Using Probability Density Functions to Evaluate Models (PDFEM, v1.0) to compare a biogeochemical model with satellite-derived chlorophyll
Name
gmd-16-4639-2023.pdf
Description
Published version
Size
6.07 MB
Format
Adobe PDF
Checksum (MD5)
d9da5e6a675899ad47f89844852c5999
Author(s)
Jönsson, Bror F
Follett, Christopher L
Bien, Jacob
Dutkiewicz, Stephanie
Hyun, Sangwon
Kulk, Gemma
Forget, Gael L
Müller, Christian
Racault, Marie-Fanny
Hill, Christopher N
Date Issued
August 18, 2023
Journal
Geoscientific Model Development
Publisher
Copernicus GmbH
Citation
Jönsson, B. F., Follett, C. L., Bien, J., Dutkiewicz, S., Hyun, S., Kulk, G., Forget, G. L., Müller, C., Racault, M.-F., Hill, C. N., Jackson, T., and Sathyendranath, S.: Using Probability Density Functions to Evaluate Models (PDFEM, v1.0) to compare a biogeochemical model with satellite-derived chlorophyll, Geosci. Model Dev., 16, 4639–4657.
Version
Final published version
Abstract
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 (mg 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.
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
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Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.5194/gmd-16-4639-2023
