Climate Interactions Research Articles

Family roots, green shoots: exploring the impact of work–family interpersonal capitalization on employee green behavior

Purpose This study aims to explore the influence of extra-workplace factors, specifically work−family interpersonal capitalization, on employee green behavior. Based on the conservation of resources theory, the research sought to understand how resources gained from positive family interactions spill over into the workplace, enhancing green behavior. In addition, the study investigated the mediating role of relational energy and the moderating effects of work green climate and environmental self-accountability, providing a nuanced understanding of the mechanisms involved. Design/methodology/approach This study used a multiwave field study combined with an experimental study to investigate the impact of work−family interpersonal capitalization on employee green behavior. Data were collected in several phases to capture changes over time and to understand causal relationships. The multiwave design allowed for observing the dynamic interplay between family and work domains, while the experimental component provided controlled conditions to validate the findings. This approach ensured robust and comprehensive analysis, integrating both real-world and experimental data. Findings The study revealed that work−family interpersonal capitalization significantly enhances employee green behavior. Relational energy emerged as a crucial mediator in this relationship. Furthermore, the study found that both work green climate and environmental self-accountability positively moderated the relationship between relational energy and green behavior. Notably, the interaction of work green climate and environmental self-accountability further strengthened this relationship, ultimately influencing the indirect effect of relational energy on employee green behavior. These findings highlight the complex interplay between personal and organizational factors in promoting sustainable practices at work. Originality/value This study provides valuable insights into the spillover effects from family to work, emphasizing the importance of considering “nongreen” factors in understanding employee green behavior. By identifying relational energy as a key mediator and uncovering the moderating roles of work green climate and environmental self-accountability, the research contributes to the broader literature on environmental sustainability and organizational behavior. The findings suggest practical implications for organizations aiming to foster green behavior, highlighting the potential of enhancing family−work interactions and cultivating a supportive green work environment.

Data-driven complementary indices and metrics for assessing national progress on climate risk and adaptation

Climate change is a complex, multidimensional issue requiring decision-making and governance supported by extensive data from social and natural systems. Large cross-country datasets are available, and various methods are used to transform this data into information relevant for policy and decision-making. Summary indices provide insights into adaptation, mitigation, vulnerability, and risk, helping track countries’ climate-related ambitions and progress. However, many existing methods for constructing indices do not fully exploit the multivariate structures within the data, leading to potential redundancies and overlaps. We develop a set of complementary, non-overlapping indices using Principal Component Analysis to capture distinct dimensions of societal and climate interactions. These data-driven indices account for underlying data structures, ensuring each provides unique and independent insights. Our analysis includes harmonized country-level datasets, metrics relevant to loss and damage, public perceptions of climate change, and projections of economic damages. The application of these indices is illustrated with dissonance metrics that assess the alignment between a country’s adaptation capacities, societal concerns, and risks. The proposed approach for index construction can be valuable across various policy contexts and for informing climate-related strategies. An online tool is provided to visualize and access the results presented in this paper.

Deciphering the role of growth regulators in enhancing plant immunity against herbivory.

Plants are central to global food production, and the pursuit of sustainability aims to enhance or preserve food quality while safeguarding the environment. Due to their immobility, plants are unable to evade unfavourable climatic setups or interactions with other living creatures. Upon their interaction with insect herbivores, plants face biotic stress, which is a constant challenge for plants, causing molecular, physiological, and biochemical changes and reducing their productivity. To combat biotic stress caused by herbivores, plants have evolved intricate defence mechanisms through growth regulators such as auxins, cytokinins, gibberellins, salicylic acid (SA), jasmonic acid (JA), ethylene (ET), abscisic acid (ABA), strigolactones and brassinosteroids. The intricate network of specific proteins, metabolites and certain phytohormones orchestrates plant defensive reactions, leading to their skilful coordination in responding to insect attacks. Comprehending the defence mechanisms holds the key to mitigating significant crop and economic losses. This review entails a comprehensive analysis of the role of growth regulators in enhancing plant immunity against herbivory, highlighting the substantial efforts by the scientific community to manage and mitigate damages from biotic stress in plants, ultimately contributing to the advancement of sustainable agriculture.

