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COLA Contribution to India’s Monsoon Mission

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

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COLA Contribution to India’s Monsoon Mission

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

  2. 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. Conceptual Model for Indian Monsoon Rainfall

  4. COLA and the Indian Monsoon Can we use this knowledge to predict the Indian monsoon? Yes, but … The Charney-Shuklahypothesis 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. The role of air-sea coupling in seasonal prediction of Asia-Pacific summer monsoon rainfall Jieshun Zhuand JagadishShukla To be submitted to Geophys. Res. Lett.

  6. 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) ATMand 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. 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.

  8. 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 …

  9. 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. ISMR=India Summer Monsoon Rainfall DelSole & Shukla 2012: GRL

  10. COLA and the Indian Monsoon • There is also evidence that other factors influence the Indian monsoon on decadal time scales

  11. Decadal SST Influences on Indian Monsoon AMV Atlantic Tripole PDV Krishnamurthy and Krishnamurthy

  12. COLA and the Indian Monsoon • … and, the Indian monsoon exhibits a rich spectrum of variability on intraseasonal to decadal and longer time scales

  13. PC1 PC1+PC2 OLR (colors) V850 (vectors) -PC1+PC2 PC2 Depiction of half a cycle of the Monsoon Intra-Seasonal Oscillation (MISO) R. Shukla and J. M. Wallace 2012

  14. 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.

  15. COLA and the Indian Monsoon • … and, there is evidence that climate change may influence the Indian monsoon

  16. Mean JJAS EIMR Ensemble Average of CCSM4, CM2.1, MPI-ESM, HadGEM2, MIROC5 EIMR (70E-110E, 10N-30N) Thanks to Bohar Singh

  17. Mean JJAS EIMR Thanks to Bohar Singh

  18. 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!

  19. 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

  20. 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. mm/day

  21. 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.

  22. How Does CFSv2 Land-Atmosphere Coupling Compare? • 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

  23. 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)?

  24. 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

  25. Multiple Analysis Ocean Initialization 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?

  26. ODA Heat Content Uncertainty (1979-2007) Heat Content Anomaly low high moderate ECMWF: ORA-S3, COMBINE-NV NCEP: GODAS, CFSR UM/TAMU: SODA GFDL : ECDA DATA SOURCE

  27. 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

  28. 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

  29. 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

  30. Ocean-Atmosphere Feedbacks Hypothesis: reducing model errors related to the coupling between atmosphere and ocean can improve monsoon rainfall forecasts

  31. 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

  32. 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

  33. 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

  34. 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. • 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 • 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

  35. Conclusion • Acollaboration 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

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