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Ticks exist on all continents and carry more zoonotic pathogens than any other type of vector. Ticks spend most of their lives in the external environment away from the host and are thus expected to be affected by changes in climate. Most empirical and theoretical studies demonstrate or predict range shifts or increases in ticks and tick-borne diseases, but there can be a lot of heterogeneity in such predictions. Tick-borne disease systems are complex, and determining whether changes are due to climate change or other drivers can be difficult. Modeling studies can help tease apart and understand the roles of different drivers of change. Predictive models can also be invaluable in projecting changes according to different climate change scenarios. However, validating these models remains challenging, and estimating uncertainty in predictions is essential. Another focus for future research should be assessing the resilience of ticks and tick-borne pathogens to climate change.

In a rapidly changing world, uncontrolled climate change worsens water scarcity disrupting hydrological cycles and hindering sustainable development. Addressing water resources vulnerability requires holistic approaches to better understand complex systems, mitigate risks from changing weather patterns, and develop adaptive water management strategies. In this study, we modeled climate change impacts on water resource vulnerability using machine learning (ML) and SWAT model based on CMIP6 Global Climate Model (GCMs) under Shared Socioeconomic Pathway (SSP). Six ML models were evaluated to reliably predict hydroclimatic events; Extremely Randomised Trees (ERT) and Categorical Boosting (CatBoost) performed best for simulating ensemble climate interactions. The statistical indicators confirmed model reliability reducing input uncertainties with bias-corrected datasets. The ensemble SWAT model simulation showed a good agreement between simulated and observed values (R2 = 93 %, NSE = 91 %, and PBIAS = -1.08 %) for calibration and (R2 = 94 %, NSE = 93 %, and PBIAS = -2.32 %) for validation periods. Furthermore, we developed a novel Hydrologic Vulnerability Index (HVI) framework based on water balance components to quantify watershed vulnerability dynamics across baseline and future scenarios. The HVI ranged from low to extreme, with maximum lower values (54.03 %) observed at baseline, indicating resilience to hydrological stress, and higher values indicating severe vulnerability (43.45 %) at SSP245, indicating extreme drought conditions. The HVI framework integrates climate projections with actionable insights, offering a comprehensive approach to sustainable water management, adaptive infrastructure, and targeted interventions. Hence, innovative policies are critical to address extreme HVIs ensuring resilience against water scarcity and ecosystem degradation. This study underscores the importance of coupling data-driven hydrological analysis with climate responsiveness for effective watershed and environmental sustainability. These results demonstrate the importance of integrating various perspectives and strategies to address both short- and long-term climatic problems, by employing adaptive management practices to ensure sufficient water and ecosystem resilience.

Wheat is a major crop worldwide, mainly cultivated for human consumption and animal feed. Grain quality is paramount in determining its value and downstream use. While we know that climate change threatens global crop yields, a better understanding of impacts on wheat end-use quality is also critical. Combining quantitative genetics with climate model outputs, we investigated UK-wide trends in genotypic adaptation for wheat quality traits. In our approach, we augmented genomic prediction models with environmental characterisation of field trials to predict trait values and climate effects in historical field trial data between 2001 and 2020. Addition of environmental covariates, such as temperature and rainfall, successfully enabled prediction of genotype by environment interactions (G × E), and increased prediction accuracy of most traits for new genotypes in new year cross validation. We then extended predictions from these models to much larger numbers of simulated environments using climate scenarios projected under Representative Concentration Pathways 8.5 for 2050-2069. We found geographically varying climate change impacts on wheat quality due to contrasting associations between specific weather covariables and quality traits across the UK. Notably, negative impacts on quality traits were predicted in the East of the UK due to increased summer temperatures while the climate in the North and South-west may become more favourable with increased summer temperatures. Furthermore, by projecting 167,040 simulated future genotype-environment combinations, we found only limited potential for breeding to exploit predictable G × E to mitigate year-to-year environmental variability for most traits except Hagberg falling number. This suggests low adaptability of current UK wheat germplasm across future UK climates. More generally, approaches demonstrated here will be critical to enable adaptation of global crops to near-term climate change.

