# Presentation: 258328

## Storm Track Predictability 

**on Seasonal to Decadal Scales**

**Gilbert P. Compo and Prashant D. Sardeshmukh**
***NOAA-CIRES Climate Diagnostics Center***

- Compo, G.P., and P.D. Sardeshmukh, 2004, J. Climate, **17**, 3701-3270.

## Outline

- ENSO has a significant effect on interannual variability of Northern Hemisphere storm tracks (*Fraedrich 1990; Hoerling and Ting 1994; Strauss and Shukla 1997; May and Bengtsson 1998; Compo et al. 2001*)
- Studying predictability and storm tracks
- Storm Track Model
- Actual and expected skill for predicting storm track anomalies
- Decadal storm track anomalies
- Observed versus expected skill

## Data and Method

*NCEP MRF9**:* T40L18
- JFM integrations: Climatological and actual and idealized ENSO	570 members
- 1950-94 integrations:	Global SSTs (GOGA)			13 members
- (monthly) 		30oN-30oS Pacific SSTs (POGA)		9 members
*NCAR CCM3**:*T42L18
- 1950-99 integrations:	Global SSTs (GOGA)			12 members
- (monthly)		Tropical SSTs (TOGA)		11 members
*NASA NSIPP**: *144X90  L34 {96X80 L34}
- 1948-04 integrations:	GOGA				9 members
- 1948-03	               :  {GOGA}				14 members
- 1948-99	               :  {GOGA with greenhouse gases}		8 members
*IRI ECHAM4.5 **:*T42L19
- 1950-03 integrations:  GOGA				24 members
*NCEP-NCAR** reanalysis dataset*:T62L28
- 1948-04

- Storm tracks are computed directly from Fourier power spectrum summed over 2.0 to 6.9 days and are also computed using an Empirical Storm Track Model.

## ENSO has a significant effect on stormtracks

**El Nino anomalous stormtracks**
**(JFM) 2 to 7 day standard deviation**

- 500 mb height

- 500 mb vertical velocity ()

- OBS

- GCM

*from Compo, Sardeshmukh, and Penland (J. Climate 2001).  *

**Is it predictable?**

- C.I. 8m

- 11-events

- 1987 SSTs 60-members

- C.I. ~20mb/day

## Combined PDF and rho-infinity

**Combined PDF ****and rho-infinity**

**Expected forecast skill**
**as a function of  **
**signal to noise ratio**** **

- 27 %

- 3 %

- Signal  0.5

***Predict • ability***
**The ability to predict**
**a *****different range of possibilities***** than the climatological range**

***from Sardeshmukh, ***
***Compo, and Penland***
***(J.Climate 2000)***

## An Empirical Linear Storm Track Model (STM)

***G*** is estimated from large (570-member) ensembles of seasonal  NCEP GCM runs with climatological mean SSTs and also observed  and idealized ENSO SST forcing.

**An Empirical Linear Storm Track Model (STM)**

*Chang and Fu (2003), Peng et al (2003), Compo and Sardeshmukh (2004)*

## Design of Study

- The linear STM is first tested for its ability to reproduce the nonlinear NCEP GCM’s
- (60-member) ensemble-mean storm track responses to warm and cold ENSO SST
- forcing in 1987 and 1989, given only the GCM’s ensemble-mean 200 mb height responses.
- It is then used to predict observed winter-mean and 5-winter-mean storm track anomalies
- during 1950-1999, given :
- The observed 200 mb height anomalies, and
- The NCEP and NCAR GCMs’ (~12-member) ensemble-mean 200 mb height
- 	responses to
- 		(a) anomalous  Global   SST forcing (GOGA), and
- 		(b) anomalous Tropical SST forcing (TOGA)
- 	**These predicted storm anomalies are interpreted as the “predictable” SST-forced **
**	****part of the anomalous storm track in each winter and 5-winter mean.**

