The remote impacts of climate feedbacks on regional climate predictability


Uncertainty in the spatial pattern of climate change is dominated by divergent predictions among climate models. Model differences are closely linked to their representation of climate feedbacks, that is, the additional radiative fluxes that are caused by changes in clouds, water vapour, surface albedo, and other factors, in response to an external climate forcing. Progress in constraining this uncertainty is therefore predicated on understanding how patterns of individual climate feedbacks aggregate into a regional and global climate response. Here we present a simple, moist energy balance model that combines regional feedbacks and the diffusion of both latent and sensible heat. Our model emulates the relationship between regional feedbacks and temperature response in more comprehensive climate models; the model can therefore be used to understand how uncertainty in feedback patterns drives uncertainty in the patterns of temperature response. We find that whereas uncertainty in tropical feedbacks induces a global response, the impact of uncertainty in polar feedbacks remains predominantly regionally confined.

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Figure 1: The relationship between feedback patterns and climate response in aquaplanet GCM simulations.
Figure 2: The climate response in the moist energy balance model for three different, stylized feedback patterns.
Figure 3: The impact of uncertainty in feedbacks patterns on uncertainty in temperature-response patterns in the moist energy balance model.


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The authors are grateful for enlightening feedback from M. Baker, A. Donohoe and P. Molnar.

Author information




G.H.R. performed the MEBM analyses and N.F. performed the AM2 integrations. All authors contributed to the interpretation of the results and to writing the manuscript.

Corresponding author

Correspondence to Gerard H. Roe.

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The authors declare no competing financial interests.

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Roe, G., Feldl, N., Armour, K. et al. The remote impacts of climate feedbacks on regional climate predictability. Nature Geosci 8, 135–139 (2015).

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