Earth Systems

Spatially Resolved Temperature Response Functions to CO2 Emissions

Abstract

Carbon dioxide (CO2) emissions affect local temperature; quantifying that local response is important for learning about the earth system, the impacts of mitigation, and adaptation needs. We assume the climate system can be represented as a time-dependent linear system, diagnosing Green's Functions for the spatial temperature response to CO2 emissions based on CMIP6 earth system models. This allows us to emulate the linear component of the temperature response to CO2. This approach is sufficient to capture the spatial temperature response of CMIP6 experiments within one standard deviation of the multimodel spread across most regions, though accuracy is lower in the Southern Ocean and the Arctic. Our approach reveals where nonlinear feedbacks are important in current CMIP6 models, and where the local system response is well represented by a time-dependent linear differential operator. It incorporates emissions path dependency and may be useful for evaluating large ensembles of emission scenarios.

Key Points

 

  • With a Green's Function approach, we emulate the linear component of the spatially resolved temperature response to CO2 emissions

  • We reproduce the temperature response well within multi-model uncertainty except in the Arctic and Southern Ocean

  • This approach allows expedient quantification of the spatial and temporal temperature response to varying CO2 emissions pathways

 

Plain Language Summary

Carbon dioxide (CO2) emissions impact surface temperature. It is well established that the global mean temperature change is proportional to the cumulative emissions of CO2. This has led to the creation of carbon budgets to reach temperature goals. We test this relationship at the spatio-temporal scale, quantifying a simple approach that estimates the local temperature response to CO2 emissions alone. We use an approach built from the Climate Model Intercomparison Project Phase 6 (CMIP6) Earth System Models, based on the concept that an additional unit of CO2 can be scaled for larger emissions and summed over time to estimate cumulative impacts. We evaluate this with additional CMIP6 experiments, showing that this approach captures the temperature response in most locations with lower accuracy in the Arctic and Southern Ocean. This type of approach may be useful to evaluate many policy scenarios and to better understand earth system processes that are represented in the models, as it takes around one second to quantify 90 years' worth of temperature change on a local computer, while Earth System Models can require weeks of runtime on supercomputers.

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

Key Points
• Identified five key sensitive parameters for methane emissions using the Sobol sensitivity analysis method.
• Parameters linked to production and diffusion present the highest sensitivities despite apparent seasonal variation.
• Fourteen out of nineteen model parameters exert negligible influence on methane emissions.

Plain Language Summary
Methane is a critical greenhouse gas, and wetlands are the largest natural source of it. Accurately predicting methane emissions from wetlands is key to tackling climate change. But these predictions, made through computer models, are seldom spot-on. Why? Because there are many factors in the models that lead to uncertain predictions. A major source of this uncertainty arises from the empirical model parameters. Just as tuning a radio dial ensures clear reception, models need properly adjusted parameters for accurate predictions. 

A sensitivity analysis was performed to determine which parameters are most crucial for accurate predictions. Instead of running the complex numerical model every time, machine learning was employed to create a faster and simpler version.

Using this approach, five parameters were pinpointed as particularly sensitive, significantly impacting the predictions. The comparison of model-predicted methane emissions with real-world measurements showed that the model performed well in some cases but needed tweaking in others. Refining these sensitive parameters with more real-world observations could make better predictions in the future.

Abstract
Methane (CH4) is globally 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.

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.

Rising Perfluorocyclobutane (PFC-318, cC4F8) Emissions in China from 2011 to 2020 Inferred from Atmospheric Observations

Abstract: Global atmospheric emissions of perfluorocyclobutane (c-C4F8, PFC-318), a potent greenhouse gas, have increased rapidly in recent years.

Combining atmospheric observations made at nine Chinese sites with a Lagrangian dispersion model-based Bayesian inversion technique, we show that PFC-318 emissions in China grew by approximately 70% from 2011 to 2020, rising from 0.65 (0.54−0.72) Gg year−1 in 2011 to 1.12 (1.05−1.19) Gg year−1 in 2020. The PFC-318 emission increase from China played a substantial role in the overall increase in global emissions during the study period, contributing 58% to the global total emission increase. This growth predominantly originated in eastern China. The regions with high emissions of PFC-318 in China overlap with areas densely populated with polytetrafluoroethylene (PTFE) factories, implying that fluoropolymer factories are important sources of PFC-318 emissions in China.

