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Research on climate anxiety is rapidly growing, with ongoing exploration of population prevalence, contributing factors, and mitigation strategies that transform anxiety into helpful action. What remains unclear is whether and how to delineate climate anxiety from mental ill health. A limited conceptualization of climate anxiety restricts efforts to identify and support those adversely affected. This paper draws on psychological and existential theories to propose a theoretical model of climate anxiety and coping, extending previous conceptualizations. The model theorizes that climate change evokes an existential conflict that manifests affectively as climate anxiety (and other emotional experiences), wherein cognitive and behavioral coping processes are activated. These processes fall on a continuum of adaptivity, depending on functional impact. Responses might range from meaningful engagement with activities that address climate change to maladaptive strategies that negatively impact personal, social, and occupational functioning. Applications of this model in research and practice are proposed. [ABSTRACT FROM AUTHOR]

The impacts of climate change increasingly show themselves in many forms in our everyday lives such as heatwaves and droughts. Drought is one of the critical events today for increasing drought frequency. This study focuses on meteorological drought because it directly affects other drought types. Hence, this study focuses on how the future drought conditions will vary under climate change effects in the Mediterranean basin (Turkey). In doing so, this study utilizes precipitation data from three General Circulation Models (GCMs) and three Regional Circulation Models (RCMs). The GCMs are CNRM-CM6, GFDL-CM4, and MPI-ESM1, while the RCMs are (RCA4)-CNRM-CM5, (Reg CM4)-GFDL-ESM2M, and (RCA4)-MPI-ESM-MR. Mitigating biases of the climate models, this study utilizes four statistical downscaling methods (SD), linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Here, the study has two purposes. The main aim of the paper here is to compare the performance of SD methods in improving the representation of observed climate variables in climate models. In addition, the study shows how different methods will affect the spatial drought distribution in the area under the SSP2 4.5 and SSP5 8.5 scenarios. Consequently, the study uses the standardized precipitation index (SPI) and Z-score index (ZSI) to quantify future drought conditions and reaches the following results. The study reveals that mild drought conditions are prevalent in the basin for future periods, and drought indices go down to − 0.55. The study also shows that different SD methods affect the results obtained by each climate model diversely. For example, while the LS method causes the most drought conditions on the results based on CNRM-CM5 and CNRM-CM6, the DM method has a similar impact on outcomes based on GFDL-CM4 and GFDL-ESM2M and causes the most drought conditions. [ABSTRACT FROM AUTHOR]

Due to global climate change, droughts are likely to become more frequent and more severe in many regions such as in South Africa. In Limpopo, observed high climate variability and projected future climate change will likely increase future maize production risks. This paper evaluates drought patterns in Limpopo at two representative sites. We studied how drought patterns are projected to change under future climatic conditions as an important step in identifying adaptation measures (e.g., breeding maize ideotypes resilient to future conditions). Thirty-year time horizons were analyzed, considering three emission scenarios and five global climate models. We applied the WOFOST crop model to simulate maize crop growth and yield formation over South Africa's summer season. We considered three different crop emergence dates. Drought indices indicated that mainly in the scenario SSP5-8.5 (2051–2080), Univen and Syferkuil will experience worsened drought conditions (DC) in the future. Maize yield tends to decline and future changes in the emergence date seem to impact yield significantly. A possible alternative is to delay sowing date to November or December to reduce the potential yield losses. The grain filling period tends to decrease in the future, and a decrease in the duration of the growth cycle is very likely. Combinations of changed sowing time with more drought tolerant maize cultivars having a longer post-anthesis phase will likely reduce the potential negative impact of climate change on maize. [ABSTRACT FROM AUTHOR]

This study establishes a calibrated SWAT (Soil and Water Assessment Tool) model for the Huntai Basin, driven by SSP126, SSP245, SSP585, and multi-model ensemble (MME) models in CMIP6 (Coupled Model Intercomparison Project-6), to investigate the effects of climate change on hydrological processes and pollution load in the Huntai Basin. The results show that the annual mean temperature and the annual precipitation will gradually increase. The nitrogen and phosphorus pollution loads in the basin exhibit a trend of decreasing-increasing-decreasing. The correlation between the nitrogen-phosphorus pollution load and the hydrological process strengthens with increasing radiative forcing. In the four scenarios, CO2 is a primary driving factor that contributes greatly to nitrogen and phosphorus pollution. The main differences are in the total driving factors, and SSP126 and SSP245 are less than those of other models. The total phosphorus and total nitrogen pollution in different climate models were higher than the average level during the benchmark period, except for ammonia nitrogen pollution, which was lower. The nitrogen and phosphorus pollution in SSP126 and SSP245 modes will reach the maximum in 2040s, and the pollution in other periods will be lower than that in SSP585 and MME scenarios. In the long run, the development state between SSP126 and SSP245 may be better appropriate for the Huntai Basin's future sustainable development. This paper analyzes the occurrence and influencing factors of nitrogen and phosphorus pollution under climate change to provide reference to the protection of water environment under changing environments. [ABSTRACT FROM AUTHOR]

Paleontological studies of the distant past and historical chronicles of the last millennia indicate that warming and cooling of the climate on our planet, replacing each other, have taken place in a continuous series. An important role in this process has been and is played by cosmic factors: variations in solar activity and parameters of the Earth's orbital motion in the gravitational field of the Sun and the planets of the Solar System. It is suggested that the currently observed climate change is associated with a decrease in the eccentricity of the Earth's orbit and the inclination of the axis of its rotation relative to the plane of the ecliptic, which has been going on for almost 30 thousand years. Both processes lead to changes in the boundaries of climatic zones. In the foreseeable future, these processes will proceed in the same direction. Therefore, the current trend towards climate change will continue. Despite the importance of addressing the problem of reducing greenhouse-gas emissions as soon as possible, this paper expresses doubts about the possibility of fending off climate warming in this way. It is proposed to focus on the development of mathematical climate models taking into account space factors and on finding the best ways to adapt industry, transport systems, and the environment to the conditions of expected climate change. [ABSTRACT FROM AUTHOR]

Climate change may cause adverse impact on agricultural production that could jeopardize food availability and security. In this paper, we investigate how changes in mean values and variability of weather variables may affect rice yield using panel data for 30 districts of Tamil Nadu State from 1971 to 2018. We estimate a fixed-effects regression model with panel-corrected standard errors. Results show that rainfall and temperature have a statistically significant impact on rice yield. Furthermore, weather variability, measured as standard deviations of temperature and rainfall, has a negative effect on rice yields. We use the Coupled Model Inter-comparison Project-5 outputs and dynamically downscaled the weather outputs using the regional climate model. The results of the climate model showed that the rise in temperature and disruptions in rainfall patterns including both excessive and deficient rainfall events in Tamil Nadu will continue in the future under different climate scenarios. Projected changes in the weather variables are likely to decrease rice yield in Tamil Nadu from 0.7 to 6.3% under the low emission scenario and 4.1 to 20.1% in the high emission scenario during 2022–2050 (relative to 1971–2018). These projections have implications for the planning and targeting of climate adaptation technologies such as drought-tolerant and flood-tolerant varieties to lessen the adverse impact of weather variability due to climate change in the future. [ABSTRACT FROM AUTHOR]

