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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 Statement: Intercomparisons 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. [ABSTRACT FROM AUTHOR]

This paper describes the new International Comprehensive Ocean–Atmosphere Data Set (ICOADS) near-real-time (NRT) release (R3.0.2), with greatly enhanced completeness over the previous version (R3.0.1). R3.0.1 had been operationally produced monthly from January 2015 onward, with input data from the World Meteorological Organization (WMO) Global Telecommunication Systems (GTS) transmissions in the Traditional Alphanumeric Codes (TAC) format. Since the release of R3.0.1, however, many observing platforms have changed, or are in the process of transitioning, to the Binary Universal Form for Representation of Meteorological Data (BUFR) format. R3.0.2 combines input data from both BUFR and TAC formats. In this paper, we describe input data sources; the BUFR decoding process for observations from drifting buoys, moored buoys, and ships; and the data quality control of the TAC and BUFR data streams. We also describe how the TAC and BUFR streams were merged to upgrade R3.0.1 into R3.0.2 with duplicates removed. Finally, we compare the number of reports and spatial coverage of essential climate variables (ECVs) between R3.0.1 and R3.0.2. ICOADS NRT R3.0.2 shows both quantitative and qualitative gains from the inclusion of BUFR reports. The number of observations in R3.0.2 increased by nearly 1 million reports per month, and the coverage of buoy and ship sea surface temperatures (SSTs) on monthly 2° × 2° grids increased by 20%. The number of reported ECVs also increased in R3.0.2. For example, observations of SST and sea level pressure (SLP) increased by around 30% and 20%, respectively, as compared to R3.0.1, and salinity is a new addition to the ICOADS NRT product in R3.0.2. Significance Statement: The International Comprehensive Ocean–Atmosphere Data Set (ICOADS) is the largest collection of surface marine observations spanning from 1662 to the present. A new version, ICOADS near-real-time 3.0.2, includes data transmitted in the Binary Universal Form for Representation of Meteorological Data (BUFR) format, in combination with Traditional Alphanumeric Codes (TAC) data. Many of the organizations that report observations in near–real time have moved to BUFR, so this update brings ICOADS into alignment with collections and archives of these international data distributions. By including the BUFR reports, the number of observations in the upgraded version of ICOADS increased by nearly one million reports per month and spatial coverage of buoy and ship SSTs increased by 20% over the previous version. [ABSTRACT FROM AUTHOR]

Arctic sea surface temperatures (SSTs) are estimated mostly from satellite sea ice concentration (SIC) estimates. In regions with sea ice the SST is the temperature of open water or of the water under the ice. A number of different proxy SST estimates based on SIC have been developed. In recent years more Arctic quality-control buoy SSTs have become available, allowing better validation of different estimates and the development of improved proxy estimates. Here proxy SSTs from different approaches are evaluated and an improved proxy SST method is shown. The improved proxy SSTs were tested in an SST analysis, and showed reduced bias and random errors compared to the Arctic buoy SSTs. Almost all reduction in errors is in the warm melt season. In the cold season the SIC is typically high and all estimates tend to have low errors. The improved method will be incorporated into an operational SST analysis. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) observations from satellite-based Advanced Very High Resolution Radiometer (AVHRR) instrument exhibit biases. Adjustments necessary for removing the AVHRR biases have been studied by progressive experiments. These experiments show that the biases are sensitive to various parameters, including the length of the input data window, the base-function empirical orthogonal teleconnections (EOTs), the ship-buoy SST adjustment, and a shift in grid system. The difference in bias adjustments due to these parameters can be as large as 0.3°-0.5°C in the tropical Pacific at the monthly time scale. The AVHRR bias adjustments were designed differently in the daily optimum interpolation SST (DOISST) and the Extended Reconstructed SST datasets that ingest AVHRR SSTs (ERSSTsat). The different AVHRR bias adjustments result in the differences in SST datasets in DOISST and ERSSTsat. Comparisons show that the SST difference between these two datasets results largely from the difference in the AVHRR bias adjustments and little from SST analysis methods in the Niño-3.4 region, as well as in the global oceans. For example, the average difference of the Niño-3.4 SSTs between DOISST and ERSSTsat is approximately 0.12°C due to the bias adjustments and is about 0.01°C due to the analysis methods. This study finds that the DOISST datasets can be improved by using the revised AVHRR bias adjustment of a wider input data window, updated EOTs, and a shifted grid system in DOISST. Improvements can also be made by including a ship-buoy SST adjustment, a zonal SST adjustment, or revised EOTs without damping in the high latitudes in ERSSTsat. [ABSTRACT FROM AUTHOR]

During June-November 2012, pronounced differences in tropical Pacific sea surface temperature (SST) anomalies were observed between three widely used SST products: the extended reconstructed SST version 3b (ERSSTv3b), and the optimum interpolation SST version 2 analyses (OISST), produced weekly (OISSTwk) and daily (OISSTdy). During June-August 2012, the Niño-3.4 SST anomaly (SSTA) index was 0.2°-0.3°C lower in ERSSTv3b than in OISSTwk and OISSTdy, while it was 0.3°-0.4°C higher from September to November 2012. Such differences in the Niño-3.4 SSTA index can impact the assessment of the status of the El Niño-Southern Oscillation, which is determined using a threshold of ±0.5°C in the Niño-3.4 SSTA index. To investigate the reasons for the differences between ERSSTv3b and OISSTdy/OISSTwk, an experimental analysis (called ERSSTsat) is created that is similar to ERSSTv3b but includes satellite-derived SSTs. However, significant differences in the Niño-3.4 SSTA index remained between ERSSTsat and OISSTdy/OISSTwk. Comparisons of ERSSTsat and OISSTdy indicate that their differences are mostly associated with the different schemes for bias adjustment applied to the satellite-based SSTs. It is therefore suggested that the differences in the Niño-3.4 SSTA index between ERSSTv3b and OISSTdy cannot be solely due to the inclusion of but by the bias adjustment methodology of satellite data in OISSTdy. Finally, the SST products are compared with observations from ships, buoys, and satellites. On the monthly time scale, the area-averaged Niño-3.4 SSTA index in the tropical Pacific is more consistent with in situ observations in ERSSTv3b than in OISSTdy. In contrast, pointwise observations across the tropical Pacific are more consistent with OISSTdy than ERSSTv3b. It is therefore suggested that the differences among SST products are partially due to a structural uncertainty of various SST estimates. [ABSTRACT FROM AUTHOR]