1. My Ocean WP18, Athens 29-30 September 2009 1 Medium Range Weather/ Monthly and Seasonal forecasts Magdalena A. Balmaseda ECMWF (UK) Climate & Seasonal Forecasts

2. My Ocean WP18, Athens 29-30 September 2009 2 WP18: Task on Climate Seasonal Forecast Participants:  Met Office  Meteo France  CMCC  ECMWF  MetNo (?) First suggestion: generalize a and split the task to 1. Seamless weather to seasonal forecasting 2. Climate Impacts

3. My Ocean WP18, Athens 29-30 September 2009 3 Overview Forecasts at different time scales: SEAMLESS prediction Seasonal Forecasts needs  Why do we want forecast at seasonal time scales?  End To End Seasonal Forecasting Systems  Initialization  Calibration Monthly Forecasts needs Weather Forecasts needs Climate Reanalysis (atmosphere/ocean/ice) needs

4. My Ocean WP18, Athens 29-30 September 2009 4 There is a clear demand for reliable seasonal forecasts:  Forecasts of anomalous rainfall and temperature at 3-6 months ahead For a range of societal, governmental, economic applications:  Agriculture  Heath (malaria, dengue,…)  Energy management  Markets, insurance  Water resource management, Huge progress in the last decade:  Operational seasonal forecasts in several centres  Pilot/Research progress for demonstrating applicability (DEMETER,IRI,EUROBRISA,…)  Build-up of community infrastructure (at WMO level)

5. My Ocean WP18, Athens 29-30 September 2009 5 The basis for extended range forecasts Forcing by boundary conditions changes the atmospheric circulation, modifying the large scale patterns of temperature and rainfall, so that the probability of occurrence of certain events deviates significantly from climatology.  Important to bear in mind the probabilistic nature of climate forecasts  How long in advance?: from seasons to decades  The possibility of seasonal forecasting has clearly been demonstrated  Decadal forecasting activities are now starting. The boundary conditions have longer memory, thus contributing to the predictability. Important boundary forcing:  SST: ENSO, Indian Ocean Dipole, Atlantic SST  Land: snow depth, soil moisture  Atmospheric composition: green house gases, aerosols,…  Ice?

6. My Ocean WP18, Athens 29-30 September 2009 6 End-To-End Seasonal forecasting System ENSEMBLE GENERATION COUPLED MODEL Forecast PRODUCTS Initialization Forward IntegrationForecast Calibration OCEAN PROBABILISTIC CALIBRATED FORECAST

7. My Ocean WP18, Athens 29-30 September 2009 7 Importance of Initialization Atmospheric point of view: Boundary condition problem  Forcing by lower boundary conditions changes the atmospheric circulation. “Loaded dice” Oceanic point of view: Initial value problem  Prediction of tropical SST: need to initialize the ocean subsurface. oEmphasis on the thermal structure of the upper ocean oPredictability is due to higher heat capacity and predictable dynamics  A simple way: ocean model + surface fluxes. o But uncertainty in the fluxes is too large to constrain the solution.  Alternative : ocean model + surface fluxes + ocean observations oUsing a data assimilation system. oThe challenge is to initialize the thermal structure –without disrupting the dynamical balances (wave propagation is important) –While preserving the water-mass characteristics

8. My Ocean WP18, Athens 29-30 September 2009 8 Real Time Ocean Observations ARGO floats XBT (eXpandable BathiThermograph) Moorings Satellite SST Sea Level

9. My Ocean WP18, Athens 29-30 September 2009 9 Coupled Hindcasts, needed to estimate model climatological PDF, require a historical ocean reanalysis Real time Probabilistic Coupled Forecast time Ocean reanalysis Quality of reanalysis affects the climatological PDF, and therefore the real-time forecast Consistency between historical and real-time initial conditions is required Re-forecasting needs an ocean re-analysis Models have errors and direct model output needs calibration

10. My Ocean WP18, Athens 29-30 September 2009 10 Ocean data assimilation improves the forecast skill (Alves et al 2003) Data Assimilation No Data Assimilation Impact of Data Assimilation Forecast Skill

11. My Ocean WP18, Athens 29-30 September 2009 11 A decade of progress on ENSO prediction Steady progress: ~1 month/decade skill gain How much is due to the initialization, how much to model development? S1 S2 S3 Half of the gain on forecast skill is due to improved ocean initialization