Global climate change is expected to have a major impact on the hydrological cycle. Understanding potential changes in future extreme precipitation is important to the planning of industrial and agricultural water use, flood control, and ecological environment protection. In this paper, we study the statistical distribution of extreme precipitation based on historical observation and various global climate models (GCMs), and predict the expected change and the associated uncertainty. The empirical frequency, generalized extreme value (GEV) distribution, and L-moment estimator algorithms are used to establish the statistical distribution relationships and the multi-model ensemble predictions are established by the Bayesian model averaging (BMA) method. This ensemble forecast takes advantage of multi-model synthesis, which is an effective measure to reduce the uncertainty of model selection in extreme precipitation forecasting. We have analyzed the relationships among extreme precipitation, return period, and precipitation durations for 6 representative cities in China. More significantly, the approach allows for establishing the uncertainty of extreme precipitation predictions. The empirical frequency from the historical data is all within the 90% confidence interval of the BMA ensemble. For the future predictions, the extreme precipitation intensities of various durations tend to become larger compared to the historic results. The extreme precipitation under the RCP8.5 scenario is greater than that under the RCP2.6 scenario. The developed approach not only effectively gives the extreme precipitation predictions, but also can be used to any other extreme hydrological events in future climate. [ABSTRACT FROM AUTHOR]

1.5 °C scenarios reported by the Intergovernmental Panel on Climate Change (IPCC) rely on combinations of controversial negative emissions and unprecedented technological change, while assuming continued growth in gross domestic product (GDP). Thus far, the integrated assessment modelling community and the IPCC have neglected to consider degrowth scenarios, where economic output declines due to stringent climate mitigation. Hence, their potential to avoid reliance on negative emissions and speculative rates of technological change remains unexplored. As a first step to address this gap, this paper compares 1.5 °C degrowth scenarios with IPCC archetype scenarios, using a simplified quantitative representation of the fuel-energy-emissions nexus. Here we find that the degrowth scenarios minimize many key risks for feasibility and sustainability compared to technology-driven pathways, such as the reliance on high energy-GDP decoupling, large-scale carbon dioxide removal and large-scale and high-speed renewable energy transformation. However, substantial challenges remain regarding political feasibility. Nevertheless, degrowth pathways should be thoroughly considered. Established climate mitigation modelling relies on controversial negative emissions and unprecedented technological change, but neglects to consider degrowth scenarios. Here the authors show that degrowth scenarios minimize many key risks for feasibility and sustainability and thus need to be thoroughly assessed. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) are used to dynamically downscale global climate models (GCMs) to provide high-resolution projections of future climate change to better inform policy and decision making at the regional scale. However, biases from GCMs are transferred to RCMs and this can limit the usefulness of the regional climate projections. This paper investigates the influence of bias correcting 4 GCMs from the Coupled Model Intercomparison Project 3, for regional climate simulations over the CORDEX-Australasia domain using Weather Research and Forecasting. The GCM outputs are bias corrected against ERA-Interim reanalysis as a surrogate truth. Results show that over decadal time scales bias correction removes large systematic precipitation and temperature biases. However, bias correction also introduced biases where there were none, introduced biases of the opposite sign, or enhanced existing biases in other regions in some instances. The dynamical mechanisms driving the changes in the biases are explored. [ABSTRACT FROM AUTHOR]

This paper demonstrates a climate change impact study on the hydrological process of a data-scarce Greek watershed. The Soil and Water Assessment Tool (SWAT) and, particularly, the ArcSWAT interface was used for the watershed simulation. The ERA-Interim reanalysis climate data regarding the period from 1981 to 2000 were used for the historical simulation of the watershed. The ArcSWAT simulated data were evaluated against the observed discharge data for the periods with the available data. The statistical evaluation confirmed the ArcSWAT model's capability in simulating the hydrological process of the research area. The climate change consequences on the hydrological components of the research area until the end of the twenty-first century were estimated by driving the ArcSWAT model with the Regional Climate Model Version 4 (RegCM4) forcing data under the extreme RCP 8.5 scenario, namely the simulations of the MPI and HadGEM2 general circulation models (GCMs), resulted from the spatio-temporal kriging approach. Based on the results, the increase in the minimum and the maximum temperature contributed to an increase in the actual evapotranspiration and the surface runoff. In contrast, the temperature increase caused a reduction in the infiltration. An increase (reduction) in the precipitation led to an increase (reduction) in the hydrological components. The climate change impact analysis of the Greek watershed showed that not only the precipitation changes but the temperature changes as well directly influence the water balance components of the research area and particularly the infiltration. [ABSTRACT FROM AUTHOR]

It is important to estimate a high-resolution spatiotemporal distribution of temperature rise under climate change for effective adaptation and mitigation planning. This paper studies the threshold crossing time (TCT) for eight Indian warming targets (IWTs) between 1.5 and 5 °C based on regional climate models (RCMs) participating in The Coordinated Regional Downscaling Experiment (CORDEX)—South Asia project. To evaluate the performance of CORDEX experiments in simulating the near-surface mean temperature, the model performance index (MPI) is used which represents that the CanESM experiment performs best for simulating Tmean over India. All India average Tmean projected by the multi-model ensemble (MME) represents that 1.5 °C TCT is about 2033 ± 3 and 2028 ± 3 in RCP4.5 and RCP8.5 scenarios, respectively. During the twenty-first century, Indian subcontinent can experience five IWTs (1.5–3.5 °C) under RCP4.5 and all eight under scenario RCP8.5. The fastest warming region in India is the western Himalayan region (North India) and north-west region (Indian Thar Desert). [ABSTRACT FROM AUTHOR]

Tropical regions are characterized by hydrological extreme events, which are likely to be exacerbated by climate change. Therefore, quantifying the extent to which climate change may damage a hydrological system becomes crucial. This paper aims to evaluate the findings from previous research on projected impacts of climate change on hydrological systems located in regions bounded by the Tropic of Cancer and the Tropic of Capricorn. It intends to provide an in-depth understanding of the climatic conditions, applied approaches, climate change impacts on future streamflow, and measures to reduce prediction uncertainty in the tropics. The review revealed that there is a significant variation in the magnitude of climate change impacts on streamflow in the tropics. The reason for the inconsistent trend prediction is that projections are heavily dependent on the trajectory of greenhouse gas emissions, climate model structural differences, and uncertainty of downscaling methods and hydrological models. Therefore, to minimize the uncertainty and maximize confidence in streamflow projections, it is essential to apply multi-member model ensembles and to clarify the adaptation strategy (coping, adjusting, or transforming). [ABSTRACT FROM AUTHOR]

A primary driver of deep-ocean mixing is breaking of internal tides generated via interactions of barotropic tides with topography. It is important to understand how the energy conversion from barotropic to internal tides responds to global warming. Here we address this question by applying a linear model of internal tide generation to coupled global climate model simulations under a high carbon emission scenario. The energy conversion to high-mode internal tides is projected to rise by about 8% by the end of the 21st century, whereas the energy conversion to low-mode internal tides remains nearly unchanged. The intensified near-bottom stratification under global warming increases energy conversion into both low and high-mode internal tides. In contrast, the intensified depth-averaged stratification reduces the modal horizontal wavenumber of internal tides, leading to increased (decreased) energy conversion into high (low)- mode internal tides. Our findings imply stronger mixing over rough topography under global warming, which should be properly parameterized in climate models for more accurate projections of future climate changes. The authors use a linear model of internal tide generation and an ensemble of coupled global climate model simulations under a high emission scenario, and find a significant enhancement of energy conversion to high-mode internal tides by the end of the 21st century. [ABSTRACT FROM AUTHOR]

