Preprints
https://doi.org/10.5194/gmd-2022-230
https://doi.org/10.5194/gmd-2022-230
Submitted as: methods for assessment of models
14 Nov 2022
Submitted as: methods for assessment of models | 14 Nov 2022
Status: this preprint is currently under review for the journal GMD.

Functional ANOVA for Carbon Flux Estimates from Remote Sensing Data

Jonathan Hobbs1, Matthias Katzfuss2, Hai Nguyen1, Vineet Yadav1, and Junjie Liu1 Jonathan Hobbs et al.
  • 1Jet Propulsion Laboratory, California Institute of Technology
  • 2Texas A&M University

Abstract. The constellation of Earth-observing satellites now produces atmospheric greenhouse gas concentration estimates across multiple years. Their global coverage is providing additional information on the global carbon cycle. These products are combined with complex inversion systems to infer the magnitude of carbon sources and sinks around the globe. Multiple factors, including the atmospheric transport model and satellite product aggregation method, can impact flux estimates. Functional analysis of variance (ANOVA) invokes a spatio-temporal statistical model to efficiently estimate common flux signals across multiple inversions, and partitions variability across the discrete factors considered. The approach is illustrated on inversion experiments with different satellite retrieval aggregation methods and identifies significant flux anomalies in the presence of mode differences across aggregation methods. Functional ANOVA is also applied to a recent flux model intercomparison project (MIP), and the relative magnitudes of transport model effects and data source (satellite versus in situ) are similar but exhibit slightly different importance for inversions over different continents.

Jonathan Hobbs et al.

Status: open (until 11 Jan 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Jonathan Hobbs et al.

Data sets

Functional ANOVA for Carbon Flux Estimates: Supporting Data Hobbs, Jonathan; Katzfuss, Matthias; Nguyen, Hai; Yadav, Vineet; Liu, Junjie https://doi.org/10.5281/zenodo.7081161

Model code and software

Functional ANOVA for Carbon Flux Estimates: Analysis Software Hobbs, Jonathan; Katzfuss, Matthias; https://doi.org/10.5281/zenodo.7080750

Jonathan Hobbs et al.

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Short summary
The cycling of carbon among the land, oceans, and atmosphere is a closely monitored process in the global climate system. These exchanges between the atmosphere and the surface can be quantified using a combination of atmospheric carbon dioxide observations and computer models. This study presents a statistical method for investigating the similarities and differences in the estimated surface/atmosphere carbon exchange when different computer model assumptions are invoked.