Table of contents

Significant differences in key results across the various climate models and integrated assessment models (IAMs) represent a critical challenge to reliable scientific findings and the robust design of climate policies, which leads to an enormous amount of attention and the urgent call for a multi-model study. In this paper, we develop an integrated literature–survey framework by combining the typical content analysis with a simple statistical analysis to systematically examine the developing trends of IAM-based multi-model studies and explore the model-robust climate policy findings; we also conduct an extended analysis to identify the role of a multi-model approach in global warming and other global change research by employing co-citation network analysis. The results reveal that multi-model comparison and ensemble are effective methods to explore reliable scientific findings and yield robust policy conclusions. The current multi-model studies are sparse as a whole, especially for IAM-based climate economic and policy research; future multi-model works, at both the global and regional levels, are therefore promising. We observe that the developed countries (the EU and the US) dominate the current multi-model study, which could be proved by the number of primary IAMs developed, frequency of models adopted, and number of works published. Addressing the risks of global warming relies on reliable scientific research and robust climate policy design, particularly for the developing large emitters, which heavily depends on consistent efforts toward primary model development and comprehensive cooperation with state-of-the-art model teams all over the world.

In recent years, a range of global flood models (GFMs) were developed, each utilizing different process descriptions as well as validation data sets and methods. To quantify the magnitude of these differences, studies assessed the performance of GFMs only on the continental and catchment level. Since the default model set-ups resulted in locally marked deviations, there is a clear need for further and especially more standardized research to not only maintain credibility, but also support the application of GFM products by end-users. Consequently, here we conceptually outline the basic requirements and challenges of a Global Flood Model Validation Framework for more standardized model validation and benchmarking. With the proposed framework we hope to encourage the much needed debate, research developments in this direction, and involvement of science with end-users. By means of the framework, it is possible to streamline the data sets used for input and validation as well as the validation approach itself. By subjecting GFMs to more thorough and standardized methods, we think their quality as well as acceptance will increase as a result, especially amongst end-users of their outputs. Otherwise GFMs may only serve a purely scientific purpose of continued 'siloed' model improvement but without practical use. Furthermore, we want to invite GFM developers to make their models more integratable which would allow for representation of more physical processes and even more detailed comparison on a model component basis. We think this is pivotal to not only improve the accuracy of model input data sets, but to focus on the core of each model, the process descriptions. Only if we know more about why GFMs deviate, are we able to improve them accordingly and develop a next generation of models, not only providing first-order estimates of flood extent but supporting the global disaster risk reduction community with more accurate and actionable information.

Urban environments and heat waves interact synergistically and aggravate the thermal environment through the urban heat island (UHI) effect. Of concern is the potential for a projected warmer future climate to further compound heat waves in urban environments. The present study investigates the interaction of a 2006 heat wave in North America with two urban environments (Phoenix and New York City (NYC)) in current climate and future climate simulations. The future climate conditions were generated using the pseudo-global warming methodology. Multiple high-resolution (3 km) simulations were performed using the weather research and forecasting (WRF) model coupled with the single-layer urban canopy model to improve representation of urban processes and we explore how irrigated green roofs and cool roofs can mitigate heat wave amplification by UHIs. To quantify heat wave intensity, an analytical model is applied to the WRF model output that considers the urban surface heat and water vapor exchanges with the atmosphere. A future, warmer climate is found to amplify the UHI intensity during heat waves in both Phoenix (21%) and NYC (48%), but the amplification is of great uncertainty as its magnitude is comparable to the temporal variability of temperatures. The increase in urban heat index can be almost completely offset by adopting irrigated green roofs in urban areas, and partially offset by adopting cool roofs.

