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Investigating Tropical Atlantic ocean variability through CAM3/CCSM3 simulation experiments, highlighting model improvements, resolution impact, and potential predictability. Findings suggest improvements in coupled models with biases remaining. The study explores the impact on Atlantic Sector and the influence on other regions.
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Tropical Atlantic Variabilityas Simulated by CAM3/CCSM3 R. Saravanan National Center for Atmospheric Research
Intercomparison of Simulated Equatorial SST(STOIC: Davey et al., 2002)
Topics • Effect of model improvements • Role of atmospheric model resolution • Potential predictability
Model integrations • Coupled Experiments: • CCSM1 (T42), CCSM2 (T42), CCSM3 (T85) • Uncoupled experiments (1950-2000): • Vanilla AMIP: CCM3 (T42), CAM3 (T42/T85) • IPCC AMIP: CAM3 (T85)
March-April-May mean SST OBS CCSM1 CCSM2 CCSM3
March-April-May SST std. Dev. OBS CCSM1 CCSM2 CCSM3
March-April-May mean PRECIP OBS CAM3/T42 CAM3/T85 CCSM3
Potential Predictability P = E/T - I/N E = ensemble-mean variance I = internal variance T = total variance (= E + I) N = no. of ensemble members Effect of model version Effect of model resolution Effect of forcings
Potential Predictability of PS MAM JJA CCM3 Vanilla AMIP CAM3 Vanilla AMIP CAM3 IPCC AMIP
Conclusions • Some improvement in the coupled model simulations of Tropical Atlantic SST • Significant biases remain, both in the mean and in the variance • Coupled CCSM3 seems to have less of a double ITCZ structure than uncoupled AMIP runs! • Potential predictability in the Atlantic Sector • Not affected by model resolution • Not affected by presence of presence of Greenhouse Gas forcings etc. • But tropical SST signal is important
CCSM2 SST Bias DJF JJA
Questions • Evidence for remote influence of Tropical Atlantic SST anomalies on the Tropical Pacific • Seen in CAM2+slab, CAM2+POP, CCM3+slab, and CCM3+MOM. How about other models? • Observational data: dominated by ENSO signal