Weather Research Articles

Risk Assessment Protocol for Existing Bridge Infrastructure Considering Climate Change

The escalating impact of climate change on global weather patterns threatens the functionality and resilience of infrastructure systems. This paper presents a rigorous risk assessment protocol tailored to existing bridge infrastructure, integrating climate change projections, structural integrity, and socioeconomic factors. The protocol’s application involves five sequential steps: selecting a bridge, disassembling the structure into components, calculating utilization factors for design and projected temperatures, evaluating severity factors encompassing structural and socioeconomic aspects, and ultimately determining an overall risk rating. To demonstrate the protocol’s effectiveness, a case study was conducted on the Westminster Drive Underpass in London, Ontario. This study shows how the protocol systematically evaluates the vulnerability of each bridge component to projected temperatures under the Representative Concentration Pathway 6.0 model. The protocol provides a holistic risk assessment by incorporating both the structural response and socioeconomic implications of failure. The results rank the bridge’s risk level and highlight the urgency of intervention. The protocol emerges as a robust tool for decision-makers, practitioners, and engineers, offering a comprehensive approach to strengthen bridge infrastructure against the challenges of climate change.

Agrarian Distress in Indian Agriculture: Causes, Socio-economic Consequences and Coping Strategies

Agrarian distress is the economic, political and social challenges faced by the farmers and rural communities due to factors such as low crop yields, fluctuating prices of agricultural produce, high input costs, indebtedness, and lack of access to credit, markets, and infrastructure. Over 8,00,000 individuals are dies by suicide each year because of various reasons. More over the suicides are happening in the families whose own land is less than 1-2 hectares, who are in debt, have been identified the most at risk for suicide. The present article aims at better understanding of the causes, consequences and possible coping strategies for the agrarian distress in India. Causes such as fluctuations in crop sales, weather and climate conditions, dependent on natural resources, etc. are discussed in detail. Consequences of agrarian stress are suicides of farmers, rural migration, food insecurity, environmental impacts and so on. Solutions such as strengthening the forward markets, comprehensive crop insurance schemes for the framers where the non-MSP (Minimum Support Price) crops also may be covered, assured price to the farmers are discussed. Many researchers argued that farmers suicides are increasing but the research is limited in the specialised area of interest. In addition to that, researchers are focussing only on the socio-economic variables rather than the psychological variables. In order to mitigate the risks out of agrarian distress, farmers should be encouraged by the government and extension service providers to take up the subsidiary occupations and secondary agricultural occupations which helps them in getting profits in the situations where the crop damages due to the extreme weather patterns. As rightly pointed out by National Bank for Agriculture and Rural Development (NABARD), distress of the framers should have a long-term potential solution such as major reforms in the existing agricultural laws rather than farm loan waivers which acts as the short-term relief to the farmers.

Impact of Polar Vortex Modes on Winter Weather Patterns in the Northern Hemisphere

This study is an additional investigation of stratosphere–troposphere coupling based on the recent stratospheric winter descriptions in five distinct modes: January, February, Double, Dynamical, and Dadiative. These modes, established in a previous study, categorize the main stratospheric winter typologies modulated by the timing of important sudden stratospheric warmings (SSWs) and final stratospheric warmings (FSWs). The novelty of this research is to investigate the Northern Annular Mode, mean sea level pressure (MSLP) anomalies in the Ural and Aleutian regions, and the decomposition of Eliassen–Palm flux into wavenumbers 1 and 2 within each mode. The results show that the January and Double modes exhibit similar pre-warming surface signals, characterized by Ural blocking and Aleutian trough events preceding weak polar vortex events. The January mode displays a positive MSLP anomaly of +395 hPa (−191 hPa) in the Ural (Aleutian) region in December, while the Double mode shows +311 hPa (−89 hPa) in November. These modes are primarily wave-1 driven, generating tropospheric responses via negative Arctic Oscillation patterns. Conversely, the February and Dynamical modes show opposite signals, with Aleutian blocking and Ural trough events preceding strong polar vortex events. In December, the February mode exhibits MSLP anomalies of +119 hPa (Aleutian) and −180 hPa (Ural), while the Dynamical mode shows +77 hPa and −184 hPa, respectively. These modes, along with important SSWs in February and dynamical FSWs, are driven by both wave-1 and wave-2 and do not significantly impact the troposphere. The Radiative mode’s occurrence is strongly related to the Aleutian blocking presence. These findings confirm that SSW timing is influenced by specific dynamical forcing related to surface precursors and underscore its importance in subsequent tropospheric responses. This study establishes a connection between early winter tropospheric conditions and upcoming stratospheric states, potentially improving seasonal forecasts in the northern hemisphere.

