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Climate change has the potential to significantly impact various aspects of Earth's climate systems, including precipitation patterns, necessitating region-specific action plans. This study examines the Wadi Al Rummah region in Qassim province, Saudi Arabia, by analyzing Intensity-Duration-Frequency (IDF) curves across six locations, utilizing observed daily precipitation data from 1986 to 2014. The nonparametric quantile mapping method was employed to adjust the outputs of eight Regional Climate Models (RCMs) within the CMIP6 ensemble. These models were evaluated under four Shared Socioeconomic Pathways (SSPs), ranging from a stringent mitigation scenario to one with very high greenhouse gas emissions. Also, two statistical tests, namely the Kolmogorov-Smirnov and Chi-Square tests, were used to assess the best-fitting distribution to estimate the maximum rainfall values. Temporal disaggregation of daily precipitation data was performed using the K-nearest neighbors (KNN) method. The IDF curves were generated for both historical and three projected future periods using Gumbel distribution, which proved to be the best-fitting statistical model, using six return periods: 2, 5, 10, 25, 50, and 100 years. Results indicate that high-emission scenarios and longer timeframes exhibit greater uncertainty in IDF projections. Additionally, rainfall intensity is expected to increase over shorter durations, with significant increases observed in Buriydah and Nabhaniyah under SSP 8.5. In contrast, Al Rass, Badayea, and Al Mithnab show mixed trends, while Unaizah shows little to no significant change. These findings emphasize the need for sustainable development and adaptive strategies to mitigate risks in Qassim province, as climate impacts are projected to intensify, particularly in the short to long term. [ABSTRACT FROM AUTHOR]

Abstract Nowadays the phenomenon of global warming is unequivocal, as confirmed by the latest reports of the IPCC and studies of the climate-change impacts on ecosystems, global economy, and populations. The effect of climate change on groundwater is a very relevant task especially for regions dependent chiefly on groundwater availability, as for the southern Italy. In such a territorial framework, to achieve a detailed hydro-climatological characterization, an Ensemble of 15 RCMs (E15) derived from the EURO-CORDEX project was analyzed considering two IPCC Representative Concentration Pathways (RCP4.5 and RCP8.5). The E15 was calibrated over the period (1950–1996) by a statistical comparison with data observed by the regional meteorological network managed by the former National Hydrological Service (SIMN), Department of Naples. The effects of climate change on air temperature (T), precipitation (P) and, consequently, on actual evapotranspiration (ETR) and effective precipitation Pe (P – ETR) were analyzed until 2100. The latter was considered as a proxy of groundwater recharge of the principal aquifer systems, represented chiefly by the karst aquifers. As a principal result, it was found that the E15 is basically able to reproduce the observed annual precipitation (OBSP) and mean annual air temperature (OBST), being characterized by a very similar frequency distribution. Accordingly, an inferential statistical approach was performed for calibrating E15 precipitation (E15P) and air temperature (E15T) based on the compensation of the difference with OBSP (+ 7%) and OBST (− 16%). The E15 projects a reduction in precipitation and an increase in air temperature under both RCPs, with a divergence point between the two scenarios occurring by about 2040. As a principal result, Pe shows declining trends for both RCP scenarios, reaching a decrease of the 11-yrs moving average down to − 20%, for RCP4.5, and − 50%, for RCP8.5, even if characterized by relevant inter-annual fluctuations.

Nowadays the phenomenon of global warming is unequivocal, as confirmed by the latest reports of the IPCC and studies of the climate-change impacts on ecosystems, global economy, and populations. The effect of climate change on groundwater is a very relevant task especially for regions dependent chiefly on groundwater availability, as for the southern Italy. In such a territorial framework, to achieve a detailed hydro-climatological characterization, an Ensemble of 15 RCMs (E15) derived from the EURO-CORDEX project was analyzed considering two IPCC Representative Concentration Pathways (RCP4.5 and RCP8.5). The E15 was calibrated over the period (1950–1996) by a statistical comparison with data observed by the regional meteorological network managed by the former National Hydrological Service (SIMN), Department of Naples. The effects of climate change on air temperature (T), precipitation (P) and, consequently, on actual evapotranspiration (ETR) and effective precipitation Pe (P – ETR) were analyzed until 2100. The latter was considered as a proxy of groundwater recharge of the principal aquifer systems, represented chiefly by the karst aquifers. As a principal result, it was found that the E15 is basically able to reproduce the observed annual precipitation (OBSP) and mean annual air temperature (OBST), being characterized by a very similar frequency distribution. Accordingly, an inferential statistical approach was performed for calibrating E15 precipitation (E15P) and air temperature (E15T) based on the compensation of the difference with OBSP (+ 7%) and OBST (− 16%). The E15 projects a reduction in precipitation and an increase in air temperature under both RCPs, with a divergence point between the two scenarios occurring by about 2040. As a principal result, Pe shows declining trends for both RCP scenarios, reaching a decrease of the 11-yrs moving average down to − 20%, for RCP4.5, and − 50%, for RCP8.5, even if characterized by relevant inter-annual fluctuations. [ABSTRACT FROM AUTHOR]

