While controls over the Earth's climate system have undergone rigorous hypothesis-testing since the 1800s, questions over the scientific consensus of the role of human activities in modern climate change continue to arise in public settings. We update previous efforts to quantify the scientific consensus on climate change by searching the recent literature for papers sceptical of anthropogenic-caused global warming. From a dataset of 88125 climate-related papers published since 2012, when this question was last addressed comprehensively, we examine a randomized subset of 3000 such publications. We also use a second sample-weighted approach that was specifically biased with keywords to help identify any sceptical peer-reviewed papers in the whole dataset. We identify four sceptical papers out of the sub-set of 3000, as evidenced by abstracts that were rated as implicitly or explicitly sceptical of human-caused global warming. In our sample utilizing pre-identified sceptical keywords we found 28 papers that were implicitly or explicitly sceptical. We conclude with high statistical confidence that the scientific consensus on human-caused contemporary climate change—expressed as a proportion of the total publications—exceeds 99% in the peer reviewed scientific literature.

This study focuses on future very hot summers associated with severe heatwaves and record-breaking temperatures in France. Daily temperature observations and a pair of historical and scenario (greenhouse gas radiative concentration pathway 8.5) simulations with the high-resolution (∼12.5 km) ALADIN regional climate model provide a robust framework to examine the spatial distribution of these extreme events and their 21st century evolution.

Five regions are identified with an extreme event spatial clustering algorithm applied to observed temperatures. They are used to diagnose the 21st century heatwave spatial patterns. In the 2070s, we find a simulated mega-heatwave as severe as the 2003 observed heatwave relative to its contemporaneous climate. A 20-member initial condition ensemble is used to assess the sensitivity of this future heatwave to the internal variability in the regional climate model and to pre-existing land surface conditions. Even in a much warmer and drier climate in France, late spring dry land conditions may lead to a significant amplification of summer extreme temperatures and heatwave intensity through limitations in evapotranspiration. By 2100, the increase in summer temperature maxima exhibits a range from 6 °C to almost 13 °C in the five regions in France, relative to historical maxima. These projections are comparable with the estimates given by a large number of global climate models.

Current anthropogenic climate change is the result of greenhouse gas accumulation in the atmosphere, which records the aggregation of billions of individual decisions. Here we consider a broad range of individual lifestyle choices and calculate their potential to reduce greenhouse gas emissions in developed countries, based on 148 scenarios from 39 sources. We recommend four widely applicable high-impact (i.e. low emissions) actions with the potential to contribute to systemic change and substantially reduce annual personal emissions: having one fewer child (an average for developed countries of 58.6 tonnes CO₂-equivalent (tCO₂e) emission reductions per year), living car-free (2.4 tCO₂e saved per year), avoiding airplane travel (1.6 tCO₂e saved per roundtrip transatlantic flight) and eating a plant-based diet (0.8 tCO₂e saved per year). These actions have much greater potential to reduce emissions than commonly promoted strategies like comprehensive recycling (four times less effective than a plant-based diet) or changing household lightbulbs (eight times less). Though adolescents poised to establish lifelong patterns are an important target group for promoting high-impact actions, we find that ten high school science textbooks from Canada largely fail to mention these actions (they account for 4% of their recommended actions), instead focusing on incremental changes with much smaller potential emissions reductions. Government resources on climate change from the EU, USA, Canada, and Australia also focus recommendations on lower-impact actions. We conclude that there are opportunities to improve existing educational and communication structures to promote the most effective emission-reduction strategies and close this mitigation gap.

The consensus that humans are causing recent global warming is shared by 90%–100% of publishing climate scientists according to six independent studies by co-authors of this paper. Those results are consistent with the 97% consensus reported by Cook et al (Environ. Res. Lett. 8 024024) based on 11 944 abstracts of research papers, of which 4014 took a position on the cause of recent global warming. A survey of authors of those papers (N = 2412 papers) also supported a 97% consensus. Tol (2016 Environ. Res. Lett. 11 048001) comes to a different conclusion using results from surveys of non-experts such as economic geologists and a self-selected group of those who reject the consensus. We demonstrate that this outcome is not unexpected because the level of consensus correlates with expertise in climate science. At one point, Tol also reduces the apparent consensus by assuming that abstracts that do not explicitly state the cause of global warming ('no position') represent non-endorsement, an approach that if applied elsewhere would reject consensus on well-established theories such as plate tectonics. We examine the available studies and conclude that the finding of 97% consensus in published climate research is robust and consistent with other surveys of climate scientists and peer-reviewed studies.

