Analysis Of Climate Data Research Articles

A Tree Ring Proxy Evaluation of Declining Causes in Pinus sylvestris L. and Pinus nigra J.F. Arnold in Northeastern Romania

Drought-induced dieback has been extensively studied in various forests habitats. We used a retrospective tree ring width (TRW), basal area increment (BAI), oxygen isotope ratios in tree ring cellulose (δ18OTR) and carbon isotope ratios in tree ring cellulose (δ13CTR) to assess causes in declining Pinus sylvestris L. and Pinus nigra J.F. Arnold. The climate data analysis indicates a significant increased trend occurred after 1980 in minimum, mean and maximum temperature and a reduced amount of precipitation compared to the 1920–1980-time scale. According to the Palmer Drought Severity Index, we found two extreme drought years (1946 and 2000) and three years with severe drought (1990, 2003 and 2012). One-way ANOVA indicated no significant difference between P. nigra and P sylvestris tree ring width, basal area increment, but a considerable difference between δ13CTR and δ18OTR. Basal area increment evaluated the climate-growth relationship most accurately, comparing to δ18OTR and δ13CTR, which explained the influences of environmental factors in tree rings formation. The δ13CTR was mainly negatively correlated with high temperatures from April-August current growing seasons. The negative correlation between δ13CTR and NDVI indices (June, August) shows a decreased carbon uptake induced by drought from summer to early autumn. The low δ18OTR signal was associated with a complex of factors, including the strong influence of heavy precipitation occurring in the growing season and a weak reaction of declined trees to resources. Species-specific responses to drought in 1990, 2003 and 2012 indicated P. sylvestris as more sensitive to drought whit higher demand for water supply in the optimal compared with P. nigra. Weak and unstable correlations in time with increasing/decreasing values in drought periods were obtained more accurately using δ18OTR compared to δ13CTR. The species-specific resilience response to drought years showed a weak resilience and resistance in P. sylvestris occurred more evident after the 2012 event compared to less sensitive P. nigra trees. Decision-makers can use presented results to reinforce specific management plans capable of protecting and changing local compositions where is the case with species more resistant to drouth.

Vulnerability of Communities to Climate Change Induced Disaster Risks and Potential Mental Health Outcomes in Isiolo County, Kenya

Purpose: The study was conducted to identify and evaluate disaster risks and mental health outcomes caused by extreme climate events. Methodology: Quantitative data was obtained from existing climate and mental health (1984-2019) records, while qualitative data was obtained from literature review of case studies and content analysis, Focus Group Discussion and household survey in four major zones for two consecutive years. ArcGIS software method explored various properties of the climate systems to infer the distribution of climate parameters, select extremes value and calculate linear trend of time series. The quantitative data was analyzed using statistical tools in Excel, IBM SPSS version 20 while climate data analysis was done using R software (version 3.21). Results: The exceedance threshold of 𝜇 = 340 𝑚𝑚 was chosen. On the other hand, mean exceedance threshold of 𝜇 = 36.50𝐶 and 𝜇 = 11.380𝐶 for minimum and maximum temperatures respectively. The rainfall band was very high or very low, deemed to create disaster risks. The results revealed that the most common disaster risks include: drought and heatwaves, strong sand storms, flash floods and floods. The duration of time, frequency and unpredictable weather variability events were above critical threshold, hence categorized as high risk, rated 1, hence fatal. Unique Contribution to Theory and Practice: The study provides historical empirical data on hazard mapping and mental health outcomes to enable policy and programmes formulation by state and nonstate actors. The study recommends development of robust environmental health procedures to diagnose mental disorders, mapping of disasters; mental disorder epidemiology and make it user friendly to advice policy, scale up solutions and accelerate evidence informed advocacy on adaptation and resilience mental health programme strategies

