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COLA 2014 - 2018

COLA 2014 - 2018. Scientific Advisory Committee Meeting 26 April 2012. Introduction Reminder Core Principles History 2014-2018 Major Themes Research 1. Framework Foundation Natural vs. Forced Uncertainty Regional 2. Evaluation National Models Stochastic Mechanisms MMIE

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COLA 2014 - 2018

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  1. COLA 2014 - 2018 Scientific Advisory Committee Meeting 26 April 2012

  2. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3, Next Generation Prediction • Broader Impacts • Conclusion Reminder from this morning … • COLA is a unique institution organized to support highly productive, excellent research, graduate education and service to the Nation • COLA’s innovative contributions are widely recognized and have significantly influenced our current understanding of climate dynamics • COLA provides leadership in climate research and education, initiates national and international research programs, and strongly influences the direction of operational climate prediction • COLA is the home of GrADS, a software package that revolutionized the practice of climate analysis when it was introduced 20 years ago and that continues to be the tool of choice for climate data analysis and visualization • Graduates of the Climate Dynamics PhD program are taking up climate research positions and helping shape the future of Earth system modeling

  3. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion COLA’s Core Principles • Provide an environment that nurtures and maintains team of productive researchers • Strive for scientific and technical excellence, rigor and integrity (peer-review)in research and education • Push envelope of innovativenumerical experimentation grounded in established theory • Serve as honest broker among major climate model developments • Bridgeresearch in climate predictabilityandoperational climate prediction • Engage in the national climate research and education enterprise

  4. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion History of Omnibus Grant • COLA Omnibus Grant I 1994-1998 • COLA Omnibus Grant II 1999-2003 • COLA Omnibus Grant III 2004-2008 • COLA Omnibus Grant IV 2009 – 2014 • SAC meeting 26-27 Sep 2011 • COLA Science Review 2007-2011 Mar 2012 • Three-year review by SAC & agencies Apr 2012 • (Hoped for) Agencies’ guidance to submit proposal Jul 2012 • COLA Omnibus proposal submissionmid-Nov 2012 • COLA Omnibus Grant V2014 – 2018 Now Future

  5. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Major Themes for COLA 2014-2018 • Basic and Applied Research • Broader Impacts and Service to the Nation

  6. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Basic and Applied Research • Continue development of a unified framework for predictability in a changing climate • Conduct systematic, rigorous multi-scale evaluation of physical processes and mechanisms of climate variability at I-S-I-D time scales in national models • Provide leadership and coordination on development of next generation (2018) seamless prediction system for operational climate forecasting for the Nation

  7. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Basic and Applied Research • Continue the development of a unified framework for predictability in a changing climate • COLA’s foundation research– Predictability of intraseasonal/seasonal/interannual variations – in a seamless, probabilistic framework • Natural and forced variability – predictability of (MJO/ISO, ENSO, AMO, PDO, …) and changes in these natural modes associated with climate change • Can test regional climate predictability estimation approach to determine minimum domain size • Characterizing and quantifying uncertainty - bothontic uncertainty (associated with randomness) and statistical uncertainty (associated with sampling) • Predictability of climate at regional scales and scientific basis for adaptation strategies

  8. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Seamless, Probabilistic Framework Identification Suggests a subclass of models • Identification: Proceeding from observations to models, as representations of relevant physics, to testing hypotheses • Estimation: Defining metrics that address predictability across time scales, quantify inferences about model parameters, and advise experimental design and evaluation • Diagnostic Checking: Run numerical experimental and evaluate fit to observations • Applies across time scales from days to decades • Applies to Predictability, Detection, and Attribution • E.g., helps find, apply overlooked or under-exploited sources of ISI predictability After Box, Jenkins, Reinsel (2008) Diagnostic Checking Check fitted model with aim of revealing model inaccuracies Estimation Use data to make inferences about model parameters

