Table of contents

Tipping points have become a key concept in research on climate change, indicating points of abrupt transition in biophysical systems as well as transformative changes in adaptation and mitigation strategies. However, the potential existence of tipping points in socio-economic systems has remained underexplored, whereas they might be highly policy relevant. This paper describes characteristics of climate change induced socio-economic tipping points (SETPs) to guide future research on SETPS to inform climate policy. We review existing literature to create a tipping point typology and to derive the following SETP definition: a climate change induced, abrupt change of a socio-economic system, into a new, fundamentally different state. Through stakeholder consultation, we identify 22 candidate SETP examples with policy relevance for Europe. Three of these are described in higher detail to identify their tipping point characteristics (stable states, mechanisms and abrupt change): the collapse of winter sports tourism, farmland abandonment and sea-level rise-induced migration. We find that stakeholder perceptions play an important role in describing SETPs. The role of climate drivers is difficult to isolate from other drivers because of complex interplays with socio-economic factors. In some cases, the rate of change rather than the magnitude of change causes a tipping point. The clearest SETPs are found on small system scales. On a national to continental scale, SETPs are less obvious because they are difficult to separate from their associated economic substitution effects and policy response. Some proposed adaptation measures are so transformative that their implementations can be considered an SETP in terms of 'response to climate change'. Future research can focus on identification and impact analysis of tipping points using stylized models, on the exceedance of stakeholder-defined critical thresholds in the RCP/SSP space and on the macro-economic impacts of new system states.

Background: Environmental changes are predicted to threaten human health, agricultural production and food security. Whilst their impact has been evaluated for major cereals, legumes and vegetables, no systematic evidence synthesis has been performed to date evaluating impact of environmental change on fruits, nuts and seeds (FN&S)—valuable sources of nutrients and pivotal in reducing risks of non-communicable disease. Methods: We systematically searched seven databases, identifying available published literature (1970–2018) evaluating impacts of water availability and salinity, temperature, carbon dioxide (CO₂) and ozone (O₃) on yields and nutritional quality of FN&S. Dose-response relationships were assessed and, where possible, mean yield changes relative to baseline conditions were calculated. Results: 81 papers on fruits and 24 papers on nuts and seeds were identified, detailing 582 and 167 experiments respectively. A 50% reduction in water availability and a 3–4dS m⁻¹ increase in water salinity resulted in significant fruit yield reductions (mean yield changes: −20.7% [95%CI −43.1% to −1.7%]; and −28.2% [95%CI −53.0% to −3.4%] respectively). A 75%–100% increase in CO₂ concentrations resulted in positive yield impacts (+37.8%; [95%CI 4.1% to 71.5%]; and 10.1%; [95%CI −30.0% to 50.3%] for fruits and nuts respectively). Evidence on yield impacts of increased O₃ concentrations and elevated temperatures (>25 °C) was scarce, but consistently negative. The positive effect of elevated CO₂ levels appeared to attenuate with simultaneous exposure to elevated temperatures. Data on impacts of environmental change on nutritional quality of FN&S were sparse, with mixed results. Discussion: In the absence of adaptation strategies, predicted environmental changes will reduce yields of FN&S. With global intake already well-below WHO recommendations, declining FN&S yields may adversely affect population health. Adaptation strategies and careful agricultural and food system planning will be essential to optimise crop productivity in the context of future environmental changes, thereby supporting and safeguarding sustainable and resilient food systems.

Marine plastic debris floating on the ocean surface is a major environmental problem. However, its distribution in the ocean is poorly mapped, and most of the plastic waste estimated to have entered the ocean from land is unaccounted for. Better understanding of how plastic debris is transported from coastal and marine sources is crucial to quantify and close the global inventory of marine plastics, which in turn represents critical information for mitigation or policy strategies. At the same time, plastic is a unique tracer that provides an opportunity to learn more about the physics and dynamics of our ocean across multiple scales, from the Ekman convergence in basin-scale gyres to individual waves in the surfzone. In this review, we comprehensively discuss what is known about the different processes that govern the transport of floating marine plastic debris in both the open ocean and the coastal zones, based on the published literature and referring to insights from neighbouring fields such as oil spill dispersion, marine safety recovery, plankton connectivity, and others. We discuss how measurements of marine plastics (both in situ and in the laboratory), remote sensing, and numerical simulations can elucidate these processes and their interactions across spatio-temporal scales.

Recent advances in shale gas development have largely outpaced efforts to manage associated waste streams that pose significant environmental risks. Wastewater management presents significant challenges in the Marcellus shale, where increasing fluid volumes concomitant with expanding development will threaten to overwhelm existing infrastructure over the next decade. In this work, we forecast growth in drilling, flowback, and produced fluid volumes through 2025 based on historic data and consider conventional and alternative disposal options to meet future demands. The results indicate that nearly 12 million m³ (74 MMbbl) of wastewater will be generated annually by 2025. Even assuming wastewater recycling rates in the region rebound, meeting increased demands for wastewater that cannot be reused due to poor quality or logistics would require significant capital investment to expand existing disposal pathways, namely treatment and discharge at centralized facilities or dedicated brine injection in Ohio. Here, we demonstrate the logistical and environmental advantages of an alternative strategy: repurposing depleted oil and gas wells for dedicated injection of wastewater that cannot otherwise be reused or recycled. Hubs of depleted wells could accommodate projected increases in wastewater volumes more efficiently than existing disposal options, primarily because the proximity of depleted wells to active production sites would substantially reduce wastewater transport distances and associated costs. This study highlights the need to reevaluate regional-scale shale wastewater management practices in the context of evolving wastewater qualities and quantities, as strategic planning will result in more socially and economically favorable options while avoiding adverse environmental impacts that have overshadowed the environmental benefits of natural gas expansion in the energy sector.

