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Iron oxide assemblages are central to many pursuits, ranging from Mars exploration to environmental remediation. Oxides and oxyhydroxides of iron both carry the special properties of color and magnetism. In this paper, we use visible-light spectroscopy and rock magnetic data collected at varying temperatures (~77–973 K) to analyze the concentrations and identities of iron oxides found in natural hematite-dominated samples that were obtained from a scientific drill core of Late Triassic red beds in the American Southwest. Our results suggest that hematite colorization of Earth materials varies from red to blue/purple as crystal size increases. Second-derivative analysis of the collected visible-light spectra allows this variation to be measured through the characteristic wavelength band position. Magnetic coercivity data indicate "hardness" differences that also may suggest smaller grain sizes are associated with redder colors. Yellowish maghemite and goethite have overlapping characteristic wavelength band positions that make it challenging to distinguish their contributions to mixed assemblages from visible-light data alone. Remanent magnetizations acquired at ~77 K and room temperature suggest the presence of hematite and a low-coercivity phase that may be maghemite and/or oxidized magnetite. However, we interpret this phase as maghemite in order to explain the changes in iron oxide concentrations indicated by visible-light intensities near ~425 nm and because the thermal demagnetization data suggest that goethite is absent from the samples. Future research that increases the resolution of hematite, maghemite, and goethite detection in experimental and natural samples will provide opportunities to refine the study of past climates and constrain soil iron availability under future changes in global moisture and temperature. Multimethod datasets improve understanding of environmental conditions that cause iron oxides assemblages to shift in phase dominance, grain size, and crystallinity. [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]

Copyright of Herald of Journalism / Habaršy Žurnalistika Seriâsy is the property of Al-Farabi Kazakh National University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Drawing on ecological economics, post-development studies, and political ecology, this paper argues that sustainable development notions have run their course within sustainability marketing debates and proposes degrowth as an alternative framework to steer disciplinary debates in new directions. We chart unexplored territory, offering sustainability marketing scholars tools to navigate degrowth-minded policies, transformative frameworks, and business models. In doing so, our work contributes to existing sustainability marketing debates in three ways: first, we respond to the paucity of studies engaging with the political economy of sustainability marketing. Second, we make visible the tensions and contradictions that arise as marketers seek to reconcile imperatives of economic growth and sustainability. Finally, we foreground degrowth as an emerging sustainability proposition, with potential for inspiring the radical set of transformations required to avert catastrophic climate change and keep global temperatures well below +2°C (relative to pre-industrial levels), as pledged in the Paris Agreement. [ABSTRACT FROM AUTHOR]

We thank the authors (hereafter referred to as CCC) for providing us an opportunity to clarify some points of our original paper. CCC list in their abstract three "key points"that we respond to in this Reply. The first comment is the central one and the most developed. It deals mainly with discussion of features of methods of spectral analysis, mainly SSA. We have quoted the sub‐parts of that comment as items 1a to 1k. The replies to comments/key points 2 and 3 are shorter. We disagree with most of the comments by CCC and explain why. We conclude that we have successfully countered CCC's criticism and shown many of their points to be unsubstantiated. The main problem seems to reside in differences concerning the literature on Singular Spectral Analysis and our use of it. Much of our response to the comments can be found in textbooks and review papers on SSA and time series analysis; we quote extensively, both in our original paper and in this response to CCC, Golyandina and Zhigljavsky (2013). Key Points: We find compelling results showing a significant co‐variability between sunspot numbers and global temperature.We do not have a mechanism to explain that forcing: it remains to be found. We do not discuss further the parts of CCC on mechanisms.The trends of temperature and sunspots (first SSA component) are similar; the second component results in an opposite trend in the last 30 years. [ABSTRACT FROM AUTHOR]

Copyright of Urban Academy/ Kent Akademisi is the property of ICAM NETWORK and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

While numerous studies focus on homogeneity in increasing the utility value of a system, its feasibility and practicality are inconclusive; even with the possible limitations of a homogeneous approach, the potential of promoting heterogeneity is still overlooked. This paper reports that the use of partitions leads to the making of controlled thermal heterogeneity, where variance in flow direction and temperature is measured in terms of energy and mass imbalance. Moreover, through a qualitative analysis using Particle Image Velocimetry (PIV) and a quantitative analysis via a soft-sensing technique, the percentage change in temperature was shown to decrease from 58.0 to 21.8 % after four rounds of residence cycle. This gradual stabilization indicates the system's advancement towards a steady-state, where a maximum of 1.0 K temperature difference is observed to be retained. The novel approach of controlling the inner state of a space using partitions allows a single space to have multiple zones that each demonstrate appropriate conditions for a unique demand; thus improving the system's efficiency in terms of energy, space and time. This concept of heightening the system's performance via the promotion of heterogeneity using partitions is applicable in, but not restricted to, the industrial sector where thermal heterogeneity in a reactor can effectively cater to the simultaneous production of multiple outputs from one vessel. [ABSTRACT FROM AUTHOR]

In investigating global patterns of biodiversity through deep time, many large-scale drivers of diversification have been proposed, both biotic and abiotic. However, few robust conclusions about these hypothesized effectors or their roles have been drawn. Here, we use a linear stochastic differential equation (SDE) framework to test for the presence of underlying drivers of diversification patterns before examining specific hypothesized drivers. Using a global dataset of observations of skeletonized marine fossils, we infer origination, extinction and sampling rates (collectively called fossil time series) throughout the Phanerozoic using a capture–mark–recapture approach. Using linear SDEs, we then compare models including and excluding hidden (i.e. unmeasured) drivers of these fossil time series. We find evidence of large-scale underlying drivers of marine Phanerozoic diversification rates and present quantitative characterizations of these. We then test whether changing global temperature, sea-level, marine sediment area or continental fragmentation could act as drivers of the fossil time series. We show that it is unlikely any of these four abiotic factors are the hidden drivers we identified, though there is evidence for correlative links between sediment area and origination/extinction rates. Our characterization of the hidden drivers of Phanerozoic diversification and sampling will aid in the search for their ultimate identities. [ABSTRACT FROM AUTHOR]

Maintaining high-quality welded connections is crucial in many industries. One of the challenges is assessing the mechanical properties of a joint during its production phase. Currently, in industrial practice, this occurs through NDT (non-destructive testing) conducted after the production process. This article proposes the use of a virtual sensor, which, based on temperature distributions observed on the joint surface during the welding process, allows for the determination of hardness distribution across the cross-section of a joint. Welding trials were conducted with temperature recording, hardness measurements were taken, and then, neural networks with different hyperparameters were tested and evaluated. As a basis for developing a virtual sensor, LSTM networks were utilized, which can be applied to time series prediction, as in the analyzed case of hardness value sequences across the cross-section of a welded joint. Through the analysis of the obtained results, it was determined that the developed virtual sensor can be applied to predict global temperature changes in the weld area, in terms of both its value and geometry changes, with the mean average error being less than 20 HV (mean for model ~35 HV). However, in its current form, predicting local hardness disturbances resulting from process instabilities and defects is not feasible. [ABSTRACT FROM AUTHOR]

