1. Prediction of extreme events at Sub-seasonal to Seasonal lead times
Frédéric Vitart, Laura Ferranti, Ivan Tsonevsky
ECMWF

2. INDEX S2S database MJO/Weather regimes in S2S database Case studies:
2010 Heat Wave over Russia
Tropical Cyclone PAM
Extreme Forecast products and Verification

3. WWRP/WCRP Sub-seasonal to Seasonal (S2S) Prediction Project
Teleconnections (C. Stan and H. Lin)
Madden-Julian Oscillation (D. Waliser and S. Woolnough)
Monsoons (H. Hendon)
Sub-Projects
Africa (A. Robertson and R. Graham)
Extremes (F. Vitart)
Verification and Products (C. Coelho)
Research Issues
Predictability
Teleconnection
O-A Coupling
Scale interactions
Physical processes
Modelling Issues
Initialisation
Ensemble generation
Resolution
O-A Coupling
Systematic errors
Multi-model combination
Needs & Applications
Liaison with SERA
(Working Group on Societal and Economic Research Applications)
S2S Database

4. S2S database
3-week behind real-time forecasts + re-forecasts (up to day 60)
Common grid (1.5x1.5 degree)
Data archived with a daily frequency (sub-daily for total precip/max and min 2mtm) in GRIB2 at ECMWF and CMA
About 80 parameters, including:
3D fields (u/v/w/z/t/q) on 10 pressure levels (up to 10 hPa)
Surface fluxes
Sea Surface temperature
Sea-ice cover (fraction)
Snow depth/density/snow fall/snow albedo

5. WWRP/WCRP S2S Database Time-range Resol. Ens. Size Freq. Hcsts
Hcst length
Hcst Freq
Hcst Size
ECMWF
D 0-46
T639/319L91 51
2/week
On the fly
Past 20y
2/weekly 11
UKMO
D 0-60
N216L85 4
daily
4/month 3
NCEP
D 0-44
N126L64
4/daily
Fix 1
ECCC
D 0-32
0.45x0.45 L40 21
weekly
BoM
T47L17 33
6/month
JMA
D 0-34
T319L60 25
3/month 5
KMA
CMA
D 0-45
T106L40
CNRM
T255L91
2/monthly 15
CNR-ISAC
0.75x0.56 L54 40
HMCR
D 0-63
1.1x1.4 L28 20 10

6. Sub-seasonal prediction of extreme events
Prediction of large-scale, long lasting events (> 1 week):
Heat/cold waves
Droughts
Flooding
Prediction of statistics of small scale events, for example:
Tropical cyclones
Tornadoes

7. MJO influences on large floods of the West Coast of North America
Madden Julian Oscillation
MJO PHASES
Locations of large floods during 1985–2010
MJO influences on large floods of the West Coast of North America
From C. Zhang, BAMS 2013

8. Bivariate Correlation with ERA Interim – Ensemble Mean
re-forecasts
All Year
DJF

9. MJO Teleconnections (S2S re-forecasts)EI
Z500 Composites
Phase pentads NDJFM
BoM 0.15
CMA 0.14
HMCR 0.13
NCEP 0.32
ISAC 0.25
CNRM 0.15
UKMO 0.28
JMA 0.22
ECCC 0.21
ECMWF 0.31

10. Predicting skill associated with the Euro-Atlantic Regimes:
NAO -
NAO +
Bom
Bom
Blocking
Atlantic Ridge
Bom
Bom
Ens. Meand anomaly correlation as a function of forecast lead time for the prediction of the 4 EA regimes.
The scores are computed for the common period of reforecast covering 12 years ( ) we have applied a 5 days running mean prior the Acc computation.
The ACC for NAO- drops below 0.5 between days while for the BL ACC drops about 2-3 days earlier (9-12 days for BL) this results is consistent with other studies.

11. Russian Heat Wave July-August 2010
Worst heat wave on record over the past 33 years (Hoag, Nature 2014)
Estimated 55,000 deaths
Wildfires, smoke, worst drought in nearly 40 years, and the loss of at least 9 million hectares of crops
ERA interim 2mtm anomalies 1-7 August 2010

12. Russian Heat Wave 2010 1% 5% 4 July 1 August 15 August
ERA Interim
Day 1-7
Day 8-14
Day 15-21 1% 5%
4 July
1 August
15 August
WEEK1: time evolution of heat wave well predicted
WEEK2 and 3: Onset and decay predicted one week too late
Timing of maximum well predicted

13. 2mtm anomalies over Russia – ECMWF reforecasts
1-7 August 2010 1% 5%
Lead
time
d26-32
d22-28
d19-25
d15-21
d12-18
d8-14
d5-11
d1-7

14. 2mtm anomalies over Russia – S2S reforecasts
1-7 August 2010
UKMO (17 Jul)
BOM (16 Jul)
ECMWF (18 Jul)
NCEP(16 Jul)
ECCC(21 Jul)
JMA(20 Jul)
HMCR(20 Jul)
CNRM(15 Jul)
ERA Interim

15. 2mtm anomalies over Russia – S2S models reforecasts
1-7 August 2010
July 18 17 15 18 21 20 20 16 18 20

16. Tropical Cyclone PAM Case Study
Formed March 6, 2015
Dissipated March 22, 2015
Hit Vanuatu islands on 13 March 2015
Most intense tropical cyclone of the south Pacific Ocean in terms of sustained winds and regarded as one of the worst natural disasters in the history of Vanuatu.

