Table of contents

Arctic feedbacks are increasingly viewed as the wild card in the climate system; but their most unpredictable and potentially dangerous aspect may lie in the human, rather than the physical, response to a warming climate. If Arctic policy is driven by agendas based on domestic resource development, the ensuing oil and gas extraction will ensure the failure of the Paris Agreement. If Arctic energy policy can be framed by the Arctic Council, however, its environmental agenda and fragmented governance structure offers the scientific community a fighting chance to determine the region's energy future. Connecting Arctic climate science to resource economics via its unique governance structure is one of the most powerful ways the scientific community can protect the Arctic region's environmental, cultural, and scientific resources, and influence international energy and climate policy.

NASA has launched the decade-long Arctic-Boreal Vulnerability Experiment (ABoVE). While the initial phases focus on field and airborne data collection, early integration with modeling activities is important to benefit future modeling syntheses. We compiled feedback from ecosystem modeling teams on key data needs, which encompass carbon biogeochemistry, vegetation, permafrost, hydrology, and disturbance dynamics. A suite of variables was identified as part of this activity with a critical requirement that they are collected concurrently and representatively over space and time. Individual projects in ABoVE may not capture all these needs, and thus there is both demand and opportunity for the augmentation of field observations, and synthesis of the observations that are collected, to ensure that science questions and integrated modeling activities are successfully implemented.

We analyze monthly tropical near surface air temperature and Mauna Loa Observatory carbon dioxide (CO₂) data within 1960–2016 to identify different carbon cycle responses for two El Nino types: El Ninos originating in the central tropical Pacific (CP El Nino) and El Ninos originating in the eastern tropical Pacific (EP El Nino). We find significant differences between the two types of El Nino events with respect to time delay of the CO₂ rise rate that follows the increase in tropical near surface air temperatures caused by El Nino events. The average time lag of the CP El Nino is 4.0 ± 1.7 months, while the mean time lag of EP El Nino is found to be 8.5 ± 2.3 months. The average lag of all considered 1960–2016 El Ninos is 5.2 ± 2.7 months. In contrast the sensitivity of the CO₂ growth rate to tropical near surface air temperature increase is determined to be about the same for both El Nino types equal to 2.8 ± 0.9 ppm yr⁻¹ K⁻¹ (or 5.9 ± 1.9 GtC yr⁻¹ K⁻¹). Our results should be useful for the understanding of the carbon cycle and constraining it in climate models.

Reducing exposure to air pollution is an important goal for many local and national governments. Disparities in air pollution exposure by race, ethnicity, and socioeconomic class are well documented; reducing these disparities is another important policy target. Meeting both goals requires tools to evaluate how emission reduction options affect average exposures and exposure disparities. Here, we consider the role of emission location in implementing control strategies, and investigate the effect of two practical, space-based approaches—low-emission zones and truck rerouting—on diesel particle levels in Southern California. We employ Eulerian grid modeling to explore the impact that emission location has on four outcomes important to policymakers: total pollution exposure, exposure efficiency (i.e. exposure impact per unit emission), exposure inequality (i.e. deviations from exposure being equally distributed across the population; unequal exposure among individuals), and exposure injustice (i.e. associations between exposure and demographic attributes such as race or ethnicity; unequal exposure among groups). Our results highlight potential trade-offs (e.g. an increase in equality but reduction in justice for interventions in some locations) as well as opportunities for 'win-win' solutions (locations for which emission reductions would reduce all four target outcomes). We find that a simple, straightforward approach—reducing emissions in neighborhoods with a high proportion of minority residents—may or may not yield the strongest benefits to environmental justice; the reason is that the straightforward approach fails to account for meteorology and where pollution travels after being emitted. In short, we demonstrate an approach that can be used to identify areas in which emissions reductions would have high efficiency and would also result in disproportionately large reductions to average exposure, exposure inequality, and exposure injustice. The approach presented here could be used to design and prioritize local or national emission reduction efforts.

The Ciénaga Grande de Santa Marta (CGSM) is one of the world's most productive tropical wetlands and one that has witnessed some of the greatest recorded dieback of mangroves. Human-driven loss of hydrologic connectivity by roads, artificial channels and water flow regulation appears to be the reason behind mangrove mortality in this ungauged wetland. In this study, we determined the CGSM's current state of hydrologic connectivity by combining a remote sensing technique, termed as Wetland Interferometric Synthetic Aperture Radar (InSAR), with a hydrologic study of river water discharge. For this research, we processed 29 ALOS-PALSAR acquisitions taken during the period 2007–2011 and generated 66 interferograms that provide information on relative surface water level changes. We found that change in water discharge upstream on the main tributary of the CGSM could explain at most 17% of the variance of the change in water level in the CGSM. Fresh water inputs into the wetland were identified only when the mean daily water discharge in the river exceeded 700 m³ s⁻¹, which corresponds to only 30% of the days during the period. The interferogram analysis also revealed that artificial channels within the wetland serve as barriers to water flow and contribute to the overall loss in hydrologic connectivity. We recommend increasing fresh water inputs from the Magdalena River by reducing water regulation of fresh water from the river and improving connectivity on either side of the artificial channels crossing the CGSM. This study emphasizes the potential of the application of wetland InSAR to determine hydrologic connectivity in wetlands that are completely or poorly ungauged and to define the necessary guidelines for wetland hydrologic restoration.

Carbon tetrachloride (CCl₄ or CTC) is an ozone-depleting substance whose emissive uses are controlled and practically banned by the Montreal Protocol (MP). Nevertheless, previous work estimated ongoing emissions of 35 Gg year⁻¹ of CCl₄ into the atmosphere from observation-based methods, in stark contrast to emissions estimates of 3 (0–8) Gg year⁻¹ from reported numbers to UNEP under the MP. Here we combine information on sources from industrial production processes and legacy emissions from contaminated sites to provide an updated bottom-up estimate on current CTC global emissions of 15–25 Gg year⁻¹. We now propose 13 Gg year⁻¹ of global emissions from unreported non-feedstock emissions from chloromethane and perchloroethylene plants as the most significant CCl₄ source. Additionally, 2 Gg year⁻¹ are estimated as fugitive emissions from the usage of CTC as feedstock and possibly up to 10 Gg year⁻¹ from legacy emissions and chlor-alkali plants.

To meet the growing demand for food, land is being managed to be more productive using agricultural intensification practices, such as the use of irrigation. Understanding the specific environmental impacts of irrigation is a critical part of using it as a sustainable way to provide food security. However, our knowledge of irrigation effects on climate is still limited to daytime effects. This is a critical issue to define the effects of irrigation on warming related to greenhouse gases (GHGs). This study shows that irrigation led to an increasing temperature (0.002 °C year⁻¹) by enhancing nighttime warming (0.009 °C year⁻¹) more than daytime cooling (−0.007 °C year⁻¹) during the dry season from 1961–2004 over the North China Plain (NCP), which is one of largest irrigated areas in the world. By implementing irrigation processes in regional climate model simulations, the consistent warming effect of irrigation on nighttime temperatures over the NCP was shown to match observations. The intensive nocturnal warming is attributed to energy storage in the wetter soil during the daytime, which contributed to the nighttime surface warming. Our results suggest that irrigation could locally amplify the warming related to GHGs, and this effect should be taken into account in future climate change projections.

