Weather Trends Research Articles

Anomalous Arctic warming linked with severe winter weather in Northern Hemisphere continents

We have extended a recently developed metric that ingests United States station data—the accumulated winter season severity index—to a global indicator based on temperature and snowfall from reanalysis output. The expanded index is analyzed to reveal relationships between Arctic air temperatures/pressures and the probability of severe winter weather across the Northern Hemisphere midlatitudes. Here we find a direct and quasilinear relationship between anomalously high Arctic temperatures/pressures and increased severe winter weather, especially in northeastern continental regions downstream of maximum regional Arctic warming. Positive temperature trends in the Arctic are associated with positive trends in severe winter weather across the continents in mid- to late-winter, coinciding with an increase in stratospheric polar vortex disruptions. During the era of rapid Arctic warming, variability has decreased over the Arctic Ocean and Europe, but has increased in Canada, the Northern US and northeast Asia, indicating more pronounced shifts in weather conditions.

Trend Analysis and Change Point Detection of Climatic Parameters in Ambedkar Nagar District of Uttar Pradesh, India

Climate change is one of the most serious challenges confronting humanity today, and its effects can be seen all over the world. The assessment of changes in the long-term weather trend is critical for developing water resource management strategies under climate change scenarios. Long-term trends for annual, monsoon, non-monsoon rainfall, rainy days, maximum temperature, and minimum temperature of Akbarpur, Allapur, Bhiti, Jalalpur, and Tanda stations in Uttar Pradesh's Ambedkar Nagar district were examined in this study. The Mann-Kendall test revealed a non-significant decreasing trend for annual and monsoon rainfall at Akbarpur, Bhiti, and Jalalpur stations with an average rate of 1.64 mm/year whereas, it shows a non-significant increasing trend for annual and monsoon rainfall at the Allapur and Tanda stations with an average rate of 3.168 mm/year. Non-monsoon rainfall showed an in-significant decreasing trend at Allapur and Jalalpur stations with an average rate of 0.415 mm/year, whereas a non-significant increasing trend was shown at Akbarpur, Bhiti, and Tanda stations with an average rate of 0.197 mm/year. Annual rainy days shows non-significant decreasing trend for all stations of Akbarpur district with an average rate of 0.117 day/year (one day decrease with in nine year). It indicates seasonal shift and increased possibility of high intensity rainfall. The impact of extreme events of rainfall on the trend was evaluated on the whole and partial series before and after the break point and it was observed that there was no significant climate change in Ambedkar Nagar district and non-significant changing year of precipitation data series was found in 1995. A significant increasing trend in maximum temperature was observed for all stations in the months of August and December, with an average rate of 0.02 0C per year, while Allapur station also shows a significant increasing trend in maximum temperature in September, with a rate of 0.012 0C per year. However, a significant decreasing trend was observed in minimum temperature in the month of May at Akbarpur, Allapur, Jalalpur, Tanda station with an average rate of 0.027 0C per year. Rainfall and temperature are a principal climatic parameter which regulates the environmental condition of a particular region by directly influencing the agricultural productivity and water resources.

Strong regional trends in extreme weather over the next two decades under high- and low-emissions pathways

Strong regional trends in extreme weather over the next two decades under high- and low-emissions pathways

Geochemistry and clay mineralogy of shales in the Sulu Orogenic Belt, East China: suggestion for continental weathering during an Early Cretaceous hyperthermal interval

