1. ECMWF long range forecast systems
Dr. Tim Stockdale
European Centre for Medium-Range Weather Forecasts

2. Outline Overview of System 4 Some recent research results
EUROSIP multi-model forecasts
Forecasts for JJA 2013
Seasonal prediction at ECMWF
History/approach
Strengths and weaknesses of S4
History of EUROSIP, including theory of multi-model
Inc DEMETER results as motivation
Also theoretical basis for mm averaging
Practical considerations – real-time risks, confidence etc
NCEP joining
Mention Paco’s assessment
Difficulty of 14 years for assessment
Show some results from Laura
New suite and products
Mention that NCEP processing suite now operational
New multi-model suite still in esuite – want to be sure everything is complete, and manage handover for MS
Example plots, esp inc Nino pdfs.
Summary
Operational system, plus benefits for research, some already, more for future. Plus meeting together, discussing, sharing expertise etc.

3. System 4 seasonal forecast model
IFS (atmosphere)
TL255L91 Cy36r4, 0.7 deg grid for physics (operational in Dec 2010)
Full stratosphere, enhanced stratospheric physics
Singular vectors from EPS system to perturb atmosphere initial conditions
Ocean currents coupled to atmosphere boundary layer calculations
NEMO (ocean)
Global ocean model, 1x1 resolution, 0.3 meridional near equator
NEMOVAR (3D-Var) analyses, newly developed.
Coupling
Fully coupled, no flux adjustments
Sea-ice based on sampling previous five years

4. Reduced mean state errors
JJA T850 bias (left), DJF U50 – stratosphere – right.
S4 bias (top), S3 (bottom) – on same scale!!
Note for T850, much reduced error in structure of T850 field over N America And surrounding oceans
These plots are typical of very many fields – almost all aspects of climate improved. Almost all!

5. Tropospheric scores One month lead Four month lead
Spatially averaged grid-point temporal ACC
One month lead
Four month lead

6. S4 extended hindcast set
Scores are smoother and systematically higher with 51 member hindcasts

7. S4 extended hindcast set
Gain over S3 is now stronger and more robust

8. More recent ENSO forecasts are better ....

9. QBO
System 4
30hPa
System 3
50hPa

10. Problematic ozone analyses

11. Snow depth limits, 1st April
Land surface
Snow depth limits, 1st April

12. Sea ice

13. Tropical storm forecasts

14. Recent Research

15. QBO
A big reduction in vertical diffusion, and a further tuning of non-orographic GWD, has given a big additional improvement in the QBO compared to S4.
Period and downward penetration match observations
Semi-annual oscillation still poorly represented

16. QBO forecasts S3 S4
New

17. NH winter forecasts
0.371
0.319

18. NH winter forecasts
Even with 101 members, ensemble mean signal not always well defined

19. NH winter forecasts
New version has weaker signal, more noise

20. NH winter forecasts
Forecast skill is above perfect model predictability limit

21. EUROSIP A European multi-model seasonal forecast system
Operational since 2005
Data archive and real-time forecast products
Initial partners: ECMWF, Met Office, Météo-France
NCEP an Associate Partner; forecasts included since 2012
Products released at 12Z on the 15th of each month
Aim is a high quality operational system
Data policy issues are always a factor in Europe
Initially, all models ran at ECMWF, comprehensive data archive; now, some models run remotely with reduced data volumes
Also more diversity in forecast/re-forecast strategy

22. Recent changes: variance scaling
Robust implementation
Limit to maximum scaling (1.4)
Weakened upscaling for very large anomalies
Improves every individual model
Improves consistency between models
Improves accuracy of multi-model ensemble mean

23. Revised Nino plumes

24. Error vs spread (uncalibrated)

25. Calibrated p.d.f. ENSO forecasts have good past performance data
We can calibrate forecast spread based on past performance
We can also allow varying weights for models
We have to be very careful not to overfit data at any point.
Represent forecast with a p.d.f.
This is the natural output of our calibration procedure
Easier visual interpretation by user
Calibration and combination in general case
Ideally apply similar techniques to all forecast values (T2m maps etc)
More difficult because less information on past (higher noise levels)
Hope to get there eventually ….. .

26. Nino 3.4 plume and p.d.f.

27. P.d.f. interpretation P.d.f. based on past errors Bayesian p.d.f.
The risk of a real-time forecast having a new category of error is not accounted for. E.g. Tambora volcanic eruption.
We plot 2% and 98%ile. Would not go beyond this in tails.
Risk of change in bias in real-time forecast relative to re-forecast.
Bayesian p.d.f.
Explicitly models uncertainty coming from errors in forecasting system
Two different systems will calculate different pdf’s – both are correct
Validation
Rank histograms show pdf’s are remarkably accurate (cross-validated)
Verifying different periods shows relative bias of different periods can distort pdf – sampling issue in our validation data.

28. Forecasts for JJA 2013

29. ECMWF forecast: ENSO
Past performance

30. EUROSIP forecast: ENSO
Past performance

31. Tercile probabilities
ECMWF forecast: JJA 2mT
Tercile probabilities
ACC skill ( )

32. ECMWF forecast: JJA precip
Tercile probabilities
ACC skill ( )