Table of contents

Snow covers Arctic and boreal regions (ABRs) for approximately 9 months of the year, thus snowscapes dominate the form and function of tundra and boreal ecosystems. In recent decades, Arctic warming has changed the snowcover's spatial extent and distribution, as well as its seasonal timing and duration, while also altering the physical characteristics of the snowpack. Understanding the little studied effects of changing snowscapes on its wildlife communities is critical. The goal of this paper is to demonstrate the urgent need for, and suggest an approach for developing, an improved suite of temporally evolving, spatially distributed snow products to help understand how dynamics in snowscape properties impact wildlife, with a specific focus on Alaska and northwestern Canada. Via consideration of existing knowledge of wildlife-snow interactions, currently available snow products for focus region, and results of three case studies, we conclude that improving snow science in the ABR will be best achieved by focusing efforts on developing data-model fusion approaches to produce fit-for-purpose snow products that include, but are not limited to, wildlife ecology. The relative wealth of coordinated in situ measurements, airborne and satellite remote sensing data, and modeling tools being collected and developed as part of NASA's Arctic Boreal Vulnerability Experiment and SnowEx campaigns, for example, provide a data rich environment for developing and testing new remote sensing algorithms and retrievals of snowscape properties.

Palm oil production has been linked to deforestation, biodiversity loss, and climate change. We explore consumer awareness of palm oil, perceptions of its environmental impact, recognition of ecolabels including the Roundtable on Sustainable Palm Oil (RSPO) ecolabel, and inclusion or avoidance of ecolabels in household shopping using a representative sample of the British population. We find consumer awareness of palm oil to be fairly high (77%), with 41% of those aware of palm oil perceiving it as 'environmentally unfriendly', more than double the level of any other vegetable oil examined. However, recognition of the RSPO ecolabel is the same as those who 'recognize' a fictitious ecolabel, making recognition indistinguishable from zero. Based on our logistic regression analysis, members of the British population most likely to actively include ecolabelled products in their weekly household shopping are those who are female, from higher socioeconomic groups, spend more than £120 per week on household shopping, and have received a Bachelors degree or higher. Despite clear benefits of environmental certification and ecolabelling, a relatively niche segment of the general population actively includes ecolabelled products in their weekly household shopping. Therefore, we recommend current policies be amended to require companies to source 100% identity preserved certified palm oil that can be traced to the plantation level to avoid having to rely on consumer decisions to enable a shift towards more responsibly-sourced palm oil. Additionally, requiring multinational companies to map and publicly disclose full supply chain information for all global operations, including palm oil suppliers and concessions, could help illuminate and discourage unsustainable practices.

While quality of life (QOL) is the result of satisfying human needs, our current provision strategies result in global environmental degradation. To ensure sustainable QOL, we need to understand the environmental impact of human needs satisfaction. In this paper we deconstruct QOL, and apply the fundamental human needs framework developed by Max-Neef et al to calculate the carbon and energy footprints of subsistence, protection, creation, freedom, leisure, identity, understanding and participation. We find that half of global carbon emissions are driven by subsistence and protection. A similar amount are due to freedom, identity, creation and leisure together, whereas understanding and participation jointly account for less than 4% of global emissions. We use 35 objective and subjective indicators to evaluate human needs satisfaction and their associated carbon footprints across nations. We find that the relationship between QOL and environmental impact is more complex than previously identified through aggregated or single indicators. Satisfying needs such as protection, identity and leisure is generally not correlated with their corresponding footprints. In contrast, the likelihood of satisfying needs for understanding, creation, participation and freedom, increases steeply when moving from low to moderate emissions, and then stagnates. Most objective indicators show a threshold trend with respect to footprints, but most subjective indicators show no relationship, except for freedom and creation. Our study signals the importance of considering both subjective and objective satisfaction to assess QOL-impact relationships at the needs level. In this way, resources could be strategically invested where they strongly relate to social outcomes, and spared where non-consumption satisfiers could be more effective. Through this approach, decoupling human needs satisfaction from environmental damage becomes more attainable.

