主管单位：中国科协技术协会 

主办单位：中国海洋湖沼学会

承办单位：中国科学院海洋研究所   

刊       期：双月刊

ISSN：     0029-814X

e-ISSN：  2523-3521

CN：        37-1149/P

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  • Physics

    Abstract：This study evaluates the performance of a Regional Ocean Modeling System-based Hybrid Coupled Model (HCMROMS) in simulating tropical instability waves (TIWs), their modulation by the El Niño-Southern Oscillation (ENSO) in the tropical Pacific Ocean, and their impacts on the mean state thermal conditions of the ocean. HCMROMS integrates the Regional Ocean Modeling System (ROMS) with a statistical atmospheric model for surface wind stress anomalies based on Singular Value Decomposition (SVD), aiming to accurately represent air-sea coupling interactions and TIWs-related ocean mesoscale processes. The model successfully reproduces the climatological state and seasonal variability of sea surface temperature (SST) in the tropical Pacific. In simulating ENSO, HCMROMS captures the quasi-three-year oscillation characteristic of ENSO. Regarding TIWs, the model accurately reproduces their main features and periods. Additionally, HCMROMS shows a significantly negative correlation between the strength of TIWs and the Niño3.4 index, being consistent with empirical analyses from observations. The model’s ability to simulate the interaction between TIWs and ENSO allows us to analyze the TIWs-related energy and heat budgets. The model’s energy budget reveals that the strength of TIWs is strongly modulated by ENSO phases. The study also examines the feedback effects of TIWs on the mean state through a heat budget analysis. These results indicate that TIWs play a crucial role in the climatological heat balance, with the amplitude being comparable to sea surface heat flux. These findings underscore the importance of accurately simulating TIWs to better represent and understand their role in the tropical Pacific climate system. Overall, HCMROMS demonstrated robust performance in representing both ENSO and TIWs, offering a reliable tool for future studies on their interactions and the broader dynamics of the tropical Pacific Ocean. The model’s precise representations of TIWs and their relationship with ENSO highlight its potential in advancing our understanding of ocean-atmosphere interactions and improving climate predictions.  

    Yinnan LI, Yang YU, Chuan GAO, Hongna WANG, Rong-Hua ZHANG

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  • Physics

    Abstract：Various physics-based dynamical and data-based statistical models have been developed for uses in predicting sea surface temperature (SST) evolution in relation to the El Niño-Southern Oscillation (ENSO) over the tropical Pacific. At present, clear limitations remain in their ENSO predictions, with predicted SST anomalies (SSTAs) being widely spread across diverse models and considerable inter-model uncertainty. Fortunately, deep learning (DL)-based modeling has recently made promising advances in ENSO prediction tasks; numerous neural networks (NNs) have been constructed for ENSO predictions. However, most NNs themselves are purely data-driven and lack constraints of the necessary physical processes in the coupled system; there are few studies in which DL models are directly integrated with physics-based dynamical models. Previously, such a new type of intermediate coupled models (ICMs) was developed by directly integrating U-Net-derived sea surface wind stress models with an intermediate ocean dynamical model (denoted as ICM-UNet), with demonstrated success in simulating ENSO evolutions in freely coupled runs. It is thus natural to take a step further for prediction applications. In this study, this new ICM-UNet is applied for retrospective ENSO predictions, the first time that such a fusion of DL atmospheric model and dynamical oceanic model with different architectures can be achieved to make ENSO predictions. The overall evaluations indicate that the ICM-UNet yields valid retrospective predictions during the period 1995–2023, confirming that the ICM-UNet is a credible ocean-atmosphere coupled model for ENSO predictions. In case studies during 2020–2023, the ICM-UNet predictions reveal that SSTAs over the equatorial Pacific evolved into a second-year cooling in late 2021 and a warming tendency in 2023, forming a three-year La Niña and an El Niño event thereafter, which is consistent with the reality. The ICM-UNet successful fusion, taking advantage of both the physical constraints due to dynamical oceanic models and nonlinear representations of wind responses due to DL capacity, further underscores the high adaptability of integrating data-driven NNs into the ocean-atmosphere coupled modeling for ENSO-related studies.  

    Shuangying DU, Rong-Hua ZHANG, Chuan GAO

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  • Physics

    Abstract：Climate changes lead to significant warming of the Southern Ocean. By analyzing observational products and objective analysis data, this study reveals that the Antarctic Intermediate Water (AAIW) shows a zonal asymmetry of heat content changes, resulting from the different regional responses to the large-scale circulations. The heat content changes show significant interannual to decadal variations superimposed on a long-term trend, mainly attributed to the heat redistribution affected by the atmospheric circulations. AAIW in the Indian sector exhibits a significant widespread warming, and AAIW in the southwest Pacific sector presents an unexpected cooling. Warming has increased by 0.4 ZJ (zettajoules, 1 ZJ=1021 J) per decade since 1979 in the Indian sector, which is equivalent to the heat gain rate of 0.04 W/m2 during 1979–2019 in the Southern Ocean. Upwelling of warm circumpolar deep water driven by upward Ekman pumping, induced by the persistent positive phase of the Southern Annular Mode in recent years, plays a leading role in promoting this warming. The unexpected cooling in the southwest Pacific sector, reaching ­0.2 ZJ per decade during 1979–2019, is due to the increasing cold water from sea ice and melting water. An increase of low-pressure anomaly facilitates the shallowing and tilting of isopycnals and the intrusion of cold water into the interior ocean, which is closely associated with the Atlantic multidecadal oscillation. The enhancement of this zonal asymmetry in the AAIW shows an increasing heat content in the Indian sector and a decreasing heat content in the Pacific sector, which implies that the Indian Ocean will become an important potential warming pool in the future.  

    Xubin NI, Ling DU, Linlin FAN, Huangyuan SHI

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  • Physics

    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 ℃, and the average root mean square error (RMSE) of 0.61, 0.72, and 0.90 ℃, respectively. Geographically, the forecast RMSE with 1-d lead time in China seas increased from south to north, and the values were 0.41 ℃ in South China Sea (SCS) and 1.31 ℃ 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.  

    Huan XU, Pengfei LIN, Xuhua CHENG, Hailong LIU, Weipeng ZHENG, Zipeng YU, Yongqiang YU, Yiwen LI, Tao ZHANG

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