Journal of the Korean earth science society (한국지구과학회지)

The Korean Earth Science Society (한국지구과학회)
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Negative Biases and Its Seasonal Differences in the Northwest Pacific Sea Surface Temperature in CMIP6 Models

기후모형(CMIP6) 해면수온의 북서태평양 음의 오차와 계절 차이

  • Euihyun Jung (Ocean Circulation & Climate Research Department, Korea Institute of Ocean Science and Technology) ;
  • Heeseok Jung (Ocean Circulation & Climate Research Department, Korea Institute of Ocean Science and Technology) ;
  • Min Ho Kwon (Ocean Climate Prediction Center, Korea Institute of Ocean Science and Technology) ;
  • Chan Joo Jang (Ocean Circulation & Climate Research Department, Korea Institute of Ocean Science and Technology)
  • 정의현 (한국해양과학기술원 해양순환기후연구부) ;
  • 정희석 (한국해양과학기술원 해양순환기후연구부) ;
  • 권민호 (한국해양과학기술원 해양기후예측센터) ;
  • 장찬주 (한국해양과학기술원 해양순환기후연구부)
  • Received : 2024.08.16
  • Accepted : 2024.10.09
  • Published : 2024.10.31
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Abstract

Accurate simulation of the sea surface temperatures in climate models is important for representing the energy scale of the ocean surface and quantifying the energy balance within the ocean-atmosphere interaction. There are several regions where these sea surface temperatures in climate model simulations show persistent biases, and the Northwest Pacific is one of the regions with negative biases in many climate models. While most studies have focused on the annual and ensemble averages of this bias, this study analyzed historical sea surface temperatures from 31 CMIP6 models to examine the pattern and magnitude of the North Pacific bias in individual models and in multi-ensemble averages over the seasons. This negative bias was observed in most of the CMIP6 models with similar spatial distributions and was present throughout the year. Seasonally, the bias is more pronounced in spring (-1.7℃) and summer (-1.8℃), and decreases slightly in fall (-1.3℃) and winter (-1.2℃). In addition, the differences between the individual models were larger in the summer and winter in the Northwest Pacific than in the other seasons.

기후 모형에서 해면수온을 정확하게 모의하는 것은 해수면의 에너지 규모를 표현하고 해양-대기 상호작용 내 에너지 균형을 정량화한다는 측면에서 중요하다. 그런데 기후 모형 모의에서 이러한 해수면 온도가 지속적인 오차를 보이는 몇몇 지역이 있고, 북서태평양은 많은 기후 모형 모의에서 음의 오차를 보이는 지역 중 하나이다. 많은 연구가 이 오차와 관련하여 수행되었지만 대부분은 오차의 연평균 및 앙상블평균에 초점을 맞추어 진행되었다. 하지만, 본 연구는 31개 CMIP6 모형의 과거 해면수온을 분석하여 다중 모형 평균 및 개별 모형의 북태평양 오차의 패턴과 그 크기를 계절별로 분석하였다. 이 음의 오차는 비슷한 공간 분포를 가진 대부분의 CMIP6 모형에서 나타나며 연중 내내 존재한다. 계절별로는 봄(-1.7℃)과 여름(-1.8℃)에 오차의 크기가 더 크고, 가을(-1.3℃)과 겨울(-1.2℃)에는 소폭 감소한다. 또한 북서태평양의 여름과 겨울에는 다른 계절에 비해 개별 모형 간의 차이가 더 크다.

Keywords

Acknowledgement

이 연구는 해양수산부의 재원으로 해양수산과학기술진흥원의 "인도양 한-미 공동관측 및 연구(PM63990)"와 "쿠로시오 해류로 인한 한반도 해양위기 대응기술개발(PM64380)" 그리고 한국해양과학기술원의 "한반도 주변해 해양변화 예측역량 강화와 해양기후 변화전망 (PEA0203)"의 지원을 받아 수행되었습니다.

