1. SST Forced Atmospheric Variability in an AGCM Arun Kumar Qin Zhang Peitao Peng Bhaskar Jha Climate Prediction Center NCEP

2. Outline MotivationMotivation Data and MethodologyData and Methodology ResultsResults Summary and ConclusionsSummary and Conclusions

3. Motivation Horel and Wallace, 1981: Planetary Scale Atmospheric Phenomenon Associated with the SO Correlation between DJF 700mb Z and SST index

4. Motivation “…What, then, are the prospects of utilizing information on equatorial SST anomalies …to improve the quality of long-range forecasts for middle latitudes?…”“…What, then, are the prospects of utilizing information on equatorial SST anomalies …to improve the quality of long-range forecasts for middle latitudes?…” -- If the strength of correlations … is limited by the high noise level inherent in seasonal averages… then the prospects of [seasonal predictions] are not encouraging -- On the other hand, if these patterns constitute blurred images resulting from our inadvertent superposition of an ensemble of shaper patterns, …, then there is hope that (seasonal prediction of)… midlatitude climate anomalies with higher degree of detail and accuracy than is now (will be) possible.

5. Motivation

6. Motivation Question: How much does the atmospheric response in the extratropical latitudes depend on details of the ENSO SST anomalies, or to SST anomalies in different ocean basins?Question: How much does the atmospheric response in the extratropical latitudes depend on details of the ENSO SST anomalies, or to SST anomalies in different ocean basins?

7. Data and Methodology For each DJF seasonal mean from 1980-2000, we have access to an 80-member ensemble of AGCM simulations forced with the observed SSTsFor each DJF seasonal mean from 1980-2000, we have access to an 80-member ensemble of AGCM simulations forced with the observed SSTs Ensemble mean for each DJF provides a good estimate of atmospheric response to that year’s SST forcingEnsemble mean for each DJF provides a good estimate of atmospheric response to that year’s SST forcing For this data set, we analyze how the ensemble mean 200- mb height response varies with SSTsFor this data set, we analyze how the ensemble mean 200- mb height response varies with SSTs

8. Data and Methodology ICsTarget AugSepOctNovDJF SepOctNovDJF OctNovDJF NovDJF 80-member Ensemble From 2002 and 2003 ICs Data is from “Seasonal Forecast Model” archives from 2002- 2003Data is from “Seasonal Forecast Model” archives from 2002- 2003 –10-member ensemble from different atmospheric initial conditions each month –Lagged ensemble from different ICs

9. Data and Methodology Difference in 200-mb eddy height climatology from December and September ICs 200-mb eddy height climatology for December ICs

10. Data and Methodology Difference in 200-mb height variance from December and September ICs 200-mb height variance for December ICs

11. Results Variance of Ensemble Means External to Internal Variance Ratio

12. Results EOF1 53%

13. Results Remaining External Variance Fractional External Variance Related to Mode1

14. Results EOF2 19%

15. Results EOF3 12%

16. Results Fraction of Variance Explained by Modes 1-3

17. Results Z = a*ΔSST + b*ΔSST 2 if ΔSST +  Z + & ΔSST -  Z - then a= (Z + - Z - ) / (2* ΔSST avg ) and b= (Z + + Z - ) / (2* ΔSST avg ) (Monahan & Dai 2004) (83+98) – (89+99)

18. Results Ensemble mean (shaded); EOF1 (contour) Ensemble mean – EOF1 DJF 1998

19. Results Ensemble mean (shaded); EOF1 (contour) Ensemble mean – EOF1 DJF 1999

20. Results -Strong Warm Cold EOF1 - Warm Strong Cold

21. Results Composite based on 1980, 81, 82 & 86

22. Results Anomaly Correlation Ensemble Mean EOF1 EOF1 + EOF2 EOF1:EOF3

23. Results AC(EOF1+EOF2) – AC(EOF1) AC(EOF1:EOF2) – AC(EOF1+EOF2)

24. Results AC (EOF1) AC (EOF1:EOF3)

25. Summary and Conclusions A large fraction of extratropical variability is indeed related to “…high noise level inherent in seasonal averages… and the prospects of [seasonal predictions] are limited.”A large fraction of extratropical variability is indeed related to “…high noise level inherent in seasonal averages… and the prospects of [seasonal predictions] are limited.” There are other modes of atmospheric response that are related to non-ENSO SSTs (e.g., EOF2), but this could be specific to the analysis period.There are other modes of atmospheric response that are related to non-ENSO SSTs (e.g., EOF2), but this could be specific to the analysis period. This (and previous) analysis has shown higher order response to ENSO extremes, but it is hard to show any definite influence averaged over all SST years. This is either because of the rarity of such events, or because of incorrect simulation by the AGCMThis (and previous) analysis has shown higher order response to ENSO extremes, but it is hard to show any definite influence averaged over all SST years. This is either because of the rarity of such events, or because of incorrect simulation by the AGCM Should be repeated with other AGCMsShould be repeated with other AGCMs