Search

Conventional nature conservation has attempted to protect biodiversity, ecological integrity (i.e., composition and biomass/abundance of native species), and ecosystem services in parallel, assuming generally positive relationships among them. However, many native species are potentially weakened by and maladapted to a new, warmer climate. Such native species may not be able to offer ecosystem services at previous levels. If the relationship between ecosystem services and native species is no longer positive due to climate change, pursuing ecological integrity could be ineffective for maximizing ecosystem services. Hence, there is room to investigate future relationships among biodiversity, ecological integrity, and ecosystem services. This investigation can be done by describing the changes in ecosystem services as a function of biodiversity and ecological integrity. Herein, biodiversity and ecological integrity can be expressed by phylogenetic diversity and species intactness indices based on abundance, respectively. According to my hypothetical figure, the metrics of biodiversity and ecological integrity are potentially not positively but rather negatively related to ecosystem services. The hypothetical figure can be examined and improved using three different approaches: computer simulations (e.g., the LANDIS‐II forest simulation model), collection of actual data (e.g., measuring ecosystem services of some sites with psychrophilic and thermophilic species), and questionnaire surveys about cultural services (e.g., using digitally manipulated photographs and generative adversarial networks). Thus, we can successfully enable practitioners to understand new relationships, which will be relevant to ecosystem management under changing climates. [ABSTRACT FROM AUTHOR]

This paper develops the notion of metonymy scenarios by exploring the social and cognitive dimensions of various creative uses of metonymy in a collection of digital banners created for the Global Climate Strike movement. The paper argues that the banners exploit existing metonymic relationships to activate dominant anthropocentric discourses in society, and to subvert them via processes of recontextualisation and reappropriation, in order to challenge system conventions and normative attitudes regarding climate change. The literature to date has not adequately considered metonymy as a dynamic and scenario-activating cognitive operation, nor has it thoroughly investigated the relationship between metonymy and irony. However, the data analysed here show that several creative uses of metonymy, including twice-true metonymy, metonymy in combination with metaphor, and the juxtaposition of different metonymies are markers of what this paper posits as metonymic mininarratives or scenarios. [ABSTRACT FROM AUTHOR]

In order to effectively model the potential impacts of future climate change, there is a requirement for climate data inputs which (a) are of high spatial and temporal resolution, (b) explore a range of future climate change scenarios, (c) are consistent with historical observations in the historical period, and (d) provide an exploration of climate model uncertainty. This paper presents a suite of climate projections for the United Kingdom that conform to these requirements: CHESS-SCAPE. CHESS-SCAPE is a 1 km resolution dataset containing 11 near-surface meteorological variables that can be used to as input to many different impact models. The variables are available at several time resolutions, from daily to decadal means, for the years 1980–2080. It was derived from the state-of-the art regional climate projections in the United Kingdom Climate Projections 2018 (UKCP18) regional climate model (RCM) 12 km ensemble, downscaled to 1 km using a combination of physical and empirical methods to account for local topographic effects. CHESS-SCAPE has four ensemble members, which were chosen to span the range of temperature and precipitation change in the UKCP18 ensemble, representing the ensemble climate model uncertainty. CHESS-SCAPE consists of projections for four emissions scenarios, given by the Representative Concentration Pathways 2.6, 4.5, 6.0 and 8.5, which were derived from the UKCP18 RCM RCP8.5 scenarios using time shifting and pattern scaling. These correspond to UK annual warming projections of between 0.9–1.9 K for RCP2.6 up to 2.8–4.3 K for RCP8.5 between 1980–2000 and 2060–2080. Little change in annual precipitation is projected, but larger changes in seasonal precipitation are seen with some scenarios projecting large increases in precipitation in the winter (up to 22 %) and large decreases in the summer (up to - 39 %). All four RCP scenarios and ensemble members are also provided with bias correction, using the CHESS-met historical gridded dataset as a baseline. With high spatial and temporal resolution, an extensive range of warming scenarios and multiple ensemble members, CHESS-SCAPE provides a comprehensive data resource for modellers of climate change impacts in the UK. The CHESS-SCAPE data are available for download from the NERC EDS Centre for Environmental Data Analysis: 10.5285/8194b416cbee482b89e0dfbe17c5786c. [ABSTRACT FROM AUTHOR]

Numerical models have been developed to investigate and understand responses of biogeochemical cycle to global changes. Steady state, when a system is in dynamic equilibrium, is generally required to initialize these model simulations. However, the spin‐up process that is used to achieve steady state pose a great burden to computational resources, limiting the efficiency of global modeling analysis on biogeochemical cycles. This study introduces a new Semi‐Analytical Spin‐Up (SASU) to tackle this grand challenge. We applied SASU to Community Land Model version 5 and examined its computational efficiency and accuracy. At the Brazil site, SASU is computationally 7 times more efficient than (or saved up to 86% computational cost in comparison with) the traditional native dynamics (ND) spin‐up to reach the same steady state. Globally, SASU is computationally 8 times more efficient than the accelerated decomposition spin‐up and 50 times more efficient than ND. In summary, SASU achieves the highest computational efficiency for spin‐up on site and globally in comparison with other spin‐up methods. It is generalizable to wide biogeochemical models and thus makes computationally costly studies (e.g., parameter perturbation ensemble analysis and data assimilation) possible for a better understanding of biogeochemical cycle under climate change. Plain Language Summary: Land carbon cycle models, a critical component of earth system models, have been developed to investigate and understand ecological responses to global changes. However, the model spin‐up process to achieve steady state are usually time‐consuming and pose great computational burden. The spin‐up problem hinders our ability to study some key issues in land carbon cycle modeling, such as sensitivity analysis. This paper applies a new SASU framework to accelerate spin‐up of Community Land Model matrix version 5. Compared with traditional native dynamic approach, SASU speeds up the spin‐up by approximately 50 times, achieving the fastest spin‐up speed than all the previously recorded methods. Our SASU method is applicable to biogeochemical models and, therefore, liberates the models for computationally costly studies, such as parameter perturbation ensemble analysis and data assimilation. Key Points: A new semi‐analytical spin‐up (SASU) framework combines the default accelerated spin‐up method and matrix analytical algorithmSASU accelerates CLIM5 spin‐up by tens of times, becoming the fastest method to our knowledgeSASU is applicable to most biogeochemical models and enables computationally costly study, for example, sensitivity analysis [ABSTRACT FROM AUTHOR]

Media are essential actors in transmitting, contesting and embedding the attitudes towards climate change, yet media performance in post-communist countries has been relatively little researched. Informed by conceptual frameworks of strategic narratives, agenda-setting and framing, this paper investigates the media coverage of climate change and environment related issues in Latvia. The paper demonstrates the representation of climate change and environment related issues in Latvian and Russian-speaking traditional and online media, using quantitative data analysis of 3753 media articles, video and audio broadcasts from August 2020 till January 2021, as well as qualitative content analysis of seven peaks. The findings reveal a significant amount of climate change and environment related articles and broadcasts in Latvian media. News agencies and public broadcasters are the most important media segments in terms of publishing, whereas online media are prior in terms of the audiences reached. International efforts emerge as a dominant theme in the media coverage, while climate change per se receives a minor journalist attention. Both observations confirm a low level of climate change domestication in the Latvian media. Media reliance on political and government information sources and prepackaged material suggests a high potential for official political narratives to spread, yet the persuasive power of strategic narratives remains blurred as the perception side is highly underreported. [ABSTRACT FROM AUTHOR]