This study aimed to predict evaporation from dam reservoirs using artificial intelligence considering climate change. Mahabad Dam, near Lake Urmia, in northwestern Iran, is used to investigate the proposed approach. There are three parts to the study presented herein. In the first part, two machine learning models, namely group method of data handling (GMDH) and least squares support vector regression (LS-SVR), were used to model the inflow to the dam reservoir. Temperature, precipitation, and inflow during the previous month from 1990 to 2017 were used as input data. In the second part, the evaporation from the dam reservoir was modeled using the adaptive neuro-fuzzy inference system (ANFIS) and optimized ANFIS using Harris hawks optimization (HHO) and the arithmetic optimization algorithm (AOA) optimization algorithms. The input parameters in this part were temperature, precipitation, inflow to the dam reservoir, along with evaporation from the dam reservoir in the previous month. In the third part, precipitation and temperature were predicted using the fifth report of IPCC based on RCP2.6, RCP4.5, and RCP8.5 scenarios for the period 2020-2040. Out of 28 models presented in the fifth report, EC-ERATH and FIO-ESM had the greatest similarity with observational data of temperature and precipitation, respectively. The results of scatter plots and Taylor's diagram showed the higher performance of LS-SVR (root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash-Sutcliffe efficiency (NSE) of 8.65, 4.69, and 0.96) compared to GMDH (RMSE, MAPE, and NSE of 11.65, 7.81, and 0.93) in modeling the inflow. Moreover, both hybrid modes (AOA-ANFIS and HHO-ANFIS) improved the performance of ANFIS in modeling the evaporation from the dam reservoir. The RMSE, MAPE, and NSE values for ANFIS were 0.56, 0.52, and 0.89, respectively, while these values for the AOA-ANFIS (RMSE, MAPE, and NSE of 0.31, 0.24, and 0.93) and HHO-ANFIS (RMSE, MAPE, and NSE of 0.20, 0.30, and 0.96) were improved remarkably. The impact of climate change reduced the inflow to the dam reservoir by about 0.45, 0.80, and 1.7 MCM in RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. Also, the effect of climate change caused the evaporation from the dam reservoir to increase by about 0.2, 0.9, and 1 MCM in RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. The findings of this study show that the correct management of dam reservoirs needs to consider the potential effects of climate change in the future. Moreover, the hybrid machine learning models used in this study can be used to predict the amount of evaporation in other reservoirs.

BACKGROUND: Oncomelania hupensis is the sole intermediate host of Schistosoma japonicum. Its emergence and recurrence pose a constant challenge to the elimination of schistosomiasis in China. It is important to accurately predict the snail distribution for schistosomiasis prevention and control.

METHODS: Data describing the distribution of O. hupensis in 2016 was obtained from the Yunnan Institute of Endemic Disease Control and Prevention. Eight machine learning algorithms, including eXtreme Gradient Boosting (XGB), support vector machine (SVM), random forest (RF), generalized boosting model (GBM), neural network (NN), classification and regression trees (CART), k-nearest neighbors (KNN), and generalized additive model (GAM), were employed to explore the impacts of climatic, geographical, and socioeconomic variables on the distribution of suitable areas for O. hupensis. Predictions of the distribution of suitable areas for O. hupensis were made for various periods (2030s, 2050s, and 2070s) under different climate scenarios (SSP126, SSP245, SSP370, and SSP585).

RESULTS: The RF model exhibited the best performance (AUC: 0.991, sensitivity: 0.982, specificity: 0.995, kappa: 0.942) and the CART model performed the worst (AUC: 0.884, sensitivity: 0.922, specificity: 0.943, kappa: 0.829). Based on the RF model, the top six important variables were as follows: Bio15 (precipitation seasonality) (33.6%), average annual precipitation (25.2%), Bio2 (mean diurnal temperature range) (21.7%), Bio19 (precipitation of the coldest quarter) (14.5%), population density (13.5%), and night light index (11.1%). The results demonstrated that the overall suitable habitats for O. hupensis were predominantly distributed in the schistosomiasis-endemic areas located in northwestern Yunnan Province under the current climate situation and were predicted to expand north- and westward due to climate change.

CONCLUSIONS: This study showed that the prediction of the current distribution of O. hupensis corresponded well with the actual records. Furthermore, our study provided compelling evidence that the geographical distribution of snails was projected to expand toward the north and west of Yunnan Province in the coming decades, indicating that the distribution of snails is driven by climate factors. Our findings will be of great significance for formulating effective strategies for snail control.

