Table of contents

The Paris Agreement of COP21 set a goal of holding global average temperature increases to below 2 °C above pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5 °C. This is particularly relevant for the African context where temperatures are likely to warm faster than the global average and where the magnitude of change will be regionally heterogeneous. Additionally, many biogeophysical and socioeconomic systems are particularly vulnerable to change in both means and extremes. In this paper we contextualise the lack of regional climate information over Africa at global warming levels (GWLs) of 1.5 and 2 °C above pre-industrial levels through a short review of the literature. We show most studies that provide information over Africa under specific GWLs have used data from global models, however global models poorly resolve local scale forcing (e.g. topography) nor the internal climate variability of a region. Although downscaling using regional climate models can address these issues we find only one paper that has used downscaled data for GWL studies over Africa. Articles in this focus collection use data from global climate models and the co-ordinated regional downscaling experiment to elucidate the regional and local scale climate responses to various warming levels. This may provide information that contributes meaningfully to the UNFCCC negotiation process and also for the development of adaptation and mitigation policies.

The carbon balance of the Amazon depends on a complex interplay between climate, soil and tree behaviour. Land surface models have difficulty in reproducing the observed biomass distribution and relationships between net productivity and biomass. A new model representing in more detail the effect of different succession stages is capable of observing this relationship (Rödig et al2018Environ. Res. Lett. 1–40). The key question for future models is how to incorporate more realistically the nutrient influences on growth and that of a future climate enriched with carbon dioxide.

With the Paris Agreement's ambition of limiting climate change to well below 2 °C, negative emission technologies (NETs) have moved into the limelight of discussions in climate science and policy. Despite several assessments, the current knowledge on NETs is still diffuse and incomplete, but also growing fast. Here, we synthesize a comprehensive body of NETs literature, using scientometric tools and performing an in-depth assessment of the quantitative and qualitative evidence therein. We clarify the role of NETs in climate change mitigation scenarios, their ethical implications, as well as the challenges involved in bringing the various NETs to the market and scaling them up in time. There are six major findings arising from our assessment: first, keeping warming below 1.5 °C requires the large-scale deployment of NETs, but this dependency can still be kept to a minimum for the 2 °C warming limit. Second, accounting for economic and biophysical limits, we identify relevant potentials for all NETs except ocean fertilization. Third, any single NET is unlikely to sustainably achieve the large NETs deployment observed in many 1.5 °C and 2 °C mitigation scenarios. Yet, portfolios of multiple NETs, each deployed at modest scales, could be invaluable for reaching the climate goals. Fourth, a substantial gap exists between the upscaling and rapid diffusion of NETs implied in scenarios and progress in actual innovation and deployment. If NETs are required at the scales currently discussed, the resulting urgency of implementation is currently neither reflected in science nor policy. Fifth, NETs face severe barriers to implementation and are only weakly incentivized so far. Finally, we identify distinct ethical discourses relevant for NETs, but highlight the need to root them firmly in the available evidence in order to render such discussions relevant in practice.

The most recent IPCC assessment has shown an important role for negative emissions technologies (NETs) in limiting global warming to 2 °C cost-effectively. However, a bottom-up, systematic, reproducible, and transparent literature assessment of the different options to remove CO₂ from the atmosphere is currently missing. In part 1 of this three-part review on NETs, we assemble a comprehensive set of the relevant literature so far published, focusing on seven technologies: bioenergy with carbon capture and storage (BECCS), afforestation and reforestation, direct air carbon capture and storage (DACCS), enhanced weathering, ocean fertilisation, biochar, and soil carbon sequestration. In this part, part 2 of the review, we present estimates of costs, potentials, and side-effects for these technologies, and qualify them with the authors' assessment. Part 3 reviews the innovation and scaling challenges that must be addressed to realise NETs deployment as a viable climate mitigation strategy. Based on a systematic review of the literature, our best estimates for sustainable global NET potentials in 2050 are 0.5–3.6 GtCO₂ yr⁻¹ for afforestation and reforestation, 0.5–5 GtCO₂ yr⁻¹ for BECCS, 0.5–2 GtCO₂ yr⁻¹ for biochar, 2–4 GtCO₂ yr⁻¹ for enhanced weathering, 0.5–5 GtCO₂ yr⁻¹ for DACCS, and up to 5 GtCO₂ yr⁻¹ for soil carbon sequestration. Costs vary widely across the technologies, as do their permanency and cumulative potentials beyond 2050. It is unlikely that a single NET will be able to sustainably meet the rates of carbon uptake described in integrated assessment pathways consistent with 1.5 °C of global warming.

We assess the literature on innovation and upscaling for negative emissions technologies (NETs) using a systematic and reproducible literature coding procedure. To structure our review, we employ the framework of sequential stages in the innovation process, with which we code each NETs article in innovation space. We find that while there is a growing body of innovation literature on NETs, 59% of the articles are focused on the earliest stages of the innovation process, 'research and development' (R&D). The subsequent stages of innovation are also represented in the literature, but at much lower levels of activity than R&D. Distinguishing between innovation stages that are related to the supply of the technology (R&D, demonstrations, scale up) and demand for the technology (demand pull, niche markets, public acceptance), we find an overwhelming emphasis (83%) on the supply side. BECCS articles have an above average share of demand-side articles while direct air carbon capture and storage has a very low share. Innovation in NETs has much to learn from successfully diffused technologies; appealing to heterogeneous users, managing policy risk, as well as understanding and addressing public concerns are all crucial yet not well represented in the extant literature. Results from integrated assessment models show that while NETs play a key role in the second half of the 21st century for 1.5 °C and 2 °C scenarios, the major period of new NETs deployment is between 2030 and 2050. Given that the broader innovation literature consistently finds long time periods involved in scaling up and deploying novel technologies, there is an urgency to developing NETs that is largely unappreciated. This challenge is exacerbated by the thousands to millions of actors that potentially need to adopt these technologies for them to achieve planetary scale. This urgency is reflected neither in the Paris Agreement nor in most of the literature we review here. If NETs are to be deployed at the levels required to meet 1.5 °C and 2 °C targets, then important post-R&D issues will need to be addressed in the literature, including incentives for early deployment, niche markets, scale-up, demand, and—particularly if deployment is to be hastened—public acceptance.

Atmospheric aerosols have complex and variable compositions and properties. While scientific interest is centered on the health and climatic effects of atmospheric aerosols, insufficient attention is given to their involvement in multiphase chemistry that alters their contribution as carriers of nutrients in ecosystems. However, there is experimental proof that the nutrient equilibria of both land and marine ecosystems have been disturbed during the Anthropocene period.

This review study first summarizes our current understanding of aerosol chemical processing in the atmosphere as relevant to biogeochemical cycles. Then it binds together results of recent modeling studies based on laboratory and field experiments, focusing on the organic and dust components of aerosols that account for multiphase chemistry, aerosol ageing in the atmosphere, nutrient (N, P, Fe) emissions, atmospheric transport, transformation and deposition. The human-driven contribution to atmospheric deposition of these nutrients, derived by global simulations using past and future anthropogenic emissions of pollutants, is put into perspective with regard to potential changes in nutrient limitations and biodiversity. Atmospheric deposition of nutrients has been suggested to result in human-induced ecosystem limitations with regard to specific nutrients. Such modifications favor the development of certain species against others and affect the overall functioning of ecosystems. Organic forms of nutrients are found to contribute to the atmospheric deposition of the nutrients N, P and Fe by 20%–40%, 35%–45% and 7%–18%, respectively. These have the potential to be key components of the biogeochemical cycles since there is initial proof of their bioavailability to ecosystems. Bioaerosols have been found to make a significant contribution to atmospheric sources of N and P, indicating potentially significant interactions between terrestrial and marine ecosystems. These results deserve further experimental and modeling studies to reduce uncertainties and understand the feedbacks induced by atmospheric deposition of nutrients to ecosystems.

Climate change is already having adverse impacts on ecosystems, communities and economic activities through higher temperatures, prolonged droughts, and more frequent extremes. However, a gap remains between public understanding, scientific knowledge about climate change, and changes in behaviour to effect adaptation. 'Serious games'—games used for purposes other than entertainment—are one way to reduce this adaptation deficit by enhancing opportunities for social learning and enabling positive action. Games can provide communities with the opportunity to interactively explore different climate futures, build capability and capacity for dealing with complex challenges, and socialise adaptation priorities with diverse publics. Using systematic review methods, this paper identifies, reviews, synthesises and assesses the literature on serious games for climate change adaptation. To determine where and how impact is achieved, we draw on an evaluation framework grounded in social learning, to assess which combinations of cognitive (knowledge and thinking), normative (norms and approaches) and relational (how people connect and network building) learning are achieved. Results show that factors influencing the overall success in influencing behaviour and catalysing learning for adaptation include generating high levels of inter- and intra- level trust between researchers, practitioners and community participants; strong debriefing and evaluation practices; and the use of experienced and knowledgeable facilitators. These results can help inform future game design, and research methodologies to develop robust ways for engaging with stakeholders and end users, and enhance learning effects for resilient climate futures.

Research on humans and the Earth system has historically occurred separately, with different teams and models devoted to each. Increasingly, however, these communities and models are becoming intricately linked. In this review, we survey the literature on integrated human-Earth system models, quantify the direction and strength of feedbacks in those models, and put them in context of other, more frequently considered, feedbacks in the Earth system. We find that such feedbacks have the potential to alter both human and Earth systems; however, there is significant uncertainty in these results, and the number of truly integrated studies remains small. More research, more models, and more studies are needed to robustly quantify the sign and magnitude of human-Earth system feedbacks. Integrating human and earth models entails significant complexity and cost, and researchers should carefully assess the costs and benefits of doing so with respect to the object of study.

Background: In response to the Paris Agreement under the United Nations Framework Convention on Climate Change, the research community was asked to estimate differences in sectoral-specific risks at 1.5 °C and 2 °C increases in global mean surface air temperature (SAT) above pre-industrial temperatures. Projections of the health risks of climate change typically focus on time periods and not on the magnitude of temperature change.

Objective: Summarize projections of health risks associated with temperature extremes and occupational heat stress, air quality, undernutrition, and vector-borne diseases to estimate how these risks would differ at increases in warming of 1.5 °C, 2 °C, and higher.

Methods: A comprehensive search strategy included English language publications since 2008 projecting health risks of climate change identified through established databases. Of 109 relevant publications, nearly all were for future time periods (e.g. in 2030 and 2050) rather than future SAT thresholds. Time periods were therefore converted to temperature changes based on the models and scenarios used.

Results: Warming of 1.5 °C is reached in about the 2030s for all multi-model means under all scenarios and warming of 2 °C is reached in about the 2050s under most scenarios. Of the 40 studies projecting risks at 1.5 and 2 °C increases of SAT, risks were higher at 2 °C for adverse health consequences associated with exposures to high ambient temperatures, ground-level ozone, and undernutrition, with regional variations. Risks for vector-borne diseases could increase or decrease with higher global mean temperatures, depending on regional climate responses and disease ecology.

Conclusions: The burden of many climate-sensitive health risks are projected to be greater at an increase of 2 °C SAT above pre-industrial temperatures than at 1.5 °C. Future projection studies should report results based on changes in global and regional mean SATs and time, to facilitate quantitative analyses of health risks and to inform the level of ambition and timing of adaptation interventions.

Since 1992, there has been a revolution in our ability to quantify the land ice contribution to sea level rise using a variety of satellite missions and technologies. Each mission has provided unique, but sometimes conflicting, insights into the mass trends of land ice. Over the last decade, over fifty estimates of land ice trends have been published, providing a confusing and often inconsistent picture. The IPCC Fifth Assessment Report (AR5) attempted to synthesise estimates published up to early 2013. Since then, considerable advances have been made in understanding the origin of the inconsistencies, reducing uncertainties in estimates and extending time series. We assess and synthesise results published, primarily, since the AR5, to produce a consistent estimate of land ice mass trends during the satellite era (1992–2016). We combine observations from multiple missions and approaches including sea level budget analyses. Our resulting synthesis is both consistent and rigorous, drawing on (i) the published literature, (ii) expert assessment of that literature, and (iii) a new analysis of Arctic glacier and ice cap trends combined with statistical modelling.

