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Drought is one of the most devastating threats to the livelihoods of the southern African population, who mainly rely on rain‐fed agriculture for income. Previous studies have highlighted that the Botswana High influences drought over the region; however, its influence on the spatial modes of drought remains unknown. This study examines the spatiotemporal structures of drought modes (DMs) over southern Africa and their link with the Botswana High in observation, reanalysis and Model for Prediction Across Scales (MPAS). To characterize droughts, the study uses the 3‐month scale standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). Spatiotemporal characteristics of the DMs are identified using empirical orthogonal function (EOF) analysis on SPI and SPEI. EOF analysis is also used to identify the spatiotemporal characteristics of the Botswana High. The relationship between each DM and the Botswana High is quantified using correlation and R2 analysis. In all the datasets (Climate Research Unit (CRU), European Centre for Medium‐Range Weather Forecasts version 5 (ERA5), 20th Century reanalysis II (20C) and MPAS), the most dominant five DMs (hereafter DM1–DM5) over southern Africa jointly explain more than 60% of the interannual variability in the 3‐month scale summer droughts for SPEI and SPI. CRU, ERA5 and MPAS agree that the Botswana High correlates with the interannual variability of DM1, with a stronger correlation in ERA5 (r = −0.85) compared to MPAS (r = −0.42) and CRU (r = −0.35). Additionally, wet years (+ve SPEI and SPI) are characterized by a weak Botswana High and drought years (−ve SPEI and SPI) by a strong Botswana High. The wet and dry years correspond to the −ve and +ve phases of El Niño–Southern Oscillation (ENSO), respectively. Given this, the results of this study suggest that the Botswana High might be a teleconnection pattern through which ENSO signals influence DM1 over the region. [ABSTRACT FROM AUTHOR]

This study analyses the climate change and technological change impact on food production in Benin using data from the agriculture ministry and Climate Analytics over the period 1995–2015 and a bio‐economic model. The results suggest that land‐use changes may depend on crop types and prevailing future conditions. With respect to crop production, the results show that the climate change effect on different crops varies from one municipality to another. Moreover, crops such as rice and soybean will benefit more from climate change in several Benin's municipalities, while maize will suffer from climate change. Moreover, results showed technological change globally mitigates the negative impact of moderate climate change. The results suggest that Beninese's government must invest in agriculture technology through agriculture and engineering high schools' installation, which could improve farmers' productivity and efficiency. [ABSTRACT FROM AUTHOR]

Maritime Antarctica's harsh abiotic conditions forged simple terrestrial ecosystems, mostly constituted of bryophytes, lichens, and vascular plants. Though biotic interactions are, together with abiotic factors, thought to help shape this ecosystem, influencing species' distribution and, indirectly, mediating their response to climate, the importance of these interactions is still fairly unknown. We modeled current and future abundance patterns of bryophytes, lichens, and vascular plants, accounting for biotic interactions and abiotic drivers, along a climatic gradient in maritime Antarctica. The influence of regional climate and other drivers was modeled using structural equation models, with and without biotic interactions. Models with biotic interactions performed better; the one offering higher ecological support was used to estimate current and future spatial distributions of vegetation. Results suggest that plants are confined to lower elevations, negatively impacting bryophytes and lichens, whereas at higher elevations both climate and other drivers influence bryophytes and lichens. Our findings strongly support the use of biotic interactions to predict the spatial distribution of Antarctic vegetation. [Display omitted] • Bryophytes, lichens, and plants constitute the bulk of Maritime Antarctic vegetation • Abiotic drivers only partially explained Antarctic vegetation abundance • Biotic interactions were key to further explain vegetation abundance patterns • These findings boost our understanding of climate change impact in Antarctica There is an urgent need to improve predictive ecological models of climate change effects in maritime Antarctic vegetation. Rocha et al. uncovered that the inclusion of biotic interactions is a vital step to better explain and predict vegetation abundance patterns. These findings contribute toward building better conservation tools for the region. [ABSTRACT FROM AUTHOR]

Extreme precipitation events have substantial implications for water resources, ecosystems, and human populations in Central Africa (CA). Consequently, understanding the spatial variability of these events is crucial for effective climate change adaptation and water management strategies. In this study, we assess the performance of the state-of-the-art global climate models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating extreme precipitation events over CA. By considering three observational datasets, we evaluated the ability of sixteen CMIP6 models as well their multi-model ensemble (MME), to capture the patterns of extreme precipitation events. We then focus on key metrics such as duration and intensity, based on a total of ten indices of extreme precipitation events, defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The results showed that the individual models as well as the MME exhibited acceptable performance in reproducing the patterns of extreme precipitation events, especially when dealing with wet-day amount, frequency, dry spell duration and persistence of extreme precipitation over the two northern CA subregions (i.e., Sudano-Sahelian and Northern Equatorial). Moreover, the analyses revealed that CMIP6 models generally lack the ability of in simulating not only the wet spell duration, but also heavy precipitation indices, particularly over the central and southeastern CA subregions (i.e., Equatorial East and Southern Equatorial, respectively). These results highlight the remaining challenges in reproducing local-scale processes governing precipitation variability and extreme precipitation events over the region. The results of this study provide an understanding of both the strengths and limitations of the CMIP6 models over CA, which would help to improve regional climate projections and strengthen the ability of decision makers to assess the future risks associated with extreme precipitation events in the region. [ABSTRACT FROM AUTHOR]

To separate the contributions of climate change and human activities to alterations in flow for the Ahar Chay watershed in Iran, change points of the normalized difference vegetation index (NDVI) and evapotranspiration (ET) were distinguished before and after significant alterations. The Budyko model and Soil and Water Assessment Tool (SWAT) were used to find the reduction in runoff. New Penman-Monteith-Leuning (PML) ET data was used to consider plant types. Results revealed an increase in NDVI coinciding with the change point in 1998, as well as ET in 1999. The significance of the Sattarkhan Dam became evident when distinguishing the impacts upstream versus downstream of the dam. Results also indicated that climate change contributed significantly to upstream flow reduction (83.5%), whereas downstream reductions were primarily linked to human-related impacts (59.7–82.5%). Assuming no dam interception, 2007 became the change point, signifying a 37.5% reduction in average flow from before to after this point due to the increased vegetation cover. [ABSTRACT FROM AUTHOR]

Social Impact Statement: For hundreds of years, Agave marmorata plants have been used in the production of alcoholic beverages in Mexico. This species is very important in small‐scale rural economies because it is a large plant, yielding five liters of mezcal. However, the production of these beverages takes place when it reaches its reproductive stage, which takes up to 35 years. Due to its slow maturation and high demand, it is considered an endangered species. Therefore, as a conservation strategy, this study proposes the creation of nurseries, genetic breeding programs, and demographic monitoring of wild populations to counteract the extraction of wild plants and, the conservation of the genetic diversity. Summary: Agave marmorata Roezl., is an endemic species distributed in the states of Oaxaca and Puebla, Mexico, and locally is widely used to produce mezcal.We assessed the genomic diversity and differentiation using the RADseq method and 29,101 high‐quality single nucleotide polymorphisms (SNPs) in wild plants and grown under three different management types (cultivated, plants used as live fences, and young plants growing in nurseries). We examined the demographic history and used species distribution modeling to understand the future of A. marmorata under scenarios of climate change.We found high levels of genomic diversity (HS = 0.229) and moderate levels of inbreeding (FIS = 0.106 and Fhat3 = 0.190). The cultivated samples harbored less genetic diversity than the wild plants. Furthermore, we estimated low differentiation between cultivated and wild localities (FST = 0.037). In the wild samples, we identified two main genetic groups, one in the East and another in the West of its distribution area. This genetic structure possibly derived from a population contraction during the Pleistocene (~216,879.75 BP) and the formation of two refugia in small areas with climatic stability. Furthermore, the demographic reconstruction indicated that A. marmorata went through a recent population expansion event, with a large current Ne (Ne = 8,009). The future climate change models indicated contrasting possible changes in its distribution range, from an increase to the reduction of its suitable habitat, differences related to model parametrization, and future levels of CO2 production.We propose conservation measures for the different management types of the species while also considering the biotic and abiotic interactions of Agave marmorata. [ABSTRACT FROM AUTHOR]

The north and central coast of Chile is influenced by El Niño-Southern Oscillation (ENSO) through oceanic and atmospheric teleconnections. However, it also experiences episodic oceanic warmings off central Chile (30°S) lasting a few months that are not necessarily associated with ENSO. These episodes, called "Chile Niño" events, besides their ecological and socio-economical impacts, have also the potential to influence tropical Pacific variability. Here, we investigate how realistically the models in the Coupled Model Intercomparison Project (CMIP, Phases 5 and 6) simulate Chile Niño/Niña (CN) events, and quantify their changes under anthropogenic forcing. Despite limitations of the global models in simulating realistically coastal upwelling dynamics, we show that they simulate reasonably well the observed spatial pattern, amplitude and seasonal evolution of CN events. They however fail to properly represent the positive skewness from observations. The analysis of a sub-group of models (36) that simulate ENSO realistically reveals that CN events increase in amplitude and variance in the future climate with no changes in their frequency of occurence. This is interpreted as resulting from compensating effects amongst changes in remote drivers and local feedbacks. In particular, ENSO variance increases while that of the South Pacific Oscillation decreases. Conversely, we found that while the Wind-Evaporation-SST feedback tends to increase and the coupling between mixed-layer depth and SST weakens, favoring the development of CN events, the thermocline and wind-SST feedbacks decrease. However, only the change in the thermocline feedback is correlated to changes in CN variance amongst the models, suggesting a dominant role of local oceanic stratification changes in constraining the sensitivity of CN to global warming. [ABSTRACT FROM AUTHOR]

