A Framework for Modeling Uncertainty in Regional Climate Change
Name
MITJPSPGC_Rpt244.pdf
Description
Main article
Size
7.26 MB
Format
Adobe PDF
Checksum (MD5)
7c535b66f74c57811cd248d9d5b3a5c6
Author(s)
Monier, Erwan
Gao, Xiang
Scott, Jeffery
Sokolov, Andrei
Schlosser, C. Adam
Date Issued
May 2013
Publisher
MIT Joint Program
Citation
Report 244
Series/Report no.
MIT Joint Program Report Series;Report 244
Abstract
In this study, we present a new modeling framework and a large ensemble of climate projections to investigate the uncertainty in regional climate change over the US associated with four dimensions of uncertainty. The sources of uncertainty considered in this framework are the emissions projections (using different climate policies), climate system parameters (represented by different values of climate sensitivity and net aerosol forcing), natural variability (by perturbing initial conditions) and structural uncertainty (using different climate models). The modeling framework revolves around the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model with an intermediate complexity earth system model (with a two-dimensional zonal-mean atmosphere). Regional climate change over the US is obtained through a two-pronged approach. First, we use the IGSM-CAM framework which links the IGSM to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Secondly, we use a pattern-scaling method that extends the IGSM zonal mean based on climate change patterns from various climate models. Results show that uncertainty in temperature changes are mainly driven by policy choices and the range of climate sensitivity considered. Meanwhile, the four sources of uncertainty contribute more equally to precipitation changes, with natural variability having a large impact in the first part of the 21st century. Overall, the choice of policy is the largest driver of uncertainty in future projections of climate change over the US.
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