Global Warming Trend Research Articles

Researching the current hydrological state of the Torey lakes region of the Ulzda-Torey internal drainage area

The research of the Torey lakes regon, Ulzda-Torey internal drainage area has established that the manifestation of the general global warming trend in the recent decades had been the moderate regional temperature shifts which induce significant changes in the hydrological states and lead to shifts of the hydrological cycle in general. It has been verified that in the discussed area the ground air temperature indications had shifted from the average negative to the average positive. In the period of 1938-1989 the air temperature had a negative value with the average of -1.0°C in 52 years. Starting from 1989 the average annual temperature indications had turned positive, namely +0.4°C in 32 years. A comparative research of the area for the 1938-2021 period has indicated that the regional air temperature rise by 2.2°C combined with the waning annual amount of atmospheric precipitation (with the average of 21%) leads to a sharp decrease of the average annual river flow (by 16%) and even down to none.

Can the Digital Economy Enable Carbon Emission Reduction: Analysis of Mechanisms and China’s Experience

With the continuous advancement of global scientific and technological capabilities, the issue of global warming caused by greenhouse gas emissions has received widespread attention from countries worldwide. Promoting carbon reduction and curbing the trend of global warming have become urgent and significant challenges for China and the world. Therefore, it is of great practical significance to explore the impact and mechanism of the digital economy on carbon reduction. This paper empirically analyzes the impact and means of the digital economy on carbon emissions using panel regression models and mediation effect models. The research indicates that the digital economy significantly impacts carbon emissions, and the following main conclusions are drawn: (1) The influence of the digital economy on carbon intensity exhibits an inverted U-shaped curve, starting with promotion and then inhibition. (2) The digital economy can affect carbon emissions through industrial structural upgrading and technological innovation. (3) Regions with a relatively high level of digital economy development also tend to have higher energy utilization efficiency, leading to a more pronounced impact of the digital economy on carbon emissions levels.

Arctic warming trends and their uncertainties based on surface temperature reconstruction under different sea ice extent scenarios

IPCC AR6 states that the neglect of recent rapid warming in the Arctic generally leads to an underestimate of global warming trends. In this study, by reconstructing the surface temperature (ST) datasets in the Arctic under different sea ice extent scenarios (Imax and Imin), we respectively evaluated the annual and seasonal warming trends and their uncertainties from 1900 to 2020. The results show that the reconstructed datasets have a good representation of the ST change trends in the Arctic. In 1900–2020, the annual warming trends in the Arctic (0.17 ± 0.031 and 0.14 ± 0.025 °C per decade under the Imax and Imin reconstruction, respectively) are roughly 1.6–1.8 times the global mean warming trends (0.10 ± 0.008 and 0.09 ± 0.008 °C per decade). While in 1979–2020, the Arctic warming trends (0.66 ± 0.100 and 0.55 ± 0.080 °C per decade) increase to 3.1–3.5 times of the global warming trend (0.19 ± 0.023 and 0.18 ± 0.023 °C per decade) for Imax and Imin, respectively, indicating that the Arctic amplification effect has been significantly enhancing in recent decades. Although the seasonal warming trends are closely related to cloud feedback mechanisms, atmospheric circulation, and ocean circulation, they are not sensitive to the different reconstruction scenarios.

Monitoring the Impact of Heat Damage on Summer Maize on the Huanghuaihai Plain, China

As an important food crop, summer maize is widely planted all over the world. Monitoring its growth and output is of great significance for world food security. With the trend of global warming and deterioration, the frequency of high temperature and heat damage affecting summer corn has been increasing in the past ten years. Therefore, there is an increasing demand for monitoring the high temperature and heat damage of summer maize. At present, there are nearly a hundred indices or methods for research on high temperature and heat damage. However, research based on the vegetation index cannot fully describe the damage caused by high-temperature thermal damage, and there is an obvious asynchrony effect. Research based on hyperspectral remote sensing has many inconveniences in data acquisition and complex physical model construction. Therefore, this study uses remote sensing data, including MODIS surface reflection data, MODIS land surface temperature products, as well as ground observation data and statistical data, combined with multiple remote sensing indices and land surface temperature, to construct a remote sensing index, LSHDI (land surface heat damage index). The LSHDI first searches for a location with the worst vegetation growth conditions in the three-dimensional feature space based on the LST (land surface temperature), the normalized difference vegetation index (NDVI), and the land surface water index (LSWI). Then, it calculates the distance between each point and this location to measure the degree of vegetation affected by high temperature and heat damage. Finally, because there is no reliable disaster verification dataset that has been published at present, this study uses soil moisture as a reference to explain the performance and stability of the LSHDI. The results showed that their coefficient of determination was above 0.5 and reached a significance level of 0.01. The LSHDI can well-reflect the high temperature and heat damage of land surface vegetation and can provide important data support and references for agricultural management departments.

