4.7 Review

An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence

Journal

REVIEWS OF GEOPHYSICS
Volume 58, Issue 4, Pages -

Publisher

AMER GEOPHYSICAL UNION
DOI: 10.1029/2019RG000678

Keywords

Climate; climate sensitivity; global warming; Bayesian methods

Funding

  1. joint U.K. BEIS/Defra Met Office Hadley Centre Climate Programme [GA01101]
  2. U.S. Department of Energy (DOE) Regional and Global Modeling Analysis program area
  3. DOE by Lawrence Livermore National Laboratory [DE-AC52-07NA27344]
  4. Australian Research Council [FL150100035, FL120100050]
  5. National Science Foundation [AGS-1752796]
  6. European Research Council (ERC) [770765]
  7. European Union's Horizon 2020 Research and Innovation Programme [820829]
  8. U.K. Natural Environment Research Council (NERC) [NE/N006038/1, NE/N006143/1]
  9. Dutch Ministry of Education, Culture and Science (OCW)
  10. Past Earth Network [EP/M008363/1]
  11. ReCoVER [EP/M008495/1]
  12. Integrated Research Program for Advancing Climate Models from MEXT, Japan
  13. NERC [NE/P019048/1, NE/H006273/1]
  14. NASA Modeling Analysis and Prediction program
  15. Australian Research Council [FL120100050] Funding Source: Australian Research Council
  16. NERC [NE/N006038/1, NE/N006143/1, NE/H006273/1, NE/P019048/1] Funding Source: UKRI

Ask authors/readers for more resources

We assess evidence relevant to Earth's equilibrium climate sensitivity per doubling of atmospheric CO2, characterized by an effective sensitivity S. This evidence includes feedback process understanding, the historical climate record, and the paleoclimate record. An S value lower than 2 K is difficult to reconcile with any of the three lines of evidence. The amount of cooling during the Last Glacial Maximum provides strong evidence against values of S greater than 4.5 K. Other lines of evidence in combination also show that this is relatively unlikely. We use a Bayesian approach to produce a probability density function (PDF) for S given all the evidence, including tests of robustness to difficult-to-quantify uncertainties and different priors. The 66% range is 2.6-3.9 K for our Baseline calculation and remains within 2.3-4.5 K under the robustness tests; corresponding 5-95% ranges are 2.3-4.7 K, bounded by 2.0-5.7 K (although such high-confidence ranges should be regarded more cautiously). This indicates a stronger constraint on S than reported in past assessments, by lifting the low end of the range. This narrowing occurs because the three lines of evidence agree and are judged to be largely independent and because of greater confidence in understanding feedback processes and in combining evidence. We identify promising avenues for further narrowing the range in S, in particular using comprehensive models and process understanding to address limitations in the traditional forcing-feedback paradigm for interpreting past changes.

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