The world's oceans are under threat from the prevalence of heatwaves caused by climate change. Despite this, there is a lack of understanding regarding their impact on seawater oxygen levels - a crucial element in sustaining biological survival. Here, we find that heatwaves can trigger low-oxygen extreme events, thereby amplifying the signal of deoxygenation. By utilizing in situ observations and state-of-the-art climate model simulations, we provide a global assessment of the relationship between the two types of extreme events in the surface ocean (0–10 m). Our results show compelling evidence of a remarkable surge in the co-occurrence of marine heatwaves and low-oxygen extreme events. Hotspots of these concurrent stressors are identified in the study, indicating that this intensification is more pronounced in high-biomass regions than in those with relatively low biomass. The rise in the compound events is primarily attributable to long-term warming primarily induced by anthropogenic forcing, in tandem with natural internal variability modulating their spatial distribution. Our findings suggest the ocean is losing its breath under the influence of heatwaves, potentially experiencing more severe damage than previously anticipated. The authors show that the global ocean is experiencing a significant rise in simultaneous heatwaves and low-oxygen extreme events that pose threats to marine life worldwide. [ABSTRACT FROM AUTHOR]

This research aimed to assess changes in mean and extreme rainfall within the Kaduna River Basin (KRB), specifically examining the implications of two Representative Concentration Pathways (RCPs)—4.5 and 8.5 scenarios. Employing a quantile mapping technique, this study corrected inherent biases in four Regional Climate Models, enabling the examination of mean precipitation and six indices capturing extreme precipitation events for the 2050s. These findings were compared against a historical reference period spanning from 1981 to 2010, considering the basin's upstream and downstream segments. Results revealed an average annual rainfall reduction under scenarios 4.5 (21.39%) and 8.5 (20.51%) across the basin. This decline exhibited a more pronounced impact on monthly rainfall during the wet season (April to October) compared to the dry season (November to March). Notably, a substantial decrement in wet indices, excluding consecutive wet days (CWD), was foreseen in both seasons for the upstream and downstream areas, signalling an impending drier climate. The anticipated rise in consecutive dry days (CDD) is poised to manifest prominently downstream attributed to global warming-induced climate change brought on by increased anthropogenic emissions of greenhouse gases. These findings accentuate a heterogeneous distribution of extreme rainfall, potentially leading to water scarcity issues throughout the KRB, especially impacting upstream users. Moreover, the projections hint at an increased risk of flash floods during intense wet periods. Consequently, this study advocates the implementation of targeted disaster risk management strategies within the KRB to address these foreseeable challenges. [ABSTRACT FROM AUTHOR]

The complex meso- and microclimatic heterogeneity inherent to mountainous regions, driven by both topographic and biotic factors, and the lack of observations, poses significant challenges to using climate models to predict and understand impacts at various scales. We present here a six-year dataset (2017–2022) of continuous climatic measurements collected at five elevations from 983 m to 2705 m above sea level in the Val Mazia - Matschertal valley in the Italian Alps. The measurements include the air temperature, relative humidity, wind speed and direction, solar radiation, soil properties, precipitation, and snow height. Collected within the European Long-Term Ecological Research program (LTER), this dataset is freely available in an open access repository. The time series may be valuable for the validation of regional climate models, atmospheric exchange modelling, and providing support for hydrological models and remote sensing products in mountain environments. Additionally, our data may be useful for research on the influence of elevation on ecological processes such as vegetation growth, plant composition, and soil biology. Beyond its utility in advancing such fundamental research, meteorological monitoring data contribute to informed socio-political decisions on climate adaptation strategies, land management, and water resource planning, enhancing the safety and resilience of mountain communities and biodiversity. [ABSTRACT FROM AUTHOR]

Given the general notion that dynamical downscaling leads to added accuracy in both historical simulations as well as climate change projections, this paper investigates its validity over India using historical data (1975-2005) from the CORDEX models and their driving global climate models (GCMs) from Coupled Model Intercomparison Project Phase 5 (CMIP5), and comparing them against observed temperature and rainfall. We find that downscaling invariably leads to an improvement in the spatial pattern of surface air temperature, but compared to the driving GCMs, the errors in magnitude after downscaling are even worse in some cases. In regard to JJAS rainfall simulations, the CMIP5 driving GCMs are found to be superior to their dynamically downscaled counterparts both in terms of spatial patterns as well as magnitude of errors. Both CMIP5 driving GCMs as well as the CORDEX models underestimate rainfall during JJAS; however, negative bias in CORDEX models is worse. Unlike the driving CMIP5 GCMs, their dynamically downscaled counterparts simulate an early onset followed by a slow and late withdrawal of the Indian summer monsoon rainfall. The frequency of occurrence of rainfall intensities is simulated well by both sets of models in the lower intensity regime (0-20 mm/day); however, for higher intensities, the driving CMIP5 GCMs underestimate whereas the CORDEX models overestimate. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of two simulations by regional climate model (RCM) ALADIN-Climate/CZ with different sizes of integration domain and their driving simulation of global climate model (GCM) ARPÉGE-Climat over central Europe during the period 1961-2010. After a brief evaluation of seasonal means of air temperature characteristics (daily mean, maximum and minimum temperature) and their variability, we focus on the ability of the simulations to represent observed changes in seasonal mean air temperature and its variability. We distinguish between intrinsic and trend-induced variability. Moreover, the dependence on the size of the integration domain and the improvement brought about by the nested RCM compared to the driving GCM are analysed. Both the evaluation of temperature means and variability and the evaluation of their changes confirm that RCM simulation on a larger domain often differs more considerably from driving GCM than simulation on a smaller domain. The larger domain simulation usually produces smaller biases for mean temperature but has worse agreement with observations in terms of variability. Further, the areas with the greatest discrepancies are often in the south-eastern region. In addition, noticeable differences between RCM and GCM simulations mostly emerge for the Alpine region and eastern regions. A possible explanation is the complex terrain and larger distance from domain boundaries. Moreover, it was analysed whether removing of the linear trend enhances the representation of changes in variability. This was not generally confirmed. Only in summer the simulations underestimated the observed increase in intrinsic variability less considerably than for total variability. However, biases were still considerable in many cases. [ABSTRACT FROM AUTHOR]

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Predictive estimates of changes in the climatological boundary of the permafrost zone as a function of the average annual air temperature in the Bolshezemelskaya tundra under various global economy scenarios until the middle of the XXI century have been obtained. The permafrost climatological boundary shift in the northeasterly direction observed in the period from 1950 to 2010 was determined from the threshold average annual air temperature. According to the adjusted scenario forecasts obtained using the climate model, it will continue in the coming decades under any global economy scenario and is an inevitable consequence of the anthropogenic influence on the climate. The study results are important for assessing the prospects and development of a long-term observation network created to monitor the permafrost state and greenhouse gas fluxes in the Russian Federation. [ABSTRACT FROM AUTHOR]

