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Overview

Overview. Office of Naval Research 24 May 2012. Introduction Welcome Goals Uniqueness Accomplishments Recent Advances Service Leadership Operations Education Critical Mass Team Scale Productivity Big Models Big Data Conclusion. Vision and Mission. VISION

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Overview

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  1. Overview Office of Naval Research 24 May 2012

  2. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion Vision and Mission VISION Global society benefits from improved understanding of climate variability and predictability and free and open access to data and research tools MISSION Basic and applied research and educating the next generation to explore, establish and quantify the predictability and prediction of intra-seasonal to decadal variability in a probabilistic framework and in the context of a changing climate

  3. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion Scientific Advisory Committee

  4. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion Scientific Advisory Committee

  5. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion Uniqueness • Critical mass of excellent climate scientists working together • Desire, capability to experiment with multiple national models • Stable, multi-agency funding and external expert advice • Scientific leadership in national/international climate research • Co-sponsorship with GMU of PhD program in Climate Dynamics • Highly-valued, widely-used software: GrADS • High-capacity in-house computing facility • Building global capacity: creating & advancing research institutions

  6. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion Advantages of COLA Uniqueness • New Ideas and Accomplishments • Recent Advances • COLA in the Nation’s Service • Educating the Next Generation of Earth System Modelers • Critical Mass

  7. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion Accomplishments over 20 Years COLA is an environment for research that enables scientists to test new ideas and pose a “credible threat” to old ones • Established scientific basis for dynamical S-I prediction • Established critical role of land surface in climate predictability • Established feasibility of reanalysis • Advanced the multi-model ensemble • Helped quantify limits of climate predictability • Land-atmosphere interactions • Ocean-atmosphere interactions • Developed framework for climate predictability and prediction • Showed that model fidelity determines model predictability • Developed and contributed GrADSto the climate research and prediction community • Organized multi-institutional, multi-model modeling projects

  8. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion Recent Advances • Established a scientific rationale for regional multi-year prediction • Discovered a new mechanism of overlooked land-driven predictability associated with spring-summer growing season transition • Determined that ocean analysis is critical to ENSO prediction accuracy and that multi-analysis ensembles are a viable method to overcome uncertainty in ocean states • Established a scientific basis for decadal prediction • Developed a method that extracts predictability at all time scales • Quantified how and why the extratropical response to ENSO changes as the global climate warms • Developed a simple conceptual model of interannual variations of Indian monsoon rainfall: linear combination of boundary-forced seasonal a statistical average of intra-seasonal variations Also – see Science Highlights

  9. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion Leading Predictable Component: Internal Multidecadal Pattern (IMP)

  10. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion IMPand the Global Warming Trend

  11. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion Mean Precipitation Change inEurope’s Growing Season: 21st C minus 20th C T159 (128-km) T1279 (16-km) “Time-slice” runs of the ECMWF IFS with observed SST for the 20th century and CMIP3 projections of SST for the 21st century at two different model resolutions.

  12. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion Future Change in Extreme Summer Drought Late 20th C to Late 21st C 4X probability of extreme summer drought in Great Plains, Florida, Yucutan, and parts of Eurasia • 10th Percentile Drought: Number of years out of 47 in a simulation of future climate (2071-2117) for which the June-August mean rainfall was less than the 5th driest year of 47 in a simulation of current climate (1961-2007). • Dirmeyer, P. A., B. A. Cash, J. L. Kinter III, T. Jung, L. Marx, C. Stan, P. Towers, N. Wedi, J. M. Adams, E. L. Altshuler, B. Huang, E. K. Jin, and J. Manganello, 2012: Evidence for enhanced land-atmosphere feedback in a warming climate. J. Hydrometeor., (in press).

  13. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion COLA in the Nation’s Service • Leadership of national and international initiatives • Direct impact on operational prediction • Honest broker role among major modeling groups

  14. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion Panels and Working Groups

