1. © Crown copyright Met Office Seasonal Forecasting EUROBRISA. Paraty, March 2007 Alberto Arribas

2. © Crown copyright Met Office The impossibility of model- only automated-products … today

3. © Crown copyright Met Office What’s a seasonal forecasts? Between art and science ….. Dynamical forecasting models Analysis of current ocean observations Statistical forecasting model Analysis of climate trends Skill assessed by past performance of the forecast methods Monthly conference of experts (forecasting, research & comms staff) (Met Office,EURO-SIP) Research studies (e.g. PREDICATE, COAPEC) What other forecasts are saying

4. © Crown copyright Met Office Based around the view that seasonal-to-decadal variability has: a few large scale patterns is a fluid-dynamical jigsaw puzzle with a few key pieces Mechanisms (models and obs): predictability! Input to operational forecasts What’s a seasonal forecasts?

5. © Crown copyright Met Office Predictability studies Perfect model skill – ECMWF model Potential skill – Atlantic SST, climate Change, El Nino and volcanoes Greenhouse gases are missing (Lineger et al. 2007) Atlantic SST response is weak (Rodwell et al. 2004) El Nino teleconnection is missing (Toniazzo and Scaife 2006) Volcanic influence is weak (Stenchikov et al. 2006) (Adam Scaife)

6. © Crown copyright Met Office GloSea4, the new Met Office Seasonal Forecasting System Expected to become operational in April 2009 and later to be integrated with our decadal system Main drivers: Role of seasonal forecasts as adaptation tool to climate change Bridge between NWP – Climate to facilitate model development SCIENTIFIC CHALLENGES

7. © Crown copyright Met Office GloSea4 scientific challenges: Coupled Initialization

8. © Crown copyright Met Office Current setup (Met Office, ECMWF): GloSea4 scientific challenges: Coupled Initialization Atmos + Land surf. (reconf. NWP) Ocean DA Coupled model perturbations Problems: -Imbalances atmosphere/ocean -Perturbations degrading analysis

9. © Crown copyright Met Office GloSea3 GloSea4 scientific challenges: Coupled Initialization

10. © Crown copyright Met Office Observations GloSea4 scientific challenges: Coupled Initialization

11. © Crown copyright Met Office GloSea4 scientific challenges: Coupled Initialization Simple coupling prescribing SSTs to both atmos and ocean models and no ocean DA

12. © Crown copyright Met Office Ocean, ice and atmosphere to see coupled model fluxes during assimilation period (no mismatch between NWP and coupled forecast fluxes) Background state for assimilation will be a coupled model state Eliminates initial shocks: Coupled model should be in balanced state at end of initialization process. GloSea4 scientific challenges: Coupled Initialization

13. © Crown copyright Met Office GloSea4 scientific challenges: Perturbations Problems of the current perturbations (to SST and wind stress) used at ECMWF and Met Office: Not relevant for extra tropics Not Flow dependant Degrading analysis

14. © Crown copyright Met Office GloSea4 scientific challenges: Perturbations Pert. Members CTRL (Bowler, Arribas et al. 2008. QJRMS)

15. © Crown copyright Met Office GloSea4 scientific challenges: Perturbations Problems of the current perturbations (to SST and wind stress) used at ECMWF and Met Office: Not relevant for extra tropics Not Flow dependant Degrading analysis GloSea4 will use a lagged approach (not perturbing a central analysis)

16. © Crown copyright Met Office Flow dependent perturbations Representing true uncertainty (spatial and magnitude) Current system Lagged approach GloSea4 scientific challenges: Perturbations

17. © Crown copyright Met Office GloSea4 scientific challenges: Model development and uncertainties

18. © Crown copyright Met Office GloSea4 is being build around HadGEM3 GloSea4 scientific challenges: Model development

19. © Crown copyright Met Office GloSea4 is being build around HadGEM3 GloSea4 scientific challenges: Model development

20. © Crown copyright Met Office HadGEM3 produces strong MJO-scale variability in OLR (comparable with observations), but weak variability in zonal wind (not shown). OLR, obs OLR, HadGEM3 Frequency-wave power spectra for OLR anomalies at MJO scales (30-100d period, wavnumbers 1-4) Andrew Marshall GloSea4 scientific challenges: Model development

