Source: Journal of Advances in Modeling Earth Systems (JAMES)
One of the fundamental metrics in climate change research is equilibrium climate sensitivity: the amount that Earth’s long-term, near-surface temperatures will change in response to a doubling of atmospheric carbon dioxide concentrations. Previous research has shown that two spatially dependent processes—the variability of sea surface temperatures and the organization of initially scattered convection (known as convective aggregation)—largely control climate sensitivity by regulating feedbacks related to clouds and water vapor that can amplify or moderate Earth’s ability to radiate heat into space.
Most modeling studies investigating the role of convective aggregation on climate sensitivity have used sea surface temperatures that are spatially uniform. But recent research has questioned whether this is appropriate and suggested that aggregation could actually depend upon sea surface temperature patterns. To better understand the relationship between these processes, Coppin and Bony conducted four simulations using the full atmospheric component of the Institut Pierre Simon Laplace Climate Model 5A (IPSL-CM5A) general circulation model coupled to a highly simplified, 10-meter-deep ocean model.
In contrast to earlier studies, which found that aggregation decreases climate sensitivity when coupled with a simplified ocean, the team instead found that aggregation can increase it. Because these results indicate that aggregation strongly interacts with sea surface temperature gradients, this study casts significant doubt on the appropriateness of using experiments with fixed and uniform sea surface temperatures to understand the effects of convective aggregation on climate sensitivity. (Journal of Advances in Modeling Earth Systems (JAMES), https://doi.org/10.1029/2018MS001406, 2018)
—Terri Cook, Freelance Writer
Citation:
Cook, T. (2019), Improving estimates of long-term climate sensitivity, Eos, 100, https://doi.org/10.1029/2019EO116981. Published on 05 March 2019.
Text © 2019. The authors. CC BY-NC-ND 3.0
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