A green economy is expected to solve the failure of the conventional economy to address climate change. It would do this by emphasising the balance between economic and environmental factors – an approach that is in line with the existing concept of Islamic finance. As a new field, research related to the green economy is still developing. This study attempts to provide quantitative information on this development. In total, Scopus lists 1183 papers, including journal articles, book chapters, and conference papers, on this subject, all produced between 1961 and 2021. This study employed the Biblioshiny package from R-studio and Excel software to synthesise and analyse this data. In addition, we identified relevancies between the green economy and Islamic finance using existing research. Regarding the results obtained, the natural fit between Islamic finance and a green economy would naturally support the implementation of the latter. Furthermore, the results show that sukuk, in particular, is a potential instrument in Islamic finance for promoting a green economy. [ABSTRACT FROM AUTHOR]

The spatio-temporal variogram is an important factor in spatio-temporal prediction through kriging, especially in fields such as environmental sustainability or climate change, where spatio-temporal data analysis is based on this concept. However, the traditional spatio-temporal variogram estimator, which is commonly employed for these purposes, is extremely sensitive to outliers. We approach this problem in two ways in the paper. First, new robust spatio-temporal variogram estimators are introduced, which are defined as M-estimators of an original data transformation. Second, we compare the classical estimate against a robust one, identifying spatio-temporal outliers in this way. To accomplish this, we use a multivariate scale-contaminated normal model to produce reliable approximations for the sample distribution of these new estimators. In addition, we define and study a new class of M-estimators in this paper, including real-world applications, in order to determine whether there are any significant differences in the spatio-temporal variogram between two temporal lags and, if so, whether we can reduce the number of lags considered in the spatio-temporal analysis. [ABSTRACT FROM AUTHOR]

The present stage of development of artificial intelligence (AI) is based on advanced technologies and modern methods and algorithms of machine learning (ML) including deep machine learning, intellectual data analysis and other fundamental scientific areas, which in turn play an important role in delivering applied solutions in almost all areas of digital economy. However, the corresponding expansion of the AI application range and complication of the class of problems, as well as of the spectrum and the volume of data used for creation of applied AI models and AI-based intelligent system call for a significant expansion of the theoretical and algorithmic basis of AI including development of ML methods that involve mathematical and physical models of objects and phenomena of domains, methods of fusion of multimodal data, methods for constructing geometric and topological components of deep neural networks, methods for modeling 3D objects, etc. In 2021, in order to cope well with these challenges, Skoltech Research Center for Applied Artificial Intelligence was created with support from the Federal Program "Artificial Intelligence." The aims of the Center are as follows: creation of a scientific and technological basis for solution of a wide range of relevant applied tasks for purposes of sustainable development of the economy of the Russian Federation including optimization problems of managerial decisions aimed at reduction of the carbon footprint and other urgent ESG tasks; environmental monitoring tasks for detection of anomalies and extreme situations forecast; assessment of economic and social risks and their dynamics caused by climatic changes; predictive analytic problems, etc. In the present paper, we describe new AI technologies, models, methods and algorithms developed in the Center, describe the main applied areas of research of the Center, and provide a briefly survey of the already developed scientific and applied results. [ABSTRACT FROM AUTHOR]

The carbon embodied in buildings is an important proportion of our emissions and needs to be radically reduced in order to support climate change mitigation. The highest proportion of embodied carbon is usually emitted during the product stage, and within the structural elements. Therefore, reducing the carbon embodied in the structural materials is likely to have a major impact. In most buildings, the majority of embodied carbon comes from steel and concrete. But although there are now hundreds of registered Environmental Product Declarations (EPD) for cements and concretes, there has been very limited independent published information comparing the embodied carbon of different concrete mixes and raw materials. This lack of comparative data limits the potential to make appropriate decisions at early design stages leading to low carbon buildings. The authors have recently conducted a review of verified EPD for concrete mixes and for concrete’s key constituents, including cement, identifying the range of carbon coefficients. This paper provides guidance on making use of the coefficient ranges provided in that research: to support the verification of EPD for concrete and its raw materials; in material selection; in assessing building level embodied carbon; in benchmarking; and in the setting of reduction targets. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel graph‐based method to classify the regions with similar climate in a local area. We refer our proposed method as graph partition based method (GPBM). Our proposed method attempts to overcome the shortcomings of the current state‐of‐the‐art methods in the literature. It has no limit on the number of variables that can be used and also preserves the nature of climate data. To illustrate the capability of our proposed algorithm, we benchmark its performance with other state‐of‐the‐art climate classification techniques. The climate data are collected from 24 synoptic stations in Fars province in southern Iran. The data include seven climate variables stored as time series from 1951 to 2017. Our results exhibit that our proposed method performs a more realistic climate classification with less computational time. It can save more information during the climate classification process and is therefore efficient in further data analysis. Furthermore, using our method, we can introduce seasonal graphs to better investigate seasonal climate changes. To the best of our knowledge, our proposed method is the first graph‐based climate classification system. [ABSTRACT FROM AUTHOR]

• The evolutionary process of pipe jacking method in Japan was reviewd. • The classification system provided by the Japan Microtunneling Association was introduced. • The application status of this method is investigated based on survey data from JSTT. • SPJ and HDSPJ have better performance for the ultra-long pipe jacking construction. • Curved pipe jacking projects account for 40% of total jacking projects, and HDSPJ is most adopted for curved pipe jacking. Pipe-jacking is one of the trenchless methods being widely used for the construction and maintenance of urban underground pipelines. As an eco-friendly construction method, it has the capacity for the installation of pipelines with diameters from 100 mm (4 in.) to 5000 mm (200 in.) covering the majority needs of municipal pipeline construction and is considered the most widely used trenchless method in Japan. However, there is little English literature that systematically introduces the research and application status of pipe jacking technology in Japan, resulting in a serious underestimation of Japanese research achievements in this field. This paper briefly reviews the origin, evolutionary stages, and classification system of the pipe jacking method in Japan, and representative technological advances are also introduced through case statistics. Meanwhile, the deficiencies in the current classification system of the International Society of Trenchless Technology (ISTT) are discussed as well. In addition, the application status of this method is investigated based on survey data from the Japan Society for Trenchless Technology (JSTT). According to the survey data from 5232 projects, a systematic analysis is carried out on the selection of pipe jacking equipment, the scale of jacking construction, the profile of curved projects, and the geological adaptability of the pipe jacking method in Japan. The emphasis of this paper is to introduce the current technical situation and analyze the development trends of the pipe-jacking method based on Japan's experience, and to popularize this method in other countries that need this simple, effective, and economical construction technique under the background of climate change. [ABSTRACT FROM AUTHOR]