ABSTRACT Understanding past weathering–climate feedback mechanisms under greenhouse conditions has significance for guiding the development of climate mitigation strategies. The continental paleoweathering state of Early Cretaceous shales in the Sulu Orogenic Belt of East China during a hyperthermal interval related to oceanic anoxic event 1a (OAE1a) was investigated via geochemical and clay mineralogical analyses of shale samples. The resulting diagrams of ternary 15Al2O3–Zr–300TiO2 discrimination, Th/Sc and Zr/Sc show that the geochemical composition was mainly controlled by source composition. This is supported by observations of low Zr contents, high indexes of compositional variability (ICV; > 1), and micropetrological characteristics. All samples showed narrow SiO2/Al2O3 and (Fe2O3 + MgO)/Al2O3 ranges that were within the predicted weathering trends of the ternary A-CN-K diagram, indicating that the shale geochemistry was not influenced by silicification and K-metasomatism. The chondrite-normalized rare-earth-element pattern suggests that all samples had the same felsic igneous parent rock. The geochemistry of whole rocks and their silicate fractions indicates that the shale had low chemical indexes of alteration (CIA) but high ICV values compared to post-Archean Australian Shale, indicating low sediment maturity and extremely weak chemical weathering in the source terrane and/or sedimentary basin under hot-dry conditions. Moreover, the clay minerals in the shale were mainly illite, suggesting low mineral weathering. Thus, during the OAE1a-related hyperthermal interval, chemical weathering was relatively weak in some mid-latitude arid inland areas. This may be mainly due to aridification decreasing water–rock reactions. Chemostratigraphic analysis suggests that the CIA, Ga/Rb, and Rb/Sr values of shales from the upper strata of the Yangjiazhuang and Shuinan formations were greater than those from lower strata. Moreover, the opposite trend in K2O/Al2O3 ratios was found. These indices suggest gradual increases in chemical weathering as the climate changed from hot-dry to warm-humid. In contrast, proxy indices of shale from the Zhifengzhuang Formation showed the opposite trend, suggesting a gradual decrease in chemical weathering as the climate changed from warm-humid to hot-dry. Considering the composition of the clay minerals, it is speculated that continental weathering was dependent on atmospheric humidity rather than temperature under the extreme greenhouse conditions, resulting in ineffective climate regulation by chemical weathering. This comprehensive study advances our understanding of the weathering–climate feedback mechanism under greenhouse regimes.

The Impact of Climate Change on Immunity and Gut Microbiota in the Development of Disease.

According to the definition provided by the United Nations, "climate change" describes the persistent alterations in temperatures and weather trends. These alterations may arise naturally, such as fluctuations in the solar cycle. Nonetheless, since the 19th century, human activities have emerged as the primary agent for climate change, primarily attributed to the combustion of fossil fuels such as coal, oil, and gas. Climate change can potentially influence the well-being, agricultural production, housing, safety, and employment opportunities for all individuals. The immune system is an important interface through which global climate change affects human health. Extreme heat, weather events and environmental pollutants could impair both innate and adaptive immune responses, promoting inflammation and genomic instability, and increasing the risk of autoimmune and chronic inflammatory diseases. Moreover, climate change has an impact on both soil and gut microbiome composition, which can further explain changes in human health outcomes. This narrative review aims to explore the influence of climate change on human health and disease, focusing specifically on its effects on the immune system and gut microbiota. Understanding how these factors contribute to the development of physical and mental illness may allow for the design of strategies aimed at reducing the negative impact of climate and pollution on human health.

Exploring the factors influencing the first singing date of a cicada, Graptopsaltria nigrofuscata: How will it be affected by climate change?

Abstract Climate change affects various scales of biotic interaction through phenological shifts. The emergence phenology of cicadas is ecologically important because these insects have large effects on the ecosystem as herbivores, as food resources, and in nutrient flux from subterranean resources. However, little is known about the weather factors affecting their emergence patterns in the field because their nymphal stages grow underground for several years. Here, we used long‐term observation data on the first singing date (i.e. the first emergence of male individuals) of Graptopsaltria nigrofuscata, recorded by the Japan Meteorological Agency and citizen scientists throughout Japan, to (1) explore the most influential weather factors across a variety of time spans on the first singing date of G. nigrofuscata and (2) determine whether the temporal trend of the first singing date could be explained by temporal weather trends caused by climate change by using a state space model. Our results indicated that higher temperatures from midsummer to early winter in the previous year are bringing the cicadas' emergence forward, and the annual increase in temperature is causing the advancement of emergence patterns. Other weather factors, such as precipitation and humidity, did not have a strong effect. Our findings suggest that increased growth rates at the nymphal stage due to warming in the previous year influence cicada emergence timing. To understand the mechanisms of how rising temperatures are advancing cicada emergence, we need an approach based on the physiology and ecology of their nymphs.

