Shi Hui, Jin Fei-Fei, Wills Robert C J, Jacox Michael G, Amaya Dillon J, Black Bryan A, Rykaczewski Ryan R, Bograd Steven J, García-Reyes Marisol, Sydeman William J
Farallon Institute, Petaluma, CA 94952, USA.
Department of Atmospheric Sciences, University of Hawaii, Honolulu, HI 96822, USA.
Sci Adv. 2022 May 6;8(18):eabm3468. doi: 10.1126/sciadv.abm3468.
Ocean memory, the persistence of ocean conditions, is a major source of predictability in the climate system beyond weather time scales. We show that ocean memory, as measured by the year-to-year persistence of sea surface temperature anomalies, is projected to steadily decline in the coming decades over much of the globe. This global decline in ocean memory is predominantly driven by shoaling of the upper-ocean mixed layer depth in response to global surface warming, while thermodynamic and dynamic feedbacks can contribute substantially regionally. As the mixed layer depth shoals, stochastic forcing becomes more effective in driving sea surface temperature anomalies, increasing high-frequency noise at the expense of persistent signals. Reduced ocean memory results in shorter lead times of skillful persistence-based predictions of sea surface thermal conditions, which may present previously unknown challenges for predicting climate extremes and managing marine biological resources under climate change.
海洋记忆，即海洋状况的持续性，是气候系统中超出天气时间尺度的可预测性的主要来源。我们表明，以海面温度异常的逐年持续性来衡量的海洋记忆，预计在未来几十年里在全球大部分地区将稳步下降。海洋记忆的这种全球下降主要是由上层海洋混合层深度因全球表面变暖而变浅所驱动的，而热力学和动力学反馈在区域上也可做出重大贡献。随着混合层深度变浅，随机强迫在驱动海面温度异常方面变得更加有效，以持久性信号为代价增加了高频噪声。海洋记忆的减少导致基于持续性的海面热状况熟练预测的提前期缩短，这可能给预测极端气候和在气候变化下管理海洋生物资源带来前所未知的挑战。