Quantitative constraints on past mean ocean temperature (MOT) critically inform our historical understanding of Earth's energy balance. A recently developed MOT proxy based on paleoatmospheric Xe, Kr, and N2 ratios in ice core air bubbles is a promising tool rooted in the temperature dependences of gas solubilities. However, these inert gases are systematically undersaturated in the modern ocean interior, and it remains unclear how air‐sea disequilibrium may have changed in the past. Here, we carry out 30 tracer‐enabled model simulations under varying circulation, sea ice cover, and wind stress regimes to evaluate air‐sea disequilibrium in the Last Glacial Maximum (LGM) ocean. We find that undersaturation of all three gases was likely reduced, primarily due to strengthened high‐latitude winds, biasing reconstructed MOT by −0.38 ± 0.37°C (1σ). Accounting for air‐sea disequilibrium, paleoatmospheric inert gases indicate that LGM MOT was 2.27 ± 0.46°C (1σ) colder than the pre‐industrial era. Plain Language Summary: The ocean plays a central role in Earth's climate system as a major reservoir of heat. Understanding how ocean heat content (OHC) changed in the past is therefore key to unraveling the history of global climate. Xenon, krypton, and nitrogen trapped in ice core air bubbles offer a means of reconstructing past OHC, because changes in global ocean temperature affect the solubilities of these gases in seawater, leading to corresponding changes in their atmospheric abundances. For example, a colder ocean can hold more xenon, meaning less xenon resides in the atmosphere. However, these gases in the ocean today are slightly out of equilibrium with the atmosphere (i.e., they are undersaturated), and it remains unclear to what extent this disequilibrium could have changed in the past. We carried out global atmosphere‐ocean model simulations, finding that undersaturation was likely reduced in the Last Glacial Maximum (LGM), a colder era of global climate ∼20,000 years ago. Our analysis suggests that a small component of the additional xenon in the colder LGM ocean arose from this change in air‐sea disequilibrium. After accounting for this effect, ice core noble gas measurements suggest a slightly warmer LGM ocean than previously thought. Key Points: Global ocean air‐sea gas exchange simulations suggest reduced undersaturation of inert gases in the Last Glacial Maximum (LGM) oceanReduced LGM undersaturation indicates a small cold bias in mean ocean temperature (MOT) reconstruction from ice core noble gasesA revised estimate of MOT, accounting for air‐sea disequilibrium, is 2.27 ± 0.46°C colder than the pre‐industrial era [ABSTRACT FROM AUTHOR]

Climate change has been a significant threat to sustainable agriculture impacting trade and food security. This research investigates the impacts of climate change on agricultural trade and food security in emerging economies focusing on Southern Africa. The research employed the dynamic panel Generalised Method of Moments (GMM) using panel data for the period 2012 to 2021 obtained from a sample of 12 Southern African countries selected based on data availability. The main independent variables to the research were climate change variables (temperature and precipitation). Other control variables included were population growth, food inflation and agricultural growth. The study found significant negative effects of climate change on agriculture trade. Whilst temperature changes were found to have insignificant effects, precipitation changes were found to have significant positive effects. The research recommends concerted efforts towards climate change adaptation and mitigation for sustainable agriculture. Based on the Findings, sustainable food security and agricultural trade Southern African emerging economies may be attained through effective climate change mitigation and adaptation approaches particularly promoting climate-smart agriculture (CSA). [ABSTRACT FROM AUTHOR]

The Great Plains of North America host a stark climatic gradient, separating the humid and well-watered eastern US from the semi-arid and arid western US, and this gradient shapes the region's water availability, its ecosystems, and its economies. This climatic boundary is largely set by the influence of two competing atmospheric circulation systems that meet over the Great Plains – the wintertime westerlies bring dominantly dry air that gives way to moist, southerly air transported by the Great Plains low-level jet in the warmer months. Climate model simulations suggest that, as CO 2 rises, this low-level jet will strengthen, leading to greater precipitation in the spring but less in the summer and, thus, no change in mean annual precipitation. Combined with rising temperatures that will increase potential evapotranspiration, semi-arid conditions will shift eastward, with potentially large consequences for the ecosystems and inhabitants of the Great Plains. We examine how hydroclimate in the Great Plains varied in the past in response to warmer global climate by studying the paleoclimate record within the Ogallala Formation, which underlies nearly the entire Great Plains and provides a spatially resolved record of hydroclimate during the globally warmer late Miocene. We use the stable isotopes of oxygen (δ18 O) as preserved in authigenic carbonates hosted within the abundant paleosol and fluvial successions that comprise the Ogallala Formation as a record of past hydroclimate. Today, and coincident with the modern aridity gradient, there is a sharp meteoric water δ18 O gradient with high (-6 ‰ to 0 ‰) δ18 O in the southern Great Plains and low (-12 ‰ to -18 ‰) δ18 O in the northern plains. We find that the spatial pattern of reconstructed late Miocene precipitation δ18 O is indistinguishable from the spatial pattern of modern meteoric water δ18 O. We use a recently developed vapor transport model to demonstrate that this δ18 O spatial pattern requires air mass mixing over the Great Plains between dry westerly and moist southerly air masses in the late Miocene – consistent with today. Our results suggest that the spatial extents of these two atmospheric circulation systems have been largely unchanged since the late Miocene and any strengthening of the Great Plains low-level jet in response to warming has been isotopically masked by proportional increases in westerly moisture delivery. Our results hold implications for the sensitivity of Great Plains climate to changes in global temperature and CO 2 and also for our understanding of the processes that drove Ogallala Formation deposition in the late Miocene. [ABSTRACT FROM AUTHOR]

Instrumental data from the pre–Industrial Revolution period are important to -understand climate change. In this paper, the observations made by the French missionary J. Amiot in present-day central Beijing during 1757–62 were processed and analyzed. The observations represent the earliest continuous dataset of meteorological records found in China that have been digitized recently. Comparisons between the Amiot annual temperature range and extreme values with modern observations showed that the observations were read at approximately 0800 and 1500 local solar time (LST) in a well-ventilated outdoor site. The daily maximum, minimum, and mean temperatures (T-max, T-min, and T-mean, respectively) during 1757–62 were determined by examining the relationship between temperature at 0800 and 1500 LST and T-max, T-min, and T-mean in modern reference series. Nearly 260 years ago, Beijing's climate was typical of an inland temperate monsoon zone with annual T-mean, annual mean T-max, and annual mean T-min being 11.5°, 17.8°, and 6.1°C, respectively; further, the temperatures did not vary considerably from the 1951–1980 temperatures, but differed evidently compared to relatively recent decades (1981–2020). The difference was larger than the magnitudes of global and regional temperature changes. Thus, climate warming since the pre–Industrial Revolution period in the urban areas of Beijing has dominantly occurred over the last four decades. Uncertainties related to the thermometer and observational conditions 260 years ago and the interpolation method used have also been discussed in this paper. [ABSTRACT FROM AUTHOR]

