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Groundwater is an essential resource for agriculture and domestic use in drought-prone regions, particularly in northwestern Bangladesh. Accurate forecast of groundwater level (GWL) fluctuations is crucial for sustainable water regulation. This work investigates the application of deep learning and dynamic system response models to forecast GWL changes in this vulnerable area. The models employed include Long Short-Term Memory (LSTM) networks, Autoregressive Moving Average (ARMA), Discrete-Time State-Space Model (n4sid), Continuous-Time State-Space Model (SSEST), Discrete-Time State-Space Model through a Regularized ARX Model Reduction (SSREGEST), and coupled ARMA-state-space models. A total of eight models were trained and tested on historical GWL data from 19 observation wells. The top-performing models at various locations delivered satisfactory results, with C, IOA, NRMSE, and MAD values ranging from 0.53 to 0.92, 0.62 to 0.95, 0.01 to 0.25, and 0.08 m to 1.09 m, respectively. Model comparison using the Entropy-Distance from Average Solution (Entropy-EDAS) method revealed that LSTM networks outperformed traditional time series (ARMA), system dynamic (n4sid, SSEST, SSREGEST), and coupled ARMA-state-space models (ARMA-n4sid, ARMA-SSEST, and ARMA-SSREGEST) in most locations, while other models exhibited varying performances across different observation wells. The varying performance across different observation wells highlights that prediction accuracy depends not only on the modeling algorithms but also on the quantity and quality of the learning and testing data. The projections generated by the best models effectively captured historical trends, providing the first-ever five-year forecasts of GWL fluctuations for the region. These projections offer valuable insights for water resource management and planning in areas vulnerable to drought and climate variability. [ABSTRACT FROM AUTHOR]

Introduction: Lyme disease is the most common vector-borne disease in the United States and Canada. The primary vector for the causative agent of Lyme disease, Borrelia burgdorferi, in the Pacific Northwest is the western blacklegged tick, Ixodes pacificus. Materials and Methods: Using active tick surveillance data from British Columbia, Canada, and Washington State, USA, habitat suitability models using MaxEnt (maximum entropy) were developed for I. pacificus to predict its current and mid-century geographic distributions. Passive surveillance data both from BC and WA were also visualized. Results: According to the constructed models, the number of frost-free days during the winter is the most relevant predictor of its habitat suitability, followed by summer climate moisture, ecoregion, and mean minimum fall temperature. The ensemble geographic distribution map predicts that the coastal regions and inland valleys of British Columbia and the Puget Lowlands of Washington State provide the most suitable habitats for I. pacificus. The density map of ticks submitted from passive surveillance data was overlaid on the current distribution map and demonstrates the correlation between numbers of submissions and habitat suitability. Mid-century projections, based on current climate change predictions, indicate a range expansion, especially of low and moderate suitability, from current distribution. Regarding Lyme disease risk, I. pacificus identified from both active and passive surveillance and tested positive for B. burgdorferi were found to be in areas of moderate to very high suitability for I. pacificus. Conclusion: According to developed models, the total suitable habitat area for I. pacificus will expand in the interior regions of British Columbia and Washington State. However, the risk remains small given relatively low infection rates among I. pacificus. Further studies are required to better understand how this might change in the future. [ABSTRACT FROM AUTHOR]

Fire plays a critical role in both the formation and degradation of ecosystems; however, there are still significant uncertainties in the estimation of burned areas (BAs). This study systematically evaluated the performance of ten global climate models (GCMs) in simulating global and regional BA during a historical period (1997–2014) using the Global Fire Emissions Database version 4.1s (GFED4s) satellite fire product. Then, six of the best models were combined using Bayesian Model Averaging (BMA) to predict future BA under three Shared Socioeconomic Pathways (SSPs). The results show that the NorESM2-LM model excelled in simulating both global annual and monthly BA among the GCMs. GFDL-ESM4 and UKESM1-0-LL of the GCMs had the highest Pearson's correlation coefficient (PCC), but they also exhibited the most significant overestimation of monthly BA variations. The BA fraction (BAF) for GCMs was over 90% for small fires (<1%). For small fires (2~10%), GFDL-ESM4(j) and UKESM1-0-LL(k) outperformed the other models. For medium fires (10–50%), CESM2-WACCM-FV2(e) was closest to GFED4s. There were large biases for all models for large fires (>50%). After evaluation and screening, six models (CESM2-WACCM-FV2, NorESM2-LM, CMCC-ESM2, CMCC-CM2-SR5, GFDL-ESM4, and UKESM1-0-LL) were selected for weighting in an optimal ensemble simulation using BMA. Based on the optimal ensemble, future projections indicated a continuous upward trend across all three SSPs from 2015 to 2100, except for a slight decrease in SSP126 between 2071 and 2100. It was found that as the emission scenarios intensify, the area experiencing a significant increase in BA will expand considerably in the future, with a generally high level of reliability in these projections across most regions. This study is crucial for understanding the impact of climate change on wildfires and for informing fire management policies in fire-prone areas in the future. [ABSTRACT FROM AUTHOR]

Many fundamental aspects of New York State's climate have already begun to change, and the changes are projected to continue—and in some cases, accelerate—throughout the 21st century. This chapter explores observed and projected changes in a variety of physical variables that relate directly to weather and climate, starting with average and extreme air temperature and proceeding to the associated effects on precipitation, extreme events, and core properties of New York's coastal and inland waters. These climate attributes and hazards lead to impacts throughout the eight sectors of this assessment. [ABSTRACT FROM AUTHOR]

Monsoons are a vital part of the agriculture and economy of India which most of its population rely on for their livelihoods. It still is not clear how climate change will impact precipitation events over India due to the complexity of accurately modelling precipitation. Using twelve Coupled Model Intercomparison Project Six (CMIP6) models, we compared their performance to observed data taken from CRU as well as looking at the future changes in precipitation until the end of the twenty first century for the six precipitation homogenous regions over India. The individual models showed varying degrees of wet and dry biases and the ensemble mean of these models showed relatively lesser bias and improved spatial correlation. Out of 12 models, NorESM and MIROC6 models outperform other models in terms of capturing the spatial variability of precipitation over the Indian region. It is also found that due to lesser moisture transport from the adjoining seas represented through vertically integrated moisture transport (VIMT) analysis, there is consistent dry bias across the models. Further a comprehensive analysis of model performance across six homogeneous precipitation regions indicates that NorESM demonstrates better performance in the CNE and HR regions, EC-Earth excels in the PR, WC, and NE regions, while CMCC shows better performance specifically in the NW region compared to other models. Shared Socioeconomic Pathways (SSPs) were used for future projections and a slight increase in June, July, August, and September (JJAS) precipitation until the end of the century with SSP5-8.5 showing the largest increase. We found an increase in precipitation of 0.49, 0.74 and 1.4 mm/day under SSP1-2.6, SSP2-4.5 and SSP5-8.5 in the far future. The northeast region was shown to receive the largest increase in precipitation (2.9 mm/day) compared to other precipitation homogenous regions and northwest will experience largest shift in precipitation. Interestingly, the number of wet days is expected to increase in the northwest region implying more VIMT towards the region. Our results indicate that monsoon precipitation extremes across all the homogenous regions will increase into the future with a higher severity under fossil-fuelled development, although the models still show large biases lowering confidence in our results. [ABSTRACT FROM AUTHOR]

Understanding the evolutionary responses of anadromous salmon and trout to climate change is critical for effective conservation planning. In this study, we conducted a comprehensive review of literature published from 2010 to 2020 to synthesize current knowledge on climate impacts to these fish populations. Specifically, we focused on 199 papers that explored evolutionary processes in response to changing environmental conditions. Our analysis revealed several key themes, including the interwoven influences of climate and human activities on genetic variation, phenotypic traits, and population dynamics. We found that geographic patterns in genetic diversity are closely linked to climatic gradients, highlighting the importance to conservation strategies of variation in existing adaptive capacity. Additionally, temporal trends in phenology, maturation age, and fecundity indicate ongoing evolutionary and plastic responses to climate change. Importantly, human activities were identified as significant drivers of maladaptation in anadromous salmon and trout populations. We emphasize the need for targeted monitoring of specific evolutionary processes to mitigate the loss of genetic diversity and enhance adaptive capacity. Our study underscores the importance of identifying and protecting areas of high genetic diversity and rare genes, particularly in regions projected to experience rapid climatic shifts. In conclusion, our findings identify strengths and gaps in the research investigating the role of evolutionary dynamics in the face of climate change. By capitalizing on new tools for sequencing, genomic analysis, and automated field data collection, we can establish baselines for tracking evolutionary responses to climate change. Better integration of evolutionary processes into projections of future climate impacts will lead to more effective strategies to ensure the long‐term resilience of these iconic fish species and other wildlife. [ABSTRACT FROM AUTHOR]

