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An assessment on short-term sea surface temperature forecast in China seas based on a global eddy-resolving dynamical forecast system.

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Title: An assessment on short-term sea surface temperature forecast in China seas based on a global eddy-resolving dynamical forecast system.
Authors: Xu, Huan1,2 (AUTHOR), Lin, Pengfei1,3 (AUTHOR) linpf@mail.iap.ac.cn, Cheng, Xuhua2 (AUTHOR), Liu, Hailong4 (AUTHOR), Zheng, Weipeng1,3 (AUTHOR), Yu, Zipeng1 (AUTHOR), Yu, Yongqiang1,3 (AUTHOR), Li, Yiwen5 (AUTHOR), Zhang, Tao1,3 (AUTHOR)
Source: Journal of Oceanology & Limnology. Mar2026, Vol. 44 Issue 2, p511-527. 17p.
Subject Terms: *Forecasting, *Ocean temperature, *Evaluation methodology, *Seasonal temperature variations, *Ocean, *Ocean circulation
Geographic Terms: China, East China Sea, South China Sea
Abstract: Short-term sea surface temperature (SST) forecasting is an essential operational task around China seas. However, the capability of short-term SST forecast from the dynamical numerical model for China seas has not been fully evaluated so far. We assessed the short-term SST forecast skill using a global eddy-resolving ocean forecast system, i.e., the LICOM Forecast System version 1.0 (LFS v1.0) for China seas in 2022 against satellite SST. Results show that LFS v1.0 was able to forecast the short-term SST variation in the study area. The SST with 1-, 7-, and 15-d lead time well captured the observed SST with average pattern correlation coefficient (PCC) of 0.94, 0.93, and 0.92 throughout 2022, the annual mean bias of the forecasted SST of 0.08, −0.16, and −0.33 °C, and the average root mean square error (RMSE) of 0.61, 0.72, and 0.90 °C, respectively. Geographically, the forecast RMSE with 1-d lead time in China seas increased from south to north, and the values were 0.41 °C in South China Sea (SCS) and 1.31 °C in the Bohai Sea (BS). In addition, LFS v1.0 showed better forecast SST abilities in the SCS and East China Sea (ECS) than those in the Yellow Sea and BS. In the ECS and SCS, the forecasted SST was less influenced by the ocean bottom topography due to accurately simulated ocean circulations like Kuroshio. The RMSEs of the SST forecasted by LFS v1.0 displayed seasonal variations, smaller in the area from the middle of boreal August to the middle of boreal December, and larger in boreal late spring and early summer. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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  Availability: 0
Header DbId: enr
DbLabel: Energy & Power Source
An: 193684598
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
  Group: Ti
  Data: An assessment on short-term sea surface temperature forecast in China seas based on a global eddy-resolving dynamical forecast system.
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  Data: <searchLink fieldCode="AR" term="%22Xu%2C+Huan%22">Xu, Huan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Pengfei%22">Lin, Pengfei</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> linpf@mail.iap.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Cheng%2C+Xuhua%22">Cheng, Xuhua</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Hailong%22">Liu, Hailong</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zheng%2C+Weipeng%22">Zheng, Weipeng</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Zipeng%22">Yu, Zipeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Yongqiang%22">Yu, Yongqiang</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Yiwen%22">Li, Yiwen</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Tao%22">Zhang, Tao</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Oceanology+%26+Limnology%22">Journal of Oceanology & Limnology</searchLink>. Mar2026, Vol. 44 Issue 2, p511-527. 17p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Ocean+temperature%22">Ocean temperature</searchLink><br />*<searchLink fieldCode="DE" term="%22Evaluation+methodology%22">Evaluation methodology</searchLink><br />*<searchLink fieldCode="DE" term="%22Seasonal+temperature+variations%22">Seasonal temperature variations</searchLink><br />*<searchLink fieldCode="DE" term="%22Ocean%22">Ocean</searchLink><br />*<searchLink fieldCode="DE" term="%22Ocean+circulation%22">Ocean circulation</searchLink>
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  Label: Geographic Terms
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  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink><br /><searchLink fieldCode="DE" term="%22East+China+Sea%22">East China Sea</searchLink><br /><searchLink fieldCode="DE" term="%22South+China+Sea%22">South China Sea</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Short-term sea surface temperature (SST) forecasting is an essential operational task around China seas. However, the capability of short-term SST forecast from the dynamical numerical model for China seas has not been fully evaluated so far. We assessed the short-term SST forecast skill using a global eddy-resolving ocean forecast system, i.e., the LICOM Forecast System version 1.0 (LFS v1.0) for China seas in 2022 against satellite SST. Results show that LFS v1.0 was able to forecast the short-term SST variation in the study area. The SST with 1-, 7-, and 15-d lead time well captured the observed SST with average pattern correlation coefficient (PCC) of 0.94, 0.93, and 0.92 throughout 2022, the annual mean bias of the forecasted SST of 0.08, −0.16, and −0.33 °C, and the average root mean square error (RMSE) of 0.61, 0.72, and 0.90 °C, respectively. Geographically, the forecast RMSE with 1-d lead time in China seas increased from south to north, and the values were 0.41 °C in South China Sea (SCS) and 1.31 °C in the Bohai Sea (BS). In addition, LFS v1.0 showed better forecast SST abilities in the SCS and East China Sea (ECS) than those in the Yellow Sea and BS. In the ECS and SCS, the forecasted SST was less influenced by the ocean bottom topography due to accurately simulated ocean circulations like Kuroshio. The RMSEs of the SST forecasted by LFS v1.0 displayed seasonal variations, smaller in the area from the middle of boreal August to the middle of boreal December, and larger in boreal late spring and early summer. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s00343-025-5066-4
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
        StartPage: 511
    Subjects:
      – SubjectFull: Forecasting
        Type: general
      – SubjectFull: Ocean temperature
        Type: general
      – SubjectFull: Evaluation methodology
        Type: general
      – SubjectFull: Seasonal temperature variations
        Type: general
      – SubjectFull: Ocean
        Type: general
      – SubjectFull: Ocean circulation
        Type: general
      – SubjectFull: China
        Type: general
      – SubjectFull: East China Sea
        Type: general
      – SubjectFull: South China Sea
        Type: general
    Titles:
      – TitleFull: An assessment on short-term sea surface temperature forecast in China seas based on a global eddy-resolving dynamical forecast system.
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            NameFull: Xu, Huan
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            NameFull: Lin, Pengfei
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            NameFull: Cheng, Xuhua
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            NameFull: Liu, Hailong
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            – D: 01
              M: 03
              Text: Mar2026
              Type: published
              Y: 2026
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