On 4–6 December 2015, storm Desmond caused very heavy rainfall in Northern England and Southern Scotland which led to widespread flooding. A week after the event we provided an initial assessment of the influence of anthropogenic climate change on the likelihood of one-day precipitation events averaged over an area encompassing Northern England and Southern Scotland using data and methods available immediately after the event occurred. The analysis was based on three independent methods of extreme event attribution: historical observed trends, coupled climate model simulations and a large ensemble of regional model simulations. All three methods agreed that the effect of climate change was positive, making precipitation events like this about 40% more likely, with a provisional 2.5%–97.5% confidence interval of 5%–80%. Here we revisit the assessment using more station data, an additional monthly event definition, a second global climate model and regional model simulations of winter 2015/16. The overall result of the analysis is similar to the real-time analysis with a best estimate of a 59% increase in event frequency, but a larger confidence interval that does include no change. It is important to highlight that the observational data in the additional monthly analysis does not only represent the rainfall associated with storm Desmond but also that of storms Eve and Frank occurring towards the end of the month.

A teleconnection between the North Atlantic Ocean and the Eurasian continent is suggested by statistical and dynamical analysis of the northern summer 500 hPa geopotential height field. This teleconnection, termed the Atlantic–Eurasian (AEA) teleconnection, has five centers of action, in the subtropical North Atlantic Ocean, northeastern North Atlantic Ocean, Eastern Europe, the Kara Sea, and north China. The AEA index (AEAI) shows that the AEA undergoes a high degree of variability from year to year, and the AEAI has an increasing trend over the last 30 years. Our results suggest that this phenomenon is a large-scale Rossby wave train that originates in the subtropical North Atlantic Ocean. We support this conclusion by the methods of stationary wave ray tracing in non-uniform horizontal basic flow, wave activity flux calculations, and numerical models. The AEA and midlatitude circumglobal teleconnection pattern manifest distinct features at the hemispheric scale, despite the anomalies associated with them bear some similarities in the northeastern North Atlantic and Eastern Europe. Regional climate variations are strongly linked to this AEA along its path through northern Eurasia.

Hydraulic fracturing (HF) and horizontal drilling have revolutionized the fossil fuel industry by enabling production from unconventional oil and gas (UOG) reserves. However, UOG development requires large volumes of water, and subsequent oil and gas production from both conventional and unconventional wells generate large volumes of produced water (PW). While PW is usually considered a waste product, its reuse may lessen demand for freshwater supplies, reduce costs for transportation and disposal, and reduce the risks for injection-induced seismicity. Whether this water is disposed of or treated and reused, both methods require significant amounts of energy. The objective of this study was to identify the primary energy demands of alternative water management strategies, and to characterize and quantify their geographic variability in four oil and gas producing basins in New Mexico using a single year of production. Results illustrate the importance of each component of each produced water management strategy in determining its total energy footprint. Based on 2015 production and water use data, the energy to extract fresh groundwater for hydraulic fracturing (34 GWh-th yr⁻¹.) exceeds the energy that would be required if the same volume of PW were treated chemically (19 GWh-th yr⁻¹.). In addition, the energy required to transport fresh water and dispose of PW (167 GWh-th yr⁻¹.) is far greater than that required to move treated PW (8 GWh-th yr⁻¹.) to a point of reuse. Furthermore, transportation distances, which contribute significantly to the total energy footprint of a given management strategy, are underestimated by nearly 50% state-wide. This indicates that reuse may be an even more energy efficient way to manage PW, even with energy-intensive treatment strategies like electrocoagulation. Reuse of PW for HF is not only more energy efficient than conventional management techniques, it also reduces both demand for scarce fresh water resources and use of disposal wells. By evaluating components of each management strategy individually, this work illustrates how the energy footprint of regional PW management can be reduced. The advent of UOG recovery in the last decade highlights the need to understand existing water management in the industry, identify opportunities and strategies for improvement, and recognize that these dynamics are likely to change into the future.

Human beings are constantly exposed to many kinds of environmental agents which affect their health and lifespan. Galactic cosmic rays (GCRs) are the main source of ionizing radiation in the lower troposphere, in which secondary products can penetrate the ground and underground layers. GCRs affect the physical–chemical properties of the terrestrial atmosphere, as well as the biosphere. GCRs are modulated by solar activity and latitudinal geomagnetic field distribution. In our ecological/populational retrospective study, we analyzed the correlation between the annual flux of local secondary GCR-induced ionization (CRII) and mortality rates in the city of Sao Paulo, Brazil, between 1951–2012. The multivariate linear regression analyses adjusted by demographic and weather parameters showed that CRII are significantly correlated with total mortality, infectious disease mortality, maternal mortality, and perinatal mortality rates (p < 0.001). The underlying mechanisms are still unclear. Further cross-sectional and experimental cohort studies are necessary to understand the biophysical mechanisms of the association found here.

China has experienced intense land use and land cover changes during the past several decades, which have exerted significant influences on climate change. Previous studies exploring related climatic effects have focused mainly on one or two specific land use changes, or have considered all land use and land cover change types together without distinguishing their individual impacts, and few have examined the physical processes of the mechanism through which land use changes affect surface temperature. However, in this study, we considered satellite-derived data of multiple land cover changes and transitions in China. The objective was to obtain observational evidence of the climatic effects of land cover transitions in China by exploring how they affect surface temperature and to what degree they influence it through the modification of biophysical processes, with an emphasis on changes in surface albedo and evapotranspiration (ET). To achieve this goal, we quantified the changes in albedo, ET, and surface temperature in the transition areas, examined their correlations with temperature change, and calculated the contributions of different land use transitions to surface temperature change via changes in albedo and ET. Results suggested that land cover transitions from cropland to urban land increased land surface temperature (LST) during both daytime and nighttime by 0.18 and 0.01 K, respectively. Conversely, the transition of forest to cropland tended to decrease surface temperature by 0.53 K during the day and by 0.07 K at night, mainly through changes in surface albedo. Decreases in both daytime and nighttime LST were observed over regions of grassland to forest transition, corresponding to average values of 0.44 and 0.20 K, respectively, predominantly controlled by changes in ET. These results highlight the necessity to consider the individual climatic effects of different land cover transitions or conversions in climate research studies. This short-term analysis of land cover transitions in China means our estimates should represent local temperature effects. Changes in ET and albedo explained <60% of the variation in LST change caused by land cover transitions; thus, additional factors that affect surface climate need consideration in future studies.

