P-3.4 Wed. October 26, 2005. Motivation Quantify the impact of interannual SST variability on the mean and the spread of Probability Density Function (PDF) of seasonal atmospheric means. Abstract
P-3.4 Wed. October 26, 2005
Analysis of Interannual variability in atmospheric signal and noise with SSTsBhaskar Jha and Arun KumarClimate Prediction Center, NCEP/NOAA, Camp Springs, MD-20746
Fig. 6. (Left panel) warm composite of spread, and (right panel) cold composite of spread.
Fig. 3. (Left panel) observed seasonal mean 200 hPa height regressed against the observed Nino 3.4 SSTs variability and (right panel) same but for the AGCM ensemble means.
Analysis of inter-annual variability in the mean and spread and its relative contributions of changes in the mean and spread to seasonal predictability
Fig. 7. (Left panel) The difference of spread of warm composite and mean spread and (right panel) same as left panel except for cold composite.
Fig. 4. (Left panel) observed 200 hPa height warm composite and (right panel) for the AGCMs. The warm SST events are defined as those when the Nino 3.4 SST index is at least one standard deviation above normal.
Fig. 1. DJF total variability of 200 hPa seasonal mean heights. Total variance is computed from the variance of all DJF for the period of 1950-2000. (Left panel) observation and (right panel) Eight AGCM. Units are in meters**2.0.
Fig. 8. Signal to Noise ratio of 200 hPa height for eight AGCMs.
Fig. 5. As in Fig. 4, but for cold composite. The cold SST events are defined as those when the Nino 3.4 SST index is at least one standard deviation below normal.
Fig. 2. Left panel shows the external variability of 200 hPa. External variability is computed from the ensemble mean of all DJFs. Right panel shows the internal variability (spread). Units are in meters**2.0. Note the difference in magnitude of the external and the internal variability.