Table of contents

Seasonally dry tropical forests (SDTF) are located in regions with alternating wet and dry seasons, with dry seasons that last several months or more. By the end of the 21st century, climate models predict substantial changes in rainfall regimes across these regions, but little is known about how individuals, species, and communities in SDTF will cope with the hotter, drier conditions predicted by climate models. In this review, we explore different rainfall scenarios that may result in ecological drought in SDTF through the lens of two alternative hypotheses: 1) these forests will be sensitive to drought because they are already limited by water and close to climatic thresholds, or 2) they will be resistant/resilient to intra- and inter-annual changes in rainfall because they are adapted to predictable, seasonal drought. In our review of literature that spans microbial to ecosystem processes, a majority of the available studies suggests that increasing frequency and intensity of droughts in SDTF will likely alter species distributions and ecosystem processes. Though we conclude that SDTF will be sensitive to altered rainfall regimes, many gaps in the literature remain. Future research should focus on geographically comparative studies and well-replicated drought experiments that can provide empirical evidence to improve simulation models used to forecast SDTF responses to future climate change at coarser spatial and temporal scales.

Cyanobacterial blooms (CBs) are generally triggered by eutrophic conditions due to anthropogenic nutrient inputs to local waters (wastewater or contaminated waters). During the bloom, some species produce toxic secondary metabolites (cyanotoxins) that are dangerous for humans and animals. Here, a multidisciplinary strategy for an early detection and constant monitoring is proposed. This strategy combines remote/proximal sensing technology with analytical/biotechnological analyses. To demonstrate the applicability of this strategy, four anthropogenically-impacted sites were selected along the Campania coast of southwestern Italy, in the so called 'Land of Fires'. The sites were observed using satellite and aircraft images during summer, 2015. Algal community composition was determined using spectrophotometric analysis for the detection of the cyanobacterial pigment phycocyanin (PC). Complementary metagenomic analysis revealed the taxonomic presence of cyanobacteria belonging to genera associated with strong eutrophic conditions.

Key elements of this strategy are the combination and integration of applying different methodological approaches such as the parallel and combined use of satellite, aerial and in-situ data, the simplified multispectral image indexing and classification for a truly efficient method in detecting early blooms of cyanobacteria. The effectiveness of the strategy has been validated also by the specific taxa of cyanobacteria found in the examined areas that confirm the assumption that cyanobacterial blooms may serve as useful bioindicators of degraded water quality in coastal ecosystems. To our knowledge this is the first time that the presence of cyanobacteria has been observed in water bodies along the Campania coast.

We assess the utility of seasonal forecasts for the energy industry by showing how recently-established predictability of the North Atlantic Oscillation (NAO) in winter allows predictability of near-surface wind speed and air temperature and therefore energy supply and demand respectively. Our seasonal prediction system (GloSea5) successfully reproduces the influence of the NAO on European climate, leading to skilful forecasts of wind speed and wind power and hence wind driven energy supply. Temperature is skilfully forecast using the observed temperature-NAO relationship and the NAO forecast. Using the correlation between forecast NAO and observed GB electricity demand, we demonstrate that skilful predictions of winter demand are also achievable on seasonal timescales well in advance of the season. Finally, good reliability of probabilistic forecasts of above/below-average wind speed and temperature is also demonstrated.

Cities have become key players in climate change mitigation policy. To develop their climate policies, cities need good assessments of their current and future emissions. We use publically available national datasets to develop an integrated approach for estimating GHG emissions at the metropolitan level over time, between multiple locations, and across sectors. We estimate consistent production-based GHG emissions for the 100 most populated metropolitan areas in the United States in 2014. We find that total 2014 metropolitan CO₂ emissions range from 4.1 million metric tons in Lancaster, Pennsylvania to nearly 170 million metric tons in the Houston, Texas; with an overall average of 27 million metric tons. The top 20 absolute emitters and top 20 per capita emitters only overlap for 9 locations. Per capita emissions also show a wide variation: from 5 metric tons per person in the Tucson, Arizona to 65 metric tons per person in the Baton Rouge, Louisiana; with an overall average of 14 metric tons per person. We also compute estimates for 2002 and 2011 and compare to our 2014 emission estimates. Across all locations analyzed, average total emissions increased by 3% and average per capita emissions decreased by 14%. Where possible, we also compare our emission estimates to those reported by the cities in their climate action plans and find an average absolute difference between our estimates and those reported by the cities of 5.6 metric tons CO₂ per person, likely due to temporal and scope differences between the two estimates. Our integrated emission estimation approach complements bottom-up approaches typically employed by municipalities and helps practitioners divert their attention and resources away from continuous emission accounting toward more impactful emission mitigation efforts.

We present observational evidence for linkages between extreme Arctic stratospheric ozone anomalies in March and Northern Hemisphere tropospheric climate in spring (March–April). Springs characterized by low Arctic ozone anomalies in March are associated with a stronger, colder polar vortex and circulation anomalies consistent with the positive polarity of the Northern Annular Mode/North Atlantic Oscillation in March and April. The associated spring tropospheric circulation anomalies indicate a poleward shift of zonal winds at 500 hPa over the North Atlantic. Furthermore, correlations between March Arctic ozone and March–April surface temperatures reveal certain regions where a surprisingly large fraction of the interannual variability in spring surface temperatures is associated with interannual variability in ozone. We also find that years with low March Arctic ozone in the stratosphere display surface maximum daily temperatures in March–April that are colder than normal over southeastern Europe and southern Asia, but warmer than normal over northern Asia, adding to the warming from increasing well-mixed greenhouse gases in those locations. The results shown here do not establish causality, but nevertheless suggest that March stratospheric ozone is a useful indicator of spring averaged (March–April) tropospheric climate in certain Northern Hemispheric regions.