Large-scale 2nd generation bioenergy deployment is a key element of 1.5 °C and 2 °C transformation pathways. However, large-scale bioenergy production might have negative sustainability implications and thus may conflict with the Sustainable Development Goal (SDG) agenda. Here, we carry out a multi-criteria sustainability assessment of large-scale bioenergy crop production throughout the 21st century (300 EJ in 2100) using a global land-use model. Our analysis indicates that large-scale bioenergy production without complementary measures results in negative effects on the following sustainability indicators: deforestation, CO₂ emissions from land-use change, nitrogen losses, unsustainable water withdrawals and food prices. One of our main findings is that single-sector environmental protection measures next to large-scale bioenergy production are prone to involve trade-offs among these sustainability indicators—at least in the absence of more efficient land or water resource use. For instance, if bioenergy production is accompanied by forest protection, deforestation and associated emissions (SDGs 13 and 15) decline substantially whereas food prices (SDG 2) increase. However, our study also shows that this trade-off strongly depends on the development of future food demand. In contrast to environmental protection measures, we find that agricultural intensification lowers some side-effects of bioenergy production substantially (SDGs 13 and 15) without generating new trade-offs—at least among the sustainability indicators considered here. Moreover, our results indicate that a combination of forest and water protection schemes, improved fertilization efficiency, and agricultural intensification would reduce the side-effects of bioenergy production most comprehensively. However, although our study includes more sustainability indicators than previous studies on bioenergy side-effects, our study represents only a small subset of all indicators relevant for the SDG agenda. Based on this, we argue that the development of policies for regulating externalities of large-scale bioenergy production should rely on broad sustainability assessments to discover potential trade-offs with the SDG agenda before implementation.

Climate change modeling relies on projections of future greenhouse gas emissions and other phenomena leading to changes in planetary radiative forcing. Scenarios of socio-technical development consistent with end-of-century forcing levels are commonly produced by integrated assessment models. However, outlooks for forcing from fossil energy combustion can also be presented and defined in terms of two essential components: total energy use this century and the carbon intensity of that energy. This formulation allows a phase space diagram to succinctly describe a broad range of possible outcomes for carbon emissions from the future energy system. In the following paper, we demonstrate this phase space method with the Representative Concentration Pathways (RCPs) as used in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5).

The resulting RCP phase space is applied to map IPCC Working Group III (WGIII) reference case 'no policy' scenarios. Once these scenarios are described as coordinates in the phase space, data mining techniques can readily distill their core features. Accordingly, we conduct a k-means cluster analysis to distinguish the shared outlooks of these scenarios for oil, gas and coal resource use. As a whole, the AR5 database depicts a transition toward re-carbonization, where a world without climate policy inevitably leads to an energy supply with increasing carbon intensity. This orientation runs counter to the experienced 'dynamics as usual' of gradual decarbonization, suggesting climate change targets outlined in the Paris Accord are more readily achievable than projected to date.

This study estimates changes in grid-wide, energy consumption caused by load shifting via cooling thermal energy storage (CTES) in the building sector. It develops a general equation for relating generator fleet fuel consumption to building cooling demand as a function of ambient temperature, relative humidity, transmission and distribution current, and baseline power plant efficiency. The results present a graphical sensitivity analysis that can be used to estimate how shifting load from cooling demand to cooling storage could affect overall, grid-wide, energy consumption. In particular, because power plants, air conditioners and transmission systems all have higher efficiencies at cooler ambient temperatures, it is possible to identify operating conditions such that CTES increases system efficiency rather than decreasing it as is typical for conventional storage approaches. A case study of the Dallas–Fort Worth metro area in Texas, USA shows that using CTES to shift daytime cooling load to nighttime cooling storage can reduce annual, system-wide, primary fuel consumption by 17.6 MWh for each MWh of installed CTES capacity. The study concludes that, under the right circumstances, cooling thermal energy storage can reduce grid-wide energy consumption, challenging the perception of energy storage as a net energy consumer.

The Paris Agreement limits global average temperature rise to 2 °C and commits to pursuing efforts in limiting warming to 1.5 °C above pre-industrial levels. This will require rapid reductions in the emissions of greenhouse gases and the eventual decarbonisation of the global economy. Wind energy is an established technology to help achieve emissions reductions, with a cumulative global installed capacity of ~486 GW (2016). Focusing on Australia, we assess the future economic viability of wind energy using a 12-member ensemble of high-resolution regional climate simulations forced by Coupled Model Intercomparison Project (CMIP) output. We examine both near future (around 2030) and far future (around 2070) changes. Extractable wind power changes vary across the continent, though the most spatially coherent change is a small but significant decrease across southern regions. The cost of future wind energy generation, measured via the Levelised Cost of Energy (LCOE), increases negligibly in the future in regions with significant existing installed capacity. Technological developments in wind energy generation more than compensate for projected small reductions in wind, decreasing the LCOE by around 30%. These developments ensure viability for existing wind farms, and enhance the economic viability of proposed wind farms in Western Australian and Tasmania. Wind energy is therefore a resilient source of electricity over most of Australia and technological innovation entering the market will open new regions for energy production in the future.

The Yangtze river basin, in South East China, experiences anomalously high precipitation in summers following El Niño. This can lead to extensive flooding and loss of life. However, the response following La Niña has not been well documented. In this study, the response of Yangtze summer rainfall to El Niño/La Niña is found to be asymmetric, with no significant response following La Niña. The nature of this asymmetric response is found to be in good agreement with that simulated by the Met Office seasonal forecast system. Yangtze summer rainfall correlates positively with spring sea surface temperatures in the Indian Ocean and northwest Pacific. Indian Ocean sea surface temperatures are found to respond linearly to El Niño/La Niña, and to have a linear impact on Yangtze summer rainfall. However, northwest Pacific sea surface temperatures respond much more strongly following El Niño and, further, correlate more strongly with positive rainfall years. It is concluded that, whilst delayed Indian Ocean signals may influence summer Yangtze rainfall, it is likely that they do not lead to the asymmetric nature of the rainfall response to El Niño/La Niña.

Oceanic and atmospheric patterns play a crucial role in modulating climate variability from interannual to multi-decadal timescales by causing large-scale co-varying climate changes. The brevity of the existing instrumental records hinders the ability to recognize climate patterns before the industrial era, which can be alleviated using proxies. Unfortunately, proxy based reconstructions of oceanic and atmospheric modes of the past millennia often have modest agreements with each other before the instrumental period, raising questions about the robustness of the reconstructions. To ensure the stability of climate signals in proxy data through time, we first identified tree-ring datasets from distant regions containing coherent variations in Asia and North America, and then interpreted their climate information. We found that the multi-decadal covarying climate patterns of the middle and high latitudinal regions around the northern Pacific Ocean agreed quite well with the climate reconstructions of the tropical and southern Pacific areas. This indicates a synchronous variability at the multi-decadal timescale of the past 430 years for the entire Pacific Ocean. This pattern is closely linked to the dominant mode of the Pacific sea surface temperature (SST) after removing the warming trend. This Pacific multi-decadal SST variability resembles the Interdecadal Pacific Oscillation.

