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Oil palms (OP) produce palm oil, a unique commodity without commercial alternatives. A serious disease of OP is basal stem rot (BSR) caused by Ganoderma boninense Pat. Climate change will likely increase BSR, thereby causing mortality of OP and reduced yields of palm oil. Work is being undertaken to produce modified OP (mOP) to resist BSR, although this will take decades for full development, if successfully produced at all. mOP will not be 100% effective, and it would be useful to know the effect of mOP on the key parameters of BSR incidence, OP mortality, and yield loss. The current paper employed CLIMEX modeling of suitable climates for OP and modeling narratives for Indonesia and Thailand. Indonesia is the largest producer of OP and Thailand is a much smaller manufacturer, and it was informative to compare these two countries. The gains from using mOP were substantial compared to the current production of some other continents and countries. The current paper, for the first time, assessed how climate change will affect BSR parameters for conventional and mOP. Greater consideration of the potential benefits of mOP is required to justify investing in the technology. [ABSTRACT FROM AUTHOR]

In recent decades, climate change has become a critical global issue with far-reaching consequences for regional climates and ecosystems. While regional climate models provide valuable information, there is a growing need for high-resolution simulations to assess local impacts. This paper addresses this gap by presenting the first simulation of a 3 km convection-permitting (CP) scenario simulation for Bulgaria. The main aim of this study is to assess different precipitation indices and their future changes for Bulgaria under the Representative Concentration Pathway 8.5 (RCP8.5) scenario following the Coordinated Regional Climate Downscaling Experiment Flagship Pilot Study protocol. The simulations are evaluated against high-resolution observations. We downscale Coupled Model Intercomparison Project 5 Global Climate Model (CMIP5 GCM) data for historical (1995–2004) and future (2089–2098) periods using a regional climate model (RCM) at 15 km grid spacing and parametrized convection. We use these fields as initial and boundary conditions for convection-permitting kilometer-scale simulations. The 15 km grid spacing driving model is used as a reference to assess the added value of the kilometer-scale simulation. Additionally, the 3 km seasonal mean and projected 2 m temperature and the winter snow water equivalent are presented. The results show that the kilometer-scale simulation shows better performance of wet-hour intensity in all seasons, wet-hour frequency in the spring, fall, and winter, and extreme precipitation (99.9th percentile of all precipitation events, p99.9) in the winter and fall. The kilometer-scale simulation improves the projected precipitation distribution and modifies the signal of the precipitation frequency, intensity, and heavy precipitation change over some areas. A positive projected change in the wet-hour intensity is expected in all seasons (13.86% in spring, MAM, 17.48% in summer, JJA, 1.97% in fall, SON, and 17.43% in winter, DJF) and in the heavy precipitation in the spring (13.14%) and winter (31.19%) in the kilometer-scale experiment. The projected increase in mean winter precipitation is accompanied by a significant decrease in mean winter snowfall over lowlands (50−70%). The convection-permitting Regional Climate Model, version 4.7.1 (RegCM4.7.1) suggests an increase in winter snowfall over the highest parts of the country, but a significant increase in the 2 m temperatures there. The results of this study are encouraging and may be of interest to the community of climate scientists and users of climate data for making reliable estimates of the local impacts of future climate change. [ABSTRACT FROM AUTHOR]

The hydrological assessment of the Vistula River basin in the near future will be a key element in the development of strategies to adapt agriculture to climate change. The Vistula River basin covers 61% of Poland's area (190,062 km2) and is mainly used for agricultural production. The aim of this study is to assess the water balance of the Vistula River basin from the perspective of 2050 based on the analysis of two climate scenarios, RCP 4.5 and RCP 8.5, and the three climate models ICHEC-EC-EARTH_KNMI-RACMO22E (A), ICHEC-EC-EARTH_DMI-HIRHAM5 (B), and ICHEC-EC-EARTH_SMHI-RCA4 (C). This paper presents the steps in the development of the SWAT model and the results of the hydrological analysis of the Vistula catchment. Calibration and validation of the model were carried out using the SUFI-2 algorithm in the SWAT-CUP programme for 2013–2018. The data used to calibrate the SWAT model are monthly flow measurements [m3/s] from the measurement station in Tczew, located near the estuary of the Vistula basin to the Baltic Sea. The summary result of the work is the results of modelling the flow of the Vistula River catchment for different climate scenarios in the 2020–2050 perspective. The average annual precipitation for all projections in 2021–2030, 2031–2040, and 2041–2050 will be higher by up to 22% (763 mm) (RCP 8.5.C for 2041–2050) compared to the 2013–2018 simulation years (624 mm). The average annual temperature for most climate projections for 2021–2030 will fall to as low as 8.7 °C (RCP 4.5.B) compared to the 2013–2018 simulation period (9.2 °C). In contrast, for all projections in 2031–2040 and 2041–2050, the average annual temperature will increase to as much as 10.3 °C (RCP 8.5.C). The simulation results for the climate projections (2020–2050) indicate that there are no clear trends of change in the water management of the Vistula River basin for the coming decades. According to scenarios RCP 4.5.A, RCP 8.5.A, and RCP 8.5.B, the annual sums of potential evapotranspiration show a slight downward trend. On the other hand, for the RCP 8.5.C and RCP 4.5.C projections and the climate change scenario RCP 4.5.B, the results obtained show a slight upward trend in the annual sum of potential evapotranspiration. For the overall evapotranspiration and potential evapotranspiration assessment for all climate projections analysed, the annual evapotranspiration total shows a clear increase compared to the 2013–2018 baseline period. The average annual actual evapotranspiration for all projections in 2021–2030, 2031–2040, and 2041–2050 will increase up to 467 mm (RCP 4.5.A—2021–2030) compared to the 2013–2018 simulation period of 401 mm. The average annual potential evapotranspiration for all projections in 2021–2030, 2031–2040, and 2041–2050 will increase up to 755 mm (RCP 8.5.C—2031–2040) compared to the 2013–2018 simulation period—616 mm. The analysis of the total runoff in all climate models for the RCP 4.5 scenario shows that the annual average total runoff tends to decrease. The results of the simulations carried out for the RCP 8.5 scenario, which are generally characterised by an increase in total runoff in subsequent years, are different. When analysing annual total runoff on a regional basis, it appears that for most of the climate projections analysed (except for the RCP 8.5.A scenario), annual runoff will be lower, especially in the lowlands in the central part of the Vistula basin. In regions where the increase in precipitation is greatest in the north-western and southern basins, higher total runoff should be expected. The analysis of the total runoff in all climate models for the RCP 4.5 scenario shows that the annual average total runoff tends to decrease. The results of the simulations carried out for the RCP 8.5 scenario, which are generally characterised by an increase in total runoff in subsequent years, are different. When analysing annual total runoff on a regional basis, it appears that for most of the climate projections analysed (except for the RCP 8.5.A scenario), annual runoff will be lower, especially in the lowlands in the central part of the Vistula basin. In regions where the increase in precipitation is greatest in the north-western and southern basins, higher total runoff should be expected. [ABSTRACT FROM AUTHOR]

Empirical evidence continues to show that climate change remains a threat to the stability of the hydrologic system. As the climate system interacts with the hydrologic cycle, one significant repercussion of global warming includes changes in water availability at both regional and local scales. Climate change adaptation is intrinsically difficult to attain due to the dynamic earth system and lack of a comprehensive understanding of future climate and its associated uncertainties. Mostly in developing countries, climate adaptation is hampered by scarcity of good quality and adequate hydro-meteorological data. This article provides a synopsis of the modelling chain applied to investigate the response of the hydrologic system under changing climate, which includes choosing the appropriate global climate models, downscaling techniques, emission scenarios, and the approach to be used in hydrologic modelling. The conventional criteria for choosing a suitable hydrological model are discussed. The advancement of emission scenarios including the latest Shared Socioeconomic Pathways and their role in climate modelling, impact assessment, and adaptation, are also highlighted. This paper also discusses the uncertainties associated with modelling the hydrological impacts of climate change and the plausible approaches for reducing such uncertainties. Among the outcomes of this review include highlights of studies on the commonly used hydrological models for assessing the impact of climate change particularly in the sub-Saharan Africa region and some specific reviews in southern Africa. Further, the reviews show that as human systems keep on dominating within the earth system in several ways, effective modelling should involve coupling earth and human systems models as these may truly represent the bidirectional feedback experienced in the modern world. The paper concludes that adequate hydro-meteorological data is key to having a robust model and effective climate adaptation measures, hence in poorly gauged basins use of artificial neural networks and satellite datasets have shown to be successful tools, including for model calibration and validation. [ABSTRACT FROM AUTHOR]

