Strategic Planning

Strategic Planning Scientific Advisory Committee 27 February 2007

“Omnibus” Funding COLAis 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 2004-2008 Predictability of Earth’s Climate Funding: ~$3M / yr (NSF - 46%; NOAA - 39%; NASA - 15%) Principal Investigator: J. Shukla Co-Investigators: T. DelSole, P. Dirmeyer, B. Huang, J. Kinter, B. Kirtman, B. Klinger, V. Krishnamurthy, V. Misra, E. Schneider, P. Schopf, D. Straus 1999-2003 Predictability and Variability of the Present Climate Funding: ~$2.75M / yr Principal Investigator: J. Shukla Co-Principal Investigators: 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-Principal Investigators: J. Kinter, E. Schneider, D. Straus

History of COLA Omnibus Grant Omnibus Grant I 1994 – 1998 Science Review 1993 – 1996 SAC & Agencies Review 6-7 Nov 1996 Agencies’ Guidance to Submit Proposal May 1997 SAC Review of Proposal 26-27 Mar 1998 Omnibus Proposal Submitted May 1998 Omnibus Grant II 1999-2003 SAC Meeting 14-15 Nov 2000 Five-Year Science Review 1997-2001 SAC & Agencies Review 21-22 Feb 2002 Agencies’ Guidance to Submit Proposal April 2002 SAC Review of Proposal 6-7 Feb 2003 Omnibus Proposal Submitted April 2003 Omnibus Grant III 2004-2008 SAC Meeting 26-27 Sep 2005 Five-Year Science Review 2002-2006 SAC & Agencies Review 26-28 Feb 2007 Agencies’ Guidance to Submit Proposal April 2007 If new omnibus proposal is invited: SAC Review of Proposal Feb 2008 Omnibus Proposal Due March 2008 Omnibus 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 • Vision • Global 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 • Mission • Establish 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 Research is inspired by: Considerations of use? NO YES Pure basic research (e.g. Bohr) Use-inspired basic research (e.g. Pasteur) YES Quest for fundamental understanding? Pure applied research (e.g. Edison) NO 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

Climate components predictable at decadal leads 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)! Requirements for Days to Decades Prediction Climate information ??? societally-relevant a decade in advance

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

Requirement for Regional-Scale Prediction:Process-Resolving Models ~1975 9 levels 1 member ~2005 26 levels 10 members needed to get ETC fluxes right (Jung, 2006) cloud(?) resolving cloud resolving Resolving Cloud Processes Requires 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

Predictability of the Physical Climate System 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 prediction Routinely 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 models Collaboration with GFDL

Predictability of the Physical Climate System Proposal Outline 0. VISION, MISSION, HYPOTHESES AND GOALS • SCIENTIFIC FOUNDATIONS FOR DYNAMICAL PREDICTION FROM DAYS TO DECADES • DYNAMICS AND MODELING OF THE TOTAL 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

Predictability of the Physical Climate System Proposal Outline 0. VISION, MISSION, HYPOTHESES AND GOALS Emphasis on both fundamental predictability and days-to-decades prediction • SCIENTIFIC FOUNDATIONS FOR DYNAMICAL PREDICTION FROM DAYS TO DECADES • DYNAMICS AND MODELING OF THE TOTAL CLIMATE SYSTEM

Predictability of the Physical Climate System Proposal Outline 0. VISION, MISSION, HYPOTHESES AND GOALS • SCIENTIFIC FOUNDATIONS FOR DYNAMICAL PREDICTION FROM DAYS TO DECADES • Intra-Seasonal, Seasonal and Interannual Predictability in a Changing Climate • Land-Climate Interaction • Decadal Time Scales • DYNAMICS AND MODELING OF THE TOTAL CLIMATE SYSTEM

SCIENTIFIC FOUNDATIONS FOR DYNAMICAL PREDICTION FROM DAYS TO DECADES Intra-Seasonal, Seasonal and Interannual Predictability in a Changing Climate Intra-Seasonal Characteristics of regimes: predictability, transitions, change Seasonal Coupled Dynamical Seasonal Prediction: roles of noise, decadal modes, climate change determining the best multi-model ensemble roles of systematic error, initial conditions error Extreme events (e.g. US droughts, Asian monsoon drought): predicting the whole PDF Interannual ENSO as a forced/damped or unstable mode: impact of changing climate Tropical Atlantic variability (TAV): Remote-forced vs. intrinsic predictability Variability in the Indian Ocean: IO dipole (IOD) and interaction with monsoons Effects 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 predictability Role of noise in land-atmosphere interaction Process-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 sets Baseline 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 interaction What 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

Predictability of the Physical Climate System Proposal Outline 0. VISION, MISSION, HYPOTHESES AND GOALS • SCIENTIFIC FOUNDATIONS FOR DYNAMICAL PREDICTION FROM DAYS TO DECADES • DYNAMICS AND MODELING OF THE TOTAL CLIMATE SYSTEM • Climate Dynamics • Multi-Model Ensembles and Predictability of Extremes: Information Theory • Toward Process-Resolving Models (topography, clouds, snow, etc.) potential collaborations with other modeling groups

Predictability of the Physical Climate System Proposal Outline 0. VISION, MISSION, HYPOTHESES AND GOALS • SCIENTIFIC FOUNDATIONS FOR DYNAMICAL PREDICTION FROM DAYS TO DECADES • Intra-Seasonal, Seasonal and Interannual Predictability in a Changing Climate Intraseasonal: Characteristics of regimes Seasonal: Coupled Dynamical Seasonal Prediction and extreme events - predicting the whole PDF Interannual: ENSO dynamics, TAV, Indian Ocean variability, and the effects of the changing climate • Land-Climate Interaction Improving 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 • Decadal Time Scales Predictable components on decadal time scales, coupled initialization, C20C, predicting days to decades, broader impacts • DYNAMICS AND MODELING OF THE TOTAL CLIMATE SYSTEM • Climate Dynamics • Multi-model Ensembles and Predictability of Extremes: Information Theory • Toward Process-Resolving Models

Strategic Planning