Climate change affects both men and women which, in turn, shapes their varied and contrasting perceptions of climate variability and change. This paper examined the gendered perceptions of climate variability and change among local communities in Queen Elizabeth National Park in Uganda. The objectives are threefold: - identify climatic shocks faced by the local communities; examine the perceptions of men and women of climate variability and change; and to compare their perceptions with empirical meteorological data. This study employed both qualitative and quantitative methods, with data collected from 215 respondents using survey, interviews and focused group discussions. From the findings, indicators of climate variability and change included reduced flooding events, occurrence of human diseases, increasing crop pests and diseases, dry spells and intensity of rains. There was increasing significant temperatures while rainfall was declining. Both male and female significantly associated with increasing temperatures and reduced flooding events. While climatic shocks affected both males and females, the impact was more pronounced depending on distinct livelihood activities and roles and responsibilities undertaken. The study concluded that people’s perceptions of climate change should be taken on by the government and integrated in the national climate programs that support people’s livelihoods and survival mechanisms. [ABSTRACT FROM AUTHOR]

Over the past 50 years, temperatures on the Qinghai-Tibet Plateau (QTP) have risen roughly twice as fast as the global average, making it the most unpredictable region of environmental change due to global warming. In this paper, an Environmental Area Index model was developed using data from the Coupled Model Intercomparison Project to assess the ecological risk faced by QTP ecosystems under the influence of climate factors. The results show that ecological risk gradually decreases from northwest to the southeast, and there are different trends in ecological risk for each class in areas with different elevation gradients. As elevation increased, the proportion of potential risk areas gradually decreased, and the proportion of high- and higher-risk areas gradually increased. We predict that in the period 2021–2100, the overall ecological risk change trend on the QTP will not be obvious, but there will be a more obvious change on the vertical gradient. In general, under the existing global climate change scenario, the ecological risk faced by the QTP show a decreasing trend under the influence of climate factors, and the decrease in ecological risks is much higher at higher elevations than at lower elevations. [ABSTRACT FROM AUTHOR]

As increasing global temperatures lead to sea level rise and associated impacts (e.g. flooding, erosion, saltwater intrusion), the relocation of people and assets away from sites of coastal risk has been viewed by some as a certainty. However, many people affected by emerging coastal changes remain in sites of residence. Here we examine the experiences of residents in three low-lying villages across two small island states: Dreketi and Karoko in Fiji, and Funafala in Tuvalu. Analysis of qualitative data from interviews shows that residents are concerned about local coastal changes, and largely attribute them to climate change. While some anticipate future relocation and retreat, and a few households have retreated short distances away from the coast, for now residents remain in and move to these sites to maintain livelihoods, practices, well-being, and sense of belonging. These are places that people value and plan to live in as long as possible. The contribution of this paper is to highlight the vernacular explanations of overlapping drivers of immobility and translocality in sites of coastal risk. It indicates the need to move away from the binaries of immobility/mobility and of trapped/voluntarily immobile populations and to examine the multiplicities of human (im)mobility. [ABSTRACT FROM AUTHOR]

Climate change is considered as one of the main environmental challenges which occurred during the last five decades. Climate change poses an additional heavy pressure on Jordan's limited water resources and the environment. This paper aimed to assess climate change in Jordan using many temperature indicators (max. T>25, max. T>30, min. T>10 and min. T<10°C). Also, the area method was used as a new approach to assess the trend in the mentioned indicators. The data collected for 30 years in 10 stations was used for calculation and analysis. The results indicated that two stations recorded a noteworthy increase in temperature above 25 and 30°C, while the others showed an insignificant increase/decrease. Only one station showed a significant increase in the min. T>10°C with an irrelevant change in the other stations. Analysis of monthly data revealed that 50, 60 and 30% of the stations showed an increase in the max. temp. (>25) in June, July and August, respectively, with a notable increase for two stations. In comparison with the minimum temperature >15°C, six of the 10 stations showed increases during June, four in July and four in August, with a significant trend in two stations in two different months. Regarding the coldest days (min.<10°C), 6 of 8 stations and 8 of 10 stations showed a decrease in the average min. monthly temperature<10°C in November and December, respectively, which indicated late commencement of the winter season and a significant change in the season's pattern. By comparing the results of the area method with those of the linear regression method, a high agreement was found between the trends in both approaches, while a substantial difference in R2 value was found. Based on the obtained results, it was concluded that the climate change in temperature is pronounced and can be determined successfully by the area method. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to assess and analyse the dynamics of flash flooding events in Slovenia. The paper examines in particular the frequency of flash floods and their seasonal distribution. The methodology is based on the analysis of historical records and modern flood data. The results of a long-term frequency analysis of 138 flash floods that occurred between 1550 and 2015 are presented. Because of the lack of adequate historical flood data prior to 1950 the main analysis is based on data for the periodbetween1951 and2015, while the analysis of data for the period between1550 and1950 is added as a supplement to the main analysis. Analysis of data for the period after 1950 shows that on average 1.3 flash floods occur each year in Slovenia. The linear trend for the number of flash floods is increasing but is not statistically significant. Despite the fact that the majority of Slovenian rivers have one of the peaks in spring and one of the lows in summer, 90% of flash floods actually occur during meteorological summer or autumn - i.e. between June and November, which shows that discharge regimes and flood regimes are not necessarily related. Because of the lack of flood records from the more distant past as well as the large variability of flash flood events in the last several decades, we cannot provide a definitive answer to the question about possible changes in their frequency and seasonality by relying solely on the detected trends. Nevertheless, considering the results of analysis and future climate change scenarios the frequency of flash floods in Slovenia could increase while the period of flash flood occurrence could be extended. [ABSTRACT FROM AUTHOR]

With increasing concern over global warming and climate change, this paper aims to investigate five rail passenger systems in Europe with respect to their greenhouse gas emissions. The countries are compared to each other to determine which are performing the best and the reasons behind this. The countries are analysed over a ten years period to investigate if there are trends forming as time progresses. The information collected is then used to produce two different forecasts for greenhouse gas emissions on the railways to look at the potential each country has for meeting European Union emissions targets. It is found that Finland, Sweden and the United Kingdom are showing very positive signs of being able to achieve the greenhouse gas emission targets in 2030 and 2050. [ABSTRACT FROM AUTHOR]

