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This study assesses the policy/legal status of both multistream residues and potential secondary products ('symbiosis products') and whether there could be environmental benefits associated with the utilization of residues from integrated pulp and paper and carbon steel mills as raw materials for such secondary products. Waste-related European Union (EU) and Finnish policy and legal instruments were reviewed to identify potential constraints for, and suggested next steps in, the development of potential process industry residue-based symbiosis products. The products were soil amendment pellets, low-grade concrete, and mine filler. A global warming potential (GWP) assessment and an exergy analysis were applied to these potential symbiosis products. Some indicative GWP calculations of greenhouse gas emissions associating similar and/or analogous products based on virgin primary raw materials, more energy-intensive processes, and the alternative treatment of these residues as wastes are also presented. This study addresses GWP, exergy, and legal aspects in a holistic manner to determine the potential environmental benefits of secondary products within the EU legal framework. The GWP assessment and exergy analysis indicate that the utilization of multistream residues causes very low environmental burdens in terms of GWP. The utilization option can have potential environmental benefits in terms of GWP through process replacement and avoided landfilling and waste treatment impacts, as well as potentially through emission reductions from product replacement if suitable and safe applications can be identified. Waste regulation does not define the legal requirements under which utilizing residues in such novel concepts as introduced in this study would be possible, nor how waste status could be removed and product-based legislation be applied to the potential products instead. [ABSTRACT FROM AUTHOR]

To investigate the relative contributions of current vs. historical factors in explaining broad-scale diversity gradients using a combination of contemporary factors and a quantitative estimate of the temporal accessibility of areas for recolonization created by glacial retreat following the most recent Ice Age. The part of the Nearctic region of North America that was covered by ice sheets during the glacial maximum 20 000 BP. We used range maps to estimate the species richness of mammals and terrestrial birds in 48 400 km2 cells. Current conditions in each cell were quantified using seven climatic and topographical variables. Historical conditions were estimated using the number of years before present when an area became exposed as the ice sheets retreated during the post-Pleistocene climate warming. We attempted to tease apart contemporary and historical effects using multiple regression, partial regression and spatial autocorrelation analysis. A measure of current energy inputs, potential evapotranspiration, explained 76–82% of the variance in species richness, but time since deglaciation explained an additional 8–13% of the variance, primarily due to effects operating at large spatial scales. Because of spatial covariation between the historical climates influencing the melting of the ice sheet and current climates, it was not possible to partition their effects fully, but of the independent effects that could be identified, current climate explained two to seven times more variance in richness patterns than age. Factors acting in the present appear to have the strongest influence on the diversity gradient, but an historical signal persisting at least 13 000 years is still detectable. This has implications for modelling changes in diversity patterns in response to future global warming. [ABSTRACT FROM AUTHOR]

Understanding the impact of various climate forcing agents, such as aerosols, on extreme precipitation is socially and scientifically vital. While anthropogenic absorbing aerosols influence Earth's energy balance and atmospheric convection, their role in extreme events remains unclear. This paper uses convective‐resolving radiative‐convective‐equilibrium simulations, with fixed solar radiation, to investigate the influence of absorbing aerosols on extreme precipitation comprehensively. Our findings reveal an underappreciated mechanism through which absorbing aerosols can, under certain conditions, strongly intensify extreme precipitation. Notably, we demonstrate that a mechanism previously reported for much warmer (hothouse) climates, where intense rainfall alternates with multi‐day dry spells, can operate under current realistic conditions due to absorbing aerosol influence. This mechanism operates when an aerosol perturbation shifts the lower tropospheric radiative heating rate to positive values, generating a strong inhibition layer. Our work highlights an additional potential effect of absorbing aerosols, with implications for climate change mitigation and disaster risk management. Plain Language Summary: Aerosols, particles suspended in the atmosphere, can interact with the incoming solar radiation by scattering or absorbing it. Aerosol species that absorb solar radiation generate local warming of the atmosphere. This local warming changes the vertical profile of temperature and by that affects cloud and precipitation development. In this paper we use idealized computer simulations to investigate the effect of absorbing aerosols on precipitation, and specifically on extreme precipitation events in the tropics. We demonstrate that under certain conditions, absorbing aerosols can strongly enhance extreme precipitation even despite reducing the mean. We show that this trend can be explained by a mechanism previously reported for much warmer climate conditions than currently found on Earth, involving heating by radiation of the lower part of the troposphere. These results have implications for climate change mitigation and disaster risk management. Key Points: The effect of absorbing aerosol on extreme precipitation is examined in idealized convective‐resolving radiative‐convective‐equilibrium simulationsAerosol perturbation that shifts the lower tropospheric radiative heating rate to positive values strongly enhances extreme precipitationThis trend is explained by a mechanism reported before for hothouse climate conditions involving a shift into an "episodic deluge" regime [ABSTRACT FROM AUTHOR]

Scafetta (2022, https://doi.org/10.1029/2022gl097716) purports to test Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models through a comparison of temperature changes over three decades. Unfortunately, the paper contains numerous conceptual and statistical errors that undermine all of the conclusions. First, no uncertainty is given for the observational temperature difference, making it impossible to assess compatibility with any model result. Second, the CMIP6 data are the ensemble means for each model, but the metric being tested is sensitive to the internal variability and so the full ensemble for each model must be used. When this is corrected, the conclusion that "all models with ECS > 3.0°C overestimate the observed global surface warming" is not sustained. Third, the statistical test in Section 2 would reject all models even in a perfect model setup given sufficient ensemble members, thus the second conclusion "that spatial t‐statistics rejects the data‐model agreement" is also not sustainable. Plain Language Summary: Comparisons of models and observations need to account from multiple sources of uncertainty in both the observations and due to the chaotic dynamics of the weather. The analyses in Scafetta (2022, https://doi.org/10.1029/2022gl097716) do not take either of these issues into account and thus the conclusions in that paper are not supportable. Key Points: Scafetta (2022) contains errors in both of the statistical tests used that make the conclusions unsupportable [ABSTRACT FROM AUTHOR]

This paper presents the methodology for the construction of the KNMI'23 national climate scenarios for the Netherlands. We have developed six scenarios, that cover a substantial part of the uncertainty in CMIP6 projections of future climate change in the region. Different sources of uncertainty are disentangled as much as possible, partly by means of a storyline approach. Uncertainty in future emissions is covered by making scenarios conditional on different SSP scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5). For each SSP scenario and time horizon (2050, 2100, 2150), we determine a global warming level based on the median of the constrained estimates of climate sensitivity from IPCC AR6. The remaining climate model uncertainty of the regional climate response at these warming levels is covered by two storylines, which are designed with a focus on the annual and seasonal mean precipitation response (a dry‐trending and wet‐trending variant for each SSP). This choice was motivated by the importance of future water management to society. For users with specific interests we provide means how to account for the impact of the uncertainty in climate sensitivity. Since CMIP6 GCM data do not provide the required spatial detail for impact modeling, we reconstruct the CMIP6 responses by resampling internal variability in a GCM‐RCM initial‐condition ensemble. The resulting climate scenarios form a detailed storyline of plausible future climates in the Netherlands. The data can be used for impact calculations and assessments by stakeholders, and will be used to inform policy making in different sectors of Dutch society. Plain Language Summary: To prepare society for the effects of future climate change, we need to know what the future climate will be like. In this paper we explain the method that is used to construct six different scenarios that describe possible future climates of the Netherlands. The scenarios make assumptions about future greenhouse gas emissions, and are based on the outcomes of climate models that simulate the response of the climate to these emissions. The KNMI'23 climate scenarios show that strongly reducing global emissions strongly reduces the expected changes in the climate of the Netherlands. In the scenario in which global emissions continue to rise until 2080, Dutch society will have to adapt to a much stronger increases in heat and precipitation extremes, increased risks of droughts with low river discharge in summer, and increased risk of flooding due to high river discharges in winter. In the coming years the climate scenario data will be used to evaluate what needs to be done to keep the country a safe place for people to live in and to thrive in, under changing climate conditions. Key Points: We present a methodology for the construction of regional climate scenarios using a storyline approach to partition uncertaintyResults from CMIP6 are reconstructed with a GCM‐RCM initial condition ensemble to produce high‐resolution scenario data for end‐usersSix scenario variants cover emission uncertainty (high, moderate, low) and uncertainty in the regional response (dry‐trending, wet‐trending) [ABSTRACT FROM AUTHOR]

As the Arctic and its rivers continue to warm, a better understanding of the possible future impacts on people would benefit from close partnership with Indigenous communities and scientists from diverse fields of study. We present efforts by the Arctic Rivers Project to conduct community‐engaged research to increase collective understanding of the historical and potential future impacts of climate change on rivers, fish, and Indigenous communities. Working in central to northern Alaska and the Yukon Territory in Canada, the project seeks to engage with Indigenous communities in ethical and equitable ways to produces science that is useful, useable, and used that may serve as an example for future research efforts. Toward this goal, we formed an Indigenous Advisory Council and together developed project‐specific knowledge co‐production protocols. This paper provides a novel model of design and implementation to co‐produce knowledge with communities across a large study domain. Plain Language Summary: The Arctic and rivers located in the Arctic and subarctic are warming due to climate change. To understand the impacts this warming will have on people, partnering with impacted Indigenous communities in the region is important. It is also important that these partnerships are ethical and equitable and produce science that is actionable. This paper discusses efforts undertaken by a specific project, the Arctic Rivers Project, to conduct ethical and equitable research with Indigenous communities and generate science that is useful to those communities. Through this research our goal is to better understand potential future impacts of climate change on rivers, fish, and Indigenous communities in central northern Alaska and the Yukon Territory in Canada. To achieve this goal, the project formed an Indigenous Advisory Council (IAC) and together developed guidelines for how we can work collaboratively with Indigenous communities. Our specific process of forming an IAC and guidelines is, to our knowledge, a new way to approach collaborative research when working across a large geographic area. We present our process here so that it may provide an example for other research efforts. Key Points: Arctic climate information can be made useful, useable, and used by equitably accounting for diverse community adaptation needs through knowledge co‐productionInstitutional and community capacity, including means and ability, is necessary for equitable knowledge co‐production to occurWe present an approach for co‐producing knowledge with Indigenous communities that can serve as an example for other scientific efforts [ABSTRACT FROM AUTHOR]

