An anomalous warm weather event in the Antarctic McMurdo Dry Valleys on 18 March 2022 created an opportunity to characterize soil biota communities most sensitive to freeze-thaw stress. This event caused unseasonal melt within Taylor Valley, activating stream water and microbial mats around Canada Stream. Liquid water availability in this polar desert is a driver of soil biota distribution and activity. Because climate change impacts hydrological regimes, we aimed to determine the effect on soil communities. We sampled soils identified from this event that experienced thaw, nearby hyper-arid areas, and wetted areas that did not experience thaw to compare soil bacterial and invertebrate communities. Areas that exhibited evidence of freeze-thaw supported the highest live and dead nematode counts and were composed of soil taxa from hyper-arid landscapes and wetted areas. They received water inputs from snowpacks, hyporheic water, or glacial melt, contributing to community differences associated with organic matter and salinity gradients. Inundated soils had higher organic matter and lower conductivity (p < .02) and hosted the most diverse microbial and invertebrate communities on average. Our findings suggest that as liquid water becomes more available under predicted climate change, soil communities adapted to the hyper-arid landscape will shift toward diverse, wetted soil communities.

Understanding soil organic carbon (SOC) distribution and its environmental controls in permafrost regions is essential for achieving carbon neutrality and mitigating climate change. This study examines the spatial pattern of SOC and its drivers in the Headwater Area of the Yellow River (HAYR), northeastern Qinghai-Xizang Plateau (QXP), a region highly susceptible to permafrost degradation. Field investigations at topsoils of 86 sites over three summers (2021-2023) provided data on SOC, vegetation structure, and soil properties. Moreover, the spatial distribution of key permafrost parameters was simulated: temperature at the top of permafrost (TTOP), active layer thickness (ALT), and maximum seasonal freezing depth (MSFD) using the TTOP model and Stefan Equation. Results reveal a distinct latitudinal SOC gradient (high south, low north), primarily mediated by vegetation structure, soil properties, and permafrost parameters. Vegetation coverage and above-ground biomass showed positive correlation with SOC, while soil bulk density (SBD) exhibited a negative correlation. Climate warming trends resulted in increased ALT and TTOP. Random Forest analysis identified SBD as the most important predictor of SOC variability, which explains 38.20% of the variance, followed by ALT and vegetation coverage. These findings likely enhance the understanding of carbon storage controls in vulnerable alpine permafrost ecosystems and provide insights to mitigate carbon release under climate change.

The aerosol scattering phase function (ASPF), a crucial element of aerosol optical properties, is pivotal for radiative forcing calculations and aerosol remote sensing detection. Current detection methods for the ASPF include multi-sensor detection, single-sensor rotational detection and imaging detection. However, these methods face challenges in achieving high-resolution full-angle measurement, particularly for small forward (i.e., less than 10 degrees) or backward (i.e., more than 170 degrees) scattering angles in open path. In this work, a full-angle ASPF detection system based on the multi-field-of-view Scheimpflug lidar technique has been proposed and demonstrated. A 450 nm continuous-wave semiconductor laser was utilized as the light source and four CMOS image sensors were employed as detectors. To detect the full-angle ASPF, four receiving units capture angular scattering signals across different angle ranges, namely 0 degrees-20 degrees, 10 degrees-96 degrees, 84 degrees-170 degrees, 160 degrees-180 degrees, respectively. The influence of the relative illumination and angular response of the used image sensors have been corrected, and a signal stitching algorithm was developed to obtain a complete 0-180 degrees angular scattering signal. Atmospheric measurements have been conducted by employing the full-angle ASPF detection system in open path. The experimental results of the ASPF have been compared with the AERONET data from the Socheongcho station and simulated ASPF based on the typical aerosol models in mainland China, showing excellent agreement. The promising results demonstrated in this work have shown a great potential for detecting the full-angle ASPF in open path.

Char and soot represent distinct types of elemental carbon (EC) with varying sources and physicochemical properties. However, quantitative studies in sources, atmospheric processes and light-absorbing capabilities between them remain scarce, greatly limiting the understanding of EC's climatic and environmental impacts. For in-depth analysis, concentrations, mass absorption efficiency (MAE) and stable carbon isotope were analyzed based on hourly samples collected during winter 2021 in Nanjing, China. Combining measurements, atmospheric transport model and radiative transfer model were employed to quantify the discrepancies between char-EC and soot-EC. The mass concentration ratio of char-EC to soot-EC (R-C/S) was 1.4 +/- 0.6 (mean +/- standard deviation), showing significant dependence on both source types and atmospheric processes. Case studies revealed that lower R-C/S may indicate enhanced fossil fuel contributions, and/or considerable proportions from long-range transport. Char-EC exhibited a stronger light-absorbing capability than soot-EC, as MAE(char) (7.8 +/- 6.7 m(2)g(-1)) was significantly higher than MAE(soot) (5.4 +/- 3.4 m(2)g(-1))(p < 0.001). Notably, MAE(char) was three times higher than MAE(soot) in fossil fuel emissions, while both were comparable in biomass burning emissions. Furthermore, MAE(soot) increased with aging processes, whereas MAE(char) exhibited a more complex trend due to combined effects of changes in coatings and morphology. Simulations of direct radiative forcing (DRF) for five sites indicated that neglecting the char-EC/soot-EC differentiation could cause a 10 % underestimation of EC's DRF, which further limit accurate assessments of regional air pollution and climate effects. This study underscores the necessity for separate parameterization of two types of EC for pollution mitigation and climate change evaluation.

