4.6 Article

The role of atmosphere feedbacks during ENSO in the CMIP3 models. Part II: using AMIP runs to understand the heat flux feedback mechanisms

期刊

CLIMATE DYNAMICS
卷 37, 期 7-8, 页码 1271-1292

出版社

SPRINGER
DOI: 10.1007/s00382-010-0895-y

关键词

ENSO; Atmospheric feedbacks; Heat flux; AMIP

资金

  1. CNRS
  2. European Community [GOCE-CT-2003-505539, ENV/244067]
  3. Met Office
  4. Natural Environment Research Council [ncas10009] Funding Source: researchfish

向作者/读者索取更多资源

Several studies using ocean-atmosphere general circulation models (GCMs) suggest that the atmospheric component plays a dominant role in the modelled El Nio-Southern Oscillation (ENSO). To help elucidate these findings, the two main atmosphere feedbacks relevant to ENSO, the Bjerknes positive feedback (mu) and the heat flux negative feedback (alpha), are here analysed in nine AMIP runs of the CMIP3 multimodel dataset. We find that these models generally have improved feedbacks compared to the coupled runs which were analysed in part I of this study. The Bjerknes feedback, mu, is increased in most AMIP runs compared to the coupled run counterparts, and exhibits both positive and negative biases with respect to ERA40. As in the coupled runs, the shortwave and latent heat flux feedbacks are the two dominant components of alpha in the AMIP runs. We investigate the mechanisms behind these two important feedbacks, in particular focusing on the strong 1997-1998 El Nio. Biases in the shortwave flux feedback, alpha (SW), are the main source of model uncertainty in alpha. Most models do not successfully represent the negative alpha(SW) in the East Pacific, primarily due to an overly strong low-cloud positive feedback in the far eastern Pacific. Biases in the cloud response to dynamical changes dominate the modelled alpha (SW) biases, though errors in the large-scale circulation response to sea surface temperature (SST) forcing also play a role. Analysis of the cloud radiative forcing in the East Pacific reveals model biases in low cloud amount and optical thickness which may affect alpha (SW). We further show that the negative latent heat flux feedback, alpha (LH), exhibits less diversity than alpha (SW) and is primarily driven by variations in the near-surface specific humidity difference. However, biases in both the near-surface wind speed and humidity response to SST forcing can explain the inter-model alpha(LH) differences.

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