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Evaporation from water surfaces is influencing many environmental science processes. Evaporation from pan data is not available as the most well-known method of determining evaporation in many areas, and therefore, the application of experimental mathematical equations in this field is considered. Most of the data for extracting these equations did not belong to cold regions, and as a result, the accuracy of these equations in cold regions has low reliability. In this study, the equations of Trabert (Meteorol Z 13:261–263, 1896), Kohler (Tech Rep Geol Survey Prof Paper 269:127–148, 1954), Kohler et al. (Evaporation from pans and lakes, US Government Printing Office, Washington, 1955), Papadakis (Soil Sci 93:76, 1961), Ântal (Evapotranspiration from com field. Term paper submitted for Dr. R. F. Dale's Agronomy 537 class. Received from personal communication with Dr. Dale (Dept. of Agronomy, Purdue, 1973), Linacre (Agric Meteorol 18:409–424, 1977), Linacre (Water Int 19:5–14, 1994) in Two default and revised modes (14 equations in total) have been used to determine evaporation in cold regions (Emberger climate classifications). Performance index (PI) as an error metric was used to analyze the efficiency of the equations. Findings show that in the absence of proper datasets for revision, Kohler et al. (Evaporation from pans and lakes, US Government Printing Office, Washington, 1955) and Papadakis (Soil Sci 93:76, 1961) equations are more proper options for determining evaporation in cold regions. The revision process increased the efficiency of all equations, with the slightest improvement for Papadakis (Soil Sci 93:76, 1961) and the most improvement for Trabert (Meteorol Z 13:261–263, 1896). According to the behavior of Papadakis (Soil Sci 93:76, 1961) equation in the revision process and its efficiency in both default and revision modes, it can be considered a firm equation in the cold region. The findings reveal that the revised equations have little efficiency difference. It indicates that in cold regions, the influence of the default parameters of the equations on efficiency is more significant than their mathematical formula of the equation. Also, the revision has led to Trabert (Meteorol Z 13:261–263, 1896) equation with relatively good results due to less relative flexibility than other equations. Therefore, it can be concluded that the essential factor to increase the reliability of the application of experimental mathematical equations in determining evaporation from the pan is the use of revised equations with data from cold regions. In addition, among all the equations studied, the revised Kohler et al. (Evaporation from pans and lakes, US Government Printing Office, Washington, 1955) equation was recognized as the most efficient equation for determining evaporation from the pan in the cold regions studied. [ABSTRACT FROM AUTHOR]

This paper applied the fuzzy function approach, combined with the ridge regression model, to produce daily rainfall projections from large-scale climate variables. This study developed a statistical downscaling model based on principal components, c-means fuzzy clustering, Volterra series, and ridge regression. The model is known, hereafter as SDC2R2. In the developed downscaling model, the use of ridge regression, instead of multiple linear regression, is proposed to downscale daily rainfall with wide range (WR) predictors. The WR predictors were applied to sufficiently incorporate climate change signals. The developed model also captured the non-linear interactions of the climate variables by applying the transformation of Volterra series realization over WR predictors. This transformation was performed by applying principal components as orthogonal filters. Further, these principal components were clustered by using c-means clustering and non-linear transformations were applied on these membership functions, to improve the prediction ability of the model. The reanalysis of climate data from the National Centres for Environmental Prediction (NCEP) was used to develop the model and was validated by using the Global Climate Model (GCM) for four locations in the Manawatu River basin. The developed model was used to obtain future daily rainfall projections from three Representative Concentrative Pathways (RCP 2.6, RCP 4.5, and RCP 8.5) scenarios from the Canadian Earth System Model (CanESM2) GCM. The performance of the model was compared with a widely used statistical downscaling model (SDSM). It was observed that the model performed better than SDSM in downscaling rainfall on a daily basis. Every scenario indicated that there is a probability of obtaining high future rainfall frequency. The results of this study provide valuable information for decision-makers since climate change may potentially impact the Manawatu basin. [ABSTRACT FROM AUTHOR]

Buildings worldwide have evolved based on local resources and skills, evolving form and orientation to ensure a productive and comfortable indoor environment. Traditional habitations rely on passive climate-responsive mechanisms and physiological resilience. At the same time, contemporary buildings rely increasingly on active mechanisms for fine-tuned convenience and comfort. Those buildings are becoming less habitable due to climate change. This paper presents an overview of research into climate-responsive building adaptation, identifying various factors determining a building's ability to regulate external climatic conditions in providing a habitable indoor environment. The review covers the ability of occupants to manage their thermal environment and adaptation mechanisms, including various adaptation strategies attributed to climate change. Besides a review of relevant research tools and methodologies, the paper also identifies future research challenges. Those challenges include but are not limited to evaluating climate classification provided by building standards given climate change, the need for region-specific climate-change vulnerability assessment of the built environment to develop specific adaptation strategies, a survey of vernacular structures to understand their inherent adaptation capacities, developing a framework to study building adaptation, etc. Thus, this review opens the possibility of further research in building adaptation. [ABSTRACT FROM AUTHOR]

This paper analyzes the relationships between the circulation regimes of the 500-hPa height (z500) and 250-hPa zonal winds (u250) in the Pacific–North America region during boreal winter, and the 45-day Northern Hemisphere oscillation in z500. The regimes were calculated using a k-means clustering applied to the leading 12 principal components of the combined z500–u250 anomaly fields. We divided the oscillation into eight arbitrary phases. The oscillation phase z500 composite maps are spatially well correlated with regime z500 composites: phases 1–2 are best correlated with the Arctic Low, phases 3–5 are best correlated with the Pacific Trough, phase 6 is best correlated with the Arctic High, and phases 7–8 are best correlated with the Alaskan Ridge. We found that these correlations are generally consistent with the regimes that tend to occur during the individual oscillation phases: the Arctic Low occurs above significance in phases 1–2, the Pacific Trough occurs above significance in phase 3, and Alaskan Ridge occurs above significance in phases 7–8. Therefore, the oscillation has a preferred order with respect to the regimes. The regime transitions indicate a pattern that moves through the Pacific Wavetrain, a regime that appears for k = 5 as a mean state. Transitions out of this regime into different regimes are preferred in different phases of the oscillation. These results imply a possible enhancement to regime prediction using the low-frequency oscillations in combination with regimes. Significance Statement: Subseasonal prediction, weather forecasting in the 2–4-week range, is important for many parts of society, e.g., water managers, emergency response units, and farmers. However, current prediction skill in this time range is low. This paper performs an initial analysis of a possible method to increase weather statistic prediction skill beyond 10 days in the winter for the Pacific–North America region. This is done by combining two ways of looking at large, long-lasting patterns of pressure systems in the atmosphere, which are associated with various weather statistics like precipitation extremes and storminess. The results indicate this method holds potential skill for enhancing subseasonal prediction. Further investigation might yield forecasting improvements in this important time range. [ABSTRACT FROM AUTHOR]

Due to their higher energy efficiency and better economic performance, distributed energy resource (DER) systems are expected to be one of the main energy supply forms in the future and have gained increasing attention in recent years. Thus, there is a need to boost our understanding of how to apply DER systems in different types of actual cases. This paper investigates a techno-economic analysis of a DER system applied in a real case in a hot-summer and cold-winter zone in China, where the climate is considered to be Cfa according to the Köppen–Geiger climate classification system. An urban underground transportation infrastructure system located in Changsha (China) was chosen to analyze the techno-economic performance of a natural-gas-fired DER system in comparison with a centralized energy system (CES). First, a scientific and reasonable application program of the natural-gas-fired DER system was developed by an overall load analysis (electric load, air-conditioner load, and domestic hot water load during the operating period). Based on this load analysis, this research combined the energy consumption and the actual operating situation and then compared and analyzed different types and capacities of equipment in this case. Moreover, a comprehensive analysis of the economic benefits was estimated by comparing the natural-gas-fired DER system with conventional CESs. Overall, the total annual cost of the DER system was reduced by 18.73%, and its additional investment can be paid back within about 2.2 years. A better economic benefit was achieved by applying the natural-gas-fired DER system in an actual case, which will help encourage the widespread application of DER systems. [ABSTRACT FROM AUTHOR]