Assessment of the Dynamical Linkages and Flow Evolution Governing Multiple Extreme Weather Events over Western North America in February 2019

Abstract We present an extended case study analysis based on observed extreme weather events (EWEs) and the planetary- and synoptic-scale variability of a persistent flow regime spanning the month of February 2019 across the North Pacific (NPAC) basin and western North America. The EWEs are clustered into two periods during February 2019: record cold and kona low conditions over Hawaii, lower elevation snow across Washington, and heavy AR-related rainfall in Southern California from 9 to 15 February; and heavy snow in Arizona and Oregon and heavy rainfall in Northern California from 21 to 28 February. From a weather regime perspective, the NPAC flow was dominated by a persistent ridge around 150°W, a retracted NPAC jet stream, repeated western NPAC (WPAC) cyclogenesis events, and frequent positively tilted troughs in the eastern NPAC and over western North America. Dynamically relevant features on the subseasonal-to-seasonal (S2S) time scale include a slowly propagating MJO signal in phases 6 and 7, a rapid NPAC jet retraction around 9 February, and subsequent eastward extension toward a climatological jet position around 21 February. On synoptic time scales, Rossby wave breaking on the southern flank of the NPAC jet and within the aforementioned persistent ridge led to the kona low formation and many of the positively tilted troughs responsible for the extreme precipitation events. In addition, frequent cyclogenesis west of the date line helped to maintain the persistent ridge strength and location through favorable heat and vorticity fluxes. The chronology and complex linkages between these aforementioned features and mechanisms are explained in depth in this paper. Significance Statement This study identified several extreme cold, rain, and snow events across the western contiguous United States and Hawaii, showed how these events are all connected to a persistent weather pattern upstream over the North Pacific basin, and identified key mechanisms for why the weather pattern was so persistent. Some of the physical mechanisms for keeping the weather pattern stagnant include anomalous convection within the tropics off the east coast of Asia, breaking waves in the atmosphere like waves breaking on a beach, and repeated cyclone development off the coast of Russia and Alaska. We hope that the results of this paper encourage others to look for similar mechanisms for similar stagnant weather patterns in a more holistic manner.

Escalating Global Threat of Heatwaves and Policy Options for Adaptation and Mitigation

This study evaluates the effects of intense and frequent heatwaves on human health, food security and infrastructure all having a direct bearing on sustainable development. It emphasizes that health issues such as cardiac and respiratory disorders, strokes, and mental health diseases are aggravated in harsh climates. This study focuses on the adverse implications of hot weather and the impact of said phenomenon on various fields of life. The aftermath of the excessive burning of fossil fuels and the emission of greenhouse gases have severely impacted the world by disrupting the economic stability of many countries around the globe. The heat waves are also responsible for low crop yield, fertilizer shortage and adverse impact on livestock. The intense and hot weather has not only caused power outages but also challenged infrastructure sustainability. The continuous usage of cooling systems is leading to urban heat islands adding more heat in the environment. This study highlights the challenges of climate change and brings attention to the need for sustainable development and practices to ensure proper heat management. After a deep insight into information and responses collected for the research, the study suggests some recommendations requiring all stakeholders including policymakers, implementing agencies, organizations and civil society to play their crucial role in devising and implementing suitable policy options and practices for adaptation and mitigation of climate change factors.

