Benchmarking of automatic quality control checks for ocean temperature profiles and recommendations for optimal sets - Simon Good; Bill Mills; Tim Boyer; Francis Bringas; Guilherme Castelão; Rebecca Cowley; Gustavo Goni; Viktor Gouretski; Catia M. Domingues (Frontiers in Marine Science, 2023)
Millions of in situ ocean temperature profiles have been collected historically using various instrument types with varying sensor accuracy and then assembled into global databases. These are essential to our current understanding of the changing state of the oceans, sea level, Earth’s climate, marine ecosystems and fisheries, and for constraining model projections of future change that underpin mitigation and adaptation solutions. Profiles distributed shortly after collection are also widely used in operational applications such as real-time monitoring and forecasting of the ocean state and weather prediction. Before use in scientific or societal service applications, quality control (QC) procedures need to be applied to flag and ultimately remove erroneous data. Automatic QC (AQC) checks are vital to the timeliness of operational applications and for reducing the volume of dubious data which later require QC processing by a human for delayed mode applications. Despite the large suite of evolving AQC checks developed by institutions worldwide, the most effective set of AQC checks was not known. We have developed a framework to assess the performance of AQC checks, under the auspices of the International Quality Controlled Ocean Database (IQuOD) project. The IQuOD-AQC framework is an open-source collaborative software infrastructure built in Python (available from https://github.com/IQuOD ). Sixty AQC checks have been implemented in this framework. Their performance was benchmarked against three reference datasets which contained a spectrum of instrument types and error modes flagged in their profiles. One of these (a subset of the Quality-controlled Ocean Temperature Archive (QuOTA) dataset that had been manually inspected for quality issues by its creators) was also used to identify optimal sets of AQC checks. Results suggest that the AQC checks are effective for most historical data, but less so in the case of data from Mechanical Bathythermographs (MBTs), and much less effective for Argo data. The optimal AQC sets will be applied to generate quality flags for the next release of the IQuOD dataset. This will further elevate the quality and historical value of millions of temperature profile data which have already been improved by IQuOD intelligent metadata and observational uncertainty information ( https://doi.org/10.7289/v51r6nsf ).

Hydrodynamics in Acute Ischemic Stroke Catheters Under Static and Cyclic Aspiration Conditions - Bryan C. Good; Francesco Costanzo; Scott D. Simon; Keefe B. Manning (Cardiovascular Engineering and Technology, 2020)

Assessing the Potential Impact of Changes to the Argo and Moored Buoy Arrays in an Operational Ocean Analysis System - Chongyuan Mao; Robert R. King; Rebecca Reid; Matthew J. Martin; Simon A. Good (Frontiers in Marine Science, 2020)

Interannual-decadal variability of wintertime mixed layer depths in the North Pacific detected by an ensemble of ocean syntheses - Takahiro Toyoda; Yosuke Fujii; Tsurane Kuragano; Naohiro Kosugi; Daisuke Sasano; Masafumi Kamachi; Yoichi Ishikawa; Shuhei Masuda; Kanako Sato; Toshiyuki Awaji; Fabrice Hernandez; Nicolas Ferry; et al. (Climate Dynamics, 2017)

Intercomparison and validation of the mixed layer depth fields of global ocean syntheses - Takahiro Toyoda; Yosuke Fujii; Tsurane Kuragano; Masafumi Kamachi; Yoichi Ishikawa; Shuhei Masuda; Kanako Sato; Toshiyuki Awaji; Fabrice Hernandez; Nicolas Ferry; Stéphanie Guinehut; Matthew J. Martin; et al. (Climate Dynamics, 2017)

Steric sea level variability (1993–2010) in an ensemble of ocean reanalyses and objective analyses - Andrea Storto; Simona Masina; Magdalena Balmaseda; Stéphanie Guinehut; Yan Xue; Tanguy Szekely; Ichiro Fukumori; Gael Forget; You-Soon Chang; Simon A. Good; Armin Köhl; Guillaume Vernieres; et al. (Climate Dynamics, 2017)

The impact of observational sampling on time series of global 0–700 m ocean average temperature: a case study - Simon A. Good (International Journal of Climatology, 2017)
The limited historical observational sampling of the ocean gives rise to uncertainty in time series of global ocean temperature anomalies calculated from those observations. Without knowledge of the true global state of the oceans, it is difficult to characterize the errors caused by these sampling issues. One way to quantify them is to use climate model data. Pseudo observational time series can be constructed from the model data using knowledge of where observations occurred. Comparison of these with time series constructed from the full model fields yields information about how observational sampling impacts time series of the temperature change in the modelled world. This can then be related back to the time series generated from the real observations. In this study, climate model data were used to investigate sampling errors in 0–700 m global average ocean temperature anomaly time series calculated using a straightforward gridding approach. The sampling had two impacts. First, sampling causes issues with constructing a climatology that is representative of the long‐term average state of the ocean. Climatology errors were shown to have the potential to cause systematically changing errors in anomaly time series. Second, some regions of the ocean were poorly observed prior to improvements brought about by the Argo project. This was found to cause spurious variability, both year to year and over multi‐year time scales. The latter had similar magnitude to the actual multi‐year variability seen in the model data but was smaller than the model's long‐term temperature change. The features of these errors depend on the ocean state and therefore varied between climate model runs. More sophisticated methods used to calculate ocean temperature time series are expected to be less impacted by sampling. Nevertheless, sampling errors will still occur and therefore this type of study is recommended even for those techniques.

Salinity changes in the World Ocean since 1950 in relation to changing surface freshwater fluxes - Nikolaos Skliris; Robert Marsh; Simon A. Josey; Simon A. Good; Chunlei Liu; Richard P. Allan (Climate Dynamics, 2014)

The roles of surface heat flux and ocean heat transport convergence in determining Atlantic Ocean temperature variability - Jeremy P. Grist; Simon A. Josey; Robert Marsh; Simon A. Good; Andrew. C. Coward; Beverly A. de Cuevas; Steven G. Alderson; Adrian L. New; Gurvan Madec (Ocean Dynamics, 2010)