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2014 - 2018. Scientific Advisory Committee Meeting 27 September 2011. COLA Contributions to the Nation’s Climate Enterprise. COLA Contributions to the Nation’s Climate Enterprise. Scientific excellence Experimenting with the Nation’s climate models

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2014 2018

2014 - 2018

Scientific Advisory Committee Meeting

27 September 2011

cola contributions to the nation s climate enterprise1
COLA Contributions to the Nation’s Climate Enterprise
  • Scientific excellence
  • Experimenting with the Nation’s climate models
  • Leading the national and international climate research community
  • Educating the next generation of experts in climate dynamics and Earth system modeling
  • Advancing scientific productivity through developing and supporting the software and data management practices
cola contributions to the nation s climate enterprise2
COLA Contributions to the Nation’s Climate Enterprise
  • Scientific excellence
  • Experimenting with the Nation’s climate models
  • Leading the national and international climate research community
  • Educating the next generation of experts in climate dynamics and Earth system modeling
  • Advancing scientific productivity through developing and supporting the software and data management practices
slide5

COLA Publications

> 500 peer-reviewed publications since 1993

cola contributions to the nation s climate enterprise3
COLA Contributions to the Nation’s Climate Enterprise
  • Scientific excellence
  • Experimenting with the Nation’s climate models
  • Leading the national and international climate research community
  • Educating the next generation of experts in climate dynamics and Earth system modeling
  • Advancing scientific productivity through developing and supporting the software and data management practices
experimentation with the nation s climate models
Experimentation with the Nation’s Climate Models

NCAR

CAM4

GFDL

AM2

GSFC

GEOS5

NCEP

GFS2

POP4

MOM4

MOM4

MOM4

CCSM4

CESM1

CFSv2

(CFSv1)

GEOS_CM

CM2.x

Multi-Model Ensemble

cola involvement with ncep ncar nmme and cfsv3
COLA Involvement with NCEP, NCAR: NMME and CFSv3
  • NMME – National Multi-Model Ensemble
    • Real-time seasonal forecast ensembles with CCSM3beinggiven to NCEP

(in collaboration with U. Miami)

    • Proposal to CPO FY12 AO: real-time seasonal forecast ensembles with CCSM4
      • 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 I-S-I 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
cola contributions to the nation s climate enterprise4
COLA Contributions to the Nation’s Climate Enterprise
  • Scientific excellence
  • Experimenting with the Nation’s climate models
  • Leading the national and international climate research community
  • Educating the next generation of experts in climate dynamics and Earth system modeling
  • Advancing scientific productivity through developing and supporting the software and data management practices
cola leadership current examples
COLA Leadership – Current Examples

Editors: Adv. Atmospheric Science (Huang)

Climate Dynamics(Schneider)

J. Climate(DelSole)

IPCC AR5 (DelSole, Lu, contributing authors)

Climate change assessement; also contributions from others (e.g. ZOD and FOD reviewers)

International Advisory Panel for Weather and Climate, India (Shukla, chair; Palmer, Uccellini, members)

Advise Indian government on weather forecasting and climate prediction (research and opertions)

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

World Climate Modeling Summit(Shukla, chair; Kinter, member)

Very successful meeting in May 2008  multiple BAMS articles in 2010

cola contributions to the nation s climate enterprise5
COLA Contributions to the Nation’s Climate Enterprise
  • Scientific excellence
  • Experimenting with the Nation’s climate models
  • Leading the national and international climate research community
  • Educating the next generation of experts in climate dynamics and Earth system modeling
  • Advancing scientific productivity through developing and supporting the software and data management practices
slide12

Education

COLAand George Mason University (GMU) established (2003) a new Ph.D. Program in Climate Dynamics in the School of Computational Sciences (SCS). Became Climate Dynamics Department in College of Sciences in 2006. Now part of Department of Atmospheric, Oceanic and Earth Sciences.

