JP

Quantifying the Compounding, Co-Existing Nature and Inequities of Physical and Transition Risks Across the United States

Abstract: Physical and transition risks across socio-environmental systems are becoming increasingly complex, multi-faceted, compounding, and span unjust societal landscapes. Multi-Sector Dynamics (MSD) explores the existence and extent that human and natural systems co-exist, interact, and co-evolve. To meet this need, we have developed an open-science, visualization platform that harmonizes, combines, overlays, and diagnoses landscapes of risks and inequities across socio-economics, human health, biodiversity, demographics, as well as the natural, managed, and built environmental systems. The platform’s current geographic focus allows for an MSD-inspired perspective that resolves combinatory-risk landscapes across the United States at the county level. Combinatory-risk indices from weighted composites of a variety of indicators are created and based on user specifications to areas-of-concern.

As a visual example – we demonstrate where “hotspots” of environmental risks compound. As separate mappings (Figure 1a), current risks to land, water availability and quality, and exposure to poor air quality exhibit features not discernably co-located. The resultant landscape of combinatory risk (Figure 1b) exhibits discernable, prominent “hotspots” across California, the Mississippi River basin, the Southeast, and Mid-Atlantic states. Concurrently, another combined transition-risk mapping indicates that the lower Mississippi River contains the largest portion of fossil energy employment along with high levels of poverty and unemployment. This highlights a potential connection between contrasting regional effects of a low-carbon energy transition. Other examples will demonstrate similar connections and compounding landscapes. Quantitative metrics will show the profound effect the incorporation of socio-demographics has on the “top 5 list” of states that experience the most severe compounding physical and transition risks, and underscore the importance of the choice in these metrics are for the interpretation and assessment of priorities into deep-dive analysis and actions.

Climate Extremes and the Future of the Power Sector in the U.S. Heartland: A Multi-Sector Dynamics Perspective

Abstract: A wide range of electric generation technologies can play a major role in future power production in the heartland of the U.S. for consumption. Different generation technologies have different vulnerabilities to a changing climate and its extremes. The cooling cycle of thermal power plants are vulnerable to rising summer temperatures that increase cooling water temperatures and subsequently add cost and possible curtailments. Drought could limit hydropower availability and further limit thermoelectric cooling. Photovoltaics are less efficient in higher temperatures, and wind resources may change or shift with the changing climate. Rising temperatures are also likely to increase summer peak demands as a result of more intense and broad use of air conditioning, even in currently cooler climates. In addition, high temperatures and high demand pose risks for failure of critical grid infrastructure, such as large power transformers. This combination of stressors raises important research questions: What is the risk of a “perfect storm” that could lead to a tipping point failure of the power system? Are some evolutions of the power sector more or less vulnerable to climate change?

As a preliminary investigation, we review existing word in the area and consider a range of realistic power generation scenarios in the US Heartland (Figure 1). We evaluate the sensitivities of various technologies and demand to climate change and associated extremes, and consider the possible range of changes in their production. We then examine the possible effects on the evolution of the power sector by mid-century in various scenarios, taking a Multi-Sectoral Dynamics perspective by focusing on the interaction of sectors (different supply technologies and different demand sectors) and the effects of multiple stressors (both gradual climate change and changes in extreme events) on the systems. Preliminarily, summer months are a more likely period for a potential “perfect storm,” where a combination of extreme heat, drought, and stagnant meteorological conditions could have significant negative effects on all technologies, while increasing peak power demands across the region. Our results are expected to help develop a research agenda to better resolve future vulnerabilities and suggest strategies to increase power sector resilience.

Exploring Injection Locations for Stratospheric Aerosol Geoengineering to Maximize Particle Lifetime in the Stratosphere

Abstract: Stratospheric Aerosol Injection (SAI) aims to mitigate climate change by releasing aerosols into the stratosphere to reflect incoming shortwave radiation. The radiative efficiency of SAI (i.e., the ratio of injected mass flux to radiative forcing) depends strongly on the stratospheric lifetime of injected particles. In this study, we use a Lagrangian trajectory model (LAGRANTO), modified to account for sedimentation, to analyze the sensitivity of particle lifetime to injection locations.

For the first time, we find that choosing injection longitude could notably increase particle lifetime in the stratosphere, especially for injections below 20 km. For example, the particles injected over the Indian Ocean (60° E to 105° E) in winter have a mean lifetime of 1.33 years, which is 23% larger than particles injected at the same altitude and latitude range (18 km, 10° S to 10° N) over the East Pacific (75° W to 120° W).

We explore four injection strategies to maximize particle lifetime in the stratosphere by selecting injection locations. Selecting injection latitude and longitude can help to achieve a lower injection altitude (more than 1 km lower) without sacrificing lifetime. For example, a uniform injection in the tropical area at 20 km has a mean particle lifetime of 2.0 years, we can select the injection latitude and longitude to lower the injection altitude by 1.5 km (at 18.5 km) to achieve a similar mean lifetime (i.e., 2.0 years). Because maximizing particle lifetime by selecting injection location will increase the interhemispheric imbalance, we designed an injection strategy that can maximize the particle lifetime in the stratosphere subjecting to the interhemispheric balance constraint.