Weathering trends in the Norian through geochemical and rock magnetic analyses from the Pignola–Abriola section (Lagonegro Basin, Italy)

Abstract. We investigated the geochemical and rock magnetic properties of the magnetostratigraphically calibrated Pignola–Abriola section (Italy) in order to understand the climatic perturbations that characterize the late Norian–early Rhaetian interval (Late Triassic). We performed experiments on anhysteretic and isothermal remanence (ARM and IRM) and on magnetic susceptibility (χ) to obtain the rock magnetic parameters necessary for our paleoclimatic investigation. An episode of increase in the relative quantity of hematite, suggesting the enhanced subaerial oxidation of iron minerals, was identified in the Norian from ∼ 217 Ma in the Alaunian up to ∼ 211 Ma in the early Sevatian, followed by a decline up to 207–206 Ma at the end of the Norian (late Sevatian). The results of geochemical and multivariate statistical analyses support a long-term increase and reduction in rock weathering, confirming and extending previous 87Sr / 86Sr data from the Pizzo Mondello section (Italy). Possible causes of these long-term weathering trends are the multiphase uplifting of the Cimmerian orogen, occurring at mid-northern latitudes along the southern margin of Asia in the Late Triassic, and/or the northward motion of Pangea across the equatorial humid belt. Rapid excursions in oxidized iron minerals have also been observed across the Norian–Rhaetian boundary, the origin of which still has to be determined.

The use of environmental data in descriptive and predictive models of vector-borne disease in North America.

Vector-borne disease incidence and burden are on the rise. Weather events and climate patterns are known to influence vector populations and disease distribution and incidence. Changes in weather trends and climatic factors can shift seasonal vector activity and host behavior, thus altering pathogen distribution and introducing diseases to new geographic regions. With the upward trend in global temperature, changes in the incidence and distribution of disease vectors possibly linked to climate change have been documented. Forecasting and modeling efforts are valuable for incorporating climate into predicting changes in vector and vector-borne disease distribution. These predictions serve to optimize disease outbreak preparedness and response. The purpose of this scoping review was to describe the use of climate data in vector-borne disease prediction in North America between 2000 and 2022. The most investigated diseases were West Nile virus infection, Lyme disease, and dengue. The uneven geographical distribution of publications could suggest regional differences in the availability of surveillance data required for vector-borne disease predictions and forecasts across the United States, Canada, and Mexico. Studies incorporated environmental data from ground-based sources, satellite data, previously existing data, and field-collected data. While environmental data such as meteorological and topographic factors were well-represented, further research is warranted to ascertain if relationships with less common variables, such as oceanographic characteristics and drought, hold among various vector populations and throughout wider geographical areas. This review provides a catalogue of recently used climatic data that can inform future assessments of the value of such data in vector-borne disease models.

Improved RepVGG ground-based cloud image classification with attention convolution

Abstract. Atmospheric clouds greatly impact Earth's radiation, hydrological cycle, and climate change. Accurate automatic recognition of cloud shape based on a ground-based cloud image is helpful for analyzing solar irradiance, water vapor content, and atmospheric motion and then predicting photovoltaic power, weather trends, and severe weather changes. However, the appearance of clouds is changeable and diverse, and their classification is still challenging. In recent years, convolution neural networks (CNNs) have made great progress in ground-based cloud image classification. However, traditional CNNs poorly associate long-distance clouds, making the extraction of global features of cloud images quite problematic. This study attempts to mitigate this problem by elaborating on a ground-based cloud image classification method based on the improved RepVGG convolution neural network and attention mechanism. Firstly, the proposed method increases the RepVGG residual branch and obtains more local detail features of cloud images through small convolution kernels. Secondly, an improved channel attention module is embedded after the residual branch fusion, effectively extracting the global features of cloud images. Finally, the linear classifier is used to classify the ground cloud images. Finally, the warm-up method is applied to optimize the learning rate in the training stage of the proposed method, making it lightweight in the inference stage and thus avoiding overfitting and accelerating the model's convergence. The proposed method is validated on the multimodal ground-based cloud dataset (MGCD) and the ground-based remote sensing cloud database (GRSCD) containing seven cloud categories, with the respective classification accuracy rate values of 98.15 % and 98.07 % outperforming those of the 10 most advanced methods used as the reference. The results obtained are considered instrumental in ground-based cloud image classification.