Power system planning aims at ensuring that sufficient supply- and demand-side assets exist to meet electricity demand at all times. For a Texas electric power system with high wind and solar penetrations, we quantify how climate change will affect supply and demand during three types of high stress periods for the power grid: high demand hours, high net demand hours, and high system ramp hours. We specifically quantify effects on demand, reductions in available thermal capacity (i.e. thermal deratings), wind and solar generation, and net demand. We estimate each using meteorological variables from five climate change projections (2041–2050) assuming Representative Concentration Pathway 8.5 and from a reference period (1996–2005). All five projections indicate that climate change will increase demand by up to 2 GWh during high demand hours (4% of demand in the reference period) and increase net demand by up to 3 GWh during high net demand periods (6% of net demand in the reference period). All five projections also indicate thermal deratings will increase during high demand and net demand periods by up to 2 GWh and high net demand ramps will increase by up to 2 GW. Overall, our results indicate compounding effects of climate change in Texas will necessitate greater investment in peak and flexible capacity.

According to established understanding, deep-water formation in the North Atlantic and Southern Ocean keeps the deep ocean cold, counter-acting the downward mixing of heat from the warmer surface waters in the bulk of the world ocean. Therefore, periods of strong Atlantic meridional overturning circulation (AMOC) are expected to coincide with cooling of the deep ocean and warming of the surface waters. It has recently been proposed that this relation may have reversed due to global warming, and that during the past decades a strong AMOC coincides with warming of the deep ocean and relative cooling of the surface, by transporting increasingly warmer waters downward. Here we present multiple lines of evidence, including a statistical evaluation of the observed global mean temperature, ocean heat content, and different AMOC proxies, that lead to the opposite conclusion: even during the current ongoing global temperature rise a strong AMOC warms the surface. The observed weakening of the AMOC has therefore delayed global surface warming rather than enhancing it.

Social Media Abstract: The overturning circulation in the Atlantic Ocean has weakened in response to global warming, as predicted by climate models. Since it plays an important role in transporting heat, nutrients and carbon, a slowdown will affect global climate processes and the global mean temperature. Scientists have questioned whether this slowdown has worked to cool or warm global surface temperatures. This study analyses the overturning strength and global mean temperature evolution of the past decades and shows that a slowdown acts to reduce the global mean temperature. This is because a slower overturning means less water sinks into the deep ocean in the subpolar North Atlantic. As the surface waters are cold there, the sinking normally cools the deep ocean and thereby indirectly warms the surface, thus less sinking implies less surface warming and has a cooling effect. For the foreseeable future, this means that the slowing of the overturning will likely continue to slightly reduce the effect of the general warming due to increasing greenhouse gas concentrations.

The high demand for cement-based materials to support building and infrastructure systems is of growing concern as the production of cement leads to significant greenhouse gas (GHG) emissions and notable resource demand. While improved efficiency of cement use has been proposed as a means to mitigate these burdens, the effects of increasing longevity of cement in-use remains a poorly studied area. This work quantitatively explores the implications of using cement for a longer in-use residence times. Specifically, this work uses dynamic material flow analysis models to quantify the in-use stock of cement in the United States from 1900 to 2015. With these models, the implications of increasing or decreasing mean longevity of in-use cement on required cement production, demand for batching water, aggregates, and energy for cement-based materials, and GHG emissions are quantified. This work shows that a 50% increase in cement longevity could have led to a 14% reduction in material resource demand and GHG emissions from concrete production in the United States, equivalent to 0.28 to 0.83 Gt of batching water, 2.9 to 7.6 Gt of aggregates, 1E + 06 to 2.3E + 06 TJ of energy, and 0.4 to 0.7 Gt of CO₂-eq emissions. This percent reduction exceeds goals for reducing GHG emissions through alternative energy resources, suggesting improving durability and longevity of in-use cement stock could be a critical means to mitigating environmental impacts.

Global mitigation efforts remain insufficient to limit the global temperature increase to well below 2 °C. While a growing academic literature analyzes this problem, perceptions of which obstacles inhibit goal attainment and which responses might be most effective seem to differ widely. This makes prioritization and agreement on the way forward difficult. To inform prioritization in global climate policy and research agendas, we present quantitative data on how 917 experts from the IPCC and the UNFCCC perceive the importance of different obstacles and response options for achieving 2 °C. On average, respondents consider opposition from special interest groups the most important obstacle and technological R&D the most important response. Our survey also finds that the majority of experts perceives a wide range of issues as important, supporting an agenda that is inclusive in terms of coverage. Average importance ratings differ between experts from the Global North and South, suggesting that balanced representation in global fora and regionally differentiated agendas are important. In particular, opposition from special interest groups is a top priority among experts from North America, Europe and Oceania. Investigating the drivers of individual importance ratings, we find little difference between experts from the IPCC and the UNFCCC, while expert's perceptions correlate with their academic training and their national scientific, regulatory, and financial contexts.