Globally, demand for food animal products is rising. At the same time, we face mounting, related pressures including limited natural resources, negative environmental externalities, climate disruption, and population growth. Governments and other stakeholders are seeking strategies to boost food production efficiency and food system resiliency, and aquaculture (farmed seafood) is commonly viewed as having a major role in improving global food security based on longstanding measures of animal production efficiency. The most widely used measurement is called the 'feed conversion ratio' (FCR), which is the weight of feed administered over the lifetime of an animal divided by weight gained. By this measure, fed aquaculture and chickens are similarly efficient at converting feed into animal biomass, and both are more efficient compared to pigs and cattle. FCR does not account for differences in feed content, edible portion of an animal, or nutritional quality of the final product. Given these limitations, we searched the literature for alternative efficiency measures and identified 'nutrient retention', which can be used to compare protein and calories in feed (inputs) and edible portions of animals (outputs). Protein and calorie retention have not been calculated for most aquaculture species. Focusing on commercial production, we collected data on feed composition, feed conversion ratios, edible portions (i.e. yield), and nutritional content of edible flesh for nine aquatic and three terrestrial farmed animal species. We estimate that 19% of protein and 10% of calories in feed for aquatic species are ultimately made available in the human food supply, with significant variation between species. Comparing all terrestrial and aquatic animals in the study, chickens are most efficient using these measures, followed by Atlantic salmon. Despite lower FCRs in aquaculture, protein and calorie retention for aquaculture production is comparable to livestock production. This is, in part, due to farmed fish and shrimp requiring higher levels of protein and calories in feed compared to chickens, pigs, and cattle. Strategies to address global food security should consider these alternative efficiency measures.

Misinformation can have significant societal consequences. For example, misinformation about climate change has confused the public and stalled support for mitigation policies. When people lack the expertise and skill to evaluate the science behind a claim, they typically rely on heuristics such as substituting judgment about something complex (i.e. climate science) with judgment about something simple (i.e. the character of people who speak about climate science) and are therefore vulnerable to misleading information. Inoculation theory offers one approach to effectively neutralize the influence of misinformation. Typically, inoculations convey resistance by providing people with information that counters misinformation. In contrast, we propose inoculating against misinformation by explaining the fallacious reasoning within misleading denialist claims. We offer a strategy based on critical thinking methods to analyse and detect poor reasoning within denialist claims. This strategy includes detailing argument structure, determining the truth of the premises, and checking for validity, hidden premises, or ambiguous language. Focusing on argument structure also facilitates the identification of reasoning fallacies by locating them in the reasoning process. Because this reason-based form of inoculation is based on general critical thinking methods, it offers the distinct advantage of being accessible to those who lack expertise in climate science. We applied this approach to 42 common denialist claims and find that they all demonstrate fallacious reasoning and fail to refute the scientific consensus regarding anthropogenic global warming. This comprehensive deconstruction and refutation of the most common denialist claims about climate change is designed to act as a resource for communicators and educators who teach climate science and/or critical thinking.

We estimate the cumulative future emissions expected to be released by coal power plants that are currently under construction, announced, or planned. Even though coal consumption has recently declined and plans to build new coal-fired capacities have been shelved, constructing all these planned coal-fired power plants would endanger national and international climate targets. Plans to build new coal-fired power capacity would likely undermine the credibility of some countries' (Intended) Nationally Determined Contributions submitted to the UNFCCC. If all the coal-fired power plants that are currently planned were built, the carbon budget for reaching the 2 °C temperature target would nearly be depleted. Propositions about 'coal's terminal decline' may thereby be premature. The phase-out of coal requires dedicated and well-designed policies. We discuss the political economy of policy options that could avoid a continued build-up of coal-fired power plants.

Proxy records have provided major insights into the variability of past climates over long timescales. However, for much of the Southern Hemisphere, the ability to identify spatial patterns of past climatic variability is constrained by the sparse distribution of proxy records. This is particularly true for mainland Australia, where relatively few proxy records are located. Here, we (1) assess the potential to use existing proxy records in the Australasian region—starting with the only two multi-century tree-ring proxies from mainland Australia—to reveal spatial patterns of past hydroclimatic variability across the western third of the continent, and (2) identify strategic locations to target for the development of new proxy records. We show that the two existing tree-ring records allow robust reconstructions of past hydroclimatic variability over spatially broad areas (i.e. > 3° × 3°) in inland north- and south-western Australia. Our results reveal synchronous periods of drought and wet conditions between the inland northern and southern regions of western Australia as well as a generally anti-phase relationship with hydroclimate in eastern Australia over the last two centuries. The inclusion of 174 tree-ring proxy records from Tasmania, New Zealand and Indonesia and a coral record from Queensland did not improve the reconstruction potential over western Australia. However, our findings suggest that the addition of relatively few new proxy records from key locations in western Australia that currently have low reconstruction skill will enable the development of a comprehensive drought atlas for the region, and provide a critical link to the drought atlases of monsoonal Asia and eastern Australia and New Zealand.

Over the past three decades, farmers in China have increasingly used fertilizers to increase paddy rice production. While this approach has eased the rising demand for food, it is unclear whether it pays off in the long-run when costs associated with environmental consequences are considered. Using two case studies in Zhejiang Province, China, this paper analyzed field-based rice yields, fertilizer inputs, nitrogen leaching and greenhouse emissions and their socioeconomic values of different farm practices. The objective was to assess the trade-offs among economic gains from increased yield and environmental consequences of different paddy rice management practices. The results indicated short-term economic gains to farmers outweigh the environmental cost concerns. However, considering the lasting environmental effects, there is a significant imbalance toward a conservative farming practice. The results further indicated that synergies can be achieved if precision management practices are adopted. It was also indicated that a large spatial variation exists in yields and environmental impacts, suggesting 'one-size fits all' policies will likely be ineffective in reducing environmental impacts. Although only two case studies were demonstrated in this study, the approach may be generalized to other geographic regions to help guide paddy farmers in similar climatic and land use environments such as those in the subtropical regions of Southeast Asia, to achieve synergic environment practices.

Accurate biomass estimation is critical to quantify the changes in biomass and carbon stocks following the restoration of degraded landscapes. However, there is lack of site-specific allometric equations for the estimation of aboveground biomass (AGB), which consequently limits our understanding of the contributions of restoration efforts in mitigating climate change. This study was conducted in northwestern Ethiopia to develop a multi-species allometric equation and investigate the spatial and temporal variation of C-stocks following the restoration of degraded landscapes. We harvested and weighed 84 trees from eleven dominant species from six grazing exclosures and adjacent communal grazing land. We observed that AGB correlates significantly with diameter at stump height D₃₀ (R² = 0.78; P < 0.01), and tree height H (R² = 0.41, P < 0.05). Our best model, which includes D₃₀ and H as predictors explained 82% of the variations in AGB. This model produced the lowest bias with narrow ranges of errors across different diameter classes. Estimated C-stock showed a significant positive correlation with stem density (R² = 0.80, P < 0.01) and basal area (R² = 0.84, P < 0.01). At the watershed level, the mean C-stock was 3.8 (±0.5) Mg C ha⁻¹. Plot-level C-stocks varied between 0.1 and 13.7 Mg C ha⁻¹. Estimated C-stocks in three- and seven-year-old exclosures exceeded estimated C-stock in the communal grazing land by 50%. The species that contribute most to C-stocks were Leucaena sp. (28%), Calpurnia aurea (21%), Euclea racemosa (20.9%), and Dodonaea angustifolia (15.8%). The equations developed in this study allow monitoring changes in C-stocks and C-sequestration following the implementation of restoration practices in northern Ethiopia over space and time. The estimated C-stocks can be used as a reference against which future changes in C-stocks can be compared.

