Earth Systems

SEBALIGEE v2: Global Evapotranspiration Estimation Replacing Hot/Cold Pixels with Machine Learning

Abstract: An open source computer algorithm, the Surface Energy Balance Algorithm for Land-Improved (SEBALI), was designed to estimate actual evapotranspiration (ET) at a basin level. In this study, we build on later versions of SEBALI/SEBALIGEE to estimate ET at a 30-m resolution for any scale application using advanced machine learning approaches (SEBALIGEE v2). We evaluate the monthly ET estimated from the new algorithm across several fluxnet sites in US, China, Italy, Belgium, Germany, and France, yielding an Absolute Mean Error (AME) of 0.41 mm/day versus 0.48 mm/day in the original SEBALIGEE. Analyses of the ET in the US indicate that the annual wheat ET decreases significantly between 2013 and 2021 (p < 0.05), accompanied by a significant air temperature increase. Net solar radiation is found to be the most influencing factor on ET of corn and soybeans with R2 values of ~0.72.

Impacts of wind power on air quality, premature mortality, and exposure disparities in the US

Abstract: Understanding impacts of renewable energy on air quality and associated human exposures is essential for informing future policy. We estimate the impacts of US wind power on air quality and pollution exposure disparities using hourly data from 2011-2017 and detailed atmospheric chemistry modeling.

Wind power associated with Renewable Portfolio Standards (RPS) in 2014 resulted in $2.0 billion in health benefits from improved air quality. 29% and 32% of these health benefits accrued to  racial/ethnic minority and low income populations respectively, below a 2021 target by the Biden administration that 40% of overall benefits of future federal investments flow to disadvantaged communities. Wind power worsened exposure disparities among racial and income groups in some states, but improved them in others.

Health benefits could be up to $8.4 billion if displacement of fossil fuel generators prioritized those with higher health damages. However, strategies that maximize total health benefits would not mitigate pollution disparities, suggesting more targeted measures are needed.

The preeminent conference for the advancement of Earth and space sciences, the AGU (American Geophysical Union) Fall Meeting draws more than 25,000 attendees from over 100 countries each year to share research findings and identify innovative solutions to complex problems. Organized around the theme “Science Leads the Future,” this year’s AGU Fall Meeting will take place in Chicago and online on December 12 - 16.

AGU Fall Meeting frames science as engine of a better future
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.

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