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Why I think there is hope for Indian Monsoon seasonal predictions

Why I think there is hope for Indian Monsoon seasonal predictions. Presenting Author: Fred Kucharski, Abdus Salam ICTP, Earth System Physics Section, Climate Variability Group, Trieste, Italy. Model set-up, experimental design

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Why I think there is hope for Indian Monsoon seasonal predictions

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  1. Why I think there is hope for Indian Monsoon seasonal predictions Presenting Author: Fred Kucharski, Abdus Salam ICTP, Earth System Physics Section, Climate Variability Group, Trieste, Italy

  2. Model set-up, experimental design There is growing evidence (Krishna Kumar et al. 2005, Kucharski et al. 2008) that an AMIP-type (or 2-tier type, AGCMs forced with observed SSTs) approach does not realistically reproduce the ENSO-Indian monsoon relationship. Continuous coupled model integrations do not reproduce the observed history of e.g. ENSO events (for example, the 1976/1977 climate shift). Consequently, we use a hybrid approach, where observed SSTs are prescribed everywhere to force the intermediate complexity model ICTP-AGCM (T30, 3.75x3.75 hor. Res.) apart from the Western Pacific and Indian Ocean (Africa to 140 E, 35S to 30N). Here the AGCM is coupled model to the MICOM OGCM (1x1 degrees horizontal resolution and 20 vertical levels). We perform an ensemble of 10 runs from 1950 to 1999, plus another 10 for a sensitivity experiment. Results published in Kucharski et al., 2007, J Climate, 20, 4255-4266, Kucharski et al., 2008, GRL, 35, L04706, Kucharski et al, 2009, QJRMS, 135, 569-579, Wang et al., Meteorolog. Z., Aug, 2009.

  3. Model climatology is not great……but not too bad either Model SST bias I would not dare to Show rainfall differences…

  4. Timeseries of IMR: SPEEDY (IO coupled) vs CRU in mm/day Indian Monsoon rain: Mean rain (JJAS) in land-points of box: 70-95E, 10-30N Corr(CRU,speedy_glob) = 0.29 Corr(CRU,speedy_MXL) = 0.50 Corr(CRU,speedy_iocoup) = 0.63 pot. CS of 0.60 from C20C models….. Lagged correlation between IMR (JJAS) and 4-month average NINO3 index for IO_coup

  5. Regression of NINO3 index onto rainfall CRU ICTP-AGCM We may conclude that ICTP-AGCM reproduces ENSO Monsoon relation well.

  6. NINO3.4 –Indian Ocean SSt connection quite wrong….

  7. Regression of tropical Atlantic Index onto precipitation, Surface wind and streamfunction CRU Also found by Rajeevan and Sridhar, GRL, 2008 Tropical Atlantic Index: Average negative SST in box 30W-20E, 20S-0N Everything consistent with Gill-Matsuno-type response as in Jin and Hoskins (1995), JAS ICTP- AGCM ICTP- AGCM ENS1- ENS2

  8. What can an institution without great computer resources (and limited modeling resources) do? The following results will be from the following paper that was never submitted…… Potential and actual predictability of Indian monsoon rainfall derived from DEMETER using a Tier 1.5 approach F. Kucharski, A. Bracco, J. Kroeger, F. Molteni, J. H. Yoo, GRL, never submitted Use DEMETER hindcasts 1960 to 2000, 3 models, 9 ensemble members each. Then bias correct, and force the ICTPAGCM with the corrected SSTs from May to October, the use HadISST from

  9. Results

  10. Nino3.4 –rainfall regressions OBS OBS-Tier1.5 DEMETER DEMETER-Tier1.5 AMIP

  11. Conclusions There is hope that state-of-the-art coupled models should be better than that……..

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