The heat storage changes (Q_t) can be a significant component of the energy balance in lakes, and it is important to account for Q_t for reasonable estimation of evaporation at monthly and finer timescales if the energy balance-based evaporation models are used. However, Q_t has been often neglected in many studies due to the lack of required water temperature data. A simple hysteresis model (Q_t = a^*Rn + b + c^*dRn/dt) has been demonstrated to reasonably estimate Q_t from the readily available net all wave radiation (Rn) and three locally calibrated coefficients (a–c) for lakes and reservoirs. As a follow-up study, we evaluated whether this hysteresis model could enable energy balance-based evaporation models to yield good evaporation estimates. The representative monthly evaporation data were compiled from published literature and used as ground-truth to evaluate three energy balance-based evaporation models for five lakes. The three models in different complexity are De Bruin-Keijman (DK), Penman, and a new model referred to as Duan-Bastiaanssen (DB). All three models require Q_t as input. Each model was run in three scenarios differing in the input Q_t (S1: measured Q_t; S2: modelled Q_t from the hysteresis model; S3: neglecting Q_t) to evaluate the impact of Q_t on the modelled evaporation. Evaluation showed that the modelled Q_t agreed well with measured counterparts for all five lakes. It was confirmed that the hysteresis model with locally calibrated coefficients can predict Q_t with good accuracy for the same lake. Using modelled Q_t as inputs all three evaporation models yielded comparably good monthly evaporation to those using measured Q_t as inputs and significantly better than those neglecting Q_t for the five lakes. The DK model requiring minimum data generally performed the best, followed by the Penman and DB model. This study demonstrated that once three coefficients are locally calibrated using historical data the simple hysteresis model can offer reasonable Q_t to force energy balance-based evaporation models to improve evaporation modelling at monthly timescales for conditions and long-term periods when measured Q_t are not available. We call on scientific community to further test and refine the hysteresis model in more lakes in different geographic locations and environments.

The dispersion of surface drifters released over a 7-year period from five locations in the southern Gulf of Mexico is described. It is shown that the drifter dispersion is strongly affected by the main mesoscale circulations features frequently observed in this area. Some of them are the anticyclonic eddies shed by the Loop Current at the eastern side of the Gulf of Mexico, and a semi-permanent cyclonic gyre at the Bay of Campeche. The results are examined further in terms of two dominant and contrasting dispersion scenarios: (i) an intense northward advection of drifters, preferentially along the western margin, and (ii) the retention of drifters in the southernmost part of the Gulf of Mexico. The results are discussed from an environmental point of view, by considering particle dispersion from point sources as an approach to study marine pollution problems.

Existing bottom-up emission inventories of methane from global oil and gas systems do not satisfactorily explain year-on-year variation in atmospheric methane estimated by top-down models. Using a novel bottom-up approach this study quantifies and attributes methane and ethane emissions from global oil and gas production from 1980 to 2012. Country-specific information on associated gas flows from published sources are combined with inter-annual variations in observed flaring of associated gas from satellite images from 1994 to 2010, to arrive at country-specific annual estimates of methane and ethane emissions from flows of associated gas. Results confirm trends from top-down models and indicate considerably higher methane and ethane emissions from oil production than previously shown in bottom-up inventories for this time period.

Commodity crop expansion has increased with the globalization of production systems and consumer demand, linking distant socio-ecological systems. Oil palm plantations are expanding in the tropics to satisfy growing oilseed and biofuel markets, and much of this expansion has caused extensive deforestation, especially in Asia. In Latin America, palm oil output has doubled since 2001, and the majority of expansion seems to be occurring on non-forested lands. We used MODIS satellite imagery (250 m resolution) to map current oil palm plantations in Latin America and determined prior land use and land cover (LULC) using high-resolution images in Google Earth. In addition, we compiled trade data to determine where Latin American palm oil flows, in order to better understand the underlying drivers of expansion in the region. Based on a sample of 342 032 ha of oil palm plantations across Latin America, we found that 79% replaced previously intervened lands (e.g. pastures, croplands, bananas), primarily cattle pastures (56%). The remaining 21% came from areas that were classified as woody vegetation (e.g. forests), most notably in the Amazon and the Petén region in northern Guatemala. Latin America is a net exporter of palm oil but the majority of palm oil exports (70%) stayed within the region, with Mexico importing about half. Growth of the oil palm sector may be driven by global factors, but environmental and economic outcomes vary between regions (i.e. Asia and Latin America), within regions (i.e. Colombia and Peru), and within single countries (i.e. Guatemala), suggesting that local conditions are influential. The present trend of oil palm expanding onto previously cleared lands, guided by roundtable certifications programs, provides an opportunity for more sustainable development of the oil palm sector in Latin America.

