Trends In Maximum Temperatures Research Articles

Long-Term Spatiotemporal Trends in Precipitation, Temperature, and Evapotranspiration Across Arid Asia and Africa

This study examines trends in precipitation (PRE), maximum temperature (TMAX), minimum temperature (TMIN), and potential evapotranspiration (PET) using the Modified Mann-Kendall test and Sen’s slope estimator between 1901 and 2022 in the arid lands of Central Asia, West Asia and North Africa. The results reveal complex spatial and temporal climate change patterns across the study area. Annual PRE shows a slight negative trend (Z = −0.881, p = 0.378), with significant decreases from 1951–2000 (Z = −3.329, p = 0.001). The temperatures exhibit strong warming trends (TMIN: Z = 9.591, p < 0.001; TMAX: Z = 8.405, p < 0.001). PET increased significantly (Z = 6.041, p < 0.001), with acceleration in recent decades. Spatially, precipitation decreased by 10% in maximum annual values, while PET increased by 10–15% in many areas. Temperature increases of 2–3 °C were observed, with TMAX rising from 36–39 °C to 39–42 °C in some MENA regions. Seasonal analysis shows winter precipitation decreasing significantly in recent years (Z = −1.974, p = 0.048), while summer PET shows the strongest increasing trend (Z = 5.647, p < 0.001). Spatial analysis revealed clear latitudinal gradients in temperature and PET, with higher values in southern regions. PRE patterns were more complex, with coastal and mountainous areas receiving more precipitation. The combination of rising temperatures, increasing PET, and variable PRE trends suggest an overall intensification of aridity in many parts of the region. This analysis provides crucial insights into the climate variability of these water-scarce areas, emphasizing the need for targeted adaptation strategies in water resource management, agriculture, and ecosystem conservation.

A rapid response process for evaluating causes of extreme temperature events in the United States: The 2023 Texas/Louisiana heat wave as a prototype

Abstract As climate attribution studies have become more common, routine processes are now being established for attribution analysis following extreme events. This study describes the prototype process being developed through a collaboration across National Oceanic and Atmospheric Administration (NOAA), including monitoring tools as well as observational and model-based analysis of causal factors. The prolonged period of extreme heat in summer 2023 over Texas, Louisiana and adjacent areas provided a proving ground for this emerging capability. This event posed unique challenges to the initial process. The extreme heat lasted for most of the summer while most heat wave metrics have been designed for 3–7 d events. The eastern portion of the affected area also occurred within the so-called summer-time daytime warming hole where the warming trend in maximum temperatures has been mitigated wholly or in part by increased precipitation. The extreme temperature coincided with a strong—but not record—precipitation deficit over the region. Both observations and climate model simulations illustrate that the temperatures for a given precipitation deficit have warmed in recent decades. In other words, meteorological droughts today are hotter than their historical analogs providing a stronger attribution to anthropogenic forcing than for temperature alone. These findings were summarized in a prototype plain language report that was distributed to key stakeholders. Based on their feedback, the monitoring and assessment tools will continue to be refined, and the project is exploring other climate model large ensembles to increase the robustness of attribution for future events.

Global warming induced spread of the highest human fascioliasis hyperendemic area

BackgroundClimate change is driving the occurrence of several infectious diseases. Within a One Health action to complement the ongoing preventive chemotherapy initiative against human fascioliasis in the Northern Bolivian Altiplano hyperendemic area, field surveys showed a geographical expansion of its lymnaeid snail vector. To assess whether climate change underlies this spread of the infection risk area, an in-depth analysis of the long-term evolution of climatic factors relevant for Fasciola hepatica development was imperative.MethodsWe used monthly climatic data covering at least a 30-year period and applied two climatic risk indices, the water-budget-based system and the wet–day index, both of verified usefulness for forecasting fascioliasis transmission in this endemic area. To reveal the long-term trends of the climatic factors and forecast indices, we applied procedures of seasonal-trend decomposition based on locally weighed regression and trend analysis on the basis of linear models. To further demonstrate the changes detected, we depicted selected variables in the form of anomalies.ResultsThis study revealed a notorious climatic change affecting most of the hyperendemic area, with a strong impact on crucial aspects of the fascioliasis transmission. Trends in maximum and mean temperatures show significant increases throughout the endemic area, while trends in minimum temperatures are more variable. Precipitation annual trends are negative in most of the localities. Trends in climatic risk indices show negative trends at lower altitudes or when farther from the eastern Andean chain. However, monthly and yearly values of climatic risk indices indicate a permanent transmission feasibility in almost every location.ConclusionsWarmer temperatures have enabled lymnaeids to colonize formerly unsuitable higher altitudes, outside the endemicity area verified in the 1990s. Further, drier conditions might lead to an overexploitation of permanent water collections where lymnaeids inhabit, favoring fascioliasis transmission. Therefore, the present preventive chemotherapy by annual mass treatments is in need to widen the area of implementation. This study emphasizes the convenience for continuous monitoring of nearby zones for quick reaction and appropriate action modification.Graphical

