1. Intra-seasonal Seasonal Interannual Intra-seasonal Seasonal Interannual ISI Research at COLA Paul Dirmeyer

2. ISI Outline Introduction COLA Multi-Model Leadership El Niño and Ocean-Driven Predictability Monsoons – Ocean/Land Contrast Land-Climate Interactions 26 April 20121COLA Scientific Advisory Committee - George Mason University

3. Introduction There is a 20+ year history at COLA in ISI climate research. ISI seamlessly bridges from weather to decadal+ climate – “Weather is the climate’s delivery system”. Question: where does climate predictability come from? It is difficult to categorize by timescale, component, phenomenon because it’s all intertwined. ISI after Ghil 2002 26 April 2012

4. COLA Leadership in Multi-Model Projects A key element of COLA’s history has been its central role in ISI multi-model and multi-institutional experiments. Institutional diversity was intrinsic in the COLA AGCM. – The original COLA model was a version of the NMC operational forecast model with parameterizations from GFDL (sub-grid atmospheric physics) and NASA (land surface). – Version 2 of the COLA AGCM coupled the NCAR dynamical core with the “COLA Physics Package” of diverse lineage. – Also COLA coupled model, Poseidon OGCM, SSiB … much institutional expertise in modeling. 26 April 20123COLA Scientific Advisory Committee - George Mason University

5. COLA Leadership in Multi-Model Projects 1984 1988 1992 1996 2000 2004 2008 2012 Atm+Ocean Atm+Land Atmosphere Land Ocean Dynamical Extended Range Forecasts (DERF) Global Soil Wetness Project (GSWP) Project to Inter-compare Land-Surface Parameterization Schemes (PILPS 2d) Experimental Long-Lead Forecast Bulletin Dynamical Seasonal Prediction (DSP) Climate of the Twentieth Century (C20C) Interactive Ensemble Retrospective ENSO Forecasts with MOM GSWP-2 + Multi-Model Land Analysis Global Land-Atmosphere System Study (GLACE) Multi-Model Ensemble (MME) Land Multi-Model Interactive Ensemble GLACE-2 Project Athena Ocean Multi-Model Analysis Multi-ODA Initialization National Multi-Model Ensemble (NMME) Nearly all projects include international participation

6. Dynamical Extended Range Forecasts (DERF) Global Soil Wetness Project (GSWP) Project to Inter-compare Land Surface Parameterization Schemes (PILPS 2d) Experimental Long-Lead Forecast Bulletin Dynamical Seasonal Prediction (DSP) Climate of the Twentieth Century (C20C) Interactive Ensemble Retrospective ENSO Forecasts with MOM GSWP-2 + Multi-Model Land Analysis Global Land-Atmosphere System Study (GLACE) Multi-Model Ensemble (MME) Land Multi-Model Interactive Ensemble GLACE-2 Project Athena Ocean Multi-Model Analysis Multi-ODA Initialization National Multi-Model Ensemble (NMME) COLA Leadership in Multi-Model Projects 1984 1988 1992 1996 2000 2004 2008 2012 GFDL NMC/NCEP Both NOAA Models NOAA model run by COLA

7. Dynamical Extended Range Forecasts (DERF) Global Soil Wetness Project (GSWP) Project to Inter-compare Land Surface Parameterization Schemes (PILPS 2d) Experimental Long-Lead Forecast Bulletin Dynamical Seasonal Prediction (DSP) Climate of the Twentieth Century (C20C) Interactive Ensemble Retrospective ENSO Forecasts with MOM GSWP-2 + Multi-Model Land Analysis Global Land-Atmosphere System Study (GLACE) Multi-Model Ensemble (MME) Land Multi-Model Interactive Ensemble GLACE-2 Project Athena Ocean Multi-Model Analysis Multi-ODA Initialization National Multi-Model Ensemble (NMME) COLA Leadership in Multi-Model Projects 1984 1988 1992 1996 2000 2004 2008 2012 CCM/CCSM NCAR Models NCAR model run by COLA or components

8. Dynamical Extended Range Forecasts (DERF) Global Soil Wetness Project (GSWP) Project to Inter-compare Land Surface Parameterization Schemes (PILPS 2d) Experimental Long-Lead Forecast Bulletin Dynamical Seasonal Prediction (DSP) Climate of the Twentieth Century (C20C) Interactive Ensemble Retrospective ENSO Forecasts with MOM GSWP-2 + Multi-Model Land Analysis Global Land-Atmosphere System Study (GLACE) Multi-Model Ensemble (MME) Land Multi-Model Interactive Ensemble GLACE-2 Project Athena Ocean Multi-Model Analysis Multi-ODA Initialization National Multi-Model Ensemble (NMME) COLA Leadership in Multi-Model Projects 1984 1988 1992 1996 2000 2004 2008 2012 GSFC NASA Models