This paper explores the nature and dynamics of adaptation and resilience in the face of a diverse and varied environmental and ecological context using the case study of South Asia's Indus Civilization (ca. 3000-1300 BC). Most early complex societies developed in regions where the climatic parameters faced by ancient subsistence farmers were varied but rain falls primarily in one season. In contrast, the Indus Civilization developed in a specific environmental context that spanned a very distinct environmental threshold, where winter and summer rainfall systems overlap. There is now evidence to show that this region was directly subject to climate change during the period when the Indus Civilization was at its height (ca. 2500-1900 BC). The Indus Civilization, therefore, provides a unique opportunity to understand how an ancient society coped with diverse and varied ecologies and change in the fundamental environmental parameters. This paper integrates research carried out as part of the Land, Water and Settlement project in northwest India between 2007 and 2014. Although coming from only one of the regions occupied by Indus populations, these data necessitate the reconsideration of several prevailing views about the Indus Civilization as a whole and invigorate discussion about human-environment interactions and their relationship to processes of cultural transformation. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to elucidate a potential use of the large samples of seasonal means that hindcasts provide for investigating different aspects of climate variability. This use of hindcasts complements their traditional uses in bias correction, real-time forecast calibration, and prediction skill assessment. For seasonal hindcast data from NCEP CFSv2 we show that a sample size 5208 for each target season is achievable. To demonstrate the utility of the proposed concept, we use this large sample dataset to illustrate how it could be used in documenting spatial variability in various moments of seasonal mean precipitation PDF over the US, and further, quantify nuances in the variations in precipitation PDF at different geographical locations with the amplitude of ENSO SSTs. It is our hope that analysis presented in this paper will accelerate utilization of seasonal hindcast datasets in furthering our understanding of different aspects of climate variability. With the advantage of the large sample size, we demonstrated that the precipitation PDF at the each grid of the CONUS can be represented by gamma distribution for a more concise and effective way to summarize precipitation variability. The availability of the large sample dataset also allowed us to analyze the statistical characteristic of the precipitation responses to the different amplitudes of ENSO SSTs. The results show that for strong warm events, enhancement in precipitation has larger amplitude than decrease in precipitation for cold events in the regions of Southern California and southeastern US. The variation of the precipitation signal over the other sub-regions including the southwestern US, mid-northwest, and mid-east shows more linear relationship with the ENSO SSTs. In response to anomalous ENSO SSTs, although the PDF of December-January-February seasonal mean precipitation anomaly is shifted from its climatological PDF, there is still a large overlap between precipitation PDFs for ENSO and its climatological counterpart. This uncertainty in seasonal mean outcomes of precipitation, therefore, limits the seasonal prediction skill. [ABSTRACT FROM AUTHOR]

Highlights • Proposed framework identifies the similarity among the weather condition over years. • A powerful domain specific weather-based decision support system (DSS) can be built using this framework. • Significant reuse of the developed content in a weather-based DSS can be achieved using this framework. Abstract Weather-based decision support systems (DSSs) are being built to improve the efficiency of the production systems in the domains of healthcare, agriculture, transport, governance and so on. Normally, a weather condition (WC) is represented by the statistical values of weather variables for a given duration (e.g. a day or a week). In a weather-based DSS, given a WC, the domain experts prepare the appropriate suggestions to improve the efficiency of the stakeholders. Normally, once the domain experts prepare a suggestion for a given WC that belongs to certain period (e.g. year or season), there is a scope to reuse the same suggestion for the similar WCs of other period(s). As a result, the performance of the DSS could be improved. In this paper, to improve reuse , we have proposed a notion of category-based WC (CWC) which is formed by using the categories of weather variables in the respective domain. By considering the context of agromet advisory service operated by India Meteorological Department (IMD) and the corresponding weather categories provided by IMD, we have analyzed the extent of reuse among CWCs by conducting the experiments on 30 years of weather data collected at Rajendranagar, Hyderabad, Telangana state. The experiments are conducted by considering two types of CWCs with the duration equal to one day and five days. By varying the number of weather variables in CWC from one to five, we have computed the extent of reuse among CWCs of different periods of the following period types: year, season, and phenophases (i.e., growth stages) of the Rice crop. The results show that there is a significant similarity among the CWCs of the given period and the CWCs of the preceding periods of each period type. For any domain including agriculture, the results provide an opportunity to improve the efficiency of weather-based agricultural DSSs by improving the reuse of the weather-based suggestions. [ABSTRACT FROM AUTHOR]

As the climate changes, it is important to understand the effects on the environment. Changes in wildland fire risk are an important example. A stochastic lattice-based wildland fire spread model was proposed by Boychuk et al. (2007), followed by a more realistic variant (Braun andWoolford, 2013). Fitting such a model to data from remotely sensed images could be used to provide accurate fire spread risk maps, but an intermediate step on the path to that goal is to verify the model on data collected under experimentally controlled conditions. This paper presents the analysis of data from small-scale experimental fires that were digitally video-recorded. Data extraction and processing methods and issues are discussed, along with an estimation methodology that uses differential equations for the moments of certain statistics that can be derived from a sequential set of photographs from a fire. The interaction between model variability and raster resolution is discussed and an argument for partial validation of the model is provided. Visual diagnostics show that the model is doing well at capturing the distribution of key statistics recorded during observed fires. [ABSTRACT FROM AUTHOR]

The paper presents the results of time-related changes in maximum temperatures in lakes. The analysis was carried out on the basis of 9 lakes located in the northern part of Poland. The analysis was based on daily water and air temperatures in the period 1971–2015. Mann–Kendall's and Sen's tests were applied to determine the directions and rates of change of maximum air and water temperatures. The average increase of maximum water temperature in analysed lakes was found to be 0.39 °C dec–1, while the warming trend of the maximum air temperature was 0.48 °C dec–1. Cluster analysis (CA) was used to group lakes characterised by similar changes of maximum water temperature. The first group included five lakes in which the values of the maximum temperature trends were 0.41 °C dec–1. In the second cluster the average value of maximum water temperature increase was smaller than in the first cluster (0.36 °C dec–1). Comparing the results of cluster analysis with morphometric data show that in the first cluster lakes are having a greater average depth, maximum depth and water transparency in comparison to the lakes of the second cluster. The study showed significant changes in the maximum annual water temperatures in post-glacial lakes located in Poland in the years 1971-2015. The highest rate of increase in monthly maximum water temperature was recorded in April and the lowest in January and February. The value of maximum water temperature trends was lower than annual air temperature trends. [ABSTRACT FROM AUTHOR]

In this paper, 1416 conventional ground-based meteorological observation stations on the mainland of China were subdivided into groups of differing spatial density. Data from each subgroup were then used to analyze variations in the tropical cyclone (TC) precipitation statistics derived from each subgroup across the mainland of China (excluding Taiwan, Hong Kong, and Macao), as well as in two regions (east China and south China) and three provinces (Guangdong, Hainan, and hiangxi) between 1981 and 2010. The results showed that for the mainland of China, total precipitation, mean annual precipitation, mean daily precipitation, and its spatial distribution were the same regardless of the spatial density of the stations. However, some minor differences were evident with respect to precipitation extremes and their spatial distribution. Overall, there were no significant variations in the TC precipitation statistics calculated from different station density schemes for the mainland of China. The regional and provincial results showed no significant differences in mean daily precipitation, but this was not the case for the maximum daily precipitation and torrential rain frequency. The maximum daily precipitation calculated from the lower-density station data was slightly less than that based on the higher-density station schemes, and this effect should be taken into consideration when interpreting regional climate statistics. The impact of station density on TC precipitation characteristics was more obvious for Hainan than for Guangdong or hiangxi provinces. In addition, the effects were greater for south China (including Guangxi Zhuang Autonomous region, Guangdong, and Hainan provinces) than east China (including Shandong, hiangsu, Zhejiang, Shanghai, Fujian, Anhui, and hiangxi provinces). Furthermore, the analysis proved that the statistical climatic characteristics began to change significantly when the station spacing was between 40 and 50 km, which are close to the mean spacing for all stations across the mainland of China. Moreover, TC areal precipitation parameters, including mean total areal precipitation and mean daily areal precipitation, also began to change significantly when the spacing was between 40 and 50 km, and were completely different when it was between 100 and 200 km. [ABSTRACT FROM AUTHOR]

