Understanding Differences in Sea Surface Temperature Intercomparisons

Author:

Huang Boyin1ORCID,Yin Xungang1,Carton James A.2,Chen Ligang2,Graham Garrett3,Liu Chunying4,Smith Thomas5,Zhang Huai-Min1

Affiliation:

1. a NOAA/National Centers for Environmental Information, Asheville, North Carolina

2. b Department of Atmospheric and Oceanic Sciences, University of Maryland, College Park, College Park, Maryland

3. c North Carolina Institute for Climate Studies, North Carolina State University, Asheville, North Carolina

4. d Riverside Technology, Inc., Asheville, North Carolina

5. e NOAA/Center for Satellite Applications and Research, College Park, Maryland

Abstract

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 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.

Funder

Climate Program Office

Publisher

American Meteorological Society

Subject

Atmospheric Science,Ocean Engineering

Reference54 articles.

1. Argo, 2000: Argo float data and metadata from Global Data Assembly Centre (Argo GDAC). SEANOE, accessed 1 February 2022, https://doi.org/10.17882/42182.

2. RAMSSA—An operational, high-resolution, Regional Australian Multi-Sensor Sea surface temperature Analysis over the Australian region;Beggs, H.,2011

3. Beggs, H., L. Qi, P. Govekar, and C. Griffin, 2020: Ingesting VIIRS SST into the Bureau of Meteorology’s operational SST analyses. 21st GHRSST Science Team Meeting, Online, EUMETSAT, https://imos.org.au/fileadmin/user_upload/shared/SRS/SST/Beggs_GHRSST-XXI_Extended_Abstract_06Sep2020.pdf.

4. A global analysis of sea surface temperature for numerical weather prediction;Brasnett, B.,1997

5. The impact of satellite retrievals in a global sea-surface-temperature analysis;Brasnett, B.,2008

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