Analysis Of Climate Data Research Articles

LAND SURFACE TEMPERATURE FORECASTING IN THE CONTEXT OF THE DEVELOPMENT OF SUSTAINABLE CITIES AND COMMUNITIES

The article examines the aspects of land surface temperature forecasting for effective planning and development of sustainable cities and communities. The relevance of the research lies in the need to develop effective approaches to the analysis and forecasting of climate data, for the timely determination of existing problems and ways to solve them, in accordance with civilizational challenges. The trends of changes in the land surface temperature from the middle of the 20th to the beginning of the 21st century were analyzed, using the example of five megacities located in different regions: Tokyo (Japan), Lagos (Nigeria), Berlin (Germany), Singapore (Singapore) and Belo Horizonte (Brazil). The results of the analysis of changes in average monthly and average annual temperatures are presented. The factors affecting the formation of the temperature regime of each of the cities are determined. The role of urbanization as a key factor in the development of the city is described, the main challenges caused by it are considered. An overview of megacities from the point of view of sustainable development was carried out. Prospects for urban development and measures aimed at reducing the urban heat island effect are considered. The role of modern technologies, in particular machine learning for predicting the land surface temperature, is described. The expediency of land surface temperature forecasting, in order to implement strategies for mitigating negative consequences and achieving the goals of sustainable development, is substantiated. The conducted research makes it possible to be convinced of the need for a responsible approach to the design and development of sustainable cities and communities. The temperature of the earth’s surface is one of the key indicators that allows you to monitor the main trends of urban life and can be an indicator of the effectiveness of strategies used to increase comfort and sustainable development.

A shifting climate: New paradigms and challenges for (early career) scientists in extreme weather research

AbstractResearch on weather and climate extremes has become integral to climate science due to their increasing societal relevance and impacts in the context of anthropogenic climate change. In this perspective we examine recent changes and evolving paradigms in the study of extreme events, emphasizing the increasingly interdisciplinary nature of research and their societal implications. We discuss the importance of understanding the physical basis of extreme events and its linkages to climate impacts, highlighting the need for collaboration across multiple disciplines. Furthermore, we explore the challenge of big climate data analysis and the application of novel statistical methods, such as machine learning, in enhancing our understanding of extreme events. Additionally, we address the engagement with different stakeholder groups and the evolving landscape of climate services and private‐sector involvement. We conclude with reflections on the risks and opportunities for early career researchers in navigating these interdisciplinary and societal demands, stressing the importance of meaningful scientific engagement, and removing barriers to inclusivity and collaboration in climate research.

Long Term Historical Precipitation and Temperature Trend Analysis for Selected Stations in Bale and East Bale Zones of Southeastern Ethiopia

Trend analysis for the long-term average temperature and precipitation of Bale and East Bale Zones during 1901 to 2016 has been performed for selected 5 stations to understand the pattern of these important meteorological features under climate change. The precipitation characterizations viz. standardized Precipitation Index has been considered for Sinana, Goro, Ginnir, Dallo menna and Agarfa stations. This study was aimed to analysis ‘’long term historical climate analysis selected stations in Bale and East Bale zones’’. The study considered station nearby kebeles was selected purposively from highland, midland and lowland districts. Standardized Precipitation Index drought index method was used to classify drought condition of the area. The findings showed that, the average temperature of the district showed an increased in the past three decades whereas the annual precipitation of the districts showed a decreasing trend with some variation from the mean in the period 1901- 2016 which was validated with 1984 – 2016 observed data. In line with this, representative respondents were considered from lowland, highland and midland agro-ecologies. Long term annual mean temperature showed increase trends by 0.17°C per decade at Sinana, Goro, Ginnir and Agarfa stations. Additionally, it was revealed more increment by 0.19°C per decade at Dallo menna station. From long term climate data analysis, it was indicated that extremely wet recorded in 1903, 1914, 1917, 1926, 1977 and 2013, while extremely dry conditions were recorded in 1919, 1938 and 1943 annual precipitation recorded for the whole period has been calculated and interpreted.

