We present a systematic study of the differences generated by coupling the same ecological–biogeochemical model to a 1°, coarse-resolution, and 1∕6°, eddy-permitting, global ocean circulation model to (a) biogeochemistry (e.g., primary production) and (b) phytoplankton community structure. Surprisingly, we find that the modeled phytoplankton community is largely unchanged, with the same phenotypes dominating in both cases. Conversely, there are large regional and seasonal variations in primary production, phytoplankton and zooplankton biomass. In the subtropics, mixed layer depths (MLDs) are, on average, deeper in the eddy-permitting model, resulting in higher nutrient supply driving increases in primary production and phytoplankton biomass. In the higher latitudes, differences in winter mixed layer depths, the timing of the onset of the spring bloom and vertical nutrient supply result in lower primary production in the eddy-permitting model. Counterintuitively, this does not drive a decrease in phytoplankton biomass but results in lower zooplankton biomass. We explain these similarities and differences in the model using the framework of resource competition theory, and find that they are the consequence of changes in the regional and seasonal nutrient supply and light environment, mediated by differences in the modeled mixed layer depths. Although previous work has suggested that complex models may respond chaotically and unpredictably to changes in forcing, we find that our model responds in a predictable way to different ocean circulation forcing, despite its complexity. The use of frameworks, such as resource competition theory, provides a tractable way to explore the differences and similarities that occur. As this model has many similarities to other widely used biogeochemical models that also resolve multiple phytoplankton phenotypes, this study provides important insights into how the results of running these models under different physical conditions might be more easily understood.

The effect of biodiversity on ecosystem functioning is one of the major questions of ecology. However, the role of phytoplankton functional diversity in ecosystem productivity and stability under fluctuating (i.e. non-equilibrium) environments remains largely unknown. Here we use a marine ecosystem model to study the effect of phytoplankton functional diversity on both ecosystem productivity and its stability for seasonally variable nutrient supply and temperature. Functional diversity ranges from low to high along these two environmental axes independently. Changes in diversity are obtained by varying the range of uptake strategies and thermal preferences of the species present in the community. Species can range from resource gleaners to opportunists, and from cold to warm thermal preferences. The phytoplankton communities self-assemble as a result of species selection by resource competition (nutrients) and environmental filtering (temperature). Both processes lead to species asynchrony but their effect on productivity and stability differ. We find that the diversity of temperature niches has a strong and direct positive effect on productivity and stability due to species complementarity, while the diversity of uptake strategies has a weak and indirect positive effect due to sampling probability. These results show that more functionally diverse phytoplankton communities lead to higher and more stable ecosystem productivity but the positive effect of biodiversity on ecosystem functioning depends critically on the type of environmental gradient.

How might climate change affect the acidification of the world’s oceans or air quality in China and india in the coming decades, and what climate policies could be effective in minimizing such impacts? To answer such questions, decision-makers routinely rely on science-based projections of physical and economic impacts of climate change on selected regions and economic sectors. But the projections they obtain may not be as reliable or useful as they appear: today’s gold standard for climate impact assessments—model intercomparison projects (MIPs)—fall short in many ways.

MIPs, which use detailed climate and impact models to assess environmental and economic effects of different climate-change scenarios, require international coordination among multiple research groups, and use a rigid modeling structure with a fixed set of climate-change scenarios. This highly dispersed, inflexible modeling approach makes it difficult to produce consistent and timely climate impact assessments under changing economic and environmental policies. In addition, MIPs focus on a single economic sector at a time and do not represent feedbacks among sectors, thus degrading their ability to produce accurate projections of climate impacts and meaningful comparisons of those impacts across multiple sectors.

To overcome these drawbacks, researchers at the MIT Joint Program on the Science and Policy of Global Change propose an alternative method that only a handful of other groups are now pursuing: a self-consistent modeling framework to assess climate impacts across multiple regions and sectors. They describe the Joint Program’s implementation of this method and provide illustrative examples in a new study published in Nature Communications.