Search

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.

Continuation of historical trends in crop yield are critical to meeting the demands of a growing and more affluent world population. Climate change may compromise our ability to meet these demands, but estimates vary widely, highlighting the importance of understanding historical interactions between yield and climate trends. The relationship between temperature and yield is nuanced, involving differential yield outcomes to warm ([Formula: see text]C) and hot ([Formula: see text]C) temperatures and differing sensitivity across growth phases. Here, we use a crop model that resolves temperature responses according to magnitude and growth phase to show that US maize has benefited from weather shifts since 1981. Improvements are related to lengthening of the growing season and cooling of the hottest temperatures. Furthermore, current farmer cropping schedules are more beneficial in the climate of the last decade than they would have been in earlier decades, indicating statistically significant adaptation to a changing climate of 13 kg·ha-1· decade-1 All together, the better weather experienced by US maize accounts for 28% of the yield trends since 1981. Sustaining positive trends in yield depends on whether improvements in agricultural climate continue and the degree to which farmers adapt to future climates.

Abstract There is considerable interest in determining whether recent changes in the temperature distribution extend beyond simple shifts in the mean. The authors present a framework based on quantile regression, wherein trends are estimated across percentiles. Pointwise trends from surface station observations are mapped into continuous spatial fields using thin-plate spline regression. This procedure allows for resolving spatial dependence of distributional trends, providing uncertainty estimates that account for spatial covariance and varying station density. The method is applied to seasonal near-surface temperatures between 1979 and 2014 to unambiguously assess distributional changes in the densely sampled North American region. Strong seasonal differences are found, with summer trends exhibiting significant warming throughout the domain with little distributional dependence, while the spatial distribution of spring and fall trends show a dipole structure. In contrast, the spread between the 95th and 5th percentile in winter has decreased, with trends of −0.71° and −0.85°C decade−1, respectively, for daily maximum and minimum temperature, a contraction that is statistically significant over 84% of the domain. This decrease in variability is dominated by warming of the coldest days, which has outpaced the median trend by approximately a factor of 4. Identical analyses using ERA-Interim and NCEP-2 yield consistent estimates for winter (though not for other seasons), suggesting that reanalyses can be reliably used for relating winter trends to circulation anomalies. These results are consistent with Arctic-amplified warming being strongest in winter and with the influence of synoptic-scale advection on winter temperatures. Maps for all percentiles, seasons, and datasets are provided via an online tool.

High temperature extremes during the growing season can reduce agricultural production. At the same time, agricultural practices can modify temperatures by altering the surface energy budget. Here we identify centennial trends towards more favourable growing conditions in the US Midwest, including cooler summer temperature extremes and increased precipitation, and investigate the origins of these shifts. Statistically significant correspondence is found between the cooling pattern and trends in cropland intensification, as well as with trends towards greater irrigated land over a small subset of the domain. Land conversion to cropland, often considered an important influence on historical temperatures, is not significantly associated with cooling. We suggest that agricultural intensification increases the potential for evapotranspiration, leading to cooler temperatures and contributing to increased precipitation. The tendency for greater evapotranspiration on hotter days is consistent with our finding that cooling trends are greatest for the highest temperature percentiles. Temperatures over rainfed croplands show no cooling trend during drought conditions, consistent with evapotranspiration requiring adequate soil moisture, and implying that modern drought events feature greater warming as baseline cooler temperatures revert to historically high extremes.

One of the many ways that climate change may affect human health is by altering the nutrient content of food crops. However, previous attempts to study the effects of increased atmospheric CO2 on crop nutrition have been limited by small sample sizes and/or artificial growing conditions. Here we present data from a meta-analysis of the nutritional contents of the edible portions of 41 cultivars of six major crop species grown using free-air CO2 enrichment (FACE) technology to expose crops to ambient and elevated CO2 concentrations in otherwise normal field cultivation conditions. This data, collected across three continents, represents over ten times more data on the nutrient content of crops grown in FACE experiments than was previously available. We expect it to be deeply useful to future studies, such as efforts to understand the impacts of elevated atmospheric CO2 on crop macro- and micronutrient concentrations, or attempts to alleviate harmful effects of these changes for the billions of people who depend on these crops for essential nutrients.

