1. © Crown copyright Met Office Case Study: Seasonal Forecasting -- Theory and Examples Emily Wallace, Chris Gordon, Alberto Arribas, David Hein Bangkok, Feb 201

2. © Crown copyright Met Office Seasonal Forecasting -- How is it possible?

3. © Crown copyright Met Office Predictability and chaos ‘chaos’ drywet The distribution is analogous to the climatology of a meteorological variable (here, rainfall). The ball drops can be seen as values corresponding to individual years. The precise bin in which a ball falls cannot be predicted (‘chaos’). If many drops are made, the ‘distribution’ of balls in the bins can be described.

4. Predictability and chaos wetdry ‘chaos’ large-scale influences © Crown copyright Met Office Individual ball drops are analogous to individual forecasts, all with similar starting points. The prediction consists in quantifying the difference between the two distributions (climatology and forecast). Example of large-scale influence: ocean temperatures The precise bin in which a ball falls still cannot be predicted (‘chaos’). The tilt of the table changes the shape of the distribution (‘predictability’). If many drops are made, the new distribution of balls in the bins can be described.

5. © Crown copyright 2011 time predictability ICs SSTs, surface, etc external forcings daysmonthsyears Sources of predictability: Initial conditions Boundary conditions (SST, soil moisture, etc); External forcing (emissions, etc) Seasonal: probabilistic forecast

6. ‘climate’ (seasonal averages), not ‘weather’ (conditions on specific days) large-area averages, not localised events range of outcomes, with probabilities attached to them (risk ) What is predictable at long range? How are long-range predictions done? statistical methods – using empirical relationships derived from historical records dynamical methods – using dynamical (climate) models

7. A recent history of improvements at UK 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-res (50 km atm. / 0.25 ocn) -NEMOVAR – 3d-Var Ocean Data Assimilation

8. Spatial resolution N216 N96

9. 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

10. © Crown copyright 2011 Example of forcing: sea surface temperature anomalies The forcing pattern is large scale and slow-varying in time. The impact is also large scale.

11. © Crown copyright Met Office Seasonal forecasting with dynamical models

12. A seasonal forecasting system requires: definition of starting point (initial conditions; data assimilation) model of the climate system description of uncertainties (ensembles) Dynamical methods: seasonal forecasting systems

13. © Crown copyright Met Office For seasonal forecasting, assimilation of ocean state is important Tropical Atmosphere Ocean array (TAO) ARGO floats SST Subsurface ocean

14. 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

15. © Crown copyright 2011 ENSO teleconnections: precipitation JJA DJF Forecast (E-L)Observed (E-L) Skilful reproductions in the tropics – even for rainfall Red = El Nino is drier Blue = El Nino is wetter

16. © Crown copyright Met Office Examples of bespoke forecast products and information

17. Forecasting the rainy seasons of Africa Regional Climate Outlook Forums (RCOFs) Regional and international experts meet to: predict prospects for the region help users understand and use the forecasts (agriculture, health, water resources, disaster risk management) train African climate scientists With DFID funding Met Office supports RCOFs in East, West and southern Africa by providing seasonal predictions and visiting experts Recent RCOF forecasts for southern, East and West African rainy seasons

18. West African Monsoon Rainfall - Example seasonal forecast- rainfall for MAM 2011 Traditionally, seasonal forecasts are expressed as changes over several months, e.g. MAM User consultation in Africa has indicated strong interest in temporal evolution of rainy season: rainy season onset end of rainy season dry spells Met Office seasonal forecast for probability of below-normal precip. in MAM 2011, issued February 2011

19. Onset of West African monsoon: skill of current seasonal prediction system (GloSea4) Michael Vellinga (After Fontaine et al. 2008) ‘Jump’ of main convective region from Guinea Coast to Sahel is well represented in model climatology Hindcast period: 1989-2002, nominal start date = 1 st May (average onset early July) Average GloSea4 Average observed (NOAA) Fraction of zonal strip (and pentad) with OLR below threshold (indicating deep convection) Output 2: Improved monthly-seasonal-decadal prediction

