Global Temperature Increase Research Articles

Retrieving the atmospheric concentrations of carbon dioxide and methane from the European Copernicus CO2M satellite mission using artificial neural networks

Abstract. Carbon dioxide (CO2) and methane (CH4) are the most important anthropogenic greenhouse gases and the main drivers of climate change. Monitoring their concentrations from space helps detect and quantify anthropogenic emissions, supporting the mitigation efforts urgently needed to meet the primary objective of the Paris Agreement, adopted at the 21st Conference of the Parties to the United Nations Framework Convention on Climate Change (UNFCCC) in 2015, to limit the global average temperature increase to well below 2 °C above pre-industrial levels. In addition, satellite observations can be used to quantify natural sources and sinks, improving our understanding of the carbon cycle. Advancing these goals is one key motivation for the European Copernicus CO2 monitoring mission CO2M. The necessary accuracy and precision requirements for the measured quantities XCO2 and XCH4 (the column-averaged dry-air mole fractions of CO2 and CH4) are demanding. According to the CO2M mission requirements, the spatial and temporal variability of the systematic errors (or spatio-temporal systematic errors) of XCO2 and XCH4 must not exceed 0.5 ppm and 5 ppb, respectively. The stochastic errors due to instrument noise must not exceed 0.7 ppm for XCO2 and 10 ppb for XCH4. Conventional so-called full-physics algorithms for retrieving XCO2 and/or XCH4 from satellite-based measurements of reflected solar radiation are typically computationally intensive and still usually require empirical bias corrections based on supervised machine learning methods. Here we present the retrieval algorithm Neural networks for Remote sensing of Greenhouse gases from CO2M (NRG-CO2M), which derives XCO2 and XCH4 from CO2M radiance measurements with minimal computational effort using artificial neural networks (ANNs). In addition, NRG-CO2M also provides estimates of both the noise-driven uncertainties and the averaging kernels of XCO2 and XCH4 for each sounding. Since CO2M will not be launched until 2026, our study exploits simulated measurements over land surfaces from a comprehensive observing system simulation experiment (OSSE) that includes realistic meteorology, aerosols, surface bidirectional reflectance distribution function (BRDF), solar-induced chlorophyll fluorescence (SIF), and CO2 and CH4 concentrations. We created a novel hybrid learning approach that combines advantages of simulation-based and measurement-based training data to ensure coverage of a wide range of XCO2 and XCH4 values, making the training data representative of future concentrations as well. The algorithm's postprocessing is designed to achieve a high data yield of about 80 % of all cloud-free soundings. The spatio-temporal systematic errors of XCO2 and XCH4 are 0.44 ppm and 2.45 ppb, respectively. The average single sounding precision is 0.41 ppm for XCO2 and 2.74 ppb for XCH4. Therefore, the presented retrieval method has the potential to meet the demanding CO2M mission requirements for XCO2 and XCH4. While the presented results are a solid proof of concept, the actual achievable quality can only be determined once NRG-CO2M is trained on real data, where it is confronted, e.g., with unknown instrument effects and systematic errors in the training truth.

Climate Change: Mitigation Strategies and Need for Adaptation

Life on Earth is under increasing threat of anthropogenic greenhouse gas (GHG) emissions. The anthropogenic activities have unequivocally caused about 1.0°C of global warming above the pre-industrial level, and it is expected to increase further and reach 1.5°C between 2030 and 2052 if the existing emission rates persist. This perilous change in the Earth’s climate is causing uncontrollable wildfires, heatwaves, floods, droughts, storms, destruction of agricultural crops, biodiversity loss, and destruction of vulnerable ecosystems. If the current trend continues, Earth will become unhabitable and a hostile place to live. It becomes necessary to call for effective climate change mitigation strategies and adaptation mechanisms. In 2015, an internationally legally binding treaty called the Paris Agreement was adopted to tackle climate change and strengthen the global response to reduce GHG emissions. The agreement aims to hold the increase in global surface temperature to below 2°C above pre-industrial levels by 2100 and pursue efforts to limit the increase to 1.5°C. The aim of this study was to theoretically determine how erratic climate patterns are threatening and deteriorating the sustainability of diverse sectors worldwide. The article reviews some key climate change mitigation strategies such as conventional mitigation (reducing fossil-based CO2 emissions), negative emissions (carbon dioxide removals), and radiative forcing geoengineering (altering the Earth’s radiative energy budget to stabilize or reduce global temperatures). The significance of the synergy between climate change mitigation and climate change adaptation has also been discussed in this article.

