Yuwei Hu1,2, Helen Beggs3 and Xiao Hua Wang1,2 1.Sino-Australian Research Centre for Coastal Management, University of New South Wales, Canberra, Australia 2.School of Science, University of New South Wales, Canberra, Australia 3.Bureau of Meteorology, Melbourne, Australia Inter-Comparison Groups Sea surface temperature (SST) climatology datasets provide the reference for observations of ocean anomalous events such as coastal upwelling and Marine Heat Waves (MHWs), which may have significant effects on the local marine ecosystem. The representativeness of the SST climatology datasets of the historical and current ocean surface states is essential to identify and predict anomalous events. Here we compare four high resolution SST climatology datasets around the Australian coast to investigate uncertainty introduced by reference SSTs to estimates of SST anomalies. The datasets studied are: (i) 0.05-degree SST Climate Change Initiative (CCI) global daily climatology for 1981-2016, 1985-2012, 1992- 2016 and 1994-2016, calculated by this study from the ESA SST CCI Analysis product version 2.0 (Rayner et al., 2019), a satellite-only SSTdepth analysis created by the UK Met Office OSTIA SST analysis system from SST CCI Along Track Scanning Radiometer (ATSR) and SST CCI Advanced Very High Resolution Radiometer (AVHRR) products (1981-2016); (ii) 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; (iii) 0.05-degree NOAA 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), and (iv) 0.1-degree BRAN SST daily climatology for 1994-2016, derived from the BRAN_2016 ocean reanalysis data generated by the Ocean Forecasting Australian Model version 3 (OFAM3) ocean model (Oke et al.,2013). Three climatology comparison groups based on these four SST products are formed (Table 1) and statistical analysis of the differences in each climatology comparison group are conducted followed by the in-situ validation using TAO/TRITON mooring SST1m climatology (https://www.pmel.noaa.gov/gtmba/taotriton- collaboration). In this study we define CCI as the a reference to compare with CRW, SSTAARS and BRAN climatology datasets, with all comparisons performed over the SSTAARS domain (Figure 4). Groups CCI_1985 – CRW, CCI_1992 – SSTAARS and CCI_1994 – BRAN are compared to reduce potential impact of the reference period on SST differences in each CCI related group. The comparison groups from (a) to (c) are listed in Table 2 together with the five main factors affecting the comparison, including whether day and night data were included, reference period, data central year, climatology calculation algorithm and whether or not the climatology represents the same SST depth. Table 1. Main attributes of the climatology datasets Acknowledgements Y. Hu a candidate in UNSW Canberra, has been financially supported by the China Scholarship Council and the top-up scholarship from UNSW Canberra since August 2017 for her PhD studies in Australia. The authors would like to thank the European Space Agency CCI Project for providing the SST CCI analysis v2.0 data, and BoM internal reviewers Chris Griffin, Frank Colberg and Grant Smith for giving very helpful comments and suggestions to this study. This work is supported by the Sino-Australian Research Centre for Coastal Management. Statistical Analysis Differences of each comparison group are calculated along with temporal relative bias (Figure 1) and standard deviation (STD, Figure 2) in each valid SSTAARS grid cell. Density distributions of differences within time bins are then calculated. The SST differences are first classified in time bins base