Statistical approximation of high-dimensional climate models

Alena Miftakhova*, Kenneth L. Judd, Thomas S. Lontzek, Karl H Schmedders

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)67-80
Number of pages14
JournalJournal of Econometrics
Volume214
Issue number1
DOIs
StatePublished - Jan 2020

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Keywords

  • Climate change
  • Greenhouse gas
  • Orthogonal polynomials
  • Single equation models

ASJC Scopus subject areas

  • Economics and Econometrics

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