This study aims to estimate the surface runoff and examine the impact of climate change on water resources in the Upper Kabul River Basin (UKRB). A hydrological model was developed using the Soil and Water Assessment Tool (SWAT) from 2009 to 2019. The monthly calibration was conducted on streamflow in six stations for the period from 2010 to 2016, and the results were validated from 2017 to 2018 based on available observed data. The hydrological sensitivity parameters were further prioritized using SWAT-CUP. The uncertainty of the model was analyzed by the 95% Prediction Uncertainty (95PPU). Future projections were analyzed for the 2040s (2030–2049) and 2090s (2080–2099) compared to the baseline period (1986–2005) under two representation concentration pathways (RCP4.5, RCP8.5). Four Regional Climate Models (RCMs) were bias-corrected using the linear scaling bias correction method. The modeling results exhibited a very reasonable fit between the estimated and observed runoff in different stations, with NS values ranging from 0.54 to 0.91 in the calibration period. The future mean annual surface runoff exhibited an increase in the 2040s and 2090s compared to the baseline under both RCPs of 4.5 and 8.5 due to an increase in annual precipitation. The annual precipitation is projected to increase by 5% in the 2040s, 1% in the 2090s under RCP4.5, and by 9% in the 2040s and 2% in the 2090s under RCP8.5. The future temperature is also projected to increase and consequently lead to earlier snowmelt, resulting in a shift in the seasonal runoff peak to earlier months in the UKRB. However, the shifts in the timing of runoff could lead to significant impacts on water availability and exacerbate the water stress in this region, decreasing in summer runoff and increasing in the winter and spring runoffs. The future annual evapotranspiration is projected to increase under both scenarios; however, decreases in annual snowfall, snowmelt, sublimation, and groundwater recharge are predicted in the UKRB. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) that produce good outputs in one region or for specific variables may underperform for others. Thereby, assessing the performance of various model simulations and their corresponding mean ensemble is critical in identifying the most suitable models. In this regard, a study was conducted to evaluate the performance of ten RCMs against observations from multiple ground-based stations in the East African Transboundary Omo Gibe River Basin, Ethiopia, during the baseline period of 1986–2005. The study evaluated the models' ability to replicate various aspects of climatic variables and their corresponding statistical indicators. The results confirmed that RCMs have varying abilities to reproduce climatic conditions across the basin. The ensembles and RACMO22T (EC-EARTH) were better at replicating the average annual precipitation distribution. Meanwhile, the CCLM4-8-17 (MPI) together with the ensembles better captured the measured precipitation annually, despite the discrepancies in the actual magnitudes. All RCMs were able to simulate the seasonal precipitation patterns effectively, with RACMO22T (EC-EARTH), CCLM4-8-17 (CNRM), RCA4 (CNRM), CCLM4-8-17 (MPI), and REMO2009 (MPI) models captured superior, excluding the maximum value. Interannual and seasonal rainfall pattern variations were more significant than variations in air temperature. Additionally, a better correlation was observed between actual and simulated precipitation at multiple separate monitoring places. The RCA4 (MPI) and CCLM4-8-17 (MPI) demonstrated reasonable minimum and maximum temperatures. The RCA4 (MIROC5) model was more effective in reproducing extreme precipitation events. However, all RCMs and their ensembles tended to overestimate the return periods of these events. In general, the research highlights the importance of selecting reliable RCMs that better replicate observed climatic settings and employing the ensemble mean of top-performing models following systematic bias adjustment for a specific application.

This study evaluates the performance of four bias correction methods based on CORDEX (coordinated regional climate downscaling experiment) domain six regional climate models (RCMs) at the Mara River Basin. A suitable bias correction method was considered to develop the future climate scenario. The performance of bias correction methods was evaluated by various statistical metrics based on the historical period and revealed that the distribution mapping (DM) techniques have strong performance under the different climatic conditions. The effectiveness of the DM method is found to be better at capturing the coefficient of variation and standard deviation of observed rainfall and temperature. Therefore, this study considers the future climate (2026–2095) from bias-corrected RCMs output using DM techniques. The results from bias-adjusted RCMs show an increase of rainfall (+118.3%) and temperature (+2.91) in the future climate under Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5. In addition, this study tested the random forest (RF) method to determine the capacity of each bias-corrected RCMs for reproducing the future rainfall and temperature under the RCP 4.5 and RCP 8.5 scenario. The results demonstrate that the RF can reproduce the climate variable with its average correlation (R2) of 0.93 for rainfall and 0.95 for temperature. HIGHLIGHTS The performance of four bias correction methods and climate simulation of six CORDEX Africa regional climate models (RCMs) was evaluated.; The statistical matrix showed that the distribution mapping (DM) method results were better when compared to the other methods.; The random forest (RF) model was performed for reproducing the climate scenario from the RCMs.; It was found that the ensemble of all RCMs showed better results for the future climate scenario.;

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Precipitation is one of the key input data for hydrological modelling and water resources assessment. Climate projections are regionally available at a finer resolution, but it is still necessary to validate such data against the observations before any application. In this study the precipitation data available from the Coordinated Regional Downscaling Experiment (CORDEX) program of the South Asian domain was examined against APHRODITE (Asian Precipitation – Highly-Resolved Observational Data Integration Towards Evaluation) precipitation, which is used as observations in the absence of in situ measurements for the data-scarce Kabul basin in Afghanistan. The evaluation methods include verification statistics as well as visual verification by mapping the data over the basin. The evaluation results showed that some of the RCMs (Regional Climate Models) are following an entirely different precipitation pattern both temporally and spatially; however, their driving GCMs might be the same. Six of the 17 RCMs examined in this study performed well for the Kabul basin. The ensemble results of these selected RCMs suggest a decreasing trend in the future precipitation under both RCPs (Representative Concentration Pathways), with variations ranging from −14% to +6% under RCP 4.5 and −17% to +8% under RCP 8.5 for most areas, especially in the central parts of the basin.

