Brown carbon (BrC) is the ubiquitous part of the atmospheric organic carbon. It absorbs solar lights and greatly impacts the Earth's radiative balance. This study examines the spectral characteristics of BrC and its radiative effect in the Dhaka South (DS) site and Dhaka North (DN) site from July 2023 to January 2024 with a high-volume particulate matter sampler on quartz filters. Spectral characteristics such as absorption coefficient (babe,), mass absorption efficiency (MAE), absorption angstrom exponent (AAE), and refractive index (Kabs-x) were determined by using a UV -visible spectrophotometer, and fluorescence emission spectra were analyzed in different pH by the fluorescent spectrophotometer. The concentrations of BrC and black carbon (BC) were determined by an aethalometer. The mean concentrations of BrC and BC in Dhaka city were 18.63 +/- 3.84 mu g 111-3 and 17.93 +/- 3.82 pg M-3, respectively. The AAE values lie in the range of 3.20-4.01 (DN) and 3.27-4.53 (DS), and the radiative forcing efficiency of BrC was obtained at 4.43 +/- 1.02 W g-1 in DN and 3.93 +/- 0.74 W g-1 in DS, indicating the presence of highly light-absorbing BrC in these locations. Average MAE and Kabs_k values were 1.55 +/- 0.45 m2g1 and 0.044 + 0.013, respectively, in DS, alternatively 1.84 +/- 0.59 m2g1 and 0.052 +/- 0.016 in DN. The fluorescence excitation-emission spectra confirmed the presence of a polyconjugate cyclic ring with multifunctional groups in the structure of BrC. Light absorption properties and fluorescence emission spectra were varied with the change of pH. As the pH increased (2-8), the AAE value decreased and MAEB,c_365 increased due to protonation or deprotonation. This study highlights that the BrC has a significant impact on the air quality as well as the Earth's radiative balance, emphasizing its strong light-absorbing properties and variability with environmental factors.

Ongoing climate warming and increased human activities have led to significant permafrost degradation on the Qinghai-Tibet Plateau (QTP). Mapping the distribution of active layer thickness (ALT) can provide essential information for understanding this degradation. Over the past decade, InSAR (Interferometric synthetic aperture radar) technology has been utilized to estimate ALT based on remotely-sensed surface deformation information. However, these methods are generally limited by their ability to accurate extract seasonal deformation and model subsurface water content of active layer. In this paper, an ALT inversion method considering both seasonal deformation from InSAR and smoothly multilayer soil moisture from ERA5 is proposed. Firstly, we introduce a ground seasonal deformation extraction model combining RobustSTL and InSAR, and the deformation extraction accuracy by considering the deformation characteristics of permafrost are evaluated, proving the effectiveness of RobustSTL in extracting seasonal deformation of permafrost. Then, using ERA5 soil moisture products, a smoothed multilayer soil moisture model for ALT inversion is established. Finally, integrating the seasonal deformation and multilayer soil moisture, the ALT can be estimated. The proposed model is applied to the Yellow River source region (YRSR) with Sentinel-1A images acquired from 2017 to 2021, and the ALT retrieval accuracy is validated with measured data. Experimental results show that the vertical deformation rate of the study area generally ranges from -30 mm/year to 20 mm/year, with seasonal deformation amplitude ranging from 2 mm to 30 mm. The RobustSTL method has the highest accuracy in extracting seasonal deformation of permafrost, with an RMSE (root mean square error) of 0.69 mm, and is capable of capturing the freeze-thaw characteristics of the active layer. The estimated ALT of the YRSR ranges from 49 cm to 450 cm, with an average value of 145 cm. Compared to the measured data, the proposed method has an average error of 37.5 cm, which represents a 21 % improvement in accuracy over existing methods.

The paper presents the strategic project of Tomsk State University devoted to studying the carbon cycle in the arctic land-shelf system. The obtained carbon cycle characteristics should be used for global climate model correction. The main objective of the consortium is to obtain new data on the variability of climatic and biological factors of various ecosystems, monitor them, and create archives of data on their dynamics. The area of the project includes the basins of the Great Siberian Rivers, and the shelf of the adjacent Arctic seas. A consortium of approximately twenty universities and research institutions was formed to study the carbon cycle in various environments, including seas, rivers, wetlands, and permafrost. In addition to studying the carbon cycle, the project also aims to develop methods for carbon sequestration and ecosystems remediation. One of such methods was developed for the assessment and cleanup of bottom sediments from oil and petroleum products as well as other hydrophobic contaminants and has been patented and tested in a series of field trials. Several special monitoring methods are described, such as novel sampling and sample laboratory processing techniques to assess microplastics in the environment; and holographic methods for underwater monitoring of the plankton behavior for early bioindication of hazards in the water area. This is particularly relevant for areas with dangerous objects, such as nuclear power plants, oil platforms, and gas pipelines. The methods of math modeling of the impact of climate change and anthropogenic factors on indigenous and local population lives were used.

