Quality Assessment of Reanalysis Data and 39 CMIP6 Models Based on In-situ Sea Temperature Observations in the Upper to Middle Layers of the Bering Sea

Liu Jiankang; Chen Hongxia

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Liu Jiankang，Chen Hongxia. Quality Assessment of Reanalysis Data and 39 CMIP6 Models Based on In-situ Sea Temperature Observations in the Upper to Middle Layers of the Bering Sea[J]. Haiyang Xuebao，2026, 48(x)：1–17

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Quality Assessment of Reanalysis Data and 39 CMIP6 Models Based on In-situ Sea Temperature Observations in the Upper to Middle Layers of the Bering Sea

Liu Jiankang^{1, 2
,},
Chen Hongxia^{1, 2, 3
,
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1.
First Institute of Oceanography, MNR, Qingdao 266061, China
2.
Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266061, China
3.
Key Laboratory of Marine Science and Numerical Modeling, MNR, Qingdao 266237, China

Received Date: 2025-07-09
Available Online: 2026-05-22

Abstract

Abstract

Based on observational data from four representative stations along the B transect in the Bering Sea during July 2012 (a cold year) and 2014 (a warm year), along with multiple sets of reanalysis data and climate model results, this study systematically evaluates the ability of different data sources to reproduce sea temperature structures from approximately 0 to 1000 meters and their performance over multiple time scales using correlation coefficients, root mean square errors (RMSE), and standard deviation as evaluation metrics. The results show that the temperature variability in the upper ocean (approximately 0–200 m) is significantly higher than in the deeper layers (below approximately 200 m). Reanalysis data generally have smaller average errors across all layers compared to climate model data. Specifically, for 2012, the error in the upper layers is about 0.3–0.5 ℃, while the model error is approximately 2 ℃; in the deep layers, the errors are about 0.1 ℃ and 1 ℃, respectively. In 2014, the errors in most models were lower than those in 2012, indicating that model performance is somewhat dependent on the climatic background. Long-term sequence analysis indicates that all data sources can reproduce the characteristic "cold winters and warm summers" seasonal cycle, but models show a systematic bias of about 1 ℃ in the middle layer temperature. On the interdecadal scale, the sea surface temperature anomaly (SSTA) shows consistent trends across data, while the middle layer temperature anomaly (MATA) exhibits time shifts of several years for extreme values. This study quantifies the error magnitude and uncertainty characteristics of different data in reproducing the upper ocean temperature structure in the Bering Sea, providing a quantitative reference for regional sea temperature variation analysis and multi-source data application.
- Bering Sea,
- Sea temperature in the upper to middle layers,
- Reanalysis data,
- CMIP6 models data

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References(33)

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