The accelerating pace of climate change poses unprecedented challenges to global ecosystems and human societies. In response, this study reviews the power of Artificial Intelligence (AI) to develop advanced predictive models for assessing the multifaceted impacts of climate change. The study used the PRISMA framework to find, assess, and combine research on using AI in predicting climate change impacts. Integrating AI techniques, such as machine learning algorithms and predictive analytics, into climate modeling provides a robust framework for understanding and projecting the complex dynamics associated with global climate change. These models exhibit a high capacity for data collection, analyzing intricate patterns and integration, including their relationships within the datasets. They enable quick and accurate predictions of future climate scenarios, scenarios testing, historical eventualities, their magnitude, and adaptation. However, challenging issues like data gaps, especially in interconnected systems such as the atmosphere, are associated. Also, AI insight translation into an actionable recommendation recognizable by the policymakers, including ethical usage, is an emerging concern. Therefore, further advances to circumvent these will include the integration of AI with physical models, developing hybrid models, and generating synthetic climatic datasets to enhance data quality and gaps. Also, AI tools are being developed to aid decision-making for policy integration. AI-based predictive modeling is restructuring and bringing reformative change to the understanding of and approach toward climatic change through AI model development. AI guarantees an unfailing plan and a resilient future with sustainable approaches that empower scientists, policymakers, and communities.

BACKGROUND: Cryptosporidiosis is a zoonotic intestinal infectious disease caused by Cryptosporidium spp., and its transmission is highly influenced by climate factors. In the present study, the potential spatial distribution of Cryptosporidium in China was predicted based on ecological niche models for cryptosporidiosis epidemic risk warning and prevention and control.

METHODS: The applicability of existing Cryptosporidium presence points in ENM analysis was investigated based on data from monitoring sites in 2011-2019. Cryptosporidium occurrence data for China and neighboring countries were extracted and used to construct the ENMs, namely Maxent, Bioclim, Domain, and Garp. Models were evaluated based on Receiver Operating Characteristic curve, Kappa, and True Skill Statistic coefficients. The best model was constructed using Cryptosporidium data and climate variables during 1986‒2010, and used to analyze the effects of climate factors on Cryptosporidium distribution. The climate variables for the period 2011‒2100 were projected to the simulation results to predict the ecological adaptability and potential distribution of Cryptosporidium in future in China.

RESULTS: The Maxent model (AUC&#x2009;=&#x2009;0.95, maximum Kappa&#x2009;=&#x2009;0.91, maximum TSS&#x2009;=&#x2009;1.00) fit better than the other three models and was thus considered the best ENM for predicting Cryptosporidium habitat suitability. The major suitable habitats for human-derived Cryptosporidium in China were located in some high-population density areas, especially in the middle and lower reaches of the Yangtze River, the lower reaches of the Yellow River, and the Huai and the Pearl River Basins (cloglog value of habitat suitability&#x2009;>&#x2009;0.9). Under future climate change, non-suitable habitats for Cryptosporidium will shrink, while highly suitable habitats will expand significantly (&#x3c7;2&#x2009;=&#x2009;76.641, P&#x2009;<&#x2009;0.01; &#x3c7;2&#x2009;=&#x2009;86.836, P&#x2009;<&#x2009;0.01), and the main changes will likely be concentrated in the northeastern, southwestern, and northwestern regions.

CONCLUSIONS: The Maxent model is applicable in prediction of Cryptosporidium habitat suitability and can achieve excellent simulation results. These results suggest a current high risk of transmission and significant pressure for cryptosporidiosis prevention and control in China. Against a future climate change background, Cryptosporidium may gain more suitable habitats within China. Constructing a national surveillance network could facilitate further elucidation of the epidemiological trends and transmission patterns of cryptosporidiosis, and mitigate the associated epidemic and outbreak risks.

