Articles | Volume 10, issue 4
https://doi.org/10.5194/esd-10-729-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-10-729-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluating climate emulation: fundamental impulse testing of simple climate models
Adria K. Schwarber
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20742, USA
Steven J. Smith
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20742, USA
Joint Global Change Research Institute, 5825 University Research Ct, College Park, MD 20740, USA
Corinne A. Hartin
Joint Global Change Research Institute, 5825 University Research Ct, College Park, MD 20740, USA
Benjamin Aaron Vega-Westhoff
Joint Global Change Research Institute, 5825 University Research Ct, College Park, MD 20740, USA
Ryan Sriver
Department of Atmospheric Sciences, University of Illinois
Urbana–Champaign, Champaign, IL 61820, USA
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- Effect of aerosol radiative forcing uncertainty on projected exceedance year of a 1.5 °C global temperature rise A. Peace et al. 10.1088/1748-9326/aba20c
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- How may solar geoengineering impact global prospects for climate change mitigation? K. Ricke & A. Harding 10.1093/oxrep/grad044
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- Reduced Complexity Model Intercomparison Project Phase 1: introduction and evaluation of global-mean temperature response Z. Nicholls et al. 10.5194/gmd-13-5175-2020
- Reduced Complexity Model Intercomparison Project Phase 2: Synthesizing Earth System Knowledge for Probabilistic Climate Projections Z. Nicholls et al. 10.1029/2020EF001900
- Minimal CMIP Emulator (MCE v1.2): a new simplified method for probabilistic climate projections J. Tsutsui 10.5194/gmd-15-951-2022
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Latest update: 10 Dec 2024
Short summary
Simple climate models (SCMs) underlie many important scientific and decision-making endeavors. This illustrates the need for their use to be rooted in a clear understanding of their fundamental responses. In this study, we provide a comprehensive assessment of model performance by evaluating the fundamental responses of several SCMs. We find biases in some responses, which have implications for decision science. We conclude by recommending a standard set of validation tests for any SCM.
Simple climate models (SCMs) underlie many important scientific and decision-making endeavors....
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