North Atlantic climate far more predictable than models imply-猫眼课题宝

DOI：

10.1038/s41586-020-2525-0

发表时间：

2020-07

期刊：

Nature

影响因子：

64.8

通讯作者：

中科院分区：

综合性期刊1区

文献类型：

作者：

D. Smith;Adam A. Scaife;R. Eade;P. Athanasiadis;A. Bellucci;I. Bethke;R. Bilbao;L. Borchert;L. Caron;F. Counillon;G. Danabasoglu;T. Delworth;F. Doblas-Reyes;N. Dunstone;V. Estella-Perez;S. Flavoni;L. Hermanson;N. Keenlyside;V. Kharin;M. Kimoto;W. Merryfield;J. Mignot;T. Mochizuki;K. Modali;P. Monerie;W. Müller;D. Nicolì;P. Ortega;K. Pankatz;H. Pohlmann;J. Robson;P. Ruggieri;R. Sospedra‐Alfonso;D. Swingedouw;Y. Wang;S. Wild;S. Yeager;X. Yang;L. Zhang

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、预测和预测至关重要。尽管大尺度温度信号的模型间一致性很高，但大气环流的动态变化非常不确定。这导致人们对未来几十年的区域预测，尤其是降水量预测的信心较低。气候系统的混乱性质也可能意味着信号的不确定性在很大程度上是不可减少的。然而，在获得进一步的观测结果之前，气候预测很难验证。在这里，我们评估了过去六十年的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是高度可预测的，尽管各个模型模拟之间缺乏一致性，并且原始模型输出的预测能力较差。至关重要的是，当前的模型低估了北大西洋涛动（北大西洋大气环流的主要变率模式）的可预测信号（总变率的可预测部分）一个数量级。因此，与完美模型相比，当前模型需要 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，我们仅选择并使用北大西洋涛动足够接近方差调整的集合平均预测北大西洋涛动的集合成员。这种方法极大地改善了欧洲和北美东部冬季气候的十年预测。对大西洋数十年变率的预测也得到改善，这表明北大西洋涛动不仅仅由大西洋数十年变率驱动。我们的结果强调需要了解为什么当前气候模型中的信噪比太小，以及纠正该模型错误将在多大程度上减少十年以上时间尺度上区域气候变化预测的不确定性。

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.

North Atlantic climate far more predictable than models imply

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