Heatwave Intensifications in Armenia: Evidence From Temporal and Spatial Analysis of Observational Data Over the Last Decades

ABSTRACTIncreasing temperatures cause the weather to become more severe. Studying how heatwaves (HWs) impact individual heat exposure and susceptibility is vital for building climate change mitigation and adaptation measures. So, this study explores the impact of HWs on individual heat exposure and susceptibility. The assessment was based on the highly complex topographic region contribution to HWs in the Republic of Armenia (RA) using data from 16 meteorological stations for the extended warm season (May–September). We developed a regional HW catchment program to estimate the HW indices' responses to climatic change and to present them in terms of topography changes. The Mann–Kendall (MK) test was used to determine the statistical significance of the trends for 10 distinct HW indicators using linear and exponential trends along with graphical interpretations. This study reveals a large‐scale, significant increasing trend in annual maximum temperatures (Tmax) and observed HWs over the period 1955–2019. The rising temperature is accompanied by an increase in HW indices, particularly at low altitudes up to 1250 m above sea level (ASL), where the main population centres and national crop production are concentrated. According to our classification, the above‐mentioned areas have faced extreme and severe increases in HW intensity, covering more than 60% of the country's territory. Moreover, not only the intensity and frequency have been identified but the HW period extension as well. This extreme increase has been found in the low‐lying highly populated and intensively cultivated areas, such as in the Ararat valley. These results have implications for future climate assessments, adaptation strategies, agriculture and public health in Armenia. The development of targeted mitigation measures and adaptation strategies informed by these findings is essential for addressing the escalating challenges posed by HWs in the region.

A study on meteorological characteristics and its consequences on human mortality in Rajkot City, Gujarat

Abstract Background: Climate change presents a crucial threat to human health. The health of human is affected by heat waves and extreme weather events. Hence, the present study was planned to explore weather conditions and their consequences on mortality of human being by accessing the meteorological and mortality data. Methodology: Meteorological data include maximum and minimum temperature, average wind speed, and total rainfall; relative humidity was obtained for January 1, 2017–December 31, 2021, from India Meteorological Department, Pune. Trend analysis of climatic variables was done. Human mortality data were obtained from January 1, 2017, to December 31, 2020, from Health Department, Rajkot Municipal Corporation, Rajkot. The impact of temperature and rainfall on mortality was observed. Results: Upward trend of maximum temperature, rainfall, and relative humidity while downward trend for minimum temperature and average wind speed was observed. Human mortality due to communicable and noncommunicable diseases was not affected by maximum temperature. The effect of rainfall on mortality due to vector-borne diseases was observed. Conclusion: The effect of rainfall on human mortality was observed. Upward and downward trend of climatic variables was not found to be statistically significant. Further study needs to be conducted by analyzing climate data over a longer duration and across a larger geographical area.

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.

Long-term meteorological characteristics and extreme climate indices over Tirupati: a rapidly developing tropical city

Tirupati’s climate has undergone significant changes in both temperature and precipitation patterns. While there has been a consistent increase in rainfall during the southwest monsoon, there is a concerning long-term trend of a decrease in total annual precipitation over the last 30 years. The city has experienced a rise in wet days during both the southwest and northeast monsoons, yet a recent decrease over the past three decades. Heavy precipitation events, particularly during the southwest monsoon, have shown a positive trend, whereas there have been no significant changes in heavy rainfall days during the northeast monsoon. Temperature trends reveal that there has been a warming scenario, with a significant positive trend in annual maximum temperatures and a consistent annual rise in mean minimum temperatures. A substantial decrease in cold and very cold days, especially during the last 30 years, suggests a broader warming trend impacting seasonal temperature variations in Tirupati. These findings highlight the complex interplay of monsoons, temperature variations, and changing precipitation patterns in Tirupati's climate over the years.

