Statistical approximation of high-dimensional climate models

GSTDTAP > 资源环境科学

DOI	10.1016/j.jeconom.2019.05.005
	Statistical approximation of high-dimensional climate models
	Alena Miftakhova, Kenneth L. Judd, Thomas S. Lontzek and Karl Schmedders
	2019
出版年	2019
国家	瑞士
领域	资源环境
英文摘要	We propose a general emulation method for constructing low-dimensional approximations of complex dynamic climate models. Our method uses artificially designed uncorrelated CO2 emissions scenarios, which are much better suited for the construction of an emulator than are conventional emissions scenarios. We apply our method to the climate model MAGICC to approximate the impact of emissions on global temperature. Comparing the temperature forecasts of MAGICC and our emulator, we show that the average relative out-of-sample forecast errors in the low-dimensional emulation models are below 2%. Our emulator offers an avenue to merge modern macroeconomic models with complex dynamic climate models.
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来源平台	Centre for Energy Policy and Economics
引用统计
文献类型	科技报告
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/329975
专题	资源环境科学
推荐引用方式 GB/T 7714	Alena Miftakhova, Kenneth L. Judd, Thomas S. Lontzek and Karl Schmedders. Statistical approximation of high-dimensional climate models,2019.