A review of the state‐of‐the‐art in short‐term Solar Power Forecasting (SPF) methodologies is presented in this paper. Over the last few years, developing and improving solar forecasting models has been the main focus of researchers, considering the need to efficiently increase their forecasting accuracy. Forecasting models aim to be used as an efficient tool to help with the stability and control of energy systems and electricity markets. Intending to further comprehend the factors affecting the quality of SPF models, this paper focuses on short‐term solar forecasting methodologies since they pose a crucial role in the daily operation and scheduling of power systems, since they focus on forecasting horizons typically ranging from 1 h to 1 day. The reviewed works are classified according to the climatic conditions, technical characteristics, and the forecasting errors of the different methodologies, providing readers with information over various different cases of SPF. Considering the need to improve the SPF efficiency, such classifications allow for important comparative conclusions to be drawn, depending on the location of each case and the meteorological data available. Future directions in the field of short‐term solar power forecasting are proposed considering the increasing development of SPF models' architecture and their field of focus. [ABSTRACT FROM AUTHOR]

Natural hazard threats have grown as a result of climate change, fast demographic development, and major urbanization. Devastating floods have occurred in several areas of the world recently, including the Kingdom of Saudi Arabia, which is located in a region with a dry environment. In arid or semi-arid regions, rapidly forming flash floods associated with debris flowing down over dry water courses leading to a potential threat to both lives and property. Being located at the coastal plain of western Saudi Arabia, Jeddah City has witnessed an unexpected amount of rainfall events in recent years. Such extreme rainfall events, integrated with other factors, namely topography, land use, surface runoff, etc., have led to flood generation, which is alarming indeed. Herein, this paper addresses the varying climatic classifications of the Kingdom, its risk and vulnerability, followed by reasoning about the impact of flash flood events and the associated casualties and property losses. Further, it reports about the existing strategies of the government and proposes a systematic way forward on how to alleviate such events in future. Thus, risk variables have been discovered and integrated in the context of climate change and rising anthropogenic strain on coastal communities to give planners and decision makers tools to assure effective and appropriate flood risk management. [ABSTRACT FROM AUTHOR]

Lucerne is regarded as the best legume crop for forage to be cultivated in South Africa. It is commonly used to produce good quality hay. It also plays an important role in soil conservation, regeneration, and crop rotation systems as it supplies substantial amounts of nitrogen to succeeding crops through symbiotic N2 fixation, which makes it the preferable choice for intensive forage production systems. Fertilizer in liquid inoculant formulations has demonstrated to contribute growth and yield increase for leguminous crops. Therefore, the aim of this paper was to determine the effects of Sinorhizobium meliloti liquid formulation inoculation on the growth, yield, and nitrogen content in lucerne. The strain RF14 (Sinorhizobium meliloti) was collected from the Agricultural Research Council at Roodeplaat (Plant Health and Protection located (East), Pretoria (South Africa). The liquid inoculant contained 6.5 × 109 viable cells mL−1. According to the Kooen–Gieger climatic classification, the experiments were conducted on two different climatic zones. The first site was in Bronkhorspruit (Blesbokfontein farm) in the Gauteng province and the second was in Hartbeesfontein (Rietfontein Farm) in the Northwest province. The results showed that lucerne inoculation with liquid inoculant formulation of Sinorhizobium meliloti significantly increased nodule number, size, growth, and yield in both bioclimatic zones. The significantly increased were compared to the negative control. The Sinorhizobium meliloti inoculant increased nitrogen accumulation in all inoculated treatments compared to the control. The finding of this research provides important information on the impact of rhizobium microbial inoculant application in the improvement of soil fertility through nodule formation. In addition, seed vigor improvement was translated in overall growth and yield increase in lucerne plants. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive overview of the modelling tools available for integrating climate change impacts into pest risk assessments (PRA), elucidating the existing methodologies and models employed to understand the potential distributions of pests based on historical data and under future climate change scenarios. We highlight the strengths and weaknesses of these models and provide commentary on their ability to identify emerging threats due to climate change accurately and adequately, considering pest establishment likelihood, host crop exposure and the distribution of impacts. The simplest methods are based on climate‐matching models, degree‐day development models and Köppen–Geiger climate classification. Correlative species distribution models derive species–environment relationships and have been applied to PRA with mixed success. When fitted models are applied to different continents they are usually challenged to extrapolate climate suitability patterns outside the climate space used to train them. Global climate change is creating novel climates, exacerbating this 'transferability' problem. Some tools have been developed to reveal when these models are extrapolating. Process‐oriented models, which focus on mechanisms and processes rather than distribution patterns, are inherently more reliable for extrapolation to novel climates such as new continents and future climate scenarios. These models, however, require more skill and generally more knowledge of the species to craft robust models. [ABSTRACT FROM AUTHOR]

Solar photovoltaics (PV) continues to grow rapidly across the world and now accounts for a very considerable proportion of all non-fossil-fuel electricity. With the continuing urgency of greenhouse gas abatement, the growth of solar PV is inevitable. Competition with other land uses and the desire to optimize the efficiency of the panels by making use of water cooling are compelling arguments for offshore floating PV (OFPV), a trend that could also benefit from the existing infrastructure recently built for offshore wind farms. Building on our earlier work, we present a larger dataset (n = 82) located around the globe to assess global yield (dis)advantages while also accounting for a modified form of water cooling of the offshore panels. Using our results regarding the Köppen–Geiger (KG) classification system and using a statistical learning method, we demonstrate that the KG climate classification system has limited validity in predicting the likely gains from OFPV. Finally, we also explore a small subset of sites to demonstrate that economics, alongside geography and technology, impacts the feasibility of locating PV panels offshore. [ABSTRACT FROM AUTHOR]

The transition from fossil fuels to "green" energy involves increasing energy efficiency from existing energy systems and reducing harmful emissions into the atmosphere. Currently, renewable energy sources (RESs) are a priority way of generating energy for Smart Grid systems that meet the requirements of efficiency and safety. Classification of regions by similar climatic characteristics helps as an effective tool for avoiding various risks when implementing a Smart Grid based on RES. In this paper, the clustering method is considered by the authors as a tool to achieve the goal of the study: the division of regions into clusters with similar climatic characteristics, which will allow to choose the most efficient RES for each specific region. The authors considered the selected climatic characteristics based on certain factors after a review of existing sources, for example, the level of solar insolation, average annual wind speed, average annual temperature, and average annual precipitation for 84 subjects of the Russian Federation. As a result, five clusters were identified by the k-means method using the Stata software. For each cluster, the characteristics and the most preferred type of RES for the implementation of Smart Grid are described. Cluster analysis based on climatic characteristics is the first stage of a comprehensive methodology for selecting the most favorable regions for the development of both a particular type of RES and Smart Grid systems in general, as proposed by the a [ABSTRACT FROM AUTHOR]