Climate risk assessments must account for a wide range of possible futures, so scientists often use simulations made by numerous global climate models to explore potential changes in regional climates and their impacts. Some of the latest‐generation models have high effective climate sensitivities (EffCS). It has been argued these "hot" models are unrealistic and should therefore be excluded from analyses of climate change impacts. Whether this would improve regional impact assessments, or make them worse, is unclear. Here we show there is no universal relationship between EffCS and projected changes in a number of important climatic drivers of regional impacts. Analyzing heavy rainfall events, meteorological drought, and fire weather in different regions, we find little or no significant correlation with EffCS for most regions and climatic drivers. Even when a correlation is found, internal variability and processes unrelated to EffCS have similar effects on projected changes in the climatic drivers as EffCS. Model selection based solely on EffCS appears to be unjustified and may neglect realistic impacts, leading to an underestimation of climate risks. Plain Language Summary: Climate impact researchers often must decide which of the many available global climate models to use for their analyses. It has been suggested models with very high climate sensitivities should be excluded from impact analyses, because their global mean temperature projections are unrealistic. However, we show that projected future changes in climatic drivers of floods, droughts, and wildfires, across many regions of the world, are not correlated with model climate sensitivity. Regional impacts depend on numerous processes and phenomena many of which are unrelated to climate sensitivity. Excluding models solely on the basis of their climate sensitivity is thus not justified, and can lead to important impacts being ignored by policymakers, with serious consequences for society. Key Points: Future changes in climatic drivers of floods, droughts, and wildfires are generally not correlated with climate sensitivity in Sixth Coupled Model Intercomparison Project modelsModel selection for impacts solely based on climate sensitivity is not justified and may lead to an underestimation of climate risksModel internal variability plays an important role in future projections of climate impact drivers [ABSTRACT FROM AUTHOR]

Coastal zones are facing intensive ecological pressures and challenges, which could vary over a wide range of spatiotemporal scales. Our limited capability to understand and especially predict this variability can lead to the misinterpretation of coastal ecological resilience. Therefore, the assessment and prediction of ecological resilience are particularly important. In this study, a new approach based on the Pressure–State–Response model is developed to assess and predict pixel-scale multi-year ecological resilience (ER) and then applied to investigate the spatiotemporal variations of ER in the China's coastal zone (CCZ) in the past few decades and predict future ER trend under various scenarios. The results show that ER in the CCZ displayed a general spatial distribution pattern of "higher in the southern half and lower in the northern half" from 1995 to 2020. Over the 25-year period, ER exhibited a declining trend. Specifically, the eastern provinces experiencing the most significant decline. The ER levels across scenarios ranked from high to low as follows: SSP1-2.6 > SSP4-3.4 > SSP2-4.5 > SSP3-7.0 > SSP5-8.5. The assessment and prediction methods designed can be applied to ER studies in other coastal zones, making it a valuable approach for broader applications. [ABSTRACT FROM AUTHOR]

Snow conditions are changing rapidly across our planet, which has important implications for wildlife managers. In Alaska, USA, the later arrival of snow is challenging wildlife managers' ability to conduct aerial fall (autumn) moose (Alces alces) surveys. Complete snow cover is required to reliably detect and count moose using visual observation from an aircraft. With inadequate snow to help generate high‐quality moose survey data, it is difficult for managers to determine if they are effectively meeting population goals and optimizing hunting opportunities. We quantified past relationships and projected future trends between snow conditions and moose survey success across 7 different moose management areas in Alaska using 32 years (1987–2019) of moose survey data and modeled snow data. We found that modeled mean snow depth was 15 cm (SD = 11) when moose surveys were initiated, and snow depths were greater in years when surveys were completed compared to years when surveys were canceled. Further, we found that mean snow depth toward the beginning of the survey season (1 November) was the best predictor of whether a survey was completed in any given year. Based on modeled conditions, the trend in mean snow depth on 1 November declined from 1980 to 2020 in 5 out of 7 survey areas. These findings, coupled with future projections, indicated that by 2055, the delayed onset of adequate snow accumulation in the fall will prevent the completion of moose surveys over roughly 60% of Alaska's managed moose areas at this time of the year. Our findings can be used by wildlife managers to guide decisions related to the future reliability of aerial fall moose surveys and help to identify timelines for development of alternate measurement and monitoring methods. [ABSTRACT FROM AUTHOR]

Background: Ehrlichiosis and anaplasmosis are vector-borne bacterial diseases produced by intracellular rickettsial species of the genus Ehrlichia and Anaplasma. Ehrlichia canis and Anaplasma spp. (A. platys and A. phagocytophilum) have reported cases of zoonotic transmision and are the main bacterial agents of canine ehrlichiosis and anaplasmosis. They normally present an asymptomatic or mild course in domestic and wild animals with some lethal cases reported. The main vector of these diseases in Europe are the castor bean tick (Ixodes ricinus) and the brown dog tick (Rhipicephalus sanguineus), although only in the latter, the main host is the domestic dogs. The aim of this work is to apply an integrative approach to convert ecological niche models (ENMs) into potential transmission risk models and understand the relative contribution of the two potential vectors (R. sanguineus and I. ricinus) to spread both diseases in the Iberian Peninsula and Balearic Islands. Results: Two ENMs meeting all criteria were successfully generated for R. sanguineus and I. ricinus with human footprint being the most relevant explanatory variable. The novelty of the study lies in the combination of independent ENMs for both species to estimate the disease transmission risk of specific bacteria (E. canis, A. platys and A. phagocytophilum). Only the transmission risk maps that had higher contribution of R. sanguineus than I. ricinus showed relevant and positive significant correlations between risk and seroprevalence in either of the two species of bacteria (R ≥ 0.4; p < 0.05). Regarding Anaplasma spp., the map having 10% contribution of I. ricinus (10I) and 90% of R. sanguineus (90R) inferred 47.4% of infected dogs in very high-risk areas. In the case of E. canis, the model showing a proportion of 25I-75R showed better validation power (53.4% of infected dogs in very high-risk areas). Conclusion: The validation approach used in this study produced a good approximation to understand the relative contribution of the two tick species in bacterial disease transmission in dogs in the Iberian Peninsula and Balearic Islands. Rhipicephalus sanguineus appears as the main transmitter of both diseases in the study area (90% and 75% for anaplasmosis and ehrlichiosis respectively), in accordance with its higher abundance and host preference. This estimate may help veterinary staff, clinicians and owners to optimize the control of these diseases in certain vulnerable areas, and thus reduce the risk of infection in risk areas. [ABSTRACT FROM AUTHOR]

Due to growing water demands and changing hydro-meteorological variables brought on by climate change, drought is becoming an increasingly serious climate concern. The Al-Baha region of Saudi Arabia is the subject of this study because it is susceptible to both agricultural and meteorological droughts. This study investigates how climate change affects patterns of drought in Al-Baha by analyzing four drought indices (Agricultural Standardized Precipitation Index (aSPI), the Standardized Precipitation Index (SPI), the Rainfall Deficiency Index (RDI), and the Effective Reconnaissance Drought Index (eRDI)) for the years 1991–2022. Analysis of rainfall data was carried out to classify drought events according to their duration, frequency, and severity. Results showed that severe droughts occurred in 2009, 2010, 2012, 2016, and 2022, with 2010 being the worst year. Results also indicated a notable decrease in precipitation, which has resulted in extended dry spells. Several indices indicate that this tendency has significant ramifications for agriculture, particularly in areas where farming is a major economic activity. In addition, the possible occurrence of hydrological drought was also observed based on the negative values for the Reservoir Storage Index (RSI) in Al-Baha. Projections for the future under two Representative Concentration Pathways (RCPs) showed notable variations in temperature and precipitation. Both the RCP4.5 (low emission) and the RCP8.5 (high emission) projection scenarios indicate that drought conditions will likely worsen further. Depending on the emission scenario, it is projected to show a temperature increase of 1–2 °C, whereas the variability in precipitation projections indicates significant uncertainty, with a reduction change in the range of 1.2–27% between 2050 and 2100. The findings highlight the urgent need for proactive adaptation strategies, effective water resource management, and the development of sophisticated drought prediction tools. Addressing these challenges is crucial for sustaining agriculture and managing water scarcity in Saudi Arabia in the face of increasing drought risk. [ABSTRACT FROM AUTHOR]