References

  1. Balaguru, K., Van Roekel, L. P., Leung, L. R., & Veneziani, M, 2021, Subtropical Eastern North Pacific SST Bias in Earth System Models. Journal of Geophysical Research: Oceans, 126(8), e2021JC017359.
  2. Boschetti, F., Feng, M., Hartog, J. R., Hobday, A. J., & Zhang, X., 2023, Sea surface temperature predictability assessment with an ensemble machine learning method using climate model simulations. Deep Sea Research Part II: Topical Studies in Oceanography, 210, 105308.
  3. Cho, A., Song, H., Tak, Y.-J., Yeh, S.-W., An, S.-I., Lee, S.-M., Ji, H.-S., & Hyun, Y.-K, 2022, Atmosphere-driven cold SST biases over the western North Pacific in the GloSea5 seasonal forecast system. Climate Dynamics, 59(9), 2571-2584.
  4. Chung, E.-S., Kim, S.-J., Lee, S.-K., Ha, K.-J., Yeh, S.-W., Kim, Y. S., Jun, S.-Y., Kim, J.-H., & Kim, D, 2024, Tropical eastern Pacific cooling trend reinforced by human activity. npj Climate and Atmospheric Science, 7, 1234567890.
  5. Feng, J., Lian, T., Chen, D., & Li, Y, 2021, The Cause of the Large Cold Bias in the Northwestern Pacific Ocean. Geophysical Research Letters, 48(18), e2021GL094616.
  6. Huo, W., Drews, A., Martin, T., & Wahl, S. (2024). Impacts of North Atlantic Model Biases on Natural Decadal Climate Variability. Journal of Geophysical Research: Atmospheres, 129(4), e2023JD039778.
  7. Jang, C. J., Park, J., Park, T., & Yoo, S, 2011, Response of the ocean mixed layer depth to global warming and its impact on primary production: a case for the North Pacific Ocean. ICES Journal of Marine Science, 68(6), 996-1007.
  8. Jo, A. R., Lee, J.-Y., Timmermann, A., Jin, F.-F., Yamaguchi, R., & Gallego, A, 2022, Future Amplification of Sea Surface Temperature Seasonality Due To Enhanced Ocean Stratification. Geophysical Research Letters, 49(9), e2022GL098607.
  9. Lafferty, D. C., & Sriver, R. L, 2023, Downscaling and bias-correction contribute considerable uncertainty to local climate projections in CMIP6. npj Climate and Atmospheric Science, 6(1), 158.
  10. Levitus, S, 1987, Rate of Change of Heat Storage of the World Ocean. Journal of Physical Oceanography, 17(4), 518-528.
  11. Luo, F., Ying, J., Liu, T., & Chen, D, 2023, Origins of Southern Ocean warm sea surface temperature bias in CMIP6 models. npj Climate and Atmospheric Science, 6(1), 127.
  12. Mass, C., Randall, C., Conrick, R., & Ovens, D, 2022, The Relationship between Northeast Pacific Sea Surface Temperatures, Synoptic Evolution, and Surface Air Temperatures over the Pacific Northwest. Weather and Forecasting, 37(10), 1741-1759.
  13. McKenna, S., Santoso, A., Sen Gupta, A., & Taschetto, A. S, 2024, Understanding Biases in Indian Ocean Seasonal SST in CMIP6 Models. Journal of Geophysical Research: Oceans, 129(2), e2023JC020330.
  14. Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V., Rowell, D. P., Kent, E. C., & Kaplan, A, 2003, Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. Journal of Geophysical Research: Atmospheres, 108(D14).
  15. Sallee, J.-B., Pellichero, V., Akhoudas, C., Pauthenet, E., Vignes, L., Schmidtko, S., Garabato, A. N., Sutherland, P., & Kuusela, M, 2021, Summertime increases in upper-ocean stratification and mixed-layer depth. Nature, 591(7851), 592-598.
  16. Wang, C., Zhang, L., Lee, S.-K., Wu, L., & Mechoso, C. R, 2014, A global perspective on CMIP5 climate model biases. Nature Climate Change, 4(3), 201-205.
  17. Wang, C., Zou, L., & Zhou, T, 2018, SST biases over the Northwest Pacific and possible causes in CMIP5 models. Science China Earth Sciences, 61(6), 792-803.
  18. Wang, D., Liu, J., Luan, Q., Shao, W., Fu, X., Wang, H., & Gu, Y, 2023, Projection of future precipitation change using CMIP6 multimodel ensemble based on fusion of multiple machine learning algorithms: A case in Hanjiang River Basin, China. Meteorological Applications, 30(5), e2144.
  19. Wu, Z., Jiang, C., Conde, M., Chen, J., & Deng, B, 2020, The long-term spatiotemporal variability of sea surface temperature in the northwest Pacific and China offshore. Ocean Sci., 16(1), 83-97.
  20. Xie, S.-P., Deser, C., Vecchi, G. A., Ma, J., Teng, H., & Wittenberg, A. T, 2010, Global Warming Pattern Formation: Sea Surface Temperature and Rainfall. Journal of Climate, 23(4), 966-986.
  21. Yashayaev, I. M., & Zveryaev, I. I, 2001, Climate of the seasonal cycle in the North Pacific and the North Atlantic oceans. International Journal of Climatology, 21(4), 401-417.
  22. Zhang, Q., Liu, B., Li, S., & Zhou, T, 2023, Understanding Models' Global Sea Surface Temperature Bias in Mean State: From CMIP5 to CMIP6. Geophysical Research Letters, 50(4), e2022GL100888.
  23. Zhu, Y., Zhang, R.-H., & Sun, J, 2020, North Pacific Upper-Ocean Cold Temperature Biases in CMIP6 Simulations and the Role of Regional Vertical Mixing. Journal of Climate, 33(17), 7523-7538.
  24. Zou, L., & Zhou, T, 2011, Sensitivity of a regional ocean-atmosphere coupled model to convection parameterization over western North Pacific. Journal of Geophysical Research: Atmospheres, 116(D18).