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«Bob Weller, Woods Hole Oceanographic Institution Ben Kirtman, University of Miami September 2, 2010 National Oceanic and Atmospheric Administration ...»

Assessment of

Intraseasonal to Interannual

Climate Prediction and Predictability

Bob Weller, Woods Hole Oceanographic Institution

Ben Kirtman, University of Miami

September 2, 2010

National Oceanic and Atmospheric Administration

The National Academies

 A private, non-profit organization charged to provide

advice to the Nation on science, engineering, and


 National Academy of Sciences (NAS) chartered in 1863;

The National Research Council (NRC) is the operating arm of the NAS, NAE, and IOM.

 NRC convenes ad hoc committees of experts who serve pro bono, and who are carefully chosen for expertise, balance, and objectivity  All reports go through stringent peer-review and must be approved by both the study committee and the institution.


1) Committee Membership and Charge

2) Motivation and Committee Approach Why Intraseasonal to Interannual (ISI) Timescales?

 What is “Predictability?” 

3) Framework for Report Sources of Predictability  Research Goals Building Blocks of ISI Forecast Systems  Improvements to Building Blocks Procedures for Operational Centers  Best Practices Committee Membership ROBERT A. WELLER (Chair), Woods Hole Oceanographic Institution ALBERTO ARRIBAS, Met Office, Hadley Centre JEFFREY L. ANDERSON, National Center for Atmospheric Research ROBERT E. DICKINSON, University of Texas LISA GODDARD, Columbia University EUGENIA KALNAY, University of Maryland BENJAMIN KIRTMAN, University of Miami


MICHAEL B. RICHMAN, University of Oklahoma R. SARAVANAN, Texas A&M University DUANE WALISER, Jet Propulsion Laboratory, California Institute of Technology BIN WANG, University of Hawaii Charge to the Committee

The study committee will:

1. Review current understanding of climate predictability on intraseasonal to interannual time scales;

2. Describe how improvements in modeling, observational capabilities, and other technological improvements have led to changes in our understanding of predictability;

3. Identify key deficiencies and gaps remaining in our understanding of climate predictability and recommend research priorities to address these gaps;

4. Assess the performance of current prediction systems;

5. Recommend strategies and best practices that could be used to assess improvements in prediction skill over time.

Why Intraseasonal to Interannual (ISI) Timescales?

 “ISI” - timescales ranging from a couple of weeks to a few years.

 Errors in ISI predictions are often related to errors in longer term climate projections  Useful for a variety of resource management decisions Many realizations/verifications possible.

What is “Predictability?”  “The extent to which a process contributes to prediction quality.”  Literature provides variety of interpretations;

committee agreed on qualitative approach.

Key aspects of committee approach  Quantitative estimates of a upper limit of predictability for the real climate system are not possible.

 Verification of forecasts provide a lower bound for predictability.

 Traditional predictability studies (e.g., twin model studies) are qualitatively useful.

Framework for Analyzing ISI Forecasting

Performance of ISI forecasting systems is based upon:

1) Knowledge of Sources of Predictability How well do we understand a climate process/phenomenon?

2) Building Blocks of Forecasting Systems To what extent do observations, data assimilation systems, and models represent important climate processes?

3) Procedures of Operational Forecasting Centers How do these centers make, document, and disseminate forecasts?

–  –  –

El Niño – Southern Oscillation (ENSO) Recommendations Regarding Sources of Predictability Many sources of predictability remain to be fully exploited by ISI forecast systems.

Criteria for identifying high-priority sources:

1) Physical principles indicate that the source has an impact on ISI variability and predictability.

2) Empirical or modeling evidence supports (1).

3) Identifiable gaps in knowledge/representation in forecasting systems.

4) Potential social value.

Six Research Goals for Sources of Predictability

–  –  –

Six Research Goals for Sources of Predictability

2) Stratosphere-troposphere interactions Improve understanding of link between stratospheric processes and ISI variability. Successfully simulate/predict sudden warming events and subsequent impacts.

–  –  –

3) Ocean-atmosphere coupling Understanding of sub-grid scale processes should be improved.

Six Research Goals for Sources of Predictability

4) Land-atmosphere feedbacks Investigate coupling strength between land and atmosphere. Continue to improve initialization of important surface properties (e.g., soil moisture).

–  –  –

5) High impact events (volcanic eruptions, nuclear exchange) Develop forecasts following rapid, large changes in aerosols/trace gases.

Six Research Goals for Sources of Predictability

6) Non-stationarity Long-term trends affecting components of climate system (e.g., greenhouse gases, land use change) can affect predictability and verification techniques.

Changes in variability may also be important.

–  –  –

Observational Networks Forecast Improvements involve each of the Building Blocks Past improvements to ISI forecasting systems have occurred synergistically.

