The nighttime ocean cool skin signal ΔT [defined as skin sea surface temperature (SST_skin) minus depth SST (SST_depth)] is investigated using 103 days of matchups between shipborne Infrared SST Autonomous Radiometer (ISAR) SST_skin and water intake SST_depth at ~7.1–9.9-m depths, in oceans around Australia. Before data analysis, strict quality control of ISAR SST_skin data is conducted and possible diurnal warming contamination is carefully minimized. The statistical distribution of ΔT, and its dependencies on wind speed, heat flux, etc., are consistent with previous findings. The overall average ΔT value is −0.23 K. It is observed that the magnitude of the cool skin signal increases after midnight and a coolest skin offset (with an average value of −0.36 K) is found at around dawn. The dependency of ΔT on SST conditions is observed. Direct warm skin events are discovered when the net heat flux direction is from the atmosphere to the ocean, which is more likely to occur at high latitudes when the air is very humid and warmer than the SST. In addition, several cool skin models are validated: one widely used physical model performs best and can capture most skin-effect trends and details; the empirical models only reflect the basic features of the observed ΔT values. If the user cannot apply the physical model (due to, e.g., the algorithm complexity or missing inputs), then the empirical parameterization in the form proposed in a 2002 study can be used. However, we recommend using a new set of parameters, calculated in this study, based on much more representative dataset, and with more rigorous quality control.

Four sea surface temperature (SST) diurnal variation (DV) models have been compared against Multifunctional Transport Satellite‐1R (MTSAT‐1R) SST measurements over the Tropical Warm Pool (TWP) region (90°E–170°E, 25°S–15°N) for 4 months from January to April 2010. The four models include one empirical model formulated by Chelle Gentemann (hereafter CG03), one physical model proposed by Zeng and Beljaars in 2005 (ZB05) and its updated version (ZB + T), and one air‐sea coupled model (the Met Office Unified Model Global Coupled configuration 2, GC2) with ZB05 warm layer scheme added on top of the standard configuration. The sensitivity of the v3 MTSAT‐1R data to the “true” changes in SST is first investigated using drifting buoys and is estimated to be 0.60 ± 0.05. This being significantly different from 1, the models are validated against MTSAT‐1R data and the same data scaled by the inverse of the sensitivity (representing an estimate of the true variability). Results indicate that all models are able to capture the general DV patterns but with differing accuracies and features. Specifically, CG03 and ZB + T underestimate strong (>2 K) DV events' amplitudes especially if we assume that sensitivity‐scaled MTSAT‐1R variability is most realistic. ZB05 can effectively capture the DV cycles under most DV and wind conditions, as well as the DV spatial distribution. GC2 tends to overestimate small‐moderate (< 2 K) DV events but can reasonably predict large DV events. One to three hour lags in warming start and peak times are found in GC2.

In situ sea surface temperature (SST) measurements play a critical role in the calibration/validation (Cal/Val) of satellite SST retrievals and ocean data assimilation. However, their quality is not always optimal, and proper quality control (QC) is required before they can be used with confidence. The in situ SST Quality Monitor (iQuam) system was established at the National Oceanic and Atmospheric Administration (NOAA) in 2009, initially to support the Cal/Val of NOAA satellite SST products. It collects in situ SST data from multiple sources, performs uniform QC, monitors the QC’ed data online, and distributes it to users. In this study, the iQuam QC is compared with other QC methods available in some of the in situ data ingested in iQuam. Overall, the iQuam QC performs well on daily-to-monthly time scales over most global oceans and under a wide variety of environmental conditions. However, it may be less accurate in the daytime a when pronounced diurnal cycle is present, and in dynamic regions, due to the strong reliance on the “reference SST check”, which employs daily low-resolution level 4 (L4) analyses with no diurnal cycle resolved. The iQuam “performance history check”, applied to all in situ platforms, is an effective alternative to the customary “black/gray” lists, available only for some platforms (e.g., drifters and Argo floats). In the future, iQuam QC will be upgraded (e.g., using improved reference field(s), with enhanced temporal and spatial resolutions). More comparisons with external QC methods will be performed to learn and employ the best QC practices.

This study has evaluated five years (2016–2020) of Himawari-8 (H8) Sea Surface Temperature (SST) Level 2 Pre-processed (L2P) data produced by the Australian Bureau of Meteorology (Bureau) against shipborne radiometer SST measurements obtained from the Infrared SST Autonomous Radiometer (ISAR) onboard research vessel RV Investigator. Before being used, all data sets employed in this study have gone through careful quality control, and only the most trustworthy measurements are retained. With a large matchup database (31,871 collocations in total, including 16,418 during daytime and 15,453 during night-time), it is found that the Bureau H8 SST product is of good quality, with a mean bias ± standard deviation (SD) of −0.12 °C ± 0.47 °C for the daytime and −0.04 °C ± 0.37 °C for the night-time. The performance of the H8 data under different environmental conditions, determined by the observations obtained concurrently from RV Investigator, is examined. Daytime and night-time satellite data behave slightly differently. During the daytime, a cold bias can be seen under almost all environmental conditions, including for most values of wind speed, SST, and relative humidity. On the other hand, the performance of the night-time H8 SST product is consistently more stable under most meteorological conditions with the mean bias usually close to zero.