Climate predictions using coupled models in different time scales, from intraseasonal to decadal, are usually affected by initial shocks, drifts, and biases, which reduce the prediction skill. These arise from inconsistencies between different components of the coupled models and from the tendency of the model state to evolve from the prescribed initial conditions toward its own climatology over the course of the prediction. Aiming to provide tools and further insight into the mechanisms responsible for initial shocks, drifts, and biases, this paper presents a novel data set developed within the Long Range Forecast Transient Intercomparison Project, LRFTIP. This data set has been constructed by averaging hindcasts over available prediction years and ensemble members to form a hindcast climatology, that is a function of spatial variables and lead time, and thus results in a useful tool for characterizing and assessing the evolution of errors as well as the physical mechanisms responsible for them. A discussion on such errors at the different time scales is provided along with plausible ways forward in the field of climate predictions.

中文翻译：

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用于比较基于动力学模型的次季节到年代际气候预测的瞬态行为的数据集

使用耦合模型在不同时间尺度（从季节内到十年）进行的气候预测通常会受到初始冲击、漂移和偏差的影响，这会降低预测技能。这些产生于耦合模型的不同组成部分之间的不一致以及模型状态在预测过程中从规定的初始条件向其自身的气候变化的趋势。为了提供工具并进一步深入了解导致初始冲击、漂移和偏差的机制，本文提出了在远程预测瞬态比对项目 LRFTIP 中开发的新数据集。该数据集是通过对可用预测年份和集合成员的后报求平均值来构建的，以形成后报气候学，这是空间变量和提前期的函数，因此是一种有用的工具，可用于表征和评估错误的演变以及造成这些错误的物理机制。提供了对不同时间尺度上的此类错误的讨论以及气候预测领域的可行方法。