Table of contents

Because of a projected surge of several billion urban inhabitants by mid-century, a rising urgency exists to advance local and strategically deployed measures intended to ameliorate negative consequences on urban climate (e.g., heat stress, poor air quality, energy/water availability). Here we highlight the importance of incorporating scale-dependent built environment induced solutions within the broader umbrella of urban sustainability outcomes, thereby accounting for fundamental physical principles. Contemporary and future design of settlements demands cooperative participation between planners, architects, and relevant stakeholders, with the urban and global climate community, which recognizes the complexity of the physical systems involved and is ideally fit to quantitatively examine the viability of proposed solutions. Such participatory efforts can aid the development of locally sensible approaches by integrating across the socioeconomic and climatic continuum, therefore providing opportunities facilitating comprehensive solutions that maximize benefits and limit unintended consequences.

This study examined the spring–summer (November, December, January and February) albedo averages and trends using a dataset consisting of 28 years of homogenized satellite data for the entire Antarctic sea ice region and for five longitudinal sectors around Antarctica: the Weddell Sea (WS), the Indian Ocean sector (IO), the Pacific Ocean sector (PO), the Ross Sea (RS) and the Bellingshausen–Amundsen Sea (BS). Time series data of the sea ice concentrations and sea surface temperatures were used to analyse their relations to the albedo. The results indicated that the sea ice albedo increased slightly during the study period, at a rate of 0.314% per decade, over the Antarctic sea ice region. The sea ice albedos in the PO, the IO and the WS increased at rates of 2.599% per decade (confidence level 99.86%), 0.824% per decade and 0.413% per decade, respectively, and the steepest increase occurred in the PO. However, the sea ice albedo in the BS decreased at a rate of −1.617% per decade (confidence level 95.05%) and was near zero in the RS. The spring–summer average albedo over the Antarctic sea ice region was 50.24%. The highest albedo values were mainly found on the continental coast and in the WS; in contrast, the lowest albedo values were found on the outer edge of the sea ice, the RS and the Amery Ice Shelf. The average albedo in the western Antarctic sea ice region was distinctly higher than that in the east. The albedo was significantly positively correlated with sea ice concentration (SIC) and was significantly negatively correlated with sea surface temperature (SST); these scenarios held true for all five longitudinal sectors. Spatially, the higher surface albedos follow the higher SICs and lower SST patterns. The increasing albedo means that Antarctic sea ice region reflects more solar radiation and absorbs less, leading to a decrease in temperature and much snowfall on sea ice, and further resulted in an increase in albedo. Conversely, the decreasing albedo leads to more solar radiation absorbing and sea ice melting, thus resulting in a decrease in albedo.

India is increasingly investing in renewable technology to meet rising energy demands, with hydropower and other renewables comprising one-third of current installed capacity. Installed wind-power is projected to increase 5-fold by 2035 (to nearly 100GW) under the International Energy Agency's New Policies scenario. However, renewable electricity generation is dependent upon the prevailing meteorology, which is strongly influenced by monsoon variability. Prosperity and widespread electrification are increasing the demand for air conditioning, especially during the warm summer. This study uses multi-decadal observations and meteorological reanalysis data to assess the impact of intraseasonal monsoon variability on the balance of electricity supply from wind-power and temperature-related demand in India. Active monsoon phases are characterized by vigorous convection and heavy rainfall over central India. This results in lower temperatures giving lower cooling energy demand, while strong westerly winds yield high wind-power output. In contrast, monsoon breaks are characterized by suppressed precipitation, with higher temperatures and hence greater demand for cooling, and lower wind-power output across much of India. The opposing relationship between wind-power supply and cooling demand during active phases (low demand, high supply) and breaks (high demand, low supply) suggests that monsoon variability will tend to exacerbate fluctuations in the so-called demand-net-wind (i.e., electrical demand that must be supplied from non-wind sources). This study may have important implications for the design of power systems and for investment decisions in conventional schedulable generation facilities (such as coal and gas) that are used to maintain the supply/demand balance. In particular, if it is assumed (as is common) that the generated wind-power operates as a price-taker (i.e., wind farm operators always wish to sell their power, irrespective of price) then investors in conventional facilities will face additional weather-volatility through the monsoonal impact on the length and frequency of production periods (i.e. their load-duration curves).

