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.
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