Predictability of the upper ocean heat content in a Community Earth System Model ensemble prediction system

• Published in: Acta oceanologica Sinica Vol. 43; no. 1; pp. 1 - 10
• Main Authors: Liu, Ting, Zhong, Wenxiu
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 2024; Springer Nature B.V
• Subjects: Climate; Climate models; Climate variability; Climatology; Data assimilation; Data collection; Earth and Environmental Science; Earth Sciences; Ecology; Engineering Fluid Dynamics; Enthalpy; Environmental Chemistry; Frequency dependence; Heat content; Lead time; Locking features; Long term memory; Marine & Freshwater Sciences; Oceanography; Oceans; Predictions; Seasonal variation; Seasonal variations; Spatial distribution; Tropical environment; Tropical environments; Upper ocean; retrospective forecast experiment; ocean heat content; prediction skill
• ISSN: 0253-505X; 1869-1099
• DOI: 10.1007/s13131-023-2239-x

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.