The entire cascade of scenario generation, global and regional climate modeling, as well as concrete measures towards climate adaptation are subject to uncertainties. An exact prediction of how the climate will change in the coming years, and how it will affect land use, is not possible. There is thus a perceived need to identify ways via which uncertainties can be addressed. Based on the need to address the research gap in this area, this paper reports the findings of a study on uncertainty in a climate change adaptation context, and how it is perceived. It consists of a multi-stakeholder survey among climate change professionals, including academic staff at universities, representatives from international agencies, members of NGOs, policymakers, and representatives of industry from 50 countries, including a balanced representation of industrialized and developing nations. The results obtained suggest that uncertainties are often a hindrance to engagement in climate change adaptation efforts, and to land management. Furthermore, there is a range of tools to reduce climate change adaptation uncertainties, whose deployment may help to address them. The paper concludes by providing a list of lessons learned and suggestions as to how uncertainty can be better communicated, and by doing so, how a reduction in the levels of climate change vulnerability may be achieved, and how land management may be fostered. [ABSTRACT FROM AUTHOR]

In atmospheric models, the representation of cloudiness is a direct linkage between the moisture amount and associated radiative forcing. This paper begins by providing a review of the parameterization of cloudiness that has been used for numerical weather predictions and climate studies. The inherent uncertainties in representing a partial fraction of clouds for radiation feedback and in evaluating it against the corresponding observations are focused. It is also stated that the major hydrometeor categories of water substances such as cloud ice and water that are responsible for cloud cover are readily available in modern weather and climate models. Inconsistencies in cloud cover and hydrometeors, even in the case of the prognostic method, are discussed. The compensating effect of cloudiness for radiative feedback is found to imply that the condensed water amount itself is more influential on the radiative forcing, rather than the accuracy of the cloudiness. Based on the above perspectives, an alternative diagnostic parameterization method is proposed, utilizing a monotonic relation between the cloud water amounts and cloudiness that are obtained from aircraft and satellite observations. The basic premise of this approach lies in the accuracy of the water substance in the models, indicating that future efforts need to be given to improvements in physical processes concerning hydrometeor properties for the accurate representation of cloud radiative feedback. [ABSTRACT FROM AUTHOR]

Analyzing the future changes in runoff is crucial for efficient water resources management and planning in arid regions with large river systems. This paper investigates the future runoffs of the headwaters of the Tarim River Basin under different emission scenarios by forcing the hydrological model SWAT-Glacier using six regional climate models from the Coordinated Regional Downscaling Experiment (CORDEX) project. Results indicate that compared to the period of 1976~2005, temperatures are projected to increase by 1.22 ± 0.72 °C during 2036~2065 under RCP8.5 scenarios, with a larger increment in the south Tianshan mountains and a lower increment in the north Kunlun Mountains. Precipitation is expected to increase by 3.81 ± 14.72 mm and 20.53 ± 27.65 mm during 2036–2065 and 2066–2095, respectively, under the RCP8.5 scenario. The mountainous runoffs of the four headwaters that directly recharge the mainstream of the Tarim River demonstrate an overall increasing trend in the 21st century. Under the RCP4.5 and RCP8.5 scenarios, the runoff is projected to increase by 3.2% and 3.9% (amounting to 7.84 × 108 m3 and 9.56 × 108 m3) in 2006–2035. Among them, the runoff of the Kaidu River, which is dominated by rainfall and snowmelt, is projected to present slightly decreasing trends of 3~8% under RCP4.5 and RCP8.5 scenarios. For catchments located in the north Kunlun Mountains (e.g., the Yarkant and Hotan Rivers which are mix-recharged by glacier melt, snowmelt, and rainfall), the runoff will increase significantly, especially in summer due to increased glacier melt and precipitation. Seasonally, the Kaidu River shows a forward shift in peak flow. The summer streamflow in the Yarkant and Hotan rivers is expected to increase significantly, which poses challenges in flood risk management. [ABSTRACT FROM AUTHOR]

Jamaica produces one of the most expensive coffees on the global market. The local specialty coffee industry plays a significant role in the island's economy and also contributes to the livelihood of smallholders—the majority of whom operate the industry's coffee farms. While climate model projections suggest that Jamaica will continue to experience a warming and drying trend, no study has assessed the future impacts of changing climatic patterns on local coffee-growing areas. This research developed a number of geospatial processing models within the ArcMap software platform to model current coffee suitability and future crop suitability across three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) and three future time periods (2021–2040, 2041–2060, and 2081–2100). The results validated current locations of coffee production and revealed that there was an observable decrease in coffee suitability across the island, across all SSP scenarios and time periods under study. Most growing regions were projected to experience declines in production suitability of at least 10%, with the most severe changes occurring in non-Blue Mountain regions under the SSP5-8.5 scenario. Implications of this projected suitability change range from decreased production volumes, increased price volatility, and disruption to market operations and livelihood incomes. The paper's findings offer stakeholders within Jamaica's coffee industry the opportunity to develop targeted adaptation planning initiatives, and point to the need for concrete decisions concerning future investment pathways for the industry. It also provides insight into other tropical coffee-growing regions around the world that are facing the challenges associated with climate change. [ABSTRACT FROM AUTHOR]

Climate–land interaction over West Africa has often been assessed using climate simulations, although the model-based approach suffers from the limitations of climate models for the region. In this paper, an alternative method based on the analysis of historical land cover data and standardized climatic indices is used to investigate climate–land interactions, in order to establish climatic conditions and their corresponding land cover area changes. The annual variation in land cover area changes and climatic changes are first estimated separately and then linked using various spatiotemporal scales. The results show that incidences of land cover change result from abrupt changes in climatic conditions. Interannual changes of −1.0–1.0 °C, 0–1.5 °C, and −0.5–0.5 °C, and up to ± 50 mm changes in precipitation and climatic water balance, lead to 45,039–52,133 km2, 20,935–22,127 km2, and approximately 32,000 km2 changes, respectively, while a ± 0.5 °C and ± 20 mm change represents normal climate conditions with changes below 20,000 km2. Conversely, conversions of cropland, forest, grassland, and shrubland are the main land cover change types affecting the climate. The results offer a basis for the re-evaluation of land cover change and climate information used in regional climate models simulating land–climate interactions over West Africa. [ABSTRACT FROM AUTHOR]

Due to climate change, hydroclimatic extremes are becoming more frequent and intense and their characterization and analysis is essential for climate modelling. One of the regions that will be most affected by these extremes is the Mediterranean coast of Spain. Therefore, this paper analyses the significant wave height (Hs), peak wave period (Tp) and sea level (SL) extremes and their correlation along the Spanish Mediterranean coast. After conducting this analysis, it is finally concluded that and adjustment of the extreme long-term distribution of Tp is urgently needed to create accurate models and projections, which must be considered in combination with the intense extremes happening in the Levantine basin when modelling this area and designing new projects. [ABSTRACT FROM AUTHOR]

The paper compares future streamflow projections for 133 catchments in the Murray–Darling Basin simulated by a hydrological model with future rainfall inputs generated from different methods informed by climate change signals from different global climate models and dynamically downscaled datasets. The results show a large range in future projections of hydrological metrics, mainly because of the uncertainty in rainfall projections within and across the different climate projection datasets. Dynamical downscaling provides simulations at higher spatial resolutions, but projections from different datasets can be very different. The large number of approaches help provide a robust understanding of future hydroclimate conditions, but they can also be confusing. For water resources management, it may be prudent to communicate just a couple of future scenarios for impact assessments with stakeholders and policymakers, particularly when practically all of the projections indicate a drier future in the Basin. The median projection for 2046–2075 relative to 1981–2010 for a high global warming scenario is a 20% decline in streamflow across the Basin. More detailed assessments of the impact and adaptation options could then use all of the available datasets to represent the full modelled range of plausible futures. [ABSTRACT FROM AUTHOR]

