Overview Office of Naval Research 24 May 2012. ONR Briefing – 24 May 2012 COLA Overview Vision and...

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ONR Briefing – 24 May 2012 COLA Overview Overview Office of Naval Research 24 May 2012

Transcript of Overview Office of Naval Research 24 May 2012. ONR Briefing – 24 May 2012 COLA Overview Vision and...

Page 1: Overview Office of Naval Research 24 May 2012. ONR Briefing – 24 May 2012 COLA Overview Vision and Mission MISSION Basic and applied research and educating.

Overview

Office of Naval Research24 May 2012

Page 2: Overview Office of Naval Research 24 May 2012. ONR Briefing – 24 May 2012 COLA Overview Vision and Mission MISSION Basic and applied research and educating.

ONR Briefing – 24 May 2012 COLA Overview 2

Vision and Mission

MISSIONBasic 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

VISIONGlobal society benefits

from improved understanding ofclimate variability and predictability

and free and open access to data and research tools

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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Scientific Advisory Committee

The SAC reviews and advises on scientific activities at COLA.

Current members selected in consultation with the Federal agencies:

J. Hurrell NCAR 2010 – ; chair 2011 – D. Lettenmaier U Washington 2011 –

T. Palmer ECMWF 2010 – S. Schubert NASA Goddard 2002 –

L. Uccellini NOAA NCEP 2007 – B. Wang U Hawaii 2005 –

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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Scientific Advisory CommitteeFormer members of the COLA SAC:

D. Anderson ECMWF 2005-2009

L. Bengtsson MPI 1998-2001

G. Branstator NCAR 1999-2010

D. Burridge ECMWF 1994-1999; 2001-2005

A. Busalacchi U Maryland 1994-1997

R. Dickinson U Texas 1998-2010; chair 2004-2010

D. Hartmann U Washington 1998-2005; chair 1999-2003

A. Leetmaa GFDL 2000-2005

R. Mechoso UCLA 1994-1998

K. Miyakoda IMGA (Italy) 1994-1997

G. North Texas A&M 1999-2005

Julia Paegle U Utah 1994-1997

J. Slingo U Reading (UK) 2005-2009

K. Trenberth NCAR 1994-1999; chair 1998-1999

J. M. Wallace U Washington 1994-1999; chair 1994-1997

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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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

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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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

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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Accomplishments over 20 YearsCOLA 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 GrADS to the climate research and prediction community• Organized multi-institutional, multi-model modeling projects

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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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

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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Leading Predictable Component: Internal Multidecadal Pattern (IMP)

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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IMP and the Global Warming TrendIntroduction

WelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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Mean Precipitation Change inEurope’s Growing Season: 21st C minus 20th C

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion“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.

T159 (128-km)

T1279 (16-km)

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Future Change in Extreme Summer Drought Late 20th C to Late 21st C

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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).

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COLA in the Nation’s Service• Leadership of national and international

initiatives• Direct impact on operational prediction• Honest broker role among major modeling

groups

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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Panels and Working GroupsIntroduction

WelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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Journal Editors: 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 Center Scientific 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

COLA Leadership – Current ExamplesIntroduction

WelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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Congratulations, Shukla!

Shukla Receives 2012 Padma Shri Award

from Government of India

Smt. Pratibha Devisingh Patil,

President of India

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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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)• Experimentation with CFS and CFSv2

– 52 papers published 2007-2011• COLA was instrumental in introducing CFS to IITM; acknowledged in published papers:

– Pokhrel et al., 2012: ENSO, IOD and IMR in CFS coupled simulations. Climate Dyn. – Chaudhari et 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

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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COLA and 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).

