Subsurface chlorophyll maxima (SCMs), observed globally in the ocean, are layers of high chlorophyll concentration below the surface. These maxima frequently appear at depths greater than the depth of maximum phytoplankton biomass, a phenomenon lacking a consistent mechanistic explanation. While the depth of the nutricline (where nutrient concentration sharply increases) correlates with SCM depth, the underlying biological processes remain unclear. Photoacclimation, the adjustment of cellular chlorophyll content in response to light, is a potential mechanism, as phytoplankton increase their chlorophyll to carbon ratio (chl:phyC) under low light to enhance light harvesting. However, chl:phyC is also influenced by nutrient availability and temperature. Existing studies have pointed to photoacclimation's role in SCM formation in some regions (e.g., subtropics), but its importance in other regions (e.g., subpolar, polar, upwelling regions) remains debated. Understanding SCM formation is critical because chlorophyll is the most commonly used metric of phytoplankton, yet it isn't a reliable proxy for biomass. This research aims to quantitatively assess the contribution of photoacclimation, driven by optimal resource allocation, to the global distribution of chlorophyll and SCMs.

Numerous studies have investigated SCMs, noting their ubiquitous presence across various oceanographic regions. Observations reveal significant regional variations in SCM depth, ranging from shallow depths in subpolar regions to deeper depths in subtropical regions. The relationship between SCM depth and nutricline depth has been recognized, suggesting a link to nutrient availability. Empirical formulae have been developed to model chl:phyC based on incubation experiments, highlighting its variability and importance in phytoplankton physiology. However, existing models often lack a mechanistic understanding of the interplay between photoacclimation, nutrient availability, and light limitation in shaping SCM distribution across diverse oceanic environments. The research builds upon previous work on optimal resource allocation theory in phytoplankton, which demonstrates how organisms allocate resources to maximize their growth rate under varying conditions. This theory explains various physiological responses observed in laboratory experiments.

The study employs a coupled 3D biogeochemical ocean circulation model. The physical component uses the Meteorological Research Institute Community Ocean Model version 3 (MRI.COM3), with a horizontal resolution of 1º longitude and 0.5° latitude (south of 64°N) and 51 vertical layers. Realistic wind stress, heat flux, and freshwater fluxes drive the model. The biological component is a 3D extension of the Flexible Phytoplankton Functional Type (FlexPFT) model. This model incorporates an optimality-based photoacclimation theory, assuming phytoplankton optimize resource allocation between nutrient uptake and light harvesting to maximize growth. The model includes phytoplankton, zooplankton, multiple nutrient forms (nitrate, ammonia, dissolved organic nitrogen), dissolved and particulate iron. The iron cycle is incorporated, considering input from dust and sediment. The model accounts for the cellular-scale processes of resource allocation among structural material, nutrient uptake (affinity and maximum uptake rate), and light harvesting (chloroplast size and chl:phyC). Instantaneous acclimation is assumed meaning resource allocation tracks environmental changes without lag. Optimized values for resource allocation fractions are calculated daily using daily-averaged environmental variables (temperature, light intensity, nitrogen concentration, iron concentration). The model uses the daily average of photosynthetically active radiation and calculates the phytoplankton growth rate at each time step, considering circadian variations in solar irradiance. The model parameters are described in detail in the supplementary materials. Model validation was conducted using satellite-observed chlorophyll concentrations, nitrate concentrations, and primary production estimates, showing reasonable agreement with observed patterns (though with some underestimation in subtropical regions due to the model's lack of nitrogen fixers).

The model successfully reproduces observed regional differences in SCM depth across various oceanographic regions (subpolar, subtropical, equatorial, Antarctic, and upwelling regions). The simulated SCM depth is closely linked to the nutricline depth, reflecting the sharp increase in nutrient concentration with depth. The study finds that SCM depth isn't solely determined by the depth of maximum phytoplankton biomass. Instead, the variation in cellular chl:phyC across the water column plays a more significant role. In subtropical regions, deep SCMs (around 118m) result from a large increase in cellular chl:phyC with depth, even though phytoplankton biomass is much lower at that depth. Conversely, in subpolar regions, shallower SCMs occur due to a relatively high surface cellular chl:phyC that reduces the impact of biomass decline with depth. The model showcases a common mechanism across various regions—the depth of maximum cellular chl:phyC, rather than maximum phytoplankton biomass, governs SCM depth. Cellular chl:phyC is determined by two factors: chloroplast chl:phyC and the fraction of resources allocated to the chloroplast. Chloroplast chl:phyC is light-dependent, increasing with decreasing light intensity until a maximal depth is reached. Resource allocation to the chloroplast is strongly influenced by nutrient availability. In low-nutrient regions (e.g., subtropics), resources are mainly allocated to nutrient uptake near the surface, leading to low surface cellular chl:phyC and deeper SCMs. In high-nutrient regions (e.g., subpolar), resource allocation is more balanced, leading to higher surface chl:phyC and shallower SCMs. The simulated global SCM distribution closely matches satellite-derived observations, correctly reproducing features like the change in SCM depth across subtropical-subpolar boundaries and the shallowing of SCMs in coastal upwelling regions. Simulated SCM depth in the Arctic and Antarctic are in line with observations.

The study demonstrates that photoacclimation, driven by an optimality-based resource allocation strategy in phytoplankton, is a dominant mechanism determining the global distribution and depth of SCMs. The model successfully explains the observed relationship between SCM depth, nutricline depth, and nutrient availability. The findings highlight that chlorophyll concentration is not a direct proxy for phytoplankton biomass, and considering photoacclimation is critical for accurate modelling and interpretation of ocean observations. The model also reveals a more nuanced understanding of how nutrient availability influences phytoplankton’s resource allocation strategies, influencing the cellular chl:phyC profile and ultimately, the formation of SCMs at varying depths. The strong link between cellular-level physiological responses and large-scale global chlorophyll patterns improves our understanding of marine ecosystem dynamics. The model has limitations (using a single generic phytoplankton species and a resolution of >10m) but is still a major step in understanding how phytoplankton respond to climate change.

This research establishes a mechanistic link between phytoplankton's photoacclimation response and the global distribution of subsurface chlorophyll maxima. The model successfully reproduces observed global SCM patterns, demonstrating the importance of considering photoacclimation driven by resource allocation strategies. This understanding is crucial for interpreting ocean observations and predicting how marine ecosystems will respond to future climate change. Future work should investigate the role of phytoplankton diversity and finer-scale processes (e.g., mesoscale eddies) to improve model accuracy and predictive capabilities.

The model employs a single generic phytoplankton species, neglecting phytoplankton community composition and potentially different photoacclimation responses across different species. The model's vertical resolution (around 10 m) limits its ability to capture finer-scale chlorophyll variations, such as those potentially caused by swimming behavior or buoyancy control. The model also doesn't explicitly account for mesoscale eddy effects on SCM depth. The model underestimates surface chlorophyll concentration in subtropical regions due to the absence of nitrogen fixers.