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Title: Uncovering the Forced Climate Response from a Single Ensemble Member Using Statistical Learning

Abstract

Internal atmospheric variability fundamentally limits predictability of climate and obscures evidence of anthropogenic climate change regionally and on time scales of up to a few decades. Dynamical adjustment techniques estimate and subsequently remove the influence of atmospheric circulation variability on temperature or precipitation. The residual component is expected to contain the thermodynamical signal of the externally forced response but with less circulation-induced noise. Existing techniques have led to important insights into recent trends in regional (hydro-) climate and their drivers, but the variance explained by circulation is often low. Here, we develop a novel dynamical adjustment technique by implementing principles from statistical learning. We demonstrate in an ensemble of Community Earth System Model (CESM) simulations that statistical learning methods, such as regularized linear models, establish a clearer relationship between circulation variability and atmospheric target variables, and need relatively short periods of record for training (around 30 years). The method accounts for, on average, 83% and 78% of European monthly winter temperature and precipitation variability at gridcell level, and around 80% of global mean temperature and hemispheric precipitation variability. We show that the residuals retain forced thermodynamical contributions to temperature and precipitation variability. Accurate estimates of the total forced response canmore » thus be recovered assuming that forced circulation changes are gradual over time. Overall, forced climate response estimates can be extracted at regional or global scales from approximately 3–5 times fewer ensemble members, or even a single realization, using statistical learning techniques. We anticipate the technique will contribute to reducing uncertainties around internal variability and facilitating climate change detection and attribution.« less

Authors:
 [1];  [2];  [3];  [4];  [4];  [3];  [3]
+ Show Author Affiliations
  1. ETH Zurich (Switzerland). Inst. for Atmospheric and Climate Science and Seminar for Statistics
  2. ETH Zurich (Switzerland). Seminar for Statistics
  3. ETH Zurich (Switzerland). Inst. for Atmospheric and Climate Science
  4. National Center for Atmospheric Research, Boulder, CO (United States)
Publication Date:
Research Org.:
University Corporation for Atmospheric Research, Boulder, CO (United States)
Sponsoring Org.:
Swiss Data Science Centre; USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF)
OSTI Identifier:
1609295
Grant/Contract Number:  
FC02-97ER62402
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Climate
Additional Journal Information:
Journal Volume: 32; Journal Issue: 17; Journal ID: ISSN 0894-8755
Publisher:
American Meteorological Society
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Meteorology & atmospheric sciences; Atmospheric circulation; Climate change; Climate prediction; Regression analysis; Statistical techniques; Climate variability

Citation Formats

Sippel, Sebastian, Meinshausen, Nicolai, Merrifield, Anna, Lehner, Flavio, Pendergrass, Angeline G., Fischer, Erich, and Knutti, Reto. Uncovering the Forced Climate Response from a Single Ensemble Member Using Statistical Learning. United States: N. p., 2019. Web. doi:10.1175/jcli-d-18-0882.1.
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Sippel, Sebastian, Meinshausen, Nicolai, Merrifield, Anna, Lehner, Flavio, Pendergrass, Angeline G., Fischer, Erich, & Knutti, Reto. Uncovering the Forced Climate Response from a Single Ensemble Member Using Statistical Learning. United States. https://doi.org/10.1175/jcli-d-18-0882.1
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Sippel, Sebastian, Meinshausen, Nicolai, Merrifield, Anna, Lehner, Flavio, Pendergrass, Angeline G., Fischer, Erich, and Knutti, Reto. Fri . "Uncovering the Forced Climate Response from a Single Ensemble Member Using Statistical Learning". United States. https://doi.org/10.1175/jcli-d-18-0882.1. https://www.osti.gov/servlets/purl/1609295.
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@article{osti_1609295,
title = {Uncovering the Forced Climate Response from a Single Ensemble Member Using Statistical Learning},
author = {Sippel, Sebastian and Meinshausen, Nicolai and Merrifield, Anna and Lehner, Flavio and Pendergrass, Angeline G. and Fischer, Erich and Knutti, Reto},
abstractNote = {Internal atmospheric variability fundamentally limits predictability of climate and obscures evidence of anthropogenic climate change regionally and on time scales of up to a few decades. Dynamical adjustment techniques estimate and subsequently remove the influence of atmospheric circulation variability on temperature or precipitation. The residual component is expected to contain the thermodynamical signal of the externally forced response but with less circulation-induced noise. Existing techniques have led to important insights into recent trends in regional (hydro-) climate and their drivers, but the variance explained by circulation is often low. Here, we develop a novel dynamical adjustment technique by implementing principles from statistical learning. We demonstrate in an ensemble of Community Earth System Model (CESM) simulations that statistical learning methods, such as regularized linear models, establish a clearer relationship between circulation variability and atmospheric target variables, and need relatively short periods of record for training (around 30 years). The method accounts for, on average, 83% and 78% of European monthly winter temperature and precipitation variability at gridcell level, and around 80% of global mean temperature and hemispheric precipitation variability. We show that the residuals retain forced thermodynamical contributions to temperature and precipitation variability. Accurate estimates of the total forced response can thus be recovered assuming that forced circulation changes are gradual over time. Overall, forced climate response estimates can be extracted at regional or global scales from approximately 3–5 times fewer ensemble members, or even a single realization, using statistical learning techniques. We anticipate the technique will contribute to reducing uncertainties around internal variability and facilitating climate change detection and attribution.},
doi = {10.1175/jcli-d-18-0882.1},
journal = {Journal of Climate},
number = 17,
volume = 32,
place = {United States},
year = {Fri Aug 09 00:00:00 EDT 2019},
month = {Fri Aug 09 00:00:00 EDT 2019}
}
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    Climate change now detectable from any single day of weather at global scale
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    • Nature Climate Change, Vol. 10, Issue 1
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