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)

Climate Dynamics FacultyFaculty (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

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

Educating the Next Generation of Earth System Modelers

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2002-2011 GMU Climate Dynamics PhD RecipientsDeepthi Achuthavarier Role of the Indian and Pacific Oceans in Indian Summer Monsoon

VariabilityResearch ScientistNASA Goddard

Whit Anderson Oceanic Sill - Overflow Systems: Investigation and Simulation with the Poseidon OGCM

Research ScientistNOAA GFDL

Kristi Arsenault (2011) Impact of Model and Observational Error on Assimilating Snow Cover Fraction Observations

Research ScientistNASA Goddard

Susan Bates The Role of the Annual Cycle in the Coupled Ocean-Atmosphere Variability in the Tropical Atlantic Ocean

Research ScientistNCAR

Robert Burgman ENSO Decadal Variability in a Tropically-Forced Hybrid Coupled Model Assistant ProfessorFlorida International University

Carlos Cruz Global Ocean Circulation Variability Induced by Southern Ocean Winds Research ScientistNASA Goddard

Meizhu Fan Low Frequency North Atlantic SST Variability: Weather Noise Forcing and Coupled Response

Research ScientistNOAA NESDIS

Xia Feng New Methods For Estimating Seasonal Potential Climate Predictability Post-doctoral Research ScientistGeorge Mason University

Laura Feudale Extreme Events in Europe & N. America During 1950-2003: An Observational & Modeling Study

Research ScientistARPA/OSER (Italy)

Youkyoung Jang (2011) The Atmospheric Influence of Tropical Diabatic Heating Associated with Developing ENSO on Indian Monsoon

Post-doctoral Research ScientistFlorida International University

Liwei Jia (2011) Robust Multi-year Predictability on Continental Scales Post-doctoral Research ScientistCOLA

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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2002-2011 GMU Climate Dynamics PhD RecipientsDaeho Jin The Impact of ENSO on the Extratropics Post-doctoral Research Scientist

University of Maryland, College Park

Jian Li (2011) SST Diurnal Variability in the CFS and its Influence on Low Frequency Variability

Research ScientistNOAA NESDIS

Julia Manganello The Influence of SST Anomalies on Low-Frequency Variability of the North Atlantic Oscillation

Research ScientistCOLA

Bala Narapusetty Impact Of Tropical Instability Waves In The Eastern Equatorial Pacific Post-doctoral Research ScientistCOLA

Xiaohua Pan Impact of Mean Climate on ENSO Simulation and Prediction Post-doctoral Research ScientistUniversity of Maryland, Baltimore County

Kathy Pegion Potential Predictability of Tropical Intraseasonal Variability in the NCEP CFS

Post-doctoral Research ScientistCIRES, University of Colorado

Mary Ellen Verona Observational Analysis and Numerical Simulation of 1997-1998 El Niño deceased

Yuri Vikhliaev Decadal Extra-Tropical Pacific Variability Post-doctoral Research ScientistNASA Goddard

Li Xu (2011) Snow Cover as a Source of Climate Predictability: Mechanisms of Snow-Atmosphere Coupling

Research ScientistCOLA

Tugrul Yilmaz Improving Land Data Assimilation Performance With A Water Budget Constraint

Post-doctoral Research ScientistU.S. Department of Agriculture

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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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 Jagadish Shukla

Offered since 200847 students enrolled in 2011

~100 students anticipated in 2012

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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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

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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“Omnibus” Funding

1994-1998 Predictability and Variability of the Present ClimateFunding: $2.25M /yr Principal Investigator: J. ShuklaCo-PIs: J. Kinter, E. Schneider, D. Straus

1999-2003 Predictability and Variability of the Present ClimateFunding: ~$2.75M / yrPrincipal Investigator: J. ShuklaCo-PIs: J. Kinter, E. Schneider, P. Schopf, D. StrausCo-investigators: P. Dirmeyer, B. Huang, B. Kirtman

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.