Climate change, with its reaching implications, has become a popular topic in recent years. Among the many aspects of climate change, one of the most pressing concerns has been identified as the impact on the terrestrial water cycle, which has a direct impact on human settlement and ecosystems. The paper begins by reviewing previous studies, and then identifies their flaws and future research directions. The effects of climate change on surface water resources in the Dhidhessa Sub-basin, Abbay Basin, Ethiopia, were studied as practices. For future potential climate change, the results of global climate models (GCMs) and high-resolution regional climate models (RCMs) from multiple climate models were combined with data from Representative Concentration Pathways (RCPs) prepared by the Intergovernmental Panel on Climate Change from the CCAFS Data Distribution Center web page. To evaluate the impacts on water resources, various distributed hydrologic models based on local underlying surfaces were developed. The future potential climate change of the Dhidhessa Sub-basin Province was evaluated by integrating RCP outputs, whereas the climate change of the Dhidhessa River was directly derived from the results of different RCP. Dhidhessa stream flow will decrease in the future compared to the baseline era. The predictions of future discharge (stream flow) were based on climate scenarios data from 1991 to 2020 and for the future with two time windows, 2044 (2030–2059) and 2084 (2070–2099), on a monthly time-step after bias correction to both precipitation and temperature in the future climate described in the under each RCP. According to model results, the quantity of surface water resources in the Dhidhessa river region will decrease over the next 100 years, the percent decrease in mean annual stream flow by 10%, in 2044, and 6.3% in 2084, respectively, making the impact of temperature increase on runoff greater than that of precipitation. The distribution of runoff would be more even across years but more uneven across years in the long-term window, implying a higher possibility of drought and flooding. In general, this study discovered that any effect on this river that results in a decrease in flow will have a direct impact on the area's ongoing water resource development and socioeconomic development. [ABSTRACT FROM AUTHOR]

The role of climate change in future streamflow is still very uncertain, especially over semi-arid regions. However, part of this uncertainty can be offset by correcting systematic climate models' bias. This paper tries to assess how the choice of a bias correction method may impact future streamflow of the Cheliff-Mactaa-Tafna (CMT) rivers. First, three correction methods (quantile mapping (QM), quantile delta mapping (QDM), and scaled distribution mapping (SDM)) were applied to an ensemble of future precipitation and temperature coming from CORDEX-Africa, which uses two Representative Concentration Pathways: RCP4.5 and RCP8.5. Then, the Zygos model was used to convert the corrected time series into streamflow. Interestingly, the findings showed an agreement between the three methods that revealed a decline in future streamflow up to [−42 to −62%] in autumn, [+31% to −11%] in winter, [−23% to −39%] in spring, and [−23% to −41%] in summer. The rate of decrease was largest when using QM-corrected model outputs, followed by the raw model, the SDM-corrected model, and finally, the QDM-corrected model outputs. As expected, the RCP presents the largest decline especially by the end of the 21st Century. [ABSTRACT FROM AUTHOR]

Energy retrofits can enhance the liveability and efficiency of historic buildings while preserving their historic and aesthetic values. However, measures like improved insulation and airtightness may increase their vulnerability to overheating and climate change may further worsen their performance in the future. This paper investigates indoor overheating risks brought by climate change in retrofitted historic buildings and proposes effective adaptation strategies. Firstly, local weather conditions are analysed to identify homogenous climatic zones. For each climatic zone, "a business-as-usual" emissions scenario is adopted, and most representative regional climate models are selected to obtain hourly output of future climate projection. A comparative study is adopted where typical alpine residential buildings, "Portici house", are simulated with regard to future energy use and indoor thermal state using the dynamic model in EnergyPlus. Energy use and indoor thermal conditions are compared before and after energy retrofit, as well as under present and future climate conditions. The results demonstrate that retrofit interventions could significantly improve energy efficiency of historic buildings in both present and future scenarios. A change in climate together with retrofit interventions will, however, result in higher risk of indoor overheating in South Tyrol. Potential negative side effects of energy retrofit could be controlled by adopting adequate shading and ventilation approaches that minimise, or eliminate, the risk of overheating during high temperature periods while optimising historic buildings' energy performance. [ABSTRACT FROM AUTHOR]

Climate variability and change greatly affect agricultural and water resource management over West Africa. This paper presents the current characteristics and projected change in regional crop water demand (CWD), irrigation requirement (IR), and water availability (WA) over West Africa. Observed and simulated daily rainfall, minimum temperature, maximum temperature, and evapotranspiration are used to derive the above agro-meteorological and hydrological variables. For future periods, high-resolution climate data from three regional climate models under two different scenarios, i.e., representative concentration pathway (RCP) 4.5 and 8.5, are considered. Evaluation of the characteristics of present-day CWD, IR, and WA indicated that the ensemble mean of the model-derived outputs reproduced the prevailing spatial patterns of CWD and IR. Moreover, the wetter part of the domain, especially along the southern coast, was correctly delineated from the drier northern regions, despite having biases. The ensemble model also simulated the annual cycle of water supply and the bimodal pattern of the water demand curves correctly. In terms of future projections, the outcomes from the study suggest an average increase in the CWD by up to 0.808 mm/day and IR by 1.244 mm/day towards the end of the twenty-first century, compared to the baseline period. The hot-spot areas, where there is higher projected increment in CWD and IR, are over Senegal, Southern Mali, and Western Burkina Faso. In most cases, WA is projected to decrease towards the end of the twenty-first century by −0.418 mm/day. The largest decline in WA is found to be over Guinea and most of the eastern parts of West Africa. Despite the current under-utilization of the existing groundwater resources, the threat of global warming in reducing future WA and increasing CWD suggested caution on the scale of irrigation schemes and management strategies. The outcomes from the study could be a crucial input for the agricultural and water managers for introducing effective measures to ensure sustainability of irrigated farm lands. [ABSTRACT FROM AUTHOR]

Recent climate changes have prompted changes in the hydrological cycle at a global scale, creating instability when predicting future climate conditions and related changes. Perturbations in global climate models have created the need to concentrate consequent changes in hydro climatic factors to comprehend the regional and territorial impacts of climate and environmental changes. Syria, as a Middle East country, is exposed to extreme climate events such as drought and flood. The aim of this study is to analyze rainfall trends in Syria in response to the likely climate change. The analysis was conducted for rainfall data collected from 71 stations distributed all over the country for the period (1991–2009). The trend analysis was performed in monthly and seasonal scales using Mann–Kendall non-parametric statistical tests. The results attained from Mann–Kendall trend analysis revealed decreasing trends at most of the stations. Additionally, rainfall analysis was conducted for the stations with significant trends for wet and dry periods, which also revealed decreasing trends at almost all the stations. From the analysis of the results, it is obvious that slight increasing trends in rainfall in Syria occurred in the fall period. However, in the winter and spring periods, significant decreasing trends have been observed at almost all the stations. This reveals that the country will suffer from shortage of water, because most rainfall occurs in the winter and spring, infrequently in fall and rarely in summer. The results are consistent with the IPCC's fifth report that predicted a decrease in rainfall in the Mediterranean and southern Asia. The results of this paper could help the management of water resources in Syria considering future climate changes. [ABSTRACT FROM AUTHOR]

Climate change is expected to increase the occurrence of droughts, with the hydrology in alpine systems being largely determined by snow dynamics. In this paper, we propose a methodology to assess the impact of climate change on both meteorological and hydrological droughts, taking into account the dynamics of the snow cover area (SCA). We also analyze the correlation between these types of droughts. We generated ensembles of local climate scenarios based on regional climate models (RCMs) representative of potential future conditions. We considered several sources of uncertainty: different historical climate databases, simulations obtained with several RCMs, and some statistical downscaling techniques. We then used a stochastic weather generator (SWG) to generate multiple climatic series preserving the characteristics of the ensemble scenario. These were simulated within a cellular automata (CA) model to generate multiple SCA future series. They were used to calculate multiple series of meteorological drought indices, the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and a novel hydrological drought index (Standardized Snow Cover Index (SSCI)). Linear correlation analysis was applied to both types of drought to analyze how they propagate and the time delay between them. We applied the proposed methodology to the Sierra Nevada (southern Spain), where we estimated a general increase in meteorological and hydrological drought magnitude and duration for the horizon 2071–2100 under the RCP 8.5 emission scenario. The SCA droughts also revealed a significant increase in drought intensity. The meteorological drought propagation to SCA droughts was reflected in an immediate or short time (1 month), obtaining significant correlations in lower accumulation periods of drought indices (3 and 6 months). This allowed us to obtain information about meteorological drought from SCA deficits and vice versa. [ABSTRACT FROM AUTHOR]

