1. COLA Contribution to India’s Monsoon Mission Monsoon Mission International Consultancy Meeting IITM, Pune September 2012 Jim Kinter Center for Ocean-Land-Atmosphere Studies

2. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India COLA and the Indian Monsoon COLA has been interested in, and making fundamental contributions to Indian monsoon research for more than two decades The Charney-Shukla (1981) hypothesis undergirds much of the research in this area …

3. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Conceptual Model for Indian Monsoon Rainfall

4. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India COLA and the Indian Monsoon Can we use this knowledge to predict the Indian monsoon? Yes, but … The Charney-Shukla hypothesis has its limitations: The boundary conditions that apply to the atmosphere are neither fixed in space and time nor external to the coupled ocean- atmosphere-land oscillations that modulate tropical circulation and rainfall …

5. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India The role of air-sea coupling in seasonal prediction of Asia-Pacific summer monsoon rainfall Jieshun Zhu and Jagadish Shukla To be submitted to Geophys. Res. Lett.

6. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Model, Experiments and Validated Datasets Model: CFS v2 Hindcast Experiments: 1) One-Tier (coupled) prediction: CFS v2 predictions starting from ECMWF ORA-S4 ocean initial conditions; 2) Two-Tier prediction: GFS (the atmospheric component of CFS v2) forced by the daily mean SST From One-Tier predictions In both predictions, (a) ATM and LND initial data from CFSRR (b) starting from every April during 1982-2009 (c) 4 ensemble members with different ATM/LND ICs Validation Dataset: CMAP precipitation analysis

7. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India

8. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India

9. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India

10. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Summary Two-Tier prediction (without coupling processes) produces higher rainfall biases and unrealistically high interannual rainfall variability in the tropical western North Pacific and some coastal regions, e.g. west of Philippines and west of the Indo-China Peninsula – suggests an important “damping” role by coupling The differences in anomaly correlation between One-Tier (coupled) and Two-Tier predictions are not significant, but RMSE is clearly larger in Two-Tier prediction in this region.

11. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India COLA and the Indian Monsoon How, then, can we predict the Indian monsoon? Statistical models have been employed for many decades, but there is now evidence that dynamical models are superior to statistical models …

12. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Dynamical Models Outperform Statistical The skill in forecasts of all- India monsoon rainfall from May ICs with dynamical models (ENSEMBLES Project) is statistically significant, and greater than empirical forecasts based on observed SST. DelSole & Shukla 2012: GRL ISMR=India Summer Monsoon Rainfall

13. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India COLA and the Indian Monsoon There is also evidence that other factors influence the Indian monsoon on decadal time scales

14. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Krishnamurthy and Krishnamurthy Decadal SST Influences on Indian Monsoon AMV PDV Atlantic Tripole

15. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India COLA and the Indian Monsoon … and, the Indian monsoon exhibits a rich spectrum of variability on intraseasonal to decadal and longer time scales

16. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India R. Shukla and J. M. Wallace 2012 OLR (colors) V850 (vectors) PC1 PC2 PC1+PC2 -PC1+PC2 Depiction of half a cycle of the Monsoon Intra-Seasonal Oscillation (MISO)

17. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India COLA and the Indian Monsoon … and the Indian monsoon is strongly influenced by details of the underlying topography and associated atmospheric circulation There is evidence that our current models are not capable of simulating (or even analyzing) this level of complexity Could this be inadequate resolution? Improper model physics? We have evidence for both possibilities.

18. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India COLA and the Indian Monsoon … and, there is evidence that climate change may influence the Indian monsoon

19. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Mean JJAS EIMR EIMR (70E-110E, 10N-30N) Thanks to Bohar Singh Ensemble Average of CCSM4, CM2.1, MPI-ESM, HadGEM2, MIROC5

20. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Mean JJAS EIMR Thanks to Bohar Singh

21. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India COLA and the Indian Monsoon All these indicators suggest that our current dynamical models, while superior to statistical models, are not fully up to the task of predicting the Indian monsoon We have separate evidence that model fidelity is positively correlated with predictability, i.e., models that more faithfully represent the mean climate are better at quantifying predictability and potentially better at making predictions WE NEED BETTER MODELS!

22. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India COLA  Monsoon Mission Land-Atmosphere Feedbacks – Hypothesis: reducing model errors related to the coupling between atmosphere and land can improve monsoon rainfall forecasts Diagnose impact of improper representation of L-A feedbacks in CFSv2 Design a superior LS initialization method that can positively influence Indian monsoon prediction skill Multiple Analysis Ocean Initialization – Hypothesis: errors in oceanic initialization are limiting prediction skill of Indo- Pacific SST anomalies on seasonal time scales  impact on Indian monsoon prediction skill Use multiple ODA method to improve initial state of Pacific and Indian Oceans Test whether oceanic anomalies in Indian Ocean add value to monsoon prediction Ocean-Atmosphere Feedbacks – Hypothesis: reducing model errors related to the coupling between atmosphere and ocean can improve monsoon rainfall forecasts Examine sensitivity of CFSv2 predictions to improved parameterization of cloud processes developed by CPT Experiment with regionally coupled model to design coupled ENSO-monsoon rainfall forecasting system

23. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Strong Drifts CFSv2 reanalysis mean precipitation during JJA (top) and the drift in the first month of reforecasts validating during JJA (bottom). There are very strong drifts in the vicinity of the northern Indian Ocean and South Asia, which have major consequences for intra-seasonal forecasts in the area with CFSv2. 23 mm/day

24. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Drift with Lead Time Reanalysis precipitation (black) is higher and has more interannual variability (whiskers) than forecasts (colors). Forecast monsoon precipitation gets weaker at longer leads. That dries the soil in those forecasts (bottom), exacerbating the problem. 24

25. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India How Does CFSv2 Land- Atmosphere Coupling Compare? 25 July index for CFSv2 with Noah is considerably weaker (+&-) than: – GSWP-2 (Land Multi-Model Ensemble) – IFS (ECMWF) run in climate mode – MERRA (NASA) reanalysis (both L-A and the land-only “replay”). Left panels from Dirmeyer (2011): GRL doi:10.1029/2011GL048268

26. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Drift in July Coupling Changes in coupling index shows strong feedbacks are well placed over NW India, but the rest of the country becomes “hot” at longer leads. These changes come because soil moisture drops - drifts into the semi-arid “sweet spot” for flux sensitivity. Could this drift contribute to reduced skill (cf GLACE-2)? 26

27. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Precipitation Validation and Soil Moisture ICs CFSR monthly precipitation rapidly decorrelates from obs. CFSR precip similarly loses correlation with initial surface soil moisture anomalies. Observed precipitation has much stronger correlation with antecedent soil. Why? Positive L-A feedback, or persistent weather regimes? 27

28. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India And Extremes? Plots show the changes in correlation when only the forecasts with the driest/wettest 20% of soil moisture ICs are used (compared to previous slide). More skill and connection of forecasts to SM IC. Observations also show even stronger correlations. Still an open question: what is the cause? 28 CFSR Extreme SM IC minus Total (June)

29. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Proposed Land-Atmosphere Feedback Investigation (Task 1) CFSv2 has weak correlation of past soil moisture to future precipitation compared to observations – Conduct specified persisted initial SM anomaly hindcasts Determine CFS atmospheric response to soil moisture – is it too weak? Does skill improve with persisted anomalies? CFSv2 mean climate significantly different from obs – Develop an anomaly-based initialization strategy for LS Consistent with CFSv2 climatology by scaling means and variances CFSRR provides a rich dataset for this development 29

30. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Multiple Analysis Ocean Initialization 30 What are the effects of uncertainty in Indian Ocean heat content on monsoon prediction? Will ensemble predictions initialized with multiple ocean analyses improve Indo- Pacific SST and monsoon predictive skills?

31. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India ECMWF: ORA-S3, COMBINE-NV NCEP: GODAS, CFSR UM/TAMU:SODA GFDL :ECDA DATA SOURCE ODA Heat Content Uncertainty (1979-2007) moderate high low Heat Content Anomaly

32. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Prediction skill of the NINO3.4 is sensitive to Ocean ICs (April ICs: 1979-2007) Predictive skill varies substantially across individual ocean ICs ES_Mean is comparable to the best of individual predictions ES_Mean is close to the upper limit set by super-ensemble diagnostics

33. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Indian Ocean SST Prediction Skill, JJAS 1982-2007 Initialized in April Multi-ocean initialization achieves higher skill than individual ocean IC cases Higher skill near Madagascar corresponds to subsurface memory

34. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India 34 Proposed Work – Task 2 Monsoon season hindcasts (Jun-Sep; 1982-present), using CFSv2 with multiple analysis ocean initialization (NCEP GODAS, CFSR; ECMWF ORA3-4) with leads from Jan to May Ocean anomaly initialization to reduce initial shock and climate drift Skill comparison with CFSRR, ECMWF S4 and ENSEMBLES Expected Results Improved prediction skill of the Indo-Pacific SST anomalies Added value to the monsoon rainfall prediction Better ensemble spread and more realistic pdf distribution

35. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Ocean-Atmosphere Feedbacks Hypothesis: reducing model errors related to the coupling between atmosphere and ocean can improve monsoon rainfall forecasts

36. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Coupled Model Development Serious errors in low clouds have been shown to affect the ocean-atmosphere interaction (e.g. Hu et al. 2011) The Stratocumulus to Cumulus Transition Climate Process Team (external to COLA) has given COLA permission to use their improved representation of shallow clouds implemented in CFS A subset of the CFSRR hindcasts will be repeated with the improved shallow cloud scheme included in CFS

37. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Improving O-A Feedbacks CGCMs, including CFSv2, have large biases in both the climatological mean and variances SST-forced two-tier prediction might be the answer, but, as shown above, it introduces errors by overestimating the variance

38. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India

39. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Improving O-A Feedbacks CGCMs, including CFSv2, have large biases in both the climatological mean and variances SST-forced two-tier prediction might be the answer, but, as shown above, it introduces errors by overestimating the variance Alternative approach of regional coupling requires knowledge of future SST, e.g., in ENSO region

40. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Summary – Task 3 Hypothesis: the best monsoon predictions will be made with models that filter out the influence of weather noise and maximize the role of the ocean initial conditions. 1.A bias-corrected CFSv2 (specified SST in tropical Pacific; mixed layer model elsewhere) will be validated against the observed record for 1982- present to determine the best specified oceanic heat flux and mixed layer model depth 2.A version of CFSv2 in which the dynamical ocean is replaced outside the tropical Pacific with the mixed layer model determined in Step 1 will be used to produce hindcasts for the same period

41. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Conclusion A collaboration between COLA and IITM is very timely and has great potential – COLA is one of the world leaders in climate modeling, but is deliberately not funded by the US agencies to do model development – IITM has launched the Monsoon Mission to improve monsoon predictions – Working together, we can dramatically advance the science of monsoon prediction

42. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Annual Budget Summary Personnel – Senior staff (Kinter, Dirmeyer, Huang, Schneider) $ 50K – (3) Post-doctoral research associates$192K – (3) PhD students$100K – Fringe benefits$ 64K Travel – Domestic $ 7K – Foreign$ 10K Indirect costs$ 0 Total cost$423K Annual inflation 3% Three-year total $1,310K

43. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India

44. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India

45. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Regional JJAS Rainfall Correlations Bng - GPCP Bng - CMAP

46. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Regional JJAS Rainfall Correlations Bng - GPCP Bng – ERA/I

47. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Regional JJAS Rainfall Correlations Bng - MERRA Bng - GPCP

48. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Ganges Basin Correlations WGGBBoBBng Chen CMAP GPCP CFSR MERRA

49. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Bangladesh Correlations WGGBBoBBng Chen CMAP GPCP CFSR MERRA

50. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India 850 hPa Circulation Regressed on Rainfall Index CFSR Regressed on GPCP GBWG C A C A

51. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India 850 hPa Circulation Regressed on Rainfall Index BngBoB CFSR Regressed on GPCP: 1979-2009 C

52. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India 850 hPa Circulation Regressed on Rainfall Index Bng: CFSR Regressed on GPCPBng: CFSR-V Regressed on CFSR-p 1979-2009

53. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India 850 hPa Circulation Regressed on Rainfall Index Bng-MERRA 1979-2009 Bng – GPCP/CFSR

54. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India 850 hPa Circulation Regressed on Rainfall Index Bng – GFDL CM3 20 th Bng – GPCP/CFSR 1979-2009

55. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India 850 hPa Circulation Regressed on Rainfall Index Bng – Athena IFS 20 th Bng – GPCP/CFSR 1961-20071979-2009

56. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India COLA and the Indian Monsoon How, then, can we predict the Indian monsoon? There is evidence that ENSO, which is substantially predictable on interannual time scales, influences on the Indian monsoon On the other hand, ENSO’s influence is thought to wax and wane on decadal time scales … or does it really?

57. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India ENSO/ISMR Relationship The “breakdown” in the ENSO/ISMR relationship may be a sampling issue. – Model ensemble mean hindcasts (solid; colors) do not exhibit the breakdown in correlation. – Individual ensemble members (dash-dot) do show apparent breakdowns when sampled like the observations. DelSole & Shukla 2012: GRL

58. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Complex Topography Western Ghats Ganges, Brahmaputra River Delta Ganges Basin Tibetan Plateau

59. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India 1979-2009 JJAS Rainfall (GPCP) Western Ghats (WG) Bangladesh (Bng) Bay of Bengal (BoB) Ganges Basin (GB) Following analysis courtesy of Ben Cash

60. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Regional JJAS Rainfall Correlations Based on GPCP WG GB

61. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Regional JJAS Rainfall Correlations Based on GPCP BngBoB

62. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Regional JJAS Rainfall Correlations Bng - GPCPBng - CFSR

63. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Regional JJAS Rainfall Regressions BoB Bng Based on GFDL CM3 Historical 1879-1909

64. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Summary There is a significant disagreement among observational estimates of the mean and variability of monsoon rainfall – Largely due to disagreements in data-sparse regions such as Myanmar and Bangladesh There are distinct centers of regional rainfall variability (with associated distinct circulation features – not shown) Some models can capture some of the patterns of variability, while other models cannot Some models are more faithful to the best observational estimates than are reanalyses!

65. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Ratio of Standard Deviation of Monthly Mean Net Surface Heat Flux CONTROL:AGCM i Total Weather Noise Significance test: In shaded regions the ratio is different from 1 at the 1% significance level <1 =1

66. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Ratio of Precipitation Anomaly Standard Deviations CGCM:AGCM Total Weather Noise <1 =1

67. COLA - KinterMonsoon Mission International Consultancy :: 11 September 2012 :: Pune, India Correlation SSTA CGCM and noise forced IE-CGCM MonthlyDecadal 100-year simulations