This study will use the Mann–Kendall (MK) non-parametric trend test to examine timing changes in spring (early May to the end of June) streamflow records observed by the Water Survey of Canada during 1985–2011 in an Arctic headwater basin in the Western Canadian Arctic. The MK test shows a general delay in the five timing measures of springtime streamflow, which are based on the 5 percentile (Q5), 10 percentile (Q10), 50 percentile (Q50), 90 percentile (Q90), and 95 percentile (Q95) dates of spring runoff, respectively. However, much stronger trend signals were clearly noted for the high percentiles than that for the low and middle percentiles, indicating different effects of hydroclimate processes working on the timing of springtime streamflow. In contrast, the earlier snowmelt onset derived from daily mean temperatures was found over the 27-year study period. In addition, multiple relationships were correlated between these five timing measures of spring runoff and five hydroclimate indicators (total snowfall, snowmelt onset, spring temperature fluctuation, spring rainfall, and spring rainfall timing) in order to identify possible causes on the changes of springtime streamflow timing. The results indicate that the differences are due to the contradictory effects of winter–spring air temperature changes, temperature fluctuation during the melting period, and spring rainfall to spring runoff. The earlier snowmelt onset, which is attributed to the winter–spring warming, and spring temperature fluctuation that works in the opposite way, result in the minor timing changes of Q5, Q10, and Q50. The increase in spring rainfall and its delayed timing have a significant impact on the dates of Q90 and Q95. Moreover, the decreased total snow accumulation over the winter season only has a minor influence on the timing of springtime streamflow.

Black carbon (BC) aerosols impact climate and air quality. Since BC from fossil versus biomass combustion have different optical properties and different abilities to penetrate the lungs, it is important to better understand their relative contributions in strongly affected regions such as South Asia. This study reports the first year-round ¹⁴C-based source apportionment of elemental carbon (EC), the mass-based correspondent to BC, using as regional receptor sites the international Maldives Climate Observatory in Hanimaadhoo (MCOH) and the mountaintop observatory of the Indian Institute of Tropical Meteorology in Sinhagad, India (SINH). For the highly-polluted winter season (December–March), the fractional contribution to EC from biomass burning (f_bio) was 53 ± 5% (n = 6) at MCOH and 56 ± 3% at SINH (n = 5). The f_bio for the non-winter remainder was 53 ± 11% (n = 6) at MCOH and 48 ± 8% (n = 7) at SINH. This observation-based constraint on near-equal contributions from biomass burning and fossil fuel combustion at both sites compare with predictions from eight technology-based emission inventory (EI) models for India of (f_bio)_EI spanning 55–88%, suggesting that most current EI for Indian BC systematically under predict the relative contribution of fossil fuel combustion. A continued iterative testing of bottom-up EI with top-down observational source constraints has the potential to lead to reduced uncertainties regarding EC sources and emissions to the benefit of both models of climate and air quality as well as guide efficient policies to mitigate emissions.

Köppen bioclimatic classification relates generic vegetation types to characteristics of the interactive annual-cycles of continental temperature (T) and precipitation (P). In addition to predicting possible bioclimatic consequences of past or prospective climate change, a Köppen scheme can be used to pinpoint biases in model simulations of historical T and P. In this study a Köppen evaluation of Coupled Model Intercomparison Project (CMIP) simulations of historical climate is conducted for the period 1980–1999. Evaluation of an example CMIP5 model illustrates how errors in simulating Köppen vegetation types (relative to those derived from observational reference data) can be deconstructed and related to model-specific temperature and precipitation biases. Measures of CMIP model skill in simulating the reference Köppen vegetation types are also developed, allowing the bioclimatic performance of a CMIP5 simulation of T and P to be compared quantitatively with its CMIP3 antecedent. Although certain bioclimatic discrepancies persist across model generations, the CMIP5 models collectively display an improved rendering of historical T and P relative to their CMIP3 counterparts. In addition, the Köppen-based performance metrics are found to be quite insensitive to alternative choices of observational reference data or to differences in model horizontal resolution.