  • Current Graduate Students
    • K. Arsenault (Shukla/Dirmeyer/Houser)
    • A. Badger (Jin)
    • G. Bucher (Boybeyi)
    • H. Chen (Schneider)
    • I. Colfescu (Schneider)
    • X. Feng (Lu)
    • A. Garuba (Klinger)
  • A. Hazra (Klinger)
  • Y. Jang (Straus)
  • L. Jia(DelSole)
  • L. Krishnamurthy (Krishnamurthy)
  • E. Lajoie (DelSole)
  • J. Li (Huang)
  • J. Nattala (Kinter)
  • E. Palipane (Lu)
  • M. Scafonas (Lu)
  • B. Singh (Krishnamurthy)
  • A. Srivastava (Shukla)
  • E. Stofferahn (Boybeyi)
  • E. Swenson (Straus)
  • L. Xu(Shukla)
  • X. Yan (DelSole)
  • Climate Dynamics Faculty
    • Faculty (0.5 FTE): Boybeyi, Chiu, DelSole, Huang,Jin, Kinter, Klinger, Lu, Schneider, Schopf, Shukla (Director, CLIM), Stan, Straus(chair, AOES)
    • Adjunct:Dirmeyer (selected for 2012 appointment), Doty, Krishnamurthy
  • Bold = 2011 graduate
cola contributions to the nation s climate enterprise6
COLA Contributions to the Nation’s Climate Enterprise
  • Scientific excellence
  • Experimenting with the Nation’s climate models
  • Leading the national and international climate research community
  • Educating the next generation of experts in climate dynamics and Earth system modeling
  • Advancing scientific productivity through developing and supporting the software and data management practices
grads is used worldwide
GrADS is Used Worldwide

77,500 downloads

February 2010 - Present

history of omnibus grant
History of Omnibus Grant
  • COLA Omnibus Grant I 1994-1998
  • COLA Omnibus Grant II 1999-2003
  • COLA Omnibus Grant III 2004-2008
    • COLA Science Review 2002-2006 Jan 2007
    • Three-year review by SAC & agencies Feb 2007
    • Agencies’ Guidance to submit proposal Jul 2007
    • COLA Omnibus proposal submitted Mar 2008
    • Funding of award delayed Jan – Sep 2009
  • COLA Omnibus Grant IV 2009 – 2014
    • SAC meeting 26-27 Sep 2011

… FUTURE …

history of omnibus grant1
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 2007-2011Mar 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

Climate

Physicsisn't a religion.  If it were, we'd have a much easier time raising money. 

- Leon Lederman

planning for 2014 2018 proposal strategy
Planning for 2014-2018 Proposal: Strategy
  • What is COLA’s niche?
    • What one thing is most important to COLA and the world, and at which COLA can be the best?
  • What are remaining and emerging scientific/technical problems that need COLA's attention?
  • Balance: basic research, applied research, education and service to the Nation

COLA

planning for 2014 2018 proposal opportunities
Planning for 2014-2018 Proposal: Opportunities
  • Ideas from recent COLA research
  • New ideas emerging in the community
  • Strategic plans of funding agencies
  • Collaborationswith partner organizations, including other programs at GMU
highlights of current cola research
Highlights of Current COLA Research
  • ISI prediction – predictability rebound
    • Develops as a result of coupling of climate system components, e.g., L-A, O-A
    • Consistent with “predictability in the midst of chaos”
  • Seamless prediction
    • Applying NWP technology for climate prediction: higher resolution is necessary but not sufficient for improving climate model fidelity
    • Unifying metrics of NWP and climate prediction quality and fidelity
  • Decadal predictability and prediction
    • Rigorous scientific basis for decadal predictability. Evidence for multi-decadal modes of variability; however, we don’t know how to initialize those modes
    • Decadal predictions with CFSv2 (CMIP5)
      • Detected predictability due to changing GHG forcing and ENSO; nothing in between
  • Hypothesis-based experimentation
    • Model fidelity determines model predictability
    • To provide feedback to model development, need experiments to test hypotheses
basis for seamless prediction
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.
seamless approaches
“Seamless” Approaches
  • “Strong” seamlessness: a unified modeling approach in which the same model code, with different grids and/or parameter settings, is used for severe weather prediction, medium-range weather prediction, ISI climate prediction and global change projections
  • Palmer: Using the constraints and insights gained from NWP to influence climate model development and the climate prediction enterprise
    • Can reduce long-standing uncertainties in climate effects of cloud-aerosol-radiation interaction, other fast processes
  • Brunet, Hurrell: Data assimilation for coupled models as a prediction and validation tool for weather and climate research
  • Running NWP models at ISI time scales
  • Running IPCC-class models to predict days to decades

