250 likes | 371 Views
SST Forced Atmospheric Variability in an AGCM. Arun Kumar Qin Zhang Peitao Peng Bhaskar Jha Climate Prediction Center NCEP. Outline. Motivation Data and Methodology Results Summary and Conclusions. Correlation between DJF 700mb Z and SST index. Motivation.
E N D
SST Forced Atmospheric Variability in an AGCM Arun Kumar Qin Zhang Peitao Peng Bhaskar Jha Climate Prediction Center NCEP
Outline • Motivation • Data and Methodology • Results • Summary and Conclusions
Correlation between DJF 700mb Z and SST index Motivation Horel and Wallace, 1981: Planetary Scale Atmospheric Phenomenon Associated with the SO
Motivation • “…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.
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?
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 SSTs • Ensemble 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 SSTs
ICsTarget Aug Sep Oct Nov DJF Sep Oct Nov DJF Oct Nov DJF NovDJF 80-member Ensemble From 2002 and 2003 ICs Data and Methodology • Data is from “Seasonal Forecast Model” archives from 2002-2003 • 10-member ensemble from different atmospheric initial conditions each month • Lagged ensemble from different ICs
Data and Methodology Difference in 200-mb eddy height climatology from December and September ICs 200-mb eddy height climatology for December ICs
Data and Methodology Difference in 200-mb height variance from December and September ICs 200-mb height variance for December ICs
Variance of Ensemble Means External to Internal Variance Ratio Results
Results EOF1 53%
Fractional External Variance Related to Mode1 Remaining External Variance Results
Results EOF2 19%
Results EOF3 12%
Results Fraction of Variance Explained by Modes 1-3
(83+98) – (89+99) Results Z = a*ΔSST + b*ΔSST2 if ΔSST+ Z+&ΔSST-Z- then a= (Z+ - Z-) / (2* ΔSSTavg) and b= (Z+ + Z-) / (2* ΔSSTavg) (Monahan & Dai 2004)
Results DJF 1998 Ensemble mean (shaded); EOF1 (contour) Ensemble mean – EOF1
DJF 1999 Results Ensemble mean (shaded); EOF1 (contour) Ensemble mean – EOF1
Results Strong Cold Cold EOF1 - Warm -Strong Warm
Results Composite based on 1980, 81, 82 & 86
Results Anomaly Correlation EOF1 Ensemble Mean EOF1 + EOF2 EOF1:EOF3
Results AC(EOF1+EOF2) – AC(EOF1) AC(EOF1:EOF2) – AC(EOF1+EOF2)
Results AC (EOF1:EOF3) AC (EOF1)
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.” • 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 AGCM • Should be repeated with other AGCMs