Influence Of Global Warming Research Articles

Response of bottom dissolved oxygen reduction to net ecosystem production observed by a wave-driven profiler in the Changjiang River Plume

Coastal hypoxia, exacerbated by the combined influence of eutrophication and global warming, presents a significant environmental challenge. However, the lag correlation between organic matter (OM) export from the upper layers and bottom dissolved oxygen (DOBOT) reduction still lack clear elucidation. This study investigated the coupling between net ecosystem production (NEP, representing the maximum OM export) and DOBOT in the Changjiang River plume (CRP), using a wave-driven profiler system. The high-resolution profiles revealed rhythmic fluctuations in water column NEP, with sediment-water exchange (−74.6%) and NEP (−4.0%) dominating DOBOT reduction. Notably, surface NEP impacts DOBOT with a lag time of 25.65 h, indicating an OM sinking speed of 1.32 mm s−1. NEP at a depth of 3.4 m exerted the most significant influence on DOBOT, explaining a 12% reduction. These findings elucidate the response mechanism of DOBOT reduction to upper OM export and provide insights for hypoxia prediction in coastal and estuarine areas.

Changes in the standard freezing depth of seasonally frozen ground in China over the last 50 years

ABSTRACT In recent years, under the influence of global warming, the freezing depth of seasonally frozen ground (SFG) has decreased at an accelerating rate. This has affected engineering and construction insofar as accurate determinations are needed of SFG areas and the amount of frost heaving. In this study, a method of deriving the E-factor using the improved Stefan formula was used to study changes in the standard freezing depth (SFD) of SFG in China. The method was used to reveal the trends and patterns of changes in the SFD of SFG over the past fifty years. The study relied on ERA5L and reanalysis data and empirical formulas for standard sites in China’s SFG regions. Raster data of the interdecadal SFD in SFG areas of China from 1971 to 2020 were generated to analyze changes in the SFD. Our results show that in the past fifty years, the SFD in China’s SFG areas has shown a downward trend, with the SFD changing at an average rate of −6.749 cm/10a years. The average SFD in the 1970s, 1980s, 1990s, 2000s, and 2010s was 111.31, 107.13, 95.69, 89.84, and 86.21 cm, respectively. From the overall performance of the SFD in different decadal periods, the SFD in central and northeast China changed sharply within the range of 35° N to 50° N in SFG regions. The boundary lines of low-value SFDs expanded sharply to the north as the latitude increased, and the boundary lines of high-value SFDs contracted sharply.

Dryland self-expansion enabled by land-atmosphere feedbacks.

Dryland expansion causes widespread water scarcity and biodiversity loss. Although the drying influence of global warming is well established, the role of existing drylands in their own expansion is relatively unknown. In this work, by tracking the air flowing over drylands, we show that the warming and drying of that air contributes to dryland expansion in the downwind direction. As they dry, drylands contribute less moisture and more heat to downwind humid regions, reducing precipitation and increasing atmospheric water demand, which ultimately causes their aridification. In ~40% of the land area that recently transitioned from a humid region into a dryland, self-expansion accounted for >50% of the observed aridification. Our results corroborate the urgent need for climate change mitigation measures in drylands to decelerate their own expansion.

Water Footprint Assessment of Agricultural Production in Bilecik Province

The freshwater sources are under serious pressure both in terms of quality characteristics due to pollution and in terms of quantity due to the increase in parameters such as temperature and evaporation under the influence of global warming. To ensure sustainable use of these resources, it is necessary to employ high-efficiency pressurized irrigation systems and cultivate plant species that are resilient to various stress factors and highly productive. In determining the water usage characteristics of plants, rapid atmospheric effects brought by climate change, plant water and temperature stress, soil moisture should be monitored, and water production indicators should be determined. In the water-intensive agricultural sector, monitoring the water footprint has become one of the important indicators in terms of ensuring water-food-energy sustainability, efficient use and fair sharing of water resources. This study aims to determine the water footprint of agricultural production in Bilecik province and its districts located in the transitional zone. Accordingly, values of crop and livestock production throughout the province and using a volume-based approach, the water footprint of crop production is estimated at 0.6 billion cubic meters (BCM), while the water footprint of livestock production is 0.5 BCM, resulting in a total agricultural water footprint of 1.1 BCM. In crop production, green water footprint constitutes 33%, blue water footprint 59%, and grey water footprint 8% of the total water footprint. The data obtained will form the basis for developing strategies in sustainable water and food management, aligned with climate change scenarios, to achieve sectoral supply-demand balance.