Spatial–Temporal Analysis of Impacts of Climate Variability on Maize Yield in Kenya

This study examined the spatial temporal impacts of climate variability on maize yield in Kenya. The maize yield data were obtained from the Kenya Maize Yield Database while climatic variable data were obtained from the Climatic Research Unit gridded Time Series (CRU TS) with a spatial resolution of 0.5° × 0.5°. The non-parametric Mann–Kendall and Sen’s slope tests showed no trend in the data for maximum temperature, minimum temperature and precipitation. The spatial maps patterns highlight the rampancy of wetter areas in the Lake Victoria basin and Highlands East of Rift Valley compared to other regions. Additionally, there is a decreasing trend in the spatial distribution of precipitation in wetter areas and an increasing trend in maximum temperature in dry areas, albeit not statistically significant. Spearman’s rank correlation test showed a strong positive correlation between maize yield and the climatic parameters for the Lake Victoria basin, Highlands East of Rift Valley, Coastal Strip and North Western Regions. The findings suggest that climate variability has a significant impact on maize yield for four out of six climatological zones. We recommend adoption of policies and frameworks that will augment adaptive capacity and build resilience to climatic changes.

Forecasting Maximum Temperature Trends with SARIMAX: A Case Study from Ahmedabad, India

Globalization and industrialization have significantly disturbed the environmental ecosystem, leading to critical challenges such as global warming, extreme weather events, and water scarcity. Forecasting temperature trends is crucial for enhancing the resilience and quality of life in smart sustainable cities, enabling informed decision-making and proactive urban planning. This research specifically targeted Ahmedabad city in India and employed the seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model to forecast temperatures over a ten-year horizon using two decades of real-time temperature data. The stationarity of the dataset was confirmed using an augmented Dickey–Fuller test, and the Akaike information criterion (AIC) method helped identify the optimal seasonal parameters of the model, ensuring a balance between fidelity and prediction accuracy. The model achieved an RMSE of 1.0265, indicating a high accuracy within the typical range for urban temperature forecasting. This robust measure of error underscores the model’s precision in predicting temperature deviations, which is particularly relevant for urban planning and environmental management. The findings provide city planners and policymakers with valuable insights and tools for preempting adverse environmental impacts, marking a significant step towards operational efficiency and enhanced governance in future smart urban ecosystems. Future work may extend the model’s applicability to broader geographical areas and incorporate additional environmental variables to refine predictive accuracy further.

Trends of maximum annual sea surface temperature in the Eastern China Seas

The increasing ocean warming due to climate change significantly threatens regional marine ecosystems by raising the frequency and severity of extreme temperature events. This study examines patterns and trends of maximum annual sea surface temperature (Tmax) in the Eastern China Seas from 1985 to 2022. The results show a significant warming trend in Tmax, exceeding the global average, with notable differences between southern and northern regions. The northern Tmax warming rate is faster, with occurrence times significantly advancing, while the southern Tmax warming rate is slower, with occurrence times significantly delayed. The southern Tmax and its timing are closely correlated with the annual maximum air temperature and its timing. In the north, Tmax timing is influenced by latent heat flux (QLH); a significant increase in August QLH inhibits the continued rise of SST, causing Tmax to advance. The study also highlights a significant increase in marine heatwaves at Tmax timing, with higher Tmax indicating a higher occurrence probability. By elucidating these Tmax trends and dynamics, our study enhances understanding of regional climate impacts, supporting targeted conservation efforts and adaptive ecosystem management strategies in the Eastern China Seas.

Climate trend analysis in the ramis catchment, upper wabi shebelle basin, Ethiopia, using the CMIP6 dataset

Climate change, a global concern, has significant implications for rainfall and temperature patterns. A critical knowledge gap exists in understanding these implications for the Ramis catchment of the Upper Wabi-Shebelle Basin. This research projects future rainfall and temperature patterns in the Ramis catchment of the Upper Wabe-Shebelle basin. The projection utilized a multimodel comprising eight distinct general circulation models from coupled model intercomparison phase six (CMIP6) data simulation. These models were selected based on their performance, considering two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5). The Quantile Mapping (QM) bias correction technique, implemented in R, was used to enhance the reliability of extracted data. The Mann‒Kendall (MK) trend test method was employed to analyze temperature and precipitation trends for two future periods, 2030–2060 and 2061–2090, annually and seasonally. The results suggest a decrease in rainfall during the spring and Winter seasons for 2030 to 2060 and 2061 to 2090 respectively, potentially leading to water scarcity that could impact crop growth and water supply. A decreasing trend in annual rainfall was observed from 2030 to 2060 under SSP2-4.5 and SSP5-8.5 scenarios. The study also uncovered significant and consistent warming trends in maximum and minimum temperatures across the Ramis catchment under both the SSP2-4.5 and SSP5-8.5 scenarios. Rainfall Anomaly Index (RAI) analysis predicts a minor increase in rainfall in the Ramis catchment from 2030 to 2090 under both scenarios, with a notable RAI decrease expected in 2043–2071, indicating potential drought conditions. These findings offer critical insights for regional climate change impact assessments across the Ramis catchment.

