Mean Minimum Temperature Research Articles

Observed time series trend analysis of climate variability and smallholder adoption of new agricultural technologies in west Shewa, Ethiopia

This study was aimed at analyzing time-series trends and variability analysis of observed rainfall and temperature records from 16 stations during the 1985–2015 period. Besides, it focused on the smallholders’ perceptions, including the analysis of their response strategies to both observed and perceived climate variability and its determinants. Both purposive and random sampling procedures were used. Mixed methods (quantitative and qualitative) of the research were used. The sample household survey method was used to collect primary data from smallholder farmers, while key informant interviews were used to gather qualitative data from the experts. A semi-structured questionnaire was used to collect quantitative data, whereas a discussion guide checklist was used to gather qualitative data. A total of 397 respondents and 24 key informants were used as sources of primary data. In addition, visits to the National Meteorology Agency of Ethiopia were made to collect recorded climate data. Descriptive statistics and econometric models were used to analyze quantitative data, whereas thematic categorization was used to analyze and report qualitative data. The results show that both the summer and annual rainfall have an increasing trend but are not statistically significant. With regards to variability, low to moderate levels of variability were recorded according to the seasons and annual rainfall. The result of the Mann-Kendall test shows that the mean minimum temperature was raised by 0.05 °C while the maximum temperature rose by 0.03 °C/30 years. The monthly maximum temperature also shows an increasing trend, with the lowest record during August (22.05 °C) and the highest in March (26.49 °C. Besides, smallholder farmers’ perceptions were in accordance with recorded station data. In response to both observed and perceived climate variability and its extremes, smallholder farmers in the study area were striving to adopt different adaptation strategies. Rain variability, frequency of extension contact, years of farming experience, access to institutions and infrastructure, wealth status, market access, and the number of active labor force within the household all hampered smallholder farmers' efforts to implement potential measures. In contrast, the high prices of chemicals and improved seed, access to credit services, land size, and limited access to accurate and timely early warning information all negatively and significantly influenced the adaptation decisions of smallholder farmers in the area. Therefore, the impacts of abnormal trends or shifts in observed rainfall and temperature patterns, along with socio-economic, technological, and behavioral factors, call for policymakers to set strategies that will enable smallholders to improve their livelihoods, ensure food security, and build resilience in the face of climatic extremes.

LONG TERM CLIMATE CHANGE ASSESSMENT DURING 1907 - 2015 IN THE KUMAUN HIMALAYA REGION, UTTARAKHAND

Climate change is considered as a significant man-made global environmental challenge currently faced by the mankind today. The warming has a direct impact on the temperature sensitive snow and ice cover, geo-hazards and water supply. Objective of this study is assess the long term climate change assessment in the Kumaun Himalaya region. Monthly temperature and rainfall IMD data of years from 1907 to 2015 has been processed in the study. Monthly trend and statistical significance of rainfall and temperature were estimated using Mann-Kendall (MK) Test and the Sen’s Slope Estimator. The results show that monthly, annual and seasonal air temperature has increasing trend at significant level during the past 110 years, as against the global average of 0.5°C. The overall monthly and annual precipitation has decreasing trend at significant level in all weather station under study except Mukteshwar. Annual mean minimum temperature of the Kumaun Himalaya region showed a very significant increasing trend and has increased by 1.026°C per 110 years during the period 1907-2015. This warming is mainly contributed by the winter and post-monsoon seasons, which have shown increasing trend at significant level as compared to other seasons in the last century. The rainfall over the region showing significant decreasing trend in monthly rainfall over the time In monsoon season, rainfall is showing decreasing trend at significant level all over the Kumaun region but winter, pre monsoon and post monsoon season showing high degree of variability in trend and magnitude of rainfall.

Spatial Distribution, Seasonal Abundance and Physio-Chemical Assessment of Mosquito Larval Breeding Sites in Mardan District, Khyber Pakhtunkhwa, Pakistan.