We present annual and pentad (five-year mean) time series for the East, West Antarctic and Greenland Ice Sheets and glaciers separately and combined. When averaged over pentads, covering the entire period considered, we obtain a monotonic trend in mass contribution to the oceans, increasing from 0.31 ± 0.35 mm of sea level equivalent for 1992–1996 to 1.85 ± 0.13 for 2012–2016. Our integrated land ice trend is lower than many estimates of GRACE-derived ocean mass change for the same periods. This is due, in part, to a smaller estimate for glacier and ice cap mass trends compared to previous assessments. We discuss this, and other likely reasons, for the difference between GRACE ocean mass and land ice trends.

Decentralization has the potential to reduce the scale of the piped distribution network needed to enable non-potable water reuse (NPR) in urban areas by producing recycled water closer to its point of use. However, tradeoffs exist between the economies of scale of treatment facilities and the size of the conveyance infrastructure, including energy for upgradient distribution of recycled water. To adequately capture the impacts from distribution pipes and pumping requirements, site-specific conditions must be accounted for. In this study, a generalized framework (a heuristic modeling approach using geospatial algorithms) is developed that estimates the financial cost, the energy use, and the greenhouse gas emissions associated with NPR (for toilet flushing) as a function of scale of treatment and conveyance networks with the goal of determining the optimal degree of decentralization. A decision-support platform is developed to assess and visualize NPR system designs considering topography, economies of scale, and building size. The platform can be used for scenario development to explore the optimal system size based on the layout of current or new buildings. The model also promotes technology innovation by facilitating the systems-level comparison of options to lower costs, improve energy efficiency, and lower greenhouse gas emissions.

Solar photovoltaic (PV) electricity generation can greatly reduce both air pollutant and greenhouse gas emissions compared to fossil fuel electricity generation. The Chinese government plans to greatly scale up solar PV installation between now and 2030. However, different PV development pathways will influence the range of air quality and climate benefits. Benefits depend on how much electricity generated from PV is integrated into power grids and the type of power plant displaced. Using a coal-intensive power sector projection as the base case, we estimate the climate, air quality, and related human health benefits of various 2030 PV deployment scenarios. We use the 2030 government goal of 400 GW installed capacity but vary the location of PV installation and the extent of inter-provincial PV electricity transmission. We find that deploying distributed PV in the east with inter-provincial transmission maximizes potential CO₂ reductions and air quality-related health benefits (4.2% and 1.2% decrease in national total CO₂ emissions and air pollution-related premature deaths compared to the base case, respectively). Deployment in the east with inter-provincial transmission results in the largest benefits because it maximizes displacement of the dirtiest coal-fired power plants and minimizes PV curtailment, which is more likely to occur without inter-provincial transmission. We further find that the maximum co-benefits achieved with deploying PV in the east and enabling inter-provincial transmission are robust under various maximum PV penetration levels in both provincial and regional grids. We find large potential benefits of policies that encourage distributed PV deployment and facilitate inter-provincial PV electricity transmission in China.

The Urban Heat Island (UHI), the tendency for urban areas to be hotter than rural regions, represents a significant health concern in summer as urban populations are exposed to elevated temperatures. A number of studies suggest that the UHI increases during warmer conditions, however there has been no investigation of this for a large ensemble of cities. Here we compare urban and rural temperatures in 54 US cities for 2000–2015 and show that the intensity of the Urban Heat Island, measured here as the differences in daily-minimum or daily-maximum temperatures between urban and rural stations or ΔT, in fact tends to decrease with increasing temperature in most cities (38/54). This holds when investigating daily variability, heat extremes, and variability across climate zones and is primarily driven by changes in rural areas. We relate this change to large-scale or synoptic weather conditions, and find that the lowest ΔT nights occur during moist weather conditions. We also find that warming cities have not experienced an increasing Urban Heat Island effect.

A global Earth system model is used to study the relationship between heat waves and surface ozone levels over land areas around the world that could experience either large decreases or little change in future ozone precursor emissions. The model is driven by emissions of greenhouse gases and ozone precursors from a medium-high emission scenario (Representative Concentration Pathway 6.0–RCP6.0) and is compared to an experiment with anthropogenic ozone precursor emissions fixed at 2005 levels. With ongoing increases in greenhouse gases and corresponding increases in average temperature in both experiments, heat waves are projected to become more intense over most global land areas (greater maximum temperatures during heat waves). However, surface ozone concentrations on future heat wave days decrease proportionately more than on non-heat wave days in areas where ozone precursors are prescribed to decrease in RCP6.0 (e.g. most of North America and Europe), while surface ozone concentrations in heat waves increase in areas where ozone precursors either increase or have little change (e.g. central Asia, the Mideast, northern Africa). In the stabilized ozone precursor experiment, surface ozone concentrations increase on future heat wave days compared to non-heat wave days in most regions except in areas where there is ozone suppression that contributes to decreases in ozone in future heat waves. This is likely associated with effects of changes in isoprene emissions at high temperatures (e.g. west coast and southeastern North America, eastern Europe).

Greenhouse gas emissions embodied in trade is a growing concern for the international community. Multiple studies have highlighted drawbacks in the territorial and production-based accounting of greenhouse gas emissions because it neglects emissions from the consumption of goods in trade. This creates weak carbon leakage and complicates international agreements on emissions regulations. Therefore, we estimated consumption-based emissions using input-output analysis and life cycle assessment to calculate the greenhouse gas emissions hidden in meat and dairy products in Hong Kong, a city predominately reliant on imports. We found that emissions solely from meat and dairy consumption were higher than the city's total greenhouse gas emissions using conventional production-based calculation. This implies that government reports underestimate more than half of the emissions, as 62% of emissions are embodied in international trade. The discrepancy emphasizes the need of transitioning climate targets and policy to consumption-based accounting. Furthermore, we have shown that dietary change from a meat-heavy diet to a diet in accordance with governmental nutrition guidelines could achieve a 67% reduction in livestock-related emissions, allowing Hong Kong to achieve the Paris Agreement targets for 2030. Consequently, we concluded that consumption-based accounting for greenhouse gas emissions is crucial to target the areas where emissions reduction is realistically achievable, especially for import-reliant cities like Hong Kong.

Georeferenced information on road infrastructure is essential for spatial planning, socio-economic assessments and environmental impact analyses. Yet current global road maps are typically outdated or characterized by spatial bias in coverage. In the Global Roads Inventory Project we gathered, harmonized and integrated nearly 60 geospatial datasets on road infrastructure into a global roads dataset. The resulting dataset covers 222 countries and includes over 21 million km of roads, which is two to three times the total length in the currently best available country-based global roads datasets. We then related total road length per country to country area, population density, GDP and OECD membership, resulting in a regression model with adjusted R² of 0.90, and found that that the highest road densities are associated with densely populated and wealthier countries. Applying our regression model to future population densities and GDP estimates from the Shared Socioeconomic Pathway (SSP) scenarios, we obtained a tentative estimate of 3.0–4.7 million km additional road length for the year 2050. Large increases in road length were projected for developing nations in some of the world's last remaining wilderness areas, such as the Amazon, the Congo basin and New Guinea. This highlights the need for accurate spatial road datasets to underpin strategic spatial planning in order to reduce the impacts of roads in remaining pristine ecosystems.

Following the adoption of the Paris Agreement, there has been an increasing interest in quantifying impacts at discrete levels of global mean temperature (GMT) increase such as 1.5 °C and 2 °C above pre-industrial levels. Consequences of anthropogenic greenhouse gas emissions on agricultural productivity have direct and immediate relevance for human societies. Future crop yields will be affected by anthropogenic climate change as well as direct effects of emissions such as CO₂ fertilization. At the same time, the climate sensitivity to future emissions is uncertain. Here we investigate the sensitivity of future crop yield projections with a set of global gridded crop models for four major staple crops at 1.5 °C and 2 °C warming above pre-industrial levels, as well as at different CO₂ levels determined by similar probabilities to lead to 1.5 °C and 2 °C, using climate forcing data from the Half a degree Additional warming, Prognosis and Projected Impacts project. For the same CO₂ forcing, we find consistent negative effects of half a degree warming on productivity in most world regions. Increasing CO₂ concentrations consistent with these warming levels have potentially stronger but highly uncertain effects than 0.5 °C warming increments. Half a degree warming will also lead to more extreme low yields, in particular over tropical regions. Our results indicate that GMT change alone is insufficient to determine future impacts on crop productivity.

As the planet's dominant land use, agriculture often competes with the preservation of natural systems that provide globally and regionally important ecosystem services. How agriculture impacts ecosystem service delivery varies regionally, among services being considered, and across spatial scales. Here, we assess the tradeoffs between four ecosystem services—agricultural production, carbon storage, biophysical climate regulation, and biodiversity—using as a case study the Amazon, an active frontier of agricultural expansion. We find that the highest values for each of the ecosystem services are concentrated in different regions. Agricultural production potential and carbon storage are highest in the north and west, biodiversity greatest in the west, and climate regulation services most vulnerable to disruption in the south and east. Using a simple optimization model, we find that under scenarios of agricultural expansion that optimize total production across ecosystem services, small increases in priority for one ecosystem service can lead to reductions in other services by as much as 140%. Our results highlight the difficulty of managing landscapes for multiple environmental goals; the approach presented here can be adapted to guide value-laden conservation decisions and identify potential solutions that balance priorities.

Irrigation modifies land-surface water and energy budgets, and also influences weather and climate. However, current earth-system models, used for weather prediction and climate projection, are still in their infancy stage to consider irrigation effects. This study used long-term data collected from two contrasting (irrigated and rainfed) nearby maize-soybean rotation fields, to study the effects of irrigation memory on local hydroclimate. For a 12 year average, irrigation decreases summer surface-air temperature by less than 1 °C and increases surface humidity by 0.52 g kg⁻¹. The irrigation cooling effect is more pronounced and longer lasting for maize than for soybean. Irrigation reduces maximum, minimum, and averaged temperature over maize by more than 0.5 °C for the first six days after irrigation, but its temperature effect over soybean is mixed and negligible two or three days after irrigation. Irrigation increases near-surface humidity over maize by about 1 g kg⁻¹ up to ten days and increases surface humidity over soybean (~ 0.8 g kg⁻¹) with a similar memory. These differing effects of irrigation memory on temperature and humidity are associated with respective changes in the surface sensible and latent heat fluxes for maize and soybean. These findings highlight great need and challenges for earth-system models to realistically simulate how irrigation effects vary with crop species and with crop growth stages, and to capture complex interactions between agricultural management and water-system components (crop transpiration, precipitation, river, reservoirs, lakes, groundwater, etc.) at various spatial and temporal scales.

Ambient air pollution in India contributes to negative health impacts and early death. Ground-based monitors often used to quantify health impacts are located in urban regions, yet approximately 70% of India's population lives in rural communities. We simulate high-resolution concentrations of fine particulate matter (PM) and ozone from the regional Community Multi-scale Air Quality model over northern India, including updated estimates of anthropogenic emissions for transportation, residential combustion and location-based industrial and electrical generating emissions in a new anthropogenic emissions inventory. These simulations inform seasonal air quality and health impacts due to anthropogenic emissions, contrasting urban versus rural regions. For our northern India domain, we estimate 463 200 (95% confidence interval: 444 600–482 600) adults die prematurely each year from PM_2.5 and that 37 800 (28 500–48 100) adults die prematurely each year from O₃. This translates to 5.8 deaths per 10 000 attributable to air pollution out of an annual rate of 72 deaths per 10 000 (8.1% of deaths) using 2010 estimates. We estimate that the majority of premature deaths resulting from PM_2.5 and O₃ are in rural (383 600) as opposed to urban (117 200) regions, where we define urban as cities and towns with populations of at least 100 000 people. These findings indicate the need for rural monitoring and appropriate health studies to understand and mitigate the effects of ambient air pollution on this population in addition to supporting model evaluation.