Initial Value Problem

Data Assimilation

Initialization

funding agencies agendas
Funding Agencies’ Agendas
  • NSF GeoVision: Develop a framework to understand and predict responses of the Earth as a system, from the space-atmosphere boundary to the core, including the influences of humans and ecosystems. 
  • NOAA Strategic Plan (paraphrased): An informed society anticipating and responding to climate and its impacts
    • Improved scientific understanding of the changing climate system and its impacts
    • Assessments that identify potential impacts and inform science, service, and stewardship decisions
    • Mitigation and adaptation choices supported by sustained, reliable, and timely climate services
    • A climate-literate public that understands its vulnerabilities to a changing climate and makes informed decisions
  • NASA Climate Strategic Plan (paraphrased):
    • Modeling, assessment, and computing for the 2013 National Assessment by the USGCRP and IPCC AR5
    • Acceleration of operational use of NASA research data to improve climate prediction and weather forecasting
    • Synthesis of NASA Earth Science observations(key mission science teams)
  • DOE BER Strategic Plan (paraphrased):
    • Develop higher-resolution models, integrate more relevant processes, and cover longer time scales
    • Improve parameterizations and basic knowledge about aerosols
    • Advance understanding of important biological interactions and feedbacks to identify possible mitigation strategies
    • Improve Earth system models (ESMs) and develop new techniques to evaluate them at both global and regional scales
    • Comprehensively compare ESM predictions with observations based on new observing technologies
major themes for cola 2014 2018
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
    • Develop and maintain The Honest Broker web page for national climate models
    • Create a multi-disciplinary institute (climate, environment, biodiversity, society) at GMU
    • Educate the next generation of climate scientists: GMU Climate Dynamics Ph.D. program
    • Contribute to National Multi-Model Ensemble (NMME) for operational seasonal forecast
    • Further develop and support GrADS; export best practices in data management
major themes for cola 2014 20181
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
basic and applied research
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 probabilistic framework
    • Natural and forced variability – predictability of (MJO/ISO, ENSO, AMO, PDO, …) and changes in these natural modes associated with climate change
    • Characterizing and quantifying uncertainty
    • Predictability of regional climate and scientific basis for adaptation strategies
characterizing and quantifying model uncertainty
Characterizing and Quantifying Model Uncertainty
  • Current approaches – e.g., MME, perturbed physics, perturbed parameters – are ad hoc "ensembles of opportunity”
  • More meaningful and rigorous way needed, e.g. ensemble data assimilation scheme for parameter estimation and stochastic parameterizations
basic and applied research1
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
    • Natural and forced variability – predictability of (MJO/ISO, ENSO, AMO, PDO, …) and changes in these natural modes associated with climate change
    • Characterizing and quantifying uncertainty
    • Predictability of regional climate and scientific basis for adaptation strategies
      • Global downscaling: 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
      • Reduce uncertainty and error due to engineering vagaries associated with limited-area approach
basic and applied research2
Basic and Applied Research
  • Conduct systematic and rigorous multi-scale evaluation of physical processes and mechanisms of variability at all time scales from intraseasonal to decadal in national models
    • Evaluate fidelity of national climate models, with actionable feedback to national model development centers
    • Evaluate relative merits of deterministically-parameterizedmulti-model ensemble predictionvs. stochastically-parameterized probabilistic prediction
    • Further explore and exploit 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.
    • Develop a multi-model interactive ensemble
basic and applied research3
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
    • 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
major themes for cola 2014 20182
Major Themes for COLA 2014-2018
  • Broader Impacts and Service to the Nation
    • Develop and maintain The Honest Broker web page for national climate models
    • Create multi-disciplinary GMUinstitute: climate, environment, biodiversity, society
    • Educate next generation of climate scientists: GMU Climate Dynamics Ph.D. program
    • Contribute to National Multi-Model Ensemble for operational seasonal forecast
    • Further develop and support GrADS; export best practices in data management
      • Design and implement new data analysis capabilities (linear algebra, sorting, defop)
      • Design and implement new data model for quasi-regular grids (swaths, icosahedral, …)
      • Explore GrADS-in-the-Cloud: mobile computing platforms for geoscience data analysis
      • Leveraging GrADS open source development model to entrain broader development community
major themes for cola 2014 20183
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
    • Develop and maintain The Honest Broker web page for national climate models
    • Create multi-disciplinary GMUinstitute: climate, environment, biodiversity, society
    • Educate next generation of climate scientists: GMU Climate Dynamics Ph.D. program
    • Contribute to National Multi-Model Ensemble for operational seasonal forecast
    • Further develop and support GrADS; export best practices in data management