This study investigated the anthropogenic influence on the temporal variability of annual precipitation for the period 1950–2005 as simulated by the CMIP5 models. The temporal variability of both annual precipitation amount (PRCPTOT) and intensity (SDII) was first measured using a metric of statistical dispersion called the Gini coefficient. Comparing simulations driven by both anthropogenic and natural forcing (ALL) with simulations of natural forcing only (NAT), we quantified the anthropogenic contributions to the changes in temporal variability at global, continental and sub-continental scales as a relative difference of the respective Gini coefficients of ALL and NAT. Over the period of 1950–2005, our results indicate that anthropogenic forcing have resulted in decreased uniformity (i.e. increase in unevenness or disparity) in annual precipitation amount and intensity at global as well as continental scales. In addition, out of the 21 sub-continental regions considered, 14 (PRCPTOT) and 17 (SDII) regions showed significant anthropogenic influences. The human impacts are generally larger for SDII compared to PRCTOT, indicating that the temporal variability of precipitation intensity is generally more susceptible to anthropogenic influence than precipitation amount. The results highlight that anthropogenic activities have changed not only the trends but also the temporal variability of annual precipitation, which underscores the need to develop effective adaptation management practices to address the increased disparity.

Rapid Arctic sea ice decline over the last few decades opens new perspectives for Arctic marine navigation. Further warming in the Arctic will promote the Northern Sea Route (NSR) as an alternative to the conventional Suez or Panama Canal routes for intercontinental shipping. Here we use both satellite data and CMIP5 ensemble of climate models to estimate the NSR transit window allowing intercontinental navigation between Atlantic and Pacific regions. To this end, we introduce a novel approach to calculate start and end dates of the navigation season along the NSR. We show that modern climate models are able to reproduce the mean time of the NSR transit window and its trend over the last few decades. The selected models demonstrate that the rate of increase of the NSR navigation season will slow down over the next few decades with the RCP4.5 scenario. By the end of the 21st century ensemble-mean estimates show an increase of the NSR transit window by about 4 and 6.5 months according to RCP4.5 and 8.5, respectively. Estimated trends for the end date of the navigation season are found to be stronger compared to those for the start date.

Given the substantial role that forests play in removing CO₂ from the atmosphere, there has been a growing need to evaluate the carbon (C) implications of various forest management and land-use decisions. Although assessment of land-use change is central to national-level greenhouse gas monitoring guidelines, it is rarely incorporated into forest stand-level evaluations of C dynamics and trajectories. To better inform the assessment of forest stand C dynamics in the context of potential land-use change, we used a region-wide repeated forest inventory (n = 71 444 plots) across the eastern United States to assess forest land-use conversion and associated changes in forest C stocks. Specifically, the probability of forest area reduction between 2002–2006 and 2007–2012 on these plots was related to key driving factors such as proportion of the landscape in forest land use, distance to roads, and initial forest C. Additional factors influencing the actual reduction in forest area were then used to assess the risk of forest land-use conversion to agriculture, settlement, and water. Plots in forests along the Great Plains had the highest periodic (approximately 5 years) probability of land-use change (0.160 ± 0.075; mean ± SD) with forest conversion to agricultural uses accounting for 70.5% of the observed land-use change. Aboveground forest C stock change for plots with a reduction in forest area was −4.2 ± 17.7 Mg ha⁻¹ (mean ± SD). The finding that poorly stocked stands and/or those with small diameter trees had the highest probability of conversion to non-forest land uses suggests that forest management strategies can maintain the US terrestrial C sink not only in terms of increased net forest growth but also retention of forest area to avoid conversion. This study highlights the importance of considering land-use change in planning and policy decisions that seek to maintain or enhance regional C sinks.

As sources of data for global forest monitoring grow larger, more complex and numerous, data analysis and interpretation become critical bottlenecks for effectively using them to inform land use policy discussions. Here in this paper, we present a method that combines big data analytical tools with Emerging Hot Spot Analysis (ArcGIS) to identify statistically significant spatiotemporal trends of forest loss in Brazil, Indonesia and the Democratic Republic of Congo (DRC) between 2000 and 2014. Results indicate that while the overall rate of forest loss in Brazil declined over the 14-year time period, spatiotemporal patterns of loss shifted, with forest loss significantly diminishing within the Amazonian states of Mato Grosso and Rondônia and intensifying within the cerrado biome. In Indonesia, forest loss intensified in Riau province in Sumatra and in Sukamara and West Kotawaringin regencies in Central Kalimantan. Substantial portions of West Kalimantan became new and statistically significant hot spots of forest loss in the years 2013 and 2014. Similarly, vast areas of DRC emerged as significant new hot spots of forest loss, with intensified loss radiating out from city centers such as Beni and Kisangani. While our results focus on identifying significant trends at the national scale, we also demonstrate the scalability of our approach to smaller or larger regions depending on the area of interest and specific research question involved. When combined with other contextual information, these statistical data models can help isolate the most significant clusters of loss occurring over dynamic forest landscapes and provide more coherent guidance for the allocation of resources for forest monitoring and enforcement efforts.