Alpine lakes in the interior of Tibet, the endorheic Changtang Plateau (CP), serve as 'sentinels' of regional climate change. Recent studies indicated that accelerated climate change has driven a widespread area expansion in lakes across the CP, but comprehensive and accurate quantifications of their storage changes are hitherto rare. This study integrated optical imagery and digital elevation models to uncover the fine spatial details of lake water storage (LWS) changes across the CP at an annual timescale after the new millennium (from 2002–2015). Validated by hypsometric information based on long-term altimetry measurements, our estimated LWS variations outperform some existing studies with reduced estimation biases and improved spatiotemporal coverages. The net LWS increased at an average rate of 7.34 ± 0.62 Gt yr⁻¹ (cumulatively 95.42 ± 8.06 Gt), manifested as a dramatic monotonic increase of 9.05 ± 0.65 Gt yr⁻¹ before 2012, a deceleration and pause in 2013–2014, and then an intriguing decline after 2014. Observations from the Gravity Recovery and Climate Experiment satellites reveal that the LWS pattern is in remarkable agreement with that of regional mass changes: a net effect of precipitation minus evapotranspiration (P-ET) in endorheic basins. Despite some regional variations, P-ET explains ~70% of the net LWS gain from 2002–2012 and the entire LWS loss after 2013. These findings clearly suggest that the water budget from net precipitation (i.e. P-ET) dominates those of glacier melt and permafrost degradation, and thus acts as the primary contributor to recent lake area/volume variations in endorheic Tibet. The produced lake areas and volume change dataset is freely available through PANAGEA (https://doi.pangaea.de/10.1594/PANGAEA.888706).

Accurate wind resource assessments are crucial in the development of wind farm projects. However, it is common practice to estimate the wind yield over the next 20 years from short-term measurements and reanalysis data of the past 20 years, even though wind climatology is expected to change under the future climate. The present work examines future changes in wind power output over Europe using an ensemble of ESMs. The power output is calculated using the entire wind speed PDF and a non-constant power conversion coefficient. Based on this method, the ESM ensemble projects changes in near-future power outputs with a spatially varying magnitude between −12% and 8%. The most extreme changes occur over the Mediterranean region. For the first time, the sensitivity of these future change in power output to the type of wind turbine is also investigated. The analysis reveals that the projected wind power changes may vary in up to half of their magnitude, depending on the type of turbine and region of interest. As such, we recommend that wind industries fully account for projected near-future changes in wind power output by taking them into account as a well-defined loss/gain and uncertainty when estimating the yield of a future wind farm.

An anticipated consequence of ongoing global warming is the intensification of the rainfall regimes meaning longer dry spells and heavier precipitation when it rains, with potentially high hydrological and socio-economic impacts. The semi-arid regions of the intertropical band, such as the Sahel, are facing particularly serious challenges in this respect since their population is strongly vulnerable to extreme climatic events. Detecting long term trends in the Sahelian rainfall regime is thus of great societal importance, while being scientifically challenging because datasets allowing for such detection studies are rare in this region. This study addresses this challenge by making use of a large set of daily rain gauge data covering the Sahel (defined in this study as extending from 20°W–10°E and from 11°N–18°N) since 1950, combined with an unparalleled 5 minute rainfall observations available since 1990 over the AMMA-CATCH Niger observatory.

The analysis of the daily data leads to the assertion that a hydro-climatic intensification is actually taking place in the Sahel, with an increasing mean intensity of rainy days associated with a higher frequency of heavy rainfall. This leads in turn to highlight that the return to wetter annual rainfall conditions since the beginning of the 2000s—succeeding the 1970–2000 drought—is by no mean a recovery towards the much smoother regime that prevailed during the 1950s and 1960s. It also provides a vision of the contrasts existing between the West Sahel and the East Sahel, the East Sahel experiencing a stronger increase of extreme rainfall.

This regional vision is complemented by a local study at sub-daily timescales carried out thanks to the 5 minute rainfall series of the AMMA-CATCH Niger observatory (12000 km²). The increasing intensity of extreme rainfall is also visible at sub-daily timescales, the annual maximum intensities have increased at an average rate of 2%–6% per decade since 1990 for timescales ranging from 5 min to 1 hour. Both visions—regional/long term/daily on the one hand, and local/27/years/sub-daily, on the other—converge to the conclusion that, rather than a rainfall recovery, the Sahel is experiencing a new era of climate extremes that roughly started at the beginning of this century.

Vegetation has a substantial impact on the local climate. Land cover changes through afforestation or deforestation can amplify or mitigate climate warming by changes in biophysical and biogeochemical mechanisms. In the montane to subalpine area of the Eastern Alps in Europe, where forests have constantly expanded in the last four decades, data of meteorological stations show a consistent reduction in incoming global radiation for the period 2000–2015. To assess the potential role of forests in contributing to such a reduction, three site pairs in Central Europe with neighbouring forest and non-forest sites were analysed. In all the pairs, a lower amount of incoming radiation was recorded at the forest site. When biophysical mechanisms such as albedo, surface roughness and Bowen ratio changes were modelled together with changes in global radiation, the total radiative forcing accounted for a rate of change in air temperature was equal to 0.032 °C ± 0.01 °C per Wm⁻². These results suggest that local climate is influenced by land cover change through afforestation both via albedo and radiation feedbacks but also by means of indirect biophysical and species-dependent mechanisms. The data obtained for the site pairs in Central Europe are finally discussed to infer the occurrence of similar forest-driven effects in the Eastern Alps which may explain part of the solar dimming observed in high elevation weather stations.

International climate policy uses global mean temperature rise limits as proxies for societally acceptable levels of climate change. These limits are informed by risk assessments which draw upon projections of climate impacts under various levels of warming. Here we illustrate that indicators used to define limits of warming and those used to track the evolution of the Earth System under climate change are not directly comparable. Depending on the methodological approach, differences can be time-variant and up to 0.2 °C for a warming of 1.5 °C above pre-industrial levels. This might lead to carbon budget overestimates of about 10 years of continued year-2015 emissions, and about a 10% increase in estimated 2100 sea-level rise. Awareness of this definitional mismatch is needed for a more effective communication between scientists and decision makers, as well as between the impact and physical climate science communities.

Due to unplanned growth, large extension and limited resources, most megacities in the developing world are vulnerable to hydrological hazards and infectious diseases caused by waterborne pathogens. Here we aim to elucidate the extent of the relation between the spatial heterogeneity of physical and socio-economic factors associated with hydrological hazards (flooding and scarcity) and the spatial distribution of gastrointestinal disease in Mexico City, a megacity with more than 8 million people. We applied spatial statistics and multivariate regression analyses to high resolution records of gastrointestinal diseases during two time frames (2007–2009 and 2010–2014). Results show a pattern of significant association between water flooding events and disease incidence in the city center (lowlands). We also found that in the periphery (highlands), higher incidence is generally associated with household infrastructure deficiency. Our findings suggest the need for integrated and spatially tailored interventions by public works and public health agencies, aimed to manage socio-hydrological vulnerability in Mexico City.

Recent studies have assessed the impacts of climate change at specific global temperature targets using relatively short (30 year ) transient time-slice periods which are characterized by a steady increase in global mean temperature with time. The Inter-Sectoral Impacts Model Intercomparison Project Phase 2b (ISIMIP2b) provides trend-preserving bias-corrected climate model datasets over six centuries for four global climate models (GCMs) which therefore can be used to evaluate the potential effects of using time-slice periods from stabilized climate state rather than time-slice periods from transient climate state on climate change impacts.

Using the H08 global hydrological model, the impacts of climate change, quantified as the deviation from the pre-industrial era, and the signal-to-noise (SN) ratios were computed for five hydrological variables, namely evapotranspiration (EVA), precipitation (PCP), snow water equivalent (SNW), surface temperature (TAR), and total discharge (TOQ) over 20 regions comprising the global land area.

A significant difference in EVA for the transient and stabilized climate states was systematically detected for all four GCMs. In addition, three out of the four GCMs indicated that significant differences in PCP, TAR, and TOQ for the transient and stabilized climate states could also be detected over a small fraction of the globe. For most regions, the impacts of climate change toward EVA, PCP, and TOQ are indicated to be underestimated using the transient climate state simulations. The transient climate state was also identified to underestimate the SN ratios compared to the stabilized climate state. For both the global and regional scales, however, there was no indication that surface areas associated with the different classes of SN ratios changed depending on the two climate states (t-test, p > 0.01).

Transient time slices may be considered a good approximation of the stabilized climate state, for large-scale hydrological studies and many regions and variables, as: (1) impacts of climate change were only significantly different from those of the stabilized climate state for a small fraction of the globe, and (2) these differences were not indicated to alter the robustness of the impacts of climate change.

In the United States, the electricity sector is a major focus for implementing policies to meet national, state, or local mandatory or voluntary CO₂ emissions reductions goals. Thus, it is important to have timely and available information on greenhouse gas emissions generated by the power sector to ensure that the policies implemented achieve intended emissions reductions. This work is the first to develop a transparent method to compute the emissions intensity for the US electricity section from 2001 through 2017 at different temporal (annual, quarterly, monthly) and regional (US, NERC, and state) levels. We find that between 2001 and 2017 the average annual CO₂ emissions intensity of electricity production in the United States decreased by 30%, from 630 g CO₂ kWh⁻¹ to 439 g CO₂ kWh⁻¹. This change in CO₂ intensity is attributable to an increase in generation from natural gas and wind accompanied by a reduction in coal-fired power generation. The decline in carbon intensity varies across regions, with the largest reduction between 2001 and 2017 from power plants in the Northeast (58%) and the smallest reduction from power plants in the Texas region (27%). In absolute terms the South-central region saw the largest decrease in emissions intensity (358 g CO₂ kWh⁻¹) and Texas saw the smallest (164 g CO₂ kWh⁻¹). We also find that replacing coal generation with natural gas or renewables has increased the monthly correlation of CO₂ intensity between regions. At the state level, Delaware saw the largest decrease in CO₂ intensity (466 g CO₂ kWh⁻¹), and Idaho is the only state that has not decreased CO₂ intensity since 2001.

Agriculture is one of the sectors that is expected to be most significantly impacted by climate change. There has been considerable interest in assessing these impacts and many recent studies investigating agricultural impacts for individual countries and regions using an array of models. However, the great majority of existing studies explore impacts on a country or region of interest without explicitly accounting for impacts on the rest of the world. This approach can bias the results of impact assessments for agriculture given the importance of global trade in this sector. Due to potential impacts on relative competitiveness, international trade, global supply, and prices, the net impacts of climate change on the agricultural sector in each region depend not only on productivity impacts within that region, but on how climate change impacts agricultural productivity throughout the world. In this study, we apply a global model of agriculture and forestry to evaluate climate change impacts on US agriculture with and without accounting for climate change impacts in the rest of the world. In addition, we examine scenarios where trade is expanded to explore the implications for regional allocation of production, trade volumes, and prices. To our knowledge, this is one of the only attempts to explicitly quantify the relative importance of accounting for global climate change when conducting regional assessments of climate change impacts. The results of our analyses reveal substantial differences in estimated impacts on the US agricultural sector when accounting for global impacts vs. US-only impacts, particularly for commodities where the United States has a smaller share of global production. In addition, we find that freer trade can play an important role in helping to buffer regional productivity shocks.

We assess the feasibility and cost-effectiveness of renewable energy alternatives to Inga 3, a 4.8-GW hydropower project on the Congo River, to serve the energy needs of the host country, the Democratic Republic of Congo (DRC), and the main buyer, South Africa. To account for a key uncertainty in the literature regarding the additional economic impacts of managing variable wind and solar electricity, we built a spatially and temporally detailed power system investment model for South Africa. We find that a mix of wind, solar photovoltaics, and some natural gas is more cost-effective than Inga 3 to meet future demand except in scenarios with pessimistic assumptions about wind technology performance. If a low load growth forecast is used, including Inga 3 in the power mix results in higher system cost across all sensitivities. In our scenarios, the effect of Inga 3 deployment on South African power system cost ranges from an increase of ZAR 4300 (US$ 330) million annually to savings of ZAR 1600 (US$ 120) million annually by 2035. A cost overrun as low as 20% makes the Inga 3 scenarios more expensive in all sensitivity cases. Including time and cost overruns and losses in transmission from DRC to South Africa make Inga 3 an even less attractive investment. For DRC, through analysis of spatial datasets representing technical, physical, and environmental constraints, we find abundant renewable energy potential: 60 GW of solar photovoltaic and 0.6–2.3 GW of wind located close to transmission infrastructure have levelized costs less than US$ 0.07 per kWh, or the anticipated cost of Inga 3 to residential consumers.

