Search Results (684)

Nutrient stoichiometry, particularly the balance of carbon (C), nitrogen (N), and phosphorus (P), plays a fundamental role in regulating freshwater ecosystem dynamics, primary production, and biogeochemical cycling. This study presents one of the first dedicated reviews to combine bibliometric mapping with ecological synthesis of C:N:P ratios in inland waters, drawing on 1004 publications indexed in the Web of Science Core Collection (2000–2025), comprising peer-reviewed articles and review articles refined by document type, language, and research area. Bibliometric mapping using VOSviewer (version 1.6.20) identified exponential growth in publications after 2010, with phosphorus dynamics and eutrophication emerging as the most-cited themes, while recent years have shown increasing attention to C:P ratios as reliable ecological indicators. Four dominant thematic clusters were identified: Nutrient Cycling and Biogeochemistry; Phytoplankton and Food Web Dynamics; Eutrophication and Water Quality; and Climate Change and Ecosystem Responses. Ecological synthesis demonstrated substantial deviations from the canonical Redfield ratio (106C:16N:1P), with pronounced stoichiometric variability across trophic states, latitudes, and ecosystem types. Case comparisons revealed high C:P ratios in Arctic and alpine lakes linked to dissolved organic carbon inputs, low N:P ratios in tropical waters that promote cyanobacterial dominance, and stable, low phosphorus concentrations in deep African lakes. These findings emphasize the significance of flexible stoichiometry in predicting ecosystem tipping points, managing harmful algal blooms (HABs), and guiding nutrient restoration strategies. By integrating bibliometric and ecological evidence, this study identifies C:P ratios as a promising candidate indicator that merits further field validation for freshwater management, while underscoring persistent research gaps in microbial stoichiometry, cross-scalar modeling, and policy uptake in the Global South. Full article

Taking the Yellow River Basin (YRB) as an example, this study explores the non-stationary drought evolution features in large river basins under climate change. This study utilized precipitation and multiple climate factor data to establish the non-stationary standardized precipitation index (NSPI) through the GAMLSS model. Combined with the run theory, Copula function and a cascaded RF-LSTM machine learning model, the drought characteristics and retrospective predictive patterns were systematically assessed. The results show that: (1) The Arctic Oscillation, the Pacific Decadal Oscillation, the Southern Oscillation and the North Pacific Index are the primary climate drivers of non-stationary precipitation variation in the YRB, with the former three being selected most frequently and NPI additionally influencing April–June and September, and their effects are both different and lagging. Compared with the traditional SPI, the NSPI assigned higher drought grades and greater severity to typical drought years (e.g., the 1974 event was rated D3 with a severity of 17.935 by NSPI versus D2 with 11.733 by SPI), and thus better captured non-stationary drought evolution. (2) The duration of droughts exhibited a decreasing trend that was not statistically significant (p > 0.05), whereas drought intensity and severity decreased significantly (p < 0.05); the peak severity showed a significant upward trend (p = 0.0078). Spatially, the northwest of the Loess Plateau was a compound core area with high severity, high frequency and long duration of droughts, while the upper reaches were mainly characterized by low severity, short duration and sudden droughts. (3) The drought risk in the YRB shows a higher frequency in the lower reaches and a lower frequency in the upper reaches. The middle and lower reaches were high-risk areas, with shorter AND-type joint exceedance return periods for moderate drought (2.46–5.83 years) and severe drought (3.77–9.15 years). The upper reaches were low-risk areas, with longer return periods reaching up to 5.83 years for moderate drought and 9.15 years for severe drought. The study shows that the NSPI, considering the driving of multiple climate factors, can more effectively identify and assess non-stationary drought risks, providing a scientific basis for drought prevention and control in river basins. Full article