Purpose: The choice of characterization model has the potential to profoundly influence LCA results and conclusions. It is therefore a requirement of ISO 14044:2006 that the selection of characterization model shall be both justified and consistent with goal and scope. The purpose of this article was to examine current practices regarding the characterization of GHG emissions and reporting as CO2 equivalent emissions. Methods: A survey of practice was conducted across articles recently published in the International Journal of Life Cycle Assessment. Each article was examined using seven predetermined questions covering reporting of CO2 equivalent emissions and justification of climate metric used, interpretation of results in relation to climate measures or goals, and the implications of choosing an alternative climate metric. Results and discussion: Of the sample of 85 articles, more than half reported GHG emissions as CO2 equivalent emissions. Of these, 81% unambiguously reported the climate metric used. Most often, there was no justification for the choice of a characterization model. Where a justification was given, the most common reason was alignment with the requirements of a program or PCR document. In some cases, the choice of characterization model was determined by the choice of impact assessment method (e.g., CML, ReCiPe). In other cases, the choice of characterization model was based on the desire to compare results to other studies. It is noted that none of the abovementioned reasons is scientific justification related to a stated climate objective. Not surprisingly, most studies made no attempt to interpret results reported as CO2 equivalent emissions in relation to climate measures or goals, and most did not discuss the potential implications of alternative climate metrics. For almost half of the articles, the choice of climate metric was assessed as potentially having major implications for decision-making or comparison to alternative systems. Conclusions: Based on this survey, it is evident that key aspects of ISO 14044:2006 are routinely not being followed. When GHG emissions are aggregated into a single impact category indicator result, there is a loss of transparency about climate impacts over time and the potential to unknowingly trade short-term climate benefits against the exacerbation of the difficulties of achieving long-term climate stabilization. As such, whenever GHG emissions are reported as CO2 equivalent emissions, it is imperative that the choice of characterization model is unambiguously reported and justified, that results are interpreted in relation to environmental outcomes, and that the potential implications of selecting alternative models are discussed. [ABSTRACT FROM AUTHOR]

BACKGROUND: Climate change significantly impacts global maize production via yield reduction, posing a threat to global food security. Disease‐related crop damage reduces quality and yield and results in economic losses. However, the occurrence of diseases caused by climate change, and thus crop yield loss, has not been given much attention. RESULTS: This study aims to investigate the potential impact of six major diseases on maize yield loss over the next 20 to 80 years under climate change. To this end, the Maximum Entropy model was implemented, based on Coupled Model Intercomparison Project 6 data. The results indicated that temperature and precipitation are identified as primary limiting factors for disease onset. Southern corn rust was projected to be the most severe disease in the future; with a few of the combined occurrence of all the selected diseases covered in this study were predicted to progressively worsen over time. Yield losses caused by diseases varied per continent, with North America facing the highest loss, followed by Asia, South America, Europe, Africa, and Oceania. CONCLUSION: This study provides a basis for regional projections and global control of maize diseases under future climate conditions. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Climate change and biodiversity conservation are two of the most urgent challenges of the twenty-first century. Current global climate models indicate that climate-related events will continue to increase in frequency and intensity, leading to severe impacts on ecosystems, particularly plant diversity. Despite the 2015 Paris Agreement, efforts to limit global warming to 1.5 degrees Celsius and secure adequate climate financing remain unsatisfactory. In addition, the National Biodiversity Strategic Action Plans (NBSAPs), a core mechanism of the Convention on Biological Diversity (CBD), have faced issues in implementation at the national level. Many NBSAPs lack clear and measurable biodiversity targets, which limits their effectiveness. This review presents a comprehensive analysis of these urgent issues, highlighting the significant challenges and deficiencies in current climate and biodiversity conservation policies. It evaluates the effectiveness of the Kew conservation strategy as a model for protecting and conserving plant diversity. Furthermore, this review underscores the pivotal role of plant tissue culture (PTC) technology in achieving plant conservation targets within the post-2020 global biodiversity framework. This review strongly supports the post-2020 global biodiversity framework and the integration of PTC into global plant conservation strategies to meet the ambitious 30-by-30 targets. This review also advocates for the establishment of the Kew-Wide Mechanism (KWM) to bolster climate resilience, reduce anthropogenic impacts on plant diversity, revitalize global conservation efforts, and accelerate ecosystem restoration in the face of ongoing climate change. Proposed as a comprehensive approach to plant conservation, the KWM offers a strategic, innovative, and scalable solution as the global economy transitions toward decarbonization. [ABSTRACT FROM AUTHOR]

East Asia has been identified as a key area at risk of precipitation increases resulting from global warming. East Asian summer monsoon has distinct spatiotemporal characteristics and the simulation characteristics of global climate models therefore needs to be evaluated closely to obtain a reliable projection of future precipitation patterns and associated extreme events. Using metrics for the spatiotemporal variability of monsoon precipitation over East Asia, this study evaluated the performance of Coupled Model Intercomparison Project Phase 6 (CMIP6) models and analyzed future projections and uncertainty factors. Spatiotemporal precipitation patterns were simulated reasonably well by CMIP6 models but with weaker rainfall amplitudes. CMIP6 models simulated more intense precipitation than the predecessor CMIP5 ones and captured observations better. In future projections, an overall precipitation increase occurs during both the northward migration of the rain band and during peak monsoon time over East Asia and the three subregions, with stronger changes under higher emission scenarios. This increase was mainly ascribed to a thermodynamic response due to increased moisture availability. Internal climate variability and model uncertainty dominated future precipitation uncertainties. Dynamic terms explained a large portion of model uncertainty due to circulation changes, and thermodynamic terms were significantly related to scenario uncertainty. [ABSTRACT FROM AUTHOR]

The uncertainty in precipitation projections across the Sahel has persisted across generations of climate models. Many projections show a zonal dipole in the sign of precipitation change, with wetting across the Central Sahel and drying across the Western Sahel. We analyze the outputs from an ensemble of current climate models to explain why some produce this dipole and understand the inter‐model variability in the transition region for these models. Models projecting the dipole tend to shift the Atlantic Intertropical Convergence Zone (ITCZ) to the south, while models that do not tend to shift the ITCZ to the north. We find that the strong relationship between the change in Sahel precipitation and surface temperature‐based indices is highly driven by the non‐dipole models. These indices do not explain most of the variance in where models transition. This suggests that understanding zonal variability in Sahel precipitation change must go beyond these temperature‐based analysis. Plain Language Summary: Precipitation across the Sahel is vital for sustaining a rapidly growing population and the region's sensitive ecosystem. Uncertainty in Sahel rainfall projections presents a significant challenge for stakeholders. At the end of the 21st century, many climate models predict a dipole pattern, characterized by a drought across western Sahel and an increased rainfall across the central Sahel. This study delves into the outputs of the current state‐of‐the‐art global climate models to investigate and explain why certain models show this drought and increased rainfall pattern across the Western and Central Sahel, respectively, while others do not. Additionally, we seek to comprehend the substantial disparity in the location where these models transition from drought to increased rainfall in this region. Our findings indicate that models depicting these spatial distribution patterns tend to position their band of maximum rainfall farther south compared to models that do not exhibit this pattern. Furthermore, we observe that the strong correlation between Sahel rainfall and temperature‐based indices is predominantly heightened by the group of models lacking this rainfall dipole pattern. This study suggests that to gain a deeper understanding of this drought and increased rainfall projections in the Sahel, we must explore factors beyond these temperature‐based indices. Key Points: Zonal intermodel variability in projections of Sahelian precipitation cannot be fully explained using traditional temperature‐based indicesModels with non‐dipole Sahel precipitation change drive strong correlations between temperature‐based indices and Sahel precipitation changeModels with (without) the zonal dipole pattern tend to produce a southward (northward) shift in the rainbands across the tropical Atlantic [ABSTRACT FROM AUTHOR]

Climate variability on centennial timescales has often been linked to internal variability of the Atlantic Meridional Overturning Circulation (AMOC). However, due to the scarceness of suitable paleoclimate proxies and long climate model simulations, large uncertainties remain on the magnitude and physical mechanisms driving centennial‐scale AMOC variability. For these reasons, we perform a systematic multi‐model comparison of centennial‐scale AMOC variability in pre‐industrial control simulations of state‐of‐the‐art global climate models. Six out of nine models in this study exhibit a statistically significant mode of centennial‐scale AMOC variability. Our results show that freshwater exchanges between the Arctic Ocean and the North Atlantic provide a plausible driving mechanism in a subset of models, and that AMOC variability can be amplified by ocean–sea ice feedbacks in the Labrador Sea. The amplifying mechanism is linked to sea ice cover biases, which could provide an observational constraint for centennial‐scale AMOC variability. Plain Language Summary: Changes in ocean circulation are often proposed as drivers of natural variations of the Earth's climate on timescales of centuries. However, it is unclear how strong these natural variations of the circulation strength, called internal variability, are in the real world, because reconstructions from the past climate are sparse and climate models are expensive to run for these long timescales. Here, we compare how the latest generation of climate models simulate internal variability of the Atlantic Meridional Overturning Circulation (AMOC)—the ocean circulation that is often thought to be responsible for Europe's comparatively mild climate—on timescales of 100–250 years. We find that many models have stronger variability on these timescales than what would be expected simply from random noise. In several models, AMOC variability appears to be driven by the release of fresh water from the Arctic Ocean and amplified by intermittent sea ice cover in the North Atlantic. However, this amplification only occurs if a model simulates a too extensive sea ice cover in winter. This mechanism shows that sea ice cover—which is easily observable—could be used to constrain variability of the AMOC on timescales longer than the observational record. Key Points: We present a robust multi‐model comparison of internal centennial‐scale AMOC variability in state‐of‐the‐art climate modelsA robust mechanism of Arctic–North Atlantic freshwater exchange is identified only in models that use the NEMO ocean componentSea ice cover biases in convective regions of the North Atlantic amplify AMOC variability and could provide an observational constraint [ABSTRACT FROM AUTHOR]

