Climate trends over the past few decades have been fairly rapid in many agricultural regions around the world, and increases in atmospheric carbon dioxide (CO 2 ) and ozone (O 3 ) levels have also been ubiquitous. The virtual certainty that climate and CO 2 will continue to trend in the future raises many questions related to food security, one of which is whether the aggregate productivity of global agriculture will be affected. We outline the mechanisms by which these changes affect crop yields and present estimates of past and future impacts of climate and CO 2 trends. The review focuses on global scale grain productivity, notwithstanding the many other scales and outcomes of interest to food security. Over the next few decades, CO 2 trends will likely increase global yields by roughly 1.8% per decade. At the same time, warming trends are likely to reduce global yields by roughly 1.5% per decade without effective adaptation, with a plausible range from roughly 0% to 4%. The upper end of this range is half of the expected 8% rate of gain from technological and management improvements over the next few decades. Many global change factors that will likely challenge yields, including higher O 3 and greater rainfall intensity, are not considered in most current assessments.Many factors will shape global food security over the next few decades, including changes in rates of human population growth, income growth and distribution, dietary preferences, disease incidence, increased demand for land and water resources for other uses (i.e. bioenergy production, carbon sequestration, and urban development), and rates of improvement in agricultural productivity. This latter factor, which we define here simply as crop yield (i.e., metric tons of grain production per hectare of land), is a particular emphasis of the plant science community, as researchers and farmers seek to sustain the impressive historical gains associated with improved genetics and agronomic management of major food crops.Sources of growth in agricultural productivity are also multifaceted and include levels of funding for public and private research and development, changes in soil quality, availability and cost of mineral fertilizers, atmospheric concentrations of CO 2 and ozone (O 3 ), and changes in temperature (T) and precipitation (P) conditions. This Update focuses on changes in weather, CO 2 , and O 3 in agricultural areas and how that has affected and will affect crop productivity. In doing so, we recognize that this is only part of the fuller story on crop productivity, which in turn is only part of the fuller story on future food security. For example, this Update is silent on the many ways that global change can influence food security via pathways other than agricultural productivity, such as by influencing human disease incidence or income growth rates.The main question of interest here is the following: how important will climate change and CO 2 be in shaping future crop yields at the global scale, relative to the many other factors that i...

Long-term warming trends across the globe have shifted the distribution of temperature variability, such that what was once classified as extreme heat relative to local mean conditions has become more common. This is also true for agricultural regions, where exposure to extreme heat, particularly during key growth phases such as the reproductive period, can severely damage crop production in ways that are not captured by most crop models. Here, we analyze exposure of crops to physiologically critical temperatures in the reproductive stage (T crit ), across the global harvested areas of maize, rice, soybean and wheat. Trends for the 1980-2011 period show a relatively weak correspondence (r = 0.19) between mean growing season temperature and T crit exposure trends, emphasizing the importance of separate analyses for T crit . Increasing T crit exposure in the past few decades is apparent for wheat in Central and South Asia and South America, and for maize in many diverse locations across the globe. Maize had the highest percentage (15%) of global harvested area exposed to at least five reproductive days over T crit in the 2000s, although this value is somewhat sensitive to the exact temperature used for the threshold. While there was relatively little sustained exposure to reproductive days over T crit for the other crops in the past few decades, all show increases with future warming. Using projections from climate models we estimate that by the 2030s, 31, 16, and 11% respectively of maize, rice, and wheat global harvested area will be exposed to at least five reproductive days over T crit in a typical year, with soybean much less affected. Both maize and rice exhibit non-linear increases with time, with total area exposed for rice projected to grow from 8% in the 2000s to 27% by the 2050s, and maize from 15 to 44% over the same period. While faster development should lead to earlier flowering, which would reduce reproductive extreme heat exposure for wheat on a global basis, this would have little impact for the other crops. Therefore, regardless of the impact of other global change factors (such as increasing atmospheric CO 2 ), reproductive extreme heat exposure will pose risks for global crop production without adaptive measures such as changes in sowing dates, crop and variety switching, expansion of irrigation, and agricultural expansion into cooler areas.