Farmers’ Perception and Adaptation Strategies to Climate Change in Central Mali

AbstractAdaptation of the agricultural sector to climate change is crucial to avoid food insecurity in sub-Saharan Africa. Farmers’ perception of climate change is a crucial element in adaptation process. The aim of this study was (i) to compare farmers’ perception of climate change with actual weather data recorded in central Mali, (ii) to identify changes in agricultural practices implemented by farmers to adapt to climate change, and (iii) to investigate the link between farmers’ perception of climate change and implementation of adaptation practices. Focus group discussions and individual surveys were conducted to identify climate-related changes perceived by farmers and agricultural adaptation strategies they consider relevant to cope with these changes. A majority (>50%) of farmers perceived an increase in temperature, decrease in rainfall, shortening of growing season, early cessation of rainfall, and increase in the frequency of dry spells at the beginning of the growing season. In line with farmers’ perception, analysis of climate data indicated (i) an increase in mean annual temperature and minimum growing season temperature and (ii) a decrease in total rainfall. Farmers’ perception of early cessation of rainfall and more-frequent drought periods were not detected by climate data analysis. To cope with the decrease in rainfall and late start of the growing season, farmers used drought-tolerant cultivars and implemented water-saving technologies. Despite a perceived warming, no specific adaptation to heat stress was mentioned by farmers. We found evidence of a link between farmers’ perception of climate change and the implementation of some adaptation options. Our study highlights the need for a dialogue between farmers and researchers to develop new strategies to compensate for the expected negative impacts of heat stress on agricultural productivity.

Novel Approach for Spatiotemporal Weather Data Analysis

Massive volumes of multidimensional array-based spatiotemporal data are generated by climate observations and model simulations. The growth in climate data leads to new opportunities for climate studies at multiple spatial and temporal scales. Managing, analyzing and processing of big climate data is considered to be challenging because of huge data volume. In this work multidimensional climate data such as precipitation and temperature are processed and analyzed in the Spark MapReduce Platform, since Spark platform is computationally more efficient than Hadoop-MapReduce Platform of same configuration. In temporal scale monthly and seasonal analysis of climate data has been carried out to understand the regional climate. The prediction of Rainfall on monthly and seasonal time scales is very much important for planning and devising agricultural strategies and disaster management, etc. As the prediction of climate state is very challenging, in this study an attempt is being made for the prediction of the rainfall using the time series analysis in the same framework. As a test case the time series approach such as Auto Regression Integrated Moving Average (ARIMA) has been used for the prediction of rainfall. The proposed approach is evaluated and found to be accurate in the analysis and prediction of climate data and this will surely guide for the researcher for better understanding of the climate and its application to multiple sectors.

АНАЛИЗ ИЗМЕНЧИВОСТИ ОСАДКОВ В ПРОВИНЦИИ ОЛЬГИН (КУБА) ЗА ПЕРИОД 1970–2020 годы

The study of trends in meteorological indicators is an important task that has received much attention at present. The study analyses changes in humidification in the eastern part of the island of Cuba (Holguín) for the period from 1970 to 2020. The study is based on a statistical analysis of climatic data to establish trends in moisture features in the area. Trends in precipitation were determined, coefficients of variation and variability were calculated, and changes in the number of days with precipitation were identified. Moisture changes were studied and it was revealed that moistening in the province is moderate and not uniform. The coefficient and balance of humidification were calculated according to long-term data. Analysis of data of change of the quantity of precipitation and days with precipitation is carried out. The moisture coefficient for the territory was calculated. The wettest (1981-1985) and the driest (1991-1995) periods have been revealed. The number of days with precipitation increased from 39 days (1970) to 150 days (2007). A systematic increase in the number of days with precipitation was detected. The maximum number of days with precipitation is in the period 2006-2010. In 2016-2020, there is a change in the structure of precipitation and a shift of maximum daily precipitation to the beginning of the year.