Seasonal river flow forecasts are beneficial for planning agricultural activities, river navigation, and for management of reservoirs for public water supply and hydropower generation. In the United Kingdom (UK), skilful seasonal river flow predictions have previously been limited to catchments in lowland (southern and eastern) regions. Here we show that skilful long-range forecasts of winter flows can now be achieved across the whole of the UK. This is due to a remarkable geographical complementarity between the regional geological and meteorological sources of predictability for river flows. Forecast skill derives from the hydrogeological memory of antecedent conditions in southern and eastern parts of the UK and from meteorological predictability in northern and western areas. Specifically, it is the predictions of the atmospheric circulation over the North Atlantic that provides the skill at the seasonal timescale. In addition, significant levels of skill in predicting the frequency of winter high flow events is demonstrated, which has the potential to allow flood adaptation measures to be put in place.

Urban agriculture requires local water to replace 'hydrologic externalities' associated with food produced outside of the local area, with an accompanying shift of the water footprint (WF) for agricultural production from rural to urban areas. Water requirements of urban agriculture have been difficult to estimate due to the heterogeneity of shading from trees and buildings within urban areas. We developed CityCrop, a plant growth and evapotranspiration (ET) model that couples a 3D model of tree canopies and buildings derived from LiDAR with a ray-casting approach to estimate spatially-explicit solar inputs in combination with local climate data. Evaluating CityCrop over a 1 km² mixed use, residential neighborhood of Vancouver Canada, we estimated median light attenuation to result in 12% reductions in both reference ET (ET_o) and crop ET (ET_c). However, median crop yields were reduced by only 3.5% relative to potential yield modeled without any light attenuation, while the median crop WF was 9% less than the WF for areas unimpeded by shading. Over the 75 day cropping cycle, median crop water requirements as ET_c were 17% less than that required for a well-watered grass (as ET_o). If all lawns in our modeled area were replaced with crops, we estimate that about 37% of the resident population could obtain the vegetable portion of their diet from within the local area over a 150 day growing season. However doing so would result in augmented water demand if watering restrictions apply to lawns only. The CityCrop model can therefore be useful to evaluate trade-offs related to urban agriculture and to inform municipal water policy development.

The effectiveness of fiscal policy to influence vehicle purchases for emissions reductions in private passenger road transport depends on its ability to incentivise consumers to make choices oriented towards lower emissions vehicles. However, car purchase choices are known to be strongly socially determined, and this sector is highly diverse due to significant socio-economic differences between consumer groups. Here, we present a comprehensive dataset and analysis of the structure of the 2012 private passenger vehicle fleet-years in six major economies across the World (UK, USA, China, India, Japan and Brazil) in terms of price, engine size and emissions distributions. We argue that choices and aggregate elasticities of substitution can be predicted using this data, enabling us to evaluate the effectiveness of potential fiscal and technological change policies on fleet-year emissions reductions. We provide tools to do so based on the distributive structure of prices and emissions in segments of a diverse market, both for conventional as well as unconventional engine technologies. We find that markets differ significantly between nations, and that correlations between engine sizes, emissions and prices exist strongly in some markets and not strongly in others. We furthermore find that markets for unconventional engine technologies have patchy coverages of varying levels. These findings are interpreted in terms of policy strategy.

Energy and water systems are interconnected. This work first characterizes 2010 primary energy demand for direct water services and local freshwater demand for energy on Maui Island, Hawaii, then investigates scenarios for future changes in these demands. The goal of this manuscript is to dissect the relationship and trends of energy–water connections to inform policymaking decisions related to water and energy planning. Analysis proceeds by inventorying water and energy flows and adjusting to a 2010 base year, then applying intensity factors for energy or water used at a given stage for a given sector to determine absolute energy and water demands for the isolated system of Maui Island. These bottom-up, intensity-based values are validated against published data where available. Maui consumes about 0.05% of its freshwater for energy (versus >6% for the US on average) and about 32% of its electricity (19% of its on-island primary energy) for direct water services (versus 8% of primary energy for the US on average). These values could change with policy choices like increased instream flows, higher wastewater treatment standards, electricity fuel mix changes, desalination, or increased biofuels production. This letter contributes a granular assessment of both energy for water and water for energy in a single isolated system, highlighting opportunities to address energy–water interdependencies in a context that could be relevant in other communities facing similar choices.

