1. Int. Conference on S2S Prediction, 10-13 Feb. 2014 1 Extra-tropical flow regimes and connections with tropical rainfall in the MINERVA experiments Franco Molteni, Frederic Vitart, Tim Stockdale, Laura Ferranti (European Centre for Medium-Range Weather Forecasts, Reading, U.K.) Susanna Corti (ISAC-CNR, Italy) Ben Cash, David Straus (COLA/George Mason Univ., USA)

2. Int. Conference on S2S Prediction, 10-13 Feb. 2014 2 The MINERVA experiments MINERVA: a COLA-ECMWF project sponsored by the NCAR Accelerated Scientific Discovery programme:  seasonal re-forecasts at T319, T639 (30yr, Nov+May IC) and T1279 (12yr, May IC) with IFS_cy38r1 + NEMO_v-3.1, run on NCAR Yellowstone HPC, 28M core-hours) Outline of results:  Predictive skill for NAO and PNA for seasonal (DJF) and month- 2 (Dec) means  Probabilistic prediction of flow regime occurrence in the sub- seasonal range for the Atlantic and Pacific sectors  Teleconnections of Indo-Pacific rainfall and NH 500 hPa height: impact on NAO long-range predictions

3. Int. Conference on S2S Prediction, 10-13 Feb. 2014 3 NAO, Dec (m2) T319 ac = 0.37 T639 ac = 0.50 ERA Ens mean Ens members Z 500 Anomaly index = 1 σ

4. Int. Conference on S2S Prediction, 10-13 Feb. 2014 4 NAO, DJF (m2-4) T319 ac = 0.26 T639 ac = 0.51 ERA Ens mean Ens members Z 500 Anomaly index = 1 σ

5. Int. Conference on S2S Prediction, 10-13 Feb. 2014 5 PNA, DJF (m2-4) T319 ac = 0.68 T639 ac = 0.66 ERA Ens mean Ens members Z 500 Anomaly index = 1 σ

6. Int. Conference on S2S Prediction, 10-13 Feb. 2014 6 Ensemble-mean NAO fc. for DJF

7. Int. Conference on S2S Prediction, 10-13 Feb. 2014 7 A re-visitation of Pacific + Atlantic regimes: methodology Data:  5-day means of 500-hPa height from ERA-Interim  Dec.1979-Mar.1980 to Dec.2012-Mar.2013 (24 pentads*34 years = 816) Definition of anomalies wrt 34-yr climate (low-pass filtered) EOF analysis on 3 domains:  Euro-Atlantic (EAT: 80W-40E, 25-85N)  Pacific – North America (PNA: 160E-80W, 25-85N)  Pacific + Atlantic (PAT = PNA + EAT, 160E-40E, 25-85N) Non-hierarchical cluster analysis using k-means algorithm  up to 6 clusters for EAT and PNA, up to 8 clusters for PAT  Significance test on signal-to-noise ratio (centroid variance / inter-cluster variance) against 500 red-noise data samples with same variance, skewness and lag-1 autocorrelation as individual PCs)  Refs.: Michelangeli et al. 1995, Straus et al. 2007

8. Int. Conference on S2S Prediction, 10-13 Feb. 2014 8 EOF-1 for the three domains

9. Int. Conference on S2S Prediction, 10-13 Feb. 2014 9 Statistics for N-cluster partitions (%) 2 cl3 cl4 cl5 cl6 cl7 clnh8cl E-AT var s/n 24.742.359.371.481.5 E-AT conf.lev 52.786.899.899.699.8 P-NA var s/n 24.243.857.969.479.1 P-NA conf.lev 76.087.698.698.899.0 P-AT var s/n 15.627.336.343.650.055.761.2 P-AT conf.lev 57.076.290.493.097.498.098.8

10. Int. Conference on S2S Prediction, 10-13 Feb. 2014 10 Euro-Atlantic 4-cluster centroids NAO+ 31.5% Atl. Ridge 22.2% Blocking 25.0% NAO- 21.3%

11. Int. Conference on S2S Prediction, 10-13 Feb. 2014 11 Pacific-North American 4-cluster centroids Pacific Trough 27.7% PNA+ 24.0% Arctic Low ( PNA- ) 27.7% Alaskan Ridge 20.6%

