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Intra-Seasonal to Inter-Annual Predictabilty and Prediction (Acknowledgements)

Intra-Seasonal to Inter-Annual Predictabilty and Prediction (Acknowledgements) Deepthi Achuthavarier Youkyoung Jang Eric Altshuler Jim Kinter Ben Cash V. Krishnamurthy Tim DelSole Sanjiv Kumar Paul Dirmeyer Julia Manganello Mike Fennessy Cristiana Stan

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Intra-Seasonal to Inter-Annual Predictabilty and Prediction (Acknowledgements)

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  1. Intra-Seasonal to Inter-Annual Predictabilty and Prediction (Acknowledgements) Deepthi Achuthavarier Youkyoung Jang Eric Altshuler Jim Kinter Ben Cash V. Krishnamurthy Tim DelSole Sanjiv Kumar Paul Dirmeyer Julia Manganello Mike Fennessy Cristiana Stan Zhichang Guo David Straus Bohua Huang Jieshun Zhu COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  2. Intra-Seasonal to Inter-Annual Predictabilty and Prediction Overarching Framework for Seasonal Predictability – COLA’s Role Role of Oceanic initial Conditions in ENSO Re-forecasts Seamless Prediction: The Role of Resolution Strategies for Doing Research with Flawed Parameterizations Predictability in a Changing Climate: Past, Present and Future COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  3. Overarching framework for Seasonal Predictability • COLA’s Role • “Predictability in the Midst of Chaos” • Scientific Basis for Seasonal Predictability • Slowly varying tropical SST and land surface act as forcing function for the seasonal mean circulation and intra-seasonal fluctuations (storm tracks, blocking, weather regimes) • Thus: • In coupled prediction, ocean and land initial conditions must be specified from observations/analyses! • Need to know the sensitivities to uncertainties in the initial conditions of atmosphere, ocean and land (land not well studied) COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  4. Slowly varying tropical SST as forcing function Bulletin of the Americal Meteorological Society Vol. 81, No. 11, November 2000 DSP and PROVOST (European partner) DSP: Multi-agency, multi-model, multi-institution Spatial Variance of midlatitude geopotential due to tropical SST forcing: Probabilistic view from ensembles Compile a large number of samples of GCM integrations, where a sample is obtained by randomly drawing one ensemble member for each calendar win- ter. (Each sample is a series of seasonal means, comparable to observations.) JFM SST time series from Maximum Correlation Analysis (SVD) between tropical Pacific SST and 500 hPa mid-latitude geopotential fields in PNA region Geopotential height variance explained computed by regression onto SST time series COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  5. Pacific North American Height variance explained by tropical SST (winter mean) COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  6. Tropical SST Forcing, seasonal mean climate and low-frequency intraseasonal fluctuations Circulation Regimes: Chaotic Variability versus SST-Forced PredictabilityDavid M. Straus, Susanna Corti, Franco MolteniJournal of ClimateVolume 20, Issue 10 (May 2007) pp. 2251-2272 Synoptic-Eddy Feedbacks and Circulation Regime AnalysisDavid M. StrausMonthly Weather ReviewVolume 138, Issue 11 (November 2010) pp. 4026-4034 Frequency of occurrence depends on SST Straus, D.M., S. Corti, and F. Molteni, 2007: J. Clim. 20, 2251-2272 Straus, D.M. 2010: Mon Wea. Rev. 138, 4026-4034 COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  7. Role of Oceanic Initial Conditions in ENSO Re-forecasts • Model CFS version 2 provided by NCEP EMC • Hindcast Experiments: 1) ATM/LND/ICE initial data from CFSRR 2) Four sets of forecasts differing in OCN initial data from ODA products: ECMWF COMBINE-NV, ECMWF ORA-S3, NCEP CFSR, NCEP GODAS 3) Anomaly Initialization for OCN initial state 4) 12-month hindcast starting 01 April for 1979-2007 (4 ensemble members) • Validation Datasets: SST -- ERSST v3. Heat Content (HC) -- Ensemble Mean (EM) of six ODAs (above 4 ODAs + SODA + GFDL ECDA) COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  8. CFSv2 SST Predictive Skill (April ICs) Correlation for ICs from 4 ODAs 2-month forecast lead 5-month forecast lead 11-month forecast lead ODA 1 ODA 2 ODA 3 ODA 4

