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Double ITCZ Phenomena in GCM’s Marcus D. Williams.

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Presentation on theme: "Double ITCZ Phenomena in GCM’s Marcus D. Williams."— Presentation transcript:

1 Double ITCZ Phenomena in GCM’s Marcus D. Williams

2 Outline The role of the ITCZ in the tropics and it’s importance to tropical circulations Reasons for the Double-ITCZ problem in the IPCC AR4 coupled GCMs A graphical look at the overestimation of precipitation compared to observations Bias in the Models ocean-atmosphere feedback Summary/Conclusions


4 The importance of studying the ITCZ The ITCZ is the region where the low-level air converges and rises into the upward branch of the Hadley/Walker circulation. The latent heat released in the ITCZ drives the Hadley/Walker circulation. Hadley/Walker circulation is the largest circulation in the troposphere. A model that fails to simulate the Hadley/Walker circulation correctly cannot be expected to provide good forecast (the climate drift problem). The location and intensity of the ITCZ affect the surface wind field, which is a critical factor in air-sea interaction-- a core component of El Nino. A large part of the difficulties in forecasting El Nino has to do with the GCMs’ failure to simulate the ITCZ correctly. Most GCMs have difficulties in correctly simulating the seasonal variation of the MJO intensity. This is largely a result of the models’ failure in correctly simulating the seasonal movement of the ITCZ. The ITCZ study has led to new insight into the monsoon and monsoon onset. The current GCMs have considerable amount of difficulty in simulating the ITCZ precipitation correctly.

5 Reasons for Double ITCZ problem Study preformed on 22 IPCC AR4 coupled GCMs and 12 Atmospheric Model Intercomparison Project (AMIP) model runs. Analysis based on 20 yr (1979-2009) simulations. Most models have some degree of the double ITCZ problem characterized as excessive precip in the off Equatorial Pacific and insufficient precip over the Equatorial Pacific. Excessive precip over the Tropics usually causes overly strong trade winds, excessive latent heat flux (LHF), and insufficient Shortwave radiation flux (SWF), leading to significant cold SST bias in much of the Tropical oceans. The double-ITCZ problem was found to be a result of three bias in the ocean-atmosphere feedback over the equatorial Pacific. 1)Excessive Bjerknes mechanism feedback 2)Overly positive SST-LHF feedback 3)Insufficient SST-SWF feedback


7 Data

8 Observations vs. Models FIG. 2. Annual mean SST (shading) and precipitation (contour) for observation and 22 IPCC AR4 CGCMs. Contour starts at 5 mm/day with an interval of 2 mm/day.

9 SST observations

10 Precipitation data

11 Zonal mean precipitation

12 Bjerknes Feedback SST gradient between the warm pool and cold tongue generates an east-west asymmetry in the atmospheric convection, precipitation, clouds, and water vapor Creates an east-west asymmetry in the total diabetic heating Forces the SLP gradient thus strengthening the trade winds (Walker circulation) Trade winds feed into the SST gradient by inducing upwelling

13 Zonal wind stress

14 Linear regression of wind stress

15 SST-surface LHF feedback Perturbation in SST affects the surface wind speed, surface air humidity, and sea-air humidity difference. Modifies the surface LHF, which in turn modifies the SST SST-LHF feedback has a different sign for different regions

16 Latent Heat Flux (LHF)

17 Linear regression of LHF

18 SST-surface SWF feedback Feedback has a different sign over the warm pool and cold tongue. Negative over the warm pool because of convection reducing SWF to the surface and cools the SST Positive over the cold pool because of the static stability over the layer causing the SST to warm Any positive feedback tends to enhance the east-west gradient Negative feedback weakens horizontal SST gradient, which weakens the Walker circulation Strong positive feedbacks tend to shift the whole system westward, leading to an excessive SST cold tongue and double ITCZ pattern

19 Zonal mean Shortwave radiation flux SWF underestimated in most models

20 Linear regression of SWF Linear regression of SWF

21 Summary

22 Conclusions

23 References

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