Local Climate Research Articles

Predictive modeling of Super El Niño through integrated local and global climate signals

Recent surges in extreme weather events underscore the critical need for advanced climate prediction models. Super El Niño (SE) events, characterized by unprecedented climatic and economic impacts, have intensified global concerns. This study introduces a novel predictive framework utilizing the Super El Niño Index (SEI), which integrates diverse datasets, including NOAA Sea Surface Temperature indices and regional data from Korea for correlation analysis and model development. Additionally, high-resolution simulations from NASA’s ECCO2 project are utilized to visualize and quantitatively analyze ocean current and SST dynamics, providing a physical basis for understanding SE-induced variability. The SEI identifies SE events with a threshold value of 75 and demonstrates its effectiveness in accurately capturing major historical events, such as those in 1982–83, 1997–98, and 2015–16, while revealing a rising trend in SEI values since 1982, likely tied to global warming. The model successfully predicted an SEI of approximately 80 for 2023, later validated as one of the five most intense SE events on record. These findings highlight the far-reaching influence of SE events beyond the equatorial Pacific, extending to regions like Korea and Japan, and emphasize the necessity for robust predictive tools to mitigate the growing frequency and intensity of SE events in a warming world.

Spatiotemporal Evolution of Urban Driving Factors and Seasonal Heat Island Response from the Perspective of Local Climate Zones: A Case Study of Xiamen City, China

Spatiotemporal Evolution of Urban Driving Factors and Seasonal Heat Island Response from the Perspective of Local Climate Zones: A Case Study of Xiamen City, China

Understanding Climate Vulnerability and Adaptation among Farmers: A Micro-Level Study from the North Western Himalayas

Climate change poses serious threats to agriculture and horticulture, particularly in ecologically fragile and climate-sensitive regions such as the North Western Himalayas of Jammu and Kashmir. This study investigates farmers’ perceptions of climate change and the adaptation strategies they employ in response. The region, marked by limited livelihood opportunities and high vulnerability to climatic extremes, remains underexplored with respect to localized climate resilience. To address this gap, an empirical study was conducted using primary data from 150 randomly selected respondents via a structured questionnaire, supplemented with secondary sources. Statistical tools such as ANOVA, chi-square tests, Spearman’s correlation, the Weighted Average Index (WAI), and the Adaptation Strategies Index (ASI) were employed. The results reveal a significant association between climate variability and farmers’ livelihoods. Major climatic stressors include erratic rainfall (WAI = 4.6), reduced snowfall (WAI = 4.1), and rising temperatures (WAI = 3.8), all of which have negatively affected agricultural productivity and farming calendars. In response, farmers are adopting adaptation practices such as mixed cropping (WAI = 4.6), crop diversification (WAI = 4.5), improved irrigation (WAI = 4.1), and enhanced post-harvest management (WAI = 4.5). However, several barriers—including limited financial resources, poor infrastructure, and weak institutional support—continue to constrain adaptive capacity. The study also identifies a strong positive correlation between deteriorating climatic conditions and rising poverty levels (ρ = 0.773, p = 0.000). Furthermore, farmers’ projections of future climate scenarios were significantly associated with their choice of adaptation strategies (Chi-square = 300.000, p = 0.000), underscoring the critical role of perception in shaping responses. These findings highlight the urgent need for policy interventions that integrate farmers’ insights into adaptation planning. Strategies such as climate-resilient crops, efficient irrigation systems, agricultural insurance, and strengthened institutional support are essential for building climate-resilient farming communities in the Himalayan region.

Advancing Local Climate Action: Insights from the Piedmont Region, Italy

While the impacts of climate change are increasingly apparent and expected to worsen in the near future, effective mitigation and adaptation strategies, plan and actions are becoming compelling. Despite the need for an integrated approach among governance levels, local authorities lie at the forefront of climate action, due to their proximity to the affected communities. However, the potential of this local climate governance is under discussion, as its efficacy is still unclear. This study focuses on the municipalities in the Piedmont region (Italy). It investigates how these municipalities tackle climate change within the consolidated framework of the Covenant of Mayors, the global-level initiative sponsored by the European Commission that since 2008 promotes and supports climate action at the local level. The assessment is based on a quali-quantitative analysis, owing to official documents and expert surveys. The results show that mitigation is rather consolidated, but it often relies on the willingness of private actors to act, hence pushing the achievement of climate targets beyond the direct control of the municipal authority. Furthermore, the study reveals a significant underdevelopment in terms of adaptation; though a certain attention for ecosystem services and wellbeing emerges, the ability to effectively act appears limited. Overall, the results hint at the inherent complexity of governing local climate-related planning, especially for municipalities, that suffer from limited funds and resources, as well as internal political instability. Although difficulties will persist, being structured in the socio-political context, we argue that local partnerships connecting different levels of governance and incorporating private actors and communities in the planning and implementation system can strengthen local resilience against global climate challenges, enhancing climate action and fostering the sustainability of local communities.