21. © Crown copyright Met Office (Figure courtesy of Tim Stockdale ) GloSea4 scientific challenges: Model uncertainties

22. © Crown copyright Met Office Stoch.Physics: RP GloSea4 scientific challenges: Model uncertainties (Bowler, Arribas et al. 2008. QJRMS)

23. © Crown copyright Met Office Stoch.Physics: SKEB1  CTRL run  SKEB run K -3 K -5/3 GloSea4 scientific challenges: Model uncertainties (Bowler, Arribas et al. 2008. submitted QJRMS)

24. © Crown copyright Met Office GloSea4 scientific challenges: Post-processing

25. © Crown copyright Met Office GloSea4 scientific challenges: Post-processing (i) Emphasis on assessing the usefulness of probabilistic forecasts to end- users (Cusack and Arribas, 2008. Mon.Wea.Rev.) (ii) Reducing sampling errors to provide more robust forecasts and estimate of ‘skill’ metrics (Cusack and Arribas, submitted Mon.Wea.Rev.)

26. © Crown copyright Met Office GloSea4 scientific challenges: Post-processing (i) Emphasis on assessing the usefulness of probabilistic forecasts to end- users (Cusack and Arribas, 2008. Mon.Wea.Rev.) (ii) Reducing sampling errors to provide more robust forecasts and estimate of ‘skill’ metrics (Cusack and Arribas, submitted Mon.Wea.Rev.) Statistical models Spatial aggregation Temporal aggregation

27. © Crown copyright Met Office GloSea4 scientific challenges: Post-processing (i) Emphasis on assessing the usefulness of probabilistic forecasts to end- users (Cusack and Arribas, 2008. Mon.Wea.Rev.) (ii) Reducing sampling errors to provide more robust forecasts and estimate of ‘skill’ metrics (Cusack and Arribas, submitted Mon.Wea.Rev.)

28. © Crown copyright Met Office GloSea4 scientific challenges: Visualization/Communication

29. © Crown copyright Met Office GloSea4 scientific challenges: Visualization/Communication 1987-2001 1971-2001 1961-1990

30. © Crown copyright Met Office GloSea4 scientific challenges: Visualization/Communication Main customer: Public Weather Service Always a message, not necessarily a signal!!! Windows of opportunity (e.g. 05/06 winter -> strong NAO)

31. Page 31© Crown copyright 2004 The need to combine forecasts with additional data ….

32. Page 32© Crown copyright 2004 1976 1995 2003 2006 Skill-calibrated combination of statistical and dynamical predictions 2004 2005 1983 1997 1999 2000 2001 2002 1988 1998 1986 1987 1993 1972 Fcst prob (May) of T > 15.5C = 27.1% (1:4) Clim prob (2007) of T > 15.5C = 17.3% (1:6) Clim prob (71-00) of T > 15.5C = 5.4% (1:19) Summer 2007 probabilities for UK. May forecast

33. Page 33© Crown copyright 2004 Akosombo dam: 1000MWatt hydro-electric power station ~50% of Ghana’s electricity Limit of catchment Lake Volta Forecasting inflow for Volta dam

34. Page 34© Crown copyright 2004 Applications: water volume inflow, lake Volta: learning to use in decision making Real-time forecasts Corr=0.69 June issue forecasts of Jul-Oct inflow Inflow model combines: Dynamical+statistical+catchment obs.