Historical Analysis of Climate Variability and Agricultural Production in Nigeria (1931-2020)

This study investigates the historical dynamics of climate variability and its impacts on agricultural production in Nigeria from 1931 to 2020. Utilizing a robust analytical framework that integrates climatic data analysis, statistical modelling, and geographic information system (GIS) techniques, the research explores trends in temperature and precipitation across different agro-ecological zones. Key findings reveal a consistent upward trend in average annual temperatures, with more pronounced warming observed in northern Nigeria, notably the Sahelian zone. Concurrently, precipitation patterns exhibit regional disparities, characterized by increased rainfall in southern regions and variable patterns in the north, including periods of severe droughts during the 1970s and 1980s. Statistical analyses employing Mann-Kendall trend tests and regression models confirm significant temperature increases and varying precipitation trends, underscoring their direct impacts on agricultural productivity. The study highlights the sensitivity of crop yields to climatic factors, demonstrating that rising temperatures adversely affect staple crops such as maize, millet, and sorghum, while adequate precipitation plays a critical role in enhancing yields. Socio-economic factors, including disparities in resource access between northern and southern regions, influence farmers’ adaptive capacity and resilience to climate variability. Adaptation strategies adopted by Nigerian farmers, such as adjusted planting schedules, crop diversification, and irrigation practices, are explored within the context of socio-economic constraints and infrastructural limitations. Spatial analysis using GIS provides insights into the differential impacts of climate variability across Nigeria, emphasizing the vulnerability of northern regions to climate shocks compared to the relative resilience of southern regions. Policy implications underscore the urgency of promoting climate-resilient agricultural practices, improving resource access, enhancing rural infrastructure, and implementing targeted capacity-building initiatives. These measures are essential for enhancing adaptive capacity, resilience, and sustainable agricultural development amidst on going climate variability and change in Nigeria.

Adapting Harvests: A Comprehensive Study of Farmers’ Perceptions, Adaptation Strategies, and Climatic Trends in Dera Ghazi Khan, Pakistan

Understanding farmers’ perceptions, attitudes, and adaptation strategies toward climate change is important for developing effective policies that support agricultural development and food security in rural communities. This study aimed to understand climatic trends over the past two decades (2003–2022), farmers’ perceptions, and adaptation strategies regarding climate change in Dera Ghazi Khan, Pakistan. The Mann–Kendall trend test identified a significant decrease in average minimum temperature (τ = −0.357, p < 0.05) and an increase in rainfall patterns (τ = 0.337, p < 0.05). A mixed-method approach is employed, utilizing a survey of 180 randomly selected farmers, focus group discussions, and climate data analysis. Ordinal and binary logistic regression models were used to analyze the influence of sociodemographic factors on farmers’ perceptions of climate change and their choice of adaptation strategies. The results indicate that farmers primarily rely on religious beliefs/prayers (65.6%) to cope with climate change, followed by seeking off-farm jobs (50%) and changing fertilizer use (42.2%). The result of the binary logistic regression indicates that among the different demographic characteristics, education, and land size significantly influence farmers’ decisions in taking adaptation measures against climate change. This study emphasizes the need for climate policies that integrate farmer knowledge with meteorological data and sociodemographic analysis to ensure a sustainable agricultural sector in Pakistan. Further research is necessary to explore more effective adaptation methods.

Comparative analysis of breeding patterns and reproductive efficiency of mares in subtropical conditions of Pakistan.

The present study aimed to evaluate and compare the overall and breed-specific seasonal breeding patterns, fertility rates, cyclicity, and follicular dynamics of Arab, Thoroughbred, and Percheron mares under the subtropical conditions of Pakistan. A retrospective analysis of climatic data and breeding records of eleven breeding studs spanning four years (2020-2023) was made to find out the overall seasonality in the breeding pattern of mares. Fifty mares of each breed (n=150 in total) were scanned by ultrasonography for a calendar year to find the cyclicity pattern and follicular dynamics (follicular growth rate, size of ovulatory follicle, and days from estrus till ovulation). The statistical analysis of breeding records demonstrated a clear pattern of seasonal breeding (p<0.05). The highest monthly foalings were noted in March (247±45.37), and overall breeding activities peaked in Spring season (p<0.05). Breed-specific results of Arab, Thoroughbred, and Percheron mares revealed that Arab mares maintained stable breeding activity throughout the year, with the highest activity in spring and peak conception rate in winter (56.25%±32.78; p>0.05). Thoroughbred mares experienced significant seasonal declines from spring to winter with a peak conception rate in winter (63.89%±27.37, p>0.05). Percherons showed the most pronounced seasonal effects, especially with a high fall conception rate (73.04%±19.61) and a sharp decrease in winter breeding metrics (p<0.05). Furthermore, Thoroughbred and Percheron mares displayed the most pronounced seasonal effects on the percentage of cyclic mares 77.3% and 56% in winters (p<0.05). Moreover, the follicular dynamics of the three breeds also exhibited significant differences (p<0.05). The current study concludes that seasonal and breed-specific variability exists among the reproductive parameters of Arab, Thoroughbred, and Percheron mares in subtropics, necessitating breed-specific reproductive management measures to maximize mare breeding efficiency.