Tree-ring records have shown a significant upward trend of late summer temperatures over the Tibetan Plateau (TP) in recent decades. More detailed instrumental observations over the TP have also shown an increase in warm extremes, especially in the high-altitude area. It is not clear whether the recent increase in occurrence of warm extremes on the TP reflects an amplification of recent temperature fluctuations or it relates to the long-term climate trend. This study aims to address the above question by analyzing long-term late summer (August–September) temperature extremes over the TP using temperature reconstructions derived from tree-ring maximum latewood density. Our results show that the recent frequency of late summer warm extremes was unprecedented over the past four centuries, while the occurrence of cold extremes in the instrumental interval was minimal. However, after the removal of long-term trend, the frequencies of both warm and cold extremes in the instrumental period are actually smaller than the pre-instrumental period, indicating a decreased variability of late summer temperatures. Thus, we conclude that the recent observed increase in warm extremes is related to the long-term warming trend, rather than an amplification of temperature variability. This finding implies that the persistent warming on the TP in the future might trigger much more frequent warm extremes with potential ecological and environmental effects.

Emission reduction from the coal-dominated power sector is vital for achieving China's carbon mitigation targets. Although the coal expansion has been slowed down due to the cancellation of and delay in new construction, coal-based power was responsible for over one third of China's energy-related CO₂ emissions by 2018. Moreover, with a technical lifetime of over 30 years, current investment in coal-based power could hinder CO₂ mitigation until 2050. Therefore, it is important to examine whether the current coal-based power planning aligns with the long-term climate targets. This paper introduces China's Nationally Determined Contribution (NDC) goals and an ambitious carbon budget along with global pathways well-below 2 degrees that are divided into five integrated assessment models, which are two national and three global models. We compare the models' results with bottom-up data on current capacity additions and expansion plans to examine if the NDC targets are in line with 2-degree pathways. The key findings are: 1. NDC goals alone are unlikely to lead to significant reductions in coal-based power generation. On the contrary, more plants may be built before 2030; 2. this would require an average of 187–261 TWh of annual coal-based power capacity reduction between 2030 and 2050 to achieve a 2 °C compatible trajectory, which would lead to the stranding of large-scale coal-based power plants; 3. if the reduction in coal power can be brought forward to 2020, the average annual coal-based power reduction required would be 104–155 TWh from 2020 to 2050 and the emissions could peak earlier; 4. early regulations in coal-based power would require accelerated promotion of alternatives between 2020 and 2030, with nuclear, wind and solar power expected to be the most promising alternatives. By presenting the stranding risk and viability of alternatives, we suggest that both the government and enterprises should remain cautious about making new investment in coal-based power sector.

Ecosystem service assessments facilitate the valuation of nature and support decision-making. Ecosystem services are connected to climate; however, ecosystem service values affected by climate change remain unclear. We mapped global ecosystem service values totaling ∼1.3 trillion international dollars for 2005. Transitions in Köppen–Geiger climate classes projected with General Circulation Models under the four IPCC Representative Concentration Pathways (RCP) were modeled providing 20 climate scenarios. The mapped global ecosystem service values were combined with the 20 climate scenarios in order to identify where and how much of the global ecosystem service value is within a climate class transition. By 2050, 252–375 billion international dollars of ecosystem service value (20%–30% of total value) are in a Köppen–Geiger climate transition for both RCP 2.6 and 8.5 scenarios. In RCP 2.6, the 2015 Paris Agreement carbon emission scenario target, Köppen–Geiger climate transitions stabilize after 2050. However, in the RCP 8.5 scenario, ecosystem service values amounting to 467–632 billion international dollars (37%–50% of total value) are in a Köppen–Geiger climate transition by 2085. These results provide an inclusive global overview of climate change impact on evaluated ecosystem services that affect populations and economies.

The impact of the Atlantic water inflow (AW inflow) into the Barents Sea on the regional cyclone activity in winter is analyzed in 10 ensemble simulations with the coupled Arctic atmosphere-ocean-sea ice model HIRHAM-NAOSIM for the 1979–2016 period. The model shows a statistically robust connection between AW inflow and climate variability in the Barents Sea. The analysis reveals that anomalously high AW inflow leads to changes in static stability and wind shear in the lower troposphere, and thus favorable conditions for cyclogenesis in the Barents/Kara Seas. The frequency of occurrence of cyclones, but particularly of intense cyclones, is increased over the Barents Sea. Furthermore, the cyclones in the Barents Sea become larger (increased radius) and stronger (increased intensity) in response to an increased AW inflow into the Barents Sea, compared to years of anomalously low AW inflow.