China's terrestrial ecosystems play an important role in the global carbon cycle. Regional contributions to the interannual variation (IAV) of China's terrestrial carbon sink and the attributions to climate variations are not well understood. Here we have investigated how terrestrial ecosystems in the four climate zones with various climate variabilities contribute to the IAV in China's terrestrial net ecosystem productivity (NEP) using modeled carbon fluxes data from six ecosystems models. Model results show that the monsoonal region of China dominates national NEP IAV with a contribution of 86% (69%–96%) on average. Yearly national NEP changes are mostly driven by gross primary productivity IAV and half of the annual variation results from NEP changes in summer. Regional contributions to NEP IAV in China are consistent with their contributions to the magnitude of national NEP. Rainfall variability dominates the NEP annual variability in China. Precipitation in the temperate monsoon climate zone makes the largest contribution (23%) to the IAV of NEP in China because of both the high sensitivity of terrestrial ecosystem carbon uptake to rainfall and the large fluctuation in the precipitation caused by the East Asian summer monsoon anomalies. Our results suggest that NEP IAV can be mainly attributed to ecosystems with larger productivity and response to precipitation, and highlight the importance of monsoon climate systems with high seasonal and interannual variability in driving internannual variation in the land carbon sink.

Accurate soil organic carbon (SOC) maps are needed to predict the terrestrial SOC feedback to climate change, one of the largest remaining uncertainties in Earth system modeling. Over the last decade, global scale models have produced varied predictions of the size and distribution of SOC stocks, ranging from 1000 to >3000 Pg of C within the top 1 m. Regional assessments may help validate or improve global maps because they can examine landscape controls on SOC stocks and offer a tractable means to retain regionally-specific information, such as soil taxonomy, during database creation and modeling. We compile a new transboundary SOC stock database for coastal watersheds of the North Pacific coastal temperate rainforest, using soil classification data to guide gap-filling and machine learning approaches to explore spatial controls on SOC and predict regional stocks. Precipitation and topographic attributes controlling soil wetness were found to be the dominant controls of SOC, underscoring the dependence of C accumulation on high soil moisture. The random forest model predicted stocks of 4.5 Pg C (to 1 m) for the study region, 22% of which was stored in organic soil layers. Calculated stocks of 228 ± 111 Mg C ha⁻¹ fell within ranges of several past regional studies and indicate 11–33 Pg C may be stored across temperate rainforest soils globally. Predictions compared very favorably to regionalized estimates from two spatially-explicit global products (Pearson's correlation: ρ = 0.73 versus 0.34). Notably, SoilGrids 250 m was an outlier for estimates of total SOC, predicting 4-fold higher stocks (18 Pg C) and indicating bias in this global product for the soils of the temperate rainforest. In sum our study demonstrates that CTR ecosystems represent a moisture-dependent hotspot for SOC storage at mid-latitudes.

We investigate ensemble-mean ('consensus') values of resolution-weighted CMIP5 multi-model simulations of 1976–2005 summer regional hydroclimates, and of their projected 2070–2099 changes under three progressively more severe representative concentration pathways greenhouse scenarios. Uncertainties in these consensus values are estimated from the cross-ensemble scatter. We analyze differences among 30 year present-day and future consensus summer hydroclimates that are averaged over three disparate regions of the United States: the semi-arid Southern Great Plains, the arid Southwest, and the humid Southeast. Our study considers the impact of several scenarios of greenhouse forcing on the regional averages of both single hydroclimatic variables and on ratios of variables which are indicative of continental drying, as well as the partitioning of surface moisture or available energy into their respective subcomponents. In all three study regions, there is a projected robust increase in surface temperature as the severity of the greenhouse scenario increases; but the regional-average hydroclimatic changes are comparatively uncertain, and often are not proportional to the change in surface warming. There is, however, a projected robust increase in continental drying that is manifested by several complementary measures, but that differs in magnitude by region. The prospect of future continental aridification should be viewed with some caution, however, since it may be a result of various shortcomings in current-generation climate models or in the specified greenhouse scenarios.