Seasonal variations of insect abundance: Correlating growth stage-specific metrics with weather patterns in Rangpur Region, Bangladesh

The disclosure of insect occurrences correlating with meteorological data is pivotal in devising effective management strategies for rice fields. The study collected data on harmful and beneficial insects, along with meteorological variables, for three consecutive rice-growing seasons in 2021 and 2022. Month-wise insect distribution were delineated, with prominent species such as green leafhopper, brown planthopper, white-backed planthopper, rice whorl maggot, damselfly, parasitic wasps and green mirid bug observed consistently. Notably, most of the harmful insects were predominantly present during March–July and September–October. The spatial distribution, further segmented based on the rice-growing seasons, revealing the Aus season's preeminence in hosting harmful insects followed by Aman and Boro. The prevalence of most of the harmful insects were in tillering stage. Beneficial insects, displayed their dominance during specific months, growth stage, emphasizing their potential role in controlling harmful species and their prevalence were increased by increased of harmful insects. Furthermore, the study elucidated the correlation between climatic factors and insect abundance, emphasizing the role of temperature, rainfall and relative humidity. Temperature metrics and relative humidity manifested significant associations with several insects both harmful and beneficial species differently. While rainfall notably correlated with rice bug, short-horned grasshoppers, yellow stem borer, and non-significant association with other insects. The study underscored mutual dependencies between predator and prey species, emphasizing the ecological balance within the agricultural ecosystem. This comprehensive analysis indicated the need for integrated pest management strategies, considering both harmful and beneficial insect dynamics, to promote sustainable practices for optimizing rice production in changing climatic scenarios.

Etiology, clinical features, and epidemiological analysis of diarrhea patients visiting a gastrointestinal clinic in a comprehensive hospital in Beijing, China, in 2023

AbstractObjectiveTo investigate the clinical features and epidemiology of diarrhea patients and analyze the current distribution of enteropathogens causing diarrhea in a comprehensive hospital in Beijing, China, in 2023.Materials and MethodsFrom April to October 2023, we enrolled patients with diarrheal diseases who visited the gastrointestinal clinic in our hospital. The patients' demographic, epidemiological, and clinical features were obtained via a questionnaire. Stool samples were examined for 20 enteropathogens by multiplex polymerase chain reaction testing.ResultsWe enrolled 260 patients; men and adults accounted for 55.77% and 95.77% of the patients, respectively. The median age was 37 years. Eighty‐four enteropathogens, 72 bacteria and 12 viruses, were identified in 74 patients. Enteroaggregative Escherichia coli was the predominant agent. Patients with and without pathogens detected in stool samples showed no significant differences in age, sex, gastrointestinal symptoms, and stool characteristics. Possible food‐related events were recorded in 57.31% of the patients. Leukocyte counts in patients with bacterial infections were higher than those of patients with viral infections and those with no detected pathogens (p < 0.05). Seasonality of bacterial distribution was observed (p < 0.05).ConclusionBacteria were predominant pathogens among the diarrhea patients. The incidence of diarrhea was related to hot weather and foodborne illness. Bacterial diarrhea may cause systemic infection. The clinical symptoms of infectious diarrhea were usually non‐specific and unrelated to the type of infection. Timely and comprehensive multi‐pathogen surveillance might be helpful to detect suspected pathogens and promote epidemic prevention and control.

Energy retrofit and climate adaptive design of dwellings in the hot arid climate: Trends and future challenges