Retraction Note: Climatic data analysis and computer data simulation of inland cities based on cloud computing and remote sensing images

Retraction Note: Climatic data analysis and computer data simulation of inland cities based on cloud computing and remote sensing images

Prediction of tornado days in the United States with deep convolutional neural networks

Tornado outbreaks in the United States have caused large numbers of human and economic losses. The analysis of gridded climate data to find spatial patterns of meteorological variables that produce tornadoes is often a time consuming and manual process requiring visual inspection. Therefore, the ability to train convolutional neural networks (CNNs) to recognize patterns in climate data that produce tornado outbreaks would be a huge benefit to climatologists. In this paper, three CNNs of increasing complexity, including LeNet-5, VGG-16, and Resnet-50 were trained to predict days with tornado outbreaks of varying size based on gridded values of convective available potential energy (CAPE), convective inhibition (CIN), storm relative helicity (HLCY), and geopotential height. All three models showed promising results in predicting large tornado outbreaks with greater than 20 tornadoes with VGG-16 using the variable CAPE having a slightly better performance, highlighting the importance of CAPE in the analysis of tornado environments.

Evaluation of Climate Change on Streamflow, Sediment, and Nutrient Load at Watershed Scale

This study evaluated changes in climatic variable impacts on hydrology and water quality in Big Sunflower River Watershed (BSRW), Mississippi. Site-specific future time-series precipitation, temperature, and solar radiation data were generated using a stochastic weather generator LARS-WG model. For the generation of climate scenarios, Representative Concentration Pathways (RCPs), 4.5 and 8.5 of Global Circulation Models (GCMs): Hadley Center Global Environmental Model (HadGEM) and EC-EARTH, for three (2021–2040, 2041–2060 and 2061–2080) future climate periods. Analysis of future climate data based on six ground weather stations located within BSRW showed that the minimum temperature ranged from 11.9 °C to 15.9 °C and the maximum temperature ranged from 23.2 °C to 28.3 °C. Similarly, the average daily rainfall ranged from 3.6 mm to 4.3 mm. Analysis of changes in monthly average maximum/minimum temperature showed that January had the maximum increment and July/August had a minimum increment in monthly average temperature. Similarly, maximum increase in monthly average rainfall was observed during May and maximum decrease was observed during September. The average monthly streamflow, sediment, TN, and TP loads under different climate scenarios varied significantly. The change in average TN and TP loads due to climate change were observed to be very high compared to the change in streamflow and sediment load. The monthly average nutrient load under two different RCP scenarios varied greatly from as low as 63% to as high as 184%, compared to the current monthly nutrient load. The change in hydrology and water quality was mainly attributed to changes in surface temperature, precipitation, and stream flow. This study can be useful in the development and implementation of climate change smart management of agricultural watersheds.

Practices and Strategies for Adaptation to Climate Variability in Family Farming. An Analysis of Cases of Rural Communities in the Andes Mountains of Colombia and Chile

Climate variability imposes greater challenges on family farming and especially on rural communities in vulnerable mountainous regions such as the Andes in Latin America. Changes in rainfall patterns and fluctuations in temperatures cause a greater frequency of extreme events, increased pests, and crop diseases, which even lead to food insecurity in communities that depend on self-production for survival. This is why strategies need to be developed to face this new scenario. Two cases of adaptation experiences to the effects of climate variability in rural communities in Chile (Araucanía Region) and Colombia (Cauca Department) were analyzed on this paper. For this, a mixed methodological approach was adopted that included the analysis of climate data, socioeconomic, and productive characterization of the communities, and a characterization of adaptation practices for both cases. The results show various ways of adapting mainly to changes in the availability and access of water for the development of agriculture and for domestic use. Likewise, it is shown that in order to be successful, the measures for facing climate variability must be part of coordinated strategies under a community-based adaptation approach and not developed in isolation.

Exposure to changes in climate in Bolivia

Bolivia, by location and topographic characteristics, is a country with high exposure to extreme climate events. Often, the country is affected by droughts and storms that result in heat waves and floods with adverse effects for the population and the economy in general. For this reason, the analysis of temporal and spatial climate data is more evident than ever to face the socio-economic and environmental impacts associated with knowledge and understanding of the climate patterns in the country. The present investigation focuses on determining the changes in climatic variables of precipitation and temperature, generating indicators on these variables' behavior, and identifying Bolivia's areas with the most significant temporal exposure to climate changes in recent decades. Thus, this study proposes a regionalization that allows to contrast and analyzes these changes. As a result, it is expected to contribute to the generation of public policies and research around the areas of risk, vulnerability, adaptation, and climate resilience of the most significant exposure areas identified in this study.

