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Deforestation as an anthropogenic driver of mercury pollution.

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.

Sustained growth of sulfur hexafluoride emissions in China inferred from atmospheric observations

Abstract: Sulfur hexafluoride (SF6) is a potent greenhouse gas. Here we use long-term atmospheric observations to determine SF6 emissions from China between 2011 and 2021, which are used to evaluate the Chinese national SF6 emission inventory and to better understand the global SF6 budget. SF6 emissions in China substantially increased from 2.6 (2.3-2.7, 68% uncertainty) Gg yr−1 in 2011 to 5.1 (4.8-5.4) Gg yr−1 in 2021.

The increase from China is larger than the global total emissions rise, implying that it has offset falling emissions from other countries. Emissions in the less-populated western regions of China, which have potentially not been well quantified in previous measurement-based estimates, contribute significantly to the national SF6 emissions, likely due to substantial power generation and transmission in that area. The CO2-eq emissions of SF6 in China in 2021 were 125 (117-132) million tonnes (Mt), comparable to the national total CO2 emissions of several countries such as the Netherlands or Nigeria.

The increasing SF6 emissions offset some of the CO2 reductions achieved through transitioning to renewable energy in the power industry, and might hinder progress towards achieving China’s goal of carbon neutrality by 2060 if no concrete control measures are implemented.

Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions

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.

Assessing Compounding Climate-Related Stresses and Development Pathways on the Power Sector in the Central U.S.

Abstract: Future configurations of the power system in the central region of the U.S. are dependent on relative costs of alternative power generation technologies, energy and environmental policies, and multiple climate-induced stresses. Higher demand in the summer months combined with compounding supply shocks in several power generation technologies can potentially cause a “perfect storm” leading to failure of the power system. Potential future climate stress must be incorporated in investment
decisions and energy system planning and operation.

We assess how projected future climate impacts on the power system would affect alternative pathways for the electricity sector considering a broad range of generation technologies and changes in demand. We calculate a “potential supply gap” metric for each pathway, system component, and sub-region of the U.S. Heartland due to climate-induced effects on electricity demand and power generation. Potential supply gaps range from 5% in the North Central region under mild changes in climate to 21% in the Lakes-Mid Atlantic region under more severe climate change.

We find increases in electricity demand to be more important in determining the size of the potential supply gap than stresses on power generation, while larger shares of renewables in the power system contribute to lower supply gaps. Our results provide a first step toward considering systemic climate impacts that may require changes in managing the grid or on potential additional  capacity/reserves that may be needed.

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