1. © Crown copyright Met Office The Met Office high resolution seasonal prediction system Anca Brookshaw – Monthly to Decadal Variability and Prediction, Met Office Hadley Centre, UK FOCRAII 2013

2. Outline configuration of latest operational system analysis of hindcasts ENSO (N)AO WNPSH rainfall ‘extremes’ tropical storms © Crown copyright Met Office

3. Outline configuration of latest operational system analysis of hindcasts ENSO (N)AO WNPSH rainfall ‘extremes’ tropical storms © Crown copyright Met Office

4. A recent history of improvements at the Met Office -Summer 2009: New generation prediction system (linked to model development) becomes operational -Nov. 2010: -Vertical high-res (L85 stratosphere / L75 ocean) -Sea-ice assimilation -May 2011: -Extension to monthly system -Nov. 2012: -Horizontal high resolution (50 km atm / 0.25 ocean) -NEMOVAR – 3d-Var ocean data assimilation © Crown copyright Met Office

5. Representation of orography ~ 120 km ~ 50 km

6. GloSea5 operational system Model version: HadGEM3 GA3.0 Resolution: N216L85 O(.25)L75 (~50 km atm.) Simulations length: 7 months Model uncertainties represented by: SKEB2 stochastic physics (Tennant et al. 2011) Initial conditions uncertainties represented by: Lagged ensemble

7. © Crown copyright Met Office Initialisation of the system Forecast (initialised daily): - Atmosphere & land surf: Met Office NWP analysis (4d-Var) - Ocean & sea-ice: NEMOVAR (3d-Var joint system for ocean, med-range, monthly and seasonal) 14-year hindcast (1996-2009): - Atmosphere & land surf: ERA-interim - Ocean & sea-ice: seasonal ODA reanalysis - Fixed start dates of 1 st, 9 th, 17 th, 25 th of each month - 3 members per start date

8. © Crown copyright Met Office Ensemble: lagged approach Seasonal Forecast: - 2 members run each day. - Seasonal forecast updated weekly by pulling together last 3 weeks (i.e. 42 members) Hindcast (for monthly-seasonal): 14 year hindcast run in real time ( 42 members run each week = 14 years x 3 members) Monthly Forecast: - 2 additional members run each day. - Monthly Forecast updated daily by pulling together last 7 days (i.e. 28 members)

9. © Crown copyright Met Office 20/06/2011 How the system runs – an example Atmos & land surf: NWP anal Ocean/sea-ice : Seasonal ODA Atmos & land surf: ERA-i Ocean: Seasonal ODA reanalysis 25/07/1996 (m1) 25/07/1997 (m1) 25/07/1998 (m1) 25/07/1999 (m1) 25/07/2000 (m1) 25/07/2001 (m1) Monday 21/06/2011 25/07/2002 (m1) 25/07/2003 (m1) 25/07/2004 (m1) 25/07/2005 (m1) 25/07/2006 (m1) 25/07/2007 (m1) Tuesday 26/06/2011 25/07/2004 (m3) 25/07/2005 (m3) 25/07/2006 (m3) 25/07/2007 (m3) 25/07/2008 (m3) 25/07/2009 (m3) Sunday Each week: 14x 7-month forecasts, 14x 2-month forecasts (for monthly forecast) and 42x 7-month hindcasts (1996-2009) 20/06/2011 21/06/2011 26/06/2011

10. Outline configuration of latest operational system analysis of hindcasts ENSO (N)AO WNPSH rainfall ‘extremes’ tropical storms © Crown copyright Met Office

11. Improving ENSO forecasts Obs The westward extension of Nino is a common error in many climate models. It affects remote regions. High-res model has better ENSO pattern and teleconnections Low resolution High resolution

12. © Crown copyright Met Office Niño3.4 SST: ACC, RMSE/spread ACC higher (good) RMSE reduced (good) May  JJANov  DJF GloSea5 (red) GloSea4 (blue)

13. © Crown copyright Met Office JJA DJF ForecastObserved Better ENSO teleconnections: precipitation Niño - Niña

