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

© 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

© Crown copyright Met Office Traditional forecasts

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

Precipitation over SE Asia, summer 1998

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

© Crown copyright Met Office Impact models: Lake inflow

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

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

© Crown copyright Met Office Tropical storms

© 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

© 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– members Tropical storm frequency per 5 x5° box June–November 2000– member ModelObservations ModelObservations

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

© Crown copyright Met Office Monsoon onset

© 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

© 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 ( /10) Observed climatology Hindcast climatology

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

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

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

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

© Crown copyright Met Office Hot and cold days

© 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

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

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

© Crown copyright Met Office The detail: Data Seasonal system: GloSea4, based on HadGEM3-ES 21 year hindcast, 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, Each member runs for 10 years Deterministic forecast is assessed using ensemble mean of 9 members. Observations: Temperature: HadGHCND Tmin and Tmax, spatially incomplete

© Crown copyright Met Office Global assessment: HadGHCND

© 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)

© Crown copyright Met Office Results

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

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

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

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

© Crown copyright Met Office Extent of the relationship - seasonally

© Crown copyright Met Office Change forecast method Forecast daily data

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

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

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

© Crown copyright Met Office A closer look at the hindcast

© Crown copyright Met Office Product for UK: Cold days

© 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

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

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

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

© Crown copyright Met Office The detail GloSea4. 19 years of hindcast 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

© Crown copyright Met Office Predictability of very wet days

© 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

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

© 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

© Crown copyright Met Office Questions and answers