Climatic Variables Research Articles

Projection of climate change impact on main climate variables and assessment of the future of Köppen–Geiger climate classification in Iran

AbstractHuman society and the environment are facing significant challenges due to climate change. Climate change is projected to impact main climate variables, such as temperature and precipitation. Changes in main climate variables affect climate classification and alter climate zone maps. In this research, first, the projection of temperature and precipitation in 30 main stations of Iran under the Shared Socioeconomic Pathways scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) from Coupled Model Intercomparison Project Phase Six (CMIP6) for the end of the twenty-first century (2071–2099) was carried out. Then, the future of climate zone maps was assessed in Iran by Köppen-Geiger climate classification. The evaluation of the model data based on observation data for the period of 1991–2020 showed an acceptable correlation, with R-square and RMSE values in the ranges of 0.67–0.96 and 2.44–8.38, respectively. Results showed that the temperature in the future period (2071–2099) will increase by 1–4.7 °C under all scenarios compared to the historical study period (1991–2020), while the precipitation will either increase or decrease depending on the season and the specific climate change scenario. Assessment of future climate classifications revealed that the BW (arid desert) and BS (semi-arid steppe) categories will increase, as classified by Köppen-Geiger, will increase. At the same time, Ds and Cs (dry summer) classifications will decrease in during the study period over Iran. These findings provide policymakers with some insights into how to deal with the impacts of climate change in the future and implement some measures now.

The Effects of Air Quality and the Impact of Climate Conditions on the First COVID-19 Wave in Wuhan and Four European Metropolitan Regions

This paper investigates the impact of air quality and climate variability during the first wave of COVID-19 associated with accelerated transmission and lethality in Wuhan in China and four European metropolises (Milan, Madrid, London, and Bucharest). For the period 1 January–15 June 2020, including the COVID-19 pre-lockdown, lockdown, and beyond periods, this study used a synergy of in situ and derived satellite time-series data analyses, investigating the daily average inhalable gaseous pollutants ozone (O3), nitrogen dioxide (NO2), and particulate matter in two size fractions (PM2.5 and PM10) together with the Air Quality Index (AQI), total Aerosol Optical Depth (AOD) at 550 nm, and climate variables (air temperature at 2 m height, relative humidity, wind speed, and Planetary Boundary Layer height). Applied statistical methods and cross-correlation tests involving multiple datasets of the main air pollutants (inhalable PM2.5 and PM10 and NO2), AQI, and aerosol loading AOD revealed a direct positive correlation with the spread and severity of COVID-19. Like in other cities worldwide, during the first-wave COVID-19 lockdown, due to the implemented restrictions on human-related emissions, there was a significant decrease in most air pollutant concentrations (PM2.5, PM10, and NO2), AQI, and AOD but a high increase in ground-level O3 in all selected metropolises. Also, this study found negative correlations of daily new COVID-19 cases (DNCs) with surface ozone level, air temperature at 2 m height, Planetary Boundary PBL heights, and wind speed intensity and positive correlations with relative humidity. The findings highlight the differential impacts of pandemic lockdowns on air quality in the investigated metropolises.

A machine learning approach to fill gaps in dendrometer data

Abstract Key message The machine learning algorithm extreme gradient boosting can be employed to address the issue of long data gaps in individual trees, without the need for additional tree-growth data or climatic variables. Abstract The susceptibility of dendrometer devices to technical failures often makes time-series analyses challenging. Resulting data gaps decrease sample size and complicate time-series comparison and integration. Existing methods either focus on bridging smaller gaps, are dependent on data from other trees or rely on climate parameters. In this study, we test eight machine learning (ML) algorithms to fill gaps in dendrometer data of individual trees in urban and non-urban environments. Among these algorithms, extreme gradient boosting (XGB) demonstrates the best skill to bridge artificially created gaps throughout the growing seasons of individual trees. The individual tree models are suited to fill gaps up to 30 consecutive days and perform particularly well at the start and end of the growing season. The method is independent of climate input variables or dendrometer data from neighbouring trees. The varying limitations among existing approaches call for cross-comparison of multiple methods and visual control. Our findings indicate that ML is a valid approach to fill gaps in individual trees, which can be of particular importance in situations of limited inter-tree co-variance, such as in urban environments.