ABSTRACT The inherent model uncertainty in precipitation projections is found to be more dominant over tropical regions thereby reducing the reliability of using them in climate change impact assessment studies. To address such issues, a subset of well performing global climate models (GCMs) can provide narrow range of possible future outcomes, which can be helpful in formulating mitigation and adaptation strategies that are more targeted and efficient. In this study, climate models are selected based on their performance in simulating relative humidity and vertical velocity since these variables play an important role in precipitation simulation and significantly contribute toward the intermodel spread. The models are evaluated by using various statistical performance measures and ranked using multi‐criteria decision‐making approaches. Finally, based on Jenks natural breaks optimization algorithm, subset of GCMs consisting of ACCESS1.0, ACCESS1.3 and INM‐CM4 models, are considered as the best possible subset for precipitation simulation over tropical land regions. Two observational precipitation datasets are further considered to investigate the effectiveness of the proposed framework. The proposed methodology is validated to be effective in identifying the best climate models since the resulting subset is capable of both capturing observed precipitation and minimizing the uncertainty in future projections. Hence, this methodology can be utilized further for performance evaluation of GCMs focusing different geography and climatic drivers. [ABSTRACT FROM AUTHOR]

ABSTRACT Low temperatures in late spring pose a potential threat to the maintenance of grain yield and quality. Despite the importance of protein and starch in wheat quality, they are often overlooked in models addressing climate change effects. In this study, we conducted multiyear environment‐controlled phytotron experiments and observed adverse effects resulting from low‐temperature stress (LTS) on plant carbon and nitrogen dynamics, grain protein and starch formation, and sink capacity. We quantified the relationships between low temperature during the jointing and booting stages and plant nitrogen uptake, grain nitrogen accumulation, grain starch accumulation, grain setting, and potential grain weight using source–sink relationship‐based methods. The LTS factor was introduced to account for the cultivar‐specific to LTS at different growth stages. Compared with the original model, the improved model produced fewer errors when simulating aboveground nitrogen accumulation, grain protein concentration, grain starch concentration, grain starch yield, grain number, and grain weight under LTS, with reductions of 60%, 71%, 73%, 58%, 50% and 65%, respectively. The improvements in the model enhance its mechanism and applicability in assessing short‐term successive frost effects on wheat grain quality. Furthermore, when using the improved model, special attention should be given to the low‐temperature sensitivity parameters. [ABSTRACT FROM AUTHOR]

Within High Mountain Asia (HMA), the annual melting of glaciers and snowpack provides vital freshwater to populations living downstream. Precipitation over HMA can directly affect the freshwater availability in this region by altering the mass balance of glaciers and snowpack. However, available reanalyses and downscaling simulations lack the resolution required to understand important glacier‐scale variations in precipitation. This study aimed to determine the current characteristics of orographic precipitation gradients (OPG) by curve‐fitting daily precipitation as a function of elevation from a 15‐year, 4‐km grid spaced Weather Research and Forecasting (WRF) model simulation focused on the Himalayan, Karakoram, and Hindu‐Kush mountain ranges. To facilitate precipitation curve‐fitting, the WRF model grid points were separated into regions of similar orientation, referred to as facets. Akaike Information Criterion‐corrected values and an F‐test p‐value identified the need for a curvature term to account for a varying OPG with elevation. Regions with similar seasonal variability were found using k $k$‐means clustering of the monthly mean OPG coefficients. The central Himalayan slope's intra‐seasonal variability of OPG depended on synoptic scale conditions, in which cyclonically‐forced heavy‐precipitation events produced strong sublinear increases in precipitation with elevation. Initial testing of precipitation estimates using monthly coefficients showed promising results in downscaling daily WRF precipitation; the daily mean absolute error at each grid point had a lower magnitude than the daily mean precipitation total, on average. Results provide a physically‐based context for machine learning algorithms being developed to predict OPG and downscale precipitation output from global climate models over HMA. Plain Language Summary: Precipitation over High Mountain Asia (HMA) affects the region's glaciers and snowpack, which are crucial to the water supply. To study HMA and understand its water cycle, a high‐resolution data set of precipitation is needed. However, the available atmospheric data sets are generally too coarse. Here, we derived fine‐scale precipitation from a regional climate model by fitting the precipitation‐elevation relationship on individual mountain faces. The change of precipitation with elevation (the orographic precipitation gradient [OPG]) varies strongly up the mountain face, with many locations following curves rather than straight lines. Although there are day‐to‐day variations in OPG, it is captured reasonably well by monthly mean values, supporting a simple strategy for increasing the resolution of precipitation in mountainous regions. Key Points: Daily orographic precipitation gradients (OPG) indicate significant curvilinearity in the dependence of precipitation on elevationA strong sublinear increase in OPG on the central Himalayan slope often indicates a cyclonically‐forced heavy‐precipitation eventOver the central Himalayan slope, downscaling with daily or monthly OPGs yielded the highest mean absolute errors during the summer season [ABSTRACT FROM AUTHOR]

Atmospheric radiative heating rate, which manifests radiative energy convergence in the atmosphere, is a fundamental factor shaping the Earth's climate and driving climate change. Compared to the radiative energy budget at the top of atmosphere or surface, the atmospheric energy budget and heating rate are less studied due to a lack of observational constraints and diagnostic tools. Motivated by growing interest in atmospheric energy budget and to facilitate the heating rate analysis, we innovate a set of radiative kernels, which quantitatively measure the sensitivity of atmospheric heating rate to different geophysical variables. When multiplied with the changes in these geophysical variables, these kernels quantify their contributions to the heating rate change. A climate change experiment of Global Climate Models (GCMs) is used to test the application of heating rate kernels. The results indicate the radiative heating rate change simulated by GCMs can be well reproduced by the kernels, validating the kernel method. The decomposition of the heating rate changes reveals the contributing mechanisms. For example, in the tropical upper troposphere, the negative heating anomaly in a warmer climate is dominated by atmospheric temperature and water vapor. Increases in both variables intensify atmospheric thermal radiation to space, partially offset by a positive heating anomaly caused by the lifting high‐cloud tops. Moreover, compared to the results corrected using the kernels, the cloud effect inferred from the radiative heating difference between clear‐ and all‐skies ("cloud radiative heating") has a non‐negligible bias, necessitating the use of kernels to quantify the cloud‐induced heating rate changes. Plain Language Summary: The climate community has developed enormous insights into the climate feedback and sensitivity with the aid of a diagnostic tool: radiative kernels, although the analyses have been largely limited to the radiation budget at the top of the atmosphere or surface. To better understand the atmospheric energy budget change, we innovated a set of radiative kernels, which can be used to isolate the effects of different geographical variables on the radiative heating rate. Thinking of it like a recipe, geographical variables are the ingredients of the heating rate distributions. While a radiation expert may use a complex radiative transfer model to precisely compute heating rate changes, the heating rate kernels make it possible for non‐experts to make similar estimates. Key Points: An innovative set of radiative kernels of heating rate has been generated based on global reanalysis atmospheric profilesThe kernels can well explain the major atmospheric radiative heating rate changes in a warming climateThe kernels are necessary for accounting for the cloud masking effect when quantifying heating rate changes caused by cloud changes [ABSTRACT FROM AUTHOR]

Cloud feedback is the largest source of uncertainty in climate sensitivity. This feedback is generally discussed in terms of radiative flux at the top of the atmosphere, but the vertical distribution of the contribution of cloud changes to the top‐of‐atmosphere cloud feedback should be discussed to understand the feedback in greater detail. We have developed a simple analysis method called "vertical profile analysis of cloud feedback," which simply uses radiative flux data from each level of the model. The analysis is applied for typical cloud regimes and the results are discussed together with cloud fraction change profiles. The advantages and disadvantages of the vertical profile analysis, compared with the commonly used International Satellite Cloud Climatology Project (ISCCP) histogram kernel method, are discussed. Our analysis has the advantage of resolving the detailed vertical profiles of the contribution to the top‐of‐atmosphere cloud feedback from the changes in cloud regimes associated with warming, such as reduced cloud cover and upward shifts of the cloud layer in subtropical low‐cloud regions as well as the increased height of the melting layer in tropical deep convection. The main disadvantage is that the vertical profile analysis cannot represent the feedback components sorted by cloud optical thickness as in the ISCCP histogram kernel method. Plain Language Summary: Rising temperature caused by global warming will depend to a large extent on how clouds will change in the future climate. It is therefore important to know in detail how clouds and radiation will change in the future, not only horizontally but also vertically, in order to understand the mechanism. We have developed a simple but useful method for analyzing the vertical profile of changes in radiation and clouds. This detailed information is useful for understanding the mechanism of changes in clouds and radiation. We have compared this analysis method with the conventional method and discussed the advantages and disadvantages of the two methods in detail, based on simulation results. Although our vertical profile analysis provides more detailed vertical profile information, both methods can be used effectively, depending on the purpose of the analysis. Key Points: A simple analysis method is proposed to obtain a detailed vertical profile of cloud feedbackApplications are shown for typical regimes including stratocumulus, shallow convection, mid‐latitude clouds, and tropical deep convectionThe advantages and disadvantages of this analysis method relative to the conventional method are presented [ABSTRACT FROM AUTHOR]

