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Intra-seasonal Seasonal Interannual

Intra-seasonal Seasonal Interannual. ISI Research at COLA. Paul Dirmeyer. ISI Outline. Introduction COLA Multi-Model Leadership El Niño and Ocean-Driven Predictability Monsoons – Ocean/Land Contrast Land-Climate Interactions. Introduction.

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Intra-seasonal Seasonal Interannual

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  1. Intra-seasonalSeasonalInterannual 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 COLA 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 afterGhil 2002

  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. COLA Scientific Advisory Committee - George Mason University

  5. COLA Leadership in Multi-Model Projects 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) Ocean Atm+Ocean Atmosphere Nearly all projects include international participation Atm+Land Land 1984 1988 1992 1996 2000 2004 2008 2012

  6. COLA Leadership in Multi-Model Projects 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) NOAA Models NMC/NCEP NOAA model run by COLA GFDL Both 1984 1988 1992 1996 2000 2004 2008 2012

  7. COLA Leadership in Multi-Model Projects 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) NCAR Models CCM/CCSM NCAR model run by COLA or components 1984 1988 1992 1996 2000 2004 2008 2012

  8. COLA Leadership in Multi-Model Projects 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) NASA Models GSFC 1984 1988 1992 1996 2000 2004 2008 2012

  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. COLA 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 COLA Scientific Advisory Committee - George Mason University

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

  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”) COLA 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. Lead (months) Lead (months) COLA Scientific Advisory Committee - George Mason University

  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. Jang & Straus 2012a,b (in review JAS, JClim) COLA Scientific Advisory Committee - George Mason University

  15. 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 No Indian Ocean Heating Indian Ocean Heating Included Wm-2 Wm-2 COLA Scientific Advisory Committee - George Mason University

  16. JJAS Anomalous 850 hPay Response ERA40 • 1997 Exp without IO • With added Indian Ocean heating the monsoon response is closer to normal, as observed! • 1997 Exp with IO COLA Scientific Advisory Committee - George Mason University

  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.

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

  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. DelSole & Shukla 2012: GRL COLA Scientific Advisory Committee - George Mason University

  21. MERRA QIBT Climo. • 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. Dry Wet ppm – normalized so global integral = 106 *Dirmeyer and Brubaker 1999; 2007 COLA Scientific Advisory Committee - George Mason University

  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. Classic monsoon areas tend to low RE values Dirmeyer et al. (in prep) COLA Scientific Advisory Committee - George Mason University

  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. COLA 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. COLA 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”… COLA Scientific Advisory Committee - George Mason University

  26. Precipitation Skill GLACE-2 Forecasts • Multi-model prediction skill: r2(realistic SM IC) minusr2(random SM IC). • Significant skill improvement over a large part of North America, especially for extreme soil moisture anomalies. Temperature Skill 10 models, 1986-1995, only forecasts in JJA considered here. Koster, Dirmeyer, Guo et al. 2010: GRL COLA 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. COLA 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… Ratio is: , where s2 is the interannual variance of the ensemble mean precipitation. COLA Scientific Advisory Committee - George Mason University

  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 19th century to today. • What is the cause of this change in predictability (also evident in temperature, not shown)? Dirmeyer et al. (in prep) COLA Scientific Advisory Committee - George Mason University

  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. Change in T2m Predictability* *Predictability defined as number of days in forecast lead 31-60 (1 June ICs) where the land ICs have significant impact on T2m interannual variance. Land Cover Change (DAlbedo) Kumar et al. (in prep) COLA Scientific Advisory Committee - George Mason University

  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. COLA Scientific Advisory Committee - George Mason University

  32. Thank You

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