It is vital to understand the distribution area of a threatened plant species for its better conservation and management planning. Satyrium nepalense (family: Orchidaceae) is a threatened terrestrial orchid species with valuable medicinal and nutritional properties. The survival of S. nepalense in wild conditions has been challenged by increasing global surface temperature. Hence, understanding the impact of climate change on its potential distribution is crucial to conserve and restore this species. In present study, Maxent species distribution modeling algorithm was used to simulate the current distribution of S. nepalense in India and predict the possible range shift in projected future climate scenarios. A set of 19 bioclimatic variables from WorldClim database were used to predict the potential suitable habitats in current climatic condition and four Representative Concentration Pathway (RCP 2.6, 4.5, 6.0, and 8.5) scenarios by integrating five General Circulation Models (GCMs) for future distribution modeling of species for the years 2050 and 2070. Furthermore, change analysis was performed to identify the suitable habitat in current and future climate for delineating range expansion (gain), contraction (loss), and stable (no change) habitats of species. The Maxent model predicted that&#x2009;~&#x2009;2.38% of the geographical area in India is presently climatically suitable for S. nepalense. The key bioclimatic variables affecting the distribution of studied species were the mean temperature of warmest quarter, mean temperature of wettest quarter, precipitation of warmest quarter, and temperature seasonality. Under future climate change scenarios, the total suitable habitat of S. nepalense will increase slightly in the Himalayan region and likely to migrate towards northward, but in the Western Ghats region, the suitable areas will be lost severely. The net habitat loss under four RCP scenarios was estimated from 26 to 39% for the year 2050, which could further increase from 47 to 60% by the year 2070. The finding of the predictive Maxent modeling approach indicates that warming climates could significantly affect the potential habitats of S. nepalense and hence suitable conservation measures need to be taken to protect this threatened orchid species in wild conditions.

In Bangladesh, projected climate change is expected to increase food demands by more frequent and intense droughts and increasing temperatures. Few investigations have studied the impact of climate variability on future rice production. Previous investigations mainly checked the sensitivity of higher air temperature and higher atmospheric carbon dioxide on rice yields. Whereas in this study, we checked the combined effects of major climatic parameters on rice. The effects of climate change on yield of a popular winter rice cultivar in Bangladesh were assessed using the biophysical simulation model ORYZA2000. This model was first validated for 2000─2008 using field experimental data from Bangladesh, with a careful test of climate data on daily basis for station-wise and reanalysis datasets. The model performance was satisfactory enough to represent crop productions in nine major rice-growing districts. Then, simulation experiments were carried out for 2046─2065 and 2081─2100. Results show 33 % reduction of average rice yields for 2046─2065 and 2081─2100 for three locations. Projected rainfall pattern and distribution will also have a negative impact on the yields by increasing water demands by 14 % in the future. The model also showed that later transplanting will have less damage under the projected climate.

Despite their incredible diversity, relatively little work has been done to assess impacts of climate change on tropical freshwater organisms. Chortiheros wesseli is a species of Neotropical cichlid (Cichlidae: Cichlinae) restricted to only a few river drainages in the Caribbean-slope of Honduras. Little is known about this species and few specimens had been collected until recently; however, our work with this species in the wild has led to a better understanding of its ecology and habitat preferences making it an excellent model for how freshwater fishes can be affected by climate change. This study assesses the distribution and habitats of Chortiheros wesseli using a combination of field data and species distribution modeling. Results indicate this species is largely limited to its current range, with no realistic suitable habitat nearby. Empirical habitat data show that this species is limited to narrow and shallow flowing waters with rapids and boulders. This habitat type is highly influenced by precipitation, which contributed the greatest influence on the models of present and future habitat suitability. Although several localities are within boundaries of national protected areas, species distribution models all predict a reduction in the range of this freshwater fish based on climate change scenarios. The likelihood of a reduced range for this species will be intensified by adverse changes to its preferred habitats.

Introduction. Crop wild relatives (CWR) play an important role in combatting threats to global food security and the adverse effects of climate change on food production. At the same time, climate change is predicted to lead to significant challenges for species survival, including Nordic CWR taxa. Aim. The modelling of future climate scenario effects on Nordic priority CWR taxa was undertaken to set priorities for in situ and ex situ conservation for both species and areas. Methods. We modelled the effect of future climate change on suitable habitats for 84 taxa under two CMIP6 Shared Socio-economic Pathways scenarios (SSP 2-4.5, and SSP 5-8.5). The present potential distribution range was compared to the status by year 2100. Results. The results revealed that even modest climate change causes negative effects in many species. There was large variation among the species' responses to climate change, but over half of the taxa showed a reduction in suitable future habitats under both SSP scenarios. Threatened and mountainous species appear to be more negatively affected than the rest of the priority CWR taxa. Conclusions. We recommend in situ conservation with active management of Nordic CWR, prioritizing ex situ collection for species vulnerable to climate change. In addition, regular monitoring of CWR populations is essential to detect diversity loss and guide management, and a comprehensive Nordic CWR in situ network, integrated with ex situ conservation, is critical for long-term species survival.