  9. Prediction Skill of the Nino3.4 Index Combine-NV CFSRSuper_Ensemble ( oC ) Leading Months Leading Months

  10. Forecast Equatorial Heat Content Anomaly vs. OBS COMBINE-NV ORA-S3 CFSR GODAS OBS COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  11. ENSO Forecast Summary • ENSO prediction skill can depend significantly on the ODA used to initialize the ocean. • The slightly worse performance of the prediction initializing from CFSR is attributed to its slight difference in the upper ocean heat content, possibly in the off-equatorial domain. COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  12. Seamless Prediction: The Role of Resolution • Are we still dependent upon and/or limited by parameterizations of convection and other processes? • The Athena Project • ECMWF Integrated Forecast System (IFS) - AGCM • - 13-month runs at a variety of horizontal resolutions: • T159 (125 km), T511 (39 km), T1279 (16 km) , T2047 (10 km) • AMIP runs (1961-2007) at a variety of horizontal resolutions • No re-tuning of convective parameterizations • NICAM (almost no parameterizations) • - Seasonal runs COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  13. GENESIS DENSITY Manganello, et al., 2011: Tropical Cyclone Climatology in a 10-km Global Atmospheric GCM: Toward Weather-Resolving Climate Modelling. OBS T2047 T1279 T511 T156 Atlantic Tropical Cyclones Track genesis in left panels Track densities in right panels Higher resolution is necessary COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  14. Power Dissipation Index North Atlantic (May-Nov 1975-2007) from AMIP and Obs black line: Observed green line – T159 (multiplied by 10) red line – T1279 (multiplied by 2) dashed line – Nino 3.4 (multiplied by -1) COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  15. Indian Monsoon JJAS Precipitation IFS (reduced to N80) 1961-2008, T2047 1990-2008 TRMM 1998-2009 (mm/day) TRMM T159 T511 T1279 T2047 Increased resolution only makes the systematic error worse ! COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  16. Strategies for Doing Research with Flawed Parameterizations Replace them: “Super-parameterization SP-CCSM” - embed a two-dimensional slab of one-dimensional cloud-resolving models in CCSM3 T42 – these replace the conventional convection parameterizations (South American Monsoon) Supplement them: Idealized added heating put into CAM3 to circumvent model’s poor moist response to SST anomalies (ENSO / Indian Monsoon relationship) Remove them: Try to resolve everything explicitly – (NICAM) Stochastic Parameterizations – Augment existing parameterizations COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  17. Oscillatory Modes in South American Monsoon System Multi-Channel Singular Spectrum Analysis of OLR Intra-Seasonal Oscillation (MJO) Inter-Seasonal Oscillation (NAO) Observation SP-CCSM: CCSM with embedded cloud-resolving models No intraseasonal oscillation CCSM period ~ 60 d period ~ 120 d

  18. Inserting idealized additional heating into CAM3 • Proxy for SST-forcing of tropics during developing warm ENSO event in JJAS • Full set of model parameterizations are retained – model can have non-linear moist feedbacks • Use idealized vertical stucture, and a realistic horizontal structure Added Heating for 1997 Monsoon No Indian Ocean Heating Indian Ocean Heating Included

  19. JJAS Mean 850 hPa Streamfunction Response ERA40 • 1997 Exp without IO • 1997 Exp with IO Note: With added IO heating the Monsoon response is closer to normal, as observed !