Struggling Thermal Stress Impacts on Growth Performance and Health Status of Newly Weaned Rabbits Using Nanoemulsion of Origanum majorana Considering the Economic Efficiency of Supplementation.

With the recent trend of global warming, HS-instigated diminishing could extremely jeopardize animal health, productivity, and farm profit. Marjoram essential oil (MEOE) is a worthy source of wide range phytogenic compounds that may improve heat tolerance, redox and inflammatory homeostasis, and immunity of newly weaned rabbits, specifically if included in the diets in a nano form. One hundred newly weaned rabbits were randomly distributed into four homogeneous groups. The first group (control group) included rabbits that received basal diet without supplementation. In contrast, the other three groups included rabbits that received basal diets supplemented with 200 (MEONE200), 400 (MEONE400), and 800 (MEONE800) mg MEONE/kg diet, respectively. Among MEONE-treated groups and control groups, MEONE400 group showed the highest (p < 0.001) growth performance traits, including final body weight, average daily gain, feed efficiency, and the performance index. Compared to the control, all MEONE-supplemented groups possessed lower rectal temperatures and respiration rates, recording the lowest values in the MEONE400 group. The oxidative stress biomarkers and immunoglobulins G and M were significantly improved in the MEONE400 and MEONE800 compared with the control and MEONE200 groups. The addition of MEONE (400 or 800 mg/kg) decreased the concentrations of serum interleukin-4 (p = 0.0003), interferon gamma (p = 0.0004), and tumor necrosis factor-α (p < 0.0001) but significantly elevated (p < 0.001) the activity of nitric oxide, amyloid A and lysozyme. Liver functions (lower concentrations of liver enzymes) were significantly improved in all MEONE-treated groups compared to the control group. There was a considerable significant effect of dietary supplementation of MEONE400 on economic efficiency. In conclusion, the addition of 400 mg/kg to the diets of newly weaned rabbits can be recommended as an affective intervention to mitigate the negative impacts of HS.

Spatiotemporal Variations of Reference Evapotranspiration and Its Climatic Driving Factors in Guangdong, a Humid Subtropical Province of South China

It is of great importance to study the changes in reference evapotranspiration (ET0) and the factors that influence it to ensure sustainable and efficient water resource utilization. Daily ET0 data calculated using the Penman–Monteith method from 37 meteorological stations located within Guangdong Province in the humid zone of southern China from 1960 to 2020 were analyzed. The trend analysis and Mann–Kendall test were used to analyze the time series changes in ET0 and major climatic factors (air temperature (T), relative humidity (RH), sunshine duration (SD), and wind speed (u2)) for over 61 years. Sensitivity and contribution analyses were used to evaluate the driving factors of ET0. The main findings of the study are as follows: (1) the trend in average annual ET0 time series in Guangdong slightly increased at a trend rate of 1.61 mm/10a over the past 61 years, with most stations experiencing an increase in ET0. During the same period, air temperature significantly increased, while RH and SD decreased; u2 also decreased. (2) Sensitivity analysis showed that ET0 was more sensitive to RH and T than SD and u2, with ET0 being most sensitive to RH in spring and winter and T in summer and autumn. (3) The contribution analysis showed that T was the dominant factor for ET0 variation in Guangdong, followed by SD. SD was found to be the dominant factor in ET0 changes in areas where the “evaporation paradox” occurred, as well as in spring and summer. The study concludes that the climate in Guangdong became warmer and drier over the past 61 years, and if the current global warming trend continues, it will lead to higher evapotranspiration and drought occurrence in the future.

