Modeling Diurnal and Intraseasonal Variability of the Ocean Mixed Layer

Abstract The intraseasonal variability of SST associated with the passage of the Madden?Julian oscillation (MJO) is well documented; yet coupled model integrations generally underpredict the magnitude of this SST variability. Observations from the Improved Meteorological Instrument (IMET) mooring in the western Pacific during the intensive observing period (IOP) of the Tropical Ocean Global Atmosphere Coupled Ocean?Atmosphere Response Experiment (TOGA COARE) showed a large diurnal signal in SST that is modulated by the passage of the MJO. In this study, observations from the IOP of the TOGA COARE and a one-dimensional (1D) ocean mixed layer model incorporating the K-Profile Parameterization (KPP) vertical mixing scheme have been used to investigate the rectification of the intraseasonal variability of SST by the diurnal cycle and the implied impact of the absence of a representation of this process on the modeled intraseasonal variability in coupled GCMs. Analysis of the SST observations has shown that the increase of the daily mean SST by the diurnal cycle of SST accounts for about one-third of the magnitude of intraseasonal variability of SST associated with the Madden?Julian oscillation in the western Pacific warm pool. Experiments from the 1D model forced with fluxes at a range of temporal resolutions and with differing vertical resolution of the model have shown that to capture 90% of the diurnal variability of SST, and hence 95% of the intraseasonal variability of SST, requires a 3-h or better temporal resolution of the fluxes and a vertical grid with an upper-layer thickness of the order of 1 m. In addition to the impact of the representation of the diurnal cycle on the intraseasonal variability of SST, the strength of the mixing across the thermocline was found to be enhanced by the proper representation of the nighttime deep mixing in the ocean, implying a possible impact of the diurnal cycle onto the mean climate of the tropical ocean.