Despite the worldwide presence of pharmaceuticals in the aquatic environment, a comprehensive picture of their aquatic risk (AR) at the global scale has not yet been produced. Here, we present a procedure to estimate ARs of human pharmaceuticals at a freshwater ecoregion level. First, we predicted country- and year-specific per capita consumption with a regression model. Second, we calculated spatially explicit freshwater concentrations via a combination of mass balance models, addressing the pharmaceutical's fate in respectively humans, wastewater treatment plants and the environment. Finally, we divided the freshwater concentrations at the level of individual freshwater ecoregions with the regulatory limit value derived from toxicity tests to come to an ecoregion-specific AR. We applied our procedure to model time-trends (1995–2015) of ARs of carbamazepine and ciprofloxacin, two widely detected and regulatory relevant human use pharmaceuticals. Our analysis of carbamazepine and ciprofloxacin showed that ARs, due to exposure to these human pharmaceuticals, typically increased 10–20 fold over the last 20 years. Risks due to carbamazepine exposure were still typically low for the time period assessed (AR < 0.1), although some more densely populated and/or arid ecoregions showed higher ARs (up to 1.1). Risks for ciprofloxacin were found to be much higher with ARs larger than 1 for 223 out of 449 freshwater ecoregions in 2015. Comparison with measured concentrations in ten river basins showed that carbamazepine concentrations were predicted well. Concentrations of ciprofloxacin, measured in four river basins, were, however, generally underestimated by our model with one to two orders of magnitude. We conclude that our procedure provides a good starting point to evaluate ARs of a wide range of human pharmaceuticals at the global scale.

Groundwater demands are growing in many arid regions, and the use of non-traditional water resources, especially during extreme droughts, is increasingly common. One non-traditional resource is deep groundwater, which we define from ∼150 m to several kilometers or more deep. We analyze 41 081 data points from 17 basins in the southwestern United States (US) to estimate the distribution of fresh and usable deep groundwater for potential human consumption and irrigation. We find the Great Basin to have the largest percentages of fresh and usable deep groundwater with 88%, 96%, and 98% of the total dissolved solids (TDS) concentrations less than 1000 ppm, 3000 ppm and 10 000 ppm respectively. Seven out of the 17 southwestern basins indicate the presence of substantial quantities of usable deep groundwater (<10 000 ppm TDS). We also find that the Great Basin and the Central Valley of California have 64% and 36%, respectively, of deep groundwater with sufficiently low toxic (Na, Cl, and B) and trace element concentrations for irrigation use without treatment, with greater percentages available for more tolerant crops. Given the potentially large deep fresh and usable groundwater volumes across the southwestern US, it is important to characterize the resource and protect it for potential use in decades and centuries to come.

Despite a downward trend in pollutant levels because of a series of emission control policies, the Pearl River Delta (PRD) region continues to suffer from a high number of fine particulate matter (PM_2.5) events and the resultant public health impacts. To effectively control PM_2.5 in the region, a comprehensive understanding of source contribution and PM_2.5 responses to various emission species is critical. We applied the Community Multiscale Air Quality Modeling System together with the high-order decoupled direct method, to simulate air quality and PM_2.5 sensitivity and examined PM_2.5 responses to emission species in the PRD region in the four seasons of 2010. We employed a concentration-response function to quantify the resultant number of premature mortalities attributable to outdoor PM_2.5. We estimated that local and transboundary air pollution (TAP) contributed 27% and 73%, respectively, of the region's PM_2.5. In absolute terms, the largest impacts from local and TAP occurred in winter. With respect to relative contributions among the different sources, regional TAP (between cities in the region) (R-TAP) and local contributions had the largest effect in summer, whereas superregional TAP (from outside of the region) contributed the most in fall and winter. Outdoor PM_2.5 pollution caused 20 160 (95% confidence interval: 5100–39 310) premature mortalities every year in the PRD region. Averaging among cities, 50%, 20%, and 30% of these deaths were attributable to S-TAP, R-TAP, and local contributions, respectively. Precursor gas emissions (i.e. NH₃, volatile organic compounds, SO₂, and NO_x) affect PM_2.5 level in a nonlinear manner; thus, individual pollutant control strategies are less effective for improving PM_2.5 pollution than an integrated strategy. On the basis of our findings, we recommend that controls for multiple emission species should be implemented to control PM_2.5 pollution in the region.

The North Atlantic Oscillation (NAO), the regional manifestation of the Arctic Oscillation (AO), dominates winter climate variability in Europe and North America. Skilful seasonal forecasting of the winter NAO/AO has been demonstrated recently by dynamical prediction systems. However, the role of initial conditions in this predictability remains unknown. Using a latest generation seasonal forecasting system and reanalysis data, we show that the initial upper stratospheric zonal wind anomaly contributes to winter NAO/AO predictability through downward propagation of initial conditions. An initial polar westerly/easterly anomaly in the upper stratosphere propagates down to the troposphere in early winter, favoring a poleward/equatorward shift of the tropospheric mid-latitude jet. This tropospheric anomaly persists well into the late winter and induces the positive/negative phase of NAO/AO in the troposphere. Our results imply that good representation of stratospheric initial condition and stratosphere-troposphere coupling in models is important for winter climate prediction.