14. Outline configuration of latest operational system analysis of hindcasts ENSO (N)AO WNPSH rainfall ‘extremes’ tropical storms © Crown copyright Met Office

15. Scaife et al., GRL, 2011 Realistic blocking frequency Benefits of higher ocean resolution: improved bias and Atlantic blocking Gulf Stream bias westerly wind bias => blocking deficit Low res 1 o No Gulf Stream bias No westerly wind bias => good blocking High res 0.25 o

16. © Crown copyright Met Office A breakthrough in predicting the NAO GloSea5 Hindcast Atlantic pressure Significant (98%) NAO skill r~0.6 (other models: approx 0.2; not stat. sig.) Opens up many possibilities for long range prediction for Europe and North America ObsForecast Retrospective winter forecasts

17. Outline configuration of latest operational system analysis of hindcasts ENSO (N)AO WNPSH rainfall ‘extremes’ tropical storms © Crown copyright Met Office

18. International collaboration to improve prediction systems Working with Chinese Meteorological Agency on West North Pacific Subtropical High Jianlong Li

19. © Crown copyright Met Office GPCP Composite rainfall with strong WNPSH Importance of West North Pacific Subtropical High Jianlong Li

20. © Crown copyright Met Office Obs Previous System New System The variability of the WNPSH is much improved in the latest system Jianlong Li

21. © Crown copyright Met Office GPCP Correlation between SH index and rainfall GloSea5 Jianlong Li

22. © Crown copyright Met Office Correlations with observations: Previous System =0.41 ---- New System=0.83 Skill predicting interannual variability of West North Pacific Subtropical High Jianlong Li

23. © Crown copyright Met Office Skill predicting interannual variability of rainfall over the Yangtse River Valley Correlations with observations: Previous System = 0.35 ---- New System= 0.69 Jianlong Li

24. Outline configuration of latest operational system analysis of hindcasts ENSO (N)AO WNPSH rainfall ‘extremes’ tropical storms © Crown copyright Met Office

25. dry Emily Wallace

26. © Crown copyright Met Office wet Emily Wallace

27. Outline configuration of latest operational system analysis of hindcasts ENSO (N)AO WNPSH rainfall ‘extremes’ tropical storms © Crown copyright Met Office

28. Tropical storm tracks: June-November 1996-2009 GloSea5 ~53 km horizontal resolution 1 member, June–November 1996–2009 Observations from JTWC June–November 1996–2009 Increase in horizontal resolution has improved the tracks of tropical storms in the Western Pacific. Joanne Camp

29. © Crown copyright Met Office Hindcast skill: Northwest Pacific June–November 1996–2009 Tropical Storm Frequency (winds ≥34 knots) Accumulated Cyclone Energy (ACE) Index GloSea5 = 0.56 Glosea4 = 0.39 Pearson’s linear correlation (ensemble mean vs observations): GloSea5: Max 30 members/year GloSea4: 9 members/year GloSea5 shows greater skill for predictions of tropical storm numbers and ACE index in the Western North Pacific basin, compared to GloSea4. GloSea5 = 0.88 GloSea4 = 0.80 Joanne Camp

30. GloSea5 storm track density: El Niño vs. La Niña La NiñaEl Niño Changes in storm genesis locations with ENSO simulated well by GloSea5. La Niña - El Niño Fewer storms Obs (JTWC) GloSea5 (ensemble mean) © Crown copyright Met Office Joanne Camp

31. © Crown copyright Met Office An international prediction system KMA KMA (Rep. of Korea) Joint seasonal forecast system Shared workload and computing costs: possibility to extend hindcast and increase resolution NCMRWF NCMRWF (India) Implementing GloSea for research

32. © Crown copyright Met Office Seamless system across timescales GloSea5 med-range (2013) Project to merge with med-range in 2013 Aim is to have a single operational system (using coupled model at the highest possible resolution) for short-range ocean, med-range, monthly and seasonal – at the end of 2013 GloSea5 decadal (2014) System to be extended – in research mode - to decadal timescales in 2013 Seamless system med-range to decadal from 2014

33. © Crown copyright Met Office Thank you.