Trends in extreme temperature indices in the Poyang Lake Basin, China
Based on daily maximum and minimum temperature records at 78 meteorological stations in the Basin of China’s largest fresh water lake (Poyang Lake Basin), the temporal and spatial variability of 11 extreme temperature indices are investigated for the period 1959–2010. The analysis indicates that the...
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ftpubman:oai:pure.mpg.de:item_1976793 2023-08-20T04:05:37+02:00 Trends in extreme temperature indices in the Poyang Lake Basin, China Tao, H. Fraedrich, K. Menz, C. Zhai, J. 2014-03-15 http://hdl.handle.net/11858/00-001M-0000-0018-6ECF-E eng eng info:eu-repo/semantics/altIdentifier/doi/10.1007/s00477-014-0863-x http://hdl.handle.net/11858/00-001M-0000-0018-6ECF-E Stochastic Environmental Research and Risk Assessment info:eu-repo/semantics/article 2014 ftpubman https://doi.org/10.1007/s00477-014-0863-x 2023-08-01T22:51:50Z Based on daily maximum and minimum temperature records at 78 meteorological stations in the Basin of China’s largest fresh water lake (Poyang Lake Basin), the temporal and spatial variability of 11 extreme temperature indices are investigated for the period 1959–2010. The analysis indicates that the annual mean of daily minimum temperature (Tmin) has increased significantly, while no significant trends were observed in the annual mean of daily maximum temperature (Tmax), resulting in a significant decrease in the diurnal temperature range. Trends and percentages of stations with significant trends in Tmin-related indices are generally stronger and higher than those in Tmax-related indices; however, no significant trends can be found in Tmax-related indices (TXMean, TX90p, TXx and TX10p) at both seasonal and annual time scale. Low correlations with Global-SST ENSO index are also detected in Tmax-related indices. Significant positive relationships can be found in Tmin-related indices (TNMean, TNx, TNn and TN90p), however, the most significant negative coefficient was also found in cold nights (TN10p) with the Global-SST ENSO index. Singular value decomposition (SVD) correlating extreme temperatures over the Poyang Lake Basin and the North Pacific SST indicates the East China Sea, Western Pacific and Bering Sea to be stronger linked with Tmin than Tmax with the first mode (SVD-1) explaining 90 and 94 % of annual Tmax and Tmin respectively. Stochastic Environmental Research and Risk Assessment Stochastic Environmental Research and Risk Assessment Look Inside Within this Article Introduction Datasets and methodology Results Summary References References Other actions Export citation Register for Journal Updates About This Journal Reprints and Permissions Add to Papers Share Share this content on Facebook Share this content on Twitter Share this content on LinkedIn Article in Journal/Newspaper Bering Sea Max Planck Society: MPG.PuRe Bering Sea Pacific Stochastic Environmental Research and Risk Assessment 28 6 1543 1553 |
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Max Planck Society: MPG.PuRe |
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language |
English |
description |
Based on daily maximum and minimum temperature records at 78 meteorological stations in the Basin of China’s largest fresh water lake (Poyang Lake Basin), the temporal and spatial variability of 11 extreme temperature indices are investigated for the period 1959–2010. The analysis indicates that the annual mean of daily minimum temperature (Tmin) has increased significantly, while no significant trends were observed in the annual mean of daily maximum temperature (Tmax), resulting in a significant decrease in the diurnal temperature range. Trends and percentages of stations with significant trends in Tmin-related indices are generally stronger and higher than those in Tmax-related indices; however, no significant trends can be found in Tmax-related indices (TXMean, TX90p, TXx and TX10p) at both seasonal and annual time scale. Low correlations with Global-SST ENSO index are also detected in Tmax-related indices. Significant positive relationships can be found in Tmin-related indices (TNMean, TNx, TNn and TN90p), however, the most significant negative coefficient was also found in cold nights (TN10p) with the Global-SST ENSO index. Singular value decomposition (SVD) correlating extreme temperatures over the Poyang Lake Basin and the North Pacific SST indicates the East China Sea, Western Pacific and Bering Sea to be stronger linked with Tmin than Tmax with the first mode (SVD-1) explaining 90 and 94 % of annual Tmax and Tmin respectively. Stochastic Environmental Research and Risk Assessment Stochastic Environmental Research and Risk Assessment Look Inside Within this Article Introduction Datasets and methodology Results Summary References References Other actions Export citation Register for Journal Updates About This Journal Reprints and Permissions Add to Papers Share Share this content on Facebook Share this content on Twitter Share this content on LinkedIn |
format |
Article in Journal/Newspaper |
author |
Tao, H. Fraedrich, K. Menz, C. Zhai, J. |
spellingShingle |
Tao, H. Fraedrich, K. Menz, C. Zhai, J. Trends in extreme temperature indices in the Poyang Lake Basin, China |
author_facet |
Tao, H. Fraedrich, K. Menz, C. Zhai, J. |
author_sort |
Tao, H. |
title |
Trends in extreme temperature indices in the Poyang Lake Basin, China |
title_short |
Trends in extreme temperature indices in the Poyang Lake Basin, China |
title_full |
Trends in extreme temperature indices in the Poyang Lake Basin, China |
title_fullStr |
Trends in extreme temperature indices in the Poyang Lake Basin, China |
title_full_unstemmed |
Trends in extreme temperature indices in the Poyang Lake Basin, China |
title_sort |
trends in extreme temperature indices in the poyang lake basin, china |
publishDate |
2014 |
url |
http://hdl.handle.net/11858/00-001M-0000-0018-6ECF-E |
geographic |
Bering Sea Pacific |
geographic_facet |
Bering Sea Pacific |
genre |
Bering Sea |
genre_facet |
Bering Sea |
op_source |
Stochastic Environmental Research and Risk Assessment |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1007/s00477-014-0863-x http://hdl.handle.net/11858/00-001M-0000-0018-6ECF-E |
op_doi |
https://doi.org/10.1007/s00477-014-0863-x |
container_title |
Stochastic Environmental Research and Risk Assessment |
container_volume |
28 |
container_issue |
6 |
container_start_page |
1543 |
op_container_end_page |
1553 |
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1774716176682713088 |