Mosquitoes (Diptera: Culicidae) are haemotophagus insects and are vectors of many arthropod-borne diseases. Present study aimed to explore species composition, seasonal abundance, spatial distribution and physio-chemical properties of larval breeding sites of mosquitoes in District Mardan, Khyber Pakhtunkhwa, Pakistan. Both adults and larvae of mosquitoes were collected through light traps, insecticide spray, mouth aspirator and larval standard dipping method in District Mardan from May to November 2017. Water samples from larval sites were physio-chemically analysed. 5078 (3704 adults and 1374 larvae) mosquito specimens were collected in Mardan, Katlang and Takhtbhai tehsils. Six species in four genera were reported. Culex pipiens (89.80%) and Armigeres subalbatus (9.20%) were the most abundant species. Diversity was high in Takhtbhai (0.29) followed by Katlang (0.28) and Mardan (0.25). Greater number of specimens were recorded in peridomestic sites (93.97%) as compared to domestic habitats (6.03%). Culex pipiens larval abundance had negative correlation with pH whereas it correlated positively with electric conductivity, salinity, and TDS (total dissolved sulphur). Mosquito abundance peaked in August and July while the lowest was in May. Their monthly abundance had positive correlation with rainfall (r= 0.5069), relative humidity (r= 0.4439) and mean minimum temperature (r= 0.2866). Number of mosquitoes was highest at low elevation < 347m asl (above sea level) in agriculture land and near to water bodies (streams). Culex pipiens being the most abundant species, was susceptible to high pH. Mosquitoes preferred habitats were at low elevation in agriculture land.

Spatial Homogenization Adjustment and Application of Weather Station Networks in Xinjiang, China

In this study, we define the S0 value (buffer zone area centred on a meteorological station) and two inhomogeneity measurement parameters, the station domain area and station network density, for 89 weather stations in the Xinjiang region, and we construct the weight coefficient of the station network according to the station domain area. Applying the weight coefficient, we calculate the mean temperature, maximum temperature, and minimum temperature in January, April, July, October, and annually in the Xinjiang region from 1961 to 2021. The results show that the S0 value of 200,000 km2 is suitable for determining the weight coefficient of the station network in the Xinjiang region. The two measurement parameters can quantitatively reflect the inhomogeneity of the distribution of 89 weather stations in the Xinjiang region. The spatial distribution density of the station network is positively proportional to the station network density and inversely proportional to the station domain area and weight coefficient of the stations. The equal-weighted average is lower than the spatially homogenized revised average, which underestimates the mean temperature in the Xinjiang region, and the spatially homogenized revised average better reflects the real temperature in the Xinjiang region. The annual and monthly mean temperatures, maximum and minimum temperatures calculated by the spatially homogenized revised average, and the equal-weighted average have the same upwards trend, and the mean temperature warming trend calculated by the two methods have differences, but the differences are not significant. The annual, January, April, July, and October minimum temperature warming trends according to the spatial homogenization revised average are greater than the maximum temperature warming trend and the mean temperature warming trend, and the annual minimum temperature warming trend is 3.3 times the annual maximum warming trend and two times the annual mean temperature.

Spatial–Temporal Pattern and Influencing Factors of Vegetation Phenology and Net Primary Productivity in the Qilian Mountains of Northwest China

Understanding how vegetation growth responds to climate change is a critical requirement for predicting future ecosystem dynamics. Global warming causes significant changes in the vegetation characteristics of mountain ecosystems, particularly affecting vegetation phenology and net primary productivity (NPP). The Qilian Mountains are located in an arid and semiarid region, and the mechanisms of vegetation phenology in response to climate change still need to be further explored. We used MODIS data (2001–2020) to extract vegetation phenology and NPP, quantitatively evaluated their spatial–temporal dynamics, and analyzed the response mechanism of vegetation phenology–climate and vegetation phenology–NPP combined with meteorological data. The results showed that from southeast to northwest, the vegetation phenology changes significantly with the change in vegetation type, with SOS (start of the growing season) advancing at a rate of −0.415 d/year, EOS (end of the growing season) and LOS (length of the growing season) delaying at a rate of 0.20 d/year and 0.374 d/year, respectively, and NPP continues to increase. There was also an elevation gradient effect, with SOS delayed by 15.6 d/km, EOS advanced by 12.02 d/km and LOS shortened by 19.24 d/km. We found that the preseason temperature and SPEI (standardized precipitation evapotranspiration index) have a strong influence on the SOS and EOS, with the mean minimum temperature being the most significant and requiring attention, while the influence of precipitation cannot be ignored. We also found that the vegetation phenology is closely related to NPP, and SOS has the most significant effect. This study will provide a scientific basis for the response mechanisms of vegetation phenology in arid and semiarid regions under climate change. It will provide a reference for the implementation of effective ecosystem management.

