1. © Crown copyright Met Office Seasonal forecasting: Not just seasonal averages! Emily Wallace November 2012

2. © Crown copyright Met Office Contents Traditional seasonal forecasts Current bespoke products Tropical storms Monsoon onset Hot and cold days Research into new products Very wet days in Malaysia

3. © Crown copyright Met Office Traditional forecasts

4. GloSea4 ensemble prediction of Nino3.4 SST anomaly from March 2010 © Crown copyright Met Office

5. Precipitation over SE Asia, summer 1998

6. Seasonal forecasts are... Broad-brush Probabilistic Large scale Useful?? Reminder:

7. © Crown copyright Met Office Impact models: Lake inflow

8. Sector specific applications: Lake Volta, Ghana © Crown copyright Met Office Corr. = 0.69 June forecasts of total July-Oct. inflow Preceding rainfall and flow predictors plus seasonal forecast predictors Fcst Obs

9. Seasonal forecasts are... Broad-brush Probabilistic Large scale Useful Wasting information?? Reminder:

10. © Crown copyright Met Office Tropical storms

11. © Crown copyright Met Office Current forecast products Deterministic forecasts Provides a best estimate and forecast range (±1 stdev interval) for: Numbers of named storms ACE index During the following 6 months Probabilistic forecasts Probability distributions Exceedance of thresholds (to aid assessment of risk) Help to quantify and communicate the inherent uncertainties in the forecast. Public forecast Tailored products

12. © Crown copyright Met Office Western North Pacific tropical storm tracks in GloSea5 Storm tracks Model storms have characteristics that are similar to observed storms: Model storms produced at same latitude Many storms last longer than 5 days. Produces straight moving and recurving tracks – important for landfall forecasts Track density Model peak in TS frequency in the SCS as in observations Tracks shifted too far north near the dateline. June–November 1996–2009 12 members Tropical storm frequency per 5 x5° box June–November 2000–2009 1 member ModelObservations ModelObservations

13. © Crown copyright Met Office Experimental multi-model seasonal tropical storm forecasts Skill (1996-2009) Tropical storms: 0.47 Typhoons: 0.62 ACE index: 0.77 No. forecast ensemble members: 93 20 13 240

14. © Crown copyright Met Office Monsoon onset

15. © Crown copyright Met Office Temporal evolution Describe “temporal evolution” with local rainfall accumulations between 18 Sep-31 Jan Express accumulation as percentage of long-term average season total Time Fraction of season total rainfall onset= 20% Average time of onset Heavy line: accumulated precip. from climatology Thin line: accumulated precip. for individual year example: early onset in individual year

16. © Crown copyright Met Office Observed mean evolution: 20 th isochrone Colours indicate time of local arrival of 20% of average season total rainfall GPCP average 18 Sep/30 Jan (1996- 2009/10) Observed climatology Hindcast climatology

17. © Crown copyright Met Office GloSea4 forecast skill ROC scores 20 th isochrone for 1 August hindcasts Early arrival:Late arrival:

18. © Crown copyright Met Office GloSea4 Forecast probabilities for 2011 Short Rains (Sep-Nov) Early onset:Late onset: Courtesy of Michael Vellinga

19. © Crown copyright Met Office Observations for 2011 Courtesy of Lizzie Good

20. © Crown copyright Met Office Relocatable Northwest monsoon: Arrival of 30 th isocrone 2011

21. © Crown copyright Met Office Hot and cold days

22. © Crown copyright Met Office What is an extreme day? E.g. 33.5°C for March E.g. 34.5°C for June 90 th percentile Extreme day

23. © Crown copyright Met Office What is an extreme day? 2006: No extreme days 2010: 56 extreme days!