Forests are essential to climate change mitigation through carbon sequestration, transpiration, and turnover. However, the quantification of climate change impacts on forest growth is uncertain and even contradictory in some regions, which is the result of spatially constrained studies. Here, we use an unprecedented network of 1.5 million tree growth records from 493 Picea abies and Pinus sylvestris stands across Europe to predict species‐specific tree growth variability from 1950 to 2016 (R2 > 0.82) and develop 21st‐century gridded projections considering different climate change scenarios. The approach demonstrates overall positive effects of warming temperatures leading to 25% projected conifer growth increases under the SPP370 scenario, but these additional carbon gains are spatially inhomogeneous and associated with geographic climate gradients. Maximum gains are projected for pines in Scandinavia, where growth trajectories indicate 50% increases by 2071–2100. Smaller but significant growth reductions are projected in Mediterranean Europe, where conifer growth shrinks by 25% in response to warmer temperatures. Our results reveal potential mitigating effects via forest carbon sequestration increases in response to global warming and stress the importance of effective forest management. [ABSTRACT FROM AUTHOR]

Climate projections for continental Europe indicate drier summers, increased annual precipitation, and less snowy winters, which are expected to cause shifts in species' distributions. Yet, most regions/countries currently lack comprehensive climate‐driven biodiversity projections across taxonomic groups, challenging effective conservation efforts. To address this gap, our study evaluated the potential effects of climate change on the biodiversity of an alpine country of Europe, Switzerland. We used a state‐of‐the art species distribution modeling approach and species occurrence data that covered the climatic conditions encountered across the full species' ranges to help limiting niche truncation. We quantified the relationship between baseline climate and the spatial distribution of 7291 species from 12 main taxonomic groups and projected future climate suitability for three 30‐year periods and two greenhouse gas concentration scenarios (RCP4.5 and 8.5). Our results indicated important effects of projected climate changes on species' climate suitability, with responses varying by the taxonomic and conservation status group. The percentage of species facing major changes in climate suitability was higher under RCP8.5 (68%) compared to RCP4.5 (66%). By the end of the century, decreases in climate suitability were projected for 3000 species under RCP8.5 and 1758 species under RCP4.5. The most affected groups under RCP8.5 were molluscs, algae, and amphibians, while it was molluscs, birds, and vascular plants under RCP4.5. Spatially, by 2070–2099, we projected an overall decrease in climate suitability for 39% of the cells in the study area under RCP8.5 and 10% under RCP4.5, while projecting an increase for 50% of the cells under RCP8.5 and 73% under RCP4.5. The most consistent geographical shifts were upward, southward, and eastward. We found that the coverage of high climate suitability cells by protected areas was expected to increase. Our models and maps provide guidance for spatial conservation planning by pointing out future climate‐suitable areas for biodiversity. [ABSTRACT FROM AUTHOR]

Existing sociological literature provides conflicting theoretical accounts of disadvantaged youth's aspirations. While structuralists and rational choice theorists contend that disadvantaged young people tend to form low aspirations in the face of limited structural opportunities, cultural sociologists maintain that disadvantaged youth construct highly aspirational imagined futures to claim their moral self-worth in the present. I argue that incorporating time frames into the study of aspirations helps resolve the tension by enabling researchers to investigate when—in what time frame—one model works better than others. I demonstrate the value of this approach using qualitative interviews with 31 eighth-grade students in China's rural Shanxi Province, where structural constraints of socioeconomic attainment undercut cultural ideals of social mobility. In this context, findings show that respondents focused on practical constraints from their academic performance and family economic strains when projecting their short-term futures (structural/rational choice model) while they constructed future selves distinctive from rural origins in their long-term futures (cultural model). I conclude by discussing this approach's implications for studying aspirations, expectations, and their relationships to educational and career outcomes. [ABSTRACT FROM AUTHOR]

Abstract Climate change has a significant impact on water resources, making it essential to re-evaluate water management strategies and incorporate climate scenarios in assessments. The Municipal Department of Aigeiros is located in the northern part of Greece. Water consumption is high in Aigeiros and the increased future temperatures projected during the summer period will create significant pressures on water resources. The water resources management study of the region is carried out using the simulations of the RCA4 Regional Climate Model (RCM) driven by the HadGEM-ES global climate model of the Met Office Hadley Centre (MOHC) under 3 different climate emission scenarios, namely RCP 2.6, RCP 4.5 and RCP 8.5. For the simulation of the urban water balance of Aigeiros, Komotini, Greece and the assessment of water demand and supply for three climate scenarios (RCP 2.6, 4.5, and 8.5) over a 30-year period, the Aquacycle software was used. The data used in the assessment included projected climatic conditions for the area (i.e., precipitation and evapotranspiration), domestic water consumption, and natural and spatial characteristics. The results indicate that drinking water demand is likely to increase in the coming decades for RCP 4.5 (1323 m3/d for 2041–2050) and RCP 8.5 (1330 m3/d for 2041–2050) scenarios compared to 2020 (1320 m3/d). However, simulations for water supply suggest an increase in groundwater recharge in the future, but also the potential for long drought periods during summer months in RCP 4.5 and RCP 8.5 scenarios. The simulation results show both the current situation and the climate scenarios and can be the reference basis for recording the different types of water consumption in urban areas. Therefore, it is possible to control and predict how much of the total consumption is due to the consumer usage profile within a household or to the irrigation needs of green areas in line with the climatic conditions, consumer behavior and technical parameters.

We compared the projected results of future hydrological changes (from 2040 to 2050) based on a 30-year weather dataset (1984–2014) in 7 rain gauges in the Jazmurian watershed with paleoclimatic changes inferred from established proxies in the region. By drawing parallels between anticipated future changes and past climatic conditions, we aim to develop a general understanding of how climate change may alter precipitation patterns in the watershed. The region is influenced by the Indian Ocean Summer Monsoon (IOSM) as well as the Mid-Latitude Westerlies (MLW). Precipitation projections for the future are derived using statistical downscaling outputs from IPCC Sixth Report models (CMIP6) under various Shared Socioeconomic Pathway (SSP) scenarios. In the western watershed, the CanESM5 model forecasts a decrease in MLW precipitation (32–20%) and an increase in IOSM precipitation (157–253%), leading to an overall rise of 0–28%. Conversely, the eastern section shows variable MLW precipitation changes (– 19% to + 20%) and a projected increase in IOSM precipitation (21–405%), resulting in a 9–120% rise. In the western region, the MPI-ESM1-2-HR model predicts a decline in MLW precipitation (30–3%) and an increase in IOSM precipitation (142–189%), with an overall rise of 9–22%. In the east, there is a decrease in MLW precipitation (23–9%) and an increase in IOSM precipitation (252–437%), leading to a rise of 44–91%. Future precipitation in the Jazmurian watershed is projected to increase despite potential decreases in MLW precipitation. Results suggest increases in the present low IOSM precipitation, leading to overall precipitation rises by 2050 compared to the historical levels. These findings highlight a trend towards wetter conditions, with the SSP 370 scenario showing the most significant rise in IOSM precipitation. The decrease in Mediterranean precipitation is consistent with findings from other GCMs and aligns with historical climate variations observed during previous warm and humid phases in the region. Nevertheless, given the strong correlation between the rise in the Earth's radiation budget and increased monsoon rainfall in the Jazmurian watershed since the late Pleistocene, we anticipate a significant rise in IOSM activity.Highlights: Findings indicate a trend towards wetter conditions across the Jazmurian watershed. GCMs forecast a decrease in MLW precipitation while IOSM precipitation increases over the region. The SSP 370 scenario shows the most significant rise in IOSM precipitation. Multi-proxy data displays intense paleo IOSM activity during past warm/wet periods in the region. Decreases in MLW precipitation align with past warm/wet period climate shifts. [ABSTRACT FROM AUTHOR]

To explore the potential impacts of climate change on precipitation and mesoscale convective systems (MCSs) in the Peruvian Central Andes, a region with complex terrain, two future convection‐permitting regional climate simulations and one historical one are conducted using the Weather Research and Forecasting (WRF) model. All simulations adopt consistent model configurations and two nested domains with grid spacings of 15 and 3 km covering the entire South America and the Peruvian Central Andes, respectively. The historical run, spanning 2014–2019, is driven by ERA5 reanalysis, and the future simulations, covering the period 2070–2080, are driven by a bias‐corrected global dataset derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble under the SSP2‐4.5 and SSP5‐8.5 emission scenarios. Results show geographically dependent changes in annual precipitation, with a consistent rise in the frequency of intense hourly precipitation across all regions examined. The western Amazon Basin shows a decrease in annual precipitation, while increases exist in parts of the Peruvian west coast and the east slope of the Andes under both future scenarios. In the warming scenarios, there is an overall increase in the frequency, precipitation intensity, and size of MCSs east of the Andes, with MCS precipitation volume increasing by up to ∼22.2%. Despite consistently enhanced synoptic‐scale low‐level jets in future scenarios, changes in low‐level dynamic convergence are inhomogeneous and predominantly influence annual precipitation changes. The increased convective available potential energy (CAPE), convective inhibition (CIN), and precipitable water (PW) in a warming climate suppress weak convection, while fostering a more unstable and moisture‐rich atmosphere, facilitating more intense convection and the formation and intensification of heavy precipitation‐producing MCSs. The study highlights the value of convection‐permitting climate simulations in projecting future severe weather hazards and informing climate adaptation strategies, especially in regions characterized by complex terrain. [ABSTRACT FROM AUTHOR]

