1. Sub-seasonal and Seasonal prediction at ECMWF
Frederic Vitart, ECMWF

2. Index Overview of ECMWF forecasting Systems
Seasonal Forecasts – Description and Summary of performances
JJA 2019 Seasonal forecasts
Sub-Seasonal forecasts - Description and Summary of performances

3. Forecasting systems at ECMWF
Products
ECMWF:
Weather and Climate
Dynamical Forecasts
Medium-Range
Forecasts
High Res up to day 10
ENS up to day 15
Sub-Seasonal
Day 10-46
Seasonal
Month 2-7
Slide 3: The monthly forecasting system fills the gap between two currently operational forecasting systems at ECMWF: medium-range weather forecasting and seasonal forecasting. Medium-range weather forecasting produces weather forecasts out to 15 days, whereas seasonal forecasting produces forecasts out to 7 months. The two systems have different physical bases. Medium-range weather forecasting is essentially an atmospheric initial value problem. Seasonal forecasting (2-7 months forecasts), on the other hand, is justified by the long predictability of the oceanic circulation (of the order of several months) and by the fact that the variability in tropical SSTs has a significant global impact on the atmospheric circulation.
Twice daily
Twice weekly
Once a month

4. European Centre for Medium-Range Weather Forecasts
From medium-range to seasonal to extended range
Extended-range
Medium-range
Seasonal Forecast
DJF 2015/16
ENS-Monthly Forecasting System
Slide 5: Forecast products at different time ranges:
Daily or sub-daily fields for the medium-range forecasting (middle)
Weekly mean anomalies for extended-range forecasting (right)
Seasonal mean or monthly means for seasonal forecasting (seasonal)
European Centre for Medium-Range Weather Forecasts

5. SEAS5 Description and summary of performance

6. European Centre for Medium-Range Weather Forecasts
Towards a unified ensemble prediction system
Seas4 2011
Seas3 2006
Seas5 Nov 2017
ECMWF Ensemble System
ECMWF Seasonal System
Advantages
Confidence in representation of relevant processes
Possibility of Seas results influencing the extended range.
Simplicity
Trades off:
Certain aspects of the initialization
Slowing the inclusion of new earth-system components (such as O3).
SEAS5 Innovations
More recent model cycle.
High resolution (ocean and atmosphere)
Sea-Ice
New ocean reanalysis ORAS5
European Centre for Medium-Range Weather Forecasts

7. See Johnson et al GMD, 2019

8. Improving teleconnections is challenging
20 years or progress in ENSO prediction at ECMWF and contribution of ocean observations
1997
System 1
2002
System 2
2006
System 3
2011
System 4
2017
SEAS5
Gain about 2 months in ENSO prediction
Without ocean data assimilation, we would lose about 15 years of progress.
But comparatively slower progress in mid-latitude seasonal skill
Improving teleconnections is challenging
From Stockdale et al, ECMWF Tech Memo 835

9. ENSO: best scores ever
RMS error improves more than correlation, due to better amplitude
ENSO forecasts a little more under- dispersive than in S4
From Stockdale et al, ECMWF Tech Memo 835

10. ENSO: comparisons
S5 beats all previous systems in all NINO regions,
SEAS5 ENSO scores clearly ahead of other EUROSIP models (Met Office, Météo-France, JMA).
From Stockdale et al, ECMWF Tech Memo 835

11. T2m Skill differences: Seas5 – System4
DJF
JJA
MAM
SON
Better skill in T2m in areas close to Sea ice
Sea Ice brings increased skill in T2m in surrounding areas
Some degradation over the North Atlantic Subpolar Gyre and downstream
From Stockdale et al, ECMWF Tech Memo 835

12. Tropical storm climatology improved
SEAS5
System 4
Increase largely due to resolution
Still have relative deficit over Atlantic
Have also discovered that stochastic physics is critical in giving large enough storm numbers
Forecast skill is broadly similar: bit better in NW Pacific, bit worse in North Atlantic.
SEAS5 low res

13. Probabilistic scores: reliability
From Stockdale et al, ECMWF Tech Memo 835

14. MSLP: actual vs potential forecast skill
SEAS5
Perfect Model
DJF
JJA
Note room for improvement in almost everywhere, except for Greenland

15. Monitoring the State of the Ocean (from ORAS5)
Warm SST anomaly over Central Pacific
Subsurface anomaly is warm as well, with deep thermocline.
But Spring Predictabilty Barrier !!!.