Tectonic–Climate Interactions Controlled the Episodic Magmatism and Exhumation of the Zheduo–Gongga Massif in the Eastern Tibetan Plateau

The Zheduo–Gongga Mountain, an enormous tower located at the boundary of the eastern Tibetan Plateau, is an ideal place to study the contribution of the climate and/or tectonics to the mountain building. Here, we report new zircon U–Pb ages, biotite 40Ar–39Ar, and apatite fission track (AFT) ages of granites along the Zhonggu transect in the northern part of the Zheduo–Gongga massif to investigate the detailed exhumation history and mechanism. The results show zircon U-Pb ages of 14.3 ± 0.3 and 11.3 ± 0.2 Ma, Biotite 40Ar–39Ar ages of 4.39 ± 0.07 and 3.62 ± 0.05 Ma, and AFT ages of ~2.6–0.9 Ma. Combining previous structural and geochronological studies, we argue that the growth and exhumation of the Zheduo–Gongga Mountain experienced the following stages. Late Oligocene–early Miocene crust shortening and magmatism marked the initiation of the crustal thickening and surface uplift during ~32–11 Ma, forming a migmatite–granitic belt along the Xianhuihe fault, in response to the northward advancing of the Indian plate into the Eurasian plates. Subsequently, the massif experienced episodic phases of exhumation with variable rates. The exhumation occurred at a rate of ~1–1.5 km/Ma with a cooling rate of 70 ± 20 °C/m.y. during ~11–5 Ma coinciding with the coeval intensification of the Asian monsoon and clockwise rotation of the Chuandian block, south of the Xianshuihe fault. During ~5–2 Ma, a phase of accelerated exhumation (~2–5 km/Ma) started, followed by a possible phase of decelerated exhumation (~1–1.5 km/Ma, corresponding to a cooling rate of 120 ± 20 °C/m.y.) since ~2 Ma, when alpine glaciations initiated due to global cooling. This study highlights the importance of tectonic deformation during ~11–5 Ma in controlling the early growth and exhumation of high mountains in the eastern Tibetan Plateau. The climate may account for the later exhumation of the Zheduo–Gongga mountain since ~5 Ma.

The LTAR Cropland Common Experiment at Upper Mississippi River Basin-Ames.

Agricultural systems evolve from the interactions of climate, crops, soils, management practices (e.g., tillage, cover crops, nutrient management), and economic risks and rewards. Alternatives to the corn (Zea mays L.)-soybean [Glycine max (L.) Merr.] (C-S) cropping systems that dominate in the US Midwest may provide more sustainable use of resources, reduce the documented environmental impacts of current C-S systems, and improve production efficiency and ecosystem services. Innovative management practices are needed to offer producers options to increase farm resilience to variable weather conditions and offset negative environmental impacts. In response to this need, the Upper Mississippi River Basin Long-Term Agroecosystem Research network site at Ames, IA, established a cropland experiment in 2016 to investigate an alternative crop management system that includes reduced tillage, cover crops, and right source, right rate, right time, and right place (4R) nitrogen (N) management. The experimental site is located on the Iowa State University Kelley Research Farm in Boone County, IA. Crop, soil, air, and tile drainage water measurements are made throughout the year using published methods for each agronomic and environmental metric. Our goal is to provide quantitative information to farmers, consultants, agribusiness partners, and state and federal agencies to help guide decisions on the effective use of alternative management practices. Future changes in experimental treatments will adopt a knowledge co-production approach whereby researchers and stakeholders will work collaboratively to identify problems, implement research protocols, and interpret results.

Regional variation in the landscape ecology of West Nile virus sentinel chicken seroconversion in Florida.