We analyze the evolution of the scientific consensus on anthropogenic global warming (AGW) in the peer-reviewed scientific literature, examining 11 944 climate abstracts from 1991–2011 matching the topics 'global climate change' or 'global warming'. We find that 66.4% of abstracts expressed no position on AGW, 32.6% endorsed AGW, 0.7% rejected AGW and 0.3% were uncertain about the cause of global warming. Among abstracts expressing a position on AGW, 97.1% endorsed the consensus position that humans are causing global warming. In a second phase of this study, we invited authors to rate their own papers. Compared to abstract ratings, a smaller percentage of self-rated papers expressed no position on AGW (35.5%). Among self-rated papers expressing a position on AGW, 97.2% endorsed the consensus. For both abstract ratings and authors' self-ratings, the percentage of endorsements among papers expressing a position on AGW marginally increased over time. Our analysis indicates that the number of papers rejecting the consensus on AGW is a vanishingly small proportion of the published research.

The Russian heatwave in 2010 killed tens of thousands of people, and was by far the worst event in Europe since at least 1950, according to recent studies and a novel universal heatwave index capturing both the duration and magnitude of heatwaves. Here, by taking an improved version of this index, namely the heat wave magnitude index daily, we rank the top ten European heatwaves that occurred in the period 1950–2014, and show the spatial distribution of the magnitude of the most recent heatwave in summer 2015. We demonstrate that all these events had a strong impact reported in historical newspapers. We further reveal that the 1972 heatwave in Finland had a comparable spatial extent and magnitude as the European heatwave of 2003, considered the second strongest heatwave of the observational era. In the next two decades (2021–2040), regional climate projections suggest that Europe experiences an enhanced probability for heatwaves comparable to or greater than the magnitude, extent and duration of the Russian heatwave in 2010. We demonstrate that the probability of experiencing a major European heatwave in the coming decades is higher in RCP8.5 than RCP4.5 even though global mean temperature projections do not differ substantially. This calls for a proactive vulnerability assessment in Europe in support of formulating heatwave adaptation strategies to reduce the adverse impacts of heatwaves.

The UK is vulnerable to wildfire, and vulnerability is likely to increase due to climate change. Whilst the risk is small compared with many other countries, recent fires have raised awareness and highlighted the potential for environmental damage and loss of property and key infrastructure. Most UK wildfires are a result of inadvertent or deliberate human action, but the environmental conditions depend on antecedent and current weather. This paper presents projections of the effects of climate change on UK wildfire danger, using a version of an operational fire danger model, UKCP18 climate projections representing low and high emissions, and several indicators of fire danger. Fire danger will increase across the whole of the UK, but the extent and variability in change varies with indicator. The absolute danger now and into the future is greatest in the south and east (the average number of danger days increases 3–4 times by the 2080s), but danger increases further north from a lower base. The variation in change across the UK for indicators based on absolute thresholds is determined by how often those thresholds are exceeded now, whilst the (lesser) variability in percentile-based indicators reflects variability in the projected change in climate. Half of the increase in danger is due to increased temperature, and most of the rest is due to projected reductions in relative humidity. Uncertainty in the magnitude of the change is due to uncertainty in changes in temperature, relative humidity, and rainfall, and there is a large difference between two of the UKCP18 climate model ensembles. Reducing emissions to levels consistent with achieving international climate policy targets significantly reduces, but does not eliminate, the increase in fire danger. The results imply that greater attention needs to be given to wildfire danger in both emergency and spatial planning, and in the development of guidelines for activities that may trigger fires. They suggest the need for the development of a fire danger system more tailored to UK conditions, and the combination of fire danger modelling with projections of sources of ignition to better estimate the change in wildfire risk.