We apply the hydrologic landscape (HL) concept to assess the hydrologic vulnerability of the western United States (U.S.) to projected climate conditions. Our goal is to understand the potential impacts of hydrologic vulnerability for stakeholder-defined interests across large geographic areas. The basic assumption of the HL approach is that catchments that share similar physical and climatic characteristics are expected to have similar hydrologic characteristics. We use the hydrologic landscape vulnerability approach (HLVA) to map the HLVA index (an assessment of climate vulnerability) by integrating hydrologic landscapes into a retrospective analysis of historical data to assess variability in future climate projections and hydrology, which includes temperature, precipitation, potential evapotranspiration, snow accumulation, climatic moisture, surplus water, and seasonality of water surplus. Projections that are beyond 2 standard deviations of the historical decadal average contribute to the HLVA index for each metric. Separating vulnerability into these seven separate metrics allows stakeholders and/or water resource managers to have a more specific understanding of the potential impacts of future conditions. We also apply this approach to examine case studies. The case studies (Mt. Hood, Willamette Valley, and Napa–Sonoma Valley) are important to the ski and wine industries and illustrate how our approach might be used by specific stakeholders. The resulting vulnerability maps show that temperature and potential evapotranspiration are consistently projected to have high vulnerability indices for the western U.S. Precipitation vulnerability is not as spatially uniform as temperature. The highest-elevation areas with snow are projected to experience significant changes in snow accumulation. The seasonality vulnerability map shows that specific mountainous areas in the west are most prone to changes in seasonality, whereas many transitional terrains are moderately susceptible. This paper illustrates how HL and the HLVA can help assess climatic and hydrologic vulnerability across large spatial scales. By combining the HL concept and HLVA, resource managers could consider future climate conditions in their decisions about managing important economic and conservation resources. [ABSTRACT FROM AUTHOR]

The likely changes to precipitation seasonality with warming are both impactful and not well understood. This work aims to describe areas that experience similar changes to seasonal precipitation irrespective of the original underlying precipitation seasonality. We train a self-organizing map on the difference between the seasonal cycle of precipitation in the past and in a high-warming future climate as represented by the Community Earth System Model, version 2, to create regions with similar changes in precipitation seasonality. This method is applied separately over land and ocean surfaces because of the differing processes leading to precipitation over each. This method indicates that future changes in seasonal precipitation are most varied in the tropics because of a southward shift in the intertropical convergence zone. The seasonal shifts found over midlatitude oceans indicate a poleward shift in atmospheric river activity. We find a correspondence between certain land-based precipitation changes and Köppen climate classification. The seasonality of large-scale and convective precipitation is examined for each region. The relationship between the seasonal changes to precipitation and associated atmospheric processes is discussed. These processes include atmospheric rivers, the intertropical convergence zone, tropical cyclones, and monsoons. [ABSTRACT FROM AUTHOR]

Precipitation regionalization, serving as a foundation for extrapolating information from gauged to ungauged sites, contributes to a comprehensive understanding of the spatial distribution of precipitation. However, existing studies have focused mainly on precipitation, neglecting the influence of climate background and meteorological circulation. Moreover, there is a lack of specialized analysis of regionalization results. This study proposes a novel approach to precipitation regionalization that considers covariates in the circulation field and periodic features. The method utilizes Barnett–Preisendorfer canonical correlation analysis coupled with principal component analysis (BPCCA) to select covariates and multivariate self-organizing map (SOM) clustering for preliminary regionalization. Wavelet decomposition is further used for regional feature analysis. The methodology is empirically applied to analyze summer precipitation in Shanxi Province, identifying homogeneous regions characterized by diverse spatiotemporal distributions. The results successfully identified 12 distinct regions of precipitation, effectively capturing the influence of topographic and atmospheric factors. According to the periodic and trend characteristics of each region at different time scales, we merged the division results on the three frequencies and six regions were ultimately differentiated. Specifically, a decreasing trend was observed in the southern and southeastern parts of Shanxi, as well as in the western part of the Lüliang Mountains. They had a significant 4-yr period mode, and the decreasing trend was more significant in the southern region. In contrast, northern Shanxi showed no trend and a significant 8-yr period mode. This proposed method presents an effective strategy for enhancing precipitation regionalization and extracting valuable information from the circulation field and multiple frequencies. [ABSTRACT FROM AUTHOR]

Rapid urbanization has altered the natural surface properties and spatial patterns, increasing the risk of urban waterlogging. Assessing the probability of urban waterlogging risk is crucial for preventing and mitigating the environmental risks associated with urban waterlogging. This study aims to evaluate the impact of different urban spatial morphologies on the probability of urban waterlogging risk. The proposed assessment framework was demonstrated in Guangzhou, a high-density city in China. Firstly, a spatial weight naive Bayes model was employed to map the probability of waterlogging risk in Guangzhou. Secondly, the World Urban Database and Access Portal Tools (WUDAPT)-based method was used to create a local climate zone (LCZ) map of Guangzhou. Then, the range of waterlogging risk and the proportion of risk levels were analyzed across different LCZs. Finally, the Theil index was used to measure the disparity in waterlogging risk exposure among urban residents. The results indicate that 16.29% of the area in Guangzhou is at risk of waterlogging. Specifically, 13.06% of the area in LCZ 2 is classified as high risk, followed by LCZ 1, LCZ 8, and LCZ 10, with area proportions of 11.42%, 8.37%, and 6.26%, respectively. Liwan District has the highest flood exposure level at 0.975, followed by Haizhu, Yuexiu, and Baiyun. The overall disparity in waterlogging exposure in Guangzhou is 0.30, with the difference between administrative districts (0.13) being smaller than the difference within the administrative districts (0.17). These findings provide valuable insights for future flood risk mitigation and help in adopting effective risk reduction strategies at urban planning level. [ABSTRACT FROM AUTHOR]

Mărculești Agricultural Research-Development Station is located in the south-east of the country, in the center of the Bărăgan plain. Throughout the years, it has developed and perfected the cultivation technologies for cereals, technical plants and fodder, in the pedoclimatic conditions of the area. According to Köppen climatic classification, Bărăgan region is part of the BSax province, the mean value for annual temperature is 10.9 0C and the mean annual value for precipitation is 480 mm. Fertile soils of chernozem type with a relatively good supply of nutrients are characteristic of this region. The present paper presents the climatic evolution in the South-eastern part of Bărăgan, based on comparison of the values of temperature and precipitation measured at the location during 2017-2022 with the multiannual mean values of these parameters. The results of the data analysis showed an increase in temperature and a decrease in precipitation, which are most likely to have negative effects on yields. Precipitations are of great importance for the autumn cultures because they provide the quick and uniform growing of the plants, and they ensure the accumulation of moisture in the soil during the winter season. Considering the obtained results, the following recommendations can be made: changes of the agricultural technology used for the main cultures in Bărăgan - autumn cereals, rape, maize and sunflower, early spring sowing, and the choice of hybrids cultivars with a medium vegetation period. [ABSTRACT FROM AUTHOR]

The latest coupled configuration of the Met Office Unified Model (Global Coupled configuration 2, GC2) is presented. This paper documents the model components which make up the configuration (although the scientific description of these components is detailed elsewhere) and provides a description of the coupling between the components. The performance of GC2 in terms of its systematic errors is assessed using a variety of diagnostic techniques. The configuration is intended to be used by the Met Office and collaborating institutes across a range of timescales, with the seasonal forecast system (GloSea5) and climate projection system (HadGEM) being the initial users. In this paper GC2 is compared against the model currently used operationally in those two systems. Overall GC2 is shown to be an improvement on the configurations used currently, particularly in terms of modes of variability (e.g. mid-latitude and tropical cyclone intensities, the Madden-Julian Oscillation and El Niño Southern Oscillation). A number of outstanding errors are identified with the most significant being a considerable warm bias over the Southern Ocean and a dry precipitation bias in the Indian and West African summer monsoons. Research to address these is ongoing. [ABSTRACT FROM AUTHOR]

