4.7 Article

The Role of Atmosphere Feedbacks during ENSO in the CMIP3 Models. Part III: The Shortwave Flux Feedback

期刊

JOURNAL OF CLIMATE
卷 25, 期 12, 页码 4275-4293

出版社

AMER METEOROLOGICAL SOC
DOI: 10.1175/JCLI-D-11-00178.1

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资金

  1. European Union EUCLIPSE project [ENV/244067]
  2. CASE grant from the Met Office
  3. Natural Environment Research Council [NE/H015698/1] Funding Source: researchfish
  4. NERC [NE/H015698/1] Funding Source: UKRI

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Previous studies using coupled general circulation models (GCMs) suggest that the atmosphere model plays a dominant role in the modeled El Nino-Southern Oscillation (ENSO), and that intermodel differences in the thermodynamical damping of sea surface temperatures (SSTs) are a dominant contributor to the ENSO amplitude diversity. This study presents a detailed analysis of the shortwave flux feedback (alpha(SW)) in 12 Coupled Model Intercomparison Project phase 3 (CMIP3) simulations, motivated by findings that alpha(SW) is the primary contributor to model thermodynamical damping errors. A feedback decomposition method, developed to elucidate the asw biases, shows that all models underestimate the dynamical atmospheric response to SSTs in the eastern equatorial Pacific, leading to underestimated alpha(SW) values. Biases in the cloud response to dynamics and the shortwave interception by clouds also contribute to errors in asw. Changes in the alpha(SW) feedback between the coupled and corresponding atmosphere-only simulations are related to changes in the mean dynamics. A large nonlinearity is found in the observed and modeled SW flux feedback, hidden when linearly calculating asw. In the observations, two physical mechanisms are proposed to explain this nonlinearity: 1) a weaker subsidence response to cold SST anomalies than the ascent response to warm SST anomalies and 2) a nonlinear high-level cloud cover response to SST. The shortwave flux feedback nonlinearity tends to be underestimated by the models, linked to an underestimated nonlinearity in the dynamical response to SST. The process-based methodology presented in this study may help to correct model ENSO atmospheric biases, ultimately leading to an improved simulation of ENSO in GCMs.

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