Transport energy demand (TED) forecasting is a crucial issue for countries like Turkey that are dependent on external resources. The accuracy and effectiveness of these forecasts are extremely important, especially for the strategies and plans to be developed. With this in mind, different forms of forecasting models were developed in the present study using the Walrus Optimizer (WO) and White Shark Optimizer (WSO) algorithms to estimate Turkey's energy consumption related to road and railway transportation modes. Additionally, another objective of this study was to examine the impacts of different transport modes on energy demand. To investigate the effect of demand distribution among transport modes on energy consumption, model parameters such as passenger-kilometers (P-km), freight-kilometers (F-km), carbon dioxide emissions (CO2), gross domestic product (GDP), and population (POP) were utilized in the development of the models. It was found that the WO algorithm outperformed the WSO algorithm and was the most suitable method for energy demand forecasting. All the developed models demonstrated a better performance level than those reported in previous studies, with the best performance achieved by the semi-quadratic model developed with the WO, showing a 0.95% MAPE value. Projections for energy demand up to the year 2035 were established based on two different scenarios: the current demand distribution among transport modes, and a demand shift from road to rail transportation. It is anticipated that the proposed energy demand models will serve as an important guide for effective planning and strategy development. Moreover, the findings suggest that a balanced distribution among transport modes will have a positive impact on transport energy and will result in lower energy requirements. [ABSTRACT FROM AUTHOR]

As urban expansion continues, the intensifying land surface temperature (LST) underscores the critical need for accurate predictions of future thermal environments. However, no study has investigated which method can most effectively and consistently predict the future LST. To address these gaps, our study employed four methods—the multiple linear regression (MLR), geographically weighted regression (GWR), random forest (RF), and artificial neural network (ANN) approach—to establish relationships between land use/cover and LST. Subsequently, we utilized these relationships established in 2006 to predict the LST for the years 2012 and 2018, validating these predictions against the observed data. Our results indicate that, in terms of fitting performance (R2 and RMSE), the methods rank as follows: RF > GWR > ANN > MLR. However, in terms of temporal stability, we observed a significant variation in predictive accuracy, with MLR > GWR > RF > ANN for the years 2012 and 2018. The predictions using MLR indicate that the future LST in 2050, under the SSP2 and SSP5 scenarios, is expected to increase by 1.8 ± 1.4 K and 2.1 ± 1.6 K, respectively, compared to 2018. This study emphasizes the importance of the MLR method in predicting the future LST and provides potential instructions for future heat mitigation. [ABSTRACT FROM AUTHOR]

Multi-decadal trends in the wintertime North Atlantic Oscillation (NAO) are under-represented by coupled general circulation models (CGCMs), consistent with a lack of autocorrelation in their NAO index series. This study proposes and tests two simple "reddening" approaches for correcting this problem in simulated indices based on simple one parameter short-term (AR; Auto-Regressive order 1) and long-term (FD; Fractional-Difference) time series filters. Using CGCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6), the FD filter successfully improves the autocorrelation structure of the NAO, and in turn the simulation of extreme trends, while the AR filter is less successful. The 1963–1993 NAO trend is the maximum 31-year trend in the historical period. Raw CGCMs underestimate the likelihood of this trend by a factor of ten but this discrepancy is corrected after reddening. CMIP6 future projections show that long-term (2024–2094) NAO ensemble mean trends systematically increase with the magnitude of radiative forcing: -2.4 to 3.5 hPa/century for low-to-high forcing after reddening (more than double the range using raw output). The related likelihood of future maximum 31year trends comparable to 1963–1993 ranges from 3 to 7% whereas none of these CMIP6 projections simulate this without reddening. Near-term projections of the next 31 years (2024–2054) are less sensitive than long term trends to the future scenario, showing weak-to-no forced trend. However, reddening increases the ensemble range by 74% (to +/-1 standard deviation/decade), which could increase/decrease regional climate change signals in the Northern Hemisphere by magnitudes that are underestimated when using raw CGCM output. [ABSTRACT FROM AUTHOR]

Central Asia (CA) is among the world's most vulnerable regions to climate change. Increasing anthropogenic greenhouse gas concentrations (GHGs) are the primary forcing of the current and future climate system for the time scale of a century. By analysing observation datasets, we show that a warming of 1.2°C led to a decrease of 20% in snow‐depth CA during the last 70 years, especially over the mountains. In recent decades, longer summer times and fewer icing days (more than 20 days·year−1) have exposed unprecedented shock to CA's climate system's components. Furthermore, we analyse 442 model simulations from Coupled Model Inter‐comparison Project Phase 5 and 6 (CMIP5, CMIP6) and show that CMIP6 simulations are generally warmer and wetter than the CMIP5 ones in CA. For instance, under the highest emission scenarios (RCP8.5 and SSP5‐8.5), CMIP6 projects a 6.1°C increase, while CMIP5 projects a 5.3°C increase, suggesting CMIP6 anticipates greater warming with high emissions. In contrast to CMIP6, the CMIP5 precipitation trends suggest a potential nonlinear relationship between increased greenhouse gas emissions and changes in precipitation, though the impact is much less pronounced than the temperature changes. Our analysis shows that CMIP6 models are more sensitive to temperature rise than CMIP5 ones. Both simulation sets' ensemble means capture well the observed warming trend. The imposed snow‐melting leads to an increase in the run‐off in the vicinity of glaciers. Such climatic shifts lead to more flooding events in CA. Given the projected warming range of 2–6°C in CA at the end of the century in various scenarios and models, such warming trends might be catastrophic in this region. The seasonal cycle of the temperature change indicates an extension of the glacier's melting period under future scenarios with fossil‐fueled development. The models' uncertainty increases for the far‐future time‐slice, and warming larger than 4°C in CA is very likely among all the models and during all the seasons if no sustainable action is taken. This study also incorporates a detailed Köppen climate classification analysis, revealing significant shifts towards warmer climate categories in Central Asia, which may have profound implications for regional hydrological cycles and water resource management, particularly in the Amu Darya and Syr Darya river basins under warmer scenario by the end of the century. The Tundra and ice cap climate categories will lose more than 60% of their coverage at the end of the century compared to the historical period in the Amu Darya and Syr Darya river basins. [ABSTRACT FROM AUTHOR]

Abstract Climate risk assessments must account for a wide range of possible futures, so scientists often use simulations made by numerous global climate models to explore potential changes in regional climates and their impacts. Some of the latest‐generation models have high effective climate sensitivities (EffCS). It has been argued these “hot” models are unrealistic and should therefore be excluded from analyses of climate change impacts. Whether this would improve regional impact assessments, or make them worse, is unclear. Here we show there is no universal relationship between EffCS and projected changes in a number of important climatic drivers of regional impacts. Analyzing heavy rainfall events, meteorological drought, and fire weather in different regions, we find little or no significant correlation with EffCS for most regions and climatic drivers. Even when a correlation is found, internal variability and processes unrelated to EffCS have similar effects on projected changes in the climatic drivers as EffCS. Model selection based solely on EffCS appears to be unjustified and may neglect realistic impacts, leading to an underestimation of climate risks.

Abstract Understanding the evolutionary responses of anadromous salmon and trout to climate change is critical for effective conservation planning. In this study, we conducted a comprehensive review of literature published from 2010 to 2020 to synthesize current knowledge on climate impacts to these fish populations. Specifically, we focused on 199 papers that explored evolutionary processes in response to changing environmental conditions. Our analysis revealed several key themes, including the interwoven influences of climate and human activities on genetic variation, phenotypic traits, and population dynamics. We found that geographic patterns in genetic diversity are closely linked to climatic gradients, highlighting the importance to conservation strategies of variation in existing adaptive capacity. Additionally, temporal trends in phenology, maturation age, and fecundity indicate ongoing evolutionary and plastic responses to climate change. Importantly, human activities were identified as significant drivers of maladaptation in anadromous salmon and trout populations. We emphasize the need for targeted monitoring of specific evolutionary processes to mitigate the loss of genetic diversity and enhance adaptive capacity. Our study underscores the importance of identifying and protecting areas of high genetic diversity and rare genes, particularly in regions projected to experience rapid climatic shifts. In conclusion, our findings identify strengths and gaps in the research investigating the role of evolutionary dynamics in the face of climate change. By capitalizing on new tools for sequencing, genomic analysis, and automated field data collection, we can establish baselines for tracking evolutionary responses to climate change. Better integration of evolutionary processes into projections of future climate impacts will lead to more effective strategies to ensure the long‐term resilience of these iconic fish species and other wildlife.

