DOE OSTI · 3029310
Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP)
Abstract
Anthropogenic climate change is unfolding rapidly, yet its regional manifestation can be obscured by internal variability. A primary goal of climate science is to identify the externally forced climate response from among the noise of internal variability. Separating the forced response from internal variability can be addressed in climate models by using a large ensemble to average over different possible realizations of internal variability. However, with only one realization of the real world, it is a major challenge to isolate the forced response directly in observations. In the Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP), contributors used existing and newly developed statistical and machine learning methods to estimate the forced response over 1950–2022 within individual realizations of the climate system. Participants used neural networks, linear inverse models, fingerprinting methods, and low-frequency component analysis, among other approaches. These methods were trained using large ensembles from multiple climate models and then applied to observations. Here, we evaluate method performance within large ensembles and investigate the estimates of the forced response in observations. Our results show that many different types of methods are skillful for estimating the forced response in climate models, though the relative skill of individual methods varies depending on the variable and evaluation metric. Methods with comparable skill in models can give a wide range of estimates of the forced response pattern in observations, illustrating the epistemic uncertainty in forced response estimates. ForceSMIP gives new insights into the forced response in observations, its uncertainty, and methods for its estimation.
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Wills, Robert C. J. [Eidgenoessische Technische Hochschule (ETH), Zurich (Switzerland)] (ORCID:0000000277762076), Deser, Clara [National Center for Atmospheric Research (NCAR), Boulder, CO (United States)], McKinnon, Karen A. [Univ. of California, Los Angeles, CA (United States)], Phillips, Adam [National Center for Atmospheric Research (NCAR), Boulder, CO (United States)], Po-Chedley, Stephen [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)], Sippel, Sebastian [Leipzig Univ. (Germany)], Merrifield, Anna L. [Eidgenoessische Technische Hochschule (ETH), Zurich (Switzerland)], Bône, Constantin [Centre National de la Recherche Scientifique (CNRS), Paris (France). Laboratoire d’Océanographie et du Climat: Expérimentations et Approches Numériques (LOCEAN); Inst. Pierre-Simon Laplace (IPSL), Paris (France); Sorbonne Univ., Paris (France); Institut de Recherche pour le Développement (IRD), Paris (France); Muséum National d'Histoire Naturelle (MNHN), Paris (France)], Bonfils, Céline [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)], Camps-Valls, Gustau [Univ. of Valencia (Spain)], Cropper, Stephen [Univ. of California, Los Angeles, CA (United States)], Connolly, Charlotte [Colorado State Univ., Fort Collins, CO (United States)], Duan, Shiheng [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)], Durand, Homer [Univ. of Valencia (Spain)], Feigin, Alexander [Russian Academy of Sciences (RAS), Nizhny Novgorod (Russian Federation)], Fernandez, Martin A. [Colorado State Univ., Fort Collins, CO (United States)], Gastineau, Guillaume [Centre National de la Recherche Scientifique (CNRS), Paris (France). Laboratoire d’Océanographie et du Climat: Expérimentations et Approches Numériques (LOCEAN); Inst. Pierre-Simon Laplace (IPSL), Paris (France); Sorbonne Univ., Paris (France); Institut de Recherche pour le Développement (IRD), Paris (France); Muséum National d'Histoire Naturelle (MNHN), Paris (France)], Gavrilov, Andrei [Univ. of Valencia (Spain); Russian Academy of Sciences (RAS), Nizhny Novgorod (Russian Federation)], Gordon, Emily [Stanford Univ., CA (United States)], Günther, Moritz [Max Planck Institute for Meteorology, Hamburg (Germany)], Höver, Maren [Eidgenoessische Technische Hochschule (ETH), Zurich (Switzerland); Univ. of Oxford (United Kingdom)], Kravtsov, Sergey [Univ. of Wisconsin, Milwaukee, WI (United States)], Kuo, Yan-Ning [Cornell Univ., Ithaca, NY (United States)], Lien, Justin [Tohoku Univ., Sendai (Japan)], Madakumbura, Gavin D. [Univ. of California, Los Angeles, CA (United States)], Mankovich, Nathan [Univ. of Valencia (Spain)], Newman, Matthew [National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States). Physical Sciences Laboratory (PSL)], Rader, Jamin [Colorado State Univ., Fort Collins, CO (United States)], Shi, Jia-Rui [New York Univ. (NYU), NY (United States)], Shin, Sang-Ik [National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States). Physical Sciences Laboratory (PSL); Univ. of Colorado, Boulder, CO (United States). Cooperative Inst. for Research in Environmental Sciences (CIRES)], Varando, Gherardo [Univ. of Valencia (Spain)]. 2026-03-19. Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP). https://doi.org/10.1175/jcli-d-25-0326.1
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