Testing a Physical Hypothesis for the Relationship Between Climate Sensitivity and Double‐ITCZ Bias in Climate Models. (16th September 2020)

• Record Type: Journal Article
• Authors: Webb, Mark J.; Lock, Adrian P.
• Is Part Of: Journal of advances in modeling earth systems. Volume 12:Number 9(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-09-16
• Subjects: Intertropical Convergence Zone -- emergent constraint -- climate sensitivity -- cloud feedback; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2019MS001999
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: Tian (2015, https://doi.org/10.1002/2015GL064119 ) found that Coupled Model Intercomparison Project Phases 3 and 5 (CMIP3 and CMIP5) climate models with too much precipitation in a region of the Southeast Pacific (due to a double‐Intertropical Convergence Zone [ITCZ] bias) tend to have lower climate sensitivities and suggested that this might form the basis of an "emergent constraint, " which could rule out lower values of climate sensitivity. However, no physical mechanism has been proposed to explain this relationship. Here we advance the hypothesis that deep convection encroaching into regions that should be dominated by shallow clouds hampers the formation of shallow clouds in the present climate and reduces the magnitude of positive low‐level cloud feedbacks, resulting in smaller values of climate sensitivity. We test this hypothesis first by performing sensitivity tests with the HadGEM2‐A aquaplanet model subject to a uniform +4 K sea surface temperature (SST) perturbation, in which we vary the degree to which deep convection associated with the single/double ITCZ extends toward subtropical low‐cloud regions. Experiments with more precipitation encroaching into the subtropics have weaker subtropical cloud radiative effects in the present‐day simulations and less positive subtropical cloud feedbacks, consistent with our hypothesis. We test this hypothesis further by looking for the predicted relationships across multimodel ensembles of SST forced Atmospheric Model Intercomparison Project (AMIP) experiments subject to a uniform +4 K SST increase. Relationships of the expected sign are found in the CMIP5 AMIP+4K experiments, but not all are statistically significant at the 5% level. We find no statistically significant support for our hypothesis in the currently available CMIP6 AMIP+4K experiments. Plain Language Summary: A previous study found that climate models with too much heavy rainfall extending from the tropics into the Southeast Pacific tend to have smaller amounts of global warming in response to increases in carbon dioxide. It has been suggested that this might mean that climate models that are more sensitive are more realistic. However, it is unclear what physical processes in the climate system might cause such a relationship. Here we propose a potential explanation for this relationship that heavy rainfall extending into regions that should be dominated by low‐level clouds is associated with conditions that make it harder to form low‐level clouds, which are known to amplify climate warming. We test this idea using two approaches. Modifying a single climate model to vary the degree to which heavy rainfall spreads out into low‐cloud regions reproduces the expected relationships. However, examination of a larger set of models shows that not all parts of the suggested explanation are supported at a statistically significant level. Furthermore, no elements of our proposed explanation are supported at a statistically significant level in a newer set of models. Key Points: We propose a mechanism for the Tian (2015, https://doi.org/10.1002/2015GL064119 ) finding that climate models with stronger double‐ITCZ biases tend to have lower climate sensitivities We hypothesize that deep convection encroaching into low‐cloud regions disrupts low clouds and diminishes positive low‐cloud feedbacks Our hypothesized relationships are found in experiments with a single model, but not all are statistically significant across models