Spatial Distribution of, and Variations in, Cold Regions in China from 1961 to 2019

In this study, on the basis of the temperature data collected at 612 meteorological stations in China from 1961 to 2019, cold regions were defined using three indicators: an average temperature of <−3.0 °C during the coldest month; less than five months with an average temperature of >10 °C; and an annual average temperature of ≤5 °C. Spatial interpolation, spatial superposition, a trend analysis, and a spatial similarity analysis were used to obtain the spatial distribution of the cold regions in China from 1961 to 2019. Then, the areas of the cold regions and the spatial change characteristics were analyzed. The results reveal that the average area of the cold regions in China from 1961 to 2019 was about 427.70 × 104 km2, accounting for about 44.5% of the total land area. The rate of change of the area of the cold regions from 1961 to 2019 was −14.272 × 104 km2/10 a, exhibiting a very significant decreasing trend. On the basis of the changes between 1991–2019 and 1961–1990, the area of China’s cold regions decreased by 49.32 × 104 km2. The findings of this study provide references for studying changes in the natural environment due to climate change, as well as for studying changes on a global scale.

Climate trends and climate change scenarios in Ho Chi Minh City

This paper reviews the trends of climate and climate change scenarios in Ho Chi Minh City (HCMC). The linear regression method is used to determine the trend and variation of past climate (1980-2019) at Tan Son Hoa station. The annual average temperature tends to increase about 0.024°C/year (r2=0.54) and the rainfall tends to increase about 6.03 mm/year (r2=0.67). For temperature scenario, by 2030 the annual average temperature in the whole city will increase from 0.80- 0.81°C (RCP4.5) and 0.92-0.98°C (RCP8.5). By 2050, it will increase 1.23-1.33°C (RCP4.5) and 1.55-1.68°C (RCP8.5). By 2100, it will increase 1.75-1.88°C (RCP4.5) and 3.20-3.55°C (RCP8.5) compared to the base period. Regarding rainfall scenario, in 2030, the city-wide average rainfall will increase by 12-21% (RCP4.5) and by 12-17% (RCP8.5). By 2050, the average rainfall is likely to increase by 13-15% (RCP4.5) and 15-17% (RCP8.5). By 2100, the average rainfall is likely to increase by 18-22% (RCP4.5) and 20-21% (RCP8.5) compared to the base period.

Modeling climate phenomenon with software grids analysis and display system in the development of the global warming module

This study aims to model climate change based on rainfall, air temperature, pressure, humidity and wind with grADS software and create a global warming module. This research uses 3D model, define, design, and develop. The results of the modeling of the five climate elements consist of the annual average temperature in Indonesia in 2009-2015 which is between 29oC to 30.1oC, the horizontal distribution of the annual average pressure in Indonesia in 2009-2018 is between 800 mBar to 1000 mBar, the horizontal distribution the average annual humidity in Indonesia in 2009 and 2011 ranged between 27-57, in 2012-2015, 2017 and 2018 it ranged between 30-60, during the East Monsoon, the wind circulation moved from northern Indonesia to the southern region Indonesia. During the west monsoon, the wind circulation moves from the southern part of Indonesia to the northern part of Indonesia. The global warming module for SMA/MA produced is feasible to use, this is in accordance with the value given by the validate of 69 which is in the appropriate category and the response of teachers and students through a 91% questionnaire.

The Influence of Climate Conditions and Meteorological Factors on the Nutritional Value of Wheat (Triticum Aestivum L.) Used for Human and Animals Nutrition, in Romania

