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Extreme high temperatures are associated with increased incidences of rail buckles. Climate change is predicted to alter the temperature profile in the United Kingdom with extreme high temperatures becoming an increasingly frequent occurrence. The result is that the number of buckles, and therefore delays, expected peryear will increase if the track is maintained to the current standard. This paper uses a combination of analogue techniques and a weather generator to quantify the increase in the number of buckles and rail related delays in the southeast of the United Kingdom. The paper concludes by assigning a cost to the resultant rise in delays and damage before making recommendations on how these effects can be mitigated. Copyright © 2008 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

This paper evaluated and compared the performance of a statistical downscaling method and a dynamical downscaling method to simulate the spatial–temporal rainfall distribution. Outputs from RegCM4 Regional Climate Model (RCM) and the CanESM2 Atmosphere–Ocean General Circulation Model (AOGCM) were selected for the data scarce Gwadar‐Ormara basin, Pakistan. The evaluation was based on the climatological average and standard deviation for historic (1971–2000) and future (2041–2070) time periods under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. The performance evaluation showed that statistical downscaling is preferred to simulate and project rainfall patterns in the study area. Additionally, the Statistical DownScaling Model (SDSM) showed low R2 values in calibration and validation of the simulations with respect to observed data for the historic period. Overall, SDSM generated satisfactory results in simulating the monthly rainfall cycle of the entire basin. In this study, RegCM4 showed large rainfall errors and missed one rainfall season in the historic period. This study also explored whether the grid‐based rainfall time series of the Asian Precipitation—Highly Resolved Observational Daily Integration Towards Evaluation (APHRODITE) dataset could be used to enlarge and complement the sample of in situ observed rainfall time series. A spatial correlogram was used for observed and APHRODITE rainfall data to assess the consistency between the two data sources, which resulted in rejecting APHRODITE data. For the future time period (2041–2070) under RCPs 4.5 and 8.5 scenarios, rainfall projections did not show significant difference for both downscaling approaches. This may relate to the driving model (CanESM2 AOGCM) and not necessarily suggests poor performance of downscaling; either statistical or dynamical. Hence, the study recommends evaluating a multi‐model ensemble including other GCMs and RCMs for the same area of study. [ABSTRACT FROM AUTHOR]

The influence of climate change on forest condition is noticeable. Forest ecosystem stress caused by climate change has already been manifested in several parts of Europe, including Poland. Thus, the main objective of this paper is to investigate for the entire area of Poland a long‐term trend and variability of forest greenness expressed as the Normalized Difference Vegetation Index (NDVI), derived from two decades (2002–2021) of remote sensing Moderate Resolution Imaging Spectroradiometer (MODIS) data. In the next step, selected meteorological elements – temperature (T), precipitation (P) and evapotranspiration (ETo), derived from ERA5‐Land reanalysis—were used to determine the influence of climatic conditions on the variability of NDVI in forests. The study documents the general greening of forests in Poland in 2002–2021. The greening is mostly visible in central‐eastern Poland, where the annual mean NDVI increased by 0.030 in 20 years, while it is weaker in the Baltic coast and in the southern edges of Poland (increase by 0.009 in 20 years). Overall, the positive, statistically significant trends in annual NDVI prevail over the negative, statistically significant trends and account for 32.5% of forest area, whereas the negative trends account for 3.9%. The study indicates an overall moderate impact of meteorological elements on variability of NDVI in forests in Poland. The most important factors affecting forest condition are P and ETo. The strongest correlations between NDVI and P and ETo reach 0.55 and are located in central Poland, in the form of a belt from western to eastern borders. [ABSTRACT FROM AUTHOR]

Extreme rain events have caused numerous issues and have had a significant impact on agriculture, human activities, ecology, infrastructure, and casualties. The theory of extreme values has been widely applied in extreme precipitation modelling and a variety of other fields. This paper employs the generalized Pareto distribution, which has been widely used to analyse extreme climates, in conjunction with the peak over thresholds approach to investigate exceedances. The occurrence of intense rainfall events in Kigali city causes severe damage to human properties, infrastructure damage, people injuries, loss of life, and other various harmful consequences. Early detection of extreme rainfall in Kigali aids in the development and implementation of strategies and measures to mitigate the negative effects of extreme rainfall before it occurs. The aim of this research is to estimate the frequency and magnitude of intense rainfall events in Kigali. The daily rainfall data from Kigali Airport station collected by Rwanda Meteorological Agency from 1990 to 2019 were applied. The results showed that as the return periods increased, so did the return levels, implying that the intensity and frequency of rainfall in Kigali will increase in the future. The model's goodness was tested, and the study suggests a model that has a non‐negative shape parameter (ξ) to be good. The study's findings are extremely important for understanding the occurrence of these events and also serve as a tool for decision‐making and the development of policies aimed at mitigating the effects of such events. [ABSTRACT FROM AUTHOR]

In climate change research, accurate temperature data are often demanded. However, affected by many factors, especially solar radiation, the accuracy of environmental air temperature measurement can be greatly reduced, since there is a difference in temperature between the environmental air and the related temperature measured by the sensor accommodated inside the radiation shield. In the paper, the term “temperature error” refers to the temperature difference described above. To improve the accuracy of the temperature data, a temperature errorcorrection method is proposed. First, a computational fluid dynamics (CFD) method is adopted to quantify the temperature errors accurately. A neural network algorithm is then applied to form a universal correction equation by fitting temperature errors calculated using the CFD method. Finally, to validate the correction equation, field observation experiments are performed. The root mean square error (RMSE) and the mean absolute error (MAE) between the temperature errors obtained experimentally using a sensor inside the DTR503A shield and the corresponding temperature errors determined by using the proposed correction method are 0.043 and 0.038°C, respectively. The RMSE and MAE for the DTR13 radiation shield are 0.049 and 0.044°C, respectively. This method may reduce the error of the temperature data to 0.05°C. If the environmental factors corresponding to the temperature data can be quantified accurately, the factors influencing the temperature error can be added to the correction method continuously. The accuracy of this correction method may be furtherly improved. [ABSTRACT FROM AUTHOR]

Against an academic and policy backdrop of interest in (and concerns about) the issue, this paper draws on a range of academic writing in various disciplines to explore visual strategies of climate change communication. The geographic scope of the investigation is the United Kingdom, with particular attention to recognizable icons of climate change in UK media and the images used in political campaigns. The paper is in two parts. The first part concentrates on various efforts to put a 'face' on the climate change issue, while part two suggests that weather and renewable energy are the dominant alternative motifs. The paper draws a basic distinction between fear-laden representations of climate change and a variety of visual efforts to use so-called inspirational imagery. All of the images reviewed suggest an affirmative answer to the question in the title, there are multiple efforts underway to move beyond polar bears and represent climate change in more creative and meaningful ways. The bigger question addressed is one raised already by photographers as well as academics, i.e. whether documentary photography (rather than particular types of images) is the more fundamental issue. The answer in the paper is that photographs are no different from other visual images in their capacity to draw attention to messages. The challenge is to use visuals creatively, in ways that prompt positive engagement with climate change without enhancing public disengagement and fatalism. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