The effect of industrial oil palm expansion on deforestation and peatland conversion in Southeast Asia has been well documented. Despite being the fastest growing producer group by area, the effects of smallholder expansion in contrast is yet to be fully understood. By combining spatial analysis with farm and farmer surveys, this article examines the types of land use changes associated with independent smallholder oil palm expansion in Indonesian Borneo. We furthermore estimate through predictive modeling how plot and smallholder characteristics influence the probability that smallholder plantation establishment involved peat- and/or forestland conversion. Results point to an increasing rate of especially peatland conversion due to rising scarcities of suitable lands on mineral soils. They also demonstrate how oil palm smallholders involved in environmentally detrimental land conversions are less likely to be experienced oil palm farmers and more likely to belong to indigenous groups, be incompliant of sustainability standards and have experienced fire. This highlights the importance of improved peatland management and targeted extension support in smallholder oil palm landscapes to both mitigate and reduce the impact of smallholder oil palm expansion.

Do food imports increase the variability of domestic food prices? The answer to this question depends on whether foreign production is more volatile than domestic production. If imports are likely to destabilize domestic prices, storing crops for future consumption may prove an appealing strategy to cope with the adverse supply effects of a more unstable climate. Unfortunately, public storage has proven to be unsustainable due to the high costs of carrying crop inventories over time and the inability of policy planners to correctly forecast changes in domestic supply. Therefore, understanding the roles of imports and stocks on domestic food price instability is important as domestic shortfalls in food production are likely to become more frequent as the world's climate becomes warmer. Using maize prices observed in 76 maize markets of 27 maize net importers across Africa, Asia and Latin America during 2000–2015, we find that, on average, a 1% increase in the ratio of imports to total consumption is correlated with a 0.29% reduction of the intra-annual coefficient of variation of maize prices; likewise a 1% increase in the amount of maize available in stocks at the beginning of the season is correlated with a 0.22% reduction in the said coefficient. We also find that climate-induced supply shocks toward mid-century may increase maize price variability in the focus countries by around 10%; these increases could be offset with similar increases in the ratio of imports to total consumption or in the stock-to-use ratio at the beginning of the crop marketing year. The fact that both imports and stocks help to stabilize domestic prices suggests that their uses should hinge on a careful cost-benefit analysis, including the risk of facing world production more variable than domestic production and the costs of carrying maize inventories over time.

Changes in mean sea level (MSL) are a major, but not the unique, cause of changes in high-percentile sea levels (HSL), e.g. the annual 99.9th quantile of sea level (among other factors, climate variability may also have huge influence). To unravel the respective influence of each contributor, we propose to use structural time series models considering six major climate indices (CI) (Artic Oscillation, North Atlantic Oscillation, Atlantic Multidecadal Oscillation, Southern Oscillation Index, Niño 1 + 2 and Niño 3.4) as well as a reconstruction of MSL. The method is applied to eight century-long tide gauges across the world (Brest (France), Newlyn (UK), Cuxhaven (Germany), Stockholm (Sweden), Gedser (Danemark), Halifax (Canada), San Francisco (US), and Honolulu (US)). The treatment within a Bayesian setting enables to derive an importance indicator, which measures how often the considered driver is included in the model. The application to the eight tide gauges outlines that MSL signal is a strong driver (except for Gedser), but is not unique. In particular, the influence of Artic Oscillation index at Cuxhaven, Stockholm and Halifax, and of Niño Sea Surface Temperature index 1 + 2 at San Francisco appear to be very strong as well. A similar analysis was conducted by restricting the time period of interest to the 1st part of the 20th century. Over this period, we show that the MSL dominance is lower, whereas an ensemble of CI contribute to a large part to HSL time evolution as well. The proposed setting is flexible and could be applied to incorporate any alternative predictive time series such as river discharge, tidal constituents or vertical ground motions where relevant.

