A learning‐based approach to regression analysis for climate data–A case of Northeast China

Jiaxu Guo, Yidan Xu, Liang Hu, Xianwei Wu, Gaochao Xu, Xilong Che
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Abstract

Global climate change is an important issue that all of humanity needs to address together. Precipitation is an important climatic feature for agricultural development and food security, and the study of precipitation and its associated climatic factors is important for the analysis of global change. As an important part of China's food production, Northeast China has a temperate monsoon climate with simultaneous rain and heat, which is favorable for crop growth. In this paper, a scientific workflow for climate data analysis with a learning‐based method is designed. Using climate data from typical models in CMIP6, a machine learning‐based approach is used to establish regression relationships between precipitation and climate variables such as temperature, humidity and wind speed in Northeast China, which is validated through a time series approach. We design a weight‐based model ensemble method and a learning‐based bias correction method, so that the ensemble model can achieve better performance. We also analyze the precipitation trends in Northeast China under the three Shared Socio‐economic Pathways (SSPs). This will help researchers to analyze the long‐term evolution and factors of climate.
基于学习的气候数据回归分析方法--以中国东北地区为例
全球气候变化是全人类需要共同应对的重要问题。降水是农业发展和粮食安全的重要气候特征,研究降水及其相关气候因子对分析全球变化具有重要意义。作为中国粮食生产的重要组成部分,东北地区属于温带季风气候,雨热同期,有利于作物生长。本文设计了一种基于学习方法的气候数据分析科学工作流程。利用 CMIP6 中典型模式的气候数据,采用基于机器学习的方法建立了中国东北地区降水与温度、湿度和风速等气候变量之间的回归关系,并通过时间序列方法进行了验证。我们设计了基于权重的模型集合方法和基于学习的偏差校正方法,从而使集合模型获得更好的性能。我们还分析了三种共享社会经济路径(SSPs)下中国东北地区的降水趋势。这将有助于研究人员分析气候的长期演变和因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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