Large-scale land acquisitions (LSLAs) have received considerable scholarly attention over the last decade, and progress has been made towards quantifying their direct impacts. There is also a growing recognition of the importance of indirect effects of LSLAs, such as 'spillover' or indirect land-use change (iLUC), and the substantial challenges they pose for attribution and quantification. In fact, the relative contributions of direct and indirect LUC associated with LSLAs are unknown. This study aims to address these knowledge gaps using Economic Land Concessions (ELCs) in Cambodia, now the most targeted country for LSLAs in Southeast Asia. We leverage findings on archetypical pathways of direct and indirect LUC in Cambodia, developed through previous mixed-methods synthesis efforts, to quantify remotely sensed forest loss to specific ELCs. During 2000–2016, Cambodia roughly 1611 kha of forest, or 22% of total forest cover. Although ELCs (as of 2016) contained roughly 16% of Cambodia's forest cover (2000), forest lost within ELC boundaries accounted for nearly 30% (476 kha) of total forest lost by 2016. Furthermore, iLUC contributed an additional 49–174 kha of forest loss (3.0%–10.7% of all forest lost in Cambodia) over the same period. Thus, iLUC contributed to Cambodia's total forest loss at the rate of 11.4%–40.8% of direct LUC caused by ELCs. Such findings suggest that the total amount of LUC caused by LSLAs may well be underestimated globally. This and related synthesis research efforts can be valuable approaches for better targeting remote sensing analyses to specific locations and time periods needed to disentangle and quantify forest loss due to direct and indirect land change processes.

Flash flood is a recurrent natural hazard with substantial impacts in the Southeast US (SEUS) due to the frequent torrential rainfalls that occur in the region, which are triggered by tropical storms, thunderstorms, and hurricanes. Flash floods are costly natural hazards, primarily due to their rapid onset. Therefore, predicting property damage of flash floods is imperative for proactive disaster management. Here, we present a systematic framework that considers a variety of features explaining different components of risk (i.e. hazard, vulnerability, and exposure), and examine multiple machine learning methods to predict flash flood damage. A large database of flash flood events consisting of more than 14 000 events are assessed for training and testing the methodology, while a multitude of data sources are utilized to acquire reliable information related to each event. A variable selection approach was employed to alleviate the complexity of the dataset and facilitate the model development process. The random forest (RF) method was then used to map the identified input covariates to a target variable (i.e. property damage). The RF model was implemented in two modes: first, as a binary classifier to estimate if a region of interest was damaged in any particular flood event, and then as a regression model to predict the amount of property damage associated with each event. The results indicate that the proposed approach is successful not only for classifying damaging events (with an accuracy of 81%), but also for predicting flash flood damage with a good agreement with the observed property damage. This study is among the few efforts for predicting flash flood damage across a large domain using mesoscale input variables, and the findings demonstrate the effectiveness of the proposed methodology.

A majority of studies suggest that elevation dependent warming (EDW) has been verified in mountainous areas. However, there is some controversy about the EDW of high mountain Asia (HMA). Based on the analysis of the data from 128 meteorological stations in the entire region for 1961–2017, we found that there was no EDW in HMA on the time scale of 1961–2017 and the spatial scale of the altitude of 3500–5000 m. The EDW in HMA is the most obvious during the period of 1998–2012. In general, after 1980, there was EDW in the altitude of 2500–5000 m. The Southeastern Tibetan Plateau always has EDW phenomenon for most of the time scales while other areas only have EDW at certain periods. Therefore, we consider that the rate of warming is higher only in specific mountain areas and time scales.

This study advances research at the intersection of environmental degradation, social stratification, and population health in the United States. Expanding the theoretical principles of power, proximity, and physiology, we hypothesize that the harmful effect of fine particulate matter on life expectancy is greater in states with higher levels of income inequality and larger black populations. To test our hypothesis, we use two-way fixed effects regression analysis to estimate the effect of a three-way interaction between fine particulate matter, income share of the top ten percent, and the percent of the population that is black on state-level average life expectancy for all US states and the District of Columbia (2000–2014). The findings support our hypothesis: the estimated effect of the three-way interaction on average life expectancy is negative and statistically significant, net of various socioeconomic and demographic controls. Using post-estimation techniques, we visually illustrate that the harmful effect of fine particulate matter on life expectancy is especially pronounced in states with both very high levels of income inequality and very large black populations. We conclude by summarizing the theoretical and substantive implications of our findings, the limitations of the study, and potential next steps in this evolving area of interdisciplinary research.

The relationship between the destructive potential of tropical cyclones (TCs) over the western North Pacific (WNP) (as quantified by the Power Dissipation Index) and El Niño events is investigated in this work. Results show that the destructive potential of TCs is significantly affected by how rapidly El Niño decays from a positive phase to a negative phase. For TCs occurring during 'slow-transforming' El Niño, more of them initiate over the southeastern part (0°–15 °N, 150 °E–180°) of the WNP and the destructive potential of TCs is usually strong. In contrast, weaker destructiveness is indicated during 'rapid-transforming' periods, with fewer TC formations in the southeastern area. This weaker destructiveness during rapid-transforming El Niño years is mainly caused by anomalously cooler upper-ocean conditions in the central Pacific, negative relative vorticity anomalies, and increased vertical wind shear in the WNP. These findings may have important implications for the seasonal prediction of TC changes in the WNP.

