Natural Ecosystems

Abstract: Phytoplankton exhibit diverse physiological responses to temperature which influence their fitness in the environment and consequently alter their community structure. Here, we explored the sensitivity of phytoplankton community structure to thermal response parameterization in a modelled marine phytoplankton community. Using published empirical data, we evaluated the maximum thermal growth rates (μmax) and temperature coefficients (Q10; the rate at which growth scales with temperature) of six key Phytoplankton Functional Types (PFTs): coccolithophores, cyanobacteria, diatoms, diazotrophs, dinoflagellates, and green algae. Following three well-documented methods, PFTs were either assumed to have (1) the same μmax and the same Q10 (as in to Eppley, 1972), (2) a unique μmax but the same Q10 (similar to Kremer et al., 2017), or (3) a unique μmax and a unique Q10 (following Anderson et al., 2021). These trait values were then implemented within the Massachusetts Institute of Technology biogeochemistry and ecosystem model (called Darwin) for each PFT under a control and climate change scenario.

Our results suggest that applying a μmax and Q10 universally across PFTs (as in Eppley, 1972) leads to unrealistic phytoplankton communities, which lack diatoms globally. Additionally, we find that accounting for differences in the Q10 between PFTs can significantly impact each PFT's competitive ability, especially at high latitudes, leading to altered modeled phytoplankton community structures in our control and climate change simulations. This then impacts estimates of biogeochemical processes, with, for example, estimates of export production varying by ~10% in the Southern Ocean depending on the parameterization.

Our results indicate that the diversity of thermal response traits in phytoplankton not only shape community composition in the historical and future, warmer ocean, but that these traits have significant feedbacks on global biogeochemical cycles.

Abstract: The combination of taxa and size classes of phytoplankton that coexist at any location affects the structure of the marine food web and the magnitude of carbon fluxes to the deep ocean. But what controls the patterns of this community structure across environmental gradients remains unclear.

Here, we focus on the North East Pacific Transition Zone, a ~ 10° region of latitude straddling warm, nutrient-poor subtropical and cold, nutrient-rich subpolar gyres. Data from three cruises to the region revealed intricate patterns of phytoplankton community structure: poleward increases in the number of cell size classes; increasing biomass of picoeukaryotes and diatoms; decreases in diazotrophs and Prochlorococcus; and both increases and decreases in Synechococcus. These patterns can only be partially explained by existing theories.

Using data, theory, and numerical simulations, we show that the patterns of plankton distributions across the transition zone are the result of gradients in nutrient supply rates, which control a range of complex biotic interactions. We examine how interactions such as size-specific grazing, multiple trophic strategies, shared grazing between several phytoplankton size classes and heterotrophic bacteria, and competition for multiple resources can individually explain aspects of the observed community structure. However, it is the combination of all these interactions together that is needed to explain the bulk compositional patterns in phytoplankton across the North East Pacific Transition Zone. The synthesis of multiple mechanisms is essential for us to begin to understand the shaping of community structure over large environmental gradients.

Abstract: Deforestation reduces the capacity of the terrestrial biosphere to take up toxic pollutant mercury (Hg) and enhances the release of secondary Hg from soils. The consequences of deforestation for Hg cycling are not currently considered by anthropogenic emission inventories or specifically addressed under the global Minamata Convention on Mercury.

Using global Hg modeling constrained by field observations, we estimate that net Hg fluxes to the atmosphere due to deforestation are 217 Mg year–1 (95% confidence interval (CI): 134–1650 Mg year–1) for 2015, approximately 10% of global primary anthropogenic emissions. If deforestation of the Amazon rainforest continues at business-as-usual rates, net Hg emissions from the region will increase by 153 Mg year–1 by 2050 (CI: 97–418 Mg year–1), enhancing the transport and subsequent deposition of Hg to aquatic ecosystems. Substantial Hg emissions reductions are found for two potential cases of land use policies: conservation of the Amazon rainforest (92 Mg year–1, 95% CI: 59–234 Mg year–1) and global reforestation (98 Mg year–1, 95% CI: 64–449 Mg year–1).

We conclude that deforestation-related emissions should be incorporated as an anthropogenic source in Hg inventories and that land use policy could be leveraged to address global Hg pollution.