The timing of fall foliage coloration, especially peak coloration, is of great importance to the climate change research community as it has implications for carbon storage in forests. However, its long-term variation and response to climate change are poorly understood. To address this issue, we examined the long-term trends and breakpoints in satellite derived peak coloration onset from 1982 to 2014 using an innovative approach that combines Singular Spectrum Analysis (SSA) with Breaks for Additive Seasonal and Trend (BFAST). The peak coloration trend was then evaluated using both field foliage coloration observations and flux tower measurements. Finally, interannual changes in peak coloration onset were correlated with temperature and precipitation variation. Results showed that temporal trends in satellite-derived peak coloration onset were comparable with both field observations and flux tower measurements of gross primary productivity. Specifically, a breakpoint in long-term peak coloration onset was detected in 25% of pixels which were mainly distributed at latitudes north of 37°N. The breakpoint tended to occur between 1998 and 2004. Peak coloration onset was delayed before the breakpoint while it was transformed to an early trend after the breakpoint in nearly all pixels. The remaining 75% of pixels exhibited monotonic trends, 35% of which revealed a late trend and 40% an early trend. The results indicate that the onset of peak coloration experienced a late trend during the 1980s and 1990s in most deciduous and mixed forests. However, the trend was reversed during the most recent decade when the timing of peak coloration became earlier. The onset of peak coloration was significantly correlated with late summer and autumn temperature in 55.5% of pixels from 1982 to 2014. This pattern of temperature impacts was also verified using field observations and flux tower measurements. In the remaining 44.5% of pixels, 12.2% of pixels showed significantly positive correlation between the onset of peak coloration and cumulative precipitation during late summer and autumn period from 1982 to 2014. Our findings can improve understanding of the impact of changes in autumn phenology on carbon uptake in forests, which in turn facilitate more reliable measures of carbon dynamics in vegetation–climate interactions models.

Tropical peatlands of the western part of insular Southeast Asia have experienced extensive land cover changes since 1990. Typically involving drainage, these land cover changes have resulted in increased peat oxidation in the upper peat profile. In this paper we provide current (2015) and cumulative carbon emissions estimates since 1990 from peat oxidation in Peninsular Malaysia, Sumatra and Borneo, utilizing newly published peatland land cover information and the recently agreed Intergovernmental Panel on Climate Change (IPCC) peat oxidation emission values for tropical peatland areas. Our results highlight the change of one of the Earth's most efficient long-term carbon sinks to a short-term emission source, with cumulative carbon emissions since 1990 estimated to have been in the order of 2.5 Gt C. Current (2015) levels of emissions are estimated at around 146 Mt C yr⁻¹, with a range of 132–159 Mt C yr⁻¹ depending on the selection of emissions factors for different land cover types. 44% (or 64 Mt C yr⁻¹) of the emissions come from industrial plantations (mainly oil palm and Acacia pulpwood), followed by 34% (49 Mt C yr⁻¹) of emissions from small-holder areas. Thus, altogether 78% of current peat oxidation emissions come from managed land cover types. Although based on the latest information, these estimates may still include considerable, yet currently unquantifiable, uncertainties (e.g. due to uncertainties in the extent of peatlands and drainage networks) which need to be focused on in future research. In comparison, fire induced carbon dioxide emissions over the past ten years for the entire equatorial Southeast Asia region have been estimated to average 122 Mt C yr⁻¹ (www.globalfiredata.org/_index.html). The results emphasise that whilst reducing emissions from peat fires is important, urgent efforts are also needed to mitigate the constantly high level of emissions arising from peat drainage, regardless of fire occurrence.

Recent trends in summer heat waves (HW) over Central-Eastern China and their atmospheric drivers are investigated using the ERA Interim re-analysis. A composite analysis shows that these events are preceded by an increase in 500 hPa geopotential height. Consequently, a subsidence anomaly develops over the region and surface solar radiation increases. An increase in the northward moisture transport from the tropical region is also found to increase specific humidity, leading to warmer night-time temperatures. Feedback effects are also important: decrease of precipitation and enhanced evaporation also increases the specific humidity and North-Westerlies due to the low pressure lead to more heat convergence. HW occurrence increases, especially during the last decade, and is largely due to an increase in the mean temperature rather than to a change in dynamics, suggesting a human influence.

Recent studies indicate that the glaciers and ice caps in Queen Elizabeth Islands (QEI), Canada have experienced an increase in ice mass loss during the last two decades, but the contribution of ice dynamics to this loss is not well known. We present a comprehensive mapping of ice velocity using a suite of satellite data from year 1991 to 2015, combined with ice thickness data from NASA Operation IceBridge, to calculate ice discharge. We find that ice discharge increased significantly after 2011 in Prince of Wales Icefield, maintained or decreased in other sectors, whereas glacier surges have little impact on long-term trends in ice discharge. During 1991–2005, the QEI mass loss averaged 6.3 ± 1.1 Gt yr⁻¹, 52% from ice discharge and the rest from surface mass balance (SMB). During 2005–2014, the mass loss from ice discharge averaged 3.5 ± 0.2 Gt yr⁻¹ (10%) versus 29.6 ± 3.0 Gt yr⁻¹ (90%) from SMB. SMB processes therefore dominate the QEI mass balance, with ice dynamics playing a significant role only in a few basins.

Haze pollution over the past four decades in Southeast Asia is mainly a result of forest and peatland fires in Indonesia. The economic impacts of haze include adverse health effects and disruption to transport and tourism. Previous studies have used a variety of approaches to assess the economic impacts of haze and the forest fires more generally. But no study has used contingent valuation to assess non-market impacts of haze on individuals. Here we apply contingent valuation to estimate impacts of haze on Singapore, one of most severely affected countries. We used a double-bounded dichotomous-choice survey design and the Kaplan-Meier-Turnbull method to infer the distribution of Singaporeans' willingness to pay (WTP) for haze mitigation. Our estimate of mean individual WTP was 0.97% of annual income (n = 390). To calculate total national WTP, we stratified by income, the demographic variable most strongly related to individual WTP. The total WTP estimate was $643.5 million per year (95% CI [$527.7 million, $765.0 million]). This estimate is comparable in magnitude to previously estimated impacts of Indonesia's fires and also to the estimated costs of peatland protection and restoration. We recommend that our results be incorporated into future cost–benefit analyses of the fires and mitigation strategies.

