Reconciling models of primary production and photoacclimation [Invited]

Abstract

Primary production and photoacclimation models are two important classes of physiological models that find applications in remote sensing of pools and fluxes of carbon associated with phytoplankton in the ocean. They are also key components of ecosystem models designed to study biogeochemical cycles in the ocean. So far, these two classes of models have evolved in parallel, somewhat independently of each other. Here we examine how they are coupled to each other through the intermediary of the photosynthesis–irradiance parameters. We extend the photoacclimation model to accommodate the spectral effects of light penetration in the ocean and the spectral sensitivity of the initial slope of the photosynthesis–irradiance curve, making the photoacclimation model fully compatible with spectrally resolved models of photosynthesis in the ocean. The photoacclimation model contains a parameter θ m , which is the maximum chlorophyll-to-carbon ratio that phytoplankton can attain when available light tends to zero. We explore how size-class-dependent values of θ m could be inferred from field data on chlorophyll and carbon content in phytoplankton, and show that the results are generally consistent with lower bounds estimated from satellite-based primary production calculations. This was accomplished using empirical models linking phytoplankton carbon and chlorophyll concentration, and the range of values obtained in culture measurements. We study the equivalence between different classes of primary production models at the functional level, and show that the availability of a chlorophyll-to-carbon ratio facilitates the translation between these classes. We discuss the importance of the better assignment of parameters in primary production models as an important avenue to reduce model uncertainties and to improve the usefulness of satellite-based primary production calculations in climate research.

Primary Production, an Index of Climate Change in the Ocean: Satellite-Based Estimates over Two Decades

“…This considerably improved the confidence with which regional primary production could be estimated, especially in those regions that were previously known to be different from others, such as the Arabian Sea and the Antarctic Ocean [110]. Yet, the need to improve P-I data coverage in large areas of the global ocean still remains (this study, Figure 1; [33,49,86]). In particular, large areas of the Pacific and Indian Oceans remain poorly sampled.…”

“…They have also been categorised as depth-integrated or resolved and as wavelength-integrated or resolved [35]. Reducing models to a canonical form helps analyse similarities and differences between models and highlights the importance of model parameters [46,47,49]. The differences between spectral and non-spectral models are systematic and significant [47], but they can be corrected for [47,50,51].…”

“…In this study, we used a spectrally-resolved model that incorporates vertical structure in chlorophyll-a concentration [2,31,44,45,52], with recent updates [49]. This model simulates changes in photosynthesis as a function of irradiance using a two-parameter photosynthesis versus irradiance (P-I) function.…”

“…All of these, along with the length of the time-series data, are key considerations when studying the variability in ocean primary production in the context of climate change. The model used here is identical to the one described in Sathyendranath et al [49] (see Appendix A for a brief description of the steps involved), but with a notable improvement to the P-I parameter assignment, based on an enhanced in situ database.…”

“…In the model of Platt and Sathyendranath [31] and Sathyendranath et al [49], ocean-colour remote-sensing products and a standard Gaussian function are used to calculate the distribution of phytoplankton biomass (B(z) in mg m −3 ) at depth. Depth profiles of chlorophyll-a were computed as shifted Gaussian functions:…”