A Copula Discretization of Time Series-Type Model for Examining Climate Data

The study presents a comparative analysis of climate data under two scenarios: a Gaussian copula marginal regression model for count time series data and a copula-based bivariate count time series model. These models, built after comprehensive simulations, offer adaptable autocorrelation structures considering the daily average temperature and humidity data observed at a regional airport in Mobile, AL.

Complex network analysis of climate and landscape satellite data to explore spatio-temporal patterns in urban environment: the case of Athens

Motivated by the significance and complexity of exploring spatiotemporal patterns - regions within an urban environment, particularly in the context of extreme heat events- this research analyzes meteorological time series through complex network analysis. The data collected for the examination area is focused on Athens, Greece, and covers sections of the city’s urban landscape. The data was obtained from the Copernicus observation component of the European Union. Initially, the time series are transformed into networks using correlation network methodology, followed by examination of the discriminative capability of the topological measures of networks degree and modularity as community - region detection methods. Of particular interest is that our findings suggest that the proposed complex network analysis can lead to the extraction of spatial urban regions closely linked to land use and building heights in corresponding areas. These results may help investigate the spatial variability of heat in the urban environment and inform urban planning and management strategies in policy decision-making regarding the intensity of urban heat throughout the city and the planning of climate change adaptation strategies.

The possibilities of GIS technologies for assessing and monitoring the climatic conditions of the Makazhoy Fepression (Chechen Republic, Russia)

Modeling based on a digital relief model of the spatiotemporal change in the amount of total solar radiation and the assessment of the contribution of locations to the distribution of meteorological elements.DEM analysis was conducted in a GIS environment to assess the spatial and temporal distribution of total solar radiation, and statistical analysis of retrospective climatic and actual (2023) meteorological data was conducted.Based on a digital model of the relief of average spatial resolution in a GIS environment, changes in the spatial and temporal magnitude of solar radiation were estimated. A prospective analysis of climatic conditions made it possible to assess the general features of the climate of the Makazhoy basin Field observations made it possible to characterize modern climatic conditions, as well as to calculate gradients.Depending on the locations, it was determined that the amount of total solar radiation in the Makazhoy basin varies by almost 3 times. Within the same altitude intervals, the difference between the slopes of the southern and northern exposures reaches up to 50 %. The smallest differences in the amount of radiation were observed on the days of the summer solstice, and the largest on the days of the winter solstice. The average annual air temperature in the altitude range of 1960–2264 m in 2023 varied from 8.6 to 5.8°, and the amount of precipitation – from 481 to 525 mm, respectively. The intra‐annual course of meteorological parameters (air temperature and leaf surface, precipitation and relative humidity) within the meteorological transect has been revealed. The data obtained make it possible to proceed to the formation of a GIS system for monitoring the climatic conditions of the carbon landfill of the Kadyrov Chechen State University.