Development of a Quality-Controlled and Homogenised Long-Term Daily Maximum and Minimum Air Temperature Network Dataset for Ireland

Accurate long-term daily maximum and minimum air temperature series are needed to assess the frequency, intensity, distribution, and duration of extreme climatic events. However, quality control and homogenisation procedures are required to minimise errors and inhomogeneities in climate series before the commencement of climate data analysis. A semi-automatic quality control procedure consisting of climate consistency, internal consistency, day-to-day step-change, and persistency tests was applied for 12 long-term series registered in Ireland from 1831–1968, Armagh Observatory (Northern Ireland) from 1844–2018, and for 21 short-term series dating to the mid-19th century. There were 976,786 observations quality-controlled, and 27,854 (2.9%) values flagged. Of the flagged records, 98.5% (n = 27,446) were validated, 1.4% (n = 380) corrected and 0.1% (n = 28) deleted. The historical long-term quality-controlled series were merged with the modern series quality-controlled by Met Éireann and homogenised using the software MASHv3.03 in combination with station metadata for 1885–2018. The series presented better homogenisation outcomes when homogenised as part of smaller regional networks rather than as a national network. The homogenisation of daily, monthly, seasonal, and annual series improved for all stations, and the homogenised records showed stronger correlations with the Central England long-term temperature series.

Climate Change Influences Basidiome Emergence of Leaf-Cutting Ant Cultivars.

Maintaining symbiosis homeostasis is essential for mutualistic partners. Leaf-cutting ants evolved a long-term symbiotic mutualism with fungal cultivars for nourishment while using vertical asexual transmission across generations. Despite the ants’ efforts to suppress fungal sexual reproduction, scattered occurrences of cultivar basidiomes have been reported. Here, we review the literature for basidiome occurrences and associated climate data. We hypothesized that more basidiome events could be expected in scenarios with an increase in temperature and precipitation. Our field observations and climate data analyses indeed suggest that Acromyrmex coronatus colonies are prone to basidiome occurrences in warmer and wetter seasons. Even though our study partly depended on historical records, occurrences have increased, correlating with climate change. A nest architecture with low (or even the lack of) insulation might be the cause of this phenomenon. The nature of basidiome occurrences in the A. coronatus–fungus mutualism can be useful to elucidate how resilient mutualistic symbioses are in light of climate change scenarios.

Feasibility of Utilizing Photovoltaics for Irrigation Purposes in Moamba, Mozambique

Agriculture plays a significant role in the labor force and GDP of Mozambique. Nonetheless, the energy source massively used for water pumping in irrigation purposes is based on fossil fuels (diesel oil). Despite the water availability and fertile soils in Moamba, Mozambique, farmers struggle with the high cost of fuels used in the pumping systems. This study was sought to analyze the feasibility of utilizing a solar photovoltaic system as a means to reduce the environmental impact caused by the diesel pumps and simultaneously alleviate the expenses regarding the use of non-environmentally friendly technologies. Site observations and interviews were undertaken in order to obtain local data regarding the water demand, current energy systems costs and distances from the source to the irrigated fields. CLIMWAT 2.0 was used for climate data acquisition and analysis. The environmental benefits, the cost effectiveness and local climate conditions show that the PV system is feasible in Moamba. Furthermore, parameters such as hydraulic energy, incident solar energy, pump efficiency and total system efficiency were used to predict the performance of the system. The results obtained are important to analyze the implementation of such energy systems.

