The role of moist processes in shortwave radiative feedback during ENSO in the CMIP5 models

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Abstract

The weak negative shortwave (SW) radiative feedback αsw during El Niño-Southern Oscillation (ENSO) over the equatorial Pacific is a common problem in the models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). In this study, the causes for the αsw biases are analyzed using three-dimensional cloud fraction and liquid water path (LWP) provided by the 17 CMIP5 models and the relative roles of convective and stratiform rainfall feedbacks in αsw are explored. Results show that the underestimate of SW feedback is primarily associated with too negative cloud fraction and LWP feedbacks in the boundary layers, together with insufficient middle and/or high cloud and dynamics feedbacks, in both the CMIP and Atmospheric Model Intercomparsion Project (AMIP) runs, the latter being somewhat better. The underestimations of SW feedbacks are due to both weak negative SW responses to El Niño, especially in the CMIP runs, and strong positive SW responses to La Niña, consistent with their biases in cloud fraction, LWP, and dynamics responses to El Niño and La Niña. The convective rainfall feedback, which is largely reduced owing to the excessive cold tongue in the CMIP runs compared with their AMIP counterparts, contributes more to the difference of SW feedback (mainly under El Niño conditions) between the CMIP and AMIP runs, while the stratiform rainfall plays a more important role in SW feedback during La Niña.

Original languageEnglish
Pages (from-to)9892-9908
Number of pages17
JournalJournal of Climate
Volume28
Issue number24
DOIs
StatePublished - 2015
Externally publishedYes

Funding

This work was supported by the CAS Strategic Priority Research Program (Grant XDA05110304), the National 973 project (Grant 2015CB954102), and the National Natural Science Foundation of China (Grants 41205079 and 41305040).

Keywords

  • Circulation/Dynamics
  • ENSO
  • Interannual variability
  • Model evaluation/performance
  • Models and modeling
  • Variability

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