Comparative Analysis of the Microbial Community Profiles of Sichuan and Guizhou Smoke-Cured Sausages Using a High-Throughput Sequencing Approach

Autochthonous microorganisms play critical roles in shaping the quality of Chinese sausages and may be influenced by local climate and/or processing conditions. The present study aimed to reveal the interprovincial differences in microbial community between Sichuan and Guizhou sausages, as well as driving factors based on high-throughput sequencing and bioinformatic analysis. The results indicated that Cobetia, Debaryomycetaceae, Kurtzmaniella, and Candida zeylanoides served as biomarkers for Sichuan sausages. In contrast, Enterococcus, unclassified Cyanobacteriales, Lactobacillales, Aspergillus vitricola, Mortierella, Fusarium, and Penicillium were identified as biomarkers for Guizhou sausages. Furthermore, salt content and moisture level showed positive correlations with Cobetia, Staphylococcus, Debaryomyces, and Kurtzmaniella, mainly found in Sichuan sausages. Conversely, pH and water activity (Aw) were positively associated with potential pathogenic bacteria (e.g., Vibrio, Cyanobacteria, Enterococcus, and Aeromonas) and fungi (e.g., Aspergillus, Fusarium, and Penicillium), which were mainly distributed in Guizhou sausages. Notably, microbial composition discrepancies between Sichuan and Guizhou sausages were primarily driven by processing conditions rather than regional climate factors. Collectively, these findings provide valuable insight for developing novel specific starters.

Aerosol Distribution Due to Wildfire in Sumatra, Indonesia Considered from Model Simulation

Open burning of biomass has occurred around the world, and emissions from biomass burning are impacting local, regional, and global air quality issues and climate change. This study focuses on severe biomass burning aerosols (BBAs) in Sumatra in September 2019. The chemical transport simulation model employed in this study is based on a meteorological field simulated by SCALE (Scalable Computing for Advanced Library and the Environmental Regional Model) for offline calculations. Simulation results are validated by using ground-based measurements and biomass burning aerosol distribution observed by JAXA/GCOM-C (Global Change Observation Mission-Climate)/SGLI (second-generation global imager). The results of this study show that the injection process in the model simulations has a significant impact on the aerosol distribution. Aerosols generated by fires can rise to higher altitudes due to the heat of the fire, but aerosols originating from surface and soil fires were found to reproduce well at less elevated injection height.

Dynamic multi-scale effect of urban spatial form on PM2.5 concentrations in different climate zones of China

ABSTRACT The enhancement of air quality continues to be a pivotal component of sustainable urban development. However, in examining the relationship between urban spatial form and air pollutants, there remains inadequate evidence for the indicative role of urban form indicators, the selection of urban scales, and the understanding of spatiotemporal variability. This study investigated spatial and temporal variations in the correlation between spatial form and PM2.5 at scales of 1 km, 2 km, and 3 km in five megacities across diverse climatic zones in China from 2019 to 2021. The results of the study corroborated the significant seasonal, scale and regional differences in the response of PM2.5 to urban spatial forms. It was further proposed that: (1) In the highly polluted cities of Zhengzhou and Shenyang, the models demonstrated a higher degree of accuracy on a smaller scale. This finding indicated that the capability of diffusion associated with pollution was less effective than that observed in Chengdu, Kunming and Shenzhen. (2) In comparison, population and building indicators were of greater significance in indicating air pollution than other factors. However, the seasonal and multi-scale variations of the important indicators indicated that there was no consistent, perfect urban form capable of eliminating pollution. (3) Therefore, it was recommended that cities with high pollution should improve the ventilated environment according to the local climate and enhance the diffusion capacity of pollution.