Systematic model inter-comparison helps to narrow discrepancies in the analysis of the future impact of climate change on agricultural production. This paper presents a set of alternative scenarios by five global climate and agro-economic models. Covering integrated assessment (IMAGE), partial equilibrium (CAPRI, GLOBIOM, MAgPIE) and computable general equilibrium (MAGNET) models ensures a good coverage of biophysical and economic agricultural features. These models are harmonized with respect to basic model drivers, to assess the range of potential impacts of climate change on the agricultural sector by 2050. Moreover, they quantify the economic consequences of stringent global emission mitigation efforts, such as non-CO₂ emission taxes and land-based mitigation options, to stabilize global warming at 2 °C by the end of the century under different Shared Socioeconomic Pathways. A key contribution of the paper is a vis-à-vis comparison of climate change impacts relative to the impact of mitigation measures. In addition, our scenario design allows assessing the impact of the residual climate change on the mitigation challenge. From a global perspective, the impact of climate change on agricultural production by mid-century is negative but small. A larger negative effect on agricultural production, most pronounced for ruminant meat production, is observed when emission mitigation measures compliant with a 2 °C target are put in place. Our results indicate that a mitigation strategy that embeds residual climate change effects (RCP2.6) has a negative impact on global agricultural production relative to a no-mitigation strategy with stronger climate impacts (RCP6.0). However, this is partially due to the limited impact of the climate change scenarios by 2050. The magnitude of price changes is different amongst models due to methodological differences. Further research to achieve a better harmonization is needed, especially regarding endogenous food and feed demand, including substitution across individual commodities, and endogenous technological change.

The last major volcanic eruptions, the Agung in 1963, El Chichon in 1982 and Pinatubo in 1991, were each associated with a cooling of the troposphere that has been observed over large continental areas and over the Western Pacific, the Indian Ocean and the Southern Atlantic. Simultaneously, Eastern tropical Pacific temperatures increased due to prevailing El Niño conditions. Here we show that the pattern of these near-surface temperature anomalies is partly reproduced with decadal simulations of the EC-Earth model initialised with climate observations and forced with an estimate of the observed volcanic aerosol optical thickness. Sensitivity experiments highlight a cooling induced by the volcanic forcing, whereas El Niño events following the eruptions would have occurred even without volcanic eruptions. Focusing on the period 1961–2001, the main source of skill of this decadal forecast system during the first 2 years is related to the initialisation of the model. The contribution of the initialisation to the skill becomes smaller than the contribution of the volcanic forcing after two years, the latter being substantial in the Western Pacific, the Indian Ocean and the Western Atlantic. Two simple protocols for real time forecasts are investigated: using the forcing of a past volcanic eruption to simulate the forcing of a new one, and applying a two-year exponential decay to the initial stratospheric aerosol load observed at the beginning of the forecast. This second protocol applied in retrospective forecasts allows a partial reproduction of the skill attained with observed forcing.

Anthropogenic land-use and land cover changes (LULCC) affect global climate and global terrestrial carbon (C) cycle. However, relatively few studies have quantified the impacts of future LULCC on terrestrial carbon cycle. Here, using Earth system model simulations performed with and without future LULCC, under the RCP8.5 scenario, we find that in response to future LULCC, the carbon cycle is substantially weakened: browning, lower ecosystem C stocks, higher C loss by disturbances and higher C turnover rates are simulated. Projected global greening and land C storage are dampened, in all models, by 22% and 24% on average and projected C loss by disturbances enhanced by ~49% when LULCC are taken into account. By contrast, global net primary productivity is found to be only slightly affected by LULCC (robust +4% relative enhancement compared to all forcings, on average). LULCC is projected to be a predominant driver of future C changes in regions like South America and the southern part of Africa. LULCC even cause some regional reversals of projected increased C sinks and greening, particularly at the edges of the Amazon and African rainforests. Finally, in most carbon cycle responses, direct removal of C dominates over the indirect CO₂ fertilization due to LULCC. In consequence, projections of land C sequestration potential and Earth's greening could be substantially overestimated just because of not fully accounting for LULCC.

Changes in the hydrological cycle are among the aspects of climate change most relevant for human systems and ecosystems. Besides trends in overall wetting or drying, changes in temporal characteristics of wetting and drying are of crucial importance in determining the climate hazard posed by such changes. This is particularly the case for tropical regions, where most precipitation occurs during the rainy season and changes in rainy season onset and length have substantial consequences. Here we present projections for changes in tropical rainy season lengths for mean temperature increase of 1.5 °C and 2 °C above pre-industrial levels. Based on multi-ensemble quasi-stationary simulations at these warming levels, our analysis indicates robust changes in rainy season characteristics in large parts of the tropics despite substantial natural variability. Specifically, we report a robust shortening of the rainy season for all of tropical Africa as well as north-east Brazil. About 27% of West Africa is projected to experience robust changes in the rainy season length with a mean shortening of about 7 days under 1.5 °C. We find that changes in the temporal characteristics are largely unrelated to changes in overall precipitation, highlighting the importance of investigating both separately.

Low productivity cattle ranching, with its linkages to rural poverty, deforestation and greenhouse gas (GHG) emissions, remains one of the largest sustainability challenges in Brazil and has impacts worldwide. There is a nearly universal call to intensify extensive beef cattle production systems to spare land for crop production and nature and to meet Brazil's Intended Nationally Determined Contribution to reducing global climate change. However, different interventions aimed at the intensification of livestock systems in Brazil may involve substantial social and environmental tradeoffs. Here we examine these tradeoffs using a whole-farm model calibrated for the Brazilian agricultural frontier state of Mato Grosso, one of the largest soybean and beef cattle production regions in the world. Specifically, we compare the costs and benefits of a typical extensive, continuously grazed cattle system relative to a specialized soybean production system and two improved cattle management strategies (rotational grazing and integrated soybean-cattle) under different climate scenarios. We found clear tradeoffs in GHG and nitrogen emissions, climate resilience, and water and energy use across these systems. Relative to continuously grazed or rotationally grazed cattle systems, the integreated soybean-cattle system showed higher food production and lower GHG emissions per unit of human digestible protein, as well as increased resilience under climate change (both in terms of productivity and financial returns). All systems suffered productivity and profitability losses under severe climate change, highlighting the need for climate smart agricultural development strategies in the region. By underscoring the economic feasibility of improving the performance of cattle systems, and by quantifying the tradeoffs of each option, our results are useful for directing agricultural and climate policy.

We use the 100-member Grand Ensemble with the climate model MPI-ESM to evaluate the controllability of mean and extreme European summer temperatures with the global mean temperature targets in the Paris Agreement. We find that European summer temperatures at 2 °C of global warming are on average 1 °C higher than at 1.5 °C of global warming with respect to pre-industrial levels. In a 2 °C warmer world, one out of every two European summer months would be warmer than ever observed in our current climate. Daily maximum temperature anomalies for extreme events with return periods of up to 500 years reach return levels of 7 °C at 2 °C of global warming and 5.5 °C at 1.5 °C of global warming. The largest differences in return levels for shorter return periods of 20 years are over southern Europe, where we find the highest mean temperature increase. In contrast, for events with return periods of over 100 years these differences are largest over central Europe, where we find the largest changes in temperature variability. However, due to the large effect of internal variability, only four out of every ten summer months in a 2 °C warmer world present mean temperatures that could be distinguishable from those in a 1.5 °C world. The distinguishability between the two climates is largest over southern Europe, while decreasing to around 10% distinguishable months over eastern Europe. Furthermore, we find that 10% of the most extreme and severe summer maximum temperatures in a 2 °C world could be avoided by limiting global warming to 1.5 °C.

Little empirical evidence on the economic value of biological control of pests at farm level is available to improve economic decision-making by farmers and policy makers. Using insect sampling and household survey in an integrated bio-economic analysis framework, this paper studies farmers' crop management practices in cotton in the North China Plain, and estimates the marginal value of natural enemies and costs of chemical insecticides to farmers. Ladybeetles (mainly Harmonia axyridis, Propylea japonica, and Coccinellaseptempunctata), the dominant natural enemy group that controls the primary pest (aphid) in cotton in our study area, provide a significant economic benefit that is unknown to the farmers. Even at the current high levels of insecticide use, an additional ladybeetle provides an economic benefit of 0.05 CNY (almost USD 0.01) to farmers. The use of broad-spectrum insecticides by farmers is alarmingly excessive, not only undermining farmers' cotton profitability but also inducing social costs as well as disruption of the natural pest suppression system. Doubling current ladybeetle density in cotton field could gain an estimated USD 300 million for cotton farmers in China, providing a strong economic case for policies to move the pest control system towards a more ecologically-based regime, with positive consequences for farm income and environmental health. With rising use of biological control service provided by natural enemies such as ladybeetles in cotton fields, significant falls in farmers' insecticide use would be expected, which could raise the value of ladybeetles and other natural enemies even further. The results indicate that there is an urgent need to rationalize inputs and move forward to improved agro-ecosystem management in smallholder farming system. Raising knowledge and awareness on the costs and value of biological pest control versus insecticides among farmers and policy makers and having effective extension service, are priorities towards achieving a more ecologically-based approach to crop protection on smallholder farms.

Mitigation scenarios to limit global warming to 1.5 °C or less in 2100 often rely on large amounts of carbon dioxide removal (CDR), which carry significant potential social, environmental, political and economic risks. A precautionary approach to scenario creation is therefore indicated. This letter presents the results of such a precautionary modelling exercise in which the models C-ROADS and En-ROADS were used to generate a series of 1.5 °C mitigation scenarios that apply increasingly stringent constraints on the scale and type of CDR available. This allows us to explore the trade-offs between near-term stringency of emission reductions and assumptions about future availability of CDR. In particular, we find that regardless of CDR assumptions, near-term ambition increase ('ratcheting') is required for any 1.5 °C pathway, making this letter timely for the facilitative, or Talanoa, dialogue to be conducted by the UNFCCC in 2018. By highlighting the difference between net and gross reduction rates, often obscured in scenarios, we find that mid-term gross CO₂ emission reduction rates in scenarios with CDR constraints increase to levels without historical precedence. This in turn highlights, in addition to the need to substantially increase CO₂ reduction rates, the need to improve emission reductions for non-CO₂ greenhouse gases. Further, scenarios in which all or part of the CDR is implemented as non-permanent storage exhibit storage loss emissions, which partly offset CDR, highlighting the importance of differentiating between net and gross CDR in scenarios. We find in some scenarios storage loss trending to similar values as gross CDR, indicating that gross CDR would have to be maintained simply to offset the storage losses of CO₂ sequestered earlier, without any additional net climate benefit.

The assessment of health impacts associated with airborne particulate matter smaller than 2.5 μm in diameter (PM_2.5) relies on aerosol concentrations derived either from monitoring networks, satellite observations, numerical models, or a combination thereof. When global chemistry-transport models are used for estimating PM_2.5, their relatively coarse resolution has been implied to lead to underestimation of health impacts in densely populated and industrialized areas. In this study the role of spatial resolution and of vertical layering of a regional air quality model, used to compute PM_2.5 impacts on public health and mortality, is investigated. We utilize grid spacings of 100 km and 20 km to calculate annual mean PM_2.5 concentrations over Europe, which are in turn applied to the estimation of premature mortality by cardiovascular and respiratory diseases. Using model results at a 100 km grid resolution yields about 535 000 annual premature deaths over the extended European domain (242 000 within the EU-28), while numbers approximately 2.4% higher are derived by using the 20 km resolution. Using the surface (i.e. lowest) layer of the model for PM_2.5 yields about 0.6% higher mortality rates compared with PM_2.5 averaged over the first 200 m above ground. Further, the calculation of relative risks (RR) from PM_2.5, using 0.1 μg m⁻³ size resolution bins compared to the commonly used 1 μg m⁻³, is associated with ±0.8% uncertainty in estimated deaths. We conclude that model uncertainties contribute a small part of the overall uncertainty expressed by the 95% confidence intervals, which are of the order of ±30%, mostly related to the RR calculations based on epidemiological data.