The Lorenz Energy Cycle (LEC) in the Taiwan Earth System Model Version 1 (TaiESM1) historical simulation is calculated and compared with ERA5 to evaluate the model performance from the thermodynamic aspect. The future change of LEC is accessed by comparing the SSP5‐8.5 and historical simulations in TaiESM1. TaiESM1 reasonably simulates the global mean, seasonal cycle, and spatial patterns of the energy reservoirs with larger values in the mean energy components and smaller in the eddy energy components. The energy cycle in TaiESM1 is about 35%–45% stronger than ERA5, except from December to February. The impact of global warming on the LEC is different at the vertical levels. The influence of meridional temperature gradient change is the dominant factor in the intensity of the energy cycle, and the change in static stability only contributes to the lower troposphere. Lifting the tropopause in the tropics increases the meridional temperature gradient and produces more zonal mean potential energy (PM) in the upper troposphere. PM is the primary driver of the LEC and leads to a more active energy cycle in the upper troposphere. As the tropical tropospheric depth increases and the mid‐latitude eddy activities become more active, more (less) energy is stored in the upper (lower) troposphere, and the energy conversion processes tend to become stronger (weaker) in the upper (lower) troposphere. Plain Language Summary: The Lorenz Energy Cycle represents the atmosphere as a heat engine. TaiESM1 is a newly developed climate model. Therefore, we evaluate the model performance and future projection from the thermodynamic aspect to understand the model's overall capability and characteristics. The global mean values of energy reservoirs are in a reasonable range. Energy reservoirs have good spatial patterns and a proper seasonal cycle, but the model has a much stronger energy cycle than reanalysis data due to a stronger meridional temperature gradient. After warming, the energy cycle tends to be stronger (weaker) in the warming scenario's upper (lower) troposphere. This research shows that TaiESM1 is a reliable research tool and provides insights for future improvements for the model development team. Key Points: The newly developed TaiESM1 can simulate atmospheric energy reservoirs regarding global and seasonal means and spatial patternsThe energy cycle in TaiESM1 is 35%–45% stronger than ERA5, mainly contributed by the more active mean flows and eddiesIn the SSP5‐8.5 scenario, the energy cycle intensifies (weakens) in the upper (lower) troposphere due to changes in temperature gradient [ABSTRACT FROM AUTHOR]

ABSTRACT Changes in climate in the South Atlantic region and adjacent regions have been described in numerous works using projections from global climate models from CMIP5 and CMIP6. This paper presents an evaluation of the ability of these models to reproduce the atmospheric circulation patterns (weather types) and their seasonal and inter‐annual variability. The analyses are performed based on the probability of occurrence of weather types in the historical period and in future projections. The scatter index and the relative entropy are the statistical parameters used to evaluate the models' performance in the historical period. Future projections consist of RCP2.6, 4.5 and 8.5 scenarios for the CMIP5 models and the SSP126, 245, 370 and 585 scenarios for the CMIP6 and are assessed at different time intervals: short term (2015–2039), mid‐term (2040–2069) and long term (2070–2100). The performance of projections is measured by analysing their consistency, that is, based on the similarity between projections of the same scenario in different models. The results show that the reproduction of the probability of occurrence of historical weather types and their seasonal and interannual variability was better performed by ACCESS1‐0, HadGEM2‐ES, HadGEM2‐CC, CMCC‐CM and MPI‐ESM‐P when assessing the models from CMIP5, and by HadGEM3‐GC31‐MM, ACCESS‐ESM1‐5, ACCESS‐ CM2 and MRI‐ESM‐P when assessing the models from CMIP6. As for future projections, only the BESM‐AO2‐5, GFDL‐ESM4 and HadGEM3‐GC31‐MM models showed inconsistency in one or more scenarios. [ABSTRACT FROM AUTHOR]

Summary Societal impact statement The European spruce bark‐beetle, Ips typographus, is a major pest of Norway spruce across mainland Europe; however, until 2018, it was considered absent from the United Kingdom (UK). The finding of a breeding population of I. typographus in Kent, England, in 2018 and subsequent findings in pheromone traps across the southeast of England, has led to an urgent need to improve understanding of environmental suitability across the UK. Two distinct published phenological models for I. typographus, developed in Sweden and Austria, were adapted for use in the UK and validated against pheromone trap data collected at the original site of infestation and in the surrounding area from 2019 to 2022. Both models captured some, but not all, of the within‐season variation in trap catches. One, the PHENIPS model, more accurately captured early season flight patterns. The climate in the southeast of England is mild enough to facilitate two generations of the beetle, while further north the pest is expected to be univoltine. At high altitude in Scotland, there is insufficient thermal warming within a season for the completion of one annual generation. Phenological modelling does not explain why the pest failed to establish in England prior to 2018 including when a large amount of infested material was imported to sawmills in the 1940s. Under 2 and 3°C global warming scenarios, we might expect to see an increase in potential voltinism across the UK in the next few decades, increasing the risk of large outbreaks of I. typographus were it allowed to establish. In 2018, a breeding population of the European spruce bark‐beetle, Ips typographus, was discovered in woodland in southeast England. Ips typographus is a major forest pest in continental Europe; however, despite previous findings at ports and sawmills, this was the first recorded infestation in the UK. The number of generations per year (voltinism) varies with latitude and altitude. Applying a phenological model, we find that the current climate in southern UK could support two generations, while temperature accumulation in parts of Scotland may be insufficient to support one generation. Global warming will increase voltinism and hence the risk of establishment. [ABSTRACT FROM AUTHOR]

Accurate climate projections are critical for various applications and impact assessments in environmental science and management. This study presents HiCPC (High-resolution CMIP6 downscaled daily Climate Projections over China), a novel dataset tailored to China's specific needs. HiCPC leverages outputs from 22 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). To address inherent biases in daily GCM simulations, an advanced Bias Correction and Spatial Disaggregation (BCSD) method is employed, using the China Meteorological Forcing Dataset (CMFD) as a reference. HiCPC offers detailed daily precipitation and temperature data across China at an enhanced spatial resolution of 0.1° × 0.1°. It covers both the historical period (1979–2014) and future projections (2015–2100) based on four CMIP6 Shared Socioeconomic Pathways (SSPs) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Upon validation, HiCPC demonstrates good performance, surpassing CMIP6 GCMs across the historical period. This reinforces its significance for essential research in climate change evaluation and its associated implications within China. [ABSTRACT FROM AUTHOR]

The challenge of selecting strategies to adapt to climate change is complicated by the presence of irreducible uncertainties regarding future conditions. Decisions regarding long-term investments in conservation actions contain significant risk of failure due to these inherent uncertainties. To address this challenge, decision makers need an arsenal of sophisticated but practical tools to help guide spatial conservation strategies. Theory asserts that managing risks can be achieved by diversifying an investment portfolio to include assets -- such as stocks and bonds -- that respond inversely to one another under a given set of conditions. We demonstrate an approach for formalizing the diversification of conservation assets (land parcels) and actions (restoration, species reintroductions) by using correlation structure to quantify the degree of risk for any proposed management investment. We illustrate a framework for identifying future habitat refugia by integrating species distribution modeling, scenarios of climate change and sea level rise, and impacts to critical habitat. Using the plains coqui (Eleutherodactylus juanariveroi), an endangered amphibian known from only three small wetland populations on Puerto Rico's coastal plains, we evaluate the distribution of potential refugia under two model parameterizations and four future sea-level rise scenarios. We then apply portfolio theory using two distinct objective functions and eight budget levels to inform investment strategies for mitigating risk and increasing species persistence probability. Models project scenario-specific declines in coastal freshwater wetlands from 2% to nearly 30% and concurrent expansions of transitional marsh and estuarine open water. Conditional on the scenario, island-wide species distribution is predicted to contract by 25% to 90%. Optimal portfolios under the first objective function -- benefit maximization -- emphasizes translocating frogs to existing protected areas rather than investing in the protection of new habitat. Alternatively, optimal strategies using the second objective function -- a risk-benefit tradeoff framework -- include significant investment to protect parcels for the purpose of reintroduction or establishing new populations. These findings suggest that leveraging existing protected areas for species persistence, while less costly, may contain excessive risk and could result in diminished conservation benefits. Although our modeling includes numerous assumptions and simplifications, we believe this framework provides useful inference for exploring resource dynamics and developing robust adaptation strategies using an approach that is generalizable to other conservation problems which are spatial or portfolio in nature and subject to unresolvable uncertainty. [ABSTRACT FROM AUTHOR]

Global warming has caused the gradual degradation of permafrost, which may affect the vegetation water uptake from different depths. However, the water utilization strategies of different vegetation species during the thawing stages of permafrost regions need further study. To elucidate these differences, we selected the permafrost region in Northeast China as study area. We analyzed the water uptake from different depths of Larix gmelinii, a deciduous coniferous tree, Pinus sylvestris var. mongolica, an evergreen tree, and Betula platyphylla, a deciduous broadleaf tree, using stable isotopes of xylem water, soil water, and precipitation from June to October 2019. The results showed that L. gmelinii primarily used shallow soil water (0-40 cm) with the highest proportion at 64.1%, B. platyphylla generally used middle soil water (40-110 cm) with the highest proportion at 55.7%, and P. sylvestris mainly used middle (40-110cm) and deep soil water (110-150 cm) with the highest proportion at 40.4% and 56.9%. The water sources from different depths exhibited more frequent changes in P. sylvestris, indicating a higher water uptake capacity from different soil depths. L. gmelinii mainly uptakes water from shallow soils, suggesting that the water uptake of this species is sensitive to permafrost degradation. This study revealed the water uptake strategies from different depths of three tree species in a permafrost region, and the results suggested that water uptake capacity of different tree species should be considered in the prediction of vegetation changes in permafrost regions under a warming climate. [ABSTRACT FROM AUTHOR]

