Medium Range Weather/ Monthly and Seasonal forecasts

1. Medium Range Weather/ Monthly and Seasonal forecasts Climate & Seasonal Forecasts Magdalena A. Balmaseda ECMWF (UK)

2. WP18: Task on Climate Seasonal Forecast • Participants: • Met Office • Meteo France • CMCC • ECMWF • MetNo (?) • First suggestion: generalize a and split the task to • Seamless weather to seasonal forecasting • Climate Impacts

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. 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. 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. COUPLED MODEL Atmosphere model Atmosphere model Atmosphere model Ocean model Ocean model Ocean model PROBABILISTIC CALIBRATED FORECAST ENSEMBLE GENERATION Forward Integration Forecast Calibration Initialization End-To-End Seasonal forecasting System Forecast PRODUCTS OCEAN

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. • Emphasis on the thermal structure of the upper ocean • Predictability is due to higher heat capacity and predictable dynamics • A simple way: ocean model + surface fluxes. • But uncertainty in the fluxes is too large to constrain the solution. • Alternative : ocean model + surface fluxes + ocean observations • Using a data assimilation system. • The challenge is to initialize the thermal structure • without disrupting the dynamical balances (wave propagation is important) • While preserving the water-mass characteristics

8. ARGO floats XBT (eXpandable BathiThermograph) Moorings Satellite SST Sea Level Real Time Ocean Observations

9. Real time Probabilistic Coupled Forecast time Ocean reanalysis Re-forecasting needs an ocean re-analysis Models have errors and direct model output needs calibration Coupled Hindcasts, needed to estimate model climatological PDF, require a historical 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

10. No Data Assimilation Data Assimilation No Data Assimilation Data Assimilation Impact of Data Assimilation Forecast Skill Ocean data assimilation improves the forecast skill (Alves et al 2003)

11. Half of the gain on forecast skill is due to improved ocean initialization S1 S2 S3 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?

12. Can we reduce the error? How much? (Predictability limit) RMSError Ensemble Spread B. Can we increase the spread by improving the ensemble generation and calibration? Ocean Observation & Reliable forecast products Forecast Systems are generally not reliable (RMS > Spread) 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

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 • Can not use the ¼ degree product directly The longer the record, the better

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. Atmospheric model Atmospheric model Wave model Wave model Ocean model Ocean model Real Time Ocean Analysis ~Real time Delayed Ocean Analysis ~12 days ECMWF: Weather and Climate Dynamical Forecasts 10-Day Medium-Range Forecasts Seasonal Forecasts Monthly Forecasts

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. 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. 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. 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. 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) • Use Sea-ice in seasonal • Initialization of ML model for monthly (resources) • Use of velocity data for the wave model in medium range. • Start implementing MyOcean software (1/4 degree ocean model/sea ice)

21. THE END