A climate model is essential for hydrological designs considering climate change, but there are still limitations in employing raw temporal and spatial resolutions for small urban areas. To solve the temporal scale gap, a temporal disaggregation method of rainfall data was developed based on the Neyman–Scott Rectangular Pulse Model, a stochastic rainfall model, and future design rainfall was projected. The developed method showed better performance than the benchmark models. It produced promising results in estimating the rainfall quantiles for recurrence intervals of less than 20 years. Overall, the analysis results imply that extreme rainfall events may increase. Structural/nonstructural measures are urgently needed for irrigation and the embankment of new water resources. [ABSTRACT FROM AUTHOR]

Currently, studies analysing the relationship between climatic factors and vegetation may present some problems, including considering insufficient spatiotemporal characteristics, contradictory results and deficiencies in reflecting vegetation changes in the future. To address this, we first analysed the spatiotemporal characteristics of the impact of climatic factors on the normalized difference vegetation index (NDVI) using a multiple regression model constructed from the perspective of water deficit. Second, the two carbon emission scenarios provided by Coupled Model Inter-comparison Project phase 5 (CMIP5) were combined to predict the future vegetation conditions in China. Finally, trend analysis, Sen's slope and cycle analysis methods were employed to explore the trend of the NDVI and its spatiotemporal characteristics in the arid and semiarid regions of China. The results show that the impact of climate change on vegetation possesses distinct spatiotemporal characteristics, and the influences are different depending on the month and location. Vegetation has greened in most arid and semiarid areas of China during historical periods. In the future, NDVI variation will show a fluctuating tendency, and increasing and declining trends for the NDVI will alternately occur monthly. Although both precipitation and temperature will increase in the future, spring vegetation will be threatened by stress in some areas due to severe drought caused by the increase in evaporation, which is caused by increasing temperature. The impact of climatic factors on the amplitude and cycle of vegetation type zones varies monthly. The impact of carbon concentration on the NDVI in vegetation type zones was not significant but was relatively large in some areas. The influence of the carbon concentration on the NDVI cycle is different each month. Carbon concentration basically has no effect on amplitude.

Abstract Drought is one of the most widespread and destructive hazards over the Loess Plateau (LP) of China. Due to climate change, extremely high temperature accompanied with drought (expressed as hot drought) may lead to intensive losses of both properties and human deaths in future. A hot drought probabilistic recognition system is developed to investigate how potential future climate changes will impact the simultaneous occurrence of drought and hot extremes (hot days exceeding certain values) on the LP. Two regional climate models, coupled with multiple bias‐correction techniques and multivariate probabilistic inference, are innovative integrated into the hot drought probabilistic recognition system to reveal the concurrence risk of droughts and hot extremes under different Representative Concentration Pathway (RCP) scenarios. The hot‐day index, TX90p, indicating the number of days with daily maximum temperature (Tmax) exceeding the 90th percentile threshold, and the Standardized Precipitation Index are applied to identify the joint risks on the LP using copula‐based methods. The results show that precipitation will increase throughout most of the LP under both RCP4.5 and RCP8.5 scenarios of 2036–2095, while Tmax may increase significantly all over the LP (1.8–2.7 °C for RCP4.5 and 2.7–3.6 °C for RCP8.5). The joint return periods of Standardized Precipitation Index and TX90p show that fewer stations will experience severe drought with long‐term hot extremes in two future scenarios. However, some stations may experience hot droughts that are more frequent and extreme, particularly certain stations in the southwest and south‐central regions of the LP with recurrence period less than 10 years.

Evidence revealed that climate change has a significant impact on grain production in China. Northeast China has abundant agricultural resources which can make the maximum contribution to national food security. This study examines the effects of climate variability and price anomalies on grain yield and land use in Northeast China. The analysis showed that different climate variability phase combinations based on Pacific Decadal Oscillation and North Atlantic Oscillation present variations in signals and different magnitude of effects over the study area. The results revealed that land use by total grain crop negatively responds to the increase in price anomalies in Heilongjiang and Jilin Provinces. To assess the impact of climate change on crop yield model, the yield models under dynamically downscaled regional climate models revealed that climate variables significantly contribute to total grain yields. In the near future, minimum temperature (− 0.26 °C under CanESM2-4.5, − 4.42 °C under HadGEM2-ES), maximum temperature (− 2.82 °C under CanESM2-4.5, − 0.84 under HadGEM2-ES), and precipitation (ranged from 3.59 to 11.10%) positively contribute to total grain yields under both models. Overall, analysis showed that climate change has a significant contribution to grain production. In conclusion, the implications for future research and policymakers have been addressed. Particularly, the importance of considering regional differences in adaptation planning in agricultural regions was also considered. [ABSTRACT FROM AUTHOR]