Intelligent Streetlight Control System Using Machine Learning Algorithms for Enhanced Energy Optimization in Smart Cities

In the USA, where large-scale city infrastructure uses huge amounts of energy, street lighting collectively makes up a large percentage of city-wide electricity consumption. As cities become smarter and greener, there is an immediate demand to update the management of public lighting networks. This research's prime objective was to create an adaptive machine learning system for streetlight control that can react automatically to the environment and human activity patterns in real time. The development of our intelligent streetlight control system led us to build a complete dataset that contains the necessary elements for context-based lighting decisions. The dataset contains contemporary, along with historical readings of ambient light intensity expressed in lux units, which delivers an essential understanding of natural illumination and lighting needs. The primary performance metric is accuracy, which indicates the number of accurately predicted instances against the number of overall predictions. Furthermore, a Confusion Matrix is utilized to present an in-depth breakdown of the outcomes of classification, illustrating the number of examples that were accurately or inaccurately classified into each class. The application of an intelligent streetlight system using machine learning is directly in line with the strategic policies of the U.S. Department of Energy (DOE), specifically its requirements regarding the modernization of the smart grid, energy efficiency, and carbon reduction. By providing real-time data-driven control of street lighting in response to environmental and usage conditions, the system makes full integration of municipal infrastructure into the smart grid possible. At the policy level, the system is an effective and pragmatic tool for municipalities looking to achieve federal and local climate action targets. Greenhouse gas (GHG) mitigation is achieved through the reduction of electricity consumption through adaptive lighting, and the machine learning function minimizes human interaction, maximizing operational autonomy and cost savings. One of the strongest ramifications of using the intelligent streetlight system is the possibility of huge cost reductions on city utility budgets.

Spatiotemporal characterization of meteorological drought and rainfall variation during El Niño/La Niña event over Bundelkhand region of India

This study analyzes the relationship between the El Niño-Southern Oscillation (ENSO) and rainfall variability in the Bundelkhand region using the Oceanic Niño Index (Niño 3.4 Index) for the period 1950–2022. Drought monitoring was conducted using the Standardized Precipitation Index (SPI) to assess the impact of ENSO on regional drought conditions. Results indicate that El Niño years correspond to a decline in rainfall, with weak and moderate El Niño events leading to deficits of − 38.62 mm and − 21.44 mm, respectively, while strong El Niño years experienced a significant reduction of − 128.8 mm. Conversely, the relationship between La Niña and rainfall is inconsistent, with moderate La Niña years showing a positive anomaly of 66.89 mm, while weak and strong La Niña years resulted in mixed effects, with anomalies of − 17.59 mm and − 56.72 mm, respectively. Spatiotemporal analysis further revealed that drought conditions persisted in Bundelkhand during the La Niña years of 1973 and 2020, highlighting the region’s unique response to ENSO events. These findings emphasize the need for localized climate assessments, as large-scale ENSO trends do not consistently predict rainfall patterns in Bundelkhand region. This study provides valuable insights for drought mitigation and early warning strategies in the region.

Heat vulnerability index mapping through principal component analysis and equal weight methods: comparing Spatial patterns at a low urban scale and local climate zones in an arid mid-size South American coastal city

Heat vulnerability index mapping through principal component analysis and equal weight methods: comparing Spatial patterns at a low urban scale and local climate zones in an arid mid-size South American coastal city

Generation of representative meteorological years through anomaly-based detection of extreme events

Typical Meteorological Years (TMYs) have long supported the building sector by integrating local climate into building design for energy, thermal comfort, and peak load assessments. As climates shift, past heat waves and cold spells signal future conditions requiring greater adaptability. This study proposes a new file generation method that preserves TMY properties while embedding extreme events. We combine three anomaly-detection methods—temperature thresholds, Graph Neural Networks (GNNs), and Extreme Value Theory (EVT)—to capture climatic deviations, detect anomalies, and model statistical extremes. An integrated hierarchical method forms the new Representative Meteorological Year (RMY) file. RMY files for six ASHRAE climate-zones consistently capture past extremes, producing worst-case scenarios for key metrics, including peak loads, indoor thermal stress, natural ventilation and outdoor comfort. The largest deviation between the TMY and RMY was a doubling of indoor thermal stress hours across all climates, while average energy use remained aligned, with a deviation of 6%.

Local adaptation has a role in reducing vulnerability to climate change in a widespread Amazonian forest lizard.

The extant genetic variation within and among taxa reflects a long history of diversification and adaptive mechanisms in response to climate change and landscape alterations. However, the velocity of current anthropogenic changes poses an imminent threat to global biodiversity. Understanding how species and populations might respond to global climate change provides valuable information for conservation in the face of these impacts. Here, we use genomic data to observe candidate loci under climate selection and test for genetic vulnerability to climate change in a widespread Amazonian ombrophilous lizard population. We found nine populations across Amazonia with a considerable amount of admixture among them. Distinct approaches of genome-environment association analyses revealed 56 candidate single-nucleotide polymorphisms (SNPs) under climatic selection, showing an east-west gradient in the adaptive landscape and a signal of local climate adaptation across the species range. According to our results, signals of local adaptation indicate that the species may not respond equally throughout its range, with some populations facing higher extinction risks. Genomic offset analysis predicts the southern and central portions of Amazonia to have a higher vulnerability to future climate change. Our findings highlight the importance of considering spatially explicit contexts with a large sampling coverage to evaluate how local adaptation and climatic vulnerability affect Amazonian forest ectothermic fauna.