12. My Ocean WP18, Athens 29-30 September 2009 12 Ocean Observation & Reliable forecast products Calibration and multi-model can increase the skill and reliability of forecasts. In a general case, even the multi-model needs calibration. Long records are needed for robust calibration and downscaling RMS ErrorEnsemble Spread A.Can we reduce the error? How much? (Predictability limit) B. Can we increase the spread by improving the ensemble generation and calibration? Forecast Systems are generally not reliable (RMS > Spread)

13. My Ocean WP18, Athens 29-30 September 2009 13 Requirements for Seasonal Data for initialization  In situ QC data (historical and real time)  SST, high resolution (historical and real time)  Sea Level Anomalies (historical and real time)  MDT  Bottom pressure (?)  Sea ice (in the future) Independent data for validation/ comparison  Sea level data (gauges)  Ocean currents (blended product)  Ocean reanalysis from other centres (including those without model) Direct 3D ocean states: depending on centre  Met-Office, INGV (OK), since they produce MyOcean reanalysis with their own systems  Meteo-France will try the ORCA2 product from Mercator  ECMWF needs ORCA1 resolution oCan not use the ¼ degree product directly The longer the record, the better

14. My Ocean WP18, Athens 29-30 September 2009 14 ECMWF and 3D ocean reanalysis from My Ocean The time scales of the project precludes the direct output of the ¼ ocean re-analysis for seasonal forecasts  Quite expensive assessment. Alternative  Interpolate ¼ into 1 deg  Use the interpolated data.  Directly: not very interesting experiment (?)  Anomaly initialization. Other alternatives:  Use My Ocean re-analysis of sea ice.  Consider other time scales (weather /monthly forecasting).  Start work on the implementation of the ¼ of degree model from MyOcean to be ready for future use of My Ocean products.

15. My Ocean WP18, Athens 29-30 September 2009 15 Delayed Ocean Analysis ~12 days Real Time Ocean Analysis ~Real time ECMWF: Weather and Climate Dynamical Forecasts ECMWF: Weather and Climate Dynamical Forecasts 10-Day Medium-Range Forecasts 10-Day Medium-Range Forecasts Seasonal Forecasts Seasonal Forecasts Monthly Forecasts Monthly Forecasts Ocean model Atmospheric model Wave model Atmospheric model Ocean model Wave model

16. My Ocean WP18, Athens 29-30 September 2009 16 Monthly forecasts Mixed layer processes are important Currently, monthly forecasts use the same ocean model configuration that seasonal Ideally, one would use a dynamical ocean model with  Higher horizontal/vertical resolution Alternative, an ocean mixed layer model can be used:  High horizontal/vertical resolution but not dynamics  High resolution initial conditions for this ML model will be needed

17. My Ocean WP18, Athens 29-30 September 2009 17 Requirements for Monthly Forecast High resolution 3D ocean re-analysis to initialize ML model  Temperature and Salinity. Velocities  Long Reanalysis and timely real-timi (within 12-24 h) Possibility 1:  Get data for a long term reanalysis. Exact period to be discussed. (1993-2005) is a possibility)  Use it to initialize the ML model and conduct a series of monthly hindcasts.  Assess skill respect initial conditions from a low resolution ocean model.  Problem: human resources are scarce Possibility 2:  Start work on the implementation of the ¼ of degree model from MyOcean to be ready for future use of My Ocean products.

18. My Ocean WP18, Athens 29-30 September 2009 18 Medium Range Weather forecasts They will benefit from better representation of air-sea interaction:  Currents coupled to the wave model  High resolution SST with diurnal cycle. Sea-ice Currently, persisted SST anomalies are used Interest in testing coupling to currents. Or at least, initialization of currents  And use persisted currents during the forecast Ideally, one would use a dynamical ocean/sea-ice model with  Higher horizontal resolution  Higher vertical resolution  But software does not exist yet Alternative, ML model as in monthly.

19. My Ocean WP18, Athens 29-30 September 2009 19 Requirements for Medium Range Possibility 1:  Use ocean currents. It involves other groups at ECMWF.  Need for skill assessment of the product. Possibility 2:  Start work on the implementation of the ¼ of degree model from MyOcean to be ready for future use of My Ocean products.

