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

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


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


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




System

Proposal Outline0. VISION, MISSION, HYPOTHESES AND GOALS


1. Intra-Seasonal, Seasonal and Interannual Predictability in a Changing Climate2. Land-Climate Interaction

3. Decadal Time Scales



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


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


DAYS TO DECADES

Proposal Outline

0. VISION, MISSION, HYPOTHESES AND GOALS


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


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


System

strategic planning scientific advisory committee 27 february 2007

Documents

omnibus proposal duemarch

omnibus proposal submittedmay

new omnibus proposal

sac agencies review21

sac agencies review26

sac agencies review6

sac meeting14

sac meeting26