An integrated database of ocean temperature and salinity observations

Abstract

Observations made by surface-based instruments and subsurface profiling instruments have, up to now, largely been treated separately, motivated by a focus on different aspects of the climate system. However, activities such as coupled climate reanalyses, long-term forecasting, satellite validation, and climate monitoring increasingly want observations from both sets of instruments. To address this, we create an ''Integrated'' Ocean Database of global in situ temperature and salinity observations covering 1900-2010, which we name HadIOD. The database merges data taken from the latest versions of the International Comprehensive Ocean-Atmosphere Data Set (surface observations) and the Met Office Hadley Centre EN dataset (subsurface observations). Duplicates introduced by this process are flagged. In addition to the measurements and their basic metadata, each observation in the database is allocated a unique identifier, quality flags and, where possible, bias corrections and uncertainty estimates. A short investigation is presented demonstrating the use, and value, of some of the bias corrections and uncertainties in the database. While this paper describes our initial attempt at building HadIOD, there is much that could be done to build on the foundations presented here. Some ideas for future development are discussed.

Vertical Redistribution of Oceanic Heat Content

“…Note the historical emphasis on measurements of the relatively warm North Atlantic Ocean and the tendency for shipborne observations to focus on lower latitudes generally, particularly in winter (see, e.g., Fig. 2 of Atkinson et al 2014). …”

Optimal Estimation of Sea Surface Temperature from AMSR-E

“…MOHC quality control (QC) flags and track flags are provided with the data. See Atkinson et al [38] for more information on the quality control.…”

Stability Assessment of the (A)ATSR Sea Surface Temperature Climate Dataset from the European Space Agency Climate Change Initiative

“…The extra-tropical moorings tend to be in coastal regions where a small change in location can have a significant impact on the SST time series. Additionally, the quality of these observations is low compared to the GTMBA (e.g., see [26,30,41]). This makes application of tests based on multiple pairwise comparisons difficult with marine data.…”

“…Atkinson et al [26] Merchant et al [18] Uncertainty due to Noise/Random Errors (K) There have been few technological changes to the GTMBA, drifting buoy and Argo observations that will have significantly impacted the quality of the data over the study period. However, the density and distribution of the observing systems has changed significantly.…”

“…Uncertainty characteristics and depth of the in situ observations following Atkinson et al [26]. Also listed are the robust standard deviation of the differences between the ESA SST CCI product and listed platforms, the median difference and number of match-ups from phase 1 of the ESA SST CCI project [18].…”