The wet bulb temperature (Tw) has gained considerable attention as a crucial indicator of heat-related health risks. Here we report south-to-north spatially heterogeneous trends of Tw in China over 1979-2018. We find that actual water vapor pressure (Ea) changes play a dominant role in determining the different trend of Tw in southern and northern China, which is attributed to the faster warming of high-latitude regions of East Asia as a response to climate change. This warming effect regulates large-scale atmospheric features and leads to extended impacts of the South Asia high (SAH) and the western Pacific subtropical high (WPSH) over southern China and to suppressed moisture transport. Attribution analysis using climate model simulations confirms these findings. We further find that the entire eastern China, that accommodates 94% of the country's population, is likely to experience widespread and uniform elevated thermal stress the end of this century. Our findings highlight the necessity for development of adaptation measures in eastern China to avoid adverse impacts of heat stress, suggesting similar implications for other regions as well. Attributing spatially heterogeneous heat stress trends to water vapor pressure changes driven by climate change-induced rapid warming in high-latitudes of East Asia, the authors predict widespread and uniform future heat stress in eastern China. [ABSTRACT FROM AUTHOR]

The Hadley circulation plays a central role in determining the location and intensity of the hydrological cycle in tropical and subtropical latitudes. Thus, the human-induced historical and projected weakening of the Northern Hemisphere Hadley circulation has considerable societal impacts. Yet, it is currently unknown how unparalleled this weakening is relative to the response of the circulation to natural forcings in past centuries. Here, using state-of-the-art climate models, we show that in contrast to the recent and future human-induced Hadley circulation weakening, natural forcings acted to intensify the circulation by cooling the climate over the last millennium. The reversal of a naturally-forced multi-centennial trend by human emissions highlights their unprecedented impacts on the atmospheric circulation. Given the amplifying effect of natural forcings on the Hadley circulation, our analysis stresses the importance of adequately incorporating natural forcings in climate model projections to better constrain future tropical climate changes. The authors find an unprecedented human-induced weakening of the Northern Hemisphere Hadley circulation in recent and coming decades, contrasting with the naturally-forced simulated strengthening over the preindustrial last millennium. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to estimate how the observed and predicted climate changes may affect the development rates and emergence of the codling moth in the southern part of the Wielkopolska region in Poland. In order to simulate the future climate conditions one of the most frequently used A1B SRES scenarios and two different IPCC climate models (HadCM3 and GISS modelE) are considered. A daily weather generator (WGENK) was used to generate temperature values for present and future climate conditions (time horizons 2020-2040 and 2040-2060). Based on the generated data set, the degree-days values were then calculated and the emergence dates of the codling moth at key stages were estimated basing on the defined thresholds. Our analyses showed that the average air surface temperature in the Wielkopolska region may increase from 2.8°C (according to GISS modelE) even up to 3.3°C (HadCM3) in the period of 2040-2060. With the warming climate conditions the cumulated degree-days values may increase at a rate of about 142 DD per decade when the low temperature threshold ( T) of 0°C is considered and 91 DD per decade when T = 10°C. The key developmental stages of the codling moth may occur much earlier in the future climate conditions than currently, at a rate of about 3.8-6.8 days per decade, depending on the considered GCM model and the pest developmental stage. The fastest changes may be observed in the emergence dates of 95% of larvae of the second codling moth generation. This could increase the emergence probability of the pest third generation that has not currently occurred in Poland. [ABSTRACT FROM AUTHOR]

Assessing the regional impact of climate change on agriculture, hydrology, and forests is vital for sustainable management. Trustworthy projections of climate change are needed to support these assessments. In this paper, 18 global climate models (GCMs) from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are evaluated for their ability to simulate regional climate change in Zhejiang Province, Southeast China. Simple graphical approaches and three indices are used to evaluate the performance of six key climatic variables during simulations from 1971 to 2000. These variables include maximum and minimum air temperature, precipitation, wind speed, solar radiation, and relative humidity. These variables are of great importance to researchers and decision makers in climate change impact studies and developing adaptation strategies. This study found that most GCMs failed to reproduce the observed spatial patterns, due to insufficient resolution. However, the seasonal variations of the six variables are simulated well by most GCMs. Maximum and minimum air temperatures are simulated well on monthly, seasonal, and yearly scales. Solar radiation is reasonably simulated on monthly, seasonal, and yearly scales. Compared to air temperature and solar radiation, it was found that precipitation, wind speed, and relative humidity can only be simulated well at seasonal and yearly scales. Wind speed was the variable with the poorest simulation results across all GCMs. [ABSTRACT FROM AUTHOR]

Extreme precipitation events lead to dramatic impacts on society and the situation will worsen under climate change. Decision-makers need reliable estimates of future changes as a basis for effective adaptation strategies, but projections at local scale from regional climate models (RCMs) are highly uncertain. Here we exploit the km-scale convection-permitting multi-model (CPM) ensemble, generated within the FPS Convection project, to provide new understanding of the changes in local precipitation extremes and related uncertainties over the greater Alpine region. The CPM ensemble shows a stronger increase in the fractional contribution from extreme events than the driving RCM ensemble during the summer, when convection dominates. We find that the CPM ensemble substantially reduces the model uncertainties and their contribution to the total uncertainties by more than 50%. We conclude that the more realistic representation of local dynamical processes in the CPMs provides more reliable local estimates of change, which are essential for policymakers to plan adaptation measures. [ABSTRACT FROM AUTHOR]

Extreme hydrological events, like floods and droughts, exert considerable effects on both human and natural systems. The frequency, intensity, and duration of these events are expected to change due to climate change, posing challenges for water resource management and adaptation. In this study, the Soil and Water Assessment Tool plus (SWAT +) model was calibrated and validated to simulate flow under future shared socioeconomic pathway (SSP2-4.5 and SSP5-8.5) scenarios in the Baro River Basin with R2 values of 0.88 and 0.83, NSE of 0.83 and 0.74, and PBIAS of 0.39 and 8.87 during calibration and validation. Six bias-corrected CMIP6 Global Climate Models (GCM) were selected and utilized to investigate the effects of climate change on the magnitude and timing of hydrological extremes. All climate model simulation results suggest a general increase in streamflow magnitude for both emission scenarios (SSP2-4.5 and SSP5-8.5). The multi-model ensemble projections show yearly flow increases of 4.8% and 12.4% during the mid-term (MT) (2041–2070) and long-term (LT) (2071–2100) periods under SSP2-4.5, and 15.7% and 35.6% under SSP5-8.5, respectively. Additionally, the analysis revealed significant shifts in the projected annual 1 day, 3 day, 7 day, and 30 day maximum flows, whereas the annual 3 day and 7 day minimum flow fluctuations do not present a distinct trend in the future scenario compared to the baseline (1985–2014). The study also evaluated the timing of hydrological extremes, focusing on low and peak flow events, utilizing the annual 7 day maximum and minimum flow for this analysis. An earlier occurrence was noted for both peak and low flow in the SSP2-4.5 scenario, while a later occurrence was observed in the SSP5-8.5 scenario compared to the baseline. In conclusion, this study showed the significant effect of climate change on river hydrology and extreme flow events, highlighting their importance for informed water management and sustainable planning. [ABSTRACT FROM AUTHOR]