Two long continuous regional climate simulations over China have been carried out using the Regional Climate Model version 4 (RegCM4) and Weather Research and Forecasting model (WRF). The Simulations are forced by NCEP Reanalysis II data with a horizontal grid spacing of 30 km during 1981–2010. It is demonstrated that the RegCM4 and WRF had pronounced temperature and precipitation downscaling ability, producing more regional details and smaller biases than the driving R2. Overall, WRF tended to better capture the temperature and precipitation pattern and magnitude, daily temperature frequency, the monsoon rain belt movement, and seasonal precipitation variations over most wet regions, while larger deficits than RegCM4 were shown over some dry regions. The extreme precipitation indices of the two RCMs were quite different, with strong regional and seasonal dependence. WRF was better at simulating the annual mean temperature and precipitation trends, with higher spatial pattern correlations. The optimal ensemble approach combining the advantages of RegCM4 and WRF showed improved simulation compared to the individual models. The optimal ensemble reduced the annual temperature biases from the two models by 15–30%, and increased the precipitation spatial pattern correlations by 0.08–0.13. Further works are needed to improve the performances of the ensemble approach by using more RCMs. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) are crucial for climate studies and may be an alternative source of meteorological data in data-scarce regions. However, the effectiveness of the numerical weather prediction (NWP) models applied in RCMs is hampered by the parameterization of unresolved physical processes in the model. A major source of uncertainties in NWP models is the parameterization of the planetary boundary layer (PBL). This study evaluates the influence of seven PBL parameterization schemes in the Weather Research and Forecasting (WRF) model on the retrieval of four meteorological variables over the Kenyan highlands. The seven PBL schemes consist of four local schemes: the Mellor-Yamada-Janjic (MYJ), Mellor-Yamada-Nakanishi-Niino (MYNN), Bougeault-Lacarrere (BouLac), quasinormal scale elimination (QNSE), and three nonlocal schemes: asymmetrical convective model version 2 (ACM2), Shin and Hong (SHIN) and Yonsei University (YSU). The forcing data for the WRF model was obtained from the fifth generation of the European ReAnalysis (ERA5) dataset. The results were validated against observational data from the Trans-African Hydro-Meteorological Observatory (TAHMO). WRF was found to simulate surface meteorological variables with spatial details coherent with the complex topography within the Kenyan highlands, irrespective of the PBL scheme. A comparison between 2-meter temperature (T2) derived from the YSU scheme and T2 from the land component of ERA5 (ERA5-Land) indicates that surface meteorological variables derived from WRF are better suited for applications over the Kenyan highlands. The choice of the PBL scheme was found to primarily influence the simulation of the 10-meter wind speed (WS10) and rainfall as opposed to T2 and the 2-meter relative humidity (RH2). The insensitivity of the 2-meter variables to the choice of the PBL scheme is attributed to the influence of the surface layer parameterization near the surface. Results from the rainfall simulation indicate that the YSU scheme provides a more realistic depiction of PBL dynamics within the study area. Hence, the YSU scheme is best suited for simulating surface meteorological variables over the Kenyan highlands.

Fifth-generation district heating and cooling (5th DHC) systems offer promising approaches to decarbonizing space heating, cooling and domestic hot water supply. By using these systems, clustered buildings combined with industrial waste heat can achieve a net-zero energy balance on a variety of time scales. Thanks to the low exergy approach, these systems are highly efficient. As part of the Smart Anergy Quarter Baden (SANBA) project, the thermal energy grid simulation tool TEGSim has been further developed and used to design an ultra-low-temperature district heating (ULTDH) network with hydraulic and thermal components fitted to the specific regional characteristics of the investigated case. Borehole thermal energy storage (BTES) used as seasonal storage ensures long-term feasibility. The annual discrepancy of input of thermal energy provided by space cooling and output of energy demanded by space heating and domestic hot water is supplied by an external low-grade industrial waste heat source. This paper presents the functionality of the simulation and shows how to interpret the findings concerning the design of all components and their interplay, energy consumption and efficiencies.

We employ a large ensemble of Regional Climate Models (RCMs) from the COordinated Regional-climate Downscaling EXperiment to explore two questions: (1) what can we know about the future precipitation characteristics over Africa? and (2) does this information differ from that derived from the driving Global Climate Models (GCMs)? By taking into account both the statistical significance of the change and the models' agreement on its sign, we identify regions where the projected climate change signal is robust, suggesting confidence that the precipitation characteristics will change, and those where changes in the precipitation statistics are non-significant. Results show that, when spatially averaged, the RCMs median change is usually in agreement with that of the GCMs ensemble: even though the change in seasonal mean precipitation may differ, in some cases, other precipitation characteristics (e.g., intensity, frequency, and duration of dry and wet spells) show the same tendency. When the robust change (i.e., the value of the change averaged only over the land points where it is robust) is compared between the GCMs and RCMs, similarities are striking, indicating that, although with some uncertainty on the geographical extent, GCMs and RCMs project a consistent future. Potential added value of downscaling future climate projections (i.e., non-negligible fine-scale information that is absent in the lower resolution simulations) is found for instance over the Ethiopian highlands, where the RCM ensemble shows a robust decrease in mean precipitation in contrast with the GCMs results. This discrepancy may be associated with the better representation of topographical details that are missing in the large scale GCMs. The impact of the heterogeneity of the GCM–RCM matrix on the results has been also investigated; we found that, for most regions and indices, where results are robust or non-significant, they are so independently on the choice of the RCM or GCM. However, there are cases, especially over Central Africa and parts of West Africa, where results are uncertain, i.e. most of the RCMs project a statistically significant change but they do not agree on its sign. In these cases, especially where results are clearly clustered according to the RCM, there is not a simple way of subsampling the model ensemble in order to reduce the uncertainty or to infer a more robust result. [ABSTRACT FROM AUTHOR]