The scientific discourse on climate change throughout the US has primarily revolved around changes in mean climate and/or climate extremes. However, little is known about the impacts of climate change on mild weather conditions despite its significant relevance to quality of life. Here, we adopt the concept of "outdoor days" defined as those relatively pleasant days when most people may enjoy outdoor activities (Choi et al., 2024). We project how climate change reshapes seasonality of US outdoor days: relatively large drops in summer, late spring, and early fall; and a significant increase in winter. However, annual outdoor days are projected to change slightly, with notable exceptions. We project relatively large drops in southeast (−23%), south (−19%), and Ohio Valley (−19%), and a significant increase in northwest (14%) toward the end of the century. Our findings have implications for quality of life in different regions, and for nation‐wide travel and tourism. Plain Language Summary: Here, we contribute to the understanding of how climate change will influence quality of life in the US by applying the concept of outdoor days—thermal comfort conditions allowing for outdoor activities, such as walking, jogging, and cycling by most people. We project using state‐of‐the‐art global climate models that climate change will shift the seasonality of outdoor days, resulting in less frequent outdoor days in summer and more frequent outdoor days in the other seasons across the country. Our results highlight specific regional hotspots in the US where annual outdoor days could significantly decrease or increase with important implication for quality of life in different climate regions of the US. Key Points: We project how climate change reshapes seasonality of US outdoor daysFuture climate change will likely result in a northwest‐southeast disparity in the projected change of annual outdoor days in the USWe provide new evidence of the impact of global warming on the quality of human life, travel, and tourism in the US [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) has been increasing since industrialization with rising greenhouse gases. However, a warming hole exists in the North Atlantic where SST has cooled by 0.4 K/century during 1900–2017. It has been argued that this cooling is due to a slowdown of the Atlantic Meridional Overturning Circulation (AMOC), and subpolar North Atlantic SST has thus been utilized to estimate AMOC variability. We assess the robustness of subpolar North Atlantic SST as a proxy for AMOC strength under historical forcing, abrupt quadrupling of CO2, and a medium future emissions pathway, finding that AMOC's fingerprint on SST depends upon forcing scenarios. AMOC is important in warming hole development during significant warming periods, although SST may introduce uncertainties for AMOC reconstruction in stabilized regimes due to diverse forcing mechanisms and decadal variability. Our results caution against using SST alone as a proxy for AMOC variability—both on paleoclimatic and contemporary time scales. Plain Language Summary: As greenhouse gas concentrations increase, the global average sea surface temperature (SST) has risen in response at a rate of 1.4 K/Century since industrialization; however, there exists an area in the North Atlantic decreasing in SST, the North Atlantic Warming Hole (NAWH). Some have argued that the cooling is due to a slowdown of the Atlantic Meridional Overturning Circulation (AMOC), a global circulation current that transports warm equatorial surface water northward. We analyze forcing scenarios using a global climate model under three differing forcing mechanisms, one similar to the real world, one where CO2 initially starts at four times pre‐industrial levels and then held constant, and one based on a medium level future emissions scenario. From this, we study the quality of North Atlantic SST as a proxy for AMOC strength. Our study finds that the impact of the AMOC on SST changes between scenarios, and thus we believe that North Atlantic SST may introduce uncertainty into paleoclimatic studies attempting to reconstruct AMOC change. Additionally, we found that a slowdown of the AMOC plays an important role in the cooling of the North Atlantic when AMOC is slowing rapidly (or "shutting down"), although has a more minimal impact in less extreme scenarios. Key Points: Sea surface temperature based fingerprint of Atlantic Meridional Overturning Circulation (AMOC) changes under different forcing scenariosAMOC in steep decline significantly contributes to North Atlantic warming hole formation, largely due to cloud radiative forcingAMOC plays a smaller role in North Atlantic SST variability in historical period and after CO2 stabilization than during significant warming [ABSTRACT FROM AUTHOR]

Based on the observations and the phase 6 of Coupled Model Intercomparison Project (CMIP6) multimodel simulations, we conducted a detection and attribution analysis for the observed changes in intensity and frequency indices of extreme precipitation during 1961–2014 over the whole of China and within distinct climate regions across the country. A space–time analysis is simultaneously applied in detection so that spatial structure on the signals is considered. Results show that the CMIP6 models can simulate the observed general increases of extreme precipitation indices during the historical period except for the drying trends from southwestern to northeastern China. The anthropogenic (ANT) signal is detectable and attributable to the observed increase of extreme precipitation over China, with human-induced greenhouse gas (GHG) increases being the dominant contributor. Additionally, we also detected the ANT and GHG signals in China's temperate continental, subtropical–tropical monsoon, and plateau mountain climate zones, demonstrating the role of human activity in historical extreme precipitation changes on much smaller spatial scales. Significance Statement: The observed intensification of extreme precipitation globally has been attributed to human influences. Here, we demonstrate that anthropogenic forcing has discernably intensified extreme precipitation over the period 1961–2014, over China and in three of its four climate zones, with human-induced greenhouse gas increases being the dominant contributor. Our results strengthen the body of evidence that greenhouse gas increases are intensifying extreme precipitation by quantifying their role in observed changes at smaller regional scales than previously reported. [ABSTRACT FROM AUTHOR]

Using the past to improve future predictions requires an understanding and quantification of the individual climate contributions to the observed climate change by aerosols and greenhouse gases (GHGs), which is hindered by large uncertainties in aerosol forcings and responses across climate models. To estimate historical aerosol responses, we apply detection and attribution methods to attribute a joint change in temperature and precipitation to forcings by combining signals of observed changes in tropical wet and dry regions, the interhemispheric temperature asymmetry, global mean temperature (GMT), and global mean land precipitation (GMLP). Fingerprints representing the climate response to aerosols (AERs) and the remaining external forcings (noAER; mostly GHG) are derived from large ensembles of historical single- and ALL-forcing simulations from three models in phase 6 of the Coupled Model Intercomparison Project and selected using a perfect model study. Results from an imperfect model study and a hydrological sensitivity analysis support combining our choice of temperature and precipitation fingerprints into a joint study. We find that diagnostics including temperature and precipitation slightly better constrain the noAER signal than diagnostics based purely on temperature or GMT-only and allow for the attribution of AER cooling (even when GMT is not included in the fingerprint). These results are robust across fingerprints from different climate models. Estimated contributions for AER and noAER agree with other published estimates including those from the most recent IPCC report. Finally, we attribute the best estimate of 0.46 K ([−0.86, −0.05] K) of aerosol-induced cooling and 1.63 K ([1.26, 2.00] K) of noAER warming in 2010–19 relative to 1850–1900 using the combined signals of GMT and GMLP. Significance Statement: Aerosols are small liquid or solid airborne particles. They are predominantly the secondary result of emissions of aerosol precursor gases emitted via industrial or natural processes. While greenhouse gases warm the climate, aerosols can have a cooling effect on the climate system, thus offsetting some of the greenhouse gas–related warming. We expect greenhouse gas concentrations in the atmosphere to continue to increase, while aerosol concentrations are likely going to decline due to their impacts on human health. Our study uses observed temperature and precipitation changes to quantify how much aerosols have offset warming from past greenhouse gas emissions. This can help constrain future predictions of global warming. [ABSTRACT FROM AUTHOR]

Land–atmospheric feedback influences the occurrence and severity of flash droughts. However, the observed and projected changes in flash droughts and associated land–atmospheric coupling have not been examined over India. Moreover, the causes of the rapid depletion of soil moisture during flash droughts are not well known. We identify major flash droughts and associated soil moisture–vapor pressure deficit (SM–VPD) coupling in India using ERA5 and simulations from global climate models (CMIP6-GCMs). The summer monsoon season (June–September) witnesses more than 60% of the flash drought events and a relatively higher rate of flash drought development. The flash drought frequency has mainly decreased during India's observed climate (1980–2019), which is projected to decline further in the future warming climate. On the other hand, the flash drought development rate has significantly increased during the observed period, which is projected to enhance further under the warming climate. SM–VPD coupling during the flash drought onset-development phase is considerably higher (threefold to fivefold) than during the normal condition (in the absence of flash drought). The high (low) SM–VPD coupling explains the faster (slower) flash drought development rate in the observed and future warming climate. The strength of SM–VPD coupling has increased in the recent period and is projected to increase further in the future warming climate. The increased SM–VPD coupling can intensify future flash droughts in India, especially during the summer monsoon season, with considerable implications for agriculture, water resources, and ecosystems. Significance Statement: This study aims to understand better the role of land–atmospheric coupling in explaining flash drought characteristics (frequency and development rate) in India. Strong land–atmospheric (SM–VPD) feedback might influence the regional weather patterns during flash drought, which often negatively impacts humans and the ecosystem. We explain the causes and drivers of increasing (decreasing) flash drought development rate (frequency) in India. The long-term change in SM–VPD coupling drives the frequency of flash drought, whereas an anomalous instantaneous change in coupling controls the flash drought development rate. More intense flash droughts contributed by the increased land–atmospheric coupling are projected in the future. Predicting the SM–VPD coupling metric can facilitate more time for preparedness, resulting in minimizing the flash drought impacts. [ABSTRACT FROM AUTHOR]

The accelerating impact of climate change on golf course conditions has led to a significant increase in pesticide dependency, underscoring the importance of innovative management strategies. The shift from Coupled Model Intercomparison Project Phase 5 (CMIP5) to the latest CMIP6 phase has drawn the attention of professionals, including engineers, decision makers, and golf course managers. This study evaluates how climate projections from CMIP6, using Canadian Earth System Models (CanESM2 and CanESM5), impact pesticide application trends on Quebec's golf courses. Through the comparison of temperature and precipitation projections, it was found that a more substantial decline in precipitation is exhibited by CanESM2 compared to CanESM5, while the latter projects higher temperature increases. A comparison between historical and projected pesticide use revealed that, in most scenarios and projected periods, the projected pesticide use was substantially higher, surpassing past usage levels. Additionally, in comparing the two climate change models, CanESM2 consistently projected higher pesticide use across various scenarios and projected periods, except for RCP2.6, which was 27% lower than SSP1-2.6 in the second projected period (PP2). For all commonly used pesticides, the projected usage levels in every projected period, according to climate change models, surpass historical levels. When comparing the two climate models, CanESM5 consistently forecasted greater pesticide use for fungicides, with a difference ranging from 65% to 222%, and for herbicides, with a difference ranging from 114% to 247%, across all projected periods. In contrast, insecticides, growth regulators, and rodenticides displayed higher AAIR values in CanESM2 during PP1 and PP3, showing a difference of 28% to 35.6%. However, CanESM5 again projected higher values in PP2, with a difference of 1.5% to 14%. [ABSTRACT FROM AUTHOR]