Wheat (Triticum aestivum L.) is the basic cereal in human and animal nutrition. Every month, wheat is harvested somewhere in the world. In Romania, a country with a temperate-continental climate, the wheat is harvested between June and July, while the sowing is carried out between September and October. Climatic and meteorological factors during these periods can influence the nutritional quality of wheat. The aim of this study was to analyse the influence of annual average temperature and the amount of precipitate on the chemical composition and on the value of metabolizable energy of the wheat, respectively. The climatic and meteorological data used in this study come from NMA database. Were analysed the periods September 2017 - July 2018 (period 1, noted with 2018 - the year of harvesting) and September 2018 - July 2019 (period 2, noted with 2019 - the year of harvesting), respectively. For the chemical analysis, the NIR (Near InfraRed spectroscopy) method was used. The calculation of metabolizable energy was performed based on the ATWATER system, a system applicable to both human and animal nutrition. The statistical analysis of the climatic and meteorological data showed that the annual average temperature for period 1 was lower compared to the temperatures of period 2. Also, the precipitations were more abundant in period 1 compared to period 2. There were no significant statistical differences for any of the climatic and meteorological factors assayed during the analyzed periods. Following the statistical correlations between the nutrients studied by chemical analysis, for those 2 periods, significant differences were observed (p <0.001). The humidity of wheat grains harvested in 2018 was higher (average = 13.03%) compared to that of grains harvested in 2019 (average = 10.72%). The protein content was lower in 2018 (average = 10.02%) than in 2019 (average = 11.04%); and similar results were obtained for the fibre content (average 2018 = 2.17%; average 2019 = 2.96%). Also, the value of metabolizable energy was lower for wheat harvested in 2018 (average = 3517.90 kcal/kg) compared to 2019 (average = 3611.04 kcal/kg). In conclusion, the results of this study highlight the influence of temperature and precipitation on the chemical composition of wheat, thus having a direct impact on the nutritional quality of this grain for human and animal nutrition.

Evidence of Climate Change Impact on Parkinson’s Disease

We have investigated the link between climate change and Parkinson’s diseases (PD) by contrasting variations between 1990 and 2016 of PD patients’ indices (prevalence, deaths, and disability-adjusted life years) and climate indices (warming and annual average temperature) for 185 countries. Countries have been clustered in four categories, depending on whether they had higher-than-median warming and higher-than-median temperature, and for each cluster variations in PD patients’ and climate indices have been studied. In the cluster of the 25 countries (home to about 900 million people) characterized by higher-than-average warming and higher-than-average temperature, we have found evidence of a correlation between more intense warming and higher variations. This statistical result is discussed and linked to other evidence reported in literature. To our knowledge, this is the first time that a statistically-sound link between climate change and the epidemiological data of PD patients has been found and documented.

Impact of Meteorological Factors on Thermokarst Lake Changes in the Beilu River Basin, Qinghai-Tibet Plateau, China (2000–2016)

Variations in weather conditions have a significant impact on thermokarst lakes, such as the sub-lake permafrost thawing caused by global warming. Based on the analysis of Landsat sensor images by ENVI TM 5.3 software, the present study quantitatively determined the area of the thermokarst lakes and the area of the single selected thermokarst lake in the Beilu River Basin from 2000 to 2016. In an effort to explore the reason for changes in the area of thermokarst lakes, this work used Pearson correlation to analyze the relationship between the area of thermokarst lakes and precipitation, wind speed, average temperature, and relative humidity as obtained from the weather station Wudaoliang. Furthermore, this study used multiple linear regression to comprehensively study the correlation between the meteorological factors and changes in the thermokarst lake area. In this case, the total lake-area changes and the single-area changes exhibited unique patterns. The results showed that the total lake area and the single selected lake area increased year by year. Furthermore, the effects of the four meteorological factors defined above on the total area of typical thermokarst lakes are different from the effects of these factors on the single selected thermokarst lake. While the total area of specific thermokarst lakes exhibited a time lag in their response to the four factors, the surface area of the selected thermokarst lake responded to these factors on time. The dominant meteorological factor contributing to total lake area variations of typical thermokarst lakes is the increasing annual average temperature. The Pearson correlation coefficient between the total area and the annual average temperature is 0.717, suggesting a statistically significant correlation between the two factors. For the selected thermokarst lake, the surface area is related to annual average temperature and wind speed. As a result, wind speed and average temperature could infer the variation law on the thermokarst lake due to the linear fitting equation between area and significant meteorological factors.

Distribution Pattern of Soil Organic Carbon and Its Regional Humification Constant in the Coastal Monsoon Region of Eastern China