Storm Daniel fueled by anomalously high sea surface temperatures in the Mediterranean

In September 2023, Cyclone Daniel formed in the Mediterranean, severely affecting Greece and Libya, and becoming the deadliest storm in Mediterranean history. The Mediterranean’s unusually high sea surface temperatures (SST) likely contributed to the cyclone’s intensity and extreme rainfall. Greece saw over 700 mm of rain in 18 h, while Libya experienced daily records exceeding 400 mm, leading to catastrophic infrastructure failures. Our hypothesis is that high SSTs provided additional energy and moisture fueling Daniel’s intensification. Using the Weather Research and Forecasting model, we conducted numerical experiments to quantify the role of high SSTs during the event. Comparing actual conditions with a counterfactual scenario, we found that the long-term global warming signal in SSTs significantly increased the storm’s intensity and precipitation. This study underscores the need to understand rising SSTs contribution to predicting and mitigate future tropical-like cyclones as global temperatures increase.

Projected phenological shifts in stratification and overturning of ice-covered Northern Hemisphere lakes

The seasonal cycle of vertical mixing is crucial for lake ecosystems, yet its future under climate change remains uncertain. While lake stratification shifts have been widely studied, the annual overturning duration changes are less clear. Using sub-daily simulations from a fully coupled numerical Earth system model, we assess phenological changes in stratification and overturning in Northern Hemisphere ice-covered lakes. We find the total stratification duration (comprising both summer and winter phases) is projected to decrease by 0.7, 4.6, and 6.9 days in 2029, 2067, and 2096, respectively, under global temperature increases of 1.5 °C, 3 °C, and 4.5 °C. Conversely, the duration of overturning is expected to increase by 0.7, 4.2, and 8 days annually. Notably, these changes are asymmetrical, with most of the overturning extension occurring in the fall, following the peak growing season. This extended overturning could affect lake ecosystems, particularly through enhanced ventilation of bottom layers and altered nutrient cycling.

Evidence and Explanation for the 2023 Global Warming Anomaly

In 2023, the rapid increase in global temperature of around 0.25 °C caught the scientific community by surprise. Its cause has been investigated largely by exploring variations on a long-term trend, with little success. Building on previous work, this paper proposes an alternative explanation—on decadal timescales, observed temperature shows a complex, nonlinear response to forcing, stepping through a series of steady-state regimes. The 2023 event is nominated as the latest in the sequence. Step changes in historical and modeled global mean surface temperatures (GMSTs) were detected using the bivariate test. Each time series was then separated into gradual (trends) and rapid components (shifts) and tested using probative criteria. For sea surface, global and land surface temperatures from the NOAA Global Surface Temperature Dataset V6.0 1880–2022, the rapid component of total warming was 94% of 0.72 °C, 78% of 1.16 °C and 74% of 1.93 °C, respectively. These changes are too large to support the gradual warming hypothesis. The recent warming was initiated in March 2023 by sea surface temperatures (SSTs) in the southern hemisphere, followed by an El Niño signal further north. Global temperatures followed, then land. A preceding regime shift in 2014 and subsequent steady-state 2015–2022 was also initiated and sustained by SSTs. Analysis of the top 100 m annual average ocean temperature from 1955 shows that it forms distinct regimes, providing a substantial ‘heat bank’ that sustains the changes overhead. Regime shifts are also produced by climate models. Archived data show these shifts emerged with coupling of the ocean and atmosphere. Comparing shifts and trends with equilibrium climate sensitivity (ECS) in an ensemble of 94 CMIP5 RCP4.5 models 2006–2095 showed that shifts had 2.9 times the influence on ECS than trends. Factors affecting this relationship include ocean structure, initialization times, physical parameters and model skill. Single model runs with skill ≥75 showed that shifts were 6.0 times more influential than trends. These findings show that the dominant warming mechanism is the sudden release of heat from the ocean rather than gradual warming in the atmosphere. The model ensemble predicted all regime changes since the 1970s within ±1 year, including 2023. The next shift is projected for 2036, but current emissions are tracking higher than projected by RCP4.5. Understanding what these changes mean for the estimation of current and future climate risks is an urgent task.