Climate change models predict temperature increases, which may affect germination, an important stage in the recruitment of individuals in agroecosystems. Therefore, it is crucial to conduct research on how temperature will impact the germination of multipurpose native species. Leucaena diversifolia (Schltdl.) Benth. is native to America and is commonly cultivated around the world due to having a high protein content in seeds, and their trees are used in agrosilvopastoral systems because they fix nitrogen and provide shade and cattle feed. However, climate change affects the critical phases of its life cycle and influences its growth, reproduction, phenology, and distribution. To assess the germination performance of Leucaena diversifolia under different temperatures throughout thermal times, we estimated germination variables and determined cardinal temperatures and thermal time; we also analysed germination and potential distribution under two climate change scenarios. We found significant variations in seed germination (78–98%) and differences in cardinal temperatures (Tb = 5.17 and 7.6 °C, To = 29.42 and 29.54 °C, and Tc = 39.45 and 39.76 °C). On the other hand, the sub-optimal and supra-optimal temperature values showed little differences: 51.34 and 55.57 °Cd. The models used showed variations in germination time for the analysed scenarios and the potential distribution. We confirm that the populations and distribution of L. diversifolia will be altered due to climate changes, but the species retains the ability to germinate under warmer conditions. [ABSTRACT FROM AUTHOR]

This article focuses on water security in Mykolaiv, a city of 0.5 million inhabitants in southern Ukraine, in the situation of scarcity of usable water resources caused by climate change and military operations. This problem arose after the Dnipro-Mykolaiv water pipeline was destroyed in April 2022 as a result of military operations and the supply of drinking water to the city was cut off. To ensure that the city's population has constant access to sufficient water of acceptable quality, a search for alternative water sources and a climate risk assessment were carried out for the new municipal water supply system from the Southern Bug River. The possible change in flow and its intra-annual distribution under the influence of climate change was modeled using the WaterGAP2 hydrological model and climate projections under the SSP1-2.6 and SSP5-P8.5 scenarios. It was found that under the SSP1-2.6 scenario, the reduction in river flow will be insignificant (up to a maximum of 14% in the far future) and there will be no restrictions on the city's water supply from this section of the river in the near (2021-2050) and far (2051-2080) period. The maximum water withdrawal for municipal water supply and the minimum environmental flow will reach their maximum value only in August (56% of the projected flow), which is not critical. Under the SSP5-8.5 scenario, in the long-term perspective of 2051-2080, the largest decrease in runoff will occur from May to October, and the water withdrawal will increase to 40-79% of the projected flow. The use of the research results not only in water management, but also in municipal administration, and their dissemination in territorial communities will contribute to the successful adaptation of socioeconomic and environmental processes in the region and can bring successful benefits not only to the economy, but also to communities. [ABSTRACT FROM AUTHOR]

A large portion of cloud scenes over the globe shows multiple layers composed of different phases, in general with ice clouds on the top and liquid water clouds beneath. Such multi‐layer (ML) clouds constitute major challenges in cloud observations and weather and climate modeling. This study improved a threshold algorithm for detecting ice‐over‐water ML clouds using geostationary satellites. Optimal thresholds were established for the spectral characteristics of the Advanced Himawari Imager (AHI) and the Advanced Geostationary Radiation Imager (AGRI), accounting for differences between land and ocean surfaces. Validation with collocated space radar and lidar measurements indicated the identification accuracies of approximately 82% over the land and 76% over the ocean. Annual distributions of ML clouds inferred by AHI and AGRI exhibited strong similarity. Furthermore, 6 years of hourly observations revealed distinct monthly and daily variations in ice‐over‐water clouds over the Asia–Pacific region. The ML cloud monthly variations were similar to those of the seasonal convection cycle, with occurrence frequencies over the typical regions higher in summer (maximum ∼27%) and lower (minimum 6%–10%) in winter. Regarding daily variations, ice‐over‐water clouds occurred more frequently around local noon over most of the six time zones (from UTC + 06 to UTC + 11) throughout all seasons. The refined spatiotemporal distribution of ML clouds, particularly the daily variations, is possible to improve our understanding of cloud vertical distributions and radiative effects, and has the potential to promote subsequent validation and parameterization of cloud overlapping in global climate modeling. Plain Language Summary: ML clouds with complex vertical structures introduce significant challenges in global climate models owing to a lack of accurate observations and universally applicable theories. While active satellite instruments provide valuable information about ML clouds, they are limited by temporal resolution and spatial discontinuity. This study improves on a passive geostationary satellite spectral imager‐based method for detecting ice‐over‐water clouds. Based on the advantages of geostationary satellites, the annual and monthly distributions, as well as daily variations of ice‐over‐water clouds, are revealed. Our results provide a valuable observational foundation for investigating ML cloud properties. Key Points: New thresholds of multi‐layer cloud detection algorithms for Advanced Himawari Imager and Advanced Geostationary Radiation Imager are introduced and validatedDaytime spatial and temporal variations of multi‐layer clouds over the Asia‐Pacific region are presentedThere are substantial daily variations (up to over 15%) on the multi‐layer cloud occurrence frequencies [ABSTRACT FROM AUTHOR]

Global climate models often simulate atmospheric conditions incorrectly due to their coarse grid resolution, flaws in their dynamics, and biases resulting from parameterization schemes. Here we document a bias in the magnitude and extent of minimum temperature extremes in the CMIP6 model ensemble, relative to ERA5. The bias is present in the southern Cascadia region (i.e., Pacific Northwestern United States and southwestern British Columbia, Canada, spanning from the coast to the Rocky Mountains), with some models showing a bias magnitude in excess of −10°C in the first percentile of daily winter minimum temperature. The sea level pressure pattern for these events is similar in CMIP6 models and ERA5, showing high anomalies in the Northeast Pacific that are indicative of an atmospheric blocking pattern and consequently more northerly flow. Though this atmospheric blocking pattern is typically concurrent with cold winter temperatures across much of North America, Rocky and Cascade mountain ranges prevent the cold air from reaching the southern Cascadia region as confirmed by the observations and reanalysis. Our results suggest that the bias in CMIP6 minimum temperatures is a result of unresolved topography in the Rockies and Cascade mountain ranges, such that the terrain does not adequately block cold air advection from reaching the southern Cascadia region. Plain Language Summary: Global climate models continue to struggle with recreating some of the observed behaviors of our Earth system for a variety of reasons. Here we document one such issue: daily minimum temperatures in Southwestern British Columbia and western Washington that are much colder than are observed. We find that these temperatures occur when extremely cold air is moved from the north into southwestern British Columbia and western Washington. In reality, the Rocky and Cascade mountain ranges prevent this air from reaching western Washington and southwestern British Columbia by acting as barriers. However, in the global climate models these mountain ranges are lower and less jagged, making it easier for cold air to move across them. Key Points: CMIP6 models show a pronounced cold bias in the coldest daily minimum temperatures for the southern Cascadia region of North AmericaWe find no evidence suggestion that differences in large‐scale dynamics are causing the cold bias in the southern Cascadia regionPoorly resolved topography in the CMIP6 models allows excessive cold advection into southern Cascadia during atmospheric blocking events [ABSTRACT FROM AUTHOR]

The assessment of trends in hydroclimatic characteristics averaged over the watershed area and changes in the fields of the hydrological cycle components in the 21st century in a large river basin of the Nizhnekamskoye Reservoir was carried out on the basis of a model approach combining regional space distributed hydrological model and global climate models. It is shown that a slight decrease by 8% is expected in the area-averaged specific runoff by the end of the century according to the Representative Concentration Pathway 8.5 scenario. However, in various parts of the watershed both a slight increase by 3–6% and a significant decrease in the mean annual runoff by 22% can occur, depending on physiographic conditions. Moreover, climatic changes can increase contrasts in the ecological status of the territory: warm regions in the steppe zone become even hotter and more arid, and the moisture availability of wetter territories in the centre and east of the basin increases even more. [ABSTRACT FROM AUTHOR]

Pine wilt disease is one of the most severe and devastating diseases affecting pine forests worldwide, resulting in huge economic losses in many countries. The pinewood nematode (PWN), Bursaphelenchus xylophilus, is the causal agent of pine wilt disease and is obligately vectored by pine sawyer beetles, of the genus Monochamus. For the disease to be present, the habitat must be suitable for the PWN, and include at least one vector species, and at least one host species. To predict its potential distribution, a model must consider all three components. However, no comprehensive study has examined the influence of climatic suitability on the distribution of this “biological complex”. This study addresses this gap by incorporating biotic interactions, specifically involving 13 vectors and 61 host plants, into projections based on the PWN model. We predicted the global potential distribution of pine wilt disease and compared it with the PWN model to highlight the importance of including biotic interactions in species distribution models under climate change. We found that the model revealed an overall trend of increasing suitability scores for both the PWN and pine wilt disease models under future climate scenarios. Furthermore, compared to the PWN model, the biotic model results in an apparent increase in suitability worldwide in the future as the climate will be more suitable to vector and host complexes, suggesting that pine wilt disease could potentially spread to other places via available hosts and vectors. Synthesis and applications. By incorporating biotic interactions, we projected a more accurate suitable area for pine wilt disease, offering valuable insights into regions at high risk for future invasions by the disease and its vectors. This information supports the development of management and early detection strategies in areas of high suitability, helping to mitigate potential economic and ecological losses. Additionally, this study introduces a novel approach for integrating biotic factors into species distribution models. [ABSTRACT FROM AUTHOR]

