Met Office Hadley Centre, Exeter, UK.
College of Engineering, Mathematics and Physical Sciences, Exeter University, Exeter, UK.
Nature. 2020 Jul;583(7818):796-800. doi: 10.1038/s41586-020-2525-0. Epub 2020 Jul 29.
Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain. This leads to low confidence in regional projections, especially for precipitation, over the coming decades. The chaotic nature of the climate system may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models, and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade.
量化气候模型中的信号和不确定性对于气候变化的检测、归因、预测和预估至关重要。尽管大尺度温度信号在模型间具有较高的一致性，但大气环流的动力变化却非常不确定。这导致人们对未来几十年的区域预测，特别是降水预测，缺乏信心。气候系统的混沌性质也可能意味着信号不确定性在很大程度上是不可减少的。然而，在进一步的观测结果可用之前，气候预测很难得到验证。在这里，我们评估了过去六十年回顾性气候模型预测，并表明北大西洋冬季气候的年代际变化具有高度可预测性，尽管个别模型模拟之间缺乏一致性，且原始模型输出的预测能力较差。至关重要的是，当前模型低估了北大西洋涛动（北大西洋大气环流的主要变化模式）可预测信号（总变异性的可预测部分），幅度达一个数量级。因此，与完美模型相比，当前模型需要多 100 倍的集合成员才能提取该信号，并且相对于其他因素，其对气候的影响被低估。为了解决这些限制，我们实施了两阶段后处理技术。我们首先调整集合平均北大西洋涛动预测的方差，以匹配可预测信号的观测方差。然后，我们仅选择并使用与方差调整后的集合平均北大西洋涛动足够接近的集合成员。这种方法极大地提高了对欧洲和北美东部冬季气候的年代际预测。大西洋多年代际变率的预测也得到了改善，这表明北大西洋涛动不仅仅是由大西洋多年代际变率驱动的。我们的研究结果强调了需要理解为什么当前气候模型中的信噪比太小，以及纠正这种模型误差在十年以上的时间尺度上会在多大程度上减少区域气候变化预测的不确定性。