Assessing Drought Vulnerability in the Brazilian Atlantic Forest Using High-Frequency Data

This research investigates the exposure of plant species to extreme drought events in the Brazilian Atlantic Forest, employing an extensive dataset collected from 205 automatic weather stations across the region. Meteorological indicators derived from hourly data, encompassing precipitation and maximum and minimum air temperature, were utilized to quantify past, current, and future drought conditions. The dataset, comprising 10,299,236 data points, spans a substantial temporal window and exhibits a modest percentage of missing data. Missing data were excluded from analysis, aligning with the decision to refrain from using imputation methods due to potential bias. Drought quantification involved the computation of the aridity index, the analysis of consecutive hours without precipitation, and the classification of wet and dry days per month. Mann–Kendall trend analysis was applied to assess trends in evapotranspiration and maximum air temperature, considering their significance. The hazard assessment, incorporating environmental factors influencing tree growth dynamics, facilitated the ranking of meteorological indicators to identify regions most exposed to drought events. The results revealed consistent occurrences of extreme rainfall events, indicated by positive outliers in monthly precipitation values. However, significant trends were observed, including an increase in daily maximum temperature and consecutive hours without precipitation, coupled with a decrease in daily precipitation across the Brazilian Atlantic Forest. No significant correlation between vulnerability ranks and weather station latitudes and elevation were found, suggesting that geographical location and elevation do not strongly influence observed dryness trends.

In-depth Exploration of Temperature Trends in Morocco: Combining Traditional Methods of Mann Kendall with Innovative ITA and IPTA Approaches

This study examines trends in minimum and maximum temperatures at various climate stations located in different regions of Morocco for a period of five decades (1970 to 2019). Mann–Kendall, Sen’s estimator, Innovative Trend Analysis (ITA) and Innovative Polygon Trend Analysis (IPTA) were used in the analysis. The results show significant fluctuations, at different time scales, between minimum and maximum temperatures at all stations. In coastal areas, such as Rabat Sale, minimum temperatures fell during January and February while other months saw increases. Average minimum temperatures in Rabat Sale tend to fall by 0.5 °C. On the other hand, maximum temperatures in Rabat Sale rose by 0.2 °C. A decrease of 0.4 °C for Tmin and 1.6 °C for Tmax were observed in higher continental regions, such as Meknes. Other stations, such as Fez Sais (0.6 °C Tmin and 2.6 °C Tmax) and Taza (1.1 °C Tmin and 2.6 °C Tmax) showed an upward trend. Trends also vary, with notable increases in minimum and maximum temperatures, indicating different climatic dynamics according to altitude and locality. In particular, the ITA highlights a significant increase in annual maximum temperatures, with a P-value < 0.05 and trend slopes ranging from 0.0015 °C per year in Rabat Sale to 0.0076 °C per year in Taza. In addition, the IPTA results confirm diversity of upward and downward trends on monthly and seasonal scales, highlighting impact of geographical factors such as proximity to sea, topography, and continentality that contribute to formation of regional microclimates. The results highlight significant impact of climate change in Morocco.

A Bayesian hierarchical spatio-temporal model for summer extreme temperatures in Spain

A statistical study was made of the summer extreme temperatures over peninsular Spain in the last forty years. Records from 158 observatories regularly distributed over Iberia with no missing data were available for the common period from 1981 to 2020. For this purpose, a hierarchical spatio-temporal model with a Gaussian copula and a generalized extreme value parametrization of the extreme events was used. The temporal trend in maximum extreme temperatures was studied making use of both a stationary model and a nonstationary one that takes into account the influence of anthropogenic climate change on extreme temperatures using the global mean temperature as a function of time for the study period. The results led to a better fit of the nonstationary model, with there being a 3.5-fold greater increase in the 20-year return level of the extreme temperatures than in that corresponding to the global mean temperature.