Residential and non‐residential buildings are a major contributor to human well‐being. At the same time, buildings cause 30% of final energy use, 18% of greenhouse gas emissions (GHGE), and about 65% of material accumulation globally. With electrification and higher energy efficiency of buildings, material‐related emissions gain relevance. The circular economy (CE) strategies, narrow, slow, and close, together with wooden buildings, can reduce material‐related emissions. We provide a comprehensive set of building stock transformation scenarios for 10 world regions until 2060, using the resource efficiency climate change model of the stock–flow–service nexus and including the full CE spectrum plus wood‐intensive buildings. The 2020–2050 global cumulative new construction ranges from 150 to 280 billion m2 for residential and 70‐120 billion m2 for non‐residential buildings. Ambitious CE reduces cumulative 2020–2050 primary material demand from 80 to 30 gigatons (Gt) for cement and from 35 to 15 Gt for steel. Lowering floor space demand by 1 m2 per capita leads to global savings of 800‐2500 megatons (Mt) of cement, 300‐1000 Mt of steel, and 3‐10 Gt CO2‐eq, depending on industry decarbonization and CE roll‐out. Each additional Mt of structural timber leads to savings of 0.4‐0.55 Mt of cement, 0.6‐0.85 Mt of steel, and 0.8‐1.8 Mt CO2‐eq of system‐wide GHGE. CE reduces 2020–2050 cumulative GHGE by up to 44%, where the highest contribution comes from the narrow CE strategies, that is, lower floorspace and lightweight buildings. Very low carbon emission trajectories are possible only when combining supply‐ and demand‐side strategies. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges. [ABSTRACT FROM AUTHOR]

Climate change projections suggest a precarious future for the Monarch butterfly (Danaus plexippus) as the suitable climatic habitat of its exclusive overwintering host Abies religiosa (oyamel, Sacred fir, a conifer endemic to Mexico) inside the Monarch Butterfly Biosphere Reserve (MBBR) is expected to disappear by 2090. Since the upper elevation limit of A. religiosa is approximately 3,500 m and the summits of mountains within the MBBR are ca. 3,550 m, we tested the feasibility of establishing A. religiosa at four locations outside its current geographic range in the MBBR, on a geographically close volcano, Nevado de Toluca at 4000 (timberline, an extreme site), 3,800, and 3,600 m (to test species range expansion upward in elevation), and at 3400 m (a reference site, slightly lower than the upper elevation limit of A. religiosa). Using existing shrubs as nurse plants to protect the seedlings from extreme temperatures, at each site we planted five to eight populations, originating between 3,100 and 3,500 m within the MBBR. After three growing seasons in the field (6 years after sowing), we found that: (a) survival and height increment declined steeply with test site elevation; (b) even at the highest sites (3,800 and 4,000 m), survival was acceptable, at 68 and 44%, respectively, although the growth was very poor at 4000 m; (c) populations responded similarly to transfer; (d) transfer effects were best accounted for by annual dryness index; (e) to compensate for the expected 2.3°C increase in mean annual temperature or 0.009 √°Cmm-1 increase of annual dryness index from the reference period (1961-1990) to the decade centered in 2060, it would be necessary to shift populations approximately 500 m to higher elevations; and (f) upward transfers to compensate for the 2.3°C increase in mean annual temperature are expected to result in height increment and survival that are approximately 47 and 21% lower, respectively, than values expected at zero transfer distance. We conclude that the establishment of A. religiosa at 3600 and 3,800 m is feasible and that planted stands could eventually serve as overwintering sites for the Monarch butterfly under projected future climates. [ABSTRACT FROM AUTHOR]

Species distribution modeling (SDM) is an essential tool in ecology and conservation for predicting species distributions based on species presence/absence data and environmental variables. The present study aimed to understand the distribution pattern and habitat suitability of Indianthus virgatus under current and future climate change scenarios (2050 and 2070) using MaxEnt (3.4.4) and Wallace Ecological Modeling (v2.1.2) tools. The study also intended to identify key environmental predictors of I. virgatus' distribution. Species occurrence data were collected from various sources, including herbarium (online and physical), field surveys, and online databases, yielding 105 unique locations in the Western Ghats (WG) of India and Sri Lanka. We used 19 bioclimatic variables and elevation data sourced from WorldClim for modeling. The MaxEnt and Wallace models showed excellent performance in predicting the distribution of I. virgatus, with area under the curve values of 0.958 (± 0.002) and 0.93, respectively. In MaxEnt modeling, Temperature Seasonality (bio4) was the most significant environmental parameter, followed by the Precipitation of the Coldest Quarter (bio19). In contrast, the Annual Mean Temperature (bio1), Temperature Seasonality (bio4), and Annual Precipitation (bio12) were among the key contributors in Wallace EcoMod. Both the models predicted relatively lesser areas in the species' distribution range as highly suitable habitats (HSH) in India and Sri Lanka. We found divergent trends in predicting I. virgatus distributions using MaxEnt and Wallace EcoMod, particularly for future projections. Nevertheless, both models predicted significant habitat loss under future climate change scenarios, especially under RCP85, with varying degrees of suitability across India and Sri Lanka. Overall, our findings on expected habitat loss under future climate change scenarios highlight the importance of conserving I. virgatus, which has already been declared critically endangered (CR) in Sri Lanka. [ABSTRACT FROM AUTHOR]

Ceracris nigricornis Walker is an insect of the Acrididae, which can harm bamboo, rice, corn, sorghum and other crops, and can cause serious economic losses. In this study, based on 234 occurrence sites of C. nigricornis obtained from the Global Biodiversity Information Facility and literature, and data of three future climate scenarios presented by CMIP6, two niche models (GARP, MaxEnt) were used to predict the suitable area of C. nigricornis in China. The result shows that the main environmental factors affecting the distribution of C. nigricornis are min temperature of coldest month (bio6), mean temperature of coldest quarter (bio11), precipitation of driest month (bio14) and precipitation of wettest quarter (bio16). From the result of MaxEnt model, it can be seen that the suitable area of C. nigricornis in China is 128.91 × 104 km2 under current scenario. It will decrease by 3.19% in the 2050s and then increase by 12.04% in the 2090s under the SSP1‐2.6 scenario, increase by 5.79% in the 2050s and then decrease by 7.53% in the 2090s under the SSP2‐4.5 scenario, and increase by 33.03% in 2050s and then decrease by 23.31% in the 2090s under SSP5‐8.5 scenario. From the result of GARP model, it can be seen that the suitable area of C. nigricornis in China is 166.09 × 104 km2 under current scenario. It will increase by 8.41% in 2050s and then continue to increase by 6.11% in 2090s under SSP1‐2.6 scenario, increase by 23.84% in the 2050s and then decrease by 0.88% in the 2090s under the SSP2‐4.5 scenario, and increase by 34.37% in 2050s and then decrease by 1.75% in 2090s under SSP5‐8.5 scenario. The boundaries of suitable areas will expand to the north and southwest of China under future climate change scenarios, specially in Sichuan, Chongqing and Yunnan. Local forestry authorities should strengthen the monitoring of bamboo forests to prevent the damage caused by the introduction of C. nigricornis. [ABSTRACT FROM AUTHOR]

Mangroves provide habitat for a diverse array of marine species, especially snails. We used a MaxEnt model to predict potential global suitable habitat for Cerithidea rhizophorarum and Telescopium telescopium in the family Potamididae. A total of 667 occurrence data were obtained from the Global Biodiversity Information Facility (GBIF) with the following sub‐data set contribution, "iNaturalist Research Grade Observations" (85%), "International Barcode of Life project (iBOL)" (7%), "FBIP: SeaKeys_SANBI: Marine images iSpot_2013" (1%), "A dataset of marine macroinvertebrate diversity from Mozambique and São Tomé and Príncipe" (1%), occurrence data of some marine invertebrates and freshwater crabs housed in the natural history collection at the National Museums of Kenya (1%), and Natural History Museum Rotterdam‐Specimens (1%). Our results showed that temperature with a contribution of above 80% in the present and future model is the most important driver of the distribution of mangrove snails. In the present and future models, the most potentially suitable habitats for C. rhizophorarum and T. telescopium were observed along coastal areas with a temperature between 20°C–21°C and 30°C, respectively. Our model predicts that by 2100, high‐suitability areas will shrink as a result of global warming. The vulnerability of mangrove snails under future climate conditions is evident in our results. Our findings contribute significant insights into the intricate relationship between mangrove habitats and mangrove snails, offering a valuable foundation for conservation initiatives aimed at safeguarding the biodiversity and ecological functions of these crucial coastal ecosystems in the face of changing global environmental conditions. [ABSTRACT FROM AUTHOR]

Climate change often facilitates biological invasions, leading to potential interactive impacts of these global drivers on freshwater ecosystems. Although climatic mitigation efforts may reduce the magnitude of these interactive impacts, we are still missing experimental evidence for such effects under multiple climate change scenarios within a multi‐trophic framework. To address this knowledge gap, we experimentally compared the independent and interactive effects of two climate change scenarios (mitigation and business‐as‐usual) and biological invasion on the biomass of major freshwater trophic groups (phytoplankton, zooplankton, periphyton, macroinvertebrates, and a native macrophyte) and the decomposition rate of allochthonous material. Among the independent effects, we found that the business‐as‐usual climate treatment resulted in lower native macrophyte biomass and higher periphyton biomass compared to the climatic baseline and mitigation treatments. This indicates the potential of climate change to alter the relative dominance of different freshwater producers and demonstrates that climate mitigation efforts can counteract these effects. Biological invasion alone increased the biomass of chironomids, a dominant macroinvertebrate group in tropical freshwater ecosystems, demonstrating a compensatory effect on climate change. Climate change and biological invasion interactively reduced the decomposition rate of allochthonous detritus, likely mediated by the feeding preference of abundant chironomids for periphytic algae associated with the presence of non‐native macrophytes. We concluded that (i) climatic mitigation can maintain climate baseline conditions in freshwater ecosystems, and (ii) the interactive effects between future climate scenarios and biological invasion are related to complex cascading interactions among trophic groups on ecosystem processes. [ABSTRACT FROM AUTHOR]