As long as buildings face various climate change impacts in recent decades, such as increased heat, particularly in the residential sector. Therefore, energy retrofit, and climate-adaptive designs may contribute significantly to climate change mitigation and adaptation strategies. At the same time, these strategies do not only improve the sustainability of dwellings/houses/residences but also contribute to broader goals of increasing energy efficiency, reducing environmental impact, offering economic benefits, and enhancing the community's resilience in the challenges they face impact from the effects of climate change. This study aims to examine and present the development of energy retrofitting, energy efficiency and climate-adaptive design for dwellings/ residential buildings in hot weather publications through bibliometric research. The research has been examined within the Web of Science™ Core Collection (W.O.S.) online database spanning from 2012 to November 2023 by using the "Title/Abstract/Keywords" category, and a comprehensive data visualisation has been conducted utilising the VOSviewer and CiteSpace programmes. The findings indicate the research trends in the literature and future challenges, and the results from these findings demonstrate the need for sustainable and energy-efficient buildings to preserve the environment and climate. These sustainable developments focus on improvements in energy retrofitting technologies, energy efficiency and saving targets, indoor thermal comfort, optimising passive design and minimising energy demand. This study will probably be a source to provide valuable insights for researchers, practitioners, experts, and policymakers to understand the implications of energy retrofitting and climate-adaptive design in hot arid climates. As well as it would offer theoretical and practical initiatives to be applied in this field.

Q-triplet characterization of atmospheric time series at Antofagasta: A missing values problem

Abstract Located in northern Chile (23.7°S, 70.4°W), Antofagasta has an exceptionally arid and stable climate characterized by minimal precipitation and consistent weather patterns. Nevertheless, despite these climate conditions being meaningful for several research and practical applications, our understanding of weather dynamics remains limited. The available meteorological data from 1969 to 2016 is analogical, which presents a significant challenge to analyze because these records are riddled with missing values, some measurements were taken at irregular measuring intervals, making it an interesting puzzle to grasp the Antofagasta’s climate scenario. To overcome this issue, we present a comprehensive statistical analysis of atmospheric temperature, pressure, and humidity time series. Our analytical approach involves the q-triplet calculation method, serving as a powerful tool to identify distinctive behavior within systems under non-equilibrium states. Our results suggest that, in general, the q-triplet values satisfy the condition q sens < 1 < q stat < q rel, a pattern that has been observed in previous studies.

Study of low-level wind shear at a Qinghai-Tibetan Plateau airport

The Qinghai-Tibetan Plateau (QTP) is recognized as the world's largest and highest plateau, characterized by intricate topography and underlying surfaces. Within this region, volatile meteorological conditions and severe synoptic systems, including thunderstorms, turbulence, sandstorms, and notably low-level wind shear (LLWS), present safety hazards to aviation operations. Therefore, a comprehensive examination of wind shear at typical QTP airports is essential. Xining Caojiapu International Airport (ZLXN) serves as a crucial transportation hub in the northeastern QTP. The frequent incidence of LLWS events due to the airport's unique geographical and meteorological features makes it an ideal location for investigating LLWS on the plateau. This study analyzed 80 pilot reports collected from 2016 to 2021 to elucidate the spatial and temporal characteristics of LLWS events at this plateau-valley airport. Subsequently, reanalysis data and observations from ground automated weather observing systems (AWOS), a geostationary satellite, a Doppler weather radar (DWR), and a Doppler wind lidar (DWL) were comprehensively employed to investigate the underlying meteorological factors and weather patterns associated with these LLWS events. The findings revealed that LLWS events at ZLXN are predominantly triggered by convective systems, followed by cold fronts and downward momentum transportation, with a smaller proportion being induced by orographic winds and turbulence. Analysis of four representative LLWS events utilizing high-resolution DWL measurements provided insights into the types of LLWS generated and their potential impacts on aircraft operations. Conceptual models, aimed at establishing a foundation for the forecasting and warning of local LLWS, were also proposed based on multiple data sources.

The ripple effects of climate change on agricultural sustainability and food security in Africa

AbstractClimate change results in lower agricultural outputs, disruption of food supply chains, and widening of the social gap between poor and rich in developing countries, with more vulnerable groups being pushed into untold poverty. This review aims to investigate the consequences of climate change on food insecurity in Africa in the context of environmental degradation. This review emphasizes the complexity of demands on food security systems due to changing climatic conditions under the four pillars (availability, accessibility, utilization, and stability). This review demonstrated the susceptibility of farm production to changes in temperature, precipitation, and weather patterns generated by climate change. In addition, this review investigated the factors shaping food insecurity, such as increasing population growth, poverty, inadequate early warning systems, and weak agricultural infrastructure. Furthermore, the review points out how climate change affects food prices and availability and the widening income gap, potentially leading to social unrest and political instability in Africa. Vulnerable populations, including impoverished, elderly, and physically challenged individuals, are at increased risk due to climate‐related health impacts. Women who face gender inequalities and socioeconomic limitations are particularly susceptible. We posit that there is a need for comprehensive strategies that address health disparities and consider vulnerable subgroups within society alongside broader measures to enhance food security in the face of climate change.

