Using feature-based verification methods to explore the spatial and temporal characteristics of the 2019 chlorophyll-<i>a</i> bloom season in a model of the European Northwest Shelf
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Published:2021-11-01
Issue:6
Volume:17
Page:1527-1543
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ISSN:1812-0792
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Container-title:Ocean Science
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language:en
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Short-container-title:Ocean Sci.
Author:
Mittermaier MarionORCID, North Rachel, Maksymczuk Jan, Pequignet ChristineORCID, Ford David
Abstract
Abstract. Two feature-based verification methods, thus far only used for the
diagnostic evaluation of atmospheric models, have been applied to compare
∼7 km resolution pre-operational analyses of chlorophyll-a
(Chl-a) concentrations to a 1 km gridded satellite-derived Chl-a
concentration product. The aim of this study was to assess the value of
applying such methods to ocean models. Chl-a bloom objects were identified in
both data sets for the 2019 bloom season (1 March to 31 July). These bloom
objects were analysed as discrete (2-D) spatial features, but also as
space–time (3-D) features, providing the means of defining the onset,
duration and demise of distinct bloom episodes and the season as a whole. The new feature-based verification methods help reveal that the model
analyses are not able to represent small coastal bloom objects, given the
coarser definition of the coastline, also wrongly producing more bloom
objects in deeper Atlantic waters. Model analyses' concentrations are
somewhat higher overall. The bias manifests itself in the size of the model
analysis bloom objects, which tend to be larger than the satellite-derived
bloom objects. The onset of the bloom season is delayed by 26 d in the
model analyses, but the season also persists for another month beyond the
diagnosed end. The season was diagnosed to be 119 d long in the model
analyses, compared to 117 d from the satellite product. Geographically,
the model analyses and satellite-derived bloom objects do not necessarily
exist in a specific location at the same time and only overlap
occasionally.
Publisher
Copernicus GmbH
Subject
Cell Biology,Developmental Biology,Embryology,Anatomy
Reference62 articles.
1. Allen, J. I. and Somerfield, P. J.: A multivariate approach to model skill
assessment, J. Mar. Syst., 76, 83-94. https://doi.org/10.1016/j.jmarsys.2008.05.009,
2009. 2. Allen, J. I., Holt, J. T., Blackford, J., and Proctor, R.: Error
quantification of a high-resolution coupled hydrodynamic-ecosystem
coastal-ocean model: Part 2. Chlorophyll-a, nutrients and SPM, J. Mar.
Syst., 68, 381-404, https://doi.org/10.1016/j.jmarsys.2007.01.005, 2007a. 3. Allen, J. I., Somerfield, P. J., and Gilbert, F. J.: Quantifying uncertainty
in high-resolution coupled hydrodynamic-ecosystem models, J. Mar. Syst.,
64, 3-14, https://doi.org/10.1016/j.jmarsys.2006.02.010, 2007b. 4. Antoine, D., André, J. M., and Morel, A.: Oceanic primary production: 2.
Estimation at global scale from satellite (Coastal Zone Color Scanner)
chlorophyll, Global Biogeochem. Cy., 10, 57-69, https://doi.org/10.1029/95GB02832, 1996. 5. Anugerahanti, P., Roy, S., and Haines, K.: A perturbed biogeochemistry model ensemble evaluated against in situ and satellite observations, Biogeosciences, 15, 6685–6711, https://doi.org/10.5194/bg-15-6685-2018, 2018.
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