Hydrological Evolution of a Lake Recharged by Groundwater in the Badain Jaran Desert Over the Past 140 years

Understanding the evolution of lakes in arid areas is very important for water resource management. Previous studies have mainly focused on lakes with runoff recharge, while the evolution of groundwater recharge lakes in hyper-arid areas is still less known. In this study, an 86 cm-long sediment core was extracted from Sayinwusu Lake, one of groundwater-recharge lakes in the southeastern Badain Jaran Desert, Northwest China. 210Pb and 137Cs dating, total organic carbon (TOC) and total nitrogen (TN) contents, and mineral content analysis were used to reconstruct the lake evolution over the past 140 years. The evolution of Sayinwusu Lake since 1880 can be divided into two periods. In the first period from 1880 to 1950, the TOC and TN contents were low, and the minerals consisted of all detrital minerals, which indicate that the lake’s primary productivity and salinity were low. During the second period from 1950 to 2018, the contents of TOC, TN, and carbonate minerals increased rapidly at the beginning of the 1950s, indicating that the lake’s primary productivity and salinity increased. Comprehensive analysis of regional climate data suggests that the increase in evaporation caused by rising temperature is an important factor affecting lake evolution in the desert. Although precipitation has increased in the arid region of Northwest China in recent decades with increasing temperature, the enhancement of the evaporation effect is much greater. As a record from groundwater recharge lakes in deserts, our study provides new insight into projecting future lake changes in hyper-arid areas.

The blessing of dimensionality for the analysis of climate data

Abstract. We give a simple description of the blessing of dimensionality with the main focus on the concentration phenomena. These phenomena imply that in high dimensions the lengths of independent random vectors from the same distribution have almost the same length and that independent vectors are almost orthogonal. In the climate and atmospheric sciences we rely increasingly on ensemble modelling and face the challenge of analysing large samples of long time series and spatially extended fields. We show how the properties of high dimensions allow us to obtain analytical results for e.g. correlations between sample members and the behaviour of the sample mean when the size of the sample grows. We find that the properties of high dimensionality with reasonable success can be applied to climate data. This is the case although most climate data show strong anisotropy and both spatial and temporal dependence, resulting in effective dimensions around 25–100.

Subarctic landscape adaptations and paleodemography: A 14,000-year history of climate change and human settlement in central Alaska and Yukon

Central Alaska and Yukon represent the longest continuously inhabited region in the Americas, with a rich record of research on technology, subsistence, and decision-making spanning over 14,000 years. Still, archaeologists debate whether the changes observed in the material and paleodemographic record can be interpreted as the result of environmental influences. This paper presents a multivariate analysis of regional occupation chronology, paleodemography, site placement, and climate data to investigate how past populations were affected by climate change. This research shows that populations were larger than expected at some of the earliest periods and that significant climatic and environmental events do not correspond with significant population decreases as researchers have previously argued. Changes in land use were gradual and unidirectional, with decreases in average site slope, increases in average site size, and consistent average site elevation through time. Trends in land use and paleodemography correspond to cultural phases assigned by regional experts, though environmental shifts undoubtedly played a role in shaping land use and culture throughout the past. This research has possible implications for our understanding of important cultural moments in the region's history, such as initial and subsequent migrations throughout the Americas.

Long Term Spatio-temporal Variations of Seasonal and Decadal Aridity in India

A comprehensive analysis of climate data (1958-2018) is carried out at the national scale in India to assess spatiotemporal variation in aridity. The aridity is analyzed using UNEP (United Nations Environment Programme) Aridity Index (AI), which is the ratio between Precipitation (P) and Potential Evapotranspiration (PET). Freely available Terra-Climate database, P and PET variables, offered an unprecedented opportunity for monitoring variations in AI and aridity index anomalies (AIA) at interseasonal and inter-decadal basis. The study also assesses longer term patterns of P and AI anomalies with vegetation anomalies. The results indicate that significant clustered areas with maximum dryness are located at west-central part of India, the state of Maharashtra. Overall, there is a gradual increase in the extent of arid zone during 60-year period and spatially maximum extent of percentage change in aridity area is observed. The change patterns of AI in India are largely driven by the changing patterns of precipitation. The maximum impact of decline in precipitation on AIA was observed during Kharif season frequently, for every 4-5 years during 1972-1992. The pattern repeated in the last few recent years (2013- 2018), the decline in precipitation resulted increased aridity. The study also reveals that the availability and usage of irrigation sources have increased from 2014 to 2018. Thus, despite of less precipitation positive vegetation has been resulted in this period. The findings are important to understand the impacts of climate change on land use pattern, and land and water resource management.