Good knowledge about on-site wind climate is necessary in wind power assessments. Due to yearly and natural variability, a few years' measurements do not give information about wind climate. Long-term reference data can be used to correct observations to better correspond a normal year. The method doing this is commonly referred to as measure-correlate-predict (MCP). Uncertainties are introduced in predicted wind climate due to method weaknesses. In this paper uncertainties connected to length and season of on-site measurements are studied. An on-site series of 29 years, different MCP-methods and reanalysis datasets have been used. An additional high-resolution model dataset has been used to study variation within a wind farm. At least 12 months of observations and a long-term reference longer than 15 year are recommended. Large seasonal influences are seen; summer periods tend to overestimate while winter periods underestimate wind climate. [ABSTRACT FROM AUTHOR]

The article discusses studies on the effects of climate variability on crop production and management. It mentions the bad weather conditions in several areas in the U. S. including in the Cedar River in Iowa and in the upper Midwest. Effects of climate variability include the increase in crop prices. Several papers on climate variability and crop management are discussed including on how El Nino Southern Oscillation affect yield distribution of crops. Another paper examines climate stationarity. An overview of the papers is provided.

Climate classification plays a fundamental role in understanding climatic patterns, particularly in the context of a changing climate. This study utilized hourly meteorological data from 36 major cities in China from 2011 to 2021, including 2 m temperature (T2), relative humidity (RH), and precipitation (PRE). Both original hourly sequences and daily value sequences were used as inputs, applying two non-hierarchical clustering methods (k-means and k-medoids) and four hierarchical clustering methods (ward, complete, average, and single) for clustering. The classification results were compared using two clustering evaluation indices: the silhouette coefficient and the Calinski–Harabasz index. Additionally, the clustering was compared with the Köppen–Geiger climate classification based on the maximum difference in intra-cluster variables. The results showed that the clustering method outperformed the Köppen–Geiger climate classification, with the k-medoids method achieving the best results. Our research also compared the effectiveness of climate classification using two variables (T2 and PRE) versus three variables, including the addition of hourly RH. Cluster evaluation confirmed that incorporating the original sequence of hourly T2, PRE, and RH yielded the best performance in climate classification. This suggests that considering more meteorological variables and using hourly observation data can significantly improve the accuracy and reliability of climate classification. In addition, by setting the class numbers to two, the clustering methods effectively identified climate boundaries between northern and southern China, aligning with China's traditional geographical division along the Qinling–Huaihe River line. [ABSTRACT FROM AUTHOR]

The present study investigates the energy efficiency of different container house configurations across thirty European locations. By employing Heating Degree Days (HDDs) and Cooling Degree Days (CDDs), the research delves into climatic zone exploration, providing a simplified climatic classification for residential purposes and comparing it with the Köppen–Geiger model. The authors use specific hourly climatic data for each location, obtained through dynamic simulations with TRNSYS v.18 software. Initially, the CDDs are calculated by using different base temperatures (comfort temperatures that minimize energy demand) tailored to the specific conditions of each case. Then, the thermal loads of container houses are evaluated in different climatic scenarios, establishing a direct correlation between climatic conditions and the energy needs of these innovative and modular housing solutions. By comparing stacked and adjacent modular configurations in container housing, particularly in post-disaster scenarios, the study underscores the importance of adaptive design to optimize energy efficiency. The analysis conducted by the authors has allowed them to propose a climate characterization model based on HDDs, CDDs, and solar irradiance, obtaining an effective novel correlation with the Köppen–Geiger classification, especially in extreme climates. The present model emerges as a powerful tool for climate characterization in residential applications, offering a new perspective for urban planning and housing design. Furthermore, the results reveal a significant correlation between climate classification and the specific energy needs of container houses, emphasizing the direct influence of regional climatic characteristics on energy efficiency, particularly in small-sized dwellings such as container houses. [ABSTRACT FROM AUTHOR]

Traditional bioclimatic classification schemes have several inherent shortcomings; they do not represent anthropogenic impact, they contain a bias for global north representation, and they lack flexibility regarding novel climates that may arise due to climate change. Here we present an alternative approach, using a machine learning approach. We combine European Space Agency Land Cover Classification data with traditional bioclimate classification climate variables, and additional variables; latitude, elevation, and topography. We utilise a random forest algorithm to create a classification system that overcomes the limitations and biases of the traditional schemes. The algorithm produced is able to predict land cover classification globally at 0.5-degree resolution with 93% accuracy. The resulting classifications account for human impact, particularly via agriculture, are informed by the topography of a region, and avoids the biases that traditional bioclimatic schemes contain. The algorithm can provide insights into the drivers of land cover change, the spatial distribution of land cover change, the potential impacts on ecosystem services and human well-being. Furthermore, the random forest model serves as a novel approach to the prediction of future land cover, and can be used to identify regions at risk of a land cover transition. Our data-based machine learning approach produces larger land-cover changes due to climate change than a traditional bioclimatic scheme, especially in sensitive regions such as Amazonia. Overall, our new approach projects approximately 17.4 million square kilometre of land-cover change per degree celsius of global warming. [ABSTRACT FROM AUTHOR]

This paper examines the role of soil moisture in quantifying drought through the development of a drought index using observed and modeled soil moisture. In Nebraska, rainfall is received primarily during the crop-growing season and the supply of moisture from the Gulf of Mexico determines if the impending crop year is either normal or anomalous and any deficit of rain leads to a lack of soil moisture storage. Using observed soil moisture from the Automated Weather Data Network (AWDN), the actual available water content for plants is calculated as the difference between observed or modeled soil moisture and wilting point, which is subsequently normalized with the site-specific, soil property–based, idealistic available water for plants that is calculated as the difference between field capacity and wilting point to derive the soil moisture index (SMI). This index is categorized into five classes from no drought to extreme drought to quantitatively assess drought in both space and time. Additionally, with the aid of an in-house hydrology model, soil moisture was simulated in order to compute model-based SMI and to compare the drought duration and severity for various sites. The results suggest that the soil moisture influence, a positive feedback process reported in many earlier studies, is unquestionably a quantitative indicator of drought. Also, the severity and duration of drought across Nebraska has a clear gradient from west to east, with the Panhandle region experiencing severe to extreme drought in the deeper soil layers for longer periods (>200 days), than the central and southwestern regions (125–150 days) or the eastern regions about 100 days or less. The anomalous rainfall years can eliminate the distinction among these regions with regard to their drought extent, severity, and persistence, thus making drought a more ubiquitous phenomenon, but the recovery from drought can be subject to similar gradations. The spatial SMI maps presented in this paper can be used with the Drought Monitor maps to assess the local drought conditions more effectively. [ABSTRACT FROM AUTHOR]

Climate change is already having impacts on biodiversity within Europe, with habitats and species needing to change and adapt to rising global temperatures and shifts in bio-climatic zones. Spatial planning represents an important intervention to further European, national and local biodiversity objectives for climate change adaptation. Drawing on case-studies of plans for inland and coastal areas, and involving stakeholder workshops, this paper reports on a trans-national study examining the scope of spatial plans in the Netherlands, England and France in addressing the impact of climate change. It concludes that spatial planning is making provision for biodiversity and dynamic natural processes. However, while good practice in planning for biodiversity under conditions of climate change is developing, systematic use is not being made of available procedures. The paper examines some of the barriers to implementation of this new policy commitment to climate change adaptation. Recommendations are made covering policy development to include climate change impacts upon biodiversity, modified procedures for plan-making and the appraisal of plans and projects. Amongst the specific measures recommended are the climate-proofing of projects and plans through the use of EIA and SEA; the integration of plans through adoption of common objectives and review of time horizons and boundaries; and an ecosystem-based planning approach. Copyright © 2008 John Wiley & Sons, Ltd and ERP Environment. [ABSTRACT FROM AUTHOR]