Climate Change Effects on Spatiotemporal Distribution of Precipitation over West Central India: A Statistical Downscaling Approach

The long-term shifts in temperatures and weather patterns are referred to as climate change. Climate change not only leads to long-term shifts in average temperatures but also changes in the spatial and temporal distribution of rainfall over continents. This study aims to predict likely changes in the rainfall pattern induced by climate change over the West Central (WC) region of India. The approach uses a statistical downscaling technique that converts coarse-scale outputs of the global climate model (GCM) to high-resolution future precipitation projections giving refined distribution. The decision support tool that uses a robust statistical downscaling technique viz. statistical downscaling model (SDSM) is used for assessing local climate change impacts. The research includes the study of likely regional climate variability, by integrating historical observational data and empirical relationships between large-scale climate variables and local weather patterns. Historical data and the SDSM, version 4.2, are employed to forecast future rainfall trends. Rainfall data from the India Meteorological Department and the National Centre for Environmental Prediction (NCEP) from 1961 to 2001 are used along with outputs from general circulation models (GCMs), viz. Hadley Centre coupled model, version 3 (HadCM3), and coupled global climate model, version 3 (CGCM3), for the period 1961–2099. Rainfall scenarios are presented for three future time periods (2011–2040, 2041–2070, and 2071–2099). The study indicates a significant increase in the mean annual precipitation across the West Central India region, particularly in the 2050s and 2080s. Mean annual rainfall is projected to rise by 10–19.4% under HadCM3 A2 and B2 scenarios. The HadCM3 indicates the month of September as the month of the highest precipitation in later time periods, whereas it is the month of August, according to CGCM3 simulations. When comparing the results of the two models, HadCM3 gives better results, as indicated by better R2 value in validation. Thus, the analysis gives climate change-induced likely changes in the spatiotemporal distribution of precipitation over the West Central India region. The insight given by the work will be useful for decision making in many sectors like agriculture, water management, disaster risk reduction, and infrastructure planning.

Improving the efficiency of aerial surveys for monitoring North American beaver population dynamics

Context The North American beaver (Castor canadensis) was extirpated from much of its range in the US in the 1800s due to fur trapping and change in land use. However, the species has recolonised much of its former range, including the US state of Ohio. Since 2013, the Ohio Division of Wildlife (ODOW) has monitored trends in beaver colony density via aerial surveys of 40 km × 40 km plots classified as low, medium, or high suitability based on the amount of wetland. Nonetheless, the current classification system may miss important correlates of beaver colony density. Aims Our study aimed to (1) identify predictors of beaver colony density (number of colonies inferred from aerial counts of lodges) across Ohio, and (2) develop a model-based classification system to improve the efficacy of monitoring efforts. Methods To predict beaver colony density in Ohio we used an aerial survey dataset of 54 plots (40 km × 40 km) collected by ODOW annually between 2013 and 2020, along with a suite of environmental, anthropogenic, and climate variables in a mixed effects modelling framework. Key results Beaver colony density was positively associated with wetland and reclaimed surface mine areas and inversely associated with the proportion of agricultural lands. There was a negative interaction between wetland and surface mines; in general, beaver colony density increased with wetland and surface mine area. However, in plots with wetland area >1000 ha, beaver colony density was weakly negatively associated with surface mine area. Using median and interquartile ranges of model-averaged predicted beaver colony density, we developed a new classification of low, moderate and high suitability for both the survey plots and the entire state of Ohio. We found that eastern Ohio had high suitability, while the central and western parts of the state had lower suitability for C. canadensis. Conclusions Our approach to identifying predictors for beaver colony density at broad spatial scales highlights the importance of reclaimed surface mines and wetlands for beaver populations, while the model-based habitat classification provides ODOW additional information for monitoring and beaver management decisions. Implications Improved C. canadensis monitoring at the landscape scale using habitat classifications that consider local conditions can both improve annual survey cost-effectiveness and facilitate the sustainable management of this recovering species.

