercomparison. All products qualitatively capture the macroscale spatial variability of observed SMB, but it is not possible to rank their relative performance because of the sparse observations at coastal regions with an elevation range from 200 to 1000 m. In terms of the absolute amount of observed snow accumulation in interior Antarctica, RACMO2.3 fits best, while the other models either underestimate (JRA-55, MERRA, ERA-Interim, and RACMO2.1) or overestimate (PMM5) the accumulation. Despite underestimated precipitation by the three reanalyses and RACMO2.1, this feature is clearly improved in JRA-55. However, because of changes in the observing system, especially the dramatically increased satellite observations for data assimilation, JRA-55 presents a marked jump in snow accumulation around 1979 and a large increase after the late 1990s. Although precipitation seasonality over the whole ice sheet is common for all products, ERA-Interim provides an unrealistic estimate of precipitation seasonality on the East Antarctic plateau, with high precipitation strongly peaking in summer. ERA-Interim shows a significant correlation with interannual variability of observed snow accumulation measurements at 28 of 29 locations, whereas fewer than 20 site observations significantly correlate with simulations by the other models. This suggests that ERA-Interim exhibits the highest performance of interannual variability in the observed precipitation.</description><identifier>ISSN: 0894-8755</identifier><identifier>EISSN: 1520-0442</identifier><identifier>DOI: 10.1175/jcli-d-15-0642.1</identifier><language>eng</language><publisher>Boston: American Meteorological Society</publisher><subject>Bias ; Climate models ; Coastal zone ; Data collection ; Hydrology ; Ice ; Precipitation ; Quality ; Sea level ; Seasonal variations ; Snow accumulation ; Trends</subject><ispartof>Journal of climate, 2016-07, Vol.29 (14), p.5317-5337</ispartof><rights>2016 American Meteorological Society</rights><rights>Copyright American Meteorological Society Jul 15, 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c401t-9671679a2a5916e7dba0ccd09a1fcff055279fdcc45eaafaaccba66b1fb6bce13</citedby><cites>FETCH-LOGICAL-c401t-9671679a2a5916e7dba0ccd09a1fcff055279fdcc45eaafaaccba66b1fb6bce13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/26385572$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/26385572$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,58238,58471</link.rule.ids></links><search><creatorcontrib>Wang, Yetang</creatorcontrib><creatorcontrib>Ding, Minghu</creatorcontrib><creatorcontrib>van Wessem, J. M.</creatorcontrib><creatorcontrib>Schlosser, E.</creatorcontrib><creatorcontrib>Altnau, S.</creatorcontrib><creatorcontrib>van den Broeke, Michiel R.</creatorcontrib><creatorcontrib>Lenaerts, Jan T. M.</creatorcontrib><creatorcontrib>Thomas, Elizabeth R.</creatorcontrib><creatorcontrib>Isaksson, Elisabeth</creatorcontrib><creatorcontrib>Wang, Jianhui</creatorcontrib><creatorcontrib>Sun, Weijun</creatorcontrib><title>A Comparison of Antarctic Ice Sheet Surface Mass Balance from Atmospheric Climate Models and In Situ Observations</title><title>Journal of climate</title><description>In this study, 3265 multiyear averaged in situ observations and 29 observational records at annual time scale are used to examine the performance of recent reanalysis and regional atmospheric climate model products [ERA-Interim, JRA-55, MERRA, the Polar version of MM5 (PMM5), RACMO2.1, and RACMO2.3] for their spatial and interannual variability of Antarctic surface mass balance (SMB), respectively. Simulated precipitation seasonality is also evaluated using three in situ observations and model intercomparison. All products qualitatively capture the macroscale spatial variability of observed SMB, but it is not possible to rank their relative performance because of the sparse observations at coastal regions with an elevation range from 200 to 1000 m. In terms of the absolute amount of observed snow accumulation in interior Antarctica, RACMO2.3 fits best, while the other models either underestimate (JRA-55, MERRA, ERA-Interim, and RACMO2.1) or overestimate (PMM5) the accumulation. Despite underestimated precipitation by the three reanalyses and RACMO2.1, this feature is clearly improved in JRA-55. However, because of changes in the observing system, especially the dramatically increased satellite observations for data assimilation, JRA-55 presents a marked jump in snow accumulation around 1979 and a large increase after the late 1990s. Although precipitation seasonality over the whole ice sheet is common for all products, ERA-Interim provides an unrealistic estimate of precipitation seasonality on the East Antarctic plateau, with high precipitation strongly peaking in summer. ERA-Interim shows a significant correlation with interannual variability of observed snow accumulation measurements at 28 of 29 locations, whereas fewer than 20 site observations significantly correlate with simulations by the other models. This suggests that ERA-Interim exhibits the highest performance of interannual variability in the observed precipitation.