Earth Systems

Using Quantile Regression to Detect Regional and Global Signals in Atmospheric Mercury Trends

Abstract: Mercury (Hg) is a neurotoxic contaminant that bioaccumulates in the marine food chain. 137 countries are now parties to the Minamata Convention on Mercury, which aims to combat growing risks of Hg pollution to human health and the environment. Atmospheric Hg trends over time serve as important indicators in evaluating the effectiveness of the Minamata Convention. However, there are several challenges associated with interpreting observed atmospheric Hg time series, including: data gaps, few long-term (>10 years) time series, analytical uncertainties in the measurements, meteorological variability, and the representativeness of measurement sites for broader spatial scales. Novel statistical techniques, including generalized additive models, dynamic linear modelling, and meteorological ensembles, have shown potential in recent atmospheric trends studies for overcoming these challenges; however, many of these promising techniques have yet to be applied to Hg time series. Harnessing such state-of-the-art statistical approaches, we analyze atmospheric Hg measurements for 1990–present in a regression-based framework to produce more accurate assessments of trends and their uncertainties. We apply quantile regression to analyze difference in Hg trends from the 5th–95thpercentiles. We hypothesize that lower percentile (e.g., 5th) trends are more indicative of trends in background Hg on the hemispheric scale, whereas higher percentile trends (e.g., 95th) are more indicative of local or regional emission changes. Indeed, observed and simulated trends from North American sites generally show strongly decreasing 95th percentile trends and stagnant 5th percentile trends, illustrating that regional emissions have decreased while hemispheric trends have stayed stagnant or increased. Using companion simulations in the global atmospheric Hg model GEOS-Chem, we analyze the sensitivity of available Hg measurement time series to trends in anthropogenic Hg emissions. The combined model–observation analysis can aid the Minamata Convention effectiveness evaluation in disentangling the anthropogenic contribution to recent Hg trends.

Economic Evaluation of Water Resources Projects under Climate Change Uncertainty: Demonstration for Investment in Lesotho

Abstract: The principles of Cost Benefit Analysis (CBA) are based on assumptions of perfect information on costs, benefits and the projection of costs and benefits in the future. In practice, these conditions do not hold, especially in the case of investment in developing countries with naturally high climatic variability, political instability, and rapid changes in demographic characteristics. This paper explores uncertainty in the financial analysis of environmental engineering projects under climate change via a bottom-up approach to economic evaluation. Using a case study of the Metolong Dam in Lesotho, which supplements water supply to a textile factory, uncertainty in climate-informed economic evaluation is explored by estimating the number of factory workers from water availability. For the project timeline of 30 years, temperature is not expected to rise more than 2 ⁰C over historical values, and precipitation changes are estimated to be within ±10% of current annual totals, so detrimental effects associated with water budgets from the hydrologic cycle may not be a significant threat near-term. However, uncertainty in water allocation rates and reservoir release which proved consequential when combined with climate change were discovered. After vulnerability analysis, the project was found to be robust to only 9.6% of the considered future scenarios. Overall, the project is judged to have minimal climate risk but high uncertainty, so flexible adaptation strategies that provide incremental robustness to the project are recommended to avoid infrastructure redundancy and waste of resources. By demonstrating the sensitivity of the economic rate of return (ERR) to uncertainty, defining project robustness using a robustness index and proposing alternative adaptation options, this study contributes to on-going efforts towards improved rational decision making under climate uncertainty.

Impacts of future climate change on global food production: perspectives from model simulations of multiple emission scenarios

Abstract: Climate change significantly impacts crop growth and production, which may undermine the resilience of global food systems. Predicting long-term dynamics of future food production under different emissions scenarios is critical for ensuing global food security. However, large variations exist in current food production projections due to uncertainties in future climate projections, their coarse resolutions, and the missing of several important processes affecting crop yield in current crop models. Here, we predicted future changes in the yields of major crops until the end of the 21st century under four emission scenarios (including Reference, Paris Forever, Paris 2°C and Paris 1.5°C), using the Dynamic Land Ecosystem Model (DLEM) as driven by the newly developed ensembles of high-resolution future climate projections (with a spatial resolution of 0.5°x 0.5°). The DLEM includes mechanistic representations of dynamic crop growth processes and agricultural management practices as affected by multiple environmental stresses, and its performance in simulating the yields of major crops has been validated at multiple scales from site to global. The developed ensembles of future climate projections were constructed with the combination of large ensembles of the MIT Integrated Global system Modeling (IGSM) zonal climate projections and multiple climate models participated in Coupled Model Intercomparison Project Phases 6 (CMIP6). Our results indicate that the projected food production varies largely both among different future scenarios and among different climate projections of the same scenario. Our study assesses the impacts of future climate change on global food systems from a risk-based perspective and quantifies the uncertainties, which have important implications for future agricultural climate change adaptation measures.

