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  • American Meteorological Society  (2)
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  • 1
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    Improved statistical method for quality control of hydrographic observations (2020)
    American Meteorological Society
    Details
    Publication Date: 2022-05-26
    Description: Author Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of the Atmospheric and Oceanic Technology 37(5), (2020): 789-806, doi:10.1175/JTECH-D-18-0244.1.
    Description: Realistic ocean state prediction and its validation rely on the availability of high quality in situ observations. To detect data errors, adequate quality check procedures must be designed. This paper presents procedures that take advantage of the ever-growing observation databases that provide climatological knowledge of the ocean variability in the neighborhood of an observation location. Local validity intervals are used to estimate binarily whether the observed values are considered as good or erroneous. Whereas a classical approach estimates validity bounds from first- and second-order moments of the climatological parameter distribution, that is, mean and variance, this work proposes to infer them directly from minimum and maximum observed values. Such an approach avoids any assumption of the parameter distribution such as unimodality, symmetry around the mean, peakedness, or homogeneous distribution tail height relative to distribution peak. To reach adequate statistical robustness, an extensive manual quality control of the reference dataset is critical. Once the data have been quality checked, the local minima and maxima reference fields are derived and the method is compared with the classical mean/variance-based approach. Performance is assessed in terms of statistics of good and bad detections. It is shown that the present size of the reference datasets allows the parameter estimates to reach a satisfactory robustness level to always make the method more efficient than the classical one. As expected, insufficient robustness persists in areas with an especially low number of samples and high variability.
    Description: This study has been conducted using EU Copernicus Marine Service Information and was supported by the European Union within the EU Copernicus Marine Service In Situ phase-I and phase-II contracts led by Ifremer. The publication was also supported by SOERE CTDO2 in France. The Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (see http://www.argo.ucsd.edu, http://argo.jcommops.org). The Argo Program is part of the Global Ocean Observing System (http://doi.org/10.17882/42182). The marine mammal data were collected and made freely available by the International MEOP Consortium and the national programs that contribute to it (see http://www.meop.net; https://doi.org/10.17882/45461). Aleix Gelabert and Dídac Costa were the skippers of the OPOO, sponsored by the Intergovernmental Oceanographic Commission (UNESCO) and Pharmaton. The BWR is a periodic oceanic race organized by the Fundació Navegació Oceànica de Barcelona (FNOB). Reviewer D. Briand provided some useful comments on the final version of the draft paper before submission.
    Description: 2020-11-04
    Keywords: Ocean ; Climatology ; Salinity ; Temperature ; Data quality control ; Oceanic variability
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
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    Global Oceans [in “State of the Climate in 2020”] (2022)
    American Meteorological Society
    Details
    Publication Date: 2022-06-09
    Description: Author Posting. © American Meteorological Society, 2021. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 102(8), (2021): S143–S198, https://doi.org/10.1175/BAMS-D-21-0083.1.
    Description: This chapter details 2020 global patterns in select observed oceanic physical, chemical, and biological variables relative to long-term climatologies, their differences between 2020 and 2019, and puts 2020 observations in the context of the historical record. In this overview we address a few of the highlights, first in haiku, then paragraph form: La Niña arrives, shifts winds, rain, heat, salt, carbon: Pacific—beyond. Global ocean conditions in 2020 reflected a transition from an El Niño in 2018–19 to a La Niña in late 2020. Pacific trade winds strengthened in 2020 relative to 2019, driving anomalously westward Pacific equatorial surface currents. Sea surface temperatures (SSTs), upper ocean heat content, and sea surface height all fell in the eastern tropical Pacific and rose in the western tropical Pacific. Efflux of carbon dioxide from ocean to atmosphere was larger than average across much of the equatorial Pacific, and both chlorophyll-a and phytoplankton carbon concentrations were elevated across the tropical Pacific. Less rain fell and more water evaporated in the western equatorial Pacific, consonant with increased sea surface salinity (SSS) there. SSS may also have increased as a result of anomalously westward surface currents advecting salty water from the east. El Niño–Southern Oscillation conditions have global ramifications that reverberate throughout the report.
    Description: Argo data used in the chapter were collected and made freely available by the International Argo Program and the national programs that contribute to it. (https://argo.ucsd.edu, https://www.ocean-ops. org). The Argo Program is part of the Global Ocean Observing System. Many authors of the chapter are supported by NOAA Research, the NOAA Global Ocean Monitoring and Observing Program, or the NOAA Ocean Acidification Program. • L. Cheng is supported by National Natural Science Foundation of China (42076202) and Strategic Priority Research Program of the Chinese Academy of Sciences (XDB42040402. • R. E. Killick is supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra. PMEL contribution numbers 5214, 5215, 5216, 5217, and 5247.
    Repository Name: Woods Hole Open Access Server
    Type: Book chapter
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