Kent, Elizabeth C., Kennedy, John J., Smith, Thomas M., Hirahara, Shoji, Huang, Boyin, Kaplan, Alexey, Parker, David E., Atkinson, Christopher P., Berry, David I., Carella, Giulia, Fukuda, Yoshikazu, Ishii, Masayoshi, Jones, Philip D., Lindgren, Fin, Merchant, Christopher J., Morak-Bozzo, Simone, Rayner, Nick A., Venema, Victor, Yasui, Souichiro and Zhang, Huai-Min (2017) A call for new approaches to quantifying biases in observations of sea-surface temperature. Bulletin of the American Meteorological Society, 98 (8), 1601-1616. (doi:10.1175/BAMS-D-15-00251.1).
Abstract
Global surface-temperature is a fundamental measure of climate change. We discuss bias estimation for sea-surface temperature and recommend the improvements to data, observational metadata, and uncertainty modeling needed to make progress.
Global surface-temperature changes are a fundamental expression of climate change. Recent, much-debated, variations in the observed rate of surface-temperature change have highlighted the importance of uncertainty in adjustments applied to sea-surface temperature (SST) measurements. These adjustments are applied to compensate for systematic biases and changes in observing protocol. Better quantification of the adjustments and their uncertainties would increase confidence in estimated surface-temperature change and provide higher-quality gridded SST fields for use in many applications.
Bias adjustments have been based either on physical models of the observing processes or on the assumption of an unchanging relationship between SST and a reference data set such as night marine air temperature. These approaches produce similar estimates of SST bias on the largest space and timescales, but regional differences can exceed the estimated uncertainty. We describe challenges to improving our understanding of SST biases. Overcoming these will require clarification of past observational methods, improved modeling of biases associated with each observing method, and the development of statistical bias estimates that are less sensitive to the absence of metadata regarding the observing method.
New approaches are required that embed bias models, specific to each type of observation, within a robust statistical framework. Mobile platforms and rapid changes in observation type require biases to be assessed for individual historic and present-day platforms (i.e., ships or buoys) or groups of platforms. Lack of observational metadata and of high-quality observations for validation and bias model development are likely to remain major challenges.
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