How landscape composition and configuration impact the distribution of multi-vector and multi-host mosquito vector-borne disease systems, such as West Nile virus (WNV), remains challenging because of complex habitat and resource requirements by hosts and vectors that affect transmission opportunities. We examined correlations between landscape composition and configuration and 2018 WNV sentinel chicken seroconversion in Florida, USA across the state and within five National Oceanic Atmospheric Administration (NOAA) bioclimatic regions to understand strength and variation of landscape effects during an elevated transmission year. Although few landscape studies have examined WNV in Florida, we expected higher percentages of residential or medium-developed landscapes and more fragmented landscapes would be positively correlated with WNV seroconversion owing to the main mosquito vector habitats and avian host distributions. However, we expected to find variation in the importance of forest, wetland, and agriculture landscapes across bioclimatic regions in the state. WNV seroconversion rates were calculated using Florida 2018 Department of Health WNV sentinel chicken seroconversion data from 187 flocks maintained by mosquito control programs. Percent land cover and edge density metrics were calculated for multiple land cover classes and within multiple buffer distances from chicken coops using 2019 National Land Cover Data. We used binomial generalized linear mixed effects models to calculate the importance of landscape metrics to WNV seroconversion. We found no statewide predictors of seroconversion, but as expected, the importance of landscape varied across regions. In the north-central part of the state, we found higher seroconversion in less populated suburban areas while higher seroconversion in south-central Florida was correlated with fragmented forested areas within 0.5 km of coops and intact woody wetland areas within 2 km of coops. This work corroborates previous findings that consistent landscape predictors of WNV are difficult to identify across broader geographic areas and sets the stage for additional work that incorporates climate and landscapes interactions for a greater understanding of WNV ecology in this geographic region.

Development of Polymorphic Index Model for Assessing Subtropical Secondary Natural Oak Forest Site Quality Under Complex Site and Climate Variables

Site and climate conditions are the key determinants controlling dominant height growth and forest productivity, both independently and interactively. Secondary natural oak forests are a typical forest type in China, especially in Hunan Province, but little is known about the site index of this forest under the complex site and climate variables in the subtropics. Based on survey data of dominant trees and site variables from 101 plots in Hunan oak natural secondary forests and climate data obtained using spatial interpolation, we used the random forest method, correlation analysis, and the analysis of variance to determine the main site and climate factors affecting oak forest dominant height and proposed a modeling method of an oak natural secondary forest site index based on the random effect of site–climate interaction type. Of the site variables, elevation affected stand dominant height the most, followed by slope direction and position. Winter precipitation and summer mean maximum temperature had the greatest impact on stand dominant height. To develop the modeling method, we created 10 popular base models but found low performance (R2 ranged from 0.1731 to 0.2030). The optimal base model was Mitscherlich form M3 (R2 = 0.1940) based on parameter significance tests. Since site and climate factors affect the site index curve, the dominant site and climate factors were combined into site types and climate types, respectively, and a nonlinear mixed-effects approach was used to simulate different site types, climate types, site–climate interaction types, and their combinations as random effects. Site–climate interaction type as a random factor enhanced model (M3.4) performance and prediction accuracy (R2 from 0.1940 to 0.8220) compared to the optimum base model. After clustering the 62 site–climate interaction types into three, five, and eight groups using hierarchical clustering, a mixed-effects model with the random effects of eight groups improved model performance (R2 = 0.8265) and applicability. The modeling method developed in this study could be used to assess a regional secondary natural oak forest site index under complex site and climate variables to evaluate the forest productivity.