First-year ice has replaced multiyear ice in the Northern Sea Route area since 2008. In this area, sea ice survival during summer substantially depends on first-year ice thickness at melt onset, and thus monitoring of first-year ice thickness in the freezing period is a key to forecasting sea ice distributions in the following summer. In this paper we introduce a new algorithm to estimate flat first-year ice draft using brightness temperature data measured by the Advanced Microwave Scanning Radiometer-2 (AMSR2). The algorithm uses a gradient ratio (GR) of 18- and 36-GHz vertically polarized brightness temperatures based on decreases in sea ice emissivity in higher AMSR2 frequency channels with thermodynamic growth associated with an increase in volume scattering. Such spectral characteristics of the emissivity are examined by comparing GR values with flat first-year ice draft extracted by mode values of in situ draft data measured by a moored ice profiling sonar. The accuracy of the daily draft estimated from GR values after applying proper noise filters is about 10 cm for a draft range of 0.4–1.2 m. [ABSTRACT FROM AUTHOR]

Abstract Recent global warming has not been ubiquitous: there are seasons, regions, and time periods with negligible or even negative air temperature trends (frequently referred to as warming holes). This paper presents a novel method enabling a proper localization of specific trend events, such as periods of warming holes, of a particularly strong warming, and of rapid transitions of trend amplitudes during the calendar year. The method consists in analyzing trends for periods of a given length (10 to 90 days) that are sliding over the year with a one day step. This allows a detailed description of the annual cycle of trends. The analysis is conducted for daily maximum and minimum temperature at 135 stations in Europe in 1961–2000. Despite an overall warming in Europe, several warming holes are uncovered during various parts of the year, not only in autumn when a warming hole has already been reported. The autumn warming hole concentrates in Eastern Europe, but it changes its strength and spatial extent: it spreads into Western Europe in September and retreats to Eastern Europe in November when it intensifies especially north of the Black Sea. Three shorter warming holes are detected: In February and March, cooling occurs in the Eastern Mediterranean and Iceland, while in early April, cooling is detected over Central, Southern, and Southeastern Europe. Another large-scale cooling occurs in Central, Northern, and Northwestern Europe in mid-June. The periods of strongest warming occur around the middle of January in Eastern Europe, in early March over almost entire Europe, and in mid-May and early August mainly over Central and Western Europe. Cluster analysis of stations with respect to the annual cycles of trends demonstrates a spatial coherence of the trends; the lack of spatial coherence points to local peculiarities or data problems of individual stations. The method of sliding seasons proves to be much more effective in the identification and localization of notable trend events than the ordinary approach of trend detection for fixed calendar seasons and/or months. Highlights • Annual course of temperature trends in Europe in 1961–2000 using "moving trends" • "Subseasons" of 10–90 days shifted by 1 day during the calendar year • Shorter subseasons = higher temporal and spatial variability of temperature trends • Greatest warming in January in NE Europe due to high interannual variability • "Warming holes" (i.e. cooling) in autumn in Central&E Europe, in June in N Europe [ABSTRACT FROM AUTHOR]

Indicator species (IS) are used to monitor environmental changes, assess the efficacy of management, and provide warning signals for impending ecological shifts. Though widely adopted in recent years by ecologists, conservation biologists, and environmental practitioners, the use of IS has been criticized for several reasons, notably the lack of justification behind the choice of any given indicator. In this review, we assess how ecologists have selected, used, and evaluated the performance of the indicator species. We reviewed all articles published in Ecological Indicators (EI) between January 2001 and December 2014, focusing on the number of indicators used (one or more); common taxa employed; terminology, application, and rationale behind selection criteria; and performance assessment methods. Over the last 14 years, 1914 scientific papers were published in EI, describing studies conducted in 53 countries on six continents; of these, 817 (43%) used biological organisms as indicators. Terms used to describe organisms in IS research included “ecological index”, “environmental index”, “indicator species”, “bioindicator”, and “biomonitor,” but these and other terms often were not clearly defined. Twenty percent of IS publications used only a single species as an indicator; the remainder used groups of species as indicators. Nearly 50% of the taxa used as indicators were animals, 70% of which were invertebrates. The most common applications behind the use of IS were to: monitor ecosystem or environmental health and integrity (42%); assess habitat restoration (18%); and assess effects of pollution and contamination (18%). Indicators were chosen most frequently based on previously cited research (40%), local abundance (5%), ecological significance and/or conservation status (13%), or a combination of two or more of these reasons (25%). Surprisingly, 17% of the reviewed papers cited no clear justification for their choice of indicator. The vast majority (99%) of publications used statistical methods to assess the performance of the selected indicators. This review not only improves our understanding of the current uses and applications of IS, but will also inform practitioners about how to better select and evaluate ecological indicators when conducting future IS research. [ABSTRACT FROM AUTHOR]

This paper provides a brief overview of the most common dating techniques applied in palaeoclimate and palaeoenvironmental studies including four radiometric and isotopic dating methods (radiocarbon, 230 Th disequilibrium, luminescence, cosmogenic nuclides) and two incremental methods based on layer counting (ice layer, varves). For each method, concise background information about the fundamental principles and methodological approaches is provided. We concentrate on the time interval of focus for the INTIMATE (Integrating Ice core, MArine and TErrestrial records) community (60–8 ka). This dating guide addresses palaeoclimatologists who aim at interpretation of their often regional and local proxy time series in a wider spatial context and, therefore, have to rely on correlation with proxy records obtained from different archives from various regions. For this reason, we especially emphasise scientific approaches for harmonising chronologies for sophisticated and robust proxy data integration. In this respect, up-to-date age modelling techniques are presented as well as tools for linking records by age equivalence including tephrochronology, cosmogenic 10 Be and palaeomagnetic variations. Finally, to avoid inadequate documentation of chronologies and assure reliable correlation of proxy time series, this paper provides recommendations for minimum standards of uncertainty and age datum reporting. [ABSTRACT FROM AUTHOR]