The CAPS framework for governance capacity building for local climate action

ABSTRACT The CAPS (CAPacity building through Shared understanding) framework presents a comprehensive approach to endogenous governance capacity building for local climate action. Combining previous research on governance capacity, shared understanding and social learning, the framework outlines the process whereby local governance capacities evolve through social learning cycles, leading to increasingly ambitious climate actions; however, previous research remains opaque regarding the specific mechanism underlying this process. To close this gap, the CAPS framework introduces the concept of shared understanding, operationalised using Protection Motivation Theory, which measures the convergence and eventual congruence of risk appraisals, coping appraisals and protective or non-protective responses among local actors. We present and explain the CAPS framework, but also offer potential applications in transdisciplinary research on local transformation processes as well as reflect on enabling conditions and challenges in terms of scope, methods, involved actors and potential counterproductive outcomes of capacity building.

Drought-induced die-off triggers species composition shift at warmer and drier edges of climate gradient in Pinus sylvestris forests of Catalonia (NE Spain)

Abstract Climate change is increasing the frequency and intensity of drought episodes, with raising forest die-off events globally. Impacts of drought on tree growth and mortality have been extensively investigated, but studies on regeneration after these episodes and their consequences for tree diversity and potential plant composition shifts are still scarce, especially analysing climate gradients. We analyse the impact of drought-induced die-offs on woody plant diversity in Pinus sylvestris forests in Catalonia (NE Spain), considering local temperature and precipitation effects. We compared plots affected by die-off to non-affected ones, surveying tree layers in 2013, 2017, and 2022, and tree recruits in 2022. Die-off plots showed greater richness and diversity in tree layer after 5 and 10 years of die-off, particularly in drier sites, while higher evenness was observed in warmer sites. Such pattern indicated a vegetation shift towards a Mediterranean-type forest, with increasing ingrowths of Quercus, Acer, Prunus, and other broadleaf trees which incorporated to tree layer, potentially replacing dead P. sylvestris. In die-off plots, species richness and evenness of larger recruits were 36% and 42% higher, respectively, than in non-affected ones. Species in tree layer and recruiting communities showed a 29% higher similarity in die-off plots than in non-affected ones, supporting that die-off provide better opportunities for recruitment by incorporation into the tree canopy and successful establishment of new recruits. These results indicate that drought-induced die-off is triggering forests transition towards a more adapted plant composition to climate change concurrent with ongoing local climate conditions.

Multidisciplinary investigations of earthflow processes in the differential erosion furrows morphostructural unit, Northern Rif (Morocco): case study of the Seikha landslide

Abstract In the Rif mountain chain, Slow earthflow processes affectthe clayey and flysch formations of the Tangier and Flysch structural units respectively. To understand the underlying geomorphological processes and the trigger-failure relationship between the local mediterranean climate conditions and the earthflow-like morphologies at the Differential Erosion Furrow (DEF) morphostructural unit, a case study is conducted at the Seikha earthflow. The methodology proposed uses a multidisciplinary approach, coupling in situ geophysical and geotechnical tests to study the geometry of the landslide and remote sensing techniques to monitor its activity. Our results indicate that on one hand, the cross-analysis of geological and geophysical results shows that landslide processes at the study area follow a typical terrestrial-style earthflow model, where the geological structures controlling the landscape’s evolution are orientated parallel to the longitudinal stress direction. Vertical and horizontal resistivity variations also allow reconstructing the retrogressive genetic processes responsible for older processes that contributed to the evolution of this hillslope in particular and the DEF morphostructural unit as a whole. On the other hand, the interpretation of multitemporal aerial photographs suggests that the Seikha landslide is in a dormant state and that its acceleration periods follow multiannual cyclic trends related to historic climate and base level fall variations. Seasonal Trends are also emphasized by SBAS (small baseline subset) inSAR (Interferometric Synthetic Aperture Radar) and borehole inclinometer results, which show evidence of slow gravitational deformation that can be accelerated during seasonal rainfall periods.