There are many uncertainties in future climate, including how the Earth may react to different types of radiative forcing, such as CO2, aerosols, and even geoengineered changes in the amount of sunlight absorbed by Earth's surface. Here, we analyze model simulations where the climate system is subjected to an abrupt change of the solar constant by ±4%, and where the atmospheric CO2 concentration is abruptly changed to quadruple and half its preindustrial value. Using these experiments, we examine how clouds respond to changes in solar forcing, compared to CO2, and feedback on global surface temperature. The total cloud response can be decomposed into those responses driven by changes in global surface temperature, called the temperature mediated cloud feedbacks, and responses driven directly by the forcing that are independent of the global surface temperature. In this paper, we study the temperature mediated cloud changes to answer two primary questions: (a) How do temperature mediated cloud feedbacks differ in response to abrupt changes in CO2 and solar forcing? And (b) Are there symmetrical (equal and opposite) temperature mediated cloud feedbacks during global warming and global cooling? We find that temperature mediated cloud feedbacks are similar in response to increasing solar and increasing CO2 forcing, and we provide a short review of recent literature regarding the physical mechanisms responsible for these feedbacks. We also find that cloud responses to warming and cooling are not symmetric, due largely to non‐linearity introduced by phase changes in mid‐to‐high latitude low clouds and sea ice loss/formation. Plain Language Summary: As the global mean temperature changes, there are changes in cloud amount, location, and thickness, which can all impact the radiative balance of the Earth. Cloud changes driven directly by global temperature change are called temperature mediated cloud feedbacks. In this paper, we study the temperature mediated cloud feedbacks that occur in model simulations where the amount of sunlight incident upon the Earth is increased or decreased abruptly, and then held constant for 150 years. We compare the cloud changes in these experiments with experiments where the CO2 concentration is similarly increased or decreased abruptly and held constant for 150 years. In doing so we find that the temperature mediated cloud feedbacks following abrupt changes in solar radiation are characteristically similar to those occurring following CO2 increase. There are however substantial differences in the temperature mediated cloud feedbacks that occur while the climate is warming versus cooling. Key Points: The temperature mediated cloud changes and feedbacks incurred by changes in solar and CO2 forcing are similarOptical depth changes at high latitudes produce substantial differences in cloud feedbacks in cooling and warming experimentsLikewise, tropical circulations respond differently in models to cooling and warming, with a stronger change in the Walker circulation in warming experiments [ABSTRACT FROM AUTHOR]

To address the central question of how climate change influences tree growth within the context of global warming, we used dendroclimatological analysis to understand the reactions of four major boreal tree species – Populus tremuloides, Betula papyrifera, Picea mariana, and Pinus banksiana– to climatic variations along a broad latitudinal gradient from 46 to 54°N in the eastern Canadian boreal forest. Tree-ring chronologies from 34 forested stands distributed at a 1° interval were built, transformed into principal components (PCs), and analyzed through bootstrapped correlation analysis over the period 1950–2003 to identify climate factors limiting the radial growth and the detailed radial growth–climate association along the gradient. All species taken together, previous summer temperature (negative influences), and current January and March–April temperatures (positive influences) showed the most consistent relationships with radial growth across the gradient. Combined with the identified species/site-specific climate factors, our study suggested that moisture conditions during the year before radial growth played a dominant role in positively regulating P. tremuloides growth, whereas January temperature and growing season moisture conditions positively impacted growth of B. papyrifera. Both P. mariana and P. banksiana were positively affected by the current-year winter and spring or whole growing season temperatures over the entire range of our corridor. Owing to the impacts of different climate factors on growth, these boreal species showed inconsistent responsiveness to recent warming at the transition zone, where B. papyrifera, P. mariana, and P. banksiana would be the most responsive species, whereas P. tremuloides might be the least. Under continued warming, B. papyrifera stands located north of 49°N, P. tremuloides at northern latitudes, and P. mariana and P. banksiana stands located north of 47°N might benefit from warming winter and spring temperatures to enhance their radial growth in the coming decades, whereas other southern stands might be decreasing in radial growth. [ABSTRACT FROM AUTHOR]

Specimen‐based data have played a central role in documenting body‐size shifts as a possible response to global warming over the last century. Identification of the drivers and patterns of these trends requires comparisons across taxa, often through meta‐analyses; however, a lack of repeatability within and interoperability (i.e. the potential for a dataset to be augmented for future research) among published studies is a major obstacle.We reviewed published studies on mammal body‐size changes in the Anthropocene, focusing on those that used museum specimens to analyse body‐size trends over time in at least one species. We assessed these papers for repeatability and interoperability with the following criteria: raw data and specimen identifiers were published and accessible, measurements were unambiguously defined, and potential sex‐ and age‐based size differences among individuals were accounted for.Most published body‐size studies have low potential for replication or augmentation; only one of 27 met all of our criteria. Although these 27 papers collectively generated an estimated 51,790 new body‐size measurements, only 1.25% (649) could be repeated or readily used in further investigations, as the remainder did not include raw data and/or specimen identifiers.Based on these findings, we recommend the following best practices in the study of body‐size trends. First, authors should explicitly define and justify all measures of size and quantify measurement error and publish all data, including measurements and specimen catalogue numbers. In addition to complying with the fundamental scientific tenet of repeatability, this minimizes redundant handling and the concomitant risk of damage to irreplaceable and often fragile museum specimens. Second, authors should test and account for the effects of demography, as some dimensions can change throughout an individual's life. Adopting these practices will improve the quality of body‐size studies, enhance the utility of extended specimen data from natural history collections, and enable researchers to conduct more expansive investigations of size trends over time. [ABSTRACT FROM AUTHOR]

The cloud feedback may result in amplification or damping of Arctic warming. Two common techniques used to diagnose the top‐of‐the‐atmosphere cloud feedback are the Adjusted Cloud Radiative Effect (AdjCRE) method and the Cloud Radiative Kernel (CRK) method. We apply both to CMIP5 and CMIP6 model data, finding that the AdjCRE calculated Arctic shortwave cloud feedback is twice as correlated with sea ice loss in CMIP5, and four times in CMIP6, as the CRK method. We find that the CRK method produces Arctic all‐sky residual percentages exceeding 20% in 15 of 18 models. We use the CRK method to decompose the feedback in CMIP5 and CMIP6 finding that its median value changed from negative to positive driven by a less‐negative cloud optical depth feedback. Despite its lack of closure, we conclude that the CRK method is better suited for Arctic SW feedbacks as it is less impacted by surface albedo changes. Plain Language Summary: The cloud feedback is the process by which cloud property changes in a warming climate can either further enhance warming or damp it. The Arctic is warming faster than the rest of the globe, and one of the largest sources of uncertainty in its climate projections is the cloud feedback. There are two popular methods to calculate the cloud feedback: the Adjusted Cloud Radiative Effect technique, and the Cloud Radiative Kernel technique. In this paper we compare the two methods in a suite of climate models by considering the extent to which changes in Arctic sea ice impact the cloud feedbacks. From this analysis we conclude that the Cloud Radiative Kernel method is less affected by sea ice loss. We then apply the Cloud Radiative Kernel technique to data from the two most recent generations of global climate models to investigate how polar day Arctic cloud feedbacks have changed between these generations. We find that the median value of these Arctic feedbacks is slightly positive in the newest generation of models, a change from slightly negative in the previous generation that is largely fueled by a weakening of the feedback associated with changes in cloud optical depth. Key Points: The Cloud Radiative Kernel method is less sensitive to surface albedo changes than the Adjusted Cloud Radiative Effect techniqueThe Cloud Radiative Kernel method provides poor radiative closure in a suite of global climate modelsThe median shortwave Arctic cloud feedback in recent climate models is slightly positive due to a weakened cloud optical depth feedback [ABSTRACT FROM AUTHOR]

Anthropogenic climate change has an unprecedented impact on ecosystems and their services, with severe consequences for human well‐being, particularly for the marginalised and vulnerable members of society in the Global South. The well‐being of communities relies not only on material and regulating services ecosystems provide but also on non‐material services.In this paper, we unravel the diverse ways that climate change impacts affect Nature's Contributions to People (NCP) and the well‐being of rural populations in four sites in Madagascar—a biodiversity hotspot but one of the economically poorest countries in the world. We conducted participatory community workshops, mapping and semi‐structured interviews with local residents across social subgroups to understand the mechanisms of climate‐related degradation and the resulting impacts on different dimensions of human well‐being through an NCP lens.We found that non‐material services are generally more often associated with well‐being effects. Climate change degrades material and non‐material services through sea level rise, biodiversity loss, drought, precipitation and temperature variability, with consequences for materials, companionship and labour, food and feed, and physical and psychological experiences. Loss of land and forests is expressed through ecological grief.The outcome of our research provides evidence‐based information to local policymakers, conservation practitioners, and climate change agencies.This information can help improve government efforts toward holistic conservation and climate change adaptation by addressing the impacts on the physical and mental well‐being of the most vulnerable communities. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

In recent years, there has been a notable increase in the frequency and intensity of natural disasters related to global warming. Large‐scale blackouts caused by natural disasters demonstrate the extreme vulnerability of power systems to these devastations. Resilience planning is the key to preparing power systems for natural disasters by enhancing the vital infrastructure's robustness. This paper proposes a new resilience improvement framework based on resiliency assessment and optimal hardening to improve the distribution system's resiliency against hurricanes. The main goal is to find the optimal distribution system line hardening solution according to reconfiguration potentials after the hurricane to minimize the distribution system cost of energy not supplied. Fragility curves and Monte Carlo simulations are used for the distribution system resilience assessment. Poles and conductors are vulnerable components of distribution system lines; therefore, two hardening strategies have been outlined using measures like replacing old poles with new poles, upgrading pole classes, and vegetation management. This method is modelled as an optimization program considering budget limitations and load priorities and implemented by a genetic algorithm on the IEEE 33‐bus standard network. The results show that optimal line hardening according to network reconfiguration potentials significantly increased the distribution system's resilience. [ABSTRACT FROM AUTHOR]