9. Predictability and Prediction on ISI Scales ENSO underpins ISI predictability and prediction. El Niño remains more “potentially predictable” than actually predictable. Spectra between models and observations still do not match (model fidelity) – this is one of the motivations for multi-model approaches. 26 April 20128COLA Scientific Advisory Committee - George Mason University

10. Brief History of ENSO Research at COLA ENSO investigation in AMIP runs (diagnostic) ENSO's dominant impact in DSP skill, mid-latitudes Predictability of ENSO Ocean dynamics and ENSO IE and the destructive role of atmospheric noise ENSO in MMEs (diagnostic) Effect of super-parameterization of convection on ENSO ENSO effect on low-frequency patterns / weather regimes Ocean MMA and the intrinsic vs. ENSO-forced variability ENSO in a changing climate and mid-latitude response to ENSO in a changing climate Pacemaker experiments Multi-ocean-analysis initialization ENSO, diabatic heating, and monsoon response 26 April 20129COLA Scientific Advisory Committee - George Mason University

11. ODA Heat Content Agreement 10 moderate high low Signal Noise Signal Noise Ocean analyses from: ECMWF (ORA-S3, COMBINE-NV), NCEP (GODAS, CFSR), UMCP/TAMU (SODA) and GFDL (ECDA). 26 April 2012COLA Scientific Advisory Committee - George Mason University 1979-2007 Zhu et al. 2012 GRL

12. What Does This Uncertainty Mean for Forecasts? One model: CFSv2 [GFSv2 (T126 L64) + MOM4 (½°×½°; ¼°lat ±10°; L40)] – 4-member ensembles: 1-4 April 1979-2007 – 12 month forecasts Four ODAs: Ocean ICs (anomaly initialization) from each: COMBINE-NV, ORA-S3, CFSR, GODAS “Fifth” ODA: Mean of the four above (“AVEoci”) 26 April 201211COLA Scientific Advisory Committee - George Mason University

13. Niño 3.4 Validation Ensemble mean performs as well or better than best single-ODA initialization run at nearly all leads for both correlation and root mean square error. AVEoci is middle of the pack – no bargain/economy. 26 April 201212COLA Scientific Advisory Committee - George Mason University Lead (months)

14. Changing ENSO/Monsoon Linkages 1997 developing El Niño (strong summer Niño3) did not translate into a poor monsoon, as expected – is the ENSO/monsoon relationship changing, and how? GCM cumulus parameterizations struggle to simulate the response to SST, so investigations based on manipulation of SST anomalies are handicapped. Solution: bypass the problem and specify “observed” diabatic heating anomalies in the atmosphere associated with the SSTs. 26 April 201213COLA Scientific Advisory Committee - George Mason University Jang & Straus 2012a,b (in review JAS, JClim)

15. No Indian Ocean Heating Indian Ocean Heating Included Inserting Idealized Heating in CAM3 Full set of model parameterizations are retained – model can have non-linear moist feedbacks Idealized vertical structure to added diabatic heating, but a realistic horizontal structure Added Heating for 1997 Monsoon 26 April 201214COLA Scientific Advisory Committee - George Mason University Wm -2

16.  1997 Exp with IO  1997 Exp without IO ERA40 With added Indian Ocean heating the monsoon response is closer to normal, as observed! 26 April 2012COLA Scientific Advisory Committee - George Mason University15 JJAS Anomalous 850 hPa  Response 

17. El Niño and Monsoons ENSO remains the “big gorilla” – the baseline source of global predictability. Other processes and elements of the climate system modulate and modify regionally. Monsoons are a classic example; particularly South Asia -For prediction, a big difficult problem with huge societal impacts on a large population. 26 April 201216

18. Monsoons Fennessy's early work with COLA AGCM Role of spring Eurasian snow cover Idealized land-sea and orographic effects Fixed sun vs fixed SST Linear prediction I-S variability South American monsoon Indian Ocean modes MJO interaction with monsoon Connection to ENSO – modulation Changes in changing climate Dynamical vs. statistical prediction of monsoon Extremes and circulation changes 26 April 201217COLA Scientific Advisory Committee - George Mason University

19. 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. 26 April 201218COLA Scientific Advisory Committee - George Mason University DelSole & Shukla 2012: GRL ISMR=India Summer Monsoon Rainfall

20. 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. 26 April 201219COLA Scientific Advisory Committee - George Mason University DelSole & Shukla 2012: GRL