  15. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion COLA Leadership – Current Examples JournalEditors: Adv. Atmospheric Science (Huang) Climate Dynamics(Schneider) J. Climate(DelSole) IPCC AR5 (DelSole, Lu, contributing authors) Climate change assessment; also contributions from others (e.g. ZOD and FOD reviewers) Int’l Advisory Panel for Weather and Climate, India (Shukla, chair; Palmer, Uccellini, members) Advise Indian government on weather forecasting and climate prediction (research and operations) NRC BASC Panel on Advancing Climate Modeling (Kinter, member) Advise US government on climate modeling strategy for 10-20 year horizon UCAR Community Advisory Committee for NCEP (Kinter, co-chair) Advise NCEP on strategic direction for 5-10 year horizon US CLIVAR PPAI Panel (Stan, member) Set agenda for Predictability, Predictions and Applications Interface Asia- Pacific Climate CenterScientific Advisory Committee (Wang & Shukla, co-chairs) Advise APCC on improving climate prediction in the Asia-Pacific region World Climate Modeling Summit(Shukla, chair; Kinter, member) Very successful meeting in May 2008  multiple BAMS articles in 2010

  16. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion Congratulations, Shukla! Smt. Pratibha DevisinghPatil, President of India Shukla Receives 2012 Padma Shri Awardfrom Government of India

  17. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion Direct Impact on Operational Prediction • Real-time seasonal forecast ensembles with CCSM3(in collaboration with U. Miami) • Forecasts provided to NCEP, IRI (tier-2 forecasts provided to IRI, APCC) • ExperimentationwithCFSandCFSv2 • 52 papers published 2007-2011 • COLA was instrumental in introducing CFS toIITM; acknowledged in published papers: • Pokhrelet al., 2012: ENSO, IOD and IMR in CFS coupled simulations. Climate Dyn. • Chaudhariet al., 2012: Model biases in NCEP CFS in IMR. Int. J. Climatology. • GrADS is in use as critical tool for operational climate prediction, including new GIS capability added specifically for CPC requirements • NMME – National Multi-Model Ensemble • Proposal to CPO FY12 AO: real-time seasonal forecast ensembles (COLA providing CCSM4; in collaboration with ESRL, GFDL, GMAO, U. Miami, NCAR, IRI, Princeton, and NCEP) • Heavy leveraging of COLA I-S-I project and results • Design of next generation operational ISI prediction model • COLA and CTB spearheading groundbreaking R2O activity • Involving research scientists from outside NCEP and including private sector input • Very successful workshop on 25-26 August 2011 • CFSv2 Evaluation Workshop planned for 30 April – 1 May 2012 at ESSIC

  18. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion • Current Graduate Students (18) • A. Badger (Dirmeyer) • G. Bucher (Boybeyi) • H. Chen (Schneider) • I. Colfescu (Schneider) • X. Feng (Lu) • A. Garuba (Klinger) • A. Hazra (Klinger) • Y. Jin (Stan) • L. Krishnamurthy (Krishnamurthy) • E. Lajoie (DelSole) • J. Nattala (Kinter) • E. Palipane (Lu) • M. Scafonas (Lu) • B. Singh (Krishnamurthy) • A. Srivastava (Shukla/Huang) • E. Stofferahn (Boybeyi) • E. Swenson (Straus) • X. Yan (DelSole) Educating the Next Generation of Earth System Modelers COLAand George Mason University (GMU)established a new Ph.D. Program in Climate Dynamics(CLIM) in 2003. CLIM is now part of the Department of Atmospheric, Oceanic and Earth Sciences (AOES). Climate Dynamics Faculty Faculty (0.5 FTE): Boybeyi, Chiu, DelSole, Dirmeyer, Huang,Jin, Kinter, Klinger, Lu, Schneider, Schopf, Shukla (Director, CLIM; founding chair of AOES), Stan, Straus(chair, AOES) Adjunct:Doty, Krishnamurthy • Bold = 2012 graduate

  19. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion

  20. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion

  21. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion CLIM 101: Global Warming - Weather, Climate and Society General Education Natural Science (non-laboratory) undergraduate course that surveys the scientific and societal issues associated with weather and climate variability and change. CLIM 101 enables students to critically examine arguments being discussed by policy makers, corporations, and the public at large. Scientific basics, potential impacts, decision-making under uncertainty, and policy challenges are all discussed. In a course capstone project, students prepare briefings to the Governor of Virginia on the impact of changing climate on various sectors of the Commonwealth’s socio-economic fabric. Instructors: Jim Kinter and JagadishShukla Offered since 2008 47 students enrolled in 2011 ~100 students anticipated in 2012

  22. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion Critical Mass • Multi-agency funding – keeping a team of excellence together • Testing hypotheses that require large resources – often not possible by individual PIs • High productivity • Working with • Large, complex models • Large complex data sets