Rising atmospheric carbon dioxide concentrations increase gaps of rice yields between low- and middle-to-high-income countries.

The rising carbon dioxide concentrations are expected to increase future rice yields. However, variations in the CO2 fertilization effect (CFE) between rice subspecies and the influence of concurrent global warming introduce uncertainty in future global rice yield projections. Here we conducted a meta-analysis of rising carbon dioxide field experiments and employed crop modelling to assess future global rice yields for the top 14 rice producing countries. We found a robust parabolic relationship between rice CFE and temperature, with significant variations between rice subspecies. Our projections indicate that global rice production in the 2050s is expected to increase by 50.32 million tonnes (7.6%) due to CFE compared with historical production. Because low-income countries will experience higher temperatures, the gaps (difference of Δyield) between middle-to-high-income and low-income countries are projected to widen from the 2030s to the 2090s under elevated carbon dioxide. These findings underscore the critical role of CFE and emphasize the necessity to increase investments in research and technology for rice producing systems in low-income countries.

Seasonal Variability in the Influence of Coastal Aquaculture Operation on Benthic–Pelagic Coupling Processes in Shallow Aquatic Ecosystems

Coastal shellfish aquaculture can influence benthic–pelagic-coupled systems because cultured species consume phytoplankton in the water column and return the captured organic matter and nutrients to the environment as biodeposits, which fall to the seafloor, affecting local sediment characteristics and the benthic community. In 2023, we conducted monthly field surveys to characterize the relationships between shellfish aquaculture and the surrounding environment by examining a range of physical and biological variables along the benthic–pelagic gradient at multiple sampling locations in relation to their distances from the aquaculture facilities in Onagawa Bay, Japan. The abundances of benthic macrofauna were dominated by polychaetes (86.3%), followed by gastropods (4.7%), malacostracans (2.7%), ophiuroids (2.1%), and bivalves (1.5%). Both benthic biomass and biodiversity were markedly higher, but the chlorophyll-a concentration of the water column and the sediment organic matter content were significantly lower at the closest proximity to the aquaculture facilities. Although the physical presence of shellfish aquaculture may effectively enhance pelagic–benthic energy fluxes, such processes may also pose a new challenge under the influence of recent global warming, causing widespread hypoxic conditions due to increased stratification in the water column accompanied by excess organic inputs from the aquaculture.

Impact of Gale Weather Events on Wind Power Generation

Abstract Due to the influence of global warming, extreme wind weather occurs frequently, especially in extreme weather such as typhoons and cold waves, problems such as wind turbine shutdown, cutting out, and sudden changes in power generation power will occur, which brings great challenges to wind power forecast and safe and stable operation of the power grid. At present, the research and analysis of strong wind weather mostly stay at the single fan level, and the causes of abnormal changes in wind power output are mostly analyzed from the physical level. However, with the large-scale access of wind power to the power grid, the impact of extreme weather on power grid operation is becoming more and more significant, and it is particularly important to study the impact of strong wind weather events on wind power cluster output. This paper summarizes various types of wind power generation weather events, then screens key meteorological factors affecting power generation under different wind weather conditions based on Gaussian search and grey correlation weighting, and finally analyzes the meteorological impact on the actual operation of wind farm stations and clusters under typhoons and cold waves respectively, aiming at the problem of huge deviations in wind power forecast caused by wind. The next research direction and suggestions are put forward.

Investigate the Spatiotemporal Evolution of Drought and Its Interaction with Atmospheric Circulation in the Yellow River Middle Basin