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Laetoli, Tanzania is one of the most important palaeontological and palaeoanthropological localities in Africa. We report on a survey of the extant terrestrial gastropod faunas of the Laetoli-Endulen area, examine their ecological associations and re-examine the utility of Pliocene fossil molluscs in palaeoenvironmental reconstruction. Standardised collecting at 15 sites yielded 7302 individuals representing 58 mollusc species. Significant dissimilarities were found among the faunas of three broad habitat types: forest, woodland/bushland and open (grassland and scattered, xeric shrubland). Overall, more species were recorded in the woodland/bushland sites than in the forest sites. Open sites were less diverse. Environmental factors contributing most strongly to the separation of habitat types were aridity index and elevation. The results are supplemented with new mollusc data from the Mbulu Plateau south of Lake Eyasi, and compared to the list of species cumulatively recorded from the Ngorongoro area. Some regional variation is apparent and historical factors may explain the absence of some fossil taxa from Laetoli today. Differences in seasonality separated upland forest sites on the Mbulu plateau from those at Lemagurut at Laetoli. Indicator species were identified for each habitat. These included several large-bodied species analogous to the Laetoli Pliocene fossil species that were then used for palaeoenvironmental reconstruction. Based on the estimated aridity index, and adopting the widely used United Nations Environment Programme (UNEP) global climate classification, the four stratigraphic subunits of the Upper Laetolil Beds (3.6–3.85 Ma) would be placed in either the UNEP's Dry Sub-humid or Semi-arid climate classes, whereas the Upper Ndolanya Beds (2.66 Ma) and Lower Laetolil Beds (3.85-<4.36 Ma) would be assigned to the Humid and Semi-arid climate classes respectively. Pliocene precipitation at Laetoli is estimated as 847–965 mm per year, refining previous estimates. This is close or slightly higher than the present mean annual precipitation, and is likely to have corresponded to a mosaic of forest, woodland and bushland within a grassland matrix consistent with other reconstructions. [ABSTRACT FROM AUTHOR]

Common bean (Phaseolus vulgaris L.) ranks among the most produced and consumed legume crops and contains essential macro- and micronutrients. Grain yield of the food crop is markedly decreased by poor management, especially a lack of additional essential nutrient elements through the application of fertilizers. In addition to the application of fertilizers, scholarly research and crop farmers have shown that the use of biofertilizer inoculants improves the yield of legume crops. The objective of this research study was to assess the effectiveness of peat-based Rhizobium tropici sp. UD5 on the growth, yield, and nitrogen concentration of common bean. The peat inoculant contained 6.5 × 109 viable cells/g. The experiment was conducted in two climatic zones, as described by the Koppen–Gieger climatic classification system. Treatments involved the peat-based inoculant Rhizobium tropici (T0 = 0 g without inoculation, T1 = 250 g of peat inoculant of strain UD5 for 50 kg seeds, T2 = 500 g of inoculant of strain UD5, and T3 = 200 g of comparative peat inoculant). The results indicated that common-bean-inoculated formulation of R. tropici sp. strain UD5 increased the following parameters compared to the controls: plant height (T1 = 18.22%, T2 = 20.41%, and T3 = 19.93% for bioclimatic zone 1; T1 = 16.78%, T2 = 20.71%, and T3 = 19.93% for bioclimatic zone 2), root length (T1 = 13.26%, T2 = 21.28%, and T3 = 19.38% for zone 1; T1 = 15.06%, T2 = 23.70%, and T3 = 19.20% for zone 2), number of nodules (T1 = 1162.57%, T2 = 1166.36%, and T3 = 1180.30% for zone 1; T1 = 1575%, T2 = 1616.5%, and T3 = 1608.25% for zone 2), size of nodules (T1 = 224.07%, T2 = 224.07%, and T3 = 208.33% for zone 1; T1 = 166.4%, T2 = 180%, and T3 = 140% for zone 2), and yield (T1 = 40.49%, T2 = 47.10%, and T3 = 45.45% for zone 1; T1 = 62.16%, T2 = 54.05%, and T3 = 58.55% for zone 2). R. tropici sp. UD5 peat inoculant formulation also increased the nitrogen concentration in leaves compared to the control (T1 = 3.75%, T2 = 1.12%, and T3 = 8.72%) in both bioclimatic zones. The findings of this study provide significant information on the positive effect of R. tropic UD5 strain peat inoculant application in the improvement of plant growth, development, and yield through the formation of nodules. [ABSTRACT FROM AUTHOR]

In this paper, we make use of the monoHölder nature of surface air temperature data to recapture the Köppen-Geiger climate classification in Europe. Using data from the European Climate Assessment and Dataset (ECA&D), we first show that the Hölder exponents of surface air temperature data are statistically related to pressure anomalies. Then, we establish a climate classification based on these Hölder exponents in such a way that it allows to recover the Köppen-Geiger climate classification. We show that the two classifications match for a vast majority of stations, and we corroborate these observations with a confirmation test. We compare these results with those obtained with another dataset (NCEP-NCAR Reanalysis Project) to show that the new classification is still well-adapted, before eventually discussing these findings. [ABSTRACT FROM AUTHOR]

In Turkey, four degree-day regions stated in the standards are used in building heating energy load calculations. Deficiencies about the degree-day regions are mentioned in some other studies. In this paper, a different climatic zone approach suggested by International Energy Agency (IEA) regarding both heating and cooling degree-day data, derived from Typical Meteorological Year (TMY) data, is used. Results are presented graphically and compared with the current regulations. The second degree-day region, according to Turkish Standard (Thermal Insulation Requirements for Buildings (TS 825)), is divided into four climatic zones with the method proposed by IEA. Difference between monthly outdoor temperature values of the second degree-day region and cities in this region is up to 6.4 °C which is high. The conclusion of this study is that the climatic variables should be evaluated in detail, and a new climatic zone study should be done regarding both heating and cooling loads. This will imply new building energy load calculations for all cities in Turkey. [ABSTRACT FROM AUTHOR]

This paper delves into the potential impact of a changing climate on the energy performance of European buildings. Research aims to provide a comprehensive evaluation of current energy requirements focusing on the envelope, considering existing regulations in national policies. Energy simulations are conducted at 94 locations across the European Union to cover the climatic variability and Koppen climate classification. The research analyzes future climate scenarios for the years 2030, 2050, and 2070, using three different Representative Concentration Pathways (RCP 2.6, 4.5, 8.5). According to a comprehensive analysis of heating, cooling, and overall energy performance, climate plays a significant role in buildings' energy balance. In moderately cool climate countries, the demand for air conditioning is projected to decrease in the years ahead. Conversely, in countries with a warm climate, there is a projected increase in the overall energy demand. Consequently, a revision of current energy regulations should be a priority. Providing insights into the relation between building design, energy efficiency, and climate change, the research identifies policy adjustments to ensure buildings can effectively respond to changing climatic conditions. A holistic and dynamic approach can support building design accounting for long-term impacts of climate change to create resilient and energy-efficient structures. • National building energy regulations of European Member States do not tackle climate change. • Over next decades, a decrease in heating demand is forecasted across all climate zones. • There is potential for a future considerable increase of cooling demand in Europe. • A reduction in total building energy demand in colder climates and an increase in warmer climates can be expected. • Revise national energy regulations, especially in warmer zones, to curb the expected rise in Europe's total energy demand. [ABSTRACT FROM AUTHOR]