Climate change is expected to impact the habitability of many places around the world in significant and unprecedented ways in the coming decades. While previous studies have provided estimates of populations potentially exposed to various climate impacts, little work has been done to assess the number of people that may actually be displaced or where they will choose to go. Here we modify a diffusion-based model of human mobility in combination with population, geographic, and climatic data to estimate the sources, destinations, and flux of potential migrants as driven by sea level rise (SLR) in Bangladesh in the years 2050 and 2100. Using only maps of population and elevation, we predict that 0.9 million people (by year 2050) to 2.1 million people (by year 2100) could be displaced by direct inundation and that almost all of this movement will occur locally within the southern half of the country. We also find that destination locations should anticipate substantial additional demands on jobs (594 000), housing (197 000), and food (783 × 10⁹ calories) by mid-century as a result of those displaced by SLR. By linking the sources of migrants displaced by SLR with their likely destinations, we demonstrate an effective approach for predicting climate-driven migrant flows, especially in data-limited settings.

As the global population increases and the climate changes, ensuring a secure food supply is increasingly important. One strategy is irrigation, which allows for crops to be grown outside their optimal climate growing regions and which buffers against climate variability. Although irrigation is a positive climate adaptation mechanism for agriculture, it has a potentially negative effect on water resources as it can lead to groundwater depletion and diminished surface water supplies. This study quantifies how crop yields are affected by climate variability and extremes and the impact of irrigation on crop yield increases under various growing-season climate conditions. To do this, we use historical climate data and county-level rainfed and irrigated crop yields for maize, soybean, winter and spring wheat over the US to analyze the relationship between climate, crop yields, and irrigation. We find that there are optimal climates, specific to each crop, where irrigation provides a benefit and other conditions where irrigation proves to have marginal, if any, benefits. Furthermore, the relationship between crop yields and climate has changed over the last decades, with a changing sensitivity in the relationship of soybean and winter wheat yields to certain climate variables, like crop reference evapotranspiration. These two conclusions have important implications for agricultural and water resource system planning, as it implies there are more optimal climate conditions where irrigation is particularly productive and regions where irrigation should be reconsidered as there is not a significant agricultural benefit and the water could be used more productively.

Industrial oil palm plantations in South East Asia have caused significant biodiversity losses and perverse social outcomes. To address concerns over plantation practices and in an attempt to improve sustainability through market mechanisms, civil society organisations and industry representatives developed the Roundtable on Sustainable Palm Oil (RSPO) in 2004. The effectiveness of RSPO in improving the sustainability of the palm oil industry is frequently debated and to date, few quantitative analyses have been undertaken to assess how successful RSPO has been in delivering the social, economic and environmental sustainability outcomes it aims to address. With the palm oil industry continuing to expand in South East Asia and significant estates being planted in Africa and South America, this paper evaluates the effectiveness of RSPO plantations compared to non-certified plantations by assessing the relative performance of several key sustainability metrics compared to business as usual practices. Using Indonesian Borneo (Kalimantan) as a case study, a novel dataset of RSPO concessions was developed and causal analysis methodologies employed to evaluate the environmental, social and economic sustainability of the industry. No significant difference was found between certified and non-certified plantations for any of the sustainability metrics investigated, however positive economic trends including greater fresh fruit bunch yields were revealed. To achieve intended outcomes, RSPO principles and criteria are in need of substantial improvement and rigorous enforcement.

The practice of planting winter cover crops has seen renewed interest as a solution to environmental issues with the modern maize- and soybean-dominated row crop production system of the US Midwest. We examine whether cover cropping patterns can be assessed at scale using publicly available satellite data, creating a classifier with 91.5% accuracy (.68 kappa). We then use this classifier to examine spatial and temporal trends in cover crop occurrence on maize and soybean fields in the Midwest since 2008, finding that despite increased talk about and funding for cover crops as well as a 94% increase in cover crop acres planted from 2008–2016, increases in winter vegetation have been more modest. Finally, we combine cover cropping with satellite-predicted yields, finding that cover crops are associated with low relative maize and soybean production and poor soil quality, consistent with farmers adopting the practice on fields most in need of purported cover crop benefits. When controlling for invariant soil quality using a panel regression model, we find modest benefits of cover cropping, with average yield increases of 0.65% for maize and 0.35% for soybean. Given these slight impacts on yields, greater incentives or reduced costs of implementation are needed to increase adoption of this practice for the majority of maize and soybean acres in the US.

Climate warming is projected to increase the regional air temperature in Southeast Alaska and alter precipitation patterns and storage, with potentially important implications for the region's aquatic ecosystems. The climate-landcover relationships influencing stream temperature have not been comprehensively evaluated in Southeast Alaskan watersheds, many of which provide spawning and rearing habitat for five species of Pacific salmon. Thus, improving our understanding of current streamwater thermal regimes is critical to assess how stream temperatures across the region may be altered by ongoing climate change. We evaluated seasonal streamwater thermal regimes in forty-seven salmon-spawning watersheds in Southeast Alaska to assess the influence of watershed geomorphic and landscape characteristics on streamwater temperature and sensitivity to variation in air temperature. Stream temperatures were measured during the 2015 water year and analyzed for winter and summer seasons. Mean summer stream temperatures ranged from 4.0 °C–17.2 °C, while mean winter stream temperatures were less variable (0.5 °C–3.5 °C). Maximum weekly average temperatures ranged from 4.3 °C–21.5 °C. Regression and time-series analyses revealed that low gradient watersheds with higher lake coverage experienced warmer summer stream temperatures and were more sensitive to air temperature fluctuations compared to streams draining watersheds with high gradients. Winter mean stream temperatures were warmer in higher gradient watersheds with greater forest and lake coverage. These findings demonstrate that streamwater thermal regimes and sensitivity to air temperature are strongly moderated by watershed geomorphology and landcover, resulting in substantial thermal heterogeneity in streams across the complex terrain characterizing the coastal temperate rainforest of Southeast Alaska.

Given their ability to provide food, raw material and alleviate poverty, oil palm (OP) plantations are driving significant losses of biodiversity-rich tropical forests, fuelling a heated debate on ecosystem degradation and conservation. However, while OP-induced carbon emissions and biodiversity losses have received significant attention, OP water requirements have been marginalized and little is known on the ecohydrological changes (water and surface energy fluxes) occurring from forest clearing to plantation maturity. Numerical simulations supported by field observations from seven sites in Southeast Asia (five OP plantations and two tropical forests) are used here to illustrate the temporal evolution of OP actual evapotranspiration (ET), infiltration/runoff, gross primary productivity (GPP) and surface temperature as well as their changes relative to tropical forests. Model results from large-scale commercial plantations show that young OP plantations decrease ecosystem ET, causing hotter and drier climatic conditions, but mature plantations (age > 8−9 yr) have higher GPP and transpire more water (up to +7.7%) than the forests they have replaced. This is the result of physiological constraints on water use efficiency and the extremely high yield of OP (six to ten times higher than other oil crops). Hence, the land use efficiency of mature OP, i.e. the high productivity per unit of land area, comes at the expense of water consumption in a trade of water for carbon that may jeopardize local water resources. Sequential replanting and herbaceous ground cover can reduce the severity of such ecohydrological changes and support local water/climate regulation.

Human subpopulations experience different exposure to persistent organic pollutants (POPs) because of differences in the structure of their food webs and the extent of environmental contamination. Here we quantify the time-variant exposure of different human populations around the world to one representative POP, namely the polychlorinated biphenyl (PCB) congener 153, based on a dynamic simulation of both global environmental fate (using the model BETR-Global) and human food chain bioaccumulation (using the model ACC-HUMAN). The approach identifies subpopulations whose diets include a carnivorous mammal as experiencing the world's highest PCB-153 exposure, i.e. the very large biomagnification potential of their food web more than makes up for the remoteness of their living environment. However, for subpopulations that do not eat warm-blooded carnivores, the proximity to sources of PCBs is more important than food web structure and environmental conditions for differentiating their exposure to PCBs.

The subnivium is a seasonal refuge that exists at the interface between the snowpack and the ground, and provides a haven for a diversity of species to survive extreme winter temperatures. Due to the fitness of many plants and animals being strongly influenced by winter conditions, much attention has been given to changes in the timing of snow cover extent and duration in seasonally snow-covered environments; however, these broad-scale characteristics do not capture the finer-scale dynamics of the subnivium. To study the factors associated with subnivium development, we quantified three critical phenophases of the subnivium: establishment, maintenance, and disintegration along a latitudinal and land cover gradient in the Great Lakes Region of North America. We hypothesized that subnivium phenophases would depend primarily on snow depth and air temperature, but that these would be mediated by latitude and land cover. We found that patterns in both establishment and disintegration were affected by latitude more than land cover, but that variability in the timing of early season snowfall events overrode the effects of both factors in subnivium establishment. In contrast, disintegration was predictably later in more northerly sites, regardless of interannual variation in weather patterns. We found that the subnivium was the result of a balance between ambient temperature, snow depth, and snow density, but that ambient temperatures constrained the system by contributing to the frequency of snowfall and inducing changes in snow depth and density. Areas in lake effect zones, characterized by high snow depths and persistent snow cover, may be the last refugia for subnivia-dependent species given the predicted shifting climate regimes of the 21st century.

Meeting the 1.5 °C goal will require a rapid scale-up of zero-carbon energy supply, fuel switching to electricity, efficiency and demand-reduction in all sectors, and the replenishment of natural carbon sinks. These transformations will have immediate impacts on various of the sustainable development goals. As goals such as affordable and clean energy and zero hunger are more immediate to great parts of global population, these impacts are central for societal acceptability of climate policies. Yet, little is known about how the achievement of other social and environmental sustainability objectives can be directly managed through emission reduction policies. In addition, the integrated assessment literature has so far emphasized a single, global (cost-minimizing) carbon price as the optimal mechanism to achieve emissions reductions. In this paper we introduce a broader suite of policies—including direct sector-level regulation, early mitigation action, and lifestyle changes—into the integrated energy-economy-land-use modeling system REMIND-MAgPIE. We examine their impact on non-climate sustainability issues when mean warming is to be kept well below 2 °C or 1.5 °C. We find that a combination of these policies can alleviate air pollution, water extraction, uranium extraction, food and energy price hikes, and dependence on negative emissions technologies, thus resulting in substantially reduced sustainability risks associated with mitigating climate change. Importantly, we find that these targeted policies can more than compensate for most sustainability risks of increasing climate ambition from 2 °C to 1.5 °C.

The adequacy of freshwater resources remains a critical challenge for a sustainable and growing society. We present a self-consistent risk-based assessment of water availability and use under future climate change and socioeconomic growth by midcentury across southern and eastern Asia (SEA). We employ large ensemble scenarios from an integrated modeling framework that are consistent across the spectrum of regional climate, population, and economic projections. We find socioeconomic growth contributes to an increase in water stress across the entire ensemble. However, climate change drives the ensemble central tendency toward an increase in water stress in China but a reduction in India, with a considerable spread across the ensemble. Nevertheless, the most deleterious unabated climate-change impact is a low probability but salient extreme increase in water stress over China and India. In these outcomes, annual withdrawals will routinely exceed water-storage capacity. A modest greenhouse gas mitigation pathway eliminates the likelihood of these extreme outcomes and also benefits hundreds of millions of people at risk to various levels of water stress increase. Over SEA we estimate an additional 200 million people under threat of facing at least heavily water-stressed conditions from climate change and socioeconomic growth, but the mitigation scenario reduces the additional population-under-threat by 30% (60 million). Nevertheless, there remains a 1-in-2 chance that 100 million people across SEA experience a 50% increase in water stress and a 1-in-10 chance they experience a doubling of water stress. Therefore, widespread adaptive measures may be required over the coming decades to meet these unavoidable risks in water shortfalls.