INDUCED PASSIVE HEATING ON THE EMERGENCE, EARLY GROWTH AND PHOTOCHEMICAL RESPONSES OF SEEDLINGS OF NATIVE MAIZE (ZEA MAYS L.) GENOTYPES FROM WARM-DRY, TEMPERATE, AND HOT AND HUMID CLIMATES

The majority of research on the effects of climate change on maize has concentrated on yield, with only a few studies focusing on seedling emergence and growth. Warmer temperatures predicted as a result of climate change will have an impact on seedlings emergence and growth. An experiment was carried out with induced passive heat with the objective of simulating the increase in temperature in emergence, initial growth and photosynthetical parameters of native genotypes of maize from three different agro-ecological zones (warm-dry, temperate, and hot and humid) climates of San Luis Potosí, México. Two different environments, Open Top Chambers (OTC) and control, were used as treatment and two genotypes for each agro-ecological zone were used. A total of 100 seeds were used in a random design with factorial arrangement for each genotype and environment (OTC and control). Abiotic variables (mean daily temperature, minimum and maximum daily temperature and the accumulated heat units) were determined and compared between the two environments and confirmed that the OTC increased temperatures and heat units. The percentage and velocity of seedling emergence and photochemical quenching (qP) were negatively affected by the effect of induced heat while the rate of growth of plants was accelareted and its plant height was increased. These results reveal dependence on the adaptation of the native genotypes. According to seedling emergence and growth, genotypes from less stress conditions (hot and humid (Huasteca)) were most affected while those from places with major variations in temperature condicions (warm-dry and temperate (Altiplano and Media respectively)) were not. We concluded the effect of induced heat diminish the seedling emergence and photochemical quenching while the growth benefited.&#x0D; Keywords: Climate change; Heat; Open top chamber; Temperature; Zea mays seedling

Variability in the minimum temperature over two centuries in the overlap region between the fringe of the Asian westerly region and the temperate continental-monsoon climate transition zone.

The overlap region between the eastern fringe of the Asian westerly region and the temperate continental-monsoon climate transition zone is sensitive to climate changes and is characterized by fragile ecosystems. Uncovering the long-term historical climate variability patterns in this region is necessary. A standardized tree-ring width chronology was constructed based on the tree-ring samples collected from four representative tree species in four typical areas in the overlap region, and the 203- to 343-year annual mean minimum temperature series in the overlap region were reconstructed. The reconstructed series overlapped well with extreme climate events and low-temperature periods recorded in historical data. Therefore, the reconstructed model is stable and reliable. As suggested by the reconstructed series, the annual average minimum temperature in the overlap region changes sharply from east to west, and the periodicity change in the overlap region shows a trend of gradually weakening from the east and west ends to the middle. In the nineteenth century, the high-latitude area was in the high-temperature period, and the entire overlap region experienced significant low-temperature periods lasting 20-45years until the 1950s. The western part had an earlier low-temperature period start time, a longer cooling duration, and a slower cooling rate than the central part. The overlap region experienced a significant warming period in approximately the last half-century, with temperatures increasing faster in the western and eastern parts than in the central part. The temperature variability in the overlap region was more intense in the last two centuries, with shorter periodicities and a larger proportion of cold periods. The central and western parts of the Asian westerly region, the mid- to high-latitude regions of the transition zone, and the overlap region experienced significant low-temperature periods or drastic cooling trends (the Little Ice Age) in the first half of the nineteenth century and significant warming trends afterwards due to global warming. The influences of these changes may have been exacerbated by the westerly circulation. The results of this study provide new insights into the use of dendroclimatology to extract temperature series in the Asian westerly region and the transition zone and a reference for research on global climate change.