Summary: Potato is a tuber that, due to its great variety, is available in the markets all year round. Moreover, it is a widely consumed product worldwide on the account of its affordability and versatility. Therefore, studying it in depth is significant from not only a nutritional or economic point of view, but also for considering its potential environmental impact. In this paper, a Life Cycle Assessment (LCA) model is developed to assess the environmental impact of a model 5 kg bag of fresh washed potatoes. It considers all phases of potato processing, from the time the potato enters the facility until it leaves the gate (gate‐to‐gate LCA). The phase of the process with the greatest environmental impact is the cleaning and classification phase, mainly due to the significant consumption of water used in the cleaning process. The general consumption and storage phases are also relevant environmental impact phases to consider. In the case of these phases, the electricity consumption resulting from the operation of the facilities and product cooling is particularly noteworthy. After establishing the critical points, based on the environmental impact perspective, this paper proposes three measures to reduce the environmental impact of the process. The application of these three measures would reduce the environmental impact by 40% in environmental impact categories such as acidification or by 32% in the global warming category. [ABSTRACT FROM AUTHOR]

A reference or "no‐feedback" radiative response to warming is fundamental to understanding how much global warming will occur for a given change in greenhouse gases or solar radiation incident on the Earth. The simplest estimate of this radiative response is given by the Stefan‐Boltzmann law as −4σTe‾3≈−3.8 ${-}4\sigma {\overline{{T}_{e}}}^{3}\approx -3.8$ W m−2 K−1 for Earth's present climate, where Te‾ $\overline{{T}_{e}}$ is a global effective emission temperature. The comparable radiative response in climate models, widely called the "Planck feedback," averages −3.3 W m−2 K−1. This difference of 0.5 W m−2 K−1 is large compared to the uncertainty in the net climate feedback, yet it has not been studied carefully. We use radiative transfer models to analyze these two radiative feedbacks to warming, and find that the difference arises primarily from the lack of stratospheric warming assumed in calculations of the Planck feedback (traditionally justified by differing constraints on and time scales of stratospheric adjustment relative to surface and tropospheric warming). The Planck feedback is thus masked for wavelengths with non‐negligible stratospheric opacity, and this effect implicitly acts to amplify warming in current feedback analysis of climate change. Other differences between Planck and Stefan‐Boltzmann feedbacks arise from temperature‐dependent gas opacities, and several artifacts of nonlinear averaging across wavelengths, heights, and different locations; these effects partly cancel but as a whole slightly destabilize the Planck feedback. Our results point to an important role played by stratospheric opacity in Earth's climate sensitivity, and clarify a long‐overlooked but notable gap in our understanding of Earth's reference radiative response to warming. Plain Language Summary: Earth's climate is stable because a warmer planet loses more energy to space, at infrared wavelengths invisible to the naked eye. The rate of change of this energy loss as the planet warms provides an estimate how Earth's energy balance responds to warming, which is simple enough to write on a small piece of paper. When scientists investigate the warming predicted by climate models, they often start from a similar but not identical calculation of how Earth's energy balance responds to warming. This calculation, based on model output, is about 15% less stabilizing than the simple pencil‐and‐paper estimate. In this paper, we explore the causes of this 15% difference between the pencil‐and‐paper estimate and the calculations using climate models. We show that the difference is primarily caused by the lack of assumed warming in climate models high in Earth's atmosphere, where temperatures are not closely linked to surface warming. This lack of warming acts as a hidden destabilizing feedback in current analysis of climate models. Key Points: Earth's reference radiative response, or "Planck feedback," is ∼0.5 W m−2 K−1 less stabilizing than a Stefan‐Boltzmann estimateWe find this deviation is mostly due to the assumed lack of stratospheric warming in calculations of the Planck feedbackThe lack of stratospheric warming serves as an implicit positive feedback in analysis of climate model warming [ABSTRACT FROM AUTHOR]

From mother nature and carbon footprints to greenhouse gasses and the race against global warming, popular discourse on environmental issues is saturated with metaphor. Some people view these metaphors as obfuscating or ineffective, while others believe they are crucial for improving climate communications and environmental attitudes. In this paper, we provide a systematic overview and evaluation of the use of English metaphors in Anglo environmental discourse, drawing on a range of empirical and popular media sources. We begin by discussing the role of metaphor in language in thought. Next, we introduce a range of metaphors used to frame discussions of (1) our relationship to nature (e.g., the earth is our common home), (2) our impact on the environment (e.g., we are knocking the climate off balance), and (3) how we should address this impact (e.g., reduce our ecological footprint). We classify these metaphors along several dimensions, including how conventional they are, how systemic they are, how emotionally impactful they are, and how aptly they capture the topics they are used to describe. From this analysis, we derive several promising candidate metaphors that may help increase public understanding and engagement with environmental issues. However, we note that such claims must be tested empirically in future research; currently, there are few large, systematic, replicable experiments in the literature assessing the impact of environmental metaphors. We conclude by offering general recommendations for using metaphors in communications about climate change and sustainability. This paper provides a systematic overview and evaluation of the use of metaphors in environmental discourse. Our analysis illuminates how certain metaphors could help increase public understanding of and engagement with issues like climate change and sustainability. [ABSTRACT FROM AUTHOR]

Changes in the frequency of temperature extremes are often attributed to global warming. The recent availability of near‐surface temperature data records from reference networks, such as the U.S. Climate Reference Network (USCRN), enables the quantification of measurement uncertainties. Within an activity of the Copernicus Climate Change Service, the estimation of the measurement uncertainty has been provided for USCRN temperature data, using metadata made available by the National Oceanic and Atmospheric Administration (NOAA). In this paper, four climate extreme indices (Frost Days, Summer Days, Ice Days, Tropical Nights) and the related uncertainties are calculated for the period 2006–2020 from the USCRN data set and compared with traditional indices. Moreover, the asymmetric USCRN measurement uncertainties are propagated to estimate the uncertainties of climate indices. The comparison shows expanded uncertainties homogeneously distributed with the latitude and typically within 15 days per year for Frost Days and within 10 days for Ice Days, while smaller uncertainties are estimated for Summer Days and Tropical Nights, with values typically within six to seven days per year. Positive uncertainties are typically larger than negative ones for all the indices. The values of Frost and Ice Days with the related uncertainties for USCRN have also been compared with the corresponding values calculated from reanalyses data, showing differences typically within 60 days for median values, quite often smaller than USCRN and inconsistent within the related uncertainties, Overall, the results show that USCRN measurement uncertainties increase confidence in the estimation of climate extreme indices and decisions for adaptation. Plain Language Summary: The relationship between the intensity and frequency of extremes and climate change as well as their attribution to human activities is fundamental for improving the assessment of risk and the elaboration of adaptation strategies. Temperature extremes are often reported and estimated using observations or model data using indices, which are widely adopted in the research community and by decision‐makers. However, the number of temperature extremes is quantified assuming input observations as perfect, whereas these are always affected by uncertainties due to instrumental noise and systematic effects that cannot be always properly accounted for. This also implies that climate extreme indices may under or over‐represent the number of temperature extremes. The advent of reference measurement networks, as well as the overall increase in observational data quality due to recent technological improvements, allows us to quantify measurement uncertainties in detail. In this paper, temperature extremes over the US are estimated from near‐surface temperature measurements provided by the USCRN network in the period 2006–2020 with related uncertainties. The use of uncertainty illustrates the range of values that climate extreme indices may assume. Possible sources of uncertainties and comparisons with data from atmospheric reanalysis are also discussed. Key Points: An extensive assessment of uncertainties for four climate extreme indices is provided using reference near‐surface temperaturesEstimate uncertainties of climate indices for reanalysis validation and quantification of extremes by propagating measurement uncertaintiesUSCRN traceable measurements with quantified uncertainties increase confidence in estimating extreme indices and decisions for adaptation [ABSTRACT FROM AUTHOR]

Global climate change and extreme weather has a profound impact on wildfire, and it is of great importance to explore wildfire patterns in the context of global climate change for wildfire prevention and management. In this paper, a wildfire spatial prediction model based on convolutional neural networks (CNNs) was constructed in the reference period (1997–2014) by using wildfire driving factors and historical burned areas derived from the Global Fire Emissions Database (GFED4s). The shifting spatial patterns of global burned areas in future scenarios for the twenty‐first century was investigated by using shared socioeconomic pathways (SSPs) published by CMIP6. Projected burned areas are analyzed by using nine climate models from CMIP6 under four SSPs (SSP126, SSP245, SSP370 and SSP585) for four defined periods. The evolution of the spatial pattern of global wildfires was further described based on terrestrial ecoregions and GFED regions. The results showed that for the reference period (1997–2014), burned areas were generally distributed in tropical and subtropical regions. The projection results exhibited a systematic increasing trend under the four SSPs from a global perspective in response to climate warming. The increasing trend for the burned area in the SSP370 and SSP585 scenarios was more obvious than that for the SSP126 and SSP245 scenarios. As the severity of the emission scenarios increases, severe wildfires will gradually shift to higher latitudes in the mid‐to‐long term (2061–2080) and long term (2081–2100). Plain Language Summary: Understanding how wildfire patterns might change under climate change is critical for developing fire management strategies. In this study, the convolutional neural networks (CNNs) regression model with a deep architecture for spatial prediction was constructed by establishing the relationship between wildfire explanatory variables and the historical burned area in the reference period. The spatial evolution of the global wildfire patterns under current and future climate change scenarios employing the proposed CNN model were investigated and were further analyzed based on the terrestrial ecoregion and GFED region. The projection results demonstrated a systematic increasing trend in burned area under the four SSPs relative to that in the reference period from a global perspective in response to climate warming. The increase in the burned area under the SSP370 and SSP585 scenarios is more obvious than that for the SSP126 and SSP245 scenarios. Key Points: The most recent CMIP6 climate models and four SSPs are used to analyze the possible changes in global wildfire occurrenceSpatial burned area patterns under current and future climate conditions are presented and analyzedIncreased emissions lead to larger burned areas under the four SSPs [ABSTRACT FROM AUTHOR]

