Impact of sub-daily wind forcing on mixed-layer depth and sea surface temperature: study using observations and a one-dimensional upper ocean model

Abstract

The impact of sub-daily wind sampling on the diurnal cycle of oceanic mixed-layer depth (MLD) and sea surface temperature (SST) is investigated using a one-dimensional upper ocean model and observations at two locations: the Central Arabian Sea (CAS) and Eastern Equatorial Indian Ocean (EEIO). Motivation to carry out this study is twofold: first, it will help in understanding the possible error in model-simulated MLD and SST due to the non-inclusion of high-temporal wind sampling; and second, it will also emphasize the requirements of temporal sampling from space-based measurements of surface winds. Temporal decorrelation analysis suggests that over a 24-hour period, auto-correlation falls rapidly in the EEIO region, whereas the fall is less even at a lag of 24 hours in CAS. Time series analysis with different sub-daily sampling rates suggests that the optimum sampling rate is three hours for MLD and SST. A suite of one-dimensional model simulations performed at the CAS and EEIO locations with sub-daily wind suggests that once-daily synoptic measurements of wind, which is the most likely scenario with one scatterometer, results in small biases but large standard deviations in MLD. In the case of SST, there is a small positive bias in the order of 0.1°C at the CAS buoy location while at the EEIO location, no such bias is observed. With two scatterometers in a constellation resulting in two observations per day, one can obtain a small standard deviation in MLD, but the bias is greater in this case. For SST, except for a small bias (about 0.1°C) at the CAS location, the distribution is mostly well-behaved Gaussian in all cases. The present study suggests the advisability of acquiring more frequent wind measurements from space-borne scatterometers. A well-coordinated satellite scatterometer constellation will help in resolving the diurnal variability and associated feedback mechanism of air–sea exchange processes, enhancing the understanding of large-scale phenomena such as the Indian summer monsoon, El Niño-southern oscillations, and the Madden–Julian oscillation.