Fatal landslides are a widespread geohazard that have affected millions of people and have claimed the lives of thousands around the globe. A change in climate has significantly increased the frequency and magnitude of rainfall, which affect the susceptibility of slopes to shallow landslides. This paper presents a methodological framework to assess the future changes in extreme and seasonal rainfall magnitudes with climate model projections. This framework was applied to project summer rainfall over Wanzhou County, China, using an ensemble of four regional climate models (RCMs) from the East Asian domain of the Coordinated Downscaling Experiment (CORDEX) under the Phase 5 Coupled Intercomparison Modeling Project (CMIP5). The results find that extreme daily rainfall was projected to decrease in the mid-21st century, with an uncertainty measured by a coefficient of variation between 5% and 25%. The mean seasonal rainfall is projected to increase in the mid-21st century up to a factor of 1.4, and up to a factor of 1.8 in the late-21st century. The variation in the mid-21st century ranged from 10% to 35%, and from 30% to 50% in the late-21st century. This case study delivered a proof-of-concept for a methodological framework to derive shallow landslide-triggering rainfall scenarios under climate change conditions. The resulting spatially distributed climate change factors (CCFs) can be used to incorporate future rainfall scenarios in slope susceptibility models and climate impact assessments. [ABSTRACT FROM AUTHOR]

Climate change is a serious and complex crisis that impacts humankind in different ways. It affects the availability of water resources, especially in the tropical regions of South Asia to a greater extent. However, the impact of climate change on water resources in Sri Lanka has been the least explored. Noteworthy, this is the first study in Sri Lanka that attempts to evaluate the impact of climate change in streamflow in a watershed located in the southern coastal belt of the island. The objective of this paper is to evaluate the climate change impact on streamflow of the Upper Nilwala River Basin (UNRB), Sri Lanka. In this study, the bias-corrected rainfall data from three Regional Climate Models (RCMs) under two Representative Concentration Pathways (RCPs): RCP4.5 and RCP8.5 were fed into the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model to obtain future streamflow. Bias correction of future rainfall data in the Nilwala River Basin (NRB) was conducted using the Linear Scaling Method (LSM). Future precipitation was projected under three timelines: 2020s (2021–2047), 2050s (2048–2073), and 2080s (2074–2099) and was compared against the baseline period from 1980 to 2020. The ensemble mean annual precipitation in the NRB is expected to rise by 3.63%, 16.49%, and 12.82% under the RCP 4.5 emission scenario during the 2020s, 2050s, and 2080s, and 4.26%, 8.94%, and 18.04% under RCP 8.5 emission scenario during 2020s, 2050s and 2080s, respectively. The future annual streamflow of the UNRB is projected to increase by 59.30% and 65.79% under the ensemble RCP4.5 and RCP8.5 climate scenarios, respectively, when compared to the baseline scenario. In addition, the seasonal flows are also expected to increase for both RCPs for all seasons with an exception during the southwest monsoon season in the 2015–2042 period under the RCP4.5 emission scenario. In general, the results of the present study demonstrate that climate and streamflow of the NRB are expected to experience changes when compared to current climatic conditions. The results of the present study will be of major importance for river basin planners and government agencies to develop sustainable water management strategies and adaptation options to offset the negative impacts of future changes in climate. [ABSTRACT FROM AUTHOR]

The Global Climate Model (GCM) run at a coarse spatial resolution cannot be directly used for climate impact studies. Downscaling is required to extract the sub-grid and local scale information. This paper investigates if the artificial neural network (ANN) is better than the widely-used regression-based statistical downscaling model (SDSM) for downscaling climate for a site in Colombo, Sri Lanka. Based on seasonal and annual model biases and the root mean squared error (RMSE), the ANN performed better than the SDSM for precipitation. This paper proposes a novel methodology for improving climate predictions by combining SDSM with neural networks. This method will allow a user to apply SDSM with a neural network model for higher skills in downscaling. The study uses the Canadian Earth System Model (CanESM2) of the IPCC Fifth Assessment Report, reanalysis from the National Center for Environmental Prediction (NCEP), and the Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE) project data as the observation. SDSM and the focused time-delayed neural network (TDNN) models are used for the downscaling. The projected annual increase for Representative Concentration Pathway (RCP) is 8.5; the average temperature is 2.83 °C (SDSM) and 3.03 °C (TDNN), and rainfall is 33% (SDSM) and 63% (TDNN) for 2080's. [ABSTRACT FROM AUTHOR]

In this paper, we investigate why current state-of-the-art chemistry-climate models underestimate the tropospheric ozone increase from the 1950s to the 1990s by approximately 50%. The accuracy of these models is vital, not only for understanding and predicting air quality globally, but also since they are used to quantify the contribution of ozone in the troposphere and lower stratosphere to climate change, where its greenhouse effect is largest. We briefly describe available northern mid-latitude ozone measurements, which include representative and reliable data from European sites that extend back to the 1950s. We use the SOCOLv3 (Solar Climate Ozone Links version 3) global chemistry-climate model to investigate the individual terms of the tropospheric ozone budget. These include: inflow from the stratosphere, dry deposition, and chemical formation and destruction. For 1960 to 2000 SOCOLv3 indicates a tropospheric ozone increase at 850 hPa over the Swiss Alps (Arosa) of 17 ppb, or around 30%. This increase is smaller than that seen in the surface ozone measurements but similar to other chemistry-climate models, including those with more complex NMVOC (Non Methane Volatile Organic Compound) schemes than SOCOLv3's. It is likely that the underestimated increase in tropospheric ozone could be explained by issues in the underlying emissions inventories used in the model simulations, with ozone precursor emissions, particularly NOx (NO + NO2), from the 1960s being too large. [ABSTRACT FROM AUTHOR]

Analysis of observations, reanalysis, and model simulations, including those using machine learning methods specifically designed for regime identification, has revealed changes in aspects of the Southern Hemisphere (SH) circulation and Australian climate and extremes over the last half-century that indicate transitions to new states. In particular, our analysis shows a dramatic shift in the metastability of the SH climate that occurred in the late 1970s, associated with a large-scale regime transition in the SH atmospheric circulation, with systematic changes in the subtropical jet, blocking, zonal winds, and storm tracks. Analysis via nonstationary clustering reveals a regime shift coincident with a sharp transition to warmer oceanic sea surface temperatures and increased baroclinicity in the large scales of the Antarctic Circumpolar Circulation (ACC), extending across the whole hemisphere. At the same time, the background state of the tropical Pacific thermocline shoaled, leading to an increased likelihood of El Niño events. The SH climate shift in the late 1970s is the first hemispheric regime shift that can be directly attributed to anthropogenic climate change. These changes in dynamics are associated with additional regional tipping points, including reductions in mean and extreme rainfall in south-west Western Australia (SWWA) and streamflow into Perth dams, and also with increases in mean and extreme rainfall over northern Australia since the late 1970s. The drying of south-eastern Australia (SEA) occurred against a background of accelerating increases in average and extreme temperatures across the whole continent since the 1990s, implying further inflection points may have occurred. Analysis of climate model simulations capturing the essence of these observed shifts indicates that these systematic changes will continue into the late 21st century under high greenhouse gas emission scenarios. Here, we review two decades of work, revealing for the first time that tipping points characteristic of regime transitions are inferred to have already occurred in the SH climate system. [ABSTRACT FROM AUTHOR]

Simple Summary: Carposina coreana Kim is the most serious pest of Cornus officinalis. In recent years, its damage to C. officinalis has become increasingly serious, causing enormous economic losses in China. Here, the maximum entropy (MaxEnt) model was used to predict the distribution of C. coreana under current climate scenarios and future climate scenarios in China with ArcGIS software. Suitable areas for C. coreana under the current climate scenarios were mainly distributed in central China, and the highly suitable areas were distributed in southern Shaanxi, southwestern Henan, and northwestern Hubei. Under future climate scenarios, the boundaries of the suitable areas for C. coreana tended to shift to northern China. Given the predictive results of this study, we can clearly see the future diffusion trend of C. coreana in China, which has important theoretical significance for the control of this pest in China. Carposina coreana is an important pest of Cornus officinalis, distributed in China, Korea, and Japan. In recent years, its damage to C. officinalis has become increasingly serious, causing enormous economic losses in China. This study and prediction of current and future suitable habitats for C. coreana in China can provide an important reference for the monitoring, early warning, prevention, and control of the pest. In this study, the potential distributions of C. coreana in China under current climate and future climate models were predicted using the maximum entropy (MaxEnt) model with ArcGIS software. The distribution point data of C. coreana were screened using the buffer screening method. Nineteen environmental variables were screened using the knife-cut method and variable correlation analysis. The parameters of the MaxEnt model were optimized using the kuenm package in R software. The MaxEnt model, combined with key environmental variables, was used to predict the distribution range of the suitable area for C. coreana under the current (1971–2000) and four future scenarios. The buffer screening method screened data from 41 distribution points that could be used for modeling. The main factors affecting the distribution of C. coreana were precipitation in the driest month (Bio14), precipitation in the warmest quarter (Bio18), precipitation in the coldest quarter (Bio19), the standard deviation of seasonal variation of temperature (Bio4), minimum temperature in the coldest month (Bio6), and average temperature in the coldest quarter (Bio11). The feature class (FC) after the kuenm package optimization was a Q-quadratic T-threshold combination, and the regularization multiplier (RM) was 0.8. The suitable areas for C. coreana under the current climate model were mainly distributed in central China, and the highly suitable areas were distributed in southern Shaanxi, southwestern Henan, and northwestern Hubei. The lowest temperature in the coldest month (Bio6), the average temperature in the coldest quarter (Bio11), and the precipitation in the warmest quarter (Bio18) all had good predictive ability. In future climate scenarios, the boundary of the suitable area for C. coreana in China is expected to shift northward, and thus, most of the future climate scenarios would shift northward. [ABSTRACT FROM AUTHOR]