When discussing the association between birth weight and air pollution, previous studies mainly focus on the maternal trimester-specific exposures during pregnancy, whereas the possible associations between birth weight and weekly-specific exposures have been largely neglected. We conducted a nested 1:4 matched case-control study in Jinan, China to examine the weekly-specific associations during pregnancy between maternal fine particulate matter (aerodynamic diameter < 2.5 μm, PM_2.5), nitrogen dioxide (NO₂), and sulfur dioxide (SO₂) exposure and birth weight, which is under a representative scenario of very high pollution levels. Ambient air monitoring data from thirteen monitoring stations and daily mean temperature data for Jinan during 2013–2016 were continuously collected. Birth data were obtained from the largest maternity and child care hospital of this city during 2014–2016. Individual exposures to PM_2.5, NO₂, and SO₂ during pregnancy were estimated using an inverse distance weighting method. Birth weight for gender-, gestational age-, and parity-specific standard score (BWGAP z-score) was calculated as the outcome of interest. Distributed lag non-linear models (DLNMs) were applied to estimate weekly-specific relationship between maternal air pollutant exposures and birth weight. For an increase of per inter-quartile range in maternal PM_2.5 exposure concentration during pregnancy, the BWGAP z-score decreased significantly during the 27th–33th gestational weeks with the strongest association in the 30th gestational weeks (standard deviation units decrease in BWGAP z-score: −0.049, 95% CI: −0.080 −0.017, in three-pollutant model). No significant association between maternal weekly NO₂ or SO₂ BWGAP z-score was observed. In conclusion, this study provides evidence that maternal PM_2.5 exposure during the 27th–33th gestational weeks may reduce the birth weight in the context of very high pollution level of PM_2.5.

Large amounts of carbon are stored in the permafrost of the northern high latitude land. As permafrost degrades under a warming climate, some of this carbon will decompose and be released to the atmosphere. This positive climate-carbon feedback will reduce the natural carbon sinks and thus lower anthropogenic CO₂ emissions compatible with the goals of the Paris Agreement. Simulations using an ensemble of the JULES-IMOGEN intermediate complexity climate model (including climate response and process uncertainty) and a stabilization target of 2 °C, show that including the permafrost carbon pool in the model increases the land carbon emissions at stabilization by between 0.09 and 0.19 Gt C year⁻¹ (10th to 90th percentile). These emissions are only slightly reduced to between 0.08 and 0.16 Gt C year⁻¹ (10th to 90th percentile) when considering 1.5 °C stabilization targets. This suggests that uncertainties caused by the differences in stabilization target are small compared with those associated with model parameterisation uncertainty. Inertia means that permafrost carbon loss may continue for many years after anthropogenic emissions have stabilized. Simulations suggest that between 225 and 345 Gt C (10th to 90th percentile) are in thawed permafrost and may eventually be released to the atmosphere for stabilization target of 2 °C. This value is 60–100 Gt C less for a 1.5 °C target. The inclusion of permafrost carbon will add to the demands on negative emission technologies which are already present in most low emissions scenarios.

Land surface phenology (LSP), the study of seasonal dynamics of vegetated land surfaces from remote sensing, is a key indicator of global change, that both responds to and influences weather and climate. The effects of climatic changes on LSP depend on the relative importance of climatic constraints in specific regions—which are not well understood at global scale. Understanding the climatic constraints that underlie LSP is crucial for explaining climate change effects on global vegetation phenology.

We used a combination of modelled and remotely-sensed vegetation activity records to quantify the interplay of three climatic constraints on land surface phenology (namely minimum temperature, moisture availability, and photoperiod), as well as the dynamic nature of these constraints. Our study examined trends and the relative importance of the three constrains at the start and the end of the growing season over eight global environmental zones, for the past three decades.

Our analysis revealed widespread shifts in the relative importance of climatic constraints in the temperate and boreal biomes during the 1982–2011 period. These changes in the relative importance of the three climatic constraints, which ranged up to 8% since 1982 levels, varied with latitude and between start and end of the growing season. We found a reduced influence of minimum temperature on start and end of season in all environmental zones considered, with a biome-dependent effect on moisture and photoperiod constraints. For the end of season, we report that the influence of moisture has on average increased for both the temperate and boreal biomes over 8.99 million km². A shifting relative importance of climatic constraints on LSP has implications both for understanding changes and for improving how they may be modelled at large scales.

Support for addressing climate change and air pollution may depend on the type of information provided to the public. We conduct a discrete choice survey assessing preferences for combinations of electricity generation portfolios, electricity bills, and emissions reductions. We test how participants' preferences change when emissions information is explicitly provided to them. We find that support for climate mitigation increases when mitigation is accompanied by improvements to air quality and human health. We estimate that an average respondent would accept an increase of 19%–27% in their electricity bill if shown information stating that either CO₂ or SO₂ emissions are reduced by 30%. Furthermore, an average respondent is willing to pay an increase of 30%–40% in electricity bills when shown information stating that both pollutants are reduced by 30% simultaneously. Our findings suggest that the type of emissions information provided to the public will affect their support for different electricity portfolios.

We provide a detailed estimate of the technical potential of rooftop solar photovoltaic (PV) electricity generation throughout the contiguous United States. This national estimate is based on an analysis of select US cities that combines light detection and ranging (lidar) data with a validated analytical method for determining rooftop PV suitability employing geographic information systems. We use statistical models to extend this analysis to estimate the quantity and characteristics of roofs in areas not covered by lidar data. Finally, we model PV generation for all rooftops to yield technical potential estimates. At the national level, 8.13 billion m² of suitable roof area could host 1118 GW of PV capacity, generating 1432 TWh of electricity per year. This would equate to 38.6% of the electricity that was sold in the contiguous United States in 2013. This estimate is substantially higher than a previous estimate made by the National Renewable Energy Laboratory. The difference can be attributed to increases in PV module power density, improved estimation of building suitability, higher estimates of total number of buildings, and improvements in PV performance simulation tools that previously tended to underestimate productivity. Also notable, the nationwide percentage of buildings suitable for at least some PV deployment is high—82% for buildings smaller than 5000 ft² and over 99% for buildings larger than that. In most states, rooftop PV could enable small, mostly residential buildings to offset the majority of average household electricity consumption. Even in some states with a relatively poor solar resource, such as those in the Northeast, the residential sector has the potential to offset around 100% of its total electricity consumption with rooftop PV.