  20. Predictability in a Changing Climate: Past, Present and Future Evolution of uncertainty (spread of pdf) from initial state  synoptic weather  intra-seasonal time scales in the fully coupled system. Questions: Does the evolution of uncertainty through atmosphere, land and ocean depend systematically on the climate: Recent past, present and future climates? What particular coupled pathways of uncertainty evolution are initiated by uncertainty in the initial land states? (Will our ability to forecast ISI time scales get better or worse in the future?) What 20th Century ISI phenomena can we re-forecast with current coupled models? COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  21. Predictability in a Changing Climate Design Considerations: Predictability and prediction skill are both model-dependent: Use both CCSM4 (1o x 1o) and CFSv2 Baseline runs from recent past, present and future climates needed. Methodologies for introducing both “small” and “large” uncertainties in land initial states are needed (unique aspect of this design) Predictability (“perfect model”) runs and predictions should be made for multiple starting times of year, with adequate ensemble size. COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  22. Predictability in a Changing Climate CCSM4 1ox1o Predictability Experiments: 50-year baseline run from pre-industrial 1850 forcing conditions and ICs 50-year baseline run from 2000 forcing and ICs 50-year baseline run from 2050 scenario forcing and ICs For each baseline run: Define four classes based on calendar date (01 Dec, 01 May, 01 Jun, 01 Jul) For each calendar date: Choose 15 key years from the appropriate baseline run, based on ENSO-criterion Each calendar date + key year define a start date from the baseline run. For each start date: Construct 14 “large” land surface perturbations (15 IC states altogether) Construct 14 “small” land surface perturbations (15 IC states altogether) For each IC state run the model for 90 days (12 months for 01 Dec, 01 Jun) COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  23. Predictability in a Changing Climate Small land surface perturbations 14 new land states must be defined for each start date from the baseline run. Subclass one: land states taken from 1,2,3, … ,7 days previous to the start date Subclass two: land states taken from 0.5, 1.5, …., 6.5 days previous (defined by linear interpolation ) Each horizontal black line represents a baseline run Each orange circle represents a key year COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  24. Predictability in a Changing Climate Large land surface perturbations 14 new land states must be defined for each start date from the baseline run. These land states are taken from the same calendar date but from the 14 other key years Each horizontal black line represents a baseline run Each column of blue circles represents a key year COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  25. Evolution of small and large land errors (1850 baseline run)Soil Moisture Root Zone (all land) Shaded region are 95% uncertainty range for respective mean Large perturbations Small perturbations Soil Moisture (root zone) rms error Common atmosphere IC forces early convergence of pdf COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  26. Evolution of small and large land errors (2000 baseline run)Soil Moisture Root Zone (all land) Shaded region are 95% uncertainty range for the respective mean Large perturbations Small perturbations Soil Moisture (root zone) rms error COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  27. Signal/Total • Initial land state has three regimes of impact on temperature predictability: CCSM-4 Days from May 1 First two weeks: steady significant global impact. Second two weeks: rapid decay of effects. Beyond 30 days: limited to a few regions.

  28. Predictability from Coupling • Top: CCSM4 (1850) correlation between initial ½ day soil moisture perturbations and 1-day T2m anomalies. • Bottom: GSWP2 seasonal index of coupling between soil moisture and evaporation. • Redshading links high land IC impacts on atmosphere (top) to strong land-atmosphere coupling (bottom).

  29. Contrary to the paradigm of rapid tropical error growth followed by early saturation, Tropical wind errors continue to grow even after day 30, and saturate later than extratropical errors. The predictability time is thus seen to be ‘greater’ in tropics than further poleward,especially for the planetary waves. We need to better understand the nature of tropical planetary waves beyond the MJO (the “background spectrum”) Results from an AGCM with specified SST COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  30. Normalized Error growth in u-rotational (1 – 60 days) 200 mb top TROPICS SH MIDLAT Planetary Waves m=1-5 Medium Waves m=6-20 PW: m = 1-5 SW: m = 6-20 850 mb bot COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  31. Normalized Error growth in u-divergent (1 – 60 days) Error growth 1 – 60 days – udiv 200 mb top TROPICS SH MIDLAT PW: m = 1-5 Planetary Waves m=1-5 SW: m = 6-20 Medium Waves m=6-20 850 mb bot COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  32. Predictability in a Changing Climate • Preliminary Results • Land-atmosphere coupling at daily time scales has the same structure as longer time sensitivites of land-atmosphere coupling • Confirmation of enhanced theoretical predictability in the tropics on a wide range of space and time scales • Little or no systematic difference seen between predictability properties based on 1850 and 2000 baseline CCSM4 runs COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  33. Intra-Seasonal to Inter-Annual • Predictabilty and Prediction • Conclusions (1) • Uncertainty in the ocean initial conditions remain a major factor in ENSO predictability • Seamless approach for Intra-seasonal to Inter-annual time scales: • High resolution is critical for coherent structures (blocking, tropical cyclones) • BUT • Model pararmeterizations remain a stumbling block • Stochastic parameterization technique to be exploited (in future work) COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

  34. Intra-Seasonal to Inter-Annual Predictabilty and Prediction Conclusions (2) Basic research using “super-parameterization” and techniques for adding idealized heating has given insights into the predictability of the Indian and South American monsoons Predictability in a Changing Climate: How do fundamental predictability properties change as the climate changes? (ongoing work) COLA Scientific Advisory Committe 2011 Intraseasonal to Interannual Research

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