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.

Examination of historical simulations from CMIP6 models shows substantial pre-industrial to present-day changes in ocean heat (ΔH), salinity (ΔS), oxygen (ΔO2), dissolved inorganic carbon (ΔDIC), chlorofluorocarbon-12 (ΔCFC12), and sulfur hexafluoride (ΔSF6). The spatial structure of the changes and the consistency among models differ among tracers: ΔDIC, ΔCFC12, and ΔSF6 all are largest near the surface, are positive throughout the thermocline with weak changes below, and there is good agreement amongst the models. In contrast, the largest ΔH, ΔS, and ΔO2 are not necessarily at the surface, their sign varies within the thermocline, and there are large differences among models. These differences between the two groups of tracers are linked to climate-driven changes in the ocean transport, with this tracer “redistribution” playing a significant role in changes in ΔH, ΔS, and ΔO2 but not the other tracers. Tracer redistribution is prominent in the southern subtropics, in a region where apparent oxygen utilization and ideal age indicate increased ventilation time scales. The tracer changes are linked to a poleward shift of the peak Southern Hemisphere westerly winds, which causes a similar shift of the subtropical gyres, and anomalous upwelling in the subtropics. This wind - tracer connection is also suggested to be a factor in the large model spread in some tracers, as there is also a large model spread in wind trends. A similar multi-tracer analysis of observations could provide insights into the relative role of the addition and redistribution of tracers in the ocean.

The balance between particulate inorganic carbon (PIC) and particulate organic carbon (POC) holds significant importance in carbon storage within the ocean. A recent investigation delved into the spatial distribution of phytoplankton and the physiological mechanisms governing their growth. Employing random forests, a machine learning technique, this study unveiled apparent relationships between POC and 10 environmental fields. In this work, we extend the use of random forests to compare how observed PIC and POC respond to environmental conditions. Our findings indicate that while both exhibit similar responses to certain environmental drivers, PIC is less sensitive to iron and more sensitive to light. Intriguingly, both PIC and POC display reduced sensitivity to CO2, contrary to previous studies, possibly due to the elevated pCO2 in our dataset. This research sheds light on the underlying processes influencing carbon sequestration and ocean productivity.

Excessive nutrient loading is a well-established driver of hypoxia in aquatic ecosystems. However, recent limnological research has illuminated the role of Chromophoric Dissolved Organic Matter (CDOM) in exacerbating hypoxic conditions, particularly in freshwater lakes. In coastal ocean environments, the influence of CDOM on hypoxia remains an underexplored area of investigation. This study seeks to elucidate the intricate relationship between CDOM and hypoxia by employing a nitrogen-based model within the context of Chesapeake Bay, a large estuary with unique characteristics including salinity stratification and the localization of hypoxia/anoxia in a 30-meter-deep channel aligned with the estuary’s primary stem. Our findings indicate that the impact of CDOM on nutrient dynamics and productivity varies significantly across different regions of Chesapeake Bay. In the upper Bay, the removal of CDOM reduces light limitation, thus promoting increased productivity, resulting in the generation of more detritus and burial, which, in turn, contributes to elevated levels of hypoxia. As we transition to the middle and lower Bay, the removal of CDOM can cause a decline in integrated primary productivity due to nutrient uptake in the upper Bay. This decrease in productivity is associated with reduced burial and denitrification, ultimately leading to a decrease in hypoxia levels. Streamflow modulates this impact. The time integral of the hypoxic volume during low-flow years is particularly sensitive to CDOM removal, while in high-flow years, it is relatively unchanged. This research underscores the necessity for a comprehensive understanding of the intricate interactions between CDOM and hypoxia in coastal ecosystems.

As phytoplankton form the base of the marine food web, understanding the controls on their abundance is fundamental to understanding marine ecology and how it might be altered by global climate change. While many Earth System Models (ESMs) predict phytoplankton biomass, it is unclear whether they properly capture the mechanistic relationships that control this quantity in the real ocean. In this paper, we used Random Forest (RF) analysis to analyze the output of ESMs and observational datasets. We gathered information from 13 ESMs and two observational datasets. The target variable was phytoplankton carbon and the predictors included environmental parameters known to influence phytoplankton, such as nutrients, light, mixed layer depth, salinity, temperature, and upwelling. We examined three questions: (1) What fractions of variability in ESMs and observations can be linked to the large-scale environmental variables simulated by ESMs? (2) What are the dominant predictors and relationships affecting phytoplankton biomass? (3) How well do ESMs simulate phytoplankton carbon and do they simulate the relationships we see in observations? We show that about 88% to 96% of the variability in observational datasets and greater than 98% in the ESMs was accounted for by variables known to influence phytoplankton biomass from large-scale environmental variables. The dominant predictors in the observational datasets were dissolved iron and shortwave radiation. The dominant predictors in the ESMs were dissolved iron, shortwave radiation, and mixed layer depth. While relationships in most of the ESMs matched the general trends seen in the observations, significant quantitative differences were seen. While the assumption made by ESMs that large-scale environmental conditions control phytoplankton biomass appears to hold in the real world, much work remains to be done to ensure that ESMs properly represent these controls.

A number of models have been developed to simulate hypoxia in the Chesapeake Bay, but these models do not agree on what processes must be included. In this study we implemented a previously published biogeochemical (BGC) code developed for open-ocean waters that includes “cryptic” microbial sulfur cycling, a process that can increase denitrification and anammox rates in anoxic waters. We ran this BGC code within the ChesROMS physical model of the Chesapeake Bay, then compared the results to those of a ChesROMS simulation with an estuarine BGC code previously implemented and calibrated in the Bay. The estuarine BGC code neglects sulfur cycling but includes burial of particulate organic matter (POM) and cycling of dissolved organic matter (DOM) and uses different values for many parameters governing phytoplankton growth and particle dynamics. At a key test site (the Bay Bridge Station), the model with sulfur cycling gives better results for oxygen and nitrate. However, it also gives a worse overprediction of ammonium-suggesting that its greater accuracy in predicting these two variables may result from cancellation of errors. By making comparisons among these two models and derivatives of them, we show that the differences in modeled oxygen and ammonium are largely due to whether or not the BGC codes include cycling of DOM and sedimentary burial of POM, while the differences in modeled nitrate are due to the other differences in the modeled biogeochemical processes (sulfur cycling/anammox/optics). Changes in parameters used in both BGC codes (in particular particle sinking velocities) tended to compensate the other differences. Predictions of hydrogen sulfide (H 2 S) within the Bay are very sensitive to the details of the simulation, suggesting that it could be a useful diagnostic.