Separating Anthropogenically‐ and Naturally‐Caused Temperature Trends: A Systematic Approach Based On Climate Memory Analysis

AbstractSeparating anthropogenic and natural contributions to global warming is crucial for understanding and predicting climate change, but it is challenging due to the complexity of the climate system. In this study, we try to address this issue by estimating the anthropogenic forcing impacts with its long‐term persistence features properly considered. In this way, a data‐driven approach is proposed to decompose the observed trends from global and regional scales into anthropogenically‐ and naturally‐caused trends. We find that there is a continuous anthropogenic global warming trend since the beginning of the last century, even during the recent global warming hiatus period. On regional scales, the anthropogenically‐forced trends among regions are found at a similar level, while their unevenly distributed warming trends among regions may be attributed to natural causes. Our findings provide new insight into climate warming, and our approach based on persistence analysis provides a new perspective for attribution studies.

A century-long analysis of global warming and earth temperature using a random walk with drift approach

Climate change poses the most significant threat to humanity today. This study examines the global warming trend by analyzing temperature changes over the past century, uncovering alarming results. Various models, including the Random Walk with Drift approach with R programming language, have been used to compare different time horizons and scenarios. This research demonstrates the importance of utilizing advanced analytical techniques to better understand climate change’s impact. The findings underscore the urgency of implementing effective climate policies to mitigate the effects of global warming and safeguard our planet’s future.

Investigating the Use of Sentinel-1 for Improved Mapping of Small Peatland Water Bodies: Towards Wildfire Susceptibility Monitoring in Canada’s Boreal Forest

Peatlands provide vital ecosystem and carbon services, and Canada is home to a significant peatland carbon stock. Global climate warming trends are expected to lead to increased carbon release from peatlands, as a consequence of drought and wildfire. Monitoring hydrologic regimes is a key in understanding the impacts of warming, including monitoring changes in small and temporally variable water bodies in peatlands. Global surface water mapping has been implemented, but the spatial and temporal scales of the resulting data products prevent the effective monitoring of peatland water bodies, which are small and prone to rapid hydrologic changes. One hurdle in the quest to improve remote-sensing-derived global surface water map quality is the omission of small and temporally variable water bodies. This research investigated the reasons for small peatland water body omission as a preparatory step for surface water mapping, using Sentinel-1 SAR data and image classification methods. It was found that Sentinel-1 backscatter signatures for small peatland water bodies differ from large water bodies, due in part to differing physical characteristics such as waves and emergent vegetation, and limitations in detectable feature sizes as a result of SAR image processing and resolution. The characterization of small peatland water body backscatter provides a theoretical basis for the development of SAR-based surface water mapping methods with high accuracy for our purposes of wildfire susceptibility monitoring in peatlands. This study discusses the implications of small peatland water body omission from surface water maps on carbon, climate, and hydrologic models.

Evaluation and Approach of Industrial Green Development: A Literature Review

With the increasing consumption of natural resources and the aggravation of environmental pollution, human beings began to realize that in order to realize the sustainable development of society, we must further examine the relationship between economic development and natural environment. In particular, it is generally believed that human activities have had a negative impact on global climate in the past hundred years, making the global warming trend increasingly obvious. If mankind wants to ease the contradiction between himself and the natural environment, we must go from the opposite to the integration of nature. Industrial green development is an attempt to integrate industrial economic operation system with natural system in order to achieve a balance. Green industry is a necessary driving force to promote green economic development and an important way to achieve ecological civilization. In order to further understand the related research of industrial greening, a new research direction is sought. Based on the summary of relevant literature, this paper firstly discusses the connotation and definition of industrial greening. Secondly, it is pointed out that ecological efficiency is an important basis for evaluating the degree of industrial green development, and the research on ecological efficiency mainly includes three aspects: theoretical interpretation, calculation and driver identification. Then, it summarizes the related ideas of industrial green path, including advanced technology application, material reduction, life cycle evaluation, green design, establishment of ecological industrial parks, etc. Finally, it points out the shortcomings of existing research and the prospects for future research.

