The role of atmosphere feedbacks during ENSO in the CMIP3 models. Part II: using AMIP runs to understand the heat flux feedback mechanisms
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Several studies using ocean–atmosphere general circulation models (GCMs) suggest that the atmospheric component plays a dominant role in the modelled El Niño-Southern Oscillation (ENSO). To help elucidate these findings, the two main atmosphere feedbacks relevant to ENSO, the Bjerknes positive feedback (μ) and the heat flux negative feedback (α), 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, μ, 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 α in the AMIP runs. We investigate the mechanisms behind these two important feedbacks, in particular focusing on the strong 1997–1998 El Niño. Biases in the shortwave flux feedback, α SW, are the main source of model uncertainty in α. Most models do not successfully represent the negative αSW 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 α 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 α SW. We further show that the negative latent heat flux feedback, α LH, exhibits less diversity than α 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 αLH differences.
KeywordsENSO Atmospheric feedbacks Heat flux AMIP
We thank Adam Scaife, Sandrine Bony, Richard Allan, Mark Ringer, Claire Barber and Fei-Fei Jin for useful discussions during the course of this work as well as support from the CORDIAL PICS from CNRS and the European Community ENSEMBLES (GOCE-CT-2003-505539, FP6) and EUCLIPSE (ENV/244067, FP7) projects. JL acknowledges support by a CASE grant from the Met Office and thanks Alejandro Bodas-Salcedo for supplying the ISCCP FD-TOA dataset. We also acknowledge the modelling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multimodel dataset. Support of this dataset is provided by the Office of Science, US Department of Energy.
- Chou S-H, Nelkin EJ, Ardizzone J, Atlas RM, Shie C-L (2001) Goddard Satellite-based Surface Turbulent Fluxes (GSSTF) Version 2 documentation. Distributed Active Archive Center (DAAC), NASA Goddard Space Flight Center, Greenbelt, MarylandGoogle Scholar
- Davey M, Huddleston M et al (2001) STOIC: a study of coupled model climatology and variability in tropical ocean regions. Clim Dyn 18:403–420Google Scholar
- Goff JA (1957) Saturation pressure of water on the new Kelvin temperature scale. Trans Am Soc Heat Vent Eng pp 347–354Google Scholar
- Goff JA, Gratch S (1946) Low-pressure properties of water from −160 to 212 °F. Trans Am Soc Heat Vent Eng pp 95–122Google Scholar
- Kim ST, Jin F-F (2010) An ENSO stability analysis. Part II: results from the 20th and 21st century simulations of the IPCC AR4 models. Clim Dyn (accepted)Google Scholar
- Marti O, Braconnot P, Dufresne J-L, Bellier J, Benshila R, Bony S, Brockmann P, Cadule P, Caubel A, Codron F, de Noblet N, Denvil S, Fairhead L, Fichefet T, Foujols M-A, Friedlingstein P, Goosse H, Grandpeix J-Y, Guilyardi E, Hourdin F, Idelkadi A, Kageyama M, Krinner G, Levy C, Madec G, Mignot J, Musat I, Swingedouw D, Talandier C (2009) Key features of the IPSL ocean atmosphere model and its sensitivity to atmospheric resolution. Clim Dyn 34(1):1–26CrossRefGoogle Scholar
- Rossow W, Walker A, Beuschel D, Roiter M (1996) International Satellite Cloud Climatology Project (ISCCP) Documentation of New Cloud Datasets, vol 737. WMO/TD-No. 737, World Meteorological Organization, pp 115Google Scholar
- Uppala SM, Kallberg P, Simmons AJ, Andrae U, Bechtold VDC, Fiorino M, Gibson JK, Haseler J, Hernandez A, Kelly GA, Li X, Onogi K, Saarinen S, Sokka N, Allan RP, Andersson E, Arpe K, Balmaseda MA, Beljaars ACM, Berg LV, Bidlot J, Bormann N, Caires S, Chevallier F, Dethof A, Dragosavac M, Fisher M, Fuentes M, Hagemann S, Holm E, Morcrette J-J, Hoskins BJ, Isaksen L, Janssen PAEM, Jenne R, McNally AP, Mahfouf J-F, Rayner NA, Saunders RW, Simon P, Sterl A, Trenberth KE, Untch A, Vasiljevic D, Viterbo P, Woollen J (2005) The ERA-40 re-analysis. Quart J R Met Soc 131:2961–3012CrossRefGoogle Scholar
- Xie S-P (2005) The shape of continents, air–sea interaction, and the rising branch of the Hadley circulation. Kluwer, Dordrecht Google Scholar