Interaction matters: Bottom-up driver interdependencies alter the projected response of phytoplankton communities to climate change.

Abstract

Phytoplankton growth is controlled by multiple environmental drivers, which are all modified by climate change. While numerous experimental studies identify interactive effects between drivers, large-scale ocean biogeochemistry models mostly account for growth responses to each driver separately and leave the results of these experimental multiple-driver studies largely unused. Here, we amend phytoplankton growth functions in a biogeochemical model by dual-driver interactions (CO2 and temperature, CO2 and light), based on data of a published meta-analysis on multiple-driver laboratory experiments. The effect of this parametrization on phytoplankton biomass and community composition is tested using present-day and future high-emission (SSP5-8.5) climate forcing. While the projected decrease in future total global phytoplankton biomass in simulations with driver interactions is similar to that in control simulations without driver interactions (5%-6%), interactive driver effects are group-specific. Globally, diatom biomass decreases more with interactive effects compared with the control simulation (-8.1% with interactions vs. no change without interactions). Small-phytoplankton biomass, by contrast, decreases less with on-going climate change when the model accounts for driver interactions (-5.0% vs. -9.0%). The response of global coccolithophore biomass to future climate conditions is even reversed when interactions are considered (+33.2% instead of -10.8%). Regionally, the largest difference in the future phytoplankton community composition between the simulations with and without driver interactions is detected in the Southern Ocean, where diatom biomass decreases (-7.5%) instead of increases (+14.5%), raising the share of small phytoplankton and coccolithophores of total phytoplankton biomass. Hence, interactive effects impact the phytoplankton community structure and related biogeochemical fluxes in a future ocean. Our approach is a first step to integrate the mechanistic understanding of interacting driver effects on phytoplankton growth gained by numerous laboratory experiments into a global ocean biogeochemistry model, aiming toward more realistic future projections of phytoplankton biomass and community composition.