This study analyzes surface air temperature (SAT) trends at 158 stations located on or above the Arctic Circle over the 2000–2024 period, aiming to assess whether recent temperature shifts could serve as indirect indicators of a slowing Atlantic Meridional Overturning Circulation (AMOC). Regression analysis reveals that only 40% of stations show statistically significant warming trends (p < 0.05), while 33% exhibit no significant trend. Applying the Pettitt and Buishand tests, we detect abrupt regime shifts at 38 stations, with breakpoints concentrated between 2009 and 2014. Notably, 36 of these stations display a weakening of the warming trend after the breakpoint: at 13 stations (including key Arctic archipelagos and the White Sea coast), an initial increase shifts to a decrease; at 17 stations, warming continues but at a slower rate; and at 6 stations (near the Bering Strait), a decrease intensifies. These spatial patterns suggest a potential fingerprint of AMOC slowdown, consistent with recent modeling studies that predict cooling in northwestern Europe and possible Little Ice Age-type environmental conditions. Our findings have implications for assessing future Arctic navigation, coastal infrastructure, and resource extraction under changing climate regimes. Full article

The Arctic is warming nearly four times faster than the global mean, driving unprecedented sea ice loss and threatening permafrost coasts and human settlements. Existing pan-Arctic vulnerability indices typically rest on satellite-era baselines and on expert-driven weighting schemes whose robustness is rarely tested. Here, we present an integrated, multi-centennial framework that jointly ingests SIBT1850 sea ice concentration (1850–2017), extended to 2024 with the NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration v6 (G02202 v6), together with ESA CCI Permafrost products (1997–2019), the Arctic Coastal Dynamics database, and pan-Arctic settlement inventories. Using non-parametric Mann–Kendall trend tests, Sen’s slope, and the Pettitt change point test across nine Seas (S1–S9), five permafrost-adjacent core seas exhibit summer Sen’s slopes of −0.105 to −0.185% yr⁻¹ with Pettitt change points clustered in 1929–1953 (mean 1936), whereas three of four support seas cluster around 1978, suggesting an approximately bimodal regime shift timing that we interpret cautiously given the limited sample. A Composite Vulnerability Index integrating six normalised indicators identifies the Chukchi (CVI = 0.630) and East Siberian (0.624) seas as the highest-priority hotspots at the SIBT1850 baseline. A satellite-era robustness check using NSIDC G02202 v6 confirms that the Chukchi–East Siberian–Laptev corridor remains in the top three highest-vulnerability basins under the 1850–2024 extension, with the Beaufort Sea retaining rank 5, validating the basin mean conclusions of the SIBT1850-based analysis. Robustness checks—PCA re-weighting, one-at-a-time and global (Sobol, PAWN) sensitivity analyses, and Monte Carlo Dirichlet perturbation—confirm that the top-two ranking is stable across weighting schemes (baseline–PCA Spearman ρ = 0.80). We explicitly avoid claiming forecasting validation, operational testing, or benchmarking against existing pan-Arctic vulnerability indices, all of which we identify as priority directions for future work. The framework provides a transparent, reproducible basis for prioritising adaptation across the Chukchi–East Siberian–Laptev corridor. Full article

Substantial uncertainties remain in climate model simulations of tropical cyclones (TCs), particularly those associated with internal climate variability. While the influence of the El Niño–Southern Oscillation (ENSO) on interannual TC variability is well established, the contribution of the Interdecadal Pacific Oscillation (IPO) to decadal-scale uncertainty is less well constrained. Although models generally reproduce IPO-related variations in tropical cyclone genesis frequency (TCGF) over the eastern North Pacific, large discrepancies persist across the broader North Pacific basin. Clarifying the role of IPO in modulating TCGF uncertainty is therefore essential for improving decadal TC projections. In this study, we analyzed a large ensemble of historical simulations from the MRI-AGCM within the d4PDF (Database for Policy Decision Making for Future Climate Change) framework. Empirical orthogonal function (EOF) analysis is applied to IPO-composited fields to identify the leading modes of intermember (100 members *60 y, 6000 times) simulation uncertainty on a decadal-scale. The results reveal that state-of-the-art models exhibit robust and spatially coherent uncertainty structures in TCGF under different IPO phases. Two leading modes are identified: (1) a South China Sea mode, closely associated with systematic precipitation biases, and (2) a zonal dipole mode between the eastern and western North Pacific, linked to the equatorward propagation of Arctic Oscillation (AO)-related variability. Misrepresentation of AO variability is found to contribute substantially to biases in simulated TCGF patterns. Comparisons with observational datasets further support the proposed mechanisms. These findings highlight the importance of improving the representation of precipitation processes and extratropical–tropical teleconnections in climate models, which is critical for enhancing the reliability of decadal predictions of North Pacific TC activity. Full article