Impact of climate change on water-related physical events, consequent human migration, and burden of drowning in India: An evidence synthesis.

Disrupted weather patterns are associated with climate change. Between 2001 and 2018, nearly 74% of disasters were water-related, including floods and cyclones. Such water-related cataclysmic events increase the risk of drowning. We aimed to map evidence on the impact of climate change on water-related physical events, associated human migration, and drowning burden in India. We searched electronic databases, government reports, and relevant websites to map evidence on water-related physical events (including but not limited to sea-level rise, glacier bursts, lake bursts, floods, rainfall, cyclones, and droughts) and consequent human migration using narrative review approach, while drowning burden through scoping review approach. We summarized the results narratively. Evidence from 48 studies and seven reports suggest that India will witness the greatest sea-level rise, significantly impacting poor coastal communities. An increase in droughts, cyclonic rainfall, storms, and floods, with increasing surface rainwater and streamflow water, due to melting glaciers is expected. Climate change-triggered migration is expected notably in northeast and south India, making farmers, drivers, street vendors, women, and youth most vulnerable. No direct evidence was identified on the impact of climate change, water-related disasters, meteorological events, or seasonal variations on drowning from India. Our study highlights a significant gap in the availability of context-specific and localized data to improve disaster response and strengthen public health systems, especially for areas most vulnerable to climate change. There is an urgent need to generate new knowledge and understanding of climate change, water-related or meteorological events, and seasonal variations' impact on drowning burden as the level of risk remains unknown.

Calm Before the Storm?

Atlantic hurricane season poses an annual threat to the billions of dollars of oil and gas hardware pumping hydrocarbons from the depths of the US Gulf of Mexico (GOM). The region’s offshore production amounts to about 1.8 million B/D—roughly 14% of the total crude output in the US, according to the US Energy Information Administration, so any disruption is felt by oil markets across the globe. Prior to the 2024 hurricane season, forecasters pegged this year as primed to be particularly intense. Colorado State University (CSU), known for its storm prognostication, estimated that the basin would experience 23 named storms, with a dozen of those becoming hurricanes (winds of 74 mph or higher), and six of those strengthening to major hurricanes (winds of 111 mph or higher). Similarly, AccuWeather meteorologists expect between 20 and 25 named storms with a possibility of 30 or more. As the season enters its busy period, the basin has only experienced five named storms to date; however, many forecasters are sticking to their initial forecasts and continue to warn of an intense September/October period. “The months of September and October should be very active across the Atlantic,” said AccuWeather lead hurricane expert Alex DaSilva. “We continue to remain very concerned about rapid intensification. Sea-surface temperatures and ocean heat content are near record levels across most of the Atlantic basin. This can act like jet fuel for tropical systems. We also continue to believe that La Niña will emerge sometime in November. When water temperatures near the equator of the eastern Pacific Ocean are lower than the long-term historical averages for an extended period, La Niña is declared. Although this is a phenomenon in the Pacific, it has ramifications for weather patterns over the Atlantic Ocean. Specifically, La Niña can reduce the wind shear across the basin. When wind shear is lower, it makes it easier for tropical systems to take shape and strengthen.” The GOM has experienced one disruption in 2024 courtesy of Hurricane Beryl. Once a Category 5 storm (winds 155 mph and higher)—and the earliest-forming Category 5 storm on record—Beryl weakened as it crossed the southern Yucatán. The storm regained Category 1 strength as it crossed the far western offshore oil patch enroute to landfall near Matagorda Bay, Texas. Shell shut in production at its Perdido spar because of the storm as well as evacuated personnel from it and its Whale facility. Chevron moved all non-essential personnel off its Anchor floating production unit, which was undergoing commissioning at the time. The last major storm to cut a path straight through the heart of the US offshore oil patch was 2021’s Hurricane Ida. Ida tore through the region as a Category 4 storm before slamming into the Louisiana coast near Port Fourchon packing 150 mph winds on 29 August. The storm prompted the shut in of almost 100% of the Gulf’s oil and gas production and caused extensive damage to major hubs in the region, including the Shell-operated West Delta Block 143 complex. The facilities, which serve as the transfer station for production from Shell’s robust Mars corridor, were off line for several months.