ALICENET – an Italian network of automated lidar ceilometers for four-dimensional aerosol monitoring: infrastructure, data processing, and applications

Abstract. ​​​​​​​Vertically resolved information on aerosol particles represents a key aspect in many atmospheric studies, including aerosol–climate interactions and aerosol impacts on air quality and human health. This information is primarily derived by lidar active remote sensing, in particular with extensive networks currently in operation worldwide. In Italy, the Institute of Atmospheric Sciences and Climate (ISAC) of the National Research Council (CNR) established the ALICENET network of automated lidar ceilometers (ALCs) in 2015. Since then, ALICENET has grown as a cooperative effort of Italian institutions dealing with atmospheric science and monitoring, and it currently includes instruments run by regional environmental protection agencies, universities, research centres, and private companies. In the current configuration, the network makes use of both single-channel ALCs and dual-channel, polarisation-sensitive-system ALCs (referred to as PLCs). The systems operate in very different environments (urban, coastal, mountainous, and volcanic areas) from northern to southern Italy, thus allowing the continuous monitoring of the aerosol vertical distribution across the country. ALICENET also contributes to the EUMETNET programme E-PROFILE, filling an Italian observational gap compared to other EU member states, which generally run extended ALC networks through national meteorological services. In this work, we present the ALICENET infrastructure and the specifically developed data processing centralised at CNR-ISAC, converting raw instrumental data into quantitative, quality-controlled information on aerosol properties ranging from attenuated backscatter to aerosol mass and vertical stratifications. This setup allows us to get insights into the 4D aerosol field over Italy with applications from near-real-time monitoring to long-term analyses, examples of which are reported in this work. Specific comparisons of the ALICENET products to independent measurements obtained with different techniques, such as particulate matter (PM) concentrations from in situ samplers and aerosol optical depth (AOD) from sun photometers, are also included here, revealing the good performances of the ALICENET algorithms. Overall, ALICENET represents a valuable resource to extend the current aerosol observational capabilities in Italy and in the Mediterranean area, and it contributes to bridging the gap between atmospheric science and its application to specific sectors, among which are air quality, solar energy, and aviation safety.

Predicting hygroscopic growth of organosulfur aerosol particles using COSMOtherm

Abstract. Organosulfur (OS) compounds are important sulfur species in atmospheric aerosol particles, due to the reduction of global inorganic sulfur emissions. Understanding the physicochemical properties, such as hygroscopicity, of OS compounds is important for predicting future aerosol–cloud–climate interactions. However, their hygroscopicity is not yet well understood due to the scarcity of authentic standards. In this work, we investigated a group of OS compounds with short carbon chains (C1–C5) and oxygen-containing functional groups in the form of sodium, potassium, or ammonium salts and their mixtures with ammonium sulfate. The hygroscopic growth factors (HGFs) of these OS compounds have been experimentally studied. Here, the HGFs were calculated from mass fraction of water that was computed using the conductor-like screening model for real solvents (COSMO-RS). A good agreement was found between the model-estimated and experimental HGFs for the studied OS compounds. This quantum-chemistry-based approach for HGF estimation will open up the possibility of investigating the hygroscopicity of other OS compounds present in the atmosphere.

Synchronous decadal climate variability in the tropical Central Pacific and tropical South Atlantic

Pantropical climate interactions across ocean basins operate on a wide range of timescales and can improve the accuracy of climate predictions. Here, we show in observations that Central Pacific (CP) El Niño-like sea surface temperature (SST) anomalies have coevolved with tropical South Atlantic SST anomalies on a quasi-decadal (~10-year) timescale over the past seven decades. During the austral autumn–winter season, decadal warm SSTs in the tropical CP effectively induce tropical SST cooling in the South Atlantic, mainly by strengthening the South Atlantic subtropical anticyclone via an extratropical atmospheric wave teleconnection in the southern hemisphere. Partially coupled pacemaker simulations corroborate the observational findings, indicating that tropical CP decadal SSTs play a primary pacing role, while Atlantic feedback is of secondary importance throughout the study period. Our results suggest that the tropical CP could be an important source of decadal predictability for tropical South Atlantic SST and the surrounding climate.