Worldwide demand for crops is increasing rapidly due to global population growth, increased biofuel production, and changing dietary preferences. Meeting these growing demands will be a substantial challenge that will tax the capability of our food system and prompt calls to dramatically boost global crop production. However, to increase food availability, we may also consider how the world's crops are allocated to different uses and whether it is possible to feed more people with current levels of crop production. Of particular interest are the uses of crops as animal feed and as biofuel feedstocks. Currently, 36% of the calories produced by the world's crops are being used for animal feed, and only 12% of those feed calories ultimately contribute to the human diet (as meat and other animal products). Additionally, human-edible calories used for biofuel production increased fourfold between the years 2000 and 2010, from 1% to 4%, representing a net reduction of available food globally. In this study, we re-examine agricultural productivity, going from using the standard definition of yield (in tonnes per hectare, or similar units) to using the number of people actually fed per hectare of cropland. We find that, given the current mix of crop uses, growing food exclusively for direct human consumption could, in principle, increase available food calories by as much as 70%, which could feed an additional 4 billion people (more than the projected 2–3 billion people arriving through population growth). Even small shifts in our allocation of crops to animal feed and biofuels could significantly increase global food availability, and could be an instrumental tool in meeting the challenges of ensuring global food security.

Global mean sea-level rise (SLR) has accelerated since 1900 from less than 2 mm yr⁻¹ during most of the century to more than 3 mm yr⁻¹ since 1993. Decision-makers in coastal countries, however, require information on SLR at the regional scale, where detection of an acceleration in SLR is difficult, because the long-term sea-level signal is obscured by large inter-annual variations with multi-year trends that are easily one order of magnitude larger than global mean values. Here, we developed a time series approach to determine whether regional SLR is accelerating based on tide gauge data. We applied the approach to eight 100-year records in the southern North Sea and detected, for the first time, a common breakpoint in the early 1990s. The mean SLR rate at the eight stations increases from 1.7 ± 0.3 mm yr⁻¹ before the breakpoint to 2.7 ± 0.4 mm yr⁻¹ after the breakpoint (95% confidence interval), which is unprecedented in the regional instrumental record. These findings are robust provided that the record starts before 1970 and ends after 2015. Our method may be applied to any coastal region with tidal records spanning at least 40 years, which means that vulnerable coastal communities still have time to accumulate the required time series as a basis for adaptation decisions in the second half of this century.

Variations in the uptake of atmospheric carbon by vegetation over India, the second-highest contributor to global greening, have enormous implications for climate change mitigation. Global studies conclude that temperature and total water storage (TWS) cause interannual variations of carbon uptake based on the correlation coefficient, which is not a causality measure. Here, we apply a statistically rigorous causality approach, Peter Clark momentary conditional independence, to the monthly observed satellite and station-based gridded dataset of India's climate and carbon uptake variables. We find no existence of causal connections from TWS to gross primary production (GPP) or net photosynthesis (PSN). Causal relationships exist from precipitation to GPP and PSN. Since shallow-rooted croplands dominate India's green cover, impacts of precipitation on carbon capture of the the land ecosystem are immediate and not via TWS. Our results identify the key climate drivers of GPP/PSN variability and highlight interactions between water and the carbon cycle in India. Our results also highlight the need for formal causal analysis using climate and earth sciences observations rather than the conventional practices of inferring causality from correlations.

The 'Greening' and subsequent desertification of the Sahara during the early to mid-Holocene is a dramatic example of natural climate change. We analyse a suite of simulations with a newly palaeo-conditioned configuration of the HadCM3 coupled model that is able to capture an abrupt desertification of North Africa during this time. We find that this model crosses a threshold of moisture availability for vegetation at around 6000 years before present. The resultant rapid reduction in vegetation cover acts to reduce precipitation through moisture recycling and surface albedo feedbacks. Precursor drying events which are not directly forced also indicate that the model is close to a critical moisture level. Similar precursor-like events appear in a Holocene record from the East of the continent, hinting that the natural system may resemble some of the properties of this model simulation. The overall response is not fundamentally altered by the inclusion of solar irradiance variations or volcanic eruptions. The simulated timing of the abrupt transition is mostly controlled by orbital forcing and local positive feedbacks, but it is also modulated to some extent by the state of the atmosphere and ocean. Comparisons with proxy records across North Africa show good agreement with the model simulations, although the simulations remain overly dry in the East. Overall, a threshold response may present a useful model of the real transition, but more high-resolution palaeoclimate records would help to discriminate among the predictions of climate models.