This study utilizes meteorological and leaf area index (LAI) data for three shared socioeconomic pathways (SSP1–2.6, SSP2–4.5, and SSP5–8.5) from four general circulation models (GCMs) of the sixth climate model intercomparison project (CMIP6) spanning from 2015 to 2099. Employing calibrated data and incorporating future land use data under three SSPs, the distributed hydrology soil vegetation model (DHSVM) is employed to simulate streamflow in the source region of the Yellow River (SRYR). The research aims to elucidate variations in streamflow across different future scenarios and to estimate extreme streamflow events and temporal distribution changes under future land use and cover change (LUCC) and climate change scenarios. The main conclusions are as follows: The grassland status in the SRYR will significantly improve from 2020 to 2099, with noticeable increases in temperature, precipitation, and longwave radiation, alongside a pronounced decrease in wind speed. The probability of flooding events increases in the future, although the magnitude of the increase diminishes over time. Both LUCC and climate change contribute to an increase in the multi-year average streamflow in the region, with respective increments of 48.8%, 24.5%, and 18.9% under SSP1–2.6, SSP2–4.5, and SSP5–8.5. Notably, the fluctuation in streamflow is most pronounced under SSP5–8.5. In SSP1–2.6, the increase in streamflow during the near future (2020–2059) exceeds that of the distant future (2059–2099). Seasonal variations in streamflow intensify across most scenarios, leading to a more uneven distribution of streamflow throughout the year and an extension of the flood season. [ABSTRACT FROM AUTHOR]

An accurate simulation and projection of future warming are needed for a proper policy response to expected climate change. We examine the simulations of the mean global and Arctic surface air temperatures by the CMIP6 (Climate Models Intercomparison Project phase 6) climate models. Most models overestimate the observed mean global warming. Only seven out of 19 models considered simulate global warming that is within ±15% of the observed warming between the average of the 2014–2023 and 1961–1990 reference period. Ten models overestimate global warming by more than 15% and only one of the models underestimates it by more than 15%. Arctic warming is simulated by the CMIP6 climate models much better than the mean global warming. The reason is an equal spread of over and underestimates of Arctic warming by the models, while most of the models overestimate the mean global warming. Eight models are within ±15% of the observed Arctic warming. Only three models are accurate within ±15% for both mean global and Arctic temperature simulations. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the spatial characteristics and duration of extreme wave events around the English coast. There are five geographic regions which are affected as coherent units under extreme wave conditions, incorporating a sixth micro-wave climate region (western Lyme Bay). Characteristic storm tracks are associated with each region. Storms affecting the East region (North Sea coast) seldom impact other areas of England, whilst in contrast, storms affecting the Southwest or Northwest also have some impact on the Southeast. Average storm duration varies from 5 h in the Northwest to 14 h on the East coast north of the Humber. Storm duration exceeding 12.5 h in the Southwest and East (northern half) near guarantees that storm waves will span High Water, when it is of most significance for beach management operations. Storms along the East coast can be associated with anticyclonic conditions, as well as low pressure systems. [ABSTRACT FROM AUTHOR]

Climate change (CC) impacts on hydrology pose significant global concerns due to their effects on water availability, thereby impacting various human activities reliant on this essential resource. This study assesses the influence of CC on the water supply in the Angostura-Bolivia basin. We employed the RegCM4 system, which develops its own regional climate models (RCMs) tailored to the Angostura basin using specific convective schemes, diverging from reliance on pre-existing RCMs, like those provided by CORDEX. Methodologically, the study involves hydrometeorological data collection and analysis, utilizing dynamic and statistical downscaling methods to refine the RCMs derived from ERA-Interim reanalysis data. Subsequently, precipitation and temperature projections are generated under CC scenarios (RCP 4.5 and RCP 8.5) for both near (2045–2055) and far (2065–2075) future periods, compared to the historical period (1981–2010). The final stage employs the HydroBID system to project future runoff, considering both perturbed and unperturbed hydrometeorological data under CC effects. The analysis of flow duration curves for 50%, 75%, and 90% exceedance probabilities reveals a significant reduction in flows across all scenarios, indicating a noteworthy impact on water availability. These findings underscore the urgency of comprehending and adapting to CC on hydrology, emphasizing the critical importance of sustainable water resource management amidst evolving climatic conditions. [ABSTRACT FROM AUTHOR]

Future climate scenarios based on regional climate models (RCMs) have been evaluated widely. However, the use of RCMs without bias correction may increase the uncertainty in the assessment of climate change impacts, especially in mountain areas. Five quantile mapping methods (QMMs) were evaluated as bias correction methods for precipitation and temperature in the historical period (1979–2005) of one local climate model and three RCMs at the Achibueno River Basin, southcentral Chile. Additionally, bias-corrected climate scenarios from 2025 to 2050 under two Representative Concentration Pathways (RCPs) were evaluated on the hydrological response of the catchment with the Soil and Water Assessment Tool (SWAT+). The parametric transformation function and robust empirical quantile were the most promising bias correction methods for precipitation and temperature, respectively. Climate scenarios suggest changes in the frequency and amount of precipitation with fluctuations in temperatures. Under RCP 2.6, partial increases in precipitation, water yield, and evapotranspiration are projected, while for RCP 8.5, strong peaks of precipitation and water yield in short periods of time, together with increases in evapotranspiration, are expected. Consequently, flooding events and increasing irrigation demand are changes likely to take place. Therefore, considering adaptation of current and future management practices for the protection of water resources in southcentral Chile is mandatory. [ABSTRACT FROM AUTHOR]

This study evaluated the impacts of climate change on hydro-meteorological droughts in the Chao Phraya River Basin (CPRB), Thailand under two Representative Concentration Pathway (RCP) scenarios (RCP4.5 and RCP8.5). We used three Reginal Climate Models (RCMs) of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment—Southeast Asia (SEACLID/CORDEX-SEA), which are bias corrected. The Soil and Water Assessment Tool (SWAT) was used to simulate streamflow for future periods. The Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) were estimated and used for drought characterization at three time scales (3, 6, and 12 months). The lag time between meteorological and hydrological droughts is approximately 1–3 months. The results suggest that the CPRB is likely to experience less frequent hydro-meteorological drought events in the future. The meteorological drought is projected to be longer, more severe, and intense. The severity of hydrological drought tends to decrease, but the intensity could increase. Climate change has been discovered to alter drought behaviors in the CPRB, posing a threat to drought monitoring and warning because droughts will be less predictable in future climate scenarios. The characterization of historical and future droughts over the CPRB is therefore valuable in developing an improved understanding of the risks of drought. [ABSTRACT FROM AUTHOR]