In an average year (1979–2016), the United States experiences nearly 1,100 tornadoes, which cause a total of 68 fatalities. Annual fatality rates have decreased since the peak in the 1920s, but there is a concern that they could start to rise again with increases in vulnerable populations and the impacts of climate change. It is possible to assess the risk of tornado fatalities using the historical record. However, the rarity of tornadoes and the short period of record may not capture the true risk. One way around this problem is to simulate thousands of years' worth of tornadoes to obtain a broader picture of risk. Previous tornado risk models have distributed tornadoes randomly or used climatology to generate realistic tornado patterns on an annual (or longer) time scale. From an operational standpoint, it would be useful to have a model that distributes tornadoes on a daily time step to enable the forecasting of potential tornado impacts on a given day. The present study introduces one such model that distributes tornadoes using information about the favourability of the atmospheric environment for tornado development: The Tornado Daily Impacts Simulator (TorDIS). The paper demonstrates model utility through 1,000 year simulations over several metropolitan areas and with a comparison between modelled and observed impacts for several high‐impact tornado days. Forecasting potential tornado impacts on a daily time step could allow emergency managers to plan ahead for high‐risk days to prioritize their resources and save lives. [ABSTRACT FROM AUTHOR]

Continuous urban development significantly transforms the ecosystem in a big city. The increasing frequency of heat waves and their influence on the rise in mortality in big cities suggest that the thermal hazard (long‐term occurrence of high air temperature) is one of the key climatic hazards of present times. The global temperature rise, reinforced in urbanized areas by the anthropogenic heat flux, leads to intensified convection processes and increased precipitation, especially torrential rain. One of the most important hydrological hazards in a big conurbation is the urban flood hazard. In this paper the identified climatic hazards occurring in a big city are analysed: the thermal hazard and the urban flood hazard. The areas currently exposed to thermal and urban flood hazards in Warsaw are identified and assessed in terms of the hazard level. The results obtained are verified with the data from meteorological measuring stations (the thermal hazard) and from the Fire Department interventions connected with rainfall and flooding (the urban flood hazard). A map of hydro‐meteorological hazards was created by combining thermal hazard and urban flood hazard maps. The approach combining the exposure to thermal and urban flood hazards, presented in this study, uses widely accessible spatial data and can be applied to any location. It can also play a significant role in assessing the adaptation of urban areas to climate change and be an important source of information on the current exposure to hydro‐meteorological hazards as well as their possible increase. [ABSTRACT FROM AUTHOR]

Quantifying and separating different sources of uncertainty helps to improve the understanding of the projected effects of climate change and can inform decision‐making in adaptation planning. This paper (1) evaluated four process‐based crop models; (2) assessed the effects of climate change on maize yield using climate change outputs from seven global climate models (GCMs) under three representative concentration pathways (RCPs); and (3) disaggregated the contributions of multiple crop models, GCMs and RCPs to overall uncertainty. All four models captured more than 80% of the variation in days to silking, maturity and yield, indicating reasonably reproduced observations. Similarly, the root mean square errors were moderate for days to silking and maturity (fewer than 4 days) and yield (0.5–0.7 t/ha). Overall, the results indicate that the models could assess grain yield at the study sites reasonably well. The results of the multiple models ensemble indicate that the maize yield will decrease by 9–11% with a probability of 72–80% on average during the period 2010–2039 relative to the baseline (1976–2005). The uncertainty in the maize‐yield simulations might arise mostly from the GCM models, followed by the crop models and RCPs, the contribution of which could be neglected relative to the other factors. Therefore, the use of a multiple crop model and a GCM ensemble is advisable in order to account properly for uncertainties in crop assessments. The uncertainty in maize‐yield simulations might arise mostly from global climate models (GCMs), followed by crop models and representative concentration pathways (RCPs), the contribution of which could be neglected relative to the other factors. Therefore, the use of a multiple crop model and a GCM ensemble is advisable in order to account properly for uncertainties in crop assessments. [ABSTRACT FROM AUTHOR]

Climate data are particularly important in Species Distribution Models ( SDMs) that are used for predicting global warming consequences on plant and animal distributions. A number of the plants and animals, especially the endangered species such as the giant panda ( Ailuropoda melanolecua), are limited in their scope of distribution due to climate changes. Thus, predicting the habitat quality distribution under climate change is important for protecting these species. In this paper the existing and potential habitats of the giant pandas are used as the study area, the calculated Habitat Suitability Index ( HSI) maps in 1989 and 2002, and climate change data in 1989, 2002, 2050 and 2099 from the IPCC are used as the data sources. A multivariable linear regression model for mapping HSI is created with the regressive results in 2002 and 1989. The HSIs of the study area in 2050 and 2099 were then mapped with the model. These maps indicate that, from 2002 to 2050, about 2.64% of the unsuitable habitat in the study area will become suitable, while about 1.5% of the suitable habitat will turn into unsuitable habitat. This leads to an increase of the suitable habitat area on the whole from 2050 to 2099: about 3.43% of the unsuitable habitat will become suitable, while about 6.59% of the suitable habitat will turn into unsuitable habitat, which leads to a decrease of the suitable habitat area on the whole. From the suitable habitat distribution, it can be seen that the suitable habitat of giant pandas gradually moves north under projected global climate change. Copyright © 2013 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

ABSTRACT This paper proposes a method for describing the distribution of observed temperatures on any day of the year such that the distribution and summary statistics of interest derived from the distribution vary smoothly through the year. The method removes the noise inherent in calculating summary statistics directly from the data thus easing comparisons of distributions and summary statistics between different periods. The method is demonstrated using daily effective temperatures (DET) derived from observations of temperature and wind speed at De Bilt, Holland. Distributions and summary statistics are obtained from 1985 to 2009 and compared to the period 1904-1984. A two-stage process first obtains parameters of a theoretical probability distribution, in this case the generalized extreme value (GEV) distribution, which describes the distribution of DET on any day of the year. Second, linear models describe seasonal variation in the parameters. Model predictions provide parameters of the GEV distribution, and therefore summary statistics, that vary smoothly through the year. There is evidence of an increasing mean temperature, a decrease in the variability in temperatures mainly in the winter and more positive skew, more warm days, in the summer. In the winter, the 2% point, the value below which 2% of observations are expected to fall, has risen by 1.2 °C, in the summer the 98% point has risen by 0.8 °C. Medians have risen by 1.1 and 0.9 °C in winter and summer, respectively. The method can be used to describe distributions of future climate projections and other climate variables. Further extensions to the methodology are suggested. [ABSTRACT FROM AUTHOR]