Real-time monitoring of marine ecosystems is crucial for global climate change research. In extreme marine environments such as the westerly regions in the Arctic and Antarctic, monitoring buoys and platforms often suffer from severe challenges, including insufficient energy supply, limited battery life, and difficult maintenance. Triboelectric nanogenerators (TENGs) offer a promising strategy for self-powered marine sensing. However, conventional tubular liquid–solid triboelectric nanogenerators (LS-TENGs) suffer from low efficiency of interfacial charge transfer due to limited contact area and excessive internal resistance, which restricts their output. In this study, a multi-tube nested liquid–solid triboelectric nanogenerator (MLS-TENG) is proposed, and the suitable filling ratio is determined through comparative experiments on structural parameters. This design significantly increases the effective contact area, reduces internal resistance, and improves synergistic charge transfer at multiple interfaces. Experimental results demonstrate that the MLS-TENG exhibits substantially improved electrical output compared with the corresponding single-tube structures. When integrated with a power management module, the capacitor charging efficiency is improved by approximately 120 times. In real sea trials, an array composed of MLS-TENG units successfully drives a self-powered sensing system, achieving stable 4G transmission of environmental parameters. This work provides a scalable structural optimization strategy for constructing high-performance blue energy-harvesting self-powered nodes for the marine Internet of Things. Full article

Drought and related climate features (aridity, water balance) in Romania since 1961 are well documented, but studies spanning longer periods are limited and typically rely on modelled or sparse observational data. This study presents an analysis of drought, water balance and aridity in Romania over 123 years (1901–2023), using monthly data from 156 weather stations included in the RoCliHom dataset. Drought evolution is analyzed using the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). Aridity is examined with the De Martonne Aridity Index. The non-parametric Mann–Kendall test is used for trend detection, which allows a fair comparison with previous studies on drought and aridity in Romania. Trend magnitude is calculated with Sen’s slope estimator. Our results show a clear increase in evapotranspiration as a sign of climate warming over the country since the beginning of the 20th century. Annual precipitation amount presents no major changes. Water balance has decreased in July and August at 40% and 85% of the locations, respectively. During the growing season, drought has intensified within the last seven, six and five decades, but there are no significant changes over the full period of study in this respect. We found strong negative correlations between SPEI and North Atlantic Oscillation, Northern Annular Mode and Arctic Oscillation teleconnection indices. The evolution over the 123-year period shows that the drought episodes that occurred in recent decades are not without precedent in the long-term climatic context. Full article

Rapid Arctic warming has significantly altered sea ice–atmosphere boundary layer processes, which low-resolution models struggle to resolve accurately. This study evaluates the historical performance (1958–2014) of four high-resolution models from CMIP6 HighResMIP—EC-Earth3P-HR, CNRM-CM6-1-HR, HadGEM3-GC3.1-HH, and Fgoals-f3-H—against ORAS5 and CMEMS reanalysis datasets and examines their physical response to rapid warming under the SSP5-8.5 scenario (2015–2025). Results show substantial intermodel differences in simulating Arctic sea ice thickness, mixed layer depth, sea surface temperature and salinity, and deep convection. HadG-EM3-GC3.1-HH and CNRM-CM6-1-HR perform best overall, reliably reproducing trends in the two major deep convection regions, meridional temperature–salinity gradients, and long-term evolution with lower biases and higher correlations. Under decadal strong warming, models generally simulate shoaling mixed layers in deep convection zones and upper-water destabilization in the Canada Basin, but responses in sea ice, eddy kinetic energy, and transect temperature–salinity vary markedly. HadGEM3-GC3.1-HH and CNRM-CM6-1-HR better represent physical quantities and ocean stratification consistent with observed real-world responses. We conclude that these two models are more suitable for studies of Arctic sea ice–atmosphere boundary layer changes and deep convection, providing a basis for high-resolution model selection and Arctic climate projection. Full article

