0333 extra-tropical cyclones and windstorms in … · extra-tropical cyclones and winter windstorms...
TRANSCRIPT
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 ([email protected]), 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.