The limitation of machine learning methods for water supply and demand forecasting: A case study for Greater Melbourne, Australia

ABSTRACT Many cities around the world have faced water scarcity due to climate change, population growth, and urbanization. Accurate water supply and demand forecasting is critical for sustainable urban water management. Machine learning (ML) models provide new possibilities for forecasting compared with traditional models in handling non-linearity. This study aims to address the efficacy of ML models, long short-term memory (LSTM), stacked LSTM, bidirectional LSTM (Bi-LSTM), and multilayer perceptron (MLP), for forecasting water supply and demand in Greater Melbourne, Australia. The ML modelling utilized daily water supply and demand, and climatic variables (rainfall and maximum temperature) recorded by Melbourne Water and the Bureau of Meteorology from 1990 to 2019. The stacked LSTM performs better than other models in forecasting with R2, RMSE, and MAPE values of 0.908, 335.74 ML, and 23.5% for water supply and 0.791, 94.88 ML, and 5.3% for water demand, respectively. The inclusion of climatic variables enhanced the accuracy of forecasting by improving R2 and reducing RMSE and MAPE. The results indicate effectiveness of ML models, particularly LSTM-based architectures, in forecasting water supply and demand. However, these models have limitations, particularly in forecasting extreme values, emphasizing the need to improve ML models for more reliable and accurate water management.

Assessing Trade-Offs and Synergies in Ecosystem Services within the Tianshan Mountainous Region

In managing ecosystem services (ESs), it is vital to understand and effectively regulate the trade-offs and synergies (ToSs) involved. This study investigates the Tianshan Mountains (TSMs), utilizing the InVEST (Integrated Valuation of ESs and Tradeoffs) model to evaluate ecosystem service changes from 2000 to 2020, while employing univariate linear regression to examine their spatiotemporal dynamics. Pearson correlation analysis was also conducted to assess how climatic variables (temperature and precipitation) and vegetation indicators (NDVI, normalized difference vegetation index) influence the overall ecosystem service benefits. The findings reveal notable spatial heterogeneity and dynamic shifts in ESs across the TSMs, with strong synergies observed between carbon storage (CS) and other services (such as habitat quality, HQ; soil conservation, SC; and water yield, WY), especially in areas experiencing increased vegetation. However, the connection between HQ and WY was comparatively weaker and occasionally exhibited negative correlations during specific periods, highlighting the intricate trade-offs among various services. The correlation analysis further showed that climate and vegetation changes significantly impact ecosystem service benefits, with declining precipitation and rising temperatures reducing these benefits, whereas higher NDVI was associated with improved service functions. Ultimately, this study emphasizes the necessity of recognizing and managing ToSs in ESs to promote sustainable regional ecosystem development.

Lesbian and Gay Population, Work Experience, and Well-Being: A Ten-Year Systematic Review.

Despite an increase in the promotion of equal opportunities at work, there is still persistent discrimination against lesbian and gay (LG) workers. In this vein, this study aimed to systematically review the research investigating the peculiarities of the work experience of LG people, particularly considering the theoretical frameworks in the approach to sexual minorities' work-related issues, as well as individual and contextual variables influencing the work experience and the impact they may have on health and well-being. We explored the PsycArticles, EMBASE, Scopus, and Web of Science electronic databases and the EBSCOHost (PsycInfo, Psychology and Behavioral Sciences Collection) scholarly search engine, between 01/01/2013 to 01/03/2023, with regards to the search terms "lgb*", "gay*", "lesbian*", "homosexual*", and "sexual minorit*", associated with "employee*", "personnel", "worker*", and "staff", and with "workplace", "work", "job", "occupation", "employment", and "career". Data were narratively synthesized and critically discussed. Of the 1584 potentially eligible articles, 140 papers contributed to this systematic review. Five main theoretical frameworks were identified: (a) minority stress, (b) sexual prejudice and stigma, (c) queer and Foucauldian paradigms, (d) social identity theories, and (e) intersectionality. Furthermore, significant individual (e.g., outness, disclosure, and work-family conflict) and contextual (e.g., heterosexist and heteronormative workplace climate and culture) variables influencing LG people's work experience were identified. This review highlights the need to develop a unified theoretical model for the construction of specific measurement tools to assess the work experience of LG people and for the implementation of interventions aimed at minimizing the effects of stigma in work contexts.