SUSTAINABLE AGRICULTURE AND CLIMATE SOLUTIONS FOR THE FUTURE OF FOOD SECURITY

Due to reasons such as the increasing world population, climate change, changes in eating habits, unbalanced nutrition, and food waste, some precautions need to be taken for the world's food future. The world population, which was 8 billion in 2023, is expected to reach 9.7 billion in 2050. If important measures are not taken, food inadequacies, negative environmental impacts, and sustainable human life will be seriously affected in response to this population increase. Full compliance with the European Green Deal targets is required for a sustainable world life. The European Green Deal includes plans to keep the global temperature increase around 1.5 ºC, to reduce greenhouse gas emissions by 55% in 2030 compared to 1990, and to become a climate-neutral continent by 2050. It is known that these targets cannot be achieved if current nutrition habits, food waste, land use, water use, and carbon emission habits continue. If habits are not changed, an additional area twice the size of India will be required to feed the growing population by 2050. Considering that food practices constitute 26% of the world's greenhouse gas emissions, significant changes need to be made, and measures need to be taken in the fields of agriculture, animal husbandry, aquaculture, and food. Foremost among these is the significant reduction of food-related emissions through changes in methods, inputs, and technology. In addition, it becomes necessary to make important changes such as returning 1/3 of the food currently wasted in the world to human consumption, a significant change in eating habits, an orientation towards plant-based inputs, and a balanced diet.

Implementation of Supervised Learning Algorithm to Predict Climate Factors in Pangkalpinang

Abstract Currently, the world is experiencing erratic climate conditions, characterized by an increase in frequency and intensity of extreme weather, as well as significant climate changes such as an increase in global temperatures. Pangkalpinang city is one of cities with the highest population density in Bangka Belitung Islands. This population density contributes to climate change through increased greenhouse gas emissions from transportation activities, industry, and high energy consumption. Conversely, climate change also has an impact on environmental conditions and the quality of life, making it important to predict climate factors to effectively manage their impact. The purpose of this research is to determine the prediction of climate factors in Pangkalpinang City. In this research, three Supervised Learning algorithm research methods were used, including Linear Regression, ANN (Artificial Neural Network), and XGBoost Regressor (Extreme Gradient Boosting Regressor). Based on the results of the research that has been conducted, it shows that the XGBoost Regressor model provides the best performance with an MSE value of 79.304 and the smallest MAPE value of 16.912. Beside it, the R-squared value is 0.654. This model shows a good ability to predict climate variables compared to other methods. This finding can be the basis for more appropriate policy making in anticipating and managing the impacts of climate change in Pangkalpinang.

Basin-scale spatio-temporal development of glacial lakes in the Hindukush-Karakoram-Himalayas

Glacial lakes are expanding exponentially in the cryospheric environment of the Hindukush-Karakoram-Himalayas (HKH). Rapid glacier melting due to an above mean global annual temperature increase in HKH is attributed as the main reason for the expansion of the glacial lakes. The rapid expansion of glacial lakes increases the risk of future Glacial Lake Outburst Floods (GLOFs) events in the HKH.In the present study, glacial lake inventories for the Indus, Ganga and Brahmaputra (IGB) river basins in the HKH were generated for 1990, 2000, 2010 and 2020 using Landsat (TM & OLI) at the sub-basin level to understand the spatio-temporal and regional patterns of glacial lakes dynamics, elevational evolution, and changes in the typology. We mapped 17,641 glacial lakes (area: 1082.57 ± 192.601 km2) in 1990, 18,206 (area: 1120.95 ± 198.49 km2) in 2000, 18,399 (area: 1147.12 ± 201.26 km2) in 2010, and 19,284 (area: 1191.81 ± 209.21 km2) in 2020. Between 1990 and 2020, IGB basins showed an increase of 9.31 % in total number and 10.09 % in total area of glacial lakes. In 2020, the Brahmaputra basin had the maximum total area (area: 763.59 ± 132.14 km2), followed by Indus basin (area: 217.47 ± 43.39 km2) and the Ganga basin (area: 210.74 ± 33.66 km2). However, between 1990 and 2020, glacial lakes in the Ganga basin (n: 22.08 %) had the highest growth rate, followed by the Indus basin (n: 14.73 %) and the Brahmaputra basin (n: 4.41 %). In 2020, 76.11 % of glacial lakes were end-moraine-dammed M(e) lakes, followed by other bedrock-dammed B(o) lakes (16.45 %), supraglacial lakes (2.79 %), lateral moraine-dammed M(l) lakes (2 %), cirque B(c) lakes (1.06 %), other moraine-dammed M(o) lakes (0.38 %), and other glacial (O) lakes (1.18 %). Given the rapid growth of glacial lakes in the region along with their likely flood volumes and damage potential in case of their failures, the present study will be of importance for disaster management authorities, an important input for detection of potentially hazardous glacial lakes and for development of mitigation strategies to minimize the impact of potential future GLOF events.