The uneven changes in potential evapotranspiration (PET) in response to temperature rise are called the 'evapotranspiration paradox' phenomenon, which is expected to intensify further under a warming climate. In this paper, we explored the spatial–temporal changes in the future 'evapotranspiration paradox' phenomenon over China and its 10 major river sub‐regions under different climate change scenarios. Thus, this paper uses four global climate model outputs under seven shared socioeconomic pathway‐based scenarios (SSP1‐1.9, SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, SSP4‐3.4, SSP4‐6.0 and SSP5‐8.5) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Considering the latest IPCC's 6th Assessment Report (AR6), this research emphasizes the 2021–2040 (near‐term), 2041–2060 (mid‐term) and 2081–2100 (long‐term) periods to anticipate the 'evapotranspiration paradox' phenomenon. In this study, PET is estimated based on the modified Penman–Monteith (P‐M) method (considering CO2). Furthermore, the paradox phenomenon in this study is defined considering two pivotal conditions: the surface temperature increases but the evaporation decreases (Type I), and the temperature decreases but the evaporation still tends to increase (Type II). The results show that there were only Type I 'evapotranspiration paradoxes' that existed in the historical period, which were dominant especially before the 1990s. Nearly 50% of the areas experienced the Type I 'evapotranspiration paradox' phenomenon that occurred during 1975–1994 and 1995–2014. Spatially, it covered 100% of the area of the Southeast River (SER) and the Liaohe River (LR) during 1975–1994 and the area of the SER, the HAR, the HHR and the LR during 1995–2014. In the future, the interdecadal growth rate of PET in China is projected to be the highest under the SSP5‐8.5 and the lowest under the SSP3‐7.0 with spatial variation. Importantly, the largest areas of approximately 36% and 45% with the Type I phenomenon are inclined to occur under the SSP1‐1.9 and SSP4‐6.0, respectively, over the long‐term period (2081–2100). The area with the Type I phenomenon will be less than 20% in the near‐term, and it is less than 12% in the mid‐term period. For the Type II evapotranspiration paradox, the uppermost 45% of the area is expected to experience the Type II phenomenon under SSP1‐1.9 during the mid‐term period, while it is 30% under SSP1‐2.6 during the long‐term period. However, this study's findings provide the scientific basis for formulating adaptation and mitigation strategies to combat 'evapotranspiration paradox'‐related extremes at regional scales. [ABSTRACT FROM AUTHOR]

In recent years, concurrent climate extreme conditions (i.e., hot‐dry, cold‐dry, hot‐wet, and cold‐wet) have led to various unprecedented natural disasters (e.g., floods, landslide, wildfire, droughts, etc.), causing significant damages to human societies and ecosystems. This is especially true for China where many unprecedented natural disasters have been reported due to the recent warming in local climate. In this paper, we focus on the issue of ultra‐extreme events (1‰ threshold) and address how future global warming would affect the climate extreme conditions in China. Specifically, to reduce the uncertainties from models, we use a downscaled and bias‐corrected CMIP6 ensemble under two continuously‐warming scenarios to evaluate the impact of global warming on ultra‐extreme events over China. The results show that, under both SSP245 and SSP585 scenarios, extreme hot conditions would become dominant in most regions of China and some regions are likely to experience over 50 extreme hot days at future warming levels. The frequency of extreme cold events is projected to be small. More frequent extreme hot‐wet events with concurrence in the same month and year would be expected for China under the continuously‐warming scenarios. This is particularly obvious for the west where more than 6 hot‐wet months are likely to take place under future warming scenarios. This may imply that more extreme heat waves and flooding events would coincide in the same month or year for China in the future. For univariate ultra‐extreme events, both extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level, particularly under the SSP245 scenario. When the global mean temperature is limited to 1.5°C rather than 2°C, the avoided impacts of hot‐wet and cold‐wet extremes concurring in the same month will be larger than those of dry‐related compound extremes. Overall, the results suggest that slowing down global warming can reduce the frequency of concurrent climate extreme conditions in China, highlighting the importance of immediate action toward carbon emission reduction. Plain Language Summary: In recent years, concurrent climate extreme conditions (e.g., hot‐dry, cold‐dry, hot‐wet, and cold‐wet) have led to various unprecedented natural disasters (e.g., floods, landslide, wildfire, droughts, etc.), causing significant damages to human societies and ecosystems. This is especially true for China where many unprecedented natural disasters have been reported due to the recent warming in local climate. In this paper, we focus on the issue of ultra‐extreme events (1‰ threshold) and address how future global warming would affect the climate extreme conditions in China. Here, we use a downscaled and bias‐corrected CMIP6 ensemble under two continuously‐warming scenarios to address this question. The results show that, under both SSP245 and SSP585 scenarios, extreme hot conditions would become dominant in most regions of China and some regions are likely to experience over 50 extreme hot days at future warming levels. Both extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level, particularly under the SSP245 scenario. Overall, the results suggest that slowing down the global warming can reduce the frequency of concurrent climate extreme conditions in China, highlighting the importance of immediate action toward carbon emission reduction. Key Points: A downscaled and bias‐corrected CMIP6 ensemble is used to evaluate the impact of global warming on ultra extreme events over ChinaMore frequent extreme hot‐wet events with concurrence in the same month and year would be expected for ChinaBoth extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level [ABSTRACT FROM AUTHOR]

The onset of periods with very high or low air temperatures has aroused general interest for a long time, which is understandable since it has many dangerous effects directly affecting humans. Such perilous climatic phenomena include exceptionally cold and exceptionally warm months, which this study investigates for all Europe and for its five physico‐geographic regions over the 190‐year period of 1831–2020. Therefore, the research in this paper includes two periods characteristic of the history of climate – the late Little Ice Age (LIA) and present‐day warming. The studies are based on average monthly air temperature values from 40 weather stations in Europe. In this paper, exceptionally cold months (ECMs) or exceptionally warm months (EWMs) are considered to have occurred when the average air temperature at a station differed from the respective long‐term average by at least two standard deviations. The highlights of the study include the identification of a drop in the number of ECMs by 20 over the entire 190‐year period, and a highly statistically significant increase in EWMs by 44 between 1980 and 2020. These changes proceeded with different intensities from one physico‐geographic region of Europe to another. [ABSTRACT FROM AUTHOR]

Global warming is one of the most challenging issues of the current era. Revolutions in industrial, Information and Communication Technology (ICT) sectors significantly contribute in increasing global warming. Green House Gases (GHG) emissions from industrial, transportation, power and other sectors cause environmental pollution, which results in climate degradation. Environmental experts are well aware of the disastrous consequences of excessive global warming; therefore, several decarbonization strategies are developed and practiced in the recent past. A widely practiced decarbonization strategy is replacing fossil fuels by Renewable Energy Sources (RES) in the power systems. Electricity consumers are also encouraged to shift their consumption load to low carbon emissions' time periods. For accomplishing this task, an estimation of future carbon emissions is required. In this paper, power system's carbon emissions are predicted accurately with the help of a novel and an efficient forecasting model. The proposed model comprises of Spearman Correlation Analysis (SCA) and Improved Shallow Denoising Autoencoder (ISDAE) based feature engineering. Forecasting is performed through an Improved Particle Swarm Optimization (IPSO) based Deep Neural Network (DNN) forecaster. In addition, a comprehensive quantification analysis is also presented in this paper. The impacts of RES integration level on the electricity price, consumption cost and Greenhouse Gases (GHG) emissions are quantified descriptively and graphically. Performance of the proposed forecasting model is evaluated by Normalized Root Mean Square Error (NRMSE), Mean Absolute Error (MAE) and Mean Square Error (MSE). Simulation results prove that the proposed model outperforms Support Vector Machine (SVM) and Multiple Linear Regression (MLR) based carbon emission forecasting models in terms of forecasting accuracy. [ABSTRACT FROM AUTHOR]

Numerical weather prediction is pushing the envelope of grid resolution at local and global scales alike. Aiming to model topography with higher precision, a handful of articles introduced unstructured vertical grids and tested them for dry atmospheres. The next step toward effective high‐resolution unstructured grids for atmospheric modeling requires that also microphysics is independent of any vertical columns, in contrast to what is ubiquitous across operational and research models. In this paper, we present a non‐column based continuous and discontinuous spectral element implementation of Kessler's microphysics with warm rain. We test the proposed algorithm against standard three‐dimensional benchmarks for precipitating clouds and show that the results are comparable with those presented in the literature across all of the tested effective resolutions. While presented for both continuous and discontinuous spectral elements in this paper, the method that we propose can be adapted to any numerical method used in other codes, as long as the code can already handle vertically unstructured grids. Plain Language Summary: The earth climate is warming faster than ever. While climate models are the tool available to scientists to forecast its future evolution, they are biased by uncertainties that are, arguably, mostly embedded in the modeling of clouds. Thanks to the advent of exascale computing, a reduction of cloud modeling uncertainties can be expected by simulating clouds at higher and higher resolutions. While uniform high resolution across the whole domain is ideal, for computational efficiency reasons scientists are likely to increase the model resolution in some regions more than others not only in the horizontal direction—which is a standard approach—but also along the vertical direction. Grid refinement in the vertical direction, however, may lead to the loss of the vertical structure of the grid columns, affecting the usability of column‐based physics packages that are used to model clouds and precipitation. To overcome this problem, we present an algorithm to solve the equations that model precipitating clouds along arbitrarily shaped grids in any spatial direction. This approach is advantageous from a modeling perspective as well as from a computational one because it allows full flexibility of the domain partitioning algorithms when hundreds of thousands of parallel processors are used. Key Points: A non‐column based spectral element approach to model precipitating clouds is presentedThe approach can be easily applied to any microphysics scheme with and without precipitationThis approach will make full physics simulations of the atmosphere on unstructured grids possible [ABSTRACT FROM AUTHOR]

