Climate Warming Research Articles

Clean Energy and Long Run Dynamics of Output, Labor and Capital Stock: Evidence from China

Clean energy has sparked the importance of a sustainable environment and economic production in China. The concerns of climate and global warming can be solved with the adoption of clean energy. This study investigates the potential long-run connection amid economic production, labor, and capital stock with clean energy employing data from the years 1990-2020 in China. The combination of long-run estimates with Granger causalit678y has been employed. The observed output indicates that there is a long-run connection among these variables in an equilibrium sense. Also, changes in clean energy are significant in explaining changes in economic production. The outputs of the causality test corroborate that clean energy is causing economic production. The estimated elasticity of clean energy with respect to production is statistically significant in the long run. The findings imply that the development of new energy technologies for clean energy should be incorporated into the energy mix to balance the supply-demand gap.

No deceleration signs in the permafrost ground subsidence four years after the 2019 fire in Northwest Territories, Canada

Abstract The circum-arctic permafrost environment is often disturbed by wildfires but could also show resilience to these disturbances. However, the increased frequency and extent of wildfires, coupled with unprecedented hot weather, have introduced greater uncertainties in the post-fire permafrost dynamics. We need to address emerging questions, e.g., How will permafrost respond to the joint effect of hot anomalies and wildfires? To what extent will post-wildfire deformation evolve? How will permafrost resilience to wildfires vary? Utilizing Interferometric Synthetic Aperture Radar (InSAR) time series analysis, we investigated the post-wildfire ground deformation around a 2019 fire scar in the lower Mackenzie Valley, Northwest Territories, Canada, where dramatic heat anomalies and severe wildfires have been recorded in recent years. The resilience of permafrost to wildfires appears to be weakened by the continuous and rapid warming after the fire, as evidenced by the year-on-year acceleration in subsidence rates. Such acceleration was never reported by previous findings that typically observed deceleration in subsidence rates four to five years after wildfires. The deformation along the line of sight (LOS) of the satellite demonstrates significant permafrost degradation induced by wildfires and exacerbated by climate warming, and the cumulative subsidence was detected up to 25 cm in the LOS direction in the upland areas and up to 10 cm in the lowland areas four years after the fire. The difference in deformation magnitude could be attributed to local factors, including ground ice, topography, and vegetation. Our study highlights the increasingly severe threat to circum-arctic permafrost due to the combined effects of wildfires and extreme heat anomalies.

Integrating remotely sensed imagery in a forest gap model to study North American boreal forests in a changing world

Abstract Boreal forests of Alaska and Western Canada are experiencing rapid climate change characterized by higher temperatures, more extreme droughts, and changing disturbance regimes, resulting in forest mortality and composition changes. Mechanistic models are increasingly important for predicting future forest trends as the region experiences novel environmental change. Previously, many process-based models have generated starting conditions by ‘spinning up’ to equilibrium. However, setting appropriate initial conditions remains a persistent challenge in using mechanistic forest models, where stochastic events and latent parameters governing tree establishment have long-lasting impacts on simulation outcomes. Recent advances in remote sensing analysis provide information that can help address this issue. We updated an individual-based gap model, the University of Virginia Forest Model Enhanced (UVAFME), to include initial conditions derived from aerial and satellite imagery at two locations. Following these updates, material legacies (e.g., trees, seed banks, soil organic layer) allowed new forest types to persist in UVAFME simulations, landscape-level forest heterogeneity increased, and forest-wide biomass estimates increased. At both study sites, initialization from remotely sensed data had a strong impact on forest cover and volume. Climate change impacts were simulated decades earlier than when the model was ‘spun up'. In Alaska’s Tanana Valley State Forest, warmer climate scenarios drove deciduous expansion, increased drought stress, and resulted in a 28% decrease in overall biomass by 2100 between historical and high emissions climate scenarios. At a lowland site in Northern British Columbia, lodgepole pine (Pinus contorta) remained dominant and became more productive with exogenous climate forcing as temperature, nutrient, and flooding limitations decreased. These case studies demonstrate a new framework for forest modeling and emphasize the advantages of integrating remotely sensed data with mechanistic models, thereby laying groundwork for future research that explores near-term impacts of non-stationary ecological change.