  23. “Omnibus” Funding • Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion COLA is a private, non-profit research institute supported by NSF (lead), NOAA and NASA through a single jointly-peer-reviewed *, jointly-funded five-year proposal. 2009-2014 Predictability of the Physical Climate System Funding: ~$3.6 M / yr Principal Investigator: Kinter Co-Investigators: Cash, DelSole, Dirmeyer, Huang, Jin, Klinger, Krishnamurthy, Schneider, Shukla, Straus 2004-2008 Predictability of Earth’s Climate Funding: ~$3.25M / yr (NSF - 46%; NOAA - 39%; NASA - 15%) Principal Investigator: Shukla Co-Investigators: DelSole, Dirmeyer, Huang, Kinter, Kirtman, Klinger, Krishnamurthy, Misra, Schneider, Schopf, Straus 1999-2003 Predictability and Variability of the Present Climate Funding: ~$2.75M / yr Principal Investigator: J. Shukla Co-PIs: J. Kinter, E. Schneider, P. Schopf, D. Straus Co-investigators: P. Dirmeyer, B. Huang, B. Kirtman 1994-1998 Predictability and Variability of the Present Climate Funding: $2.25M /yr Principal Investigator: J. Shukla Co-PIs: J. Kinter, E. Schneider, D. Straus * Thanks to our peers and the agencies

  24. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion “Omnibus” Funding COLA is viewed as a major interagency National center of excellence: • Box 5-1 Major Interagency Programs • U.S. Climate Change Science Program (CCSP) • U.S. Weather Research Program (USWRP) • National Space Weather Program (NSWP) • Center for Ocean-Land-Atmosphere Studies (COLA) 2006

  25. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion Pres., IGES (1993-present) Dir. COLA (1993-2004) Exec. Dir. COLA (1993-2004) Dir. COLA (2005-present) Your most precious possessions are the people you have working there, and what they carry around in their heads, and their ability to work together. - Robert Reich Over 400 person-years experience together

  26. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion

  27. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion COLA Publications > 550 peer-reviewed publications since 1993

  28. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion Experimentation with the Nation’s Climate Models GMAO GEOS5 NCAR CAM4 NCEP GFS2 GFDL AM2 CICE4 CLM4 CICE SIS SIS (mod) NOAH LM2 Catchment MOM4 MOM4 MOM4 POP4 CPL7 ESMF FMS ESMF CCSM4 CESM1 CFSv2 (CFSv1) GEOS_CM CM2.x Multi-Model Ensemble

  29. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion Big Data at COLA • Large data sets: • CMIP5: samples from PIControl, Historical, AMIP, Decadal, RCP4.5, and RCP8.5 for 18 models (10TB; 33K data sets) • People from other labs (IRI, CCCma) are asking COLA for the data!! • CFSv2: All NCEP seasonal hindcasts; decadal predictions • NMME: 7 models, 25 years of hindcasts • Athena: ECMWF and JAMSTEC ultra-high resolution models • Atmospheric and oceanic reanalyses: NCEP, NARR, MERRA, CFS-R, ERA-40, ERA-Interim, ESRL-20C, JRA, GSWP, GODAS, SODA, ECDA, ORA-S3, ORA-S4, and COMBINE-NV • COLA recently adopted a more cost-effective design to isolate and curate frequently used static data (shared) and promote best practices among data curators, users

  30. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion GrADS • COLA’s long-term, stable, multi-agency funding enables GrADSto be both nimbly responsive to user needs and dedicated to long-term design and planning. • GrADS is an essential tool for COLA research and data management (at COLA and at NCAR) and essential to the climate analysis research and operations community. • GrADS has over 86,000 users worldwide • No comparable non-commercial geoscience software • GrADSfigures are frequently found in weather and climate journals • GrADSis used to generate images on many weather and climate web pages hosted by NOAA, NASA, Universities, and a variety of International Agencies (http://iges.org/grads/gotw.html) • Open source development model has inspired investment in GrADSby other groups, notably the OpenGrADSand PyGrADSprojects at NASA Goddard

  31. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion GrADS: 86,000 Users Worldwide Downloads from COLA February 2010 - Present

  32. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion GrADS: On Your Favorite Web Sites Climate Prediction Center Arctic Oscillation Monitor http://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/hgt.shtml

  33. Introduction • Welcome • Goals • Uniqueness • Accomplishments • Recent Advances • Service • Leadership • Operations • Education • Critical Mass • Team • Scale • Productivity • Big Models • Big Data • Conclusion Summary • NSF, NOAA and NASA can take credit for creating COLA: 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

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