The response of precipitation extremes to climate change is considered using results from theory, modeling, and observations, with a focus on the physical factors that control the response. Observations and simulations with climate models show that precipitation extremes intensify in response to a warming climate. However, the sensitivity of precipitation extremes to warming remains uncertain when convection is important, and it may be higher in the tropics than the extratropics. Several physical contributions govern the response of precipitation extremes. The thermodynamic contribution is robust and well understood, but theoretical understanding of the microphysical and dynamical contributions is still being developed. Orographic precipitation extremes and snowfall extremes respond differently from other precipitation extremes and require particular attention. Outstanding research challenges include the influence of mesoscale convective organization, the dependence on the duration considered, and the need to better constrain the sensitivity of tropical precipitation extremes to warming.

Assessments of impacts of climate change and future projections over the Indian region, have so far relied on a single regional climate model (RCM) - eg., the PRECIS RCM of the Hadley Centre, UK. While these assessments have provided inputs to various reports (e.g., INCCA 2010; NATCOMM2 2012), it is important to have an ensemble of climate projections drawn from multiple RCMs due to large uncertainties in regional-scale climate projections. Ensembles of multi-RCM projections driven under different perceivable socio-economic scenarios are required to capture the probable path of growth, and provide the behavior of future climate and impacts on various biophysical systems and economic sectors dependent on such systems. The Centre for Climate Change Research, Indian Institute of Tropical Meteorology (CCCR-IITM) has generated an ensemble of high resolution downscaled projections of regional climate and monsoon over South Asia until 2100 for the Intergovernmental Panel for Climate Change (IPCC)using a RCM (ICTP-RegCM4) at 50 km horizontal resolution, by driving the regional model with lateral and lower boundary conditions from multiple global atmosphere-ocean coupled models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The future projections are based on three Representation Concentration Pathway (RCP) scenarios (viz., RCP2.6, RCP4.5, RCP8.5) of the IPCC.

Mediterranean ecosystems such as those found in California, Central Chile, Southern Europe, and Southwest Australia host numerous, diverse, fire-adapted micro-ecosystems. These micro-ecosystems are as diverse as mountainous conifer to desert-like chaparral communities. Over the last few centuries, human intervention, invasive species, and climate warming have drastically affected the composition and health of Mediterranean ecosystems on almost every continent. Increased fuel load from fire suppression policies and the continued range expansion of non-native insects and plants, some driven by long-term drought, produced the deadliest wildfire season on record in 2018. As a consequence of these fires, a large number of structures are destroyed, releasing household chemicals into the environment as uncontrolled toxins. The mobilization of these materials can lead to health risks and disruption in both human and natural systems. This article identifies drivers that led to a structural weakening of the mosaic of fire-adapted ecosystems in California, and subsequently increased the risk of destructive and explosive wildfires throughout the state. Under a new climate regime, managing the impacts on systems moving out-of-phase with natural processes may protect lives and ensure the stability of ecosystem services.

Due to climate change the frequency and intensity of extreme rainfall events, which contribute to urban flooding, are expected to increase in many places. These floods can damage transport infrastructure and disrupt mobility, highlighting the need for cities to adapt to escalating risks. Reinforcement learning (RL) serves as a powerful tool for uncovering optimal adaptation strategies, determining how and where to deploy adaptation measures effectively, even under significant uncertainty. In this study, we leverage RL to identify the most effective timing and locations for implementing measures, aiming to reduce both direct and indirect impacts of flooding. Our framework integrates climate change projections of future rainfall events and floods, models city-wide motorized trips, and quantifies direct and indirect impacts on infrastructure and mobility. Preliminary results suggest that our RL-based approach can significantly enhance decision-making by prioritizing interventions in specific urban areas and identifying the optimal periods for their implementation. Our framework is publicly available: \url{https://github.com/MLSM-at-DTU/floods_transport_rl}.