Understanding seasonal climatic conditions is critical for better management of resources such as water, energy, and agriculture. Recently, there has been a great interest in utilizing the power of Artificial Intelligence (AI) methods in climate studies. This paper presents cutting-edge deep-learning models (UNet++, ResNet, PSPNet, and DeepLabv3) trained by state-of-the-art global CMIP6 models to forecast global temperatures a month ahead using the ERA5 reanalysis dataset. ERA5 dataset was also used for fine-tuning as well performance analysis in the validation dataset. Ten different setups (with CMIP6 and CMIP6 + ERA5 fine-tuning) including six meteorological parameters (i.e., 2 m temperature, 10 m eastward component of wind, 10 m northward component of wind, geopotential height at 500 hPa, mean sea-level pressure, and precipitation flux) and elevation were used with both four different algorithms. For each model 14 different sequential and nonsequential temporal settings were used. The mean absolute error (MAE) analysis revealed that UNet++ with CMIP6 with 2 m temperature + elevation and ERA5 fine-tuning model with “Year 3 Month 2” temporal case provided the best outcome with an MAE of 0.7. Regression analysis over the validation dataset between the ERA5 data values and the corresponding AI model predictions revealed slope and R² values close to 1 suggesting a very good agreement. The AI model predicts significantly better than the mean CMIP6 ensemble between 2016 and 2021. Both models predict the summer months more accurately than the winter months. [ABSTRACT FROM AUTHOR]

The continued and envisioned large-scale integration of renewable energy sources as a reaction to rising global temperatures and climate change will need a readily available DC grid to increase commissioning and operating efficiency. The effective operation of these grids is predicated on the correct control of its main control points. A plethora of DC-DC converters that find use in DC microgrids act as the main control points. DC-DC converters are non-linear and can operate in different modes with completely unique characteristics. To utilise classical control techniques, laborious equivalent linear models are derived for DC-DC converters using averaging modelling schemes. The application and limitations of these modelling techniques are well captured in the available literature. The most common limitation of the available modelling schemes is that more focus is dedicated to converter attributes like order, functionality and operating mode, even when optimal power flow and voltage regulation within the DC network are of more interest. Structure-based modelling techniques like the use of basic building blocks nullify converter attributes in the modelling process which translates to modelling efficiency. In light of the merits seen with the use of basic building blocks when modelling converters in CCM, the current study extends these merits to converters operation in DCM. Similar to modelling converters in CCM, modelling techniques that are available in the literature continue to consider converter attributes in the modelling process for DCM operation. Moreover, the two modes of operation are treated as unique entities and often modelled in a non-unified manner, which compromise modelling efficiency since the same converter can operate in a different state solely based on loading. The aim is to increase modelling efficiency but also nullify operating mode in the modelling process. The same basic building blocks are now modelled as two-port networks for DCM operation and adopted based on the exact configuration of a specified converter to compute its steady-state and dynamic models. All the advantages seen when modelling converters in CCM using basic building blocks are retained and augmented when considering DCM operation. Thus, any converter with well-defined basic building blocks can be easily modelled solely based on the connection of constituent basic building blocks. [ABSTRACT FROM AUTHOR]

The Fennoscandian boreal and mountain regions harbour a wide range of vegetation types, from boreal forest to high alpine tundra and barren soils. The area is facing a rise in air temperature above the global average and changes in temperature and precipitation patterns. This is expected to alter the Fennoscandian vegetation composition and change the conditions for areal land use such as forestry, tourism and reindeer husbandry. In this study we used a unique high-resolution (3 km) climate scenario with considerable warming resulting from strongly increasing carbon dioxide emissions to investigate how climate change can alter the vegetation composition, biodiversity and availability of suitable reindeer forage. Using a dynamical vegetation model, including a new implementation of potential reindeer grazing, resulted in simulated vegetation maps of unprecedented high resolution for such a long time period and spatial extent. The results were evaluated at the local scale using vegetation inventories and for the whole area against satellite-based vegetation maps. A deeper analysis of vegetation shifts related to statistics of threatened species was performed in six "hotspot" areas containing records of rare and threatened species. In this high-emission scenario, the simulations show dramatic shifts in the vegetation composition, accelerating at the end of the century. Alarmingly, the results suggest the southern mountain alpine region in Sweden will be completely covered by forests at the end of the 21st century, making preservation of many rare and threatened species impossible. In the northern alpine regions, most vegetation types will persist but shift to higher elevations with reduced areal extent, endangering vulnerable species. Simulated potential for reindeer grazing indicates latitudinal differences, with higher potential in the south in the current climate. In the future these differences will diminish, as the potentials will increase in the north, especially for the summer grazing grounds. These combined results suggest significant shifts in vegetation composition over the present century for this scenario, with large implications for nature conservation, reindeer husbandry and forestry. [ABSTRACT FROM AUTHOR]

The temporal relationship between global glaciations and the Great Oxidation Event (GOE) suggests that climate change played an important role in Earth's oxygenation. The potential role of temperature is captured by the stratigraphic proximity between glacial deposits and sediments containing mass‐independent fractionation of sulfur isotopes (MIF‐S). We use a time‐dependent one‐dimensional photochemical model to investigate whether temperature changes associated with global glaciations can drive oscillations in atmospheric O2 levels and MIF‐S production across the GOE. We find that extreme climate change can cause atmospheric O2 to oscillate between pre (<10−6 times the present atmospheric level, PAL) and post‐GOE (>10−5 PAL) levels. Post‐glacial hot‐moist greenhouse climates lead to post‐GOE O2 levels because the abundant H2O vapor and oxidizing radicals drive the depletion of reduced species. This pattern is generally consistent with the MIF‐S signal observed in the sedimentary record, suggesting a link between global glaciations and O2 oscillations across the GOE. Plain Language Summary: The Great Oxidation Event was the most significant environmental and chemical transformation in Earth's history, marking the first time oxygen accumulated in the atmosphere around 2.4 billion years ago. Oxygen increased from below one millionth (low) to at least one‐thousandth of a percent (intermediate) of the present oxygen concentration during this event. However, measurements of geochemical oxygen proxies suggest that oxygen levels oscillated between low and intermediate levels before stabilizing after this event. The first rise of atmospheric oxygen occurred during a period of extreme climate variability indicated by the presence of glacial rock deposits around this time. In this study, we use a time‐dependent photochemical model to show that extreme temperature changes caused by global glaciations can drive oscillations in atmospheric oxygen levels across the Great Oxidation Event (GOE). Our results can help explain why atmospheric oxygen shows drastic changes across the GOE in a way that is consistent with the geochemical record. Key Points: Across the Great Oxidation Event (GOE), extreme climate change linked to global glaciations can drive oscillations in atmospheric O2 levels and mass‐independent fractionation of sulfur isotopes (MIF‐S) productionGlacial climates and hot‐moist greenhouse climates were likely characterized by pre‐GOE and post‐GOE O2 levels respectivelyTemperature changes associated with global glaciations can help explain the MIF‐S record across the GOE [ABSTRACT FROM AUTHOR]