Improving agricultural productivity is a foundational sustainability challenge in the 21st century. Large-scale land acquisitions (LSLAs) have important effects on both well-being and the environment in the Global South. Their impacts on agricultural productivity and subsequent effects on farm incomes, food-security and the distribution of these outcomes across households remain under-investigated. In particular, prior studies do not sufficiently attend to the mechanistic nature of changes in household agricultural practices that affect LSLA outcomes. To address these challenges, we use a novel household dataset and a quasi-experimental design to estimate household-level changes in agricultural productivity and other LSLA outcomes in Tanzania. We use causal mediation analysis to assess how four common mechanisms—contract farming, land loss, market access and technology adoption around LSLAs—influence agricultural productivity. We find that households near LSLAs exhibit 20.2% (95% CI: 3.1%–37.3%) higher agricultural productivity, primarily due to increased crop prices and farmer selection of high-value crops. Importantly, the direction and magnitude of effect sizes associated with the different mechanisms vary. The presence of contract farming explains 18.1% (95% CI: 0.56%, 47%) of the effect size in agricultural productivity, whereas land loss reduces agricultural productivity by 26.8% (95% CI: −71.3%, −4.0%). Market access and technology adoption explain little to no portion of the effect size on agricultural productivity. Despite higher agricultural productivity mediated by contract farming, we do not find increased household incomes or food security. Plausible explanations include limited market access, higher crop prices restricting food access and elite capture of contract farming concentrating income effects to a few households. Our results stand in contrast to assumptions that technological spillovers occur through LSLAs and are the principal drivers of LSLA-induced agricultural transformation. We find instead that access to contract farming and high-value crops lead to greater agricultural productivity, but also that benefits related to these mechanisms are unequally distributed.

High- and low pressure systems of the large-scale atmospheric circulation in the mid-latitudes drive European weather and climate. Potential future changes in the occurrence of circulation types are highly relevant for society. Classifying the highly dynamic atmospheric circulation into discrete classes of circulation types helps to categorize the linkages between atmospheric forcing and surface conditions (e.g. extreme events). Previous studies have revealed a high internal variability of projected changes of circulation types. Dealing with this high internal variability requires the employment of a single-model initial-condition large ensemble (SMILE) and an automated classification method, which can be applied to large climate data sets. One of the most established classifications in Europe are the 29 subjective circulation types called Grosswetterlagen by Hess & Brezowsky (HB circulation types). We developed, in the first analysis of its kind, an automated version of this subjective classification using deep learning. Our classifier reaches an overall accuracy of 41.1% on the test sets of nested cross-validation. It outperforms the state-of-the-art automatization of the HB circulation types in 20 of the 29 classes. We apply the deep learning classifier to the SMHI-LENS, a SMILE of the Coupled Model Intercomparison Project phase 6, composed of 50 members of the EC-Earth3 model under the SSP37.0 scenario. For the analysis of future frequency changes of the 29 circulation types, we use the signal-to-noise ratio to discriminate the climate change signal from the noise of internal variability. Using a 5%-significance level, we find significant frequency changes in 69% of the circulation types when comparing the future (2071–2100) to a reference period (1991–2020).