An LSTM deep learning framework for history-based tornado prediction using meteorological data and damage assessment using NDVI anomalies

Extreme weather patterns can affect ground and satellite sensors before and after their occur. This study focused on tornadoes that occurred on December 10 and 11, 2021 in the state of Kentucky. The main goal of this research was to develop a deep learning algorithm based on history to predict this phenomenon. Four scenarios were created based on temperature, air pressure, wind speed and their combination. The temperature-based scenario shows high accuracy and shows the time series of temperature rise several degrees before the tornado. In the second step, the normalized difference vegetation index (NDVI) anomaly was calculated and classified for Mayfield city. Severe NDVI anomalies showed high consistency with enhanced Fujita scale and ultra-high-resolution satellite imagery, with a correlation greater than 0.9.

Analysis of Nationwide Steep Slope Survey Results for the Proposal of Improvement Measures in Korea

The risk and damage caused by steep slope collapses within the Korean Peninsula are increasing because of abnormal weather patterns resulting from climate change. Consequently, there is a growing need for proactive disaster management, leading to the registration and management of steep slopes prone to collapse within the jurisdiction through nationwide surveys. The necessity for domestic steep slope management has recently become apparent although the management system has some issues in establishing and refining it to reflect reality. In this study, the appropriateness of the results obtained from the 2022 nationwide survey of steep slope areas was evaluated utilizing the characteristics of steep slopes (types, structures, heights, lengths, slopes, and landowners.), disaster risk (disaster risk grades and risk factors), and risk mitigation measures (structural and nonstructural), to propose improvement measures. Through this, we identified problems with the methodology for selecting target areas, funding for steep slope management, impact assessment of disasters, and results of reviewing non-structural measures, thereby proposing improvements in the management of privately owned steep-slope areas. By improving the survey system, which is an essential element for responding to steep slope disasters, to reflect reality, the costs of damage and recovery caused by steep-slope collapses are expected to significantly decrease.

Examining the evolution of extreme precipitation event using reanalysis and the observed datasets along the Western Ghats

AbstractIn recent decades, India has witnessed an increase in the intensity, frequency, and spread of extreme weather events. The widespread increase in extreme precipitation over the Western Coast of India is a matter of great concern. The factors contributing to such devastating extreme precipitation remain unclear due to the variability present in meteorological and oceanic variables and associated large‐scale circulations. Using reanalysis and observed datasets, we attempted to identify a combination of dynamic, thermodynamic, and cloud microphysics processes contributing to the anomalous precipitation from August 1 to 10, 2019 against its climatology. Our key findings highlight the crucial role of warm sea surface temperatures (anomaly >1.4°C), outgoing longwave radiation (anomaly <−50 W·m−2), and atmospheric temperature (anomaly over the ocean is >1.6°C) in enhancing the moisture‐holding capacity of the atmosphere by almost 10%. This elevated moisture, propelled by intensified low‐level winds (anomalies exceeding 4 m·s−1), leads to a shift from ocean to land. Notably, we observe that vertical updrafts (anomalies >−0.4 m·s−1) contribute to increased atmospheric instability and moisture convergence. The presence of an ample amount of cloud hydrometeors, with anomalies surpassing 2.5 × 10−4 kg·kg−1, establishes conditions conducive to sustained intense precipitation. Our findings deepen our understanding of the complex relationships between ocean and atmospheric dynamics, and wind patterns, and emphasize their pivotal influence on regional weather patterns and land surface hydrology.

