A framework for modeling uncertainty in regional climate change
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 emissio...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Springer Netherlands
2016
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| Online Access: | http://hdl.handle.net/1721.1/103370 https://orcid.org/0000-0001-5533-6570 |
| _version_ | 1811083793259298816 |
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| author | Monier, Erwan Gao, Xiang Scott, Jeffery R. Sokolov, Andrei P. Schlosser, Courtney Adam |
| author2 | Massachusetts Institute of Technology. Joint Program on the Science & Policy of Global Change |
| author_facet | Massachusetts Institute of Technology. Joint Program on the Science & Policy of Global Change Monier, Erwan Gao, Xiang Scott, Jeffery R. Sokolov, Andrei P. Schlosser, Courtney Adam |
| author_sort | Monier, Erwan |
| collection | MIT |
| description | 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 IGSM-CAM 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. |
| first_indexed | 2024-09-23T12:39:24Z |
| format | Article |
| id | mit-1721.1/103370 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:39:24Z |
| publishDate | 2016 |
| publisher | Springer Netherlands |
| record_format | dspace |
| spelling | mit-1721.1/1033702022-10-01T10:19:26Z A framework for modeling uncertainty in regional climate change Monier, Erwan Gao, Xiang Scott, Jeffery R. Sokolov, Andrei P. Schlosser, Courtney Adam Massachusetts Institute of Technology. Joint Program on the Science & Policy of Global Change Monier, Erwan Gao, Xiang Scott, Jeffery R. Sokolov, Andrei P. Schlosser, Courtney Adam 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 IGSM-CAM 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. United States. Environmental Protection Agency. Climate Change Division (Cooperative Agreement #XA-83600001) 2016-06-28T19:18:28Z 2016-06-28T19:18:28Z 2014-06 2013-01 2016-05-23T09:36:08Z Article http://purl.org/eprint/type/JournalArticle 0165-0009 1573-1480 http://hdl.handle.net/1721.1/103370 Monier, Erwan, Xiang Gao, Jeffery R. Scott, Andrei P. Sokolov, and C. Adam Schlosser. "A framework for modeling uncertainty in regional climate change." Climatic Change 131:1 (2015), pp.51–66. https://orcid.org/0000-0001-5533-6570 en http://dx.doi.org/10.1007/s10584-014-1112-5 Climatic Change Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf Springer Netherlands Springer Netherlands |
| spellingShingle | Monier, Erwan Gao, Xiang Scott, Jeffery R. Sokolov, Andrei P. Schlosser, Courtney Adam A framework for modeling uncertainty in regional climate change |
| title | A framework for modeling uncertainty in regional climate change |
| title_full | A framework for modeling uncertainty in regional climate change |
| title_fullStr | A framework for modeling uncertainty in regional climate change |
| title_full_unstemmed | A framework for modeling uncertainty in regional climate change |
| title_short | A framework for modeling uncertainty in regional climate change |
| title_sort | framework for modeling uncertainty in regional climate change |
| url | http://hdl.handle.net/1721.1/103370 https://orcid.org/0000-0001-5533-6570 |
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