Abstract: Methane (CH4) is the second most critical greenhouse gas after carbon dioxide, contributing to 16-25% of the observed atmospheric warming. Wetlands are the primary natural source of methane emissions globally. However, wetland methane emission estimates from biogeochemistry models contain considerable uncertainty. One of the main sources of this uncertainty arises from the numerous uncertain model parameters within various physical, biological, and chemical processes that influence methane production, oxidation, and transport. Sensitivity Analysis (SA) can help identify critical parameters for methane emission and achieve reduced biases and uncertainties in future projections. This study performs SA for 19 selected parameters responsible for critical biogeochemical processes in the methane module of the Energy Exascale Earth System Model (E3SM) land model (ELM). The impact of these parameters on various CH4 fluxes is examined at 14 FLUXNET- CH4 sites with diverse vegetation types. Given the extensive number of model simulations needed for global variance-based SA, we employ a machine learning (ML) algorithm to emulate the complex behavior of ELM methane biogeochemistry. ML enables the computational time to be shortened significantly from 6 CPU hours to 0.72 milliseconds, achieving reduced computational costs. We found that parameters linked to CH4 production and diffusion generally present the highest sensitivities despite apparent seasonal variation. Comparing simulated emissions from perturbed parameter sets against FLUXNET-CH4 observations revealed that better performances can be achieved at each site compared to the default parameter values. This presents a scope for further improving simulated emissions using parameter calibration with advanced optimization techniques like Bayesian optimization.

Abstract: Phytoplankton exhibit diverse physiological responses to temperature which influence their fitness in the environment and consequently alter their community structure. Here, we explored the sensitivity of phytoplankton community structure to thermal response parameterization in a modelled marine phytoplankton community. Using published empirical data, we evaluated the maximum thermal growth rates (µmax) and temperature coefficients (Q10; the rate at which growth scales with temperature) of six key Phytoplankton Functional Types (PFTs): coccolithophores, cyanobacteria, diatoms, diazotrophs, dinoflagellates, and green algae. Following three well-documented methods, PFTs were either assumed to have (1) the same µmax and the same Q10 (as in to Eppley, 1972) (2) a unique µmax but the same Q10 (similar to Kremer et al. 2017) or (3) a unique µmax and a unique Q10 (following Anderson et al. 2021). These trait values were then implemented within the MIT biogeochemistry and ecosystem model (called Darwin) for each PFT under a control and climate change scenario.

Our results suggest that applying a µmax and Q10 universally across PFTs (as in Eppley, 1972) leads to unrealistic phytoplankton communities, which lack diatoms globally. Additionally, we find that accounting for differences in the Q10 between PFTs can significantly impact each PFT’s competitive ability, especially at high latitudes, leading to altered modeled phytoplankton community structures in our control and climate change simulations. This then impacts estimates of biogeochemical processes, with, for example, estimates of export production varying by ~10% in the Southern Ocean depending on the parameterization.

Our results indicate that the diversity of thermal response traits in phytoplankton not only shape community composition in the contemporary and future, warmer ocean, but that these traits have significant feedbacks on global biogeochemical cycles.
 

Duration

Two years

Motivation

• Under a global, low-carbon economy driven by hydrogen-based energy technologies, leakages at unprecedented scales are inevitable.

• Atmospheric H2 is largely controlled naturally by global soil sinks. The secondary H2 sink is reaction with atmospheric hydroxyl radical (OH).

• Soil micro-biotic & geophysical processes have nonlinear effects on H2 uptake controlled by temperature and moisture. These controls can weaken future soil H2 consumption under climate change.

Abstract: We present a self-consistent, large ensemble, high-resolution global dataset of long-term future climate, which accounts for the uncertainty in climate system response to anthropogenic emissions of greenhouse gases and in geographical patterns of climate change. The dataset is developed by applying an integrated spatial disaggregation (SD) - bias-correction (BC) method to climate projections from the MIT Integrated Global System Modeling (IGSM) framework. Four emissions scenarios are considered that represent energy and environmental policies and commitments of potential future pathways, namely, Reference, Paris Forever, Paris 2°C and Paris 1.5°C. The dataset contains nine key meteorological variables on a monthly scale from 2021 to 2100 at a spatial resolution of 0.5°x 0.5°, including precipitation, air temperature (mean, minimum and maximum), near-surface wind speed, shortwave and longwave radiation, specific humidity, and relative humidity.

We demonstrate the dataset’s ability to represent climate-change responses across various regions of the globe.

This dataset can be used to support regional-scale climate-related impact assessments of risk across different applications that include hydropower, water resources, ecosystem, agriculture, and sustainable development.

Pages

Subscribe to Natural Ecosystems