Nitrogen (N) availability is a key regulator of carbon (C) cycling in terrestrial ecosystems. Anthropogenic N input, such as N deposition and fertilization, increases N availability in soil, which has important implications for an ecosystem's C storage and loss. Soil respiration (Rs), which is the second largest C flux from terrestrial ecosystems to the atmosphere, plays an important role in terrestrial C cycles. The direction and magnitude of the responses of Rs and its components to N addition have been widely evaluated, but it remains unclear how these processes change across multiple N addition levels. Here we conducted a two-year field experiment to examine the changes of Rs and its autotrophic respiration (Ra) and heterotrophic respiration (Rh) components along a gradient of eight N levels (0, 1 2, 4, 8, 16, 24, 32 g m⁻² yr⁻¹) in a Tibetan alpine steppe, and used structural equation modeling (SEM) to explore the relative contributions of biotic and abiotic variables and their direct and indirect pathways regulating the Ra and Rh. Our results indicated that both Rs and Ra exhibited first increasing and then subsequent decreasing trends at the threshold of 8 g N m⁻² yr⁻¹. In contrast, the Rh declined linearly with the N addition rate continuously increasing. SEM analysis revealed that, among various environmental factors, soil temperature was the most important one modulating Rs, which not only had a direct effect on the two Rs components, but also indirectly regulated the Ra and Rh via root and microbial biomass. These findings suggest that the nonlinear response patterns of Rs should be considered for better predicting terrestrial C balance, given that anthropogenic N input to the terrestrial ecosystems is increasing continuously.

Power lines are increasingly widespread across many regions of the planet. Although these linear infrastructures are known for their negative impacts on bird populations, through collision and electrocution, some species take advantage of electricity pylons for nesting. In this case, estimation of the net impact of these infrastructures at the population level requires an assessment of trade-offs between positive and negative impacts. We compiled historical information (1958–2014) of the Portuguese white stork Ciconia ciconia population to analyze long-term changes in numbers, distribution range and use of nesting structures. White stork population size increased 660% up to 12000 breeding pairs between 1984 and 2014. In the same period, the proportion of nests on electricity pylons increased from 1% to 25%, likely facilitated by the 60% increase in the length of the very high tension power line grid (holding the majority of the nests) in the stork's distribution range. No differences in breeding success were registered for storks nesting on electricity pylons versus other structures, but a high risk of mortality by collision and electrocution with power lines was estimated. We discuss the implications of this behavioral change, and of the management responses by power line companies, both for stork populations and for managers.

A ground-based field campaign was conducted during the summer of 2011, 10 km east of the Israeli coast, aimed at studying small, warm convective clouds. During the campaign, clouds were detected on days that were predicted to be cloud-free by standard forecasting methods. Moreover, the clouds' bases were often much lower than the estimated lifting condensation level. Detailed air parcel model simulations revealed that such small non-buoyant clouds can form only if the convective motion is driven by perturbations in the relative humidity in the middle of the boundary layer, rather than by temperature perturbations near the surface. Furthermore, cloud base height exhibited weak sensitivity to the initial elevation of the parcel, suggesting that it serves as an accumulation point for many relative-humidity-perturbed thermodynamic trajectories. Such a mechanism is likely to be common under atmospheric conditions of a hot and humid boundary layer capped by a strong inversion layer.

Climate warming and increasing variability challenges the electricity supply in warm seasons. A good quantitative representation of the relationship between warm-season electricity consumption and weather condition provides necessary information for long-term electricity planning and short-term electricity management. In this study, an extended version of cooling degree days (ECDD) is proposed for better characterisation of this relationship. The ECDD includes temperature, residual temperature and specific humidity effects. The residual temperature is introduced for the first time to reflect the building thermal inertia effect on electricity consumption. The study is based on the electricity consumption data of four multiple-street city blocks and three office buildings.

It is found that the residual temperature effect is about 20% of the current-day temperature effect at the block scale, and increases with a large variation at the building scale. Investigation of this residual temperature effect provides insight to the influence of building designs and structures on electricity consumption. The specific humidity effect appears to be more important at the building scale than at the block scale. A building with high energy performance does not necessarily have low specific humidity dependence. The new ECDD better reflects the weather dependence of electricity consumption than the conventional CDD method.

The politically contentious issue of calculating countries' contributions to climate change is strongly dependent on methodological choices. Different principles can be applied for distributing efforts for reducing human-induced global warming. According to the 'Brazilian Proposal', industrialized countries would reduce emissions proportional to their historical contributions to warming. This proposal was based on the assumption that the political process would lead to a global top-down agreement. The Paris Agreement changed the role of historical responsibilities. Whereas the agreement refers to equity principles, differentiation of mitigation efforts is delegated to each country, as countries will submit new national contributions every five years without any international negotiation. It is likely that considerations of historical contributions and distributive fairness will continue to play a key role, but increasingly so in a national setting. Contributions to warming can be used as a background for negotiations to inform and justify positions, and may also be useful for countries' own assessment of what constitutes reasonable and fair contributions to limiting warming. Despite the fact that the decision from COP21 explicitly rules out compensation in the context of loss and damage, it is likely that considerations of historical responsibility will also play a role in future discussions. However, methodological choices have substantial impacts on calculated contributions to warming, including rank-ordering of contributions, and thus support the view that there is no single correct answer to the question of how much each country has contributed. There are fundamental value-related and ethical questions that cannot be answered through a single set of calculated contributions. Thus, analyses of historical contributions should not present just one set of results, but rather present a spectrum of results showing how the calculated contributions vary with a broad set of choices. Our results clearly expose some of the core issues related to climate responsibility.

