Publication Type
Journal Article
Version
publishedVersion
Publication Date
8-2018
Abstract
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.
Discipline
Sociology
Publication
Advances in Atmospheric Sciences
Volume
35
Issue
8
First Page
899
Last Page
904
ISSN
0256-1530
Identifier
10.1007/s00376-017-7259-z
Publisher
Springer Verlag (Germany)
Citation
WILLIAMSON, Fiona, ALLAN, Rob, REN, Guoyu, LEE, Tsz-cheung, LUI, Wing-Hong, KUBOTA, Hisayuki, MATSUMOTO, Jun, LUTERBACHER, Jurg, WILKINSON, Clive, & WOOD, Kevin.(2018). Collating historic weather observations for the East Asian region: Challenges, solutions, and reanalyses. Advances in Atmospheric Sciences, 35(8), 899-904.
Available at: https://ink.library.smu.edu.sg/soss_research/2645
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s00376-017-7259-z