1. ECMWF training course, 2006: Predictability on the seasonal timescale Predictability on the Seasonal Timescale Tim Stockdale and Franco Molteni Seasonal Forecast Section European Centre for Medium-Range Weather Forecasts

2. ECMWF training course, 2006: Predictability on the seasonal timescale Contents Sources and limits of seasonal predictability  El Nino is predictable, and has a major impact on the global atmosphere  Many other factors, not all understood; limits of empirical methods Seasonal forecasting with ocean-atmosphere GCMs  Benefits of an ensemble forecast with a comprehensive numerical model  Outline of ECMWF system and handling of model bias  The problem of model error Performance of the ECMWF system  El Nino forecasts  Atmosphere behaviour  Validation and operational use

3. ECMWF training course, 2006: Predictability on the seasonal timescale Basic ideas of seasonal predictability Internal variability of atmosphere is mostly unpredictable Boundary conditions and “external” forcings may give partial/probabilistic predictability The boundary/external forcing must be predictable!  Either slowly changing (eg CO 2 trend, maybe decadal variability)  Or dynamically predictable (eg El Nino)  Or have at least a moderate persistence timescale (eg large soil moisture anomalies)

4. ECMWF training course, 2006: Predictability on the seasonal timescale Sources of seasonal predictability  KNOWN TO BE IMPORTANT:  El Nino variability- biggest single signal  Other tropical ocean SST- important regional impacts  Local land surface conditions- e.g. soil moisture in spring  Climate change (all forms)- especially important in mid-latitudes  OTHER FACTORS:  Mid-latitude ocean temperatures- still controversial  Remote soil moisture/snow cover - not well established  Volcanic eruptions- definitely important for large events  Sea ice anomalies- local effects are clear  Stratospheric QBO- possible tropospheric impact  Dynamic memory of atmosphere- most likely on monthly timescale  Solar cycle- questionable statistical connections

5. ECMWF training course, 2006: Predictability on the seasonal timescale Methods of seasonal forecasting Empirical forecasting schemes  Use past observational record and statistical methods  Work with observed data instead of error-prone numerical models  Limited number of past cases means that they work best when observed variability is dominated by a single source of predictability   A non-stationary climate is problematic  Two-tier forecast systems  First predict SST anomalies (ENSO or global; dynamical or statistical)  Use ensemble of atmospheric GCMs to predict global response  Problems if SST variability is mainly forced by atmospheric variability  Single-tier GCM forecasts  Include comprehensive range of sources of predictability  Predict joint evolution of SST and atmosphere flow  Estimate uncertainty of future SST, important for prob. forecasts  Model errors are an issue! 

6. ECMWF training course, 2006: Predictability on the seasonal timescale ICTP hindcast of Indian summer rainfall (land points 70-95 E, 10-30N) vs. CRU data Prescribed global observed SST mixed layer in IO, obs. SST elsewhere corr = 0.28 corr = 0.44

7. ECMWF training course, 2006: Predictability on the seasonal timescale ICTP hindcast of Indian summer rainfall (land points 70-95 E, 10-30N) vs. CRU data Dynamical ocean model (MICOM 2.9) in the IO, observed SST elsewhere corr = 0.62

8. ECMWF training course, 2006: Predictability on the seasonal timescale ECMWF coupled model (System 2) IFS (atmosphere)  T L 95L40 Cy23r4, 1.875 deg grid for physics (operational in 2001)  Initialized from ECMWF operational system  Prognostic clouds, fully interactive soil moisture, convection, radiation, …. HOPE (ocean)  Global ocean model, but sea-ice prescribed from climatology  1 x 1 deg at mid-latitudes, 0.3 deg meridional near equator; 29 levels  Initialized by improved Optimal Interpolation analysis scheme OASIS (coupler)  Coupling once per 24 hours (so no diurnal cycle in ocean)  No flux correction or other constraints (except specified sea-ice)

9. ECMWF training course, 2006: Predictability on the seasonal timescale ECMWF coupled model (System 3, soon) IFS (atmosphere)  T L 159L62 Cy30r1/2, 1.125 deg grid for physics  Full set of singular vectors from EPS to perturb atmosphere initial conditions.  Ocean currents coupled to atmosphere boundary layer calculations HOPE (ocean)  Essentially the same ocean model  A lot of extra work in improving the ocean analyses OASIS (coupler)  Better treatment of sea-ice, but still no proper model

10. ECMWF training course, 2006: Predictability on the seasonal timescale ECMWF forecast strategy Initialize coupled system (see Magdalena’s talk)  Aim is to start system close to reality. Accurate SST is particularly important, plus ocean sub-surface. Run an ensemble forecast (see Magdalena’s talk)  Generate an ensemble on the 1st of each month, with perturbations to represent the uncertainty in the initial conditions; run forecasts for 6 months Remove systematic error  Forecasts have considerable systematic error  Estimate this error from a set of previous forecasts, which define the model climatology.  Model climatology is a function of date and forecast lead-time.  Linear assumption is not correct, but seems to work reasonably well.

11. ECMWF training course, 2006: Predictability on the seasonal timescale Creating the ensemble Wind perturbations  Perfect wind would give a good ocean analysis, but uncertainties are significant. We represent these by adding perturbations to the wind used in the ocean analysis system. SST perturbations  SST uncertainty is not negligible  SST perturbations added to each ensemble member at start of forecast. Atmospheric unpredictability  Atmospheric ‘noise’ soon becomes the dominant source of spread in an ensemble forecast. This sets a fundamental limit to forecast quality.  To account for uncertainties in physical parametrizations, we activate ‘stochastic physics’.