Impact of Climate Change and Human Activities on the Ecological Sensitivity of the Yangtze River Economic Belt

The increasing climate change and human activities exert their influence on the ecological sensitivity of the region individually and interactively. Therefore, a clear understanding of the impact of climate change and human activities on ecological sensitivity will enhance the resilience of the regional ecological environment and the level of sustainable development. This study took the Yangtze River Economic Belt, the first demonstration zone of China's ecological civilization construction, as the research object. Based on the meteorological, remote sensing, and statistical data of 130 cities in the whole region from 2001 to 2021, an index system of climate change, human activities, and ecological sensitivity was constructed. Response surface methodology （RSM） was used to explore the effects of climate and anthropogenic single factors and interactions on the ecological sensitivity in each region. The results showed that： ① The ecological sensitivity value of the belt fluctuated and rose in time, rising by 2.2% from 2001 to 2021. In terms of space, the overall spatial distribution was "high in the north and low in the south." In 2021, the proportion of severely and extremely sensitive cities in the Yangtze River Economic Belt reached nearly 50%. ② For a single factor, the distribution of the effect of the same factor had certain characteristics： The areas where the single factors of economic development, rainfall, and temperature had a positive impact on the ecological sensitivity were concentrated in the areas with higher or faster economic development, along and south of the Yangtze River. For the interaction factors, the effect of 78.6% of the factors on the ecological sensitivity was negative interaction, and the change of one factor level would change the direction of the effect of the other factor on the regional sensitivity. ③ The comprehensive ecological management area of the Yangtze River Economic Belt was divided based on the ecological sensitivity and climate sensitivity. The governance areas that needed priority improvement were clustered within the three urban agglomerations and their northern adjacent areas, which meant that the ecological sensitivity and climate sensitivity of a city had spillover effects. This study is expected to provide inspiration for the economic zone and even the national and global efforts in the field of regional ecological governance.

Land cover and burn severity dynamics of the Ogan Komering Ilir peatlands from 2015 to 2023 using sar and optical datasets

Land cover changes and wildfires have had an increasing impact on the Ogan Komering Ilir Peatland ecosystems in South Sumatra, Indonesia. This study aims to understand the peatland land cover and burn severity dynamics from 2015 to 2023. The random forest method was applied to classify land cover, while the differenced Normalized Burn Ratio (dNBR) was used for mapping fire severity. We combined various satellite data to classify land cover, consisting of Landsat-8, Sentinel-1, and Sentinel-2. Landsat-8 or Sentinel-2 images were also used for the dNBR calculation. We revealed complex climate, human, and restoration interactions in land cover and burn severity fluctuations over 273,799 hectares of the study area from 2015 to 2023. The 2015 El Niño-induced drought led to 21,754 fire hotspots and 2.01% of the area in high-severity burns. In 2016, it reduced tree cover by 10.18% and increased bare/sparse vegetation by 6.11%. The 2019 El Niño event led to 7,893 fire hotspots, lessening unburned areas and worsening burns. Due to the extreme effects of the 2015 drought, restoration efforts between 2016 and 2020 significantly decreased fire hotspots in 2016. Tree cover stabilized, reaching 48.46% by 2020, whereas unburned areas rose to 69.46% in 2018, showing good recovery and lower fire severity. In 2021-2023, fire hotspots were modest relative to El Niño years but increased in 2023. After 2020, tree cover decreased, but other land cover classes fluctuated. Therefore, continual monitoring and adaptive management are critical for reducing negative consequences and increasing ecosystem resilience.

