Climate — Open Access Journal

Maximum (T_max), minimum (T_min), mean (T_mean) air temperature and diurnal temperature range (DTR) trends on a seasonal and annual time scale are evaluated from data recorded at nine Spanish weather stations during the period 1950–2011. Temporal and spatial variability in temperatures and in the diurnal temperature range (DTR) are presented. The non-parametric Theil-Sen approach and the Mann-Kendall test are used to evaluate anomaly temperature trends and their statistical significance, respectively. An air temperature reduction in Spain between 1950 and 1980 emerges and significant warming is observed between 1980 and 2011. On a seasonal scale, the weakest trends (mostly insignificant at the 5% confidence level) are noted during autumn, while the strongest warming rates were found during summer and spring. The rate of change between 1950 and 2011 in T_max, T_min and T_mean was 1.6 °C, 1.1 °C and 1.3 °C, respectively. DTR trends showed a decrease on the Mediterranean coast and a small change in northern, Atlantic and rural areas. The spatial distribution of annual and seasonal trends was plotted as isoline maps and strong trend gradients from the south to the north of the country are observed. DTR values were negatively correlated with relative humidity and precipitation and positively correlated with sunshine hours. Full article

In the wake of important economic reforms and an ongoing agrarian transition, non-timber forest products, most notably black cardamom, have emerged as significant trade options for ethnic minority farmers in the mountainous Sino-Vietnamese borderlands. Yet, after a series of harsh winters had already crippled cardamom harvests in the 2000s, extreme weather in 2016 decimated the cardamom plantations of hundreds of farming households. Drawing from sustainable livelihoods, livelihood diversification, and vulnerability literatures, we investigate the multiple factors shaping how these harvest failures have affected ethnic minority cultivator livelihoods. Focusing on four case study villages, two in Yunnan, and two in northern Vietnam, we analyse the coping and adaptation strategies Hmong, Yao, Hani, and Yi minority farmers have adopted. We find that farmers’ decisions and strategies have been rooted in a complex ensemble of factors including their degree of market access, other livelihood opportunities available to them, cultural traditions and expectations, and state development strategies. Moreover, we find that in recent years the Chinese and Vietnamese states have stood-by as affected cultivators have struggled to reorganize their livelihoods, suggesting that the impacts of extreme weather events might even serve state projects to further agrarian transitions in these borderlands. Full article

In The Gambia, climate change has affected, and continues to affect, the agriculture sector. Thus, there is a need to develop and understand effective agricultural adaptation policies. The present study used protection motivation theory to describe farmers’ adoption of climate change adaptation measures in the Central River Region of The Gambia. Primary data were collected in eight communities of the region. A transect walk was conducted, followed by a survey of farmers (n = 283). Perception data collected referred back to the past 20 years, with stated implementation addressing current adaptation practices. Results showed that the perception variables, namely, severity, ability to withstand, and internal barriers, were significantly correlated with protection motivation, while protection motivation and stated implementation for water conservation technique were strongly correlated. Structural equation modeling confirmed the mediation role of protection motivation between farmers’ “stated implementation” of adaptation measures and their perception of climate variability. A decrease in soil water storage capacity, degradation of the quality of soil surface structure, and a decrease of the length of the growing season are all factors that motivate farmers to implement an adaptation measure. The cost of the implementation and farmers’ vulnerability are factors that prevent implantation of adaptation measures. This study suggested that farmers’ resilience should be improved and adaptation measures should be subsidized in order to make them more accessible to farmers. Full article

According to modern concepts, the main natural sources of dust in the atmosphere are dust storms and associated dust devils—rotating columns of rising dust. The impact of dust and aerosols on climate change in the past, present and future is one of the poorly understood and, at the same time, one of the fundamental elements needed for weather and climate forecasting. The purpose of this review is to describe and summarise the results of the study of dust devils in the Earth’s atmosphere. Special attention is given to the description of the 3D structures, the external flows and atmospheric gradients of temperature that lead to the generation and maintenance of the dust devils. Full article

The study of climate, in such a diverse climatic region as the Caucasus, is necessary in order to evaluate the influence of local factors on the formation of temperature and precipitation regimes in its various climatic zones. This study is based on the instrumental data (temperatures and precipitation) from 20 weather stations, located on the territory of the Caucasian region during 1961–2011. Mathematical statistics, trend analysis, and rescaled range Methods were used. It was found that the warming trend prevailed in all climatic zones, it intensified since the beginning of global warming (since 1976), while the changes in precipitation were not so unidirectional. The maximum warming was observed in the summer (on average by 0.3 °C/10 years) in all climatic zones. Persistence trends were investigated using the Hurst exponent H (range of variation 0–1), which showed a higher trend persistence of annual mean temperature changes (H = 0.8) compared to annual sum precipitations (H = 0.64). Spatial-correlation analysis performed for precipitations and temperatures showed a rapid decrease in the correlation between precipitations at various weather stations from R = 1 to R = 0.5, on a distance scale from 0 to 200 km. In contrast to precipitation, a high correlation (R = 1.0–0.7) was observed between regional weather stations temperatures at a distance scale from 0 to 1000 km, which indicates synchronous temperature changes in all climatic zones (unlike precipitation). Full article