35. Page 35© Crown copyright 2004

36. © Crown copyright Met Office Thanks / Obrigado / Gracias All questions welcome

37. © Crown copyright Met Office Combining information When enough people can collect, re-use and distribute public sector information, people organise around it in new ways, creating new enterprises and new communities... In the past, only large companies, government or universities were able to re-use and recombine information. Now, the ability to mix and 'mash' data is far more widely available. (Mayo and Steinberg, 2007) http://www.cabinetoffice.gov.uk/newsroom/news_releases/2007/070625_info_res.asp

38. © Crown copyright Met Office

40. Long-range forecasts CGCM initialisation issue Better to have fairly smooth evolution but starting from ‘wrong’ state, or to start near observed state but have more shock/drift?? Several options to consider. daily 20C isotherm depths No data assim in initial ocean (top) GloSea with data assim (bottom) UCL CASE students: Jamie Jackson: initialisation theory in simple coupled model (year 2) Peter Kowalski: North Atlantic re-emergence effect simple model (year 1)

41. © Crown copyright Met Office Development Timeline Dec09Dec08Feb08 Pre-GloSea4: Experimental system 09/08: Final configuration GloSea4 (Vert/Hor res.) 01/09: GloSea4 ready (init, stoch.phys, cal.) 05/09: GloSea4 operational acceptance Evaluation of performance (Seasonal Skill index)

42. © Crown copyright Met Office GloSea4 Initialization + IC unc. Model unc. Model dev. Calibration GloSea4 (Maff) (Drew, Margaret) (Alberto) (Stephen, Anna) (Stephen, Margaret) PACE, ENSEMBLES, DYNAMITE

43. © Crown copyright Met Office GloSea4 System design Atmos + Land surf. (reconf. NWP) Ocean DA at ORCA1 res Coupled DA HadGEM3 + Stoch phys. Post-proc. 1 (sampling issues) Atmos + Land surf. (reconf. ERA-40) NEMO hindcast (driven by ERA-40) at ORCA1 res Coupled DA? HadGEM3 + Stoch phys. Post-proc. 1 (sampling issues) Post-proc. 2 (bias correction) PRODUCTS Linux serversSupercomputer Note: No perturb. to ICs. System run every week with Stoch. Phys.

44. © Crown copyright Met Office GloSea4, the new Met Office Seasonal Forecasting System Technical infrastructure (Maff) Design considerations: As smooth as possible to transfer between research and operations. (e.g. creative use of SCS task filtering.) UI changes kept to a minimum Mimic operational file systems in research mode Self-correcting in event of failure, where possible Scripting kept out of SCS: can then be properly version controlled Use of automatic documentation tools, and clear user/implementation guides: anyone can easily find their way around the system FCM repository: easy control of script versions passed to operations. Branching => bug fixing and suite development. Also issue tracking.

45. © Crown copyright Met Office GloSea4 scientific challenges: Coupled Initialization

46. © Crown copyright Met Office GloSea4, the new Met Office Seasonal Forecasting System Technical infrastructure (Maff)

47. © Crown copyright Met Office GloSea4, the new Met Office Seasonal Forecasting System Coupled atmosphere/ocean initialization

48. © Crown copyright Met Office Late winter cold Europe response with many El Niños Teleconnection pathway from the Pacific into Europe El Niño composites from a troposphere- stratosphere-mesosphere model (L60HadGAM1): ENSO – NAO pathway Filling of the polar cyclone (DJF 50hPa Z) negative NAO in late winter (JFM PMSL) Sarah Ineson Downward propagation

49. © Crown copyright Met Office Impact of vertical resolution on seasonal forecasts for Europe  HadGEM2-A 15-member ensemble hindcasts (Dec-Apr) for 15 winters, 1962-2005  Investigate impact on UK / European climate in standard model (L38) and high-top model with stratospheric resolution (L60) of: - ENSO- North Atlantic SST tripole - stratospheric warmings- Volcanic eruptions (example below) Z50, obs Z50, L60 Next steps:  Delve a little deeper into the models’ weak polar vortex response (common problem)  Determine hindcast skill in L60 vs L38 Full summary of results on next slide… Andrew Marshall

50. © Crown copyright Met Office Current setup: GloSea4 scientific challenges: Coupled Initialization Atmos + Land surf. (reconf. NWP) Ocean DA at ORCA1 res Coupled DA HadGEM3 + Stoch phys.

51. © Crown copyright Met Office GloSea4 scientific challenges: Coupled Initialization Atmosphere and ocean start-dump Atmosphere (NWP-DA) Land surface (NWP-DA) SST (Ostia) Ocean (FOAM-DA)