Drought is one of the most widespread and destructive hazards over the Loess Plateau (LP) of China. Due to climate change, extremely high temperature accompanied with drought (expressed as hot drought) may lead to intensive losses of both properties and human deaths in future. A hot drought probabilistic recognition system is developed to investigate how potential future climate changes will impact the simultaneous occurrence of drought and hot extremes (hot days exceeding certain values) on the LP. Two regional climate models, coupled with multiple bias‐correction techniques and multivariate probabilistic inference, are innovative integrated into the hot drought probabilistic recognition system to reveal the concurrence risk of droughts and hot extremes under different Representative Concentration Pathway (RCP) scenarios. The hot‐day index, TX90p, indicating the number of days with daily maximum temperature (Tmax) exceeding the 90th percentile threshold, and the Standardized Precipitation Index are applied to identify the joint risks on the LP using copula‐based methods. The results show that precipitation will increase throughout most of the LP under both RCP4.5 and RCP8.5 scenarios of 2036–2095, while Tmax may increase significantly all over the LP (1.8–2.7 °C for RCP4.5 and 2.7–3.6 °C for RCP8.5). The joint return periods of Standardized Precipitation Index and TX90p show that fewer stations will experience severe drought with long‐term hot extremes in two future scenarios. However, some stations may experience hot droughts that are more frequent and extreme, particularly certain stations in the southwest and south‐central regions of the LP with recurrence period less than 10 years. Key Points: A modeling system is developed to investigate future characteristics of simultaneous occurrence of drought and hot extremesPrecipitation projections from PRICIS and RegCM models were jointly corrected by the multidimensional copula modelResults show that some stations in partial area of Loess Plateau may experience hot droughts that are more frequent and extreme in future [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) serve as an important tool to perform high resolution climate simulations for a limited area. However, there remains a crucial issue regarding the efficacy of RCMs that whether they are able to produce added value (AV) in representing the regional climatic features compared to their coarse scale driving Global climate models (GCMs). The present study aims at assessing the AV of a set of RCM simulations performed under the Coordinated Regional Climate Downscaling Experiments‐South Asia (CORDEX‐SA) compared with their respective driving GCMs in representing the Indian summer monsoon (ISM) precipitation and its dynamical features. For this purpose, the performance of RCMs and GCMs is compared for the present day climate (1970–2005) against observations and reanalysis datasets in terms of various ISM features, that is, the spatial distribution of precipitation, the evolution of vertical shear of zonal wind and the onset phase of ISM in terms of delayed or early arrival of monsoon. The RCMs show a clear improvement upon their corresponding driving GCMs in simulating spatial features of precipitation distribution that are characteristically associated with ISM. Regionally, the RCMs also show skill in capturing the precipitation amount as a reduction in bias is found compared with their driving GCMs. There is a significant improvement in the simulation of onset dates of ISM by only some of the experiments particularly, RegCM4 driven with IPSL‐CM5A‐LR. The study concludes that the present set of CORDEX‐SA RCM simulations do indeed add value to their driving GCMs for a number of features associated with the ISM precipitation but this value varies with region, driving GCM and the particular feature of ISM under focus. Simulated climatology (1970–2005) of JJAS mean precipitation (mm day−1) by (A) IMD observation, (a1–l1) GCMs and their corresponding (a2–l2) CORDEX‐SA RCMs grouped together in a box. The orographic feature in spatial variability of ISM is evident in IMD—a characteristic which seems to be better captured here by RCMs as compared to their parent GCMs. [ABSTRACT FROM AUTHOR]

Regional climate model (RCM) simulations are being increasingly used for climate change impact assessments, but their application is challenging due to considerable biases inherited from global climate model (GCM) simulations and generated from dynamical downscaling processes. This study assesses the biases in NARCliM (NSW and ACT regional climate modelling) simulations and quantifies the consequence of the climate biases in the downstream assessment of climate change impact on wheat crop system, using the Agricultural Production System sIMulator (APSIM). Results showed that post‐processing bias‐corrected temperature and rainfall data from NARCliM had small annual mean biases but large biases in the crop growing season (CGS). During the CGS, the mean bias error of rainfall was generally positive for rainfall probability and negative for intensity, which subsequently resulted in APSIM simulating negative biases for runoff and deep drainage and positive bias in soil evaporation. Bias in soil water balance and water availability resulted in less plant transpiration and less N uptake, ultimately, leading to large negative biases in crop yields. A simple bias correction of the simulated crop yield driven by RCMs could result in a largely consistent distribution with those generated with APSIM simulations forced by observed climate. Our results showed that RCM simulation biases could confound with the climate change signal and produced an unreliable estimate of the effects of the changes in climate and farm management variables on crop yields. The results suggested that RCM simulations with the current bias correction on the RCM‐simulated outputs applied on an annual basis were inadequate for climate change assessments which involve biophysical models. Our study highlights the need for improved RCM simulations by eliminating the systemic biases associated with rainfall characteristics, although suitable post‐processing bias correction on a seasonal or monthly basis may result in improved RCM simulations for agricultural impacts of climate change. RCM simulations with post‐processing bias correction of RCM outputs on an annual basis were inadequate for climate change assessments with biophysical models. Our study highlights the need for improved RCM simulations by eliminating the systemic biases associated with rainfall characteristics, although suitable post‐processing bias correction on a seasonal or monthly basis may result in improved RCM simulations for agricultural impact analysis of climate change. [ABSTRACT FROM AUTHOR]

The ability of an ensemble of five regional climate models (hereafter RCMs) under Coordinated Regional Climate Downscaling Experiments-South Asia (hereafter, CORDEX-SA) in simulating the key features of present day near surface mean air temperature (Tmean) climatology (1970-2005) over the Himalayan region is studied. The purpose of this paper is to understand the consistency in the performance of models across the ensemble, space and seasons. For this a number of statistical measures like trend, correlation, variance, probability distribution function etc. are applied to evaluate the performance of models against observation and simultaneously the underlying uncertainties between them for four different seasons. The most evident finding from the study is the presence of a large cold bias (−6 to −8 °C) which is systematically seen across all the models and across space and time over the Himalayan region. However, these RCMs with its fine resolution perform extremely well in capturing the spatial distribution of the temperature features as indicated by a consistently high spatial correlation (greater than 0.9) with the observation in all seasons. In spite of underestimation in simulated temperature and general intensification of cold bias with increasing elevation the models show a greater rate of warming than the observation throughout entire altitudinal stretch of study region. During winter, the simulated rate of warming gets even higher at high altitudes. Moreover, a seasonal response of model performance and its spatial variability to elevation is found. [ABSTRACT FROM AUTHOR]