Exploring the Drivers Influencing Multidimensional Alpha and Beta Diversity of Macroinvertebrates in Mountain Streams

Understanding the driving mechanisms of diversity across multiple dimensions is a fundamental task in biodiversity conservation. Here, we examined the alpha and beta diversity of macroinvertebrates in the taxonomic, functional, and phylogenetic dimensions in mountain streams of northwestern Hubei Province, China. We also assessed how much local environmental, land use, climatic, and spatial variables affected these diversities. We found that (1) there were generally weak or no correlations of alpha and beta diversity between taxonomic, functional, and phylogenetic dimensions; (2) compared to both functional and phylogenetic beta diversity, which was mainly determined by nestedness, taxonomic beta diversity was mostly molded by turnover and was much higher; and (3) local environmental variables predominantly influenced taxonomic and functional dimensions of alpha and beta diversity, while spatial factors primarily drove phylogenetic dimension. These results suggest that regulating local habitats is crucial for lotic biodiversity conservation efforts, though spatial processes cannot be overlooked. Furthermore, our findings verify the supplemental role of functional and phylogenetic data in enriching insights provided by taxonomic data alone. This underscores the importance of a multidimensional approach for a more nuanced understanding of community assembly mechanisms, which is crucial for efficient ecosystem management and biodiversity conservation.

Assessment of climatic and environmental parameters on fish abundance of an afro-tropical reservoir

This study assessed the impacts of climatic parameters (rainfall and temperature) and environmental variables (transparency, depth, chloride, TS, TSS, TOC) on the abundance of commercially important fish species in Esa-Odo. The research question was to find out if climatic and environmental factors influence the abundance of fish species in an afro-tropical reservoir. Fish species were collected on a monthly basis for two annual cycles covering both dry and rainy seasons. GIS-based climatic data was used to determine the relationship between climatic conditions and fish species abundance. Results showed that the highest abundance of fish was recorded during the rainy season, with Oreochromis niloticus being the most dominant species throughout the sampling period. Trend analysis revealed that variations in climate and environmental parameters influenced the abundance of different fish species in the reservoir. Mann-Kendal analysis indicated that an increase in rainfall led to an increase in reservoir depth, a decrease in transparency levels, and a reduction in temperature, with a Sen’s slope value of -38. Additionally, CCA and correlation matrix results demonstrated that climate and environmental parameters significantly influenced fish species abundance. The study emphasized the importance of climatic and environmental factors in the abundance of fish species in the reservoir, providing valuable information for future research on fishery resources. Governments and stakeholders were urged to prioritize the conservation and management of the reservoir’s fish population to prevent declines.

Leaf mass per area: An investigation into the application of the ubiquitous functional trait from a paleobotanical perspective.

Leaf mass per area (LMA) is a widely used functional trait in both neobotanical and paleobotanical research that provides a window into how plants interact with their environment. Paleobotanists have used site-level measures of LMA as a proxy for climate, biome, deciduousness, and community-scale plant strategy, yet many of these relationships have not been grounded in modern data. In this study, we evaluated LMA from the paleobotanical perspective, seeking to add modern context to paleobotanical interpretations and discover what a combined modern and fossil data set can tell us about how LMA can be best applied toward interpreting plant communities. We built a modern data set by pulling plant trait data from the TRY database, and a fossil data set by compiling data from studies that have used the petiole-width proxy for LMA. We then investigated the relationships of species-mean, site-mean, and site-distribution LMA with different climatic, phylogenetic, and physiognomic variables. We found that LMA distributions are correlated with climate, site taxonomic composition, and deciduousness. However, the relative contributions of these factors are not distinctive, and ultimately, LMA distributions cannot accurately reconstruct the biome or climate of an individual site. The correlations that make up the leaf economics spectrum are stronger than the correlations between LMA and climate, phylogeny, morphospace, or depositional environment. Fossil LMA should be understood as the culmination of the influences of these variables rather than as a predictor.

