Monthly and seasonal forecasts at ECMWF: operational plans and prospects from current research

1. Monthly and seasonal forecasts at ECMWF: operational plans and prospects from current research Franco Molteni withM. Balmaseda, L. Ferranti, K. Mogensen, T. Palmer, T. Stockdale, F. Vitart European Centre for Medium-Range Weather Forecasts, Reading, U.K.

2. Overview • Results from ongoing experimentation on the development of Seasonal Forecast System-4 (caution: work in progress!!) • Prospects for the extension of the monthly forecast: progress with the simulation of the MJO and its teleconnections Vitart and Molteni, MWR 2009; QJRMS 2010, under rev. ; Vitart GRL 2009. • Beyond System-4: what can we expect from the inclusion of a dynamical sea-ice model? Results from simulations for summer 2007 and 2008. Balmaseda, Ferranti, Molteni, Palmer,QJRMS 2010, under rev.

3. ECMWF Seasonal forecast system (Sys-3) IFS 31R1 1.1 deg. 62 levels HOPE ~ 1. deg. lon 1./0.3 d. lat. OASIS-2 TESSEL Ens. Forecasts Initial Con. 4-D variational d.a. Gen. of Perturb. System-3 CGCM Multivar. O.I.

4. ECMWF Seasonal Fc. System 4: main features • New ocean model : NEMO v. 3.0 + 3.1 coupling interface • ORCA-1 configuration (~1-deg. resol., ~0.3 lat. equatorial refinement) • 42 vertical levels, 20 levels with z < 300 m • Variational ocean data assimilation (NEMOVAR) • 3-D var with inner and outer loop • Collaboration with CERFACS, UK Met Office, INRIA • First re-analysis (1957-2009), no assim. of sea-level anomalies • Second re-analysis and real-time system including SLA • IFS model cycle: 36r3 or 36r4 (36r1 currently operational) • New physics package, including HTESSEL land-surface scheme, snow model (with EC-Earth), new land surface initialization • New formulation for prescribed sea-ice concentration • Sampling from most recent years

5. 2010 time-line • Further tests of recent IFS cycles (1st/2nd quarter) • Set up real-time ocean analysis system (1st/2nd quarter) • Definition of final configuration (2nd quarter) • Start production of hindcasts (summer) • Validation of hindcasts and definition of operational products (late summer/fall) • Operational implementation (winter 2010/11)

6. Ocean Re-Analysis with NEMO at ECMWF • Using NEMO/NEMOVAR • Model configuration: ORCA1, smooth coastlines, closed Caspian Sea. • Forced by ERA40 (until 1989) + ERA Interim (after 1989) • Assimilates Temperature/Salinity from EN3 • Strong relaxation to SST (OI_v2) • Online bias correction scheme • Preliminary ocean re-analysis 1957-2009 • This used the EN3_v2a dataset, where the XBT were not corrected • First ensemble reanalysis 1957-2009 • 5 ensemble members (perturbations to wind, initial conditions, observation coverage) • Corrected XBT

7. Ocean Re-Analysis with NEMO • Data assimilation helps the convergence of the solution • Periods of large differences between Assim and Control • 1970’s • Post 2000’s

8. An outstanding modelling issue: tropical wind biases Biases in 850 hPa streamfunction (obs SST, DJF 1963-2006) ERA40 31r1 (sys3) – ERA 36r1 - ERA

