Is there one Indian Monsoon in IPCC AR4 Coupled Models?

1. Is there one Indian Monsoon in IPCC AR4 Coupled Models? Massimo A. Bollasina – AOSC658N, 3 Dec 2007

2. Framework • The ASM is a challenging testbed for models, both coupled or run with observed SST (e.g., CMIP, AMIP, CLIVAR/IMP, etc.), given the complex feedbacks among land-ocean-atmosphere • Several intercomparison studies focused around the AM region and the tropical Pacific have been carried out recently with IPCC AR4 models (as part of the WCRP-CMIP3), including: • South Asian monsoon and ENSO in simulations (Annamalai et al., 2007) • South Asian monsoon precipitation variability, simulated and projected (Kripalani et al., 2007) • Double ITCZ and ocean-atmosphere feedback analysis (Lin, 2007) • Global precipitation characteristics (Dai, 2006) • Agreements in the simulated global water cycle (Waliser et al., 2007) • Atmospheric hydrological cycle in the Tropics (Wang and Lau, 2007) • Enso evolution and teleconnections (Joseph and Nigam, 2006)

3. Motivation Precipitation is one of the most important climate variables It is also a key variable for the assessment of model skill, being the result of a variety of physical processes. Errors in the simulated precipitation often reflect deficiencies in the representation of these processes in the model Precipitation, in particular summer rainfall (~75% of the total annual amount), is also of major importance for the Indian monsoon region This work aims at addressing these questions: • How well do IPCC AR4 CGCMs simulate the spatial and temporal patterns of the mean annual cycle of precipitation in the Indian monsoon region? • Are there sistematic biases and/or commondeficiencies? • Are there skills at regional scale? • Which is the link between precipitation and SSTs?

4. Climate of the Twentieth Century • 25 state-of-the-art global coupled models participated in the AR4, 7 considered • Focus is on the retrospective integrations used to simulate the climate of the twentieth century in coupled models (20c3m) • The 20c3m simulations attempt to reproduce the overall climate variations during 1850-present with best estimates of natural (e.g., solar radiations, volcanic aerosols) and anthropogenic (e.g., GHGs, sulfate aerosols, ozone) forcings • Multiple realizations are available for each participating model from the LLNL Program for Climate Model Diagnostics and Intercomparison (PCMDI) archives. • Only the first run (run 1) is analyzed • Period: 1979-1999 (21 years) at monthly scale

5. Data Used • Models: • 4 from the US: • NCAR Community Climate System Model version 3 (NCAR CCSM3), • GFDL Coupled Model version 2.1 (GFDL-CM2.1) • NCAR Parallel Coupled Model (PCM), • GISS model (GISS-EH) • 2 from EU: • UKMO Hadley Centre Coupled Atmosphere–Ocean General Circulation Model version 3 (HadCM3) • MPI for Meteorology ECHAM5/MPI - Ocean Model (OM) • 1 from JAPAN: • CCSR(UT)/NIES/JAMSTEC Model for Interdisciplinary Research on Climate version 3.2 (MIROC3.2) • To facilitate intercomparison, datasets regridded to R30 (~2.25°x3.75°) • Validating datasets: GPCP (2.5°x2.5°), ERA40 (2.5°x2.5°), HadSST (1°x1°), AIR

6. Climate Models Analyzed

7. Seasonal mean (Jun-Sep) precipitation (mm day-1) and 850 hPa winds (m s-1)

8. Seasonal mean (Jun-Sep) standard deviation of precipitation (mm day-1)

9. Zonally averaged precipitation (mm day-1) between 70°-100°E as a function of latitude

10. Annual cycle of monthly precipitation (mm day-1) over India (land points) and the Indian Ocean (60°-100°E; 5°-30°N)

11. Root Mean Square Difference (RMSD; mm day-1) and Spatial Correlation of monthly precipitation over (60°-100°E; 0°-30°N) with respect to GPCP

12. Annual cycle of Standard Deviation of monthly precipitation (mm day-1) over India (land points) and the Indian Ocean (60°-100°E; 5°-30°N)

13. Seasonal mean (Jun-Sep) 1000-300 hPa vertically integrated stationary moisture flux (kg m-1 s-1) and its convergence (mm day-1; shaded)

14. Latitude-height cross-section between 75°-85°E of winds (streamlines) and vertical velocity (Pa s-1 ;multiplied by 100)

15. Trend of Jun-Sep precipitation (mm day-1year-1)

16. Seasonal lead-lag correlations between JJA precipitation over India and SST over the Indian Ocean based on monthly data (Observations: All-India Rainfall and HadSST) Lag -6 = DJF SST leading JJA PCP; Lag -3 = MAM SST leading JJA PCP

17. Lag 0 = JJA SST and JJA PCP; Lag +3 = SON SST lagging JJA PCP

18. Amplitude and phase (arrows) of the climatological mean annual cycle of Precipitation

19. Amplitude and phase (arrow) of the climatological mean annual cycle of SST

20. Lead-lag correlations between JJAS precipitation and local SST based on monthly data (Observations: GPCP and HadSST) - Lag is referred to SST (± 1 month) R = 0.22 is 95% conf. level

22. Conclusions • A realistic simulation of precipitation is a very challenging task, considering it is the result of many dynamical and physical processes and feedbacks • Current coupled models, although improved considerably with respect to the versions used only few years ago, still show large deficiencies and biases in the simulation of regional patterns and variability of precipitation • A wide spread of responses among the models exists and many features of the mean monsoon are not correctly simulated • Regional-scale hydroclimate simulation is still a very challenging task • Caution has to be used in interpreting modeling results • A significant improvement is desirable (e.g., orographical precipitation, air-sea interaction), both for temporal and spatial characteristics of precipitation