Frozen soil, covering most of the Tibetan Plateau (TP), critically influences land surface and climate simulations. Although some studies have made advancements in simulations, further investigation into the distinct mechanisms underlying relevant parameterization schemes remains essential. This study compares two frozen soil permeability schemes in Noah-MP (NY06: high-permeability; Koren99: low-permeability) to elucidate their distinct hydrological mechanisms. Although significant disparities exist in the simulation of soil water and ice content between the two schemes in permafrost regions, the simulated soil water content in the shallow layer exhibits similarity. Their underlying physical processes behind this similarity differ fundamentally: Koren99 relies on cross-seasonal ice melt recharge, whereas NY06 depends more on current-season precipitation and snowmelt. With greater soil depth, soil water differences progressively propagate downward, amplifying variations in hydraulic conductivity, and soil memory effects become increasingly dominant. Meanwhile, the Koren99 scheme more effectively impedes bottom-up melting water transport than top-down effect. However, the aforementioned disparities are not apparent in seasonally frozen soil. Notable disparities also exist in simulated evapotranspiration and surface runoff over permafrost regions, particularly during the summer months. This research investigates the differences in water transport within frozen soil over the TP, elucidates the distinct hydrological mechanisms underlying different frozen soil permeability schemes, and highlights that similar soil hydrothermal simulations are associated with different physical processes, leading to varying degrees of effectiveness in soil memory. Furthermore, this research elucidates the dual role of soil ice (permeability restriction and water storage) in hydrological processes, providing a theoretical basis for improving frozen soil parameterization.

Carbonaceous aerosols play a crucial role in air pollution and radiative forcing, though their light-absorbing and isotopic characteristics remain insufficiently understood. This study analyzes optical absorption and isotopic composition in PM10 and PM2.5 particles from primary emission sources, focusing on traffic-related and solid fuel categories. We analyzed key optical properties, including the Angstrom absorption exponent (AAE), the contributions of black carbon (BC) and brown carbon (BrC) to total light absorption and the mass absorption efficiencies (MAE) of carbonaceous aerosols. AAE values were lower for traffic emission sources (0.9 to 1.3) than solid fuel emission sources (1.5 to 3), with similar values for both particle sizes. BrC contributions were more prominent at shorter wavelengths and were notably higher in solid fuel emission sources (61% to 88%) than in traffic emission sources (8% to 40%) at 405 nm. MAE values of BC at 405 nm were 2 to 20 times higher than BrC across different emissions. Particle size significantly affect MAE(BC) with PM2.5 higher when compared to PM10. Emissions from diesel concentrate mixer and raw coal burning exhibited the highest MAE(BC) for PM2.5 and PM10, respectively. Conversely, Coke had the lowest MAE(BC) but the highest MAE(BrC) for both sizes. Traffic emissions showed more stable carbon isotope ratios (delta C-13) enrichment (-29 parts per thousand to -24 parts per thousand) than solid fuels (-31 parts per thousand to -20 parts per thousand). delta C-13 of solid fuel combustion, unlike traffic sources, is found to be independent of size variation. These findings underscore the importance of source and size-specific aerosol characterization for unregulated emission sources.

In recent years, increasing wildfire activity in the western United States has led to significant emissions of smoke aerosols, impacting the atmospheric energy balance through their absorption and scattering properties. Single scattering albedo (SSA) is a key parameter that governs these radiative effects, but accurately retrieving SSA from satellites remains challenging due to limitations in sensor resolution, low sensitivity of traditional remote sensing methods, and uncertainties in radiative transfer modeling, particularly from surface reflectance and aerosol characterization. Smoke optical properties evolve rapidly after emission, influenced by fuel type, combustion conditions, and chemical aging. Accurate SSA retrieval near the source thus requires high-temporal-resolution satellite observations. Critical Reflectance (CR) method provides this capability by identifying a unique reflectance value at which top-of-atmosphere (TOA) reflectance becomes insensitive to aerosol loading and primarily reflects aerosol absorption. SSA can be retrieved from this critical reflectance. This study presents a geostationary-based CR method using the Advanced Baseline Imager (ABI) on GOES-R satellites. The approach leverages ABI's high temporal (5-10 min) and spatial (3 km) resolution, consistent viewing geometry, and wide coverage. A tailored look-up table, based on an AOD-dependent smoke model for North America, links CR to SSA. Case studies show strong agreement with AERONET measurements, with retrieval differences mostly within 0.01-well below AERONET's +/- 0.03 uncertainty. The method captures temporal and spatial variations in smoke absorption and demonstrates robustness across daylight hours. This GEO-based CR approach offers an effective tool for high-resolution SSA retrieval, contributing to improved aerosol radiative forcing estimates and climate modeling.

