We analyze for the first time all 16 Coupled Model Intercomparison Project Phase 5 models with explicit marine ecological modules to identify the common mechanisms involved in projected phytoplankton biomass, productivity, and organic carbon export changes over the twenty-first century in the RCP8.5 scenario (years 2080–2099) compared to the historical scenario (years 1980–1999). All models predict decreases in primary and export production globally of up to 30 % of the historical value. We divide the ocean into biomes using upwelling velocities, sea-ice coverage, and maximum mixed layer depths. Models generally show expansion of subtropical, oligotrophic biomes and contraction of marginal sea-ice biomes. The equatorial and subtropical biomes account for 77 % of the total modern oceanic primary production (PP), but contribute 117 % to the global drop in PP, slightly compensated by an increase in PP in high latitudes. The phytoplankton productivity response to climate is surprisingly similar across models in low latitude biomes, indicating a common set of modeled processes controlling productivity changes. Ecological responses are less consistent across models in the subpolar and sea-ice biomes. Inter-hemispheric asymmetries in physical drivers result in stronger climate-driven relative decreases in biomass, productivity, and export of organic matter in the northern compared to the southern hemisphere low latitudes. The export ratio, a measure of the efficiency of carbon export to the deep ocean, decreases across low and mid-latitude biomes and models with more than one phytoplankton type, particularly in the northern hemisphere. Inter-model variability is much higher for biogeochemical than physical variables in the historical period, but is very similar among predicted 100-year biogeochemical and physical changes. We include detailed biome-by-biome analyses, discuss the decoupling between biomass, productivity and export across biomes and models, and present statistical significance and consistency across models using a novel technique based on bootstrapping combined with a weighting scheme based on similarity across models.
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