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Using Machine Learning to Identify Novel Hydroclimate States

PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES(2022)

Columbia Univ | NASA

Cited 3|Views17
Abstract
Anthropogenic climate change is expected to alter drought risk in the future. However, droughts are not uncommon or unprecedented, as documented in tree-ring-based reconstructions of the summer average Palmer drought severity index (PDSI). Using an unsupervised machine-learning method trained on these reconstructions of pre-industrial climate, we identify outliers: years in which the spatial pattern of PDSI is unusual relative to 'normal' variability. We show that in many regions, outliers are more frequently identified in the twentieth and twenty-first centuries. This trend is more pronounced when the regional drought atlases are combined into a single global dataset. By definition, outlier patterns at the 10% level are expected to occur once per decade, but from 1950 to 2000 more than 6 years per decade are identified as outliers in the global drought atlas (GDA). Extending the GDA through 2020 using an observational dataset suggests that anomalous global drought conditions are present in 80% of years in the twenty-first century. Our results indicate, without recourse to climate models, that the world is more frequently experiencing drought conditions that are highly unusual in the context of past natural climate variability.This article is part of the Royal Society Science+ meeting issue 'Drought risk in the Anthropocene'.
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machine learning,climate change,drought
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Drought Risk in the Anthropocene

PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES 2022

被引用1

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要点】:使用机器学习方法识别新的水文气候状态,发现近年来全球干旱条件明显增多,超出了过去自然气候变异的范围。

方法】:使用无监督机器学习方法,训练基于树轮重建数据的模型,识别与“正常”变异相比较异常的干旱模式。

实验】:将区域性干旱数据集组合成全球数据集,从1950年到2000年,每个十年内超过6年被识别为异常值。使用观测数据从GDA延伸至2020年,表明在21世纪80%的年份中存在异常全球干旱条件。表明无需借助气候模型,世界正越来越频繁地经历高度异常的干旱条件。