  9. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Basis for Seamless Prediction • WCRP, 2005:  The world climate research programmestrategic framework2005-2015. WMO/TD-No. 1291. • Palmer, T. N.and co-authors, 2008: Toward seamlessprediction: Calibration of climate change projections using seasonal forecasts. Bull. Amer. Meteor. Soc., 89, 459–470. • Dole, R., 2008: Linking weather and climate. Synoptic-Dynamic Meteorology and Weather Analysis and Forecasting. Meteor. Monogr., 55, Amer. Meteor. Soc., 297-348. • Hurrell, J., and co-authors, 2009: A unifiedmodeling approach to climate system prediction. Bull. Amer. Meteor. Soc., 90, 1819-1832. • Brunet, G., and co-authors, 2010: Collaboration of the weather and climate communitiesto advance subseasonal-to-seasonal prediction. Bull. Amer. Meteor. Soc., 91, 1397-1406. • Hazeleger, W., and co-authors, 2010: EC-Earth: A seamlessEarth-system predictionapproach in action. Bull. Amer. Meteor. Soc., 91, 1357-1364. • Shukla, J., and co-authors, 2010: Toward a new generation of world climate research and computing facilities. Bull. Amer. Meteor. Soc., 91, 1407–1412. • Shapiroand co-authors 2010: An Earth-system predictioninitiativefor the twenty-first century. Bull. Amer. Meteor. Soc., 91, 1377-1388.

  10. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion The chain is only as strong as its weakest link. Use ISI Forecasts to quantify the strength of links 1-3 Palmer et al. 2008

  11. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Time and Space Scales • Critical (fast) processes in weather and climate • Cloud – aerosols – radiation interaction • Diapycnal mixing of oceanic eddies • Land surface – PBL interaction Hurrell et al. 2009

  12. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Predictability of ISI VariationsOverlooked or Under-Exploited Sources of PredictabilityPredictability of the Total Climate System • Sensitivity to small uncertainties in initial conditions of all components of the system • Atmosphere ( dynamics + water vapor + ozone) • Ocean (SST, salinity, thermocline) • Hydrosphere (cloud water, soil moisture) • Focus on observational limitations for accurate land ICs • Cryosphere (sea-ice extent & volume, snow cover & depth) • Biosphere (vegetation cover) • Evaluation of quality of predictions in all components

  13. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Predictability of ISI VariationsAtmospheric Examples • Influence of planetary scale snow cover on seasonal predictability • Hypothesis: Mechanism involves stratosphere  Need model with “good” stratosphere or experiments to nudge the stratosphere • Influence of tropical sub-seasonal fluctuations on extra-tropical IS predictability (blocking, NAO, response to ENSO) • Hypothesis: Details in tropics matter  Need experiments to nudge the tropics • Experiments with “super-parameterization” (imbedded cloud resolving models in lieu of convective parameterization) • Influence of extratropical instabilities on the MJO • Experiments to nudge the extra-tropical circulation (a la Ferranti et al. 1998) • Influence of long-term trends in sea-ice on the circulation • ‘Time-slice’ experiments with specified sea-ice, using current and projected SST • Influence of future mean state and boundary conditions on tropical and extratropicalatmospheric predictability • Need experiments to test the effects separately • N.B. Many of these experiments require high spatial resolution

  14. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Influence of Sea Ice Trends Arctic Sea Ice Volume (1000 km3) 1979-2005 Mean Annual Cycle Arctic Sea Ice Volume (1000 km3) 1979-2012 Anomaly PIOMAS HadGEM2-CC “historical” PIOMAS HadGEM2-CC “historical” EXAMPLE: Francis & Vavrus (2012) have noted slower Rossby wave progression, consistent with sea ice loss  need GCM experiments to test this hypothesis

  15. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Attribution • Definition: • What aspect(s) of antecedent states anticipate a given class of events (e.g. weather regime)? • What confluence of factors leads to a given event? (e.g. extreme) • At what lead time? • “Inverse” predictable component analysis: What state of the system maximizes the predictability of a given pattern? • Stripping: Removing essential features that make hindcasts reproduce events that stand out from the climatological PDF • Degrading the model • Altering the initial conditions