Influence of Different Spacing and Organic Manure on the Yield and Economics of Groundnut (Arachis hypogea L.)

Aim: The trail was taken to find out the influence of different spacing and organic manure on the yield and economics of the groundnut (Arachis hypogea L.)&#x0D; Study Design: Factorial randomized block design.&#x0D; Place and Duration of Study: The experiment was taken during the kharif season of 2021 at G.H. Raisoni University at Agronomy farm, Chhindwara, M.P.&#x0D; Methodology: The set of nine treatment combination consist of three different spacing and three different OM which is applied at 12 kg/ha of FYM, at 6 kg/ha VC and at 6 kg/ha of PM. The experiment was carried out in Factorial Randomized Block Design with three replication and the treatment combination is T1: S1O1 (30 cm x 10 cm + FYM), T2: S1O2 (30 cm x 10 cm + VC), T3: S1O3 (30 cm x 10 cm + PM), T4: S2O1 (30 cm x 15 cm + FYM), T5: S2O2 (30 cm x 15 cm +VC), T6: S2O3 (30 cm x 15 cm + PM), T7: S3O1 (45 cm x 10 cm + FYM), T8: S3O2 (45 cm x 10 cm + VC) and T9: S3O3 (45 cm x 10 cm + PM) and total number of plots is 27. At the time of experiment, the maximum mean of temperature was 41.60C whereas the minimum mean temperature was 5.680C. The economics of groundnut crop was calculated as per the fundamental market prices of the input and produced during the kharif season 2021.&#x0D; Results: Maximum no. of pods/plant (30.67), length of pod (2.72), no. of kernels/pod (2.07), seed index (43.22 g), shelling percentage (74.76 %), pod weight/plant (18.66 g), pod yield/plot (1.53 kg), pod yield (63.01 kg/ha), kernel yield (23.45 kg/ha), haulm yield (39.56 kg/ha) and harvest index (24.07 %) were recorded with T3–30 cm x 10 cm with Poultry manure at 30, 60 and 90 DAS.&#x0D; Conclusion: Based on this experiment, it was said that with spacing 30cm x 10cm in combination with poultry manure gives the maximum and significantly higher kernel yield (23.45 kg/ha), gross return Rs.1,72,022.00/ha) as well as highest net return Rs.99,367.00)/ha).

Assessing the impact of climate change on the agricultural economy in Thailand: an empirical study using panel data analysis.

This study estimates the impact of climate change on the economic growth of the agricultural sector and its variability using a panel dataset from 1995 to 2019 for 76 provinces in Thailand. The panel data analysis consists of unit root tests for identifying stationary characteristics, autoregressive distributed lag (ARDL) bounds for analyzing cointegration, and pool mean group (PMG) estimation for detecting long-run and short-run effects. The cointegration results indicate the existence of long-run equilibrium in the agricultural economy and its variability to climatic and non-climatic variables. Results from the PMG estimation suggest that extreme weather events have a negative impact on the agricultural economy, but increased total rainfall has a positive association with the agricultural economy. The increases in mean average and mean minimum temperatures will reduce the variability of agricultural growth. The obtained results suggest that the productivity of agricultural households and water resources increases the agricultural revenue and reduces its variability for long-term development in the agricultural sector of Thailand.

