Improved probabilistic twenty-first century projections of sea surface temperature over East Asian marginal seas by considering uncertainty owing to model error and internal variability

Jongsoo Shin, Roman Olson, Soon Il An

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In this study, probabilistic future changes in sea surface temperature (SST) over East Asian marginal seas between historical (1971–2000) and late twenty-first century (2061–2100) periods are calculated by using both unweighted and weighted averaging methods. Unlike most previous studies, the present study considers uncertainty caused by internal variability and model error, which could reduce the credible intervals. Here, marginal seas are divided into three regions of Yellow Sea, South Sea, and East/Japan Sea, and the projections are computed separately for January–February–March (JFM), April–May–June, July–August–September (JAS), and October–November–December seasons. Our results show that the SSTs for the three regions are projected to increase by about 1–3 K and 2–6 K under the representative concentration pathway (RCP) 4.5 and the RCP8.5 scenarios, respectively, in terms of the 90% credible intervals. The future SST change over the Yellow and the East/Japan seas is larger than that over the South Sea, which is similar to recent observed trends. SSTs are expected to increase more in JAS than in JFM for all three regions. Before making the projections, the method is tested in a suite of one-at-a-time cross-validation experiments. The method well-calibrated results as measured by the 90% posterior credible intervals.

Original languageEnglish
Pages (from-to)6075-6087
Number of pages13
JournalClimate Dynamics
Volume53
Issue number9-10
DOIs
Publication statusPublished - 2019 Nov 1

Bibliographical note

Funding Information:
The authors acknowledge the World Climate Research Programme?s Working Group on Coupled Modelling, which established CMIP, and are grateful for the climate modeling groups listed in Table?1 of this paper for producing and making available their model output. For CMIP, the U.S. Department of Energy?s Program for Climate Model Diagnosis and Intercomparison provided coordinated support and led the development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2018R1A5A1024958).?R. Olson acknowldeges support from the Institute for Basic Science (project code IBS-R028-D1).

Funding Information:
The authors acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which established CMIP, and are grateful for the climate modeling groups listed in Table 1 of this paper for producing and making available their model output. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provided coordinated support and led the development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2018R1A5A1024958). R. Olson acknowldeges support from the Institute for Basic Science (project code IBS-R028-D1).

Publisher Copyright:
© 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

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