The fifth phase of the Coupled Model Intercomparison Project (CMIP5) will produce a state-of-the- art multimodel dataset designed to advance our knowledge of climate variability and climate change. Researchers worldwide are analyzing the model output and will produce results likely to underlie the forthcoming Fifth Assessment Report by the Intergovernmental Panel on Climate Change. Unprecedented in scale and attracting interest from all major climate modeling groups, CMIP5 includes “long term” simulations of twentieth-century climate and projections for the twenty-first century and beyond. Conventional atmosphere–ocean global climate models and Earth system models of intermediate complexity are for the first time being joined by more recently developed Earth system models under an experiment design that allows both types of models to be compared to observations on an equal footing. Besides the longterm experiments, CMIP5 calls for an entirely new suite of “near term” simulations focusing on recent decades and the future to year 2035. These “decadal predictions” are initialized based on observations and will be used to explore the predictability of climate and to assess the forecast system's predictive skill. The CMIP5 experiment design also allows for participation of stand-alone atmospheric models and includes a variety of idealized experiments that will improve understanding of the range of model responses found in the more complex and realistic simulations. An exceptionally comprehensive set of model output is being collected and made freely available to researchers through an integrated but distributed data archive. For researchers unfamiliar with climate models, the limitations of the models and experiment design are described.

Faced by the realities of a changing climate, decision makers in a wide variety of organizations are increasingly seeking quantitative predictions of regional and local climate. An important issue for these decision makers, and for organizations that fund climate research, is what is the potential for climate science to deliver improvements—especially reductions in uncertainty—in such predictions? Uncertainty in climate predictions arises from three distinct sources: internal variability, model uncertainty, and scenario uncertainty. Using data from a suite of climate models, we separate and quantify these sources. For predictions of changes in surface air temperature on decadal timescales and regional spatial scales, we show that uncertainty for the next few decades is dominated by sources (model uncertainty and internal variability) that are potentially reducible through progress in climate science. Furthermore, we find that model uncertainty is of greater importance than internal variability. Our findings have implications for managing adaptation to a changing climate. Because the costs of adaptation are very large, and greater uncertainty about future climate is likely to be associated with more expensive adaptation, reducing uncertainty in climate predictions is potentially of enormous economic value. We highlight the need for much more work to compare (a) the cost of various degrees of adaptation, given current levels of uncertainty and (b) the cost of new investments in climate science to reduce current levels of uncertainty. Our study also highlights the importance of targeting climate science investments on the most promising opportunities to reduce prediction uncertainty.

Climate change is predicted to become a major threat to biodiversity in the 21st century, but accurate predictions and effective solutions have proved difficult to formulate. Alarming predictions have come from a rather narrow methodological base, but a new, integrated science of climate-change biodiversity assessment is emerging, based on multiple sources and approaches. Drawing on evidence from paleoecological observations, recent phenological and microevolutionary responses, experiments, and computational models, we review the insights that different approaches bring to anticipating and managing the biodiversity consequences of climate change, including the extent of species' natural resilience. We introduce a framework that uses information from different sources to identify vulnerability and to support the design of conservation responses. Although much of the information reviewed is on species, our framework and conclusions are also applicable to ecosystems, habitats, ecological communities, and genetic diversity, whether terrestrial, marine, or fresh water.

The Working Group II contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) provides a comprehensive assessment of the scientific literature relevant to climate change impacts, adaptation and vulnerability. The report recognizes the interactions of climate, ecosystems and biodiversity, and human societies, and integrates across the natural, ecological, social and economic sciences. It emphasizes how efforts in adaptation and in reducing greenhouse gas emissions can come together in a process called climate resilient development, which enables a liveable future for biodiversity and humankind. The IPCC is the leading body for assessing climate change science. IPCC reports are produced in comprehensive, objective and transparent ways, ensuring they reflect the full range of views in the scientific literature. Novel elements include focused topical assessments, and an atlas presenting observed climate change impacts and future risks from global to regional scales. Available as Open Access on Cambridge Core.