12. Int. Conference on S2S Prediction, 10-13 Feb. 2014 12 Probabilistic prediction of regime occurrence jB j [X (t) ] : binary index of j-th cluster occurrence = (0, 1) jFrom cluster analysis: B j [X i ] Probabilistic index of cluster occurrence based on kernel estimator: jj P j [X (t) ] = Σ i K [X (t) – X i ] B j [X i ] Multi-normal Kernel function K = exp { - | X (t) – X i | 2 / (h s) 2 } s 2 = internal variance of clusters h = kernel width (0.25, 0.35, 0.50) jFrom time series of Bj [X(t)] for analysis and ensemble members, we compute 5-day and 15-day CRPS and mean abs. error of the ens. mean, as well as the associated skill scores : SS = 1. – S/S clim

13. Int. Conference on S2S Prediction, 10-13 Feb. 2014 13 T319: Skill score based on CRPS, all EAT clusters 1 Nov (d0) 1 Jan (d61) Score for 5-day means Score for 5-day means, 3-point filter Score for 15-day means 0.8 0.4 0.0

14. Int. Conference on S2S Prediction, 10-13 Feb. 2014 14 T319: Skill scores based on CRPS and MAE, all clusters CRPS EAT MAE CRPS PNA MAE

15. Int. Conference on S2S Prediction, 10-13 Feb. 2014 15 T319: Skill scores based on CRPS, 4 Eur-Atl. clusters NAO+ 31.5% Atl. Ridge 22.2% Blocking 25.0% NAO- 21.3%

16. Int. Conference on S2S Prediction, 10-13 Feb. 2014 16 T639: Skill scores based on CRPS, 4 Eur-Atl. clusters NAO+ 31.5% Atl. Ridge 22.2% Blocking 25.0% NAO- 21.3%

17. Int. Conference on S2S Prediction, 10-13 Feb. 2014 17 T319: Skill scores based on CRPS, 4 Pac.-N.Am. clusters Arctic Low 27.7% Alaskan Ridge 20.6%

18. Int. Conference on S2S Prediction, 10-13 Feb. 2014 18 T639: Skill scores based on CRPS, 4 Pac.-N.Am. clusters Arctic Low 27.7% Alaskan Ridge 20.6%

19. Int. Conference on S2S Prediction, 10-13 Feb. 2014 19 Skill for 15d-mean fc of NAO +/- regime indices NAO+ NAO-

20. Int. Conference on S2S Prediction, 10-13 Feb. 2014 20 Local correlation SST – precip, DJF 1981-2008

21. Int. Conference on S2S Prediction, 10-13 Feb. 2014 21 Precip. teleconnections in DJF: GPCP 2.2

22. Int. Conference on S2S Prediction, 10-13 Feb. 2014 22 Precip. teleconnections in DJF: System 4 (from Nov.)

23. Int. Conference on S2S Prediction, 10-13 Feb. 2014 23 Z 500 _hPa vs. precip: ERA-Int. and System-4 ERA Sys4

24. Int. Conference on S2S Prediction, 10-13 Feb. 2014 24 Correlations of Indo-Pac. rainfall and NAO (DJF) WCIO obs EIWP obs NAO obs WCIO T319 T639 EIWP T319 T639 NAO T319 T639 WCIO-0.140.35-0.54 -0.48 0.21 EIWP-0.14-0.06-0.54 -0.48 -0.18 -0.14 NINO4W0.19-0.82-0.080.59 0.55 -0.81 -0.80 0.24 0.20

25. Int. Conference on S2S Prediction, 10-13 Feb. 2014 25 Impact of tropical rainfall correlation on teleconnections Cov. (Z500, WCIO) DJF Cov. (Z500, NINO4W) cor = 0.0 cor = 0.19 (obs) cor = 0.55

26. Int. Conference on S2S Prediction, 10-13 Feb. 2014 26 Teleconnections with WCIO and NINO4 rainfall, DJF WCIO T319 T639 Nino4 T319 T639

27. Int. Conference on S2S Prediction, 10-13 Feb. 2014 27 Summary  On seasonal timescale, T639 has the same predictive skill as T319 for PNA, but a (notably) higher skill for NAO; the NAO skill improvement is also seen in month-2 means.  On the sub-seasonal scale, considerable difference in predictive skill are found for different flow regimes. In the Euro-Atlantic sector, the NAO+ and Atlantic Ridge regimes are more predictable than NAO- and Blocking.  T639 shows a better skill than T319 in predicting the NAO+ regime occurrence, while skill for NAO- shows a stronger drop at day 20~30  For Indo-Pacific rainfall, the MINERVA runs (as Sys-4) show stronger links between rainfall over the Western Indian Ocean and over the Maritime Continents / Central Pacific than those found in GPCP data. As a result, extratropical teleconnection patterns from these three tropical regions look more similar than in observations, and the NAO – Indian Ocean rainfall connection is underestimated. This problem is alleviated in T639 wrt T319, but only by 10~15%.