Flammable futures—storylines of climatic impacts on wildfire events and palm oil plantations in Indonesia

Abstract Wildfire events are driven by complex interactions of climate and anthropogenic interventions. Predictions of future wildfire events, their extremity, and their impact on the environment and economy must consider the interactions between these drivers. Economic policy and land use decisions influence the susceptibility of an area to climate extremes, the probability of burning, and future decision making. To better understand how climate-driven drought events and adaptation efforts affect burned area, agricultural production losses, and land use decisions, we developed a storyline approach centered on Indonesia’s 2015 fire events, which saw significant (>5%) production losses of palm oil. We explored analogous events under three warming conditions and two storylines (multi-model ensemble mean climate change and high impact). We employed a model chain consisting of CMIP6 climate modeling to quantify climate change impacts, a wildfire climate impacts and adaptation model (FLAM) to predict burned areas, and the Global Biosphere Management Model (GLOBIOM) to predict resultant production losses and socio-economic consequences in the oil palm sector in Indonesia and, by extension, the EU. FLAM is a mechanistic fire model used to reproduce and project wildfires based on various criteria and input variables; GLOBIOM is a global economic land use model, which assesses land use competition and provides economic impacts based on scenario data. We found that total burned area and production loss can increase by up to 25% and lead to local price increases up to 70%, with only minor differences beyond 2.5 degrees of warming. Our results highlight the importance of considering the interactions of future warming, drought conditions, and extreme weather events when predicting their impacts on oil palm losses and burned area. This study sets the stage for further exploration on the impacts of land management policies on local and international environments and economies in the context of global warming.

The relationship of δD and δ18O in soil water and its implications for soil evaporation across distinct rainfall years in winter wheat field in the North China Plain

Soil evaporation plays a key role in regulating local climate and water loss. Stable isotope ratios of water (²H/¹H and ¹⁸O/¹⁶O) are effective tracers for studying water flux. This study examines three isotope-based indicators deuterium excess (d-excess), the slope of the soil water evaporation line (SEL), and line-conditioned excess (lc-excess) across three wheat growing seasons: wet, ordinary, and dry years. The influencing factors of d-excess, SEL, and lc-excess, respectively, soil, vegetation, and meteorology, were analyzed using various methods. Wheat yields varied significantly, reaching 6.69 t ha⁻¹ in wet years, 8.66 t ha⁻¹ in dry years, and 9.28 t ha⁻¹ in ordinary years. The lc-excess was highest in ordinary years, and d-excess peaked during dry years. A negative correlation between d-excess and SEL slope, and between lc-excess and SEL slope, was observed in dry and ordinary years (P<0.05), but not in wet years (P>0.05). Multivariate regression showed that net radiation (Rn) was the primary factor influencing SEL, contributing 54.19 %, 11.58 %, and 29.27 % in wet, dry, and ordinary years, respectively. Leaf area index (LAI) was the most significant factor affecting lc-excess (37.91 % in wet years, 32.22 % in dry, and 30.92 % in ordinary years). Vapor pressure deficit (VPD) affected d-excess in wet and ordinary years, while air (Ta) and soil temperature (Ts) were key in dry years. Variation partitioning revealed meteorological factors primarily influenced SEL, lc-excess, and d-excess in wet years, while soil, vegetation, and climate interactions had greater effects in dry and ordinary years. The lc-excess, integrating multiple factors, is a better indicator of soil evaporation than SEL.

Does the work environment matter in shaping English as a foreign language teachers’ teaching for creativity: A mixed‐methods investigation into the roles of perceived climate, peer group interaction, and supervisory relationship

AbstractTeachers play a critical role in fostering students’ creativity, especially in English as a foreign language (EFL) classes, known as teaching for creativity (TfC). Despite this, no comprehensive study has explored how the work environment influences EFL teachers’ TfC. Therefore, this study investigates how various holistic work environment factors affect TfC among Chinese EFL teachers. Drawing on the dynamic componential model of creativity, the study employs a mixed‐methods approach, combining quantitative data from a survey of 406 EFL teachers analyzed using partial least squares structural equation modeling in Smart PLS 3, and qualitative insights from semi‐structured interviews with 20 EFL teachers analyzed in MAXQDA 2022. The quantitative results reveal that perceived climate and peer group interaction positively and significantly impact TfC, whereas supervisory relationship (SR) does not show significant effects. The qualitative findings validate these outcomes and offer deeper insights into how PC and PGI specifically facilitate or impede TfC, alongside explanations for the non‐significant role of SR. Additionally, the qualitative analysis identifies another influential factor on TfC: teacher–student interaction. These findings carry theoretical and practical implications for teacher educators and the professional development of EFL teachers.