Feasibility study on substitution of SF6 insulated current transformer with environment-friendly natural gas

Many SF6 gas-insulated current transformers (CT) are used in the power grid. The global warming potential (GWP) of SF6 is 23,900 times that of CO2, and the leakage of SF6 will aggravate the global warming trend. It is a hot issue to carry out the study of environment-friendly gas substitution in the power grid. This paper systematically studied the voltage withstand, partial discharge, and temperature rise characteristics of dry air, N2, and CO2 gas-insulated CTs. Through the study, the permissible critical pressures in environment-friendly gas-insulated CTs were determined to be 0.9 MPa for dry air, 0.7 MPa for N2, and 0.8 MPa for CO2, respectively. The partial discharge intensity was less than 5 pC for environment-friendly gas-insulated CTs at 0.3 MPa∼0.8 MPa pressure. The experimental results of temperature rise characteristics showed that under the condition of a 600A rated operating current, the maximum temperature rise of the CTs was about 80 °C, and the maximum temperature rise happened at the conductive rod. The experimental results verified that environment-friendly gases could meet the requirements of insulation and temperature rise. Due to the increase of pressure in CTs, the action pressure of the explosion-proof membrane should be changed to meet the mechanical strength requirements. It is recommended that when using 0.9 MPa dry air, the action pressure should be designed as 1.8 MPa; when using 0.7 MPa N2, the pressure should be designed as 1.4 MPa; when using 0.8 MPa CO2, the pressure should be designed as 1.6 MPa. The results of this paper can provide a reference for development of environment-friendly gas-insulated CTs.

Performance-based sub-selection of CMIP6 models for impact assessments in Europe

Abstract. We have created a performance-based assessment of CMIP6 models for Europe that can be used to inform the sub-selection of models for this region. Our assessment covers criteria indicative of the ability of individual models to capture a range of large-scale processes that are important for the representation of present-day European climate. We use this study to provide examples of how this performance-based assessment may be applied to a multi-model ensemble of CMIP6 models to (a) filter the ensemble for performance against these climatological and processed-based criteria and (b) create a smaller subset of models based on performance that also maintains model diversity and the filtered projection range as far as possible. Filtering by excluding the least-realistic models leads to higher-sensitivity models remaining in the ensemble as an emergent consequence of the assessment. This results in both the 25th percentile and the median of the projected temperature range being shifted towards greater warming for the filtered set of models. We also weight the unfiltered ensemble against global trends. In contrast, this shifts the distribution towards less warming. This highlights a tension for regional model selection in terms of selection based on regional climate processes versus the global mean warming trend.

Soil enzyme kinetics and thermodynamics in response to long-term vegetation succession

Our current knowledge regarding soil organic matter (SOM) turnover during vegetation succession is often limited to conventional C decomposition models. However, microbial enzyme-mediated SOM degradation and nutrient cycling are mainly reflected in the kinetic parameters of these enzymes. Changes in the composition and structure of plant communities are typically accompanied by alterations in soil ecological functions. Therefore, it is important to clarify the kinetic parameters of soil enzymes and their temperature sensitivity in response to vegetation succession, especially under the current trend of climate change-related global warming; however, these are still understudied. Here, we examined the kinetic parameters of soil enzymes, their temperature sensitivity, and their associations with environmental variables over long-term (approximately 160 years) vegetation succession on the Loess Plateau using a space-for-time substitution method. We found that the kinetic parameters of soil enzymes changed significantly during vegetation succession. Specific response characteristics varied depending on the enzyme. Overall, the temperature sensitivity (Q10, 0.79–1.87) and activation energy (Ea, 8.69–41.49 kJ·mol−1) remained stable during long-term succession. Compared with N-acetyl-glucosaminidase and alkaline phosphatase, β-glucosidase was more sensitive to extreme temperatures. In particular, two kinetic parameters (i.e., maximum reaction rate, Vmax; half-saturation constant, Km) of β-glucosidase were decoupled at low (5 °C) and high (35 °C) temperatures. Overall, Vmax was the primary determinant of variations of enzyme catalytic efficiency (Kcat) during succession, and soil total nutrients had a greater impact on Kcat than available nutrients. Our results suggested that soil ecosystems played an increasingly important role as a C source during long-term vegetation succession, as indicated by the positive responses of the C cycling enzyme Kcat, while the factors related to soil N and P cycling remained relatively stable.