XCDAT: A Python Package for Simple and Robust Analysis of Climate Data

xCDAT: A Python Package for Simple and Robust Analysis of Climate Data

Machine Learning for Pedestrian-Level Wind Comfort Analysis

(1) Background: Artificial intelligence (AI) and machine learning (ML) techniques are being more widely employed in the field of wind engineering. Nevertheless, there is a scarcity of research on the comfort of pedestrians in terms of wind conditions with respect to building design, particularly in historic sites. (2) Objectives: This research aims to evaluate ML- and computational fluid dynamics (CFD)-based pedestrian wind comfort (PWC) analysis outputs using a novel method that relies on the sophisticated handling of image data. The goal is to propose a novel assessment method to enhance the efficiency of AI models over different urban scenarios. (3) Methodology: The stages include the analysis of climate data, CFD analysis with OpenFOAM, ML analysis using Autodesk Forma, and comparisons of the CFD and ML results using a novel image similarity assessment method based on the SSIM, MSE, and PSNR metrics. (4) Conclusions: This study effectively demonstrates the considerable potential of utilizing ML as a supplementary tool for evaluating PWC. It maintains a high degree of accuracy and precision, allowing for rapid and effective assessments. The methodology for precise comparison of two visual outputs in the absence of numerical data allows for more objective and pertinent comparisons, as it eliminates any potential distortions. (5) Recommendations: Additional research can explore the integration of ML models with climate data and different case studies, thus expanding the scope of wind comfort studies.

Responsible Artificial Intelligence for Climate Action: A Theoretical Framework for Sustainable Development

Climate change poses an urgent and significant challenge, with far-reaching impacts already affecting our planet, and projections indicating worsening conditions in the future. The concept of sustainable development aims to meet present needs while safeguarding the ability of future generations to meet their own requirements. However, climate change's effects on sustainable development are of paramount concern, as they amplify issues like poverty, food insecurity, and environmental degradation, affecting economic growth, social progress, and environmental protection. Taking immediate action to mitigate climate change and implement sustainable practices is crucial to ensuring a habitable planet for future generations. In this context, Responsible Artificial Intelligence (RAI) emerges as a promising direction, striving for ethical and responsible technology use in diverse sustainable development tasks. RAI proves to be a robust candidate for empowering climate change mitigation and adaptation efforts. This study introduces a theoretical RAI framework designed to support climate action by responsibly enabling more accurate predictions and analysis of climate data, enhancing energy efficiency, and reducing greenhouse gas emissions. The framework emphasizes the need for interdisciplinary collaboration among policymakers, scientists, and technicians to develop RAI solutions that advance sustainable development and alleviate the adverse impacts of climate change. Unlike previous works, this research presents a novel perspective on the principles of RAI that explicitly consider climate-related aspects. By laying the foundations of AI research to bolster our fight against climate change, this article establishes essential pillars that encourage further advancements in this critical endeavor.

Understanding drivers of wildfires in California

This research study investigates the intricate relationship between El Niño events, precipitation patterns, and wildfires in California over the past two decades. California, known for its susceptibility to wildfires, has experienced significant variations in precipitation influenced by the El Niño-Southern Oscillation (ENSO) phenomenon. The objective of this research is to examine the impact of El Niño events on California's precipitation patterns and subsequently assess their association with wildfire occurrences. Predicting wildfire and precipitation wildfires are important to California’s significant agriculture production and better preparing for possible droughts or wildfires. To achieve this objective, a comprehensive analysis of climate data spanning the last 20 years was conducted. Historical records of El Niño episodes, obtained from reputable sources, were measured by correlation with precipitation data acquired from meteorological databases. Additionally, wildfire occurrence and severity data were collected from relevant agencies and organizations to be measured for correlation. As it turns out, while there was some positive correlation with El Nino and precipitation, there was little direct correlation between El Nino and wildfires. This means that there are many more complex factors to be assessed when figuring out causes of wildfires in addition to El Nino. The findings of this research will contribute to a better understanding of the main causes of wildfires in California. The results can inform policymakers, land managers, and disaster response agencies in developing more effective strategies to mitigate the risks associated with wildfires. Additionally, the study will provide valuable insights into the influence of climate variability on fire-prone ecosystems, aiding in the prediction and management of future wildfire events.

Climate Dynamics over Kerala, India: Insight from a Century-long Temperature and Rainfall Data Analysis

Historical climate data analysis is of great significance in climate change adaptation and mitigation planning at global as well as regional levels. This article attempted to study the long-term trends of temperature and rainfall across the districts in Kerala, India. CRU monthly time series data of rainfall and temperature data spanning from 1901-2022 were used for the analysis. Mann-Kendall test and Sen’s Slop estimator were applied to detect the presence and magnitude of the trend, and Pettitt’s homogeneity test was used to find the climate change point in temperature time series data. The analysis found a significant and positive temperature trend across all districts in Kerala, with temperature increases ranging from 0.0086°C/year to 0.0102°C/year. In terms of rainfall trends, June and January experienced a significant decrease, while July and September saw a significant increase over the years. The year 1976 was identified as the point of climate change. It was observed that there was an increase in the southwest and Northeast monsoons in the post-period, with higher variability in the latter. Winter rainfall notably decreased during the post-period. The variability in climate parameters identified in this study could impact crop cycles and agricultural productivity, requiring further investigation at a micro-level for effective adaptation and mitigation strategies for the state.