Climate conditions have a strong influence on the distribution of many natural communities; thus, the influence of global climate change may alter ecosystems. High-altitude wetlands in the Central Andes of Argentina (CAA) provide relevant ecosystem benefits and promote human activities. However, a degradation of Andean wetlands has been observed and documented in the last decades. Projecting the impacts of climate change on future distribution of wetlands is an essential subject in ecological studies. In this work, the present and future suitability for wetland systems in a pristine section of the CAA using spatial distribution modeling under low and high-emission scenarios are explored. The studied wetlands are strongly driven by bioclimatic variables such as mean annual temperature, precipitation, and its seasonality. Projections show that most of the currently occupied areas will modify under future climate conditions. Changes in temperature and precipitation patterns will decrease the potential suitability in low elevation areas for the next decades for the species inhabiting there, especially south of 29 °S. Additionally, future warmer climatic conditions, greater temperature variability, and reduction in precipitation would probably affect the snow cover and the available water supply which are key limiting factors for the distribution of Andean wetlands. This research is a contribution to understanding possible effects of climate change on high-altitude ecosystems. Mitigation measures for conservation of wetlands in CAA are immediately required to compensate for the impact of climate change under future environmental conditions.

Streamflow simulations from the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b) were analyzed to evaluate future changes in surface water resources over northern Patagonia, a region that contributes significantly to the total hydropower production of Argentina. Ten global hydrological models (GHMs), forced by four general circulation models, effectively capture the winter streamflow maximum in the Negro river basin. However, most of them face challenges in simulating the late-spring pulse due to a misrepresentation of temperature over the higher elevations of the Andes. We quantified the future streamflow evolution using a multi-model ensemble from a subset of the best-performing GHMs under the RCP2.6 and RCP6.0 emission scenarios for two temporal horizons. According to the multi-model ensemble, there is a projected decrease in the annual streamflow of the analyzed rivers, which is more important considering the RCP6.0 scenario during the late 21st century, reaching up to −40% relative to the 1979–2005 reference period. This reduction is attributed to the projected precipitation decline in the headwaters of the Negro river basin in response to changes in the surface pressure patterns. These results have implications for regional water authorities for the development of adaptation plans considering future demand projections. HIGHLIGHTS A set of global hydrological models (GHMs) from the ISIMIP2b project was used to characterize streamflow changes in the Comahue region.; A decrease in the annual streamflow is projected along the 21st century, particularly for the Neuquén river.; Uncertainty is mainly linked to the global climate models used to drive the GHMs.; Hydropower generation and irrigation for agriculture are expected to face future reductions.;

This study evaluates the performance of 16 models sourced from the coupled model intercomparison project phase 6 (CMIP6) in simulating marine heatwaves (MHWs) in the South China Sea (SCS) during the historical period (1982–2014), and also investigates future changes in SCS MHWs based on simulations from three shared socioeconomic pathway (SSP) scenarios (SSP126, SSP245, and SSP585) using CMIP6 models. Results demonstrate that the CMIP6 models perform well in simulating the spatial-temporal distribution and intensity of SCS MHWs, with their multi-model ensemble (MME) results showing the best performance. The reasonable agreement between the observations and CMIP6 MME reveals that the increasing trends of SCS MHWs are attributed to the warming sea surface temperature trend. Under various SSP scenarios, the year 2040 emerges as pivotal juncture for future shifts in SCS MHWs, marked by distinct variations in changing rate and amplitudes. This is characterized by an accelerated decrease in MHWs frequency and a notably heightened increase in mean intensity, duration, and total days after 2040. Furthermore, the projection results for SCS MHWs suggest that the spatial pattern of MHWs remains consistent across future periods. However, the intensity shows higher consistency only during the near-term period (2021–2050), while notable inconsistencies are observed during the medium-term (2041–2070) and long-term (2071–2100) periods under the three SSP scenarios. During the near-term period, the SCS MHWs are characterized by moderate and strong events with high frequencies and relatively shorter durations. In contrast, during the medium-term period, MHWs are also characterized by moderate and strong events, but with longer-lasting and more intense events under the SSP245 and SSP585 scenarios. However, in the long-term period, extreme MHWs become the dominant feature under the SSP585 scenario, indicating a substantial intensification of SCS MHWs, effectively establishing a near-permanent state. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) module deployment has surged globally as a part of the transition towards a decarbonized electricity sector. However, future climate change presents issues for module degradation due to prolonged exposure to outdoor conditions. Here, we identify key degradation mechanisms of monocrystalline‐silicon (mono‐Si) modules and empirically model their degradation modes under various climate scenarios. Modules tend to degrade faster due to the thermal degradation mechanism. We estimate that the weighted average degradation rate will increase up to 0.1%/year by 2059. On assessing the impacts of module degradation on future PV power generation and levelized cost of energy, we project up to 8.5% increase in power loss that leads to ~10% rise in future energy price. These results highlight the need to climate‐proof PV module design through careful material selection and improvements in the module manufacturing process. In particular, we recommend the use of heat dissipation techniques in modules to prevent degradation due to overheating. [ABSTRACT FROM AUTHOR]

While the ubiquity and importance of narratives for human adaptation is widely recognized, there is no integrative framework for understanding the roles of narrative in human adaptation. Research has identified several cognitive and social functions of narratives that are conducive to well-being and adaptation as well as to coordinated social practices and enculturation. In this paper, we characterize the cognitive and social functions of narratives in terms of active inference, to support the claim that one of the main adaptive functions of narrative is to generate more useful (i.e., accurate, parsimonious) predictions for the individual, as well as to coordinate group action (over multiple timescales) through shared predictions about collective behavior. Active inference is a theory that depicts the fundamental tendency of living organisms to adapt by proactively inferring the causes of their sensations (including their own actions). We review narrative research on identity, event segmentation, episodic memory, future projections, storytelling practices, enculturation, and master narratives. We show how this research dovetails with the active inference framework and propose an account of the cognitive and social functions of narrative that emphasizes that narratives are for the future--even when they are focused on recollecting or recounting the past. Understanding narratives as cognitive and cultural tools for mutual prediction in social contexts can guide research on narrative in adaptive behavior and psychopathology, based on a parsimonious mechanistic model of some of the basic adaptive functions of narrative. [ABSTRACT FROM AUTHOR]

Marine heatwaves (MHWs) are oceanic conditions characterized by extremely high sea surface temperature (SST) anomalies that last for several days to years. Because MHWs have devastating effects on marine ecosystems and significant impacts on fisheries, understanding future MHWs is important for adapting to upcoming climate changes. In this study, we examined future changes in MHWs in the northwestern Pacific Ocean (18–53ºN, 117ºE–170ºW) under two CO2 emission scenarios using a high-resolution ensemble (four members for each scenario) simulation product using a high-resolution ocean model that satisfactorily resolves the Kuroshio, Kuroshio Extension, and SST fronts. Following global warming, MHWs based on a threshold in the historical period (1981–2005) will increase and intensify (i.e., occur with higher SST anomalies than before). In the historical period, the annual MHW days ranged from 20 to 34 days. Annual MHW days increase to 63–313 days (188 days–all year round) depending on the region under the high CO2 mitigation (emission) scenario at the end of the twenty-first century of 2076–2100. Furthermore, we investigated the spatial details of future MHWs. Future MHWs reflect the magnitude of SST variability in addition to that of sea surface warming in the twenty-first century; future MHWs are less frequent and more intense in the subtropical–subarctic frontal zone with large SST variability than in other regions. [ABSTRACT FROM AUTHOR]

Coastal zones are facing intensive ecological pressures and challenges, which could vary over a wide range of spatiotemporal scales. Our limited capability to understand and especially predict this variability can lead to the misinterpretation of coastal ecological resilience. Therefore, the assessment and prediction of ecological resilience are particularly important. In this study, a new approach based on the Pressure–State–Response model is developed to assess and predict pixel-scale multi-year ecological resilience (ER) and then applied to investigate the spatiotemporal variations of ER in the China’s coastal zone (CCZ) in the past few decades and predict future ER trend under various scenarios. The results show that ER in the CCZ displayed a general spatial distribution pattern of “higher in the southern half and lower in the northern half” from 1995 to 2020. Over the 25-year period, ER exhibited a declining trend. Specifically, the eastern provinces experiencing the most significant decline. The ER levels across scenarios ranked from high to low as follows: SSP1-2.6 > SSP4-3.4 > SSP2-4.5 > SSP3-7.0 > SSP5-8.5. The assessment and prediction methods designed can be applied to ER studies in other coastal zones, making it a valuable approach for broader applications.