Weather extremes are often associated with atmospheric blocking, but how the underlying physical processes leading to blocking respond to climate change is not yet fully understood. Here we track blocks as upper-level negative potential vorticity (PV) anomalies and apply a Lagrangian analysis to 100 years of present-day (∼2000) and future (∼2100, under the RCP8.5 scenario) climate simulations restarted from the Community Earth System Model–Large Ensemble Project runs (CESM-LENS) to identify different physical processes and quantify how their relative importance changes in a warmer and more humid climate. The trajectories reveal two contrasting airstreams that both contribute to the formation and maintenance of blocking: latent heating in strongly ascending airstreams (moist processes) and quasi-adiabatic flow near the tropopause with weak radiative cooling (dry processes). Both are reproduced remarkably well when compared against ERA-Interim reanalysis, and their relative importance varies regionally and seasonally. The response of blocks to climate change is complex and differs regionally, with a general increase in the importance of moist processes due to stronger latent heating (+1 K in the median over the Northern Hemisphere) and a larger fraction (+15%) of strongly heated warm conveyor belt air masses, most pronounced over the storm tracks. Future blocks become larger (+7%) and their negative PV anomaly slightly intensifies (+0.8%). Using a Theil–Sen regression model, we propose that the increase in size and intensity is related to the increase in latent heating, resulting in stronger cross-isentropic transport of air with low PV into the blocking anticyclones. Our findings provide evidence that moist processes become more important for the large-scale atmospheric circulation in the midlatitudes, with the potential for larger and more intense blocks.

As the energy transition accelerates and renewable energy technologies become cost-competitive with fossil fuels in many countries, the availability of finance could become a bottleneck. Integrated assessment models (IAMs) and other macro-economic transition (MET) models typically do not feature detailed financial markets and do not sufficiently consider financing barriers and opportunities for the transition to carbon neutrality. While progress has been made in the representation of financial markets in macro-models since the financial crisis of 2008 the focus has been on financial (in)stability of the financial sector, not its ability to finance investment projects in the energy transition. Hence, a crucial gap remains, preventing macro model-based analysis of financing barriers and policy interventions that may accelerate the energy transition. In this article we review how state-of-the-art macro-economic models consider the financial sector. From this review we identify what elements are still missing to adequately model the financial dynamics and challenges for the energy transition specifically. Based on a discussion of relevant parts of the finance literature, we then propose four steps to improve the representation of finance in global IAMs and MET models more generally.

Farmers' perception of climate change is crucial in adaptation intention and process. However, farmers' perceptions may not be timely, accurate and systematically consistent with the direction and significance of observational records. Although some research compared farmers' perceptions and climate data, little attention has been paid to comprehensibly analyse both data sources discrepancies based on empirical studies results. By combining bibliometrics and a systematic review approach, we identify which approaches are used to compare perceived and observed data, how both patterns have been mutually evolved, which factors determine their (in)consistency, and if their accordance and robustness affect farmers' adaptive capacity. We analyse a portfolio of 147 papers collected from the Scopus library catalogue since 2000. The bibliometric analysis was coupled with an exploratory analysis of 98 papers selected from the original portfolio. The literature is extensive, fast-growing, and spans several disciplines. We identify four consolidated research lines: (a) perceived risk and farmers' adaptive capacity nexus, (b) crop vulnerability due to temperature increase and erratic rainfall patterns, (c) forecasting use and influence in farmers' decisions, and (d) climate change awareness conditioning farmers' profiles. Nonetheless, we observe some research gaps: (a) a conceptual mismatch in 'normal pattern' or 'drought' meaning, (b) poor or limited data from meteorological stations, (c) overlook or oversimplification of local knowledge in describing perception, (d) farmers' memory weaknesses to keep track of climate alterations, and (e) a geographical dissonance in favour of Global South regions. Our science-metric study also reveals some research questions to be consolidated: Can the perception of extreme events increase climate change awareness? Can greater awareness reduce discrepancy with observed data? How do heuristics and socio-psychological filters influence farmers' awareness and interpretation of climate data? We suggest putting major efforts into reinforcing these research lines as part of a novel domain-dependent trend to reduce the discrepancy.

