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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.

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. This paper presents MOSAIKS, a system for planet-scale prediction of multiple outcomes using satellite imagery and machine learning (SIML). MOSAIKS generalizes across prediction domains and has the potential to enhance accessibility of SIML across research disciplines.

Understanding the impact of climate change on human migration is critical for policymakers. Yet climate change can both incentivize people to migrate and reduce their ability to move, making its effect on human migration ambiguous. We propose an approach to studying migration that combines causal inference methods with cross-validation techniques to reliably estimate effects of weather on migration within and across borders. This approach highlights the key role of migrant demographics in the weather-migration relationship. We show that allowing weather effects to differ by age and education improves out-of-sample performance by a factor of five or more compared with a homogeneous effect. Demographic heterogeneity is critical in explaining this discrepancy. Projections based on our empirical estimates indicate that the effects of climate change on future cross-border migration will be an order of magnitude larger for most demographics than the average effect, but differing responses across groups largely offset one another. Migration responses to climate are demographically heterogeneous. Accounting for age and education greatly improves predictions, with demographic-specific effects often an order of magnitude larger than population wide averages.

Recent innovations in task-agnostic imagery featurization have lowered the computational costs of using machine learning to predict ground conditions from satellite imagery. These methods hold particular promise for the development of imagery-based monitoring systems in low-income regions, where data and computational resources can be limited. However, these relatively simple prediction pipelines have not been evaluated in developing-country contexts over time, limiting our understanding of their performance in practice. Here, we compute task-agnostic random convolutional features from satellite imagery and use linear ridge regression models to predict maize yields over space and time in Zambia, a country prone to severe droughts and crop failure. Leveraging Landsat and Sentinel 2 satellite constellations, in combination with district-level yield data, our model explains 83% of the out-of-sample maize yield variation from 2016 to 2021, slightly outperforming a model trained on Normalized Difference Vegetation Index (NDVI) features, a common remote sensing approach used by practitioners to monitor crop health. Our approach maintains an R2 score of 0.74 when predicting temporal variation alone, while the performance of the NDVI-based approach drops to an R2 of 0.39. Our findings imply that this task-agnostic featurization can be used to predict spatial and temporal variation in agricultural outcomes, even in contexts with limited ground truth data. More broadly, these results point to imagery-based monitoring as a promising tool for assisting agricultural planning and food security, even in contexts where computationally expensive methodologies remain out of reach.

Observational analyses consistently find that yields of major rainfed crops increase with temperature up to a threshold of approximately 32°^{\\circ}$ C, above which they reduce sharply. Two damage pathways have been suggested to explain this relationship: that high temperatures directly stress crops and drive yield loss, or that high temperatures induce water stress in crops, which in turn drives yield loss. Here we explore a third pathway: that soil water stress limits both agricultural productivity and evaporative cooling, giving rise to the nonlinear relationship between temperature and yield. Determining which of these pathways underpins the yield‐temperature relationship is important for predicting future crop productivity because climate change is expected to alter the co‐variability between temperature and water availability. To examine this third pathway, we use cumulative growing‐season transpiration from an idealized land surface model as a proxy for yield. This approach reproduces the observed yield‐temperature relationship, even though the model includes no mechanisms that limit productivity at high temperatures. In experiments where the influence of temperature on soil moisture is suppressed, yields still decline during hot, dry periods in a manner consistent with the observations. We conclude that water stress, and its influence on evaporative cooling, temperature, and agricultural productivity, drives the yield‐temperature relationship found in crops that experience episodic water stress. This framework explains the muted sensitivity of irrigated yields to high atmospheric temperatures, and suggests that future yield outcomes depend more critically on changes in rainfall than suggested by estimates that attribute yield losses primarily to temperature variations. Plain Language Summary Why do observational analyses consistently find that crop yields decrease dramatically at high temperatures? Using a model that includes no direct representation of heat stress on agricultural productivity, we reproduce the observed relationships between crop yields and high temperatures in both irrigated and non‐irrigated fields. This suggests that soil water availability, through its dual influence on crop productivity and temperature, is the origin of this long‐observed nonlinear relationship. Our conclusion that soil moisture underpins the relationship between temperature and yield suggests that long‐term rainfall trends, which are uncertain in climate model projections, will exert a strong control on yield outcomes under climate change. Management of water resources, in addition to adaptation to high temperatures, is vital for ensuring long‐term food security. Key Points Water stress causes yield reductions that are correlated with, but not caused by, high temperatures Distinct yield‐temperature relationships in irrigated and non‐irrigated fields can be explained by differing levels of water stress Future yield projections using historical yield‐temperature relationships may overstate damages if water stress is the primary driver of yield loss