Based on the multi-model ensemble mean of CMIP6 simulations, the future changes in frequency, intensity and duration of Compound (both daytime and nighttime) heatwaves (HWs) in summer over China at various global warming levels (GWLs) under the SSP3-7.0 and SSP5-8.5 are assessed. HWs over China become more frequent and hotter, and the duration of HWs becomes longer compared to those in the recent climate. The magnitudes of these changes are primarily dependent on GWLs, but they are not very sensitive to the scenarios. At 4 ℃ GWL, the frequency of HWs increases by more than fivefold under both scenarios, and the intensity (duration) of HWs averaged under the two scenarios is 2.28 ℃ hotter (3.59 days longer) than the one in the recent climate over the entire China. Meanwhile, the maximum duration of HW events can reach more than 25 days in summer in comparison with 8 days in the recent climate. The changes in HW properties are regionally dependent at the four GWLs. For example, the largest increase in HW frequency is over the Northwest China, the largest increase in intensity in HWs is seen over the Northeast and Northwest, and the largest increase in HW duration is over the Southwest China. The extreme rare events (50-year and 100-year events) in the recent climate would become the norm over China and four sub-regions at 4 ℃ GWL. Overall, seasonal mean warming dominates the changes in HW properties over China at the different GWLs. The seasonal mean warming in summer across China is related to the increases of longwave radiation, partly due to increase in greenhouse gas forcing and partly resulted from increased water vapor and the increase of shortwave radiation (under the SSP5-8.5) over eastern China related to decreases in aerosols and total cloud cover. Furthermore, the regional variations in the water vapor over China are consistent with atmospheric circulation changes. The seasonal mean surface warming results in enhanced upward sensible and latent heat fluxes, leading to increased summer mean daily maximum and minimum of near-surface air temperature and the enhancement of HWs properties over the entire China. Changes of shortwave radiation tend to play a weaker role for surface warming under the SSP3-7.0 than those under the SSP5-8.5, which is related to increased aerosol changes under the SSP3-7.0. [ABSTRACT FROM AUTHOR]

The Arctic is a global warming 'hot‐spot' that is experiencing rapid increases in air and ocean temperatures and concomitant decreases in sea ice cover. These environmental changes are having major consequences on Arctic ecosystems. All Arctic endemic marine mammals are highly dependent on ice‐associated ecosystems for at least part of their life cycle and thus are sensitive to the changes occurring in their habitats. Understanding the biological consequences of changes in these environments is essential for ecosystem management and conservation. However, our ability to study climate change impacts on Arctic marine mammals is generally limited by the lack of sufficiently long data time series. In this study, we took advantage of a unique dataset on hooded seal (Cystophora cristata) movements (and serum samples) that spans more than 30 years in the Northwest Atlantic to (i) investigate foraging (distribution and habitat use) and dietary (trophic level of prey and location) habits over the last three decades and (ii) predict future locations of suitable habitat given a projected global warming scenario. We found that, despite a change in isotopic signatures that might suggest prey changes over the 30‐year period, hooded seals from the Northwest Atlantic appeared to target similar oceanographic characteristics throughout the study period. However, over decades, they have moved northward to find food. Somewhat surprisingly, foraging habits differed between seals breeding in the Gulf of St Lawrence vs those breeding at the "Front" (off Newfoundland). Seals from the Gulf favoured colder waters while Front seals favoured warmer waters. We predict that foraging habitats for hooded seals will continue to shift northwards and that Front seals are likely to have the greatest resilience. This study shows how hooded seals are responding to rapid environmental change and provides an indication of future trends for the species—information essential for effective ecosystem management and conservation. [ABSTRACT FROM AUTHOR]

Understanding the present and future features of atmospheric rivers (ARs) is critical for effective disaster prevention and mitigation efforts. This study comprehensively assesses the performance of ARs in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) models on both seasonal and interannual timescales within the historical period and investigates the future projection of ARs under different emission scenarios on a global scale. The multi‐model mean results obtained using the PanLu detection algorithm consistently exhibit agreement with the observational AR climatology and capture interannual fluctuations as well as the relationships with large‐scale drivers. The future projections reveal increased AR frequency, intensity, duration, and spatial extent and decreased landfall intervals with regional variations and seasonal fluctuations. Besides, the AR frequency increase will accelerate around the middle of the century, attributed to a non‐linear rise in surface temperature. Furthermore, mid‐latitude ARs are gradually shifting toward higher latitudes in both hemispheres under SSP585, with Greenland experiencing a substantial increase in AR frequency and AR‐induced precipitation. The hydrological implications arising from more frequent ARs are manifested more prominently in AR‐induced heavy precipitation (HP), with regions historically characterized by lower AR occurrence also receiving a higher percentage of precipitation from ARs. At last, an incremental decomposition highlights the dominant role of thermal effects and relatively limited contributions from dynamical effects in AR changes. Besides, the interplay between regionally divergent temperature amplification results in different dynamically driven AR responses across the globe. Plain Language Summary: Understanding atmospheric rivers (ARs) is crucial for disaster prevention and mitigation. This study evaluates the performance of CMIP6 simulation on historical ARs on seasonal and interannual timescales, assesses their future projection under different emission scenarios, and examines their hydrological implications. The seasonal and interannual AR patterns in models are consistent with the observations. Future projections reveal that ARs will become more frequent, intense, and longer‐lasting while the time between landfall AR events will be shorter, with the magnitude varying by region and season. Interestingly, AR frequency is expected to increase even faster after the middle of the century due to a non‐linear rise in Earth surface temperature. The study also shows that mid‐latitude ARs are shifting toward higher latitudes, bringing more precipitation to the polar regions. The changes in AR characteristics can lead to increased risks of heavy precipitation, especially in regions where ARs are relatively not frequent in the past time. The study also shows that the changes in AR characteristics are primarily contributed by the increase in atmospheric moisture. The contribution from the changes in wind patterns is limited and varies across different regions as a result of regionally divergent temperature amplification due to global warming. Key Points: CMIP6 simulates atmospheric rivers (ARs) well in seasonal and interannual timescales globallyARs will play a more significant role in the hydrological cycle as global warming alters various AR characteristicsThermal effects dominate AR changes globally, while dynamical effects associated with temperature rise vary regionally [ABSTRACT FROM AUTHOR]

Climate change has been linked to the escalating frequency, duration, and intensity of heat waves in the Mediterranean region, intensifying health concerns for the general populace. Urban environments face elevated health risks due to concentrated populations and the urban heat island effect, further amplifying nighttime heat conditions. This study aims to project changes in heat wave characteristics and the associated population exposure risk in a large Mediterranean city, Thessaloniki, Greece. High-resolution climate simulations, using the WRF model, were conducted for three 5-year periods (2006–2010, 2046–2050, 2096–2100) under the RCP8.5 emission scenario, covering Thessaloniki with a 2 km grid. By the end of the century, Thessaloniki is projected to experience over 60 annual heat wave days, compared to ~8 in the present climate, while some episodes were found to persist beyond 30 days. The relative risk during heat wave days is expected to rise, which is primarily due to nighttime heat stress. Interestingly, the results indicate that minimum apparent temperature might be a more reliable indicator in predicting heat-related mortality compared to maximum apparent temperature. These findings emphasize the growing importance of informed heat mitigation and adaptation strategies and healthcare preparedness in urban areas facing escalating heat-related health challenges. [ABSTRACT FROM AUTHOR]

While the ubiquity and importance of narratives for human adaptation is widely recognized, there is no integrative framework for understanding the roles of narrative in human adaptation. Research has identified several cognitive and social functions of narratives that are conducive to well-being and adaptation as well as to coordinated social practices and enculturation. In this paper, we characterize the cognitive and social functions of narratives in terms of active inference, to support the claim that one of the main adaptive functions of narrative is to generate more useful (i.e., accurate, parsimonious) predictions for the individual, as well as to coordinate group action (over multiple timescales) through shared predictions about collective behavior. Active inference is a theory that depicts the fundamental tendency of living organisms to adapt by proactively inferring the causes of their sensations (including their own actions). We review narrative research on identity, event segmentation, episodic memory, future projections, storytelling practices, enculturation, and master narratives. We show how this research dovetails with the active inference framework and propose an account of the cognitive and social functions of narrative that emphasizes that narratives are for the future—even when they are focused on recollecting or recounting the past. Understanding narratives as cognitive and cultural tools for mutual prediction in social contexts can guide research on narrative in adaptive behavior and psychopathology, based on a parsimonious mechanistic model of some of the basic adaptive functions of narrative.