9. Hindcasts with IFS 36r1: SST bias (1) Start: 1 Nov. 1989/2008 Verify: Dec-Feb System 3 IFS 36r1 T159/L91 + NEMO

10. Hindcasts with IFS 36r1: SST bias (2) Start: 1 May 1989/2008 Verify: Jun-Aug System 3 IFS 36r1 + NEMO

11. Hindcasts with IFS 36r1: SST bias (3) Start: 1 May 1989/2008 Verify: Sep-Nov System 3 IFS 36r1 + NEMO

12. Hindcasts with IFS 36r1: bias in Z500 Start: 1 Nov. 1989/2008 Verify: Dec-Feb System 3 IFS 36r1 + NEMO

13. Hindcasts with 36r1: Ens-mean ACC, SST Sys3 36r1 T159/L91+ NEMO

14. Impact of vertical res.: Ens-mean ACC, SST 36r1 T159/L62 + NEMO 36r1 T159/L91+ NEMO

15. Impact of horizontal res.: Ens-mean ACC, SST 36r1 T255/L91 + NEMO 36r1 T159/L91+ NEMO

16. Hindcasts with IFS 36r1: Ens-mean ACC, Z500 Start: 1 Nov. 1989/2008 Verify: Dec-Feb System 3 IFS 36r1 + NEMO

17. Hindcasts with IFS 36r1: Ens-mean ACC, T_2m Start: 1 Nov. 1989/2008 Verify: Dec-Feb System 3 IFS 36r1 + NEMO

18. Hindcasts with IFS 36r1: Ens-mean ACC, T_2m Start: 1 May 1989/2008 Verify: Jun-Aug System 3 IFS 36r1 + NEMO

19. Work to do and outstanding issues NEMOVAR • Inclusion of altimeter data • Implementation of real-time suite Coupled model • Further tests at higher horizontal resolution (T255) • Bias in tropical winds: • Wait for a “better” IFS cycle (how better?) • Test flux correction for wind stress/heat fluxes (as a diagnostic tool; an option for Sys-4 ?)

20. Coupled forecast at TL159 Initial condition Day 32 EPS Integration at T399 Coupled forecast at TL255 Initial condition Day 32 Day 10 Heat flux, Wind stress, P-E Ocean only integration Unified EPS/monthly forecasts at ECMWF Original “stand-alone” monthly system: 32-day EPS/monthly system since March 2008: Further resolution upgrade to T639/T319 on 26 January 2010

21. June monsoon rainfall over India: EPS-monthly (from 15 May): ACC = 0.57Sys-3 from 1 May /1 June (avail. on 15): ACC = 0.29/0.50 46-day exp. from the 15thof each month, 1989-2008

22. MJO impact on JJA precipitation in 46-day EPS EPS ERA-In Wheeler- Hendon 2004 Phase 2-3 Phase 4-5 Phase 6-7 Phase 8-1

23. MJO impact on tropical storm frequency, Aug-Oct. EPS ERA-In

24. MJO impact on DJF Z_500hPa in 46-day EPS

25. MJO impact on DJF Z_500hPa: internal variability

26. Impact of MJO on N.Atl. regime frequency (Cassou 2008)

27. Impact of MJO on NAO+ frequency in 46-day EPS

28. Impact of MJO on forecast reliability T_850 > upper tercile, fc. day 19-25 Blue line: no MJO in IC Red line: MJO in IC

29. Conclusions (1) • In terms of modelling/analysis infrastructure, the development of a new seasonal forecast system based on NEMO/ NEMOVAR is close to completion. • The tropical wind biases arising on seasonal and longer timescales in recent IFS cycles lead to a significant cold bias in the tropical Pacific SST; in turn, this causes a reduction in predictability of west-Pacific SST and associated teleconnections. • Performance in the tropical Indian and Atlantic oceans is comparable or moderately better than System-3; biases in the Southern Ocean SST and NH winter circulation are substantially reduced. • Recent changes in physical parametrizations have, on the other hand, substantially improved the simulation of tropical intra-seasonal variability (e.g. MJO) on weekly/monthly time-scales and beyond. • The improved MJO simulation leads to increased predictive skill in both the tropics and extratropics on the 15-45 day range. Forecast reliability in the NH extratr. is strongly dependent on MJO conditions

30. Arctic sea-ice variability • The summers of 2007-2008 have seen unprecedented anomalies in the Arctic ice extension • The ECMWF Seasonal Forecast system does not represent interannual variations of the sea-ice. Would the SF over Europe improve if Arctic sea-ice anomalies were predicted? Images from the National Snow and Ice Data Center: http://www.nsidc.org/sotc/sea_ice.html