Keep cool in a changing climate: an integrated modelling procedure for costeffective mitigation of rising temperatures in rural landscapes

Rising temperatures may negatively impact rural landscapes in temperate climates due to reduced yields in agriculture and forestry, an increased risk of biodiversity loss, changes in the local climate and a decrease in recreational value. One promising way to mitigate increasing land surface temperatures (LST) in rural landscapes is to implement land-use and land-cover changes as adaptation measures that retain precipitation in soils, water bodies, and groundwater to allow vegetation to evaporate more water to reduce LST in summer. We develop an integrated modelling procedure to identify cost-effective spatially differentiated adaptation measures in agriculture and forestry to mitigate LST increases. We define cost-effective adaptation in a landscape as maximizing LST mitigation for given costs. The procedure combines the results of a model that predicts the spatially differentiated effects of adaptation measures on LST with the results of an economic model that estimates the respective spatially differentiated costs in an optimisation algorithm. We demonstrate how the procedure works by applying it to the Elbe-Elster-county in Germany. We find that a substantial share of results can only be explained by considering spatially differentiated costs and mitigation impacts and not average values showing the importance of taking into account costs and impacts of measures in a spatially differentiated manner. We also compare results from our integrated modelling procedure with a (purely natural science) approach that selects those adaptation measures first which perform best in terms of LST mitigation and find that our approach leads to a better heat mitigation effect by a factor of 3.5 – 4.8.

Heat stress induced by irrigation over the US Great Plains and related uncertainties

Abstract Irrigation plays a crucial role in agricultural production across the US Great Plains. Meanwhile, it is a key driver of local and regional climate due to its influence on energy and water exchange between land surface and atmosphere. Despite the irrigation-induced evaporative cooling on temperature alone, how irrigation affects summer heat stress—a combination of temperature and humidity can become a concern to public health—is not well understood. This study examines the potential impacts of irrigation practices on summer temperature and heat extremes in the Great Plains using a set of sensitivity experiments conducted with the Weather Research & Forecasting (WRF) model for 10 growing seasons. Results show that intensive irrigation lowers the ambient temperature, but the increased humidity from enhanced evapotranspiration, especially during the extreme hot and dry summers, can possibly elevate the risks of heat stress in the heavily irrigated area and its surroundings. The response of humid heat extremes to irrigation depends on the heat metrics used in the assessment. For variables like wet-bulb temperature, wet-bulb globe temperature, and equivalent temperature, irrigation leads to significantly intensified humid heat extremes by up to 5 °C and increased heatwave frequency by 3 events year−1. In contrast, metrics like the heat index and environmental stress index suggest that irrigation mitigates heat intensity by decreasing the temperature metrics by up to 1 °C. Given the importance of irrigation in Great Plains agriculture in a changing climate, these uncertainties underscore the urgent need to connect heat metrics with health outcomes to better address heat mitigation in rural communities.

Local government debt, digital finance and climate change: Study from the perspective of green innovation

Local government debt, digital finance and climate change: Study from the perspective of green innovation

Environmental actions, support for policy, and information’s provision: experimental evidence from the US

We investigate the effectiveness of providing prospective versus retrospective information on local climate change to promote pro-environmental behavior and support for green policies. A randomized experiment on a representative sample of American adults finds that providing prospective local climate change information is most effective at increasing pro-environmental actions and policy support, regardless of partisanship. The impact of this information provision increases when individuals feel responsible for addressing climate change, have young children, and trust the central government. Prospective information on local climate change can thus contribute to promoting the vast support for the necessary actions to address the climate crisis.

Assessing seasonality and mobility from a fragmented faunal assemblage: the case of Amud Cave (Israel)

In this paper we investigate the seasonality of site occupation at Amud Cave (Israel). This site presents a long sedimentary sequence featuring two main late Middle Paleolithic occupation phases (70 – 55 Ka BP) rich in anthropogenic remains, and displaying very high densities of lithic artifacts, faunal remains and evidence of combustion. The abundance of these features appears high relative to the thickness of the sequence, thus raising the question of the duration and frequency of site occupation(s). We aim here to unravel this issue implementing a multiproxy approach to the dental fraction of the faunal assemblage. Combining teeth replacement and wear patterns coupled with micro- and mesowear analyses, we provide insights into the seasonality of occupation and hunting strategies at the site. We found that hunting activities at Amud Cave were conducted mainly in the course of the winter months with possible expansion into the late fall and early spring. This result is consistent with regional and local climate reconstructions and resources procurement locations evidenced in previous works. Assuming a fast depositional rate, we suggest that the sequence results from frequent seasonal returns to the site, forming a palimpsest of short-term occupations. This scenario is compatible with settlements and mobility patterns inferred for the late Middle Paleolithic in the Southern Levant.