Dietary deficiencies of zinc and iron are a substantial global public health problem. An estimated two billion people suffer these deficiencies, causing a loss of 63 million life-years annually. Most of these people depend on C3 grains and legumes as their primary dietary source of zinc and iron. Here we report that C3 grains and legumes have lower concentrations of zinc and iron when grown under field conditions at the elevated atmospheric CO2 concentration predicted for the middle of this century. C3 crops other than legumes also have lower concentrations of protein, whereas C4 crops seem to be less affected. Differences between cultivars of a single crop suggest that breeding for decreased sensitivity to atmospheric CO2 concentration could partly address these new challenges to global health.

Preparing for climate change requires an understanding of the degree to which global warming has regional implications. Here we document a strong relationship between the magnitude and extent of warming and explain its origin using a simple model based on binomial statistics. Applied to HadCRUT5 instrumental observations, the model shows that 96% of interannual variability in the proportion of regions experiencing anomalous warmth over the last century can be explained on the basis of the magnitude of global mean surface temperature (GMST) anomalies. The model performs similarly well when applied to a variety of unforced and forced model simulations and represents a general thermodynamic link between global and local warming on annual timescales. Our model predicts that, independent of the baseline that is chosen, 95% of the globe is expected to experience above‐average annual temperatures at 0.7°C of GMST warming, and 99% at 1.0°C of warming. Plain Language Summary: Whereas many studies focus on global mean surface temperature (GMST), it is also critical to understand the spatial patterns of this warming in preparing for a warmer world. Using a simple statistical model, we describe a strong relationship between GMST and the spatial extent of above‐average temperature anomalies each year. This relationship is shown to hold across various model simulations and historical temperature data, capturing both annual and decadal trends. We can use our model alongside projections to gain insight into the extent of warming variations. For instance, 1°C GMST warming from a given baseline climate generally corresponds to a world in which 99% of places across the globe will be anomalously warm every year. Key Points: A simple statistical model relates the proportion of Earth's area that is anomalously warm to global mean surface temperature (GMST)The model predicts 96% of the interannual variance in the proportion of anomalous warmth between 1900 and 2021 on the basis of GMST1.0°C of GMST warming corresponds to 99% of the globe being anomalously warm, independent of baseline [ABSTRACT FROM AUTHOR]

Existing estimates of sea surface temperatures (SSTs) indicate that, during the early twentieth century, the North Atlantic and northeast Pacific oceans warmed by twice the global average, whereas the northwest Pacific Ocean cooled by an amount equal to the global average.sup.1-4. Such a heterogeneous pattern suggests first-order contributions from regional variations in forcing or in ocean-atmosphere heat fluxes.sup.5,6. These older SST estimates are, however, derived from measurements of water temperatures in ship-board buckets, and must be corrected for substantial biases.sup.7-9. Here we show that correcting for offsets among groups of bucket measurements leads to SST variations that correlate better with nearby land temperatures and are more homogeneous in their pattern of warming. Offsets are identified by systematically comparing nearby SST observations among different groups.sup.10. Correcting for offsets in German measurements decreases warming rates in the North Atlantic, whereas correcting for Japanese measurement offsets leads to increased and more uniform warming in the North Pacific. Japanese measurement offsets in the 1930s primarily result from records having been truncated to whole degrees Celsius when the records were digitized in the 1960s. These findings underscore the fact that historical SST records reflect both physical and social dimensions in data collection, and suggest that further opportunities exist for improving the accuracy of historical SST records.sup.9,11. Correction of oddities in the historical record of sea surface temperatures reveals that some basin-wide climate variations were an artefact of systematic biases that stem, in part, from Japanese records being truncated to whole numbers when the records were digitized., Author(s): Duo Chan [sup.1] , Elizabeth C. Kent [sup.2] , David I. Berry [sup.2] , Peter Huybers [sup.1] Author Affiliations: (1) Department of Earth and Planetary Sciences, Harvard University, Cambridge, [...]