During prolonged droughts, information is needed about when and how the extreme event is likely to terminate. A drought termination framework based on historical data comprising current rate and historical ensemble approaches is presented here for assessing the prospects of groundwater level recovery. The current rate approach is evaluated across all initialisation months in the historical record and provides reasonable estimates for the duration of recovery from relatively severe groundwater level deficiencies in some slowly responding boreholes. The utility of the framework is demonstrated through a near-real-time application to 30 groundwater boreholes in England and Wales from October 2017 onwards. Recovery during winter 2017/18 was considered unlikely, as some aquifers required increases in groundwater levels that have occurred seldom, if ever before, in long historical records. Data to February 2018 confirmed the success of these pre-winter outlooks. Recovery by mid- to late-2018 or beyond was more likely; slow rates of recovery by October 2017 and increasing return periods of effective rainfall required for recovery over timeframes in the summer half-year underlined the importance of winter rainfall and suggested that the historical ensemble may underestimate the duration of recovery. There was moderate confidence for a delay in recovery beyond the end of 2018 in some slowly responding Chalk boreholes in south-central and eastern England. There is considerable potential for the transferability of the drought termination framework beyond the UK wherever there are sufficient historical data. The two approaches provide limited information in distinctly different circumstances and their relevance and value may differ in space and time, suggesting their complimentary use as the most robust way to incorporate information on the prospects for groundwater level recovery into existing seasonal forecasting services, supporting decision-making by water managers during prolonged droughts.

While it is understood that cities generate the majority of carbon emissions, for most cities, towns, and rural areas around the world no carbon footprint (CF) has been estimated. The Gridded Global Model of City Footprints (GGMCF) presented here downscales national CFs into a 250 m gridded model using data on population, purchasing power, and existing subnational CF studies from the US, China, EU, and Japan. Studies have shown that CFs are highly concentrated by income, with the top decile of earners driving 30%–45% of emissions. Even allowing for significant modeling uncertainties, we find that emissions are similarly concentrated in a small number of cities. The highest emitting 100 urban areas (defined as contiguous population clusters) account for 18% of the global carbon footprint. While many of the cities with the highest footprints are in countries with high carbon footprints, nearly one quarter of the top cities (41 of the top 200) are in countries with relatively low emissions. In these cities population and affluence combine to drive footprints at a scale similar to those of cities in high-income countries. We conclude that concerted action by a limited number of local governments can have a disproportionate impact on global emissions.

The availability of climate model experiments under three alternative scenarios stabilizing at warming targets inspired by the COP21 agreements (a 1.5 °C not exceed, a 1.5 °C with overshoot and a 2.0 °C) makes it possible to assess future expected changes in global yields for two staple crops, wheat and maize. In this study an empirical model of the relation between crop yield anomalies and temperature and precipitation changes, with or without the inclusion of CO₂ fertilization effects, is used to produce ensembles of time series of yield outcomes on a yearly basis over the course of the 21st century, for each scenario. The 21st century is divided into 10 year windows starting from 2020, within which the statistical significance and the magnitude of the differences in yield changes between pairs of scenarios are assessed, thus evaluating if and when benefits of mitigations appear, and how substantial they are. Additionally, a metric of extreme heat tailored to the individual crops (number of days during the growing season above a crop-specific threshold) is used to measure exposure to harmful temperatures under the different scenarios. If CO₂ effects are not included, statistically significant differences in yields of both crops appear as early as the 2030s but the magnitude of the differences remains below 3% of the historical baseline in all cases until the second part of the century. In the later decades of the 21st century, differences remain small and eventually stop being statistically significant between the two scenarios stabilizing at 1.5 °C, while differences between these two lower scenarios and the 2.0 °C scenario grow to about 4%. The inclusion of CO₂ effects erases all significant benefits of mitigation for wheat, while the significance of differences is maintained for maize yields between the higher and the two lower scenarios, albeit with smaller benefits in magnitude. Changes in extremes are significant within each of the scenarios but the differences between any pair of them, even by the end of the century are only on the order of a few days per growing season, and these small changes appear limited to a few localized areas of the growing regions. These results seem to suggest that for globally averaged yields of these two grains the lower targets put forward by the Paris agreement does not change substantially the expected impacts on yields that are caused by warming temperatures under the pre-existing 2.0 °C target.

Results from an 25 regional climate model simulations from the Coordinated Regional Downscaling Experiment Africa initiative are used to assess the projected changes in temperature and precipitation over southern Africa at two global warming levels (GWLs), namely 1.5 °C and 2.0 °C, relative to pre-industrial values, under the Representative Concentration Pathway 8.5. The results show a robust increase in temperature compared to the control period (1971–2000) ranging from 0.5 °C–1.5 °C for the 1.5 °C GWL and from 1.5 °C–2.5 °C, for the 2.0 °C GWL. Areas in the south-western region of the subcontinent, covering South Africa and parts of Namibia and Botswana are projected to experience the largest increase in temperature, which are greater than the global mean warming, particularly during the September–October–November season. On the other hand, under 1.5 °C GWL, models exhibit a robust reduction in precipitation of up to 0.4 mm day⁻¹ (roughly 20% of the climatological values) over the Limpopo Basin and smaller areas of the Zambezi Basin in Zambia, and also parts of Western Cape, South Africa. Models project precipitation increase of up to 0.1 mm day⁻¹ over central and western South Africa and in southern Namibia. Under 2.0 °C GWL, a larger fraction of land is projected to face robust decreases between 0.2 and 0.4 mm day⁻¹ (around 10%–20% of the climatological values) over most of the central subcontinent and parts of western South Africa and northern Mozambique. Decreases in precipitation are accompanied by increases in the number of consecutive dry days and decreases in consecutive wet days over the region. The importance of achieving the Paris Agreement is imperative for southern Africa as the projected changes under both the 1.5 °C, and more so, 2.0 °C GWL imply significant potential risks to agricultural and economic productivity, human and ecological systems health and water resources with implied increase in regional water stresses.

There is a general lack of information about the potential effects of 1.5, 2 or more degrees of global warming on the regional climates within Africa, and most studies that address this use data from coarse resolution global models. Using a large ensemble of CORDEX Africa simulations, we present a pan-African overview of the effects of 1.5 and 2 °C global warming levels (GWLs) on the African climate. The CORDEX simulations, consistent with their driving global models, show a robust regional warming exceeding the mean global one over most of Africa. The highest increase in annual mean temperature is found over the subtropics and the smallest one over many coastal regions. Projected changes in annual mean precipitation have a tendency to wetter conditions in some parts of Africa (e.g. central/eastern Sahel and eastern Africa) at both GWLs, but models' agreement on the sign of change is low. In contrast to mean precipitation, there is a consistent increase in daily precipitation intensity of wet days over a large fraction of tropical Africa emerging already at 1.5 °C GWL and strengthening at 2 °C. A consistent difference between 2 °C and 1.5 °C warmings is also found for projected changes in annual mean temperature and daily precipitation intensity. Our study indicates that a 0.5 °C further warming (from 1.5 °C–2 °C) can indeed produce a robust change in some aspects of the African climate and its extremes.

We analyze the potential effect of global warming levels (GWLs) of 1.5 °C and 2 °C above pre-industrial levels (1861−1890) on mean temperature and precipitation as well as intra-seasonal precipitation extremes over the Greater Horn of Africa. We used a large, 25-member regional climate model ensemble from the Coordinated Regional Downscaling Experiment and show that, compared to the control period of 1971−2000, annual mean near-surface temperature is projected to increase by more than 1 °C and 1.5 °C over most parts of the Greater Horn of Africa, under GWLs of 1.5 °C and 2 °C respectively. The highest temperature increases are projected in the northern region, covering most parts of Sudan and northern parts of Ethiopia, and the lowest temperature increases are projected over the coastal belt of Tanzania. However, the projected mean surface temperature difference between 2 °C and 1. 5 °C GWLs is higher than 0.5 °C over nearly all land points, reaching 0.8 °C over Sudan and northern Ethiopia. This implies that the Greater Horn of Africa will warm faster than the global mean.

While projected changes in precipitation are mostly uncertain across the Greater Horn of Africa, there is a substantial decrease over the central and northern parts of Ethiopia. Additionally, the length of dry and wet spells is projected to increase and decrease respectively. The combined effect of a reduction in rainfall and the changes in the wet and dry spells will likely impact negatively on the livelihoods of people within the coastal cities, lake regions, highlands as well as arid and semi-arid lands of Kenya, Tanzania, Somalia, Ethiopia and Sudan. The probable impacts of these changes on key sectors such as agriculture, water, energy and health sectors, will likely call for formulation of actionable policies geared towards adaptation and mitigation of the impacts of 1.5 °C and 2 °C warming.

Throughout the Euro-Arctic region of Russia (Murmansk region), there is a substantial increase of metal concentrations in water, which are related to local discharges from the metallurgical and mining industry, transboundary pollution, as well as indirect leaching of elements by acid precipitation. This study collates data to investigate the relationship between surface water contamination by metals, and fish and human health. Fish are used as a biological indicator to show the impact of water pollution by metals on the ecosystem's health. The etiology of fish and human diseases are related to the water pollution and accumulation of metals in organisms. High concentrations of Ni and Cd in water drives an accumulation of these elements in organs and tissues of fish, especially in kidneys. The relation between the accumulation of Ni in kidneys and the development of fish nephrocalcinosis and fibroelastosis was established. Statistical analysis demonstrated that human populations in cities close in proximity to smelters show the highest incidence of disease. The results of histological, clinical, and post-mortem examination of patients shows the highest content of toxic metals, especially Cd, in livers and kidneys. Our complex investigation of a set of disorders observed in fish and human populations indicates that there is a high probability that the negative impact on human health is caused by prolonged water contamination by heavy metals. As a novel finding, this paper shows that based on the similarity of pathological processes and bioaccumulation of metals in fish and humans, examining the content of heavy metals in fish can be used to confirm etiology and evaluate the potential risk to human health by pollution of surface waters.

Few studies have examined changing snow seasonality in Central Asia. Here, we analyzed changes in the seasonality of snow cover across Kyrgyzstan (KGZ) over 14 years from 2002/03–2015/16 using the most recent version (v006) of MODIS Terra and Aqua 8 day snow cover composites (MOD10A2/MYD10A2). We focused on three metrics of snow seasonality—first date of snow, last date of snow, and duration of snow season—and used nonparametric trends tests to assess the significance and direction of trends. We evaluated trends at three administration scales and across elevation. We used two techniques to assure that our identification of significant trends was not resulting from random spatial variation. First, we report only significant trends (positive or negative) that are at least twice as prevalent as the converse trends. Second, we use a two-stage analysis at the national scale to identify asymmetric directional changes in snow seasonality. Results show that more territory has been experiencing earlier onset of snow than earlier snowmelt, and roughly equivalent areas have been experiencing longer and shorter duration of snow seasons in the past 14 years. The changes are not uniform across KGZ, with significant shifts toward earlier snow arrival in western and central KGZ and significant shifts toward earlier snowmelt in eastern KGZ. The duration of the snow season has significantly shortened in western and eastern KGZ and significantly lengthened in northern and southwestern KGZ. Duration is significantly longer where the snow onset was significantly earlier or the snowmelt significantly later. There is a general trend of significantly earlier snowmelt below 3400 m and the area of earlier snowmelt is 15 times greater in eastern than western districts. Significant trends in the Aqua product were less prevalent than in the Terra product, but the general trend toward earlier snowmelt was also evident in Aqua data.