The Mediterranean Sea is a climate change hotspot since it provides a magnified warming signal. Heavily populated areas (e.g., Spanish Mediterranean coasts) are vulnerable to negative socio-economic impacts. This is particularly important for climate-related economic sectors such as coastal tourism, the focus of this paper. To promote a sustainable development of these activities and provide key information to stakeholders, it is necessary to anticipate changes in climate. Thus, it is fundamental to use climate modelling tools which account for air-sea interactions, which largely determine the climate signal of the Mediterranean coasts. In this paper, a set of regional air-sea coupled climate model simulations from Med-CORDEX are used to (i) study the climatic conditions on the Spanish Mediterranean coasts in the next decade(s) and (ii) to assess the possibility of extending the coastal tourist season towards spring-fall. We show that climate conditions are getting warmer and drier in the area, especially in summer. Heat waves and heavy precipitation will become more frequent. Thermal discomfort will increase in summer and summer conditions are extending towards spring and fall. Our work remarks the urgent need of adaptation measures of the sector, including the extension of the high tourist season to spring-fall, especially in the long term. We make a special effort to compile a set of adaptation measures for stakeholders. This study is part of the project ECOAZUL-MED, which aims to create a climate service tool to optimize the management of relevant sectors of the blue economy in the Spanish Mediterranean coasts.

The impact of climatic change on the summer monsoon season is studied to understand the rainfall pattern towards the end of the century utilizing the Coupled Model Intercomparison Project, Phase 6 (CMIP6) released by the World Climate Research Programme (WCRP). The analysis of model simulations from CMIP6 was carried out using 64 years of the historical period (1951–2014) and future projections till the end of the century (2015–2100). The models are compared with observed daily rainfall data from the APHRODITE (Asian Precipitation‐Highly‐Resolved Observational Data Integration Towards Evaluation). The analysis revealed that most of the models show an overestimation in the annual cycle of rainfall in the historical period; however, a few of them underestimate the values. The majority of them capture the onset signal of the summer monsoon in early June, along with a good seasonality in the daily rainfall climatology. The simulations that are coherent with the observational data sets are selected on the basis of the Taylor diagram for future projections in four scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5). The projections of the aforementioned scenarios are taken from the model outputs of EC‐Earth3‐Veg‐LR, INM‐CM4‐8, INM‐CM5‐0, MIROC‐ES2L and MPI‐ESM1‐2‐HR. The selected models exhibit far greater agreement among the 30 models when it comes to the features of rainfall during the summer monsoon. We have given more emphasis on summer monsoon rainfall in the historical and future projection periods since the trends are becoming more chaotic, as reported in observational studies. Over the Indian subcontinent, all of the chosen scenarios show an increased frequency of intense rainfall events with varying decadal and multidecadal features. Central India and west coastal belts are showing positive trends in extreme rainfall events towards the end of the century. At the turn of the century, the southern peninsular region experienced a decline in monsoon precipitation, whereas central India experienced an increase. The severity of rainfall variations during the monsoon season and trends in extremes are increasing as we move from the low‐emission scenario to the high‐emission scenario. [ABSTRACT FROM AUTHOR]

Identifying droughts and accurately evaluating drought impacts on vegetation growth are crucial to understanding the terrestrial carbon balance across China. However, few studies have identified the critical drought thresholds that impact China's vegetation growth, leading to large uncertainty in assessing the ecological consequences of droughts. In this study, we utilize gridded surface soil moisture data and satellite‐observed normalized difference vegetation index (NDVI) to assess vegetation response to droughts in China during 2001–2018. Based on the nonlinear relationship between changing drought stress and the coincident anomalies of NDVI during the growing season, we derive the spatial patterns of satellite‐based drought thresholds (TSM) that impact vegetation growth in China via a framework for detecting drought thresholds combining the methods of feature extraction, coincidence analysis, and piecewise linear regression. The TSM values represent percentile‐based drought threshold levels, with smaller TSM values corresponding to more negative anomalies of soil moisture. On average, TSM is at the 8.7th percentile and detectable in 64.4% of China's vegetated lands, with lower values in North China and Jianghan Plain and higher values in the Inner Mongolia Plateau. Furthermore, TSM for forests is commonly lower than that for grasslands. We also find that agricultural irrigation modifies the drought thresholds for croplands in the Sichuan Basin. For future projections, Earth System Models predict that more regions in China will face an increasing risk for ecological drought, and the Hexi Corridor‐Hetao Plain and Shandong Peninsula will become hotspots of ecological drought. This study has important implications for accurately evaluating the impacts of drought on vegetation growth in China and provides a scientific reference for the effective ecomanagement of China's terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

Compound wind and precipitation extremes (CWPEs) amplify risk to human health, socio‐economic and ecological systems relative to their single extreme meteorological events. Given the rise in weather and climate extremes resulting from global warming, it is crucial to evaluate the ability of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to capture this bivariate compound event and explore projected changes of CWPEs in the future under different climate‐change scenarios—the Shared Socioeconomic Pathway (SSP) scenarios. In this study, we first evaluate 14 CMIP6 models at a global scale using the ERA5 reanalysis data set spanning 1979–2014. Overall, some of the CMIP6 models, especially the multi‐model ensemble mean (MMEM), can reasonably capture CWPEs during the historical period, with more CWPEs in the northern and southern hemispheres during their respective cold seasons. However, the MMEM tends to overestimate CWPEs in some land areas and show underestimation in some oceanic regions. Then we compute projected changes of CWPEs in periods 1 (2041–2070) and 2 (2071–2100) under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios. Low emission scenarios effectively mitigate the long‐term increase in future CWPEs. The occurrence of CWPEs will change significantly with the increase of emissions during period 2, particularly in polar regions. Finally, we quantify the uncertainty for global future projections of CWPE changes. The main sources of uncertainty are internal variability and model uncertainty, but the contribution of scenario uncertainty will increase as time progresses. Overall, our results provide useful information to cope with CWPEs' global impact, emphasizing the importance of incorporating the compound nature of weather and climate extremes in future climate projections. [ABSTRACT FROM AUTHOR]

Water resources in Morocco have been severely influenced by climate change and prolonged drought, particularly in the pre-Saharan zone. The Ziz watershed faces increasing pressure due to the high demographic growth, increased demand for water, excessive groundwater consumption, and investment in agriculture. But how long will water resources withstand these problems? This study, therefore, enters into the context of the assessment of water resources and estimates their vulnerability using the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Standardized Groundwater Index (SGI), on data from the Ziz watershed from 1986 to 2016. Additionally, climate projections were utilized to simulate the future SGI from 2017 to 2100. The Water Evaluation and Planning System (WEAP) was employed to evaluate changes in Land Use and Land Cover (LULC) during the period of 1992–2020, and to generate future scenarios for land class inflows and outflows from 2017 to 2100, in comparison to the reference period of 1986–2016, thereby incorporating the SSP climate scenarios. The results indicate that the Ziz Basin experienced significant drought events in 1986–1989 and 2000–2003. The SPI and SPEI significantly correlated with SGI in some monitoring wells and with specific accumulation periods. The LULC analysis showed an increase in agricultural land and urban land and a decrease in barren or sparse land. Climate data analysis and scenarios predict that under SSP5-8.5, minimum and maximum temperatures will increase by 2.61 °C and 2.93 °C, respectively, and precipitation will decrease by 30% over this century. This substantial shift in climate conditions is reflected in the decline in SGIs, especially in the long term under SSP5-8.5. Water availability will decrease during this century under SSP3-7.0 and SSP5-8.5, as reflected in reduced land class inflows and increased outflows. These findings emphasize the need for stakeholders to implement integrated water governance for sustainability in the Ziz watershed. [ABSTRACT FROM AUTHOR]

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The fragile ecology of the Yellow River basin (YRB) is vulnerable to global warming and human activities. As the second largest river in China, the YRB covers the subhumid, semi‐arid, arid and alpine climate zones, with considerably spatial differences in vegetation cover (VC), thus demonstrating substantial variations in response to climate change. In this study, characteristics of VC variations in different climate zones of the YRB during 1982–2019 were analysed, impacts of climate change and human activities on VC variations were identified, and possible VC variations in the future were projected under different climate scenarios. Results show that NDVI in the YRB significantly increased at a rate of 2.3 × 10−3 year−1 from 1982 to 2019, especially in the middle reaches of the YRB. Temperature is the main driver of VC variations in the source region of the YRB (alpine zone), wherein the warming trend contributes to the greening. The internal variability of VC is also influenced by temperature, with a marginal effect of precipitation minus evaporation (P − E). In the middle and lower reaches of the YRB consisting of arid, semi‐arid, and subhumid zones, VC benefited only from the warming trend and had a less relationship with the internal variability of temperature, whereas the internal variability of VC was influenced by P − E and had a weaker relationship with the P − E trend. Contributions of climate change to VC variations in the source region and middle and lower reaches of the YRB are approximately 62%, 42%, and 27%, respectively. Human activities considerably impact the ecological environment but mostly play a positive role. In the future, VC will increase in the YRB, especially under the SSP5‐8.5 scenario, and VC reveals the most prominent changes in the lower reaches. This study suggests that climate change might be favourable to VC in YRB, and emphasizes that human activities should adapt to climate change in an orderly manner. [ABSTRACT FROM AUTHOR]

