Collating historic weather observations for the East Asian region: Challenges, solutions, and reanalyses

Historic instrumental weather observations, made on land or at sea from as early as the 17th century (e.g., Camuffo etal., 2010), are integral to extending our understanding of the decadal and centennial variations of Earth’s climate and forcomparison with paleo-proxy data. The potential of such dat...

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Main Authors: WILLIAMSON, Fiona, ALLAN, Rob, REN, Guoyu, LEE, Tsz-cheung, LUI, Wing-Hong, KUBOTA, Hisayuki, MATSUMOTO, Jun, LUTERBACHER, Jurg, WILKINSON, Clive, WOOD, Kevin
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Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/soss_research/2645
https://ink.library.smu.edu.sg/context/soss_research/article/3902/viewcontent/Williamson2018_Article_CollatingHistoricWeatherObserv.pdf
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spelling sg-smu-ink.soss_research-39022018-08-24T02:06:34Z Collating historic weather observations for the East Asian region: Challenges, solutions, and reanalyses WILLIAMSON, Fiona ALLAN, Rob REN, Guoyu LEE, Tsz-cheung LUI, Wing-Hong KUBOTA, Hisayuki MATSUMOTO, Jun LUTERBACHER, Jurg WILKINSON, Clive WOOD, Kevin Historic instrumental weather observations, made on land or at sea from as early as the 17th century (e.g., Camuffo etal., 2010), are integral to extending our understanding of the decadal and centennial variations of Earth’s climate and forcomparison with paleo-proxy data. The potential of such data is shown to best effect when used in dynamical 4D globalreanalyses to reconstruct climate patterns and fluctuations over more than 250 years, improving climate projections and con-tributing to climate change detection and attribution studies. This longer temporal dimension permits the resolution of morerealizations of decadal to multi-decadal climate variations (Bengtsson et al., 2007). The reduction of errors in reanalyses de-pends heavily on the homogeneity and geographic and temporal coverage of the data assimilated into them, particularly whendownscaling climate change simulations. For some regions of the world, a paucity of observational data requires a global,multi-disciplinary effort to source and recover previously unknown repositories of instrumental weather observations, and topreserve them in digital formats suitable for modern-day use. This is the premise behind ACRE (Atmospheric CirculationReconstructions over the Earth) China—a dedicated effort within the wider CSSP (Climate Science for Service Partnership)China project. 2018-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soss_research/2645 info:doi/10.1007/s00376-017-7259-z https://ink.library.smu.edu.sg/context/soss_research/article/3902/viewcontent/Williamson2018_Article_CollatingHistoricWeatherObserv.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School of Social Sciences eng Institutional Knowledge at Singapore Management University Sociology
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Sociology
spellingShingle Sociology
WILLIAMSON, Fiona
ALLAN, Rob
REN, Guoyu
LEE, Tsz-cheung
LUI, Wing-Hong
KUBOTA, Hisayuki
MATSUMOTO, Jun
LUTERBACHER, Jurg
WILKINSON, Clive
WOOD, Kevin
Collating historic weather observations for the East Asian region: Challenges, solutions, and reanalyses
description Historic instrumental weather observations, made on land or at sea from as early as the 17th century (e.g., Camuffo etal., 2010), are integral to extending our understanding of the decadal and centennial variations of Earth’s climate and forcomparison with paleo-proxy data. The potential of such data is shown to best effect when used in dynamical 4D globalreanalyses to reconstruct climate patterns and fluctuations over more than 250 years, improving climate projections and con-tributing to climate change detection and attribution studies. This longer temporal dimension permits the resolution of morerealizations of decadal to multi-decadal climate variations (Bengtsson et al., 2007). The reduction of errors in reanalyses de-pends heavily on the homogeneity and geographic and temporal coverage of the data assimilated into them, particularly whendownscaling climate change simulations. For some regions of the world, a paucity of observational data requires a global,multi-disciplinary effort to source and recover previously unknown repositories of instrumental weather observations, and topreserve them in digital formats suitable for modern-day use. This is the premise behind ACRE (Atmospheric CirculationReconstructions over the Earth) China—a dedicated effort within the wider CSSP (Climate Science for Service Partnership)China project.
format text
author WILLIAMSON, Fiona
ALLAN, Rob
REN, Guoyu
LEE, Tsz-cheung
LUI, Wing-Hong
KUBOTA, Hisayuki
MATSUMOTO, Jun
LUTERBACHER, Jurg
WILKINSON, Clive
WOOD, Kevin
author_facet WILLIAMSON, Fiona
ALLAN, Rob
REN, Guoyu
LEE, Tsz-cheung
LUI, Wing-Hong
KUBOTA, Hisayuki
MATSUMOTO, Jun
LUTERBACHER, Jurg
WILKINSON, Clive
WOOD, Kevin
author_sort WILLIAMSON, Fiona
title Collating historic weather observations for the East Asian region: Challenges, solutions, and reanalyses
title_short Collating historic weather observations for the East Asian region: Challenges, solutions, and reanalyses
title_full Collating historic weather observations for the East Asian region: Challenges, solutions, and reanalyses
title_fullStr Collating historic weather observations for the East Asian region: Challenges, solutions, and reanalyses
title_full_unstemmed Collating historic weather observations for the East Asian region: Challenges, solutions, and reanalyses
title_sort collating historic weather observations for the east asian region: challenges, solutions, and reanalyses
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/soss_research/2645
https://ink.library.smu.edu.sg/context/soss_research/article/3902/viewcontent/Williamson2018_Article_CollatingHistoricWeatherObserv.pdf
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