Coral reefs are at risk due to various global and local anthropogenic stressors that impact the health of reef ecosystems worldwide. The most recent climate models predict that climate change will increase the frequency and intensity of tropical storms. This increased storm occurrence and strength will likely compromise coral reef structures and habitats for reef-dwelling organisms, including across the Florida Keys Reef Tract (FKRT), the most extensive tropical reef system along the US coast. While several recent studies reveal the chronic impacts of tropical storms on corals, relatively little is known about the effects of major storm events on coral growth and how these effects vary over spatiotemporal scales. Here, I characterize the skeletal growth of two common Caribbean reef-building coral species, Siderastrea siderea and Pseudodiploria strigosa , before and after Hurricane Irma to investigate the storm's impact on coral skeletal growth on inner and outer reefs of the FKRT. Coral cores were extracted from both species at four inner and four outer reef sites in May 2015, before Hurricane Irma struck the Florida Keys in September 2017. Subsequently, 33 micro-cores were collected in May 2019, two years after the storm traversed our previously cored coral colonies. A three-way ANOVA model with storm, species, and reef location as the three factors was used to assess the impact of the storm on each of three growth parameters: skeletal density, linear extension, and calcification rates. Results reveal no difference in the coral annual skeletal growth parameters pre- and post-Hurricane Irma, although previously quantified differences in these growth parameters across species and location were observed. However, analysis of the "yearly" change in annual skeletal growth parameters showed significant differences in skeletal density across groups before and after Hurricane Irma, but not for linear extension and calcification rates. Our findings improve an understanding of the impacts of tropical storms on coral skeletal growth and offer new insights into how we can employ corals' innate growth capacities to help conserve coral reefs under climate change. [ABSTRACT FROM AUTHOR]

The agricultural sector is vulnerable to extreme droughts, particularly in regions with limited water infrastructure. This study aims to analyze the future characteristics of droughts in Mille watershed under climate change. It employs three drought indices: the Reconnaissance Drought Index, Streamflow Drought Index, and Agricultural Standardized Precipitation Index. The analysis uses baseline data (1985–2014) and future data (2041–2100) downscaled from the Coupled Model Inter-comparison Project 6. Three Global Climate Models like MIROC-6, CMCC, and MRI were used under two Shared Socioeconomic Pathways Scenarios (SSP2-4.5 and SSP5-8.5). The Soil and Water Assessment Tool model was employed to simulate future streamflow for two time periods (the 2050s and the 2080s). The study found that the most severe Reconnaissance Drought Index events in the future will occur in Kombolcha, Mille, Batie, and Sirinka, with magnitudes and frequencies in the 2050s of (-1.51, 4.76%), in 2058 (-1.49, 6.16%), in the 2063s (-1.52,5.65%), and in 2066 (-1.5, 4.48%). However, on an annual scale, extreme and severe hydrological drought events are projected for 2088 (-2.0) and 2091 (-1.51) under the SSP2-4.5 scenario. Agricultural droughts are expected to be more frequent in Mille and Batie compared to other stations. Overall, the Reconnaissance Drought Index indicates a significant drought trend in the past, with an anticipated increase in the frequency and severity of future droughts. The study area is expected to experience relatively fewer hydrological drought events. These findings highlight the need for effective monitoring systems and timely identification of drought events, which are crucial for making informed decisions regarding drought mitigation and water management, particularly in the water-dependent agricultural sector. [ABSTRACT FROM AUTHOR]

Climate change is one of the most critical factors impacting hydrological dynamic systems. This study investigated how climate change influences the hydrological dynamics within the Siliana watershed in northwestern Tunisia, employing the Soil and Water Assessment Tool (SWAT) model. The analysis compared streamflow patterns for the future period (2046–2072) with a baseline period (1979–2005). Simulations were carried out using four combinations of regional and global climate models from EURO-CORDEX, based on two Representative Concentration Pathways (RCP4.5 and RCP8.5). The results indicate a projected annual precipitation decrease of 22% with RCP4.5 and 27% with RCP8.5, accompanied by a temperature rise of up to 7 °C under RCP8.5. Streamflow is anticipated to decrease by 44% under RCP4.5 and 69% under RCP8.5. Extreme events show intensified high flows of shorter durations and increased low flows. Analysis using the Standardized Precipitation Evapotranspiration Index (SPEI) revealed longer and more intense droughts. Under the RCP8.5 scenario, 24% of the watershed faces extreme drought, while 76% experiences severe drought conditions. These findings highlight notable changes in hydrological indicators, emphasizing the urgent need for adaptive strategies in water resource management within the Siliana Basin to mitigate the effects of climate change. [ABSTRACT FROM AUTHOR]

The aim of this study is to determine the effect of climate change on reference evapotranspiration (ETo) and sunflower and wheat evapotranspiration (ETs and ETw, respectively) in the Trakya Region of Türkiye. ETo Calculator (version 3.2) and CROPWAT 8.0 were used to compute ETo and ET in the reference period (1970–1990), short- (2016–2025), mid- (2046–2055), and long- (2076–2085) terms. Additionally, ETo was tested in 2012 and ETo was simulated for every 1 °C temperature increase up to 5 °C in the reference period. Calculated ETo and ET values for the future were compared with the reference period. For the future, climate data estimated by RegCM3 Regional Climate Model, A2 scenario were used. While the average ETo value of the reference period was 3.3 mm day−1, it was 3.0 mm day−1 in 2012. Compared to the reference period, ETo values change by −3% (3.2 mm day−1), 9% (3.6 mm day−1), and 21% (4.0 mm day−1) in the short-, mid-, and long-term, respectively. The 575 mm ET deficit calculated during the vegetation period of sunflower in the model reference period was forecasted to change by −11% (514 mm), +15% (660 mm), and +25% (721 mm) in the short-, mid-, and long-term, respectively. For wheat, while 59 mm of excess water was calculated in the reference period, it became 193 mm (+227%) in the short-term and a water deficit of 8 mm (−113%) and 6 mm (−110%) in the mid- and long-term, respectively. In addition, it is estimated that there will be an increase of 0.1 mm day−1 (4%) in ETo values for each 1 °C temperature increase compared to the reference period (1970–1990). It was concluded that climate change in the Trakya Region will not significantly affect wheat farming; however, it will cause a serious water deficit in sunflower production. [ABSTRACT FROM AUTHOR]

Climate change caused predominately by carbon dioxide (CO2) from fossil fuel use is a critical issue for our future. It is incumbent on science educators to learn about it and teach it in ways that illustrate the power of science to understand climatic changes and model past, present, and possible climate futures. It is equally important for educators to address alternative explanations that do not cause present warming. We provide sufficient background to understand the effects of atmospheric CO2 on climate, how we know past values of both CO2 and temperature, and how mathematical models lead to a quantitative understanding and predictions of increases in temperature on doubling atmospheric CO2. We discuss alternative but incorrect explanations for present warming that are employed by those who use misinformation and disinformation to take attention away from the main cause, the burning of fossil fuels. Guided student activities are provided to help students develop an understanding of the causes and strategies to mitigate the worst effects of climate change. We must provide students with a sound understanding of climate change to empower them with the knowledge to make effective choices. Informed students can develop into agents of change as we prepare them to live in their changing climate futures. [ABSTRACT FROM AUTHOR]

Downscaling global climate models (GCMs) provides crucial high-resolution data needed for informed decision-making at regional scales. However, there is no uniform approach to select the most suitable GCMs. Over Southeast Asia (SEA), observations are sparse and have large uncertainties, complicating GCM selection especially for rainfall. To guide this selection, we apply a standardised benchmarking framework to select CMIP6 GCMs for dynamical downscaling over SEA, addressing current observational limitations. This framework identifies fit-for-purpose models through a two-step process: (a) selecting models that meet minimum performance requirements in simulating the fundamental characteristics of rainfall (e.g. bias, spatial pattern, annual cycle and trend) and (b) selecting models from (a) to further assess whether key precipitation drivers (monsoon) and teleconnections from modes of variability are captured, i.e. the El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). GCMs generally exhibit wet biases, particularly over the complex terrain of the Maritime Continent. Evaluations from the first step identify 19 out of 32 GCMs that meet our minimum performance expectations in simulating rainfall. These models also consistently capture atmospheric circulations and teleconnections with modes of variability over the region but overestimate their strength. Ultimately, we identify eight GCMs meeting our performance expectations. There are obvious, high-performing GCMs from allied modelling groups, highlighting the dependency of the subset of models identified from the framework. Therefore, further tests of model independence, data availability and future climate change spread are conducted, resulting in a final subset of two independent models that align with our a priori expectations for downscaling over the Coordinated Regional Climate Downscaling Experiment –Southeast Asia (CORDEX-SEA). [ABSTRACT FROM AUTHOR]

High‐resolution climate projections are valuable resources for understanding the regional impacts of climate change and developing appropriate adaptation/mitigation strategies. In this study, we developed a 10‐km gridded hydrometeorological dataset over India by dynamic downscaling of the bias‐corrected Community Earth System Model (CESMv1) climate projections under RCP8.5 scenario using the state‐of‐the‐art Weather Research and Forecasting (WRF) model. The downscaled CESM dataset (DSCESM) is archived in the World Data Center for Climate (WDCC) portal at three temporal resolutions (daily, monthly and monthly climatology) for current (2006–2015), mid‐century (2041–2050) and end‐century (2091–2100) periods. The dataset includes 2‐m air temperature, total accumulated precipitation, wind speed, relative humidity, sensible and latent heat fluxes, along with surface shortwave and outgoing longwave radiation. All the DSCESM variables were evaluated against reanalysis data and station observations for the period 2006–2015. This dataset can help us quantitatively understand regional climate change in India. It can also be used in conjunction with agricultural, hydrological, fire and other application models for climate change impact assessment on various sectors to help develop effective adaptation/mitigation strategies. [ABSTRACT FROM AUTHOR]