We assess the impact of three bias correction approaches on present day means and extremes, and climate change signal, for six climate variables (precipitation, minimum and maximum temperature, radiation, vapour pressure and mean sea level pressure) from dynamically downscaled climate simulations over Queensland, Australia. Results show that all bias‐correction methods are effective at removing systematic model biases, however the results are variable and season‐dependent. Importantly, our results are based on fully independent cross‐validation, an advantage over similar studies. Linear scaling preserves the climate change signals for temperature, while quantile mapping and the distribution‐based transfer function modify the climate change signal and patterns of change. The Perkins score for all the values above the 95th percentile and below the 5th percentile was used to evaluate how well the climate model matches the observational data. Bias correction improved Perkins score for extremes for some variables and seasons. We rank the bias‐correction methods based on the Kling–Gupta efficiency (KGE) score calculated during the validation period. We find that linear scaling and empirical quantile mapping are the best approaches for Queensland for mean climatology. On average, bias correction led to an improvement in the KGE score of 23% annually. However, in terms of extreme values, quantile mapping and statistical distribution‐based transfer function approaches perform best, and linear scaling tends to perform worst. Our results show that, except linear scaling, all approaches impact the climate change signal. [ABSTRACT FROM AUTHOR]

This paper reviews the geo-engineering approach to tackling climate change. The failure of the 15 United Nations Framework Convention on Climate Change Conference of the Parties (COP15) to obtain a legally binding emissions reduction agreement makes the deployment of geo-engineering solutions an increasingly attractive proposition. This review looks at a variety of global and local approaches to geo-engineering covering solar radiation management and carbon cycle engineering and attempts to assess the feasibility of the technologies from an engineering perspective. However, despite the plethora of ideas generated by the science community, it still appears that much work remains to be done in the initial engineering assessment of these techniques and this is a major hurdle to overcome before any geo-engineering scheme can be fully considered. Hence, the paper concludes by calling for the instigation of national and international programmes of research at the feasibility level, to inform discussions regarding future possible deployment of small scale, local geo-engineering and adaptation measures. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

A weatherpatternbased multiple regression model to derive future possible changes in the level of the higher temporal resolution spectrum of heavy precipitation has been developed. The temporal spectrum was described using statistical precipitation amounts as a function of the events duration 1–24 h and return period once in 5 yr to once in 100 yr. The principle of the method consists in projecting a statistical relationship between the parameters of a transformed Gumbel distribution theoretical extreme value distribution and the distribution of classes of objective weather patterns to time slices in the near future of climate. Changes in distribution parameters were constructed in the model from changes in the distribution of weather patterns. Possible change signals were calculated for the catchment of the Weißeritz River Ore Mountains, Germany for the time slices centred around 2025 2011–2040 and 2050 2036–2065 as changes versusthe reanalyses of the reference period 1961–2000 May–September. For the climate conditions to be expected in the near future IPCC A1B scenario, increases in the amounts of heavy precipitation, i.e. decreases in the return periods of equal amounts of heavy precipitation from the reference period, were obtained for the entire temporal spectrum covered by this paper. Overall, the change signals derived on the basis of a concept of weather patterns seem plausible because they represent a possible continuation of the already observed increase in frequency and intensification of events of heavy precipitation in the extended study area. Copyright © 2009 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

ABSTRACT The amount of sunshine weighs heavily in our perception of the weather. It is largely determined by cloud cover, especially that at the low-to-medium level. Therefore, when reviewing the Hong Kong Observatory's weather symbol forecasting product, verification on the low-to-medium cloud field is carried out against the synoptic observations at the Hong Kong International Airport. Several metrics are used to examine the different aspects of the forecasts, and consideration is given to the non-Gaussian nature of the reported cloud amount for a fairer assessment. Based on the data from January to mid-August 2015, the median of the forecasts from the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System is found to outperform the other model forecasts, particularly when the performance is examined by forecast day. This paper presents these results and also discusses the potential of using the field in deriving site-specific weather symbol forecasts. [ABSTRACT FROM AUTHOR]

The Italian territory is located at the heart of one of the global hot spots of climate change, where the implementation of climate‐smart land management practices is imperative to guarantee the present and future maintenance of ecosystem functions as well as the sustainability of human socioeconomic activities. The project HIGHLANDER (HIGH performance computing to support smart LAND sERvices) led by Cineca aims at building a comprehensive and multi‐sector framework for land‐management decision‐making in Italy. The project relies on high quality information on different components of the landscape, with a focus on climate‐driven processes, and state‐of‐the‐art computing infrastructures. The HIGHLANDER Data Portal maximizes the impact of HIGHLANDER results by providing access to data products and services. In this article, we describe the architectural features of the platform, as well as the available HIGHLANDER datasets and downstream applications. [ABSTRACT FROM AUTHOR]

Owing to climate change, abnormal climate phenomena occur frequently, damaging aging highway facilities. Therefore, it is necessary to develop adaptation projects for highway systems. Adaptation projects focus on mitigating the impact of climate change on highway facilities and enabling safe use. The present study aims to develop quantified evaluation indicators that are necessary for planning and selecting appropriate climate change adaptation projects. By analysing previous studies, the most important factors to be considered in the evaluation process of climate change adaptation projects were defined, and 20 preliminary evaluation indicators were divided into three layers. A feasibility study for each class was performed on the preliminary evaluation indicators by an expert panel; 16 evaluation indicators were selected through a feasibility study, and the weight of each indicator was calculated using the analytical hierarchy process (AHP) method. The priority of the evaluation indicators was service improvement through adaptation projects (weight 8.76), cost reduction and job creation (weight 8.46), and climate change impact reduction and vulnerability reduction (weight 8.31). These quantified evaluation indicators can help organizations that manage highways respond to climate change and establish adaptation projects. [ABSTRACT FROM AUTHOR]

ABSTRACT The present paper concerns the impact of climate on building heating energy consumption for different types of energy in Tianjin, a large city in northern China. The results show that heating energy consumption is dominantly related to dry bulb temperature ( DBT), but the impacts vary with energy types and time scales (day, month or year). DBT can explain 97.7% of the coal consumption at different time scales and 83.0-89.7% of the natural gas consumption. DBT can also account for 25% of the daily and monthly heating electricity consumption and 89% of the annual heating electricity consumption. These results suggest that different measures for building energy conservation should be taken because of the differing climate impacts for different energy types. Especially, likely increase in temperature in the future should be considered to improve the energy efficiency of buildings. In addition, different models should be used to predict building energy consumption at different time scales. This would be helpful for energy policy makers and energy providers for adjusting energy use strategies. [ABSTRACT FROM AUTHOR]

ABSTRACT Heavy rain events or rainstorms of various scales often occur in Hong Kong from April to September. In extremes cases, they lead to serious landslides and flash floods resulting in loss of life, and damage to property and infrastructure. With climate change, extreme rain events are projected to be more frequent and more intense in this region. A system to gauge and label the severity of rainstorms would be useful for identifying and communicating extreme conditions in warning and disaster prevention operations. In parallel, a better understanding of the characteristics of rainstorms would also be important for impact assessments and engineering design purposes. This paper formulates a practical framework to index the magnitude and severity of rainstorms in Hong Kong. An index termed as the Severity Index combining the magnitude of different rainstorm attributes is devised for quantifying the overall severity of rainstorms. This index is flexible to allow tuning for specific applications. [ABSTRACT FROM AUTHOR]

