Strategic Planning Scientific Advisory Committee 27 February 2007.
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Transcript of Strategic Planning Scientific Advisory Committee 27 February 2007.
Strategic Planning
Scientific Advisory Committee27 February 2007
“Omnibus” Funding
1994-1998 Predictability and Variability of the Present Climate
Funding: $2.25M /yr Principal Investigator: J. ShuklaCo-Principal Investigators: J. Kinter, E. Schneider, D. Straus
1999-2003 Predictability and Variability of the Present Climate
Funding: ~$2.75M / yrPrincipal Investigator: J. ShuklaCo-Principal Investigators: J. Kinter, E. Schneider, P. Schopf, D.
StrausCo-investigators: P. Dirmeyer, B. Huang, B. Kirtman
2004-2008 Predictability of Earth’s ClimateFunding: ~$3M / yr (NSF - 46%; NOAA - 39%; NASA - 15%)Principal Investigator: J. ShuklaCo-Investigators: T. DelSole, P. Dirmeyer, B. Huang, J. Kinter, B.
Kirtman, B. Klinger, V. Krishnamurthy, V. Misra, E.
Schneider, P. Schopf, D. Straus
COLA is 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
History of COLA Omnibus Grant
Omnibus Grant I 1994 – 1998Science Review 1993 – 1996SAC & Agencies Review 6-7 Nov 1996Agencies’ Guidance to Submit Proposal May 1997SAC Review of Proposal 26-27 Mar 1998Omnibus Proposal Submitted May 1998Omnibus Grant II 1999-2003SAC Meeting 14-15 Nov 2000Five-Year Science Review 1997-2001SAC & Agencies Review 21-22 Feb 2002Agencies’ Guidance to Submit Proposal April 2002 SAC Review of Proposal 6-7 Feb 2003Omnibus Proposal Submitted April 2003Omnibus Grant III 2004-2008 SAC Meeting 26-27 Sep 2005Five-Year Science Review 2002-2006SAC & Agencies Review 26-28 Feb 2007Agencies’ Guidance to Submit Proposal April 2007
If new omnibus proposal is invited:SAC Review of Proposal Feb 2008Omnibus Proposal Due March 2008Omnibus Grant IV (anticipated) 2009-2013
History of COLA Omnibus Grant
Omnibus Grant I 1994 – 1998Science Review 1993 – 1996SAC & Agencies Review 6-7 Nov 1996Agencies’ Guidance to Submit Proposal May 1997SAC Review of Proposal 26-27 Mar 1998Omnibus Proposal Submitted May 1998Omnibus Grant II 1999-2003SAC Meeting 14-15 Nov 2000Five-Year Science Review 1997-2001SAC & Agencies Review 21-22 Feb 2002Agencies’ Guidance to Submit Proposal April 2002 SAC Review of Proposal 6-7 Feb 2003Omnibus Proposal Submitted April 2003Omnibus Grant III 2004-2008 SAC Meeting 26-27 Sep 2005Five-Year Science Review 2002-2006SAC & Agencies Review 26-28 Feb 2007Agencies’ Guidance to Submit Proposal April 2007
If new omnibus proposal is invited:SAC Review of Proposal Feb 2008Omnibus Proposal Due March 2008Omnibus Grant IV (anticipated) 2009-2013
History of COLA Omnibus Grant
Omnibus Grant I 1994 – 1998Science Review 1993 – 1996SAC & Agencies Review 6-7 Nov 1996Agencies’ Guidance to Submit Proposal May 1997SAC Review of Proposal 26-27 Mar 1998Omnibus Proposal Submitted May 1998Omnibus Grant II 1999-2003SAC Meeting 14-15 Nov 2000Five-Year Science Review 1997-2001SAC & Agencies Review 21-22 Feb 2002Agencies’ Guidance to Submit Proposal April 2002 SAC Review of Proposal 6-7 Feb 2003Omnibus Proposal Submitted April 2003Omnibus Grant III 2004-2008 SAC Meeting 26-27 Sep 2005Five-Year Science Review 2002-2006SAC & Agencies Review 26-28 Feb 2007Agencies’ Guidance to Submit Proposal April 2007
If new omnibus proposal is invited:SAC Review of Proposal Feb 2008Omnibus Proposal Due March 2008Omnibus Grant IV (anticipated) 2009-2013
2005 SAC Meeting• SAC recommendations:
– Plan strategically to focus and prioritize activities leading up to next five-year proposal
– Address emerging issues in predictability and the interface with climate change
• COLA conducted several strategic planning sessions in 2006– Establish vision, mission, and core values– Identify assets, core competencies and
opportunities– Plan themes and outline for 2009-2013
omnibus proposal
Vision, Mission, Core Values
• VisionGlobal society benefits from use-inspired basic research on climate variability predictability and change and the free access to data and tools to perform that research
• MissionEstablish and quantify the predictability of seasonal to decadal variations of the Earth’s climate, including the effects of global change
• Core values • People and teamwork• Scientific and technical excellence• Scientific integrity (through peer-reviewed
publication)
• Innovative experimentation
Basic vs. Applied Research
Pure basic research(e.g. Bohr)
Research is inspired by:
Quest for fundamental
understanding?
