气候变化探测和归因的直接方法
A direct approach to detection and attribution of climate change
摘要：
我们在这里提出了一种新的统计学习方法检测和归因（D&A）的气候变化。传统的最优D&A研究试图直接从模型模拟中对观察结果进行建模，但由于高维性，实际上这是具有挑战性的。降维技术通常使用经验正交函数来降低维度，但是由于这些技术是无监督的，因此所考虑的降低的空间有点任意。在这里，我们提出了一种监督的方法，我们预测一个给定的外部强迫，例如，人为强迫，直接从气候变量的空间格局，并使用预测的强迫作为检验统计量的D&A。我们希望即使在其他外力分布发生变化的情况下，预测也能很好地工作，例如，太阳或火山强迫，并因此从分布鲁棒性的角度制定优化问题。
英文摘要：
We present here a novel statistical learning approach for detection and attribution (D&A) of climate change. Traditional optimal D&A studies try to directly model the observations from model simulations, but practically this is challenging due to high-dimensionality. Dimension reduction techniques reduce the dimensionality, typically using empirical orthogonal functions, but as these techniques are unsupervised, the reduced space considered is somewhat arbitrary. Here, we propose a supervised approach where we predict a given external forcing, e.g., anthropogenic forcing, directly from the spatial pattern of climate variables, and use the predicted forcing as a test statistic for D&A. We want the prediction to work well even under changes in the distribution of other external forcings, e.g., solar or volcanic forcings, and therefore formulate the optimization problem from a distributional robustness perspective.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
