Extra-tropical flow regimes and connections with tropical rainfall i n the MINERVA experiments

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. 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. NAO, Dec (m2) T319 ac = 0.37 ERA Ens mean Ens members Z 500 Anomaly index = 1 σ T639 ac = 0.50

4. NAO, DJF (m2-4) T319 ac = 0.26 ERA Ens mean Ens members Z 500 Anomaly index = 1 σ T639 ac = 0.51

5. PNA, DJF (m2-4) T319 ac = 0.68 ERA Ens mean Ens members Z 500 Anomaly index = 1 σ T639 ac = 0.66

6. 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

7. Statistics for N-cluster partitions (%)

8. Euro-Atlantic 4-cluster centroids NAO+ 31.5% Atl. Ridge 22.2% Blocking 25.0% NAO- 21.3%

9. Pacific-North American 4-cluster centroids Arctic Low ( PNA- ) 27.7% Alaskan Ridge 20.6% Pacific Trough 27.7% PNA+ 24.0%

10. Probabilistic prediction of regime occurrence • Bj [X(t)] : binary index of j-th cluster occurrence = (0, 1) • From cluster analysis: Bj [Xi] • Probabilistic index of cluster occurrence based on kernel estimator: Pj [X(t)] = ΣiK [X(t) – Xi] Bj [Xi] • Multi-normal Kernel function K = exp { -|X(t) – Xi|2 / (h s)2 } s2 = internal variance of clusters h = kernel width (0.25, 0.35, 0.50) • From 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/Sclim

11. T319: Skill score based on CRPS, all EAT clusters Score for 5-day means Score for 5-day means, 3-point filter Score for 15-day means 0.8 0.4 0.0 1 Nov (d0) 1 Jan (d61)

12. T319: Skill scores based on CRPS and MAE, all clusters CRPS EAT MAE CRPS PNA MAE

13. T319: Skill scores based on CRPS, 4 Eur-Atl. clusters NAO+ 31.5% Atl. Ridge 22.2% Blocking 25.0% NAO- 21.3%

14. T639: Skill scores based on CRPS, 4 Eur-Atl. clusters NAO+ 31.5% Atl. Ridge 22.2% Blocking 25.0% NAO- 21.3%

15. T319: Skill scores based on CRPS, 4 Pac.-N.Am. clusters Arctic Low 27.7% Alaskan Ridge 20.6%

16. T639: Skill scores based on CRPS, 4 Pac.-N.Am. clusters Arctic Low 27.7% Alaskan Ridge 20.6%

17. Skill for 15d-mean fc of NAO +/- regime indices NAO- NAO+

18. Local correlation SST – precip, DJF 1981-2008

19. Precip. teleconnections in DJF: GPCP 2.2

20. Precip. teleconnections in DJF: System 4 (from Nov.)

21. Z 500_hPavs.precip: ERA-Int. and System-4 ERA Sys4

22. Correlations of Indo-Pac. rainfall and NAO (DJF)

23. Impact of tropical rainfall correlation on teleconnections Cov. (Z500, WCIO) DJF Cov. (Z500, NINO4W) cor = 0.0 cor = 0.19 (obs) cor = 0.55

24. Teleconnections with WCIO and NINO4 rainfall, DJF Nino4 T319 T639 WCIO T319 T639

25. 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%.