Sustainable agricultural intensification requires irrigation methods and strategies to minimize yield penalties while optimizing water, land and energy use efficiencies. We assessed, from a silo-based and integrated water-energy-food (WEF) nexus perspective, the performance of irrigation technologies in different agro-climatic regions. Secondary to this, we assessed the impact of adopting systematic approaches such as the WEF nexus on improving efficiency in irrigated agriculture through irrigation modernization. The evidence-based perspectives of silo-based performances individually considered the metrics of yield (Y), water use efficiency (WUE), and energy productivity (EP). The WEF nexus approach applied sustainability polygons to integrate the three metrics into a nexus index representing the holistic performance of the irrigation technologies. Silo-based performance in temperate regions suggests net gains for WUE (+1.10 kg m⁻³) and Y (+6.29 ton ha⁻¹) when transitioning from furrow to sprinkler irrigation, with a net loss in EP (−3.82 ton MJ⁻¹). There is potential for a net loss on EP (−3.33 ton MJ⁻¹) when transitioning from furrow to drip system in temperate regions. The best performance of irrigation technologies in dry regions in water, energy and food silos was achieved by sprinkler, drip and furrow irrigation systems, respectively. Thus, appraising irrigation technologies from a silos perspective promotes individual silos, which renders an unsustainable picture of the performance of irrigation systems. The integrative WEF nexus approach successfully highlighted the trade-offs and synergies in the nexus of water, energy and food in irrigated agriculture. Drip irrigation led all irrigation technologies in WEF nexus performance in dry (21.44 unit²), tropical (23.98 unit²), and temperate regions (47.28 unit²). Overall, the irrigation modernization pathway to drip technology from either furrow or sprinkler systems improves irrigated agriculture's WEF nexus performance in all three regions for more crop per drop per joule per hectare under climate change. This can promote inclusive and sustainable irrigation development within the planetary boundaries.

Climate change poses significant risks to large-scale infrastructure systems and brings considerable uncertainties that challenge historical planning approaches. Here we focus on how climate awareness might be better incorporated into planning and decision-making in the electric power sector. To do so, we consider lessons from decision science literature where researchers have specifically focused on how to make better decisions under uncertainty. We perform a three-part review: of decision science literature on best practices for decision-making under uncertainty, of industry practice documents to understand how new uncertainties may affect the types of decisions electric utilities are making today, and of literature on electricity sector planning under climate change to assess how lessons from decision science might fit into sector-specific analyses. We discuss how characterizations of uncertainty from the decision science literature might guide approaches in the electricity sector to appropriately capture climate-related uncertainties. We also distill three key ideas from the decision science literature that can be incorporated into electricity sector planning to manage these new uncertainties: robustness, adaptive planning, and multi-stakeholder engagement. We offer example recommendations for how these key ideas might be incorporated into one essential type of planning activity, capacity expansion.

A brief historical narrative of coral responses to climate change exposures is followed by a review of evidence. I trace the history of investigations and summarize the findings from 112 multiple-site field studies that examined environmental exposure variables and coral bleaching and mortality response relationships. A total of 59 environmental variables in six topic areas were studied of which excess thermal exposure was the most common topic and variable. Investigations were broadly classified into two categories; those focused on either excess thermal stress thresholds (TM) or on continuous variables (VM). The TM investigations considered a total of 28 variables, but only 1.7 ± 1.3 (SD) variables per publication, and only 11% completed a variable selection process that competed variables for fit or parsimony. The 65 VM publications considered 59 variables, more variables per publication (4.1 ± 4.3), and 43% of the studies followed a variable selection procedure. TM investigations received more citation and were most frequently used to identify future climate change impacts and sanctuaries. VM investigations often report excess heat threshold variables as weak single predictors of coral bleaching and mortality. Coral responses to exposure favors mechanisms of causation that are additive and interactive; specifically, the interactions between chronic and acute stresses within the geographic and habitat contexts of local environmental and coral genetic histories. Some of the potentially most important variables for predicting coral responses to exposure have seldom been studied or modeled. The implication is that the future status and health of coral reefs will be better than predicted by TMs. Moreover, impacts and sanctuaries are expected to be patchy and influenced by space, time, genetics, and taxa heterogeneity that will reflect a mix of avoidance, resistance, and recovery processes and their associated sanctuary locations.