Citizen science is a research practice that relies on public contributions of data. The strong recognition of its educational value combined with the need for novel methods to handle subsequent large and complex data sets raises the question: Is citizen science effective at science? A quantitative assessment of the contributions of citizen science for its core purpose – scientific research – is lacking. We examined the contribution of citizen science to a review paper by ornithologists in which they formulated ten central claims about the impact of climate change on avian migration. Citizen science was never explicitly mentioned in the review article. For each of the claims, these ornithologists scored their opinions about the amount of research effort invested in each claim and how strongly the claim was supported by evidence. This allowed us to also determine whether their trust in claims was, unwittingly or not, related to the degree to which the claims relied primarily on data generated by citizen scientists. We found that papers based on citizen science constituted between 24 and 77% of the references backing each claim, with no evidence of a mistrust of claims that relied heavily on citizen-science data. We reveal that many of these papers may not easily be recognized as drawing upon volunteer contributions, as the search terms “citizen science” and “volunteer” would have overlooked the majority of the studies that back the ten claims about birds and climate change. Our results suggest that the significance of citizen science to global research, an endeavor that is reliant on long-term information at large spatial scales, might be far greater than is readily perceived. To better understand and track the contributions of citizen science in the future, we urge researchers to use the keyword “citizen science” in papers that draw on efforts of non-professionals. [ABSTRACT FROM AUTHOR]

Assessing the implications of paleoclimatic and paleoenvironmental data at temporal and spatial scales that would have directly intersected with human decision-making and activity is a fundamental archaeological challenge. This paper addresses this challenge by presenting a spatial and temporal downscaling method that can provide quantitative high-spatio-temporal-resolution estimates of the local consequences of climatic change. Using a case study in Provence (France) we demonstrate that a centennial-scale Mediterranean-wide model of Holocene climate, in conjunction with modern geospatial and climate data, can be used to generate explicit and solidly-grounded monthly estimates of temperature, precipitation, and cloudiness at landscape scales and with annual resolution, enabling consideration of climate variability at human scales and meeting the data requirements of socioecological models focused on human activity. While the results are not reconstructions – that is, particular values are single realizations, consistent with the coarse-grained data but not individually empirically derived nor unique solutions – they provide a more suitable basis for assessing the human consequences of climate change than can coarse-grained data. [ABSTRACT FROM AUTHOR]

This paper describes the repackaging and consistent distribution of the world's major atmospheric and oceanic reanalyses. It also presents examples of the usefulness of examining multiple reanalyses. This service will make it much easier for anybody using reanalysis to access multiple datasets using an approach similar to that of phase 5 of the Coupled Model Intercomparison Project (CMIP5). Experienced users as well as students will find the standardized formatted data convenient to use. [ABSTRACT FROM AUTHOR]

In this paper, the regional climate model REMO is used to investigate the added value of downscaling low resolutions global climate models (GCMs) and the climate change projections over Central Africa. REMO was forced by two GCMs (EC-Earth and MPI-ESM), for the period from 1950 to 2100 under the Representative Concentration Pathway 8.5 scenario. The performance of the REMO simulations for current climate is compared first with REMO simulation driven by ERA-Interim reanalysis, then by the corresponding GCMs in order to determine whether REMO outputs are able to effectively lead to added value at local scale. We found that REMO is generally able to better represent some aspects of the rainfall inter-annual variability, the daily rainfall intensity distribution as well as the intra-seasonal variability of the Central African monsoon, though few biases are still evident. It is also found that the boundary conditions strongly influences the spatial distribution of seasonal 2-m temperature and rainfall. From the analysis of the climate change signal from the present period 1976-2005 to the future 2066-2095, we found that all models project a warming at the end of the twenty-first century although the details of the climate change differ between REMO and the driving GCMs, specifically in REMO where we observe a general decrease in rainfall. This rainfall decrease is associated with delayed onset and anticipated recession of the Central African monsoon and a shortening of the rainy season. Small-scales variability of the climate change signal for 2-m temperature are usually smaller than that of the large-scales climate change part. For rainfall however, small-scales induce change of about 70% compared to the present climate statistics. [ABSTRACT FROM AUTHOR]

Desert margins preserve evidence of more extensive arid conditions in the past and represent global hot spots with respect to climate change. The balance of scientific evidence now suggests that dryland landscape reactivation did not correspond simply to ice age aridity peaks as previously assumed. This paper provides a new perspective on, and more nuanced understanding of, desert marginal landscapes as palaeoenvironmental archives. The two case studies represent ends of a sediment availability spectrum. Both environments experience substantial spatial and temporal variability in deposition, with implications for reconstructing past environments at individual sites. Recently generated chronostratigraphic data from the dry subhumid Dobrogea loess, Romania, are synthesised to provide quantitative insights into the spatial and temporal organisation of loess plateaux. Loess accumulation peaks during the LGM and MIS4, but not at all sites. Accumulation rates vary substantially between sites. Comparison of geographical context yields no clear driving mechanism for this variability. Secondly, two transverse lunette dunes in semi-arid Willandra Lakes, Australia, yield comparable chronostratigraphies and distribution of stratigraphic units. A first attempt is made to identify changes in wind regime through time on the Australian desert margins, and suggests that high latitude climate forcing may have acted upon semi-arid Australia over millennial scales. The novel perspectives generated from the new datasets provide unprecedented insights into past climate circulation and land surface processes along desert margins. These new perspectives, coupled with the new geochronological and quantitative palaeoclimate proxy tools now available, represent the next frontier for palaeoenvironmental research on desert margins. [ABSTRACT FROM AUTHOR]

This paper presents a spatiotemporal analysis performed by applying a Bayesian hierarchical model on NORA10 data of significant wave height. The model has previously been applied to corrected ERA-40 data of significant wave height and was generally found to perform well. However, a new set of high-resolution significant wave height data has recently become available, which is believed to be an improvement compared to the C-ERA-40 data, and a similar spatiotemporal analysis is performed on this new data set. NORA10 differs from C-ERA-40 in various ways and in particular the spatial resolution is significantly increased. Hence, one main motivation for the study presented in this paper is to investigate how the model performs on data with very high spatial resolution. In particular, the model contains a separate component for identifying long-term trends in the wave climate, possibly due to climate change. For the C-ERA-40 data, significant increasing trends were detected in the selected area in the North Atlantic Ocean. These trends are not reproduced in the NORA10 data, but there are differences in geographical and temporal coverage that may, at least partly, explain such differences. The new analysis and the results pertaining to the NORA10 data are presented in this paper. [ABSTRACT FROM AUTHOR]

Climate change shifts the distributions of a set of climatic variables, including temperature, precipitation, humidity, wind speed, sunshine duration, and evaporation. This paper explores the importance of those additional climatic variables other than temperature and precipitation. Using the county-level agricultural data from 1980 to 2010 in China, we find that those additional climatic variables, especially humidity and wind speed, are critical for crop growth. Therefore, omitting those variables is likely to bias the predicted impacts of climate change on crop yields. In particular, omitting humidity tends to overpredict the cost of climate change on crop yields, while ignoring wind speed is likely to underpredict the effect. Our preferred specification indicates that climate change is likely to decrease the yields of rice, wheat, and corn in China by 36.25%, 18.26%, and 45.10%, respectively, by the end of this century. [ABSTRACT FROM AUTHOR]