We evaluated the potential impact of future climate change on spring maize and single-crop rice in northeastern China (NEC) by employing climate and crop models. Based on historical data, diurnal temperature change exhibited a distinct negative relationship with maize yield, whereas minimum temperature correlated positively to rice yield. Corresponding to the evaluated climate change derived from coupled climate models included in the Coupled Model Intercomparison Project Phase 5 (CMIP5) under the Representative Concentration Pathway 4.5 scenario (RCP4.5), the projected maize yield changes for three future periods [2010-39 (period 1), 2040-69 (period 2), and 2070-99 (period 3)] relative to the mean yield in the baseline period (1976-2005) were 2.92%, 3.11% and 2.63%, respectively. By contrast, the evaluated rice yields showed slightly larger increases of 7.19%, 12.39%, and 14.83%, respectively. The uncertainties in the crop response are discussed by considering the uncertainties obtained from both the climate and the crop models. The range of impact of the uncertainty became markedly wider when integrating these two sources of uncertainty. The probabilistic assessments of the evaluated change showed maize yield to be relatively stable from period 1 to period 3, while the rice yield showed an increasing trend over time. The results presented in this paper suggest a tendency of the yields of maize and rice in NEC to increase (but with great uncertainty) against the background of global warming, which may offer some valuable guidance to government policymakers. [ABSTRACT FROM AUTHOR]

This paper provides a review of paleoclimate modeling activities in China. Rather than attempt to cover all topics, we have chosen a few climatic intervals and events judged to be particularly informative to the international community. In historical climate simulations, changes in solar radiation and volcanic activity explain most parts of reconstructions over the last millennium prior to the industrial era, while atmospheric greenhouse gas concentrations play the most important role in the 20th century warming over China. There is a considerable model-data mismatch in the annual and boreal winter temperature change over China during the mid-Holocene [6000 years before present (ka BP)], while coupled models with an interactive ocean generally perform better than atmospheric models. For the Last Glacial Maximum (21 ka BP), climate models successfully reproduce the surface cooling trend over China but fail to reproduce its magnitude, with a better performance for coupled models. At that time, reconstructed vegetation and western Pacific sea surface temperatures could have significantly affected the East Asian climate, and environmental conditions on the Qinghai-Tibetan Plateau were most likely very different to the present day. During the late Marine Isotope Stage 3 (30-40 ka BP), orbital forcing and Northern Hemisphere glaciation, as well as vegetation change in China, were likely responsible for East Asian climate change. On the tectonic scale, the Qinghai-Tibetan Plateau uplift, the Tethys Sea retreat, and the South China Sea expansion played important roles in the formation of the East Asian monsoon-dominant environment pattern during the late Cenozoic. [ABSTRACT FROM AUTHOR]

This work is executed to predict the variation in global temperature and greenhouse gas (GHG) emissions resulting from climate change and global warming, taking into consideration the natural climate cycle. A mathematical model was developed using a Recurrent Neural Network (RNN) with Long–Short-Term Memory (LSTM) model. Data sets of global temperature were collected from 800,000 BC to 1950 AD from the National Oceanic and Atmospheric Administration (NOAA). Furthermore, another data set was obtained from The National Aeronautics and Space Administration (NASA) climate website. This contained records from 1880 to 2019 of global temperature and carbon dioxide levels. Curve fitting techniques, employing Sin, Exponential, and Fourier Series functions, were utilized to reconstruct both NOAA and NASA data sets, unifying them on a consistent time scale and expanding data size by representing the same information over smaller periods. The fitting quality, assessed using the R-squared measure, ensured a thorough process enhancing the model's accuracy and providing a more precise representation of historical climate data. Subsequently, the time-series data were converted into a supervised format for effective use with the LSTM model for prediction purposes. Augmented by the Mean Squared Error (MSE) as the analyzed loss function, normalization techniques, and refined data representation from curve fitting the LSTM model revealed a sharp increase in global temperature, reaching a temperature rise of 4.8 °C by 2100. Moreover, carbon dioxide concentrations will continue to boom, attaining a value of 713 ppm in 2100. In addition, the findings indicated that the RNN algorithm (LSTM model) provided higher accuracy and reliable forecasting results as the prediction outputs were closer to the international climate models and were found to be in good agreement. This study contributes valuable insights into the trajectory of global temperature and GHG emissions, emphasizing the potential of LSTM models in climate prediction. [ABSTRACT FROM AUTHOR]

A recent rise in the global brewery sector has increased the demand for high-quality, late summer hops. The effects of ongoing and predicted climate change on the yield and aroma of hops, however, remain largely unknown. Here, we combine meteorological measurements and model projections to assess the climate sensitivity of the yield, alpha content and cone development of European hops between 1970 and 2050 CE, when temperature increases by 1.4 °C and precipitation decreases by 24 mm. Accounting for almost 90% of all hop-growing regions, our results from Germany, the Czech Republic and Slovenia show that hop ripening started approximately 20 days earlier, production declined by almost 0.2 t/ha/year, and the alpha content decreased by circa 0.6% when comparing data before and after 1994 CE. A predicted decline in hop yield and alpha content of 4–18% and 20–31% by 2050 CE, respectively, calls for immediate adaptation measures to stabilize an ever-growing global sector. The effects of climate change on the yield and aroma of beer hops remains unknown. Here the authors demonstrate a climate-induced decline in the quality and quantity of traditional aroma hops across Europe and calls for urgent adaptation measures to stabilize international market chains. [ABSTRACT FROM AUTHOR]

Almost all climate time series have some degree of nonstationarity due to external forces of the observed system. Therefore, these external forces should be taken into account when reconstructing the climate dynamics. This paper presents a novel technique in predicting nonstationary time series. The main difference of this new technique from some previous methods is that it incorporates the driving forces in the prediction model. To appraise its effectiveness, three prediction experiments were carried out using the data generated from some known classical dynamical models and a climate model with multiple external forces. Experimental results indicate that this technique is able to improve the prediction skill effectively. [ABSTRACT FROM AUTHOR]

The Grid-point Atmospheric Model of IAP LASG version 2 (GAMIL2) has been developed through upgrading the deep convection parameterization, cumulus cloud fraction and two-moment cloud microphysical scheme, as well as changing some of the large uncertain parameters. In this paper, its performance is evaluated, and the results suggest that there are some significant improvements in GAMIL2 compared to the previous version GAMIL1, for example, the components of the energy budget at the top of atmosphere (TOA) and surface; the geographic distribution of shortwave cloud radiative forcing (SWCF); the ratio of stratiform versus total rainfall; the response of atmospheric circulation to the tropical ocean; and the eastward propagation and spatiotemporal structures of the Madden Julian Oscillation (MJO). Furthermore, the indirect aerosols effect (IAE) is −0.94 W m, within the range of 0 to −2 W m given by the IPCC 4th Assessment Report (2007). The influence of uncertain parameters on the MJO and radiation fluxes is also discussed. [ABSTRACT FROM AUTHOR]

Sea ice is an important component in the Earth's climate system. Coupled climate system models are indispensable tools for the study of sea ice, its internal processes, interaction with other components, and projection of future changes. This paper evaluates the simulation of sea ice by the Flexible Global Ocean-Atmosphere-Land System model Grid-point Version 2 (FGOALS-g2), in the fifth phase of the Coupled Model Inter-comparison Project (CMIP5), with a focus on historical experiments and late 20th century simulation. Through analysis, we find that FGOALS-g2 produces reasonable Arctic and Antarctic sea ice climatology and variability. Sea ice spatial distribution and seasonal change characteristics are well captured. The decrease of Arctic sea ice extent in the late 20th century is reproduced in simulations, although the decrease trend is lower compared with observations. Simulated Antarctic sea ice shows a reasonable distribution and seasonal cycle with high accordance to the amplitude of winter-summer changes. Large improvement is achieved as compared with FGOALS-g1.0 in CMIP3. Diagnosis of atmospheric and oceanic forcing on sea ice reveals several shortcomings and major aspects to improve upon in the future: (1) ocean model improvements to remove the artificial island at the North Pole; (2) higher resolution of the atmosphere model for better simulation of important features such as, among others, the Icelandic Low and westerly wind over the Southern Ocean; and (3) ocean model improvements to accurately receive freshwater input from land, and higher resolution for resolving major water channels in the Canadian Arctic Archipelago. [ABSTRACT FROM AUTHOR]