Author Correction: Energy from buildings is key to a warming climate

Author Correction: Energy from buildings is key to a warming climate

Highly inhomogeneous interactions between background climate and urban warming across typical local climate zones in heatwave and non-heatwave days

Abstract Urban heat island (UHI) in conjunction with heatwave (HW) leads to exacerbation of thermal stress in urban areas. Prior research on UHI and HW has predominantly concentrated on examining the thermal conditions at the surface and near-surface, with few investigations extending to the radiative and dynamical interactions of UHI and HW, particularly with a focus on the inhomogeneities across local climate zones (LCZs). Here, we analyse the temperature disparity between HW and non-HW conditions across LCZs in the Sydney area by quantifying the contributions of individual radiative and dynamical processes using the coupled surface-atmosphere climate feedback-response analysis method (CFRAM). Three moist HW events in 2017, 2019, and 2020 are simulated using the Weather Research and Forecasting (WRF) model coupled with the Single-Layer Urban Canopy Model (SLUCM). It is found that the maximum surface and 900-hPa temperature difference between HW and non-HW days may reach up to 10 K, with the increased net solar radiation during HWs being comparable to the typical level of anthropogenic heat flux in urban areas. It is also found that the reduction of clouds, the presence of vapor, and the increase of sensible heat contribute to the warming effect to various degrees, with the contribution of clouds being the most dominant. Conversely, the generation of dry convection and the increase of latent heat flux lead to cooling effects, with the latter being more dominant and capable of causing up to 10 K surface temperature difference between LCZ1 (compact high-rise) and LCZ9 (sparsely built). The differences in the contributions of climate feedback processes across different LCZs become more evident during more severe and humid HWs. These findings underscore the necessity of implementing local climate zone-tailored heat mitigation strategies.

Feedback mechanisms controlling Antarctic glacial-cycle dynamics simulated with a coupled ice sheet–solid Earth model

Abstract. The dynamics of the ice sheets on glacial timescales are highly controlled by interactions with the solid Earth, i.e., the glacial isostatic adjustment (GIA). Particularly at marine ice sheets, competing feedback mechanisms govern the migration of the ice sheet's grounding line (GL) and hence the ice sheet stability. For this study, we developed a coupling scheme and performed a suite of coupled ice sheet–solid Earth simulations over the last two glacial cycles. To represent ice sheet dynamics we apply the Parallel Ice Sheet Model (PISM), and to represent the solid Earth response we apply the 3D VIscoelastic Lithosphere and MAntle model (VILMA), which, in addition to load deformation and rotation changes, considers the gravitationally consistent redistribution of water (the sea-level equation). We decided on an offline coupling between the two model components. By convergence of trajectories of the Antarctic Ice Sheet deglaciation we determine optimal coupling time step and spatial resolution of the GIA model and compare patterns of inferred relative sea-level change since the Last Glacial Maximum with the results from previous studies. With our coupling setup we evaluate the relevance of feedback mechanisms for the glaciation and deglaciation phases in Antarctica considering different 3D Earth structures resulting in a range of load-response timescales. For rather long timescales, in a glacial climate associated with the far-field sea-level low stand, we find GL advance up to the edge of the continental shelf mainly in West Antarctica, dominated by a self-amplifying GIA feedback, which we call the “forebulge feedback”. For the much shorter timescale of deglaciation, dominated by the marine ice sheet instability, our simulations suggest that the stabilizing sea-level feedback can significantly slow down GL retreat in the Ross sector, which is dominated by a very weak Earth structure (i.e., low mantle viscosity and thin lithosphere). This delaying effect prevents a Holocene GL retreat beyond its present-day position, which is discussed in the scientific community and supported by observational evidence at the Siple Coast and by previous model simulations. The applied coupled framework, PISM–VILMA, allows for defining restart states to run multiple sensitivity simulations from. It can be easily implemented in Earth system models (ESMs) and provides the tools to gain a better understanding of ice sheet stability on glacial timescales as well as in a warmer future climate.