Global warming contributes to an increased frequency and severity of droughts. Drought emerges as a highly prevalent natural calamity, distinguished by its formidable disruptive impact and the capacity to trigger considerable economic setbacks. Understanding the spatiotemporal characteristics of droughts and clarifying the driving role of atmospheric circulation on droughts is vital for agricultural, hydrological, ecological, and socio-economic systems. Leveraging meteorological data from 36 stations in the middle reaches of the Yellow River Basin from 1961 to 2020, we employed the Standardized Precipitation Evapotranspiration Index (SPEI) to calculate drought occurrence. Concurrently, we explored the influence of atmospheric circulation on the SPEI. The findings of our study underscore a concerning trend of worsening drought conditions within the study area. We discovered a significant correlation between the duration and severity of drought (R = 0.83, p < 0.001); longer durations often corresponded to higher levels of severity. Turning our attention to atmospheric dynamics, the Nino Eastern Pacific index (NE) emerged as a critical driver of SPEI dynamics (the contribution of NE to SPEI was 0.22), significantly impacting drought patterns. In conclusion, the study significantly contributes to our comprehension of the evolving drought patterns under the influence of global warming. The findings can provide valuable information for water resource management and drought disaster control.

Intensified surface chlorophyll responses to the Indian Ocean Dipole under greenhouse warming

The Indian Ocean Dipole (IOD) has been proposed to be a key driver of biological processes in the Indian Ocean (IO) in the present climate. Given the expected influence of global warming on both the properties of the IOD and the biogeochemistry within the IO, a key question arises: How will the relationship between the IOD and surface chlorophyll evolve in a warming climate? Here, utilizing simulations from the Coupled Model Intercomparison Project Phase 6 Earth System models, our findings reveal a notable intensification in the IOD-chlorophyll relationship under greenhouse warming. This intensification is linked to an increase in surface chlorophyll during the June to November period of positive IOD years in the southeastern IO (SEIO). Interestingly, our analysis indicates a substantial rise in IOD-related chlorophyll levels in a warming climate, despite a marked decrease in IOD-induced upwelling in the SEIO. The shallower thermocline leads to an increase in the mean nutrient concentration in the subsurface layer, thereby facilitating an enhanced anomalous nutrient supply to the surface layer, which contributes to increased surface chlorophyll. Our study highlights the consequential effects of IOD on chlorophyll dynamics and underscores the need for improved coupled models to advance our understanding of biophysical interactions in the IO in response to global warming.

Continuous warming drives the colonization dynamics of periphytic ciliate fauna in marine environments.

In order to evaluate the influence of global warming on the ecosystem processes in marine environments, the changes in colonization dynamics of periphytic microbiota were studied using the periphytic ciliate communities as the test organism fauna under a continuous warming gradient of 22℃ (control), 25℃, 28℃, 31℃, and 34 ℃. The results demonstrated that (1) the test ciliate communities generally showed a similar temporal pattern in within the colonization process under the water temperatures from 22 up to 28℃; however, (2) the colonization dynamics were significantly changed, and the fitness of colonization curves to the MacArthur-Wilson model equation was failed under the temperature increased by 6 ℃, and (3) the loading or assimilative capacity of the test aquatic ecosystem was decreased with the increase of water temperature. Therefore, this study suggests that continuous warming may significantly drive the colonization dynamics of periphytic ciliates in marine ecosystems.

Temperature regulates sex determination and growth in the paralichthid flatfishCalifornia halibut.

California halibut (Paralichthys californicus) is a candidate species for aquaculture and stock enhancement. These applications rely on sex control, either to maximize the production of faster growing females or to match sex ratios in the wild. Other paralichthids exhibit temperature-dependent sex determination (TSD), but the presence and pattern of TSD is not well defined in California halibut. Juvenile California halibut were cultured at three distinct temperatures (15°C, 19°C, and 23°C) through the developmental period presumed to be thermosensitive based on findings from congeners. Sex ratios were quantified in each treatment using phenotypic sex identification techniques applied early (molecular biomarkers; 51-100 mm total length [TL]) and late (visual examination of the gonads; ≥100 mm TL) in the juvenile phase. Both techniques indicated similar sex determination trends at each temperature, with overall sex ratios assessed as 49.9% maleat 15°C, 74.5% maleat 19°C, and 98.2% maleat 23°C. Growth rates were highest at 23°C and lowest at 15°C, with intrinsically fast- and slow-growing individuals at all temperatures. At 15°C and 19°C, females comprised a higher proportion among the fast growers than they did among the slow growers. These data show that California halibut exhibit TSD, with temperatures of 19°C and 23°C masculinizing fish while 15°C appears to produce a 1:1 sex ratio. This study will help optimize sex ratios and growth in hatcheries through thermal manipulation. Furthermore, the developed biomolecular tools and identified temperature thresholds will be important in future work to understand the influence of global warming on wild population demographics.