The seasonal cycle and interannual variability in the tropical oceans simulated by three versions of the Flexible Ocean-Atmosphere-Land System (FGOALS) model (FGOALS-g1.0, FGOALS-g2 and FGOALSs2), which have participated in phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5), are presented in this paper. The seasonal cycle of SST in the tropical Pacific is realistically reproduced by FGOALS-g2 and FGOALSs2, while it is poorly simulated in FGOALS-g1.0. Three feedback mechanisms responsible for the SST annual cycle in the eastern Pacific are evaluated. The ocean-atmosphere dynamic feedback, which is successfully reproduced by both FGOALS-g2 and FGOALS-s2, plays a key role in determining the SST annual cycle, while the overestimated stratus cloud-SST feedback amplifies the annual cycle in FGOALS-s2. Because of the serious warm bias existing in FGOALS-g1.0, the ocean-atmosphere dynamic feedback is greatly underestimated in FGOALS-g1.0, in which the SST annual cycle is mainly driven by surface solar radiation. FGOALS-g1.0 simulates much stronger ENSO events than observed, whereas FGOALS-g2 and FGOALSs2 successfully simulate the observed ENSO amplitude and period and positive asymmetry, but with less strength. Further ENSO feedback analyses suggest that surface solar radiation feedback is principally responsible for the overestimated ENSO amplitude in FGOALS-g1.0. Both FGOALS-g1.0 and FGOALS-s2 can simulate two different types of El Ni-no events - with maximum SST anomalies in the eastern Pacific (EP) or in the central Pacific (CP) - but FGOALS-g2 is only able to simulate EP El Ni-no, because the negative cloud shortwave forcing feedback by FGOALS-g2 is much stronger than observed in the central Pacific. [ABSTRACT FROM AUTHOR]

Rapid intensification (RI) is an essential process in the development of strong tropical cyclones and a major challenge in prediction. RI in offshore regions is more threatening to coastal populations and economies. Although much effort has been devoted to studying basin-wide temporal-spatial fluctuations, variations of global RI events in offshore regions remain uncertain. Here, we show that compared with open oceans, where the annual RI counts do not show significant changes, offshore areas within 400 km of the coastline have experienced a significant increase in RI events, with the count tripling from 1980 to 2020. Furthermore, thermodynamic environments present more favorable conditions for this trend, and climate models show that global ocean warming has enhanced such changes. This work yields an important finding that an increasing threat of RI in coastal regions has occurred in the preceding decades, which may continue under a future warming climate. Rapid intensification of tropical cyclones has been occurring closer to land since 1980 due to climate change. This shift can degenerate the forecast skill and increase the danger that tropical cyclones pose to coastal regions. [ABSTRACT FROM AUTHOR]

In this study, we assessed responses of adaptation options to possible climate change scenarios on maize growth and yield by using projections of 20 coupled ensemble climate models under two representative concentration pathways (RCPs) 4.5 and 8.5 by means of a DSSAT model. Growth and yield simulations were made across present and future climate conditions using the hybrid maize variety (Shone). Subsequently, simulated yields were compared with farmer' average and on-farm trial yields. Results showed that on-farm trial yield (5.1–7.3 t ha−1) lay in between farmers' average yield (2.9–5 t ha−1) and water-limited potential yield (6.3–10.6 t ha−1). Maize yields achieved in farmers' fields are projected to decline towards mid-century and further towards the end of the century regardless of the adaptation options compared with baseline in low potential clusters. Results of a combination of adaptation options including February planting, use of 64 kg ha−1 N and conservation tillage provided yield advantage of 5.8% over the 30 cm till under medium GHGs emission scenario during mid-century period at Shamana. Mulching with 5 t ha−1 was projected to produce a 4–5% yield advantage in the Hawassa cluster during the mid-century period regardless of changes in tillage or planting window. Under a high GHGs emission scenario, over 13.4% yield advantage was projected in the Bilate cluster due to conservation tillage and June planting during the mid-century period. In the Dilla cluster, the use of 10 t ha−1 mulch, conservation tillage and early planting (February) would result in a 1.8% yield advantage compared with the control either in medium or high GHGs emission scenarios. Thus, the most promising and least risky practices among simulated strategies were the use of nitrogen and mulching in combination with tillage or planting date adjustment. However, adaptation options remained least promising and highly risky if not integrated with mulching or nitrogen use. Hence, the negative impacts of future climate change and subsequent yield gaps would be reduced by optimizing the application of nitrogen, mulch and their interaction with planting date and tillage in high and low potential areas of maize production. [ABSTRACT FROM AUTHOR]

Qunf Cave oxygen isotope (δ18Oc) record from southern Oman is one of the most significant of few Holocene Indian summer monsoon cave records. However, the interpretation of the Qunf δ18Oc remains in dispute. Here we provide a multi-proxy record from Qunf Cave and climate model simulations to reconstruct the Holocene local and regional hydroclimate changes. The results indicate that besides the Indian summer monsoon, the North African summer monsoon also contributes water vapor to southern Oman during the early to middle Holocene. In principle, Qunf δ18Oc values reflect integrated oxygen-isotope fractionations over a broad moisture transport swath from moisture sources to the cave site, rather than local precipitation amount alone, and thus the Qunf δ18Oc record characterizes primary changes in the Afro-Asian monsoon regime across the Holocene. In contrast, local climate proxies appear to suggest an overall slightly increased or unchanged wetness over the Holocene at the cave site. Southern Oman speleothem oxygen isotope and multi-proxy data reveal diverse changes in the Afro-Indian summer monsoon circulations and local hydroclimate conditions during the Holocene, confirming climate model simulations. [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) are limited in their representation of regional climates. Thus, the selection and downscaling of the most suitable models for regional/local studies are crucial prior to climate change impact studies. This study addressed the selection and downscaling of GCM models from 100 ensembles each from the Shared Socioeconomic Pathways (SSP4.5 and SSP8.5) emission scenarios from the CMIP6 archive using an advanced envelop-based selection approach for Northern Nigeria. We used 2021–2050 as the short-term and 2051–2080 as the long-term study periods. The selection approach revealed that CanESM5 models are more skilful in simulating the warm and wet season, while HadGEM3-GC31-LL in the warm and dry season, whereas MPI-ESM1-2-HR and MPI-ESM1-2-LR are skilful in the cold and dry season. Furthermore, we downscaled the three most skilled models from each season and calculated their spatial averages over Northern Nigeria to provide a more precise illustration of the temperature and precipitation patterns. Under the SSP4.5 emission scenario, the ensemble mean of the downscaled and the (raw) GCMs projected about 13% (8–17%) and 20% (11–35%) increase in average annual precipitation during the short-term and long-term periods, respectively. Similarly, for SSP8.5, the models projected about 23% (5–38%) and 41% (29–60%) increase in the average annual precipitation during short-term and long-term periods respectively. For the temperature, under SSP4.5, the GCMs projected a 1.1 °C (0.26–1.6 °C) and 2.5 °C (0.87–4.04 °C) increase in average annual temperature for short-term and long-term periods respectively. Similarly, an increase of 1.2 °C (0.01–1.78 °C) and 2.7 °C (0.01–4.3 °C) is projected for SSP8.5 during the short-term and long-term periods respectively. These findings can be used for climate impact studies in the region. [ABSTRACT FROM AUTHOR]