What Lies Beneath: A Development-Oriented Auditing Approach to Understand Organizations Beyond the Surface of Hard-Control.

Auditing procedures aim to improve educational quality in vocational education and training. Auditing approaches often focus on checking for compliance to rules and standards. Through dialogue, development-oriented audits are thought to inspire soft-control, a form of control that aims to address what is beneath the surface of mere compliance. These kinds of audits offer opportunities to demonstrate ethical leadership as part of an ethical culture. It is expected that an ethical culture variables positively influence working climate variables and ultimately intrinsic motivation. In this study, conducted as part of the (Hermanussen et al., 2022) study, we employed structural equation modeling to test if model behavior and sanctionability positively influenced perceived autonomy, perceived relatedness, trust, and self-efficacy, and ultimately intrinsic motivation. The study was conducted at eight different Dutch secondary vocational education and training organizations including 1223 participants. Results demonstrate a good model fit, χ2 = .12, df = 3, p = .989, GFI = 1.00, AFGI = 1.00, and RMSEA <.001. All presumed effects were significant. The strongest effects include model behavior on autonomy (.49) and autonomy on intrinsic motivation (.45). Future research should employ designs and analyses that are able to account for a multilevel structure in educational organizations.

Evaluating UKCP18-Based weather files for overheating assessment using building simulation: A case study for a flat in london

Global warming and net zero transition are the two biggest challenges currently faced by the building industry in the UK. While the net zero transition primarily focuses on the problems of energy efficiency and heat decarbonization, the rise of global temperature imposes a significant threat to the health and wellbeing of occupants and the industry is obliged to make buildings climate-resilient by testing their designs using future weather files. To improve the quality of the current weather files, a new project has been commissioned by CIBSE to revisit the data and the methodology employed for creating future weather files and produce new CIBSE weather files using the latest UK Climate Projections released in 2018 (UKCP18). In this study, we evaluate the newly produced weather files for overheating risk using building simulation. Two different batches of weather files were curated. The first batch was produced primarily using the existing methodology for creating the UKCP09 based weather files, with an adjustment to accommodate new features of the UKCP18 and an improved procedure for morphing the solar radiation data. The second batch was created through an improved morphing process to better emulate the characteristics of distributions of climatic variables. The differences between the existing UKCP09 and new UCKP18 based weather files are compared by evaluating overheating metrics. The new weather files enable robust building performance assessment against future climate conditions under different scenarios and will play an important role in designing climate-resilient buildings and delivering a net zero built environment. Practical applications As the extreme weather events resulting from climate change become more frequent and intense, they pose significant challenges to the resilience of the built environment and severe threats to the health and wellbeing of the occupants. Climate data, which serves as the foundation for climate risk assessment, plays a critical role in helping the building sector to achieve climate resilience through the means of performance assessment and the channel of regulatory compliance. In this study, the revised future weather files created using the latest UKCP18 climate projections are presented and evaluated using building simulation, as part of the weather file testing programme for quality assurance. The revision of the CIBSE weather files according to the latest climate science, i.e. UKCP18, will enable the building industry to quantify overheating risks with more accurate climate assumptions and better inform decision making about risk mitigation and climate adaptation.