A potential decline in irrigation due to groundwater depletion or insufficient surface water would not only directly affect agriculture, but also could alter surface climate. In this study we investigated how loss of irrigation affects heat wave frequency, duration, and intensity across fifteen heat wave indices (HINs) using a regional climate model that incorporated dynamic crop growth. Averaged across all indices, loss of irrigation increased heat wave frequency, duration, and intensity. In the United States, irrigation effects on heat waves were statistically significant over irrigated cropland for the majority of HINs, but in non-irrigated regions, the effects were significant only for a few HINs. The heat index temperature metrics that include humidity were less sensitive to loss of irrigation due to the trade-off between increased temperature and decreased humidity. Using the same temperature metric but different temperature thresholds resulted in qualitatively similar effects on heat waves. Regions experiencing strong groundwater depletion, such as the southern high plains, may suffer more and longer heat waves with reduced irrigation.

Effects of climate change on the worst case scenario of a storm surge induced by a super typhoon in the present climate are investigated through the case study of Typhoon Haiyan. We present the results of our investigation on super-typhoon Haiyan by using a super high resolution (1 km grid) regional model that explicitly handles cloud microphysical processes. As the parent model, we adopted the operational weekly ensemble experiments (60 km grid) of the Japan Meteorological Agency, and compared experiments using sea surface temperatures and atmospheric environmental parameters from before the beginning of anthropogenic climate change (150 years ago) with those using observed values throughout the typhoon. We were able not only to represent the typhoon's intensity but also to evaluate the influences of climate change on worst case storm surges in the Gulf of Leyte due to a typhoon with high robustness. In 15 of 16 ensemble experiments, the intensity of the simulated worst case storm in the actual conditions was stronger than that in a hypothetical natural condition without historical anthropogenic forcing during the past 150 years. The intensity of the typhoon is translated to a disaster metric by simulating the storm surge height by using a shallow-water long-wave model. The result indicates that the worst case scenario of a storm surge in the Gulf of Leyte may be worse by 20%, though changes in frequency of such events are not accounted for here.

Observations show that the global mean surface temperature has increased steadily since the 1950s and this warming trend is particularly strong and linear over land after 1979. This paper analyzes the relationship between surface temperature trends observed over land for the period 1979–2012 and enhanced vegetation index (EVI), a satellite measured vegetation greenness index, by large-scale ecoregion. The land areas between 50°S and 50°N are classified into various large-scale ecoregions based on the climatological EVI values. The regional mean temperature trends exhibit significant spatial dependence on the regional mean EVI. In general, the warming rate increases dramatically with decreasing EVI, with the strongest warming rate seen over the driest ecoregions. When anthropogenic and natural forcings are included, climate models are generally able to reproduce observed major features of the spatial dependence. When only natural forcings are used, none of the observed features are simulated. Furthermore, the simulated temperature changes in the latter are mostly far outside the range of those in the former. These results suggest stronger warming amplification over drier ecoregions in the context of global warming, pointing mainly to human influence.

Several climate frameworks have included the role of carbon storage in natural landscapes as a potential mechanism for climate change mitigation. This has resulted in an incentive to grow and maintain intact long-lived forest ecosystems. However, recent research has suggested that the influence of albedo-related radiative forcing can impart equal and in some cases greater magnitudes of climate mitigation compared to carbon storage in forests where snowfall is common and biomass is slow-growing. While several methodologies exist for relating albedo-associated radiative forcing to carbon storage for the analysis of the tradeoffs of these ecosystem services, they are varied, and they have yet to be contrasted in a case study with implications for future forest management. Here we utilize four methodologies for calculating a shadow price for albedo radiative forcing and apply the resulting eight prices to an ecological and economic forest model to examine the effects on optimal rotation periods on two different forest stands in the White Mountain National Forest in New Hampshire, USA. These pricing methodologies produce distinctly different shadow prices of albedo, varying from a high of 9.36 × 10⁻⁴ and a low of 1.75 × 10⁻⁵ $w⁻¹yr⁻¹ in the initial year, to a high of 0.019 and a low of 3.55 × 10⁻⁴ $w⁻¹yr⁻¹ in year 200 of the simulation. When implemented in the forest model, optimal rotation periods also varied considerably, from a low of 2 to a high of 107 years for a spruce-fir stand and from 35 to 80 years for a maple-beech-birch stand. Our results suggest that the choice of climate metrics and pricing methodologies for use with forest albedo alter albedo prices considerably, may substantially adjust optimal rotation period length, and therefore may have consequences with respect to forest land cover change.