Climate change is occurring across the world, with effects varying by ecosystem and region but already occurring quickly in high-latitude and high-altitude regions. Biotic interactions are important in determining ecosystem response to such changes, but few studies have been long-term in nature, especially in the High Arctic. Mesic tundra plots on Svalbard, Norway, were subjected to grazing at two different intensities by captive Barnacle geese from 2003–2005, in a factorial design with warming by Open Top Chambers. Warming manipulations were continued through 2014, when we measured vegetation structure and composition as well as growth and reproduction of three dominant species in the mesic meadow. Significantly more dead vascular plant material was found in warmed compared to ambient plots, regardless of grazing history, but in contrast to many short-term experiments no difference in the amount of living material was found. This has strong implications for nutrient and carbon cycling and could feed back into community productivity. Dominant species showed increased flowering in warmed plots, especially in those plots where grazing had been applied. However, this added sexual reproduction did not translate to substantial shifts in vegetative cover. Forbs and rushes increased slightly in warmed plots regardless of grazing, while the dominant shrub, Salix polaris, generally declined with effects dependent on grazing, and the evergreen shrub Dryas octopetala declined with previous intensive grazing. There were no treatment effects on community diversity or evenness. Thus despite no changes in total live abundance, a typical short-term response to environmental conditions, we found pronounced changes in dead biomass indicating that tundra ecosystem processes respond to medium- to long-term changes in conditions caused by 12 seasons of summer warming. We suggest that while high arctic tundra plant communities are fairly resistant to current levels of climate warming, underlying ecosystem processes are beginning to change. In addition, even short bouts of intense herbivory can have long-term consequences for some species in these communities.

Aerosols emitted by landscape fires affect many climatic processes. Here, we combined an aerosol–climate model and a coupled climate–carbon model to study the carbon cycle and climate effects caused by fire-emitted aerosols (FEA) forcing at the top of the atmosphere and at the surface. This forcing ('best guess' present-day values of −0.10 and −1.3 W m⁻² at the top of the atmosphere and surface, respectively) had a predominant cooling influence that altered regional land carbon stocks on decadal timescales by modifying vegetation productivity and soil–litter decomposition. Changes in regional land and ocean carbon stocks became much stronger for FEA forcing acting on multi-century timescales; this occurred because carbon stocks responded to the forcing itself on such timescales and also due to gradual effects on the climate (e.g. through increased sea ice cover) that further affected the carbon cycle. Carbon increases and decreases in different regions partly offset each other, so that absolute changes in global land, atmosphere, and ocean stocks were all <2 Pg C after 30 years of FEA forcing and <6 Pg C after more than 1000 years of FEA forcing. FEA-caused changes in land carbon storage did not substantially modify the magnitude of FEA emissions, suggesting there is no consequential regional-scale positive feedback loop between these two elements. However, we found indications that the FEA-caused cooling from frequently-burning regions in Africa and Australia increased land carbon stocks in eastern South America and equatorial Asia, respectively. This suggests the potential for remote carbon cycle effects from regions emitting large amounts of fire aerosols.

Warming in the Arctic has resulted in a lengthening of the growing season and changes to the distribution and composition of tundra vegetation including increased biomass quantities in the Low Arctic. Biomass has commonly been estimated using broad-band greenness indices such as NDVI; however, vegetation changes in the Arctic are occurring at spatial scales within a few meters. The aim of this paper is to assess the ability of hyperspectral remote sensing data to estimate biomass quantities among different plant tissue type categories at the North Slope site of Ivotuk, Alaska. Hand-held hyperspectral data and harvested biomass measurements were collected during the 1999 growing season. A subset of the data was used as a training set, and was regressed against the hyperspectral bands using LASSO. LASSO is a modification of SPLS and is a variable selection technique that is useful in studies with high collinearity among predictor variables such as hyperspectral remote sensing. The resulting equations were then used to predict biomass quantities for the remaining Ivotuk data. The majority of significant biomass-spectra relationships (65%) were for shrubs categories during all times of the growing season and bands in the blue, green, and red edge wavelength regions of the spectrum. The ability to identify unique biomass-spectra relationships per community is decreased at the height of the growing season when shrubs obscure lower-lying vegetation such as mosses. The results of this study support previous research arguing that shrubs are dominant controls over spectral reflectance in Low Arctic communities and that this dominance results in an increased ability to estimate shrub component biomass over other plant functional types.