Soils are an important pool for storing organic carbon. Soil organic carbon (SOC) content is generally considered as an important indicator to evaluate farmland soil quality. The loss of SOC causes soil degradation and reduces the sustainability of farmland. In order to reveal the distribution pattern of SOC in the coastal monsoon area of eastern China, and to clarify the macro dominant factors of SOC accumulation caused by temperature and precipitation, this paper analyzed the distribution pattern of SOC in the coastal monsoon area of eastern China by using the SOC data collected from the national soil testing and formula fertilization data set, and discussed the effects of temperature and precipitation on SOC content. According to the provincial administrative divisions, the distribution of SOC in the coastal monsoon areas of eastern China from Heilongjiang Province to Hainan Province was calculated. According to the annual average temperature < 10.18℃, 10.18 ℃ ~ 20.95℃, > 20.95℃, annual average precipitation 0 ~ 400 mm, 400 ~ 800 mm, > 800 mm, the study area was divided into different regions, and the effects of annual average temperature and annual average precipitation on SOC content were studied. In the region with annual average temperature less than 10.18℃, the temperature had a negative correlation with the accumulation of organic carbon, and the ratio of precipitation and temperature had a positive correlation with the accumulation of SOC; In the region of 10.18℃~20.95℃, the annual average temperature and annual average precipitation had a significant positive correlation, and the ratio of precipitation and temperature had a positive correlation with the accumulation of SOC; In the region > 20.95℃, the temperature had a negative correlation with the accumulation of organic carbon, and the ratio of precipitation and temperature was not related to the accumulation of SOC. In the range of 0 ~ 400 mm of annual average precipitation, the temperature had a positive correlation with the accumulation of SOC, and the ratio of precipitation and temperature had a negative correlation with the accumulation of SOC; In the range of 400 ~ 800 mm of annual average precipitation, the temperature had a negative correlation with the accumulation of SOC, and the ratio of precipitation and temperature had a positive correlation with the accumulation of SOC; In the region > 800 mm of annual average precipitation, the temperature had a positive correlation with the accumulation of organic carbon before the annual average temperature of 20.95℃, and after the annual average temperature of 20.95℃, the temperature had a negative correlation with the accumulation of organic carbon, and the ratio of precipitation and temperature had no significant effect on the accumulation of SOC. On the macro scale, the annual average temperature and precipitation had significant effects on the distribution pattern of SOC in the coastal monsoon area of eastern China. According to the influence of annual average temperature and annual average precipitation on SOC accumulation, a comprehensive model based on the annual average temperature and annual average precipitation on SOC accumulation is established. Through regression verification of the model, the correlation coefficient, r = 0.9998**, the cubic curve equation could better simulated the relationship between the predicted value and the real value of SOC, r = 0.7048**,the model can reflect the cumulative effect of annual average temperature and annual average precipitation on SOC accumulation Combined with the impact.

Evolution of meteorological factors during 1980–2015 in the Daqing River Basin, North China

Precipitation and temperature data, such as the homogeneity, trend, abrupt change, and periodicity, obtained at 40 meteorological stations in the Daqing River Basin from 1980 to 2015 are analyzed using the Mann–Kendall method, anomaly accumulation, Rescaled range analysis (R/S analysis) and wavelet transform. The regularity of climate change is studied to provide guidelines for the rational utilization of water resources. The results show that the annual precipitation has an insignificant upward trend and suddenly changes in 2007. The precipitation evolution can be divided into three types of periodicity, that is, 22–32, 8–16, and 3–7 year time scales, where the 28 year scale is the first main period of precipitation change. The annual average temperature shows a notable upward trend, with 1992 as the change year. The annual average temperature can be divided into three types of periodicity, that is, the 25–32, 14–20, and 5–10 year time scales, where the 28 year scale is the first main period of temperature change. In conclusion, the climate of the Daqing River Basin gradually turns into humid and hot climate. The results provide valuable reference for the assessment of the effects of climate change, and the management of water resources.

Day-to-day temperature variability reduces economic growth

&lt;p&gt;Elevated annual average temperature has been found to impact macro-economic growth. However, various fundamental elements of the economy are affected by deviations of daily temperature from seasonal expectations which are not well reflected in annual averages. Here we show that increases in seasonally adjusted day-to-day temperature variability reduce macro-economic growth independent of and in addition to changes in annual average temperature. Combining observed day-to-day temperature variability with subnational economic data for 1,537 regions worldwide over 40 years in fixed-effects panel models, we find that an extra degree of variability results in a five percentage-point reduction in regional growth rates on average. The impact of day-to-day variability is modulated by seasonal temperature difference and income, resulting in highest vulnerability in low-latitude, low-income regions (12 percentage-point reduction). These findings illuminate a new, global-impact channel in the climate&amp;#8211;economy relationship that demands a more comprehensive assessment in both climate and integrated assessment models.&lt;/p&gt;