(e.g., with new observations comes the need for model improvement and expansion of DA system) Improvements to Building Blocks

1) Errors in dynamical models should be identified and corrected. Sustained observations and process studies are needed. Observations (top) and Model (bottom)  Examples:

* double intertropical convergence zone * poor representation of cloud processes  Climate Process Teams serve as a useful model for bringing together modelers and observationlists  Other programmatic mechanisms should be explored (e.g. facilitating testing of increased model resolution) SST (shading); precipitation (contours) Improvements to Building Blocks

2) Continue to develop and employ statistical techniques, especially nonlinear methods.

Statistical methods are useful in making predictions, assessing forecast performance, and identifying errors in dynamical models.

Cutting-edge nonlinear methods provide the opportunity to augment these statistical tools.

3) Statistical methods and dynamical models are complementary and should be pursued.

Using multiple prediction tools leads to improved forecasts.

Examples of complementary tools:

 Model Output Statistics  Stochastic Physics  Downscaling techniques Improvements to Building Blocks

4) Multi-model ensemble forecast strategies should be pursued, but standards and metrics should be developed.

–  –  –

Improvements to Building Blocks

5) For operational forecast systems, state-of-the-art data assimilation systems should be used (e.g. 4-D Var, Ensemble Kalman Filters, or hybrids).

Operational data assimilation systems should be expanded to include more data, beginning with ocean observations.

–  –  –

Relationship between Research and Operations Collaboration has expanded knowledge of ISI processes and improved performance of ISI forecasts.

Collaboration is necessary BOTH:

 between research and operational scientists  among research scientists; linking observations, model development, data assimilation, and operational forecasting.

–  –  –

1) Improve the synergy between research and operational communities.

 Workshops targeting specific forecast system improvements, held at least annually  Short-term appointments to visiting researchers  More rapid sharing of data, data assimilation systems, and models  Dialog regarding new observational systems Best Practices

2) Establish publically-available archives of information associated with forecasts Includes observations, model code, hindcasts,  forecasts, and verifications.

Will allow for quantification and tracking of  forecast improvement.

Bridge the gap between operational centers  and forecast users involved in making climate-related management decisions or conducting societally-relevant research.

3) Minimize the subjective components of operational ISI forecasts.

Best Practices

4) Broaden and make available forecast metrics.

 Multiple metrics should be used;

No perfect metric exists.

 Assessment of probabilistic information is important.

 Metrics that include information on the distribution of skill in space and time are also useful.

–  –  –

Case studies illustrate how improvements of building blocks of ISI forecasting system led to an improved representation of a source of predictability.

–  –  –

Adoption of Best Practices:

• requires stable support for research gains to be integrated into operations;

• establishes an institutional infrastructure that is committed to doing so;

• will establish “feedbacks” that guide future investments in making observations, developing models, and aiding decision-makers (i.e., BEYOND “traditional” operations);

32represents a continuous improvement process.

• Closing Thoughts  Empirically, few revolutionary jumps in forecasting performance  Best Practices are “enabling” mechanisms  Improvements should be “synergistic;” they should be designed to encourage and enable strong feedbacks among operations, research, model development, observations, and decision science

For more information:

–  –  –

Report is available online at www.nap.edu.

Image Credits Slide 6 Grand Coolee Dam – Bonneville Power Administration; Wheat field – USDA; Cloud fraction image - M. Wyant, R. Wood, C. Bretherton, C. R. Mechoso, Pre-VOCA 9 soil moisture – Vinnikov and Yeserkepova, J..Climate, 4, 66-79 (1991); Volcano impact – Robock and Mao, J. Climate, 8, 1086-1103 (1995); ENSO – CPC/NCEP/NOAA 11 MJO – Waliser et al., BAMS, 87, 425-431 (2005) 12 Stratosphere – Mark Baldwin, NWRA 13 ENSO - McPhaden (2004), BAMS, 85, 677–695 14 Soil moisture – Seneviratne et al., Earth-sci. Reviews, 99, 3-4, 125-161 (2010) 15 Volcano – USGS 16 Keeling curve – Scripps Institute of Oceanography 17 Buoy – NOAA; Model globe - NOAA 18 SST graph – Balmaseda et al., Proceedings of Oceanobs’09, ESA Pub. WPP-306, (2009) 19 Double-ITCZ - Lin (2007) J. Climate, 20, 4497–4525.

21 MME – Jin et al., Climate Dynamics, 31, 647-664 (2008) 22 Satellite obs - ECMWF 24 Sources are from the respective organizations 25 Flooding – NRCS; Volcano – NASA; Drought – NESDIS; Moscow sun - BBC 26 ENSO curves – IRI; NCEP flow chart – Jon Gottschalck, NCEP 28 National Archives 29 Pdf’s - IRI

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