Tokyo Bay is a semienclosed coastal system located in the center of Japan. Eutrophication has progressed since 1950, resulting in red tides and blue tides appearing frequently. This Data Paper reports the abundance of phytoplankton species sampled monthly from April 1998 to March 2019 at 17 stations in Tokyo Bay. Monitoring has been carried out by Chiba Prefectural Government. Phytoplankters were enumerated by microscopy as numbers of cells or colonies. The abundance of each species was expressed as cells per milliliter of seawater. The total cell density ranged from 102 to 106 cells·ml−1. The dominant taxa were diatoms, such as Chaetoceros spp., Skeletonema spp., and Thalassiosira spp. in most of the months. The dominant diatom species reached concentrations of 106 cells·ml−1. Microflagellates or Cryptomonadaceae were also abundant, especially in winter, reaching 106 cells·ml−1. These data can be used to appreciate how anthropogenic disturbances such as eutrophication and global warming affect the density and community composition of phytoplankton. The detailed Metadata for this abstract published in the Data Paper section of the journal is available in MetaCat in JaLTER at http://db.cger.nies.go.jp/JaLTER/metacat/metacat/ERDP-2021-03.1/jalter-en. [ABSTRACT FROM AUTHOR]

In this study, CMIP6 and single model initial‐condition large ensemble (SMILE) simulations were applied to reveal possible changes in the spatial coverage (RSC) of summer extreme precipitation (≥99th percentile) in China. Four different approaches were applied to provide a comprehensive assessment of RSC changes. Results indicate that the trend of RSC in summer for the period 1961–2020 in China is underestimated by raw simulations of CMIP6 multi‐model ensembles. Further analyses suggest that the confidence in model simulations reflecting the observed change can be improved based on the two observation constraint approaches and SMILE‐based approach. In addition, the reliability of the spatial distribution can also be improved. Projection results indicate that the RSC of summer extreme precipitation increases consistently with the increment of global warming across the different approaches. Among them, the results based on observational constraints and the SMILE‐based approach show enhanced reliability and present larger RSC changes than by directly using CMIP6 ensembles. In conclusion, extreme precipitation in summer is expected to become more widespread in China as the level of global warming increases. Plain Language Summary: Extreme precipitation causes severe impacts on agriculture, economy, and human health. However, it is projected by models to change in China but with low consistency. Thus, this paper provides an assessment of the projected characteristics of the ratio of spatial coverage (RSC) for extreme precipitation in China combined with multiple evidence and approaches. CMIP6 and single model initial‐condition large ensemble (SMILE) simulations are applied. Four approaches based on the statistics principle and physical interpretability are conducted to comprehensively assess change in RSC. Results reveal that extreme precipitation will become more widespread in China. Among them, results based on observation constraint approaches and SMILE‐based approach, which show enhanced accuracy and reliability, present larger RSC than directly using CMIP6 ensembles. It indicates that extreme precipitation will be more widespread in China combined with multiple evidence and methods. This study provides some scientific foundation for climate mitigation and adaptation. Key Points: Observed trend in RSC in China increased significantly in summer for the period 1961–2020 but is underestimated by CMIP6 multi‐model ensemblesConfidence is improved based on observation constraint and SMILE‐based approaches to raw model simulationsExtreme precipitation in summer will become more widespread, as supported by multiple evidence lines and approaches [ABSTRACT FROM AUTHOR]

Global warming is a non‐uniform process across space and time. This opens the door to a heterogeneous relationship between CO2$$ {\mathrm{CO}}_2 $$ and temperature that needs to be explored going beyond the standard analysis based on mean temperature. We revisit this topic through the lens of a new class of factor models for high‐dimensional panel data, called quantile factor models. This technique extracts quantile‐dependent factors from the distributions of temperature across a wide range of stable weather stations in the northern and southern hemispheres over 1959–2018. In particular, we test whether the (detrended) growth rate of CO2$$ {\mathrm{CO}}_2 $$ concentrations helps to predict the underlying factors of the different quantiles of the distribution of (detrended) temperatures in the time dimension. We document that predictive association is greater at the lower and medium quantiles than at the upper quantiles of temperature in all stations, and provide some conjectures about what could be behind this non‐uniformity. These findings complement recent results in the literature documenting steeper trends in lower temperature levels than in other parts of the spatial distribution. This paper is part of the Economica 100 Series. Economica, the LSE "house journal" is now 100 years old. To commemorate this achievement, we are publishing 100 papers by former students, as well as current and former faculty. Juan J Dolado graduated from the lSE with an MSc in 1981. [ABSTRACT FROM AUTHOR]

Single‐model initial‐condition large ensembles are powerful tools to quantify the forced response, internal climate variability, and their evolution under global warming. Here, we present the CMIP6 version of the Max Planck Institute Grand Ensemble (MPI‐GE CMIP6) with currently 30 realizations for the historical period and five emission scenarios. The power of MPI‐GE CMIP6 goes beyond its predecessor ensemble MPI‐GE by providing high‐frequency output, the full range of emission scenarios including the highly policy‐relevant low emission scenarios SSP1‐1.9 and SSP1‐2.6, and the opportunity to compare the ensemble to complementary high‐resolution simulations. First, we describe MPI‐GE CMIP6, evaluate it with observations and reanalyzes and compare it to MPI‐GE. Then, we demonstrate with six application examples how to use the power of the ensemble to better quantify and understand present and future climate extremes, to inform about uncertainty in approaching Paris Agreement global warming limits, and to combine large ensembles and artificial intelligence. For instance, MPI‐GE CMIP6 allows us to show that the recently observed Siberian and Pacific North American heatwaves would only avoid reaching 1–2 years return periods in 2071–2100 with low emission scenarios, that recently observed European precipitation extremes are captured only by complementary high‐resolution simulations, and that 3‐hourly output projects a decreasing activity of storms in mid‐latitude oceans. Further, the ensemble is ideal for estimates of probabilities of crossing global warming limits and the irreducible uncertainty introduced by internal variability, and is sufficiently large to be used for infilling surface temperature observations with artificial intelligence. Plain Language Summary: Climate model simulations that start from different initial states and differ only due to the chaos in the climate system are used extensively to quantify the forced climate response, variability intrinsic to the climate system, and their change under global warming. Here, we present a new version of the Max Planck Institute Grand Ensemble (MPI‐GE CMIP6) that is run as part of the latest generation of climate models. This single‐model ensemble currently consists of 30 realizations for the historical period 1850–2014 and for five scenarios of possible future climates until 2100. The power of MPI‐GE CMIP6 goes beyond its predecessor by not only providing monthly mean but also 3‐hourly to daily model output, the full range of future scenarios including the two highly policy‐relevant scenarios that were designed to match the Paris Agreement global warming limits of 1.5 and 2°C, and the opportunity to compare the low‐resolution ensemble to simulations of the same model version with higher horizontal resolution. In this paper, we describe the new ensemble and demonstrate with application examples how to use its power. Key Points: The Max Planck Institute Grand Ensemble in its CMIP6 version (MPI‐GE CMIP6) is a 30‐member initial‐condition large ensemble with up to 3‐hourly model output and five emission scenariosThe ensemble is specifically suited to investigate climate extremes and Paris Agreement global warming limitsMPI‐GE CMIP6 adequately represents heat extremes, while precipitation extremes are captured by complementary high‐resolution simulations [ABSTRACT FROM AUTHOR]

Adsorption‐based carbon capture has been recognized as an attractive method for mitigating global warming. Metal–organic frameworks (MOFs) are promising candidate adsorbents for this purpose due to their high adsorption uptake and selectivity for carbon dioxide. However, in real‐world applications, such as direct air capture, the presence of moisture in the feed gas may pose a grand challenge for CO2 adsorption in MOFs. This paper aims to address the issue of water–CO2 co‐adsorption in MOFs and present screening criteria for selecting MOFs that preferentially adsorb CO2 under humid conditions. First, we uncover a comprehensive overview of CO2–water co‐adsorption characteristics of various MOFs. Then, the high‐throughput screening methods are summarized. Both computational and experimental efforts have been dedicated to identify the promising MOFs for humid CO2 capture. According to the screening results and adsorption mechanism, the optimal preparation strategies are proposed to modulate the effect of water on CO2 uptake in MOFs. Finally, current MOF‐based CO2 capture prototypes are presented to evaluate their practical feasibility and performance. This work could offer valuable guidance for the development and application of MOFs for CO2 capture in the presence of water and inspire further research in this field. [ABSTRACT FROM AUTHOR]

The urgent need for carbon‐neutral structures arises from the construction industry's contributions to global carbon emissions and the unprecedented rate of global warming. This paper focuses on steel tied‐arch bridges and explores the possibilities of achieving carbon‐neutral design. Firstly, a scientometric study is conducted to identify key focus areas and emerging research trends in this field. The study encompasses publications that emphasize minimizing either the cost of structures or their detrimental impact to the environment. Taking a steel tied‐arch bridge as an example, we subsequently examine its embodied carbon under different design and manufacturing factors. The study reveals that structural design modifications and the use of high‐strength steel can significantly reduce the global warming potential of the bridge's superstructure. The paper then discusses current methods to reduce (or even cancel out) the embodied carbon associated with the bridge's steel superstructure and concrete substructure. It is demonstrated that the tied‐arch bridge's total embodied carbon can be reduced to up to 82%. [ABSTRACT FROM AUTHOR]

Environmental impact awareness in civil engineering is nowadays an important concern. The last message from the IPCC (Intergovernmental Panel on Climate Change) was clear: significant actions are mandatory and urgent to achieve the objective to limit global warming to 1.5°C. The building sector is one of the most polluting industrial sectors for which economically viable solutions must be found to cut the emissions. The development of new production technologies can contribute to this aim by creating innovative and more sustainable materials. Amongst the new materials that appear on the steel market, high strength steels are a nice example as they offer higher strength to weight ratio of structural steel elements, resulting in material savings, lighter foundations, easier transportation, and erection. These multiple advantages explain why the use of high strength steels could lead to both significant carbon emission and cost savings. However, the production of such steels uses more alloying elements and sometimes they require more production energy than for regular grades. The aim of this paper is to estimate the relative prices and relative carbon emissions of high strength steels to evaluate whether they constitute sustainable materials. This paper demonstrates that, in many cases, the increase of relative prices and carbon emissions as function of yield strength can be negligible by contrast to the weight savings induced by using high strength steels. [ABSTRACT FROM AUTHOR]