Climate change, with increased temperatures and varied rainfall, poses a great challenge to food security around the world. Appropriately assessing the impacts of climate change on crop productivity and understanding the adaptation potential of agriculture to climate change are urgently needed to help develop effective strategies for future agriculture and to maintain food security. In this study, we studied future maize yield changes under 1.5 °C (2018–2037) and 2 °C (2044–2063) warming scenarios and investigated the adaptation potential across China's Maize Belt by optimizing the sowing date and cultivar using the APSIM-Maize model. In comparison to the baseline scenario, under the 1.5 °C and 2 °C warming scenarios, we found that without adaptation, maize yields would increase in the relatively cool regions with a single-cropping system but decrease in other regions. However, in comparison with the baseline scenario, under the 1.5 °C and 2 °C warming scenarios with adaptation, maize yields would increase by 11.1%–53.9% across the study area. Across the maize belt, compared with the baseline scenario, under warming of 1.5 °C, the potential sowing window would increase by 2–17 d, and under warming of 2 °C, this sowing window would increase by 4–26 d. The optimal sowing window would also be significantly extended in the regions with single-cropping systems by an average of 10 d under the 1.5 °C warming scenario and 12 d under the 2 °C warming scenario. Late-maturing cultivar achieved higher yield than early-middle maturing cultivars in all regions except the north part of Northeast China. Adjusting the sowing date by increasing growth-period precipitation contributed more (44.5%–96.7%) to yield improvements than shifting cultivars (0%–50.8%) and climate change (−53.1% to 23.0%) across all maize planting regions except in the wet southwestern parts of the maize belt. The differences among the maize planting regions in terms of high adaptation potential provide invaluable information for policymakers and stakeholders of maize production to set out optimized agricultural strategies to safeguard the supply of maize.

The ecological risk associated with urbanization is of great concern where multiple stressors and risk receptors co-exist. Probabilistic risk characterization methods were rarely applied in past urban ecological risk assessments because of the difficulties in the derivation of theoretical probability distribution functions and the definite integral calculation. Therefore, we proposed a new method which is based on computer simulation and able to facilitate the calculation of risk probabilities. This method quantifies multiple ecological risk-related indicators using ecological models, implements Monte Carlo simulation to calculate the risk probability of single indicators, and applies the copula model to calculate the joint risk probability of multiple indicators. We conducted an assessment of urban ecological risk related to urban surface water environment in Beijing as a case study to validate this method. The results show that the means of surface runoff risk probability, total nitrogen pollutant load risk probability, and comprehensive (joint) risk probability were 0.33, 0.44, and 0.23, respectively, in the areas within Beijing Sixth Ring Road. All three types of risk were at moderate levels in the study areas, but exhibited high spatial heterogeneity and urban–suburban gradient. The average contributions of the three risk types were 25% (surface runoff risk), 32% (total nitrogen pollutant load risk), and 43% (comprehensive risk), indicating that the joint risk was overall the major risk type. In conclusion, our method considering multiple indicators and their probabilistic attributes can handle the uncertainties in ecological models and thus has potential to evaluate different types of urban ecological risks.

The Yellow River Delta (YRD) has been experiencing substantial climatic, hydrological, and anthropogenic stresses, and a sound understanding of the regime shift in its hydroclimate–vegetation system is of fundamental importance for maintaining the health and stability of its regional ecosystems. This study constructs and analyzes a 34-year-dataset (1982–2015) of hydro–climatic variables and satellite-based Normalized Difference Vegetation Index (NDVI) in the YRD. A seasonal-trend decomposition technique based on loess (STL), and a structural change analysis were coupled to detect regime shifts of regional hydroclimate and vegetation in the YRD from 1982 through to 2015. During this period the YRD exhibited a significant warmer–drier–greening trend and experienced four regime shifts of its hydroclimate–vegetation system, with the four shift periods roughly centered in 1989, 1998, 2004, and 2012. Partial correlation analysis revealed that temperature was the dominant factor promoting vegetative growth in spring and autumn (all P_NDVI-TEM greater than 0.65), and streamflow impacted the NDVI mainly in summer. Temperature and precipitation were the dominant controls of vegetative growth during the growing season prior to 2002, and thereafter precipitation and streamflow alternately became the main moisture-influencing factors of vegetative growth. Streamflow played an important complementary role on vegetative growth, particularly in near riverine areas when drought exceeds a certain threshold. Additionally, climate shifts determined the changing trend of NDVI across the region, while the effect of land use change is localized and predominant in the northeastern part of the study region. These findings offer an insight into appropriate water regulation of the Yellow River and on climatic adaptation within the YRD.

Multi-gas climate agreements rely on a methodology (widely referred to as 'metrics') to place emissions of different gases on a CO₂-equivalent scale. There has been an ongoing debate on the extent to which existing metrics serve current climate policy. Endpoint metrics (such as global temperature change potential GTP) are the most closely related to policy goals based on temperature limits (such as Article 2 of the Paris Agreement). However, for short-lived climate forcers (SLCFs), endpoint metrics vary strongly with time horizon making them difficult to apply in practical situations. We show how combining endpoint metrics for a step change in SLCF emissions with a pulse emission of CO₂ leads to an endpoint metric that only varies slowly over time horizons of interest. We therefore suggest that these combined step-pulse metrics (denoted combined global warming potential CGWP and combined global temperature change potential CGTP) can be a useful way to include short and long-lived species in the same basket in policy applications—this assumes a single basket approach is preferred by policy makers. The advantage of a combined step-pulse metric for SLCFs is that for species with a lifetime less than 20 years a single time horizon of around 75 years can cover the range of timescales appropriate to the Paris Agreement. These metrics build on recent work using the traditional global warming potential (GWP) metric in a new way, called GWP*. We show how the GWP* relates to CGWP and CGTP and that it systematically underestimates the temperature effects of SLCFs by up to 20%. These step-pulse metrics are all more appropriate than the conventional GWP for comparing the relative contributions of different species to future temperature targets and for SLCFs they are much less dependent on time horizon than GTP.

