Global mean surface air temperature (T_global) variability on subdecadal timescales can be of substantial magnitude relative to the long‐term global warming signal, and such variability has been associated with considerable environmental and societal impacts. Therefore, probabilistic foreknowledge of short‐term T_global evolution may be of value for anticipating and mitigating some course‐resolution climate‐related risks. Here we present a simple, empirically based methodology that utilizes only global spatial patterns of annual mean surface air temperature anomalies to predict subsequent annual T_global anomalies via partial least squares regression. The method's skill is primarily achieved via information on the state of long‐term global warming as well as the state and recent evolution of the El Niño–Southern Oscillation and the Interdecadal Pacific Oscillation. We test the out‐of‐sample skill of the methodology using cross validation and in a forecast mode where statistical predictions are made precisely as they would have been if the procedure had been operationalized starting in the year 2000. The average forecast errors for lead times of 1 to 4 years are smaller than naïve benchmarks on average, and they perform favorably relative to most dynamical Global Climate Models retrospectively initialized to the observed state of the climate system. Thus, this method can be used as a computationally efficient benchmark for dynamical model forecast systems.

中文翻译：

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短期年度全球温度变化的经验预测

相对于长期的全球变暖信号，年代际时间尺度上的全球平均地表温度（T _global）的变化可能会很大，而这种变化与相当大的环境和社会影响相关。因此，短期T_全球演变的概率_性预测可能对预测和减轻某些与气候变化有关的过程分辨率风险具有价值。在这里，我们介绍一种简单的基于经验的方法，该方法仅利用年平均地表气温异常的全球空间格局来预测随后的年_全球T值。通过偏最小二乘回归得到异常。该方法的技能主要通过有关长期全球变暖的状态以及厄尔尼诺-南方涛动和年代际太平洋涛动的状态和最新演变的信息来实现。我们使用交叉验证并在预测模式下测试该方法的样本外技能，在该模式下，与从2000年开始实施该程序一样，进行准确的统计预测。提前期的平均预测误差1至4年的平均时间比纯朴的基准时间要短，并且相对于追溯到初始化的观测到的气候系统状态的大多数动态全球气候模型而言，它们的表现都不错。从而，