Organisms are experiencing higher average temperatures and greater temperature variability because of anthropogenic climate change. Some populations respond to changes in temperature by shifting their ranges or adjusting their phenotypes via plasticity and/or evolution, while others go extinct. Predicting how populations will respond to temperature changes is challenging because extreme and unpredictable climate changes will exert novel selective pressures. For this reason, there is a need to understand the physiological mechanisms that regulate organismal responses to temperature changes. In vertebrates, glucocorticoid hormones mediate physiological and behavioral responses to environmental stressors and thus are likely to play an important role in how vertebrates respond to global temperature changes. Glucocorticoids have cascading effects that influence the phenotype and fitness of individuals, and some of these effects can be transmitted to offspring via trans- or intergenerational effects. Consequently, glucocorticoid-mediated responses could affect populations and could even be a powerful driver of rapid evolutionary change. Here, we present a conceptual framework that outlines how temperature changes due to global climate change could affect population persistence via glucocorticoid responses within and across generations (via epigenetic modifications). We briefly review glucocorticoid physiology, the interactions between environmental temperatures and glucocorticoid responses, and the phenotypic consequences of glucocorticoid responses within and across generations. We then discuss possible hypotheses for how glucocorticoid-mediated phenotypic effects might impact fitness and population persistence via evolutionary change. Finally, we pose pressing questions to guide future research. Understanding the physiological mechanisms that underpin the responses of vertebrates to elevated temperatures will help predict population-level responses to the changing climates we are experiencing. [ABSTRACT FROM AUTHOR]

Bioenergetics models estimate ectotherm growth, production, and prey consumption – all key for effective ecosystem management during changing global temperatures. Based on species‐specific allometric and thermodynamic relationships, these models typically use the species' lab‐derived optimum temperatures (physiological optimum) as opposed to empirical field data (realized thermal niche) that reflect actual thermal experience. Yet, dynamic behavioral thermoregulation mediated by biotic and abiotic interactions may provide substantial divergence between physiological optimum and realized thermal niche temperatures to significantly bias model outcomes. Here, using the Wisconsin bioenergetics model and in‐situ year‐round temperature data, we tested the two approaches and compared the maximum attainable lifetime weight and lifetime prey consumption estimates for two salmonid species with differing life histories. We demonstrate that using the realized thermal niche is the better approach because it eliminates significant biases in estimates produced by the physiological optimum. Specifically, using the physiological optimum, slower‐growing Salvelinus namaycush maximum attainable lifetime weight was underestimated, and consumption overestimated, while fast‐growing Oncorhynchus tshawytscha maximum attainable weight was overestimated. While the physiological optimum approach is useful for theoretical studies, our results demonstrate the critical importance that models used by management utilize up‐to‐date system‐ and species‐specific field data representing actual in‐situ behaviors (i.e., realized thermal niche). [ABSTRACT FROM AUTHOR]

The Report: Intergovernmental Panel on Climate Change (IPCC) set a goal global warming of 1.5°C on global temperature change. Extreme climate changes have increased the demand for electricity consumption by people and enterprises. In fact, China's total power generation in 2019 exceeded 25% of the world's amount, and its thermal power generation accounted for more than 70%. Although past research on electricity efficiency seldom discusses the issue of climate change, the topic still remains important. This research thus considers extreme temperature days (climate change variable) as exogenous variable and uses the Two-Stage Meta Under exogenous undesirable EBM model to examine power efficiency in China. The results are as follows. (1) In the west only Qinghai's GDP, CO2, PM2.5, and electricity consumption have technology gap ratio (TGR) efficiency values of 1 in the 5 years. (2) China's electricity consumption has the same trend with the TGR efficiency of CO2 and is higher than PM2.5. (3) The national overall efficiency, meta overall efficiency, and TGR overall efficiency are the worst in China's west region. [ABSTRACT FROM AUTHOR]

Urgent emission reduction and community adaptation efforts are necessary to avert catastrophic climate-change harms. To assess our nation's progress toward such efforts, this Article develops a comprehensive structural analysis of U.S. climate policy at the federal, state, and local levels. It observes that current climate policies reflect disparate federal, state, and local strategies around emissions regulation, emission reduction subsidies, adaptation, and liability approaches. The Article then analyzes the dynamics between federal, state, and local strategies in these policy areas. This examination leads to some surprising conclusions. Under current policy alignments, further emission regulation measures do not appear to be realistic policy options. Though such regulatory measures have long been considered the most efficient climate interventions, this analysis suggests they have little near-term prospect for further deployment. Rather, current dynamics among the states and federal government indicate that previously second- and third-choice policies, like subsidies and liability measures, have greater potential for expansion. Thus, these less-favored policy approaches may represent the best hopes for pressing emission reduction efforts. Further, the analysis suggests that while most climate adaptation policy is implemented at the local level, federal adaptation policies require the more immediate attention. Because federal adaptation policies reflect a deferential funding strategy, where the federal government attempts to match support with state and local policy preferences, altering federal programs to better recognize state and local choices will enhance adaptation efforts at all levels of government. [ABSTRACT FROM AUTHOR]

Projections of precipitation extremes over land are crucial for socioeconomic risk assessments, yet model discrepancies limit their application. Here we use a pattern-filtering technique to identify low-frequency changes in individual members of a multimodel ensemble to assess discrepancies across models in the projected pattern and magnitude of change. Specifically, we apply low-frequency component analysis (LFCA) to the intensity and frequency of daily precipitation extremes over land in 21 CMIP-6 models. LFCA brings modest but statistically significant improvements in the agreement between models in the spatial pattern of projected change, particularly in scenarios with weak greenhouse forcing. Moreover, we show that LFCA facilitates a robust identification of the rates at which increasing precipitation extremes scale with global temperature change within individual ensemble members. While these rates approximately match expectations from the Clausius-Clapeyron relation on average across models, individual models exhibit considerable and significant differences. Monte Carlo simulations indicate that these differences contribute to uncertainty in the magnitude of projected change at least as much as differences in the climate sensitivity. Last, we compare these scaling rates with those identified from observational products, demonstrating that virtually all climate models significantly underestimate the rates at which increases in precipitation extremes have scaled with global temperatures historically. Constraining projections with observations therefore amplifies the projected intensification of precipitation extremes as well as reducing the relative error of their distribution. [ABSTRACT FROM AUTHOR]

Cement is one of the highest energy-consuming and emission generating industries around the world. To reduce greenhouse emissions, several mitigation measures have been proposed, and their effectiveness is estimated. However, estimates of the global temperature change potential of the cement industry have seldom been performed. Hence, in this study, we propose a new framework that estimates CO2 emissions and other seven pollutants to estimate temperature change potential from the cement industry. The underlying framework uses system dynamics, where the effectiveness of four mitigation measures, i.e., a shift in demand, newer methodologies to produce clinker, use of energy efficiency improvements, and implementation of renewable energy, are explored. The results indicate that renewable sources of energy show highest mitigation potential. The cement industry has contributed to an increase in 2 mK temperature since 1990, which is likely to grow up to 14.8 mK by 2050 if no mitigation measures are applied. Energy efficiency improvements by extensions of perform achieve and trade scheme can reduce 0.33 mK from the Indian cement industry. This paper provides a unique opportunity for estimating temperature influence of the cement industry, which can be further implemented for other countries. [ABSTRACT FROM AUTHOR]

