References:

Multiple sectors of society, e.g. risk transfer, disaster reduction

management, planning for disruption of transport, etc. would

benefit enormously from potential skill of seasonal forecasts,

e.g. November initialised winter (DJF) forecasts.

We therefore assess

1. the climatological representation and prediction skill of

extra-tropical cyclones and winter windstorms in

seasonal prediction models

2. the benefits and limitations of using the North Atlantic

Oscillation (NAO) as predictor for European winter

windstorms on a seasonal time scale

Extra-tropical Cyclones and Windstorms in Seasonal Prediction Models

Simon Wild1 (sbw206@bham.ac.uk), Daniel J. Befort1, Antje Weisheimer2,3, Jeff R. Knight4,

Hazel E. Thornton4, Julia F. Lockwood4, Leon Hermanson4 and Gregor C. Leckebusch1

1. Motivation 3. Results

2. Event Identification

AGU Fall Meeting, 2016

A23H – 0333

Cyclone Track

Wind Track

Windstorm “Daria” (24-26th Jan 1990); Shadings: number of exceedances of 98th percentile of wind speed during lifetime of storm

• Windstorm identification

and tracking algorithm

according to Leckebusch et

al., (2008) based on ex-

ceedances of 98th percentile

of near-surface wind speeds.

• Cyclone identification and

tracking algorithm according

to Murray and Simmonds,

(1991) based on Laplacian

of MSLP.

Our analyses cover the core winter months:

December – February; from 1992/93 to 2011/12

Reanalysis: • ECMWF ERA Interim (Dee et al., 2011)

Seasonal Prediction Model Suites: • ECMWF System 3, 41 Members (Anderson et al., 2007)

• ECMWF System 4, 51 Members (Molteni et al., 2011)

• Met Office HadGEM GA3, 24 Members (MacLachlan et al., 2014)

• Good agreement of spatial climatological distributions of extra-tropical cyclones and windstorms in comparison with reanalysis • Some biases present depending on the investigated model and region • Positive and significant skill in fore-casting the winter season frequency of extra-tropical cyclones and windstorms

• The NAO as predictor for windstorms can be beneficial in some regions while forecast skill of seasonal predictions might be lost elsewhere. [1] Anderson, D. et al., 2007: Development of the ECMWF seasonal forecast System 3. ECMWF Tech. Memo. , 503, 1-58

[2] Dee, D.P. et al., 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. QJRMS, 137, 553-597 [3] Leckebusch, G.C. et al., 2008: Development and application of an objective storm severity measure for the NE-Atlantic region. MeteorZ., 17(5), 575-587

[4] MacLachlan, C. et al., 2015: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system. QJRMS , 141 (689), 1072-1084 [5] Molteni, F. et al., 2011: The new ECMWF seasonal forecast system (System 4). ECMWF Tech. Memo. , 656, 1-51 [6] Murray, R. et al., 1991: A numerical scheme for tracking cyclone centres from digital data. Part I. Australian Meteorological Magazine, 39(3), 155-166

4. Summary

1

Extra-tropical Cyclones: Mean Sea Level Pressure (MSLP), 6 hourly

Windstorms: Wind Speed at 925hPa, 12 hourly

Row 1 (a-b): Direct forecast of windstorms Windstorms in Seasonal Models vs. Windstorms in ERA – Interim

Row 3 (g-i): Difference (Row 2 minus Row 1) Blue: direct is better Red: NAO based is better

Row 2 (d-f): NAO-regressed forecast of windstorms Regressed Windstorms in Seasonal Models vs. Windstorms in ERA - Interim

Regression Slope: Windstorm Trackdensity vs. NAO in ERA-Interim

d

DATA

A

B

Climatology: All Cyclones

# Cyclones per Winter

Climatology: Windstorms

# Windstorms per Winter

Prediction Skill: All Cyclones Prediction Skill: Windstorms

B

a-f) Kendall Rank Correlation (dots: statistical significance p<0.05) g-i) Difference of Correlation Values

A

B

2 3

4

A Climatology: Strongest 5% Cyclones

# Cyclones per Winter

Prediction Skill: Strongest 5% Cyclones

Kendall Rank Correlation (dots: statistical significance p<0.05)

Befort et al. 2017, QJRMS to be submitted

Extra-tropical Cyclones and Windstorms in Seasonal Prediction Models

Simon Wild, Daniel J. Befort, Antje Weisheimer, Jeff R. Knight, Hazel E.

Thornton, Julia F. Lockwood, Leon Hermanson and Gregor C. Leckebusch

Severe extra-tropical cyclones (ETC) and associated extreme wind speeds are the

predominant cause for severe damages and large insured losses in the majority of European

countries. Reliable seasonal forecasts of ETC and windstorms (WS) would thus have great

social and economical benefits.

In this study we analyse the climatological representation and seasonal prediction skill of

ETC and WS in state-of-the-art multi-member seasonal prediction systems, namely ECMWF-

System3, ECMWF-System4 and Met Office – HadGEM-GA3 in the core winter months

(DJF). ETC identification is based on the Laplacian of the MSLP whilst WS identification is

based on near-surface wind speeds.

All data sets show good agreement of spatial climatological distributions of ETC and WS in

comparison with reanalysis data (ERA-Interim). There are however both positive and

negative biases present depending on the model and region analysed. All seasonal prediction

systems show widely small to moderate positive skill in forecasting the winter season

frequency of ETC and WS over the Northern Hemisphere. The skill is highest for ETC at the

downstream end of the Pacific stormtrack and for WS at the downstream end of the Atlantic

stormtrack. We thus find significant skill for high impact WS affecting several European

regions.

Focussing on European WS, we linearly regress the interannual WS frequency onto the North

Atlantic Oscillation (NAO) in the reanalysis data and apply this relation to the seasonal

forecast models. We find that NAO – predicted WS show also generally positive skill over

most parts of Western Europe. Compared to the directly identified and tracked WS the skill is

slightly enhanced over parts of the UK and North Sea. We find however lower skill in other

Western European regions primarily along the nodal line of the NAO. This suggests that

using the NAO as the solely predictor for WS can be beneficial in some regions while

forecast skill of seasonal predictions might be lost elsewhere.