Heat waves are considered one of the most noteworthy extreme events all over the world due to their crucial impacts on both society and the environment. For the present article, a relatively new heat wave index, which was primarily introduced for the study of extreme warming conditions over Australia (Excess Heat Factor (EHF, hereafter)), was applied over Greece (eastern Mediterranean) for a 55-year period in order to examine its applicability to a region with different climatic characteristics (compared to Australia) and its ability to define previous exceptional heat waves. The computation of the EHF index for the period 1958–2012 demonstrated that, during the warm period of the year (June, July, August, and September (JJAS)), Greece experiences approximately 20 days per year with positive anomalous conditions (EHF > 0) with positive statistically significant trends for all stations under study. Moreover, an average of 128 spells with a duration of 3 to 10 consecutive days with positive EHF values were found during the examined 55-year period. As the duration of the spell was extended, their frequency lessened. Finally, it was found that the EHF index not only detected, identified, and described efficiently the characteristics of the heat waves, but it also provided additional useful information regarding the impact of these abnormal warming conditions on the human ability to adapt to them. Full article

This paper assessed the current and mid-century trends in rainfall and temperature over the Mono River watershed. It considered observation data for the period 1981–2010 and projection data from the regional climate model (RCM), REMO, for the period 2018–2050 under emission scenarios RCP4.5 and RCP8.5. Rainfall data were interpolated using ordinary kriging. Mann-Kendall, Pettitt and Standardized Normal Homogeneity (SNH) tests were used for trends and break-points detection. Rainfall interannual variability analysis was based on standardized precipitation index (SPI), whereas anomalies indices were considered for temperature. Results revealed that on an annual scale and all over the watershed, temperature and rainfall showed an increasing trend during the observation period. By 2050, both scenarios projected an increase in temperature compared to the baseline period 1981–2010, whereas annual rainfall will be characterized by high variabilities. Rainfall seasonal cycle is expected to change in the watershed: In the south, the second rainfall peak, which usually occurs in September, will be extended to October with a higher value. In the central and northern parts, rainfall regime is projected to be characterized by late onsets, a peak in September and lower precipitation until June and higher thereafter. The highest increase and decrease in monthly precipitation are expected in the northern part of the watershed. Therefore, identifying relevant adaptation strategies is recommended. Full article

The impact of natural hazards on agriculture in Indonesia is becoming increasingly severe. Therefore, improving farmers’ capacity to undertake risk coping strategies is essential to maintaining their prosperity. The objective of this study was to investigate the determinants of farmers’ decisions on ex ante and ex post coping strategies in rural West Java, Indonesia. The study was based on a field survey of 180 farmers conducted in the Garut district from July to October 2017. The study used the protection motivation theory framework and applied three econometric models: binomial logit model, zero truncated Poisson regression model, and multinomial logit model. Most farmers (74.4%) adopted ex ante coping strategies. They were characterized as having higher risk aversion per capita expenditure and disaster experience, but lower discount rates and percentage of damage and locations in downstream and midstream areas. Coping appraisal perceptions were found to be important factors in the risk coping analysis. Four determinants of the decision on the number of ex ante coping strategies adopted were: per capita expenditure, land size, disaster experience, and access to financial institutions. The most common ex post coping strategy adopted by farmers was the middle-stress type. Full article

Severity of drought in California (U.S.) varies from year-to-year and is highly influenced by precipitation in winter months, causing billion-dollar events in single drought years. Improved understanding of the variability of drought on decadal and longer timescales is essential to support regional water resources planning and management. This paper presents a soft-computing approach to forecast the Palmer Drought Severity Index (PDSI) in California. A time-series of yearly data covering more than two centuries (1801–2014) was used for the design of ensemble projections to understand and quantify the uncertainty associated with interannual-to-interdecadal predictability. With a predictable structure elaborated by exponential smoothing, the projections indicate for the horizon 2015–2054 a weak increase of drought, followed by almost the same pace as in previous decades, presenting remarkable wavelike variations with durations of more than one year. Results were compared with a linear transfer function model approach where Pacific Decadal Oscillation and El Niño Southern Oscillation indices were both used as input time series. The forecasted pattern shows that variations attributed to such internal climate modes may not provide more reliable predictions than the one provided by purely internal variability of drought persistence cycles, as present in the PDSI time series. Full article

