A review of uncertainty in in situ measurements and data sets of sea surface temperature

Abstract

Archives of in situ sea surface temperature (SST) measurements extend back more than 160 years.Quality of the measurements is variable, and the area of the oceans they sample is limited, especially early in the record and during the two world wars. Measurements of SST and the gridded data sets that are based on them are used in many applications so understanding and estimating the uncertainties are vital. The aim of this review is to give an overview of the various components that contribute to the overall uncertainty of SST measurements made in situ and of the data sets that are derived from them. In doing so, it also aims to identify current gaps in understanding. Uncertainties arise at the level of individual measurements with both systematic and random effects and, although these have been extensively studied, refinement of the error models continues. Recent improvements have been made in the understanding of the pervasive systematic errors that affect the assessment of long-term trends and variability. However, the adjustments applied to minimize these systematic errors are uncertain and these uncertainties are higher before the 1970s and particularly large in the period surrounding the Second World War owing to a lack of reliable metadata. The uncertainties associated with the choice of statistical methods used to create globally complete SST data sets have been explored using different analysis techniques, but they do not incorporate the latest understanding of measurement errors, and they want for a fair benchmark against which their skill can be objectively assessed. These problems can be addressed by the creation of new end-to-end SST analyses and by the recovery and digitization of data and metadata from ship log books and other contemporary literature.

Optimal Estimation of Sea Surface Temperature from AMSR-E

“…The data and quality control of these observations are described in Good et al [36]. In this study, the uppermost temperature observations from the Argo observations have been used, which have a typical depth of 5 m [40] and a very high accuracy, with uncertainties of 0.002 • C [41,42]. Both Argo and drifting buoy observations have previously been used for algorithm development and validation studies [43][44][45][46].…”

Uncertainties in the Estimation of Global Observational Network Datasets of Precipitation over West Africa

“…Kennedy [6] submitted that systematic (resulting in uncertainties in the calculated precipitation climatology) and the uncertainty and the spread amongst multiple data sets vary according to regions as well as seasons [15,21,22]. The studies further revealed that uncertainties in the calculated precipitation climatology defined relative to their climatological means are generally larger in the dry regions and/or local dry seasons.…”

“…An error in the estimate is the amount of inaccuracy in the estimate compared with a known standard value [3][4][5]. Sources of uncertainty in observational measurements are often broadly categorized as (statistical) random or systematic errors [6,7]. Random observational errors are associated with the observed frequency distributions in the primary data.…”

“…They occur for many reasons ranging from misreading of the pointer instrument, rounding errors, the difficulty of reading the instrument to a precision higher than the smallest marked gradation, incorrectly recorded values, errors in transcription from written to digital sources, and sensor noise among others. The contribution of random independent errors to the uncertainty on the average/ensemble of a number of datasets is much smaller than the contribution of random error to the uncertainty on a single observation, even in the most sparsely observed years [6]. Nonetheless, where observations are few, random observational errors can be an important component of the total uncertainty and can, in principle, be made arbitrarily small by repeated measurement or large enough sample size [7].…”

Global Daily High-Resolution Satellite-Based Foundation Sea Surface Temperature Dataset: Development and Validation against Two Definitions of Foundation SST

“…The diurnal variations of upper surface temperature, however, increase during daytime usually within a top of several meters in oceans [15]. In a review of the uncertainty of in situ SST datasets, Kennedy [16] mentioned diurnal variation of temperature as one uncertainty factor and reported that variety in observation time can lead to spurious changes in the data due to the diurnal cycle of temperature. Thus, cases of such uncertainty and pseudo-variability (pseudo-SST fronts) could be contaminated in satellite-based SST data (e.g., [17]).…”