This paper argues that a uniform global tax-like price on carbon emissions, whose revenues each country retains, can provide a focal point for a reciprocal common climate commitment, whereas quantity targets, which do not nearly so readily present such a single focal point, tend to rely ultimately on individual quantity commitments. The paper postulates the conceptually useful allegory of a futuristic 'World Climate Assembly' (WCA) that votes for a single worldwide price on carbon emissions via the basic democratic principle of one person, one vote majority rule. A WCA-like uniform price-tax counters self-interest by incentivizing countries or agents to internalize the externality because each WCA agent's higher abatement cost from a higher emissions price is counterbalanced by that agent's extra benefit from inducing all other WCA agents to simultaneously lower their emissions in response to the higher price. The paper derives fresh insights and new simple formulae that relate each emitter's most-preferred world price of carbon to the world 'social cost of carbon' (SCC), and further relates the WCA-voted world price of carbon to the world SCC. Some implications are discussed. The overall methodology of the paper is a mixture of mostly classical with some behavioural economics. [ABSTRACT FROM AUTHOR]

In response to global warming, ozone is predicted to increase aloft due to stratospheric cooling but decrease in the tropical lower stratosphere. The ozone reductions have been primarily attributed to a strengthening Brewer‐Dobson circulation, which upwells ozone‐poor air. Yet, this paper finds that strengthening upwelling only explains part of the reduction. The reduction is also driven by tropospheric expansion under global warming, which erodes the ozone layer from below, the low ozone anomalies from which are advected upwards. Strengthening upwelling and tropospheric expansion are correlated under global warming, making it challenging to disentangle their relative contributions. Therefore, chemistry‐climate model output is used to validate an idealized model of ozone photochemistry and transport with a tropopause lower boundary condition. In our idealized decomposition, strengthening upwelling and tropospheric expansion both contribute at leading order to reducing tropical ozone. Tropospheric expansion drives bottom‐heavy reductions in ozone, which decay in magnitude into the mid‐stratosphere. Plain Language Summary: The ozone layer absorbs ultraviolet light otherwise harmful to life. Due to compliance with the Montreal Protocol, the ozone layer is generally recovering from depletion. But, at the same time, global warming is predicted to impact ozone, increasing ozone in the upper stratosphere and decreasing ozone in the tropical lower stratosphere. These decreases are typically argued to result from a strengthening of tropical stratospheric upwelling under global warming. Yet, this paper shows that in addition to contributions from strengthening upwelling, much of the ozone loss arises from a deepening of the troposphere under global warming. The deepening of the troposphere erodes the ozone layer from below, with the low ozone anomalies in the eroded region subsequently transported upwards by the background upwelling. Deepening of the troposphere therefore helps to explain the predicted ozone reductions throughout the tropical lower stratosphere. Key Points: Global warming reduces ozone in the tropical lower stratosphere, an effect typically attributed to strengthening stratospheric upwellingYet, global warming also deepens the troposphere, which erodes the ozone layer and reduces transport of ozone into the lower stratosphereAlong with strengthening upwelling, tropospheric expansion contributes at leading order to reductions in tropical lower stratospheric ozone [ABSTRACT FROM AUTHOR]

Blooms of various types of seaweeds have been reported worldwide, with recent expansions in surface waters. While most of the expansions have been attributed to eutrophication due mainly to human activities, any potential role of climate change is unclear. Here we show that, in the East China Sea and Yellow Sea, increased biomass of Sargassum horneri (S. horneri, brown seaweed) from 2000 to 2021 appears to be caused primarily by ocean warming, as S. horneri prefers a certain temperature range to grow. In contrast, while increases of Ulva prolifera (U. prolifera, green seaweed) in the same regions might also be related to ocean warming, during the same period, human activities such as coastal aquaculture or seaweed mitigation may muddle such effects. With the projected ocean warming in the next decades, we hypothesize that S. horneri blooms may occur earlier during the year and may continue to expand in the future. Plain Language Summary: Seaweed macroalgae blooms have been reported around the world in the last two decades, yet the exact reasons are often unclear. This paper attempts to solve this puzzle for the Sargassum and Ulva seaweeds in the East China Sea and Yellow Sea using long‐term observations and laboratory‐based seaweed physiology measurements. The paper shows not only how seaweeds have expanded in the two marginal seas in the past four decades, but also how a warming ocean (due to climate variability) and nutrient enrichment (due mainly to human activities) controlled such expansions. Among 63 large marine ecosystems (LMEs) in the world, the East China Sea (where seaweeds expanded) is one of the three LMEs that experienced "super‐fast" warming. Given the IPCC‐projected seawater temperature rise of 1.6°C by 2050, will these seaweeds continue to expand? Key Points: Multi‐sensor satellite data show significant and near‐synchronized expansions of Sargassum horneri and Ulva prolifera in the past 38 yearsOcean warming appears to be a main factor behind the recent expansions of S. horneri because of its physiological requirementOcean warming may also play a role in U. prolifera expansions, but the effect appears confounded by human mitigation efforts [ABSTRACT FROM AUTHOR]

The Qinghai‐Tibet Plateau (QTP) is the largest permafrost region in the world at low and middle latitudes and high elevation. Permafrost is being degraded on the QTP due to global warming, which has a significant effect on regional climate, hydrological, and ecological processes. This paper provides a summary of recent progress in methods used in permafrost research, the permafrost distribution, and basic data relevant to permafrost research on the QTP. The area of permafrost was 1.32 × 106 km2 over the QTP, which accounts for approximately 46% of the QTP. Moreover, simulation studies of the hydrothermal process and permafrost change were reviewed and evaluated the effect of permafrost degradation on hydrological and ecological processes. The results revealed that the effects of permafrost on runoff were closely related to soil temperature, and the effect of permafrost degradation on the carbon cycle requires further study. Finally, current challenges in simulation of permafrost change processes on the QTP were discussed, emphasizing that permafrost degradation under climate change is a slow and non‐linear process. This review will aid future studies examining the mechanism underlying the interaction between permafrost and climate change, and environmental protection in permafrost regions on the QTP. [ABSTRACT FROM AUTHOR]

This paper presents the response to anthropogenic forcing in the GISS‐E2.1 climate models for the 21st century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The experiments were performed using an updated and improved version of the NASA Goddard Institute for Space Studies (GISS) coupled general circulation model that includes two different versions for atmospheric composition: A non‐interactive version (NINT) with prescribed composition and a tuned aerosol indirect effect and the One‐Moment Aerosol model (OMA) version with fully interactive aerosols which includes a parameterized first indirect aerosol effect on clouds. The effective climate sensitivities are 3.0°C and 2.9°C for the NINT and OMA models, respectively. Each atmospheric version is coupled to two different ocean general circulation models: The GISS ocean model (E2.1‐G) and HYCOM (E2.1‐H). We describe the global mean responses for all future scenarios and spatial patterns of change for surface air temperature and precipitation for four of the marker scenarios: SSP1‐2.6, SSP2‐4.5, SSP4‐6.0, and SSP5‐8.5. By 2100, global mean warming ranges from 1.5°C to 5.2°C relative to 1,850–1,880 mean temperature. Two high‐mitigation scenarios SSP1‐1.9 and SSP1‐2.6 limit the surface warming to below 2°C by the end of the 21st century, except for the NINT E2.1‐H model that simulates 2.2°C of surface warming. For the high emission scenario SSP5‐8.5, the range is 4.6–5.2°C at 2100. Due to about 15% larger effective climate sensitivity and stronger transient climate response in both NINT and OMA CMIP6 models compared to CMIP5 versions, there is a stronger warming by 2100 in the SSP emission scenarios than in the comparable Representative Concentration Pathway (RCP) scenarios in CMIP5. Changes in sea ice area are highly correlated to global mean surface air temperature anomalies and show steep declines in both hemispheres, with the largest sea ice area decreases occurring during September in the Northern Hemisphere in both E2.1‐G (−1.21 × 106 km2/°C) and E2.1‐H models (−0.94 × 106 km2/°C). Both coupled models project decreases in the Atlantic overturning stream function by 2100. The largest decrease of 56%–65% in the 21st century overturning stream function is produced in the warmest scenario SSP5‐8.5 in the E2.1‐G model, comparable to the reduction in the corresponding CMIP5 GISS‐E2 RCP8.5 simulation. Both low‐end scenarios SSP1‐1.9 and SSP1‐2.6 also simulate substantial reductions of the overturning (9%–37%) with slow recovery of about 10% by the end of the 21st century (relative to the maximum decrease at the middle of the 21st century). Plain Language Summary: The projections of future climate change are uncertain because they are dependent on different possible scenarios of human‐caused emissions and their interaction with natural forcings, internal climate variability, and inter‐model differences. This paper presents the results of the climate model of the NASA Goddard Institute for Space Studies, GISS‐E2.1, for the anthropogenically forced climate response for the twenty first century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The sensitivity of the model response to different magnitudes of anthropogenic forcings in the twenty‐first‐century scenarios were performed using two different versions for the atmospheric composition and two different ocean general circulation models. Compared to CMIP5 GISS‐E2 versions, the CMIP6 GISS‐E2.1 climate model shows a stronger warming by 2100 in comparable scenarios due to larger effective climate sensitivity and transient climate response. Both climate models with two different ocean components project decreases in the Atlantic overturning stream function by 2100. Key Points: GISS E2.1 model with different configurations is used to carry out 134 Shared Socioeconomic Pathway (SSP) experimentsGISS‐E2.1 climate model shows a stronger warming by 2,100 in comparable Representative Concentration Pathway scenarios in CMIP5 due to larger effective climate sensitivity and stronger transient climate responseBoth coupled models, E2.1‐G and E2.1‐H, project decreases in the Atlantic overturning stream function by 2100 with the largest decrease in the warmest scenario SSP5‐8.5 in the E2.1‐G model [ABSTRACT FROM AUTHOR]