Rail infrastructure is vulnerable to extreme weather events, resulting in damage and delays to networks. The impact of heat is a major concern for the London Underground (LU) by Transport for London (TfL) both now and in future, but existing studies are limited to passenger comfort on the deep tube and do not focus on infrastructure or the vast majority of the network, which is in fact above ground. For the first time, the present empirical study examines quantitatively the statistical relationship between LU delays (by synthesizing 2011-2016 industry data) with air temperature data (from Met Office archives). A range of testing shows strong statistical relationships between most delay variables and high temperatures, though not causality. Relationships were found between high temperatures and delays associated with different asset classes on different LU lines. Track-related delays, often the focus of high-temperature research (i.e. track buckling), show a relationship, although this is small relative to delays caused by other assets. Using UK Climate Projections 2009 (UKCP09) and assuming a similar future performance indicates that the share of annual delays owed to temperatures > 24°C may increase in frequency and length, depending on the emissions scenario. Recommendations include extending the analysis to the LU asset scale and considering the local environment to understand failure causality in order to mitigate future heat risk. A review of how TfL and other infrastructure operators capture delays for future analysis is necessary to facilitate climate resilience benchmarking between networks. [ABSTRACT FROM AUTHOR]

It is widely known that precipitation is a key variable of the hydrological cycle that is strongly affected by recent climate changes. Therefore, there is a growing interest in research activities focused on alteration of rainfall regime, as it conditions the planning of countermeasures against flood and landslide hazards. The available literature about precipitation tendencies over Italian peninsula offers a limited number of studies about recent changes of extreme events and precipitation intensity. This work aims at adding a contribution to fill this research gap, investigating the changes in rainfall regime observed over the 2002–2021 period in the Campania region (southern Italy). To pursue this aim, a dataset including daily precipitation records collected at 107 stations was analysed both through 11 indices developed by the Expert Team on Climate Change Detection and Indices and through the Standardized Precipitation Index in order to detect signals of changes in extreme events and to assess tendencies towards drier or wetter conditions. The Theil‐Sen method and the Mann–Kendall non‐parametric test were employed to evaluate the trends and their statistical significance. The main results emerging from this work are (i) an increasing tendency in precipitation intensity and in the frequency of occurrence of heavy rainfall events in autumn, mainly in the northern part of the region and in the mountainous areas, (ii) an upward trend of the duration of the longest wet spell in the coastal areas and (iii) an increasing trend of dry spells in spring and in summer in the Gulf of Salerno. [ABSTRACT FROM AUTHOR]

Understanding changes in evaporation rates is expected to be of great importance for water resource planning and management. This paper examines pan evaporation data as an example of the detection and attribution of trends in climate variables. Records of pan evaporation ( Epan), rainfall ( R), radiation ( SR), wind speed ( WS), temperature ( Ta) and humidity ( RH) for the period 1973-2008 were collected from the International Institute of Tropical Agriculture (IITA) in Ibadan, Nigeria. Mann-Kendall trend and Sen's slope tests were performed on the respective meteorological variables and a variability index (VI) was also computed for these. The results showed that annually Epan, SR and WS significantly decrease ( P < 0.001) while R, Ta, and RH showed insignificant increasing trends in the last four decades. Epan and SR decrease at the rate of 8.3 mm year−2 and 37.8 MJ m−2 year−1, respectively. Similar to Epan, SR decreased significantly in all the months ( P < 0.01) and the reduction ranged from 5.1% per decade in March to 9.3% per decade in August. The result of VI showed that the decrease in Epan and other explanatory variables is rather recent. Regression between Epan and other variables indicates that about 30, 15 and 6% of its variance can be explained by SR, WS and VPD (vapour pressure deficit), respectively. The possible roles of dust-haze known as 'harmattan' winds and monsoon clouds in attenuating SR and hence reducing Epan are discussed. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Extreme weather events can have severe consequences for the local environment and population. Projected future changes in climate (e.g. UKCP09) indicate that North West England is likely to experience an increasing frequency and intensity of meteorological extremes, leading to flooding, heat waves and storms. Consequently, it is important that the region enhances its preparedness for such events. This paper explores the possibility of developing quantifiable climate risk indices for the case study area of Greater Manchester, using a combination of archival research and statistical analysis of past climate data. For extremes which are the function of a single meteorological variable (e.g. heat waves, pluvial flooding and heavy snowfall) the thresholds proved to be reliable and skillful. Days with maximum daily temperature greater than or equal to 29.2 °C, daily snowfall amount greater than or equal to 6 cm or maximum gust speed greater than or equal to 60 knots are found to be indicative of weather-related impacts which have in the past affected human health/well-being, have caused damage to the urban infrastructure or have severely disrupted services. Extreme events which are the result of a more complex interaction between variables (e.g. drought, freezing conditions) were less well captured by applying the thresholds associated with a single variable in isolation. Such critical threshold indices can be used in conjunction with future projections of climate change to establish weather-related risk for the future. This risk-based approach can subsequently be integrated to climate change adaptation strategies and development planning to ensure future preparedness. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Atmospheric aerosols are solid and liquid particles suspended in air. Aerosols have important consequences for global climate, ecosystem processes and human health due to their various sizes and chemical compositions. In this paper, nearly 23 years of aerosol optical thickness (AOT) data from operational satellite AVHRR observations over the global ocean have been used to study the latitudinal variations of the linear long-term trends (LLT) of zonal mean AOT. For the LLT of zonally averaged AOT over the global ocean, their significance generally falls below the 95% confidence level. However, the LLT of zonal mean AOT for the individual oceans, such as the Atlantic Ocean (AO), Indian Ocean (IO), and West Pacific Ocean (WPO), can easily pass 95% confidence level in some latitude belts (e.g. 5-25°N for IO, 33-40°N for WPO, 30-50°N for AO) and may produce zonally averaged solar dimming/brightening accordingly in cloudless conditions. The results will help us to understand the latitudinal features of the decadal changes in aerosol loadings, as well as surface solar radiation, better. Published in 2011 by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Slippery roads due to ice and snow are a major cause of road traffic accidents in Sweden during the winter. This paper investigates the hypothesis that as the climate becomes increasingly milder there will be a reduction in the number of accidents in winter. Two months are compared in this analysis; one colder and drier than average, the other warmer and wetter than average. Despite the differences in weather between the 2 years, there was approximately the same number of accidents in both cases, although the exact cause of these accidents varied. It is concluded that using the warmer month as a temporal analogue, the accident rate in Sweden will not fall under current climate change scenarios. This result is attributed to the fact that drivers become more complacent in milder weather conditions where the risk of slippery roads is reduced. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The fascination of seasonal climate forecasting, of which El Niño forecasting is the prime example, comes from its multifaceted character. Not only does it pose interesting new challenges for the climate scientific community but also it is naturally linked to a great variety of socioeconomic applications. Seasonal climate forecasts are indeed becoming a most important element in some policydecision making systems, especially within the context of climate change adaptation. Thus, seriously considering the management of risks posed by the variability of climate on the seasonal to interannual time scale is key to achieving the longer terms goals of climate change adaptation strategy. This review paper explores the main components needed to construct a seasonal forecasting system, from the physical basis of climate seasonal predictions, to the tools used for producing them, to the importance of assessing their skill, to their use in risk management decisionmaking. Future challenges are also examined. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