Abstract. Atmospheric inversion models have the potential to quantify CO2 fluxes at regional, sub-continental scales by taking advantage of near-surface CO2 mixing ratio observations collected in areas with high flux variability. This study presents results from a series of regional geostatistical inverse models (GIM) over North America for 2004, and uses them as the basis for an inter-comparison to other inversion studies and estimates from biospheric models collected through the North American Carbon Program Regional and Continental Interim Synthesis. Because the GIM approach does not require explicit prior flux estimates and resolves fluxes at fine spatiotemporal scales (i.e. 1° × 1°, 3-hourly in this study), it avoids temporal and spatial aggregation errors and allows for the recovery of realistic spatial patterns from the atmospheric data relative to previous inversion studies. Results from a GIM inversion using only available atmospheric observations and a fine-scale fossil fuel inventory were used to confirm the quality of the inventory and inversion setup. An inversion additionally including auxiliary variables from the North American Regional Reanalysis found inferred relationships with flux consistent with physiological understanding of the biospheric carbon cycle. Comparison of GIM results with bottom-up biospheric models showed stronger agreement during the growing relative to the dormant season, in part because most of the biospheric models do not fully represent agricultural land-management practices and the fate of both residual biomass and harvested products. Comparison to earlier inversion studies pointed to aggregation errors as a likely source of bias in previous sub-continental scale flux estimates, particularly for inversions that adjust fluxes at the coarsest scales and use atmospheric observations averaged over long periods. Finally, whereas the continental CO2 boundary conditions used in the GIM inversions have a minor impact on spatial patterns, they have a substantial impact on the continental carbon budget, with a difference of 0.8 PgC yr−1 in the total continental flux resulting from the use of two plausible sets of boundary CO2 mixing ratios. Overall, this inter-comparison study helps to assess the state of the science in estimating regional-scale CO2 fluxes, while pointing towards the path forward for improvements in future top-down and bottom-up modeling efforts.

Abstract.A series of synthetic data experiments is performed to investigate the ability of a regional atmospheric inversion to estimate grid-scale CO 2 fluxes during the growing season over North America. The inversions are performed within a geostatistical framework without the use of any prior flux estimates or auxiliary variables, in order to focus on the atmospheric constraint provided by the nine towers collecting continuous, calibrated CO 2 measurements in 2004. Using synthetic measurements and their associated concentration footprints, flux and model-data mismatch covariance parameters are first optimized, and then fluxes and their uncertainties are estimated at three different temporal resolutions. These temporal resolutions, which include a four-day average, a four-day-average diurnal cycle with 3-hourly increments, and 3-hourly fluxes, are chosen to help assess the impact of temporal aggregation errors on the estimated fluxes and covariance parameters. Estimating fluxes at a temporal resolution that can adjust the diurnal variability is found to be critical both for recovering covariance parameters directly from the atmospheric data, and for inferring accurate ecoregion-scale fluxes. Accounting for both spatial and temporal a priori covariance in the flux distribution is also found to be necessary for recovering accurate a posteriori uncertainty bounds on the estimated fluxes. Overall, the results suggest that even a fairly sparse network of 9 towers collecting continuous CO 2 measurements across the continent, used with no auxiliary information or prior estimates of the flux Correspondence to: A. M. Michalak (amichala@umich.edu) distribution in time or space, can be used to infer relatively accurate monthly ecoregion scale CO 2 surface fluxes over North America within estimated uncertainty bounds. Simulated random transport error is shown to decrease the quality of flux estimates in under-constrained areas at the ecoregion scale, although the uncertainty bounds remain realistic. While these synthetic data inversions do not consider all potential issues associated with using actual measurement data, e.g. systematic transport errors or problems with the boundary conditions, they help to highlight the impact of inversion setup choices, and help to provide a baseline set of CO 2 fluxes for comparison with estimates from future real-data inversions.

[1] This study presents monthly CO 2 fluxes from 1997 to 2001 at a 3.75°latitude  5°l ongitude resolution, inferred using a geostatistical inverse modeling approach. The approach focuses on quantifying the information content of measurements from the NOAA-ESRL cooperative air sampling network with regard to the global CO 2 budget at different spatial and temporal scales. The geostatistical approach avoids the use of explicit prior flux estimates that have formed the basis of previous synthesis Bayesian inversions and does not prescribe spatial patterns of flux for large, aggregated regions. Instead, the method relies strongly on the atmospheric measurements and the inferred spatial autocorrelation of the fluxes to estimate sources and sinks and their associated uncertainties at the resolution of the atmospheric transport model. Results show that gridscale estimates exhibit high uncertainty and relatively little small-scale variability, but generally reflect reasonable fluxes in areas that are relatively well constrained by measurements. The aggregated continental-scale fluxes are better constrained, and estimates are consistent with results from previous synthesis Bayesian inversion studies for many regions. Observed differences at the continental scale are primarily attributable to the choice of a priori assumptions in the current work relative to those in other synthesis Bayesian studies. Overall, the results indicate that the geostatistical inverse modeling approach is able to estimate global fluxes using the limited atmospheric measurement network without relying on assumptions about a priori estimates of the flux distribution. As such, the method provides a means of isolating the information content of the atmospheric measurements, and thus serves as a valuable tool for reconciling top-down and bottom-up estimates of CO 2 flux variability.Citation: Mueller, K. L., S. M. Gourdji, and A. M. Michalak (2008), Global monthly averaged CO 2 fluxes recovered using a geostatistical inverse modeling approach: 1. Results using atmospheric measurements,