Climate Predicts NDVI Better Than Plant Functional Group Attributes Along a Latitudinal Gradient in Nunavik

ABSTRACTAimThis study aims to describe the latitudinal pattern in plant functional groups' (PFGs') biomass and cover in Nunavik to test whether PFG attributes are better Normalized Difference Vegetation Index (NDVI) predictors than climate.LocationThe study spans a 700‐km latitudinal gradient from the lichen woodland to prostrate shrub tundra vegetation zones across Nunavik, Canada.TaxonOur analysis focuses on the following PFGs: erect and prostrate shrubs, herbaceous plants, bryophytes, and lichens.MethodsBiomass and cover data of the different PFGs were sampled in 40 sites distributed across the latitudinal gradient. NDVI data were obtained through remote sensing, while climatic, permafrost depth, and surficial deposits were derived from various databases. The PFG models were built to explore relationships between average NDVI (2016–2020) at the sampling site and ecological attributes such as PFG biomass or cover but also other variables such as surficial deposits and permafrost depth. A second series of models, the climatic models, were built using only climatic variables such as seasonal temperature and precipitation.ResultsThe most parsimonious PFG model was built with the biomass data of erect shrubs, herbaceous plants, bryophytes, and lichens and included surficial deposits and permafrost depth (R2 = 0.74). This biomass model performed better than the most parsimonious cover model (cover of erect shrubs and herbaceous, surficial deposits, permafrost depth; R2 = 0.63). However, the most parsimonious climatic model (fall temperature, annual, and winter precipitations) exhibited superior predictive power compared to the ecological ones (R2 = 0.87).ConclusionsPFG models built with PFGs aboveground biomass or cover are good predictors of NDVI of the plant formations sampled along the latitudinal gradient in Nunavik. Despite the intrinsic association between NDVI and vegetation attributes, our study emphasizes the importance of the regional climate in the control of primary productivity in Arctic and subarctic ecosystems. This study provides new insights into the interpretation of NDVI data and enhances our understanding of Arctic vegetation responses under rapid climate change. Furthermore, it underscores the balance between climatic drivers and ecological dynamics in shaping fragile Arctic ecosystems.

Phytoclimatological study in the high plains of Sétif: application of the bioclimatic classification system (Algeria)

The Global Bioclimatic Classification System (WBCS) by Rivas-Martínez is an effective approach to reflect and study the role of climate in determining the potential distribution of natural vegetation at various temporal and spatial scales. In particular, the diversity of physical characteristics in the Setifian high plains leads to the complexity of large-scale phytogeographic belts. Isobioclimates, unique combinations of bioclimatic indices (continentality, ombrotype, and thermotype), have been created for the study area based on Rivas-Martínez's global bioclimatic classification system for current and future climate scenarios, using the parallel climate model from CMIP5 (Coupled Model Intercomparison Project) under the intermediate greenhouse gas emission scenario (RCP4.5). Precipitation and temperature data from the "WorldClim" dataset were used as the data source. These climatic variables were reduced to a spatial resolution of 1 km. The objective is to understand how the climate is evolving in this region, what the consequences are, and what the future holds for these natural ecosystems. A phytosociological study complemented by satellite image analysis reveals a high diversity of pre-forestry formations. The digitally mapped results were used to 1) assess the relative redistribution of isobioclimates and the magnitude of change, 2) identify and locate the new projected isobioclimates, and 3) explore the compositional changes in vegetation types among analogous isobioclimatic zones. The ordination of the relationships between vegetation and bioclimates shows a strong correlation between Rivas-Martinez indices and the distribution and composition of vegetation. Changes in ombrotype, thermotype, bioclimate, ombrotype, and isobioclimate differed between the current and future series. The resulting map presents 9 isobioclimatic units, which are advantageous for confirming the diagnosis of bioclimatic characteristics and describing the relationships between climatic variables and the corresponding distributions of natural vegetation. We identified eight different isobioclimates for the current period (1979-2013) and seven for the future period (2050), three thermotype horizons for both the current and future periods, and finally, four ombrotype horizons for the period (1979-2013), while two ombrohorizons represent the period (2050). This approach is useful for explaining territorial diversity in environmental applications related to ecological adaptation assessment, nature conservation, landscape regionalization, and planning.

Lameness in Adult Sheep and Goats in Greece: Prevalence, Predictors, Treatment, Importance for Farmers.