Agricultural production statistics underpin diverse research efforts and development activities. Yet despite their critical importance, efforts to collate, update, and harmonize detailed sub-national agricultural production statistics are frequently redundant and incomplete due to the substantial time, effort, and resources required. The persisting lack of coordination and standards in the food systems data community wastes valuable resources and hinders advances in action-oriented food systems knowledge. Here we introduce the HarvestStat sub-national data consortium as an open-source, collaborative, and transparent model to overcome these challenges. HarvestStat is collaboratively producing publicly available databases and datasets for the food systems community and the broader environmental and sustainability sciences by moving beyond closed and disjointed data-gathering efforts. We are guided by core principles of complete data openness—prioritizing high standards of quality assurance; active inclusion—emphasizing involvement from local experts; and collaboration—fostering engagement across communities of data producers and users. We extend an open global call to action, inviting organizations and individuals to engage in advancing this critical agenda.

How summertime temperature variability will change with warming has important implications for climate adaptation and mitigation. CMIP5 simulations indicate a compound risk of extreme hot temperatures in western Europe from both warming and increasing temperature variance. CMIP6 simulations, however, indicate only a moderate increase in temperature variance that does not covary with warming. To explore this intergenerational discrepancy in CMIP results, we decompose changes in monthly temperature variance into those arising from changes in sensitivity to forcing and changes in forcing variance. Across models, sensitivity increases with local warming in both CMIP5 and CMIP6 at an average rate of 5.7 ([3.7, 7.9]; 95% c.i.) × 10−3°C per W m−2 per °C warming. We use a simple model of moist surface energetics to explain increased sensitivity as a consequence of greater atmospheric demand (∼70%) and drier soil (∼40%) that is partially offset by the Planck feedback (∼−10%). Conversely, forcing variance is stable in CMIP5 but decreases with warming in CMIP6 at an average rate of −21 ([−28, −15]; 95% c.i.) W2 m−4 per °C warming. We examine scaling relationships with mean cloud fraction and find that mean forcing variance decreases with decreasing cloud fraction at twice the rate in CMIP6 than CMIP5. The stability of CMIP6 temperature variance is, thus, a consequence of offsetting changes in sensitivity and forcing variance. Further work to determine which models and generations of CMIP simulations better represent changes in cloud radiative forcing is important for assessing risks associated with increased temperature variance. Plain Language Summary CMIP5 models show that, in the Northern Hemisphere midlatitudes, summertime temperature variability increases as the surface warms, indicating a compound risk of extreme hot months that have important implications for climate adaptation and mitigation. CMIP6 models, however, show only a moderate increase in temperature variability that is unrelated to warming. To understand this intergenerational discrepancy in CMIP results, we develop a framework to decompose changes in temperature variability into contributions from changes in the variability of external forcing and changes in the sensitivity of temperature to that forcing. We find that both CMIP5 and CMIP6 models show consistent increases in sensitivity as the surface warms, which we demonstrate to arise mainly from warming and drying using a simple diagnostic model. Changes in forcing variability, however, differ between CMIP5 and CMIP6. Whereas, forcing variability is stable in CMIP5, it decreases substantially with warming in CMIP6 and offsets the effect of sensitivity growth. Hence, although midlatitude land surface tends to become more sensitive in all models, whether temperature variability will increase with warming remains uncertain and relies on how forcing variability changes. Key Points Summer temperature variance increases with warming in Northern Hemisphere midlatitudes in CMIP5 but not in CMIP6 We develop a decomposition framework and a simple model to explain differences in variance changes across models Temperature sensitivity increases in both CMIP5 and CMIP6 but is offset by lower forcing variance in CMIP6

Solar radiation management is increasingly considered to be an option for managing global temperatures 1 , 2 , yet the economic effects of ameliorating climatic changes by scattering sunlight back to space remain largely unknown 3 . Although solar radiation management may increase crop yields by reducing heat stress 4 , 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. Analysis of the El Chichón and Mount Pinatubo volcanic eruptions suggests that solar radiation management strategies using stratospheric sulfate aerosols would do little to counterbalance the effects of climate change on global crop yields.

In recent years, people have interpreted scientific information about the black-tailed prairie dog (Cynomys ludovicianus) in various, and sometimes conflicting, ways. Political complexity around the relationship among black-tailed prairie dogs, agricultural interests, and wildlife has increased in recent years, particularly when prairie dogs occur on publicly owned lands leased to private entities for livestock grazing. Some have proposed that estimates of prairie dog (Cynomys spp.) numbers from 1900 are inflated, that prairie dog grazing is not unique (other grazers have similar affects on vegetation), and that prairie dogs significantly reduce carrying capacity for livestock and wildlife. We address all these issues but concentrate on the degree of competition between prairie dogs and ungulates because this motivates most prairie dog control actions. We conclude that the available information does not justify holding distribution and numbers of prairie dogs at a level that is too low to perform their keystone ecological function. We further conclude that it is especially important that prairie dogs be sufficiently abundant on public lands to perform this function.

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.