31. Sensitivity exp. on response to sea-ice anomalies 1 May - 30Sep 2007 & 2008, 40-m. ensembles • A1: Sys3 AGCM with prescribed (obs.) SST, observed sea-ice concentration • A2: Sys3 AGCM with prescribed (obs.) SST, climatological sea-ice concentration • C1: Sys3 CGCM, predicted SST, observed sea-ice concentration • C2: Sys3 CGCM, predicted SST, climatological sea-ice concent. • P1: Sys3 CGCM with prescribed SST in NW Atlantic only, observed sea-ice concentration • P2: Sys3 CGCM with prescribed SST in NW Atlantic only, climatological sea-ice concentration AGCM response : A1 – A2 CGCM response : C1 - C2, P1 – P2

32. Z500 JA 2007: Obs-Clim Ice Z500 JA 2008: Obs-Clim Ice Observed Anomalies Impact on 500 hPa geop. height in AGCM 2007 2008 Atmos model (uncoupled)

33. Z500 JA 2007: Obs-Clim Ice Z500 JA 2008: Obs-Clim Ice Z sensitivity: Obs-Clim JA 2008 Z sensitivity: Obs-Clim JA 2008 Coupled model How is the response in coupled-model experiments? 2007 2008 Atmos model (uncoupled) Exp B: Coupled integrations with climatological and Observed ice extension. 2007 & 2008.

34. Z500: Uncoupled - Coupled Z500: Partial Coupling - Coupled SST bias in the Gulf Stream region may explain a large part of the coupled-model systematic errors over the North Atlantic Sector Impact of SST bias in the Gulf Stream region on Z_500 Differences in mean 500hPa height in July-August

35. Z sensitivity: UNCOUPLED Z sensitivity: Prescribed Gulf Stream Z sensitivity: COUPLED Correcting the SST bias in the Gulf Stream region changes the atmospheric response to the prescribed sea-ice anomaly Impact on the response to the 2008 Arctic ice anomaly

36. Is the response to sea-ice dependent on the “real” SST? • Variable SST ensembles with AGCM: • 5 member ensembles, initialized in May 1987-2006 (20 years) • Performed with 2007 and climatological sea-ice concentration • 1000 40-member sub-ensembles with variable SST generated by randomly selecting 2 out of 5 ensemble members for each year • 1000 40-member-mean response to 2007 sea-ice anomaly with variable SST are obtained • The 40-member response with 2007 SST can be compared with the population of 1000 responses with variable SST: does it belong to the same population? • Yes: the SST of the same year (2007) does not induce a statistically significant difference in the response to sea-ice • No: the response to sea-ice anomaly is significantly dependent on the SST of the same year

37. Is the response to sea-ice dependent on the “real” SST? • Project the Z500 responses on EOFs of monthly means in Jul-Aug • Compute PDF of variable-SST response in PC1-PC2 plane The PC1 response with the 2007 SST (blue cross) has just a 2.3 % probability of belonging to the variable-SST response distribution

38. Conclusions (2) • Experiments indicate that the observed Arctic ice anomalies in summers 2007 and 2008 had a significant impact on the atmospheric circulation over the North Atlantic sector. • The incorrect representation of the Gulf Stream in the coupled model is partly responsible for the biases in the atmospheric circulation over the North Atlantic sector in the coupled model. The SST bias in the Gulf Stream region also affects the response of the atmosphere to anomalous sea-ice concentration. • In general, the response of the atmosphere to the sea-ice anomaly depends on the SST state. The sign of the response with actual, observed SST may be opposite to the average response with climatological or randomly-selected SST. • A posteriori bias correction of model biases is inadequate in the presence of a non-linear response to sea-ice anomaly; a better simulation of western boundary currents is needed in coupled models.