Anthropogenic aerosols have a net cooling effect on climate and also cause adverse health effects by degrading air quality. In this global-scale sensitivity study, we used a combination of the aerosol-climate model ECHAM-HAMMOZ and the University of Victoria Earth System Climate Model to assess the climate and health effects of aerosols emissions from three Representative Concentration Pathways (RCP2.6, RCP4.5, and RCP8.5) and two new (LOW and HIGH) aerosol emission scenarios derived from RCP4.5, but that span a wider spectrum of possible future aerosol emissions. All simulations had CO₂ emissions and greenhouse gas forcings from RCP4.5. Aerosol forcing declined similarly in the standard RCP aerosol emission scenarios: the aerosol effective radiative forcing (ERF) decreased from −1.3 W m⁻² in 2005 to between −0.1 W m⁻² and −0.4 W m⁻² in 2100. The differences in ERF were substantially larger between LOW (−0.02 W m⁻² in 2100) and HIGH (−0.8 W m⁻²) scenarios. The global mean temperature difference between the simulations with standard RCP aerosol emissions was less than 0.18 °C, whereas the difference between LOW and HIGH reached 0.86 °C in 2061. In LOW, the rate of warming peaked at 0.48 °C per decade in the 2030s, whereas in HIGH it was the lowest of all simulations and never exceeded 0.23 °C per decade. Using present-day population density and baseline mortality rates for all scenarios, PM_2.5-induced premature mortality was 2 371 800 deaths per year in 2010 and 525 700 in 2100 with RCP4.5 aerosol emissions; in HIGH, the premature mortality reached its maximum value of 2 780 800 deaths per year in 2030, whereas in LOW the premature mortality at 2030 was below 299 900 deaths per year. Our results show potential trade-offs in aerosol mitigation with respect to climate change and public health as ambitious reduction of aerosol emissions considerably increased warming while decreasing mortality.

Since the 'Paris agreement' in 2015 there has been much focus on what a +1.5 °C or +2 °C warmer world would look like. Since the focus lies on policy relevant global warming targets, or specific warming levels (SWLs), rather than a specific point in time, projections are pooled together to form SWL ensembles based on the target temperature rather than emission scenario. This study uses an ensemble of CMIP5 global model projections to analyse how well SWL ensembles represent the stabilized climate of global warming targets. The results show that the SWL ensembles exhibit significant trends that reflect the transient nature of the RCP scenarios. These trends have clear effect on the timing and clustering of monthly cold and hot extremes, even though the effect on the temperature of the extreme months is less visible. In many regions there is a link between choice of RCP scenario used in the SWL ensemble and climate change signal in the highest monthly temperatures. In other regions there is no such clear-cut link. From this we conclude that comprehensive analyses of what prospects the different global warming targets bring about will require stabilization scenarios. Awaiting such targeted scenarios we suggest that prudent use of SWL scenarios, taking their characteristics and limitations into account, may serve as reasonable proxies in many situations.

Climate change is expected to exacerbate the frequency of drought-induced tree mortality world-wide. To better predict the associated change of species composition and forest dynamics on various scales and develop adequate adaptation strategies, more information on the mechanisms driving the often observed patchiness of tree die-back is needed. Although forest-edge effects may play an important role within the given context, only few corresponding studies exist. Here, we investigate the regional die-back of Scots pine in Franconia, Germany, after a hot and dry summer in 2015, thereby emphasizing possible differences in mortality between forest edge and interior. By means of dendroecological investigations and close-range remote sensing, we assess long-term growth performance and current tree vitality along five different forest-edge distance gradients. Our results clearly indicate a differing growth performance between edge and interior trees, associated with a higher vulnerability to drought, increased mortality rates, and lower tree vitality at the forest edge. Prior long-lasting growth decline of dead trees compared to live trees suggests depletion of carbon reserves in course of a long-term drought persisting since the 1990s to be the cause of regional Scots pine die-back. These findings highlight the forest edge as a potential focal point of forest management adaptation strategies in the context of drought-induced mortality.

In Tanzania, a majority of rural residents cook using firewood-based three-stone-fire stoves. In this study, quantitative performance differences between technologically advanced improved cooking stoves and three-stone-fire stoves are analysed.

We test the performance of improved cooking stoves and three-stone-fire stoves using local cooks, foods, and fuels, in the semi-arid region of Dodoma in Tanzania. We used the cooking protocol of the Controlled Cooking Test following a two-pot test design. The findings of the study suggest that improved cooking stoves use less firewood and less time than three-stone-fire stoves to conduct a predefined cooking task.

In total, 40 households were assessed and ask to complete two different cooking tasks: (1) a fast cooking meal (rice and vegetables) and (2) a slow cooking meal (beans and rice). For cooking task 1, the results show a significant reduction in firewood consumption of 37.1% by improved cooking stoves compared to traditional three-stone-fire stoves; for cooking task 2 a reduction of 15.6% is found. In addition, it was found that the time needed to conduct cooking tasks 1 and 2 was significantly reduced by 26.8% and 22.8% respectively, when improved cooking stoves were used instead of three-stone-fire-stoves.

We observed that the villagers altered the initial improved cooking stove design, resulting in the so-called modified improved cooking stove. In an additional Controlled Cooking Test, we conducted cooking task 3: a very fast cooking meal (maize flour and vegetables) within 32 households. Significant changes between the initial and modified improved cooking stoves regarding firewood and time consumption were not detected.

However, analyses show that both firewood and time consumption during cooking was reduced when large amounts (for 6–7 household members) of food were prepared instead of small amounts (for 2–3 household members).

Land-use decisions are made at the local level. They are influenced both by local factors and by global drivers and trends. These will most likely change over time e.g. due to political shocks, market developments or climate change. Hence, their influence should be taken into account when analysing and projecting local land-use decisions. We provide a set of mid-term scenarios of global drivers (until 2030) for use in regional and local studies on agriculture and land-use. In a participatory process, four important drivers are identified by experts from globally distributed regional studies: biofuel policies, increase in preferences for meat and dairy products in Asia, cropland expansion into uncultivated areas, and changes in agricultural productivity growth. Their impact on possible future developments of global and regional agricultural markets are analysed with a modelling framework consisting of a global computable general equilibrium model and a crop growth model. The business as usual (BAU) scenario causes production and prices of crops to rise over time. It also leads to a conversion of pasture land to cropland. Under different scenarios, global price changes range between −42 and +4% in 2030 compared to the BAU. An abolishment of biofuel targets does not significantly improve food security while an increased agricultural productivity and cropland expansion have a stronger impact on changes in food production and prices.

The fate of live forest biomass is largely controlled by growth and disturbance processes, both natural and anthropogenic. Thus, biomass monitoring strategies must characterize both the biomass of the forests at a given point in time and the dynamic processes that change it. Here, we describe and test an empirical monitoring system designed to meet those needs. Our system uses a mix of field data, statistical modeling, remotely-sensed time-series imagery, and small-footprint lidar data to build and evaluate maps of forest biomass. It ascribes biomass change to specific change agents, and attempts to capture the impact of uncertainty in methodology. We find that:

 • A common image framework for biomass estimation and for change detection allows for consistent comparison of both state and change processes controlling biomass dynamics.

 • Regional estimates of total biomass agree well with those from plot data alone.

 • The system tracks biomass densities up to 450–500 Mg ha⁻¹ with little bias, but begins underestimating true biomass as densities increase further.

 • Scale considerations are important. Estimates at the 30 m grain size are noisy, but agreement at broad scales is good. Further investigation to determine the appropriate scales is underway.

 • Uncertainty from methodological choices is evident, but much smaller than uncertainty based on choice of allometric equation used to estimate biomass from tree data.

 • In this forest-dominated study area, growth and loss processes largely balance in most years, with loss processes dominated by human removal through harvest. In years with substantial fire activity, however, overall biomass loss greatly outpaces growth.