Enhanced root zone soil moisture monitoring using multitemporal remote sensing data and machine learning techniques

Accurate root zone soil moisture (RZSM) estimation using remote sensing (RS) in areas with dense vegetation is essential for real-time field monitoring and precise irrigation scheduling. Traditional methods often face challenges due to the dense crop cover and the complexity of soil and climate interactions. These challenges include the coarse spatial resolution of available soil moisture products, the influence of vegetation and surface roughness, and the difficulty of estimating RZSM from surface data. Aiming to overcome these limitations, two RZSM estimation methods were developed by combining synthetic aperture radar (SAR) data from Sentinel-1 (VV and VH polarizations) and optical and thermal RS data from Landsat-8. These data sources were used in conjunction with various machine learning (ML) models such as M5-pruned (M5P), support vector regression (SVR), extreme gradient boosting (XGBoost), and random forest regression (RFR) to improve the accuracy of soil moisture estimation. In addition to RS data, soil physical and hydraulic properties, meteorological variables, and topographical parameters were selected as inputs to the ML models for estimating the RZSM of sugarcane crops in Khuzestan, Iran. This study identified the temperature vegetation dryness index (TVDI) as a critical parameter for estimating RZSM in combination with the Sentinel-1 SAR data under high vegetation conditions. In both methods, the RFR algorithm outperformed, with similar performance, the XGBoost, SVR, and M5P algorithms in estimating soil surface moisture (R2 = 0.89, RMSE = 0.04 cm3cm−3). However, the accuracy of the RFR algorithm decreased with increasing depth for both the optical-thermal and combined SAR and optical-thermal RS data. This decrease was more pronounced in the combined approach, particularly for the root zone, where the RMSE reached approximately 0.073 cm3cm−3. Accordingly, the key findings demonstrated that the optical-thermal RS data outperformed the SAR RS data for retrieving RZSM in high-vegetated areas. However, combining TVDI with SAR data is a substantial improvement that opens a new path in radar-based RZSM estimation methods under high vegetation conditions.

Spatial pattern of forest aboveground biomass and its environmental influencing factors in Qinling-Daba Mountains, central China

Accurate estimation of forest aboveground biomass (AGB) is crucial for understanding and managing forest ecosystems in the context of global environmental change, and also provides a scientific basis for national and regional ecological planning and carbon emission reduction policies. In order to investigate the regional pattern of forest AGB and its influencing factors in central China, a total of 469 sample plots were measured along four sample transects and on six mountains in field survey. The results showed that: 1) Two longitudinal distribution patterns of forest AGB were found, and one was that the AGB in the Qinling Mountains and the Daba Moutains gradually decreased from east to west, and the other was that the AGB in the areas between the two mountains gradually increased from east to west. The latitudinal distribution pattern of the forest AGB showed an "increasing–decreasing-increasing–decreasing" trend from south to north. The altitudinal distribution pattern showed a "first increasing-then decreasing" pattern with increasing altitude. 2) The influence of each factor on the spatial pattern of forest AGB was manifested as temperature > precipitation > HAI > topography, indicating that the spatial pattern of forest AGB in central China was the result of the interaction of climate, human activities and natural factors.

