Sea surface temperature (SST) climatology datasets provide the reference for observations of ocean anomalous events such as coastal upwelling and Marine Heat Waves (MHWs). The representativeness of the SST climatology datasets of the historical and current ocean surface states is essential to identify and predict anomalous events that may cause severe impacts on the local ecosystem. Here we compare three high resolution SST climatology datasets around the Australian coast to investigate the uncertainty introduced by the references to current estimates of SST trends.  The datasets studied are: (i) 0.02 degree SST Atlas of the Australian Regional Seas (SSTAARS), a pixel-wise daily climatology for 1992-2016 (Wijffels et al., 2018), based on the 0.02-degree bias-corrected version 2 Integrated Marine Observing System (IMOS) one-day composite night-time AVHRR SST; (ii) 0.02 degree ReefTemp Next Generation (RTNG; http://www.bom.gov.au/environment/activities/reeftemp/reeftemp.shtml) monthly climatology for 2002-2011, calculated from 0.02-degree bias-corrected version 1 IMOS one-day composite night-time AVHRR SST; and (iii) 0.05 degree Coral Reef Watch (CRW) global monthly climatology for 1985–2012 (https://coralreefwatch.noaa.gov/satellite/coraltemp.php), derived from the MyOcean OSTIA Reanalysis (1985-2002) and NOAA Geo-Polar Blended SST reanalysis (2002–2012).  The differences between pairs of climatology datasets are presented, followed by four kinds of statistical analysis based on the data density distribution pattern along latitude, longitude, time and temperature bands.  The impact of the climatology calculation algorithm is evaluated using the 0.1 degree BRAN 2016 ocean reanalysis data (1994-2016), as SSTAARS uses a parametric model fitting model rather than the common averaging method used by RTNG and CRW.  A more sophisticated SST climatology dataset generated from the new Climate Change Initiative (CCI) SST version 2 analyses is planned to be added to the comparison, along with in-situ data from the NOAA In Situ Quality Monitor (iQUAM) for climatology validation.