Phytoplankton carbon biomass represents a critical indicator for marine ecosystem function. Here, we examine changes in surface PHYC (PHYCOS) within a suite of Earth System Models participating in the Coupled Model Intercomparison Project, Phase 6 (CMIP6), comparing an extreme climate change case with a preindustrial control simulation. While most models produce mutually consistent spatiotemporal patterns of PHYCOS, they diverge under climate change. We train machine learning emulators on the model output and feed these emulators different combinations of preindustrial and climate-altered inputs. Feeding climate-altered environmental predictors to the emulator trained with preindustrial inputs allows us to explain the majority of change away from the Arctic, suggesting that climate change largely shifts the location and timing of particular conditions. Maintaining constant inputs while varying the emulator, however, shows the impact of shifts in apparent relationships between environmental drivers and phytoplankton biomass, particularly in the Arctic. Low-latitude divergence in the relative biomass change is driven by differences in the response to nutrients, with some models showing influence of temperature. Differences in high-latitude relative biomass changes are driven by strong intermodel differences in the response to light, mixed layer depth, and nutrients. Relative to observations, models show much stronger sensitivity to light and macronutrients, suggesting that they may overrespond to climate change. However, this may be mitigated by unrealistically weak responses to iron.