Assessing Smallholder Farmers’ Perception on Climate Variability in Relation to Climatological Evidence: A Case Study of Benguene in the Sudanian Zone of Mali

Sudanian zone is one of the regions in West Africa most confronted with high climate variability, challenging agricultural sustainability and rural livelihoods. Despite multiple research efforts devoted to exploring how farmers in this region perceive climate variability and how perceptions relate to adaptive responses, much remains to be done. This study examined farmers’ perception of climate variability compared with scientific evidence in the terroir of Benguene, between 1983 and 2018. Data was collected from twenty-nine (29) farmland heads (representing 49% of the total farmland heads in the village) through a survey conducted in October 2019. Monthly temperature data from ground measurement and daily rainfall data extracted from the gridded African Rainfall Climatology version 2 (ARC-2) time series was used. Descriptive statistic was used to analyse survey data. Climate data analysis included Mann Kendall trend analysis and computation of growing season parameters (Onset, Duration and Cessation) and its trend. In the end, a conformity index was used to compare farmers’ perceptions to climatological evidence. Conformity graph showed that apart from temperature, the farmers have low, to a weak perception of the other variables used to assess their perception. Results showed that many smallholder farmers have observed a rise in temperature (90%), delay onset (93%) and early cessation (93%) of rainfall. The analysis of the climate data showed high variability in the climate during the study period as observed in the increasing trends in temperature (p-value of 0.0007) and in the rainfall (p-value of 0.0001). Concerning the season parameters, the results show increasingly early-onset (June 1st ± 22 days) increasingly late cessation (October 09th ± 14 days) and consequently a long duration of the rainy season (130 ± 27 days). Conversely, farmers thought the opposite. These results imply the urgent need to increase smallholder farmers awareness of climate variability and change by providing climate information for better adaptation

The Potential of Stormwater Management in Addressing the Urban Heat Island Effect: An Economic Valuation

Urban green infrastructure (UGI) within sustainable stormwater management provides numerous benefits to urban residents, including urban heat island (UHI) mitigation. Cost–benefit analyses (CBA) for UGI have been conducted at neighborhood level with a focus on stormwater management, but valuations of reductions in heat-related hospitalizations and mortality are lacking. These benefits create significant social value; the quantification thereof is essential for urban planning in providing a scientific foundation for the inclusion of UGI in UHI mitigation strategies. This study assesses the potential of three UGI scenarios developed for an urban neighborhood in Berlin, Germany. First, climate data analyses were conducted to determine the cooling effects of tree drains, facade greening, and green roofs. Second, a CBA was performed for each scenario to value UHI mitigation by estimating the damage costs avoided in reduced heat-related hospitalizations and fatalities, using the net present value (NPV) and benefit–cost ratio (BCR) as indicators of economic feasibility. The results indicate heat mitigation capabilities of all three UGI types, with tree drains achieving the strongest cooling effects. Regarding economic feasibility, all scenarios achieve positive NPVs and BCRs above one. The findings confirm the potential of stormwater management in mitigating UHI and generating substantial social value.

Analyzing the Impact of Climate Change on Cotton Yield Using Spatial Analysis and Statistical Modeling in the Indus River Basin, Pakistan

Cotton is an excessive growing fiber crop in the world. In Pakistan, climate warming is hurting the cotton crop. To identify countermeasures of climate change impacts on crop production, it is needed to explore the changes in crop yield and their relationship to climate change. This study, we conducted spatial analysis of cotton yield and climate data in the Indus River basin in Pakistan from 1989- 2018. We analyzed the trend in cotton production indices and climate variables using the Mann-Kendal nonparametric test, and the impact of climate change on cotton yield through Pearson’s correlation and best-subset multivariate linear regression model. Sunshine hours positively impacted on cotton yield, but an increasing trend in average temperature, minimum temperature and air relative humidity in main crop areas had a negative impact on the cotton crop. Our results illustrate the relationship between the cotton yield and climate change variables on regional basis in the Indus River basin, Pakistan. This research could provide a reference for understanding the intolerance of cotton in the Indus River basin, Pakistan to climate change.