An increase in energy demand and consumption is one of the significant challenges of the world community. Global climate change and temperature rise can significantly affect energy demand, especially in the building sector. Green passive design strategies (GPDS) such as green roof and green wall are considered a passive energy-saving technology which can deal with further climate change in near future. This paper compares the energy demand and CO2 emissions of a building with different structural scenarios during the current (2000–2019) and future climatic conditions (the 2050s) in two hot-dry (Kerman) and hot-humid (BandarAbbas) climate samples in Iran. The base case, green roof, and green wall modeling of the selected building have been developed by DesignBuilder software. Results revealed that 61% of the annual energy consumption of Kerman is related to the heating sector, while it will be changed to 47% under the effect of climate change and based on RCP2.6. However, 99% of the annual energy consumption of BandarAbbas belongs to cooling demand and it will not change by 2050s. Also, the maximum heating and cooling energy demand were calculated for the base building. Based on the results, green wall has more efficiency in optimizing total energy consumption compared to green roof in both climate types. On the other hand, GPDS are more efficient to optimize heating energy demand in comparison with cooling energy demand. Furthermore, the green wall strategy has better performance in reducing CO2 emissions as well. Accordingly, CO2 emissions reduce in Kerman by 2.73% and 2.93% by the implementation of the green wall during the observation period and 2050s, respectively. Meanwhile, this strategy can reduce CO2 emissions by only 1% per year in BandarAbbas during all studied periods. [ABSTRACT FROM AUTHOR]

This paper is an introduction to the new package in R called smoots (smoothing time series), developed for data-driven local polynomial smoothing of trend-stationary time series. Functions for data-driven estimation of the first and second derivatives of the trend are also built-in. It is first applied to monthly changes of the global temperature. The quarterly US-GDP series shows that this package can also be well applied to a semiparametric multiplicative component model for non-negative time series via the log-transformation. Furthermore, we introduced a semiparametric Log-GARCH and a semiparametric Log-ACD model, which can be easily estimated by the smoots package. Of course, this package applies to suitable time series from any other research area. The smoots package also provides a useful tool for teaching time series analysis, because many practical time series follow an additive or a multiplicative component model. [ABSTRACT FROM AUTHOR]

This paper aims to better understand why there was a global warming pause in 2000–2015 and why the global mean surface temperature (GMST) has risen again in recent years. We present and statistically analyze substantial time-series observed datasets of global lower-stratospheric temperature (GLST), troposphere–stratosphere temperature climatology, global land surface air temperature, GMST, sea ice extent (SIE) and snow cover extent (SCE), combined with modeled calculations of GLSTs and GMSTs. The observed and analyzed results show that GLST/SCE has stabilized since the mid-1990s with no significant change over the past two and a half decades. Upper-stratospheric warming at high latitudes has been observed and GMST or global land surface air temperature has reached a plateau since the mid-2000s with the removal of natural effects. In marked contrast, continued drastic warmings at the coasts of polar regions (particularly Russia and Alaska) are observed and well explained by the sea-ice-loss warming amplification mechanism. The calculated GMSTs by the parameter-free quantum-physics warming model of halogenated greenhouse gases (GHGs) show excellent agreement with the observed GMSTs after the natural El Niño southern oscillation and volcanic effects are removed. These results have provided strong evidence for the dominant warming mechanism of anthropogenic halogenated GHGs. The results also call for closer scrutiny of the assumptions made in current climate models. [ABSTRACT FROM AUTHOR]

This paper documents the experimental setup and general features of the coupled historical and future climate simulations with the first version of the US Department of Energy (DOE) Energy Exascale Earth System Model (E3SMv1.0). The future projected climate characteristics of E3SMv1.0 at the highest emission scenario (SSP5-8.5) designed in the Scenario Model Intercomparison Project (ScenarioMIP) and the SSP5-8.5 greenhouse gas (GHG) only forcing experiment are analyzed with a focus on regional responses of atmosphere, ocean, sea ice, and land. Due to its high equilibrium climate sensitivity (ECS of 5.3 K), E3SMv1.0 is one of the Coupled Model Intercomparison Project phase 6 (CMIP6) models with the largest surface warming by the end of the 21st century under the high-emission SSP5-8.5 scenario. The global mean precipitation change is highly correlated with the global temperature change, while the spatial pattern of the change in runoff is consistent with the precipitation changes. The oceanic mixed layer generally shoals throughout the global ocean. The annual mean Atlantic meridional overturning circulation (AMOC) is overly weak with a slower change from ∼11 to ∼6 Sv (Sverdrup) relative to other CMIP6 models. The sea ice, especially in the Northern Hemisphere, decreases rapidly with large seasonal variability. We detect a significant polar amplification in E3SMv1.0 from the atmosphere, ocean, and sea ice. Comparing the SSP5-8.5 all-forcing experiment with the GHG-only experiment, we find that the unmasking of the aerosol effects due to the decline of the aerosol loading in the future projection period causes transient accelerated warming in the all-forcing experiment in the first half of the 21st century. While the oceanic climate response is mainly controlled by the GHG forcing, the land runoff response is impacted primarily by forcings other than GHG over certain regions, e.g., southern North America, southern Africa, central Africa, and eastern Asia. However, the importance of the GHG forcing on the land runoff changes grows in the future climate projection period compared to the historical period. [ABSTRACT FROM AUTHOR]

Over the last century, the global mean surface air temperature (SAT) has experienced two periods of warming slowdown (hiatuses), namely 1940–1975 and 1998–2012, as well as showing well-defined interdecadal oscillations. Previous studies have focused mainly on the most recent hiatus, and little is known about the period between 1940 and 1975. From the point of view of the sea surface temperature (SST), there are two aspects of interest; i.e., the climatological SST and SST variability. In this paper, observational and modelling evidence is used to show that, compared with the climatological SST, SST variability has been the main cause of the slowdown in rate of increase in SAT since 1940. In addition, the observational data and simulation results show that SST variability had a greater impact on the slowdown in rate of increase in SAT from 1940 to 1975 (− 1.2 × 10−3 °C/year) than from 1998 to 2012 (− 5.7 × 10−3 °C/year). The SAT change over the period 1940–1975 (1.0 × 10−4 °C/year) was less affected by the climatological SST forcing experiment than that over the period 1998–2012 (− 5.0 × 10−4 °C/year). Comparing with 1940–1975, the SAT change over the period 1998–2012 was much affected by the global SAT long-term warming. The distributions of wind stress and atmospheric pressure both indicate that, although the eastern Pacific Ocean played an important role in influencing the global SAT trend between 1998 and 2012, it made little contribution to changes in global SAT between 1940 and 1975. In addition, from the perspective of seasonality, the interdecadal variation of SAT over these two periods was a seasonally dependent phenomenon. Over the period 1940–1975, the annual SAT trend essentially followed the summer SAT trend, whereas between 1998 and 2012, winter was the dominant season of annual SAT change. [ABSTRACT FROM AUTHOR]