An emergent consequence of climate change is its potential impacts on transatlantic flight routes and durations, with studies suggesting that future optimal paths will likely lead to decreased (increased) eastbound (westbound) journey times. However, these earlier works all rely on a class of flight planning algorithms (based on the so‐called 'shooting method') that are fundamentally different to those typically used by the aviation industry (based on the so‐called 'network method'). To help resolve any ambiguities associated with these differences and better align both the academic and operational communities, we have therefore conducted an evaluation of methods of estimating time‐optimal flight routes to relate the result obtained from the shooting method with the result obtained from the network method, using identical data from an ensemble of eight different climate models included within the CMIP5 project. Our findings suggest that, contrary to the existing literature, journey times will likely be shorter, irrespective of direction of travel and season, although the magnitude of this difference is negligible given the typical duration of transatlantic flights. Trajectory prediction (TP) results are largely dependent on the characteristics of the ensemble, their method of generation, the projected climate scenario and the temporal periods considered. Importantly, we conclude the choice of TP models is not a crucial factor in assessing the impact of climate change on flight durations—as long as this sample size (i.e., number of trajectories used in deriving statistics) is large. [ABSTRACT FROM AUTHOR]

ABSTRACT In this study, the annual and seasonal crop evapotranspiration at the spatial level of the Carpathian Region were evaluated over 1961-2010. The temperature, precipitation and actual evapotranspiration grid monthly climate data and land cover were analysed and processed on a seasonal basis to compute the annual crop evapotranspiration. The land cover evapotranspiration rate was assigned through evapotranspiration coefficients from the literature. Geographical Information System ( GIS) techniques, such as conversions from vector to raster data and the 'Raster Calculator' function, were used to assess the spatial distribution of the crop evapotranspiration at a regional scale. In particular, two datasets from different periods (1961-1990 and 1990-2010) were used to compute the seasonal and annual crop evapotranspiration for the Carpathian region. The results of climate parameters indicate a rise in temperature and crop evapotranspiration values between the first and the second period. In addition, significant spatial changes were observed with a shift of maximum values from south to north. [ABSTRACT FROM AUTHOR]

Global warming is projected to have major implications on global health. It is however not yet clear how this will translate to impacts on the healthcare system. By linking changes in temperature with changes in required bed days at a hospital level, through the use of a simple bed model, we quantify the projected impacts UK hospitals will need to adapt to. We show that there is already a local peak of bed days required in the main summer months due to hot temperatures. The results further show that there will be a significant increase during the main summer in both the mean and maximum number of beds needed, but a non‐significant decrease during the peak winter months. These changes lead to a more constant need of care of the year and shift the seasonal cycle of lowest hospital needs. [ABSTRACT FROM AUTHOR]

Low-likelihood weather events can cause dramatic impacts, especially when they are unprecedented. In 2020, amongst other high-impact weather events, UK floods caused more than £300 million damage, prolonged heat over Siberia led to infrastructure failure and permafrost thawing, while wildfires ravaged California. Such rare phenomena cannot be studied well from historical records or reanalysis data. One way to improve our awareness is to exploit ensemble prediction systems, which represent large samples of simulated weather events. This ‘UNSEEN’ method has been successfully applied in several scientific studies, but uptake is hindered by large data and processing requirements, and by uncertainty regarding the credibility of the simulations. Here, we provide a protocol to apply and ensure credibility of UNSEEN for studying low-likelihood high-impact weather events globally, including an open workflow based on Copernicus Climate Change Services (C3S) seasonal predictions. Demonstrating the workflow using European Centre for Medium- Range Weather Forecasts (ECMWF) SEAS5, we find that the 2020 March–May Siberian heatwave was predicted by one of the ensemble members; and that the record-shattering August 2020 California-Mexico temperatures were part of a strong increasing trend. However, each of the case studies exposes challenges with respect to the credibility of UNSEEN and the sensitivity of the outcomes to user decisions. We conclude that UNSEEN can provide new insights about low-likelihood weather events when the decisions are transparent, and the challenges and sensitivities are acknowledged. Anticipating plausible low likelihood extreme events and uncovering unforeseen hazards under a changing climate warrants further research at the science-policy interface to manage high impacts. [ABSTRACT FROM AUTHOR]

Adapting to climate change in the water sector requires abandoning two crucial assumptions. First, that the climate represented in the instrumental record is representative of the future. Instead, future water resource planning cannot be based on old measurements (or sequences derived from attaching change factors to instrumental data) and it should be recognized that stationarity is no longer viable, and, second, that climate modelling can be expected to give precise and certain predictions of the future. Instead, probabilistic projections of the future that take into account the full range of uncertainty should form the basis of robust climate change adaptation plans. As a response to the first assumption, it is suggested that stochastic weather generators represent a particularly useful approach to understanding the impacts of future climate change on water resources at a catchment scale, particularly given the recent release of 'science-hidden' tools such as the UKCP09 weather generator. With regards to the second assumption, it is suggested that modelling activity should identify the range of plausible futures to develop probabilities of risk, using those robust decision-making techniques which can gauge the performance of potential adaptation strategies. The best practice for delivering a replicable and practical hydroclimatological impact assessment for UK water resources at a catchment scale is identified, and an hypothetical example is outlined. It is suggested that although augmenting the resilience of water resources to climate change on a catchment scale is dependent on using the correct modelling tools, the robustness of the method with which that information is used to make adaptation decisions is equally as important. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Water resources management and agriculture planning models require a statistical synthesis of some rainfall features, in particular those representing dry atmospheric conditions. The bigger the basin, the more important these features become, as is the case of the La Plata Basin LPB.This paper focuses on the precipitation variability in the large LPB in South America, analysing the number of months per year with low rainfall and the sequences of months with low rainfall, their theoretical distributions and stability, which are needed as input for the models mentioned above.Long time series are used to analyse the lowfrequency variability and the relative importance of decadal variability. Changes are evident in the number of months per year with low rainfall, with a decrease of about 20 in the period after 1970.Theoretical distribution models binomial and geometric are fitted to these empirical distributions, and the regional variability of the fitting parameters is shown. In practically the entire region, the goodnessoffit of the two theoretical models considered is statistically satisfactory.The temporal variability of the parameters of the theoretical binomial p and geometric α distributions is analysed, in excluding subperiods of 10 and 5 years, respectively. The results show lowfrequency variability overlapped on a decadal variability, with low homogeneous regional behaviour.The distribution models have proven to be efficient for frequency adjustments of the rainfall properties studied. These results are an acceptable and necessary input to decision models in LPB. They also make it possible to infer effects of climate change. Copyright © 2008 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The insurance industry uses mathematical models to estimate the risks due to future natural catastrophes. For climate-related risks, historical climate data are a key ingredient used in making the models. Historical data for temperature and sea level often show clear and readily quantified climate change driven trends, and these trends would typically be accounted for when building risk models by adjusting earlier values to render the earlier data relevant to the future climate. For other climate variables, such as rainfall in many parts of the world, the questions of whether there are climate change driven trends in the historical data, and how to quantify them if there are, are less simple to answer. We investigate these questions in the context of European rainfall with a specific focus on how to deal with the uncertainty around trend estimates. We compare 10 empirical methodologies that one might use to model and predict trends, including traditional statistical testing and alternatives to statistical testing based on standard methods from model selection and model averaging. We emphasize prediction and risk assessment, rather than detection of trends, as our goal. Viewed in terms of this goal, the methods we consider each have qualitative and quantitative advantages and disadvantages. Understanding these advantages and disadvantages can help risk modellers make a choice as to which method to use, and based on the results we present, we believe that in many common situations model averaging methods, as opposed to statistical testing or model selection, are the most appropriate. [ABSTRACT FROM AUTHOR]