Spatial Patterns and Magnitudes of Agriculturally Pertinent Climate Indicators across Agroecosystems in Türkiye

Abstract This research utilized NASA–Prediction of Worldwide Energy Resource (POWER) datasets to quantify and map long-term annual and growing-season mean maximum, minimum, and mean air temperatures (Tmax, Tmin, and Tavg); diurnal temperature range (DTR); growing degree-days (thermal unit) (GDD); seasonal total precipitation; mean daily precipitation; incoming shortwave radiation Rs; relative humidity (RH); wind speed u2; saturation and actual vapor pressures (es and ea); vapor pressure deficit (VPD); grass- and alfalfa-reference evapotranspiration (ETo and ETr); and aridity index (AI) over the nine agricultural zones of Türkiye (Turkey). In addition to the latitudinal influence, the influence of continentality and oceanity effects and physiographic features were evident in the spatial patterns of all climate indicators. Tmin was the most spatially variable indicator at both scales, which can have significant impact on nighttime respiration, potentially reducing agroecosystem productivity. At the annual scale, climate indicators that showed coefficient of variation (CV) greater than 0.20 were GDD, Tavg, VPD, AI, and ea. Growing-season mean indicators with CV &gt; 0.20 were GDD, AI, VPD, total precipitation, mean daily precipitation, and ea. The Rs showed the least spatial variability at both scales. Annual-scale mean CV (0.21) was 7% greater than that the growing season (0.19). To the best of our knowledge, the analyses, information, and resources presented here are the first to quantify agricultural-zone-specific climate indicators during the growing season in Türkiye and are invaluable for decision-making on issues at the intersection of meteorology, agriculture, water resources, and hydrology.

Analysis of the annual pollen integral in Albuquerque, New Mexico, shows a negative trend with temperatures for Juniper, Cottonwood, Elm, and Mulberry

The goal of this study is to determine if the annual pollen integral (APIn) for the top tree allergens in the City of Albuquerque is correlated with meteorological variables. This analysis would be the first of its kind for this area. We used 17 consecutive years from 2004 to 2020 and data collected by the city of Albuquerque using a Spore Trap (Burkard) volumetric air sampler in a location designed to represent a typical desert environment. The pollen studied include Juniper, Elm, Ash, Cottonwood, and Mulberry. We found a negative linear correlation with early summer temperatures of the previous year and APIn for Elm, Cottonwood, and Mulberry, and early fall temperatures for Juniper. Linear regression models developed for Elm, Cottonwood, and Mulberry used the monthly mean maximum temperature for the month of June of the prior year as the independent variable to yield a R squared statistic (R2) of 0.88, 0.91 and 0.78, respectively. For Juniper, the average monthly mean minimum temperature for the previous September and October served as the independent variable and yielded the R2 value of 0.80. We also observed a positive trend for the annual maximum temperature over time and a negative trend for the total APIn. Summers in New Mexico are hot and dry, and they may be getting hotter and drier because of climate change. Our analysis predicts that climate change in this area may lead to reduced allergies if temperatures continue to increase and if precipitation patterns remain the same.

Predicting Modified Fournier Index by Using Artificial Neural Network in Central Europe.

The Modified Fournier Index (MFI) is one of the indices that can assess the erosivity of rainfall. However, the implementation of the artificial neural network (ANN) for the prediction of the MFI is still rare. In this research, climate data (monthly and yearly precipitation (pi, Ptotal) (mm), daily maximum precipitation (Pd-max) (mm), monthly mean temperature (Tavg) (°C), daily maximum mean temperature (Td-max) (°C), and daily minimum mean temperature (Td-min) (°C)) were collected from three stations in Hungary (Budapest, Debrecen, and Pécs) between 1901 and 2020. The MFI was calculated, and then, the performance of two ANNs (multilayer perceptron (MLP) and radial basis function (RBF)) in predicting the MFI was evaluated under four scenarios. The average MFI values were between 66.30 ± 15.40 (low erosivity) in Debrecen and 75.39 ± 15.39 (low erosivity) in Pecs. The prediction of the MFI by using MLP was good (NSEBudapest(SC3) = 0.71, NSEPécs(SC2) = 0.69). Additionally, the performance of RBF was accurate (NSEDebrecen(SC4) = 0.68, NSEPécs(SC3) = 0.73). However, the correlation coefficient between the observed MFI and the predicted one ranged between 0.83 (Budapest (SC2-MLP)) and 0.86 (Pécs (SC3-RBF)). Interestingly, the statistical analyses promoted SC2 (Pd-max + pi + Ptotal) and SC4 (Ptotal + Tavg + Td-max + Td-min) as the best scenarios for predicting MFI by using the ANN–MLP and ANN–RBF, respectively. However, the sensitivity analysis highlighted that Ptotal, pi, and Td-min had the highest relative importance in the prediction process. The output of this research promoted the ANN (MLP and RBF) as an effective tool for predicting rainfall erosivity in Central Europe.

