Correction for Systematic Errors in the Global Dataset of Temperature Profiles from Mechanical Bathythermographs

Author:

Gouretski Viktor1,Cheng Lijing2

Affiliation:

1. International Center for Climate and Environmental Science, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

2. International Center for Climate and Environmental Science, Institute of Atmospheric Physics, and Center for Ocean Mega-Science, Chinese Academy of Sciences, Beijing, China

Abstract

AbstractA homogeneous, consistent, high-quality in situ temperature dataset covering some decades in time is crucial for the detection of climate changes in the ocean. For the period from 1940 to the present, this study investigates the data quality of temperature profiles from mechanical bathythermographs (MBT) by comparing these data with reference data obtained from Nansen bottle casts and conductivity–temperature–depth (CTD) profilers. This comparison reveals significant systematic errors in MBT measurements. The MBT bias is as large as 0.2°C before 1980 on the global average and reduces to less than 0.1°C after 1980. A new empirical correction scheme for MBT data is derived, where the MBT correction is country, depth, and time dependent. Comparison of the new MBT correction scheme with three schemes proposed earlier in the literature suggests a better performance of the new schemes. The reduction of the biases increases the homogeneity of the global ocean database being mostly important for climate change–related studies, such as the improved estimation of the ocean heat content changes.

Funder

President's International Fellowship Initiative (PIFI).

National Key R&D Program

Publisher

American Meteorological Society

Subject

Atmospheric Science,Ocean Engineering

Reference38 articles.

1. Mechanical bathythermographs;Casciano,1967

2. Time, probe type and temperature variable bias corrections to historical expendable bathythermograph observations;Cheng;J. Atmos. Oceanic Technol.,2014

3. XBT science: Assessment of instrumental biases and errors;Cheng;Bull. Amer. Meteor. Soc.,2016

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