Worldwide, climate classification schemes are useful for characterizing the biological potentials of terrestrial ecosystems. Among these schemes, the Köppen-Geiger global climate classification has increasingly been used in a wide range of environmental fields. However, the resulting climate information is insufficient to fully portend the dynamics of ecosystem components such as vegetation gradients and water resources. To enhance the interpretability of this climate information, research-driven frameworks are needed to connect terrestrial vegetation and water resource signals to the Köppen-Geiger climate classes. Hence, this study developed a probabilistic framework for characterizing vegetation and water resources variability within the Köppen-Geiger climate classification system. The framework combines an application of entropy theory with multivariate logistic models, and it uses variables including half-degree gridded precipitation, surface temperature, leaf area index, and liquid water equivalence anomalies. Explicitly, entropy-based disorder index (DI) values are quantified for individual variables and thresholds of DI percentiles are used to discretize vegetation and water resources variability zones at the global scale. Multivariate logistic models are later applied to grid-level DI zone attributes and long-term average values to predict Köppen-Geiger climate classes. The statistical evaluation sustained variable models' likelihoods (0.04≤McFadden's pseudo-R2≤0.92) but consistent estimates (0.87≤Count R2≤0.99) within the global climate classes. The developed framework could be an avenue to improve the interpretability of Köppen-Geiger climate classes by providing a probabilistic insight into vegetation and water resources change at regional, continental, or global levels. [ABSTRACT FROM AUTHOR]

The observed continuous rise in the frequency of extreme heat events in South America (SA) poses a serious challenge for public health. However, there is a lack on the understanding of the large-scale and long-term variability and trends of thermal stress in this continent. Accordingly, here we developed the first comprehensive bioclimatology of thermal stress over SA during the past four decades. Consecutive heat stress hours were analyzed using the Universal Thermal Climate Index (UTCI) from ERA5-HEAT reanalysis according to the Köppen–Geiger climate classification and also focusing on the 31 most populated cities of SA. Results show an inland/coastline contrast and a marked latitudinal northward increase in the number of hours under heat stress. Heat stress hotspots are located mostly around the Amazon, northern and central parts of SA with 26–35% of the hours between 1979 and 2020 under strong heat stress. The annual number of hours within heat stress increased significantly between 1979 and 2020, varying from + 1.16 h/year to + 8.25 h/year depending on the Köppen–Geiger class. The past 20 years (2000 forward) presented not only more consecutive hours under heat stress than the previous two decades in all the analyzed cities, but also a higher persistence of such conditions. The bioclimatology of thermal stress developed here may provide important guidelines to decision-makers for exploring adaptation strategies to increase societal resilience. [ABSTRACT FROM AUTHOR]

This article investigates the hygrothermal properties of earth-based materials by analyzing experimental data from 88 articles spanning 32 countries worldwide. The focus is determining effective techniques for leveraging the use of excavated soil in construction, particularly emphasizing enhancement of hygrothermal comfort in specific climates. Based on statistical analysis, the study presents a comprehensive classification of earth production techniques, incorporating additives, and examines their impacts on hygrothermal properties of excavated soils. Additionally, it explores the intricate relationship between the climatic conditions of a region and the chosen earth-material production techniques. The analysis aims to propose standard parameters for earthen materials and identify gaps in both methods and experimental studies. Therefore, this study will provide valuable insights by proposing new design tools (ternary diagrams) to maximize the use of excavated soils in construction practices. The proposed diagrams illustrate the intricate relation linking either hygrothermal properties, the climate zone, and manufacturing techniques, or the relation between the most studied manufacturing techniques (compaction, fibered, and stabilization) and expected dry thermal conductivity. Thereby, results from this meta-analysis and critical review will contribute to advancing sustainable construction practices. [ABSTRACT FROM AUTHOR]

Background: Previous studies provided some evidence of meteorological factors influence seasonal influenza transmission patterns varying across regions and latitudes. However, research on seasonal influenza activities based on climate zones are still in lack. This study aims to utilize the ecological-based Köppen Geiger climate zones classification system to compare the spatial and temporal epidemiological characteristics of seasonal influenza in Chinese Mainland and assess the feasibility of developing an early warning system. Methods: Weekly influenza cases number from 2014 to 2019 at the county and city level were sourced from China National Notifiable Infectious Disease Report Information System. Epidemic temporal indices, time series seasonality decomposition, spatial modelling theories including Moran's I and local indicators of spatial association were applied to identify the spatial and temporal patterns of influenza transmission. Results: All climate zones had peaks in Winter-Spring season. Arid, desert, cold (BWk) showed up the first peak. Only Tropical, savannah (Aw) and Temperate, dry winter with hot summer (Cwa) zones had unique summer peak. Temperate, no dry season and hot summer (Cfa) zone had highest average incidence rate (IR) at 1.047/100,000. The Global Moran's I showed that average IR had significant clustered trend (z = 53.69, P < 0.001), with local Moran's I identified high-high cluster in Cfa and Cwa. IR differed among three age groups between climate zones (0–14 years old: F = 26.80, P < 0.001; 15–64 years old: F = 25.04, P < 0.001; Above 65 years old: F = 5.27, P < 0.001). Age group 0–14 years had highest average IR in Cwa and Cfa (IR = 6.23 and 6.21) with unique dual peaks in winter and spring season showed by seasonality decomposition. Conclusions: Seasonal influenza exhibited distinct spatial and temporal patterns in different climate zones. Seasonal influenza primarily emerged in BWk, subsequently in Cfa and Cwa. Cfa, Cwa and BSk pose high risk for seasonal influenza epidemics. The research finds will provide scientific evidence for developing seasonal influenza early warning system based on climate zones. [ABSTRACT FROM AUTHOR]

The recent U.S. National Academies report, Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, was unequivocal in recommending the need for the development of a North American Multimodel Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multimodel ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation and has proven to produce better prediction quality (on average) than any single model ensemble. This multimodel approach is the basis for several international collaborative prediction research efforts and an operational European system, and there are numerous examples of how this multimodel ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test bed (CTB) NMME workshops (18 February and 8 April 2011), a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data are readily available (e.g., ) and in graphical format from CPC (). Moreover, the NMME forecast is already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, and presents an overview of the multimodel forecast quality and the complementary skill associated with individual models. [ABSTRACT FROM AUTHOR]

The identification and prediction of drought events depend on the integrity of the dataset employed. Streamflow is a good indicator of surface water availability and has dominated the literature on frequency analysis of hydrological droughts and water management. However, gauged measurements are impaired by climate and land use changes, especially in large, modified watersheds. Hence, their use in drought prediction is limited because they may violate the assumption of stationarity unless a naturalized observation series is obtained. In this paper, a land surface model is used to generate runoff in the Rio Grande/Río Bravo del Norte basin. Land use land cover is kept constant so that changes are a result of climatological variations. The river threads across several climatic zones; therefore the basin is divided into different regions for analysis. Using statistical theory of runs, water deficit duration and severity and drought interarrival time are extracted from 3-month Standardized Runoff Index series for each region and for the whole basin. Copulas are used to develop joint distribution functions of water deficit duration and severity. Nine copulas, two from the extreme value family and seven from the one-parameter families of the Archimedean copulas, are tested. Different copulas are deemed suitable for each region because they are subject to different climatologic conditions, which affect the nature of deficit events, especially at the extremes. Conditional probability models of duration and severity are developed and compared for two regions-one experiencing alpine climate at the headwaters and one in the semiarid portion of the basin. Further, conditional probabilities derived from model runoff are compared with that based on observed precipitation. Finally, univariate and conditional return periods based on duration and severity are computed for the basin and their significance in long-term water resources planning is discussed. [ABSTRACT FROM AUTHOR]

