Evaluation of ECHAM5 General Circulation Model using ISCCP simulator Swati Gehlot & Johannes Quaas Max-Planck-Institut für Meteorologie Hamburg, Germany.

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Presentation transcript:

Evaluation of ECHAM5 General Circulation Model using ISCCP simulator Swati Gehlot & Johannes Quaas Max-Planck-Institut für Meteorologie Hamburg, Germany

2 Outline Motivation Sub-column sampler Results Conclusion Outline Background and motivation Model and data used Inclusion of sub-column sampler within the ISCCP simulator Results and discussion Conclusion

3 Motivation Global climate models work at a much coarser scale to resolve cloud processes and hence they are “parameterized”, leading to uncertainty In order to have confidence in the cloud parameterizations, the evaluation studies for GCM clouds are very essential ISCCP satellite data provides an adequately long time series for cloud climatology and microphysics This study focuses on application of ISCCP satellite simulator for detailed cloud diagnostics from ECHAM5 atmospheric GCM and model evaluation with comparison to observations Outline Motivation Model Sub-column sampler Results Conclusion

4 Model and data used Model evaluation studies for ECHAM5 GCM ‐ Simulations with T63 spectral resolution ‐ Additional module of ISCCP satellite simulator Analysis of diurnal cycle of convection using ECHAM5 data - Comparison of ISCCP-type cloud cover diagnosed in the model with satellite data - Focus on high and convective clouds - Analysis of ISCCP histograms – model Vs observations Model verification studies using satellite data ‑ International Satellite Cloud Climatology Project (ISCCP) ‑ MODerate Resolution Imaging Spectroradiometer (MODIS) Outline Motivation Model Sub-column sampler Results Conclusion

5 ISCCP simulator Additional module of ISCCP simulator is coupled with ECHAM5 to create ISCCP like cloud types in the model output Low Mid High ISCCP cloud types classification ISCCP cloud fraction histogram distribution Outline Motivation Model Sub-column sampler Results Conclusion

6 Need of a sub-column sampler GCM Grid cell: km Typical grid box in a GCM with inhomogeneous distribution of clouds within it Figure from A Tompkins, ECMWF, 2005 Outline Motivation Model Sub-column sampler Results Conclusion

7 Sub-grid scale variability: conventional approach ~500m ~200km grid box Typical model grid box (cloud overlap) Outline Motivation Model Sub-column sampler Results Conclusion

8 Sub-column sampler ~500m ~200km grid box Dealing with horizontal cloud inhomogeneity and vertical overlap of clouds in the model grid box using stochastic cloud generator (based on Räisänen et al, QJRMS 2004) (Stochastically generated independent sub columns) Outline Motivation Model Sub-column sampler Results Conclusion

9 Case study over four tropical regions Area 1 – Africa 00 o S to 30 o S and 00 o E to 30 o E Area 2 – Amazon 00 o S to 30 o S and 25 o W to 55 o W Area 3 – India 00 o N to 30 o N and 60 o E to 90 o E Area 4 – Indonesia 10 o N to 20 o S and 90 o E to 120 o E Outline Motivation Model Sub-column sampler Results Conclusion

10 Case study India: Diurnal cycle Outline Motivation Model Sub-column sampler Results Conclusion Diurnal cycle for India: model, ISCCP data, and MODIS data

11 Case study India: ISCCP histograms Outline Motivation Model Sub-column sampler Results Conclusion Diurnal average ISCCP cloud fraction histograms: comparison of the model output, ISCCP data, and MODIS data

12 Case study Africa: Diurnal cycle Diurnal cycle for Africa: model, ISCCP data, and MODIS data Outline Motivation Model Sub-column sampler Results Conclusion

13 Case study Africa: ISCCP histograms Outline Motivation Model Sub-column sampler Results Conclusion Diurnal average ISCCP cloud fraction histograms: comparison of the model output, ISCCP data, and MODIS data

14 Global JJA averaged histograms (land and sea) Outline Motivation Model Sub-column sampler Results Conclusion Diurnal average ISCCP cloud fraction histograms: comparison of the model output, ISCCP data, and MODIS data

15 Conclusion ECHAM5 model is evaluated using ISCCP simulator containing sub-grid variability information The model simulates the total cloud cover quite reasonably for the land and sea areas An overestimation of high and deep convective clouds is seen on comparison with ISCCP observations The model as well as ISCCP data miss a large amount of mid- cloud cover compared to MODIS observations Underestimation of low clouds in the model when compared to observations ISCCP and MODIS data show large discrepancies, particularly for land areas Outline Motivation Model Sub-column sampler Results Conclusion

16 Outline Motivation Model Sub-column sampler Results Conclusion Thank you Swati Gehlot Max-Planck-Institut für Meteorologie Hamburg, Germany

17 Spare Sheets Outline Motivation Model Sub-column sampler Results Conclusion

18 Geographical distribution of convective clouds Outline Motivation Model Sub-column sampler Results Conclusion

19 Sub-grid cloud generator Outline Motivation Model Sub-column sampler Results Conclusion Stochastic cloud generator for generating random sub-columns in a model grid cell Initialized with GCM grid mean values of cloud fraction, liquid water and ice Vertical variance by maximum-random cloud overlap assumption for cloud fraction and cloud condensate Horizontal variance of total cloud water, using the Tompkins cloud scheme with beta distribution PDF The generated sub-columns consist at each level of entirely clear sky, or entirely cloudy sky with constant cloud condensate Tested with 100 sub columns, found reasonable global distributions of ISCCP variables.

20 Case study Amazon: Diurnal cycle Outline Motivation Model Sub-column sampler Results Conclusion

21 Case study Amazon: ISCCP histograms Outline Motivation Model Sub-column sampler Results Conclusion

22 Case study Indonesia: Diurnal cycle Outline Motivation Model Sub-column sampler Results Conclusion

23 Case study Indonesia: ISCCP histograms Outline Motivation Model Sub-column sampler Results Conclusion

24 Summary: Diurnal cycle Outline Motivation Model Sub-column sampler Results Conclusion Four tropical regions comprising of land and sea areas are analyzed for evaluation of diurnal cycle of ISCCP clouds (JJA04) The amplitudes of diurnal cycle for model TCC varies between 5-17% (land) and 3- 12% (sea) compared to ISCCP data TCC which lies between 10-13% (land) and 3-13% (sea). The land areas are slightly overestimated where as the sea areas are relatively well simulated by the model For all the regions (land/sea), the high cloud cover (HCC) is overestimated in the model (8% in Africa to 43% in Indonesia) The low cloud cover (LCC) is underestimated in the model in the range of 7% (in Amazon, fig 3) to 19% (in Indonesia, fig 5) compared to the ISCCP satellite observations. The model underestimates the mid-cloud cover (MCC) with a range of 2% (in Africa, fig 3) to 10% (in Indonesia, fig 5) compared to the ISCCP satellite observations The reasonable computation of ISCCP TCC is due to the cancellation of errors by the overestimation of HCC and the underestimation of LCC and MCC

25 Summary: ISCCP histograms Outline Motivation Model Sub-column sampler Results Conclusion For all the test areas, the model simulated ISCCP histograms were computed using diurnal average cloud amount for JJA 2004 The model diagnoses larger amount of high clouds (for eg. India), and the histograms reveal that these are optically very thin The model misses much of the low cloud cover (LCC) and the mid-cloud cover (MCC), when histograms are compared with ISCCP observations The globally averaged histograms show that the sea area is relatively well simulated in the model compared to the land area The ISCCP simulator shows a decent agreement with the COSP simulator in terms of distribution of clouds in the ECHAM5 model