An assessment of the projection of near surface air temperature over the Himalayan region from the COordinated Regional Climate Downscaling EXperiment- South Asia (hereafter, CORDEX-SA) regional climate model (RCM) experiments have been carried out for different Representative Concentration Pathway (RCP) scenarios. The purpose of this study is to assess the probable future changes in the mean temperature climatology and its long term trend for different seasons under greenhouse gas forcing scenarios for different seasons till the end of 21st century. A number of statistical measures such as changes in mean climatology, long term trend and probability distribution function have been used in order to detect the signals of changes in climate. Moreover, the associated uncertainties among different model experiments and their ensemble in space, time and different seasons in particular have been quantified. Despite of strong cold bias in the model experiments over Himalayan region (Nengker et al., 2017), statistically significant strong rate of warming (0.03–0.09 °C/year) across all the seasons and RCPs have been projected by all the models and their ensemble. Season specific response towards the warming is indicated by ensemble under future climate while ON season shows comparable magnitude of warming than DJF. Such warming intensifies with the increase in the radiative forcing under a range of greenhouse gas scenarios from RCP2.6 to RCP8.5. In addition to this, a wide range of spatial variability and disagreements in the trend magnitude between different models describes the uncertainty associated with the model projections and scenarios. A substantial seasonal response to warming with respect to elevation was also found, as DJF season followed by ON portrays highest rate of warming, specifically at higher elevation sites such as western Himalayas and northern part of central Himalayas. The different elevation classes respond differently to the projected future warming under different RCPs and seasons. Such higher warming during DJF may have consequences as changes in the fractional distribution of the solid and liquid precipitation as well as the melting of the glacial deposits with ultimately affecting the streamflow response and water resources in the downstream areas. [ABSTRACT FROM AUTHOR]

An assessment of the projection of minimum and maximum air temperature over the Indian Himalayan region (IHR) from the COordinated Regional Climate Downscaling EXperiment- South Asia (hereafter, CORDEX-SA) regional climate model (RCM) experiments have been carried out under two different Representative Concentration Pathway (RCP) scenarios. The major aim of this study is to assess the probable future changes in the minimum and maximum climatology and its long-term trend under different RCPs along with the elevation dependent warming over the IHR. A number of statistical analysis such as changes in mean climatology, long-term spatial trend and probability distribution function are carried out to detect the signals of changes in climate. The study also tries to quantify the uncertainties associated with different model experiments and their ensemble in space, time and for different seasons. The model experiments and their ensemble show prominent cold bias over Himalayas for present climate. However, statistically significant higher warming rate (0.23–0.52 °C/decade) for both minimum and maximum air temperature (T min and T max ) is observed for all the seasons under both RCPs. The rate of warming intensifies with the increase in the radiative forcing under a range of greenhouse gas scenarios starting from RCP4.5 to RCP8.5. In addition to this, a wide range of spatial variability and disagreements in the magnitude of trend between different models describes the uncertainty associated with the model projections and scenarios. The projected rate of increase of T min may destabilize the snow formation at the higher altitudes in the northern and western parts of Himalayan region, while rising trend of T max over southern flank may effectively melt more snow cover. Such combined effect of rising trend of T min and T max may pose a potential threat to the glacial deposits. The overall trend of Diurnal temperature range (DTR) portrays increasing trend across entire area with highest magnitude under RCP8.5. This higher rate of increase is imparted from the predominant rise of T max as compared to T min . [ABSTRACT FROM AUTHOR]

It is highly probable that the precipitation and temperature changes induced by global warming projected for the 21st century will affect the regime of Carpathian Basin rivers, e.g. that of River Maros. As the river is an exceptionally important natural resource both in Hungary and Romania it is necessary to outline future processes and tendencies concerning its high and low water hydrology in order to carry out sustainable cross-border river management. The analyses were based on regional climate models (ALADIN and REMO) using the SRES A1B scenario. The modelled data had a daily temporal resolution and a 25 km spatial resolution, therefore beside catchment scale annual changes it was also possible to assess seasonal and spatial patterns for the modelled intervals (2021- 2050 and 2071-2010). Those periods of the year are studied in more detail which have a significant role in the regime of the river. The study emphasizes a decrease in winter snow reserves and an earlier start of the melting period, which suggest decreasing spring flood levels, but also a temporally more extensive flood season. Changes in early summer precipitation are ambiguous, and therefore no or only slight changes in runoff can be expected for this period. Nevertheless, it seems highly probable that during the summer and especially the early autumn period a steadily intensifying water shortage can be expected. The regime of the river is also greatly affected by human structures (dams and reservoirs) which make future, more detailed modelling a challenge.

Skills in reproducing monthly rainfall over Calabria (southern Italy) have been validated for the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) satellite data, the E-OBS dataset and 13 Global Climate Model-Regional Climate Model (GCM-RCM) combinations, belonging to the ENSEMBLES project output set. To this aim, 73 rainfall series for the period 1951−1980 and 79 series for the period 1981−2010 have been selected from the database managed by Multi-Risk Functional Centre of the Regional Agency for Environmental Protection (Regione Calabria). The relative mean and standard deviation errors, and the Pearson correlation coefficient have been used as validation metrics. Results showed that CHIRPS satellite data (available only for the 1981−2010 validation period) and RCMs based on the ECHAM5 Global Climate performed better both in mean error and standard deviation error compared to other datasets. Moreover, a slight appreciable improvement in performance for all ECHAM5-based models and for the E-OBS dataset has been observed in the 1981−2010 time-period. The whole validation-and-assessment procedure applied in this work is general and easily applicable where ground data and gridded data are available. This procedure might help scientists and policy makers to select among available datasets those best suited for further applications, even in regions with complex orography and an inadequate amount of representative stations.

