State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, 310012, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
2.
School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
Funds:
the National Key R&D Program of China under contract No. 2020YFA0608803, the Scientific Research Fund of the Second Institute of Oceanography, MNR No. QNYC2101, the National Natural Science Foundation of China under contract No. 42105052; the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) under contract No. SML2021SP310, and the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) under contract No. 311021001.
Upper ocean heat content (OHC) has been widely recognized as a crucial precursor to high-impact climate variability, especially for that being indispensable to the long-term memory of the ocean. Assessing the predictability of OHC using state-of-the-art climate models is invaluable for improving and advancing climate forecasts. Recently developed retrospective forecast experiments, based on a Community Earth System Model ensemble prediction system, offer a great opportunity to comprehensively explore OHC predictability. Our results indicate that the skill of actual OHC predictions varies across different oceans and diminishes as the lead time of prediction extends. The spatial distribution of the actual prediction skill closely resembles the corresponding persistence skill, indicating that the persistence of OHC serves as the primary predictive signal for its predictability. The decline in actual prediction skill is more pronounced in the Indian and Atlantic Oceans than in the Pacific Ocean, particularly within tropical regions. Additionally, notable seasonal variations in the actual prediction skills across different oceans align well with the phase-locking features of OHC variability. The potential predictability of OHC generally surpasses the actual prediction skill at all lead times, highlighting significant room for improvement in current OHC predictions, especially for the North Indian Ocean and the Atlantic Ocean. Achieving such improvements necessitates a collaborative effort to enhance the quality of ocean observations, develop effective data assimilation methods, and reduce model bias.
Figure 1. Anomaly correlation coefficients of OHC with lead month.
Figure 2. Same as Fig. 1, but for persistence.
Figure 3. Anomaly correlation coefficients for the area-averaged OHC in the (a) Pacific Ocean, (b) Indian Ocean, and (c) Atlantic Ocean. The “EP”, “CP”, “NP”, “SP”, “SI”, “NI”, “SA” and “NA” represent the eastern tropical Pacific (10°S–10°N, 170°–90°W), the western tropical Pacific (10°S–10°N, 120°–180°E), the northeastern subtropical Pacific (20°–60°N, 180°–120°W), the southern Pacific (20°–60°S, 180°–90°W), the southern Indian Ocean (10°–40°S, 40°–120°E), the northern Indian Ocean (10°–25°N, 50°–110°E), the southern Atlantic Ocean (10°–60°S, 80°–0°W), and the northern Atlantic Ocean (10°–60°N, 60°–0°W), respectively.
Figure 4. Anomaly correlation coefficients of the area-averaged OHC with the lead and target months in different oceans. For (a) the western tropical Pacific (10°S–10°N, 120°–180°E), (b) the eastern Pacific (10°S–10°N, 170°–90°W), (c) the northern Pacific (20°–60°N, 180°–120°W), (d) the southern Pacific (20°–60°S, 180°–90°W), (e) the northern Indian Ocean (10°–25°N, 50°–110°E), (f) the southern Indian Ocean (10°–40°S, 40°–120°E), (g) the northern Atlantic Ocean (10°–60°N, 60°–0°W), (h) the southern Atlantic Ocean (10°–60°S, 80°–0°W).
Figure 5. Phase-locking features of the area-averaged OHC in different oceans (same as Fig. 4).
Figure 6. Potential correlation of OHC with lead month.
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