Modelling primary production: multitude of theories, or multitude of languages?

Abstract. Marine primary production, converting approximately 50 GtC per year, is an important component of the global carbon cycle, and a major determinant of past, present and future climate. Large-scale, long-term estimates of marine primary production rely primarily on two types of models: satellite-based models that make extensive use of remote-sensing data, and ecosystem models providing numerical simulation of ecological processes embedded in general ocean circulation models. Intercomparison exercises of model outputs (both within and across the two model types) have consistently revealed high discrepancies between estimated global ocean primary production, including divergent magnitudes and even opposite trends. Comparisons of model results with in-situ observations have also revealed large uncertainties in marine primary production estimates. These uncertainties limit the applications of these models, especially in the climate context, where an important question is whether climate change will drive significant future changes in regional or global primary production. Both satellite-based and ecosystem model equations rely on a range of fixed parameters, whose values need to be carefully estimated and tested. In this paper, we suggest that such model parameters represent an underappreciated but important source of inter-model differences. With the proliferation of both satellite and in situ observations of relevant variables at global scales and the availability of powerful statistical tools in data assimilation and machine learning, we argue that time is right to systematically examine model parameters and gain insights into how they may vary spatially and temporally. We emphasize that such spatio-temporal variability can be easily theoretically justified for the models with complexity similar to the satellite models, or the ecosystem models commonly used within Earth System Models (ESMs) in climate studies. We argue that the spatially and temporally varying parameter values provide a strong reason to anticipate unification of models, which would otherwise appear structurally different. A better understanding of model parameter roles could therefore reduce discrepancies among the primary production models and improve the reliability of marine primary production projections.