skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Uncertainty Quantification in Climate Modeling and Projection

Journal Article · · Bulletin of the American Meteorological Society
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [1];  [8]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Univ. of Texas, Austin, TX (United States)
  3. Abdus Salam International Center for Theoretical Physics, Trieste (Italy)
  4. Met Office Hadley Centre for Climate Science and Services, Exeter (United Kingdom)
  5. Beijing Normal Univ. (China)
  6. Pennsylvania State Univ., University Park, PA (United States)
  7. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Arlington, VA (United States)
  8. Univ. of New Mexico, Albuquerque, NM (United States)
+ Show Author Affiliations

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.

Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE Office of Science (SC); Abdus Salam International Centre for Theoretical Physics (ICTP); International Union of Geodesy and Geophysics (IUGG)
Grant/Contract Number:
AC05-76RL01830
OSTI ID:
1327127
Report Number(s):
PNNL-SA-115173; KP1703020
Journal Information:
Bulletin of the American Meteorological Society, Vol. 97, Issue 5; Conference: Workshop on Uncertainty Quantification in Climate Modeling and Projection , Trieste (Italy), 13-17 Jul 2015; ISSN 0003-0007
Publisher:
American Meteorological SocietyCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 38 works
Citation information provided by
Web of Science

Cited By (12)

Assessing Parameter Importance of the Weather Research and Forecasting Model Based On Global Sensitivity Analysis Methods journal May 2018
Estimation of economic losses from tropical cyclones in China at 1.5 °C and 2.0 °C warming using the regional climate model COSMO-CLM journal September 2018
Characterizing the Relative Importance Assigned to Physical Variables by Climate Scientists when Assessing Atmospheric Climate Model Fidelity journal July 2018
Regression with small data sets: a case study using code surrogates in additive manufacturing journal March 2018
Assessment of Uncertainties in Projected Temperature and Precipitation over the Arabian Peninsula Using Three Categories of Cmip5 Multimodel Ensembles journal November 2017
Parametric Sensitivity and Uncertainty Quantification in the Version 1 of E3SM Atmosphere Model Based on Short Perturbed Parameter Ensemble Simulations journal December 2018
The state of the art and fundamental aspects of regional climate modeling in South America: Climate modeling in South America
  • Ambrizzi, Tércio; Reboita, Michelle Simões; da Rocha, Rosmeri Porfírio
  • Annals of the New York Academy of Sciences, Vol. 1436, Issue 1 https://doi.org/10.1111/nyas.13932
journal July 2018
Conducting robust ecological analyses with climate data journal July 2017
Estimating the Impact of Artificially Injected Stratospheric Aerosols on the Global Mean Surface Temperature in the 21th Century journal October 2018
Evaluating model outputs using integrated global speleothem records of climate change since the last glacial journal January 2019
Regionally refined test bed in E3SM atmosphere model version 1 (EAMv1) and applications for high-resolution modeling journal January 2019
Regression with Small Data Sets: A Case Study using Code Surrogates in Additive Manufacturing report April 2017

Similar Records

Related Subjects

54 ENVIRONMENTAL SCIENCES