16. Seasonal Forecast of SST
ECMWF:
-Weak ENSO.
-Large scale warming with isolated cold anomalies (Eastern boundaries in NH and western boundaries in SH)
ECMWF
C3S multi-model
-Probability of large ENSO, but also cold anomaly.
-Large spread due to model diversity.
-Large scale warming. Also pronounced warming over Arctic (mainly from 1 model)
C3S-multi model

17. Seasonal Forecast T2m and Precip
ECMWF
C3S-multi model
Large scale warming, over sea and land.
T2m warm anomalies over North East Siberia (opposite to last year).
Multi-Model produces warmer and more homogenesous anomalies than ECMWF.
Enhanced Precip over Eq. Pacific, and ITCZ (north of Equator). Not really ENSO.
Supressed Precip over Maritime Continent and Extra Eq. North Western Pac. (Philipines)
Enhanced Precip over South and East China Sea

18. Sub-Seasonal Forecasts

19. ECMWF extended-range forecasts
A 51-member ensemble is integrated for 46 days twice a week (Mondays and Thursdays at 00Z)
Atmospheric component: IFS with the latest operational cycle and with a Tco639L91 (~18 km) resolution up to day 15 and Tco319L91 (~36 km) after day 15.
Ocean-atmosphere coupling from day 0 to NEMO (about 1/4 degree) every hour.
Initial conditions::
Atmosphere: Operational 4-D var analysis + SVs+ 25 EDA perturbations
Ocean: 5-member 3D-Var analyses (NEMOVAR) + wind stress perturbations
ECMWF extended-range forecasts
Slide 19: Description of the ensemble forecasting system. Twice a week, the coupled model is integrated forward to make a 46 day forecast with 51 different initial conditions, in order to create a 51-member ensemble.

20. The ENS re-forecast suite to estimate the M-climate51
Tco639
L91
Tco319
2018 11
… May
2017 5
2016
2015
1998
…..
Initial conditions:
ERA Interim+
ORAS5 ocean Ics+
Soil reanalysis
Perturbations:
SVs+EDA(2018)+SPPT
20y
Slide 21: Model climatology: An 11 member ensemble is run starting the same day and month as the real-time but over the past 20 years. The ECMWF monthly forecasts are calibrated by using 3 consecutive sets of re-forecasts. In total this represents a 660-member ensemble which is compared to the 51-member real-time ensemble.

21. The ECMWF extended-range forecasts
Slide 22: Anomaly maps are similar to seasonal forecasting charts, but with weekly means instead of monthly means. Over each point of the map, atmospheric variables such as 2-metre temperature, total precipitation, mean sea-level pressure or surface temperature, have been averaged over a weekly period and also over the 51 members of the real-time forecast and the 660 members of the back statistics. The plots display the difference between the ensemble mean of the real-time forecast and the ensemble mean of the back-statistics. The product therefore displays the shift of the forecast ensemble mean from the estimated "climatological" mean (created from ensemble runs over the past 20 years). In addition, a Wilcoxon-Mann-Whitney test (WMW-test, see for instance Wonacott and Wonacott 1977) has been applied to estimate whether the ensemble distribution of the real-time forecast is significantly different from the ensemble distribution of the back-statistics. Regions where the WMW-test displays a significance less than 90% are blank. Regions where the WMW-test displays a significance exceeding 95% are delimited by a solid contour (blue or red depending on whether the anomaly is positive or negative respectively). The blanking of "non-significant" shifts does not mean that there is no signal in the blanked regions, but only that, with the particular sampling we have, we cannot be sure that there is a signal. For this reason, there are likely to be many areas where a signal is real but remains undetected.
Anomalies (temperature, precipitation..) -

22. Tropical cyclone activity
The ECMWF monthly forecasting system
Tropical cyclone activity
Slide 24: Forecast of tropical cyclone activity. This plot shows the probability of tropical storm strike within 300 km predicted by the monthly forecast starting on 23 April 2018 and for the period day

23. MJO Forecasts
Slide 25: An example of real-time MJO forecast. The grey line represents the evolution of the MJO over the 30 days preceding the forecast. The black lines represents the ensemble mean forecast. Each dot represents the position of the MJO at day 1,5,10,15,20 in the EOF1/EOF2 phase space. See slides 15 and 16 for more details.