Climate Trends and Wheat Yield in Punjab, Pakistan: Assessing the Change and Impact

Climate change has made weather patterns less predictable, making situations more challenging for farmers throughout the production process. This study investigates the impact of climatic variables (maximum and minimum temperature, rainfall, humidity at 8 AM and 5 PM) and fertilizer application on wheat production in Bahawalnagar district, a major wheat producing region of Punjab, Pakistan. The study utilizes the Mann–Kendall and multiple linear regression analysis to check climatic trends and identify the factors influencing wheat yield from 1991 to 2022. The study utilized a regression model to compare actual and predicted wheat yields. The results showed a decreasing trend in rainfall and an increasing trend in both maximum and minimum temperatures during the wheat growing season. Sen’s slope values for maximum temperature (0.037), minimum temperature (0.007), humidity at 8 AM (0.275), and humidity at 5 PM (0.167) indicate the direction and magnitude of trends. The regression model explained about 92% of the variance in the wheat yield. The regression analysis of humidity at both 8 AM (p = 0.001) and 5 PM (p = 0.001) shows a significant positive correlation with wheat yield. Fertilizer use exhibited a significant positive association with wheat yield (β = 9.58). Fertilizer application for wheat crops increased from 112.4 kg/ha in 1991 to 284.3 kg/ha in 2021. The regression model identifies that the average wheat yield loss from 1991 to 2022 is approximately 0.1208 t/ha per year because of the influence of climatic factors. The study findings underscore the importance of the utilization of adaptive agricultural practices that can ensure food security and improve agricultural sustainability in the region.

Asymmetric warming has different impacts on vegetation growth across various vegetation types on the Tibetan Plateau

ABSTRACT Global warming could affect vegetation growth, while land surface temperature has exhibited an asymmetric warming pattern over the past 50 years. The Tibetan Plateau, known as “the roof of the world,” experiences the surface warming almost twice as high as the global average. However, previous research has largely overlooked the impacts of this asymmetrical warming on vegetation growth and the temporal changes in these impacts. In this study, we assess the effects of asymmetric warming on vegetation growth at regional and different vegetation types by using partial correlation analysis, and reveal the temporal changes of impacts strength using time moving window. The results showed that there had been a significant greening trend (1.01 × 10−3 yr−1, p < 0.01) in the Normalized Difference Vegetation Index (NDVI) during the growing season, as well as significant warming trends in both maximum temperatures (Tmax, 0.0354°C yr−1, p < 0.01) and minimum temperatures (Tmin, 0.0333°C yr−1, p < 0.01) during 2000–2021. Under the background of asymmetric warming, the grassland showed stronger impacts of Tmin than Tmax on NDVI, while the opposite effects were observed for forests. Over time, the response of NDVI to Tmax and Tmin has intensified. This study highlights importance of asymmetric warming in the projection of climate change on vegetation in similarly vulnerable ecosystems worldwide.

Modulated Trends in Arctic Surface Air Temperature Extremes as a Fingerprint of Climate Change

Abstract Strengthened by polar amplification, Arctic warming provides direct evidence for global climate change. This analysis shows how Arctic surface air temperature (SAT) extremes have changed throughout time. Using ERA5, we demonstrate a pan-Arctic (&gt;60°N) significant upward SAT trend of +0.62°C decade−1 since 1979. Due to this warming, the warmest days of each month in the 1980s to 1990s would be considered average today, while the present coldest days would be regarded as normal in the 1980s to 1990s. Over 1979–2021, there was a 2°C (or 7%) reduction of pan-Arctic SAT seasonal cycle, which resulted in warming of the cold SAT extremes by a factor of 2 relative to the SAT trend and dampened trends of the warm SAT extremes by roughly 25%. Since 1979, autumn has seen the strongest increasing trends in daily maximum and minimum temperatures, as well as counts of days with SAT above the 90th percentile and decreasing trends in counts of days with SAT below the 10th percentile, consistent with rapid Arctic sea ice decline and enhanced air–ocean heat fluxes. The modulated SAT seasonal signal has a significant impact on the timing of extremely strong monthly cold and warm spells. The dampening of the SAT seasonal fluctuations is likely to continue to increase as more sea ice melts and upper-ocean warming persists. As a result, the Arctic winter cold SAT extremes may continue to exhibit a faster rate of change than that of the summer warm SAT extremes as the Arctic continues to warm. Significance Statement As a result of global warming, the Arctic Ocean’s sea ice is receding, exposing more and more areas to air–sea interactions. This reduces the range of seasonal changes in Arctic surface air temperatures (SAT). Since 1979, the reduced seasonal SAT signal has decreased the trend of warm SAT extremes by 25% over the background warming trend and doubled the trend of cold SAT extremes relative to SAT trends. A substantial number of warm and cold spells would not have been identified as exceptional if the reduction of the Arctic SAT seasonal amplitudes had not been taken into account. As the Arctic continues to warm and sea ice continues to diminish, seasonal SAT fluctuations will become more dampened, with the rate of decreasing winter SAT extremes exceeding the rate of increasing summer SAT extremes.