  16. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural/Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Basic and Applied Research • Continue the development of a unified framework for predictability in a changing climate • COLA’s foundation research– Predictability of intraseasonal/seasonal/interannual variations – in a seamless, probabilistic framework • Natural and forced variability – predictability of (MJO/ISO, ENSO, AMO, PDO, …) and changes in these natural modes associated with climate change • Can test regional climate predictability estimation approach to determine minimum domain size • Characterizing and quantifying uncertainty - bothontic uncertainty (associated with randomness) and statistical uncertainty (associated with sampling) • Predictability of climate at regional scales and scientific basis for adaptation strategies

  17. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Predictability of Climate at Regional Scales • Systematic exploration of benefits of higher spatial resolution • Explore impact of resolving eddies on prediction skill • E.g. TIW  ENSO • E.g. Extratropical SST gradients and cyclogenesis/frontogenesis (Greenland tip jets) • Using multiple, moderate-resolution models to estimate future climate surface conditions (SST, sea ice, etc.), apply these as lower boundary conditions to an NWP-resolution global model to compute future climate at regional scales, all over the globe • Continue fruitful collaborations with ECMWF and JAMSTEC to conduct numerical experiments with high-resolution atmospheric models (e.g. 2012 NCAR CISL Accelerated Scientific Discovery opportunity) • Explore uncertainty and error due to engineering vagaries associated with limited-area approach

  18. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Basic and Applied Research • Conduct systematic and rigorous multi-scale evaluation of physical processes and mechanisms of climate predictability at ISID time scales in national models • Evaluate fidelity of national climate models, with actionable feedback to national model development centers • Identical experiments with multiple models • Evaluate relative merits of deterministically-parameterizedmulti-model ensemble prediction vs. stochastically-parameterized probabilistic prediction • Further explore and exploit mechanisms of ISID predictability • Develop a multi-model interactive ensemble

  19. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Basic and Applied Research • Conduct systematic and rigorous multi-scale evaluation of physical processes and mechanisms of climate predictability at ISID time scales in national models • Evaluate fidelity of national climate models, with actionable feedback to national model development centers • Identical experiments with multiple models • Evaluate relative merits of deterministically-parameterizedmulti-model ensemble predictionvs. stochastically-parameterized probabilistic prediction • Further explore and exploit mechanisms of ISID predictability • Develop a multi-model interactive ensemble

  20. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Deterministic MME vs. Probabilistic Prediction • Understanding and evaluating different Model Error Representations (MER) • Multi-Model Ensemble (MME) • Multi-Physics Ensembles (MPE) • Stochastic Parameterization: Atmosphere (SPA) or Ocean (SPO) • Can mimic the setup of the MME hindcasts: • E.g.. How does a MPE of CESM match up to the NMME? • E.g., How does it affect the NMME scores if we replace the CCSM or CESM runs with our MPE runs? Add them as new members? • Potential partnerships with other groups working on this problem • CESM: AMWG already supports multiple physics options and has ongoing effort to add a stochastic parameterization option to CAM • NCEP: CFS-SPA development collaboration • DART: Data assimilation approach to estimation of stochastic parameters • U. Oxford and ECMWF: Stochastic parameterization in IFS

  21. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Basic and Applied Research • Conduct systematic and rigorous multi-scale evaluation of physical processes and mechanisms of climate predictability at ISID time scales in national models • Evaluate fidelity of national climate models, with actionable feedback to national model development centers • Identical experiments with multiple models • Evaluate relative merits of deterministically-parameterizedmulti-model ensemble prediction vs. stochastically-parameterized probabilistic prediction • Further explore and exploit mechanisms of ISID predictability • Develop a multi-model interactive ensemble

  22. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Mechanisms of ISID Predictability • IS: Predictability rebound (L-A, O-A, I-A interactions) • Example: Linking work on land-PBL interaction to work on predictability rebound • Example: Understand role of interactions between the oceanic western boundary currents and storm tracks on weather and climate time scales • SI: Origin of inter-event diversity of ENSO events • (ISI)D: What is the “inertia” of weather-noise forced variability?