Evaluating the Impact of Phase Change Materials and Double-Skin Façades on Energy Performance in Office Buildings Under Climate Change Scenarios: A Case Study in Iran

The potential for energy efficiency in office buildings is critical, especially in regions facing rapid climate change impacts. This study investigates the use of phase change materials (PCMs) and double-skin façades (DSFs) to optimize energy performance in office buildings in Iran, a country with significant energy demands for heating and cooling. Utilizing Building Information Modeling (BIM) and EnergyPlus 24.1.0 software, we evaluated energy consumption trends across climate scenarios from 1981 to 2030. The findings underscore the rising energy demand due to global temperature increases and demonstrate that integrating PCMs and DSFs can mitigate energy consumption. This research highlights the importance of region-specific building strategies to achieve energy-efficient designs and contributes practical insights for developing sustainable energy policies in Iran.

A Methodological Approach to the Study of Retroreflective Pavements

Climate change, principally driven by human activities, has led to an increase in global temperature, which is predicted to continue rising in the coming years. This temperature increase is even more pronounced in urban areas due to the heat island effect. This phenomenon is highly influenced by the presence of paved streets made with bituminous mixtures, which are characterised by their high solar radiation absorption capacity. Bituminous mixtures retain and re-emit a large amount of heat that intensifies the urban heat island effect. The novelty of this work is to measure retroreflective properties of bituminous mixtures that present a highly textured surface. In this context, the aim of this study is to evaluate the retroreflectance of different bituminous mixtures for use as pavement surfaces, focusing on the influence of colour and different types of aggregates. For this, total and directional reflectance measurements were conducted to determine the retroreflectance of these mixtures, with the purpose of mitigating the heat island effect in urban environments without affecting users through reflected solar radiation. The results show the retroreflective capacity of the designed mixtures within the visible spectrum, especially those manufactured with light-coloured aggregates and synthetic binders pigmented with titanium dioxide. Thus, the retroreflectance of the lighter mixtures range from 37.9% at a 0° entrance angle to 68.9% at 60°, while the black mixtures exhibit values between 5.1% and 8.4%.

Does the Future Look Irrigated? Evaluating the Likelihood of Irrigation Adoption Within Alabama

Farmers have time and again adopted new methods or technologies. However, recent increases in global temperatures and occurrences of extreme weather events, call for an urgency to address and reduce the risks associated with climate change. Irrigation is a key adaptation that reduces crop heat stress and enhances agricultural production. Alabama is considered water-rich but lately has experienced increased rainfall variability and temperature extremes. Various state-wide initiatives to increase irrigation have been implemented, but adoption remains limited. Existing studies have explored factors in

Deciphering Paleocene‐Eocene Thermal Maximum Climatic Dynamics: Insights From Oxygen and Hydrogen Isotopes in Clay Minerals of Paleosols From the Southern Pyrenees