Health impacts of smoke exposure in South America: Increased risk for populations in the Amazonian Indigenous territories

Abstract: Smoke particulate matter emitted by fires in the Amazon Basin poses a threat to human health. Past research on this threat has mainly focused on the health impacts on countries as a whole or has relied on hospital admission data to quantify the health response. Such analyses do not capture the impact on people living in Indigenous territories close to the fires and who often lack access to medical care and may not show up at hospitals. Here we quantify the premature mortality due to smoke exposure of people living in Indigenous territories across the Amazon Basin. We use the atmospheric chemistry transport model GEOS-Chem to simulate PM2.5 from fires and other sources, and we apply the latest epidemiological data to estimate the effects on public health. We estimate that smoke from fires in South America accounted for ~12,000 premature deaths each year from 2014-2019 across the continent, with about ~230 of these deaths occurring in Indigenous lands. Put another way, smoke exposure accounts for 2 premature deaths per 100,000 people per year across South America, but 4 premature deaths per 100,000 people in the Indigenous territories. However, Bolivia and Brazil are hotspots and deaths in indigenous territories in these countries are 9 and 12 per 100,000 people, respectively. Our analysis shows that smoke PM2.5 from fires has a detrimental effect on human health across South America, with a disproportionate impact on people living in Indigenous territories.

Background nitrogen dioxide (NO2) over the United States and its implications for satellite observations and trends: roles of aircraft, wildfires, and nitrate photolysis

Abstract: Tropospheric nitrogen dioxide (NO2) measured from satellites has been widely used to track anthropogenic NOx emissions, but its retrieval and interpretation can be complicated by the free tropospheric background to which satellite measurements are particularly sensitive. Observations from the OMI satellite instrument over the contiguous US (CONUS) shows no trend after 2009, despite sustained decreases in anthropogenic NOx emissions, implying an important and rising contribution from the free tropospheric background. Here we use the GEOS-Chem chemical transport model applied to simulation of OMI NO2 to better understand the sources and trends of background NOx over CONUS. Previous model underestimate of the background is largely corrected by the consideration of aerosol nitrate photolysis and by using a new aircraft emission inventory. The increase in aircraft emissions over the past decades not only increases the background NO2 but also affects the satellite retrieval by altering the NO2 vertical profile. Increasing wildfire emissions also contributed to the post-2009 increase in the NO2 background over the western US.

Advances in Simulating the Global Spatial Heterogeneity of Air Quality and Source Sector Contributions: Insights into the Global South

Abstract: High-resolution simulations are essential to resolve fine-scale air pollution patterns due to localized emissions, nonlinear chemical feedbacks, and complex meteorology. However, high-resolution global simulations of air quality remain rare, especially of the Global South. Here, we exploit recent developments to the GEOS-Chem community model in its high performance implementation (GCHP) to conduct one-year simulations in 2015 at cubed-sphere C360 (~ 25km) and C48 (~ 200km) resolutions. We investigate the resolution dependence of population exposure and sectoral contributions to surface PM2.5 and NO2 focusing on understudied regions. Our results indicate pronounced spatial heterogeneity with global mean population-weighted normalized root mean square error (PW-NRMSE) at C48 of for primary (50% - 105%) and secondary (26% - 36%) PM2.5 species. Under-represented regions are more sensitive to spatial resolution resulting from sparse pollution hotspots, with PW-NRMSE for PM2.5 in the Global South (34%) 1.3 times higher than globally (25%). The spatial heterogeneity in southern cities (50%) is substantially higher than the more typically clustered northern cities (27%). High-resolution simulations also change the relative importance of emission sectors for both black carbon and NO2 in the Global South. Overall, spatial gradients of population exposure and sectoral contributions are artificially reduced with coarse simulations, especially in the Global South.

