Preprints
https://doi.org/10.5194/egusphere-2022-849
https://doi.org/10.5194/egusphere-2022-849
 
08 Nov 2022
08 Nov 2022
Status: this preprint is open for discussion.

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

Bror Fredrik Jönsson1, Christopher Follett2, Jacob Bien3, Stephanie Dutkiewicz2, Sangwon Hyun3, Gemma Kulk1, Gael Forget2, Christian Müller4, Marie-Fanny Racault1,6, Christopher Nigel Hill2, Thomas Jackson1, and Shubha Sathyendranath1,5 Bror Fredrik Jönsson et al.
  • 1Plymouth Marine Laboratory, Prospect Place, Plymouth, PL1 3DH, United Kingdom
  • 2Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
  • 3Data Sciences and Operations, University of Southern California, Los Angeles, California, USA
  • 4LMU/HMGU Munich, Flatiron Institute, New York
  • 5National Centre for Earth Observation, Plymouth Marine Laboratory, Plymouth, PL1 3DH, United Kingdom
  • 6School of Environmental Sciences, University of East Anglia, NR4 7TJ, United Kingdom

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: open (until 03 Jan 2023)

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 reply

Bror Fredrik Jönsson et al.

Bror Fredrik Jönsson et al.

Viewed

Total article views: 210 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
143 61 6 210 2 2
  • HTML: 143
  • PDF: 61
  • XML: 6
  • Total: 210
  • BibTeX: 2
  • EndNote: 2
Views and downloads (calculated since 08 Nov 2022)
Cumulative views and downloads (calculated since 08 Nov 2022)

Viewed (geographical distribution)

Total article views: 207 (including HTML, PDF, and XML) Thereof 207 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Dec 2022
Download
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.