Regional Nonlinear Relationships Across the United States Between Drought and Tree‐Ring Width Variability From a Neural Network
Regional Nonlinear Relationships Across the United States Between Drought and Tree‐Ring Width Variability From a Neural Network
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DOI：
10.1029/2020gl092090
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发表时间：
2021-06
影响因子：
5.2
通讯作者：
A. Trevino;A. Stine;P. Huybers
中科院分区：
文献类型：
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作者：
A. Trevino;A. Stine;P. Huybers
神经网络以前应用于从树木年轮重建气候指数，但相对于更标准的线性方法，在技能上显示出混合的结果。研究了一个两层神经网络，用于重建美国各地夏季自校准的帕尔默干旱严重指数（scPDSI）。如果用效率系数来评估，使用神经网络的重建在75%的网格盒上比线性方法更熟练，而在使用Pearson相关系数时，使用神经网络的重建在54%的网格盒上比线性方法更熟练。重建技能的提高与捕获非线性增长-气候关系的网络有关。特别是在西南地区，非线性响应函数反映了在潮湿条件下生长对水分的敏感性逐渐降低，这与水分应力的减轻相一致。这些结果表明，在过去的两个世纪里，美国西南部干旱的发生率相对较低，但相对稳定。
Neural networks were previously applied to reconstruct climate indices from tree rings but showed mixed results in skill relative to more standard linear methods. A two‐layer neural network is explored for purposes of reconstructing summertime self‐calibrated Palmer Drought Severity Index (scPDSI) across the contiguous United States. Reconstructions using neural networks are more skillful than a linear approach at 75% of the gridboxes if evaluated by the coefficient of efficiency and at 54% when using the Pearson correlation coefficient. The increased reconstruction skill is related to the network capturing nonlinear growth‐climate relationships. In the Southwest, in particular, a nonlinear response function captures a diminishing sensitivity of growth to moisture under wetter conditions, consistent with alleviation of moisture stress. These results indicate somewhat less‐severe and more‐stable incidences of drought over the past two centuries in the U.S. Southwest.