Genetic improvements in heat tolerance of wheat provide a potential adaptation response to long-term warming trends, and may also boost yields in wheat-growing areas already subject to heat stress. Yet there have been few assessments of recent progress in breeding wheat for hot environments. Here, data from 25 years of wheat trials in 76 countries from the International Maize and Wheat Improvement Center (CIMMYT) are used to empirically model the response of wheat to environmental variation and assess the genetic gains over time in different environments and for different breeding strategies. Wheat yields exhibited the most sensitivity to warming during the grain-filling stage, typically the hottest part of the season. Sites with high vapour pressure deficit (VPD) exhibited a less negative response to temperatures during this period, probably associated with increased transpirational cooling. Genetic improvements were assessed by using the empirical model to correct observed yield growth for changes in environmental conditions and management over time. These 'climatecorrected' yield trends showed that most of the genetic gains in the high-yield-potential Elite Spring Wheat Yield Trial (ESWYT) were made at cooler temperatures, close to the physiological optimum, with no evidence for genetic gains at the hottest temperatures. In contrast, the Semi-Arid Wheat Yield Trial (SAWYT), a lower-yielding nursery targeted at maintaining yields under stressed conditions, showed the strongest genetic gains at the hottest temperatures. These results imply that targeted breeding efforts help us to ensure progress in building heat tolerance, and that intensified (and possibly new) approaches are needed to improve the yield potential of wheat in hot environments in order to maintain global food security in a warmer climate.

There is increased interest in understanding urban greenhouse gas (GHG) emissions. To accurately estimate city emissions, the influence of extraurban fluxes must first be removed from urban greenhouse gas (GHG) observations. This is especially true for regions, such as the U.S. Northeastern Corridor‐Baltimore/Washington, DC (NEC‐B/W), downwind of large fluxes. To help site background towers for the NEC‐B/W, we use a coupled Bayesian Information Criteria and geostatistical regression approach to help site four background locations that best explain CO2 variability due to extraurban fluxes modeled at 12 urban towers. The synthetic experiment uses an atmospheric transport and dispersion model coupled with two different flux inventories to create modeled observations and evaluate 15 candidate towers located along the urban domain for February and July 2013. The analysis shows that the average ratios of extraurban inflow to total modeled enhancements at urban towers are 21% to 36% in February and 31% to 43% in July. In July, the incoming air dominates the total variability of synthetic enhancements at the urban towers (R2 = 0.58). Modeled observations from the selected background towers generally capture the variability in the synthetic CO2 enhancements at urban towers (R2 = 0.75, root‐mean‐square error (RMSE) = 3.64 ppm; R2 = 0.43, RMSE = 4.96 ppm for February and July). However, errors associated with representing background air can be up to 10 ppm for any given observation even with an optimal background tower configuration. More sophisticated methods may be necessary to represent background air to accurately estimate urban GHG emissions.

Responses to public health threats presented by the global COVID-19 pandemic dramatically altered daily activities in cities around the world, including in the Los Angeles and Washington DC/Baltimore metropolitan areas. Researchers have attempted to determine the extent to which CO 2 emissions were impacted by the pandemic, linking changes in emissions to processes and sectors using different types of activity data and baselines for comparisons (Le Quéré et al., 2020;Liu et al., 2020;Zheng et al., 2020). One study shows that CO 2 emissions declined by 3.9% globally in the first 4 months in 2020, attributing half of this decline to changes in traffic and mobility (Le Quéré et al., 2020). Unlike these studies, which use only activity data to estimate declines, here we also use atmospheric CO 2 observations to detect when and how emissions were impacted, and focus on CO 2 emissions reductions at the city scale.Our analysis relies on high-accuracy atmospheric CO 2 observations from urban networks, building on a recently published study that used lower-accuracy CO 2 sensors to estimate COVID-19 related impacts for the San Francisco Bay area (Turner et al., 2020). Here, we evaluate impacts in two separate metropolitan areas: Los Angeles and Washington DC/Baltimore, allowing for an inter-comparison between two large urban regions. In Los Angeles and Washington DC/Baltimore, traffic congestion and commuting play dominant