A framework for modeling uncertainty in regional climate change

Joint Program Reprint • Journal Article
A framework for modeling uncertainty in regional climate change
Monier, E., X. Gao, J.R. Scott, A.P. Sokolov and C.A. Schlosser (2015)
Climatic Change, 131(1): 51-66 (doi:10.1007/s10584-014-1112-5)

Reprint 2014-10 [Download]

Abstract/Summary:

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 United States (US) associated with four dimensions of uncertainty. The sources of uncertainty considered in this framework are the emissions projections, global climate system parameters, natural variability and model structural uncertainty. The modeling framework revolves around the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model with an Earth System Model of Intermediate Complexity (EMIC) (with a two-dimensional zonal-mean atmosphere). Regional climate change over the US is obtained through a two-pronged approach. First, we use the IGSMCAM framework, which links the IGSM to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Second, we use a pattern-scaling method that extends the IGSM zonal mean based on climate change patterns from various climate models. Results show that the range of annual mean 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 end-of-century precipitation changes, with natural variability dominating until 2050. For the set of scenarios used in this study, the choice of policy is the largest driver of uncertainty, defined as the range of warming and changes in precipitation, in future projections of climate change over the US.

© 2014 Springer Science+Business Media

Citation:

Monier, E., X. Gao, J.R. Scott, A.P. Sokolov and C.A. Schlosser (2015): A framework for modeling uncertainty in regional climate change. Climatic Change, 131(1): 51-66 (doi:10.1007/s10584-014-1112-5) (http://dx.doi.org/10.1007/s10584-014-1112-5)
  • Joint Program Reprint
  • Journal Article
A framework for modeling uncertainty in regional climate change

Monier, E., X. Gao, J.R. Scott, A.P. Sokolov and C.A. Schlosser

2014-10
131(1): 51-66 (doi:10.1007/s10584-014-1112-5)

Abstract/Summary: 

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 United States (US) associated with four dimensions of uncertainty. The sources of uncertainty considered in this framework are the emissions projections, global climate system parameters, natural variability and model structural uncertainty. The modeling framework revolves around the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model with an Earth System Model of Intermediate Complexity (EMIC) (with a two-dimensional zonal-mean atmosphere). Regional climate change over the US is obtained through a two-pronged approach. First, we use the IGSMCAM framework, which links the IGSM to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Second, we use a pattern-scaling method that extends the IGSM zonal mean based on climate change patterns from various climate models. Results show that the range of annual mean 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 end-of-century precipitation changes, with natural variability dominating until 2050. For the set of scenarios used in this study, the choice of policy is the largest driver of uncertainty, defined as the range of warming and changes in precipitation, in future projections of climate change over the US.

© 2014 Springer Science+Business Media

Supersedes: 

A Framework for Modeling Uncertainty in Regional Climate Change