Late-Holocene ecosystem dynamics on Weizhou Island, China: A pollen and historical record of climate change and anthropogenic disturbances

Understanding the mechanisms behind landscape diversity changes on islands, particularly due to historical human interventions, remains a critical challenge in paleoecology. This study addresses this gap by utilizing over 160 pollen samples combined with multi-proxy data and historical records to reconstruct the late-Holocene ecological and environmental dynamics of Weizhou Island, China, over the last 1400 years. Our analyses reveal the evolution of ecosystem diversity and vegetation succession amid climatic fluctuations and anthropogenic pressures, identifying four distinct phases of transformation: (1) A warm and dry climate from 1400 to 850 cal yr BP fostering a savanna ecosystem; (2) A shift to a North subtropical seasonal rainforest under a more humid climate between 850 and 210 cal yr BP, with increased vegetation diversity; (3) The initiation of agriculture and aquaculture, along with a cooler climate from 210 cal yr BP to 1970 CE, marked by human-induced landscape alterations; and (4) A significant transformation from a once-diverse ecosystem to artificial protective forests from 1970 CE to the present. By correlating environmental indicators with historical accounts across eight Chinese dynasties, our study provides detailed insights into the climatic and human factors shaping the island's history. The findings demonstrate that while climatic shifts are primary drivers of biodiversity changes, anthropogenic disturbances also play a significant role. This research underscores the resilience of ecosystems to both climatic and human pressures and emphasizes the importance of integrating historical context into environmental studies.

Impacts of climate change on the transcriptional dynamics and timing of bud dormancy release in Yoshino‐cherry tree

Societal Impact StatementThe iconic Yoshino cherry tree in Japan is experiencing shifts in its blossom timing due to climate warming. To develop a genetically informed predictive model for bud dormancy release, we examined seasonal gene expression in Yoshino cherry trees at three different locations. Our experiments, coupled with the analysis of DORMANCY‐ASSOCIATED MADS‐box (DAM) genes, highlighted DAM4 as the most reliable indicator for the rate of bud dormancy release. Our study demonstrated that seasonal gene expression profiles serve as a valuable indicator for forecasting the timing of dormancy release, benefiting Japanese traditions and providing insights into the biological impacts of climate change.Summary The Yoshino cherry tree Cerasus × yedoensis ‘Somei‐yoshino’ stands out as an iconic springtime symbol in Japan. For the Yoshino cherry trees to bloom in the spring, dormant buds must undergo a period of exposure to low temperatures, allowing them to break dormancy. Key genes related to dormancy release, known as DORMANCY‐ASSOCIATED MADS‐box (DAM), have been extensively studied. However, it remains unclear how these genes function in natural environments to regulate the timing of bud dormancy release. To develop a genetically informed predictive model for bud dormancy release, we explored seasonal changes in genome‐wide gene expression profiles in the Yoshino cherry trees at three distinct sites in Japan. Five distinct genome‐wide transcription profiles, subjectively named as modes—early summer, summer, autumn, winter, and spring—were identified, with the winter and spring modes observed when the daily mean temperature was below approximately 10°C. Our experiments of bud dormancy release, along with the assessment of expression profiles of DAM genes, have revealed that among the six DAM genes, DAM4 expression profile is the most indicative of the rate of bud dormancy break. Our estimates suggest that, on average, the tree needs to be exposed to temperatures below 10.1°C for 61.1 days to suppress DAM4 expression to the threshold required for bud dormancy release. Our projections for the timing of bud dormancy release indicated a delay of approximately 2.3 days per decade from 1990 to 2020. Our study demonstrated that gene expression serves as a valuable indicator for forecasting the timing of dormancy release.