Resolving the debate surrounding the nature and controls of seasonal variation in the structure and metabolism of Amazonian rainforests is critical to understanding their response to climate change. In situ studies have observed higher photosynthetic and evapotranspiration rates, increased litterfall and leaf flushing during the Sunlight-rich dry season. Satellite data also indicated higher greenness level, a proven surrogate of photosynthetic carbon fixation, and leaf area during the dry season relative to the wet season. Some recent reports suggest that rainforests display no seasonal variations and the previous results were satellite measurement artefacts. Therefore, here we re-examine several years of data from three sensors on two satellites under a range of sun positions and satellite measurement geometries and document robust evidence for a seasonal cycle in structure and greenness of wet equatorial Amazonian rainforests. This seasonal cycle is concordant with independent observations of solar radiation. We attribute alternative conclusions to an incomplete study of the seasonal cycle, i.e. the dry season only, and to prognostications based on a biased radiative transfer model. Consequently, evidence of dry season greening in geometry corrected satellite data was ignored and the absence of evidence for seasonal variation in lidar data due to noisy and saturated signals was misinterpreted as evidence of the absence of changes during the dry season. Our results, grounded in the physics of radiative transfer, buttress previous reports of dry season increases in leaf flushing, litterfall, photosynthesis and evapotranspiration in well-hydrated Amazonian rainforests.

Water use by plant communities across years of varying water availability indicates how terrestrial water balances will respond to climate change and variability as well as to land cover change. Perennial biofuel crops, likely grown mainly on marginal lands of limited water availability, provide an example of a potentially extensive future land cover conversion. We measured growing-season evapotranspiration (ET) based on daily changes in soil profile water contents in five perennial systems—switchgrass, miscanthus, native grasses, restored prairie, and hybrid poplar—and in annual maize (corn) in a temperate humid climate (Michigan, USA). Three study years (2010, 2011 and 2013) had normal growing-season rainfall (480–610 mm) whereas 2012 was a drought year (210 mm). Over all four years, mean (±SEM) growing-season ET for perennial systems did not greatly differ from corn (496 ± 21 mm), averaging 559 (±14), 458 (±31), 573 (±37), 519 (±30), and 492 (±58) mm for switchgrass, miscanthus, native grasses, prairie, and poplar, respectively. Differences in biomass production largely determined variation in water use efficiency (WUE). Miscanthus had the highest WUE in both normal and drought years (52–67 and 43 kg dry biomass ha⁻¹ mm⁻¹, respectively), followed by maize (40–59 and 29 kg ha⁻¹ mm⁻¹); the native grasses and prairie were lower and poplar was intermediate. That measured water use by perennial systems was similar to maize across normal and drought years contrasts with earlier modeling studies and suggests that rain-fed perennial biomass crops in this climate have little impact on landscape water balances, whether replacing rain-fed maize on arable lands or successional vegetation on marginal lands. Results also suggest that crop ET rates, and thus groundwater recharge, streamflow, and lake levels, may be less sensitive to climate change than has been assumed.

Extreme heat events are associated with spikes in mortality, yet death rates are on average highest during the coldest months of the year. Under the assumption that most winter excess mortality is due to cold temperature, many previous studies have concluded that winter mortality will substantially decline in a warming climate. We analyzed whether and to what extent cold temperatures are associated with excess winter mortality across multiple cities and over multiple years within individual cities, using daily temperature and mortality data from 36 US cities (1985–2006) and 3 French cities (1971–2007). Comparing across cities, we found that excess winter mortality did not depend on seasonal temperature range, and was no lower in warmer vs. colder cities, suggesting that temperature is not a key driver of winter excess mortality. Using regression models within monthly strata, we found that variability in daily mortality within cities was not strongly influenced by winter temperature. Finally we found that inadequate control for seasonality in analyses of the effects of cold temperatures led to spuriously large assumed cold effects, and erroneous attribution of winter mortality to cold temperatures. Our findings suggest that reductions in cold-related mortality under warming climate may be much smaller than some have assumed. This should be of interest to researchers and policy makers concerned with projecting future health effects of climate change and developing relevant adaptation strategies.

