Statistical Approximation of High-Dimensional Climate Models

19 Pages Posted: 19 Dec 2016 Last revised: 4 Jan 2017

See all articles by Alena Miftakhova

Alena Miftakhova

University of Zurich - Department of Business Administration

Kenneth L. Judd

Stanford University - The Hoover Institution on War, Revolution and Peace; Center for Robust Decisionmaking on Climate & Energy Policy (RDCEP); National Bureau of Economic Research (NBER)

Thomas S. Lontzek

University of Zurich; Center for Robust Decisionmaking on Climate & Energy Policy (RDCEP)

Karl Schmedders

University of Zurich

Date Written: December 15, 2016

Abstract

In many studies involving complex representation of the Earth's climate, the number of runs for the particular model is highly restricted and the designed set of input scenarios has to be reduced correspondingly. Furthermore, many integrated assessment models, in particular those focusing on intrinsic uncertainty in social decision-making, suffer from poor representations of the climate system ue to computational constraints.In this study, using emission scenarios as input and the temperature anomaly as a predicted response variable, we construct low-dimensional approximations of high-dimensional climate models, as represented by MAGICC. In order to extract as much explanatory power as possible from the high-dimensional climate models, we construct orthogonal emissions scenarios that carry minimum repetitive information. Our method is especially useful when there is pressure to keep the number of scenarios as low as possible. We demonstrate that temperature levels can be inferred immediately from the CO2 emissions data within a one-line model that performs very well on conventional scenarios. Furthermore, we provide a system of equations that is ready to be deployed in macroeconomic optimization models. Thus, our study enhances the methodology applied in the emulation of complex climate models and facilitates the use of more realistic climate representations in economic integrated assessment models.

Keywords: Climate Change, Greenhouse Gas, Single Equation Models

JEL Classification: Q54, C20

Suggested Citation

Miftakhova, Alena and Judd, Kenneth L. and Lontzek, Thomas S. and Schmedders, Karl, Statistical Approximation of High-Dimensional Climate Models (December 15, 2016). Swiss Finance Institute Research Paper No. 16-76. Available at SSRN: https://ssrn.com/abstract=2887292

Alena Miftakhova

University of Zurich - Department of Business Administration ( email )

Moussonstrasse 15
Zurich, CH-8044
Switzerland

Kenneth L. Judd

Stanford University - The Hoover Institution on War, Revolution and Peace ( email )

Stanford, CA 94305-6010
United States

Center for Robust Decisionmaking on Climate & Energy Policy (RDCEP) ( email )

5735 S. Ellis Street
Chicago, IL 60637
United States

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Thomas S. Lontzek

University of Zurich ( email )

Switzerland

Center for Robust Decisionmaking on Climate & Energy Policy (RDCEP) ( email )

5735 S. Ellis Street
Chicago, IL 60637
United States

Karl Schmedders (Contact Author)

University of Zurich ( email )

Moussonstrasse 15
Z├╝rich, CH-8044
Switzerland
+41 (0)44 634 3770 (Phone)

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