Statistical Emulation of Climate Model Projections Based on Precomputed GCM RunsSource: Journal of Climate:;2013:;volume( 027 ):;issue: 005::page 1829Author:Castruccio, Stefano
,
McInerney, David J.
,
Stein, Michael L.
,
Liu Crouch, Feifei
,
Jacob, Robert L.
,
Moyer, Elisabeth J.
DOI: 10.1175/JCLI-D-13-00099.1Publisher: American Meteorological Society
Abstract: he authors describe a new approach for emulating the output of a fully coupled climate model under arbitrary forcing scenarios that is based on a small set of precomputed runs from the model. Temperature and precipitation are expressed as simple functions of the past trajectory of atmospheric CO2 concentrations, and a statistical model is fit using a limited set of training runs. The approach is demonstrated to be a useful and computationally efficient alternative to pattern scaling and captures the nonlinear evolution of spatial patterns of climate anomalies inherent in transient climates. The approach does as well as pattern scaling in all circumstances and substantially better in many; it is not computationally demanding; and, once the statistical model is fit, it produces emulated climate output effectively instantaneously. It may therefore find wide application in climate impacts assessments and other policy analyses requiring rapid climate projections.
|
Collections
Show full item record
contributor author | Castruccio, Stefano | |
contributor author | McInerney, David J. | |
contributor author | Stein, Michael L. | |
contributor author | Liu Crouch, Feifei | |
contributor author | Jacob, Robert L. | |
contributor author | Moyer, Elisabeth J. | |
date accessioned | 2017-06-09T17:08:19Z | |
date available | 2017-06-09T17:08:19Z | |
date copyright | 2014/03/01 | |
date issued | 2013 | |
identifier issn | 0894-8755 | |
identifier other | ams-79969.pdf | |
identifier uri | http://onlinelibrary.yabesh.ir/handle/yetl/4222807 | |
description abstract | he authors describe a new approach for emulating the output of a fully coupled climate model under arbitrary forcing scenarios that is based on a small set of precomputed runs from the model. Temperature and precipitation are expressed as simple functions of the past trajectory of atmospheric CO2 concentrations, and a statistical model is fit using a limited set of training runs. The approach is demonstrated to be a useful and computationally efficient alternative to pattern scaling and captures the nonlinear evolution of spatial patterns of climate anomalies inherent in transient climates. The approach does as well as pattern scaling in all circumstances and substantially better in many; it is not computationally demanding; and, once the statistical model is fit, it produces emulated climate output effectively instantaneously. It may therefore find wide application in climate impacts assessments and other policy analyses requiring rapid climate projections. | |
publisher | American Meteorological Society | |
title | Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs | |
type | Journal Paper | |
journal volume | 27 | |
journal issue | 5 | |
journal title | Journal of Climate | |
identifier doi | 10.1175/JCLI-D-13-00099.1 | |
journal fristpage | 1829 | |
journal lastpage | 1844 | |
tree | Journal of Climate:;2013:;volume( 027 ):;issue: 005 | |
contenttype | Fulltext |