Passive cooling technology is currently a hot research topic because it can achieve cooling without external energy input, significantly reduce cooling costs and mitigate global warming. At present, passive cooling technology is mainly used in construction, transportation, electronic equipment, and other fields, but most passive cooling materials do not have gas barrier performance, which limits the application of passive cooling technology in inflatable structures such as gas film buildings, blimp bladders and other fields, so a material that has both passive cooling and gas barrier performance is needed. In this paper, PXTPU composites were prepared by melt blending mica‐TiO2 with thermoplastic polyurethane (TPU). PXTPU not only achieves passive cooling above 10°C with the excellent reflectivity of TiO2 in mica‐TiO2, but also achieves 61.1% or even 74.5% enhancement of gas barrier capacity with the high orientation of lamellar mica in mica‐TiO2. In addition, PXTPU also exhibited good thermal stability and mechanical properties exceeding 31.2 MPa breaking strength and 210.6% breaking elongation. Therefore, PXTPU composites show great potential in inflatable structures for outdoor work, expanding the application field of passive cooling technology. [ABSTRACT FROM AUTHOR]

Due to the increasing global warming, it is anticipated that the number and severity of natural disasters will increase in the coming years. In this regard, this paper proposes a planning model to improve the resilience of distribution systems against natural disasters. A mathematical model is developed to determine the optimal locations of remote‐controlled switches (RCSs), distributed generation units (DGs), and tie lines in distribution systems with complex topologies or lateral branches. Simultaneous occurrence of multiple faults is considered to better simulate the extreme events. Moreover, the concept of multi‐microgrids is used to supply the maximum possible interrupted load after the fault occurrence. To manage the system risk and different failure scenarios, the conditional value at risk (CVaR) is added to the planning model. The optimization model has been formulated as a mixed integer linear programming (MILP) problem, which can be easily solved using various commercial solvers and give the global optimal solution. Finally, to illustrate the effectiveness of the proposed planning model on improving the distribution system resilience, it is implemented on the IEEE 33‐bus system using different case studies and sensitivity analyses. [ABSTRACT FROM AUTHOR]

Cirrus clouds provide a significant radiative forcing on the Earth's climate system. This paper looks at the connection in space and time between monthly mean lightning activity observed from the Lightning Imaging Sensor on board the International Space Station, and the global monthly mean cirrus cloud cover obtained from the MERRA‐2 reanalysis product. The correlation coefficient between the global monthly mean cloud optical thickness of the cirrus clouds (clouds at altitudes above the 400 hPa pressure levels) with the monthly mean lightning flash counts is 0.84, implying that monthly mean lightning can explain 70% of monthly variability of the global high cloud optical thickness. In addition, lightning amount explains nearly 60% of the monthly mean global area coverage of cirrus clouds. Given these statistically significant connections between lightning and cirrus clouds, we propose using global lightning data as an additional tool for monitoring monthly variability of cirrus clouds. Plain Language Summary: Cirrus clouds are one of the most important components maintaining the Earth's radiation budget. They reflect shortwave radiation from the Sun while absorbing the longwave radiation from the Earth. The net cloud radiative forcing for cirrus clouds results a warming of the climate. More/less cirrus clouds result in more/less warming of the planet. The moisture for the formation of cirrus clouds in the upper atmosphere is transported there in large part via deep convective storms, many associated with lightning activity and hence defined as thunderstorms. An increasing in cirrus clouds in a warmer atmosphere will amplify the initial warming. We explored in this study the relationship between global cirrus cloud coverage and global lightning activity. The results of the research show that 60%–70% of the monthly variability in global cirrus clouds can be explained by mean global lightning activity. Key Points: Cirrus clouds are one of the essential components in the atmosphere, with many important feedbacks on the radiation balance of EarthMonthly mean lightning can explain 70% of the monthly variability of the global high cloud optical thicknessLightning explains nearly 60% of the monthly mean global area coverage of cirrus clouds [ABSTRACT FROM AUTHOR]

The response of temperate glaciers to rapid climate warming is reflected in the geomorphology (landsystem) resulting from snout recession. This paper develops a locally diverse process‐form model of active temperate glaciers through mapping and quantification of historical landscape change on the Fjallsjökull‐Hrútárjökull foreland, SE Iceland. Quantification of volumetric and areal changes on the foreland are based on time series of archival aerial images for the period 1945–1998, high‐resolution satellite imagery for 2014 and digital elevation models of differences derived from time series of UAV imagery for the years 2014, 2016, 2018, 2019 and 2022. Landscape change and glacier snout behaviour since 1945 highlights the importance of azonal and potentially intrazonal signatures in temperate glacial landsystems, particularly: (1) the development and collapse of partially supraglacial outwash fans to produce outwash heads fronting depositional overdeepenings; and (2) the emergence of ice‐cored eskers that record the evolution of englacial drainage networks operating over overdeepenings. Such landform assemblages are manifested as substantial ice‐cored/hummocky terrains, a characteristic of deglaciating forelands that is likely to be widely replicated wherever ice‐contact glacifluvial assemblages create outwash heads that act as depositional overdeepenings. Due to its significantly greater supraglacial debris cover, complete de‐icing of the Hrútárjökull snout in response to post‐1945 warming was delayed until around 2014. This constitutes a prime example of incremental stagnation, which in a rapidly warming climate has resulted in significant landscape change (land surface elevation collapse of 0.8 m a−1) over the last 8 years. [ABSTRACT FROM AUTHOR]

A confluence of concerns about tropical forest loss, global warming, and social inequality drive calls to transform land use governance. Yet there is widespread debate about what must be transformed, by whom, and how. The increasing equation of transformation with ambitious, quantitative global targets, such as "net zero emissions" or "zero deforestation" has gained widespread appeal as a means to inspire action and hold powerful actors to account. However presenting targets themselves as the end goals of transformation, obscures both the means of achieving them and the social and environmental values that legitimate them. The escalation of targets for land use, in particular, is disconnected from targeted geographies, lacks accountability to socially diverse knowledge and priorities, and is readily appropriated by powerful actors at multiple scales. This paper argues instead, for an equity‐based approach to transformation that reveals how unequal power distorts both the ends and the means of global governance. We illustrate this argument with five case‐study "vignettes" in Indonesia, Ghana, Peru, and Brazil that reveal how de‐contextualized, target‐based thinking has reinforced state and corporate control over resources at the expense of local access, while largely failing to deliver the promised environmental outcomes. We conclude that equity‐focused, case study research is critical not only to unpack the local consequences of pursuing global targets, but also to make visible alternative efforts to achieve deeper socio‐environmental transformations. [ABSTRACT FROM AUTHOR]

In this paper, we find new evidence for the carbon futures volatility prediction by using the spillovers of fossil energy futures returns as a powerful predictor. The in‐sample results show that the spillovers have a significantly positive effect on carbon futures volatility. From the out‐of‐sample analysis with various loss functions, we find that fossil energy return spillovers significantly outperform the benchmark and show better forecasting performance than the competing models using dimension reduction, variable selection, and combination approaches. The predictive ability of the spillovers also holds in long‐term forecasting and does not derive from other carbon‐related variables. It can bring substantial economic gains in the portfolio exercise within carbon futures. Finally, we provide economic explanations on the predictive ability of the fossil energy return spillover by the channels of the carbon emission uncertainty and the investor sentiment on the warming climate. [ABSTRACT FROM AUTHOR]

This paper examines the changes of El Niño–Southern Oscillation (ENSO) associated winter rainfall relationship in Southeast Asia (SEA) in the historical period (1981–2010) and future period (2071–2100) based on the Coupled Model Intercomparison Project Phase 6 (CMIP6) models simulations. Twenty general circulation models (GCMs) are chosen from the Scenario Model Intercomparison Project (ScenarioMIP) highest emission scenario—Shared Socioeconomic Pathway (SSP) 5‐8.5. This study focuses only on the GCMs that have been able to replicate the consistent associated patterns observed, referred to as "teleconnection‐consistent models" (TCMs). During historical period, ENSO has significant negative association with SEA winter rainfall, particularly over Indochina mainland, Maritime Continent (MC) and northern Borneo. The significant negative‐associated teleconnection affected area has increased under future climate, particularly over southern part of South China Sea (SCS). The weakening of south westerlies at Western North Pacific (WNP) centred near 20°N reduces the moisture transport from Indochina into south of China, increase the negative correlated affected area in the SEA regions above equator. The northeasterlies/easterlies that prevailed from the WNP anticyclone (AC) brings significant moisture flux into southern Borneo, causing increment of significant positive‐associated moisture flux convergence to that region. The weakening of upward motion in TCM results in less warm and moist air rising over SEA MC, reducing the rainfall availability. The 1° eastward expansion of WC downward branch eastern edge leads to suppression of convective activities, increasing the dry affected area. The results suggest that the ENSO has a larger influence on the negative‐associated SEA rainfall and atmospheric circulation in the December–January–February (DJF) late‐21st century when compared to the 1980–2010. [ABSTRACT FROM AUTHOR]

Summary: The dedication of the international anaesthetic community to reducing the environmental impact of healthcare is important and to be celebrated. When this is underpinned by robust science, it has the potential to make a real difference. However, volatile anaesthetic agents have been widely promoted in the medical literature as damaging to the climate, leading to a drive to remove them from clinical practice. This is based on notional 'CO2‐equivalent' values created using the simple emission metric known as the global warming potential. Here, we assert that when proper consideration is given to the science of climate change, volatile anaesthetic gas emissions cannot be simply equated to real carbon dioxide emissions, and that their climate impact is vanishingly small. This paper gives anaesthetists a framework to make informed choices founded on climate science and calls for attention to be refocused on the urgent need to reduce the real carbon dioxide emissions associated with healthcare. [ABSTRACT FROM AUTHOR]

