Using Probability Density Functions to Evaluate Models (PDFEM, v1.0) to compare a biogeochemical model with satellite-derived chlorophyll

Jönsson, Bror Fredrik, Follett, Christopher L., Bien, Jacob, Dutkiewicz, Stephanie, Hyun, Sangwon, Kulk, Gemma, Forget, Gael L., Müller, Christian, Racault, Marie-Fanny ORCID:, Hill, Christopher N., Jackson, Thomas and Sathyendranath, Shubha (2023) Using Probability Density Functions to Evaluate Models (PDFEM, v1.0) to compare a biogeochemical model with satellite-derived chlorophyll. Geoscientific Model Development, 16 (16). 4639–4657. ISSN 1991-9603

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Global biogeochemical ocean models are invaluable tools to examine how physical, chemical, and biological processes interact in the ocean. Satellite-derived ocean color properties, on the other hand, provide observations of the surface ocean, with unprecedented coverage and resolution. Advances in our understanding of marine ecosystems and biogeochemistry are strengthened by the combined use of these resources, together with sparse in situ data. Recent modeling advances allow the simulation of the spectral properties of phytoplankton and remote sensing reflectances, bringing model outputs closer to the kind of data that ocean color satellites can provide. However, comparisons between model outputs and analogous satellite products (e.g., chlorophyll a) remain problematic. Most evaluations are based on point-by-point comparisons in space and time, where spuriously large errors can occur from small spatial and temporal mismatches, whereas global statistics provide no information on how well a model resolves processes at regional scales. Here, we employ a unique suite of methodologies, the Probability Density Functions to Evaluate Models (PDFEM), which generate a robust comparison of these resources. The probability density functions of physical and biological properties of Longhurst's provinces are compared to evaluate how well a model resolves related processes. Differences in the distributions of chlorophyll a concentration (mgĝ€¯m-3) provide information on matches and mismatches between models and observations. In particular, mismatches help isolate regional sources of discrepancy, which can lead to improving both simulations and satellite algorithms. Furthermore, the use of radiative transfer in the model to mimic remotely sensed products facilitates model-observation comparisons of optical properties of the ocean.

Item Type: Article
Additional Information: Acknowledgements: This work has been carried out under the auspices of the Simons Collaboration on Computational Biogeochemical Modeling of Marine Ecosystems (CBIOMES), which seeks to develop and apply quantitative models of the structure and function of marine microbial communities at seasonal and basin scales. Funding for this work has been provided by the Simons Foundation (grant nos. 549947 SS; 553242, 549939 JB; 827829 CLF). Additional support from the National Centre for Earth Observations of the UK is also acknowledged.
Uncontrolled Keywords: earth and planetary sciences(all),modelling and simulation,sdg 14 - life below water ,/dk/atira/pure/subjectarea/asjc/1900
Faculty \ School: Faculty of Science > School of Environmental Sciences
University of East Anglia Research Groups/Centres > Theme - ClimateUEA
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Depositing User: LivePure Connector
Date Deposited: 30 Jun 2023 09:30
Last Modified: 25 Sep 2023 08:30
DOI: 10.5194/gmd-16-4639-2023


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