Rainfall thresholds under which forests grow in Central Africa are lower than those of Amazonia and southeast Asia. Attention is thus regularly paid to rainfall whose seasonality and interannual variability has been shown to control Central African forests' water balance and photosynthetic activity. Nonetheless, light availability is also recognized as a key factor to tropical forests. Therefore this study aims to explore the light conditions prevailing across Central Africa, and their potential impact on forests' traits. Using satellite estimates of hourly irradiance, we find first that the four main types of diurnal cycles of irradiance extracted translate into different levels of rainfall, evapotranspiration, direct and diffuse light. Then accounting for scale interactions between the diurnal and annual cycles, we show that the daily quantity and quality of light considerably vary across Central African forests during the annual cycle: the uniqueness of western Central Africa and Gabon in particular, with strongly light-deficient climates especially during the main dry season, points out. Lastly, using an original map of terra firme forests, we also show that most of the evergreen forests are located in western Central Africa and Gabon. We postulate that despite mean annual precipitation below 2000 mm yr⁻¹, the light-deficient climates of western Central Africa can harbour evergreen forests because of an extensive low-level cloudiness developing during the June–September main dry season, which strongly reduces the water demand and enhances the quality of light available for tree photosynthesis. These findings pave the way for further analyses of the past and future changes in the light-deficient climates of western Central Africa and the vulnerability of evergreen forests to these changes.

The effective detection of global urban expansion is the basis of understanding urban sustainability. We propose a fully convolutional network (FCN) and employ it to detect global urban expansion from 1992–2016. We found that the global urban land area increased from 274.7 thousand km²–621.1 thousand km², which is an increase of 346.4 thousand km² and a growth by 1.3 times. The results display a relatively high accuracy with an average kappa index of 0.5, which is 0.3 higher than those of existing global urban expansion datasets. Three major advantages of the proposed FCN contribute to the improved accuracy, including the integration of multi-source remotely sensed data, the combination of features at multiple scales, and the ability to address the lack of training samples for historical urban land. Thus, the proposed FCN has great potential to effectively detect global urban expansion.

Past reductions of anthropogenic aerosol concentrations in Europe and North America could have amplified Arctic warming. In the future the impact of air pollution policies may differ, because the major anthropogenic sources of atmospheric aerosols are increasingly located in Asia. In this study numerical experiments evaluating only direct aerosol effects on atmospheric temperatures indicate that, while reduced carbon dioxide (CO₂) emissions weaken Arctic warming, direct radiative forcing effects by reductions of anthropogenic aerosol concentrations, additional to those obtained by lower CO₂ emissions, can either amplify or diminish it. Interactions between regionally modified radiation in Asia and internal climate variability may differently initiate and sustain atmospheric planetary waves propagating into the Arctic. In a nonlinear manner planetary waves may redistribute atmospheric and oceanic meridional heat fluxes at the high latitudes and either amplify or diminish Arctic warming in 2050. Lower CO₂ concentrations might apparently contribute to reduce the interactions between the Arctic system and the lower latitudes, thus reducing the influence of strong air quality measures in Asia on the Arctic amplification of global warming. While past and present air pollution policies could have amplified Arctic warming, in the future the effects from atmospheric pollution reductions are less certain, depending on the future CO₂ concentrations, and requiring improved simulations of changing aerosol concentrations and their interactions with clouds in Asia and the Arctic.

An assessment is carried out of future changes in the agroclimatic conditions and crop productivity of spring wheat across the major grain-growing regions of European Russia. Calculations are conducted using a crop model driven by high-resolution, multiple realization regional climate change simulations under the IPCC RCP8.5 scenario. It has been shown that this scenario can lead to a future decrease in the yield of spring wheat by 9.1 ± 2.3% (2030–2039), 10.3 ± 3.2% (2050–2059), and 18.9 ± 2.8% (2090–2099) as compared with the yield at the end of XX century. In the southeastern part of the region, where there are major spring wheat areas, the projected yield change is expected to be –6.7 ± 3.0% by 2050–2059 and −21.5 ± 3.1% by 2090–2099. Fertile lands in the Central Black-Earth region (southwest of Moscow) will suffer even more due to a significant increase in the aridity and decrease of the growing season. There the projected drop in yield is simulated to be 15.8 ± 5.1% by 2050–2059 and 32.9 ± 3.4% by 2090–2099. However, the major losses in total production (gross yield) are expected to occur in the Privolzhsky Federal District where the spring wheat areas amount to 87% of the total cultivated area in the region.