A Slowdown in Translation Speed of Western North Pacific Tropical Cyclones Undergoing Rapid Intensification

AbstractThis study examines long‐term trends in western North Pacific (WNP) tropical cyclones (TCs) experiencing rapid intensification (RI) from 1971 to 2022. Although there is only a weak slowdown for all intensifying WNP TCs, the average translation speed for RI TCs has significantly decelerated over the RI main development region (7.5°–25°N, 115°–160°E). This slowdown is primarily due to increasing RI probabilities for slower‐moving TCs. By contrast, the RI probability of faster‐moving TCs remains virtually unchanged. These differences in RI trend probabilities between slow‐moving and fast‐moving TCs are primarily linked to a deepening of the WNP mixed layer. TC‐induced sea surface temperature cooling tends to weaken when the mixed layer is deep. During the intensification stage, the deeper mixed layer is more critical for slower‐moving TCs than for faster‐moving TCs. Our findings suggest that RI probabilities for slow‐moving WNP TCs may continue to increase in a future warming climate.

Amazon severe drought in 2023 triggered surface water loss

Abstract The Amazon underwent a severe austral springtime drought attributed to the onset of El Niño in 2023 and the warmer North Atlantic, Indian, and North Pacific Oceans. The Amazon rivers, lakes, small streams, wetlands, and reservoirs quickly lowered their water level below historical records due to decreased rainfall and warmth in the region. Based on satellite imagery, this study presents the first estimate of the water loss extent in the 4.2 million square km of the Brazilian Amazon biome (60% of the Amazon total biome) in 2023. We estimated the loss of 3.3 million hectares of surface water relative to 2022, with an overall accuracy of 92%. The surface water losses were concentrated in the states of Amazonas (59.4%) and Pará (25.5), adding up to 2.8 million hectares. The warmer and drier climate in the region affected the main rivers in the Amazon. Among them, the Solimões, Rio Negro, Purus, Acre, and Rio Branco suffered extreme drops in their levels in some regions, resulting in a high negative impact on the aquatic biodiversity, yet estimated only in local areas. A total of 1.14 million hectares of surface water loss (i.e., 35%) was detected within Protected Areas territories, affecting extractivist, indigenous African-Brazilian, and fishing and traditional communities. Proximity analysis revealed that 75% of the 2023 surface water loss was within 25 km of small towns, 48%, and 65.8% at 50 km from indigenous villages and urban areas, respectively. Our results reinforce people's vulnerability to climate change, anticipating a plausible adverse impact scenario in the Amazon region and urging solutions to adaptation and mitigation. Therefore, an integrated monitoring system based on climate and water dynamics from satellite and ground stations is necessary to improve understanding of the problem for timing response of climatic change negative impacts.

Fertilization and tillage influence on soil organic carbon fractions: A global meta-analysis

Soilorganic carbon(SOC), as the largest terrestrial carbon pool, plays an important role in global carbon cycling, which is significantly impacted by agricultural practices. However, our ability to accurately detect and predict the impacts of fertilization and tillage onSOC dynamicsis still limited. Investigating the effects of fertilization and tillage on different SOC fractions [i.e. mineral-associated organic carbon (MAOC), particulate organic carbon (POC), free POC (frPOC), occluded POC (oPOC), coarse POC (cPOC), and fine POC (fiPOC)]can aid in the understanding of overall SOC accumulation and stabilization. To this end, we evaluated the fertilization and tillage influences on SOC fractions through a global meta-analysis. We also quantified the role of environmental and agronomic factors in modulating these effects. Fertilization increased SOC fractions by mean percent change (MPC)13 %-77 %, while tillage decreased by MPC 4 %-63 %. Among them, cPOC was the most sensitive to fertilization, while frPOC had the highest sensitivity to tillage. MAOC was the least sensitive to both practices. The application of organic fertilizer increased MAOC, SOC, and POC the most (MPC 20 %-77 %), while mineral-organic fertilizer increased frPOC, oPOC, fiPOC, and cPOC the most (MPC 81 %-126 %). Fertilization in alkaline soils with warm and humid (MAT=16–24 °C, MAP>1000 mm) climate could maximally increase SOC contents from various fractions in surface layer (<20 cm depth), particularly when the altitude was 500–1000 m. However, tillage in acidic soils with low temperatures and rainy climate (MAT=8–16 °C, MAP>1000 mm) reduced the contents of SOC fractions the most in deep layer (>40 cm depth), especially at altitudes greater than 2000 m. Whether under fertilization or tillage, POC (occupying 62 %-74 %) consistently contributed more to SOC than MAOC (26 %-38 %). Overall, we suggest that SOC fractions should be prioritized over total SOC when evaluating the effects of site-specific management strategies on carbon sequestration in agricultural lands.