This study explores the critical issue of understanding the potential impacts of climate change on the habitat suitability of the highly endangered forest-dwelling Mangshan pit viper (Protobothrops mangshanensis) in China. Through the application of the MaxEnt model, high-resolution bioclimatic datasets, and species occurrence data, the research aims to elucidate the spatial and temporal dynamics of P. mangshanensis distribution from the present to the years 2050 and 2070. Through the integration of three climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and exploring different shared socioeconomic pathway (SSP) scenarios (SSP126, SSP370, and SSP585), the study seeks to provide comprehensive insights into the potential variations in habitat suitability under diverse future climate conditions. The methodology employed involves the construction of the MaxEnt model utilizing the BioClim dataset and 83 species occurrence points. The SSP scenarios mentioned above represent future climate change scenarios, and the accuracy of the model is evaluated using the area under the receiver-operating characteristic (ROC) curve (AUC). Key findings reveal that the MaxEnt model exhibits high accuracy (AUC = 0.998), pinpointing the current suitable habitat for P. mangshanensis to be confined to the Mangshan area within the Nanling Mountains, covering an approximate area of 1023.12 km2. However, projections based on future climate scenarios suggest notable shifts in habitat suitability dynamics. While potential suitable habitats may emerge in the northwest of the current range, the existing suitable habitats are anticipated to undergo significant reduction or even complete disappearance. Notably, precipitation during the driest month emerges as a critical determinant influencing the distribution of the species. In conclusion, the study underscores the exacerbating impact of climate change on habitat deterioration and survival risks for P. mangshanensis, emphasizing the urgent need for conservation measures to safeguard the remaining suitable habitats for this endangered species. The implications of these findings are far-reaching, with the anticipated contraction of the snake's range potentially leading to its disappearance and increased habitat fragmentation. By shedding light on the potential distributional changes of P. mangshanensis in Mangshan, the research provides valuable insights for informing targeted conservation strategies and policy interventions aimed at mitigating the adverse effects of climate change on endangered species. [ABSTRACT FROM AUTHOR]

Cloud dynamics and their response to future climate change continue to present a significant source of uncertainty in climate predictions. Besides the average cloud properties, the diurnal cloud cycle (DCC) exerts a substantial influence on Earth's energy balance by reflecting solar radiation during the daytime and continuously absorbing and reemitting longwave radiation throughout the whole day. Previous studies have demonstrated that climate models exhibit certain discrepancies in simulating the DCC; however, less research attention has been paid to the patterns of these DCC biases and their impacts on modeling the Earth's energy balance. Here, we employ satellite data to compare DCC patterns in Coupled Model Intercomparison Project Phase 5 (CMIP5) and their latest versions in CMIP6 at both regional and global scales. We found that some of the latest climate models tend to have larger DCC biases when using satellite observations as the references, and the radiative effects due to DCC changes account for nearly 50% of the changes in total cloud radiative effects (CREs), suggesting that the DCC biases play a significant role in modelingthe global energy budget. We therefore call for improving cloud parameterization schemes with particular attention to their diurnal cycle to reduce their impacts on future climate projections. [ABSTRACT FROM AUTHOR]

A combination of annual peak water demand due to seasonal population spikes along with small and shallow aquifers has prompted an assessment of the region's watersheds as operating at a net water deficit. This study uses the Soil and Water Assessment Tool (SWAT) to simulate historical drought conditions in the Chipuxet watershed in Rhode Island, USA. The calibrated and validated model uses the Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) as well as an Indicators of Hydrological Alteration (IHA) calculation to determine the frequency and severity of historical droughts and to simulate climate change conditions developed through a downscaled climate model selection. The output data for the historical and climate change scenarios were analyzed for drought frequency and severity. Results indicate that water stress will increase in both low-emission (RCP4.5) and high-emission (RCP8.5) scenarios. Additionally, the SMDI and ETDI show that RCP8.5 climate scenarios will have more severe deficits. Finally, IHA data indicate that zero-flow days and low-flow durations increase under all climate scenarios. [ABSTRACT FROM AUTHOR]

The Chinese government attaches great importance to climate change adaptation and has issued relevant strategies and policies. Overall, China's action to adapt to climate change remains in its infancy, and relevant research needs to be further deepened. In this paper, we study the future adaptive countermeasures of Shenzhen city in the Pearl River Delta in terms of climate change, especially urban flood risk resilience. Based on the background investigation of urban flood risk in Shenzhen, this paper calculates the annual precipitation frequency of Shenzhen from 1953 to 2020, and uses the extreme precipitation index as a quantitative indicator to analyze the changes in historical precipitation and the impact of major flood disasters in Shenzhen city in previous decades. Based on the six kinds of model data of the scenario Model Inter-comparison Project (MIP) in the sixth phase of the Coupled Model Inter-comparison Project (CMIP6), uses the Taylor diagram and MR comprehensive evaluation method to evaluate the ability of different climate models to simulate extreme precipitation in Shenzhen, and the selected models are aggregated and averaged to predict the climate change trend of Shenzhen from 2020 to 2100. The prediction results show that Shenzhen will face more severe threats from rainstorms and floods in the future. Therefore, this paper proposes a resilience strategy for the city to cope with the threat of flood in the future, including constructing a smart water management system and promoting the development of a sponge city. Moreover, to a certain extent, it is necessary to realize risk transfer by promoting a flood insurance system. [ABSTRACT FROM AUTHOR]

Assessment of climate change on reservoir inflow is important for water and power stressed countries. Projected climate is subject to uncertainties related to climate change scenarios and Global Circulation Models (GCMs). This paper discusses the consequences of climate change on discharge. Historical climatic and gauging data were collected from different stations within a watershed. Bias correction was performed on GCMs temperature and precipitation data. After successful development of the hydrological modeling system (SWAT) for the basin, streamflow was simulated for three future periods (2011-2040, 2041-2070, and 2071-2100) and compared with the baseline data (1981-2010) to explore the changes in different flow indicators such as mean flow, low flow, median flow, high flow, flow duration curves, temporal shift in peaks, and temporal shifts in center-of-volume dates. From the results obtained, an overall increase in mean annual flow was projected in the basin under both RCP 4.5 and RCP 8.5 scenarios. Winter and spring showed a noticeable increase in streamflow, while summer and autumn showed a decrease in streamflow. High flows were predicted to increase, but median flow was projected to decrease in the future under both scenarios. Flow duration curves showed that the probability of occurrence of high flow is likely to be more in the future. It was also noted that peaks were predicted to shift from May to July in the future, and the center-of-volume date of the annual flow may vary from -11 to 23 days in the basin, under both RCP 4.5 and RCP 8.5. As a whole, the Mangla basin will face more floods and less droughts in the future due to the projected increase in high and low flows, decrease in median flows and greater temporal and magnitudinal variations in peak flows. These outcomes suggest that it is important to consider the influence of climate change on water resources to frame appropriate guidelines for planning and management. [ABSTRACT FROM AUTHOR]

The East China Shelf Seas, comprising the Bohai Sea, the Yellow Sea, and the shelf region of East China Sea, play significant roles among the shelf seas of the Western North Pacific Ocean. The projection of sea surface temperature (SST) changes in these regions is a hot research topic in marine science. However, this is a very difficult task due to the lack of available long-term projection data. Recently, with the high development of simulation technology based on numerical models, the model intercomparison projects, e.g., Phase 5 of the Climate Model Intercomparison Project (CMIP5), have become important ways of understanding climate changes. CMIP5 provides multiple models that can be used to estimate SST changes by 2100 under different representative concentration pathways (RCPs). This paper developed a CMIP5-based SST investigation framework for the projection of decadal and seasonal variation of SST in East China Shelf Seas by 2100. Since the simulation results of CMIP5 models may have degrees of errors, this paper uses hydrological observation data from World Ocean Atlas 2018 (WOA18) for model validation and correction. This paper selects seven representative ones including ACCESS1.3, CCSM4, FIO-ESM, CESM1-CAM5, CMCC-CMS, NorESM1-ME, and Max Planck Institute Earth System Model of medium resolution (MPI-ESM-MR). The decadal and seasonal SST changes in the next 100 years (2030, 2060, 2090) are investigated by comparing with the present analysis in 2010. The experimental results demonstrate that SST will increase significantly by 2100: the decadal SST will increase by about 1.55 °C, while the seasonal SST will increase by 1.03–1.95 °C. [ABSTRACT FROM AUTHOR]