The global health burden from wildfire smoke is expected to worsen under climate change, yet we lack quantitative estimates of the economic costs of increased mortality and hospital admissions for cardiovascular and respiratory conditions. Using a quantitative wildfire risk assessment framework and a 12-member climate model ensemble, we find a median increase in wildfire smoke health costs of 1–16% by 2070 across diverse landscapes in south-eastern Australia. Ensemble maximum cost increases (5–38%) often exceed abatements from fuel treatment, while costs decline moderately (0–7%) for the ensemble minimum. Unmitigated climate change will increase the health burden of wildfire smoke and undermine prescribed burning effectiveness. [ABSTRACT FROM AUTHOR]

El Niño-Southern Oscillation (ENSO) teleconnections exhibit a strong dependency on seasonally and intraseasonally varying mean states, leading to impactful short-term variations in regional climate. The North Atlantic Oscillation (NAO)-ENSO relation is a typical example, in that its phase relationship reverses systematically between the early and late winter. Here based on observations and an ensemble of atmosphere-only climate model simulations, we reveal that this NAO phase reversal occurs synchronously in early January, showing strong abruptness. We demonstrate that this abrupt NAO phase reversal is caused by the change in ENSO-induced Rossby wave-propagating direction from northeastward to southeastward over the northeastern North American region, which is largely governed by a climatological alteration of the local jet meridional shear. We also provide evidence that the North Atlantic intrinsic eddy–low-frequency flow feedback further amplifies the NAO responses. This ENSO-related abrupt NAO change offers an avenue for intraseasonal climate forecasting in the Euro-Atlantic region. [ABSTRACT FROM AUTHOR]

Background: Projecting future streamflow variation or the hydrological impact of climate change plays a pivotal role in the sustainable implication of planning water resources management. Therefore, this study predicts the potential of climate change's impact on hydrological components in the Upper Awash Basin (UAB). The study applied a statistical downscaling model (SDSM) to generate future high-resolution climate data from the climate model output of the Canadian Second Generation Earth System Model (CanESM2) and the National Centers for Environmental Prediction (NCEP) under the representative concentration pathways (RCP4.5 and RCP8.5) scenarios. To analyze the trend of future rainfall and temperature, non-parametric Mann-Kendall, Modified Mann-Kendall tests, Sen's slope estimator, and changing point (Pettit) tests were used. The output of downscaled climate data is used as input to a calibrated and validated Soil and Water Assessment Tool (QSWAT) model to assess the impact of future climate change on UAB hydrology. Results: The results show that annual rainfall and temperature are significantly increased (p < 0.05) in the UAB under RCP4.5 and 8.5 for the model ensemble mean for both short- and long-term scenarios. The change in the rainfall, the maximum and minimum temperature is mostly visible in the second period (the 2060s). Climate change is likely to cause persistent decreases in surface runoff (SUR_Q) and increases in actual evapotranspiration (ET) under all climate scenarios in the three periods. Reduction in SUR_Q despite an increase in rainfall could be due to an increment in both temperature and ET. The study also identified inconsistent seasonal changes in projected future precipitation that considerably impact overall climatic conditions. Conclusions: This research is essential to develop an interdisciplinary approach that integrates environmental policies for the coherent use and management of water resources for future climate change and ecological protection in the basin, including other similar basins. [ABSTRACT FROM AUTHOR]

Climate change affects ecosystems, agriculture, human health, forestry, and water resource availability. This study is mainly aimed at assessing the climate change effect on the water resources of the Kessie Watershed in the Upper Blue Nile Basin, Ethiopia. The updated Coupled Model Intercomparison Project Phase 6 (CMIP-6) data outputs were used. The three climate model outputs: ACESS_ESM1-5, FGOALS_g3, and GFDL_ESM4 with two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5) scenarios, were used. The climate model output rainfall and temperature data were downscaled to the station level through bias correction. The catchment hydrology was represented by the SWAT—Soil and Water Assessment Tool—through calibration and validation. Future temperatures and rainfall change were evaluated by the Mann–Kendall trend test and Sen's slope estimator. Future climate change trend analysis and streamflow simulation were done on two time horizons: the 2050s (2041–2070) and the 2080s (2071–2100). The baseline streamflow data (1985–2014) were used as a reference. The global climate model projection data indicated mean annual precipitation and temperatures show a slight increase for the future in both scenarios for all climate model outputs. According to the SSP2-4.5 and SSP5-8.5 scenarios, respectively, mean annual precipitation is expected to increase by 5% and 4.89% in the 2050s and 10.13% and 6.8% in the 2080s based on ACCESS_ESM1-5; 4.7% and 3.8% in the 2050s and 4.3% and 4.84% in the 2080s based on FGOALS_g3; and 4.67% and 3.81% in the 2050s and 4.67% and 3.81% based on GFDL_ESM4 models data. Yearly average maximum temperature may increase by 3.62 °C and 1.87 °C in the 2050s and 3.31 °C and 2.99 °C in the 2080s based on ACCESS ESM1-5, 1.76 °C and 1.25 °C in the 2050s and 3.44 °C and 2.61 °C in the 2080s based on FGOALS-g3, and 2.15 °C and 3.83 °C in the 2050s and 1.37 °C and 2.66 °C in the 2080s based on GFDL-ESM4 model data. Similarly, the mean annual minimum temperature is also expected to increase by 2.73 °C and 1.90 °C in the 2050s and 5.63 °C and 4.52 °C in the 2080s based on ACCESS ESM1-5, 3.04 °C and 2.43 °C in the 2050s and 3.55 °C and 4.36 °C in the 2080s based on FGOALS-g3, and 2.31 °C and 3.29 °C in the 2050s, and 3.16 °C and 3.87 °C in the 2080s based on GFDL-ESM4 model data. The streamflow is also expected to increase. In the 2050s, simulated annual streamflow is expected to increase from 12.1 to 21.8% and 9.8 to 15.4% in SSP2-4.5 and SSP5-8.5, respectively, whereas in the 2080s, the change is expected to increase from 15.14 to 24.08% and 13.08 to 41% in SSP2-4.5 and SSP5-8.5, respectively. Future water resource potential of the case study watershed seems able to support irrigation and other projects. [ABSTRACT FROM AUTHOR]

Understanding the relationship between tropical cyclone (TC) precipitation and sea surface temperature (SST) is essential for both TC hazard forecasting and projecting how these hazards will change in the future due to climate change. This work untangles how global TC precipitation is impacted by present-day SST variability (known as apparent scaling) and by long-term changes in SST caused by climate change (known as climate scaling). A variety of datasets are used including precipitation and SST observations, realistic climate model simulations, and idealized climate model simulations. The apparent scaling rates depend on precipitation metric; examples shown here have ranges of 6.1 to 9.5% per K versus 5.9 to 9.8% per K for two different metrics. The climate scaling is estimated at about 5% per K, which is slightly less than the atmospheric moisture scaling based on thermodynamic principles of about 7% per K (i.e., the Clausius–Clapeyron scaling). The apparent scaling is greater than the climate scaling, which implies that the relationship between TC precipitation and present-day SST variability should not be used to project the long-term response of TC precipitation to climate change. [ABSTRACT FROM AUTHOR]

