LASG, Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS), Beijing

1. An extended analysis of atmospheric responses over the tropical Pacific: results from atmospheric GCMs Chunqiang Wu, Tianjun Zhou, Dezheng Sun Email: wucq@mail.iap.ac.cn LASG, Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS), Beijing UAW, Tokyo, Jul. 2, 2008

2. 1 Background and Data 2 Thermodynamic response Conclusions 3 Contents Net surface heat flux, Clear sky green house effect, Cloud radiative forcings, Latent heat flux, etc.

3. Background (I) Stephen 2005 J. Clim.

4. Background (II) Two Methods: Method 1: Checked the differences in the feedbacks of water vapor and clouds in global warming among different models. (Wetherald and Manabe,1989; Cess et al. 1990, 1996) • What are the responses in a longer time period and in more models? • What are these biases related to? Method 2:Compared the response of water vapor and clouds to SST changes over the time scales for which observational data are available. (Sun and Held 1996, Soden 1997, Held and Soden 2000) model inter-comparison Individual feedback But, without OBS validation Recent result: Current AGCMs tend to overestimate the water vapor feedback and underestimate the cloud albedo feedback. (Sun et al., 2006)

5. Data Data: Observation: ISCCP FD, OAFLux,NCEP1 Model:AMIP type model results (16 models, for clarity , only 7 typical models are presented). Period:1985 to 1998 Method: Regress the corresponding components to SST anomaly over the cold tongue region (5oS-5oN, 150oE-110oW).

6. 1 Background and Data 2 Thermodynamic response Conclusions 3 Contents

7. Variables in this section • Gaclear sky greenhouse effect • LWCRFlongwave cloud radiative forcing • SWCRFshortwave cloud radiative forcing • Daatmospheric energy transport Da is defined as the difference of net longwave/shortwave radiation, latent/sensible heat flux at the surface and at the top of the atmosphere. • Fsnet surface heat flux Fs ~ Ga+LWCRF+SWCRF+Da+black body emission

8. Overview of responses 3) Response of short wave radiative forcing gives rise to the largest uncertainty among models. But not all models under estimate this response. 4) The uncertainty from the response of atmospheric energy transport is also very large, which, mainly, is from the response of latent heat flux. 1) All models, except MPI ECHAM5, underestimate the response of net surface heat flux to El Nino warming. 2) All models overestimate the response of clear sky green house effect to El Nino warming.

9. Net surface heat flux damps the change of SST over the equatorial Pacific during ENSO Net surface heat flux MPI model reasonably reproduces the response of net surface heat flux Others have weak responses, most of them have positive responses over the central equatorial Pacific

10. The response of clear sky greenhouse effect is similar to the ENSO pattern. It relates to the change of water vapor and temperature profile to El Nino warming. Models simulate a similar pattern to the observation, but with a much large magnitude, especially over the central equatorial Pacific. Clear sky greenhouse effect

11. The percentage response of water vapor at the middle level troposphere contributes to the bias in clear sky greenhouse effect. Water vapor and temperature profile

12. In observation, the negative response of short wave radiative forcing located in the central equatorial region. Over other regions, there are slight positive responses. Shortwave radiative forcing Models tend to under estimate the negative response over the central equatorial Pacific. Also, there is some unrealistic positive response over the eastern equatorial Pacific.

13. Positive for downward In observation, latent heat flux decreases over the eastern central equatorial Pacific during El Nino warming, while the response over the western equatorial Pacific is weak. Latent heat flux (hfls) Latent heat flux ~ specific humidity difference and surface wind speed Models can capture the negative response over the eastern part well, but the response in the western part is positive.

14. Qs: surface saturation specific humidity Qair: surface air specific humidity Specific humidity difference (Qs-Qair) Models show similar response pattern to the observation. Except MRI model, others under estimate the response.

15. Surface wind speed response is over estimated over the central equatorial Pacific, which is corresponding to the bias of latent heat flux Surface wind speed (10m)

16. 1 Background and Data 2 Thermodynamic response Conclusions 3 Contents

17. All models overestimates the response of clear sky greenhouse effect to El Nino warming, which is related to the excessive response of water vapor in the middle level troposphere . • Most models show deficient shortwave response, which is related to the response of convection. • All models have excessive positive latent heat flux (downward) response in the central tropical Pacific, which is caused by excessive surface wind speed response to El Nino warming. Conclusions

18. Thank You! Suggestions and Comments Please!