Controls of lithology and climate over chemical weathering trends: new insights from the precipitation-dominated Dokriani glacier, central Himalaya, India

Abstract The chemical composition of meltwater-draining Himalayan glacierized basins reflects the dominance of carbonic acid in weathering of silicate and carbonate minerals, yet the role of sulfuric acid-mediated reactions in the mineral weathering and ionic release is still unclear. Here, we present a long-term study (1992–2018) of chemical weathering characteristics of a precipitation-dominated glacierized basin (Dokriani glacier) of central Himalaya. By using new and reprocessed datasets of major ions from the glacial/subglacial zones of the glacier, we suggest that two-thirds of the dissolved load of the meltwater derives from sulfuric acid-mediated weathering of minerals and rocks. We observed a clear control of carbonic acid-mediated reactions in the early ablation periods, while sulfuric acid-mediated reactions dominate in peak and late ablation periods. The slopes and intercepts in best-fit regressions of [*Ca2+ + *Mg2+ vs *SO42− and HCO3−] and [HCO3− vs *SO42−] in meltwater were following the stoichiometric parameters of sulfide oxidation coupled to carbonate dissolution reactions. The glaciers of the central and western Himalaya are in good agreement with the present estimates. We contend that the bedrock lithology has limited or second-order effects over the ionic release from Himalayan glaciers and surmise that these patterns are broadly applicable to the other orogenic systems of the world.

Comparison of the Proposed Rainfall Prediction Model Designed using Data Mining Techniques with the Existing Rainfall Prediction Methods

Weather prediction is a very old practice and people are doing predictions about weather much before the discovery of the weather measuring instrument. In ancient times, people give weather predictions by observing the sky for a long time and patterns of the stars at night. Things are a bit different now. People more relay on the past trends and patterns followed by the weather parameters. Data mining and machine leaning is used to analysis the historical weather trends by analyzing weather data using various Data mining techniques. In this paper three rainfall prediction model based on data mining techniques are proposed and compared with the other rainfall prediction model. The comparison has been done on the basis of accuracy, precision, Recall and RMSE. The proposed models are based on ensemble methods such as bagging, boosting, and stacking. Ensemble methods are used to enhance the overall performance and accuracy of the prediction. In both bagging and boosting based proposed rainfall prediction models, artificial neural network is used as a base leaner and daily weather data from the year 1988 to 2022 is used. In stacking based proposed rainfall prediction model, random forest, Logistic regression, and K-Nearest neighbor are used as base leaners or level -0 learners and Artificial neural network is used as Meta model.

John Gadbury's 32‐year record of daily London weather (1668–1700)

AbstractGadbury’s published diary ‘Nauticum Astrologicum…’ covers 32 years of daily London weather from 1668–1700, in manuscript at the Bodleian library, Oxford. Transcription to spreadsheets has allowed the analysis of weather trends using about 41 weather terms. Eight‐point wind roses begin on 11 May 1673, augmented with partial records for most of 1671–1673. Diagrammatic summaries for weather and temperature terms are plotted annually in half‐month frequency bins. The 32‐year record is roughly divided into phases with approximate breaks at 1680 and 1690.

Utilization of Machine Learning Algorithms for Precision Agriculture: Enhancing Crop Selection

Agriculture stands as a crucial economic driver, playing a pivotal role in fostering economic progress. Understanding the dynamics of the agricultural system is imperative for ensuring food security. Even as technological strides like vertical farming emerge, conventional farming practices and beliefs continue to hold sway. This study delves into fundamental aspects such as soil composition, pH levels, humidity, and rainfall, employing a range of machine learning models including kernel naive Bayes, Gaussian naive Bayes, linear support vector machine (SVM), quadratic discriminant analysis, and quadratic SVM. The primary objective is to provide insightful crop recommendations to farmers. Accurate crop forecasting is paramount for optimizing agricultural methodologies and maintaining a consistent food supply. By leveraging historical weather trends, soil quality, and crop production data, machine learning algorithms proficiently anticipate crop yields. The outcomes of this investigation have the potential to refine crop management practices and reinforce food security measures.

Purple soil inheritance and source of parent rock material and tectonic background in the Sichuan Basin, China

ABSTRACT The parent rock inheritance of the purple soils in the Sichuan Basin is evident, and this is due to factors that are directly related to the parent rocks’ characteristics. Fundamentally, the nature of purple soils is determined by the lithology, chemical composition, weathering, and soil formation of parent rocks, whereas the provenance, and tectonic context of rocks during their diagenesis have a significant influence on their rock attributes. In this study, rocks from four sedimentary layers of the Cretaceous and Jurassic eras – k1c, J3p, J3s, J2s in the Sichuan Basin – as well as the overlying soils were examined, and experimental determinations of the samples’ physical characteristics and chemical elemental composition were made to confirm the parent rock inheritance of purple soils. The essential weathering indices ICV, CIAcorr, WIP, and Na/K were used to study the weathering characteristics of the rocks and soils, and the A-CN-K ternary diagram was used to assess the weathering trends of the rocks. The findings demonstrate that purple soil exhibits significant parent rock inheritance due to its rapid physical soil formation process and weak chemical weathering process. That the rocks of the four sedimentary layers in the study area have all reached a moderate degree of weathering, their original source components are quartzose sedimentary which may have been derived from feldspar source rocks. The tectonic background of the source rocks may be mainly continental arc and active continental margin environment, there may be an oceanic arc tectonic environment.