AbstractThis study focuses on the Paleocene‐Eocene Thermal Maximum (PETM), a hyperthermal event characterized by a rapid increase in global temperature (5–8°C) over 20 ka, in the Southern Pyrenean Foreland Basin. Although there is evidence of increased flood discharge and erosion in the Southern Pyrenees, how paleoclimatic conditions and weathering evolved remains to be assessed. This study focuses on the catchment scale climatological changes recorded in the clay minerals of floodplain paleosols, giving insights into how rainfall affected the weathering regime during and after the hyperthermal event. The oxygen (δ18O) and hydrogen (δD) isotope compositions were analyzed in two clay fractions in paleosols of the Esplugafreda continental section. The clay minerals comprise a dominant smectite‐rich assemblage, which indicates a predominantly seasonal, semi‐arid climate throughout the section. Two positive excursions in the δ18O record during the Pre‐Onset Excursion (POE) and the Syn‐PETM support an increase in air near‐surface temperature. Using the δD and δ18O smectite fractionation factors, we estimate a Mean Annual Air Temperature (MAAT) of 24.2 ± 1.0°C for the POE and 27.0 ± 0.8°C for the Syn‐PETM. The δD values show a relatively stable composition during the climatic disturbance, which suggests that this mid‐latitude catchment was overall characterized by low yearly rainfall, with a peak in extreme events during the body of the PETM and a trend toward aridification during the recovery phase of the PETM, supported by the paleosol morphotype. These climatic conditions suggest a kinetically controlled weathering regime, where physical transport of the sediments played a primary role as a denudation mechanism.

Reductions in atmospheric levels of non-CO2 greenhouse gases explain about a quarter of the 1998-2012 warming slowdown

The observed global mean surface temperature increase from 1998 to 2012 was slower than that since 1951. The relative contributions of all relevant factors including climate forcers, however, have not been comprehensively analyzed. Using a reduced-complexity climate model and an observationally constrained statistical model, here we find that La Niña cooling and a descending solar cycle contributed approximately 50% and 26% of the total warming slowdown during 1998-2012 compared to 1951-2012. Furthermore, reduced ozone-depleting substances and methane accounted for roughly a quarter of the total warming slowdown, which can be explained by changes in atmospheric concentrations. We identify that non-CO2 greenhouse gases played an important role in slowing global warming during 1998-2012.

Robustness assessment of climate policies towards carbon neutrality: A DRO-IAMS approach

There are plenty of uncertainties in the integrated climate-economic system including parameter uncertainty and model uncertainty, which significantly challenges the assessment of climate goals committed in the Paris Agreement pledges. In this study, we develop a robustness assessment framework of climate policy by effectively coupling the distributionally robust optimization (DRO) methodology with integrated assessment models (IAMs), termed DRO-IAMS framework, where “S” emphasizes the multiple IAMs being incorporated. Our approach determines a safeguarding probability for the achievement of carbon-neutrality target through the worst-case Conditional Value-at-Risk (CVaR) criterion by effectively capturing the fat-tail effect and exploiting its tractability. Leveraging a discrete support of uncertain parameters over which the objective value of global temperature increase (GTI) can be readily accessible using the IAMs, our developed DRO-IAMS framework effectively circumvents the difficulty in utilizing analytically the black-box-featured IAMs, and achieves a comprehensive and more flexible fashion in integrating the DRO (e.g, moment, ϕ-divergence, and Wasserstein ambiguity sets) and IAMs (e.g., DICE, FUND, and E3METL) to cope with parameter- and model uncertainties in climate policy assessment. Our results suggest that parameter uncertainty and model uncertainty — as critical issues that can have significant impacts on the warming and economic performance of policies — could incur biased assessment for the realization of climate targets. Our proposed DRO-IAMS approach — by its design — is shown to be able to effectively mitigate such issues by pursuing stricter mitigation efforts, and can produce more reliable assessments for typical climate policies than the common sampling-based approaches.

Global renewable energy transition in fossil fuel dependent regions

The worldwide shift towards renewable energy, as defined by the United Nations' Sustainable Development Goals (SDGs) in 2015, is a crucial milestone in achieving a sustainable future. The core focus of SDG 7 is to advance the accessibility, dependability, and environmentally-friendly nature of energy, aiming to substantially enhance the proportion of renewable energy sources and enhance energy efficiency. Although there have been significant developments in renewable energy, notably in solar and wind technologies, the rate of transition is still inadequate to achieve climate objectives. This study examines the imperative of implementing policy changes and integrated strategies to align global and local goals, while tackling the issues of resource allocation and the pressing need to transition away from fossil fuels in order to mitigate global temperature increase. Case studies showcase effective national endeavors, such as Brazil's Proalcool, Germany's Energiewende, and India's ambitious renewable energy objectives, illustrating diverse strategies for bolstering renewable energy capability. The report also analyzes the economic consequences for emerging nations that heavily rely on fossil fuels and the expected effects of reduced fossil fuel consumption. This article offers a detailed analysis of the current advancements, difficulties, and variations across different regions in the field of renewable energy. It gives valuable information on the future of global renewable energy plans, highlighting the importance of unified worldwide endeavors to attain a sustainable energy future.