Climate change (CC) is a complex phenomenon that has the potential to significantly alter marine, terrestrial, and freshwater ecosystems worldwide. Global warming of 2°C is expected to be exceeded during the 21st century, and the frequency of extreme weather events, including floods, storms, droughts, extreme temperatures, and wildfires, has intensified globally over recent decades, differently affecting areas of the world. How CC may impact multiple food safety hazards is increasingly evident, with mycotoxin contamination in particular gaining in prominence. Research focusing on CC effects on mycotoxin contamination in edible crops has developed considerably throughout the years. Therefore, we conducted a comprehensive literature search to collect available studies in the scientific literature published between 2000 and 2023. The selected papers highlighted how warmer temperatures are enabling the migration, introduction, and mounting abundance of thermophilic and thermotolerant fungal species, including those producing mycotoxins. Certain mycotoxigenic fungal species, such as Aspergillus flavus and Fusarium graminearum, are expected to readily acclimatize to new conditions and could become more aggressive pathogens. Furthermore, abiotic stress factors resulting from CC are expected to weaken the resistance of host crops, rendering them more vulnerable to fungal disease outbreaks. Changed interactions of mycotoxigenic fungi are likewise expected, with the effect of influencing the prevalence and co‐occurrence of mycotoxins in the future. Looking ahead, future research should focus on improving predictive modeling, expanding research into different pathosystems, and facilitating the application of effective strategies to mitigate the impact of CC. [ABSTRACT FROM AUTHOR]

As an active ecological agent in the boreal forest, wildfires have many important impacts on the underlying permafrost. However, investigations of post‐fire changes in the features of ecosystem‐protected permafrost are extremely limited in the patchy permafrost regions, Northeast China. Additionally, the impacts of wildfires on patchy permafrost are complicated in this region. In this paper, based on field surveys and observations, permafrost features and their influencing factors were analyzed in the area burned in 2006. The results showed notably lower soil temperatures in shrub‐wetland (NWH1, unburned and NWH2, light burn) in comparison with those in the birch‐larch forest (NWH3, light burn and NWH4, moderate burn). Changes in shallow ground temperatures (0–4 m in depth) were significant. Mean annual soil temperatures (MAST) and annual maximum soil temperature at the light‐burn NWH2 site were 0.3–5.2°C higher than those of unburned NWH1, but annual minimum soil temperatures, 0.3–2.1°C lower. MASTs at the moderately burned NWH4 were 0.6–1.6°C higher than those at the light‐burn NWH3. The active layer thickness (ALT) was greater at burned sites, and; at the moderately burned site, only seasonal frost was found when 13 years after the burn. The contents of soil organic carbon and total nitrogen were the highest at the unburned NWH1 from 0.0 to 0.4 m in depth, followed by those at the lightly‐burned NWH2 and NWH3, and the lowest at the moderately‐burned NWH4. Climate warming, forest fires, and changing local factors (e.g., soil moisture contents, snow cover, soil texture and drainage conditions) worked together to increase ground temperature and ALT. These research results could provide important scientific basis for the protection of boreal forest and wetlands and the ecosystem‐protected permafrost under a warming climate. [ABSTRACT FROM AUTHOR]

Global, continental and regional maps of concentrations, stocks and fluxes of natural resources provide baseline data to assess how ecosystems respond to human disturbance and global warming. They are also used as input to numerous modelling efforts. But these maps suffer from multiple error sources and, hence, it is good practice to report estimates of the associated map uncertainty so that users can evaluate their fitness for use.We explain why quantification of uncertainty of spatial aggregates is more complex than uncertainty quantification at point support because it must account for spatial autocorrelation of the map errors. Unfortunately, this is not done in a number of recent high‐profile studies. We describe how spatial autocorrelation of map errors can be accounted for with block kriging, a method that requires geostatistical expertise. Next, we propose a new, model‐based approach that avoids the numerical complexity of block kriging and is feasible for large‐scale studies where maps are typically made using machine learning. Our approach relies on Monte Carlo integration to derive the uncertainty of the spatial average or total from point support prediction errors. We account for spatial autocorrelation of the map error by geostatistical modelling of the standardized map error.We show that the uncertainty strongly depends on the spatial autocorrelation of the map errors. In a first case study, we used block kriging to show that the uncertainty of the predicted topsoil organic carbon in France decreases when the support increases. In a second case study, we estimated the uncertainty of spatial aggregates of a machine learning map of the above‐ground biomass in Western Africa using Monte Carlo integration. We found that this uncertainty was small because of the weak spatial autocorrelation of the standardized map errors.We present a tool to get realistic estimates of the uncertainty of spatial averages and totals of natural resource maps. The method presented in this paper is essential for parties that need to evaluate whether differences in aggregated environmental variables or natural resources between regions or over time are statistically significant. [ABSTRACT FROM AUTHOR]

Wildland fire is expected to increase in response to global warming, yet little is known about future changes to fire regimes in Europe. Here, we developed a pyrogeography based on statistical fire models to better understand how global warming reshapes fire regimes across the continent. We identified five large‐scale pyroregions with different levels of area burned, fire frequency, intensity, length of fire period, size distribution, and seasonality. All other things being equal, global warming was found to alter the distribution of these pyroregions, with an expansion of the most fire prone pyroregions ranging respectively from 50% to 130% under 2° and 4°C global warming scenarios. Our estimates indicate a strong amplification of fire across parts of southern Europe and a subsequent shift toward new fire regimes, implying substantial socio‐ecological impacts in the absence of mitigation or adaptation measures. Plain Language Summary: Pyroregions represent the typical range of area burned, fire frequency, intensity, and seasonality that prevail in a region over long periods of time. Pyroregions are thus of great interest given their potential utility to determine regional fire patterns and foresee future alterations in response to global warming. Previous research has investigated the effects of global warming focusing on burned area and fire frequency, yet changes in fire regimes are often overlooked. In this paper, we examined the effects of global warming on pyroregions in Europe. We identified five large‐scale pyroregions reflecting different fire regimes. Future climate projections indicated an increase in fire activity and subsequent expansion of fire prone pyroregions across parts of southern Europe under warmer and drier conditions. The most fire prone pyroregions presented a spatial expansion ranging respectively from 50% to 130% under 2° and 4°C global warming scenarios with potential impacts on society. Limiting global warming would substantially reduce the expansion of the fire prone pyroregions in Europe. Key Points: This is the first study to project future changes in fire regimes on a pan‐European scaleOur projections point to an intensification and expansion of the most fire prone pyroregions in southern Europe under a warmer climateLimiting global warming would substantially reduce the expansion of the area at risk and the transition toward more intense fire regimes [ABSTRACT FROM AUTHOR]

Solar energy is the basic source of renewable energy, and it is being used for controlling global pollution/warming. As the Cross nanofluid is very useful for cooling solar devices, in this paper analysis of the global warming effect is investigated by incorporating the nonlinear thermal radiation over the exponentially extendable surface because it plays a major role related to solar energy absorption of nanofluid. Furthermore, the mathematical modeling of Cross nanofluid involving magnetic effect and diffusion is discussed by using the fact of chemical reaction. Chemical reaction finds astonishing applications in pollution studies, chemical processing equipment, and polymer production. As a result of this study, it is noticed that more magnetized conducting fluid controls the motion of fluids for both cases of shear thinning and shear thickening. Brownian motion parameter Nb affects the rate of the random motion of nanoparticles. Increased Nb temperature also increases due to these random movements of nanoparticles. That is the reason why pollutant nanoparticles spread in air as a result of global warming increase. [ABSTRACT FROM AUTHOR]

The appearance of new Arctic islands and straits as the result of landscape transformation due to tidewater glacier recession under climate warming continues. Analysis of available maps and satellite images has served herein as the main research method. Six new islands, each covering an area of 0.4–248 km2, appeared in the years 2018–2021, and are described in this paper. Three other new islands, ranging in area from 0.4 to 3.2 km2, appeared along the Greenland coast in 1997, 2001, and 2013, and are described here, and they have not yet been described elsewhere. Seven of the nine aforementioned new islands appeared in Greenland and the European Arctic, where all earlier 35 new islands and straits had appeared due to ice melting. However, two of these appeared in the Severnaya Zemlya area in 2018, becoming the first new islands in the Asiatic (Siberian) Arctic. Additional new islands are now at an advanced stage of formation, and several will appear in the 2020s in the case of a stabilization of the current climate or further warming. [ABSTRACT FROM AUTHOR]

Black carbon (BC) absorbs solar radiation, increasing the buoyancy and vertical ascent of absorbing aerosol in the atmosphere. This self‐lofting process has been observed for individual plumes in the troposphere and lower stratosphere but here we show it occurring at broader scales through enhanced large‐scale ascent over BC‐rich regions. This is demonstrated in a pair of simulation using the UKESM1 Earth‐System model where BC aerosols were modeled either with or without the ability to absorb radiation. With absorption included the annual global mean concentration of BC in the upper troposphere and lower stratosphere (8–22 km) rose by up to 50% and the column loading over some remote oceanic regions more than doubled. The increase in aerosol height was particularly notable over the southeast Atlantic where biomass burning aerosol from Africa was elevated up to 1 km higher when their absorption was included. Similar effects were seen over the Arctic where the absorbing haze was transported in at higher levels and surface concentrations were halved. The absorption by BC also increased ascent over southern Asia, which tended to thicken the Asian brown cloud during the dry season but in the wet season enhancing ascent promoted deep convection and had the tendency to deplete the aerosol through wash‐out. We conclude that representing aerosol absorption accurately is important in simulating the vertical distribution, transport and abundance of aerosol in the Earth‐system that will affect their interactions with climate. Plain Language Summary: Black carbon is an important component of atmospheric aerosols as it absorbs solar radiation thereby heating the atmosphere and potentially warming climate. The localized heating can also affect clouds and atmospheric motions making the regional and climate effects more complex and uncertain. The vertical distribution and geographic spread of the aerosol is also key to how it interacts with the climate system. In this paper we highlight the fact that the absorption taking place in BC aerosols can affect its vertical ascent in the atmosphere and therefore how it becomes distributed across the globe. This self‐lofting process was demonstrated in a climate model and was particularly important in aiding the elevation and downwind transport of absorbing smoke layers from regions such as central Africa, as well as increasing the amount of BC reaching the upper troposphere and lower stratosphere. Key Points: Absorption of solar radiation helps to raise the altitude of black carbon aerosol by increasing buoyancy and vertical ascentThis self‐lofting mechanism has been captured in a climate model where radiative heating affects the large‐scale ascent and circulationSelf‐lofting increased the altitude and long‐range transport of black carbon to remote regions and the upper troposphere [ABSTRACT FROM AUTHOR]