24. © Crown copyright Met Office Percentile approach is locally relevant Hamilton et al, 2012, JGR A global assessment

25. © Crown copyright Met Office The detail: Data Seasonal system: GloSea4, based on HadGEM3-ES 21 year hindcast, 1989-2009 Each member runs for 6 months Deterministic forecast is assessed using ensemble mean of 9 members. Decadal system: DePreSys, based on HadCM3 (can also be used for seasonal forecasting) 46 year hindcast, 1960-2005 Each member runs for 10 years Deterministic forecast is assessed using ensemble mean of 9 members. Observations: Temperature: HadGHCND Tmin and Tmax, spatially incomplete

26. © Crown copyright Met Office Global assessment: HadGHCND

27. © Crown copyright Met Office The detail: Methodology Temp: Tmin, Tmax at 10 th and 90 th percentiles All regridded to 3.75deg x 2.5deg (resolution of obs) Extremes are counted from daily data Then smoothed to 18.75° x 17.50° (5x7 boxes) Average Spearman’s rank correlation coefficient over all combinations Seasonal skill is assessed over the 4 seasons: Dec-Jan (DJF), Mar-May (MAM), Jun-Aug (JJA) and Sep-Nov (SON)

28. © Crown copyright Met Office Results

29. © Crown copyright Met Office Extremes Mean Difference Seasonal temperature: Skill of extremes vs. mean -0.3 0.30 South east Asia average: 0.59 South east Asia average: 0.66 Grey=missing

30. © Crown copyright Met Office Is the daily data really providing additional information?

31. © Crown copyright Met Office Relationship between extremes and mean: Hot days and annual mean temperature

32. © Crown copyright Met Office Relationship in South East Asia - seasonally Number of days exceeding Temperature DJF

33. © Crown copyright Met Office Extent of the relationship - seasonally

34. © Crown copyright Met Office Change forecast method Forecast daily data

35. © Crown copyright Met Office Change forecast method Inferring the number of exceedances from the predicted seasonal mean anomaly Number of days exceeding Temperature

36. © Crown copyright Met Office Difference Comparing methods Extremes counted from daily data Extremes inferred from seasonal mean -0.3 0.3 0 South east Asia average: 0.59 South east Asia average: 0.49 Grey=missing

37. © Crown copyright Met Office Daily data from model gives no skilful information Extremes counted from daily data Extremes inferred from mean Percentile Spearman’s

38. © Crown copyright Met Office A closer look at the hindcast

39. © Crown copyright Met Office Product for UK: Cold days

40. © Crown copyright Met Office Temperature extremes summary Extremes are predictable on seasonal and decadal timescales. In general predictability comes from the strong relationship between the seasonal mean and the number of extremes

41. © Crown copyright Met Office Predictability of daily precipitation extremes…a first look

42. © Crown copyright Met Office Very wet days (90 th percentile) 0 very wet days 10 very wet days

43. © Crown copyright Met Office Jolly wet days (90 th percentile) 0 very wet days 10 very wet days

44. © Crown copyright Met Office The detail GloSea4. 19 years of hindcast 1996-2007 Obs: APHRODITE (up to 2007) 4 seasons: MAM, JJ, ON, ND All regridded to 3.75deg x 2.5deg Dry or very wet days are counted from daily data No smoothing before calculation of skill Spearman’s rank correlation coefficient

45. © Crown copyright Met Office Predictability of very wet days

46. © Crown copyright Met Office Very wet days: Skill of Total precip : Number of very wet days MAM JJ ON ND Similar skill to that of seasonal total precipitation

47. © Crown copyright Met Office A closer look at the hindcast Malaysian Peninsular Oct-Nov forecasts of very wet days

48. © Crown copyright Met Office Conclusions The Met Office is predicting more user-relevant variables Tropical storms: Analysis shows that skilful predictions could be made for the western North Pacific basin Monsoon onset: A useful product in Africa – possible to relocate to South East Asia Hot and cold days: predictable at seasonal lead time. Predictability linked to seasonal mean temperature predictability Very wet days: Predictable over South East Asia. Collaboration needed for best results

49. © Crown copyright Met Office Questions and answers