The Relationship between Climate, Agriculture and Land Cover in Matopiba, Brazil (1985–2020)

Climate change has been at the forefront of discussions in the scientific, economic, political, and public spheres. This study aims to analyze the impacts of climate change in the Matopiba region, assessing its relationship with land cover and land use, soybean crop production and yield, and ocean–atmosphere anomalies from 1985 to 2020. The analysis was conducted in four parts: (1) trends in annual and intra-annual climate changes, (2) the spatiotemporal dynamics of land cover and use, (3) the spatiotemporal dynamics of soybean production and yield, and (4) the relationship between climate change, agricultural practices, land cover and use, and ocean–atmosphere anomalies. Statistical analyses, including Mann–Kendall trend tests and Pearson correlation, were applied to understand these relationships comprehensively. The results indicate significant land cover and use changes over 35 years in Matopiba, with municipalities showing increasing soybean production and yield trends. There is a rising trend in annual and intra-annual maximum temperatures, alongside a decreasing trend in annual precipitation in the region. Intra-annual climate trends provide more specific insights for agricultural calendar planning. No correlation was found between the climate change trends and soybean production and yield in the evaluated data attributed to genetic and technological improvements in the region. The North Atlantic Ocean shows a positive correlation with soybean agricultural variables. Evidence suggests soybean production and yield growth under climate change scenarios. This study highlights soybeans’ adaptation and climate resilience in the Matopiba region, providing valuable insights for regional agricultural development and contributing to further research in environmental, water-related, social, and economic areas of global interest.

Variability and Changes in Climate in Northern Uganda

Variability and changes in climate are generally expected to occur. However, there remain gaps on dynamics of expected regional variations in climatic changes. This study assessed historic and projected climatic conditions up to the year 2033. The study hypothesized that temperature rather than rainfall significantly increased for the period 1980-2010 and rainfall rather than temperature is likely to decrease significantly by 2033 for Gulu District in northern Uganda. To determine historic climatic trends, rainfall and temperature data were obtained from Uganda National Meteorological Authority (UNMA) while for future climate, the PRECIS (Providing Regional Climates for Impact Studies) modelled data based on projected conditions at a 50 km spatial resolution was used. These data sets were subjected to trend analysis and the differences in means were detected at a 95% confidence level. Contrary to the evidences from other empirical studies, results generally indicated decreasing rainfall for the period 1980-2010. However, the decrease was not significant (P &gt; 0.05) while both historic mean annual maximum and minimum temperature trends showed a statistically significant increase (P&lt;0.05). Projections for 2033 reveal a significant decrease in rainfall (P &lt; 0.05) while both maximum and minimum temperature will remain quasi uniform

Understanding climate change dynamics in the Godavari middle sub-basin using parametric and non-parametric models

Climate change is considered a long-term change in precipitation, temperature and other meteorological variables. The pattern of meteorological variables is changing due to anthropogenic activities globally. Climate change has posed threat to natural and human systems. Thus, assessing and forecasting climate variability have become imperative for making resources sustainable and society resilient. This study examined trend and forecasted climate change using parametric and non-parametric methods in the Godavari Middle Sub-basin, India. Mann-Kendall and Sen's slope methods were utilized to analyze trend and magnitude of meteorological variables such as rainfall, maximum and minimum temperature, mean wind speed, mean evaporation and relative humidity. Forecasting of meteorological variables was carried out using the autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) models. Increasing trend in maximum and minimum temperature was observed at various level of significance. Decreasing trend was observed in mean evaporation at 0.05 level of significance. Decreasing trend in wind speed was also recorded in the sub-basin. February, March, April, June, October and December have shown increasing trend in relative humidity. Total monthly rainfall has shown decreasing trend in the south-eastern part of the sub-basin. Forecast of meteorological variables have also shown decrease in rainfall, increase in maximum and minimum temperature during 2017–2027 creating the sub-basin more prone to dry climate condition. Thus, a policy intervention-oriented climate action plan for lessening the impact of climate change is required in the sub-basin.