Background Improved data linkages between diverse environment and health datasets have the potential to provide new insights into the health impacts of environmental exposures, including complex climate change processes. Initiatives that link and explore big data in the environment and health arenas are now being established. Objectives To encourage advances in this nascent field, this article documents the development of a web browser application to facilitate such future research, the challenges encountered to date, and how they were addressed. Methods A ‘storyboard approach’ was used to aid the initial design and development of the application. The application followed a 3-tier architecture: a spatial database server for storing and querying data, server-side code for processing and running models, and client-side browser code for user interaction and for displaying data and results. The browser was validated by reproducing previously published results from a regression analysis of time-series datasets of daily mortality, air pollution and temperature in London. Results Data visualisation and analysis options of the application are presented. The main factors that shaped the development of the browser were: accessibility, open-source software, flexibility, efficiency, user-friendliness, licensing restrictions and data confidentiality, visualisation limitations, cost-effectiveness, and sustainability. Conclusions Creating dedicated data and analysis resources, such as the one described here, will become an increasingly vital step in improving understanding of the complex interconnections between the environment and human health and wellbeing, whilst still ensuring appropriate confidentiality safeguards. The issues raised in this paper can inform the future development of similar tools by other researchers working in this field. [ABSTRACT FROM AUTHOR]

In this paper we introduce a novel functional clustering method, the Bagging Voronoi K-Medoid Aligment (BVKMA) algorithm, which simultaneously clusters and aligns spatially dependent curves. It is a nonparametric statistical method that does not rely on distributional or dependency structure assumptions. The method is motivated by and applied to varved (annually laminated) sediment data from lake Kassjön in northern Sweden, aiming to infer on past environmental and climate changes. The resulting clusters and their time dynamics show great potential for seasonal climate interpretation, in particular for winter climate changes. [ABSTRACT FROM AUTHOR]

Abstract: Climate change is expected to alter rainfall regimes across most parts of the world. The implications of this could be more severe in arid environments where rainfall is limited and highly variable in space and time. However, lack of good quality data, of sufficient record length and spatial coverage usually restricts model development and performance geared towards assessing the effects of climate change in these areas. This paper presents an analysis of rainfall and climate data in order to determine the time of change in rainfall series and identify possible correlations between rainfall and temperature. In addition, the paper aims to make predictions of future rainfall patterns in Botswana. This is achieved by using historical rainfall and climate data from rainfall stations spread across Botswana from 1965 to 2008. In addition, large scale reanalysis data from NCAR/NCEP and El Nino Southern Oscillation (ENSO) data were used to augment the limited observed spatial climate data series when developing a rainfall model. Temperature and ENSO indices have been used to predict rainfall regimes for the present climate. Based on these, the effects of climate change were quantified using a stochastic generalised linear rainfall model (GLM) driven by outputs of global climate models (GCMs). The results indicate that temperature is a significant rainfall predictor in Botswana compared to ENSO indices. [Copyright &y& Elsevier]

Abstract: Several numerical landscape evolution models (LEMs) have been developed to date, and many are available as open source codes. Most are written in efficient programming languages such as Fortran or C, but often require additional code efforts to plug in to more user-friendly data analysis and/or visualization tools to ease interpretation and scientific insight. In this paper, we present an effort to port a common core of accepted physical principles governing landscape evolution directly into a high-level language and data analysis environment such as Matlab. SIGNUM (acronym for Simple Integrated Geomorphological Numerical Model) is an independent and self-contained Matlab, TIN-based landscape evolution model, built to simulate topography development at various space and time scales. SIGNUM is presently capable of simulating hillslope processes such as linear and nonlinear diffusion, fluvial incision into bedrock, spatially varying surface uplift which can be used to simulate changes in base level, thrust and faulting, as well as effects of climate changes. Although based on accepted and well-known processes and algorithms in its present version, it is built with a modular structure, which allows to easily modify and upgrade the simulated physical processes to suite virtually any user needs. The code is conceived as an open-source project, and is thus an ideal tool for both research and didactic purposes, thanks to the high-level nature of the Matlab environment and its popularity among the scientific community. In this paper the simulation code is presented together with some simple examples of surface evolution, and guidelines for development of new modules and algorithms are proposed. [Copyright &y& Elsevier]

• Adverse impacts of sea-level rises are not limited to a landward shift in shoreline. • Shallow groundwater can impact inland infrastructure kilometers from the coastline. • About 2/3 of the stormdrain length is subject to seawater & groundwater intrusion. • Coastal stressors can increase flood magnitude, frequency, and duration. • Compound flooding volume increases up to 6 times by a 2 m sea-level rise. Low-lying coastal areas are susceptible to multiple types of flooding from marine, subsurface, and surface sources. The co-occurrence of a rainfall event with high sea level, which raises coastal groundwater tables conjointly, may result in a compound flooding event, which could be much more widespread and dangerous than that of an individual flooding source. Focusing on Imperial Beach as a low-elevation coastal community in California, this paper provides a generalized methodology to determine the vulnerability of coastal stormdrain systems to compound seawater, groundwater, and stormwater flooding under a changing climate. Although marine inundation by itself is expected to flood only 7% of the study area by 2100 under the most pessimistic scenario (i.e. 2 m rise), our results show that more than 20% of the study area's subterranean stormdrain system is already at the risk of subsurface flooding at current sea levels. While marine inundation is a concern near the coastline as sea level rises, the results show that seawater intrusion into the stormdrain system can impact areas kilometers away from the coastline. The consequences of sea-level rise (SLR) can be exacerbated by an under-resourced stormdrain system, caused by groundwater infiltration through system defects. As such, by a 2 m rise in current sea-level, the flooding volume may double in an ideal system (i.e., no defects) while this ratio can increase six fold in a slightly defective stormdrain system (with 0.25 % porosity systemwide). The continuous simulations of the stormdrain system performance indicate that flood events will be more destructive and frequent under these circumstances. [ABSTRACT FROM AUTHOR]

This paper is devoted to the synchronization of synoptic processes in the atmosphere with tidal oscillations in the Earth's rotational speed discovered by the author. The causes and effects of the synchronization are explained. Data are given on the monthly revolution of the Earth, tidal oscillations in the Earth's rotational speed, and the main lunisolar cycles. An explanation is proposed for the causes of the Mul'tanovskii elementary synoptic periods and the manifestations of 4- and 8-year cycles in the climate system. It is shown that the 35-year Brückner cycle is attributed to the beat of the annual solar (365 days) and annual lunar (355 days) oscillations in meteorological characteristics. An explanation is proposed for how the lunisolar tides may affect the air temperature, which is based on the interaction of the gravitational lunisolar tides with the radiation conditions in the atmosphere (due to changes in the cloud cover). A discussion is given of the relationship between the fluctuations in the climatic characteristics and the change in the Earth's rotational speed on decadal time scales. [ABSTRACT FROM AUTHOR]