A significant fraction of anthropogenic CO₂ emissions is assimilated by tropical forests and stored as biomass, slowing the accumulation of CO₂ in the atmosphere. Because different plant tissues have different functional roles and turnover times, predictions of carbon balance of tropical forests depend on how earth system models (ESMs) represent the dynamic allocation of productivity to different tree compartments. This study shows that observed allocation of productivity, biomass, and turnover times of main tree compartments (leaves, wood, and roots) are not accurately represented in Coupled Model Intercomparison Project Phase 5 ESMs. In particular, observations indicate that biomass saturates with increasing productivity. In contrast, most models predict continuous increases in biomass with increases in productivity. This bias may lead to an over-prediction of carbon uptake in response to CO₂ or climate-driven changes in productivity. Compartment-specific productivity and biomass are useful benchmarks to assess terrestrial ecosystem model performance. Improvements in the predicted allocation patterns and turnover times by ESMs will reduce uncertainties in climate predictions.

Recently, energy use in the urban residential sector of China has drastically increased due to higher incomes and urbanization. The fossil fuels dominant energy supply has since worsened the air quality, especially in urban areas. In this study we estimate the future energy service demands in Chinese urban residential areas, and then use an AIM/Enduse model to evaluate the emission reduction potential of CO₂, SO₂, NO_x and PM. Considering the climate diversity and its impact on household energy service demands, our analysis is down-scaled to the provincial-level. The results show that in most of the regions, penetration of efficient technologies will bring CO₂ emission reductions of over 20% compared to the baseline by the year 2030. Deployment of energy efficient technologies also co-benefits GHG emission reduction. However, efficient technology selection appears to differ across provinces due to climatic variation and economic disparity. For instance, geothermal heating technology is effective for the cold Northern areas while biomass technology contributes to emission reduction the most in the warm Southern areas.

Smoldering peat fires in Indonesia are responsible for large quantities of trace gas and particulate emissions. However, to date no satellite remote sensing technique has been demonstrated for the identification of smoldering peat fires. Fires have two distinct combustion phases: a high temperature flaming and low temperature smoldering phases. The flaming phase temperature is approximately twice that of the smoldering phase. This temperature differential results in a spectral displacement of the primary radiant emissions of the two combustion phases. It it is possible to exploit this spectral displacement using widely separated wavelength ranges. This paper examines active fire features found in short-wave infrared (SWIR) and long-wave infrared (LWIR) nighttime Landsat data collected on peatlands in Sumatra and Kalimantan. Landsat 8's SWIR bands are on the leading edge of flaming phase radiant emissions, with only minor contribution from the smoldering phase. Conversely, Landsat 8's LWIR bands are on the trailing edge of smoldering phase radiant emissions. After examining the LWIR fire features, we conclude that they are the result of smoldering phase combustion. This has been confirmed with field validation. Detection limits for smoldering peat fires in Landsat 8 is in the 40–90 m² range. These results could lead to improved management of peatland fires and emission modeling.

Distributions and optical characteristics of aerosols were continuously observed with a polarization-sensitive (532 nm), Mie-scattering (532 and 1064 nm) and Raman-scattering (607 nm) lidar and a sky radiometer in Phimai, Thailand. Polarization lidar measurements indicated that high concentration plumes of spherical aerosols considered as biomass burning smoke were often observed in the dry season. Plumes of non-spherical aerosols considered as long-range transported soil dust from Africa, the Middle East, or Northeast Asia were occasionally observed. Furthermore, low-concentration non-spherical aerosols were almost always observed in the atmospheric mixing layer. Extinction coefficient profiles of spherical aerosols and non-spherical dust exhibited different diurnal variations, and spherical aerosols including smoke were distributed in higher altitudes in the mixing layer and residual layer. The difference can be explained by hygroscopic growth of smoke particles and buoyancy of the smoke. Analysis of seasonal variations of optical properties derived from the Raman lidar and the sky radiometer confirmed that the lidar ratio, aerosol optical depth, and Angstrom exponent were higher in the dry season (October–May) and lower in the wet season (June–September). The single scattering albedo was lower in the dry season. These seasonal variations are explained by frequent biomass burning in the dry season consistent with previous studies in Southeast Asian region. At the same time, the present work confirmed that soil dust was a major aerosol component in Phimai, Thailand.