This study presents a comprehensive assessment of longwave radiation variability in the Arctic based on unique measurements collected at the North Pole drifting station SP-28 in 1987. The primary objective is to compare these historical observations with modern datasets from the Surface Heat Budget of the Arctic Ocean (SHEBA, 1997–1998) and the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC, 2019–2020) to evaluate long-term changes in the Arctic radiation regime. Continuous longwave radiation measurements were obtained using high-precision spectral pyrgeometers to identify Arctic haze. The results show that in 1987, Arctic haze layers enhanced the downward longwave flux by 15–20 W·m⁻² and increased atmospheric emissivity. In contrast, MOSAiC observations reveal emissivity values that closely match aerosol-free model calculations, indicating a substantial decline in Arctic haze and the diminishment of radiatively significant aerosol layers. This shift is in alignment with the long-term reduction of global anthropogenic sulfur dioxide emissions across the Northern Hemisphere. Full article

Rising temperatures, driven by global climate change, are profoundly altering high-latitude ecosystems, influencing vegetation phenology and productivity. However, understanding the long-term, nuanced responses of these ecosystems remains a critical challenge. Soil warming experiments have served as useful tools for understanding these shifts. However, many of these studies have relied on a single measure, predominantly the Normalized Difference Vegetation (NDVI), measured at a single level of warming. This approach often fails to separate structural greening from underlying physiological responses. To address these gaps, this study provided a comprehensive snapshot assessment of growing season vegetation dynamics in a subarctic grassland ecosystem in Iceland that had been exposed to continuous geothermal soil warming for over 60 years. Using high-resolution multispectral drone imagery, twelve different vegetation indices (VIs) were derived to assess not only greenness but also physiological stress and photosynthetic efficiency across a range of mean annual soil temperatures (MATs). Using linear regression and redundancy analysis (RDA), the responses of these indices to warming and their relationships with other environmental drivers, such as standing biomass and plant nutrient concentrations (nitrogen and phosphorus), were analyzed. The results revealed significant positive linear relationships between most of the indices and MATs across the 5 to 11 °C range. This indicated that higher MATs led to increased biomass and structural growth, without revealing any significant thresholds or tipping points in vegetation response within the observed warming range. However, the Photochemical Reflectance (PRI) showed a significant negative relationship with warming, suggesting a decoupling between structural greening and photosynthetic light-use efficiency. Furthermore, RDA results indicated that, while most of the VIs were primarily driven by biomass, the decline in PRI was likely a compounding effect of physical canopy self-shading and plant phosphorus constraints. Ultimately, this study demonstrated that, while these subarctic grasslands exhibited local evidence of “Arctic greening” under further warming, multispectral drone remote sensing could detect underlying physiological adjustments and nutrient constraints that traditional greenness indices might overlook, providing a more nuanced understanding of ecosystem response. Full article

