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Abstract The 1991–2020 climate normals for sea surface temperature (SST) are computed based on the NOAA Daily Optimum Interpolation SST dataset. This is the first time that high‐resolution SST normals with global coverage can be achieved in the satellite SST era. Normals are one of the fundamental parameters in describing and understanding weather and climate and provide decision‐making information to industry, public, and scientific communities. This product suite includes SST mean, standard deviation, count and extreme parameters at daily, monthly, seasonal and annual time scales on 0.25° spatial grids. The main feature of the SST mean state revealed by the normals is that in the Tropics, the Indo‐Pacific Ocean is dominated by the warm pool (SST ≥ 28°C) while the eastern Pacific is characterized by the cold tongue (SST ≤ 24°C); in the midlatitudes, SSTs are in zonal patterns with high meridional gradients. Daily SST standard deviations are generally small (<1.0°C) except in frontal zones (>1.5°C) mostly associated with ocean currents such as the Gulf Stream, Kuroshio and Equatorial Currents. Compared to the 1982–2011 climatology, the 1991–2020 mean SSTs increased over most global areas but obvious cooling is seen in the Southern Ocean, eastern tropical South Pacific Ocean and North Atlantic warming hole. The Indo‐Pacific warm pool (IPWP) is found to have strengthened in both intensity and coverage since 1982–2011. By a count parameter criterion of ≥300 days annually with SST ≥ 28°C, the IPWP coverage increased 33% from 1982–2011 to 1991–2020. The global mean SST of 1991–2020 is warmer than that of 1982–2011, and the warming rate over 1991–2020 doubles that over 1901–2020. 

Abstract Our study shows that the intercomparison among sea surface temperature (SST) products is influenced by the choice of SST reference, and the interpolation of SST products. The influence of reference SST depends on whether the reference SSTs are averaged to a grid or in pointwise in situ locations, including buoy or Argo observations, and filtered by first-guess or climatology quality control (QC) algorithms. The influence of the interpolation depends on whether SST products are in their original grids or preprocessed into common coarse grids. The impacts of these factors are demonstrated in our assessments of eight widely used SST products (DOISST, MUR25, MGDSST, GAMSSA, OSTIA, GPB, CCI, CMC) relative to buoy observations: (i) when the reference SSTs are averaged onto 0.25° × 0.25° grid boxes, the magnitude of biases is lower in DOISST and MGDSST (<0.03°C), and magnitude of root-mean-square differences (RMSDs) is lower in DOISST (0.38°C) and OSTIA (0.43°C); (ii) when the same reference SSTs are evaluated at pointwise in situ locations, the standard deviations (SDs) are smaller in DOISST (0.38°C) and OSTIA (0.39°C) on 0.25° × 0.25° grids; but the SDs become smaller in OSTIA (0.34°C) and CMC (0.37°C) on products’ original grids, showing the advantage of those high-resolution analyses for resolving finer-scale SSTs; (iii) when a loose QC algorithm is applied to the reference buoy observations, SDs increase; and vice versa; however, the relative performance of products remains the same; and (iv) when the drifting-buoy or Argo observations are used as the reference, the magnitude of RMSDs and SDs become smaller, potentially due to changes in observing intervals. These results suggest that high-resolution SST analyses may take advantage in intercomparisons. Significance StatementIntercomparisons of gridded SST products be affected by how the products are compared with in situ observations: whether the products are in coarse (0.25°) or original (0.05°–0.10°) grids, whether the in situ SSTs are in their reported locations or gridded and how they are quality controlled, and whether the biases of satellite SSTs are corrected by localized matchups or large-scale patterns. By taking all these factors into account, our analyses indicate that the NOAA DOISST is among the best SST products for the long period (1981–present) and relatively coarse (0.25°) resolution that it was designed for.