24. Skill of the ECMWF Monthly Forecasting System
ROC score: 2-meter temperature in the upper tercile
Day 5-11
Day 12-18
Day 19-25
Day 26-32
Slide 27: Maps of ROC scores of the probability that 2-meter temperature averaged over the period day is in the upper tercile. Only the scores over land points are shown. The terciles have been defined from the model climatology. The verification period is Oct 2004-May Red areas indicate areas where the ROC score exceeds 0.5 (better than climatology). This plot shows that the coupled model performs better than climatology for the period days For the period days 19-26, the skill is much lower than for days 12-18, as expected. The red is largely dominating overall, suggesting that the model generally performs better than climatology at this time scale. Europe seems to be a difficult region, with very low skill at this time range. Tropical regions display the strongest skill after 30 days, suggesting that the coupled model at this time range starts to behave more like seasonal forecasting.

25. Skill of the ECMWF Monthly Forecasting System
2-meter temperature in upper tercile - Day 12-18
ROC score
Reliability diagram
Persistence of day 5-11
Day 12-18
Monthly forecast day 12-18
Slide 28: The toughest test for monthly forecasting is a comparison with the persistence of the medium-range forecasts. The top left plot shows the ROC diagram obtained with the monthly forecasting system for days (red) and the persistence of the probabilities of days 5-11 (blue). The event is the probability that 2-meter temperature is in the upper tercile. The top right panel represents the reliability diagram of the monthly forecast for day (blue line) and persistence of day 5-11 (red line). This slide demonstrates that the monthly forecast of days performs better than persisting the medium range forecast, and therefore can be useful. The difference is statistically significant according to WMW test. This result is also valid for all the other variables and other probabilistic events. It can be concluded that the model show some skill over the northern Extratropics at this time range.
The bottom panels show the same figures for the time range day and the persistence of day 5-18.
Day
19-25
Persistence of day 5-18
Monthly forecast day 19-32

26. Slide 30: Evolution of RPSS for 2-metre temperature (top panel) and total precipitation (bottom panel) from 2004
to 2016 (year of model implementation) for day against ERA Interim (red curve) and Station data (blue curve) over the northern Extratropics.

27. MJO skill scores
Slide 20: ECMWF has produced monthly forecasts routinely since March This figure shows the evolution of MJO skill scores since The MJO skill scores (bivariate correlations) have been computed on the model hindcasts produced during a complete year. For instance, 2002 indicates the hindcasts that were produced from March 2002 until March corresponds to the hindcasts produced from March 2011 until March The blue line indicates the day when the MJO bivariate correlation reaches 0.5. The red line indicates the day when the MJO bivariate correlation reaches 0.6 and the brown line indicates the day when the MJO bivariate correlation reaches 08. This suggests that the MJO skill scores have significantly improved over the past 10 years. If we consider the 0.6 correlation as a measure of MJO predictability, then the gain is of about 9 days of predictability.

28. Slide 21: MJO bivariate correlation between ensemble mean and ERA Interim. This plots shows a model intercomparison of MJO skill scores between 10 model re-forecasts from the S2S database. The re-forecast period is The orange (yellow) bars indicate when the bivariate correlation reaches 0.6 (0.5). The black vertical bars represent the 95% level of confidence. This plot suggests that current state-of-the-art operational models have, on average, skill to predict the evolution of an MJO event up to 3 weeks.

29. Impact of ocean/atmosphere coupling
RPSS over NH
MJO Bivariate Correlation
U50
T850
Slide 31: impact of ocean-atmosphere coupling on MJO bivariate correlation (right panel) and Norther Extratropics RPSS of U50 (top left panel) and T850 (bottom right panel).
Coupled
WEEK1
WEEK2
WEEK3
WEEK4
Obs SSTs
Obs SSTs
Pers SSTs
Coupled
80 case, starting on 1st Feb/May/Aug/Nov

30. SUMMARY
SEA5 is overall more skilful than previous ECMWF seasonal forecasting systems, particularly for ENSO prediction. Improvements in the high latitudes are due to the introduction of a sea-ice model (LIM2).
46-day forecasts are produced twice a week. The MJO forecast horizon has improved and reaches now week 5. Skill of 2m-temperature prediction in mid-latitudes is often low but it is higher than climatology and persistence. Ocean-atmosphere coupling is important for this time range.