The emergence of urban heat as a climate-induced health stressor is receiving increasing attention among researchers, practitioners, and climate educators. However, the measurement of urban heat poses several challenges with current methods leveraging either ground based, in situ observations, or satellite-derived surface temperatures estimated from land use emissivity. While both techniques contain inherent advantages and biases to predicting temperatures, their integration may offer an opportunity to improve the spatial resolution and global application of urban heat measurements. Using a combination of ground-based measurements, machine learning techniques, and spatial analysis, we addressed three research questions: (1) How much do ambient temperatures vary across time and space in a metropolitan region? (2) To what extent can the integration of ground-based measurements and satellite imagery help to predict temperatures? (3) What landscape features consistently amplify and temper heat? We applied our analysis to the cities of Baltimore, Maryland, and Richmond, Virginia, and the District of Columbia using geocomputational machine learning processes on data collected on days when maximum air temperatures were above the 90th percentile of historic averages. Our results suggest that the urban microclimate was highly variable across all of the cities—with differences of up to 10 °C between coolest and warmest locations at the same time—and that these air temperatures were primarily dependent on underlying landscape features. Additionally, we found that integrating satellite data with ground-based measures provided highly accurate and precise descriptions of temperatures in all three study regions. These results suggest that accurately identifying areas of extreme urban heat hazards for any region is possible through integrating ground-based temperature and satellite data. Full article

Probabilistic models are useful tools in understanding rainfall characteristics, generating synthetic data and predicting future events. This study describes the results from an analysis on comparing the probabilistic nature of daily, monthly and seasonal rainfall totals using data from 1327 rainfall stations across Australia. The main objective of this research is to develop a relationship between parameters obtained from models fitted to daily, monthly and seasonal rainfall totals. The study also examined the possibility of estimating the parameters for daily data using fitted parameters to monthly rainfall. Three distributions within the Exponential Dispersion Model (EDM) family (Normal, Gamma and Poisson-Gamma) were found to be optimal for modelling the daily, monthly and seasonal rainfall total. Within the EDM family, Poisson-Gamma distributions were found optimal in most cases, whereas the normal distribution was rarely optimal except for the stations from the wet region. Results showed large differences between regional and seasonal ϕ-index values (dispersion parameter), indicating the necessity of fitting separate models for each season. However, strong correlations were found between the parameters of combined data and those derived from individual seasons (0.70–0.81). This indicates the possibility of estimating parameters of individual season from the parameters of combined data. Such relationship has also been noticed for the parameters obtained through monthly and daily models. Findings of this research could be useful in understanding the probabilistic features of daily, monthly and seasonal rainfall and generating daily rainfall from monthly data for rainfall stations elsewhere. Full article

Nepal has experienced recent changes in two crucial climatic variables: temperature and precipitation. Therefore, climate-induced water security concerns have now become more pronounced in Nepal as changes in temperature and precipitation have already altered some hydrological processes such as the river runoff in some river systems. However, the linkage between precipitation patterns and streamflow characteristics are poorly understood, especially in small rivers. We analysed the temporal trends of temperature, precipitation, and extreme indices of wet and dry spells in the Rosi watershed in Central Nepal, and observed the temporal patterns of the streamflow of the Rosi river. We also examined the linkages between the average and extreme climate indices and streamflow. We found that the area has warmed up by an average of 0.03 °C/year, and has seen a significant decline in precipitation. The dry spell as represented by the maximum length of the dry spell (CDD) and the magnitude of dryness (AII) has become more pronounced, while the wet spell as represented by the number of heavy rainfall days (R5D) and the precipitation intensity on wet days (SDII) has diminished significantly. Our analysis shows that recent changes in precipitation patterns have affected the streamflow of the Rosi river, as manifested in the observed decline in annual and seasonal streamflows. The decrease in the availability of water in the river is likely to have severe consequences for water security in the area. Full article

This work focuses on trend analysis of rainfall, evaporation, temperature, relative humidity, wind speed, and sunshine duration over the Ouémé Delta in Bénin. Eight temperature based indices and fifteen rainfall based indices are computed from 1960 to 2016. Moreover, maximum 1, 2, 3, 5, and 10 days precipitation indices were computed at the monthly scale. Trends are detected at 0.05 confidence level, using a combination of Mann-Kendall and prewhitened Mann-Kendall test. Partial correlation and stepwise regression are used to detect the set of meteorological variables that influence pan evaporation in Ouémé Delta. Results showed intensification of heavy rainfall over Ouémé Delta. Moreover, a significant increasing trend is detected in temperature. As consequence, diurnal temperature significantly decreases as proof of the global warming. Average pan evaporation showed a significant slither increasing trend over the area. Change in pan evaporation can be explained by wind speed and sunshine duration that hold almost 50% of pan evaporation variance. As future temperature is going to be increasing, pan evaporation may increase considerably. So, adaptation measures have to be reinforced in the Ouémé Delta area where farmer are used to rainfed agriculture for food security. Moreover, Ouémé Delta plan have to be developed for it resources sustainability. Full article