Abstract: Over the past decade, soil moisture products derived from microwave remote sensing data have gained increasing attention for the monitoring of agricultural, hydrological and climate processes. ERS-1 and ERS-2 scatterometer-derived time series of data available since 1992 enable the user to analyze the change of soil moisture patterns over time and to monitor events deviating from the normal. In this paper, ERS scatterometer-derived soil moisture products are compared with known occurrences of heavy rain and drought related to the strong El Niño (EN) event of 1997/1998. We investigated globally if the typical EN-related short term climate changes can be depicted in soil moisture data. EN consequences and their manifestation in the data are presented for the Americas, Africa and Asia. Furthermore, the weather situation during the EN event in China is compared to the scatterometer-derived soil moisture data set in more detail. It could be observed that most EN-related climate variations on the different continents are clearly reflected by remote sensing soil moisture data. Heavy rain and drought conditions reported in daily media, science papers, and forecasts by the can be observed in anomaly data sets showing the deviation of soil moisture from the average. This also applies for the strong EN-related flood, which hit China in 1998 and following La Niña-related droughts. These analyses thus serve to demonstrate that information relevant to climatology is contained in the satellite scatterometer-derived soil moisture product, as well as in anomaly time series, derived from the existing soil moisture time series. Our procedure thus consisted of soil moisture time series calculation, soil moisture anomaly calculation, creation of time series and anomaly maps and the comparison of anomalies with meteorological data on three continents for the time period of outstanding El Nino years. The soil moisture data of TU Wien is available via the internet free of charge. [Copyright &y& Elsevier]

This paper, which presents an annotated checklist of the ‘lower Nymphalidae’ (Libytheinae, Danainae, Satyrinae, Charaxinae), is the fourth in a series on the butterfly fauna of Mount Kilimanjaro. Four genera of lower Nymphalidae (Danaus, Amauris, Bicyclus, Charaxes), with a total of 11 included species, are known or believed to occur within the main forest zone, from c. 1800 to 2800 m. Of these, only three species ofCharaxes(Charaxesberkeleyi, Charaxes ansorgei, Charaxes xiphares) may be restricted locally to this primary forest. The lower slopes fauna, below 1800 m, is considerably richer, with a total of 11 genera and 41 species listed (8 species of which extend into the forest zone). Possible additional species, dubious earlier records, problems with African subspecies ofDanaus chrysippus, a need for more work on certain Satyrinae, and classification of the genusCharaxesare discussed. An identification key to the subfamilies of Nymphalidae, and the 19 genera of Libytheinae, Danainae, Satyrinae, Charaxinae that occur in Tanzania, together with a key to the adults of all the species of these four subfamilies considered to occur or have occurred on Kilimanjaro, with 206 colour images, are included as online Supplementary Information. [ABSTRACT FROM PUBLISHER]

The thermal gas mass flow meter is an important meter used in industrial measurement. When the environmental temperature changes, the measured gas physical parameters change correspondingly and the thermal gas mass flow meter output signal is affected, causing large measurement error. The influence of gas temperature on the sheathed probe measurements is analyzed in this paper based on experiments and heat transfer theory using a three dimensional probe and gas heat transfer mathematical model based on the heat conduction equation. The probe heat transfer process is analyzed under convection heat transfer coupling conditions. The experimental data were analyzed and compared against the theoretical results, with a maximum average relative error of only 4.56%. The rationality of the theoretical method is thus verified. [ABSTRACT FROM AUTHOR]

The subject of this paper is the residential buildings’ thermal performance and comfort in the city of São Paulo, taking into account the climate changes and the greater vulnerability of the elderly related to the environmental conditions. The aim is the evaluation of thermal performance and comfort in residential buildings under the RCP 8.5 scenario from the IPCC Fifth Assessment Report, as well as under the heat wave occurred in January and February 2014. This study is based on real case studies and computer simulations carried out using EDSL TAS. Three elderly dwellings were surveyed and monitored aiming the comparison between measurements and computer simulation to calibrate the models. For the computer simulations, simulated and measured climate data were employed. Results were analyzed comparing comfort conditions in the different climate scenarios and among the case studies, following two adaptive comfort indexes selected for this study. Analysis revealed a tendency of change in comfort conditions following the progression of the climate scenarios, showing an increase in heat sensation and a decrease in cold sensation. The results show that the combination of both phenomena, climate change and heat wave, may lead to a potential effect of heat discomfort. [ABSTRACT FROM AUTHOR]

Increase in temperature extremes is one of the main expected impacts of climate change, as well as one of the first signs of its occurrence. Nevertheless, results emerging from General Circulation Models, while sufficient for large scales, are not enough for forecasting local trends and, hence, the IPCC has called for local studies based on on-site data. Indeed, it is expected that climate extremes will be detected much earlier than changes in climate averages. Heat waves are among the most important and least studied climate extremes, however its occurrence has been only barely studied and even its very definition remains controversial. This paper discusses the observed changes in temperature trends and heat waves in Northwestern Mexico, one of the most vulnerable regions of the country. The climate records in two locations of the region are analyzed, including one of the cities with extreme climate in Mexico, Mexicali City in the state of Baja California and the Yaqui River basin at Sonora State using three different methodologies. Results showed clear trends on temperature increase and occurrence of heat waves in both of the study zones using the three methodologies proposed. As result, some policy making suggestion are included in order to increase the adaptability of the studied regions to climate change, particularly related with heat wave occurrence. [ABSTRACT FROM AUTHOR]

Large-scale climate history of the past millennium reconstructed solely from tree-ring data is prone to underestimate the amplitude of low-frequency variability. In this paper, we aimed at solving this problem by utilizing a novel method termed “MDVM”, which was a combination of the ensemble empirical mode decomposition (EEMD) and variance matching techniques. We compiled a set of 211 tree-ring records from the extratropical Northern Hemisphere (30–90°N) in an effort to develop a new reconstruction of the annual mean temperature by the MDVM method. Among these dataset, a number of 126 records were screened out to reconstruct temperature variability longer than decadal scale for the period 850–2000 AD. The MDVM reconstruction depicted significant low-frequency variability in the past millennium with evident Medieval Warm Period (MWP) over the interval 950–1150 AD and pronounced Little Ice Age (LIA) cumulating in 1450–1850 AD. In the context of 1150-year reconstruction, the accelerating warming in 20th century was likely unprecedented, and the coldest decades appeared in the 1640s, 1600s and 1580s, whereas the warmest decades occurred in the 1990s, 1940s and 1930s. Additionally, the MDVM reconstruction covaried broadly with changes in natural radiative forcing, and especially showed distinct footprints of multiple volcanic eruptions in the last millennium. Comparisons of our results with previous reconstructions and model simulations showed the efficiency of the MDVM method on capturing low-frequency variability, particularly much colder signals of the LIA relative to the reference period. Our results demonstrated that the MDVM method has advantages in studying large-scale and low-frequency climate signals using pure tree-ring data. [ABSTRACT FROM AUTHOR]