The “New Arctic” regime represents a prominent climatic feature of the Arctic Ocean under global warming, characterized by persistently low summer sea ice extent, a marked reduction in sea ice thickness, and an expansion of open water areas at high latitudes. As a key indicator of the Arctic sea ice system, the spatiotemporal evolution of sea ice thickness and its underlying driving mechanisms remain incompletely understood. Using reanalysis datasets and remote sensing observations, this study identifies major abrupt shifts in Arctic sea ice thickness under the New Arctic regime, reveals the spatiotemporal distribution characteristics of winter sea ice thickness, and examines the driving factors from both thermodynamic and dynamic perspectives. The results show that the evolution of Arctic sea ice thickness can be divided into three phases: a high-level period during the “Traditional Arctic” (1979–1992), a rapid thinning period during the New Arctic transition (1993–2012), and a low-level stabilization period in the New Arctic regime (2013–2023). The first EOF mode of winter sea ice thickness depicts a spatially consistent thinning pattern across the entire Arctic, with the most significant reduction occurring in the multi-year ice regions north of the Canadian Arctic Archipelago and Greenland. The second EOF mode exhibits an out-of-phase variation between the Atlantic and Pacific sectors of the Arctic, accompanied by a shrinking amplitude and weakened regional oscillations. The coupling between surface air temperature and sea ice thickness displays distinct phase dependence: their negative correlation is strongest during the transition period (r = −0.78, p < 0.001) but becomes statistically insignificant in the New Arctic regime. Sea ice motion speed exhibits an overall accelerating trend, which extends from the marginal seasonal ice zones toward the high-latitude multi-year ice regions, accompanied by a notably enhanced sensitivity of sea ice motion to wind forcing. Sea ice volume flux through the Fram Strait is primarily controlled by ice motion speed, whose contribution to the flux is approximately 2.6 times that of ice thickness. The recovery of ice drift speed offsets the thinning of sea ice cover, leading to a partial rebound in volume flux during the New Arctic steady state. This study identifies the evolutionary patterns and drivers of Arctic sea ice thickness under the New Arctic regime, providing a scientific basis for further understanding the changes in the Arctic climate system and associated air–sea ice interactions. Full article

Based on monthly meteorological observations from 57 stations in Jiangsu Province during 1961–2022, the Standardized Precipitation Evapotranspiration Index (SPEI) was calculated to characterize regional dry–wet variability. Empirical Orthogonal Function (EOF) analysis was applied to extract the dominant spatially coherent dry–wet modes, and cross-wavelet analysis was further employed to examine, in the time–frequency domain, the mode-specific responses to multiscale climate drivers, including the El Niño–Southern Oscillation (ENSO), Sunspot Number (SSN), Arctic Oscillation (AO), and Pacific Decadal Oscillation (PDO). The results show that dry–wet variability in Jiangsu Province is primarily organized by a regionally coherent mode (EOF1, explaining 56.3% of the total variance) and a north–south dipole mode (EOF2, explaining 17.8%), with the zero-value line of EOF2 closely aligned with the Huaihe River–Subei Irrigation Canal climatic transition zone. The temporal coefficient of EOF1 (PC1) exhibits a significant regime shift around 2013, followed by a pronounced wetting trend across the entire region. This change may reflect recent hydroclimatic adjustments in the study area, although the present study does not attempt a formal attribution of the respective thermal and precipitation contributions. In contrast, the temporal coefficient of EOF2 (PC2) undergoes an abrupt change around 1980, indicating a transition of the spatial dry–wet pattern from “southern drought–northern flood” to “southern flood–northern drought,” broadly consistent with an interdecadal climatic transition. Cross-wavelet analysis further reveals that PC1 is closely associated with ENSO at interannual timescales, with a lag of approximately 4–6 months, while its long-term variability shows time–frequency coherence with SSN. PC2 also exhibits time–frequency coherence with SSN at longer timescales, with an apparent phase transition around the 1980s; however, this low-frequency signal should be interpreted cautiously because the underlying physical mechanism remains uncertain. Overall, this study shows that dry–wet variability in Jiangsu Province is organized by two leading spatial modes with distinct temporal evolution and scale-dependent climate linkages. These findings provide new evidence for understanding hydroclimatic variability in monsoon transition zones and offer a basis for spatially differentiated drought–flood risk assessment. Full article