Forests are increasingly impacted by climate change, affecting tree growth and carbon sequestration. Tree-ring width, closely related to tree growth, is a key climate proxy, yet models describing ring width or growth often lack comprehensive environmental data. This study assesses ERA5-Land data for tree-ring width prediction compared to automatic weather station observations, emphasizing the value of extended and global climate data. We analyzed 723 site-averaged and detrended tree-ring chronologies from two broadleaved and two gymnosperm species across Europe, integrating them with ERA5-Land climate data, CO2 concentration, and a drought index (SPEI12). A subset was compared with weather station data. For modelling interannual variations of tree-ring width we used linear models to assess parameter importance. ERA5-Land and weather-station-based models performed similarly, maintaining stable correlations and consistent errors. Models based on meteorological data from weather stations highlighted SPEI12, sunshine duration, and temperature extremes, while ERA5-Land models emphasized SPEI12, dew-point temperature (humidity), and total precipitation. CO2 positively influenced the growth of gymnosperm species. ERA5-Land facilitated broader spatial analysis and incorporated additional factors like evaporation, snow cover, and soil moisture. Monthly assessments revealed the importance of parameters for each species. Our findings confirm that ERA5-Land is a reliable alternative for modeling tree growth, offering new insights into climate-vegetation interactions. The ready availability of underutilized parameters, such as air humidity, soil moisture and temperature, and runoff, enables their inclusion in future growth models. Using ERA5-Land can therefore deepen our understanding of forest responses to diverse environmental drivers on a global scale.

Understanding the relationship between soil moisture and vegetation is crucial for future projections of ecosystem and water resources. While their hysteresis loop relationship, which arises from their asynchrony in intra-annual variation, remains underexplored. This study used the hysteresis loop type and area (Ah) to characterize the relationship between root zone soil moisture (RZSM) and normalized difference vegetation index (NDVI) across China from 1986 to 2015, and examined its ecological implications. The results identified four types of hysteresis loops. The clockwise loop, with a delayed single peak of RZSM relative to NDVI, was primarily found in north China and the Qinghai-Tibet Plateau, indicating severe water limitation during early growth period. The counterclockwise loop, with an advanced single peak of RZSM relative to NDVI, was common in southeast China's forest, suggesting a shift towards energy limitation. The 8-shaped loop, resulting from double peaks in either RZSM or NDVI due to climate change (e.g., snowmelt) and human disturbance (e.g., irrigation and crop harvest), was observed in northwest China's glaciers and croplands in south and northeast China. The multicrossed loop, marked by multimodal intra-annual variations in both RZSM and NDVI, was predominantly found in northwest China's barren lands. Additionally, from 1986 to 2015, this study observed a shift from 8-shaped or multi-crossed loops to clockwise or counterclockwise loops in some regions like the Yellow River Basin, implying a trend of revegetation. Furthermore, a higher Ah generally indicated more severe water limitation or greater mismatch between RZSM and NDVI. Significant changes in Ah, such as increases in the Yellow River Basin, suggested intensified water limitations, while decreases in southeast and northwest China pointed to an earlier peak of the growing and rainy seasons. This study provides insights into the dynamic interactions between soil moisture and vegetation, offering valuable guidance for ecological management across diverse ecosystems.

Surface soil moisture (SSM) is a key limiting factor for vegetation growth in alpine meadow on the Qinghai-Tibetan Plateau (QTP). Patches with various sizes and types may cause the redistribution of SSM by changing soil hydrological processes, and then trigger or accelerate alpine grassland degradation. Therefore, it is vital to understand the effects of patchiness on SSM at multi-scales to provide a reference for alpine grassland restoration. However, there is a lack of direct observational evidence concerning the role of the size and type of patches on SSM, and little is known about the effects of patches pattern on SSM at plot scale. Here, we first measured SSM of typical patches with different sizes and types at patch scale and investigated their patterns and SSM spatial distribution through unmanned aerial vehicle (UAV)-mounted multi-type cameras at plot scale. We then analyzed the role of the size and type of patchiness on SSM at both patch and plot scales. Results showed that: (1) in situ measured SSM of typical patches was significantly different (P < 0.01), original vegetation patch (OV) had the highest SSM, followed by isolate vegetation patch (IV), small bare patch (SP), medium bare patch (MP) and large bare patch (LP); (2) the proposed method based on UAV images was able to estimate SSM (0-40 cm) with a satisfactory accuracy (R-2 = 0.89, P < 0.001); (3) all landscape indices of OV, with the exception of patch density, were positively correlated with SSM at plot scale, while most of the landscape indices of LP and IV showed negative correlations (P < 0.05). Our results indicated that patchiness intensified the spatial heterogeneity of SSM and potentially accelerated the alpine meadow degradation. Preventing the development of OV into IV and the expansion of LP is a critical task for alpine meadow management and restoration.