The effects of global warming and geoengineering on annual precipitation and its seasonality over different parts of the world are examined using the piControl, 4xCO₂ and G1 simulations from eight global climate models participating in the Geoengineering Model Intercomparison Project. Specifically, we have used relative entropy, seasonality index, duration of the peak rainy season and timing of the peak rainy season to investigate changes in precipitation characteristics under 4xCO₂ and G1 scenarios with reference to the piControl. In a 4xCO₂ world, precipitation is projected to increase over many parts of the globe, along with an increase in both the relative entropy and seasonality index. Further, in a 4xCO₂ world the increase in peak precipitation duration is found to be highest over the subpolar climatic region. However, over the tropical rain belt, the duration of the peak precipitation period is projected to decrease. Furthermore, there is a significant shift in the timing of the peak precipitation period by 15 days–2 months (forward) over many parts of the Northern Hemisphere except for over a few regions, such as North America and parts of Mediterranean countries, where a shift in the precipitation peak by 1–3 months (backward) is observed. However, solar geoengineering is found to significantly compensate many of the changes projected in a 4xCO₂ scenario. Solar geoengineering nullifies the precipitation increase to a large extent. Relative entropy and the seasonality index are almost restored back to that in the control simulations, although with small positive and negative deviations over different parts of the globe, thus, significantly nullifying the impact of 4xCO₂. However, over some regions, such as northern parts of South America, the Arabian Sea and Southern Africa, geoengineering does not significantly nullify changes in the seasonality index seen in 4xCO₂. Finally, solar geoengineering significantly compensates the changes in timing of the peak and duration of the peak precipitation seen in 4xCO₂.

Global wood demand is projected to increase with accompanying intensification in forest management practices. There are concerns that intensive management practices such as whole-tree harvest (WTH) and shortened rotation lengths could risk the long-term productivity and carbon sink capacity of forest ecosystems. The historical (1915–2005) and future (2005–2095) development of five Scots pine (Pinus sylvestris) and five Norway spruce (Picea abies) stands were simulated across a long latitudinal gradient in Europe. The responses of above- and belowground carbon and nutrient cycles to changing forest management and climate were simulated using a biogeochemical ecosystem model and a dynamic litter and soil carbon model. The uncertainty deriving from the inter-annual climate variability was quantified by Monte Carlo simulations. The biogeochemical model estimated the historical stand development similarly to measurement-based estimates derived from growth and yield tables, supporting the validity of the modelling framework. Stand productivity increased drastically in 2005–2095 as a result of climate change. The litter and soil carbon and nitrogen stocks decreased as a result of WTH while its effect on the biomass carbon stock was positive. This indicates that the microbial controls of post-harvest on stand productivity require further research. Shortened rotation length reduced the carbon stock of biomass more than that of litter and soil. The response of the litter and soil carbon stock to forest management was very similar irrelevant of the model used demonstrating the pattern to be robust. Forest management dominated over the impacts of climate change in the short term.

Two of the most consequential developments affecting US politics are (1) the growing influence of private philanthropy, and (2) the large-scale production and diffusion of misinformation. Despite their importance, the links between these two trends have not been scientifically examined. This study employs a sophisticated research design on a large collection of new data, utilizing natural language processing and approximate string matching to examine the relationship between the large-scale climate misinformation movement and US philanthropy. The study finds that over a twenty year period, networks of actors promulgating scientific misinformation about climate change were increasingly integrated into the institution of US philanthropy. The degree of integration is predicted by funding ties to prominent corporate donors. These findings reveal new knowledge about large-scale efforts to distort public understanding of science and sow polarization. The study also contributes a unique computational approach to be applied at this increasingly important, yet methodologically fraught, area of research.