Herbarium specimens provide reliable estimates of phenological responses to climate at unparalleled taxonomic and spatiotemporal scales

Understanding the effects of climate change on the phenological structure of plant communities will require measuring variation in sensitivity among thousands of co‐occurring species across regions. Herbarium collections provide vast resources with which to do this, but may also exhibit biases as sources of phenological data. Despite general recognition of these caveats, validation of herbarium‐based estimates of phenological sensitivity against estimates obtained using field observations remains rare and limited in scope. Here, we leveraged extensive datasets of herbarium specimens and of field observations from the USA National Phenology Network for 21 species in the United States and, for each species, compared herbarium‐ and field‐based estimates of peak flowering dates expected under standardized temperature conditions, and of sensitivity of peak flowering time to geographic and interannual variation in mean minimum temperatures (TMIN). We found strong agreement between herbarium‐ and field‐based estimates for standardized peak flowering time (r = 0.91, p &lt; 0.001) and for the direction and magnitude of sensitivity to both geographic TMIN variation (r = 0.88, p &lt; 0.001) and interannual TMIN variation (r = 0.82, p &lt; 0.001). This agreement was robust to substantial differences between datasets in 1) the long‐term TMIN conditions observed among collection and phenological monitoring sites and 2) the interannual TMIN conditions observed in the time periods encompassed by both datasets for most species. Our results show that herbarium‐based sensitivity estimates are reliable among species spanning a wide diversity of life histories and biomes, demonstrating their utility in a broad range of ecological contexts, and underscoring the potential of herbarium collections to enable phenoclimatic analysis at taxonomic and spatiotemporal scales not yet captured by observational data.

Adjusting sowing date improves the photosynthetic capacity and grain yield by optimizing temperature condition around flowering of summer maize in the North China Plain.

Adjusting the sowing date to optimize temperature conditions is a helpful strategy for mitigating the adverse impact of high temperature on summer maize growth in the North China Plain (NCP). However, the physiological processes of variation in summer maize yield with sowing date-associated changes in temperature conditions around flowering remain to be poorly understood. In this study, field experiments with two maize varieties and three sowing dates (early sowing date, SD1, 21 May; conventional sowing date, SD2, 10 June; delay sowing date, SD3, 30 June) were conducted at Xinxiang of Henan Province in 2019 and 2020. Early sowing markedly decreased the daily mean temperature (Tmean), maximum temperature (Tmax), and minimum temperature (Tmin) during pre-silking, while delay sowing markedly decreased those temperatures during post-silking. Under these temperature conditions, both varieties under SD1 at 12-leaf stage (V12) and silking stage (R1) while under SD3 at R1 and milking stage (R3) possessed significantly lower malondialdehyde (MDA) content in leaf due to higher activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) compared to SD2. Therefore, SD1 at V12 and R1 stages and SD3 at R1 and R3 stages for both varieties showed significantly higher photosynthetic capacity, including higher SPAD, Fv/Fm, Pn, Tr, and Gs, which promoted greater pre-silking dry matter (DM) accumulation for SD1 to increase the kernel number, and promoted greater post-silking DM accumulation for SD3 to increase the kernel weight, eventually increased the grain yield of SD1 and SD3 compared to SD2. Results of regression analysis demonstrated that Tmean, Tmax, and Tmin values from V12 to R1 stages lower than 26.6, 32.5, and 20.3°C are necessary for improving the kernel number, while Tmean, Tmax, Tmin, and accumulated temperature (AT) values from R1 to R3 stages lower than 23.2, 28.9, 17.3, and 288.6°C are necessary for improving the kernel weight. Overall, optimal temperature conditions around flowering can be obtained by early (21 May) or delay (30 June) sowing to improve the kernel number or kernel weight due to improved photosynthetic capacity, eventually increasing the grain yield of summer maize in the NCP.

