Using Neural Networks to Correct Historical Climate Observations

Biases in expendable bathythermograph (XBT) instruments have emerged as a leading uncertainty in reconstructions of historical ocean heat content change and therefore climate change. Corrections for these biases depend on the type of XBT used; however, this is unspecified for 52% of the historical XBT profiles in the World Ocean Database. Here, we use profiles of known XBT type to train a neural network that can classify probe type based on three covariates: profile date, maximum recorded depth, and country of origin. Whereas previous studies have shown an average classification skill of 77%, falling below 50% for some periods, our new algorithm maintains an average skill of 90%, with a minimum of 70%. Our study illustrates the potential for successfully applying machine learning approaches in a wide variety of instrument classification problems in order to promote more homogeneous climate data records.

An Updated Estimate of Salinity for the Atlantic Ocean Sector Using Temperature–Salinity Relationships

Simultaneous temperature and salinity profile measurements are of extreme importance for research; operational oceanography; research and applications that compute content and transport of mass, heat, and freshwater in the ocean; and for determining water mass stratification and mixing rates. Historically, temperature profiles are much more abundant than simultaneous temperature and salinity profiles. Given the importance of concurrent temperature and salinity profiles, several methods have been developed to derive salinity solely based on temperature profile observations, such as expendable bathythermograph (XBT) temperature measurements, for which concurrent salinity observations are typically not available. These empirical methods used to date contain uncertainties as a result of temporal changes in salinity and seasonality in the mixed layer, and are typically regionally based. In this study, a new methodology is proposed to infer salinity in the Atlantic Ocean from the water surface to 2000-m depth, which addresses the seasonality in the upper ocean and makes inferences about longer-term changes in salinity. Our results show that when seasonality is accounted for, the variance of the residuals is reduced in the upper 150 m of the ocean and the dynamic height errors are smaller than 4 cm in the whole study domain. The sensitivity of the meridional heat and freshwater transport to different empirical methods of salinity estimation is studied using the high-density XBT transect across 34.5°S in the South Atlantic Ocean. Results show that accurate salinity estimates are more important on the boundaries, suggesting that temperature–salinity compensation may be also important in those regions.

An Algorithm for Classifying Unknown Expendable Bathythermograph (XBT) Instruments Based on Existing Metadata

Time-varying biases in expendable bathythermograph (XBT) instruments have emerged as a key uncertainty in estimates of historical ocean heat content variability and change. One of the challenges in the development of XBT bias corrections is the lack of metadata in ocean profile databases. Approximately 50% of XBT profiles in the World Ocean database (WOD) have no information about manufacturer or probe type. Building on previous research efforts, this paper presents a deterministic algorithm for assigning missing XBT manufacturer and probe type for individual temperature profiles based on 1) the reporting country, 2) the maximum reported depth, and 3) the record date. The criteria used are based on bulk analysis of known XBT profiles in the WOD for the period 1966–2015. A basic skill assessment demonstrates a 77% success rate at correctly assigning manufacturer and probe type for profiles where this information is available. The skill rate is lowest during the early 1990s, which is also a period when metadata information is particularly poor. The results suggest that substantive improvements could be made through further data analysis and that future algorithms may benefit from including a larger number of predictor variables.

The Impact of Improved Thermistor Calibration on the Expendable Bathythermograph Profile Data

Expendable bathythermograph (XBT) data provide one of the longest available records of upper-ocean temperature. However, temperature and depth biases in XBT data adversely affect estimates of long-term trends of ocean heat content and, to a lesser extent, estimates of volume and heat transport in the ocean. Several corrections have been proposed to overcome historical biases in XBT data, which rely on constantly monitoring these biases. This paper provides an analysis of data collected during three recent hydrographic cruises that utilized different types of probes, and examines methods to reduce temperature and depth biases by improving the thermistor calibration and reducing the mass variability of the XBT probes.The results obtained show that the use of individual thermistor calibration in XBT probes is the most effective calibration to decrease the thermal bias, improving the mean thermal bias to less than 0.02°C and its tolerance from 0.1° to 0.03°C. The temperature variance of probes with screened thermistors is significantly reduced by approximately 60% in comparison to standard probes. On the other hand, probes with a tighter weight tolerance did not show statistically significant reductions in the spread of depth biases, possibly because of the small sample size or the sensitivity of the depth accuracy to other causes affecting the analysis.