Interactions of climate, topography, and soil factors can enhance the effect of a single factor on spring phenology in the arid/semi-arid grasslands of China

Understanding the interactions among climate, topography, and soil factors on vegetation spring phenology can help reveal the feedback mechanisms of vegetation ecosystems in response to environmental conditions and climate change. Nonetheless, the coupling effects of these environmental elements on vegetation start of season (SOS) are still poorly understood. We took Xinjiang Province, a typical arid/semi-arid region, as the research area and quantitatively analyzed the explanatory power and interactions of climate, topography, and soil factors on grassland SOS through the geographical detector model based on the data extracted by Timesat 3.3 software. The results showed that: (1) From 2001 to 2020, early SOS predominantly occurred from 75 to 105 days in Northern Xinjiang. While later SOS was widespread in Southern Xinjiang from 105 to 135 days. The area of the SOS advanced region was larger than that of the delayed region; (2) There was obvious spatial hierarchical heterogeneity between SOS and climate, topography, and soil factors. Altitude, maximum temperature, and soil type were the primary drivers of SOS changes in the study area (P < 0.001); (3) The interactions among environmental factors strengthened the explanatory power of individual factors, either through mutual reinforcement or non-linear effects. The interactions between elevation and climate factors were particularly significant, with an explanatory power above 0.3. This study highlighted the importance of the coupling effects of environmental variables on SOS and suggested that drives should be considered more comprehensively in future analyses.

Impact of Enso on Indian Monsoon and Food Production

El Nino is a big ocean-atmosphere climate interaction It happens when Sea Temperatures (SST) warm up in central and eastern parts of the Equatorial Pacific. Whereas La Nina is the opposite event of it, when ocean SST cools down in those same areas. Together, El Nino and La Nina are known as the El Nino Southern Oscillation Index (O). Usually, El Nino brings warm and dry weather to southern &amp; eastern inland regions of India, Australia, Indonesia, Philippines, Malaysia, and the central Pacific islands. The variations of Indian summer monsoon Rainfall (ISMR) are linked to changes in sea surface temperatures over the equatorial Pacific &amp; Indian Oceans. ENSO events really affect summer rainfall in India and major droughts have faced during El Nino events. When El Nino kicks in, it often leads to weak monsoons &amp; higher temperatures in India. This raises the chances of droughts, which could upset crop production &amp; water supply in India. The lack of rain because of El Nino causes crop production to drop, especially short-duration Kharif crops. This situation can lead to higher inflation rates &amp; slower GDP growth, since agriculture is such a huge part of India’s economy. Therefore, this paper provide information about how El Nino affects climate throughout various regions of India, especially regarding monsoons and growing food.

Globally assessing how evapotranspiration feedbacks govern the impacts of multi-year droughts

Multi-year droughts constitute a natural experiment for testing the hydrological legacy across different climate regimes. Evapotranspiration (ET) responses to drought have not been fully elucidated because the underlying mechanisms involve complex interactions of climate, soil, and vegetation. This study collected monthly hydrometeorological data during 1982–2011 from 1069 catchments worldwide to investigate multi-year droughts and their impacts on ET and to analyse the underlying mechanisms of ET. As expected, multi-year droughts generally induced ET reductions across different climate regimes, while ET exhibited asymmetric hydrological responses with a greater portion of precipitation (P) being partitioned into ET in water-limited regions, and a smaller portion of P being partitioned in ET in energy-limited regions. Multi-year droughts induced soil moisture droughts and leaf area index decreases, which often led to increases in the vapour pressure deficit, while inconsistent responses occurred for air temperature. Interestingly, when long-term catchment wetness [i.e., (precipitation − streamflow) / precipitation] exceeded 0.50 we found that catchment wetness played a vital role in controlling the response of catchment properties to climate changes. During the multi-year droughts, attribution analysis revealed the enhanced role of available water (represented by P) in controlling ET, followed by available energy and vegetation. Although vegetation played vital role in controlling ET in energy-limited regions, it was P that controlled ET during drought periods. Taken together, these findings indicate that ET responses and their underlying mechanisms induced by multi-year droughts vary under different climate regimes. Specifically, a two-stage mechanism drove the ET responses in wet catchments, suggesting that catchments with higher catchment wetness (>0.50) could more effectively mitigate the impact of multi-year droughts.