Energy systems support technical solutions fulfilling the United Nations' Sustainable Development Goal for clean water and sanitation (SDG6), with implications for future energy demands and greenhouse gas emissions. The energy sector is also a large consumer of water, making water efficiency targets ingrained in SDG6 important constraints for long-term energy planning. Here, we apply a global integrated assessment model to quantify the cost and characteristics of infrastructure pathways balancing SDG6 targets for water access, scarcity, treatment and efficiency with long-term energy transformations limiting climate warming to 1.5 °C. Under a mid-range human development scenario, we find that approximately 1 trillion USD2010 per year is required to close water infrastructure gaps and operate water systems consistent with achieving SDG6 goals by 2030. Adding a 1.5 °C climate policy constraint increases these costs by up to 8%. In the reverse direction, when the SDG6 targets are added on top of the 1.5 °C policy constraint, the cost to transform and operate energy systems increases 2%–9% relative to a baseline 1.5 °C scenario that does not achieve the SDG6 targets by 2030. Cost increases in the SDG6 pathways are due to expanded use of energy-intensive water treatment and costs associated with water conservation measures in power generation, municipal, manufacturing and agricultural sectors. Combined global spending (capital and operational expenditures) to 2030 on water, energy and land systems increases 92%–125% in the integrated SDG6-1.5 °C scenarios relative to a baseline 'no policy' scenario. Evaluation of the multi-sectoral policies underscores the importance of water conservation and integrated water–energy planning for avoiding costs from interacting water, energy and climate goals.

The mixture of socio-economic classes, ethnicities, and cultures that characterizes many cosmopolitan urban areas can contribute to unequally perceived impacts of extreme weather events and, hence, need and responsibility for adaptation. Awareness of these differences is, as we argue, decisive for effective adaptation. This study explores the relationship between person-specific, socio-economic characteristics that are frequently associated with social vulnerability and the perception of current affectedness by extreme weather events, future impact severity as well as adaptation need and adaptation responsibility. We use a large online questionnaire survey from New York City studying two extreme weather events, heatwaves and heavy rainstorms. We find that previous harm is the most important factor across all tested models for both weather events. However, previous harm and affectedness do not well explain the perception of future impacts, whereas they correspond to views about adaptation responsibility; respondents who felt significantly more affected in the past perceive the community to be in charge of adaptation. Women (during both weather events) and the elderly (during heatwaves) state largest affectedness during past events, and see the community as being responsible for future adaptation. Hispanic and African American respondents, on the other hand, were identified to perceive adaptation to be more of an individual task—potentially related to previous experience with (a lack of) local government services in their areas. Our findings evoke equity questions, and can aid urban decision makers aiming to implement effective and just adaptation measures, targeting vulnerable socio-economic groups in New York City and potentially other cosmopolitan areas.

In the last years a large number of weather driven extreme events has occurred worldwide with unprecedented socio-economic impacts and are expected to increase, in both frequency and intensity, under future global-warming conditions. In this context early identification and predictability of such events are paramount as they mostly affect several socio-economic activities. Despite the effort in monitoring and evaluation of these extreme events, a quantitative assessment of their interaction is still a challenge. We propose to analyze if the occurrence of extremely hot days/nights in the summer is preceded by drought events in spring and early summer throughout the Mediterranean area. This was investigated by correlating the number of hot days and nights in the regions hottest months with a drought indicator on the prior months. Drought characterization was performed using both the Standardized Precipitation Evaporation Index (SPEI) and the Standardized Precipitation Index (SPI) for the 3-, 6- and 9-months time scales, considering the period 1980–2014 with a spatial resolution of 0.5°. The number of hot days and nights per month (NHD and NHN, respectively) is determined for the same period and spatial resolution. Results show that the most frequent hottest months for the Mediterranean region occur in July and August. Most regions exhibit statistically significant negative correlations, i.e. high (low) NHD/NHN following negative (positive) SPEI/SPI values, and thus a potential for NHD/NHN early warning. This analysis allowed to identify the Iberian Peninsula, northern Italy, northern Africa and the Balkans as the main hotspots of predictability of extreme hot temperatures in the summer preceded by the occurrence of drought events in the spring or early summer.