Climate change will likely impact wind and solar resources. As power systems increasingly shift towards wind and solar power, these resource changes will increasingly impact power system operations. We assess how power system operations will be affected by climate change impacts on wind and solar resources by generating wind and solar generation profiles for a reference period and five climate change projections. We then run a unit commitment and economic dispatch model to dispatch a high-renewable generator fleet with these profiles. For climate change projections, we use 2041–2050 output from five global climate models (GCMs) for Representative Concentration Pathway 8.5 for Texas, our study system. All five GCMs indicate increased wind generation potential by 1%–4% under climate change in Texas, while three and two GCMs indicate increased and decreased solar generation potential, respectively, by up to 1%. Uneven generation potential changes across time result in greater changes in dispatched generation by fuel type. Notably, nuclear generation decreases across GCMs by up to 7%, largely in low-demand (winter) months when nuclear plants, which have a high minimum stable load, must reduce their generation to avoid overgeneration. Increased wind and/or solar generation result in reduced system CO₂ emissions and electricity production costs across four of the five GCMs by 8–16 million tons and $216–516 million, or by 2% and 1%, respectively. Future research should assess the atmospheric and climate dynamics that underlie such changes in power system operations.

Despite low levels of agreement that climate change is caused primarily by humans, respondents to a survey of climate change beliefs and adoption of climate-mitigative practices among beef and grain producers in Alberta, Canada, indicate a high level of adoption of several agricultural practices with climate-mitigative benefits. Respondents' motivations for adoption of climate-mitigative practices rarely include the belief that climate change is caused by humans, but rather expectations for economic benefits, improvements in soil quality, and biodiversity, among other things. The strongest predictor of mitigative practice adoption is a learning orientation, defined as valuing improvement, research, learning, and innovation, followed by a conservation orientation that values land stewardship. Predictors are not consistent across practices; however, in some but not all cases adoption is predicted by climate change norms, or assumption of personal responsibility to address climate change, and other predictors vary by practice as well.

Climate models have been making significant progress encompassing an increasing number of complex feedback mechanisms from natural ecosystems. Permafrost thaw and subsequent induced greenhouse gas emissions, however, remain a challenge for climate models at large. Deducing permafrost conditions and associated greenhouse gas emissions from parameters that are simulated in climate models would be a helpful step towards estimating emission budgets from permafrost regions. Here we use a regional climate model with a 5 km horizontal resolution to assess future potential methane (CH₄) emissions over presently unglaciated areas in Greenland under an RCP8.5 scenario. A simple frost index is applied to estimate permafrost conditions from the model output. CH₄ flux measurements from two stations in Greenland; Nuuk representing sub-Arctic and Zackenberg high-Arctic climate, are used to establish a relationship between emissions and near surface air temperature. Permafrost conditions in Greenland change drastically by the end of the 21st century in an RCP8.5 climate. Continuous permafrost remains stable only in North Greenland, the north-west coast, the northern tip of Disko Island, and Nuussuaq. Southern Greenland conditions only sustain sporadic permafrost conditions and largely at high elevations, whereas former permafrost in other regions thaws. The increasing thawed soil leads to increasing CH₄ emissions. Especially the area surrounding Kangerlussuaq, Scoresby Land, and the southern coast of Greenland exhibit potentially high emissions during the longer growing season. The constructed maps and budgets combining modelled permafrost conditions with observed CH₄ fluxes from CH₄ promoting sites represent a useful tool to identify areas in need of additional monitoring as they highlight potential CH₄ hot spots.

There is an increasing need for monitoring schemes that help understand the evolution of the global biodiversity crisis and propose solutions for the future. Indicators, including temporal baselines, are crucial to measure the change in biodiversity over time, to evaluate progress towards its conservation and sustainable use and to set conservation priorities. They help design and monitor national and regional policies on biodiversity; they also feed into national reporting on international agreements such as the Convention on Biological Diversity and the Sustainable Development Goals. We analyse the methodological approach of five small African projects resulting from a call to promote indicator development, improve monitoring capacity and strengthen the science-policy interface in the field of biodiversity. We compared their approach to existing guidance provided by the international community, specifically the Biodiversity Indicators Partnership. To this end, we assess whether internationally recommended steps are effectively applied to national/local biodiversity monitoring in selected developing countries. We also present lessons learnt from workshop interactions between partners involved in these projects. Through our pilot projects we identified data availability and data accessibility, together with the involvement of stakeholders, as critical steps in indicator development. Moreover, there is a need for a better awareness and a wider application of the indicator concept itself. Hence, training of key actors both in the policy and science spheres is needed to operationalize indicators and ensure their continuity and sustainability. We hope that these case studies and lessons learnt can stimulate and support countries in the Global South to formulate policy-relevant biodiversity indicators.