The observed Eurasian winter surface cooling from the 1990s to the early 2010s, which is contrary to global warming, has been extensively studied. Previous studies revealed that the surface cooling trend has significantly weakened in the past decade. Based on large‐ensemble simulations, this study reveals that the weakening of Eurasian surface cooling is primarily driven by the atmospheric internal variability, which coincides with the weakening of Arctic mid‐tropospheric warming and Eurasian mid‐tropospheric cooling. Negative Arctic Oscillation (−AO) and Ural blocking (UB) in combination dominate the intensity of Arctic mid‐tropospheric warming and Eurasian mid‐tropospheric cooling. In the future, there is a possibility that the severe Eurasian cooling trend with comparable magnitude to that during 1990–2013 may reemerge accompanied with Arctic mid‐tropospheric warming, in response to the decadal strengthening of −AO and UB. This may occur before the 2050s, when the atmospheric internal variability is able to overwhelm the effects of greenhouse gases. Plain Language Summary: The significant Eurasian surface cooling trend observed in winters from the 1990s to the early 2010s has significantly weakened in the past decade. It coincides with the weakened trends of Arctic mid‐tropospheric warming and Eurasian mid‐tropospheric cooling. In a warming world, we suggest that the weakening of Arctic mid‐tropospheric warming and Eurasian mid‐tropospheric and surface cooling are primarily caused by the atmospheric internal variability. The weakened trends of negative Arctic Oscillation (−AO) and Ural blocking (UB) in recent years contribute to the weakened trends of Arctic mid‐tropospheric warming and Eurasian cooling. There is a possibility that the atmospheric internal variability may overwhelm the effects of increasing greenhouse gases (GHG) sometime before the 2050s, though the increasing GHG dominate the long‐term increase of air temperature. Then, the strong negative trend of AO and intensified UB may lead to the reemergence of the severe Eurasian cooling trend before the 2050s. Key Points: The significant Arctic mid‐tropospheric warming and Eurasian cooling observed in winters before the 2010s has weakened in the past decadeThe combined effects of Arctic Oscillation and Ural blocking dominate the intensity of Arctic mid‐tropospheric warming and Eurasian coolingStrong Eurasian cooling may reemerge before the 2050s if the atmospheric internal variability overwhelms the effects of greenhouse gases [ABSTRACT FROM AUTHOR]

Predicting current and future wildfire frequency and size is central to wildfire control and management. Multiple fire danger indices (FDIs) that incorporate weather and fuel conditions have been developed and utilized to support wildfire predictions and risk assessment. However, the scale‐dependent performance of individual FDIs remains poorly understood, which leads to large uncertainty in the estimated fire sizes under climate change. Here, we calculate four commonly used FDIs over the conterminous United States using high‐resolution (4 km) climate and fuel data sets for the 1984–2019 period. The relationships of these four FDIs to the observed wildfire sizes show that higher values of FDIs correlate to larger total fire sizes; this correlation is more robust at larger spatial scales. Sensitivity analysis indicates that the daily minimum relative humidity and precipitation are the most important drivers of the annual mean fire danger. In the instances of extreme fire danger, wind speed becomes a critical factor and should be considered in the calculation of the FDI. To assess the impact of climate change on future fire size, we calculate the present‐day and end‐of‐century FDIs using the 12 km regional climate model simulations. The four FDIs generally predict consistent changes in future fire potential, suggesting an overall higher fire potential in conjunction with a prolonged wildfire season in future climate. Regionally, the four FDIs also reveal similar seasonal patterns as the enhancement arises mostly in spring and summer over the southwest US while in summer and fall over the northern and eastern US. Plain Language Summary: Fire danger index (FDI), a measure used to assess the risk and severity, relies on a combination of weather and fuel conditions. Multiple FDIs have been frequently used to predict and manage the risk of wildfire. However, it is unclear how well these indices work at different scales, causing uncertainty in predicting the likelihood of fire ignition and the potential size of a fire. Here, we analyzed four commonly used FDIs and conducted sensitivity analysis (SA) to determine their most important drivers. SA results indicate that daily minimum relative humidity, precipitation, and wind speed as the most important drivers. In addition, FDIs highly correlate with total fire size at annual and conterminous United States (CONUS) scale and such correlation decreases at finer spatial and temporal scales. We also used a regional climate model simulation to derive FDIs for current and future climate conditions. Our results suggest an overall increase in fire potential and a prolonged wildfire season in the future climate. Moreover, the enhanced fire frequency is projected to occur in spring and summer over the southwest US whereas in summer and fall over the northern and eastern CONUS. Key Points: The correlation between fire danger indices (FDIs) and observed wildfire size are scale dependentSensitivity analysis indicates that the daily minimum relative humidity and wind speed are the most important drivers of fire dangerCoupling FDIs with regional climate model simulation show higher fire potential in US in future climate [ABSTRACT FROM AUTHOR]

Coupled glacio-hydrological models have recently become a valuable method for predicting the hydrological response of catchments in mountainous regions under a changing climate. While hydrological models focus mostly on processes of the non-glacierized part of the catchment with a relatively simple glacier representation, the latest generation of standalone (global) glacier models tend to describe glacier processes more accurately by using new global datasets and explicitly modeling ice-flow dynamics. Yet, to the authors' knowledge, existing catchment-scale coupled glacio-hydrological models either do not include these most recent advances in glacier modeling or are simply not available to other users. By making use of the capabilities of the free, distributed, physically-based Water Flow and Balance Simulation Model (WaSiM) and the Open Global Glacier Model (OGGM), a coupling scheme is developed to bridge the gap between global glacier representation and local catchment hydrology. The WaSiM-OGGM coupling scheme is used to further assess the impacts under future climates on the glaciological and hydrological processes in the Gepatschalm catchment (Austria), by considering a combination of three climate projections under the Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5. Additionally, the results are compared to the original WaSiM model with the integrated Volume-Area (VA) scaling approach for modeling glaciers. Although both models (WaSiM with VA scaling and WaSiM-OGGM coupling scheme) perform very similar during the historical simulations (1971–2010), large discrepancies arise when looking into the future (2011–2100). In terms of runoff, the VA scaling model suggests a reduction of the mean monthly peak between 10–19%, whereas a reduction of 26–41% is computed by the coupling scheme. Similarly, results suggest that glaciers will continuously retreat until 2100. By the end of the century, between 20–43% of the 2010 glacier area will remain according to the VA scaling model, but only 1–23% is expected to remain with the coupling scheme. The results from the WaSiM-OGGM coupling scheme raises awareness of including more sophisticated glacier evolution models when performing hydrological simulations at the catchment scale in the future. As the WaSiM-OGGM coupling scheme is released as open-source software, it is accessible to any interested modeler with limited or even no glacier knowledge.

Abstract Predicting current and future wildfire frequency and size is central to wildfire control and management. Multiple fire danger indices (FDIs) that incorporate weather and fuel conditions have been developed and utilized to support wildfire predictions and risk assessment. However, the scale‐dependent performance of individual FDIs remains poorly understood, which leads to large uncertainty in the estimated fire sizes under climate change. Here, we calculate four commonly used FDIs over the conterminous United States using high‐resolution (4 km) climate and fuel data sets for the 1984–2019 period. The relationships of these four FDIs to the observed wildfire sizes show that higher values of FDIs correlate to larger total fire sizes; this correlation is more robust at larger spatial scales. Sensitivity analysis indicates that the daily minimum relative humidity and precipitation are the most important drivers of the annual mean fire danger. In the instances of extreme fire danger, wind speed becomes a critical factor and should be considered in the calculation of the FDI. To assess the impact of climate change on future fire size, we calculate the present‐day and end‐of‐century FDIs using the 12 km regional climate model simulations. The four FDIs generally predict consistent changes in future fire potential, suggesting an overall higher fire potential in conjunction with a prolonged wildfire season in future climate. Regionally, the four FDIs also reveal similar seasonal patterns as the enhancement arises mostly in spring and summer over the southwest US while in summer and fall over the northern and eastern US.