Statistical approximation of high-dimensional climate models
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 scenar...
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| Veröffentlicht in: | Journal of econometrics 2020-01, Vol.214 (1), p.67-80 |
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| container_title | Journal of econometrics |
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| creator | Miftakhova, Alena Judd, Kenneth L. Lontzek, Thomas S. Schmedders, Karl |
| description | 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. |
| doi_str_mv | 10.1016/j.jeconom.2019.05.005 |
| format | Article |
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| ispartof | Journal of econometrics, 2020-01, Vol.214 (1), p.67-80 |
| issn | 0304-4076 1872-6895 |
| language | eng |
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| subjects | Approximation Averages Carbon dioxide Climate change Emissions control Emulation Forecasting Greenhouse gas Orthogonal polynomials Single equation models |
| title | Statistical approximation of high-dimensional climate models |
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