</description><subject>Bias</subject><subject>Climate models</subject><subject>Coastal zone</subject><subject>Data collection</subject><subject>Hydrology</subject><subject>Ice</subject><subject>Precipitation</subject><subject>Quality</subject><subject>Sea level</subject><subject>Seasonal variations</subject><subject>Snow accumulation</subject><subject>Trends</subject><issn>0894-8755</issn><issn>1520-0442</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNo9kN1LwzAUxYMoOKfvvggBnztzsyZtH2v9mkz2MH0Ot2nCOrpmS1LB_96OiU-HA797LucQcgtsBpCJh63u2qRJQCRMpnwGZ2QCgrOEpSk_JxOWF2mSZ0JckqsQtowBl4xNyKGkldvt0bfB9dRZWvYRvY6tpgtt6HpjTKTrwVsc3QeGQB-xw3401rsdLePOhf3G-JGvunaHcaRcY7pAsW_ooqfrNg50VQfjvzG2rg_X5MJiF8zNn07J18vzZ_WWLFevi6pcJjplEJNCZiCzAjmKAqTJmhqZ1g0rEKy2lgnBs8I2WqfCIFpErWuUsgZby1obmE_J_Sl3791hMCGqrRt8P75UkAPwPAchRoqdKO1dCN5YtfdjDf-jgKnjsOq9Wi7UkwKhjsOqY_Dd6WQbovP_PJfzXIiMz38BmXt3fA</recordid><startdate>20160715</startdate><enddate>20160715</enddate><creator>Wang, Yetang</creator><creator>Ding, Minghu</creator><creator>van Wessem, J. 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M. ; Schlosser, E. ; Altnau, S. ; van den Broeke, Michiel R. ; Lenaerts, Jan T. M. ; Thomas, Elizabeth R. ; Isaksson, Elisabeth ; Wang, Jianhui ; Sun, Weijun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c401t-9671679a2a5916e7dba0ccd09a1fcff055279fdcc45eaafaaccba66b1fb6bce13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Bias</topic><topic>Climate models</topic><topic>Coastal zone</topic><topic>Data collection</topic><topic>Hydrology</topic><topic>Ice</topic><topic>Precipitation</topic><topic>Quality</topic><topic>Sea level</topic><topic>Seasonal variations</topic><topic>Snow accumulation</topic><topic>Trends</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Yetang</creatorcontrib><creatorcontrib>Ding, Minghu</creatorcontrib><creatorcontrib>van Wessem, J. 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M.</au><au>Schlosser, E.</au><au>Altnau, S.</au><au>van den Broeke, Michiel R.</au><au>Lenaerts, Jan T. M.</au><au>Thomas, Elizabeth R.</au><au>Isaksson, Elisabeth</au><au>Wang, Jianhui</au><au>Sun, Weijun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Comparison of Antarctic Ice Sheet Surface Mass Balance from Atmospheric Climate Models and In Situ Observations</atitle><jtitle>Journal of climate</jtitle><date>2016-07-15</date><risdate>2016</risdate><volume>29</volume><issue>14</issue><spage>5317</spage><epage>5337</epage><pages>5317-5337</pages><issn>0894-8755</issn><eissn>1520-0442</eissn><abstract>In this study, 3265 multiyear averaged in situ observations and 29 observational records at annual time scale are used to examine the performance of recent reanalysis and regional atmospheric climate model products [ERA-Interim, JRA-55, MERRA, the Polar version of MM5 (PMM5), RACMO2.1, and RACMO2.3] for their spatial and interannual variability of Antarctic surface mass balance (SMB), respectively. Simulated precipitation seasonality is also evaluated using three in situ observations and model intercomparison. All products qualitatively capture the macroscale spatial variability of observed SMB, but it is not possible to rank their relative performance because of the sparse observations at coastal regions with an elevation range from 200 to 1000 m. In terms of the absolute amount of observed snow accumulation in interior Antarctica, RACMO2.3 fits best, while the other models either underestimate (JRA-55, MERRA, ERA-Interim, and RACMO2.1) or overestimate (PMM5) the accumulation. Despite underestimated precipitation by the three reanalyses and RACMO2.1, this feature is clearly improved in JRA-55. However, because of changes in the observing system, especially the dramatically increased satellite observations for data assimilation, JRA-55 presents a marked jump in snow accumulation around 1979 and a large increase after the late 1990s. Although precipitation seasonality over the whole ice sheet is common for all products, ERA-Interim provides an unrealistic estimate of precipitation seasonality on the East Antarctic plateau, with high precipitation strongly peaking in summer. ERA-Interim shows a significant correlation with interannual variability of observed snow accumulation measurements at 28 of 29 locations, whereas fewer than 20 site observations significantly correlate with simulations by the other models. This suggests that ERA-Interim exhibits the highest performance of interannual variability in the observed precipitation.</abstract><cop>Boston</cop><pub>American Meteorological Society</pub><doi>10.1175/jcli-d-15-0642.1</doi><tpages>21</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Bias Climate models Coastal zone Data collection Hydrology Ice Precipitation Quality Sea level Seasonal variations Snow accumulation Trends |
title | A Comparison of Antarctic Ice Sheet Surface Mass Balance from Atmospheric Climate Models and In Situ Observations |
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