Extreme warming events can profoundly alter the transmission dynamics of mosquito-borne diseases by affecting mosquito life-history traits (e.g. survival, growth and reproduction). At local scales, temperatures are determined largely by vegetation structure and can be dramatically altered by drivers of land-use change (e.g. forest conversion). Disturbance activities can also hinder the buffering capacity of natural habitats, making them more susceptible to seasonal climate variation and extreme weather events (e.g. droughts). In experiments spanning three years, we investigated the interactive effects of tropical forest conversion and climate on fine-scale temperature, and the consequences for mosquito larval development. This study was conducted in the northern Malaysian Bornean state of Sabah using local Aedes albopictus mosquitoes; important vectors of dengue, chikungunya and Zika viruses. We demonstrate that variation in temperatures due to forest conversion dramatically increases development rates in Ae. albopictus mosquitoes. However, this effect was mediated by an El Niño Southern Oscillation (ENSO) drought event. In normal years, mean temperatures did not differ between land-use types, however mosquitoes reared in oil palm plantations typically emerged 2–3 days faster than in logged forests. During an ENSO drought, mean temperatures did differ between land-use types, but surprisingly this did not result in different mosquito development rates. Driving this idiosyncratic response may be the differences in daily temperature fluctuations between the land-use types that either push mosquito larvae towards optimal development, or over the thermal optimum, thereby reducing fitness. This work highlights the importance of considering the synergistic effects of land-use and seasonal climate variations for predicting the thermal response of a key mosquito life-history trait driving disease transmission dynamics.

There is growing concern globally about the occurrence of anthropogenic organic contaminants in the environment, including pharmaceuticals and personal care products. This concern extends to groundwater, which is a critical water resource in Europe, and its protection is a priority to the European Commission, the European Union (EU) Member States and national agencies across Europe. Maintaining good groundwater status supports improved public health, economic growth and sustains groundwater dependant ecosystems. A range of measures have been introduced for regulating several substances that have impacted groundwater (e.g. nitrate and pesticides). However, these measures only cover a small fraction of anthropogenic substances that could pollute groundwater. Monitoring for these unregulated substances is currently very limited or not carried out at all. Therefore, a coordinated European-wide approach is needed to identify, monitor and characterise priority substances or groups of substances that have the potential to pollute groundwater. This evidence base is critical for policy development and controls on these currently unregulated substances. The European Commission highlighted this as a need during the review of the EU Groundwater Directive Annexes in 2014, when the requirement to develop a Groundwater Watch List (GWWL) was established. This paper describes the approach that has been developed through a voluntary initiative as part of the EU CIS Working Group Groundwater to establish the voluntary EU GWWL. The process for developing the GWWL is one that has brought together researchers, regulators and industry, and is described here for the first time. A summary of the key principles behind the methodology is presented as well as results from pilot studies using per- and polyfluoroalkyl substances and pharmaceuticals. These explore and support the viability of the GWWL process, an important step towards its adoption and its future use for groundwater protection across Europe.

The uncertainties in sea ice extent (total area covered by sea ice with concentration >15%) derived from passive microwave sensors are assessed in two ways. Absolute uncertainty (accuracy) is evaluated based on the comparison of the extent between several products. There are clear biases between the extent from the different products that are of the order of 500 000 to 1 × 10⁶ km² depending on the season and hemisphere. These biases are due to differences in the algorithm sensitivity to ice edge conditions and the spatial resolution of different sensors. Relative uncertainty is assessed by examining extents from the National Snow and Ice Data Center Sea Ice Index product. The largest source of uncertainty, ∼100 000 km², is between near-real-time and final products due to different input source data and different processing and quality control. For consistent processing, the uncertainty is assessed using different input source data and by varying concentration algorithm parameters. This yields a relative uncertainty of 30 000–70 000 km². The Arctic minimum extent uncertainty is ∼40 000 km². Uncertainties in comparing with earlier parts of the record may be higher due to sensor transitions. For the first time, this study provides a quantitative estimate of sea ice extent uncertainty.