20. My Ocean WP18, Athens 29-30 September 2009 20 Summary Extend the name of the task to seamless prediction, to include medium range and seasonal. Separate task on climate impacts. Direct products from TACS will be used by the different partners. Ready to provide assessment for different time scales URD  From ECMWF can include Atmospheric Re-analysis, Medium Range weather forecast, monthly and seasonal Use of 3D reanalysis  Not prescriptive. Depending of group.  Assessment of seasonal from CMCC, MetOffice and Meteo-France  ECMWF may not provide assessment of 3D products for seasonal. It could (needs further discussion/iterations. Resource dependent) oUse Sea-ice in seasonal oInitialization of ML model for monthly (resources) oUse of velocity data for the wave model in medium range. oStart implementing MyOcean software (1/4 degree ocean model/sea ice)

21. My Ocean WP18, Athens 29-30 September 2009 21 THE END

22. My Ocean WP18, Athens 29-30 September 2009 22 1.No observing system is redundant Example: the Pacific, where Argo, moorings and altimeter still complement. Lessons for other basins. Implications of the missing TAO data for the on-going El Nino 2.The altimeter is the only OS contributing to the North Subtropical Atlantic. Argo is the only OS contributing the skill on the Indian Ocean. 3.There are obvious problems in the Eq Atlantic: model error, assimilation, and possibly insufficient observing system Assessing the Ocean Observing System The assessment depends on the quality of the coupled model Sign of progress: a decade ago the OSES with Seasonal Forecasts were not considered a useful evaluation tool. Long records are needed for results to be significant: Any observing system needs to stay in place for a long time before any assessment is possible. So far impact on forecasts of SST only. Impact on atmospheric variables next From Balmaseda and Anderson 2009 See also Fujii et al 2008

23. My Ocean WP18, Athens 29-30 September 2009 23 Importance of Real Time Ocean ReAnalyses From Xue et al CWP. Not operational yet

24. My Ocean WP18, Athens 29-30 September 2009 24 Multi-Model Seasonal forecasts of Tropical Cyclones AtlE-Pac Obs July-November W-Pac 1987-2004 2005 Vitart et al, GRL 2007 Multi-model Forecasts: 1 st June 2005: JASON

25. My Ocean WP18, Athens 29-30 September 2009 25 What is the value of a long historical record? Example from the Medium Range Weather Forecasts (TIGGI) Impact of Increased ensemble size versus longer calibration period (Continuous Rank Probability Skill Score, T-2m Europe) A longer calibration period has larger impact than increasing the ensemble size. From Hagerdorn 2008

26. My Ocean WP18, Athens 29-30 September 2009 26 Predicting for users: end-to-end 63 ………… 62 4 3 2 1 Climate forecast ………… 63 62 4 3 2 1 Downscaling 63 ………… 62 4 3 2 1 Application model 0 Forecast probability of T or PP Forecasts probability of e.g. crop yield 0 non-linear transformation

27. My Ocean WP18, Athens 29-30 September 2009 27 5-month lead fcst Obs Corr. skill Prediction of Dengue Risk transmission: 5 month lead time From EUROBRISA http://eurobrisa.cptec.inpe.br/ Numerical Model+ Calibration + Dengue model Forecast issued in Nov 1997, valid for Apr 1998

28. My Ocean WP18, Athens 29-30 September 2009 28 D1 Time (days) 111210987654321 BRT ocean analysis: D1-12NRT ocean analysis: D1 Assimilation at D1-12 Assimilation at D1-5 Operational Ocean Analysis Schedule BRT ( Behind real time ocean analysis): ~12 days delay to allow data reception For seasonal Forecasts. Continuation of the historical ocean reanalysis NRT (Near real time ocean analysis):~ For Var-EPS/Monthly forecasts

29. My Ocean WP18, Athens 29-30 September 2009 29 There is a clear demand for reliable seasonal forecasts:  Forecasts of anomalous rainfall and temperature at 3-6 months ahead For a range of societal, governmental, economic applications:  Agriculture  Heath (malaria, dengue,…)  Energy management  Markets, insurance  Water resource management, Huge progress in the last decade:  Operational seasonal forecasts in several centres  Pilot/Research progress for demonstrating applicability (DEMETER,IRI,EUROBRISA,…)  Build-up of community infrastructure (at WMO level)

30. My Ocean WP18, Athens 29-30 September 2009 30 The basis for extended range forecasts Forcing by boundary conditions changes the atmospheric circulation, modifying the large scale patterns of temperature and rainfall, so that the probability of occurrence of certain events deviates significantly from climatology.  Important to bear in mind the probabilistic nature of climate forecasts  How long in advance?: from seasons to decades  The possibility of seasonal forecasting has clearly been demonstrated  Decadal forecasting activities are now starting. The boundary conditions have longer memory, thus contributing to the predictability. Important boundary forcing:  SST: ENSO, Indian Ocean Dipole, Atlantic SST  Land: snow depth, soil moisture  Atmospheric composition: green house gases, aerosols,…  Ice?