Fires in Indonesia release excessive carbon and are exacerbated during drier El Niño years. The recent 2015 fires were affected by an extended drought caused by a strong El Niño event. This led to severe haze conditions across Southeast Asia, resulting in adverse socioeconomic and health impacts. Here, we evaluate the social and environmental factors that contributed to the 2015 extreme fires in Riau, Jambi and South Sumatra. We developed proxy variables for plausible drivers of fire which contribute either as a predisposing condition or as an ignition source for fires. We evaluated how these variables influenced fire count at an administrative regency-level and fire occurrence at a pixel-level (1 km²). We used generalized linear mixed effect models to model fire count at the regency-level and boosted regression trees to model fire occurrence at the pixel-level. Rainfall, slope and population density were the most important variables predicting fires at both levels. Economic variables such as the proportion of small-scale (<10 ha) and medium-scale (10–100 ha) plantation landholdings, and the reported use of fires to clear agricultural lands in villages were important in explaining fire count at the regency-level. At the pixel-level, distance from roads and the number of recorded burns over peatlands were important in explaining fire occurrence. The main influence of rain on fires corroborates with previous studies, and highlights the importance of establishing an early warning system for droughts to better prevent and manage future extreme fire events. Mitigation efforts for future fires, especially during El Niño years, can focus on identifying high-risk areas using environmental data on rainfall, slope, peatlands, and previously burnt peat areas, as well as social data related to population density, access to roads, extents of small- and medium-plantation landholdings, and village-level propensity to burn land for agriculture.

Arctic winters have become increasingly warmer and rainier. Where permafrost prevails, winter rain (or rain-on-snow) is known to occasionally cause extensive ice layers at the snow/ground interface, i.e. 'basal ice' or 'ground ice', with potentially large ecological and socio-economic implications. However, an overall lack of field data has so far restricted our predictive understanding of the environmental conditions shaping spatiotemporal variation in basal ice. Here, we use time-series of spatially replicated snowpack measurements from coastal (Ny-Ålesund area; 2000–2017) and central Spitsbergen (Nordenskiöld Land; 2010–2017), Svalbard, to analyze spatiotemporal patterns in basal ice and how they are linked with topography, weather, snowpack and climate change. As expected, both the spatial occurrence and thickness of basal ice increased strongly with the annual amount of winter rain. This effect was modified by accumulated snowfall; a deeper snowpack restricts ice formation following a minor rain event, but enhances ice formation following heavy rain due to an increased contribution of snowmelt. Accordingly, inter-annual variation in snow depth was negatively related to basal ice thickness. Annual fluctuations in basal ice thickness were strongly correlated in space (average correlation ρ = 0.40; 0–142 km distance between plots) due to strong spatial correlation in winter rain (ρ = 0.62; 14–410 km distance between meteorological stations). Models of basal ice based on meteorological time-series (1957–2017) suggested that ice-free winters (i.e. mean basal ice <0.1 cm) had virtually not occurred since 1998, whereas such winters previously (1957–1998) occurred every three–four years on average. This detected cryosphere regime shift was linked to a parallel climate regime shift with increased winter rain amounts. Svalbard is regarded a bellwether for Arctic winter climate change. Our empirical study may therefore provide an early warning of future changes in high-arctic snowpacks.

Permafrost thaw alters subsurface flow in boreal regions that in turn influences the magnitude, seasonality, and chemical composition of streamflow. Prediction of these changes is challenged by incomplete knowledge of timing, flowpath depth, and amount of groundwater discharge to streams in response to thaw. One important phenomenon that may affect flow and transport through boreal hillslopes is development of lateral perennial thaw zones (PTZs), the existence of which is here supported by geophysical observations and cryohydrogeologic modeling. Model results link thaw to enhanced and seasonally-extended baseflow, which have implications for mobilization of soluble constituents. Results demonstrate the sensitivity of PTZ development to organic layer thickness and near-surface factors that mediate heat exchange at the atmosphere/ground-surface interface. Study findings suggest that PTZs serve as a detectable precursor to accelerated permafrost degradation. This study provides important contextual insight on a fundamental thermo-hydrologic process that can enhance terrestrial-to-aquatic transfer of permafrost carbon, nitrogen, and mercury previously sequestered in thawing watersheds.