Quantifying the impact of terrestrial carbon and nutrient discharge on the global-ocean carbon cycle over seasonal-to-interannual timescales

Abstract: Large amounts of carbon and nutrients are delivered to the coastal and pelagic ocean by the Land-Ocean Aquatic Continuum (LOAC). The LOAC refers to the major biogeochemical pathway whereby river and groundwater discharge is connected to coastal systems. Terrestrial loads fluxed through the LOAC system likely play a key role in the carbon cycle of the global ocean. For example, riverine carbon export may be responsible for global-ocean outgassing of roughly 0.45 Pg C yr-1. While the significance of terrestrial exports is commonly accepted in the community, quantification of these fluxes over seasonal-to-interannual timescales is still lacking. To address this deficiency, we parameterize contemporary terrestrial carbon and nutrient export in the ECCO-Darwin global-ocean biogeochemistry state estimate and evaluate the subsequent sensitivity of air-sea CO2 fluxes at regional and global scales from 1995 to 2017. We compute daily riverine export by combining the GlobalNEWS2.0 watershed model with point-source freshwater discharge from the JRA55-do atmospheric reanalysis. Additionally, we derive carbon exports from coastal wetlands (i.e., mangroves and marshes) from ecosystem primary production and the associated soil organic carbon. We evaluate our simulated ocean biogeochemistry and air-sea CO2 fluxes using in-situ and remotely-sensed observations in the coastal ocean. We quantify the impact of terrestrial exports to the global ocean by comparing our new simulation with a baseline simulation that does not include terrestrial carbon and nutrient export. Our study highlights the importance of improving the representation of terrestrial fluxes in global-ocean biogeochemistry models for the accurate simulation of ocean carbon cycling, biogeochemistry, and ecology.

Increase in global and East Asia Nitrogen trifluoride (NF3) emissions inferred from atmospheric observations

Abstract: Nitrogen trifluoride (NF3) is a very powerful long-lived greenhouse gas (GHG), with a global warming potential on a 100-year timescale of ∼16,600. NF3 is widely used in the manufacture of semiconductors, photovoltaic (PV) cells, and flat panel displays. Here we investigate global and regional NF3 emission rates in East Asia, using atmospheric observations from five AGAGE background monitoring stations (Mace Head, Ireland Trinidad Head, California, Ragged Point, Barbados, Cape Grim, Tasmania, Cape Matatula, Samoa) and Gosan, South Korea combined with an inverse modeling approach based on the global 3-D atmospheric chemical transport model (GEOS-Chem). We find that global NF3 emissions have grown from 1.73 0.13 Gg yr-1 ( one standard deviation) in 2014 to 2.91 0.23 Gg yr-1 in 2020, with an average annual increase of 8% yr-1. This rise in global emission is mainly attributable to East Asia (South China, Northeast China, Japan, and South Korea), where emissions increased from 1.4 0.86 Gg yr-1 to 1.49 Gg yr-1. Due to increasing demand for electronic device manufacture, especially flat panel displays, NF3 emissions are expected to increase further in the future.

The Climate Consensus Network - Creating Capacity for Climate Outreach Within our Universities

Abstract: Many scientists desire to create a more informed public on the topic of anthropogenic climate change but have limited time and energy to contribute to this cause. The Climate Consensus is a new 501(c)(3) nonprofit organization that seeks to create capacity for such work through scholarship and stipend awards, and by creating a formal network across academic institutions and departments for resource sharing. We are a group of concerned students, faculty, and staff from 12 teaching- and research-focused universities who are promoting a culture shift toward the prioritization of public outreach on the topic of climate change. We are encouraging upcoming and established scientists to engage in dialog with their local communities while garnering philanthropic and grant support for these efforts. By building capacity for the next generation of scientists to speak up for science and our future, we have an immense opportunity to shape public opinion on this topic and to motivate real action.

Sensitivity Analysis and Uncertainty Quantification of E3SM Methane model using Machine Learning

Abstract: Methane (CH4) is the second most important Greenhouse gas after carbon dioxide, accounting for 16-25% of atmospheric warming to date. However, large uncertainty exists in methane emissions estimates using biogeochemistry models. This uncertainty arises largely because CH4 dynamics depend on multiple physical, biological, and chemical processes and a large number of uncertain model parameters. Sensitivity analysis (SA) can help not only identify important parameters for methane emission, but also achieve reduced biases and uncertainties in future projections. In this study, SA is performed for the pre-selected critical parameters of methane biogeochemistry module within the Energy Exascale Earth System Model (E3SM) land model (ELM). Considering the large number of model simulations typically required for the variance-based SA, we employ a machine learning algorithm, namely, Gaussian process regression, to construct a surrogate model that enables emulating the behaviour of ELM methane biogeochemistry and conducting a full variance-based SA with much reduced computational costs but barely any loss in accuracy. We examine the sensitivity of CH4 emission to a large set of parameters at multiple FLUXNET-CH4 sites of different vegetation types. Our results will provide useful insights into the key parameters and processes that drive the uncertainty in methane emissions at different sites and the consistency of parametric uncertainties across vegetation types, soil types, climatic zones, and seasons.

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