A practical guide to climate econometrics: Navigating key decision points in weather and climate data analysis

A practical guide to climate econometrics: Navigating key decision points in weather and climate data analysis

Impact of Drought in Northeastern Algeria: Comparative Study of the SPI and SPEI Índices

Purpose: The study aimed to evaluate drought trends in Annaba, Algeria, considering global warming and extreme weather, using quantitative indices for improved drought comprehension and prediction. Theoretical Framework:The study assesses changes in drought frequency and severity in the Annaba region of Algeria due to global warming and extreme weather conditions. It employs quantitative indices to enhance drought understanding and prediction. Methodology: The study analyzes temperature and precipitation data (1981-2021) from an Annaba weather station to assess drought characteristics.Standardized indices (SPI, SPEI) and extreme value analysis (Generalized Pareto Distribution) are used to quantify drought intensity, duration, and temperature impacts. Findings: Our analyses reveal a critical divergence in drought assessment tools. While stable monthly temperatures suggest a constant influence on evapotranspiration, the SPEI index (including temperature) indicates increasing drought compared to the precipitation-only SPI. This highlights the importance of considering temperature alongside precipitation for accurate drought assessment. Research Practical and Social Implication: This study emphasizes the significance of considering the interplay among temperature, precipitation, and evapotranspiration for better understanding and predicting drought changes in the Annaba region, Algeria. These insights are crucial for sustainable water resource management and climate change adaptation in the region. Originality/Value:This study distinguishes itself by its comprehensive analysis of long-term climate data to examine drought trends in a specific region (Annaba, Algeria) severely impacted by climate change. By comparing the effectiveness of two drought indices (SPEI and SPI), it offers valuable insights into how to improve drought assessments by considering the role of temperature in evapotranspiration. This research enriches the existing literature on drought management and climate change adaptation, providing a valuable case study for similar arid and semi-arid regions worldwide.

GNSS signal ray-tracing algorithm for the simulation of satellite-to-satellite excess phase in the neutral atmosphere

Traditionally, GNSS space-based and ground-based estimates of tropospheric conditions are performed separately. It leads to limitations in the horizontal (e.g., a single space-based radio occultation profile covers a 300 km slice of the troposphere) and vertical resolution (e.g., ground-based estimates of troposphere conditions have spacing equal to stations’ distribution) of the tropospheric products. The first stage to achieve an integrated model is to create an effective 3D ray-tracing algorithm for the satellite-to-satellite (radio occultation) path reconstruction. We verify the consistency of the simulated data with the RO observations from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-1) Data Analysis and Archive Center (CDAAC) in terms of excess phase and bending angle. The results show that our solution provides an effective RO excess phase, with a relative error varying from 35% at the height of 25–30 km (1.0–1.5 m) to 0.5% at heights 5–10 km (0.1–1 m) and 14 to 2% at heights below 5 km (2–14 m). The bending angle retrieval on simulated data attained for high-resolution ray-tracing, bias lower than 2% with respect to the observed bending angle. The optimal solution takes about 1 s for one transmitter–receiver pair with a tangent point below 5 km altitude. The high-resolution processing solution takes 3 times longer.