Our investigation reveals an emission factor of approximately 3.02 g of byproduct PFC-318 emissions per kilogram of hydrochlorofluorocarbon-22 (HCFC-22) feedstock use in the production of tetrafluoroethylene (TFE) (for PTFE production) and hexafluoropropylene (HFP) if we assume all HCFC-22 produced for feedstock uses in China are pyrolyzed to produce PTFE and HFP. Further facility-level sampling and analysis are needed for a more precise evaluation of emissions from these factories.

Substantial increase in perfluorocarbons CF4 (PFC-14) and C2F6 (PFC-116) emissions in China

Abstract: The perfluorocarbons tetrafluoromethane (CF4, PFC-14) and hexafluoroethane (C2F6, PFC-116) are potent greenhouse gases with near-permanent atmospheric lifetimes relative to human timescales, and global warming potentials thousands of times that of  CO2.

Using long-term atmospheric observations from a Chinese network and an inverse modelling approach (top-down method), we determined that CF4 emissions in China increased from 4.7 (4.2-5.0, 68% uncertainty interval) Gg yr-1 in 2012 to 8.3 (7.7-8.9) Gg yr-1 in 2021, and C2F6 emissions in China increased from 0.74 (0.66-0.80) Gg yr-1 in 2011 to 1.32 (1.24-1.40) Gg yr-1 in 2021, both increasing by approximately 78%. Combined emissions of CF4 and C2F6 in China reached 78 Mt CO2-eq in 2021. The absolute increase in emissions of each substance in China between 2011-2012 and 2017-2020 was similar to (for CF4), or greater than (for C2F6), the respective absolute increase in global emissions over the same period.  Substantial CF4 and C2F6 emissions were identified in the less populated western regions of China, probably due to emissions from the expanding aluminum industry in these resource-intensive regions. It is likely that the aluminum industry dominates CF4 emissions in China, while the aluminum and semiconductor industries both contribute to C2F6 emissions.

Based on atmospheric observations, this study validates the emission magnitudes reported in national bottom-up inventories and provides insights into detailed spatial distributions and emission sources beyond what is reported in national bottom-up inventories.

Significance: We investigate the emissions of two potent greenhouse gases, tetrafluoromethane (CF4, PFC-14) and hexafluoroethane (C2F6, PFC-116), in China.

Based on atmospheric observations within China, we report substantial increases in CF4 and C2F6 emissions in China over the last decade. These increases in national emissions are sufficient to explain the entire increases in global emissions over the same period. We suggest that substantial CF4 and C2F6 emissions could be due to by-product emissions from the aluminum industry in the less populated and less economically developed western regions in China.

The findings highlight the importance of mitigating CF4 and C2F6 emissions in China and provide guidance for directing mitigation strategies towards specific regions and/or industries.

Climate and Air Quality Impact of Using Ammonia as an Alternative Shipping Fuel

Abstract: As carbon-free fuel, ammonia has been proposed as an alternative fuel to facilitate maritime decarbonization. Deployment of ammonia-powered ships is proposed as soon as 2024. However, NOx, NH3 and N2O from ammonia combustion could impact air quality and climate. In this study, we assess whether and under what conditions switching to ammonia fuel might affect climate and air quality. We use a bottom–up approach combining ammonia engine experiment results and ship track data to estimate global tailpipe NOx, NH3 and N2O emissions from ammonia-powered ships with two possible engine technologies (NH3–H2 (high NOx, low NH3 emissions) vs pure NH3 (low NOx, very high NH3 emissions) combustion) under three emission regulation scenarios (with corresponding assumptions in emission control technologies), and simulate their air quality impacts using GEOS–Chem High Performance global chemical transport model.

We find that the tailpipe N2O emissions from ammonia-powered ships have climate impacts equivalent to 5.8% of current shipping CO2 emissions. Globally, switching to NH3–H2 engines avoids 16,900 mortalities from PM2.5 and 16,200 mortalities from O3 annually, while the unburnt NH3 emissions (82.0 Tg NH3 yr-1) from pure NH3 engines could lead to 668,100 additional mortalities from PM2.5 annually under current legislation. Requiring NH3 scrubbing within current Emission Control Areas leads to smaller improvements in PM2.5-related mortalities (22,100 avoided mortalities for NH3–H2 and 623,900 additional mortalities for pure NH3 annually), while extending both Tier III NOx standard and NH3 scrubbing requirements globally leads to larger improvement in PM2.5-related mortalities associated with a switch to ammonia-powered ships (66,500 avoided mortalities for NH3–H2 and 1,200 additional mortalities for pure NH3 annually).

Our findings suggest that while switching to ammonia fuel would reduce tailpipe greenhouse gas emissions from shipping, stringent ammonia emission control is required to mitigate the potential adverse effects on air quality.

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