AI based temperature reduction effect model of fog cooling for human thermal comfort: Climate adaptation technology

This study developed a gradient-boosted regression tree-based artificial intelligence (AI) model––temperature reduction effect AI model (TREAM)––to determine the temperature reduction effect of fog cooling that varies with weather conditions. According to the trend of global warming, our society is suffering from serious damage from urban heat islands, especially negatively affecting human health and thermal comfort. Therefore, it is very important to develop and evaluate adaptive technology for providing pleasant thermal comfort to humans. This study select fog cooling as adaptive technology for human thermal comfort, and indoor and outdoor simulation were performed using STAR CCM+, a computational fluid dynamics(CFD) program. Moreover, transient analysis to validate the outdoor model of CFD and parametric study to identify the correlation between the environmental factors and fog cooling were performed. When initial temperature set as 45 °C at a relative humidity of 90% and wind speed of 1 m/s, a temperature reduction of 8.92% can be obtained. Regardless of the temperature and humidity conditions, the temperature reduction effect of fog cooling was ∼1% if the wind speed increased above 5 m/s. This study contributed to the quantitative analysis of the temperature reduction effect according to the change of environmental factors.

Global Warming Research Based on GA-BP Neural Network Model

Since the industrial revolution, global carbon emissions have continued to increase, and after entering the 21st century, the trend of global warming has become more and more obvious. This paper first use the collected 10-year monthly average temperature data and divide them into two groups: 2012.3~2022.2 and 2022.3~2022.10, then conduct the Mann-Whitney U test, and find that the temperature increase in March 2022 did not increase significantly; then we three models are established to predict the future global temperature, the first model is ARIMA, the second model is BP neural network, and the third model is GA-BP neural network. Three models are used to predict the time when the future temperature will reach 20°C, ARIMA predicts the time is 2050, BP is 2046, and GA-BP is 2045. Finally, this paper use MAE, MAPE, and RMSE to evaluate the accuracy of the model, and find that GA-BP has the highest accuracy, followed by BP, and ARIMA is the worst.

Soil texture and microorganisms dominantly determine the subsoil carbonate content in the permafrost-affected area of the Tibetan Plateau.

Under climate warming conditions, storage and conversion of soil inorganic carbon (SIC) play an important role in regulating soil carbon (C) dynamics and atmospheric CO2 content in arid and semi-arid areas. Carbonate formation in alkaline soil can fix a large amount of C in the form of inorganic C, resulting in soil C sink and potentially slowing global warming trends. Therefore, understanding the driving factors affecting carbonate mineral formation can help better predict future climate change. Till date, most studies have focused on abiotic drivers (climate and soil), whereas a few examined the effects of biotic drivers on carbonate formation and SIC stock. In this study, SIC, calcite content, and soil microbial communities were analyzed in three soil layers (0-5 cm, 20-30 cm, and 50-60 cm) on the Beiluhe Basin of Tibetan Plateau. Results revealed that in arid and semi-arid areas, SIC and soil calcite content did not exhibit significant differences among the three soil layers; however, the main factors affecting the calcite content in different soil layers are different. In the topsoil (0-5 cm), the most important predictor of calcite content was soil water content. In the subsoil layers 20-30 cm and 50-60 cm, the ratio of bacterial biomass to fungal biomass (B/F) and soil silt content, respectively, had larger contributions to the variation of calcite content than the other factors. Plagioclase provided a site for microbial colonization, whereas Ca2+ contributed in bacteria-mediated calcite formation. This study aims to highlight the importance of soil microorganisms in managing soil calcite content and reveals preliminary results on bacteria-mediated conversion of organic to inorganic C.

Recent Advances and Challenges in the Inverse Identification of Thermal Diffusivity of Natural Ice in China

The ice thermal parameters are the key to reasonably simulating ice phenology, distribution, and thickness, but they have always been a “vulnerable group” in ice research. Technically, it may seem simple to obtain accurate ice thermal property parameters, but in reality, there are numerous impact factors, requiring a rigorous research process. In the 1980s, the thermal conductivity of ice was explored in the field and laboratory, after which there has been no significant progress in China. In this century, mathematics is introduced, after which the inversion identification and analysis with the time-series data of the vertical temperature profiles of ice layers by in situ testing are carried out. The in situ thermal diffusivities of different natural ices were obtained and cross-validated with the inversion identification results. Both natural freshwater ice and sea ice exhibited differences in the thermal diffusivity of the pure ice chosen for the current simulations due to impurities within the unfrozen water among the ice crystals, but the trends are consistent with the results of a small number of laboratory tests on different types of saltwater frozen ice. In this paper, the inversion identification results of the thermal diffusivity of typical ice were selected, and the factors constraining the thermal diffusivities were analyzed. The importance of parameterizing the thermal diffusivity in the phase transition zone of ice under the trend of global warming was illustrated. Future research ideas on the physical mechanism, application value, and parameterization scheme of the thermal diffusivity of natural ice were envisaged.