21. MERRA QIBT We apply the quasi-isentropic back trajectory method* to MERRA data and observed precipitation to estimate sources of surface evaporation supplying precipitation over all land locations 60°S-90°N. Example (left) of 1979-2005 JJA moisture source for rain over the DC area, the 3 driest years, and the 3 wettest years. The “blobs” are effectively PDFs – we can use relative entropy to compare. 26 April 201220COLA Scientific Advisory Committee - George Mason University *Dirmeyer and Brubaker 1999; 2007 Climo. Dry Wet ppm – normalized so global integral = 10 6

22. Drought Years vs. Climatology Recall RE=0 if two distributions are identical. Maps show RE between climatological evaporative moisture source calculated at each point and the source for the 3 driest years. Small values ≈ circulation changes are not associated with drought. Must be another cause. 26 April 201221COLA Scientific Advisory Committee - George Mason University Classic monsoon areas tend to low RE values Dirmeyer et al. (in prep)

23. Wet Years Signal Wet years show similar large-scale patterns. Note that the highest RE values are usually over arid regions – require a circulation change to bring in moisture. 26 April 201222COLA Scientific Advisory Committee - George Mason University

24. “Relative Empathy” With Circulation The ratio of the REs (log of ratio shown) indicates “droughts” are more likely than “floods” to be associated with circulation changes (different evaporative sources). Implies wet spells are either more locally driven or more random in nature Time-scales come into play also. 26 April 201223COLA Scientific Advisory Committee - George Mason University

25. Our Evolving Understanding of Land-Climate Interactions They could matter… – Land cover change (deforestation, desertification, etc.) – Soil moisture sensitivity studies (perturbed BCs, e.g. GLACE) – Breaking the water cycle (specified SM, flux replacement) They do matter... – GSWP-1; realistic SM BCs improve simulation, “wrong year” degrades simulation – GLACE-2; realistic SM ICs improve hindcasts How it works… – Feedback pathways, coupling indices, “rebound”… 26 April 201224COLA Scientific Advisory Committee - George Mason University

26. GLACE-2 Forecasts Multi-model prediction skill: r 2 (realistic SM IC) minus r 2 (random SM IC). Significant skill improvement over a large part of North America, especially for extreme soil moisture anomalies. Temperature Skill Precipitation Skill 10 models, 1986-1995, only forecasts in JJA considered here. Koster, Dirmeyer, Guo et al. 2010: GRL 26 April 2012COLA Scientific Advisory Committee - George Mason University

27. Predictability in a Changing Climate We have begun exploring systematically ISI predictability and prediction using CCSM4 – Long 50-year simulations for current, pre-industrial and RCP85. – 15 years chosen for ensemble “forecasts” with randomized land ICs vs. small (“realistic” or similar to the climate series being forecast) perturbations (May, June, July and December ICs). – Can separate land IC role from ocean/atmosphere, and see how roles change in a changing climate. This is a “perfect model” study – plan to do actual forecast experiments for current climate scenario. 26 April 201226COLA Scientific Advisory Committee - George Mason University

28. Land ICs Signal The impact of “realistic” land surface initialization on the first week of the forecast (signal/signal ratio) is evident in precipitation over land. There is a hint that stronger impacts are present in the current climate than in pre-industrial… 26 April 201227COLA Scientific Advisory Committee - George Mason University Ratio is:, where  2 is the interannual variance of the ensemble mean precipitation.

29. Sensitivity to Changing Climate Over most land areas, in all four months examined, the positive impact of “realistic” land initialization on the simulation has increased (signal/signal ratios increase) from 19 th century to today. What is the cause of this change in predictability (also evident in temperature, not shown)? 26 April 201228COLA Scientific Advisory Committee - George Mason University Dirmeyer et al. (in prep)

30. Is Land Cover Change the Driver? We find a suspicious correspondence between the pattern of improved predictability from land ICs (top) and the pattern of prescribed land use change from 1850 to 2000 scenarios. We are still exploring this link. 26 April 201229COLA Scientific Advisory Committee - George Mason University Change in T 2m Predictability* Land Cover Change (  Albedo) *Predictability defined as number of days in forecast lead 31-60 (1 June ICs) where the land ICs have significant impact on T 2m interannual variance. Kumar et al. (in prep)

31. Summary ISI is an integral and enduring element of COLA’s research mission. ISI is far from a solved problem – progress is being made on many fronts, and there is still much we do not fully understand. We continue to explore the role of the slowly-varying boundary conditions (ocean and land) in climate predictability and prediction. Climate change adaptation is only meaningful if our models can capture the changing regional impacts of ENSO and other boundary-forced climate anomalies. 26 April 201230COLA Scientific Advisory Committee - George Mason University

32. Thank You Thank You