The performance of eight National Center for Environmental Prediction (NCEP2) reanalysis-driven regional climate models (RCMs), seven from the North American Regional Climate Change Program (NARCCAP) and one from the Coordinated Regional Downscaling Experiment (CORDEX), in simulating the 1980_2004 climate of western Canada was assessed at a number of spatial and temporal scales. Results indicated that the RCMs were more successful at capturing the seasonal spatial distribution of mean temperature than precipitation and that inaccuracies in the spatial distribution of the summer climate moisture index were likely due to the errors in precipitation distribution and amount. All RCMs performed less well in simulating summer precipitation, most likely due to continued problems with the simulation of convective precipitation. At the grid box scale, quantile_quantile (q_q) plots for temperature indicated that all RCMs showed very similar distributions to observed but with warm or cold biases, and errors in the simulation of a number of temperature-based extremes indices were related to these biases. For precipitation, q_q plots indicated that most RCMs overestimated precipitation totals, and while tending to follow the observed quantiles at smaller precipitation amounts, they diverged at larger precipitation totals. Performance in simulating the precipitation-based extremes indices depended largely on whether or not a RCM over-or under-estimated precipitation totals _ with those RCMs simulating too much precipitation underestimating the number of consecutive dry days and dry day persistence, and vice versa. Despite improvements in RCM resolution and parameterisation schemes, this work indicates that the simulation of precipitation in particular is still problematic in western Canada. This implies that scenarios of climate change constructed from RCM output require some form of bias correction to be of most use in impacts studies. [ABSTRACT FROM AUTHOR]

Mesoscale regional climate models (RCMs), the primary tool for climate predictions, have recently increased in sophistication and are being run at increasingly higher resolutions to be also used in climate impact studies on ecosystems, particularly in agricultural crops. As satellite remote sensing observations of the earth terrestrial surface become available for assimilation in RCMs, it is possible to incorporate complex land surface processes, such as dynamics of state variables for hydrologic, agricultural and ecologic systems at the smaller scales. This study focuses on parameterization of vegetation characteristics specifically designed for high resolution RCM applications using various remote sensing products, such as Advanced Very High Resolution Radiometer (AVHRR), Système Pour l’Observation de la Terre-VEGETATION (SPOT-VGT) and Moderate Resolution Imaging Spectroradiometer (MODIS). The primary vegetative parameters, such as land surface characteristics (LCC), fractional vegetation cover (FVC), leaf area index (LAI) and surface albedo localization factors (SALF), are currently presented over the Nakdong River Watershed domain, Korea, based on 1-km remote sensing satellite data by using the Geographic Information System (GIS) software application tools. For future high resolution RCM modeling efforts on climate-crop interactions, this study has constructed the deriving parameters, such as FVC and SALF, following the existing methods and proposed the new interpolation methods to fill missing data with combining the regression equation and the time series trend function for time-variant parameters, such as LAI and NDVI data at 1-km scale.

ABSTRACT In this study, version 4 of the International Centre for Theoretical Physics (ICTP) regional climate model named RegCM4 is used to perform 6 years simulation including 1 year for spin-up (from January 2001 to December 2006) over Central Africa using four convective schemes: The Emmanuel scheme ( MIT), the Grell scheme with Arakawa-Schulbert closure assumption ( GAS), the Grell scheme with Fritsch-Chappell closure assumption ( GFC) and the Anthes-Kuo scheme (Kuo). We have investigated the ability of the model to simulate precipitation, surface temperature, wind and aerosols optical depth. Emphasis in the model results were made in December-January-February ( DJF) and July-August-September ( JAS) periods. Two subregions have been identified for more specific analysis namely: zone 1 which corresponds to the sahel region mainly classified as desert and steppe and zone 2 which is a region spanning the tropical rain forest and is characterized by a bimodal rain regime. We found that MIT scheme shows a better index of agreement in temperature, but it generally has a tendency to an overestimation of other simulated parameters. Overall, GAS scheme is more suitable to downscale the aforementioned parameters except temperature, as well as the diurnal cycle of precipitation everywhere over the study domain irrespective of the seasons. In JAS, model results are similar in the representation of regional wind circulation. Apart from the MIT scheme, all the convective schemes give the same tendencies in aerosols optical depth simulations. Additional experiment reveals that the use of BATS instead of Zeng scheme to calculate ocean flux appears to improve the quality of the model simulations. [ABSTRACT FROM AUTHOR]

Ignition delay times of four ternary blends of n -heptane/ iso -octane/toluene, referred to as Toluene Primary Reference Fuels (TPRFs), have been measured in a high-pressure shock tube and in a rapid compression machine. The TPRFs were formulated to match the research octane number (RON) and motor octane number (MON) of two high-octane gasolines and two prospective low-octane naphtha fuels. The experiments were carried out over a wide range of temperatures (650–1250 K), at pressures of 10, 20 and 40 bar, and at equivalence ratios of 0.5 and 1.0. It was observed that the ignition delay times of these TPRFs exhibit negligible octane dependence at high temperatures ( T > 1000 K), weak octane dependence at low temperatures ( T < 700 K), and strong octane dependence in the negative temperature coefficient (NTC) regime. A detailed chemical kinetic model was used to simulate and interpret the measured data. It was shown that the kinetic model requires general improvements to better predict low-temperature conditions and particularly requires improvements for high sensitivity (high toluene concentration) TPRF blends. These datasets will serve as important benchmark for future gasoline surrogate mechanism development and validation. [ABSTRACT FROM AUTHOR]