Assessing Building Energy Savings and the Greenhouse Gas Mitigation Potential of Green Roofs in Shanghai Using a GIS-Based Approach

Climate change can significantly affect building energy use and associated greenhouse gas (GHG) emissions in urban areas, as fossil fuels remain a significant energy source. Green roofs can offer multiple benefits to the urban environment, but their effects on GHG mitigation have not been fully investigated, especially under climate change. This study assessed green roofs’ contribution to GHG mitigation by saving building energy and absorbing CO2 under the present (2017–2019) and future (2049–2051) climate scenarios (SSP2-45 and SSP5-85) in Shanghai, China, at the city and township scale. A Geographic Information System (GIS)-based spatial statistical method was developed based on climate change modeling and building energy simulation. The results suggested that installing green roofs can effectively save building energy regardless of building type, yet the amount of savings can vary depending on the weather conditions within the city. The contribution analysis indicated that most saved building energy was attributed to the Heating, Ventilation, and Cooling (HVAC) system, with more energy saved under warmer climate scenarios in the future, particularly during the summer months. More energy was saved from shopping malls on an annual and monthly scale, regardless of the climate scenarios and weather zones. Finally, a case study indicated installing green roofs on all five types of buildings (office, hotel, hospital, shopping mall, apartment) of less than 50 m in height can reduce 8.28% of the CO2 emitted during the building operation stage in the entire city under the present climate scenario. The annual CO2 reduction varied with the location of townships, ranging from 2.18% to 13.78%, depending on the composition of building types and local weather conditions in Shanghai. This study offered policymakers a reference on the environmental benefits and investment values of installing green roofs in large cities.

Increasing exposure of global croplands productivity to growing season heatwaves under climate warming

Abstract Growing season heatwaves that occur simultaneously over global croplands can negatively impact global food baskets. The long-term changes of growing season heatwaves, as well as their impacts on croplands productivity, are crucial to food security, but remain unclear. Here, we investigated changes in the frequency, intensity and magnitude of growing season heatwaves from the past to the future over the global croplands, based on observations and Coupled Model Intercomparison Project Phase 6 models. We introduced an index, gross primary productivity (GPP) exposure, as a proxy of the overall impact of heatwaves on cropland productivity. The results show that the frequency and intensity of growing season heatwaves have increased since 1950 and will continue throughout the 21st century. The increase of the annual accumulated magnitude of growing season heatwaves in the future is mainly contributed by the increase of heatwave frequency. This leads to a global-scale increase in the GPP exposure to growing season heatwaves, with Asia, North America, and Europe being the most affected. The continued increase in GPP exposure is dominated by increases in heatwaves rather than GPP itself. Under the lower emission scenario SSP1-2.6, the global cropland GPP exposure will reduce by 86.11% and 330.47% relative to that under SSP2-4.5 and SSP5-8.5 scenarios, respectively, by the end of 21st century. Our results provide crucial insights into potential impacts of heatwaves on cropland productivity and hence food security.

