Sea surface temperature (SST) measurements from the geostationary satellite Himawari-8 Advanced Himawari Imager (AHI) are compared with in situ skin SSTs derived from shipboard Infrared SST Autonomous Radiometers (ISAR) in the Australian region. The mean bias and standard deviation of the differences between Himawari-8 AHI and ISAR skin SST of best quality are 0.09 K and 0.30 K, with total matchups numbering 2701. Shipboard bulk SST measurements at depths between around 7.1 and 9.9 meters are compared with the matchups in a case study. Analyses show significant differences between skin and bulk SST measurements of maximum value 2.23 K under conditions of high diurnal warming. The results also demonstrate that Himawari-8 AHI skin SST with high temporal resolution has the ability to accurately measure diurnal warming events.

Sea surface temperature (SST) products that can resolve fine scale features, such as sub-mesoscale eddies, ocean fronts and coastal upwelling, are increasingly in demand. In response to user requirements for gap-free, highest spatial resolution, best quality and highest accuracy SST data, the Australian Bureau of Meteorology (BoM) produces operational, real-time Multi-sensor SST level 3 products by compositing SST from Advanced Very-High-Resolution Radiometer (AVHRR) sensors on Meteorological Operational satellite (MetOp)-B and National Oceanic and Atmospheric Administration (NOAA) 18, along with SST from Visible Infrared Imaging Radiometer Suite (VIIRS) sensors on the Suomi National Polar-orbiting Partnership (Suomi NPP) and NOAA 20 polar-orbiting satellites for the Australian Integrated Marine Observing System (IMOS) project. Here we discuss our method to combine data from different sensors and present validation of the satellite-derived SST against in situ SST data. The Multi-sensor Level 3 Super Collated (L3S) SSTs exhibit significantly greater spatial coverage and improved accuracy compared with the pre-existing IMOS AVHRR-only L3S SSTs. When compared to the Geo Polar Blended level 4 analysis SST data over the Great Barrier Reef, Multi-sensor L3S SST differs by less than 1 ${}^{°}$C while exhibiting a wider range of SSTs over the region. It shows more variability and restores small-scale features better than the Geo Polar Blended level 4 analysis SST data. The operational Multi-sensor L3S SST products are used as input for applications such as IMOS OceanCurrent and the BoM ReefTemp Next-Generation Coral Bleaching Nowcasting service and provide useful insight into the study of marine heatwaves and ocean upwelling in near-coastal regions.

Sea surface temperature (SST) climatology data sets provide the reference for observations of ocean anomalous events, which may have significant effects on the local marine ecosystem. The representativeness of the SST climatologies of the historical and current ocean surface states is essential to identify and predict anomalous events. Here, we compare five high‐resolution SST climatology data sets around the Australian coast to investigate the uncertainty introduced by the reference SSTs to current estimates of SST anomalies. The data sets studied are (a) 0.05° global daily climatology calculated by this study from the ESA SST Climate Change Initiative (CCI) Analysis v2.0 product (1981–2016); (b) 0.25° global daily climatology for 1981–2016 derived from the NCEI daily AVHRR_OI SST; (c) 0.02° SST Atlas of the Australian Regional Seas (SSTAARS), a pixel‐wise daily climatology for 1992–2016; (d) 0.05° NOAA Coral Reef Watch (CRW) global monthly climatology for 1985–2012; and (e) 0.1° global daily climatology for 1994–2016 derived from the BRAN_2016 ocean reanalysis. The climatology data sets formed from SST CCI analysis v2.0 data are used as the reference to compare data sets (b)–(e) based on the same reference time period and central year. This study indicates that climatologies (a) and (e) would be suitable for use as a reference over the Australian region. However, for smaller spatial scales or where a night‐only SST climatology is required, the 2‐km daily SSTAARS would also be suitable. The feature resolution of the climatology data sets (a) and (e) is significantly coarser than SSTAARS climatology.

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.

As the ocean has warmed, in recent decades marine heatwaves (MHWs) have emerged as a major threat to marine ecosystems and ecosystem services, presenting challenges for management of marine fisheries, aquaculture, tourism, and conservation, including for marine protected areas (MPAs). An MHW is a period of unusually high ocean temperatures, often defined as ocean temperatures that are warmer than 90% of the previous observations for a given time of year. MHWs along Australia’s coastal regions have led to mass coral bleaching on the Great Barrier Reef, damage to kelp forests and seagrass meadows in Western Australia, shifts in species, and fish and invertebrate mortality, all creating pressures on fisheries management (as reviewed in Smith et al., 2023). Understanding how climate change influences ocean extremes and impacts societal and natural values is key for evaluating future risks. Growing concerns around the effects of MHWs on marine industries, food security, ecosystem dynamics, and conservation efforts led to the development of MHW response plans for Tasmania and New South Wales during the summer of 2023/24 (Hobday et al., 2024).