Prediction of the diurnal warming of sea surface temperature using an atmosphere‐ocean mixed layer coupled model

Abstract

[1] An atmosphere-ocean mixed layer coupled model is developed to predict the diurnal variability of sea surface temperature (SST). For this purpose, a new mixed layer model is developed, which is able to reproduce realistic temperature profiles under the various atmospheric conditions, ranging from the formation of a diurnal thermocline under strong wind to the appearance of strong near surface stratification under weak wind. The predicted diurnal warming of SST (DSST) from the model is compared with satellite and buoy data in various aspects, including scatterplots, time series, and probability density functions of DSST, in order to examine the predictability. The model performance is also compared with other model results. In addition the diurnal variation of temperature profiles below the sea surface, whose information is not available from satellite data, is investigated based on model output.

Effects of the diurnal cycle in solar radiation on the tropical Indian Ocean mixed layer variability during wintertime Madden-Julian Oscillations

“…These findings have greatly improved our understanding of the role of the diurnal cycle in the tropical climate system. Among the existing studies, however, investigations of diurnal ocean variation are mainly for the western Pacific warm pool region [ Shinoda and Hendon , ; Bernie et al ., ; Shinoda , ] or the Atlantic Ocean [ Pimentel et al ., 2008; Guemas et al ., ], whereas coupled model studies focus primarily on the general effects of diurnal coupling on the mean structure and low‐frequency variability of the climate [ Danabasoglu et al ., ; Bernie et al ., ; Noh et al ., ; Oh et al ., ; Masson et al ., ; Guemas et al ., ]. In the present study, we examine the effects of diurnal cycle on the intraseasonal SST variability in the TIO region where many winter MJO events originate, which has not yet been sufficiently explored by previous researches.…”

Comparison of SST Diurnal Variation Models Over the Tropical Warm Pool Region

“…This low correlation has been noted in 2011), a correlation of 0.31 between the modeled and buoy dSSTs is found, although it should be noted that their DV model, meteorological data and study domain differ from this TWP study. A possible reason might be that the surface forcing from the models does not match exactly with the real ocean, in spite of the resemblance in the large-scale weather pattern (Noh et al, 2011). Compared with the original MTSAT-1R data, CG03 and ZB 1 T dSST max data are slightly cold biased by 20.16 and 20.20 K, respectively.…”

“…Kawai and Wada () provided a systematic review of many of the above models along with several others and suggested that most models are able to resolve the general DV patterns but with differing accuracies and other issues. Other DV model types also include, but are not restricted to, transilient models (e.g., Soloviev & Lukas, ), air‐sea coupled models (e.g., Noh et al, ), and physical‐empirical hybrid models (e.g., Gentemann et al, ; Weihs & Bourassa, ).…”

“…Kawai and Wada (2007) provided a systematic review of many of the above models along with several others and suggested that most models are able to resolve the general DV patterns but with differing accuracies and other issues. Other DV model types also include, but are not restricted to, transilient models (e.g., Soloviev & Lukas, 2013), air-sea coupled models (e.g., Noh et al, 2011), and physical-empirical hybrid models (e.g., Gentemann et al, 2009;Weihs & Bourassa, 2014). This paper compares four DV models against version 3 Multifunctional Transport Satellite-1R (MTSAT-1R) SST measurements (produced by the Australian Bureau of Meteorology, hereafter the Bureau) over the Tropical Warm Pool (TWP) region (defined in this study as 908E-1708E, 258S-158N) for 4 months from January to April 2010.…”

Effects of Australian Summer Monsoon on Sea Surface Temperature Diurnal Variation Over the Australian North‐Western Shelf

“…A number of numerical models are available to understand DV events and predict the variability of the DV process. They range from turbulence closure models (e.g., Karagali et al, 2017;Mellor & Yamada, 1982) to Geophysical Research Letters 10.1002/2017GL075008 statistical models (e.g., Gentemann et al, 2003;Price et al, 1987;Webster et al, 1996), to air-sea coupled models (e.g., Noh et al, 2011), and to physical-empirical hybrid models (e.g., Gentemann et al, 2009;Weihs & Bourassa, 2014). In essence, the balance between the net heat flux and wind-driven turbulent mixing over a daily cycle determines the magnitude of SST DV.…”