Effectiveness of Cool and Green Roofs Inside and Outside Buildings in the Brazilian Context

Several studies have assessed the thermal performance of green and cool roofs. However, few have comprehensively addressed Brazilian buildings and climates, considering indoor and outdoor environments. Considering three Brazilian cities, this study aims to assess the performance of green and cool roofs compared with traditional fibre cement roofs in a typical multifamily residential building. Energy consumption, thermal comfort, and outside surface temperature were assessed using computer simulation. The results show that the cool roofs performed better in cities with warmer climates (e.g., Cfa and Aw), reducing electricity consumption by up to 24.8% compared with traditional roofs. Green roofs are better suited for colder climates (e.g., Cfb), with up to 28.2% energy savings. Green roofs provided the highest percentage of thermal comfort hours in all climates. Cool and green roofs provided hourly reductions in outside roof surface temperature of up to 16.5 °C and 28.4 °C, respectively, compared with the traditional roof. This work reinforces that the choice between these two roof types for each city depends on the parameter used for comparison. Based on the relevant information applied to Brazilian buildings and representative climates presented, this work provided recommendations for urban planning policies and building regulations in Brazil.

Evaluation of the influence of climatic changes on the degradation of the historic buildings

While climatic change has been a widely studied topic, its impact on cultural heritage degradation remains a gap to overcome. The environment can contribute to the degradation of historic buildings and materials decay, especially temperature and humidity. So, characterizing the micro-climates of a landmark zone and understanding the influence of the recovery layers, building disposition, and street characteristics on environmental parameters are the first steps to investigating resilience strategies for heritage conservation and micro-climates. Thus, this paper presents a new approach to assess the influence of climatic warming on historic building degradation, combining multiscale experimental data and numerical modeling. The environmental parameters of the historic center of Aracati downtown, such as concentration of CO2, relative humidity, temperature, and air condition, were characterized and used together with urban volumetry to discuss the influence of on the urban microclimate and relate the data to the physical degradation of cultural heritage. An uncrewed aerial vehicle was used to register the volumetry of the historic center, and the data was used to simulate the wind conditions. Following this, numerical analysis was used to investigate the rising damp and thermal tension distributions under temperature variation over the years (from 2023 to 2100). The methodology’s applicability was demonstrated in recurrence with the Nosso Senhor do Bonfim Church, one of the most representative heritage constructions from Aracati downtown. Finally, the new methodology contributed to understanding how urban volumetry contributes to the climatic conditions of a historic area and demonstrates that the increase of the temperature can significantly affect the rising damps and the development of stress tension.

Negative Regulatory Role of Non-Coding RNA Vvi-miR3633a in Grapevine Leaves and Callus under Heat Stress

The grapevine, a globally significant fruit and an essential fruit tree species in China, is vulnerable to the adverse effects of high temperatures. Understanding the roles of microRNA and transcription factors in plant development and stress resistance is crucial for mitigating the impact of high temperature on grape growth and yield. This study investigates the response of miRNA to high-temperature stress in grape leaves. The expression level of Vvi-miR3633a was found to be inhibited under heat treatment in both Thompson seedless and Shen yue varieties, while its potential target genes (Vv-Atg36 and Vv-GA3ox2) were induced. Through transgenic overexpression experiments, it was demonstrated that Vvi-miR3633a plays a role in thermal response by affecting the expression of target genes. Furthermore, under heat stress conditions, overexpression of Vvi-miR3633a in grape callus decreased heat resistance compared to the control group (CK). The study also revealed that the target genes of Vvi-miR3633a regulate the expression of oxidase synthesis genes VvSOD and VvCAT, leading to reduced oxidase synthesis which may compromise the oxidation system. Additionally, the expression level of heat shock proteins in the transgenic lines was changed compared to the control (CK). Overall, this research provides valuable insights into understanding the molecular mechanisms involved in different crossing/breeding programs to produce heat-resistant grape varieties. Such varieties can be appropriate to propagate in warm climate areas with high temperature conditions.