17. March 2015 MJO
Forecasts starting on 26 Feb 2015

18. Modulation of tropical cyclone density anomaly by MJO
MJO Phase 2-3
MJO Phase 4-5
MJO Phase 6-7
MJO Phase 8-1
OBS
ECMWF
NCEP
JMA
BoM
Modulation of TC activity by the MJO. The plots show the anomaly of TC density as a function of MJO phase for 5 different S2S models and the multi-model combination for the time range day The figure shows a remarkable agreement with observations and suggest that all the model simulate very well the modulation of TCs by the MJO. This is an encouraging result for TC prediction
Multi

19. Probability of a TC strike within 300 km
Prediction from ECMWF
Probability of a TC strike within 300 km
09 March 2015 Day 1-7
02 March 2015 Day 8-14
23 February 2015 Day 15-21
16 February 2015 Day 22-28

20. Tropical Cyclone Pam case study
Multi-model prediction of TC strike probability anomalies March 2015
(NCEP/ECMWF/BoM/JMA/CMA)
2015/02/19 day 19-25
2015/02/26 day 12-18
PAM case study: Tropical cyclone strike (within 300 km) probability anomaly (relative to model climatology) for the forecasts starting on 19 February 2015 (left panel) and 26 February 2016 (right panel) and verifying on 9-15 March During the 9-15 March 2015, Pam, the strongest tropical cyclone of the season, hit Vanuatu (black dot on the map) with devastating consequences.
The multi-model forecasts (combination of NCEP/ECMWF/BoM/JMA and CMA) shows an increased risk of tropical storm strike in the area of Vanuatu at both time ranges.
In fact the anomaly map is consistent with the impact of an MJO event (in this case it was the strongest MJO event on record) on TC density. During the week the MJO active phase was over the west Pacific, which is consistent with less TC activity over the Indian ocean and more TC activity over the western pacific, and the possibility of twin TC genesis (over the northern and Southern hemisphere). The model seems able to predict the increased risk of a twin TC.

21. Impact of Resolution - Tropical cyclone PAM - 9-15 March 2015
Probability of a TC strike within 300 km
Day
Day 19-25
64km
32km
16km
110km+ SP
Verification

22. Verification of extreme events in S2S database
Main issues:
Rarity of extreme events in observations
Low number of ensemble members in most S2S re-forecasts
Low frequency of ensemble reforecasts in some S2S models
Short common re-forecast period
S2S real-time forecasts are more suitable for verification of extreme events, but period covered is small (archived only from 1st Jan 2015)

23. Relative Operating Characteristics (ROC) score Decile Probabilities – May 2015- April 2016 - NH
ECMWF real-time forecasts Day 12-18
Probability to be in lower decile
Probability to be in upper decile
Persistence of previous week
Forecast

24. 2m temp Cumulative Distribution Function ensemble predictions for 29 June - 5 July Climate June June June June (15-21d) (12-18d) (8-14d) (5-11d)
Cumulative Distribution function. area = [ 50., -10. ,38, 15 ]
Observed anomaly

25. 2m temp Extreme Forecast Index
forecast range:12-18days verifying 8-14 August 2016
Ncep Ecmwf
JMA Ukmo

26. EFI skill assessment Preliminary results based on ECMWF system:

27. Example of Attribution: March 2013 Cold wave over Europe
2mtm anomalies ERA Interim
ECMWF - strong MJOs
NCEP - strong MJOs
ECMWF - Weak MJOs
NCEP - Weak MJOs
Slide 34: An example of MJO impact on the Northern Extratropical weather. March 2013 was exceptionally cold over western Europe and the northern part of Asia. This extreme event coincided with an MJO propagation in the western Pacific, which could explain the negative NAO which developed in that period and which is conducive to cold temperature anomalies over western Europe (see slide 24).
The ECMWF monthly forecast starting on 14 February 2013 was successful in predicting the onset of this event up to 4 weeks in advance, although the ensemble mean anomalies are weaker than the observed anomalies. To evaluate the impact of the MJO, a composite of the 10 ensemble members which predicted the strongest MJO event in the western Pacific (middle panel) and a composite of the 10 ensemble members which did not predict an MJO in the western Pacific (bottom panel) were produced. Results suggest that 10 ensemble members with the strongest MJO produced 2-meter temperature anomaly forecasts over the northern Extratropics consistent with analysis (top panel). On the other hand, the 10 ensemble members which did not predict the MJO event produced bad 2-metre anomaly forecasts over Europe. This result suggests that the MJO event played a key role in the successful prediction of the cold event over Europe.

28. Conclusions
The S2S database can be a useful resource for case studies, skill assessment and also attribution of extreme events
S2S models display skill to predict MJO up to 3-4 weeks. However impact on NAO is weaker than in re-analysis and skill to predict European blocking (important for summer heat wave prediction) and NOA- (important with winter cold waves) is limited to 2 weeks.
Russian heat wave 2010: S2S model forecasts provided indications of an exceptional warm anomaly more than 10 days in advance.
Tropical cyclone PAM: High probabilities of TC strike 2-3 weeks in advance, most likely because of the impact of the strongest MJO on record on TC activity
Preliminary verification of ECMWF model suggests some useful skill for decile and EFI extended-range prediction

29. Regimes based on clustering of daily anomalies for 29 cold seasons (1980-2008)
500 hPa geopotential
Obtain well-known Euro-Atlantic regime patterns
The low frequency variability in European temperatures have been often associated with the frequency of occurrence of these well-known Euro-Atlantic flow regimes.
The positive and negative phase of the North Atlantic oscillation the Scandinavian blocking and the Atlantic ridge.
The large spatial scale and the low-frequency nature of such flow patterns are the key attributes for successful predictions at the sub-seasonal time-scale. In fact, circulation patterns like the NAO are often associated with global teleconnections through propagation of Rossby wave trains.
‘k means’ clustering applied to EOF pre-filtered data (retaining 80% of variance)
m2s2

30. Heat Wave Prediction in ECMWF re-forecasts
ERA Interim
Monthly Forecast Day 12-18