Climate Mission Outcome

1. Overview of Climate Predictions and Projections Program • Climate Mission Outcome • A predictive understanding of the global climate system on time scales of weeks to decades with quantified uncertainties sufficient for making informed and reasoned decisions (Criteria for success: progress measured by indicators of predictive understanding and skill scores)

2. Future New and Improved Products (preliminary modeling work going on for most of these already) • Climate • Improved intraseasonal to seasonal to decadal forecasts • Scenarios for future climate mitigation and adaptation studies • Assessments of potential for abrupt changes - surprises • Utilization of Earth System models in expanding product suite • Water resource & drought forecasts including nutrient runoff • Climate – related health and disease forecasts • Projections of sea level changes • Ecosystems • Ecological assessments and predictions from climate change • Fisheries productivity forecasts that incorporate the effects of climate • Improved assessments of sea level change on coastal resources and ecosystems • Forecasts and mitigation strategies related to air/water quality and quantity in coastal zone • Weather and Water • Improved 10-14 day forecasts • Regional and continental scale air-quality and atmospheric chemistry predictions • Improved forecasts for water resources (droughts, floods) including interactions with estuaries and coasts

3. Functional Structure of Predictions and Projections Program (Seasonal to Interannual Component Shown) New and Improved Products Operational Forecasts Information Products Test Bed - transition to operations • Systematic Research forecasts and applications (Research PMs) • establish systematic research multi-model SI prediction activity • establish multi-model Hydrological prediction system • Test application models – drought, fire, water • Improve consolidation tools • Routine Attribution reports • Multi-model-based predictability studies • Predictability studies • Experimental predictions • Studies supporting process research • Data Distribution capability Model & Data Assimilation System Development – in Environmental Modeling Program • Process research, hypothesis testing and diagnostic studies • Targeted efforts for improving climate models (CPTs, parameterizations,…) • Field experiments in support of model improvements & CPTs • global tropical interactions with new focus on Indo-Pacific and Atlantic regions • Monsoon related studies • Emerging applications (coastal ecosystems; air quality; fisheries,…) Observations, reanalyses, forcings research

4. What can lead to improvements in S/I forecasts - our strategy • Develop a (community) research strategy (FY06/Q2) • Improved dynamical prediction models • Enhanced use of ensemble information from a single model • Multi-model ensembles • Improved empirical prediction tools • Improvements in consolidation procedures • Improved SST predictions • Climate Nowcasts (Dynamical OCN) • Predictability beyond ENSO SSTs

5. A Number of Approaches can Improve Skill Scores: Example - Objective Consolidation Tool Pink: Operational Forecasts (avg. score ~ 17) Blue: Objective consolidation forecast tool (avg. score~23)

6. Proposed Structure for Improving Skill of SI Forecasts: Metric for incorporation into operations: improves skill over period of operational forecasts Operational SI Forecasts/Skill Objective Consolidation Tool OCN CFS Empirical Methods Assessment Research Foci Dynamical OCN Multimodel CDC/IRI/… Research SI Forecasts/Skill Objective Consolidation Tool

7. Priorities Next 1-5 years – resulting from our & CLIVAR planning – in 5 year research plans - NCEP needs? • Seasonal to Interannual (working towards regional capabilities) • Improve skill of SI predictions • Establish systematic community based multi-model forecasting capability/infrastructure • Incorporate impacts of Indo-Pacific and Atlantic SST anomalies • Develop dynamical understanding of trends – incorporate in forecasts • Implement routine attribution capability • Develop seasonal hydrological forecasting capability – (a national drought prediction experiment) • Predictive understanding of influence of climate on environment (a new focus) • Coastal ecosystems and fisheries regimes • Decadal to Centennial- working towards regional capabilities where possible • Develop experimental decadal trends forecasts resulting from predictive understanding of anthropogenic and natural variations (Atlantic focus) – also links directly to SI predictions • Attribution of climate of 20th C to natural versus anthropogenic influences • Understanding past decadal variability & abrupt changes • Reduce uncertainty in future projections • Implement earth system modeling capability • Intraseasonal Forecasting • Improve week2 skill scores • Develop capability to predict extremes for weeks 2,3,4. • Predictive understanding of climate on statistics of extremes (hurricanes & others)

8. Uses of Multi-model Ensembles Research - forecasts and “AMIP” runs - A distributed activity Climate Testbed - centralized activity Application models: hydrology, etc. Attribution and predictability studies Research forecasts Operations • The above need to be more systematic and be linked to other national/international activities • COPES • CliPAS (APEC-Korea) • C20C runs • others

9. MM Ensemble for Attribution and Predictability Assessments • What NOAA supported activities currently exist • Seasonal Diagnostics Consortium • Continuously updated AMIP runs forced with global SSTs. Participating models are from NCEP, GFDL, CDC (running CCM3), IRI, GMAO, and ECPC • Although updating the AMIP runs is a distributed activity, centralized collection of data and display of basic results is done at CPC • C20C simulations with different natural and anthropogenic forcings • Need to formalize predictability studies and link to NCPO research programs

10. MM Ensemble for Predictions • What NOAA supported activities currently exist: • MM ensemble predictions at IRI (based on tier-2 approach with skill assessments for participating models obtained from AMIP simulations) • Empirical-Dynamical SI prediction System at CDC (based on a set of tier-2 AMIP model runs) • Both are distributed approaches. Both are pragmatic in the sense that there is no consistent set of hindcasts. There are strong ties with the multi-model attribution and predictability assessment activities. • Need to have a formal comparison of these forecasts with the operational approaches

11. MM Ensembles for Predictions: Future • What more is desirable • A multi-model tier-1 prediction capability that would include several national coupled models • A consensus among participating entities as to what is required to achieve a 1-tier multi-model ensemble goal, e.g., • What should be the length for the hindcasts? • Need for a consistent ODA? • Minimum size of ensemble for each coupled model? • Distributed or centralized activity? • What gets implemented on Test Bed? • What can be achieved under the available resource? And if enough resources are not available, does meeting requirements halfway still beneficial (e.g., reduced length of hindcasts)? OR it HAS to be “an all or nothing approach.” • MM for regional downscaling (S-I, CC scenarios) • Linking to application models, e.g., hydrological predictions