The Correlation between Rainfall, Temperature, Relative Humidity, and Rice Field Productivity in Aceh Besar

Various factors could affect rice field productivity, such as climate, management practices, and soil properties. Aceh Besar had experienced long drought, higher temperature, shifted seasons, and the decrease yield of rice productivity due to climate change. This research aimed to analyze the correlation between climate variables and rice field productivity, such as rainfall and mean, minimum, and maximum temperatures, relative humidity in Aceh Besar District. The monthly climate data and the rice field productivity data were employed for 10 (2011-2020) and 8 (2011-2018) consecutive years, respectively. The correlation between the climate variables were calculated using Pearson coefficient correlation. The results showed that rainfall and maximum temperature were positively correlated, as well as rainfall and relative humidity. In contrary, rainfall and mean temperature, rainfall and minimum temperature, rainfall and rice field productivity were negatively correlated. The latest indicating that rainfall did not impact the rice field productivity in Aceh Besar. It was quite contradictive to the reality in the field that significantly experiencing the long drought, higher temperature, shifted seasons and the decrease yield of rice field productivity. This was due to the lack of climate data employed that required longer period preferably 30 to 50 years which was not available.

Effects of Future Climate Change on Citrus Quality and Yield in China

As the world’s most widely cultivated fruit, citrus in China is increasingly suffering from ongoing climate change, which affects the sustainability of agricultural systems and social economy. In this study, we linked climate factors to citrus quality and yield and established projection models to elucidate the impact of future climate change. Then, we used the ensemble mean of 19 Coupled Model Intercomparison Project 6 (CMIP6) models to project the 2021–2040 and 2041–2060 climate changes relative to the historical baseline 1995–2014 period under different shared socioeconomic pathways scenarios (SSP2-4.5, SSP5-8.5). The results show that the monthly mean diurnal temperature range in July had the greatest influence on quality, and monthly mean temperature in October, monthly mean relative humidity in October, monthly mean minimum temperature in November and monthly mean maximum temperature in September had the greatest influence on yield at the growth and ripening stages. Moreover, the quality and yield of citrus present different characteristics in terms of change in cultivation areas in the future. The quality of Sichuan, Zhejiang and Fujian Provinces in China will become significantly better, however, Hubei, Guangdong and Guangxi Provinces it will become worse. Surprisingly, yield will increase in all plantations due to future suitable climate conditions for citrus growth and ripening.

Аssessment of the influence of physical factors of the external environment on the heat feeling of a human in the conditions of Kamchatka

OBJECTIVE: Characterize the climate impact on the population of Petropavlovsk-Kamchatsky regarding warmth sensation in different periods of the year.MATERIALS AND METHODS: The length of a warm and cold period of the year was determined. Based on mean monthly temperatures, relative humidity, wind speed, maximum wind and minimum temperature, taken from the website of weather condition records in Petropavlovsk-Kamchatsky for 2010–2020, the equivalent-effective temperature (EET) has been calculated that allows to estimate the warmth sensation of a man, stripped to the waist.RESULTS AND DISCUSSION: According to the average temperature, the warm period is in June-September, the cold one is 8 months. As for EET, thermal comfort is in July-August; it’s cool in June and September; it’s cool/moderately cool in October; it’s moderately cool in April-May and November; it’s very chilly in December-March. Speaking about minimum temperature and maximum wind, it’s warm in August, warm/cold in September; 10 months is a cold period of the year. By comparing EET, it’s cool in July and August; it’s moderately cool in May, June, September and October; it’s very chilly in April and November, moderately cold in December and March and cold in January. In February conditions are moderately cold/cold.CONCLUSION: Amidst high humidity the wind produces a meaningful effect on a bioclimatic discomfort.

