Uncertainty Quantification in Climate Modeling and Projection
Abstract
The projection of future climate is one of the most complex problems undertaken by the scientific community. Although scientists have been striving to better understand the physical basis of the climate system and to improve climate models, the overall uncertainty in projections of future climate has not been significantly reduced (e.g., from the IPCC AR4 to AR5). With the rapid increase of complexity in Earth system models, reducing uncertainties in climate projections becomes extremely challenging. Since uncertainties always exist in climate models, interpreting the strengths and limitations of future climate projections is key to evaluating risks, and climate change information for use in Vulnerability, Impact, and Adaptation (VIA) studies should be provided with both well-characterized and well-quantified uncertainty. The workshop reported herein aimed at providing participants, many of them from developing countries, information on strategies to quantify the uncertainty in climate model projections and assess the reliability of climate change information for decision-making. The program included a mixture of lectures on fundamental concepts in Bayesian inference and sampling, applications, and hands-on computer laboratory exercises employing software packages for Bayesian inference, Markov Chain Monte Carlo methods, and global sensitivity analyses. The lectures covered a range of scientific issues underlying the evaluationmore »
- Authors:
-
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Univ. of Texas, Austin, TX (United States)
- Abdus Salam International Center for Theoretical Physics, Trieste (Italy)
- Met Office Hadley Centre for Climate Science and Services, Exeter (United Kingdom)
- Beijing Normal Univ. (China)
- Pennsylvania State Univ., University Park, PA (United States)
- Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Arlington, VA (United States)
- Univ. of New Mexico, Albuquerque, NM (United States)
- Publication Date:
- Research Org.:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); Abdus Salam International Centre for Theoretical Physics (ICTP); International Union of Geodesy and Geophysics (IUGG)
- OSTI Identifier:
- 1327127
- Report Number(s):
- PNNL-SA-115173
Journal ID: ISSN 0003-0007; KP1703020
- Grant/Contract Number:
- AC05-76RL01830
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Bulletin of the American Meteorological Society
- Additional Journal Information:
- Journal Volume: 97; Journal Issue: 5; Conference: Workshop on Uncertainty Quantification in Climate Modeling and Projection , Trieste (Italy), 13-17 Jul 2015; Journal ID: ISSN 0003-0007
- Publisher:
- American Meteorological Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES
Citation Formats
Qian, Yun, Jackson, Charles, Giorgi, Filippo, Booth, Ben, Duan, Qingyun, Forest, Chris, Higdon, Dave, Hou, Z. Jason, and Huerta, Gabriel. Uncertainty Quantification in Climate Modeling and Projection. United States: N. p., 2016.
Web. doi:10.1175/BAMS-D-15-00297.1.
Qian, Yun, Jackson, Charles, Giorgi, Filippo, Booth, Ben, Duan, Qingyun, Forest, Chris, Higdon, Dave, Hou, Z. Jason, & Huerta, Gabriel. Uncertainty Quantification in Climate Modeling and Projection. United States. https://doi.org/10.1175/BAMS-D-15-00297.1
Qian, Yun, Jackson, Charles, Giorgi, Filippo, Booth, Ben, Duan, Qingyun, Forest, Chris, Higdon, Dave, Hou, Z. Jason, and Huerta, Gabriel. Fri .
"Uncertainty Quantification in Climate Modeling and Projection". United States. https://doi.org/10.1175/BAMS-D-15-00297.1. https://www.osti.gov/servlets/purl/1327127.
@article{osti_1327127,
title = {Uncertainty Quantification in Climate Modeling and Projection},
author = {Qian, Yun and Jackson, Charles and Giorgi, Filippo and Booth, Ben and Duan, Qingyun and Forest, Chris and Higdon, Dave and Hou, Z. Jason and Huerta, Gabriel},
abstractNote = {The projection of future climate is one of the most complex problems undertaken by the scientific community. Although scientists have been striving to better understand the physical basis of the climate system and to improve climate models, the overall uncertainty in projections of future climate has not been significantly reduced (e.g., from the IPCC AR4 to AR5). With the rapid increase of complexity in Earth system models, reducing uncertainties in climate projections becomes extremely challenging. Since uncertainties always exist in climate models, interpreting the strengths and limitations of future climate projections is key to evaluating risks, and climate change information for use in Vulnerability, Impact, and Adaptation (VIA) studies should be provided with both well-characterized and well-quantified uncertainty. The workshop reported herein aimed at providing participants, many of them from developing countries, information on strategies to quantify the uncertainty in climate model projections and assess the reliability of climate change information for decision-making. The program included a mixture of lectures on fundamental concepts in Bayesian inference and sampling, applications, and hands-on computer laboratory exercises employing software packages for Bayesian inference, Markov Chain Monte Carlo methods, and global sensitivity analyses. The lectures covered a range of scientific issues underlying the evaluation of uncertainties in climate projections, such as the effects of uncertain initial and boundary conditions, uncertain physics, and limitations of observational records. Progress in quantitatively estimating uncertainties in hydrologic, land surface, and atmospheric models at both regional and global scales was also reviewed. The application of Uncertainty Quantification (UQ) concepts to coupled climate system models is still in its infancy. The Coupled Model Intercomparison Project (CMIP) multi-model ensemble currently represents the primary data for assessing reliability and uncertainties of climate change information. An alternative approach is to generate similar ensembles by perturbing parameters within a single-model framework. One of workshop’s objectives was to give participants a deeper understanding of these approaches within a Bayesian statistical framework. However, there remain significant challenges still to be resolved before UQ can be applied in a convincing way to climate models and their projections.},
doi = {10.1175/BAMS-D-15-00297.1},
journal = {Bulletin of the American Meteorological Society},
number = 5,
volume = 97,
place = {United States},
year = {Fri Jun 03 00:00:00 EDT 2016},
month = {Fri Jun 03 00:00:00 EDT 2016}
}
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