The present study refers to an extensive investigation of lameness performed countrywide in Greece, on 325 sheep and 119 goat farms. The specific objectives of this work were to present data on the occurrence of lameness on sheep and goat farms and to identify variables (including variables related to climatic factors) associated with the disorder on the farms. Farms were visited and animals on the farm were assessed for the presence of lameness; further, an interview was carried out with the farmer to obtain information regarding practices applied on the farm. Climatic variables at the location of each farm were derived from NASA research. The within farm prevalence rate varied from 0.0% to 25.0% in sheep flocks and from 0.0% to 30.0% in goat herds. The mean ± standard error (median (interquartile range)) within farm prevalence rate among sheep farms was 1.9% ± 0.2 (0.0% (0.0%)); among goat farms, it was 2.6% ± 0.5% (0.0% (0.0%)). Multivariable analysis for within farm prevalence of lameness revealed three significant predictors in sheep farms: application of vaccination against foot-rot, increased precipitation at the farm location and longer annual grazing period for sheep, and one in goat farms: increased precipitation at the farm location. Treatment of lameness involved mostly administration of antibiotics (on 104 farms); the antibiotics administered most often were lincomycin (on 69 farms) and oxytetracycline (on 33 farms). There was a tendency for higher median within farm prevalence of lameness among farms where no antibiotic administration was practiced. Finally, 6.2% of sheep farmers and 4.2% of goat farmers considered lameness as an important health problem for the animals, specifically the third and fifth most important problem on the respective farms.

Cross-Country Assessment of Socio-Ecological Drivers of COVID-19 Dynamics in Africa: A Spatial Modelling Approach

Understanding how countries’ socio-economic, environmental, health status, and climate factors have influenced the dynamics of COVID-19 is essential for public health, particularly in Africa. This study explored the relationships between African countries’ COVID-19 cases and deaths and their socio-economic, environmental, health, clinical, and climate variables. It compared the performance of Ordinary Least Square (OLS) regression, the spatial lag model (SLM), the spatial error model (SEM), and the conditional autoregressive model (CAR) using statistics such as the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and coefficient of determination (R2). Results showed that the SEM with the 10-nearest neighbours matrix weights performed better for the number of cases, while the SEM with the maximum distance matrix weights performed better for the number of deaths. For the cases, the number of tests followed by the adjusted savings, Gross Domestic Product (GDP) per capita, dependence ratio, and annual temperature were the strongest covariates. For deaths, the number of tests followed by malaria prevalence, prevalence of communicable diseases, adjusted savings, GDP, dependence ratio, Human Immunodeficiency Virus (HIV) prevalence, and moisture index of the moistest quarter play a critical role in explaining disparities across countries. This study illustrates the importance of accounting for spatial autocorrelation in modelling the dynamics of the disease while highlighting the role of countries’ specific factors in driving its dynamics.

Efficacy of Automating the Analysis of TripAdvisor Data in Tourism and Climate Studies

ABSTRACT The viability and competitiveness of tourism locations depends, in part, on climate. Netnographic research utilising TripAdvisor reviews can objectively evaluate tourists’ climatic sensitivity and the meteorological factors most important to them. Manually reading and coding hundreds of reviews is time-consuming. Web-scraping can automate this process and analyse hundreds of thousands of reviews nationwide to evaluate climatic influence more comprehensively. This paper compares the outputs of manually collected and web-scraped TripAdvisor reviews, evaluating the efficacy of a web-scraping approach. The total number of climatic mentions captured varied by a statistically insignificant 3.56%, demonstrating a reasonable efficacy of web-scraping. Out of 26,687 TripAdvisor reviews, 1,163 mentioned climate. This 4.4% mean sensitivity to climatic variables is statistically significantly different to the 7.9% yielded from manual data collection. The two methodologies had minor statistically insignificant differences in climatic references at 19 destinations, ranging from 0.42% to 8.26%. A webscraping approach is thus possible but should be used with caution.