Urban climatology is a complex field owed to the intersecting parameters. In city planning, neighborhood fabric and vegetation plays a great role in modifying arid microclimates. This work presents an approach to enable urban designers to find the optimum land use parameters to achieve pedestrian thermal comfort. In this study, a model was developed based on ENVI-met simulations of two urban and suburban sites in Cairo, Egypt. Initial design parameters were; compactness degree, grass coverage, leaf area density, trees ground coverage, and asphalt and buildings areas. After regression analysis, the step-wise algorithm succeeded in creating the best fit of 94% R² and 92% adjusted R². The suggested Urban Design Comfort Model (UDCM) was examined using MATLAB to find the optimum design parameters. Optimum values were applied to generate primitive urban configurations using Grasshopper. The primitives were simulated again in ENVI-met to validate UDCM. The resulted value of Physiological Equivalent Temperature, PET at peak time was reduced from the initial result of ENVI-met (42.3 °C) in both sites to reach (38.7 °C) then (36.8 °C) after refinement with extra foliage. This approach, as a tool for urban designers, not only facilitates and speeds up urban form design process on a passive basis, but also provides deep insights on the development of UDCM considering all different city transects rather than two. Full article

Climate change is anticipated to influence future wildfire activity in complicated, and potentially unexpected ways. Specifically, the probability distribution of wildfire size may change so that incidents that were historically rare become more frequent. Given that fires in the upper tails of the size distribution are associated with serious economic, public health, and environmental impacts, it is important for decision-makers to plan for these anticipated changes. However, at least two kinds of structural uncertainties hinder reliable estimation of these quantities—those associated with the future climate and those associated with the impacts. In this paper, we incorporate these structural uncertainties into projections of very-large fire (VLF)—those in the upper 95th percentile of the regional size distribution—frequencies in the Continental United States during the last half of the 21st century by using Bayesian model averaging. Under both moderate and high carbon emission scenarios, large increases in VLF frequency are predicted, with larger increases typically observed under the highest carbon emission scenarios. We also report other changes to future wildfire characteristics such as large fire frequency, seasonality, and the conditional likelihood of very-large fire events. Full article

Climate change is an unprecedented risk that humans have not previously experienced. It is accepted that people are generally worried about global warming. However, it is also a fact that there is a small but increasing number of climate change skeptics. These skeptics do not believe that there is any risk, nor are they concerned with other worrisome facts related to climate change. Skeptics regard the present scientific findings supporting climate change as false artefacts. Our study aimed to explore the factors that influence climate skepticism. In this work, to make a regression model, we established environmental skepticism as a dependent variable and included sociodemographic factors, values, and perception factors as the three independent variables. Also, to examine their roles indirectly, we regarded values as moderators. The results show that, in terms of values, ideology, environmentalism, religiosity, two kinds of cultural biases, and science and technology (S&T) optimism influence skepticism at the individual level, whereas, in terms of perception factors, perceived risk, perceived benefit, and negative affect have an impact. Also, values such as ideology, religiosity, environmentalism, and cultural biases play a moderating role that facilitates, buffers, or changes the effect of psychometric variables on an individual’s skepticism. Full article

The effects of neighborhood-scale land use and land cover (LULC) properties on observed air temperatures are investigated in two regions within Los Angeles County: Central Los Angeles and the San Fernando Valley (SFV). LULC properties of particular interest in this study are albedo and tree fraction. High spatial density meteorological observations are obtained from 76 personal weather-stations. Observed air temperatures were then related to the spatial mean of each LULC parameter within a 500 m radius “neighborhood” of each weather station, using robust regression for each hour of July 2015. For the neighborhoods under investigation, increases in roof albedo are associated with decreases in air temperature, with the strongest sensitivities occurring in the afternoon. Air temperatures at 14:00–15:00 local daylight time are reduced by 0.31 °C and 0.49 °C per 1 MW increase in daily average solar power reflected from roofs per neighborhood in SFV and Central Los Angeles, respectively. Per 0.10 increase in neighborhood average albedo, daily average air temperatures were reduced by 0.25 °C and 1.84 °C. While roof albedo effects on air temperature seem to exceed tree fraction effects during the day in these two regions, increases in tree fraction are associated with reduced air temperatures at night. Full article