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)

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) meansProbabilistic prediction of flow regime occurrence in the sub-seasonal range for the Atlantic and Pacific sectorsTeleconnections of Indo-Pacific rainfall and NH 500 hPa height: impact on NAO long-range predictions

Int. Conference on S2S Prediction, 10-13 Feb. 2014 3

NAO, Dec (m2)

T319ac = 0.37

T639ac = 0.50

ERAEns mean

Ens members

Z 500Anomaly

index = 1 σ

Int. Conference on S2S Prediction, 10-13 Feb. 2014 4

NAO, DJF (m2-4)

T319ac = 0.26

T639ac = 0.51

ERAEns mean

Ens members

Z 500Anomaly

index = 1 σ

Int. Conference on S2S Prediction, 10-13 Feb. 2014 5

PNA, DJF (m2-4)

T319ac = 0.68

T639ac = 0.66

ERAEns mean

Ens members

Z 500Anomaly

index = 1 σ

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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

Int. Conference on S2S Prediction, 10-13 Feb. 2014 9

Statistics for N-cluster partitions (%)

2 cl 3 cl 4 cl 5 cl 6 cl 7 clnh 8cl

E-AT var s/n

24.7 42.3 59.3 71.4 81.5

E-AT conf.lev

52.7 86.8 99.8 99.6 99.8

P-NA var s/n

24.2 43.8 57.9 69.4 79.1

P-NA conf.lev

76.0 87.6 98.6 98.8 99.0

P-AT var s/n

15.6 27.3 36.3 43.6 50.0 55.7 61.2

P-AT conf.lev

57.0 76.2 90.4 93.0 97.4 98.0 98.8

Int. Conference on S2S Prediction, 10-13 Feb. 2014 10

Euro-Atlantic 4-cluster centroids

NAO+31.5%

Atl. Ridge22.2%

Blocking25.0%

NAO-21.3%

Int. Conference on S2S Prediction, 10-13 Feb. 2014 11

Pacific-North American 4-cluster centroids

PacificTrough27.7%

PNA+24.0%

Arctic Low( PNA- )27.7%

Alaskan Ridge20.6%

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Probabilistic prediction of regime occurrence

• Bjj [X(t)] : binary index of j-th cluster occurrence = (0, 1)

• From cluster analysis: Bjj [Xi]

• Probabilistic index of cluster occurrence based on kernel estimator:

Pjj [X(t)] = Σi K [X(t) – Xi] Bjj [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 Bjj [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

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 meansScore for 5-day means, 3-point filter

Score for 15-day means0.8

0.4

0.0

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T319: Skill scores based on CRPS and MAE, all clusters

CRPS

EAT

MAE

CRPS

PNA

MAE

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T319: Skill scores based on CRPS, 4 Eur-Atl. clusters

NAO+31.5%

Atl. Ridge22.2%

Blocking25.0%

NAO-21.3%

Int. Conference on S2S Prediction, 10-13 Feb. 2014 16

T639: Skill scores based on CRPS, 4 Eur-Atl. clusters

NAO+31.5%

Atl. Ridge22.2%

Blocking25.0%

NAO-21.3%

Int. Conference on S2S Prediction, 10-13 Feb. 2014 17

T319: Skill scores based on CRPS, 4 Pac.-N.Am. clusters

ArcticLow

27.7%

Alaskan Ridge20.6%

Int. Conference on S2S Prediction, 10-13 Feb. 2014 18

T639: Skill scores based on CRPS, 4 Pac.-N.Am. clusters

ArcticLow

27.7%

Alaskan Ridge20.6%

Int. Conference on S2S Prediction, 10-13 Feb. 2014 19

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

NAO+

NAO-

Int. Conference on S2S Prediction, 10-13 Feb. 2014 20

Local correlation SST – precip, DJF 1981-2008

Int. Conference on S2S Prediction, 10-13 Feb. 2014 21

Precip. teleconnections in DJF: GPCP 2.2

Int. Conference on S2S Prediction, 10-13 Feb. 2014 22

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

Int. Conference on S2S Prediction, 10-13 Feb. 2014 23

Z 500_hPa vs. precip: ERA-Int. and System-4

ERA

Sys4

Int. Conference on S2S Prediction, 10-13 Feb. 2014 24

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

WCIOobs

EIWPobs

NAOobs

WCIOT319T639

EIWPT319T639

NAOT319T639

WCIO -0.14 0.35 -0.54-0.48

0.210.21

EIWP -0.14 -0.06 -0.54-0.48

-0.18-0.14

NINO4W 0.19 -0.82 -0.08 0.590.55

-0.81-0.80

0.240.20

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Impact of tropical rainfall correlation on teleconnections

Cov. (Z500, WCIO)

DJF

Cov.(Z500, NINO4W)

cor = 0.0 cor = 0.19 (obs) cor = 0.55

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Teleconnections with WCIO and NINO4 rainfall, DJF

WCIO

T319

T639

Nino4

T319

T639

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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%.