Forecasting crop yields is becoming increasingly important under the current context in which food security needs to be ensured despite the challenges brought by climate change, an expanding world population accompanied by rising incomes, increasing soil erosion, and decreasing water resources. Temperature, radiation, water availability and other environmental conditions influence crop growth, development, and final grain yield in a complex nonlinear manner. Machine learning (ML) techniques, and deep learning (DL) methods in particular, can account for such nonlinear relations between yield and its covariates. However, they typically lack transparency and interpretability, since the way the predictions are derived is not directly evident. Yet, in the context of yield forecasting, understanding which are the underlying factors behind both a predicted loss or gain is of great relevance. Here, we explore how to benefit from the increased predictive performance of DL methods while maintaining the ability to interpret how the models achieve their results. To do so, we applied a deep neural network to multivariate time series of vegetation and meteorological data to estimate the wheat yield in the Indian Wheat Belt. Then, we visualized and analyzed the features and yield drivers learned by the model with the use of regression activation maps. The DL model outperformed other tested models (ridge regression and random forest) and facilitated the interpretation of variables and processes that lead to yield variability. The learned features were mostly related to the length of the growing season, and temperature and light conditions during this time. For example, our results showed that high yields in 2012 were associated with low temperatures accompanied by sunny conditions during the growing period. The proposed methodology can be used for other crops and regions in order to facilitate application of DL models in agriculture.

There is a wide consensus within policy, practice, and academic circles that the adoption of modern cooking options can benefit sub-Saharan Africa. Numerous studies have examined the various demographic, socioeconomic and institutional factors affecting the adoption of clean cooking options. However, most such studies did not properly consider how geographic and environmental factors and fuel availability can affect stove adoption. In this study we use a transect-based approach, from an area of high fuelwood abundance (a state forest) to an area of high fuelwood scarcity (the semi-arid interior of Muranga county) and a peri-urban area with many fuel options (the peri-urban area of Kiambu county). We survey 400 randomly selected households along the two transects from enumeration areas used in the Kenyan national census to understand how factors intersect to affect the adoption of improved biomass stoves as primary stoves. A probit analysis suggests that stove adoption depends not only on demographic and socioeconomic factors (e.g., income, education), but also on geographical and environmental factors that reflect biomass availability and accessibility, and market access. Female-headed households tend to have lower rates of improved biomass stove adoption, largely due to lower income and related enabling factors (e.g., education, land size). Through path analysis we identify that such households can improve their opportunities to adopt improved biomass stoves through better access to credit services and participation in social groups. Overall, this study suggests the need for non-uniform and spatially explicit stove promotion strategies informed by fuelwood availability and accessibility, and market access considerations. Such strategies that are conscious of local contexts could catalyze the large-scale adoption of clean cooking options in Kenya, and elsewhere on the continent.

Under the Paris Agreement, parties self-determine their mitigation ambition level by submitting Nationally Determined Contributions (NDCs). Extant assessments find that the collective ambition of current pledges is not line with the Agreement's goals and that individual ambition varies greatly across countries, but there have not been attempts at explaining this variation. This paper identifies several potential drivers of national climate ambition, and tests whether these can account for differences in the ambition level of countries' mitigation targets under the Paris Agreement. After outlining theorized relationships between a set of domestic political characteristics and climate policy ambition, regression analysis is used to assess the effects of different potential drivers across a dataset of 170 countries. We find that a country's level of democracy and vulnerability to climate change have positive effects on NDC ambition, while coal rent and GDP have negative effects. Our findings suggest that these objective factors are more important than subjective factors, while the most influential subjective factor is the cosmopolitanism-nativism value dimension.

Heat waves in the Arctic may strongly impact environment and local communities. Recently several indices have been proposed for monitoring environmental changes in the Arctic, but heat waves have not been addressed. By applying a structured approach for evaluating occurrences of periods with exceptionally high temperatures, this study demonstrates that in the last decades there was an increase of heat wave occurrences over the terrestrial Arctic. The increase is mainly over the Canadian Arctic Archipelago and Greenland that are surrounded by ocean undergoing a sea-ice melting trend, while the Eurasian Arctic shows no significant change in heat wave occurrence. Since 2002 the probability of experiencing heat waves in the Arctic has been similar or even higher than in the middle and low latitudes and heat waves have already started to increasingly threaten local vegetation, ecology, human health and economy.

Large and severe wildfires are an observable consequence of an increasingly arid American West. There is increasing consensus that human communities, land managers, and fire managers need to adapt and learn to live with wildfires. However, a myriad of human and ecological factors constrain adaptation, and existing science-based management strategies are not sufficient to address fire as both a problem and solution. To that end, we present a novel risk-science approach that aligns wildfire response decisions, mitigation opportunities, and land management objectives by consciously integrating social, ecological and fire management system needs. We use fire-prone landscapes of the US Pacific Northwest as our study area, and report on and describe how three complementary risk-based analytic tools—quantitative wildfire risk assessment, mapping of suppression difficulty, and atlases of potential control locations—can form the foundation for adaptive governance in fire management. Together, these tools integrate wildfire risk with fire management difficulties and opportunities, providing a more complete picture of the wildfire risk management challenge. Leveraging recent and ongoing experience integrating local experiential knowledge with these tools, we provide examples and discuss how these geospatial datasets create a risk-based planning structure that spans multiple spatial scales and uses. These uses include pre-planning strategic wildfire response, implementing safe wildfire response balancing risk with likelihood of success, and alignment of non-wildfire mitigation opportunities to support wildfire risk management more directly. We explicitly focus on multi-jurisdictional landscapes to demonstrate how these tools highlight the shared responsibility of wildfire risk mitigation. By integrating quantitative risk science, expert judgement and adaptive co-management, this process provides a much-needed pathway to transform fire-prone social ecological systems to be more responsive and adaptable to change and live with fire in an increasingly arid American West.