Our results complement SAI studies with GCMs to inform future injection strategy design. The modified LAGRANTO model uses 3-hourly ERA5 data as input, which provides a better estimate of stratospheric transport than GCMs. But the LAGRANTO model cannot model aerosol dynamics nor the response of the climate to SAI.

Large Mitigation Potential of Smoke PM2.5 in the US from Human-Ignited Wildfires and Agricultural Fires

Abstract: Increasing fire activity and the associated degradation in air quality in the United States has been indirectly linked to human activity via climate change. In addition, the direct attribution of fires to human or natural causes provides potential for near term smoke mitigation. We quantify the contribution of agricultural fires and human ignited wildfires to smoke emissions in the United States using the GFED4s inventory combined with the US Forest Service Fire Program Analysis-Fire Occurrence Database. We use the GEOS-Chem model to simulate how fires driven by these two human levers impact fire particulate matter under 2.5 microns (PM2.5) concentrations in the contiguous United States (CONUS) from 2003 to 2018. We find that these human-driven fires dominate fire PM2.5 in both a high fire and human ignition year (2018) and low fire and human ignition year (2003). Across CONUS, human drivers of fire account for more than 80% of the population-weighted exposure and premature deaths associated with fire PM2.5. These findings indicate that a large portion of the smoke exposure and impacts in CONUS are driven by human activities with large mitigation potential that could be the focus of future management choices and policymaking.

Impact of climate variability and change on the ozone response to NOx emissions reductions

Abstract: Climate variability may introduce a range of air quality and health outcomes for a given emission control policy. While the influence of meteorology on air quality is well established, shifting climate baselines may affect the predicted response of air pollution to emission changes. When using air quality models for decision-making, it is important to quantify how various sources of uncertainty and variability affect the likelihood that a prospective emission control strategy will achieve a given air quality target. Here, we leverage an ensemble approach to assess how climate variability and change impact the sensitivity of ozone to nitrogen oxides (NOx) emissions reductions. We consider three sources of variability: (1) inter-annual variability caused by natural forcings such as year-to-year variations in solar radiation, (2) unforced natural climate variability due to inherent stochasticity in the climate system, (3) external climate forcing uncertainty due to a range of possible future greenhouse gas emission trajectories. We use climate fields from a five-member initial condition ensemble of the Massachusetts Institute of Technology Integrated Global System Model linked to the Community Atmosphere Model (MIT IGSM-CAM) to drive offline simulations of the high-performance GEOS-Chem chemical transport model (GCHP). Analysis of the simulated ozone response to a 10% reduction in anthropogenic NOx emissions reveals the largest spread in polluted urban areas. In these areas, the ensemble standard deviation and the inter-annual standard deviation of the ozone response can be on the order of 10-30% of the ensemble and multi-year means. In most areas, future climate variability does not induce a change in ozone photochemical regime. However, currently planned ozone control strategies in urban areas that experience a transitional or mixed photochemical regime are less robust to climate uncertainty.

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.

Implications of Policy Design and Socioeconomic Uncertainties for Energy Transitions and Sectoral Responses

Abstract: Studies exploring energy transitions typically focus on a single or small set of scenarios, often with idealized policy assumptions (e.g. with global carbon pricing and significant negative emissions). However, there are countless possible ways the future could unfold, with different implications for energy transitions. In this work, we develop a probabilistic multi-sector coupled human-natural system model and explore both deep uncertainty about climate policy design and parametric uncertainty about socioeconomic assumptions, and the implications of those uncertainties for energy transitions and sectoral responses. To reflect policy design uncertainty, we utilize a set of increasingly stringent global emissions pathways comprised of increasingly stringent regional GHG constraints, and consider both “Optimistic” and “Pessimistic” design conditions that represent deep uncertainties for climate strategy, including whether or not there is international emissions trading, coverage of land use emissions and availability of carbon dioxide removal technologies. For each of these scenarios, we then run large ensembles of our model, sampling from probability distributions for uncertain socioeconomic parameters (e.g. productivity growth, population, technology costs, fossil resources). Using this approach, we can quantify uncertainty in the future energy mix and sectoral responses (e.g. emissions, output and energy use) and how that uncertainty shifts for different policy design assumptions.

Results suggest many possible energy mixes are consistent with a given global emissions pathway, and the policy design has significant implications for future energy mixes. In particular, whether or not international emissions trading is allowed results in vastly different amounts of BECCS and afforestation pursued globally, which in turn affects how much fossil energy can continue to be used and decarbonization strategies employed in different regions and sectors. This approach demonstrates the importance of considering uncertainty when planning for energy transitions and that planning for a single future is risky.

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.

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