On deep learning-based bias correction and downscaling of multiple climate models simulations
On deep learning-based bias correction and downscaling of multiple climate models simulations
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DOI：
10.1007/s00382-022-06277-2
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发表时间：
2022-04
期刊：
影响因子：
4.6
通讯作者：
Fang Wang;D. Tian
中科院分区：
文献类型：
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作者：
Fang Wang;D. Tian
偏差校正和缩小尺度的气候模式模拟需要重建观测的空间和互变量依赖性。然而，现有的单变量偏差校正方法往往不能考虑这种依赖性。虽然已经开发了多变量偏倚校正方法来解决这个问题，但由于各种假设，它们并不总是优于单变量方法。在这项研究中，使用耦合模式相互比较项目第6阶段（CMIP 6）的20个最先进的耦合大气环流模式（GCM）日平均，最高和最低温度（Tmean，Tmax和Tmin），我们全面评估了超分辨率深度残差网络（SRDRN）深度学习模型用于气候降尺度和偏差校正。SRDRN模型将20个具有单通道或多通道输入输出通道的GCM序列依次叠加，利用不同GCM序列与观测值的相对关系有效地消除了模型的偏差，并保留了模型的变量间相关性，用于多元偏差校正。它通过深入提取空间特征并根据观测结果对日常模拟进行调整来纠正空间依赖性的偏差。对于单变量SRDRN，与分位数Delta映射（QDM）方法相比，它大大减少了Tmeanin空间，时间以及极值的较大偏差。对于多变量SRDRN，它比动态最优传输校正（dOTC）方法表现得更好，降低了Tmax和Tmin的较大偏差，但也再现了观察结果的互变量依赖性，其中QDM和dOTC显示出不切实际的伪影（Tmax <Tmin）。对基于深度学习的方法的进一步研究可能会将气候模型偏差校正和缩小尺度带到下一个水平。
Bias correcting and downscaling climate model simulations requires reconstructing spatial and intervariable dependences of the observations. However, the existing univariate bias correction methods often fail to account for such dependences. While the multivariate bias correction methods have been developed to address this issue, they do not consistently outperform the univariate methods due to various assumptions. In this study, using 20 state-of-the-art coupled general circulation models (GCMs) daily mean, maximum and minimum temperature (Tmean, Tmaxand Tmin) from the Coupled Model Intercomparison Project phase 6 (CMIP6), we comprehensively evaluated the Super Resolution Deep Residual Network (SRDRN) deep learning model for climate downscaling and bias correction. The SRDRN model sequentially stacked 20 GCMs with single or multiple input-output channels, so that the biases can be efficiently removed based on the relative relations among different GCMs against observations, and the intervariable dependences can be retained for multivariate bias correction. It corrected biases in spatial dependences by deeply extracting spatial features and making adjustments for daily simulations according to observations. For univariate SRDRN, it considerably reduced larger biases of Tmeanin space, time, as well as extremes compared to the quantile delta mapping (QDM) approach. For multivariate SRDRN, it performed better than the dynamic Optimal Transport Correction (dOTC) method and reduced greater biases of Tmaxand Tminbut also reproduced intervariable dependences of the observations, where QDM and dOTC showed unrealistic artifacts (Tmax< Tmin). Additional studies on the deep learning-based approach may bring climate model bias correction and downscaling to the next level.