Hydrological cycle amplification imposes spatial patterns on the climate change response of ocean pH and carbonate chemistry

Abstract. ​​​​​​​Ocean CO2 uptake and acidification in response to human activities are driven primarily by the rise in atmospheric CO2 but are also modulated by climate change. Existing work suggests that this “climate effect” influences the uptake and storage of anthropogenic carbon and acidification via the global increase in ocean temperature, although some regional responses have been attributed to changes in circulation or biological activity. Here, we investigate spatial patterns in the climate effect on surface ocean acidification (and the closely related carbonate chemistry) in an Earth system model under a rapid CO2-increase scenario and identify a different driving process. We show that the amplification of the hydrological cycle, a robustly simulated feature of climate change, is largely responsible for the spatial patterns in this climate effect at the sea surface. This “hydrological effect” can be understood as a subset of the total climate effect, which includes warming, hydrological cycle amplification, circulation, and biological changes. We demonstrate that it acts through two primary mechanisms: (i) directly diluting or concentrating dissolved ions by adding or removing freshwater and (ii) altering the sea surface temperature, which influences the solubility of dissolved inorganic carbon (DIC) and acidity of seawater. The hydrological effect opposes acidification in salinifying regions, most notably the subtropical Atlantic, and enhances acidification in freshening regions such as the western Pacific. Its single strongest effect is to dilute the negative ions that buffer the dissolution of CO2, quantified as alkalinity. The local changes in alkalinity, DIC, and pH linked to the pattern of hydrological cycle amplification are as strong as the (largely uniform) changes due to warming, explaining the weak increase in pH and DIC seen in the climate effect in the subtropical Atlantic Ocean.

Measuring the extent of trees outside of forests: a nature-based solution for net zero emissions in South Asia

Abstract To reduce emissions of carbon and other greenhouse gases on a pathway that does not overshoot and keeps global average temperature increase to below the 1.5 °C target stipulated by the Paris Agreement, it shall be necessary to rely on nature-based solutions with atmospheric removals. Without activities that create removals by carbon sequestration it will not be possible to balance residual emissions. Policies that focus solely on reducing deforestation will only lower future emissions. On the other hand, activities that include regeneration or regrowth of tree biomass can be used to create net-zero emissions through carbon sequestration and atmospheric removals now. New methods demonstrated here using high resolution remote sensing and deep machine learning enable analyses of carbon stocks of individual trees outside of forests (TOF). Allometric scaling models based on tree crowns at very high spatial resolution (<0.5 m) can map carbon stocks across large landscapes of millions of trees outside of forests. In addition to carbon removals, these landscapes are also important to livelihoods for millions of rural farmers and most TOF activities have the capacity to bring more countries into climate mitigation while also providing adaptation benefits. Here were present a multi-resolution, multi-sensor method that provides a way to measure carbon at the individual tree level in TOF landscapes in India. The results of this analysis show the effectiveness of mapping trees outside of forest across a range of satellite data resolution from 0.5 m to 10 m and for measuring carbon across large landscapes at the individual tree scale.

Root physiological and morphology processes co-regulate the growth of Chinese-fir saplings in response to warming and precipitation reduction in the sub-tropical regions

