| description abstract | A method is developed to quantify systematic errors in two types of sea surface temperature (SST) observations: bucket and engine-intake measurements. A simple linear model is proposed where the SST measured using a bucket is cooled or warmed by a fraction of the air?sea temperature difference and the SST measured using an engine intake has a constant bias. The model is applied to collocated nighttime observations made at moderate wind speeds, allowing the effects of solar radiation and strong vertical gradients in the upper ocean to be neglected. The analysis is complicated by large random errors in all of the variables used. To estimate coefficients in this model, a novel type of linear regression, where errors in two variables are correlated with each other, is introduced. Because of the uncertainty in a priori estimates of the error covariance matrix, a Bayesian analysis of the regression problem is developed, and maximum likelihood approximations to the posterior distributions of the model parameters are obtained. Results show that the temperature change in bucket SST resulting from the air?sea temperature difference can be detected. The analysis suggests that bucket SST may be in error by a fraction from 0.12° ± 0.02° to 0.16° ± 0.02°C of the air?sea temperature difference. When this temperature change of the bucket SST is accounted for, a warm bias in engine-intake SST in the mid- to late 1970s and the 1980s was found to be smaller than that suggested by previous studies, ranging between 0.09° ± 0.06° and 0.18° ± 0.05°C. For the early 1990s the model suggests that the engine-intake SSTs may have a cold bias of ?0.13° ± 0.07°C. | |
