The Global Wind Oscillation April-May 2007

Edward Berry NOAA/NWS Dodge City, Kansas

Klaus Weickmann NOAA/ESRL/PSD, Boulder, Colorado

High Plains Conference Hastings, NE

August 16th, 2007

> WB(2007) proposed a Global Synoptic Dynamic Model of subseasonal atmospheric variabilty (GSDM)

> GSDM considers the interaction of 3-4 different time scales including the MJO recurrence time

> Purpose of GSDM is to extend current thinking beyond the MJO

> The Global Wind Oscillation (GWO) objectively represents a non-oscillatory component of the GSDM

Background

Outline of Talk

* Review the GSDM

* Introduce the GWO * Case Study

* Summary

* Risk Assessment Plots

There is no Cookbook!!!

• Subseasonal Forecasting Tools– Numerical Models

• ensembles (multi-model)• ocean coupled

– Statistical Models• composites• regressions

– Statistical-Dynamical• linear inverse models• analogues of forecasts

– Global Synoptic-Dynamic Model (GSDM)

• Weather Climate Linkage– Regime transitions– Extreme events

eddies

convection

mountains

What is the GSDM?

Evolutionary framework for weather- climate diagnosis and forecasting

Core subseasonal time scales - fast, slow and “quasi-o”; also ENSO, etc.

Physical Processes – Tropical Convection (tropical OLR modes)– Momentum Transports (eddy vs. HC/FC)– Torques (Mountain and friction)

New way to evaluate model predictions Used to keep pulse on evolving climate

state: from synoptic storms to decadal shifts

GLOBAL SYNOPTIC DYNAMIC MODEL (GSDM)Each stage shows spatial structure of 3-4 time scales

Wave energy dispersion favors high impact weather across USA Plains

Below normal temperatures possible across central and eastern USA

High Impact weather event possible along USA west coast

Heavy precipitation event possibleSouthwest USA

What is the GWO?

• Quasi-phase space plot of normalized AAM against normalized AAM tendency

• Similar to MJO phase space plots such as WH(2004)• Objectively represents portion of GSDM most closely

linked with AAM variations – Tropical Convection (tropical OLR modes) implicitly– Momentum Transports (eddy vs. HC/FC)– Torques (Mountain and friction)

• Captures behaviors such as poleward propagation of zonal mean wind anomalies

• Provides considerable independent information on variations of the global circulation

Stage 1

Stage 2

Stage 3

Stage 4

Relative AAM Tendency

Relative AAM

4/35/11

4/9 5/164/26

5/26

4/16

5/22

Case Study: Week-2 Forecast for 18-25 May 2007

Western USA Trough when models forecasted a ridge

Severe storms across the Plains

GWO provided additional predictive insight

MAY 11

#1

#2

EE

E

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EE

WW

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E#1 #2

4/95/16

4/3 5/114/26 5/264/16 5/22

H

HH

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L LL

LL LH

L

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LHH LH

L H

LL

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May 7

May 11

250mb Daily Mean Vector Wind Anomalies

L

L LL

H

HH

H

H

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H

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L HHL

L

L

May 15

May 19

H

H

250mb Daily Mean Vector Wind Anomalies

H

H

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L

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HL L L H

L

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HL

May 23

250mb Daily Mean Vector Wind Anomalies

PDF for AMJ GSDM Stage 2 vs. 4 Western USA 850 Ta

Probs ordinate

Std dev abscissa

Stage 2

Stage 4

-4.25 +4.25-1.75

• GSDM provides link between weather and climate

• GWO is objective representing a portion of the GSDM

• Risk assessment maps based on the GSDM are being developed

• Present efforts include HydroMet Testbed for west coast floods and CPC Global Tropical HA

• Subseasonal forecasts (weeks 1-4) must be probabilistic and verified accordingly

Summary and Conclusions

QUESTIONS?

Summary and ConclusionsSummary and Conclusions

GSDM provides link between weather GSDM provides link between weather and climate and climate

GWO is objective representing a portion GWO is objective representing a portion of the GSDMof the GSDM

Risk assessment maps based on the Risk assessment maps based on the GSDM are being developed GSDM are being developed

Present efforts include HydroMet Present efforts include HydroMet Testbed for west coast floods and CPC Testbed for west coast floods and CPC Global Tropical HAGlobal Tropical HA

Subseasonal forecasts (weeks 1-4) must Subseasonal forecasts (weeks 1-4) must be probabilistic and verified accordinglybe probabilistic and verified accordingly

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11May

11May

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W

W

E

W

W EW WE

#1 #2

4/3 5/114/9 5/164/26 5/264/16 5/22

#1 #2

4/3 4/9 5/115/164/26 5/264/16 5/22

#1

#2

4/3 4/9 5/115/164/26

5/264/165/22