Dynamics of extreme climatic variables for viticulture in the main zones of ampelocenosis of the Krasnodar region

Modern climate changes affect all branches of agriculture. Everywhere there is an increase in air temperature, changes in precipitation, an increase in extreme weather events. Since the productive lifespan of a grape plant is 30-40 years, it is necessary to assess climatic changes in order to create a variety adapted to changes. The purpose of the research is to assess changes in extreme heat supply and relative humidity in the main viticultural areas of the Krasnodar region. The average values of extreme heat supply and relative humidity variables of two climatological periods of 1961–1990 and 1991–2020, their changes over time and the course of variable’s anomalies of 1991–2020 compared with the average values of 1961–1990 are calculated. An increase in the absolute maximum air temperature by 0.2–1.6 °C for the period 1991–2020 was noted compared to the previous period, with the exception of Novorossiysk (decrease by 1.4 °C); an increase in the average absolute maximum air temperature by 1.5–2.5 °C, an increase in the number of days with a maximum air temperature above +35 °C by 1.0–2.3 days; a decrease in the average relative humidity of April–October by 0.7–2.7 % and an increase in the number of days with a minimum relative humidity of less than 30 % over the summer by 0.8–5.4 days. The variability of these variables over time for the period 1991–2020 is consistent with the change in the average. An increase in the absolute maximum was established (by 0.65–0.9 °C/10 years), the number of days with a maximum air temperature above +35 °C (by 0.8–1.1 days/10 years), the number of days with a minimum relative humidity of less than 30% over the summer (by 1.2–7.2 days/10 years); decrease in the average relative humidity of April–October (by 0.5–6.5 %/10 years). These changes indicate an increase in climate extremes and the frequency of unfavorable conditions for grapes in the summer, which requires an adjustment of the assortment.

Forecasting changes in the earth’s climate system by instrumental measurements and paleodata in the phase-time region, consistent with changes in the barycentric motions of the sun. Part 1

The relevance of the research is due to the need to establish the root cause of climate change on Earth and predict changes in heliocosmic, climatic variables, natural disasters by intramental measurements and paleodata, as time series, for long intervals of the time horizon in the phase domain, taking into account their cyclicity and interdependence of changes. Purpose of research: application of the time series analysis method of the time series to establish the strength of the influence of the barycentric movements of the Sun on the variability of heliocosmic, climatic variables, natural disasters and fires, high-precision prediction of variables in the phase-time domain for long horizons of hundreds and thousands of years by instrumental measurements and paleodata. Objects of research: time series of changes in heliocosmic and climatic variables, natural disasters and fires, curves of changes in climatic changes obtained by analysis of ice cores in Antarctica and bottom sediments in Antarctica. Methods of research: wavelet phase-frequency and phase-time analysis of images of heliocosmic and climatic variables, natural disasters and fires; calculation of the consistency of changes in selected groups on a set of phase-time characteristics of variables in a sliding mode. Main results of research: In the images of wavelet phase-time functions of many variables constructed from observations in 1600–2010, Jose periods of ~178 years were found, which are contained in changes in the characteristics of barycentric movements of the Sun, solar activity, solar constant, CO2,N2OEl Nino, solar wind, the level of the Caspian Sea, temperature in Greenland, the speed of rotation of the Earth, snow accumulation rates in the Indian Ocean sector of Antarctica, global temperature; cyclicity of changes in heliocosmic and climatic variables are consistent with changes in Baricentre; there is a significant difference in the consistency of changes in the phase-time characteristics of variables obtained at the southern hot and northern cold latitudes of the planet by about 2.1 times; in the northern part of the planet, changes in variables are more chaotic, due to the different influence of changes in the Baricentre variable, the magnetic fields of the Sun and the Earth on the variability of variables at different latitudes of the planet; significant variability and resonances of the phase characteristics of heliocosmic and climatic variables are observed by changes in Baricentre on the graphs of coordinated changes in groups of variables in the observed and predicted time intervals. The studies reveal multiple oscillatory responses of the Earth’s climate system to the impact of the Baricentre variable due to the heterogeneity of its components and the Earth’s magnetosphere in space. Changes in the set of phase-time characteristics of variables on the same graph in the observed and predicted time intervals in the interval ± π are displayed as changes in autowaves characteristic of self-organizing systems, characterizing climatic changes in environments in combination with the influence of the variable Baricentre.