Considerations of use?
YES
NO
YESNO
Use-inspired basic research(e.g. Pasteur)
Pure applied research
(e.g. Edison)
Pasteur’s Quadrant, Donald E. Stokes, 1997
Assets, Core Competencies, and Opportunities
• Assets• Excellent team of scientists• High-quality in-house and remote computing resources and
data sets• Long experience in climate dynamics modeling and analysis
• Core competencies • Evaluation of and experimentation with Nation’s climate
models• Scientific leadership in seasonal-to-interannual predictability• PhD education in Climate Dynamics at GMU• GrADS and GDS: Highly valued, widely used information
technology
• Opportunities• The world has accepted global climate change; however,
society needs to progress from global mean, time-mean, century-end projections to regional-scale, time-varying next-decade predictions
• Tools: new National models (CCSM-3+, CFS-2, GEOS-5)• Contributing to new WCRP strategy for next 10+ years (COPES)
Climate of the Next Decade• IPCC assessment reports provide strong evidence
that• global climate is changing• human activity is part of the cause
• However, society needs information of a different sort:
• Climate of the next decade (or two) to match the planning horizon
• end-of-century results can’t answer risk assessment or mitigation questions
• Regionally-specific climate information• global mean values can’t answer national or state planning
questions
• Changes in weather, intraseasonal, seasonal, and interannual climate
• Modes of variability • Droughts, floods, extremes at various time scales
• Society needs predictions of the total climate system from days to decades, at regional scales, with estimates of probabilities and uncertainties
• Different requirements for developed (U.S.) and developing countries
• Mission statement for all US climate research -- how will COLA contribute?
Requirements for Days to Decades Prediction
• Identify and quantify what is predictable at decadal lead times: decadal modes, role of noise, multi-scale interaction, preferred geographic regions or seasons etc.
• Assemble a probabilistic prediction system• Develop initialization techniques and initial conditions
for decadal prediction• Generate retrospective research-quality climate data
sets and decadal predictions • Address issue of process-resolving models• Determine if predicting these elements could provide
societally-relevant information with 10-20 year lead times to address long-term planning & risk-management issues
Requirements for Days to Decades Prediction
Climate componentspredictable at decadal leads
Climate information ??? societally-relevant
a decade in advance
Lorenz once said that there are three questions about predictability:
• What do we want to predict? • What can we predict? • Is there anything in common between the two?
It may happen that what we want to predict is hardest to predict (e.g. regional water cycle)!
Steps Toward Days to Decades Prediction
For each time scale of interest: – Identify the climate phenomena that
occur– Identify the places and times of the year
where these phenomena occur– Identify the physical process(es) involved– Identify the likely origin of predictability
For example …
Steps Toward Days to Decades Prediction
~19759 levels1 member
~200526 levels10 members
cloud(?)resolving cloud
resolving
needed toget ETC fluxes right(Jung, 2006)
Requirement for Regional-Scale Prediction:
Process-Resolving Models
Resolving Cloud ProcessesRequires Million-Fold Increase
in Computing Resources
COLA Omnibus, 2009-2013:
Predictability of the Physical Climate System:
Scientific Foundations for Dynamical Prediction from Days to
Decades
Scientific Questions
What limits predictability at all time scales from days to decades? Is there a fundamental limit? What is the role of model error? Initial conditions error? It took 30 years to determine the fundamental growth rate of NWP error -- Can we accelerate progress toward quantifying the fundamental limit of climate predictability?
What aspects of the total climate system (global troposphere, stratosphere, world oceans, sea ice, land surface state, vegetation, snow) are predictable in which geographic regions, for which seasons, and how does that change in the future? For the current generation of climate models and observing systems? Future generations?
Seamless prediction: Does scale interaction enhance predictability? For example, does improved prediction of intraseasonal variations improve seasonal forecasts?