Understanding long-term variations in precipitation is crucial for identifying the effects of climate change and addressing hydrological and water management issues. This study examined the trends of the mean and four extreme precipitation indices, which are the max 1-day precipitation amount, the max 5-day precipitation amount, the consecutive wet days, and the consecutive dry days, for historical observations (1971–2000) and two future periods (2041–2060/2081–2100) under RCP2.6 and RCP8.5 emission scenarios over the Nile River Basin (NRB) at 11 major stations. Firstly, the empirical quantile mapping procedure significantly improved the performance of all RCMs, particularly those with lower performance, decreasing inter-model variability and enhanced seasonal precipitation variability. The Mann–Kendall test was used to detect the trends in climate extreme indices. This study reveals that precipitation changes vary across stations, scenarios, and time periods. Addis Ababa and Kigali anticipated a significant increase in precipitation across all periods and scenarios, ranging between 8–15% and 13–27%, respectively, while Cairo and Kinshasa exhibited a significant decrease in precipitation at around 90% and 38%, respectively. Wet (dry) spells were expected to significantly decrease (increase) over most parts of the NRB, especially during the second period (2081–2100). Thereby, the increase (decrease) in dry (wet) spells could have a direct impact on water resource availability in the NRB. This study also highlights that increased greenhouse gas emissions have a greater impact on precipitation patterns. This study's findings might be useful to decision makers as they create NRB-wide mitigation and adaptation strategies to deal with the effects of climate change. [ABSTRACT FROM AUTHOR]

Climate change is predicted to impact corn yields. Previous studies analyzing these impacts differ in data and modeling approaches and, consequently, corn yield projections. We analyze the impacts of climate change on corn yields using two statistical models with different approaches for dealing with county-level effects. The first model, which is novel to modeling corn yields, uses a computationally efficient spatial basis function approach. We use a Bayesian framework to incorporate both parametric and climate model structural uncertainty. We find that the statistical models have similar predictive abilities, but the spatial basis function model is faster and hence potentially a useful tool for crop yield projections. We also explore how different gridded temperature datasets affect the statistical model fit and performance. Compared to the dataset with only weather station data, we find that the dataset composed of satellite and weather station data results in a model with a magnified relationship between temperature and corn yields. For all statistical models, we observe a relationship between temperature and corn yields that is broadly similar to previous studies. We use downscaled and bias-corrected CMIP5 climate model projections to obtain detrended corn yield projections for 2020–2049 and 2069–2098. In both periods, we project a decrease in the mean corn yield production, reinforcing the findings of other studies. However, the magnitude of the decrease and the associated uncertainties we obtain differ from previous studies. [ABSTRACT FROM AUTHOR]

Climate change may significantly impact the availability and quality of water resources in dam reservoirs by potentially altering the hydrological regime of lake tributaries and the corresponding flow–duration curves. Hydrological models driven by climate projections (downscaled to the watershed scale and bias corrected to eliminate systematic errors) are effective tools for assessing this potential impact. To assess the uncertainty in future water resource availability, resulting from the inherent uncertainty in climate model projections, an ensemble of climate models and different climate scenarios can be considered. The reliability and effectiveness of this approach were illustrated by analyzing the potential impact of climate change on the water availability at Brugneto Lake in northern Italy. This analysis was based on climate projections derived from an ensemble of 13 combinations of General Circulation Models and Regional Climate Models under two distinct scenarios (RCP4.5 and RCP8.5). The semi-distributed HEC-HMS model was adopted to simulate the hydrological response of the basin upstream of the lake. The hydrological model parameters were calibrated automatically via the PEST software package using the inflows to the lake, estimated through a reverse level pool routing method, as observed values. Future water availability was predicted for short- (2010–2039), medium- (2040–2069), and long-term (2070–2099) periods. The results indicate that the uncertainty in reservoir inflow is primarily due to the uncertainty in future rainfall. A moderate reduction in water availability is expected for Brugneto Lake by the end of the current century, accompanied by modifications in the flow regime. These changes should be considered when planning future adaptation measures and adjusting reservoir management rules. [ABSTRACT FROM AUTHOR]

The Cantareira Water Production System (CWPS), which supplies water to the most populous region in Brazil, is facing significant challenges due to hydroclimate change, thus threatening its water security. This research integrates data from climate models and field observations with hydrological modeling, aimed at quantifying trends in key variables of the hydrological cycle. The GFDL-CM4 climate model, the most suitable for the study area, was employed to generate runoff data under both current conditions and future scenarios (SSP2-4.5 and SSP5-8.5). Our analysis reveals an increasing trend in the frequency of dry hydrological years. The Standard Precipitation Index (SPI) and Drought Magnitude (DM) confirm an increase in both the occurrence and duration of droughts in future scenarios. The runoff in all basins was reduced, causing a substantial decrease in minimum flows of 16.9%, medium flows of 11.8%, and high flows of 9.2% for the SSP5-8.5 scenario. This research introduces an approach to hydroclimate impact assessment, combining rigorous data analysis with advanced modeling techniques. Our findings not only provide a comprehensive understanding of the challenges faced by the CWPS, but also offer critical quantitative insights essential for developing effective public policies and adaptive strategies for sustainable water resource management. [ABSTRACT FROM AUTHOR]

Addressing the impacts of climate change requires, first of all, understanding the mechanisms driving changes, especially at the regional scale. In particular, policymakers and other stakeholders need physically robust climate change information to drive societal responses to a changing climate. This study analyses late 21st-century (2071–2100) precipitation projections for the Congo Basin under representative concentration pathway (RCP) 8.5, using the Rossby Centre Regional Climate Model (RCM) RCA4. Specifically, we examine the impact of the RCM formulation (reduction of turbulent mixing) on future change in seasonal mean precipitation by comparing the results of the modified model version (RCA4-v4) with those of the standard version (RCA4-v1) used in CORDEX (Coordinated Regional Climate Downscaling Experiment). The two RCM versions are driven by two global climate models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5). The results show that seasonal precipitation is largely affected by modifications in the atmospheric column moisture convergence or divergence, and, in turn, associated with changes in the dynamic (ΔDY) and thermodynamic (ΔTH) components of the moisture-budget equation. Projected decreased precipitation in the dry seasons (December–January–February and June–July–August) is linked to increased moisture divergence driven by dynamic effects (changes in circulation), with most experiments showing ΔDY as the main contributor (>60%) to the total moisture budget. Overall, precipitation is projected to increase in the wet seasons (March–April–May and September–October–November), which can be attributed to both dynamic and thermodynamic effects, but with a larger thermodynamic contribution (changes in specific humidity, ΔTH > 45%), compared to the dynamic one (ΔDY > 40%). Through a comparison of the two model versions, we found that the formulation (reducing turbulent mixing) and boundary conditions (driving GCM) strongly influence precipitation projections. This result holds substantial value for ensuring the fitness of models for future projections intended for decision-makers. [ABSTRACT FROM AUTHOR]

The impact of global climate change is a challenge to the sustainability of many ecosystems, including soil systems. However, the performance of soil properties under future climate was rarely assessed. Therefore, this study was carried out to evaluate selected soil processes under climate change using an agri-environmental modeling approach to Sri Lanka. The Agricultural Production Systems Simulator (APSIM) model was used to simulate soil and plant-related processes using recent past (1990–2019) and future (2041–2070) climates. Future climate data were obtained for a regional climate model (RCM) under representative concentrations pathway 4.5 scenarios. Rainfalls are going to be decreased in all the tested locations under future climate scenarios while the maximum temperature showcased rises. According to simulated results, the average yield reduction under climate change was 7.4%. The simulated nitrogen content in the storage organs of paddy declined in the locations (by 6.4–25.5%) as a reason for climate change. In general, extractable soil water relative to the permanent wilting point (total available water), infiltration, and biomass carbon lost to the atmosphere decreased while soil temperature increased in the future climate. This modeling approach provides a primary-level prediction of soil dynamics under climate change, which needs to be tested using fieldwork. [ABSTRACT FROM AUTHOR]

As organisations, museums are responsible for conserving, protecting, and displaying artwork and artefacts. Museum buildings must deliver an environment that will continue to provide this facility for both current and future generations. This research focused on presenting a museum with quantifiable and measurable data to help with climate adaptation planning. A methodology was developed using monitored data. Subhourly data for both indoor and outdoor temperature and humidity spanning the years 2012–2021 was used to produce a daily maximum, daily minimum, and daily average dataset. A sensitivity analysis determined which years to use to derive the indoor-outdoor relationships used in climate modelling. Future impacts were calculated using UK Climate Projections 2018 (UKCP18) data (12 models on a 2.2 km scale), as published by the Met Office Hadley Centre. The data contained within the 12 models was overlayed with the relationships derived to calculate the projected indoor temperature and humidity conditions within the museum. The results presented indicate that temperature and humidity conditions are projected to exceed design conditions more frequently in the coming decades. Consequently, adaptation plans must consider the potential impacts that include indoor environmental deterioration, leading to discomfort and health implications, increased energy costs, and system upgrade costs, as well as the potential for accelerated degradation of artwork and artefacts. [ABSTRACT FROM AUTHOR]

