Geographically resolved social cost of anthropogenic emissions accounting for both direct and climate-mediated effects.

The magnitude and distribution of physical and societal impacts from long-lived greenhouse gases are insensitive to the emission source location; the same is not true for major coemitted short-lived pollutants such as aerosols. Here, we combine novel global climate model simulations with established response functions to show that a given aerosol emission from different regions produces divergent air quality and climate changes and associated human system impacts, both locally and globally. The marginal global damages to infant mortality, crop productivity, and economic growth from aerosol emissions and their climate effects differ by more than an order of magnitude depending on source region, with certain regions creating global external climate changes and impacts much larger than those felt locally. The complex distributions of aerosol-driven societal impacts emerge from geographically distinct and region-specific aerosol-climate interactions, estimation of which is enabled by the full Earth System Modeling Framework used here.

More accurate specification of water supply shows its importance for global crop production.

Warming temperatures tend to damage crop yields, yet the influence of water supply on global yields and its relation to temperature stress remains unclear. Here we use satellite-based measurements to provide empirical estimates of how root zone soil moisture and surface air temperature jointly influence the global productivity of maize, soybeans, millet and sorghum. Relative to empirical models using precipitation as a proxy for water supply, we find that models using soil moisture explain 30-120% more of the interannual yield variation across crops. Models using soil moisture also better separate water-supply stress from correlated heat stress and show that soil moisture and temperature contribute roughly equally to historical variations in yield. Globally, our models project yield damages of -9% to -32% across crops by end-of-century under Shared Socioeconomic Pathway 5-8.5 from changes in temperature and soil moisture. By contrast, projections using temperature and precipitation overestimate damages by 28% to 320% across crops both because they confound stresses from dryness and heat and because changes in soil moisture and temperature diverge from their historical association due to climate change. Our results demonstrate the importance of accurately representing water supply for predicting changes in global agricultural productivity and for designing effective adaptation strategies.

A generalizable and accessible approach to machine learning with global satellite imagery.

Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its accessibility and use. We show that a single encoding of satellite imagery can generalize across diverse prediction tasks (e.g., forest cover, house price, road length). Our method achieves accuracy competitive with deep neural networks at orders of magnitude lower computational cost, scales globally, delivers label super-resolution predictions, and facilitates characterizations of uncertainty. Since image encodings are shared across tasks, they can be centrally computed and distributed to unlimited researchers, who need only fit a linear regression to their own ground truth data in order to achieve state-of-the-art SIML performance.

Global evidence for ultraviolet radiation decreasing COVID-19 growth rates.

With nearly every country combating the 2019 novel coronavirus (COVID-19), there is a need to understand how local environmental conditions may modify transmission. To date, quantifying seasonality of the disease has been limited by scarce data and the difficulty of isolating climatological variables from other drivers of transmission in observational studies. We combine a spatially resolved dataset of confirmed COVID-19 cases, composed of 3,235 regions across 173 countries, with local environmental conditions and a statistical approach developed to quantify causal effects of environmental conditions in observational data settings. We find that ultraviolet (UV) radiation has a statistically significant effect on daily COVID-19 growth rates: a SD increase in UV lowers the daily growth rate of COVID-19 cases by ∼1 percentage point over the subsequent 2.5 wk, relative to an average in-sample growth rate of 13.2%. The time pattern of lagged effects peaks 9 to 11 d after UV exposure, consistent with the combined timescale of incubation, testing, and reporting. Cumulative effects of temperature and humidity are not statistically significant. Simulations illustrate how seasonal changes in UV have influenced regional patterns of COVID-19 growth rates from January to June, indicating that UV has a substantially smaller effect on the spread of the disease than social distancing policies. Furthermore, total COVID-19 seasonality has indeterminate sign for most regions during this period due to uncertain effects of other environmental variables. Our findings indicate UV exposure influences COVID-19 cases, but a comprehensive understanding of seasonality awaits further analysis.

Atmospheric opacity has a nonlinear effect on global crop yields.

Agricultural impacts of air pollution, climate change and geoengineering remain uncertain due to potentially offsetting changes in the quantity and quality of sunlight. By leveraging year-to-year variation in growing-season cloud optical thickness, I provide nonlinear empirical estimates of how increased atmospheric opacity alters sunlight across the Earth's surface and how this affects maize and soy yields in the United States, Europe, Brazil and China. I find that the response of yields to changes in sunlight from cloud scattering and absorption is consistently concave across crops and regions. An additional day of optimal cloud cover, relative to a clear-sky day, increases maize and soy yields by 0.4%. Changes in sunlight due to changes in clouds have decreased the global average maize and soy yields by 1% and 0.1% due to air pollution and may further decrease yields by 1.8% and 0.4% due to climate change.

Estimating global agricultural effects of geoengineering using volcanic eruptions.

Solar radiation management is increasingly considered to be an option for managing global temperatures1,2, yet the economic effects of ameliorating climatic changes by scattering sunlight back to space remain largely unknown3. Although solar radiation management may increase crop yields by reducing heat stress4, the effects of concomitant changes in available sunlight have never been empirically estimated. Here we use the volcanic eruptions that inspired modern solar radiation management proposals as natural experiments to provide the first estimates, to our knowledge, of how the stratospheric sulfate aerosols created by the eruptions of El Chichón and Mount Pinatubo altered the quantity and quality of global sunlight, and how these changes in sunlight affected global crop yields. We find that the sunlight-mediated effect of stratospheric sulfate aerosols on yields is negative for both C4 (maize) and C3 (soy, rice and wheat) crops. Applying our yield model to a solar radiation management scenario based on stratospheric sulfate aerosols, we find that projected mid-twenty-first century damages due to scattering sunlight caused by solar radiation management are roughly equal in magnitude to benefits from cooling. This suggests that solar radiation management-if deployed using stratospheric sulfate aerosols similar to those emitted by the volcanic eruptions it seeks to mimic-would, on net, attenuate little of the global agricultural damage from climate change. Our approach could be extended to study the effects of solar radiation management on other global systems, such as human health or ecosystem function.