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Title: Effects of Ensemble Configuration on Estimates of Regional Climate Uncertainties

Abstract

Internal variability in the climate system can contribute substantial uncertainty in climate projections, particularly at regional scales. Internal variability can be quantified using large ensembles of simulations that are identical but for perturbed initial conditions. Here we compare methods for quantifying internal variability. Our study region spans the west coast of North America, which is strongly influenced by El Niño and other large-scale dynamics through their contribution to large-scale internal variability. Using a statistical framework to simultaneously account for multiple sources of uncertainty, we find that internal variability can be quantified consistently using a large ensemble or an ensemble of opportunity that includes small ensembles from multiple models and climate scenarios. The latter also produce estimates of uncertainty due to model differences. Here, we conclude that projection uncertainties are best assessed using small single-model ensembles from as many model-scenario pairings as computationally feasible, which has implications for ensemble design in large modeling efforts.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]; ORCiD logo [1]
  1. Univ. of Washington, Seattle, WA (United States)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1439661
Alternate Identifier(s):
OSTI ID: 1417237
Report Number(s):
PNNL-SA-131956
Journal ID: ISSN 0094-8276; KP1703010
Grant/Contract Number:  
AC05-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
Geophysical Research Letters
Additional Journal Information:
Journal Volume: 45; Journal Issue: 2; Journal ID: ISSN 0094-8276
Publisher:
American Geophysical Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; climate; uncertainty

Citation Formats

Goldenson, N., Mauger, G., Leung, L. R., Bitz, C. M., and Rhines, A. Effects of Ensemble Configuration on Estimates of Regional Climate Uncertainties. United States: N. p., 2018. Web. doi:10.1002/2017GL076297.
Goldenson, N., Mauger, G., Leung, L. R., Bitz, C. M., & Rhines, A. Effects of Ensemble Configuration on Estimates of Regional Climate Uncertainties. United States. doi:10.1002/2017GL076297.
Goldenson, N., Mauger, G., Leung, L. R., Bitz, C. M., and Rhines, A. Thu . "Effects of Ensemble Configuration on Estimates of Regional Climate Uncertainties". United States. doi:10.1002/2017GL076297. https://www.osti.gov/servlets/purl/1439661.
@article{osti_1439661,
title = {Effects of Ensemble Configuration on Estimates of Regional Climate Uncertainties},
author = {Goldenson, N. and Mauger, G. and Leung, L. R. and Bitz, C. M. and Rhines, A.},
abstractNote = {Internal variability in the climate system can contribute substantial uncertainty in climate projections, particularly at regional scales. Internal variability can be quantified using large ensembles of simulations that are identical but for perturbed initial conditions. Here we compare methods for quantifying internal variability. Our study region spans the west coast of North America, which is strongly influenced by El Niño and other large-scale dynamics through their contribution to large-scale internal variability. Using a statistical framework to simultaneously account for multiple sources of uncertainty, we find that internal variability can be quantified consistently using a large ensemble or an ensemble of opportunity that includes small ensembles from multiple models and climate scenarios. The latter also produce estimates of uncertainty due to model differences. Here, we conclude that projection uncertainties are best assessed using small single-model ensembles from as many model-scenario pairings as computationally feasible, which has implications for ensemble design in large modeling efforts.},
doi = {10.1002/2017GL076297},
journal = {Geophysical Research Letters},
number = 2,
volume = 45,
place = {United States},
year = {2018},
month = {1}
}

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