Taken together, our methods represent a unique combination of elements foundational to an operational landscape-scale forest biomass monitoring program.

The effects of human disturbance on biodiversity can be mediated by environmental conditions, such as water availability, climate and nutrients. In general, disturbed, dry or nutrient-depleted soils areas tend to have lower taxonomic diversity. However, little is known about how these environmental conditions affect functional composition and intraspecific variability in tropical dry forests. We studied a seasonally dry tropical forest (SDTF) under chronic anthropogenic disturbance (CAD) along rainfall and soil nutrient gradients to understand how these factors influence the taxonomic and functional composition. Specifically we evaluated two aspects of CAD, wood extraction and livestock pressure (goat and cattle grazing), along soil fertility and rainfall gradients on shrub and tree traits, considering species turnover and intraspecific variability. In addition, we also tested how the traits of eight populations of the most frequent species are affected by wood extraction, livestock pressure, rainfall and soil fertility. In general, although CAD and environmental gradients affected each trait of the most widespread species differently, the most abundant species also had a greater variation of traits. Considering species turnover, wood extraction is associated with species with a smaller leaf area and lower investment in leaf mass, probably due to the indirect effects of this disturbance type on the vegetation, i.e. the removal of branches and woody debris clears the vegetation, favouring species that minimize water loss. Livestock pressure, on the other hand, affected intraspecific variation: the herbivory caused by goats and cattle promoted individuals which invest more in wood density and leaf mass. In this case, the change of functional composition observed is a direct effect of the disturbance, such as the decrease of palatable plant abundance by goat and cattle herbivory. In synthesis, CAD, rainfall and soil fertility can affect trait distribution at community and species levels, which can have significant implications for the ecosystem functioning of SDTF under increasing levels of disturbance, climate change and soil nutrient depletion.

Mexican oak forests (genus Quercus) are frequently used for traditional charcoal production. Appropriate management programs are needed to ensure their long-term use, while conserving the biodiversity and ecosystem services, and associated benefits. A key variable needed to design these programs is the spatial distribution of standing woody biomass. A state-of-the-art methodology using small format aerial photographs was developed to estimate the total aboveground biomass (AGB) and aboveground woody biomass suitable for charcoal making (WSC) in intensively managed oak forests. We used tree crown area (CA_ap) measurements from very high-resolution (30 cm) orthorectified small format digital aerial photographs as the predictive variable. The CA_ap accuracy was validated using field measurements of the crown area (CA_f). Allometric relationships between: (a) CA_ap versus AGB, and (b) CA_ap versus WSC had a high significance level (R² > 0.91, p < 0.0001). This approach shows that it is possible to obtain sound biomass estimates as a function of the crown area derived from digital small format aerial photographs.

Meeting agricultural demand in the face of a changing climate will be one of the major challenges of the 21st century. California is the single largest agricultural producer in the United States but is prone to extreme hydrologic events, including multi-year droughts. Ventura County is one of California's most productive growing regions but faces water shortages and deteriorating water quality. The future of California's agriculture is dependent on our ability to identify and implement alternative irrigation water sources and technologies. Two such alternative water sources are recycled and desalinated water. The proximity of high-value crops in Ventura County to both dense population centers and the Pacific Ocean makes it a prime candidate for alternative water sources. This study uses highly localized spatial and temporal data to assess life-cycle energy use, life-cycle greenhouse gas emissions, operational costs, applied water demand, and on-farm labor requirements for four high-value crops. A complete switch from conventional irrigation with groundwater and surface water to recycled water would increase the life-cycle greenhouse gas emissions associated with strawberry, lemon, celery, and avocado production by approximately 14%, 7%, 59%, and 9%, respectively. Switching from groundwater and surface water to desalinated water would increase life-cycle greenhouse gas emissions by 33%, 210%, 140%, and 270%, respectively. The use of recycled or desalinated water for irrigation is most financially tenable for strawberries due to their relatively high value and close proximity to water treatment facilities. However, changing strawberry packaging has a greater potential impact on life-cycle energy use and greenhouse gas emissions than switching the water source. While this analysis does not consider the impact of water quality on crop yields, previous studies suggest that switching to recycled water could result in significant yield increases due to its lower salinity.

Russia and Ukraine are countries with relatively large untapped agricultural potentials, both in terms of abandoned agricultural land and substantial yield gaps. Here we present a comprehensive assessment of Russian and Ukrainian crop production potentials and we analyze possible impacts of their future utilization, on a regional as well as global scale. To this end, the total amount of available abandoned land and potential yields in Russia and Ukraine are estimated and explicitly implemented in an economic agricultural sector model. We find that cereal (barley, corn, and wheat) production in Russia and Ukraine could increase by up to 64% in 2030 to 267 million tons, compared to a baseline scenario. Oilseeds (rapeseed, soybean, and sunflower) production could increase by 84% to 50 million tons, respectively. In comparison to the baseline, common net exports of Ukraine and Russia could increase by up to 86.3 million tons of cereals and 18.9 million tons of oilseeds in 2030, representing 4% and 3.6% of the global production of these crops, respectively. Furthermore, we find that production potentials due to intensification are ten times larger than potentials due to recultivation of abandoned land. Consequently, we also find stronger impacts from intensification at the global scale. A utilization of crop production potentials in Russia and Ukraine could globally save up to 21 million hectares of cropland and reduce average global crop prices by more than 3%.

The Warm Arctic–cold Siberia surface temperature pattern during recent boreal winter is suggested to be triggered by the ongoing decrease of Arctic autumn sea ice concentration and has been observed together with an increase in mid-latitude extreme events and a meridionalization of tropospheric circulation. However, the exact mechanism behind this dipole temperature pattern is still under debate, since model experiments with reduced sea ice show conflicting results. We use the early twentieth-century Arctic warming (ETCAW) as a case study to investigate the link between September sea ice in the Barents–Kara Sea (BKS) and the Siberian temperature evolution. Analyzing a variety of long-term climate reanalyses, we find that the overall winter temperature and heat flux trend occurs with the reduction of September BKS sea ice. Tropospheric conditions show a strengthened atmospheric blocking over the BKS, strengthening the advection of cold air from the Arctic to central Siberia on its eastern flank, together with a reduction of warm air advection by the westerlies. This setup is valid for both the ETCAW and the current Arctic warming period.

This paper develops a methodology to assess the resource requirements of inclusive urban development in India and compares those requirements to current community-wide material and energy flows. Methods include: (a) identifying minimum service level benchmarks for the provision of infrastructure services including housing, electricity and clean cooking fuels; (b) assessing the percentage of homes that lack access to infrastructure or that consume infrastructure services below the identified benchmarks; (c) quantifying the material requirements to provide basic infrastructure services using India-specific design data; and (d) computing material and energy requirements for inclusive development and comparing it with current community-wide material and energy flows. Applying the method to ten Indian cities, we find that: 1%–6% of households do not have electricity, 14%–71% use electricity below the benchmark of 25 kWh capita-month⁻¹; 4%–16% lack structurally sound housing; 50%–75% live in floor area less than the benchmark of 8.75 m² floor area/capita; 10%–65% lack clean cooking fuel; and 6%–60% lack connection to a sewerage system. Across the ten cities examined, to provide basic electricity (25 kWh capita-month⁻¹) to all will require an addition of only 1%–10% in current community-wide electricity use. To provide basic clean LPG fuel (1.2 kg capita-month⁻¹) to all requires an increase of 5%–40% in current community-wide LPG use. Providing permanent shelter (implemented over a ten year period) to populations living in non-permanent housing in Delhi and Chandigarh would require a 6%–14% increase over current annual community-wide cement use. Conversely, to provide permanent housing to all people living in structurally unsound housing and those living in overcrowded housing (<5 m cap⁻²) would require 32%–115% of current community-wide cement flows. Except for the last scenario, these results suggest that social policies that seek to provide basic infrastructure provisioning for all residents would not dramatically increasing current community-wide resource flows.