Although the Urmia Lake has undergone remarkable drought conditions within the past decades mainly due to climate change, drought studies covering the entire Urmia Lake Basin and all drought aspects are lacking. The present study investigated the spatial and temporal drought conditions in the Urmia Lake Basin for the past (1988-2017) and future (2021-2050 and 2051-2080) periods using five general circulation models (GCMs) under the IPCC (Intergovernmental Panel on Climate Change) scenarios RCP 2.6, RCP 4.5, and RCP 8.5. The standardized precipitation index (SPI) and standardized precipitation and evapotranspiration index (SPEI) were compared. The SPEI predicted more drought events than the SPI, and it seemed to be a more suitable drought index than the SPI for the basin. In general, the future periods would encounter less drought conditions in terms of significant drought trends and duration than the observed period under all scenarios, but the frequency of quarterly severe drought events in the future periods would be higher than in the observed period. Furthermore, the stations Urmia (western bank) and Tabriz and Maragheh (eastern banks) would face the highest frequency of different types of quarterly drought events in the future periods compared with the observed period. The predicted high frequencies of drought events for the future periods can intensify the current low water level situation of the Urmia Lake, which seriously threatens all types of ecosystems in the basin. Therefore, serious actions need to be taken into account for efficient ecosystem and water resources management in the basin. [ABSTRACT FROM AUTHOR]

The objective of the current study was to project changes in extreme temperature indices over Iran by the end of the 21st Century based on the Coupled Model Intercomparison Project phase 5 (CMIP5) simulations. We used six extreme temperature indices recommended and defined by the Expert Team on Climate Change Detection and Indices from a set of 18 CMIP5 model simulations from different modelling centres. The extreme temperature indices simulation results of the models were compared with the gridded extreme temperature indices observations of the Asfazari National Database with a spatial resolution of 15 × 15 km during the period 1962–2005. For comparison purposes, the CMIP5 model data and the Asfazari data were regridded to a uniform horizontal resolution 0.5° × 0.5°grid using a bilinear interpolation scheme before further processing. After first validating the model performance by computing bias, future changes in the period 2006–2100 were projected. The modified Mann Kendall trend test and the Sen slope estimator test were used for evaluating the trend of indices and estimating the change rate, respectively, at a 95% confidence level. The results show that the minimum of the daily minimum temperature (TNn) and the minimum of the daily maximum temperature (TXn) will increase in the future. Warming of night time temperature indices (TNn and TN90p) are projected to be stronger than those of daytime temperature indices (TXn and TX90p). Warming is not uniform across the country, with considerably more warming in the western and southern parts. In terms of the frequency of frost days, a decrease is generally projected in the 21st Century. The largest decrease is projected for the Zagros mountain ranges in the west and the mountainous region in the northeastern part of the country with a magnitude of around 8–10 days.decade−1. The number of heat wave days is expected to increase sharply from about 13 days in the reference period (1963–2005) to 100 days by mid-century to more than 235 days by the end of the century under Representative Concentration Pathway 8.5. [ABSTRACT FROM AUTHOR]

In this study spatial and temporal variations of climatic growing season parameters in western Anatolia, Turkey, during 1966-2015 are analysed. The beginning of the growing season (end of the growing season) for all threshold values of 5, 7 and 10°C is delayed (advances) from the southwest to the northeast over the study area. The growing season length (GSL) increases from northeast to southwest. The growing season start (GSS), growing season end (GSE) and GSL are strongly correlated with elevation, low-elevation stations having earlier GSS, later GSE and longer GSL than stations at higher elevations. The regional average GSS shifted 7.3, 10.0 and 10.5 days earlier, while the regional average GSE moved 4.8, 6.1 and 6.2 days later, increasing the GSL by 12.0, 16.6 and 15.9 days for 5, 7 and 10°C, respectively. A comparison of mean values between subperiods documented significant changes at more than 50% of stations for GSS and GSL from the subperiod 1977-1997 to the subperiod 1998-2015 and almost no significant change between 1966-1976 and 1977-1997 for all thresholds. Correlation analysis showed that the East Atlantic Pattern was the main driver for GSS and the North Sea--Caspian Pattern for GSE, followed by the East Atlantic/Western Russia Pattern and the North Atlantic Oscillation, respectively. Monthly and annual precipitation totals and precipitation distribution within a year have not changed significantly, suggesting that air temperature has become more influential on GSS and GSE. [ABSTRACT FROM AUTHOR]

In this study, a climatological analysis of daily new snow cover data from 93 meteorological stations (10-2,400 m above sea level) over Turkey during the winter (December-March) period of 1967-2006 is presented. Series of snow cover depth (SCD) (≥1 cm) and number of snowy days (SDs) were analysed to determine the variability and statistically significant trends in the snow climatology. The results showed an inverse relation between SDs and SCD over the central (CAR) and eastern (EAR) Anatolian regions. While positive trends in SDs were observed in the CAR (77% of all stations), the new SCD decreased in time. On the other hand, the intensity of the daily snowfall over the EAR increased due to decreasing (increasing) SD (SCD) trends in the region, especially between the 500 and 1,000 m layer. It was found that higher SCDs occur in the EAR during winter with positive patterns in the east Atlantic/western Russia. The daily mean sea level pressure composite maps of high SCD (>5 cm) show that strong northeasterly flows enable the penetration of cold polar air to the EAR due to the interaction between the Siberian high-pressure centre over Europe and the low-pressure centre over the Caspian Sea. These results help further understanding of the regional variation of the snowfall pattern in Turkey and its response to global climatic change. [ABSTRACT FROM AUTHOR]

The active phase of the East Asian summer monsoon (EASM) in Taiwan during May and June, known as Meiyu, produces substantial precipitation for water uses in all sectors of society. Following a companion study that analysed the historical increase in the Meiyu precipitation, the present study conducted model evaluation and diagnosis based on the EASM lifecycle over Taiwan. Higher and lower skill groups were identified from 17 Couple Model Intercomparison Project Phase 5 (CMIP5) models, with five models in each group. Despite the difference in model performance, both groups projected a substantial increase in the Meiyu precipitation over Taiwan. In the higher skill group, weak circulation changes and reduced low‐level convergence point to a synoptically unfavourable condition for precipitation. In the lower skill group, intensified low‐level southwesterly winds associated with a deepened upper level trough enhance moisture pooling. Thus, the projected increase in Meiyu precipitation will likely occur through the combined effects of (1) the extension of a strengthened North Pacific anticyclone enhancing southwesterlies; and (2) more systematically, the Clausius–Clapeyron relationship that increases precipitation intensity in a warmer climate. The overall increase in the Meiyu precipitation projected by climate models of variable performance supports the observed tendency toward more intense rainfall in Taiwan and puts its early June 2017 extreme precipitation events into perspective. [ABSTRACT FROM AUTHOR]