Subtropical China is projected to experience elevated temperature greater than the mean global temperature increase and is accompanied by reduced precipitation. The plasticity of roots to changing environment strongly influences ecosystem feedbacks to climate change. However, knowledge gaps on the individual and combined effects of warming and precipitation reduction on root systems hinder our ability to accurately predict the growth and adaptability of forests under future climate change. To examine the effects of warming (W) and precipitation reduction (P) on roots physiology and morphology of Chinese-fir saplings, we used a randomized complete block design with factorial soil warming (ambient, ambient + 5℃) and precipitation reduction (ambient, ambient-50 %) treatments. A full excavation method was adopted to obtain roots, then we measured the root physiology (osmoregulatory substances, oxidant substances, protective enzymes, endogenous hormones), morphology (specific root length, SRL; surface root area, SRA; root tissue density, RTD). The content of carbon and nitrogen, isotopes (δ13C and δ15N); soil temperature, soil moisture and sapling growth were also measured. We found that compared with the control, W decreased the abscisic acid (IAA) content; P increased the contents of hydrogen peroxide (H2O2) and proline (Pro), and decreased the contents of IAA and cytokinin (CTK); warming plus precipitation reduction (WP) increased the Pro content, and decreased the contents of IAA and CTK. In addition, the effects of W and P on root morphology varied with soil depth and root diameter class. W, P, and WP all increased fine root SRL and SRA in deep soil. Warming and precipitation reduction could affect physiological traits (e.g. non-enzymatic substances and antioxidant enzymes) and subsequently morphological traits via influencing soil environment and root tissue chemistry. Collectively, the results indicated that Chinese-fir saplings responded to warming and precipitation reduction by comprehensive regulation of the non-enzymatic substances (e.g., osmotic substances and endogenous hormones) of fine roots and changing root morphological characteristics in deep soil.

From eco-anxiety to eco-hope: surviving the climate change threat.

As the average global temperature increases, the effects of climate change worsen, through effects on worsening extreme events as well as exacerbating political, economic, and social turmoil (wars, conflicts, and migrations). This poses an existential risk to the survival of humans and non-humans. These effects are visible due to the impact on people's mental health and psychophysical well-being. This article aims to explore the growing phenomenon of psychoterratic syndromes, with focus on the effect of eco-anxiety on mental health. Furthermore, the relationship between eco-anxiety and behavior response (both individual and collective) in the climate crisis era is outlined. A research with interdisciplinary approach was carried out for recent literature and articles relating to psychoterratic syndromes and the effects of climate change on mental health. The article explores the effects of climate change on mental health, including various research on the onset of new emotions in response to psychological effects to climate change, called psychoterratic syndromes (such as eco-anxiety, climate anxiety, solastalgia, eco-grief). Among these, eco-anxiety is the most popular term used for describing how people feel about climate change. However, the paradigm that described eco-anxiety only as a pathological emotion needs to be changed. The article emphasizes the positive effect of eco-emotions and the need to stimulate people to move from a state of anxiety, which could bring apathy and resignation, toward eco-hope. Eco-hope could be an adaptive coping mechanism in people and communities, which is key to preventing, mitigating, and protecting mental and planetary health.

The Effect of Adding Green and Black Tea Waste Extracts on Rumen Fermentation Parameters by In Vitro Techniques

The increase in global temperatures over the past few decades due to greenhouse gas emissions has raised concerns and necessitated further research in climate change mitigation and adaptation. Methane is a prominent greenhouse gas that significantly contributes to climate change, with a substantial amount generated through fermentation processes occurring in the rumen of ruminant animals. The potential of plant secondary metabolites, especially those derived from tannin-rich plants, warrants investigation to modify rumen fermentation and mitigate methane emissions in livestock diets. The objective of this study was to assess the impact of extracts obtained from green and black tea waste on rumen fermentation dynamics and gas (methane) production, utilizing in vitro methods. For this purpose, rumen fluid was collected from two fistulated sheep and subjected to three treatments: (1) a basal diet (control), (2) a basal diet + green tea waste extract (5% of dry matter), (3) a basal diet + black tea waste extract (5% of dry matter). The study assessed the effects of incorporating extracts from green and black tea waste on various parameters, including digestibility, protozoa population, ammonia nitrogen levels, volatile fatty acids, and methane gas production following a 24-h incubation period. Statistical analysis of the data was conducted using SAS software within a completely randomized design framework. The findings indicated that the addition of green and black tea waste extracts significantly decreased methane gas production (p < 0.05), protozoa count (p < 0.05), and ammonia nitrogen concentrations in rumen fluid (p < 0.05) when compared to the control group. The addition of green and black tea waste extracts has significantly altered the concentration of VFAs in rumen fluid (p < 0.05). Specifically, the addition of green tea waste extract has led to a highly significant reduction in acetic acid, (p < 0.01) and the addition of both extracts has resulted in a significant increase in propionic acid (p < 0.05). Consequently, the results suggest that the inclusion of green and black tea waste extracts in livestock diets may effectively mitigate methane emissions in the rumen, thereby reducing feed costs and reducing environmental pollution.