The effects of environmental factors on zooplankton within the marginal ice zone (MIZ) of the Barents Sea remain poorly understood. To address this knowledge gap, we investigated mesozooplankton communities across the central, northern, and northeastern regions in April 2016. Abundance and biomass ranged from 90 to 997 individuals m⁻³ and from 1.1 to 48.6 mg dry mass m⁻³ (0.3 to 13.6 g dry mass m⁻²), respectively. Oithona similis was the most abundant taxon, while calanoid copepods, including Calanus spp., Metridia longa, and Pseudocalanus spp., dominated total biomass. The spatial distribution of mesozooplankton communities was closely linked to the physical properties of water masses. Cluster analysis identified two distinct assemblages associated with Polar Front Water and Arctic Water. Redundancy analysis and generalized linear models identified temperature, mean salinity, mean chlorophyll a concentration, and sea ice concentration as significant predictors of abundance and biomass. The dominance of older life stages within major copepod taxa indicated a winter status for the mesozooplankton community. Regional and temporal comparisons of mesozooplankton biomass with prior May–June data from central and northwestern areas highlighted higher productivity in the northern and northeastern MIZ. This increase is potentially related to the warming trends observed in the Arctic since the 2000s. Our research provides novel insights into Arctic marine zooplankton assemblages and serves as a valuable baseline for future ecological monitoring and modeling of the Barents Sea ecosystem in the context of global climate change. Full article

Microplastics (MPs) and microfibres (MFs) are widespread contaminants that are found in natural environments worldwide. Although their presence has been documented in Arctic snow, sea ice and marine systems, data on their occurrence in Greenland glacier surface ice remain limited. Because of their small size, persistence, and mobility, MPs and MFs pose significant risks to both habitats and species, reaching even the most remote areas. Monitoring these environments is crucial for assessing the extent of pollution, while dissemination activities are essential for transferring scientific knowledge to local communities and fostering active engagement in adopting sustainable behaviours. A preliminary survey was conducted on a glacier in Greenland, collecting samples along the routes travelled by the Extreme E staff during the electric off-road racing series expedition in the region. Preliminary results confirmed the presence of MPs and MFs in the study area with high abundances. Fibrous and small-sized microparticles were the most prevalent types detected. The most common synthetic material was polyethylene terephthalate (PET), while natural and regenerated MFs were predominantly cellulosic. A deeper understanding of MP and MF contamination in extreme environments was achieved, highlighting the importance of environmental education and public awareness as key tools in mitigating pollution and promoting sustainable strategies. The integration of different sectors can synergistically promote sustainability efforts and address the urgent challenges of climate change and environmental pollution. Full article

Rapid changes in Arctic summer sea ice exert substantial influences on the polar climate system, maritime navigation, and resource exploitation, while subseasonal-to-seasonal (S2S) prediction of sea ice state remains highly uncertain. Using daily observations and reanalysis data of sea ice concentration (SIC) and thickness (SIT) from 1979 to 2023, together with concurrent atmospheric and oceanic fields, this study develops a multivariate linear Markov model to perform S2S predictions of Arctic summer sea ice. Sensitivity experiments with different variable combinations, weighting strategies, and modal truncation schemes are conducted, and predictive skill is systematically evaluated against persistence and climatological baselines. Results indicate that the model exhibits stable forecast skill without pronounced error accumulation at extended lead times. SIC predictability is primarily governed by its intrinsic spatiotemporal persistence and is significantly modulated by oceanic thermodynamic forcing, particularly sea surface temperature and surface net energy flux, highlighting a pronounced oceanic memory effect. In contrast, local atmospheric dynamic variables provide limited incremental skill. For SIT, predictability is dominated by its own historical state, with SIC contributing marginal short-term improvement and air–sea coupling exerting weak influence. Overall, the proposed framework effectively extracts dominant predictable signals with clear physical interpretability, providing a computationally efficient statistical approach for S2S prediction of Arctic summer sea ice. Full article