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© Crown copyright 2007 Monthly-Seasonal forecasting Alberto Arribas Monthly to Decadal group, Met Office Hadley Centre Geneva, December 2011.

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Presentation on theme: "© Crown copyright 2007 Monthly-Seasonal forecasting Alberto Arribas Monthly to Decadal group, Met Office Hadley Centre Geneva, December 2011."— Presentation transcript:

1 © Crown copyright 2007 Monthly-Seasonal forecasting Alberto Arribas Monthly to Decadal group, Met Office Hadley Centre Geneva, December 2011

2 Our strategy for monthly- seasonal forecasting - Unified monthly-seasonal system, fully integrated with model development process - Using latest available model version (from NWP to monthly-seasonal) - Focus on understanding physical mechanisms: Aim is to improve the model faster to increase forecast skill faster

3 Paper documenting the system when launched in 2009: The GloSea4 Ensemble Prediction System for Seasonal Forecasting. Arribas et al, 2011 Mon. Wea. Rev., 139, 1891–1910. GloSea4 (Global Seasonal Forecasting system version 4)

4 Link with model development means frequent model upgrades and hindcast run in real-time (as forecast) Consequence: shorter hindcast Hindcast size Update frequency

5 GloSea4 history Summer 2009: GloSea4 starts GA 1.0 N96L38 Orca(1)L42 Hindcast: November 2010: Model upgrade GA 2.0 N96L85 Orca(1)L75 Sea-ice initialisation Hindcast: March 2011: Daily forecast Daily initialisation Monthly system Arribas etal, 2011

6 GloSea4 plans November 2011: Model upgrade GA 3.0 N96L85 Orca(1)L75 Summer 2012: Model upgrade GA 4.0 N216L85 Orca(0.25)L75 ~ 50km (mid-lat)

7 Current operational system Model version: HadGEM3 GA2.0 Resolution: N96L85 O(1)L75 (~120 km, ~ 1 dg) Simulations length: 7 months Model uncertainties represented by: SKEB2 stochastic physics Initial conditions uncertainties represented by: Lagged ensemble

8 Initialisation Forecast (daily): Atmosphere & land surf: NWP analysis Ocean & sea-ice: Seasonal ODA (Optimal Interpolation) 14-year Hindcast ( ): 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

9 Ensemble: lagged approach Seasonal Forecast: 2 members run each day. Forecast updated every week: 42 members (last 3 weeks) Bias corrected using hindcast (~168 members) Hindcast (for monthly-seasonal): 14 year hindcast run in real time, 3 members per year and start date completed every week (i.e. 42 members/week) Monthly Forecast: 2 members each day. Forecast updated daily: 28 members (last 7 days) Bias corrected using hindcast (~168 members)

10 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/ /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/ /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 ( ) 20/06/ /06/ /06/2011

11 An international system... KMA (Rep. of Korea) Joint seasonal forecast system Shared workload and computing costs: possibility to extend hindcast and increase resolution NCMRWF (India) – implementing GloSea for research

12 Our approach to data sharing Happy to make NON real-time forecasts (at least 1- month old) and hindcast freely available for research Africa: ~ 6 centres currently analysing data from GloSea4 hindcasts China Meteorological Agency (hindcast) Japan (hindcast and non real-time fcst) UK Universities (hcst and non real-time fcst) etc

13 Real time data... Real time forecast and hindcast data (same set of seasonal forecasting standard products) supplied to: WMO Lead Centre EUROSIP




17 ENSO and ENSO teleconnections

18 Nino 3.4 SST

19 From May From Nov ACC RMSE / Spread Nino 3.4 SST

20 ENSO Telecon. (precip) Observations L85 - GloSea4 JJA DJF

21 An example of a recently improved physical mechanism: ENSO – Europe teleconnection

22 Importance of vertical resolution: ENSO teleconn. January- February anomalies, NCEP Toniazzo and Scaife 2006 Obs Moderate el Nino Obs Stong el Nino L38 model – moderate Nino L38 model – stong Nino

23 January- February anomalies, NCEP Toniazzo and Scaife 2006 Moderate el Nino Stong el Nino L85 model – moderate Nino L85 model – stong Nino Importance of vertical resolution: Stratospheric model


25 Future improvements: higher resolution

26 ENSO teleconnections with Indian monsoon Obs L38 L85

27 N216 N96 Future developments: Benefits of higher spatial resolution Obs This error is common to many climate models It affects remote regions N216 has better ENSO pattern and teleconnections Sarah Ineson, Dave Rowell

28 Benefits of higher resolution: Improved Atlantic Blocking Gulf Stream Bias Wly wind bias => Blocking Deficit No Gulf Stream Bias No Wly wind bias => Good Blocking New Model Scaife et al., Geophys. Res. Lett., submitted. Blocking Frequency 1 degree ocean 0.25 degree ocean

29 MJO



32 Other recent improvements: Arctic sea-ice

33 Forecasting Arctic Sea-ice observations Hindcast Forecast

34 2011 Arctic Sea-ice forecast

35 Products from the GloSea4 system

36 We are a WMO Global Producing Centre Member of EUROSIP Contributor to WMO Lead Centre Products: hurricanes, water management, etc Main customer for monthly-seasonal is UK Government

37 Public forecast for 2011 Released 26 th May 2011 Near- to above-normal activity predicted Observed to dateNovember forecastSeason total Tropical storms16218 ACE index

38 Main forecast product for government

39 ENSO prediction from April-Sep (EUROSIP) AprMayJun JulAugSep Inputs into forecast

40 Euro-SIP Output Ensemble mean PMSL Anomalies: November GloSea4ECMWF MeteoFrance France Inputs into forecast

41 GPC output PMSL anom PretoriaMontreal Toulouse Tokyo Seoul Melbourne Washington CPTEC Exeter Beijing ECMWF Moscow Inputs into forecast

42 Government product: Fig. 1

43 Government product: Fig. 2

44 Government product: Fig. 3






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