This article examines static-data assumptions trapped in water rights and, separately, in larger interstate river compacts in the American West. These reflect assumptions of scalar stationarity embedded in water codes in western states. State water adjudications sort how much water is being used, but the resulting data are often publicly unavailable and unchanged. Interstate river compacts often divide fixed, erroneous river flow data. River compact data, based on early 20th century optimistic estimates of river flow, have not changed in policy language. At both the micro- and the macro-scale, these separate data remain fixed, complicating water management in the American West.

Alaska's Yukon-Kuskokwim Delta (YKD) is one of the warmest parts of the Arctic tundra biome and tundra fires are common in its upland areas. Here, we combine field measurements, Landsat observations, and quantitative cover maps for tundra plant functional types (PFTs) to characterize multi-decadal succession and landscape change after fire in lichen-dominated upland tundra of the YKD, where extensive wildfires occurred in 1971–1972, 1985, 2006–2007, and 2015. Unburned tundra was characterized by abundant lichens, and low lichen cover was consistently associated with historical fire. While we observed some successional patterns that were consistent with earlier work in Alaskan tussock tundra, other patterns were not. In the landscape we studied, a large proportion of pre-fire moss cover and surface peat tended to survive fire, which favors survival of existing vascular plants and limits opportunities for seed recruitment. Although shrub cover was much higher in 1985 and 1971–1972 burns than in unburned tundra, tall shrubs (>0.5 m height) were rare and the PFT maps indicate high landscape-scale variability in the degree and persistence of shrub increase after fire. Fire has induced persistent changes in species composition and structure of upland tundra on the YKD, but the lichen-dominated fuels and thick surface peat appear to have limited the potential for severe fire and accompanying edaphic changes. Soil thaw depths were about 10 cm deeper in 2006–2007 burns than in unburned tundra, but were similar to unburned tundra in 1985 and 1971–1972 burns. Historically, repeat fire has been rare on the YKD, and the functional diversity of vegetation has recovered within several decades post-fire. Our findings provide a basis for predicting and monitoring post-fire tundra succession on the YKD and elsewhere.

Since the mid-1800s pinyon-juniper (PJ) woodlands have been encroaching into sagebrush-steppe shrublands and grasslands such that they now comprise 40% of the total forest and woodland area of the Intermountain West of the United States. More recently, PJ ecosystems in select areas have experienced dramatic reductions in area and biomass due to extreme drought, wildfire, and management. Due to the vast area of PJ ecosystems, tracking these changes in woodland tree cover is essential for understanding their consequences for carbon accounting efforts, as well as ecosystem structure and functioning. Here we present a carbon monitoring, reporting, and verification (MRV) system for characterizing total aboveground biomass stocks and flux of PJ ecosystems across the Great Basin. This is achieved through a two-stage remote sensing approach by first using spatial wavelet analysis to rapidly sample tree cover from very high-resolution imagery (1 m), and then training a Random Forest model which maps tree cover across the region from 2000 to 2016 using temporally-segmented Landsat spectral indices obtained from the LandTrendr algorithm in Google Earth Engine. Estimates of cover were validated against field data from the SageSTEP project (R² = 0.67, RMSE = 10% cover). Biomass estimated from cover-based allometry was higher than estimates from the Forest Inventory and Analysis Program (FIA) at the plot-level (bias = 5 Mg ha⁻¹ and RMSE = 15.5 Mg ha⁻¹) due in part to differences in tree-level biomass allometrics. County-level aggregation of biomass closely matched estimates from the FIA (R² = 0.97) after correcting for bias at the plot level. Even after many previous decades of encroachment, we find forest area (i.e. areas with ≥10% cover) increasing at a steady rate of 0.46% per year, but 80% of the 9.86 Tg increase in biomass is attributable to infilling of existing forest. This suggests that the known consequences of encroachment such as reduced water availability, impacts to biodiversity, and risk of severe wildfire may have been increasing across the region in recent years despite the actions of sagebrush steppe restoration initiatives.