Turbulence is one of the major weather hazards to aviation. Studies have shown that clear‐air turbulence may well occur more frequently with future climate change. Currently the two World Area Forecast Centres use deterministic models to generate forecasts of turbulence. It has been shown that the use of multi‐model ensembles can lead to more skilful turbulence forecasts. It has also been shown that the combination of turbulence diagnostics can also produce more skilful forecasts using deterministic models. This study puts the two approaches together to expand the range of diagnostics to include predictors of both convective and mountain wave turbulence, in addition to clear‐air turbulence, using two ensemble model systems. Results from a 12 month global trial from September 2016 to August 2017 show the increased skill and economic value of including a wider range of diagnostics in a multi‐diagnostic multi‐model ensemble. [ABSTRACT FROM AUTHOR]

Changes in precipitation patterns as a result of increased atmospheric water vapour in a warming environment over the Indian region have been reported in the past few decades. However, these studies have not focused much on exploring the changes in atmospheric water vapour in a changing climate. The present study focuses on examining the variability of integrated precipitable water (IPW) over the Indian region derived from the Modern Era Retrospective‐Analysis for Research and Application (MERRA 2) model in a warming environment. The results suggest that the IPW has increased significantly in recent decades. Changes in the IPW are consistent with changes in regional warming over India. The results also point out an increase of about 4.98% ± 1.26% in the IPW for a 1°C increase in regional warming. The variability of IPW has been examined in a warming climate. An increase in regional temperature shows coherent variation with IPW. A decadal increase of about 1.69% has been reported in the IPW. [ABSTRACT FROM AUTHOR]

Evapotranspiration and water availability are driven by changing climate and land cover parameters. In the present study, climatological records and land cover data were analysed simultaneously to accomplish the spatial distributions of climate change effects on water resources in Varanasi district, north India. Humidity–aridity was assessed by Lang's rain factor and De Martonne's aridity index, based on mean monthly rainfall and air temperature from seven meteorological stations. The climate change effect on water resources was evaluated using a 5 × 5 matrix that includes water availability and the aridity index by considering two time periods: 1941–1970 (1950s) and 1971–2000 (1980s). The methodology is based on seasonal crop evapotranspiration (ETc) (initial, mid‐season, end season and cold season) and annual water availability calculations. The high values (≤ 1,045 mm) of ETc were identified during the mid‐season stage. Water availability indicates decreases in the maximums from 718 to 636 mm during the two analysed periods, with a negative impact at the spatial scale. Lang's rain factor (< 40) indicates an arid climate in the northwest, west, east and central parts of the district and a humid climate (Lang's rain factor > 40) in the south. De Martonne's aridity index indicates rapid aridization from south to north (28.3 in the 1950s and 25.6 in the 1980s). The high and very high climate effects on water resources in Varanasi district were found mainly in the crop lands, while in the urban areas the climate effect is low. The much affected area by climate change and land cover was depicted during the recent period (1980s). This statement was proved also by the Mann and Kendall test, which indicates a negative trend for annual precipitation at all stations (for the period 1941–2000), while the mean annual temperature had a positive trend for four stations. These findings suggest that climate change had a negative effect on water resources during the last 60 years in the study area. [ABSTRACT FROM AUTHOR]

The present study analyses the impacts of past and future climate change on extreme weather events for southern parts of Canada from 1981 to 2100. A set of precipitation and temperature‐based indices were computed using the downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) multi‐model ensemble projections at 8 km resolution over the 21st Century for two representative concentration pathway (RCP) scenarios: RCP4.5 and RCP8.5. The results show that this region is expected to experience stronger warming and a higher increase in precipitation extremes in future. Generally, projected changes in minimum temperature will be greater than changes in maximum temperature, as shown by respective indices. A decrease in frost days and an increase in warm nights will be expected. By 2100 there will be no cool nights and cool days. Daily minimum and maximum temperatures will increase by 12 and 7°C, respectively, under the RCP8.5 scenario, when compared with the reference period 1981–2000. The highest warming in minimum temperature and decrease in cool nights and days will occur in Ontario and Quebec provinces close to the Great Lakes and Hudson Bay. The highest warming in maximum temperature will occur in the southern parts of Alberta and Saskatchewan. Annual total precipitation is expected to increase by about 16% and the occurrence of heavy precipitation events by five days. The highest increase in annual total precipitation will occur in the northern parts of Ontario and Quebec and in western British Columbia. [ABSTRACT FROM AUTHOR]

The study was carried out to assess meteorological drought on the basis of the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) evaluated in future climate scenarios. Yazd province, located in an arid region in the centre of Iran, was chosen for analysis. The study area has just one synoptic station with a long‐term record (56 years). The impact of climate change on future drought was examined by using the CanESM2 of the CMIP5 model under three scenarios, that is, representative concentration pathways RCP2.6, RCP4.5 and RCP8.5. Given that a drought is defined by several dependent variables, the evaluation of this phenomenon should be based on a multivariate analysis. For this purpose, two main characteristics of drought (severity and duration) were extracted by run theory in a past (1961–2016) and future (2017–2100) period based on the SPI and SPEI, and studied using copula theory. Three functions, that is, Frank, Gaussian and Gumbel copula, were selected to fit with drought severity and duration. The results of the bivariate analysis using copula showed that, according to both indicators, the study area will experience droughts with greater severity and duration in future as compared with the historical period, and the drought represented by the SPEI is more severe than that associated with the SPI. Also, drought simulated using the RCP8.5 scenario was more severe than when using the other two scenarios. Finally, droughts with a longer return period will become more frequent in future. [ABSTRACT FROM AUTHOR]

This study assessed the growth of wheat under climate change conditions (increases in CO2 and temperature) in southwestern Iran. Future climate data were projected using long‐term climate data for radiation and minimum and maximum temperatures for the baseline period 1980–2010. The future climate scenarios made use of delta changes from the Coupled Model Intercomparison Project Phase 5 (CMIP5) in the R package of the Agricultural Model Intercomparison and Improvement Project for 2040–2070. After generating the weather data, the Agricultural Production System Simulator wheat model was applied to assess the potential impact of climate change on wheat yield and the relative growth rate, crop growth rate, total dry weight and leaf area index. Averaged across all locations, an increase of 1.6 and 2.3°C in annual temperature was projected under representative concentration pathway 4.5 (RCP4.5) and RCP8.5, respectively. The decrease in the average length of the growing season was 7.5 and 9.3% under RCP4.5 and RCP8.5, respectively, in the mid‐21st century with respect to baseline. The mean cumulative radiation will decrease by 11.6 and 14.3% averaged across all sites for RCP4.5 and RCP8.5, respectively. Grain yield will decrease at all sites for both RCPs. The greatest decrease in wheat yield was projected for Izeh under RCP4.5 and RCP8.5 (7.9 and 9.3%, respectively) for 2040–2070 relative to baseline. A greater grain yield at baseline than for the RCP8.5 and RCP4.5 scenarios was closely related to higher leaf area index, relative growth rate and crop growth rate values during the reproductive phase. Thus, these parameters can be defined as indicators of crop yield. [ABSTRACT FROM AUTHOR]