  23. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Basic and Applied Research • Conduct systematic and rigorous multi-scale evaluation of physical processes and mechanisms of climate predictability at ISID time scales in national models • Evaluate fidelity of national climate models, with actionable feedback to national model development centers • Identical experiments with multiple models • Evaluate relative merits of deterministically-parameterizedmulti-model ensemble prediction vs. stochastically-parameterized probabilistic prediction • Further explore and exploit mechanisms of ISID predictability • Develop a multi-model interactive ensemble

  24. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion AM3 CAM5 GFS GEOS5 GEOS SfcFluxes4 Sfc Fluxes1 Sfc Fluxes2 SfcFluxes3 SfcFluxesM SST OGCM Ensemble of M AGCMs all receive same OGCM-output SST each day GMAO CESM NCEP GFDL … … … … N4 N1 N2 N3 average (1, …, N); N = N1+N2+N3+N4 • MMIE can be very useful infrastructure for identical experiments with multiple models and for interchanging component models • Is IE the best possible prediction in the sense that unpredictable variability is filtered? Average N daily surface fluxes Ensemble Mean Sfc Fluxes OGCM receives ensemble average of AGCM output fluxes each day Multi-Model Interactive Ensemble

  25. Driver CAM GFS GEOS GFDL-AM time POP CLM CICE processors Multi-Model Interactive Ensemble • MMIE can be very useful infrastructure for identical experiments with multiple models and for interchanging component models • Is IE the best possible prediction in the sense that unpredictable variability is filtered? Infrastructure created by PetaApps collaboration of COLA, NCAR, U. Miami

  26. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Basic and Applied Research • Provide leadership for and coordination of development of next generation (2018) seamless prediction system for Nation’s operational climate forecasting • Build on systematic evaluation of CFSv2 and other national models • Developoptimal methods of initializing high-resolution coupled models (e.g. nudging ocean state) • Define research and development pathway, including close integration of weather and climate model development • Address operational, R2O and O2R issues (user requirements, code support, data distribution, etc.) • Consider coupling of model development, reanalysis and initializationefforts • Facilitate coordination among national modeling centers and with operational prediction facility

  27. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Broader Impacts and Service to the Nation • Create multi-disciplinary GMUinstitute in sustainability:climate, environment, biodiversity, and society • Educate next generation of climate scientists: Climate Dynamics Ph.D. program • Facilitate annual national multi-model workshop to bring together modeling groups, climate researchers and climate services providers and stakeholders • Contribute to National Multi-Model Ensemble for operational seasonal forecasts • Develop and maintain The Honest Broker web page for national climate models • Further develop and support GrADS; export best practices in data management

  28. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion GrADS Developments • New data analysis capabilities • Linear algebra • Statistics • Sorting • defop • New data model for quasi-regular grids (swaths, icosahedral, …) • Expanded plug-compatibility • Leverage GrADS open source development model to entrain broader development community • Explore GrADS-in-the-Cloud: mobile computing platforms for geoscience data analysis

  29. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Major Themes for COLA 2014-2018 • Basic and Applied Research • Continue development of a unified framework for predictability in a changing climate • Conduct systematic, rigorous multi-scale evaluation of physical processes and mechanisms of climate variability at I-S-I-D time scales in national models • Provide leadership and coordination on development of next generation (2018) seamless prediction system for operational climate forecasting for the Nation • Broader Impacts and Service to the Nation • Create multi-disciplinary GMUinstitute: climate, environment, biodiversity, society • Educate next generation of climate scientists: GMU Climate Dynamics Ph.D. program • Facilitate annual national multi-model workshop to bring together modeling groups, climate researchers and climate services providers and stakeholders • Develop and maintain The Honest Broker web page for national climate models • Contribute to National Multi-Model Ensemble for operational seasonal forecast • Further develop and support GrADS; export best practices in data management

  30. Introduction • Reminder • Core Principles • History • 2014-2018 • Major Themes • Research • 1. Framework • Foundation • Natural vs. • Forced • Uncertainty • Regional • 2. Evaluation • National Models • Stochastic • Mechanisms • MMIE • 3. Next Generation Prediction • Broader Impacts • Conclusion Questions?

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