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Highlights: [•] We have successfully downscaled RCM rainfall to gauge scale recapturing statistics. [•] We devised novel techniques for recorrelation and quantile transformation of data. [•] We have recorrelated downscaled gauge rainfall to match observed correlations. [•] We show that recorrelated gauge values recapture large-scale spatial correlations. [•] We validated downscaled RCM rainfall at gauge sites over five regions in South Africa. [ABSTRACT FROM AUTHOR]

The present study is aimed to: (a) project future low flow conditions in the five largest river basins in Germany, and (b) to account for the projections uncertainties. The eco-hydrological model SWIM was driven by different regional climate models (REMO, CCLM, and Wettreg) to simulate daily river discharges in each study basin. The 50-year low flow was estimated for the period 1961 to 2000, and its return period was assessed for two scenario periods, 2021-2060 and 2061-2100, using the generalized extreme value distribution. The 50-year low flow is likely to occur more frequently in western, southern, and parts of central Germany after 2061, as suggested by more than or equal to 80% of the model runs. The current low flow period (from August to September) may be extended until late autumn at the end of this century. The return period of 50-year deficit volume shows a similar temporal and spatial pattern of change as for the low flow, indicating slightly less severe conditions with lower confidence. When compared with flood projections for the same area using the same models, the severer low flows projected in this study appear more pronounced, consistent, and have lower uncertainty. [ABSTRACT FROM AUTHOR]

Abstract: Downscaled global climate models can be used to investigate the regional impacts of future emission scenarios. The downscaled models can be validated by how well they can reproduce past, observed climate. This paper investigates the possible future climate of Lake Kariba in Northwestern Zimbabwe under the Intergovernmental Panel on Climate Change''s (IPCC) A2 and B1 emission scenarios for the near (2046–2065) and far future (2081–2100). Nine models were employed and all reflected past climate (1961–2009) well, accurately reproducing the annual cycle and capturing the unimodal annual rainy season and temperatures summer maxima and winter minima. The models project a drier climate with an average decrease in rainfall ranging from 3% to 27.46% with the exception of increased rainfall around the early rainy period (October–November) under A2 and in March under both scenarios for both the near and far future periods. Models predict that both minimum and maximum temperatures will increase by an average of 3.17–3.42°C. A polynomial (order 3) model was used to investigate the implications of the future climate projections on the sardine fish Limnothrissa miodon (Kapenta) production based on the median of all nine models for future climate. If all conditions remain the same the Kapenta fish production in Lake Kariba could continue to decline. Options and strategies for fishery management that can boost Kapenta fish production are suggested in the wake of such possible, detrimental future climate changes. [Copyright &y& Elsevier]

The article focuses on the analysis of the skills of seven regional climate models (RCM) in the reproduction of mean daily temperatures for the Central England region. It notes that three airflow indices were used during the examination of RCM's ability to reproduce. Comparison among the frequency distribution of each index, the relationships between the daily airflows indices and temperature, as well as the ability of models to reproduce is discussed.

The present paper analyses and quantifies the sensitivity of snowpack to climate change, and assesses implications for snow processes in the central Pyrenees under the climatic conditions projected by a set of 9 regional climate models (RCMs) for the end of the 21st century, tinder the IPCC emission scenario SRES A2. The methodology was based on comparison of the snow series obtained by simulating the energy balance of a snow surface driven by climatic conditions recorded during the period from 1996 to 2006, with snowy series obtained by simulations for the same period that included various changes in magnitude for each of the climatic drivers (Sensitivity analysis); and the changes projected by the RCMs to the end of the 21st century. Results showed a marked sensitivity of snowpack duration and thickness to shifts in temperature, precipitation and solar radiation. RCMs suggest that the most significant changes expected in the study area are related to temperature, which is the main parameter responsible for the predicted changes in future snow processes. A large coherence was found among the simulations made using the projections of the 9 regional climate models. Comparison with respect to current conditions indicated a decrease of 50 to 60% in maximum snow water equivalent, the occurrence at least 1 mo earlier of the maximum snow water equivalent, and a reduction in the duration of the snowpack by around 2 mo. In addition, the 3 snow parameters will be subject to a marked increase in inter-annual variability compared to the observed conditions. [ABSTRACT FROM AUTHOR]

In this study, output of the Hadley Centre Regional Circulation Model (RCM) (HadRM3P, 0.44° × 0.44° resolution) was used as input to the Canadian Forest Fire Weather Index (FWI) for the present and 2 future IPCC climate scenarios (Special Report on Emissions Scenarios [SRES], A2 and B2 scenarios). The aim was to investigate the effects of climate change on fire risk (number of days with fire risk, length of fire risk season, etc.) for the EU Mediterranean countries. Results indicated a general increase in fire risk in both future scenarios over the whole study area, The increase in fire risk was mainly due to 3 components: (1) increase in the number of years with fire risk; (2) increase in the length of the season with fife risk; (3) increase of extreme events (e.g. total number of days with FWI >45 and episodes with FWI >45 for 7 consecutive days) during the fire season. As expected, A2 scenario showed a greater increase in risk than B2 scenario. These general increases in fire risk may have a very strong impact in areas where forest land cover is high (e.g. the Alps region in Italy, the Pyrenees in Spain and mountains of the Balkan region). [ABSTRACT FROM AUTHOR]