Abstract The climatological annual cycle in surface air temperature, defined by its amplitude and phase lag with respect to solar insolation, is one of the most familiar aspects of the climate system. Here, the authors identify three first-order features of the spatial structure of amplitude and phase lag and explain them using simple physical models. Amplitude and phase lag 1) are broadly consistent with a land and ocean end-member mixing model but 2) exhibit overlap between land and ocean and, despite this overlap, 3) show a systematically greater lag over ocean than land for a given amplitude. Based on previous work diagnosing relative ocean or land influence as an important control on the extratropical annual cycle, the authors use a Lagrangian trajectory model to quantify this influence as the weighted amount of time that an ensemble of air parcels has spent over ocean or land. This quantity explains 84% of the space–time variance in the extratropical annual cycle, as well as features 1 and 2. All three features can be explained using a simple energy balance model with land and ocean surfaces and an advecting atmosphere. This model explains 94% of the space–time variance of the annual cycle in an illustrative midlatitude zonal band when incorporating the results of the trajectory model. The aforementioned features of annual variability in surface air temperature thus appear to be explained by the coupling of land and ocean through mean atmospheric circulation.

Abstract The climatological annual cycle in surface air temperature, defined by its amplitude and phase lag with respect to solar insolation, is one of the most familiar aspects of the climate system. Here, the authors identify three first-order features of the spatial structure of amplitude and phase lag and explain them using simple physical models. Amplitude and phase lag 1) are broadly consistent with a land and ocean end-member mixing model but 2) exhibit overlap between land and ocean and, despite this overlap, 3) show a systematically greater lag over ocean than land for a given amplitude. Based on previous work diagnosing relative ocean or land influence as an important control on the extratropical annual cycle, the authors use a Lagrangian trajectory model to quantify this influence as the weighted amount of time that an ensemble of air parcels has spent over ocean or land. This quantity explains 84% of the space–time variance in the extratropical annual cycle, as well as features 1 and 2. All three features can be explained using a simple energy balance model with land and ocean surfaces and an advecting atmosphere. This model explains 94% of the space–time variance of the annual cycle in an illustrative midlatitude zonal band when incorporating the results of the trajectory model. The aforementioned features of annual variability in surface air temperature thus appear to be explained by the coupling of land and ocean through mean atmospheric circulation.

Identifying the causes of historical trends in relative sea level--the height of the sea surface relative to Earth's crust--is a prerequisite for predicting future changes. Rates of change along the eastern coast of the USA (the US East Coast) during the past century were spatially variable, and relative sea level rose faster along the Mid-Atlantic Bight than along the South Atlantic Bight and the Gulf of Maine. Past studies suggest that Earth's ongoing response to the last deglaciation.sup.1-5, surface redistribution of ice and water.sup.5-9 and changes in ocean circulation.sup.9-13 contributed considerably to this large-scale spatial pattern. Here we analyse instrumental data.sup.14,15 and proxy reconstructions.sup.4,12 using probabilistic methods.sup.16-18 to show that vertical motions of Earth's crust exerted the dominant control on regional spatial differences in relative sea-level trends along the US East Coast during 1900-2017, explaining most of the large-scale spatial variance. Rates of coastal subsidence caused by ongoing relaxation of the peripheral forebulge associated with the last deglaciation are strongest near North Carolina, Maryland and Virginia. Such structure indicates that Earth's elastic lithosphere is thicker than has been assumed in other models.sup.19-22. We also find a substantial coastal gradient in relative sea-level trends over this period that is unrelated to deglaciation and suggests contributions from twentieth-century redistribution of ice and water. Our results indicate that the majority of large-scale spatial variation in long-term rates of relative sea-level rise on the US East Coast is due to geological processes that will persist at similar rates for centuries.Vertical motions of Earth's crust had the greatest effect on regional spatial differences in relative sea-level trends along the eastern coast of the USA during 1900-2017, explaining most of the large-scale spatial variance in regional rates of sea-level rise., Author(s): Christopher G. Piecuch [sup.1] , Peter Huybers [sup.2] , Carling C. Hay [sup.3] , Andrew C. Kemp [sup.4] , Christopher M. Little [sup.5] , Jerry X. Mitrovica [sup.2] , [...]