The primary innovation of this study lies in the development of an integrated modeling framework that combines downscaled climate projections, land-use-change simulations, and copula-based risk analysis. This framework allows for the assessment of localized sub-seasonal and seasonal drought hazards under future scenarios. The BCC-CSM1-1 climate model projections from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset are utilized to represent the future climate for 2025–2060 under RCP 4.5 and 8.5 scenarios. The CA-Markov model is employed to predict future land-use-change distributions. The climate–land use–drought modeling nexus enables the generation of refined spatio-temporal projections of meteorological and hydrological drought risks in the Yellow River Basin (YRB) in the future period of 2025–2060. The results highlight the increased vulnerability of the upper YRB to sub-seasonal meteorological droughts, as well as the heightened sub-seasonal hydrological drought risks in the Loess Plateau. Furthermore, downstream areas experience escalated seasonal hydrological drought exposure due to urbanization. By providing actionable insights into localized future drought patterns, this integrated assessment approach advances preparedness and climate adaptation strategies. The findings of the study enhance our understanding of potential changes in this integral system under the combined pressures of global climate change and land use shifts. [ABSTRACT FROM AUTHOR]

The ability of soil moisture (SM) to affect precipitation (P) is a vital part of the water-energy cycles. Accurately quantifying this coupling enhances the ability to predict hydroclimatic extremes like floods and droughts. In this study, the ability of soil moisture to affect precipitation (SM-P) is characterized by two parts: the influence of soil moisture on evapotranspiration (SM-ET), and the influence of evapotranspiration on precipitation (ET-P). We determined localized ET-P by incorporating the coupling between latent heat flux (LH) and LCL height, to optimize the estimation of the SM-P. This approach links SM more closely to P by considering the influence of surface fluxes. The results indicate that CMIP6 models exhibited the anticipated hotspot patterns for the three coupling metrics in transition regions. However, we observed that climate models generally exhibit weaker SM-P coupling compared to reanalysis models. Both SM-ET and SM-P showcase higher values wherein wet climate regions during dry years, and the converse occurs in dry regions. Due to sensitivity to climate change, the ET-P exhibits a more pronounced upward trend in the future. This study helps understand P's response to SM shifts in climate models, crucial for predicting hydrological extremes and coupled global warming impact. [ABSTRACT FROM AUTHOR]

The advancements in global climate modeling achieved within the CMIP6 framework have led to notable enhancements in model performance, particularly with regard to spatial resolution. However, the persistent requirement for refined techniques, such as dynamically or statistically downscaled methods, remains evident, particularly in the context of precipitation variability. This study centered on the systematic application of a bias-correction technique (quantile mapping) to four designated CMIP6 models: CNRM-ESM2-6A, IPSL-CM6A-LR, MIROC6, and MRI-ESM2-0. The selection of these models was informed by a methodical approach grounded in previous research conducted within the southern–southeastern region of Mexico. Diverse performance evaluation metrics were employed, including root-mean-square difference (rmsd), normalized standard deviation (NSD), bias, and Pearson's correlation (illustrated by Taylor diagrams). The study area was divided into two distinct domains: southern Mexico and the southeast region covering Tabasco and Chiapas, and the Yucatan Peninsula. The findings underscored the substantial improvement in model performance achieved through bias correction across the entire study area. The outcomes of rmsd and NSD not only exhibited variations among different climate models but also manifested sensitivity to the specific geographical region under examination. In the southern region, CNRM-ESM2-1 emerged as the most adept model following bias correction. In the southeastern domain, including only Tabasco and Chiapas, the optimal model was again CNRM-ESM2-1 after bias-correction. However, for the Yucatan Peninsula, the IPSL-CM6A-LR model yielded the most favorable results. This study emphasizes the significance of tailored bias-correction techniques in refining the performance of climate models and highlights the spatially nuanced responses of different models within the study area's distinct geographical regions. [ABSTRACT FROM AUTHOR]

This study was motivated by the high reliance on hydropower plants (HPPs) developed and planned along the river Nile and the fact that drought events are the most imminent and drastic threats to Uganda's power production. The study aimed to assess HPPs' resilience and the effectiveness of selected adaptation measures. The climate, land, energy, and water system (CLEWs) framework was employed to assess resilience amidst competing water demands and stringent environmental flow requirements. Under extreme dry conditions, power generation could plummet by 91% over the next 40 years, which translates into an annual per capita consumption of 19 kWh, barely sufficient to sustain a decent socioeconomic livelihood. During arid conditions, climate models predicted an increase in streamflow with increasing radiative forcing. Restricting the ecological flow to 150 m3/s could improve generation by 207%. In addition, if planned power plants were to be built 5 years ahead of schedule, the normalized mean annual plant production could increase by 23%. In contrast, increasing reservoir volumes for planned power plants will have no significant impact on generation. The path to HPP resilience could entail a combination of diversifying the generation mix, installing generators with varying capacities, and incorporating adjustable orifices on reservoirs. [ABSTRACT FROM AUTHOR]

Climate change can lead to significant environmental and societal impacts; for example, through increases in the amount and intensity of rainfall with the associated possibility of flooding. Twenty-first-century climate change simulations for Great Britain reveal an increase in heavy precipitation that may lead to widespread soil loss by rising the likelihood of surface runoff. Here, hourly high-resolution rainfall projections from a 1.5 km ('convection-permitting') regional climate model are used to simulate the soil erosion response for two periods of the century (1996–2009 and a 13-year future period at ~2100) in the "Rother" catchment, West Sussex, England. Modeling soil erosion with EROSION 3D, we found a general increase in sediment production (off-site erosion) for the end of the century of about 43.2%, with a catchment-average increase from 0.176 to 0.252 t ha−1 y−1 and large differences between areas with diverse land use. These results highlight the effectiveness of using high-resolution rainfall projections to better account for spatial variability in the assessment of long-term soil erosion than other current methods. [ABSTRACT FROM AUTHOR]

This review identifies a critical problem in the fundamental physics of current climate models. The large greenhouse effect of rising CO2 assumed in climate models is assessed by six key observations from ground- and satellite-based measurements. This assessment is enhanced by statistical analyses and model calculations of global or regional mean surface temperature changes by conventional climate models and by a conceptual quantum physical model of global warming due to halogen-containing greenhouse gases (halo-GHGs). The postulated large radiative forcing of CO2 in conventional climate models does not agree with satellite observations. Satellite-observed warming pattern resembles closely the atmospheric distribution of chlorofluorocarbons (CFCs). This review helps understand recent remarkable observations of reversals from cooling to warming in the lower stratosphere over most continents and in the upper stratosphere at high latitudes, surface warming cessations in the Antarctic, North America, UK, and Northern-Hemisphere (NH) extratropics, and the stabilization in NH or North America snow cover, since the turn of the century. The complementary observation of surface temperature changes in 3 representative regions (Central England, the Antarctic, and the Arctic) sheds new light on the primary mechanism of global warming. These observations agree well with not CO2-based climate models but the CFC-warming quantum physical model. The latter offers parameter-free analytical calculations of surface temperature changes, exhibiting remarkable agreement with observations. These observations overwhelmingly support an emerging picture that halo-GHGs made the dominant contribution to global warming in the late 20th century and that a gradual reversal in warming has occurred since ~2005 due to the phasing out of halo-GHGs. Advances and insights from this review may help humans make rational policies to reverse the past warming and maintain a healthy economy and ecosystem. [ABSTRACT FROM AUTHOR]

In this paper, we study how uncertainties weighing on the climate system impact the optimal technological pathways the world energy system should take to comply with stringent mitigation objectives. We use the TIAM-World model that relies on the TIMES modelling approach. Its climate module is inspired by the DICE model. Using robust optimization techniques, we assess the impact of the climate system parameter uncertainty on energy transition pathways under various climate constraints. Unlike other studies we consider all the climate system parameters which is of primary importance since: (i) parameters and outcomes of climate models are all inherently uncertain (parametric uncertainty); and (ii) the simplified models at stake summarize phenomena that are by nature complex and non-linear in a few, sometimes linear, equations so that structural uncertainty is also a major issue. The use of robust optimization allows us to identify economic energy transition pathways under climate constraints for which the outcome scenarios remain relevant for any realization of the climate parameters. In this sense, transition pathways are made robust. We find that the abatement strategies are quite different between the two temperature targets. The most stringent one is reached by investing massively in carbon removal technologies such as bioenergy with carbon capture and storage (BECCS) which have yields much lower than traditional fossil fuelled technologies. [ABSTRACT FROM AUTHOR]