The Role of Coupled Sea Surface Temperatures in the Simulation of the Tropical Intraseasonal Oscillation

This study compares the tropical intraseasonal oscillation (TISO) variability in the Geophysical Fluid Dynamics Laboratory (GFDL) coupled general circulation model (CGCM) and the stand-alone atmospheric general circulation model (AGCM). For the AGCM simulation, the sea surface temperatures (SSTs) were specified using those from the CGCM simulation. This was done so that any differences in the TISO that emerged from the two simulations could be attributed to the coupling process and not to a difference in the mean background state. The comparison focused on analysis of the rainfall, 200-mb velocity potential, and 850-mb zonal wind data from the two simulations, for both summer and winter periods, and included comparisons to analogous diagnostics using NCEP–NCAR reanalysis and Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) rainfall data. The results of the analysis showed three principal differences in the TISO variability between the coupled and uncoupled simulations. The first was that the CGCM showed an improvement in the spatial variability associated with the TISO mode, particularly for boreal summer. Specifically, the AGCM exhibited almost no TISO variability in the Indian Ocean during boreal summer—a common shortcoming among AGCMs. The CGCM, on the other hand, did show a considerable enhancement in TISO variability in this region for this season. The second was that the wavenumber–frequency spectra of the AGCM exhibited an unrealistic peak in variability at low wavenumbers (1–3, depending on the variable) and about 3 cycles yr−1(cpy). This unrealistic peak of variability was absent in the CGCM, which otherwise tended to show good agreement with the observations. The third difference was that the AGCM showed a less realistic phase lag between the TISO-related convection and SST anomalies. In particular, the CGCM exhibited a near-quadrature relation between precipitation and SST anomalies, which is consistent with observations, while the phase lag was reduced in the AGCM by about 1.5 pentads (∼1 week). The implications of the above results, including those for the notions of “perfect SST” and “two tier” experiments, are discussed, as are the caveats associated with the study’s modeling framework and analysis.