Background: The Paris Agreement aims at minimizing threats of climate change by keeping global temperature rise well below 2 degrees Celsius above the pre-industrial level and to pursue efforts to limit the rise to 1.5 degrees Celsius. The Representative Concentration Pathways (RCPs) are developed to investigate GHG emission pathways. RCP2.6 focuses on limiting the global temperature rise to less than 2 degrees Celsius. This paper assesses the impacts of carbon price and CCS on energy and GHG emissions in Thailand. The no carbon price (T0) and the carbon price pathways are compared. In addition, the net-zero emissions and year are discussed. Results: The decarbonized energy system with low-carbon power generation and increased electricity usage in the final energy consumption is the main pillar of GHG mitigation. Imposing carbon prices; increasing solar, wind, and biomass electricity generation; energy efficiency improvements in power generation; and energy savings in the industry and the building sectors, will be the key options for clean power generation in the carbon prices (CT) scenarios. Renewable electricity, coal and natural gas, coupled with CCS and bio-energy with CCS (BECCS) will be utilized significantly to curb GHG emissions. The increase of renewable energy and the electrification of end-use plays a key role in reducing GHG emissions. Fuel switching from diesel to biodiesel, energy efficiency improvement and electric pick-ups and trucks will help reducing GHG emissions in the transport sector. Conclusions: There are three major policy implications to meet Thailand's 2 degrees Celsius target. First, carbon prices will be the mechanism to accelerate the transformation in the energy sector. Wind and solar electricity will be key pillars of clean electricity in 2050. Policy-makers should update the renewable electricity plans to meet Thailand's 2 degrees Celsius target in 2050. Second, coal- and gas-fired plants, and BECCS will become important options in reducing CO2 emissions. The policy-makers should investigate the application of CCS in the power sector and the storage location. Third, a major transformation in the transport sector is critically needed. Liquid biofuel and electrification in pick-ups, sedans, and trucks will help reduce GHG emissions. [ABSTRACT FROM AUTHOR]

Theory indicates that tropical cyclone (TC) intensity should respond to environmental temperature changes near the surface and in the TC outflow layer. While the sensitivity of TC intensity to sea surface temperature is well understood, less is known about the role of upper-level stratification. In this paper, we combine historical data analysis and idealised modelling to explore the extent to which historical low-level warming and upper-level stratification can explain observed trends in the TC intensity distribution. Observations and modelling agree that historical global environmental temperature changes coincide with higher lifetime maximum intensities. Observations suggest the response depends on the TC intensity itself. Hurricane-strength storms have intensified at twice the rate of weaker storms per unit surface and upper-tropospheric warming, and we find faster warming of low-level temperatures in hurricane environments than the tropical mean. Idealised simulations respond in the expected sense to various imposed changes in the near-surface temperature and upper-level stratification representing present-day and end-of-century thermal profiles and agree with TCs operating as heat engines. Removing upper-tropospheric warming or stratospheric cooling from end-of-century experiments results in much smaller changes in potential intensity or realised intensity than between present day and the end of the century. A larger proportional change in thermodynamic disequilibrium compared to thermodynamic efficiency in our simulations suggests that disequilibrium, not efficiency, is responsible for much of the intensity increase from present day to the end of the century. The limited change in efficiency is attributable to nearly constant outflow temperature in the simulated TCs among the experiments. Observed sensitivities are generally larger than modelled sensitivities, suggesting that observed TC intensity change responds to a combination of the temperature change and other environmental factors. [ABSTRACT FROM AUTHOR]

Extreme maximum temperatures during Australian spring can have deleterious impacts on a range of sectors from health to wine grapes to planning for wildfires but are studied relatively little compared to spring rainfall. Spring maximum temperatures in Australia have been rising over recent decades, and it is important to understand how Australian spring maximum temperatures develop in the present and warming climate. Australia's climate is influenced by variability in the tropics and extratropics, but some of this influence impacts Australia differently from winter to summer and, consequently, may have different impacts on Australia as spring evolves. Using linear regression analysis, this paper explores the atmospheric dynamics and remote drivers of high maximum temperatures over the individual months of spring. We find that the drivers of early spring maximum temperatures in Australia are more closely related to low-level wind changes, which in turn are more related to the Southern Annular Mode than variability in the tropics. By late spring, Australia's maximum temperatures are proportionally more related to warming through subsidence than low-level wind changes and more closely related to tropical variability. This increased relationship with the tropical variability is linked with the breakdown of the subtropical jet through spring and an associated change in tropically forced Rossby wave teleconnections. An improved understanding of how the extratropics and tropics project onto the mechanisms that drive high maximum temperatures through spring may lead to improved sub-seasonal prediction of high temperatures in the future. [ABSTRACT FROM AUTHOR]

Single model initial-condition large ensembles (LEs) are a useful approach to understand the roles of forced responses and internal variability in historical and future climate change. Here, we produce one of the largest ensembles thus far using the MIROC6 coupled atmosphere–ocean global climate model (MIROC6-LE). The total experimental period of MIROC6-LE is longer than 76 000 years. MIROC6-LE consists of a long preindustrial control run, 50-member historical simulations, 8 single forcing historical experiments with 10 or 50 members, 5 future scenario experiments with 50 members and 3 single forcing future experiments with 50 members. Here, we describe the experimental design. The output data of most of the experiments are freely available to the public. This dataset would be useful to a wide range of research communities. We also demonstrate some examples of initial analyses. Specifically, we confirm that the linear additivity of the forcing-response relationship holds for the 1850–2020 trends of the annual mean values and extreme indices of surface air temperature and precipitation by analyzing historical fully forced runs and the sum of single forced historical runs. To isolate historical anthropogenic signals of annual mean and extreme temperature for 2000–2020 relative to 1850–1900, ensemble sizes of 4 and 15, respectively, are sufficient in most of the world. Historical anthropogenic signals of annual mean and extreme precipitation are significant with the 50-member ensembles in 76 % and 69 % of the world, respectively. Fourteen members are sufficient to examine differences in changes in annual mean values and extreme indices of temperature and precipitation between the shared socioeconomic pathways (ssp), ssp585 and ssp126, in most of the world. Ensembles larger than 50 members are desirable for investigations of differences in annual mean and extreme precipitation changes between ssp126 and ssp119. Historical and future changes in internal variability, represented by departures from the ensemble mean, are analyzed with a focus on the El Niño/Southern Oscillation (ENSO) and global annual mean temperature and precipitation. An ensemble size of 31 is large enough to detect ENSO intensification from preindustrial conditions to 1951–2000, from 1951–2000 to 2051–2100 in all future experiments, and from low- to high-emission future scenario experiments. The single forcing historical experiments with 27 members can isolate ENSO intensification due to anthropogenic greenhouse gas and aerosol forcings. Future changes in the global mean temperature variability are discernible with 23 members under all future experiments, while 50 members are not sufficient for detecting changes in the global mean precipitation variability in ssp119 and ssp126. We also confirm that these temperature and precipitation variabilities are not precisely analyzed when detrended anomalies from the long-term averages are used due to interannual climate responses to the historical natural forcing, which highlights the importance of large ensembles for assessing internal variability. [ABSTRACT FROM AUTHOR]