Analysis systems incorporating atmospheric observations could provide a powerful tool for validating fossil fuel CO₂ (ffCO₂) emissions reported for individual regions, provided that fossil fuel sources can be separated from other CO₂ sources or sinks and atmospheric transport can be accurately accounted for. We quantified ffCO₂ by measuring radiocarbon (¹⁴C) in CO₂, an accurate fossil-carbon tracer, at nine observation sites in California for three months in 2014–15. There is strong agreement between the measurements and ffCO₂ simulated using a high-resolution atmospheric model and a spatiotemporally-resolved fossil fuel flux estimate. Inverse estimates of total in-state ffCO₂ emissions are consistent with the California Air Resources Board's reported ffCO₂ emissions, providing tentative validation of California's reported ffCO₂ emissions in 2014–15. Continuing this prototype analysis system could provide critical independent evaluation of reported ffCO₂ emissions and emissions reductions in California, and the system could be expanded to other, more data-poor regions.

Extreme summer heat waves are known to induce foliar and stem mortality in temperate forest ecosystems, yet our mechanistic knowledge of physiological thresholds for damage is lacking. Current spatiotemporal simulations of forest growth responses to climate change fail to explain the variability between co-occurring tree species to climate extremes, indicating a need for new model frameworks that include mechanistic understanding of trait-specific responses. In this context, using manipulative heat wave (hw) experiments we investigated ecophysiological responses and physiological recovery in four co-occurring temperate tree species of the southeastern United States including three deciduous angiosperms: southern red oak (Quercus falcata Michx.), shumard oak (Q. shumardii Buckl.) and, tulip-poplar (Liriodendron tulipifera L.) and one evergreen conifer: eastern white pine (Pinus strobus L.). The objectives were to investigate inter-specific differences in ecophysiological responses to hw events to understand mechanistic differences in resilience that may be useful for future model development. Two-year-old, well-irrigated potted saplings were exposed to progressively increasing extreme hw diurnal cycles followed by a recovery cycle, with peak midday air temperature increasing from 37 °C to a maximum of 51 °C on the third day of the hw. Plants were assessed for various photosynthetic and water use responses, chlorophyll fluorescence and photosystem-II (PSII) activity, leaf temperature and foliar pigments. Intense heat caused progressive down-regulation in net photosynthesis, but the stomata remained operational, which helped cool leaves through loss of latent heat. Even though whole plant transpiration increased for all species, the rate plateaued at higher hw events that allowed leaf temperature to exceed 45 °C, well beyond the optimal range. A significant increase in non-photochemical quenching over the hw cycles was evident in all species though indications of both transient and chronic PSII damage were evident in the most heat sensitive species, pine and tulip poplar. The oaks, especially Q.falcata, showed greater thermotolerance than other species with a higher threshold for photodamage to PSII, rapid overnight recovery of photoinhibition and minimal heat-induced canopy necrosis. We conclude that these co-occurring tree species exhibit large variability in thermotolerance and in their capability to repair both transient and chronic photodamage. Our results indicate that extreme heat induced damage to PSII within the leaf chloroplasts may be a mechanistic trait that can be used to project how different species respond to extreme weather events.

Extreme winter events that damage vegetation are considered an important climatic cause of arctic browning—a reversal of the greening trend of the region—and possibly reduce the carbon uptake of northern ecosystems. Confirmation of a reduction in CO₂ uptake due to winter damage, however, remains elusive due to a lack of flux measurements from affected ecosystems. In this study, we report eddy covariance fluxes of CO₂ from a peatland in northern Norway and show that vegetation CO₂ uptake was delayed and reduced in the summer of 2014 following an extreme winter event earlier that year. Strong frost in the absence of a protective snow cover—its combined intensity unprecedented in the local climate record—caused severe dieback of the dwarf shrub species Calluna vulgaris and Empetrum nigrum. Similar vegetation damage was reported at the time along ~1000 km of coastal Norway, showing the widespread impact of this event. Our results indicate that gross primary production (GPP) exhibited a delayed response to temperature following snowmelt. From snowmelt up to the peak of summer, this reduced carbon uptake by 14 (0–24) g C m⁻² (~12% of GPP in that period)—similar to the effect of interannual variations in summer weather. Concurrently, remotely-sensed NDVI dropped to the lowest level in more than a decade. However, bulk photosynthesis was eventually stimulated by the warm and sunny summer, raising total GPP. Species other than the vulnerable shrubs were probably resilient to the extreme winter event. The warm summer also increased ecosystem respiration, which limited net carbon uptake. This study shows that damage from a single extreme winter event can have an ecosystem-wide impact on CO₂ uptake, and highlights the importance of including winter-induced shrub damage in terrestrial ecosystem models to accurately predict trends in vegetation productivity and carbon sequestration in the Arctic and sub-Arctic.

Timber harvest from tropical regions generates seven billion dollars annually in exports and is estimated to occur across 20% of the area of remaining tropical forests. This timber harvesting is estimated to account for more than one in eight of all greenhouse gas emissions from tropical forests. Yet there is currently no means to independently estimate extracted volumes and associated greenhouse gas emissions.

In this study, we built upon an earlier paper that used an automated algorithm applied to LiDAR to accurately identify area of timber harvest impact in the categories of roads/decks, skid trails and gaps. This algorithm was applied to 2014 harvest areas in four concessions in Kalimantan, Indonesia. In two of these concessions, total harvested timber volumes and greenhouse gas emissions were measured and calculated in the field using data from 188 harvested and extracted trees.

In order to relate remote sensing data with the estimated extracted volumes, we calculated factors that linked extracted timber volumes with greenhouse gas emissions, and applied three different regression equations. The parameters of the most accurate equation were the areas of roads, skid trails and gaps, explaining 87% of the variation in the data. For situations where rivers are used in place of roads for extracting timber and for instances of non-mechanized, often illegal logging, a second equation was created in which only skid trail and gap attribute data were used, and in this equation 86% of the variation was accounted. The final equation, intended for use in scenarios where LiDAR data are not available but moderate resolution imagery could be used, associated length of roads only with extracted volumes. In this case, 78% of the variation was explained.

Application of the first equation permitted estimation of extracted volumes and associated greenhouse gas emissions from two additional logging concessions. We discuss the application of these equations to areas that have been identified as illegal logging concessions, and propose that these may be applied to larger regions across the country.

These equations offer a way to estimate volumes of timber extraction when no ground data is available, and to calculate greenhouse gas emissions associated with extracted volumes, providing a simple methodology useful across forested tropical countries.

Permafrost (perennially frozen) soils store vast amounts of organic carbon (C) and nitrogen (N) that are vulnerable to mobilization as dissolved organic carbon (DOC) and dissolved organic and inorganic nitrogen (DON, DIN) upon thaw. Such releases will affect the biogeochemistry of permafrost regions, yet little is known about the chemical composition and source variability of active-layer (seasonally frozen) and permafrost soil DOC, DON and DIN. We quantified DOC, total dissolved N (TDN), DON, and DIN leachate yields from deep active-layer and near-surface boreal Holocene permafrost soils in interior Alaska varying in soil C and N content and radiocarbon age to determine potential release upon thaw. Soil cores were collected at three sites distributed across the Alaska boreal region in late winter, cut in 15 cm thick sections, and deep active-layer and shallow permafrost sections were thawed and leached. Leachates were analyzed for DOC, TDN, nitrate (NO₃⁻), and ammonium (NH₄⁺) concentrations, dissolved organic matter optical properties, and DOC biodegradability. Soils were analyzed for C, N, and radiocarbon (¹⁴C) content. Soil DOC, TDN, DON, and DIN yields increased linearly with soil C and N content, and decreased with increasing radiocarbon age. These relationships were significantly different for active-layer and permafrost soils such that for a given soil C or N content, or radiocarbon age, permafrost soils released more DOC and TDN (mostly as DON) per gram soil than active-layer soils. Permafrost soil DOC biodegradability was significantly correlated with soil Δ¹⁴C and DOM optical properties. Our results demonstrate that near-surface Holocene permafrost soils preserve greater relative potential DOC and TDN yields than overlying seasonally frozen soils that are exposed to annual leaching and decomposition. While many factors control the fate of DOC and TDN, the greater relative yields from newly thawed Holocene permafrost soils will have the largest potential impact in areas dominated by organic-rich soils.

Urban material resource requirements are significant at the global level and these are expected to expand with future urban population growth. However, there are no global scale studies on the future material consumption of urban areas. This paper provides estimates of global urban domestic material consumption (DMC) in 2050 using three approaches based on: current gross statistics; a regression model; and a transition theoretic logistic model. All methods use UN urban population projections and assume a simple 'business-as-usual' scenario wherein historical aggregate trends in income and material flow continue into the future. A collation of data for 152 cities provided a year 2000 world average DMC/capita estimate, 12 tons/person/year (±22%), which we combined with UN population projections to produce a first-order estimation of urban DMC at 2050 of ~73 billion tons/year (±22%). Urban DMC/capita was found to be significantly correlated (R² > 0.9) to urban GDP/capita and area per person through a power law relation used to obtain a second estimate of 106 billion tons (±33%) in 2050. The inelastic exponent of the power law indicates a global tendency for relative decoupling of direct urban material consumption with increasing income. These estimates are global and influenced by the current proportion of developed-world cities in the global population of cities (and in our sample data). A third method employed a logistic model of transitions in urban DMC/capita with regional resolution. This method estimated global urban DMC to rise from approximately 40 billion tons/year in 2010 to ~90 billion tons/year in 2050 (modelled range: 66–111 billion tons/year). DMC/capita across different regions was estimated to converge from a range of 5–27 tons/person/year in the year 2000 to around 8–17 tons/person/year in 2050. The urban population does not increase proportionally during this period and thus the global average DMC/capita increases from ~12 to ~14 tons/person/year, challenging resource decoupling targets.

Despite sustained declines in Amazon deforestation, forest degradation from logging and fire continues to threaten carbon stocks, habitat, and biodiversity in frontier forests along the Amazon arc of deforestation. Limited data on the magnitude of carbon losses and rates of carbon recovery following forest degradation have hindered carbon accounting efforts and contributed to incomplete national reporting to reduce emissions from deforestation and forest degradation (REDD+). We combined annual time series of Landsat imagery and high-density airborne lidar data to characterize the variability, magnitude, and persistence of Amazon forest degradation impacts on aboveground carbon density (ACD) and canopy structure. On average, degraded forests contained 45.1% of the carbon stocks in intact forests, and differences persisted even after 15 years of regrowth. In comparison to logging, understory fires resulted in the largest and longest-lasting differences in ACD. Heterogeneity in burned forest structure varied by fire severity and frequency. Forests with a history of one, two, and three or more fires retained only 54.4%, 25.2%, and 7.6% of intact ACD, respectively, when measured after a year of regrowth. Unlike the additive impact of successive fires, selective logging before burning did not explain additional variability in modeled ACD loss and recovery of burned forests. Airborne lidar also provides quantitative measures of habitat structure that can aid the estimation of co-benefits of avoided degradation. Notably, forest carbon stocks recovered faster than attributes of canopy structure that are critical for biodiversity in tropical forests, including the abundance of tall trees. We provide the first comprehensive look-up table of emissions factors for specific degradation pathways at standard reporting intervals in the Amazon. Estimated carbon loss and recovery trajectories provide an important foundation for assessing the long-term contributions from forest degradation to regional carbon cycling and advance our understanding of the current state of frontier forests.

While the global carbon budget (GCB) is relatively well constrained over the last decades of the 20th century [1], observations and reconstructions of atmospheric CO₂ growth rate present large discrepancies during the earlier periods [2]. The large uncertainty in GCB has been attributed to the land biosphere, although it is not clear whether the gaps between observations and reconstructions are mainly because land-surface models (LSMs) underestimate inter-annual to decadal variability in natural ecosystems, or due to inaccuracies in land-use change reconstructions.

As Eurasia encompasses about 15% of the terrestrial surface, 20% of the global soil organic carbon pool and constitutes a large CO₂ sink, we evaluate the potential contribution of natural and human-driven processes to induce large anomalies in the biospheric CO₂ fluxes in the early 20th century. We use an LSM specifically developed for high-latitudes, that correctly simulates Eurasian C-stocks and fluxes from observational records [3], in order to evaluate the sensitivity of the Eurasian sink to the strong high-latitude warming occurring between 1930 and 1950. We show that the LSM with improved high-latitude phenology, hydrology and soil processes, contrary to the group of LSMs in [2], is able to represent enhanced vegetation growth linked to boreal spring warming, consistent with tree-ring time-series [4]. By compiling a dataset of annual agricultural area in the Former Soviet Union that better reflects changes in cropland area linked with socio-economic fluctuations during the early 20th century, we show that land-abadonment during periods of crisis and war may result in reduced CO₂ emissions from land-use change (44%–78% lower) detectable at decadal time-scales.