Cfr (v2024.1.26): a Python package for climate field reconstruction

Abstract. Climate field reconstruction (CFR) refers to the estimation of spatiotemporal climate fields (such as surface temperature) from a collection of pointwise paleoclimate proxy datasets. Such reconstructions can provide rich information on climate dynamics and provide an out-of-sample validation of climate models. However, most CFR workflows are complex and time-consuming, as they involve (i) preprocessing of the proxy records, climate model simulations, and instrumental observations; (ii) application of one or more statistical methods; and (iii) analysis and visualization of the reconstruction results. Historically, this process has lacked transparency and accessibility, limiting reproducibility and experimentation by non-specialists. This article presents an open-source and object-oriented Python package called cfr that aims to make CFR workflows easy to understand and conduct, saving climatologists from technical details and facilitating efficient and reproducible research. cfr provides user-friendly utilities for common CFR tasks such as proxy and climate data analysis and visualization, proxy system modeling, and modularized workflows for multiple reconstruction methods, enabling methodological intercomparisons within the same framework. The package is supported with extensive documentation of the application programming interface (API) and a growing number of tutorial notebooks illustrating its usage. As an example, we present two cfr-driven reconstruction experiments using the PAGES 2k temperature database applying the last millennium reanalysis (LMR) paleoclimate data assimilation (PDA) framework and the graphical expectation–maximization (GraphEM) algorithm, respectively.

Multivariate analysis of long-term climate data in connection with yield, earliness and the problem of global warming.

Climate change is the key challenge to agriculture in the XXI century. Future agricultural techniques in the Russian Federation should involve the optimization of land utilization. This optimization should apply algorithms for smart farming and take into consideration possible climate variations. Due to timely risk assessment, this approach would increase profitability and production sustainability of agricultural products without extra expenditures. Also, we should ground farming optimization not on available empirical data encompassing limited time intervals (month, year) or human personal evaluations but on the integral analysis of long-term information bodies using artificial intelligence. This article presents the results of a multivariate analysis of meteorological extremes which caused crop failures in Eastern and Western Europe in last 2600 years according to chronicle data and paleoreconstructions as well as reconstructions of heliophysical data for the last 9000 years. This information leads us to the conclusion that the current global warming will last for some time. However, subsequent climate changes may go in any direction. And cooling is more likely than warming; thus, we should be prepared to any scenario. Plant breeding can play a key role in solving food security problems connected with climate changes. Possible measures to adapt plant industry to the ongoing and expected climate changes are discussed. It is concluded that future breeding should be based on the use of highly adapted crops that have already been produced in pre-breeding programs, ready to meet future challenges caused by potential climate change.

Present day and future urban cooling enabled by integrated water management

The process of urbanisation has increased public health risks due to urban heat, risks that will be further exacerbated in future decades by climate change. However, the growing adoption of integrated water management (IWM) practices (coordinated stormwater management of water, land, and resources) provides an opportunity to support urban heat amelioration through water supply provision and irrigated and vegetated infrastructure that can provide cooling benefits. This study examines the thermal impacts of future implementations of IWM for nine Australian cities based on a review of Government policy documents in the present and over two future time frames (2030 and 2050) under different greenhouse gas emission scenarios (SSPs 1.2-6, 3.7-0 and 5.8-5). Statistical analysis of the future climate data using historical data shows that future warming is nuanced, with changes variable in both time and place, and with extremes becoming more pronounced in future. We have developed a unique approach to morph the future climate projections onto historical data (derived from the ERA5 Reanalysis product) for the 2010-2020 period. Additionally, we use locally appropriate Local Climate Zones (LCZs) for Australian cities, resulting from a holistic and global approach that is widely adopted by the urban climate modelling community. We developed scenarios for business-as-usual as well as implementation of moderate and high levels of IWM across each of the Australian LCZs and modelled them using TARGET (The Air temperature Response to Green infrastructure Evaluation Tool). Results generated at the LCZ level are aggregated to Australian statistical areas (SA4, the largest sub-city area) and city-wide levels. The thermal impacts associated with the various degrees of IWM were marked and geographically differentiated, depending on the climatic characteristics of the various cities. For the current climate, high IWM intervention provided reductions in annual mean daily maximum temperature ranging from -0.77°C in Darwin, up to -1.86°C in Perth. Generally, the drier southern cities of Sydney, Canberra, Albury, Melbourne, Adelaide, and Perth produced the greatest thermal response to implementation of IWM and the more tropical cities with higher rainfalls the least response. For some southern cities cooling was &amp;gt; -3.0°C at the time of maximum summer temperatures. Interestingly high levels of IWM in winter produced modest warming of minimum overnight temperatures, especially for the cooler southern cities. The cooling benefits of IWM were seen across all future climate scenarios and are a real opportunity to offset-projected temperature increases resulting from climate change.