Prediction and Predictability of the Global Atmosphere-Ocean System from Days to Decades

1. Prediction and Predictability of the Global Atmosphere-Ocean Systemfrom Days to Decades Presenters: Keith Thompson, Hal Ritchie, George Boer

2. Overview of proposal accepted for funding by CFCAS Science: Proposal must be scientifically sound Targeted: Clear contribution must be made National: Clear contribution to a national effort Expertise: Capacity and capability to deliver Collaboration: Number of partners preferred Funding: Need for CFCAS funds and leverage

3. Overall Goal and Approach Goal: Improve predictions of the ocean and atmosphere on time-scales of days to decades, and space scales of tens of km to global Approach: Improvements in data assimilative models of the ocean and atmosphere, and a better understanding of the physical mechanisms that permit, and limit, predictability

4. Motivation The Need • Government departments such as EC, DFO and DND need the products that our network will develop (e.g. an interactive ocean to extend the predictability of NWP models, ocean initial conditions for seasonal predictions and climate simulations, open boundary conditions for regional marine ecosystem models, nowcasts of ocean state). • The public, decision-makers and policy-makers all need information based on the best possible science. The Opportunity • New data streams (e.g. Argo, sea surface topography). • Momentum resulting from significant Canadian investment in research programs such as Clivar and GODAE.

5. Structure of the Proposal • The proposal built on two themes distinguished by time scale: • Theme I: Days to Seasons • Theme II: Seasons to Decades • and the network will work toward a seamless prediction capability that bridges the time-scales • The two themes reflect: • the expertise in both weather and climate modelling and prediction available in Canada • the potential advantages of multi-model approaches • the developing international activities in THORPEX and WCRP COPES melding weather and climate prediction • .

6. Theme I Projects Sub-theme I.1: Ocean Modelling and Data Assimilation • Suppression of bias and drift in ocean model components • Statistics of observed variability for model testing and improvement • Multivariate assimilation of altimeter and Argo data • Ocean reanalysis and forecasting • Modelling and assimilation of sea ice Sub-theme I.2: Coupled AO Modeling and Data Assimilation • Assimilation into coupled models • Studies on joint assimilation into coupled models • Simulation and prediction of variability using a coupled Tropical -Pacific global atmosphere model

7. Mean Sea Surface Topography From Space Observed from space Comparison with model What is the message here?

8. Theme II Projects Sub-theme II.1: Analysis and Mechanisms • Tropical Modes: El Niño - Southern Oscillation and the MJO • Pacific Decadal Oscillation, Southern and Northern Annular Modes Sub-theme II.2: Predictability of the Coupled System • Potential predictability of current and future climates • Prognostic predictability from ensembles of coupled model simulations Sub-theme II.3: Prediction • Coupled Model Initialization • The Coupled Model Historical Forecasting Project • Forecast Combination, Calibration and Verification

9. Initial coupled model NINO3.4 SSTprediction attempts using the CCCma CGCM 1982-83 El Nino 1997-98 El Nino Obs. Obs. SST ANOM Ensemble Avg. SST ANOM Ensemble Avg. 1988-1989 La Nina 1975-1976 La Nina Ensemble Avg. Obs. SST ANOM SST ANOM Obs. Ensemble Avg. 1 2 3 4 5 6 7 8 9 10 11 12 Forecast range (months) 1 2 3 4 5 6 7 8 9 10 11 12 Forecast range (months)

10. Science Criterion: Proposal must be scientifically sound Theme I Theme II • Analysis and mechanisms • Predictability of the coupled system • Diagnostic • Prognostic • Coupled forecast initialization • Coupled historical forecasting project • Verification • Ocean data assimilation • Ocean Analysis and forecasting • Regional • Global • Role of eddies • Applications • Data assimilation • Coupling • Analysis methods • Modes of variability • Limits to predictability • Value of forecasts

11. Summary of Science Plan • Tackling an important scientific and technical issue: Prediction and predictability of the atmosphere ocean system on time-scales of days to decades. Part of an international effort involving many major research centers. Connections to GODAE, CLIVAR, THORPEX, WCRP COPES initiative. • Clear, feasible research plan based on 15 projects organized in two themes. Joint activities, and mechanisms for collaboration between themes, have been identified. • Network brings together Canadian research groups that have previously worked independently (e.g. atmosphere and ocean data assimilators, east coast regional ocean modellers and west coast climate modellers) leading to synergistic collaborations. • Builds on previous Canadian research efforts and investments in support of Clivar, GODAE and Argo.

12. The Network will … • Build on existing expertise in universities and government in ocean, atmosphere and coupled modelling • Accelerate development of Canadian expertise in the assimilation of ocean data and the generation of ocean products • Produce research leading to skillful seasonal-interannual-decadal predictions of social and economic value to Canada • Contribute new knowledge to the coupled predictability problem spanning time-scales of days to decades • Move Canada toward a seamless prediction capability • Make a strong contribution in the training of highly qualified personnel

13. Partners and Interactions • The network has 3 major partners: EC, DFO and DND • It also has international links (Clivar, GODAE, THORPEX, Mercator) • Co-applicants represent 7 universities. Each has a strong research record and plays a clearly defined role. • The co-applicants have diverse backgrounds e.g. • 8 out of the16 co-applicants are adjuncts • 9 of the co-applicants are in Theme I, and 7 in Theme II • 6 co-applicants have primarily atmospheric expertise, 8 oceanographic, and 2 interdisciplinary Given the co-applicants’ diverse backgrounds, there is great potential for interactions that will cross traditional boundaries, and lead to cross fertilization of ideas and techniques

14. Tang (UNBC) Demirov (MUN) Myers (UA) Foreman, Hsieh (UBC) Boer, Flato, Fyfe, Merryfield (UVic) Derome (McGill) Gauthier (UQAM) Greatbatch, Ritchie, Thompson, Wright (Dal) Stacey (RMC) The Need for a Network Approach Institution Discipline Theme Atmos Adjunct Theme I Academic Ocean Theme II

15. Expertise and Experience • Co-applicants have the required range of expertise: ocean and atmospheric modelling and assimilation, NWP and climate prediction, dynamics, parameterizations and skill assessment • Past involvement in large, collaborative and successful research programs and projects • Experience with technology transfer and development of operational systems (e.g. NWP, seasonal climate predictions and storm surge forecasting) • Ongoing involvement in international programs

16. Need for Funds and Leveraging • 77% of funds for training of HQP • No duplication of existing funds • Builds on, and feeds into, existing and planned government initiatives (e.g. seasonal prediction, intergovernmental initiative, Argo) • Highly leveraged

17. Reverse Impact Statement: What if the study is not funded? • Canada will slip further behind those nations with operational weather forecast centers that are beginning to use coupled models. In five years CMC will still be issuing 1-season forecasts using a 2-tier approach, even though their competitors are issuing multi-season forecasts based on coupled models. • Development of research, and an operational capability, in ocean data assimilation will not accelerate. • Canada will not advance significantly in operational ocean forecasting over the next 5 years and will not be able to respond adequately to the growing demand for ocean products. • Ocean products will have to be imported and this could be both costly and ineffective in terms of meeting Canada’s specific needs.