## Testing the linear STM

**GCM’s**
**60-member**
**ensemble mean**
**200 mb height**
**response**
**Linear STM’s **
**500 mb ******
**Storm track **
**response**
**GCM’s **
**ensemble-mean**
**500 mb ******
**Storm track **
**response**

**1989 **
**La Nina **

**1987 **
**El Nino **

- C.I. 20 m for height,  ~ 10 mb/day for storm track** **

- 0.9

- 0.9

## Correlation of winter mean and model storm track

***Correlation of winter mean and model storm track***

**Skill of**
**winter-mean**
**storm track**
**anomaly**
**“****predictions”**
**made by**
**the linear**
**STM**
**Predictable**
**part is**
**associated**
**mostly with **
**tropical**
**SST forcing**

**CI:  0.15**
**Starting **
**at 0.25**

***using observed***
***200mb height***

***using GCM ensemble mean 200mb height***

- Global SSTs

- Tropical SSTs

## SST-forced storm track predictability (local anomaly correlation)

- y=Gx
- 50 winters

**SST-forced storm track predictability **(local anomaly correlation)

- 1987 El Nino

- 1989 La Nina
- 60 MRF members

- GCM sensitivity

- Case dependence

## Skill in Predicting Observed Anomalous Storm Tracks

**Skill in Predicting Observed Anomalous Storm Tracks**

- More skillful years than just ENSO over PNA (26).

- 5%

- _PNA Sector_
- Skill of GOGA and TOGA significant and strongly
- associated with ENSO.
- _North-Atlantic/Europe_
- Lesser skill

## Decadal storm track anomalies

- Decadal storm track variations reported in several studies (Hurrell and van Loon 1997, Graham and Diaz 2001, Chang and Fu 2002, 2003, Harnik and Chang 2003).
- Omega stormtrack not reported.
- Relationship to global SST variations not reported.
- Use STM to examine consistency between GCM and observed 5-winter mean stormtrack anomalies.

## Skill in Simulating 5-winter mean Observed Stormtracks

**Skill in Simulating 5-winter mean Observed Stormtracks**

- _PNA Sector_
- Skill of GOGA and TOGA significant and strongly associated with low-frequency ENSO.
- _North-Atlantic/Europe_
- No significant skill

- 5%

## Observed vs expected skillfor Northern Hemisphere Storm Track Anomalies

**In Pacific sector,**
**actual skill is consistent **
**with signal to noise ratios**
**but not in Atlantic sector....**
**Is this because of errors in:**
**  ****1. Storm Track Model, or**
**  ****2. GCMs’ 200 mb Z response**
**in the Atlantic?**
**Latter is more likely,**
**given the STM’s reproduction **
**of the GCM’s ensemble mean**
**Atlantic storm tracks in **
**1987 and 1989.**

- Symbols show actual pattern correlations

## SST-forced storm track skill is very similar in older and newer GCMs... 

- y=Gx
- 50+ winters

**SST-forced storm track skill ****is very similar in older and newer GCMs... **

- 12 members

- 12 members

- Local anomaly correlation

## But actual skill of newer models is not necessarily more consistent with signal to noise ratios! 

**For newer models,**
**In Pacific sector,**
**actual skill is consistent **
**with signal to noise ratios only for large *****S***
**and not at all in Atlantic sector....**

- MRF9
- CCM3

- ECHAM
- NSIPP

- Symbols show actual pattern correlations

## Summary

**Our linear STM can reproduce a nonlinear GCM’s storm track response to ENSO, given only the GCM’s 200 mb height response. **
**2.   The linear STM has been used to estimate the local and regional predictability of  winter-mean and 5-winter-mean storm track**
**	****anomalies. There is substantial predictability in the Pacific sector, much less so in the Atlantic sector.**
**3. ****	****Most of this predictability is associated with tropical SST forcing.  **
**The predictability estimates are more reliable in the Pacific than **
**	****in the Atlantic sector, where they are inconsistent with estimated signal to noise ratios, ****even for newer models****.**