Polar amplification appears in response to greenhouse gas forcing, which has become a focus of climate change research. However, polar amplification has not been systematically investigated over the Earth's three poles (the Arctic, Antarctica, and the Third Pole). An index of polar amplification is employed, and the annual and seasonal variations of land surface temperature over the Earth's three poles are examined using MODIS (Moderate Resolution Imaging Spectroradiometer) observations for the period 2001–2018. As expected, the warming of the Arctic is most conspicuous, followed by the Third Pole, and is weakest in Antarctica. Compared to the temperature changes for the global land region, positive polar amplification appears in the Arctic and the Third Pole on an annual scale, whereas Antarctic amplification disappears, with a negative amplification index of −0.72. The polar amplification for the Earth's three poles shows seasonal differences. Strong Arctic amplification appears in boreal spring and winter, with a surface warming rate of more than 3.40 times the global mean for land regions. In contrast, the amplification of the Third Pole is most conspicuous in boreal summer. The two poles located in the Northern Hemisphere have the weakest amplification in boreal autumn. Differently from the positive amplification for the Arctic and the Third Pole in all seasons, the faster variations in Antarctic temperature compared to the globe only appear in austral autumn and winter, and the amplification signal is negative in these seasons, with an amplification index of −1.68 and −2.73, respectively. In the austral winter, the strong negative amplification concentrates on West Antarctica and the coast of East Antarctica, with an absolute value of amplification index higher than 5 in general. Generally, the polar amplification is strongest in the Arctic except from June to August, and Antarctic amplification is the weakest among the Earth's three poles. The Earth's three poles are experiencing drastic changes, and the potential influence of climate change should receive attention. [ABSTRACT FROM AUTHOR]

The regional climate impacts of hypothetical future emissions scenarios can be estimated by combining Earth system model simulations with a linear pattern scaling model such as MESMER (Modular Earth System Model Emulator with spatially Resolved output), which uses estimated patterns of the local response per degree of global temperature change. Here we use the mean trend component of MESMER to emulate the regional pattern of the surface temperature response based on historical single-forcer and future Shared Socioeconomic Pathway (SSP) CMIP6 (Coupled Model Intercomparison Project Phase 6) simulations. Errors in the emulations for selected target scenarios (SSP1–1.9, SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) are decomposed into two components, namely (1) the differences in scaling patterns between scenarios as a consequence of varying combinations of external forcings and (2) the intrinsic time series differences between the local and global responses in the target scenario. The time series error is relatively small for high-emissions scenarios, contributing around 20 % of the total error, but is similar in magnitude to the pattern error for lower-emissions scenarios. This irreducible time series error limits the efficacy of linear pattern scaling for emulating strong mitigation pathways and reduces the dependence on the predictor pattern used. The results help guide the choice of predictor scenarios for simple climate models and where to target for the introduction of other dependent variables beyond global surface temperature into pattern scaling models. [ABSTRACT FROM AUTHOR]

The study of temperature change in major countries of the world since the 1980s is a key scientific issue given that such data give insights into the spatial differences of global temperature change and can assist in combating climate change. Based on the reanalysis of seven widely accepted datasets, which include trends in climate change and spatial interpolation of the land air temperature data, the changes in the temperature of major countries from 1981 to 2019 and the spatial-temporal characteristics of global temperature change have been assessed. The results revealed that the global land air temperature from the 1980s to 2019 varied at a rate of 0.320°C/10a, and exhibited a significantly increasing trend, with a cumulative increase of 0.835°C. The mean annual land air temperature in the northern and southern hemispheres varied at rates of 0.362°C/10a and 0.147°C/10a, respectively, displaying significantly increasing trends with cumulative increases of 0.828°C and 0.874°C, respectively. Across the globe, the rates of change of the mean annual temperature were higher at high latitudes than at middle and low latitudes, with the highest rates of change occurring in regions at latitudes of 80°–90°N, followed by regions from 70°–80°N, then from 60°–70°N. The global land surface air temperature displayed an increasing trend, with more than 80% of the land surface showing a significant increase. Greenland, Ukraine, and Russia had the highest rates of increase in the mean annual temperature; in particular, Greenland experienced a rate of 0.654°C/10a. The regions with the lowest rates of increase of mean annual temperature were mainly in New Zealand and the equatorial regions of South America, Southeast Asia, and Southern Africa, where the rates were <0.15°C/10a. Overall, 136 countries (93%), out of the 146 countries surveyed, exhibited a significant warming, while 10 countries (6.849%) exhibited no significant change in temperature, of which 3 exhibited a downward trend. Since the 1980s, there have been 4, 34 and 68 countries with levels of global warming above 2.0°C, 1.5°C and 1.0°C, respectively, accounting statistically for 2.740%, 23.288% and 46.575% of the countries examined. This paper takes the view that there was no global warming hiatus over the period 1998–2019. [ABSTRACT FROM AUTHOR]

Data-driven prediction of time series is significant in many scientific research fields such as global climate change and weather forecast. For global monthly mean temperature series, considering the strong potential of deep neural network for extracting data features, this paper proposes a data-driven model, ResGraphNet, which improves the prediction accuracy of time series by an embedded residual module in GraphSAGE layers. The experimental results of a global mean temperature dataset, HadCRUT5, show that compared with 11 traditional prediction technologies, the proposed ResGraphNet obtains the best accuracy. The error indicator predicted by the proposed ResGraphNet is smaller than that of the other 11 prediction models. Furthermore, the performance on seven temperature data sets shows the excellent generalization of the ResGraphNet. Finally, based on our proposed ResGraphNet, the predicted 2022 annual anomaly of global temperature is 0.74722 °C, which provides confidence for limiting warming to 1.5 °C above pre-industrial levels. [ABSTRACT FROM AUTHOR]

The failure of states to take the necessary actions to prevent global temperatures from soaring may be interpreted as more than an act of environmental negligence. In terms of a knowing imposition of harm, it also represents an act of domination. That is, a deliberate denial of rights to a safe, democratic, and sustainable future. This paper notes the role played by institutional power in preserving this system of domination and in shaping the discursive spaces in which carbon energy options continue to be vigorously defended even in the face of mounting evidence of their danger. Yet as signs of eco-distress grow stronger, so too does a questioning of the legitimacy of this power order. This paper examines how youth employ 'communicative power', as the product of a common will formed in non-coercive communication, to counter this domination and reinterpret climate change as the product of dysfunctional decision-making and 'abnormal' justice relations between generations. It notes the significance of these actors' mobilization efforts to societal processes of learning about democracy's better potentialities and capacities to transform society from within (via law). [ABSTRACT FROM AUTHOR]

The discrepancy between simulated and observed globally averaged monthly atmospheric concentrations of carbon dioxide could be attributed either to deficiencies in the observation network or to inadequacies in the global carbon cycle models. This paper shows that model results could be brought closer to observations by improving model components that describe the seasonal changes in the storage of quickly decaying fractions of litter. [ABSTRACT FROM AUTHOR]