Revealing the injustice and factors that affect the resilience responses of residents in the full period of heat waves

Due to the influence of global warming and the urban heat island effect, heat wave resilience is particularly important for residents. In this study, we constructed a full-period evaluation system for heat wave resilience comprising three phases of preparation, resistance, and recovery from the social level in order to assess the phenomenon of heat injustice in heat waves and provide feasible heat action strategies. The results obtained by clustering, variance, and correlation analyses of the data showed the following. (1) Inequality was found in the resilience of residents with different social attributes in each phase of the heat waves. (2) The social attributes of vulnerable groups were heterogeneous in various phases of heat waves. (3) Environmental perception factors that affected resilience were heterogeneous in different phases of heat waves. Therefore, policies should focus on vulnerable groups in each phase of heat waves, and the roles of governments, neighborhoods, and individuals in heat action programs need to be clarified. The results obtained in this study provide useful evidence for developing long-term heat management and heat injustice mitigation strategies.

The Possibility of Estimating the Permafrost’s Porosity In Situ in the Hydrocarbon Industry and Environment

Global warming firstly influences the permafrost regions where numerous and rich world hydrocarbon deposits are located. Permafrost thawing has caused severe problems in exploring known hydrocarbon deposits and searching for new targets. This process is also dangerous for any industrial and living regions in cold regions. Knowledge of permafrost’s ice and unfrozen water content is critical for predicting permafrost behavior during the water–ice transition. This is especially relevant when ice and permafrost are melting in many regions under the influence of global warming. It is well known that only part of the formation’s pore water turns into ice at 0 °C. After further lowering the temperature, the water phase transition continues, but at gradually decreasing rates. Thus, the porous space is filled with ice and unfrozen water. Laboratory data show that frozen formations’ mechanical, thermal, and rheological properties strongly depend on the moisture content. Hence, porosity and temperature are essential parameters of permafrost. In this paper, it is shown that by combining research in three fields, (1) geophysical exploration, (2) numerical modeling, and (3) temperature logging, it is possible to estimate the porosity of permafrost in situ. Five examples of numerical modeling (where all input parameters are specified) are given to demonstrate the procedure. This investigation is the first attempt to quantitatively analyze permafrost’s porosity in situ.

Investigating global warming's influence on food security in Benin: in-depth analysis of potential implications of climate variability on maize production

Climate change has emerged as a pressing concern affecting nations worldwide, particularly within the African continent, including Benin. Given that maize stands as a staple cereal in Benin, this research to assess the impact and predict the effects of climate change on maize production by the year 2050. To attain this objective, an assortment of data encompassing climatic conditions, demographic factors, fertilizer application levels, and emissions of environmental pollutants has been collected and analyzed. The data analysis based on ARDL and ARIMA models has unveiled those variables such as emissions of CO2 and CH4 through food waste, peak temperatures, precipitation patterns, and rural population density exert considerable immediate influence over maize production volumes. The predictive models portend an upswing in national maize production volume, albeit accompanied by a probable decline in per capita availability. Policies aimed at controlling activities that generate high levels of air pollutants should be formulated to increase production capacities.

Investigation of the Structural Behavior of Reinforced Concrete Beams at Elevated Temperatures.

Reinforced concrete structures encounter a range of detrimental external factors over their operational lifespan. One of them is the elevated temperature effect due to fires. Conversely, due to the influence of global warming, temperatures are on the rise worldwide, leading to an increase in fire incidents. Owing to the increasing rates of construction and fire incidents, it becomes imperative to investigate the durability of reinforced concrete members when exposed to high temperatures. This experimental study aims to assess the structural behavior of reinforced concrete beams following exposure to elevated temperatures. To accomplish this goal, concrete cube specimens, steel rebars, pull-out specimens, and reinforced concrete beams were exposed to elevated temperatures of up to 800 °C and then allowed to cool in air. Following this, all specimens were subjected to testing in accordance with the relevant codes and standards. Test results were analyzed through comparison. In a comprehensive examination of the results, it is evident that the concrete compressive strength experiences an approximately 55% reduction at 600 °C. Meanwhile, there is no notable decrease in the yield strength of the steel at this temperature. However, at 800 °C, steel yield strength decreases by nearly 30%, while the compressive strength of the concrete decreases by a significant 82%. This indicates a substantial reduction in the load-bearing capacity of the beam specimens due to concrete deterioration and the subsequent decline in the bonding performance between concrete and steel rebars.

