Reducing uncertainties in climate projections with emergent constraints: Concepts, Examples and Prospects

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1007/s00376-019-9140-8.

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Authors

Florent Brient 

Abstract

Models disagree on a significant number of responses to climate change, such as climate feedback, regional changes, or the strength of equilibrium climate sensitivity. Emergent constraints aim to reduce these uncertainties by finding links between the inter-model spread in an observable predictor and climate projections. In this paper, the concepts underlying this framework are recalled with an emphasis on the statistical inference used for narrowing uncertainties, and a review of emergent constraints found in the last two decades. Potential links between highlighted predictors are explored, especially those targeting uncertainty reductions in climate sensitivity, cloud feedback, and changes of the hydrological cycle. Yet the disagreement across emergent constraints suggests that the spread in climate sensitivity can not be significantly narrowed. This calls for weighting the realism of emergent constraints by quantifying the level of physical understanding explaining the relationship. This would also permit more efficient model evaluation and better targeted model development. In the context of the upcoming CMIP6 model intercomparison a growing number of new predictors and uncertainty reductions is expected, which call for robust statistical inferences that allow cross-validation of more likely estimates.

DOI

https://doi.org/10.31223/osf.io/qwbyt

Subjects

Atmospheric Sciences, Climate, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics

Keywords

climate models, climate change, Climate sensitivity, emergent constraint, CMIP

Dates

Published: 2019-06-28 03:49

Last Updated: 2019-09-11 07:51

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License

Academic Free License (AFL) 3.0

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