12. ECMWF training course, 2006: Predictability on the seasonal timescale Remove systematic error Model drift is typically comparable to signal  Both SST and atmosphere fields Forecasts are made relative to model climatology  Model climate estimated from 15 years of forecasts (1987-2001), all of which use a 5 member ensemble.  Model climate has both a mean and a distribution, allowing us to estimate eg tercile boundaries.  Model climate is a function of start date and forecast lead time.  EXCEPTION: Nino SST indices are bias corrected to absolute values, and anomalies are displayed wrt a 1971-2000 climate. Implicit assumption of linearity  We implicitly assume that an anomaly in the model forecast relative to the model climate corresponds to the expected anomaly in an unbiased forecast relative to the true climate, despite differences between model and true climate.  Most of the time, this assumption seems to work pretty well.

13. ECMWF training course, 2006: Predictability on the seasonal timescale SST bias is a function of lead time and season

14. ECMWF training course, 2006: Predictability on the seasonal timescale

18. Despite SST bias and other errors, anomalies in the coupled system can be remarkably similar to those obtained using observed (unbiased) SSTs …..

19. ECMWF training course, 2006: Predictability on the seasonal timescale … and can also verify well against observations

20. ECMWF training course, 2006: Predictability on the seasonal timescale Model errors are serious Models have errors other than bias  Eg weak variability in system 2 and recent test versions Errors in model climate interact with errors in model variability  Specific example in Nino 4 region  Impact of artificial change in mean wind stress Our forecast errors are larger than they should be  With respect to internal variability estimates and (sometimes) other prediction systems Benefits of multi-model ensembles (see Antje’s and Paco’s talks)  Gets round some of the model-specific error  Fundamentally, though, we still need better models

21. ECMWF training course, 2006: Predictability on the seasonal timescale Variability of zonal wind in System 2 is strongly damped, even in the early months when SST is close to observed

22. ECMWF training course, 2006: Predictability on the seasonal timescale SST variability in Nino 3 is correspondingly weak

23. ECMWF training course, 2006: Predictability on the seasonal timescale Experiments with an artificial time-invariant zonal wind-stress addition along the equator, designed to make the ocean mean state in the coupled system more realistic. Compared to ‘heat-flux’ experiments, the emphasis is on altering the sub-surface structure and hence sensitivity of the SST to sub-surface anomalies. Sensitivity of forecasts to the mean state

24. ECMWF training course, 2006: Predictability on the seasonal timescale The result is increased amplitude of SST anomalies, especially in the east Pacific, and in this case a small increase in forecast skill. Increasing the amplitude of SST anomalies by this method is very robust – stronger upwelling in the mean state = stronger SST anomalies. Improving skill is more sensitive to the details of the adjustment, however.

25. ECMWF training course, 2006: Predictability on the seasonal timescale In Nino 4, System 2 has much bigger errors than its estimate of the predictability limit (red solid cf red dashed). In fact, a simple statistical model can give much better results (blue) (The statistical model is a cross-validated linear regression on the analysed zonal mean heat content of the equatorial ocean and the intial value of Nino 4 SST. The coupled model has significantly more information available, but model errors are degrading its performance.) Size of forecast errors: example of Nino-4

26. ECMWF training course, 2006: Predictability on the seasonal timescale Fortunately for us, the GCM is still significantly ahead of the statistical model in Nino 3, at least for shorter lead times.

27. ECMWF training course, 2006: Predictability on the seasonal timescale Operational seasonal forecasts ECMWF runs a 40 member ensemble forecast  Initial conditions are valid for 0Z on the 1 st of a month  Forecast is created typically on the 11 th /12 th  Forecast release date is 12Z on the 15 th. Range of operational products  Plots of basic fields on web  Raw data in MARS  Formal dissemination of real time forecasts now available Multi-model system now running  UKMO and Meteo-France both run systems at ECMWF  Full set of output is archived, derived products and plots are produced  Operational multi-model products still to be decided

28. ECMWF training course, 2006: Predictability on the seasonal timescale Example forecast products A few examples only – see web pages for full details and assessment of skill Note: Significance values on plots  A lot of variability in seasonal mean values is due to chaos  Ensembles are large enough to test whether any apparent signals are real shifts in model the pdf  We use the w-test, non-parametric, based on the rank distribution  NOT related to past levels of skill Note: Proper usage  Interpretation must be done carefully  Serious applications of the forecasts should use the actual values of the forecast and climate distributions, not just visual charts.  Model errors are significant, and must be accounted for in real applications; techniques are not yet well developed.

29. ECMWF training course, 2006: Predictability on the seasonal timescale The forecast showed in the 2004 training course ….

30. ECMWF training course, 2006: Predictability on the seasonal timescale

33. Tercile probabilities 15th percentile probabilities

34. ECMWF training course, 2006: Predictability on the seasonal timescale

35. And how do we do? ROC score is one measure of the valid information contained within probability forecasts

36. ECMWF training course, 2006: Predictability on the seasonal timescale Blue is good, yellow is bad But based on 15 years, sampling error is substantial for local values, so spatial distribution is unreliable except where skill is high Upper tercile

37. ECMWF training course, 2006: Predictability on the seasonal timescale Precipitation is generally noisier …..

38. ECMWF training course, 2006: Predictability on the seasonal timescale

43. Some final comments Plenty of scope for improving model forecasts  Nino SST forecasts still worse than predictability limits  Model errors still large compared to natural variability  Initial conditions broadly OK in Pacific, but overall there are still problems Model output → forecast users  Calibration and presentation of forecast information  Potential for multi-model ensembles Possible sources of improvements  New observing systems in tropical oceans (e.g. Indian Ocean)  New satellite measurements of land-surface variables  Higher spatial resolution in both ocean and atmosphere models  New approaches to physical parametrization (e.g. quasi-explicit convection)