20. Predicted and observed variability of onset date Onset date = first day of pentad in which latitude strip with max activity moves (and stays) north of 10°N Distribution of simulated & observed onset dates (1989-2002) GloSea4 mean onset date and standard deviation similar to observed

21. GloSea4 Forecast probabilities for 2011 Short Rains (Sep-Nov) Early onset:Late onset: Courtesy of Michael Vellinga

22. Observations for 2011 Plots courtesy of Lizzie Good Courtesy of Michael Vellinga

23. Monsoon forecasts – what next? Engage with users in Africa to improve format and uptake of probabilistic ‘onset forecasts’ Explore further products for temporal information: dry spells, cessation Apply method in other monsoon regions to explore model biases, forecast skill at seasonal lead times etc.

24. Lake Volta dam, Akosombo 1000MWatt facility: provides ~50% of Ghana’s electricity Remainder comes from oil-fired power stations, plus imports (small) Rainfall has strong seasonal dependency: peak months June-September Inflow prediction needed to assess likely requirement for oil-fired generation …thus lake-level is monitored closely!

25. Background The Volta River Authority (VRA) previously predicted lake inflow using only preceding observed rainfall in the catchment and observed flow in main tributaries – there was no input from seasonal forecasts of rainfall Met Office developed a regression technique that includes all relevant information – preceding observed rainfall and flows AND seasonal forecast components Because the catchment is large and the target season is long, there is not too much sensitivity to temporal and geographical precision – thus it is a well posed ‘problem’ for seasonal prediction

26. Corr=0.3 2 RMSE=6 0.7% Corr=0.6 9 RMSE=4 1.7% Preceding rainfall/flow predictors only Preceding rainfall/flow predictors plus seasonal forecast predictors Verification timeseries for June forecasts of total July-Oct. inflow forecast verification Skill benefits of the new system

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

28. Obs Previous System New System The variability of the WNPSH is much improved in the latest system

29. SH index and rainfall Correlations with observations: Previous System =0.41 ---- New System=0.83 Skill predicting interannual variability of West North Pacific Subtropical High

30. SH index and rainfall Skill predicting interannual variability of rainfall over the Yangtse River Valley Correlations with observations: Previous System = 0.35 ---- New System= 0.69

31. Clear messages on crop impacts using high resolution projections from Hadley Centre regional climate models Clear messages on future temperatures from the application of a regional climate model Precipitation projections are less certain, giving climate change information with different levels of confidence Application of this information to assess climate change impacts on crops still provides clear messages for the need for adaptation

32. © Crown copyright Met Office Temperature changes over Caribbean land areas in two climate projections High resolution modelling delivers consistent message on large warming over land even with different sea temperature changes Temperature changes >3K by 2080s under the B2 scenario

33. © Crown copyright Met Office The message on precipitation change is less clear Precipitation changes of up to +/- 20% or greater by 2080s under the B2 scenario

34. © Crown copyright Met Office Clear impact on Caribbean crops in 2050s with +2ºC and+/-20% precip. Table: Simulated crop yields under current climate and with a 2 ºC temperature increase accompanied by either a 20% increase or decrease in rainfall.

35. International collaboration International collaboration is important in production of forecasts (e.g. WMO Lead Centre – monthly or seasonal products) Current Met Office Hadley Centre and partners have a focus on Europe (MO), East Asia (KMA) and South Asia (NCMRWF) We are keen to work with scientists in SE Asia to develop more useful products from the new seasonal prediction systems Last year an agreement signed with Singapore (covering NWP, seasonal and climate timescales) © Crown copyright Met Office

36. Summary Met Office Hadley Centre and its partners are making improved long range prediction systems one of their key objectives Better and more useful products are beginning to emerge (temperature & rainfall extremes, water management, monsoon onset, tropical storms etc.) Keen to work with Thailand in developing products from these prediction systems for your region © Crown copyright Met Office

37. Questions and answers