Rainfall Erosivity Characteristics during 1961–2100 in the Loess Plateau, China

Rainfall erosivity, which signifies the inherent susceptibility of soil erosion induced by precipitation, plays a fundamental role in formulating a comprehensive soil loss equation (RUSLE). It stands as a crucial determinant among the foundational factors considered in a comprehensive soil loss equation’s establishment. Nonetheless, the prediction and quantification of future alterations in rainfall erosivity under the influence of global warming have been relatively limited. In this study, climate change was widely evaluated and 10 preferred global climate models in the Loess Plateau were selected by using the data sets of 27 models simulating climate change and the CN05.1 data set provided by the latest CMIP6. The monthly precipitation forecast data were obtained by using the delta downscaling method. Combined with trend analysis, significance test, and coefficient of variation, the annual rainfall erosivity during 1961–2100 under four SSP scenarios was analyzed and predicted. Among the 27 GCM models used in this paper, the most suitable climate models for simulating monthly precipitation in the Loess Plateau were CMCC-CM2-SR5, CMCC-ESM2, TaiESM1, EC-Earth3, EC-Earth-Veg-LR, INM-CM4-8, CAS-ESM2-0, EC-Earth-Veg, ACCESS-ESM1-5, and IPSL-CM6A-LR. In comparison to the base period (1961–1990), during the historical period (1961–2014), the average annual rainfall erosivity on the Loess Plateau amounted to 1259.64 MJ·mm·hm−2·h−1·a−1, showing an insignificant downward trend. In the northwest of Ningxia, Yulin City and Yanan City showed a significant upward trend. In the future period (2015–2100), the annual rainfall erosivity continues to constantly change and increase. The potential average increase in rainfall erosivity is about 13.48–25.86%. In terms of spatial distribution, most areas showed an increasing trend. Among these regions, the majority of encompassed areas within Shanxi Province, central Shaanxi, and Inner Mongolia increased greatly, which was not conducive to soil and water conservation and ecological environment construction. This study offers a scientific reference for the projected future erosivity characteristics of the Loess Plateau.

Influence of global warming and industrialization on coral reefs: A 600-year record of elemental changes in the Eastern Red Sea

The Red Sea has been recognized as a coral reef refugia, but it is vulnerable to warming and pollution. Here we investigated the spatial and temporal trends of 15 element concentrations in 9 coral reef sediment cores (aged from the 1460s to the 1980s AD) to study the influence of global warming and industrialization on the Eastern Red Sea coral reefs. We found Na, Ca, Cr, Fe, Co, Ni, and Sr concentrations were higher in the northern Red Sea (i.e., Yanbu), whereas Mg, P, S, Mn, and Cd concentrations were higher in the southern Red Sea (i.e., Thuwal & Al Lith) reef sediments. In the central (i.e., Thuwal) to southern (i.e., Al Lith) Red Sea, the study revealed diverse temporal trends in element concentrations. However, both reef sedimentation rates (−36.4 % and −80.5 %, respectively) and elemental accumulation rates (−49.4 % for Cd to −12.2 % for Zn in Thuwal, and −86.2 % for Co to −61.4 % for Cu in Al Lith) exhibited a declining pattern over time, possibly attributed to warming-induced thermal bleaching. In the central to northern Red Sea (i.e., Yanbu), the severity of thermal bleaching is low, while the reef sedimentation rates (187 %), element concentrations (6.7 % for S to 764 % for Co; except Na, Mg, Ca, Sr, and Cd), and all elemental accumulation rates (190 % for Mg to 2697 % for Co) exponentially increased from the 1970s, probably due the rapid industrialization in Yanbu. Our study also observed increased trace metal concentrations (e.g., Cu, Zn, and Ni) in the Thuwal and Al Lith coral reefs with severe bleaching histories, consistent with previous reports that trace metals might result in decreased resistance of corals to thermal stress under warming scenarios. Our study points to the urgent need to reduce the local discharge of trace metal pollutants to protect this biodiversity hotspot.

