This Tropospheric Ozone Assessment Report (TOAR) on the current state of knowledge of ozone metrics of relevance to vegetation (TOAR-Vegetation) reports on present-day global distribution of ozone at over 3300 vegetated sites and the long-term trends at nearly 1200 sites. TOAR-Vegetation focusses on three metrics over vegetation-relevant time-periods across major world climatic zones: M12, the mean ozone during 08:00–19:59; AOT40, the accumulation of hourly mean ozone values over 40 ppb during daylight hours, and W126 with stronger weighting to higher hourly mean values, accumulated during 08:00–19:59. Although the density of measurement stations is highly variable across regions, in general, the highest ozone values (mean, 2010–14) are in mid-latitudes of the northern hemisphere, including southern USA, the Mediterranean basin, northern India, north, north-west and east China, the Republic of Korea and Japan. The lowest metric values reported are in Australia, New Zealand, southern parts of South America and some northern parts of Europe, Canada and the USA. Regional-scale assessments showed, for example, significantly higher AOT40 and W126 values in East Asia (EAS) than Europe (EUR) in wheat growing areas (p < 0.05), but not in rice growing areas. In NAM, the dominant trend during 1995–2014 was a significant decrease in ozone, whilst in EUR it was no change and in EAS it was a significant increase. TOAR-Vegetation provides recommendations to facilitate a more complete global assessment of ozone impacts on vegetation in the future, including: an increase in monitoring of ozone and collation of field evidence of the damaging effects on vegetation; an investigation of the effects on peri-urban agriculture and in mountain/upland areas; inclusion of additional pollutant, meteorological and inlet height data in the TOAR dataset; where not already in existence, establishing new region-specific thresholds for vegetation damage and an innovative integration of observations and modelling including stomatal uptake of the pollutant.

A variety of tools have emerged with the goal of mapping the current delivery of ecosystem services and quantifying the impact of environmental changes. An important and often overlooked question is how accurate the outputs of these models are in relation to empirical observations. In this paper we validate a hydrological ecosystem service model (InVEST Water Yield Model) using widely available data. We modelled annual water yield in 22 UK catchments with widely varying land cover, population and geology, and compared model outputs with gauged river flow data from the UK National River Flow Archive. Values for input parameters were selected from existing literature to reflect conditions in the UK and were subjected to sensitivity analyses. We also compared model performance between precipitation and potential evapotranspiration data sourced from global- and UK-scale datasets. We then tested the transferability of the results within the UK by additional validation in a further 20 catchments. Whilst the model performed only moderately with global-scale data (linear regression of modelled total water yield against empirical data; slope=0.763, intercept=54.45, R(2)=0.963) with wide variation in performance between catchments, the model performed much better when using UK-scale input data, with closer fit to the observed data (slope=1.07, intercept=3.07, R(2)=0.990). With UK data the majority of catchments showed <10% difference between measured and modelled water yield but there was a minor but consistent overestimate per hectare (86m(3)/ha/year). Additional validation on a further 20 UK catchments was similarly robust, indicating that these results are transferable within the UK. These results suggest that relatively simple models can give accurate measures of ecosystem services. However, the choice of input data is critical and there is a need for further validation in other parts of the world.

In support of the first Tropospheric Ozone Assessment Report (TOAR) a relational database of global surface ozone observations has been developed and populated with hourly measurement data and enhanced metadata. A comprehensive suite of ozone data products including standard statistics, health and vegetation impact metrics, and trend information, are made available through a common data portal and a web interface. These data form the basis of the TOAR analyses focusing on human health, vegetation, and climate relevant ozone issues, which are part of this special feature. Cooperation among many data centers and individual researchers worldwide made it possible to build the world’s largest collection of in-situ hourly surface ozone data covering the period from 1970 to 2015. By combining the data from almost 10,000 measurement sites around the world with global metadata information, new analyses of surface ozone have become possible, such as the first globally consistent characterisations of measurement sites as either urban or rural/remote. Exploitation of these global metadata allows for new insights into the global distribution, and seasonal and long-term changes of tropospheric ozone and they enable TOAR to perform the first, globally consistent analysis of present-day ozone concentrations and recent ozone changes with relevance to health, agriculture, and climate. Considerable effort was made to harmonize and synthesize data formats and metadata information from various networks and individual data submissions. Extensive quality control was applied to identify questionable and erroneous data, including changes in apparent instrument offsets or calibrations. Such data were excluded from TOAR data products. Limitations of a posteriori data quality assurance are discussed. As a result of the work presented here, global coverage of surface ozone data for scientific analysis has been significantly extended. Yet, large gaps remain in the surface observation network both in terms of regions without monitoring, and in terms of regions that have monitoring programs but no public access to the data archive. Therefore future improvements to the database will require not only improved data harmonization, but also expanded data sharing and increased monitoring in data-sparse regions.

