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Use of models in detection and attribution of climate change

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  • Gabriele Hegerl
  • Francis Zwiers

Abstract

Most detection and attribution studies use climate models to determine both the expected ‘fingerprint’ of climate change and the uncertainty in the estimated magnitude of this fingerprint in observations, given the climate variability. This review discusses the role of models in detection and attribution, the associated uncertainties, and the robustness of results. Studies that use observations only make substantial assumptions to separate the components of observed changes due to radiative forcing from those due to internal climate variability. Results from observation‐only studies are broadly consistent with those from fingerprint studies. Fingerprint studies evaluate the extent to which patterns of response to external forcing (fingerprints) from climate model simulations explain observed climate change in observations. Fingerprints are based on climate models of various complexities, from energy balance models to full earth system models. Statistical approaches range from simple comparisons of observations with model simulations to multi‐regression methods that estimate the contribution of several forcings to observed change using a noise‐reducing metric. Multi‐model methods can address model uncertainties to some extent and we discuss how remaining uncertainties can be overcome. The increasing focus on detecting and attributing regional climate change and impacts presents both opportunities and challenges. Challenges arise because internal variability is larger on smaller scales, and regionally important forcings, such as from aerosols or land‐use change, are often uncertain. Nevertheless, if regional climate change can be linked to external forcing, the results can be used to provide constraints on regional climate projections. WIREs Clim Change 2011 2 570–591 DOI: 10.1002/wcc.121 This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models

Suggested Citation

  • Gabriele Hegerl & Francis Zwiers, 2011. "Use of models in detection and attribution of climate change," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 2(4), pages 570-591, July.
  • Handle: RePEc:wly:wirecc:v:2:y:2011:i:4:p:570-591
    DOI: 10.1002/wcc.121
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    Cited by:

    1. Feng Wang & Dominique Arseneault & Étienne Boucher & Fabio Gennaretti & Shulong Yu & Tongwen Zhang, 2022. "Tropical volcanoes synchronize eastern Canada with Northern Hemisphere millennial temperature variability," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Brian J. Reich & Shu Yang & Yawen Guan & Andrew B. Giffin & Matthew J. Miller & Ana Rappold, 2021. "A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications," International Statistical Review, International Statistical Institute, vol. 89(3), pages 605-634, December.
    3. Nicolas Misailidis Stríkis & Plácido Fabrício Silva Melo Buarque & Francisco William Cruz & Juan Pablo Bernal & Mathias Vuille & Ernesto Tejedor & Matheus Simões Santos & Marília Harumi Shimizu & Ange, 2024. "Modern anthropogenic drought in Central Brazil unprecedented during last 700 years," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    4. Dapeng Li & Feiyang Pan & Jia He & Zhiwei Xu & Dandan Tu & Guoliang Fan, 2023. "Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning," Papers 2303.11716, arXiv.org.
    5. Ross McKitrick, 2024. "Total least squares bias in climate fingerprinting regressions with heterogeneous noise variances and correlated explanatory variables," Environmetrics, John Wiley & Sons, Ltd., vol. 35(2), March.

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