Copyright of Tecnología y Ciencias del Agua is the property of Instituto Mexicano de Tecnologia del Agua (IMTA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Climate classifications of climate models' outputs have been used to assess environmental changes but systematic analyses of the differences between models, scenarios and classification methods are scarce. Here, the results of applying the most commonly used climate classifications to the outputs of 47 Global Climate Models (GCM) of different physical parameterizations and varied grid size are presented. The extent and intensity of changes for present climate, three different Representative Pathways Scenarios (RCP26, RCP45 and RCP85) and three increasingly-fine classification methods show that there is a consensus between models, and that climate classifications are indeed useful tools to translate physical climatology variables into environmental changes. The main conclusions are that climate classifications can indeed be used to gauge model performance at several grid sizes and that the classification method does not decisively affects the potential global changes in future climates under increasing greenhouse gas emissions. The analyses also reveal that there are several uncertainties that are not attributable to model grid size or to limitations in the reference datasets but more likely to deficiencies in the physics of the models. Highlights • This paper provides a systematic analysis of climate classifications for present and future climates. • Several classifications methods are used to illustrate the consensus between models and observations. • Overall, global models show consistent changes but uncertainties persist with large differences in individual model skill. [ABSTRACT FROM AUTHOR]

As a charter member of the Association of American Geographers and a well-respected scholar in academic meteorology, Robert DeCourcy Ward was an important figure in the earth sciences tradition of early American academic geography. The purpose of this paper is twofold: (1) to elaborate on the contributions of Ward to modern geographical climatology; and (2) to examine some of his writing beyond scientific topics as likely cause for the lack of attention to his geographical work today. Ward’s work across a range of spatial scales from local thunderstorm events to sea breeze circulations to global climatic classification represented a ‘bridge’ between scholars in the meteorology and geography communities. His emphasis on the conceptual understanding of climate as a dynamic rather than static entity represented a paradigm shift for which he has not been credited. He was a true and early advocate for study of the relationship between people and the environment. A thorough review of Ward’s works suggests that his environmental deterministic philosophy and writings on topics outside of physical geography, particularly immigration reform and eugenics, may have contributed to his current obscurity as an important early practitioner of our discipline. We suggest that, in general, the lessons of history, both triumphant and tragic, should never be forgotten, and it is in this framework that we believe that Ward should be remembered. [ABSTRACT FROM PUBLISHER]

In this paper, a Wind Direction Change Index (WI), which can describe four-dimensional spatiotemporal changes of the atmospheric circulation objectively and quantitatively, is defined to study its evolution and seasonal variation. The first four modes can be obtained by EOF expansion of the zonally averaged WI. The first mode reveals the basic spatial distribution of the annually averaged WI. The second mode reflects the quasi-harmonic parts of the WI deviations. Tropical, subtropical and extratropical monsoon areas can be clearly reflected by this mode. The third mode reflects the non-harmonic parts of the WI deviations. It shows the so-called February reverse in stratospheric atmosphere as well as the asymmetric seasonal changes from spring to fall and from fall to spring due to both the land-sea distribution contrast between the Northern and Southern Hemispheres and the nonlinear effect of atmospheric and ocean fluids. The fourth mode reveals the northward advancing of the global reversed wind fields from spring to summer and their southward withdrawal from summer to autumn. [ABSTRACT FROM AUTHOR]

Drought in spring and early summer has been shown to precede anomalous hot summer temperature. In particular, drought in the Mediterranean region has been recently shown to precede and to contribute to the development of extreme heat in continental Europe. In this paper, this mechanism is investigated by performing integrations of a regional mesoscale model at the scale of the European continent in order to reproduce hot summer inception, starting with different initial values of soil moisture south of 46°N. The mesoscale model is driven by the large-scale atmospheric conditions corresponding to the 10 hottest summers on record from the European Climate Assessment dataset. A northward progression of heat and drought from late spring to summer is observed from the Mediterranean regions, which leads to a further increase of temperature during summer in temperate continental Europe. Dry air formed over dry soils in the Mediterranean region induces less convection and diminished cloudiness, which gets transported northward by occasional southerly wind, increasing northward temperature and vegetation evaporative demand. Later in the season, drier soils have been established in western and central Europe where they further amplify the warming through two main feedback mechanisms: 1) higher sensible heat emissions and 2) favored upper-air anticyclonic circulation. Drier soils in southern Europe accelerate the northward propagation of heat and drying, increasing the probability of strong heat wave episodes in the middle or the end of the summer. [ABSTRACT FROM AUTHOR]

The aim of this paper is to determine how an atmosphere with enhanced mean-state baroclinity can support weaker baroclinic wave activity than an atmosphere with weak mean-state baroclinity. As a case study, a Last Glacial Maximum (LGM) model simulation previously documented to have reduced baroclinic storm activity, relative to the modern-day climate (simulated by the same model), despite having an enhanced midlatitude temperature gradient, is considered. Several candidate mechanisms are evaluated to explain this apparent paradox. A linear stability analysis is first performed on the jet in the modern-day and the LGM simulation; the latter has relatively strong barotropic velocity shear. It was found that the LGM mean state is more unstable to baroclinic disturbances than the modern-day mean state, although the three-dimensional jet structure does stabilize the LGM jet relative to the Eady growth rate. Next, feature tracking was used to assess the storm track seeding and temporal growth of disturbances. It was found that the reduction in LGM eddy activity, relative to the modern-day eddy activity, is due to the smaller magnitude of the upper-level storms entering the North Atlantic domain in the LGM. Although the LGM storms do grow more rapidly in the North Atlantic than their modern-day counterparts, the storminess in the LGM is reduced because storms seeding the region of enhanced baroclinity are weaker. [ABSTRACT FROM AUTHOR]

The present paper examines ways in which the seasonal cycle influences the evolution of El Niño in the tropical Pacific. The following hypotheses and associated physical mechanisms are investigated: (i) Hypothesis 1 (H1)—the seasonal warming of the cold tongue early in the calendar year (January–April) favors the initial growth of an event; (ii) hypothesis 2 (H2)—during an event, the warm surface waters migrating in the western basin from the Southern to the Northern Hemisphere during the northern spring (April–May) trigger enhanced convection along the equator, which contributes to reinforce the event; and (iii) hypothesis 3 (H3)—the warm surface waters returning in the western basin from the Northern to the Southern Hemisphere toward the end of the calendar year (November–January) favor the demise of ongoing events. Hypothesis-validation experiments are performed with a coupled atmosphere–ocean general circulation model (CGCM)—the tropical Pacific version of the University of California, Los Angeles (UCLA) CGCM. The anomaly-coupling technique is applied, in which the simulated seasonal cycle and interannual variability can be separated and artificially modified to highlight the aspect targeted for examination, thus allowing for comparisons of simulations in which seasonal conditions in the CGCM’s atmospheric component are either fixed or time varying. The results obtained in the experiments are supportive of hypotheses H1 and H3. No supportive evidence is found for the validity of hypothesis H2. [ABSTRACT FROM AUTHOR]