Introduction of high‐performing crop cultivars and crop/soil water management practices that increase the stomatal uptake of carbon dioxide and photosynthesis will be instrumental in realizing the United Nations Sustainable Development Goal (SDG) of achieving food security. To date, however, global assessments of how to increase crop yield have failed to consider the negative effects of tropospheric ozone, a gaseous pollutant that enters the leaf stomatal pores of plants along with carbon dioxide, and is increasing in concentration globally, particularly in rapidly developing countries. Earlier studies have simply estimated that the largest effects are in the areas with the highest ozone concentrations. Using a modelling method that accounts for the effects of soil moisture deficit and meteorological factors on the stomatal uptake of ozone, we show for the first time that ozone impacts on wheat yield are particularly large in humid rain‐fed and irrigated areas of major wheat‐producing countries (e.g. United States, France, India, China and Russia). Averaged over 2010–2012, we estimate that ozone reduces wheat yields by a mean 9.9% in the northern hemisphere and 6.2% in the southern hemisphere, corresponding to some 85 Tg (million tonnes) of lost grain. Total production losses in developing countries receiving Official Development Assistance are 50% higher than those in developed countries, potentially reducing the possibility of achieving UN SDG2. Crucially, our analysis shows that ozone could reduce the potential yield benefits of increasing irrigation usage in response to climate change because added irrigation increases the uptake and subsequent negative effects of the pollutant. We show that mitigation of air pollution in a changing climate could play a vital role in achieving the above‐mentioned UN SDG, while also contributing to other SDGs related to human health and well‐being, ecosystems and climate change.

Increasing both crop productivity and the tolerance of crops to abiotic and biotic stresses is a major challenge for global food security in our rapidly changing climate. For the first time, we show how the spatial variation and severity of tropospheric ozone effects on yield compare with effects of other stresses on a global scale, and discuss mitigating actions against the negative effects of ozone. We show that the sensitivity to ozone declines in the order soybean > wheat > maize > rice, with genotypic variation in response being most pronounced for soybean and rice. Based on stomatal uptake, we estimate that ozone (mean of 2010–2012) reduces global yield annually by 12.4%, 7.1%, 4.4% and 6.1% for soybean, wheat, rice and maize, respectively (the “ozone yield gaps”), adding up to 227 Tg of lost yield. Our modelling shows that the highest ozone‐induced production losses for soybean are in North and South America whilst for wheat they are in India and China, for rice in parts of India, Bangladesh, China and Indonesia, and for maize in China and the United States. Crucially, we also show that the same areas are often also at risk of high losses from pests and diseases, heat stress and to a lesser extent aridity and nutrient stress. In a solution‐focussed analysis of these results, we provide a crop ideotype with tolerance of multiple stresses (including ozone) and describe how ozone effects could be included in crop breeding programmes. We also discuss altered crop management approaches that could be applied to reduce ozone impacts in the shorter term. Given the severity of ozone effects on staple food crops in areas of the world that are also challenged by other stresses, we recommend increased attention to the benefits that could be gained from addressing the ozone yield gap.

Ecosystem services modelling tools can help land managers and policy makers evaluate the impacts of alternative management options or changes in land use on the delivery of ecosystem services. As the variety and complexity of these tools increases, there is a need for comparative studies across a range of settings, allowing users to make an informed choice. Using examples of provisioning and regulating services (water supply, carbon storage and nutrient retention), we compare three spatially explicit tools - LUCI (Land Utilisation and Capability Indicator), ARIES (Artificial Intelligence for Ecosystem Services) and InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs). Models were parameterised for the UK and applied to a temperate catchment with widely varying land use in North Wales. Although each tool provides quantitative mapped output, can be applied in different contexts, and can work at local or national scale, they differ in the approaches taken and underlying assumptions made. In this study, we focus on the wide range of outputs produced for each service and discuss the differences between each modelling tool. Model outputs were validated using empirical data for river flow, carbon and nutrient levels within the catchment. The sensitivity of the models to land-use change was tested using four scenarios of varying severity, evaluating the conversion of grassland habitat to woodland (0-30% of the landscape). We show that, while the modelling tools provide broadly comparable quantitative outputs, each has its own unique features and strengths. Therefore the choice of tool depends on the study question.