Climate change impact on the habitat distribution of umbrella species presents a critical threat to the entire regional ecosystem. This is further perilous if the species is economically important. Sal (Shorea robusta C.F. Gaertn.), a climax forest forming Central Himalayan tree species, is one of the most valuable timber species and provides several ecological services. Sal forests are under threat due to over-exploitation, habitat destruction, and climate change. Sal's poor natural regeneration and its unimodal density-diameter distribution in the region illustrate the peril to its habitat. We, modelled the current as well as future distribution of suitable sal habitats under different climate scenarios using 179 sal occurrence points and 8 bioclimatic environmental variables (non-collinear). The CMIP5-based RCP4.5 and CMIP6-based SSP245 climate models under 2041–2060 and 2061–2080 periods were used to predict the impact of climate change on sal's future potential distribution area. The niche model results predict the mean annual temperature and precipitation seasonality as the most influential sal habitat governing variables in the region. The current high suitability region for sal was 4.36% of the total geographic area, which shows a drastic decline to 1.31% and 0.07% under SSP245 for 2041–60 and 2061–80, respectively. The RCP-based models predicted more severe impact than SSP; however, both RCP and SSP models showed complete loss of high suitability regions and overall shift of species northwards in the Uttarakhand state. We could identify the current and future suitable habitats for conserving sal population through assisted regeneration and management of other regional issues. [ABSTRACT FROM AUTHOR]

Owning to climate changes and other anthropogenic interventions, the frequency and return levels of extreme flows cannot be treated as stationary. This study quantifies the changes in return levels by fitting the stationary and nonstationary frequency distribution models for annual and seasonal high and low flows of upper Euphrates basin during historical and two projection periods. The projected flows of three selected sub-basins in the upper Euphrates were obtained using HBV-light hydrological model forced by projected daily precipitation and temperature driven from and the most representative regional climate model (RCM) for the study area within the CORDEX-Euro domain. During the historical period, analyses suggest that half of the stations exhibited an increase in return levels (up to 13.5% with GEV), while the remaining four sub-basins experience a decrease in the return levels (up to 27% with GEV). With few exceptions, most of the sub-basins experience an increase (up to 80% with GEV) in return levels of yearly low flows. With few exceptions, the return level of low flows for all seasons generally increases; however, directional variability for high flows is observed. The changes in return levels of yearly projected flows are consistent with historical results in indicating the direction of impact for both near future and far future periods. A decrease in return levels (up to 35% and 22%) in yearly high flows and an increase (up to 100% and 40%) during the near future and far future, respectively. It is projected that the floods of a given return level in the selected sub-basins are becoming milder across all three periods. However, some directional inconsistencies are noted in nonstationarity impact at seasonal scale through projection periods. On yearly basis, the decreasing high flow along with increasing low flow quantiles suggests that the range of extreme occurrences is shrinking. The nonstationarities in high flows as well as low flows should be incorporated in design and development of water resources schemes in the study area. [ABSTRACT FROM AUTHOR]

Heatwaves are becoming more frequent under climate change and can lead to thousands of excess deaths. Adaptation to extreme weather events often occurs in response to an event, with communities learning fast following unexpectedly impactful events. Using extreme value statistics, here we show where regional temperature records are statistically likely to be exceeded, and therefore communities might be more at-risk. In 31% of regions examined, the observed daily maximum temperature record is exceptional. Climate models suggest that similar behaviour can occur in any region. In some regions, such as Afghanistan and parts of Central America, this is a particular problem - not only have they the potential for far more extreme heatwaves than experienced, but their population is growing and increasingly exposed because of limited healthcare and energy resources. We urge policy makers in vulnerable regions to consider if heat action plans are sufficient for what might come. The global risk of record-breaking heatwaves is assessed, with the most at-risk regions identified. It is shown that record-smashing events that currently appear implausible could happen anywhere as a result of climate change. [ABSTRACT FROM AUTHOR]

Climate models project an increase in drought and aridity in many regions in response to greenhouse gas concentrations in the atmosphere. In areas with complex topography, such as the Canary Islands, elevation gradients may play an important role in future changes. Convection-permitting climate simulations driven by data from three global climate models included in the Coupled Model Intercomparison Project (CMIP5) have been performed for the Canary Islands. A significant increase in the duration and severity of drought is projected by the end of the twenty-first century (2070–2099), relative to the recent past (1980–2009), under intermediate and high emissions scenarios. In addition, the percentage of land affected by droughts, on average, would increase considerably, covering up to 96% in the higher elevations, in the business-as-usual scenario. These changes and the increase in aridity are more pronounced at higher altitudes due to a clear dependence of temperature rise as a function of elevation and a substantial decrease in precipitation. [ABSTRACT FROM AUTHOR]

Projections of future streamflow extremes are subjected to various sources of uncertainties. Uncertainties arising from different stages in the modelling process of extremes contribute to the overall uncertainty in the final estimates. In this study, some important sources of uncertainties that occur in the modelling chain for projection of future extremes are analysed. The contribution of climate models, hydrological models, representative concentration pathways (RCPs) and the parameters of the extreme value distribution to the overall uncertainty in the streamflow return levels of the Chaliyar river basin in Kerala, India, was quantified using analysis of variance. 5, 10, 25, and 50-year return levels for the near (2021–2050) and far (2070–2099) future periods were computed. Results of the study indicate that the median values of return levels for the near (0.6–13.5%) and far (21.5–37.3%) future periods are higher than the corresponding values for the observed data. Also the uncertainty ranges of the return levels in the near (2–2.4 times) and far (3–3.7 times) future for different return periods are higher compared to that of the observed. The contribution to uncertainties in the return levels from climate models, parameters of the extreme value distribution employed and hydrological models is higher than that from the RCPs. The contribution of parameter uncertainty increases with the return period and that of the climate models reduces with the return period for the two future periods considered in this study, indicating that parameter uncertainty is an important source of uncertainty and has to be considered while predicting the future extremes. The contribution to uncertainty by the hydrological models and by the RCPs does not exhibit much variation for different return periods in both the future time periods considered. Understanding the uncertainties in future extremes would help to devise proper adaptation strategies to mitigate the probable impacts of climate change. [ABSTRACT FROM AUTHOR]

The phloem-limited bacteria, "Candidatus Liberibacter asiaticus" and "Ca. L. americanus", are the causal pathogens responsible for Huanglongbing (HLB). The Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera: Liviidae) is the principal vector of these "Ca. Liberibacter" species. Though Tamarixia radiata Waterston (Hymenoptera: Eulophidae) has been useful in biological control programmes against D. citri, information on its global distribution remains vague. Using the Climate Change Experiment (CLIMEX) model, the potential global distribution of T. radiata under the 2050s, 2070s, and 2090s for Special Report on Emissions Scenarios A1B and A2 was defined globally. The results showed that habitat suitability for T. radiata covered Africa, Asia, Europe, Oceania, and the Americas. The model predicted climate suitable areas for T. radiata beyond its presently known native and non-native areas. The new locations predicted to have habitat suitability for T. radiata included parts of Europe and Oceania. Under the different climate change scenarios, the model predicted contraction of high habitat suitability (EI > 30) for T. radiata from the 2050s to the 2090s. Nevertheless, the distribution maps created using the CLIMEX model may be helpful in the search for and release of T. radiata in new regions. [ABSTRACT FROM AUTHOR]