Past studies suggest that forest fires contribute significantly to the formation of ozone in the troposphere. However, the emissions of ozone precursors from wildfires, and the mechanisms involved in ozone production from boreal fires, are very complicated. Moreover, an evaluation of the role of forest fires is prevented by the lack of direct observations of the ozone precursor, nitrogen oxides (NOx), and large uncertainties exist in the emissions inventories currently used for modelling. A comprehensive understanding of the important processes and factors involving wildfires has thus been unobtainable. We made 16 year consistent analyses of NOx emissions from boreal wildfires by using satellite observations of tropospheric nitrogen dioxides (NO₂) from 1996 to 2011. We report substantial interannual variability of tropospheric NO₂ originating from large boreal fires over Siberia in 1998, 2002, 2003, 2006, and 2008; and over Alaska in 2004, 2005, and 2009. Monthly comparisons of NO₂ enhancements with fire radiative power (FRP) show reasonably strong correlation, suggesting that FRP is a better proxy than burned area for boreal fire NOx emissions. We provide space-based constraints on NOx emission factors (EFs) for Siberian and Alaskan fires. Although the associated uncertainty is relatively large, the derived EFs fall into a in reasonably agreeable range with those previously determined by in situ ground-based and airborne observations over these regions.

Four-year ground-level measurements of the two primary greenhouse gases (carbon dioxide (CO₂) and methane (CH₄)) were conducted at Dongsha Island (DSI), situated in the northern South China Sea (SCS), from March 2010 to February 2014. Their mean mixing ratios are calculated to be 396.3 ± 5.4 ppm and 1863.6 ± 50.5 ppb, with an annual growth rate of +2.19 ± 0.5 ppm yr^–1 and +4.70 ± 4.4 ppb yr^–1 for CO₂ and CH₄, respectively, over the study period. Our results suggest that the Asian continental outflow driven by the winter northeast monsoon could have brought air pollutants into the northern SCS, as denoted by significantly elevated levels of 6.5 ppm for CO₂ and 59.6 ppb for CH₄, which are greater than the marine boundary layer references at Cape Kumukahi (KUM) in the tropical northern Pacific in January. By contrast, the summertime CH₄ at DSI is shown to be lower than that at KUM by 19.7 ppb, whereas CO₂ is shown to have no differences (<0.42 ppm in July) during the same period. Positive biases of the Greenhouse Gases Observing Satellite (GOSAT) L4B data against the surface measurements are estimated to be 2.4 ± 3.4 ppm for CO₂ and 43.2 ± 36.8 ppb for CH₄. The satellite products retrieved from the GOSAT showed the effects of anthropogenic emissions and vegetative sinks on land on a vertical profiling basis. The prevailing southeasterly winds originating from as far south as the equator or Southern Hemisphere pass through the lower troposphere in the northern SCS, forming a tunnel of relatively clean air masses as indicated by the low CH₄ mixing ratios observed on the DSI in summer.

Freshwater marshes have been shown to be strong sinks for carbon dioxide (CO₂) on an annual basis relative to other wetland types; however it is likely that these ecosystems are also strong emitters of methane (CH₄), reducing their carbon (C) sequestration potential. Multiyear C balances in these ecosystems are necessary therefore to determine their contribution to the global C cycle. Despite this, the number of multiyear studies in marshes is few, with, to the best of our knowledge, only one other Northern marsh C balance reported. This study presents five years of eddy covariance flux measurements of CO₂, and four years of warm-season chamber measurements of CH₄ at a cool-temperate Typha angustifolia marsh. Annual average cumulative net ecosystem exchange of CO₂ (NEE) at the marsh was −224 ± 54 g C m⁻² yr⁻¹ (±SD) over the five-year period, ranging from −126 to −284 g C m⁻² yr⁻¹. Enhancement of the ecosystem respiration during warmer spring, autumn and winter periods appeared the strongest determinant of annual NEE totals. Warm season fluxes of CH₄ from the Typha vegetation (avg. 1.0 ± 1.2 g C m⁻² d⁻¹) were significantly higher than fluxes from the water surface (0.5 ± 0.4 g C m⁻² d⁻¹) and unvegetated mats (0.2 ± 0.2 g C m⁻² d⁻¹). Air temperature was a primary driver of all CH₄ fluxes, while water table was not a significant correlate as water levels were always at or above the vegetative mat surfaces. Weighting by the surface cover proportion of water and vegetation yielded a net ecosystem CH₄ emission of 127 ± 19 g C m⁻² yr⁻¹. Combining CO₂ and CH₄, the annual C sink at the Mer Bleue marsh was reduced to −97 ± 57 g C m⁻² yr⁻¹, illustrating the importance of accounting for CH₄ when generating marsh C budgets.

