Preprints
https://doi.org/10.5194/egusphere-2022-849
https://doi.org/10.5194/egusphere-2022-849
08 Nov 2022
 | 08 Nov 2022

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

Bror Fredrik Jönsson, Christopher Follett, Jacob Bien, Stephanie Dutkiewicz, Sangwon Hyun, Gemma Kulk, Gael Forget, Christian Müller, Marie-Fanny Racault, Christopher Nigel Hill, Thomas Jackson, and Shubha Sathyendranath

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 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, 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 facilitate model-observation comparisons of optical properties of the ocean.

Bror Fredrik Jönsson et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-849', Lester Kwiatkowski, 05 Dec 2022
  • RC2: 'Comment on egusphere-2022-849', Marcello Vichi, 05 Feb 2023
  • AC1: 'Comment on egusphere-2022-849', Bror Jonsson, 22 Mar 2023

Bror Fredrik Jönsson et al.

Bror Fredrik Jönsson et al.

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Short summary
While biogeochemical models and satellite-derived ocean-color data provide unprecedented information it is problematic to compare them. Here, we present a new approach based on comparing probability density distributions of model and satellite properties to assess model skills. We also introduce Earth Mover Distances as a novel and powerful metric to quantify the misfit between models and observations. We find that how 3D chlorophyll fields are aggregated can be a significant source of error.