Given the magnitude of soil carbon stocks in northern ecosystems, and the vulnerability of these stocks to climate warming, land surface models must accurately represent soil carbon dynamics in these regions. We evaluate soil carbon stocks and turnover rates, and the relationship between soil carbon loss with soil temperature and moisture, from an ensemble of eleven global land surface models. We focus on the region of NASA's Arctic-Boreal vulnerability experiment (ABoVE) in North America to inform data collection and model development efforts. Models exhibit an order of magnitude difference in estimates of current total soil carbon stocks, generally under- or overestimating the size of current soil carbon stocks by greater than 50 PgC. We find that a model's soil carbon stock at steady-state in 1901 is the prime driver of its soil carbon stock a hundred years later—overwhelming the effect of environmental forcing factors like climate. The greatest divergence between modeled and observed soil carbon stocks is in regions dominated by peat and permafrost soils, suggesting that models are failing to capture the frozen soil carbon dynamics of permafrost regions. Using a set of functional benchmarks to test the simulated relationship of soil respiration to both soil temperature and moisture, we find that although models capture the observed shape of the soil moisture response of respiration, almost half of the models examined show temperature sensitivities, or Q10 values, that are half of observed. Significantly, models that perform better against observational constraints of respiration or carbon stock size do not necessarily perform well in terms of their functional response to key climatic factors like changing temperature. This suggests that models may be arriving at the right result, but for the wrong reason. The results of this work can help to bridge the gap between data and models by both pointing to the need to constrain initial carbon pool sizes, as well as highlighting the importance of incorporating functional benchmarks into ongoing, mechanistic modeling activities such as those included in ABoVE.

Temperature is a dominant factor driving arctic and boreal ecosystem phenology, including leaf budburst and gross primary production (GPP) onset in Alaskan spring. Previous studies hypothesized that both accumulated growing degree day (GDD) and cold temperature (chilling) exposure are important to leaf budburst. We test this hypothesis by combining both satellite and aircraft vegetation measurements with the Community Land Model Version 4.5 (CLM), in which the end of plant dormancy depends on thermal conditions (i.e. GDD). We study the sensitivity of GPP onset of different Alaskan deciduous vegetation types to a GDD model with chilling requirement (GC model) included. The default CLM simulations have a 1–12 d earlier day of year GPP onset over Alaska vegetated regions compared to satellite constrained estimates from the Polar Vegetation Photosynthesis and Respiration Model. Integrating a GC model into CLM shifts the phase and amplitude of GPP. During 2007–2016, mean GPP onset is postponed by 5 ± 7, 4 ± 8, and 1 ± 6 d over Alaskan northern tundra, shrub, and forest, respectively. The GC model has the greatest impact during warm springs, which is critical for predicting phenology response to future warming. Overall, spring GPP high bias is reduced by 10%. Thus, including chilling requirement in thermal forcing models improves northern high-latitude phenology, but leads to other impacts during the growing season which require further investigation.

Large-scale, high-severity wildfires are a major challenge to the future social-ecological sustainability of fire-adapted forest ecosystems in the American West. Managing forests to mitigate this risk is a collective action problem requiring landowners and stakeholders within multi-ownership landscapes to plan and implement coordinated restoration treatments. Our research question is: how can we promote collective action to reduce wildfire risk and restore fire-resilient forests in the American West? To address this question we draw on collective action theory to produce an environmental public good (fire-resilient forests), and empirical examples of collective action from six projects that are part of the US Forest Service–Natural Resources Conservation Service Joint Chiefs' Landscape Restoration Partnership. Our findings are based on qualitative, semi-structured interviews conducted with 104 individuals who were purposively selected to represent the diverse stakeholders involved in these projects. Fostering collective action to restore fire-resilient forests entails getting as many landowners (especially large landowners) to participate in wildfire risk reduction as possible to increase its areal extent; and landowner coordination in planning and implementing strategically-designed restoration treatments to optimize their effectiveness. We identify factors that enabled and constrained landowner participation and coordination in the Joint Chiefs' projects. Based on our findings and theory about when collective action will emerge, we specify a suite of practices to promote collective action for wildfire risk reduction across property boundaries, emphasizing incentives and enabling conditions. These include proactive education and outreach targeting landowners; multi-stakeholder processes with broad landowner representation to develop coordinated management approaches; financial and technical assistance to support fuels treatments on all ownerships within similar time frames; strong partnerships; and using common forestry professionals to plan and implement treatments on different ownerships (especially private lands). Our findings can inform cross-boundary management for landscape-scale conservation and restoration in other contexts.

During the last 50 years, humanity's Ecological Footprint has increased by nearly 190% indicating a growing unbalance in the human-environment relationship, coupled with major environmental and social changes. Our ability to live within the planet's biological limits requires not only a major re-think in how we produce and distribute 'things', but also a shift in consumption activities. Footprint calculators can provide a framing that communicates the extent to which an individual's daily activities are compatible with our One Planet context. This paper presents the findings from the first international study to assess the value of personal Footprint calculators in guiding individuals towards sustainable consumption choices. It focuses specifically on Global Footprint Network's personal Footprint calculator, and aims to understand the profile of calculator users and assess the contribution of calculators to increasing individual awareness and encouraging sustainable choices. Our survey of 4245 respondents show that 75% of users resided in 10 countries, 54% were aged 18–34 years and had largely used the calculator within an educational context (62%). The calculator was considered a valuable tool for knowledge generation by 91% of users, and 78% found it useful to motivate action. However, only 23% indicated the calculator provided them with the necessary information to make actual changes to their life and reduce their personal Footprint. The paper discusses how and why this personal Footprint calculator has been effective in enhancing individuals' understanding of the environmental impact of their actions, framing the scale of the problem and empowering users to understand the impacts of different lifestyle choices. Those individual-level and system-level changes needed to generate global sustainability outcomes are also discussed. Similar to other calculators, a gap is also identified in terms of this calculator facilitating individuals to convert new knowledge into action.