We investigate climatic changes that have occurred in the Arctic over the period 1982–2017 through examination of ten observational cryospheric time series, and develop a new quantitative composite Arctic climate change index (ACCI). Using Factor Analysis highlights joint trends of winter temperature increases and sea ice loss, tundra shifts, and secondarily summer sea ice loss, spring snow loss, and Greenland land ice loss. An Arctic-wide atmospheric circulation index (Arctic Oscillation) was not selected as a joint contributor. Distinct Arctic change began in 1990 and the trend increases after 2005 to the end of the series. That most variables of the collection project onto a single pattern of change suggests that the Arctic is responding as a coherent system over the previous three decades. However, no single index exclusively tracks change in the Arctic, a conclusion that emerges from a multivariate analysis. A composite quantitative index (ACCI) is useful to document the covariability of systematic Arctic change.

The Yukon–Kuskokwim (YK) Delta is a region of discontinuous permafrost in the subarctic of southwestern Alaska. Many wildfires have occurred in the YK Delta between 1971–2015, impacting vegetation cover, surface soil moisture, and the active layer. Herein, we demonstrate that the remotely sensed active layer thickness (ReSALT) algorithm can resolve the post-fire active layer dynamics of tundra permafrost. We generated a stack of Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar interferograms over a study region in the YK Delta spanning 2007–2010. We applied ReSALT to this stack of interferograms to measure seasonal subsidence associated with the freezing and thawing of the active layer and subsidence trends associated with wildfire. We isolated two wildfire-induced subsidence signatures, associated with the active layer and the permafrost layer. We demonstrate that InSAR is sensitive to increases in active layer thickness following wildfire, which recovers to pre-fire values after approximately 25 years. Simultaneously, we show that fire gradually thins the permafrost layer by 4 m, which recovers to pre-fire thickness after 70 years.

Improvement in the efficiency of farmland utilization and multiple cropping systems are of prime importance for achieving food security in China. Therefore, spatially-explicit analysis detecting trends of cropping intensity are important preconditions for sustainable agricultural development. However, knowledge about the spatiotemporal dynamics of cropping intensity in China remains limited. In this study, we generated annual cropping intensity maps in China during 2000–2015 using a rule-based algorithm and MOD09A1 time series imagery. We then analyzed the spatio-temporal changes of cropping intensity. The results showed single-cropping and double-cropping areas were about 1.28 ± 0.027 × 10⁶ km² and 0.52 ± 0.027 × 10⁶ km² in China in 2015 and their areas were relatively stable from 2000–2015. However, cropping intensity had substantial spatial changes during 2000–2015. About 0.164 ± 0.026 × 10⁶ km² of single-cropping area was converted to double-cropping area, which mainly occurred in the Huang-Huai-Hai Region. About 0.193 ± 0.028 × 10⁶ km² of double-cropping area was converted to single-cropping area, which mainly occurred in the southern part of China. About 85% of croplands with decreases in cropping intensity were located in the southern part of China, and about 80% of croplands with increases in cropping intensity was distributed in the Huang-Huai-Hai Region and the northern part of the Middle and Lower Reaches of the Yangtze River region (p < 0.05). The landscapes of different cropping systems tended to be homogenized in major agricultural production regions.

The original raw dataset used to generate this work contained a number of duplicate entries—roughly 7% of the total farm fields. The substantive majority of these were from one large farm that had conducted their operations in a way that caused duplication as a side effect in our data generation process. Unfortunately, as the error was in the raw dataset, its correction required a re-run of the entire data pipeline, resulting in numerous small downstream changes. With respect to the most important numbers, the accuracy of the classifier went down slightly from 91.5% to 91.2% measured in absolute terms but increased from 0.68 to 0.74 measured by kappa. The trend in cover cropped acres grew slightly stronger, and the yield effects in maize and soybean moved from 0.65% to 0.71% and 0.35% to 0.29% respectively. None of the overall conclusions of the work have materially changed. Below, we provide all changes to the applicable sections of the original manuscript in bold underscore (or strikethrough) where applicable, in addition to modified versions of the corresponding figures and supplementary materials.