Land use, land use change, and forestry accounted for two-thirds of Brazil's greenhouse gas emissions profile in 2005. Amazon deforestation has declined by more than 80% over the past decade, yet Brazil's forests extend beyond the Amazon biome. Rapid expansion of cropland in the neighboring Cerrado biome has the potential to undermine climate mitigation efforts if emissions from dry forest and woodland conversion negate some of the benefits of avoided Amazon deforestation. Here, we used satellite data on cropland expansion, forest cover, and vegetation carbon stocks to estimate annual gross forest carbon emissions from cropland expansion in the Cerrado biome. Nearly half of the Cerrado met Brazil's definition of forest cover in 2000 (≥0.5 ha with ≥10% canopy cover). In areas of established crop production, conversion of both forest and non-forest Cerrado formations for cropland declined during 2003–2013. However, forest carbon emissions from cropland expansion increased over the past decade in Matopiba, a new frontier of agricultural production that includes portions of Maranhão, Tocantins, Piauí, and Bahia states. Gross carbon emissions from cropland expansion in the Cerrado averaged 16.28 Tg C yr⁻¹ between 2003 and 2013, with forest-to-cropland conversion accounting for 29% of emissions. The fraction of forest carbon emissions from Matopiba was much higher; between 2010–2013, large-scale cropland conversion in Matopiba contributed 45% of total Cerrado forest carbon emissions. Carbon emissions from Cerrado-to-cropland transitions offset 5%–7% of the avoided emissions from reduced Amazon deforestation rates during 2011–2013. Comprehensive national estimates of forest carbon fluxes, including all biomes, are critical to detect cross-biome leakage within countries and achieve climate mitigation targets to reduce emissions from land use, land use change, and forestry.

In the face of climate change, predicting and understanding future fire regimes across Canada is a high priority for wildland fire research and management. Due in large part to the difficulties in obtaining future daily fire weather projections, one of the major challenges in predicting future fire activity is to estimate how much of the change in weather potential could translate into on-the-ground fire spread. As a result, past studies have used monthly, annual, or multi-decadal weather projections to predict future fires, thereby sacrificing information relevant to day-to-day fire spread. Using climate projections from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), historical weather observations, MODIS fire detection data, and the national fire database of Canada, this study investigated potential changes in the number of active burning days of wildfires by relating 'spread days' to patterns of daily fire-conducive weather. Results suggest that climate change over the next century may have significant impacts on fire spread days in almost all parts of Canada's forested landmass; the number of fire spread days could experience a 2-to-3-fold increase under a high CO₂ forcing scenario in eastern Canada, and a greater than 50% increase in western Canada, where the fire potential is already high. The change in future fire spread is critical in understanding fire regime changes, but is also imminently relevant to fire management operations and in fire risk mitigation.

Approximately 95% of households in Mozambique burn solid fuels for cooking, contributing to elevated indoor and outdoor fine particulate matter (PM_2.5) concentrations and subsequent health and climate impacts. Little is known about the potential health and climate benefits of various approaches for expanding the use of cleaner stoves and fuels in Mozambique. We use state-of-the-science methods to provide a first-order estimation of potential air pollution-related health and climate benefits of four illustrative scenarios in which traditional cooking fires and stoves are displaced by cleaner and more efficient technologies. For rural areas, we find that a 10% increase in the number of households using forced draft wood-burning stoves could achieve >2.5 times more health benefits from reduced PM_2.5 exposure (200 avoided premature deaths and 14 000 avoided disability adjusted life years, DALYs, over a three-year project lifetime) compared to natural draft stoves in the same households, assuming 70% of households use the new technology for both cases. Expanding use of LPG stoves to 10% of households in five major cities is estimated to avoid 160 premature deaths and 11 000 DALYs from reduced PM_2.5 exposure for a three-year intervention, assuming 60% of households use the new stove. Advanced charcoal stoves would achieve ∽80% of the PM_2.5-related health benefits of LPG stoves. Approximately 2%–5% additional health benefits would result from reduced ambient PM_2.5, depending on the scenario. Although climate impacts are uncertain, we estimate that all scenarios would reduce expected climate change-related temperature increases from continued solid fuel use by 4%–6% over the next century. All results are based on an assumed adjustment factor of 0.8 to convert from laboratory-based emission reduction measurements to exposure reductions, which could be optimistic in reality given potential for continued use of the traditional stove. We conclude that cleaner cooking stoves in Mozambique can achieve health and climate benefits, though both are uncertain and local information about baseline and intervention PM_2.5 exposure levels are needed.

Caribbean tropical forests are subject to hurricane disturbances of great variability. In addition to natural storm incongruity, climate change can alter storm formation, duration, frequency, and intensity. This model-based investigation assessed the impacts of multiple storms of different intensities and occurrence frequencies on the long-term dynamics of subtropical dry forests in Puerto Rico. Using the previously validated individual-based gap model ZELIG-TROP, we developed a new hurricane damage routine and parameterized it with site- and species-specific hurricane effects. A baseline case with the reconstructed historical hurricane regime represented the control condition. Ten treatment cases, reflecting plausible shifts in hurricane regimes, manipulated both hurricane return time (i.e. frequency) and hurricane intensity. The treatment-related change in carbon storage and fluxes were reported as changes in aboveground forest biomass (AGB), net primary productivity (NPP), and in the aboveground carbon partitioning components, or annual carbon accumulation (ACA). Increasing the frequency of hurricanes decreased aboveground biomass by between 5% and 39%, and increased NPP between 32% and 50%. Decadal-scale biomass fluctuations were damped relative to the control. In contrast, increasing hurricane intensity did not create a large shift in the long-term average forest structure, NPP, or ACA from that of historical hurricane regimes, but produced large fluctuations in biomass. Decreasing both the hurricane intensity and frequency by 50% produced the highest values of biomass and NPP. For the control scenario and with increased hurricane intensity, ACA was negative, which indicated that the aboveground forest components acted as a carbon source. However, with an increase in the frequency of storms or decreased storms, the total ACA was positive due to shifts in leaf production, annual litterfall, and coarse woody debris inputs, indicating a carbon sink into the forest over the long-term. The carbon loss from each hurricane event, in all scenarios, always recovered over sufficient time. Our results suggest that subtropical dry forests will remain resilient to hurricane disturbance. However carbon stocks will decrease if future climates increase hurricane frequency by 50% or more.