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The Great Plains of North America host a stark climatic gradient, separating the humid and well-watered eastern US from the semi-arid and arid western US. First studied in detail by John Wesley Powell, this gradient shapes the region's ecosystems, economies, and the availability of water across the landscape. This gradient is largely set by the influence of two competing atmospheric circulation systems that meet over the Great Plains – the wintertime westerlies bring dominantly dry air that gives way to moist, southerly air transported by the Great Plains Low-Level Jet in the warmer months. Climate model simulations suggest that, as CO 2 rises, this low-level jet will strengthen, leading to greater precipitation in the spring, but less in the summer and, thus, no change in mean annual precipitation. Combined with rising temperatures that will increase potential evapotranspiration, semi-arid conditions will shift eastward, with potentially large consequences for the ecosystems and inhabitants of the Great Plains. We examine how hydroclimate in the Great Plains varied in the past in response to warmer global climate by studying the paleoclimate record within the Ogallala Formation, which underlies nearly the entire Great Plains and provides a spatially resolved record of hydroclimate during the globally warmer late Miocene. We use the stable isotopes of oxygen (δ18 O) as preserved in authigenic carbonates hosted within the abundant paleosol and fluvial successions that comprise the Ogallala Formation as a record of past hydroclimate. Today, and coincident with the modern aridity gradient, there is a sharp meteoric water δ18 O gradient with high (−6 to 0 ‰) δ18 O in the southern Great Plains and low (−12 to −18 ‰) δ18 O in the northern Plains. We find that the spatial pattern of reconstructed late Miocene precipitation δ18 O is indistinguishable from the spatial pattern of modern meteoric water δ18 O. We use a recently developed vapor transport model to demonstrate that this δ18O spatial pattern requires air mass mixing over the Great Plains between dry westerly and moist southerly air masses in the late Miocene – consistent with today. Our results suggest that the spatial extent of these two atmospheric circulation systems have been largely unchanged since the late Miocene and any strengthening of the Great Plains Low-Level Jet in response to warming has been isotopically masked by proportional increases in westerly moisture delivery. Our results hold implications for the sensitivity of Great Plains climate to changes in global temperature and CO 2 and also for our understanding of the processes that drove Ogallala Formation deposition in the late Miocene. [ABSTRACT FROM AUTHOR]

Rapid and ongoing climate change increases global temperature, impacts feeding, and reproduction in insects. The olfaction plays an important underlying role in these behaviors in most insect species. Here, we investigated how changing temperatures affect odor detection and ensuing behavior in three drosophilid flies: Drosophila novamexicana, D. virilis and D. ezoana, species adapted to life in desert, global, and subarctic climates, respectively. Using a series of thermal preference assays, we confirmed that the three species indeed exhibit distinct temperature preferences. Next, using single sensillum recording technique, we classified olfactory sensory neurons (OSNs) present in basiconic sensilla on the antenna of the three species and thereby identified ligands for each OSN type. In a series of trap assays we proceeded to establish the behavioral valence of the best ligands and chose guaiacol, methyl salicylate and isopropyl benzoate as representatives of a repellent, attractant and neutral odor. Next, we assessed the behavioral valence of these three odors in all three species across a thermal range (10-35 °C), with flies reared at 18 °C and 25 °C. We found that both developmental and experimental temperatures affected the behavioral performance of the flies. Our study thus reveals temperature-dependent changes in odor-guided behavior in drosophilid flies. Drosophila novamexicana, D. virilis, and D. ezoana are fly species that have respectively adapted to life in desert, global, or subarctic climates. These drosophilid species also exhibit distinct odor-guided behaviors in response to altered developmental or experimental temperatures. [ABSTRACT FROM AUTHOR]

This article presents the results of research on the changes and variability of snow cover in Kraków in the 100-year period 1921/22–2020/21 and in its two sub-periods covering the years of the slow and rapid territorial, urban and industrial development of Kraków (respectively, 1921/22–1960/61 and 1961/62–2020/21). The long-term variability of the number of days with snow cover, the maximum depth of the snow layer, the dates of the beginning and end of snow cover duration in the winter season, the potential snow cover duration and the index of snow cover stability were analysed. The directions of changes in the snow cover in the last 100 winter seasons in Kraków correspond to the global changes in air temperature presented in the latest IPCC reports: until the end of the 1950s there were no significant trends, or only small trends were observed, whereas from the beginning of the 1960s faster changes in the snow cover duration and maximum seasonal snow depth have been visible. In the last 60 years (1961/62–2020/21), the impact of global changes in Kraków has been joined by the impact of territorial, demographic and industrial development of the city, causing significant negative trends in snow cover with relative values of less than −9% · 10 years−1, both in the case of snow cover duration and its maximum depth in the winter season; these changes are statistically significant. Throughout the whole 100-year period (1921/22–2020/21) and in its second part (1961/62–2020/21), a decrease in snow cover stability has also been observed. [ABSTRACT FROM AUTHOR]

1-Introduction Carbon dioxide emissions caused by human activities, industrial activities and other sources are increasing day by day. The increase of this gas as an important greenhouse gas can cause fundamental changes in global temperature and climate change. Soil respiration is one of the main natural ways of releasing carbon dioxide into the atmosphere in terrestrial ecosystems, and by studying it, one can gain a lot of knowledge about the global carbon cycle. Tree species can influence soil microbial respiration by affecting biogeochemical processes through the amount of litterfall. On the other hand, the different directions of the trees, due to the changes in the radiation angle, which affects the amount of moisture and temperature, probably cause different responses of soil microbial respiration. 2-Methodology In this research, spatial changes of soil microbial respiration by integrating geographical aspects (north, west, east, south) and five different distances (0.5, 1, 1.5, 2 and 2.5 meters) from the tree trunk for the study of soil microbial respiration in three different oak species (Quercus infectoria Oliv., Quercus brantii Lindl. and Quercus libani Oliv.) were carried out in Baneh, Armardeh forests. Three oak trees were selected in the types with the dominant percentage of the desired species (almost identical in terms of height, diameter at breast and canopy). Soil microbial respiration was recorded using Anderson method and moisture in the laboratory and the temperature of the soil were also recorded at the same time sampling at the sample site (soil samples were taken from a depth of 0-10 cm). One-way analysis of variance and Tukey's test were used to check the significance of the difference in the measured characteristics between different oak species. 3-Results: The results showed that there was no significant difference in the parameters of microbial respiration and moisture between the different aspects of each of the different oak species. The results of soil temperature also showed a significant difference in Quercus brantii species between different aspects, and no significant difference was observed in Quercus infectoria and Quercus libani species. On the other hand, there were significant differences between different distances from the trunk. Also, the results of comparing the average soil microbial respiration between different species were not significant, but the results of comparing the average between different oak species at different distances showed differences between the three species. The main environmental factors in this study included soil temperature and soil moisture, which were significantly affected by spatial heterogeneity. The average soil temperature related to Quercus brantii species had a significant difference in different aspects, which could be due to the difference in the intensity of lighting during the day and the aspects of the sunlight and the change of shade over time. There was a difference in soil temperature at different distances in all three species, and this could be due to the low canopy density of the Zagros forests. 4-Discussion & Conclusions Single trees affect the substrates on which they grow and may leave their traces on the soil through the influence of the canopy and roots. The "single-tree influence" concept envisions the soil landscape as a mosaic of small patches that are spatially and genetically linked to individual trees. There are two aspects to this concept: first, within the circle of influence of each tree, soil properties vary predictably in relation to distance from the trunk and crown edge, and second, at any location relative to the trunk and crown, the degree of feature development varies as well. Soil properties are different among tree species. In other words, the organic matter produced by any tree species is different and the related grass cover contains different proportions of nutrients, lignin and secondary metabolites, which determine the quality of organic matter and the rate of decomposition. This study also showed that the spatial heterogeneity in soil temperature and moisture content may be intrinsic mechanisms that drive differences in soil microbial respiration. These findings can deepen our understanding of changes in microbial respiration and soil respiration and clarify the ecological role of different species for us. [ABSTRACT FROM AUTHOR]