Verifying the Theory of Climate Affecting Lethality of COVID-19 by an Analysis in Two Climatic Zones of Chile

Introduction: The study of seasonal influences on the COVID-19 pandemic can take advantage of the unique position of Chile and its different climatic profiles in the north-south extension. The purpose is to verify the influence of seasonal climate changes on the COVID-19 in the temperate and sub-arctic areas of Chile. Methods: We monitored the evolution of CFR in temperate versus sub-boreal regions, reporting from the John Hopkins University COVID-19 Center on the CFR in each province in midwinter, spring, and early summer. Results: CFR worsened from mid-winter to mid-spring in the temperate zone of Chile, while in the sub-boreal area the CFR improves in the same period, (Kruskal Wallis Test, p=0.004). In the temperate zone after the increase in late winter-early spring, CRF tends to stabilize; on the contrary in the sub-boreal zone, there is a more marked tendency to worsen the CFR at the same time (Kruskal Wallis Test, p=0.010). The temperate zone of Chile shows a CFR increasing until spring-like temperate Europe, unlike Europe CFR does not decrease in summer, but the mean minimum temperature in temperate Chile is lower in summer than in temperate Europe. In Patagonian, CFR remains stable or drops from winter to spring but increases in early summer. Conclusion: The temperate and sub-boreal zones of Chile have a markedly different CFR variation profile during the COVID-19 pandemic.

Climate Change Prediction Using ARIMA Model

Abstract: It is a challenging task to forecast weather data accurately. The temperature change has important implications for business and economic activity. Effective management of global climate change impacts will depend upon accurate and costeffective forecasts. This paper univariate statistic techniques to model the properties of a world mean temperature dataset to develop a parsimonious forecasting model for managerial decision-making over the short-term horizon and the ARIMAbased prognostication tool has been developed by implementing the ARIMA algorithm in python. Although the model is estimated on global temperature data, the methodology could even be applied to temperature data at more localized levels. The statistical techniques include seasonal and non-seasonal unit root testing with and without structural breaks as well as ARIMA and SARIMA modelling. This paper helps us to predict the air temperature, which is the main problem of global warming. Prediction of the likely impact of climate change on monthly mean maximum and minimum temperature in Tamilnadu. Time-series techniques to develop a parsimonious model of global mean temperature change that can be used to forecast over the short-term horizon (5- 10) years. Keywords: Global warming, Forecasting, temperature

How Well Does the ERA5 Reanalysis Capture the Extreme Climate Events Over China? Part II: Extreme Temperature

The fifth-generation atmospheric reanalysis of the European Center for Medium-Range Weather Forecasts (ERA5) is the latest reanalysis product. However, the reliability of ERA5 to capture extreme temperatures is still unclear over China. Hence, based on conventional meteorological station data, a new criterion (DISO) was used to validate the ERA5 capturing extreme temperature indices derived from the Expert Team on Climate Change Detection and Indices (ETCCDI) across the six subregions of China on different timescales. The conclusions are as follows: the original daily temperatures (mean temperature, maximum temperature, and minimum temperature) can be well reproduced by ERA5 reanalysis over China. ERA5 tends to exhibit more misdetection for the duration of extreme temperature events than extreme temperature intensity and frequency. In addition, ERA5 performed best in the summer and worst in the winter, respectively. The trend of absolute indices (e.g., TXx and TNx), percentile-based indices (e.g., TX90p, TX10p, TN90p, and TN10p), and duration indices (e.g., WSDI, CSDI, and GSL) can be captured by ERA5, but ERA5 failed to capture the tendency of the diurnal temperature range (DTR) over China. Spatially, ERA5 performs well in southeastern China. However, it remains challenging to accurately recreate the extreme temperature events in the Tibetan Plateau. The elevation difference between the station and ERA5 grid point contributes to the main bias of reanalysis temperatures. The accuracy of ERA5 decreases with the increase in elevation discrepancy.