Efforts to estimate plant productivity using satellite data can be frustrated by the presence of cloud cover. We developed a new method to overcome this problem, focussing on the high-arctic archipelago of Svalbard where extensive cloud cover during the growing season can prevent plant productivity from being estimated over large areas. We used a field-based time-series (2000−2009) of live aboveground vascular plant biomass data and a recently processed cloud-free MODIS-Normalised Difference Vegetation Index (NDVI) data set (2000−2014) to estimate, on a pixel-by-pixel basis, the onset of plant growth. We then summed NDVI values from onset of spring to the average time of peak NDVI to give an estimate of annual plant productivity. This remotely sensed productivity measure was then compared, at two different spatial scales, with the peak plant biomass field data. At both the local scale, surrounding the field data site, and the larger regional scale, our NDVI measure was found to predict plant biomass (adjusted R² = 0.51 and 0.44, respectively). The commonly used 'maximum NDVI' plant productivity index showed no relationship with plant biomass, likely due to some years having very few cloud-free images available during the peak plant growing season. Thus, we propose this new summed NDVI from onset of spring to time of peak NDVI as a proxy of large-scale plant productivity for regions such as the Arctic where climatic conditions restrict the availability of cloud-free images.

Mangroves are ecologically and economically important forested wetlands with the highest carbon (C) density of all terrestrial ecosystems. Because of their exceptionally large C stocks and importance as a coastal buffer, their protection and restoration has been proposed as an effective mitigation strategy for climate change. The inclusion of mangroves in mitigation strategies requires the quantification of C stocks (both above and belowground) and changes to accurately calculate emissions and sequestration. A growing number of countries are becoming interested in using mitigation initiatives, such as REDD+ (reducing emissions from deforestation and forest degradation), in these unique coastal forests. However, it is not yet clear how methods to measure C traditionally used for other ecosystems can be modified to estimate biomass in mangroves with the precision and accuracy needed for these initiatives. Airborne Lidar (ALS) data has often been proposed as the most accurate way for larger scale assessments but the application of ALS for coastal wetlands is scarce, primarily due to a lack of contemporaneous ALS and field measurements. Here, we evaluated the variability in field and Lidar-based estimates of aboveground biomass (AGB) through the combination of different local and regional allometric models and standardized height metrics that are comparable across spatial resolutions and sensor types, the end result being a simplified approach for accurately estimating mangrove AGB at large scales and determining the uncertainty by combining multiple allometric models. We then quantified wall-to-wall AGB stocks of a tall mangrove forest in the Zambezi Delta, Mozambique. Our results indicate that the Lidar H100 height metric correlates well with AGB estimates, with R² between 0.80 and 0.88 and RMSE of 33% or less. When comparing Lidar H100 AGB derived from three allometric models, mean AGB values range from 192 Mg ha⁻¹ up to 252 Mg ha⁻¹. We suggest the best model to predict AGB was based on the East Africa specific allometry and a power-based regression that used Lidar H100 as the height input with an R² of 0.85 and an RMSE of 122 Mg ha⁻¹ or 33%. The total AGB of the Lidar inventoried mangrove area (6654 ha) was 1 350 902 Mg with a mean AGB of 203 Mg ha⁻¹ ±166 Mg ha⁻¹. Because the allometry suggested here was developed using standardized height metrics, it is recommended that the models can generate AGB estimates using other remote sensing instruments that are more readily accessible over other mangrove ecosystems on a large scale, and as part of future carbon monitoring efforts in mangroves.

There is growing evidence of ongoing changes in the statistics of intra-seasonal rainfall variability over large parts of the world. Changes in annual total rainfall may arise from shifts, either singly or in a combination, of distinctive intra-seasonal characteristics –i.e. rainfall frequency, rainfall intensity, and rainfall seasonality. Understanding how various ecosystems respond to the changes in intra-seasonal rainfall characteristics is critical for predictions of future biome shifts and ecosystem services under climate change, especially for arid and semi-arid ecosystems. Here, we use an advanced dynamic vegetation model (SEIB-DGVM) coupled with a stochastic rainfall/weather simulator to answer the following question: how does the productivity of ecosystems respond to a given percentage change in the total seasonal rainfall that is realized by varying only one of the three rainfall characteristics (rainfall frequency, intensity, and rainy season length)? We conducted ensemble simulations for continental Africa for a realistic range of changes (−20% ~ +20%) in total rainfall amount. We find that the simulated ecosystem productivity (measured by gross primary production, GPP) shows distinctive responses to the intra-seasonal rainfall characteristics. Specifically, increase in rainfall frequency can lead to 28% more GPP increase than the same percentage increase in rainfall intensity; in tropical woodlands, GPP sensitivity to changes in rainy season length is ~4 times larger than to the same percentage changes in rainfall frequency or intensity. In contrast, shifts in the simulated biome distribution are much less sensitive to intra-seasonal rainfall characteristics than they are to total rainfall amount. Our results reveal three major distinctive productivity responses to seasonal rainfall variability—'chronic water stress', 'acute water stress' and 'minimum water stress' - which are respectively associated with three broad spatial patterns of African ecosystem physiognomy, i.e. savannas, woodlands, and tropical forests.

Macreadie et al (this issue; M2017Environ. Res. Lett.) challenged the conclusion presented by Johannessen and Macdonald (2016  Environ. Res. Lett.) that global estimates of carbon sequestration by seagrass meadows were too high. Here we clarify our global calculation, respond to M2017's criticisms and explain how the persistent misunderstandings about sediment dynamics within the Blue Carbon community continue to lead to overestimates of carbon sequestration in seagrass meadows. We point out that, although seagrasses appear to have a local effect on carbon sequestration compared with nearby barren sediments, their preferred substrate (slowly-accumulating, coarse sediment) makes them among the least effective coastal environments for burying carbon. We conclude with a proposal for the development of robust international protocols to quantify carbon burial in seagrass meadows that account for sediment accumulation, sediment mixing and carbon remineralization.

This addendum adds to the analysis presented in 'Understanding risks in the light of uncertainty: low-probability, high-impact coastal events in cities' Abadie et al (2017 Environ. Res. Lett. 12 014017). We propose to use the framework developed earlier to enhance communication and understanding of risks, with the aim of bridging the gap between highly technical risk management discussion to the public risk aversion debate. We also propose that the framework could be used for stress-testing resilience.