This paper develops a generalizable systems framework to analyze the food-energy-water (FEW) nexus from an urban systems perspective, connecting in- and trans-boundary interactions, quantifying multiple environmental impacts of community-wide FEW provisioning to cities, and visualizing FEW supply-chain risks posed to cities by the environment. Delhi's community-wide food demand includes household consumption by socio-economic-strata, visitors- and industrial food-use. This demand depends 90%, 76%, and 86% on trans-boundary supply of FEW, respectively. Supply chain data reveal unique features of trans-boundary FEW production regions (e.g. irrigation-electricity needs and GHG intensities of power-plants), yielding supply chain-informed coupled energy-water-GHG footprints of FEW provisioning to Delhi. Agri-food supply contributes to both GHG (19%) and water-footprints (72%–82%) of Delhi's FEW provisioning, with milk, rice and wheat dominating these footprints. Analysis of FEW interactions within Delhi found >75% in-boundary water-use for food is for urban agriculture and >76% in-boundary energy-use for food is from cooking fuels. Food waste-to-energy and energy-intensity of commercial and industrial food preparation are key data gaps. Visualizing supply chains shows >75% of water embodied in Delhi's FEW supply is extracted from locations over-drafting ground water. These baseline data enable evaluation of future urban FEW scenarios, comparing impacts of demand shifts, production shifts, and emerging technologies and policies, within and outside of cities.

Human impacts increasingly affect the global hydrological cycle and indeed dominate hydrological changes in some regions. Hydrologists have sought to identify the human-impact-induced hydrological variations via parameterizing anthropogenic water uses in global hydrological models (GHMs). The consequently increased model complexity is likely to introduce additional uncertainty among GHMs. Here, using four GHMs, between-model uncertainties are quantified in terms of the ratio of signal to noise (SNR) for average river flow during 1971–2000 simulated in two experiments, with representation of human impacts (VARSOC) and without (NOSOC). It is the first quantitative investigation of between-model uncertainty resulted from the inclusion of human impact parameterizations. Results show that the between-model uncertainties in terms of SNRs in the VARSOC annual flow are larger (about 2% for global and varied magnitude for different basins) than those in the NOSOC, which are particularly significant in most areas of Asia and northern areas to the Mediterranean Sea. The SNR differences are mostly negative (−20% to 5%, indicating higher uncertainty) for basin-averaged annual flow. The VARSOC high flow shows slightly lower uncertainties than NOSOC simulations, with SNR differences mostly ranging from −20% to 20%. The uncertainty differences between the two experiments are significantly related to the fraction of irrigation areas of basins. The large additional uncertainties in VARSOC simulations introduced by the inclusion of parameterizations of human impacts raise the urgent need of GHMs development regarding a better understanding of human impacts. Differences in the parameterizations of irrigation, reservoir regulation and water withdrawals are discussed towards potential directions of improvements for future GHM development. We also discuss the advantages of statistical approaches to reduce the between-model uncertainties, and the importance of calibration of GHMs for not only better performances of historical simulations but also more robust and confidential future projections of hydrological changes under a changing environment.

Ensuring food security requires food production and distribution systems function throughout disruptions. Understanding the factors that contribute to the global food system's ability to respond and adapt to such disruptions (i.e. resilience) is critical for understanding the long-term sustainability of human populations. Variable impacts of production shocks on food supply between countries indicate a need for national-scale resilience indicators that can provide global comparisons. However, methods for tracking changes in resilience have had limited application to food systems. We developed an indicator-based analysis of food systems resilience for the years 1992–2011. Our approach is based on three dimensions of resilience: socio-economic access to food in terms of income of the poorest quintile relative to food prices, biophysical capacity to intensify or extensify food production, and the magnitude and diversity of current domestic food production. The socio-economic indicator has a large variability, but with low values concentrated in Africa and Asia. The biophysical capacity indicator is highest in Africa and Eastern Europe, in part because of a high potential for extensification of cropland and for yield gap closure in cultivated areas. However, the biophysical capacity indicator has declined globally in recent years. The production diversity indicator has increased slightly, with a relatively even geographic distribution. Few countries had exclusively high or low values for all indicators. Collectively, these results are the basis for global comparisons of resilience between countries, and provide necessary context for developing generalizations about resilience in the global food system.

A subset of Sustainable Development Goals pertains to improving people's living standards at home. These include the provision of access to electricity, clean cooking energy, improved water and sanitation. We examine historical progress in energy access in relation to other living standards. We assess regional patterns in the pace of progress and relative priority accorded to these different services. Countries in sub-Saharan Africa would have to undergo unprecedented rates of improvement in energy access in order to achieve the goal of universal electrification by 2030. World over, access to clean cooking fuels and sanitation facilities consistently lag improved water and electricity access by a large margin. These two deprivations are more concentrated among poor countries, and poor people in middle income countries. They are also correlated to health risks faced disproportionately by women. However, some Asian countries have been able to achieve faster progress in electrification at lower income levels compared to industrialized countries' earlier efforts. These examples offer hope that future efforts need not be constrained by historical rates of progress.