Progress and future prospects of decadal prediction and data assimilation: a review

Decadal prediction, also known as “near-term climate prediction”, aims to forecast climate changes in the next 1–10 years and is a new focus in the fields of climate prediction and climate change research. It lies between seasonal-to-interannual predictions and long-term climate change projections, combining the aspects of both the initial value problem and external forcing problem. The core technique in decadal prediction lies in the accuracy and efficiency of the assimilation methods used to initialize the model, which aims to provide the model with accurate initial conditions that incorporate observed internal climate variabilities. The initialization of decadal predictions often involves assimilating oceanic observations within a coupled framework, in which the observed signals are transmitted through the coupled processes to other components such as the atmosphere and sea ice. However, recent studies have increasingly focused on exploring coupled data assimilation (CDA) in coupled ocean–atmosphere models, based on which it has been suggested that CDA has the potential to significantly enhance the skill of decadal predictions. This paper provides a comprehensive review of the research status in three aspects of this field: initialization methods, the predictability and prediction skill for decadal climate prediction, and the future development and challenges for decadal prediction. 年代际预测, 也称为“近期气候预测”, 旨在预测未来1-10年内的气候变化, 是气候预测和气候变化研究领域的一个新关注点.它位于季节至年际预测和长期气候变化预测之间, 结合了初值问题和外部强迫问题的两个方面.年代际预测的核心技术在于用于模式初始化的同化方法的准确性和效率, 其目标是为模式提供准确的初始条件, 其中包含观测到的气候系统内部变率.年代际预测的初始化通常涉及在耦合框架内同化海洋观测, 其中观测到的信号通过耦合过程传递到其他分量, 如大气和海冰.然而, 最近的研究越来越关注在海洋-大气耦合模式中探索耦合数据同化 (CDA) , 有人认为CDA有潜力显著提高年代际预测技巧.本文综合评述了该领域的三个方面的研究现状:初始化方法,年代际气候预测的可预测性和预测技巧, 以及年代际预测的未来发展和挑战.