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Title: Underestimating Internal Variability Leads to Narrow Estimates of Climate System Properties

Abstract

Probabilistic estimates of climate system properties often rely on the comparison of model simulations to observed temperature records and an estimate of the internal climate variability. In this study, we investigate the sensitivity of probability distributions for climate system properties in the Massachusetts Institute of Technology Earth System Model to the internal variability estimate. In particular, we derive probability distributions using the internal variability extracted from 25 different Coupled Model Intercomparison Project Phase 5 models. We further test the sensitivity by pooling variability estimates from models with similar characteristics. We find the distributions to be highly sensitive when estimating the internal variability from a single model. When merging the variability estimates across multiple models, the distributions tend to converge to a wider distribution for all properties. This suggests that using a single model to approximate the internal climate variability produces distributions that are too narrow and do not fully represent the uncertainty in the climate system property estimates.

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [4]
+ Show Author Affiliations
  1. Department of Meteorology and Atmospheric SciencePennsylvania State University University Park PA USA, Now at Cooperative Institute for Research in the AtmosphereColorado State University Fort Collins CO USA
  2. Department of Meteorology and Atmospheric SciencePennsylvania State University University Park PA USA, Earth and Environmental Systems InstitutePennsylvania State University University Park PA USA
  3. Joint Program on the Science and Policy of Global ChangeMassachusetts Institute of Technology Cambridge MA USA
  4. Joint Program on the Science and Policy of Global ChangeMassachusetts Institute of Technology Cambridge MA USA, Now at Department of Land, Air, and Water ResourcesUniversity of California Davis CA USA
Publication Date:
Research Org.:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Org.:
USDOE Office of Science (SC); National Science Foundation (NSF)
OSTI Identifier:
1566269
Alternate Identifier(s):
OSTI ID: 1566270; OSTI ID: 1610275
Grant/Contract Number:  
FG02-94ER61937
Resource Type:
Published Article
Journal Name:
Geophysical Research Letters
Additional Journal Information:
Journal Name: Geophysical Research Letters Journal Volume: 46 Journal Issue: 16; Journal ID: ISSN 0094-8276
Publisher:
American Geophysical Union
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; geology; climate sensitivity; uncertainty quantification; calibrating global net radiative forcing; internal climate variability; climate/Earth system feedbacks; calibrating transient climate response

Citation Formats

Libardoni, Alex G., Forest, Chris E., Sokolov, Andrei P., and Monier, Erwan. Underestimating Internal Variability Leads to Narrow Estimates of Climate System Properties. United States: N. p., 2019. Web. doi:10.1029/2019GL082442.
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Libardoni, Alex G., Forest, Chris E., Sokolov, Andrei P., & Monier, Erwan. Underestimating Internal Variability Leads to Narrow Estimates of Climate System Properties. United States. doi:10.1029/2019GL082442.
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Libardoni, Alex G., Forest, Chris E., Sokolov, Andrei P., and Monier, Erwan. Tue . "Underestimating Internal Variability Leads to Narrow Estimates of Climate System Properties". United States. doi:10.1029/2019GL082442.
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@article{osti_1566269,
title = {Underestimating Internal Variability Leads to Narrow Estimates of Climate System Properties},
author = {Libardoni, Alex G. and Forest, Chris E. and Sokolov, Andrei P. and Monier, Erwan},
abstractNote = {Probabilistic estimates of climate system properties often rely on the comparison of model simulations to observed temperature records and an estimate of the internal climate variability. In this study, we investigate the sensitivity of probability distributions for climate system properties in the Massachusetts Institute of Technology Earth System Model to the internal variability estimate. In particular, we derive probability distributions using the internal variability extracted from 25 different Coupled Model Intercomparison Project Phase 5 models. We further test the sensitivity by pooling variability estimates from models with similar characteristics. We find the distributions to be highly sensitive when estimating the internal variability from a single model. When merging the variability estimates across multiple models, the distributions tend to converge to a wider distribution for all properties. This suggests that using a single model to approximate the internal climate variability produces distributions that are too narrow and do not fully represent the uncertainty in the climate system property estimates.},
doi = {10.1029/2019GL082442},
journal = {Geophysical Research Letters},
number = 16,
volume = 46,
place = {United States},
year = {2019},
month = {8}
}
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DOI: 10.1029/2019GL082442
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    Works referencing / citing this record:

    Estimates of climate system properties incorporating recent climate change
    journal, January 2018

    • Libardoni, Alex G.; Forest, Chris E.; Sokolov, Andrei P.
    • Advances in Statistical Climatology, Meteorology and Oceanography, Vol. 4, Issue 1/2
    • DOI: 10.5194/ascmo-4-19-2018
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