The Role of Organic Farming for Climate Change Mitigation and Sustainable Development: A Review

Climate Change, exacerbated by human activities such as fossil fuel combustion and greenhouse gas emissions like carbon dioxide (CO2), is a pressing global crisis that disrupts long-term weather patterns, ecosystems, and human well-being. Organic agriculture offers a viable solution to mitigate these effects. By lowering greenhouse gas emissions and enhancing soil carbon sequestration, organic farming strengthens ecosystem resilience. Practices such as eliminating synthetic fertilizers and pesticides, rotating crops, and utilizing organic waste foster robust agricultural systems capable of enduring extreme weather and water scarcity. This paper provides an in-depth analysis of how organic farming contributes to climate change mitigation, detailing practices that support soil carbon sequestration, reduce nitrogen oxide (N2O) emissions, and boost biodiversity. It also addresses challenges such as emissions measurement and consumer behaviour, emphasizing the need for further research and supportive policies. Organic agriculture not only advances sustainable development but also promotes food security and environmental sustainability in the face of climate change.

Innovative Approaches for Climate-Resilient Farming: Strategies against Environmental Shifts and Climate Change

Climate change, driven predominantly by human activities such as burning fossil fuels and deforestation, is causing significant alterations to global weather patterns. This phenomenon results in more frequent and severe weather events, including prolonged droughts, intense rainfall, and shifting temperature regimes. Such changes pose a substantial threat to agriculture, a sector highly dependent on stable climatic conditions. Crop yields are increasingly unpredictable due to these extreme weather events and shifting growing seasons, impacting food security and livelihoods worldwide. To address these challenges, climate resilience agriculture has emerged as a pivotal solution. This approach involves adopting farming practices and technologies designed to withstand and adapt to changing climatic conditions. Strategies include diversifying crop varieties, precision agriculture, climate smart agriculture, nano biochar application, coated fertilizer applications, natural farming, site specific nutrient management, mulching, precise water management, seed bombing, direct seeding in rice, implementing soil conservation methods etc. Additionally, climate resilience agriculture promotes the integration of traditional knowledge with modern scientific advancements to enhance ecosystem robustness and productivity. By fostering adaptive capacity and reducing vulnerability, climate resilience agriculture aims to safeguard food systems and sustain agricultural productivity in the face of ongoing climate disruptions.

Architectural traditions in the structures built by cooperative weaver birds.

Humans cooperate to build complex structures with culture-specific architectural styles. However, they are not the only animals to build complex structures nor to have culture. We show that social groups of white-browed sparrow weavers (Plocepasser mahali) build structures (nests for breeding and multiple single-occupant roosts for sleeping) that differ architecturally among groups. Morphological differences are consistent across years and are clear even among groups with territories a few meters apart. These repeatable differences are not explained by among-group variation in local weather conditions, bird size, tree height, or patterns of genetic relatedness. Architectural styles are also robust to the immigration of birds from other groups.

Leveraging Artificial Intelligence to Address Climate Change

The paper explores how AI-enabled utilizing data analytics and machine learning methodologies enables deeper insights into the intricate patterns and behaviors of climate dynamics by analysing amounts of various data, integrating information from various origins, like satellite imagery, and the sensory data is processed to reveal meaningful insights for better understanding and informed actions. These can inform any policy decisions and facilitate more targeted interventions to mitigate the impacts of the climate conditions. The work discussed here in this research provided sources focuses on leveraging artificial intelligence (AI) and machine learning (ML) to address climate change challenges. Studies emphasize AI-driven strategies for climate change adaptation and including predicting various changes in the environment, and changes in the weather patterns. The research highlights the importance of weather conditions, and change in the weather patterns, and in developing effective AI-powered climate change in the adaptation strategies. And accordingly, these studies shows how effectively different AI and ML models like LSTM, ANN, CNN in improving the climate predictions and understanding the weather. AI and ML technologies in enhancing the changes in the weather, mitigation.