* Thanks to our peers and the agencies

2009-2014 Predictability of the Physical Climate SystemFunding: ~$3.6 M / yrPrincipal Investigator: KinterCo-Investigators: Cash, DelSole, Dirmeyer, Huang, Jin, Klinger,

Krishnamurthy, Schneider, Shukla, Straus

2004-2008 Predictability of Earth’s ClimateFunding: ~$3.25M / yr (NSF - 46%; NOAA - 39%; NASA - 15%)Principal Investigator: ShuklaCo-Investigators: DelSole, Dirmeyer, Huang, Kinter, Kirtman, Klinger,

Krishnamurthy, Misra, Schneider, Schopf, Straus

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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“Omnibus” FundingCOLA is viewed as a major interagency National center of excellence:

2006

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)

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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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

COLA Multi-Agency Funded Core Team Staff Member Highest Degree Joined COLA

Scientific Staff1 J. Shukla * Sc.D. MIT (1976); Ph.D. BHU (1971) 19832 J. L. Kinter III * Ph.D. Princeton (1984) 1984 3 E. Altshuler M.S. Maryland (1996) 19984 B. Cash Ph.D. Penn State (2000) 20025 T. DelSole * Ph.D. Harvard (1993) 19976 P. A. Dirmeyer * Ph.D. Maryland (1992) 1993 7 B. E. Doty * B.S. N. Illinois (1978) 19848 M. J. Fennessy M.S. SUNY-Albany (1980) 1984

9 Z. Guo Ph.D. Ohio State (2002) 2002

10 B. Huang * Ph.D. Maryland (1992) 1993

11 B. Klinger * Ph.D. MIT-WHOI (1992) 2000

12 V. Krishnamurthy * Ph.D. M.I.T. (1985) 1985

13 J. Lu * Ph.D. Dalhousie (2003) 200814 L. Marx M.S. MIT (1977) 198315 D. A. Paolino M.S. Illinois (1980) 1983

16 E. K. Schneider * Ph.D. Harvard (1976) 1984

17 C. Stan * Ph.D. Colorado State (2004) 200518 D. M. Straus * Ph.D. Cornell (1977) 1984

Information Systems Staff1 J. M. Adams M.S. Washington (1993) 20002 C. Steinmetz Ph.D. Purdue (1991) 19983 T. Wakefield B.S. Maryland (2004) 2000

• Also affiliated with George Mason University

Exec. Dir. COLA (1993-2004)Dir. COLA (2005-present)

Pres., IGES (1993-present) Dir. COLA (1993-2004)

Over 400 person-years experience together

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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COLA Publications > 550 peer-reviewed publications since 1993

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 20110

10

20

30

40

50

60

70

80

90

100

In review

CTRs

In press

Published

IntroductionWelcomeGoals

Uniqueness

Accomplishments

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ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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Multi-Model Ensemble

CCSM4CESM1

CFSv2(CFSv1)

CM2.x GEOS_CM

Experimentation with the Nation’s Climate Models

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

POP4

NCARCAM4

GMAOGEOS5

NCEPGFS2

MOM4

GFDLAM2

MOM4 MOM4

CLM4 NOAH CatchmentCICE4 SIS (mod)

SIS CICELM2

ESMF FMS ESMFCPL7

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Big Data at COLA• Large data sets:

• CMIP5: samples from PIControl, Historical, AMIP, Decadal, RCP4.5, and RCP8.5 for 18 models (10 TB; 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

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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ONR Briefing – 24 May 2012 COLA Overview 30

• COLA’s long-term, stable, multi-agency funding enables GrADS to 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

– GrADS figures are frequently found in weather and climate journals

– GrADS is 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 GrADS by other groups, notably the OpenGrADS and PyGrADS projects at NASA Goddard

GrADSIntroduction

WelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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GrADS: 86,000 Users Worldwide

Downloads from COLA February 2010 - Present

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion

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GrADS: On Your Favorite Web SitesIntroduction

WelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusionhttp://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/hgt.shtml

Climate Prediction CenterArctic Oscillation Monitor

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ONR Briefing – 24 May 2012 COLA Overview 33

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

IntroductionWelcomeGoals

Uniqueness

Accomplishments

Recent Advances

ServiceLeadershipOperations

Education

Critical MassTeamScaleProductivity Big ModelsBig Data

Conclusion