Reconstructing tropical monthly sea surface temperature variability by assimilating coral proxy datasets

Coral reconstruction often serves as a major proxy of high-resolution sea surface temperature (SST) variability beyond the instrumental era. However, coral reconstructions are sparse and are usually studied for interannual variability, with few studies on the monthly features. In this study, we reconstruct the monthly SST spatial field by applying the paleoclimate data assimilation method to the coral records of the latest CoralHydro2k data set for the instrument period of 1880–2000. A comparison with observed SST variability shows that our assimilated tropical SST variability performs reasonably well for the seasonal cycle and monthly ENSO characteristics, notably the phase-locking and onset timing, and more realistic spatial fields relative to the model simulations. This study suggests the feasibility of applying paleoclimate data assimilation to reconstruct the monthly SST in the historical period.

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Publication Details

Journal
npj Climate and Atmospheric Science
Published
2024-10-28
DOI
https://doi.org/10.1038/s41612-024-00816-w
Citations
4
Primary Topic
Oceanographic and Atmospheric Processes
Type
article
Field-Weighted Citation Impact
1.92
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article

Reconstructing tropical monthly sea surface temperature variability by assimilating coral proxy datasets

Leilei Jiang, Mi Yan, Wan‐Ping Hu, Weiyi Sun et al.
4 citations
npj Climate and Atmospheric Science
Oceanographic and Atmospheric Processes
1.92
article

Reconstructing tropical monthly sea surface temperature variability by assimilating coral proxy datasets

Leilei Jiang, Mi Yan, Wan‐Ping Hu, Weiyi Sun, Benyue Li, Qin Wen, Liang Ning, Fangmiao Xing, Lili Lei, Yanmin Qin, Haohao Sun, Zhengyu Liu, Kefan Chen, Jian Liu, Fen Wu
article en
4 citations

Abstract

Coral reconstruction often serves as a major proxy of high-resolution sea surface temperature (SST) variability beyond the instrumental era. However, coral reconstructions are sparse and are usually studied for interannual variability, with few studies on the monthly features. In this study, we reconstruct the monthly SST spatial field by applying the paleoclimate data assimilation method to the coral records of the latest CoralHydro2k data set for the instrument period of 1880–2000. A comparison with observed SST variability shows that our assimilated tropical SST variability performs reasonably well for the seasonal cycle and monthly ENSO characteristics, notably the phase-locking and onset timing, and more realistic spatial fields relative to the model simulations. This study suggests the feasibility of applying paleoclimate data assimilation to reconstruct the monthly SST in the historical period.

npj Climate and Atmospheric ScienceVol. 7(1)
Guangxi University (CN), Nanjing Normal University (CN), Chinese Academy of Sciences (CN), University of Massachusetts Amherst (US), Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application (CN), Institute of Earth Environment (CN), The Ohio State University (US), Nanjing University (CN)
Life below water
Openalex Percentile: Top 15%
Oceanographic and Atmospheric Processes
1.92
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