Delay impacts of ant nests on plant reproductive phenology in a temperate steppe.

Increasing ant abundance associated with climate warming has been observed in temperate ecosystems. However, how enhanced ant activity affects plant development and phenology remains unclear. In this study, individuals of a perennial forb (Potentilla tanacetifolia) during flowering with and without an ant (Proformica) nest were marked to explore the impacts of ant nests on plant growth and phenology in a temperate steppe on the Mongolian Plateau. Ant nests delayed the start and end flowering dates simultaneously and, consequently, had no effect on flowering duration. However, presence of an ant nest postponed the fruiting date of individuals. Nests further increased numbers of aborted flowers and thus decreased fruit set. These observations suggest that the delayed flowering phenology disrupted synchrony between plant reproduction and pollinators and thus reduced pollination efficiency under ant nests. Given the increasing abundance of ants with rising temperatures under climate warming, plant delayed reproductive phenology and reduced reproductive output will potentially have negative consequences for plant fitness and plant-arthropod interactions, with consequent impacts on resistance and resilience of perennial species in temperate steppes under climate change.

Global offshore wind turbine detection: a combined application of deep learning and Google earth engine

ABSTRACT As a renewable energy source, ocean wind energy plays an important role in addressing challenges such as global energy shortages and climate warming. In the past decade, the offshore wind power industry has developed rapidly. However, its development has also inevitably affected local social, economic and environmental aspects. Therefore, a timely understanding of offshore wind power dynamics development is crucial for its healthy and sustainable development. Based on this, this study designs and develops a more economical, reliable and real-time offshore wind turbine (OWT) extraction method by combining deep learning and the Google Earth Engine (GEE) cloud computing platform. The method consists of two main steps. The first part utilizes multiple semantic segmentation models to construct a multi-model detection method to initially detect OWTs. The second part utilizes the GEE cloud computing platform for installation time detection and secondary purification processing of the preliminary results. The results show that the number of global OWTs reached 13,609 by 2023, and the accuracy of the detection results reached 99.93%. China has been the fastest-growing country in offshore wind power in the last decade, from installing only 4 units in 2015 to installing 6,775 units in 2023 and surpassing the UK in 2020 and becoming the country building the most OWTs worldwide. Currently, 85% of the world’s OWTs are located in China and European North Sea waters. Additionally, other regions have great potential for offshore wind development. Finally, this study provides the world’s most up-to-date and complete OWT dataset, which can provide data support for research on marine ecological and environmental protection, marine spatial planning, and socioeconomic benefits.

Global decline in microbial-derived carbon stocks with climate warming and its future projections

Abstract Soil organic carbon (SOC) represents the largest terrestrial pool of organic carbon and is indispensable for mitigating climate change and sustaining soil fertility. As a major component of stable SOC, microbial-derived carbon (MDC) accounts for approximately half of the total SOC and has repercussions on climate feedback. However, our understanding of the spatial and temporal dynamics of MDC stocks is limited, hindering assessments of the long-term impacts of global warming on persistent SOC sequestration in the soil‒atmosphere C cycle. Here, we compiled an extensive global dataset and employed ensemble machine learning techniques to forecast the spatial-temporal dynamics of MDC stocks across 93.4% of the total global land area from 1981 to 2018. Our findings revealed that for every 1°C increase in temperature, there was a global decrease of 6.7 Pg in the soil MDC stock within the predictable areas, equivalent to 1.4% of the total MDC stock or 0.9% of the atmospheric C pool. The tropical regions experienced the most substantial declines in MDC stocks. We further projected future MDC stocks for the next century based on shared socioeconomic pathways, showing a global decline in MDC stocks with a potential 6–37 Pg reduction by 2100 depending on future pathways. We recommend integrating the response of MDC stocks to warming into socioeconomic models to enhance confidence in selecting sustainable pathways.

Thermal acclimation of ecosystem processes to climate warming.

Thermal acclimation of ecosystem processes to climate warming.