Air Quality—Meteorology Correlation Modeling Using Random Forest and Neural Network

Under the global warming trend, the diffusion of air pollutants has intensified, causing extremely serious environmental problems. In order to improve the air quality–meteorology correlation model’s prediction accuracy, this work focuses on the management strategy of the environmental ecosystem under the Artificial Intelligence (AI) algorithm and explores the correlation between air quality and meteorology. Xi’an city is selected as an example. Then, the theoretical knowledge is explained for Random Forest (RF), Backpropagation Neural Network (BPNN), and Genetic Algorithm (GA) in AI. Finally, GA is used to optimize and predict the weights and thresholds of the BPNN. Further, a fusion model of RF + BP + GA is proposed to predict the air quality and meteorology correlation. The proposed air quality–meteorology correlation model is applied to forest ecosystem management. Experimental analysis reveals that average temperature positively correlates with Air Quality Index (AQI), while relative humidity and wind speed negatively correlate with AQI. Moreover, the proposed RF + BP + GA model’s prediction error for AQI is not more than 0.32, showing an excellently fitting effect with the actual value. The air-quality prediction effect of the meteorological correlation model using RF is slightly lower than the real measured value. The prediction effect of the BP–GA model is slightly higher than the real measured value. The prediction effect of the air quality–meteorology correlation model combining RF and BP–GA is the closest to the real measured value. It shows that the air quality–meteorology correlation model using the fusion model of RF and BP–GA can predict AQI with the utmost accuracy. This work provides a research reference regarding the AQI value of the correlation model of air quality and meteorology and provides data support for the analysis of air quality problems.

Influence of dyke-type causeway on Urmia Lake (NW Iran); insights from water physico-chemical parameters seasonal (2019) changes

The Urmia Lake in NW Iran, the world’s second-largest hypersaline lake, has been exposed to rapid water level fall in the last two decades due to water resources mismanagement, building up of dams on inlet rivers, and the causeway construction in the lake. This human intervention has divided the lake into northern and southern parts and caused an extreme disturbance in the hydro-chemical and hydrodynamic systems, namely current direction reversal.To fully understand the current situation, water depth and lake surface and deepwater physico-chemical parameters (i.e. density, temperature, acidity, electrical conductivity, and total dissolved solids) are analysed in the wet and dry seasons of 2019. In the wet season, deepwaters have a higher density than surface waters, and the northern part of the lake contains denser water compared to its southern counterpart. The formation of density gradients creates an anticlockwise direction lake current. Deepwaters show higher electrical conductivity and total dissolved solids than surface waters in the wet season. Unlike the wet season, the lake water becomes more homogenised due to wave action and evaporation in the dry season. Our study clearly demonstrates that the water passageway of the causeway does not allow for a complete yearly balance between water and sediment exchange. Results suggest that the northern and southern parts of the lake have almost independent hydro-chemical and hydrodynamic systems. In the undeniable reality of global warming trends, anthropogenic interventions into sensible ecologic systems need to be better planned if we were to prevent the ecological disasters we now have to face.

Long-term forecasting of thermal and humidity actions on buildings

Global warming leads to significant long-term changes in climate loads and effects on structures, which must be considered when designing buildings. In this study, loads on buildings and structures were examined to evaluate long-term changes in temperature and humidity of the atmospheric air and to develop a method for forecasting the estimated values of these effects. According to the data obtained via weather stations in Ukraine, it was determined that long-term changes in thermal and humidity actions at different weather stations are qualitatively similar but vary numerically by a significant extent. The increase in air temperature is generally consistent with the known trend of global warming, and relative humidity is characterized by a decrease in the variance at almost constant average annual value. The proposed method of identifying and describing trends in long-term changes in climatic influences can be used in the development of regulatory documents and in directly designing buildings and structures by considering the predicted changes in climatic influences during the future life of buildings.