The devastating socioeconomic impacts of recent droughts in the Western Cape have intensified the quest for future drought mitigation measures. While ongoing global warming may increase atmospheric evaporative demand and worsen drought conditions, most studies on drought in the Western Cape have overlooked the role of potential evapotranspiration (PET). The present study examines the role of PET on future drought characteristics, focusing on four river catchments. Two drought indices, the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), were analysed. The capability of the Co‐ordinated Regional Downscaling Experiment simulations to reproduce the drought characteristics was evaluated by comparing present‐day climate simulations with observations. The impacts of different global warming levels (GWLs) on the SPI and SPEI projections were assessed using self‐organizing map (SOM) classifications. The results project that a robust drying signal across the Western Cape, but the magnitudes of the projections, which vary across the river catchments, increase with the GWLs. The changes in the drought intensity and frequency are weaker when using the SPI than the SPEI, suggesting that SPI projections may underestimate the influence of global warming on drought because they do not account for the influence of PET. The SOM classification confirms the differences between the two drought indices reveals the drought patterns that are not seen in the drought ensemble means. Nevertheless, the study underscores the need to mitigate future drought impacts over the Western Cape, but with careful consideration on the drought index used in characterizing the droughts. [ABSTRACT FROM AUTHOR]

Predicting the distribution of suitable habitats for species under climate change is essential for conservation planning. In the past three decades, the number of Persian squirrels (Sciurus anomalus) and the area covered by the Persian oak (Quercus brantii) have decreased due to various factors such as habitat degradation. Habitat suitability of the above interacting species was assessed under the current conditions and future (2050) climate change scenarios in western Iran, using a maximum entropy model (MaxEnt). The model performed well with an average area under the curve of above 0.9 for the two species. Precipitation in winter was the factor that contributed most to habitat suitability of both species. The MaxEnt projection for the year 2050 revealed a reduction in the area of suitable habitats for both species. Based on the outputs from the Hadley Centre for Climate Prediction and Research Coupled Model (HadCM) scenarios, the fundamental niche (the range of environmental conditions in which the species can survive) of the Persian squirrel will reduce by between 0.12 and 1.1%. However, under the Centre National de Recherches Météorologiques Coupled Model scenarios, small gains in potential habitats may be observed. The area of suitable habitats for the oak was reduced under all scenarios except HadCM (representative concentration pathway 2.6). The results showed that the loss of suitable habitats for both species was more evident in the northern part of the range where the best habitats currently exist. In contrast, small gains in suitable habitats may be observed in the southern and eastern part of the range. [ABSTRACT FROM AUTHOR]

Groundwater recharge depends generally on precipitation. In this study, a geographical information system (GIS) procedure was applied to assess the climate change effect on groundwater recharge in the Grand Est region of France. The analysis comprises high‐resolution climate models that reflect the long‐term climatological regime. The hydrological properties include aquifers, land‐cover and terrain morphology data, which were used to develop the potential infiltration map of the study area. Two parameters, the De Martonne aridity index and effective precipitation, were combined into a 5 × 5 matrix to assess the climate change effect on groundwater recharge during the past (1990s), present (2020s) and future (2050s) periods. The present and future intense aridization and the depletion of the effective precipitation (< 650 mm) reveal the negative effects of climate change on aquifers' recharge in the Grand Est region. The areas with high and very high climate effects will increase in the 2020s and 2050s. These areas extend mainly in the western, north‐central and northeastern parts of the region occupying the Rhine, Aube and Marne valleys. The medium effect could be found in the central, southern and northwestern parts, while the low impact on groundwater recharge was verified in the northwestern and southeastern parts of the region. The area with low effects of climate change extends mostly in the Vosges and Ardennes mountains. These findings contribute to the long‐term hydrogeological studies in the Grand Est region. [ABSTRACT FROM AUTHOR]

The study presents observed changes in climate extremes using daily precipitation and temperature data over 24 stations, covering the three climatic zones (Guinea coast, Savannah and Sahel) of Nigeria for the period 1971–2013. The data were homogenized with Expert Team on Climate Change Detection Indices (ETCCDI) RHtests version 4 software. RClimDex version 1.0 software was used to calculate 17 of the ETCCDI recommended precipitation and temperature extreme indices. The spatio‐temporal variation in the observed trends was analysed over each of the climatic zone. Results show a significant increase in the frequencies of warm spell, warm days and nights and decreasing cold spell, cold days and nights over the three climatic zones. A significant increase in annual total precipitation was found in some stations across the Guinea coast and Sahel zones. Changes in consecutive dry days and consecutive wet days are non‐significant in most stations. Also, a significant increase in extremely wet days was observed in a few stations across the three climatic zones. The implication of the observed warming could, however, result in thermal discomfort of lives in areas with significant positive trends. This could also exert pressure on the economy's power sector, as energy demand for cooling will increase. The increase in total annual precipitation will potentially be favourable for hydropower generation and increase the availability of the potable water supply for both industrial and domestic uses in the country. However, the increase in consecutive dry days and the decrease in consecutive wet days are dangerous for agricultural practices and, hence, food security. [ABSTRACT FROM AUTHOR]

This exploratory research examines the impacts of changing climate on the vulnerable US‐Affiliated Pacific Islands (USAPI) from the perspective of the Intergovernmental Panel on Climate Change's (IPCC) Fifth Assessment Report (AR5) coupled with General Circulation Models (GCMs). Island‐wide projections of future climate change (e.g. temperature, rainfall, and net water flux) were made using the latest IPCC AR5 GCMs protocol (Coupled Model Intercomparison Project Phase—CMIP5) with 38 GCMs with up to 105 model runs. A review was also made of studies on model‐based future projections of the El Niño–Southern Oscillation (ENSO). The CMIP5 model's results clearly illustrate that the past trend in temperature (1950–2017) is rising while the rainfall trend remains more or less static. It is also clear from the projections that the long‐term trend for temperature rise is fast and significant, while the trend for rainfall and net water flux (P‐E) rise appears to be slow and marginal. On the perspective of CMIP5 model's evaluation for the USAPI region, the temperature projections are found to be promising, while the rainfall projection potentials, despite some limitations, are also encouraging. The prime concerns for future disruptions in the USAPI region are the consequences of increasing frequency of the ENSO and related rainfall activities. The long‐term warming signal may further complicate the problem. Therefore, the currently water‐stressed islands and low‐lying atolls in the Federated States of Micronesia (FSM) and Republic of Marshalls Islands (RMI) are particularly vulnerable to El Niño‐related heat stress or drought and La Niña‐related inundations or flooding. In both cases, the future demand‐oriented climate‐sensitive water resources sector will be severely affected. A climate‐information‐based comprehensive water resources management plan (for the 2030s) is therefore essential with more detailed ENSO‐related climate information and impacts in terms people can understand and respond to. [ABSTRACT FROM AUTHOR]