This study adopts a multi-level approach to examine the extent to which state- and household-level factors shape residential energy consumption in the United States, focusing on efficiency improvement and affluence. Analyzing the 2009 Residential Energy Consumption Survey, state-level energy efficiency data from the American Council for an Energy-Efficient Economy (ACEEE), and other sources, we find that state context significantly influences energy consumption at the household level. Households in states scoring high on energy efficiency consume significantly less residential energy than those in states scoring low on the measure. At the household level, the analysis reveals mixed relationships between investment in energy efficiency technologies and residential energy consumption, as some measures of efficiency technology are negatively related to residential energy consumption, others are positively related to it. In regard to affluence, state-level measures do not emerge as significant predictors of residential energy consumption. At the household level, however, affluence drives residential energy consumption, which, in turn, is a significant driver of carbon dioxide emissions. Our study makes an important contribution to the social scientific literature on energy consumption, illuminating distinct relationships at different levels. To the best of our knowledge, this is the first study that simultaneously examines the impacts of factors measured at both the household (micro) and state (meso) levels.

The increasing global demand for farmland products is placing unprecedented pressure on the global agricultural system and its water resources. Many regions of the world, that are affected by a chronic water scarcity relative to their population, strongly depend on the import of agricultural commodities and associated embodied (or virtual) water. The globalization of water through virtual water trade (VWT) is leading to a displacement of water use and a disconnection between human populations and the water resources they rely on. Despite the recognized importance of these phenomena in reshaping the patterns of water dependence through teleconnections between consumers and producers, their effect on global and regional water resources has just started to be quantified. This review investigates the global spatiotemporal dynamics, drivers, and impacts of VWT through an integrated analysis of surface water, groundwater, and root-zone soil moisture consumption for agricultural production; it evaluates how virtual water flows compare to the major 'physical water fluxes' in the Earth System; and provides a new reconceptualization of the hydrologic cycle to account also for the role of water redistribution by the hidden 'virtual water cycle'.

Heat is the number one weather-related killer in the United States and indoor exposure is responsible for a significant portion of the resulting fatalities. Evolving construction practices combined with urban development in harsh climates has led building occupants in many cities to rely on air conditioning (AC) to a degree that their health and well-being are compromised in its absence. The risks are substantial if loss of AC coincides with a hot weather episode (henceforth, a heat disaster). Using simulations, we found that residential buildings in many US cities are highly vulnerable to heat disasters—with more than 50 million citizens living in cities at significant risk. This situation will be exacerbated by intensification of urban heat islands, climate change, and evolving construction practices. It is therefore crucial that future building codes consider thermal resiliency in addition to energy efficiency.

We examine the potential for climate change to impact fertility via adaptations in human behavior. We start by discussing a wide range of economic channels through which climate change might impact fertility, including sectoral reallocation, the gender wage gap, longevity, and child mortality. Then, we build a quantitative model that combines standard economic-demographic theory with existing estimates of the economic consequences of climate change. In the model, increases in global temperature affect agricultural and non-agricultural sectors differently. Near the equator, where many poor countries are located, climate change has a larger negative effect on agriculture. The resulting scarcity in agricultural goods acts as a force towards higher agricultural prices and wages, leading to a labor reallocation into this sector. Since agriculture makes less use of skilled labor, climate damage decreases the return to acquiring skills, inducing parents to invest less resources in the education of each child and to increase fertility. These patterns are reversed at higher latitudes, suggesting that climate change may exacerbate inequities by reducing fertility and increasing education in richer northern countries, while increasing fertility and reducing education in poorer tropical countries. While the model only examines the role of one mechanism, it suggests that climate change could have an impact on fertility, indicating the need for future work on this important topic.

We use a multiple-regime panel smooth transition regression to examine the economic and climatic sources of the nonuniform relationship between El Niño Southern Oscillation (ENSO) and maize yields around the globe. While the yield effect is predominantly observed in strongly teleconnected countries, it is amplified in lower income countries, which we attribute to possible lack of resilience to ENSO—induced weather shocks. Both El Niño-like and La Niña-like conditions result in maize yield reduction, but it is during El Niño events when maize yields drop by up to 20% in most affected countries. Because in many of these countries maize is an important agricultural crop, the presented results are of interest to researchers and policy makers in the areas of world nutrition and international aid. Moreover, because larger share of maize is produced by high income weakly teleconnected countries, the observed geographic heterogeneity of the El Niño impact offers possible benefits from global risk sharing. These findings also offer insights to climate change economics, as possible increased frequency of the ENSO cycle may negatively impact maize production in strongly teleconnected low income countries.