Urbanization and land use changes alter the nitrogen (N) cycle, with critical consequences for continental freshwater resources, coastal zones, and human health. Sewage and poor watershed management lead to impoverishment of inland water resources and degradation of coastal zones. Here we review the N contents of rivers of the three most important watersheds in South America: the Amazon, La Plata, and Orinoco basins. To evaluate potential impacts on coastal zones, we also present data on small- and medium-sized Venezuelan watersheds that drain into the Caribbean Sea and are impacted by anthropogenic activities. Median concentrations of total dissolved nitrogen (TDN) were 325 μg L⁻¹ and 275 μg L⁻¹ in the Amazon and Orinoco basins, respectively, increasing to nearly 850 μg L⁻¹ in La Plata Basin rivers and 2000 μg L⁻¹ in small northern Venezuelan watersheds. The median TDN yield of Amazon Basin rivers (approximately 4 kg ha⁻¹ yr⁻¹) was larger than TDN yields of undisturbed rivers of the La Plata and Orinoco basins; however, TDN yields of polluted rivers were much higher than those of the Amazon and Orinoco rivers. Organic matter loads from natural and anthropogenic sources in rivers of South America strongly influence the N dynamics of this region.

Assessing the removal of nitrogen (temporary and permanent) in large river basins is complex due to the dependency on climate, hydrological and physical characteristics, and ecosystems functioning. Measurements are generally limited in number and do not account for the full integration of all processes contributing to nitrogen retention in the river basin. However, the estimation of nitrogen retention by the ecosystems is crucial to understanding the nitrate water pollution and the N₂O emissions to the atmosphere, as well as the lag time between the implementation of agri-environmental measures to reduce nitrogen pollution and the improvement of water quality. Models have often been used to understand the dynamics of the river basin system. The objective of this study was to assess nitrogen retention in a large river basin, the Seine basin (∼65 000 km², in France), through the application of three models with different levels of complexity developed for different specific purposes: the GREEN, SWAT and RiverStrahler models. The study analyses the different modelling approaches and compares their estimates of water nitrogen retention over an 11-year period. Then reflexions on the role played by nitrogen retention by aquatic ecosystems in integrated nutrient management are presented. The results of this study are relevant for the understanding of nitrogen retention processes at the large river basin scale and for the analysis of mitigation measure scenarios designed to reduce nitrogen impacts on aquatic ecosystems and climate.

Environmental change poses risks to societies, including disrupting social and economic systems such as migration. At the same time, migration is an effective adaptation to environmental and other risks. We review novel science on interactions between migration, environmental risks and climate change. We highlight emergent findings, including how dominant flows of rural to urban migration mean that populations are exposed to new risks within destination areas and the requirement for urban sustainability. We highlight the issue of lack of mobility as a major issue limiting the effectiveness of migration as an adaptation strategy and leading to potentially trapped populations. The paper presents scenarios of future migration that show both displacement and trapped populations over the incoming decades. Papers in the special issue bring new insights from demography, human geography, political science and environmental science to this emerging field.

This study aims to determine the variability and trends of tropical biomass burning, tropospheric ozone levels from 2005–2012 in Thailand and the ozone transport from the surrounding regions. Intense biomass burning and tropospheric ozone in this area have a seasonal variability with the maximum generally occurring during the dry season. The northern part of Thailand was observed to have high tropospheric ozone during the dry peak season in April. Forward trajectory analysis determined that ozone sources due to biomass burning in the northern and western surrounding regions (Myanmar, Laos and India) enhance the tropospheric ozone column in northern Thailand. Seasonal variations were also seen for the middle and northeastern regions of Thailand. During August, most biomass burning occurs in Indonesia and Malaysia. However, forward trajectory analysis showed that the effect in the tropospheric ozone column level in the southern part of Thailand is minimal from these regions. Eight-year trends of tropospheric ozone column were also calculated for the different regions of Thailand. However, statistical analysis showed that these trends were not significant. The interannual variability of the tropospheric ozone column concentrations due to El Niño Southern Oscillation were also investigated. It was observed that the best correlation of the tropospheric ozone column with the Oceanic Niño Index (ONI) occured when ONI was advanced 3 months for the north, northeast and south regions of Thailand and 4 months for the middle region of Thailand.