What is the optimal combination of models to predict means? Extremes? Current models have huge limitations, e.g. for regional water cycle need to develop a multi-model ensemble combination that produces the best forecast
Predictability of the Physical Climate
System
Multi-Model National Models Framework
CFS (NOAA) Bridges the gap between NWP and S-I predictionRoutinely evaluated in real-time seasonal prediction mode Collaboration with NCEP (+ Climate Test Bed)CCSM (NSF)Bridges the gap between S-I predictability studies and global climate change studies (e.g. COLA S-I predictions w/ CCSM-3)Collaboration with NCAR (model development)GEOS (NASA)Bridges the gap between coupled modeling and assimilating space-based observations (atmosphere and ocean)Collaboration with GMAO (+ MAP program)GFDL (NOAA)??? SAC (2005) recommended ≤ 3 modelsCollaboration with GFDL
Predictability of the Physical Climate
System
Proposal Outline
0. VISION, MISSION, HYPOTHESES AND GOALS
1. SCIENTIFIC FOUNDATIONS FOR DYNAMICAL PREDICTION FROM DAYS TO DECADES
2. DYNAMICS AND MODELING OF THE TOTAL CLIMATE SYSTEM
Predictability of the Physical Climate
System
• Expands research horizon to decades• Takes advantage of COLA’s uniqueness: innovative methodologies for studying predictability, including evaluation of total physical climate system, mechanistic experiments, predictable component analysis
Proposal Outline
0. VISION, MISSION, HYPOTHESES AND GOALSEmphasis on both fundamental predictability and days-to-decades prediction
1. SCIENTIFIC FOUNDATIONS FOR DYNAMICAL PREDICTION FROM DAYS TO DECADES
2. DYNAMICS AND MODELING OF THE TOTAL CLIMATE SYSTEM
Predictability of the Physical Climate
System
Proposal Outline0. VISION, MISSION, HYPOTHESES AND GOALS
1. SCIENTIFIC FOUNDATIONS FOR DYNAMICAL PREDICTION FROM DAYS TO DECADES
1. Intra-Seasonal, Seasonal and Interannual Predictability in a Changing Climate2. Land-Climate Interaction
3. Decadal Time Scales
2. DYNAMICS AND MODELING OF THE TOTAL CLIMATE SYSTEM
Predictability of the Physical Climate
System
Intra-Seasonal, Seasonal and Interannual Predictability in a Changing Climate
Intra-SeasonalCharacteristics of regimes: predictability, transitions, change
SeasonalCoupled Dynamical Seasonal Prediction:
roles of noise, decadal modes, climate changedetermining the best multi-model ensembleroles of systematic error, initial conditions error
Extreme events (e.g. US droughts, Asian monsoon drought): predicting the whole PDF
InterannualENSO as a forced/damped or unstable mode: impact of changing climateTropical Atlantic variability (TAV): Remote-forced vs. intrinsic predictability Variability in the Indian Ocean: IO dipole (IOD) and interaction with monsoonsEffects of changing climate on interannual variability
SCIENTIFIC FOUNDATIONS FOR DYNAMICAL PREDICTION FROM
DAYS TO DECADES
Land-Climate Interaction
Improving the coupled land-atmosphere response from days to decades
Extended uncoupled diagnostics
Multi-scale water cycle predictabilityRole of noise in land-atmosphere interactionProcess-scale land-atmosphere interaction - is Alan Betts
right?
Impacts of vegetation variability and change on climate predictability
Initializing the land: soil moisture, vegetation, snow & land ice to demonstrate impact of land surface on prediction skill
Improve and extend global land-surface data setsBaseline forcing data set
Pursue strategies to reduce dry-down in coupled L-A models
SCIENTIFIC FOUNDATIONS FOR DYNAMICAL PREDICTION FROM
DAYS TO DECADES
Decadal Time Scales
What are the decadal predictable components?We have evidence that the Atlantic MOC has memory … Can we find and quantify memory in other processes?(potential collaboration with GFDL, others)
Coupled initialization of total climate system(collaboration with NCAR, GMAO)
Attribution of sources of climate anomalies of past 150 years (continuation of C20C project)
Predicting days to decades - multi-scale interactionWhat limits predictability beyond interannual time scales? How often does a decadal prediction have to be initialized (monthly,seasonally, annually)?
Broader impacts: Identifying high-risk climate-related issues
SCIENTIFIC FOUNDATIONS FOR DYNAMICAL PREDICTION FROM
DAYS TO DECADES
Proposal Outline
0. VISION, MISSION, HYPOTHESES AND GOALS
1. SCIENTIFIC FOUNDATIONS FOR DYNAMICAL PREDICTION FROM DAYS TO DECADES
2. DYNAMICS AND MODELING OF THE TOTAL CLIMATE SYSTEM1. Climate Dynamics2. Multi-Model Ensembles and Predictability of Extremes: Information Theory3. Toward Process-Resolving Models (topography, clouds, snow, etc.)
potential collaborations with other modeling groups
Predictability of the Physical Climate
System
Proposal Outline0. VISION, MISSION, HYPOTHESES AND GOALS
1. SCIENTIFIC FOUNDATIONS FOR DYNAMICAL PREDICTION FROM DAYS TO DECADES1. Intra-Seasonal, Seasonal and Interannual Predictability in a Changing Climate
Intraseasonal: Characteristics of regimesSeasonal: Coupled Dynamical Seasonal Prediction and extreme events - predicting the whole PDFInterannual: ENSO dynamics, TAV, Indian Ocean variability, and the effects of the changing climate
2. Land-Climate InteractionImproving the coupled land-atmosphere response, multi-scale water cycle predictability, vegetation variability and change, initializing the land, global land-surface data sets, and strategies to reduce dry-down
3. Decadal Time ScalesPredictable components on decadal time scales, coupled initialization, C20C, predicting days to decades, broader impacts
2. DYNAMICS AND MODELING OF THE TOTAL CLIMATE SYSTEM1. Climate Dynamics2. Multi-model Ensembles and Predictability of Extremes: Information Theory3. Toward Process-Resolving Models
Predictability of the Physical Climate
System