Our study points to key processes that may need to be improved in LSMs and LUC datasets in order to better represent decadal variability in the land CO₂ sink, and to better constrain the GCB during the pre-observational record.

Global-scale hydrological models are routinely used to assess water scarcity, flood hazards and droughts worldwide. Recent efforts to incorporate anthropogenic activities in these models have enabled more realistic comparisons with observations. Here we evaluate simulations from an ensemble of six models participating in the second phase of the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP2a). We simulate monthly runoff in 40 catchments, spatially distributed across eight global hydrobelts. The performance of each model and the ensemble mean is examined with respect to their ability to replicate observed mean and extreme runoff under human-influenced conditions. Application of a novel integrated evaluation metric to quantify the models' ability to simulate timeseries of monthly runoff suggests that the models generally perform better in the wetter equatorial and northern hydrobelts than in drier southern hydrobelts. When model outputs are temporally aggregated to assess mean annual and extreme runoff, the models perform better. Nevertheless, we find a general trend in the majority of models towards the overestimation of mean annual runoff and all indicators of upper and lower extreme runoff. The models struggle to capture the timing of the seasonal cycle, particularly in northern hydrobelts, while in southern hydrobelts the models struggle to reproduce the magnitude of the seasonal cycle. It is noteworthy that over all hydrological indicators, the ensemble mean fails to perform better than any individual model—a finding that challenges the commonly held perception that model ensemble estimates deliver superior performance over individual models. The study highlights the need for continued model development and improvement. It also suggests that caution should be taken when summarising the simulations from a model ensemble based upon its mean output.

Climate extremes are widely projected to become more severe as the global climate continues to warm due to anthropogenic greenhouse gas emissions. These extremes often cause the most severe impacts on society. Therefore, the extent to which the extremes might change at regional level as the global climate warms from current levels to proposed policy targets of 1.5 and 2.0 °C above preindustrial levels need to be understood to allow for better preparedness and informed policy formulation. This paper analysed projected changes in temperature and precipitation extremes at 1.0, 1.5 and 2.0 °C warming over Botswana, a country highly vulnerable to the impacts of climate change. Projected changes in temperature extremes are significantly different from each other at the three levels of global warming, across three main climatic zones in the country. Specifically, at 2.0 °C global warming relative to preindustrial, for the ensemble median: (a) country average warm spell duration index increases by 80, 65, 62 days per year across different climatic zones, approximately three (and two) times the change at 1.0 (1.5) °C; (b) cold night (TN10P) and cold day (TX10P) frequencies decrease by 12 and 9 days per year across all regions, respectively, while hot nights (TN90P) and hot days (TX90P) both increase by 8–9 days across all regions. Projected changes in drought-related indices are also distinct at different warming levels. Specifically: (a) projected mean annual precipitation decreases across the country by 5%–12% at 2 °C, 3%–8% at 1.5 °C and 2%–7% at 1.0 °C; (b) dry spell length (ALTCDD) increases by 15–19 days across the three climatic zones at 2.0 °C, about three (and two) times as much as the increase at 1.0 (1.5) °C. Ensemble mean projections indicate increases in heavy rainfall indices, but the inter-model spread is large, with no consistent direction of change, and so changes are not statistically significant. The implications of these changes in extreme temperature and precipitation for key socio-economic sectors are explored, and reveal progressively severe impacts, and consequent adaptation challenges for Botswana as the global climate warms from its present temperature of 1.0 °C above preindustrial levels to 1.5 °C, and then 2.0 °C.

The measurement of carbon exchange between vegetation and the atmosphere is vital to quantify the impact of environmental variables on the carbon sequestration capacity of forests, and to predict how they will respond to future climate. In this study we use proximal remote sensing, defined as observations made from non-contact radiometric or imaging sensors in close proximity to the forest canopy (10–20 m), as an intermediate upscaling tool between direct measurements of carbon fluxes and satellite-derived estimations of primary productivity in a tropical dry forest (TDF) in Jalisco, Mexico. Two broad-band vegetation indices (VIs), the normalized difference VI and the enhanced vegetation index 2 (EVI2), were calculated from proximally sensed canopy properties, validated with field estimates of the fraction of absorbed photosynthetically active radiation by photosynthetic tissue (f_APARgreen), and compared to estimates of gross primary productivity (GPP) and net ecosystem exchange of CO₂, measured from a flux tower. The VIs captured the phenology of the TDF, both under typical summer rainfall and during an atypically-dry wet season in El Niño of 2009. The VIs also tracked a secondary leaf-flushing in the dry season of 2010. Our study suggests that (1) VIs are the best predictors of gross carbon uptake, able to explain up to 86% of variations in GPP; (2) VIs are accurate predictors of the photosynthetic capacity of green tissue, able to explain up to 99% of f_APARgreen variation; and (3) VIs and soil water content can be used to develop an empirical model that captures the seasonal trajectory of GPP from high respiration after the rain pulses, to rapid leaf development, and finally to slow senescence as the soil dries out. Proximal remote sensing constitutes a useful tool to link field-base measurements of carbon fluxes to satellite- or airborne-derived estimates of carbon exchange.

Central Asia has been rapidly changing in multiple ways over the past few decades. Increases in temperature and likely decreases in precipitation in Central Asia as the result of global climate change are making one of the most arid regions in the world even more susceptible to large-scale droughts. Global climate oscillations, such as the El Niño–Southern Oscillation, have previously been linked to observed weather patterns in Central Asia. However, until now it has been unclear how the different climate oscillations act simultaneously to affect the weather and subsequently the vegetated land surface in Central Asia. We fit well-established land surface phenology models to two versions of MODIS data to identify the land surface phenology of Central Asia between 2001 and 2016. We then combine five climate oscillation indices into one regression model and identify the relative importance of each of these indices on precipitation, temperature, and land surface phenology, to learn where each climate index has the strongest influence. Our analyses illustrate that the North Atlantic Oscillation, the East Atlantic/West Russia pattern, and the Atlantic Multi-Decadal Oscillation predominantly influence temperature in the northern part of Central Asia. We also show that the Scandinavia index and the Multivariate ENSO index both reveal significant impacts on the precipitation in this region. Thus, we conclude that the land surface phenology across Central Asia is affected by several climate modes, both those that are strongly linked to far northern weather patterns and those that are forced by southern weather patterns, making this region a 'climate change hotspot' with strong spatial variations in weather patterns. We also show that regional climate patterns play a significant role in Central Asia, indicating that global climate patterns alone might not be sufficient to project weather patterns and subsequent land surface changes in this region.

The Paris Agreement achieved in December 2015 established that the signatory countries should pursue to hold the increase in global average temperature to below 2 °C relative to the preindustrial period and to strive to limit the temperature increase to 1.5 °C below the preindustrial period. The potential changes in tropical cyclones over the basin making landfall over southern Africa under the key global temperature goals have not been thoroughly investigated. Using the Coordinated Regional Downscaling Experiment-Africa regional climate models, we downscale six global climate models of the Coupled Model Inter-comparison Project Phase 5 to high resolution. This serves towards studying changes in tropical cyclone tracks over the South West Indian Ocean under different extents of global warming (1.5 °C, 2 °C and 3 °C of warming with respect to pre-industrial conditions). It is projected that the number of tropical cyclones making landfalls over southern Africa under global warming will decrease, with 2 °C being a critical threshold, after which the rate of cyclone frequency with further temperature increases no longer has a diminishing effect. Fewer cyclones may bring benefits and reduce damage to the southern African region. Although a decrease in damages associated with flood events is desirable, general decreases in tropical cyclone and tropical lows may also be associated with decreased rainfall over the Limpopo River basin and southern, central and northern Mozambique (with negative impacts on dryland agriculture).

In this study, we investigate changes in temperature and precipitation extremes over West and Central Africa (hereafter, WAF domain) as a function of global mean temperature with a focus on the implications of global warming of 1.5 °C and 2 °C according the Paris Agreement. We applied a scaling approach to capture changes in climate extremes with increase in global mean temperature in several subregions within the WAF domain: Western Sahel, Central Sahel, Eastern Sahel, Guinea Coast and Central Africa including Congo Basin.

While there are several uncertainties and large ensemble spread in the projections of temperature and precipitation indices, most models show high-impact changes in climate extremes at subregional scale. At these smaller scales, temperature increases within the WAF domain are projected to be higher than the global mean temperature increase (at 1.5 °C and at 2 °C) and heat waves are expected to be more frequent and of longer duration. The most intense warming is observed over the drier regions of the Sahel, in the central Sahel and particularly in the eastern Sahel, where the precipitation and the soil moisture anomalies have the highest probability of projected increase at a global warming of 1.5 °C. Over the wetter regions of the Guinea Coast and Central Africa, models project a weak change in total precipitation and a decrease of the length of wet spells, while these two regions have the highest increase of heavy rainfall in the WAF domain at a global warming of 1.5 °C. Western Sahel is projected by 80% of the models to experience the strongest drying with a significant increase in the length of dry spells and a decrease in the standardized precipitation evapotranspiration index. This study suggests that the 'dry gets drier, wet gets wetter' paradigm is not valid within the WAF domain.

Wynes and Nicholas (2017aEnviron. Res. Lett.12 1–9) recently published an article that reviewed academic and grey literature to identify the most impactful individual actions for reducing carbon emissions in developed countries, identifying having 'one fewer child' as by far the most impactful action. This action was recommended with little context considering its controversial nature. We argue that there are three issue-areas that Wynes and Nicholas should have engaged with to improve the clarity of their recommendations and reduced the potential for misunderstanding, which are (1) the extent to which individual actions in one's private life can address climate change in relation to collective actions and actions in the professional sphere (2) the role of overconsumption in driving climate change and (3) the extent to which family planning is a human right. We also suggest that engagement with these issue-areas are a step towards a better practice in academic writing on population as an environmental issue.

In their comment piece, Laycock and Lam (Environ. Res. Lett.13 068001) focused on the importance for reducing emissions of actions beyond individual choices and overconsumption, and raise the issue of family planning as a human right. Here we respond that both individual and collective actions, in private and professional life, are important to reducing emissions to near zero in the next few decades. While we do not argue that individual actions will be sufficient to achieve this profound transformation, we believe that they can be helpful towards this goal, and also note from our own observations that we see personal, professional, and collective actions as often mutually reinforcing rather than contradictory. Regarding overconsumption, we reiterate that our study was designed to illustrate the decisive role that consumption patterns play in driving greenhouse gas emissions, based on the understanding that wealthy, high-carbon individuals are responsible for a disproportionately large share of emissions. Regarding the ethics of family planning, we fully agree with Laycock and Lam (and international agreements) that family planning is a private decision. We give examples of our careful public communication around this issue to provide this context and thank them for the opportunity to do so.

In their comment piece, Laycock and Lam provide insight into the discussion of high-impact climate actions, especially concerning the effect of communicating about family size. Here we respond to their thoughts on the scope of our research, including (1) the importance of actions beyond the private individual level, (2) the importance of overconsumption to an individual's emissions and (3) the ethics of communications pertaining to the planning of family size.

Wynes and Nicholas (2017) argue that the most effective action to reduce individual greenhouse gas (GHG) emissions is to have one fewer child. We raise methodological concerns about the way in which the authors attribute responsibility for emissions: they rely on multiple counting when calculating the emissions of future generations, and they exclude scenarios in which global emission trajectories become net-zero or negative. This may distort recommendations from policy makers and educators who rely on their study. We propose an alternative way of attributing responsibility that avoids multiple counting. Investigating the implications of having children under this proposal with regards to the full range of different scenarios, including likelihood analyses, calls for further studies.