Coordinated optimization design of buildings and regional integrated energy systems based on load prediction in future climate conditions

The influence of global warming on building performance necessitates its consideration during the design phase. The conventional approach of separately designing the buildings and energy systems may not yield the globally optimal solution. To address this issue, this study proposes a coordinated optimization design approach for buildings and regional integrated energy systems (RIES) under future climate conditions. Firstly, a future regional load prediction model based on a Random Forest algorithm optimized by the Grey Wolf Optimizer is established. Next, a bi-level optimization model is proposed, with the upper level optimizing the building envelope to minimize costs and carbon emissions, and the lower level determining the optimal configuration capacity and operating strategy of RIES for cost minimization. Multi-objective grey wolf optimizer and CPLXE are employed to address the problem. Finally, the proposed method is validated using a case study of an office area in Jinan. The results show that the method can reduce costs by 14.6 % and carbon emissions by 50.93 % compared to the prototype building area with independent production systems, highlighting the potential of synergistic design between buildings and RIES. In addition, different optimization results were obtained under typical meteorological year, shared socioeconomic pathway (SSP) 245, and SSP585 climate scenarios, indicating the impact of future climate on building and RIES design. Therefore, it is necessary to consider climate change in future design work.

Reconstruction of Holocene precipitation patterns and vegetation evolution in the North China Plain: Deciphering the relative influence of climate and anthropogenic forcing

In this paper, we investigate Holocene precipitation changes in northern China to improve the understanding of the East Asian summer monsoon (EASM) and provide an environmental context for the development of Chinese civilisation. Our study is based on an analysis of 260 pollen samples from Holocene lacustrine sediments at Ningjinpo in northern China, and employs the biomisation method to reconstruct regional vegetation types and vegetation changes, and to decipher their driving factors, by incorporating regional climatic data and information on human activities. Results indicate that, in the early Holocene (10,000–8100 cal yr B.P.), regional climate was dry, with annual precipitation of 470 mm. In the middle Holocene (8100–3000 cal yr B.P.), a rapid increase was observed in precipitation, reaching 580 mm. During the late Holocene (2600–600 cal yr B.P.), climate became drier again, with annual precipitation decreasing to 480 mm. Between 10,000–600 cal yr B.P., vegetation exhibited distinct successional patterns dominantly influenced by climate, transitioning from meadow-steppe to forest, and then back to meadow-steppe. After 600 cal yr B.P., vegetation succession decoupled from climate change, with human activities emerging as the primary driving force, as indicated by the substantial increase in anthropogenic pollen and charcoal concentrations. In the future, under the combined influence of global warming and intensified human activity, the implementation of scientific environmental planning measures will become increasingly important for the restoration and sustainable development of regional vegetation ecosystems.

Drought Prediction with Feature Enhanced LSTM Model using Metaheuristic Optimization Algorithms

The impact of drought builds on all three fronts of economy, environment, and society is devastating. Predicting its arrival and duration is highly important to arrange any sort of mitigation plans. The association of detailed relationship between multiple variables makes drought prediction a highly complex task. Especially influence of global warming, polar sea extent variations and their influence on overall ocean temperature have altered the seasonal rainfall behaviors all over the world. In the midst of it, predictions centered on the history of rainfall levels become inaccurate. The proposed system is an optimized deep learning prediction model integrating indigenous knowledge (IK) is proposed to predict the drought. IK expressed in human language is translated using fuzzy function and fed to an improved Long Short Term Memory (LSTM) model. The LSTM model hyperparameters are optimized using a hybrid of Particle Swarm Optimization (PSO) with firefly to produce the meta-heuristics algorithm which will provide the best performance in presence of integration of IK features into modern meteorological features which solves the problem of local minima in LSTM hyperparameter optimization. The performance of the proposed results were tested compared with the meteorological information gathered by the Karnataka Natural Disaster Monitoring Centre (KNDMC) for the district named Chitradurga of the Karnataka state in India. The proposed system which is Indigenous Knowledge merged along the cross model attention network can produce at least 1.4% higher Nash–Sutcliffe model efficiency coefficient (NSE) and 30% lower Mean Absolute Error (MAE) in the prediction of Standard Precipitation Index (SPI) compared to Convolution Neural Networks (CNN) and LSTM based time series prediction models.