The necessity of having windows on any building's façade is not questionable. However, not every window is suitable for any building. The selection of an adequate window must include the analysis of various factors—the most important ones are the type of window (e.g., single or double glazing); filling gas in cavities (e.g., air, argon or some other gas); and placing, i.e., orientation of a window on a façade (facing north, south, or east, etc.). The research presented in this paper is dealing with the calculation of the window thermal loading for the cities of Kragujevac and Bor in Serbia and Žilina in Slovakia. These three cities were selected because they belong to different climate regions, according to the Köppen–Geiger climatic classification. The first two cities in Serbia belong to the same region Cf with difference only in the category of summer—Kragujevac Cfa and Bor Cfb—while the third city—Žilina in Slovakia—belongs to the Dfb region. The calculated thermal loading through the window was obtained as a sum of the thermal loading due to the heat conduction and thermal loading due to the solar radiation. The objective was to find the optimal window construction and orientation of a building's façade for each of these cities, by varying the type of the window, its frame material and the filling gas. The results show that for the first two cities in Serbia, there is a difference in the window frame material in the optimal window construction, while for the third city (Žilina in Slovakia), the results are the same as for the second city (Bor in Serbia) despite the fact that they belong to different climate regions (Cfb and Dfb, respectively). These results support the fact that the climate affects the optimal window construction for any city/region in the world. [ABSTRACT FROM AUTHOR]

How strong is the resemblance between two states of the atmosphere leading to the same outcome? In the following pages this question was addressed by studying the resemblance of the surface pressure fields, obtained by NCEP reanalyses, corresponding to “intense” precipitation events over 5 Italian climatic areas. Such “intense” precipitation events were selected by means of 45 daily precipitation records covering the period from 1951 to 2000. Two pressure patterns that resemble one another are termed soft-analogues in this paper. The mathematical measures used to assess the difference between two pressure patterns were: the root-mean-square difference, the correlation, and the S1 score. The comparison outcomes were categorized into classes according to the previous measures. Once a couple of the pressure patterns were found to be good soft-analogues (that was a rare occurrence), these pressure patterns were compared with all pressure patterns excluded by the initial selection being not associated with intense precipitation events. The few soft-analogues associated with “intense” precipitation events were found to be also soft-analogue to several, and sometimes a great deal of, pressure patterns not associated with the selected “intense” precipitation events. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of pluviometric precipitation time series obtained from 45 data collection points, with daily rain measurements (in mm), from 45 counties within the nine climatic regions of the state of São Paulo, mostly for the period of 1950–1997. São Paulo State (Brazil) is situated between the inter-tropical climatic zone, dominated by tropical and equatorial masses, and the sub-tropical climatic zone controlled by tropical and polar air masses. Due to this location the State can be divided into three climatic zones: south-east and south-west, which are permanently humid, and north with a well defined dry period. Rescaled range analysis, or R/S analysis, has been used to investigate the scale properties of the time series. We found two clear values for the Hurst exponent, one for a period of 10 to 200 days (H1), and another for a period above 210 days (H2). The value of H1 lies between 0.6 to 0.7 for all climatic zones indicating characteristics of persistence. However, H2 is (i) close to 0.5 for the littoral area, showing random characteristics, (ii) close to 0.6 for mountain areas, indicating persistence characteristics, and (iii) close to 0.4 for the plane area, irrespective of whether the climate is dry or humid, and shows anti-persistence characteristics. [ABSTRACT FROM AUTHOR]

A government-funded scheme, the UK Climate Change Communications Initiative (UKCCCI), has provided money for organisations to deliver projects that attempt to impact positively on people's attitudes towards climate change. This devolution of communications is a relatively novel approach after previous centralised campaigns. This paper considers what size of target audience is most appropriate for attitude change initiatives. It compares data from a regional UKCCCI project aimed at residents of two counties with a nationally representative dataset. Regional data are also analysed to see if there are differences in attitudes within the two-counties target audience. The study suggests that attitude change interventions must strike a balance between personalisation of information and the higher cost of targeting smaller groups with more specific material. [ABSTRACT FROM AUTHOR]

Aim The objective of this paper is to explore the relationships that exist between vegetation and modern pollen rain in the open, largely treeless landscape of subarctic Greenland. The implications of these results for the interpretation of fossil pollen assemblages from the time of the Norse landnám are then examined. Location The study area is the sheep farming district of Qassiarsuk in the subarctic, subcontinental vegetational and climatic zone of southern Greenland (61° N, 45° W). Between c.ad 1000–1500 this region was contained within the Norse Eastern Settlement. Methods Detrended Correspondence Analysis (DCA) of harmonized plant–pollen data sets is used to compare plant cover in 64 vegetation quadrats with pollen assemblages obtained from moss polsters at matching locations. Presence/absence data are also used to calculate indices of association, over- and under-representation for pollen types. Results Good correspondence between paired vegetation–pollen samples occurs in many cases, particularly in locations where Salix glauca– Betula glandulosa dwarf shrub heath is dominant, and across herbaceous field boundaries and meadows. Pollen samples are found to be poor at reflecting actual ground cover where ericales or Juniperus communis are the locally dominant shrubs. Dominant or ubiquitous taxa within this landscape ( Betula, Salix and Poaceae) are found to be over-represented in pollen assemblages, as are several of the ‘weeds’ generally accepted as introduced by the Norse settlers. Main conclusions Due to their over-representation in the pollen rain, many of the Norse apophytes and introductions (e.g. Rumex acetosa and R. acetosella) traditionally used to infer human activity in Greenland should be particularly sensitive indicators for landnám, allowing early detection of Norse activity in fossil assemblages. Pteridophyte spores are found to be disassociated with the ground cover of ferns and clubmosses, but are over-represented in pollen assemblages, indicating extra-local or regional sources and long residence times in soil/sediment profiles for these microfossils. A pollen record for Hordeum-type registered in close proximity to a field containing barley suggests that summer temperatures under the current climatic regime are, at least on occasion, sufficient to allow flowering. [ABSTRACT FROM AUTHOR]

A simple theoretical model is proposed to clarify how synoptic-scale waves drive the life cycle of the North Atlantic Oscillation (NAO) with a period of nearly two weeks. This model is able to elucidate what determines the phase of the NAO and an analytical solution is presented to indicate a high similarity between the dynamical processes of the NAO and zonal index, which is not derived analytically in previous theoretical studies. It is suggested theoretically that the NAO is indeed a nonlinear initial-value problem, which is forced by both preexisting planetary-scale and synoptic-scale waves. The eddy forcing arising from the preexisting synoptic-scale waves is shown to be crucial for the growth and decay of the NAO, but the preexisting low-over-high (high-over-low) dipole planetary-scale wave must be required to match the preexisting positive-over-negative (negative-over-positive) dipole eddy forcing so as to excite a positive (negative) phase NAO event. The positive and negative feedbacks of the preexisting dipole eddy forcing depending upon the background westerly wind seem to dominate the life cycle of the NAO and its life period. An important finding in the theoretical model is that negative-phase NAO events could be excited repeatedly after the first event has decayed, but for the positive phase downstream isolated dipole blocks could be produced after the first event has decayed. This is supported by observed cases of the NAO events presented in this paper. In addition, a statistical study of the relationship between the phase of the NAO and blocking activity over Europe in terms of the seasonal mean NAO index shows that blocking events over Europe are more frequent and long-lived for strong positive-phase NAO years, indicating that the positive-phase NAO favors the occurrence of European blocking events. [ABSTRACT FROM AUTHOR]

Landfilling in South Africa is controlled by a set of statutory Minimum Requirements, based on a landfill classification system. Landfills are classified according to the type of waste, the projected final size of the landfill and the climate. Climate is important as climatic conditions in South Africa vary from humid sub-tropical in the east, to semi-arid on the central plateau, to semi-desert in the west. Anti-pollution measures are closely related to climate and size, with the pollution potential of small, general (i.e. domestic) waste landfills in dry climates being regarded as negligible. At the other end of the scale, large landfills where hazardous wastes are disposed have a serious pollution potential and must be designed as containment systems. The paper describes the method currently in use for deciding on the climatic classification of a site, followed by the new method that will be adopted when the latest revision of the Minimum Requirements appears shortly. [ABSTRACT FROM AUTHOR]