Anomalous weathering trends during the Early Silurian warming: Implications for the biotic crisis and recovery

The Late Ordovician mass extinctions were divided into two distinct stages, with the second stage occurring during the Late Hirnantian. However, biotic recovery was delayed until the Aeronian and the triggering factors are still unknown. To comprehend the connections between paleoclimate, marine redox states, and biotic recovery in the Early Silurian, we analyzed temporal variations in chemical weathering intensity using multi-geochemical proxies for the shales of the Longmaxi Formation in South China. The results reveal a significant increase in chemical weathering intensity from the Rhuddanian warming phase to the Aeronian cooling phase. This increase is anomalous, considering it coincides with the Early Silurian's climate cooling. This study posits that Rhuddanian sediments exhibited low chemical weathering intensity and more congruent weathering, while Aeronian sediments were characterized by intermediate chemical weathering intensity and incongruent weathering. Given that the gradual enrichment of oxygen within bottom waters can be supported by Corg/P and Corg-S-Fe, this study suggests that congruent weathering-induced high paleoproductivity is the primary factor accelerating anoxia in the Rhuddanian. In summary, the persistent marine anoxia of seawater thus triggered the deposition of organic-rich shales and delayed biotic recovery in the Rhuddanian.

Provenance of early Permian Li-rich claystone from Central Yunnan, South China: Constrained by Sr-Nd-Pb isotopes, geochemistry, and zircon U–Pb ages

A new lithium-rich claystone from the Lower Permian has been detected in Central Yunnan, Southwestern China that could be great potential for Lithium (Li) exploration, yet the origin of the claystone is still being debated. To study it further, new geochronological, geochemical, and Sr–Nd–Pb isotopic analyses were systematically conducted on the Daoshitou Formation (P1d) claystone, as well as on the underlying strata. Through detrital zircon U–Pb dating of the claystone in the Daoshitou Formation, three major age peaks were revealed, at ∼960 Ma, ∼790 Ma, ∼550 Ma, respectively, along with three minor peaks of ∼2500 Ma, ∼1500 Ma, and ∼1200 Ma. Detrital zircons in the claystone have age spectra that closely match those of the Lower Ordovician sedimentary rocks in Central Yunnan, indicating these rocks are the major contributor of detrital zircons. However, the rock source determined by geochemical features decouples with the zircon source determined by detrital zircon data. The A–CN–K ternary diagram obviously reveals analogous predicted weathering trends between the Weining Formation (C2w) and Daoshitou Formation, suggesting a potential provenance link. Furthermore, the weathering trends of samples from three drill cores indicate that the transformation of clay mineral (smectite–illite–chlorite/kaolinite) may facilitate Li enrichment. Smectite, as the precursor mineral for Li enrichment, typically forms and preserves in a slightly alkaline environment, which suggests that the Li-rich claystone is likely to formed in this environment. Low Na/Li and K/Li ratios indicate low cation exchange selectivity in sedimentary environments, which promote the proliferation of adsorption sites for Li on smectite. The weathering products from the Weining Formation, composed of impure carbonates, could provide a slightly alkaline environment with low ion strength for the adsorption of Li. In addition, Sr–Nd–Pb isotopic compositions also reveal the significant similarity between the Daoshitou Formation and Weining Formation. Therefore, we suggest that the weathering of the Weining Formation provided the primary material to Daoshitou Formation claystone.

