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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]