Awareness and Utilization of Various Weather Based Advisory Services in Western Zone of Tamil Nadu, India

Weather-centric crop advisory services offer timely updates on weather trends, crop condition, and recommended actions, empowering farmers to make knowledgeable choices regarding diverse crop management techniques. This ultimately results in improved yields and augmented income. This study was carried out to find the Awareness and Utilization level of various Weather Agro advisory services disseminated through various sources among the farmers. Ex post facto research Design was followed in the study. The Salem and Coimbatore district were purposively selected because dissemination of weather information to the farmers in the district through District Agromet units (DAMU). Based on simple random sampling technique 120 respondents were taken for the study. The responses were interpreted using frequency and percentage analysis. Correlation was used to find the relationship between the independent variables and dependent variables using the Statistical packages for Social Sciences (SPSS) software. The result indicates that there was medium level of awareness about 67.5 per cent of respondents and utilization level of about 71.67 per cent of respondents. The variables such as education, extension agency contact, use of media tools, source of weather information are significant and positively correlated to Awareness and utilization The Weather forecast information credibility and accuracy can be increased to high level of awareness and utilization by providing the credible weather data on timely basis.

How does the environment in and around the home impact social care and health outcomes for older people?

Reducing the burden of falls and fall-related admissions to hospital and care homes is an important policy area. Falls cause significant injury leading to a reduced quality of life. We wanted to know if the environment around people’s homes changes the risk of falls for older people in Wales.
 We linked routinely collected, anonymised health data on frailty, dementia, hospital admissions, care home admission, and A&E attendance in the Secure Anonymised Information Linkage Databank. We also linked individual level demographic data and household level neighbourhood walkability and accessibility metrics detailing the built environment (e.g. access to services, greenspace). We capture change in the built environment and house moves between January 2010 and December 2017. Using unadjusted and adjusted cox regression models we will assess how the risk and severity of a fall changes in relation to the built environment.
 We have created a dynamic national e-cohort linking data on frailty, dementia, hospital admissions, care home admission, A&E attendance and built environment measures for people living in Wales aged 60 and above between January 2010 and December 2017. At the conference we will report summary statistics for each quarter as well as the overall population. We will also report unadjusted cox model odds ratios, as well as fully adjusted models. We will adjust for age, sex, urbanicity (urban/rural), deprivation (WIMD), dementia diagnosis, and seasonal weather trends. We will also report odds ratios for our stratification analysis where we will investigate whether associations vary by urbanicity and deprivation.
 With an aging population, it is becoming more important to help the current and future older population age healthily, preventing adverse health outcomes before they happen. This research will help guide policy and resource allocation to support people staying at home and have a more fulfilling life for longer.

RELATING WEATHER TRENDS AND RESIDENCE LENGTH TO LOCAL CLIMATE CHANGE CONCERNS IN KNOXVILLE, TENNESSEE

ABSTRACT Climate change beliefs and concerns are closely tied to socio-demographic traits and personal experience with extreme weather events. Here, we compare survey participants’ perceptions of extreme weather trends in Knoxville, Tennessee, with observed trends in extreme weather during their residency. We also assess the relationship between climate change concerns and weather perceptions. We find that perceived weather trends, including hot summer days and cold/snowy winters, matched observed trends for some—but not all—residents, depending in part on the length of their residency. Perceived weather trends were also related to level of climate change concerns, belief in its anthropogenic nature, and some socio-demographic characteristics, including income. Our results suggest that as cities in the Southeast continue to grow in population and residents experience the effects of Earth’s changing climate, these factors will play an important role in extreme weather perceptions and attribution to climate change.

Identification of extreme weather and their correlation on soybean production in Garut regency

The phenomenon of extreme weather events as a result of the impact of climate change can cause threats to agricultural systems, including soybean (Glycine max L.). Soybean is the main source of vegetable protein, which is sensitive and vulnerable to climate change. Therefore, research has been carried out through the identification analysis of changes in extreme weather events and analyzed for their correlation with soybean crops in Garut Regency to determine the effect of extreme weather elements on soybean production. The method used in this research is descriptive quantitative, using trend analysis on extreme weather with data on extreme weather elements such as maximum rainfall, maximum temperature, minimum temperature, wet spell, dry spell, the largest wind speed, and trend analysis on soybean production and productivity. Data for the research were obtained from BUTPAAG LAPAN Garut Regency, Garut Regency Agriculture Office, and other related sources. The correlation analysis used is the Pearson correlation with a significance level of 5%. The results showed that climate change impacts extreme weather changes in the Garut Regency area, with increasing extreme weather trends. However, extreme weather changes were not significantly correlated with soybean production. In this research, only the maximum rainfall and the largest wind speed were significantly correlated with soybean productivity.Keywords: correlation analysis, trend analysis, extreme weather, Garut regency, soybean production.