The Arctic experiences rapid climate change, but our ability to predict how this will influence plant communities is hampered by a lack of data on the extent to which different species are associated with particular environmental conditions, how these conditions are interlinked, and how they will change in coming years. Increasing temperatures may negatively affect plants associated with cold areas due to increased competition with warm-adapted species, but less so if local temperature variability is larger than the expected increase. Here we studied the potential drivers of vegetation composition and species richness along coast to inland and altitudinal gradients by the Nuuk fjord in western Greenland using hierarchical modelling of species communities (HMSC) and linear mixed models. Community composition was more strongly associated with random variability at intermediate spatial scales (among plot groups 500 m apart) than with large-scale variability in summer temperature, altitude or soil moisture, and the variation in community composition along the fjord was small. Species richness was related to plant cover, altitude and slope steepness, which explained 42% of the variation, but not to summer temperature. Jointly, this suggests that the direct effect of climate change will be weak, and that many species are associated with microhabitat variability. However, species richness peaked at intermediate cover, suggesting that an increase in plant cover under warming climatic conditions may lead to decreasing plant diversity.

Freeze-thaw-induced N2O pulses could account for nearly half of annual N2O fluxes in cold climates, but their episodic nature, sensitivity to snow cover dynamics, and the challenges of cold-season monitoring complicate their accurate estimation and representation in global models. To address these challenges, we combined in situ automated high-frequency flux measurements with cross-ecoregion soil core incubations to investigate the mechanisms driving freeze-thaw-induced N2O emissions. We found that deepened snow significantly amplified freeze-thaw N2O pulses, with these similar to 50-day episodes contributing over 50% of annual fluxes. Additionally, freeze-thaw-induced N2O pulses exhibited significant spatial heterogeneity, ranging from 3.4 to 1184.1 mu g N m(-2) h(-1) depending on site conditions. Despite significant spatiotemporal variation, our results indicated that 68%-86% of this variation can be explained by shifts in controlling factors: from water-filled pore space (WFPS), which drove anaerobic conditions, to microbial constraints as snow depth increases. Below 43% WFPS, soil moisture was the overwhelmingly dominant driver of emissions; between 43% and 66% WFPS, moisture and microbial attributes (including denitrifying gene abundance, nitrogen enzyme kinetics, and microbial biomass) jointly triggered N2O emissions pulses; above 66% WFPS, microbial attributes, particularly nitrogen enzyme kinetics, prevailed. These findings suggested that maintaining higher soil moisture served as a trigger for activating microbial activity, particularly enhancing nitrogen cycling. Furthermore, we showed that hotspots of freeze-thaw-induced N2O emissions were linked to high root production and microbial activity in cold and humid grasslands. Overall, our study highlighted the hierarchical control of WFPS and microbial processes in driving freeze-thaw-induced N2O emission pulses. The easily measurable WFPS and microbial attributes predictable from plant and soil properties could forecast the magnitude and spatial distribution of N2O emission hot moments under changing climate. Integrating these hot moments, particularly the dynamics of WFPS, into process-based models could refine N2O emission modeling and enhance the accuracy of global N2O budget prediction.

Alpine treelines ecotones are critical ecological transition zones and are highly sensitive to global warming. However, the impact of climate on the distribution of treeline trees is not yet fully understood as this distribution may also be affected by other factors. Here, we used high-resolution satellite images with climatic and topographic variables to study changes in treeline tree distribution in the alpine treeline ecotone of the Changbai Mountain for the years 2002, 2010, 2017, and 2021. This study employed the Geodetector method to analyze how interactions between climatic and topographic factors influence the expansion of Betula ermanii on different aspect slopes. Over the past 20 years, B. ermanii, the only tree species in the Changbai Mountain tundra zone, had its highest expansion rate from 2017 to 2021 across all the years studied, approaching 2.38% per year. In 2021, B. ermanii reached its uppermost elevations of 2224 m on the western aspects and 2223 m on the northern aspects, which are the predominant aspects it occupies. We also observed a notable increase in the distribution of B. ermanii on steeper slopes (> 15 degrees) between 2002 and 2021. Moreover, we found that interactions between climate and topographic factors played a more significant role in B. ermanii's expansion than any single dominant factor. Our results suggest that the interaction between topographic wetness index and the coldest month precipitation (Pre(1)), contributing 91% of the observed variability, primarily drove the expansion on the southern aspect by maintaining soil moisture, providing snowpack thermal insulation which enhanced soil temperatures, decomposition, and nutrient release in harsh conditions. On the northern aspect, the interaction between elevation and mean temperature of the warmest month explained 80% of the expansion. Meanwhile, the interaction between Pre(1) and mean temperature of the growing season explained 73% of the expansion on the western aspect. This study revealed that dominant factors driving treeline upward movement vary across different mountain aspects. Climate and topography play significant roles in determining tree distribution in the alpine treeline ecotone. This knowledge helps better understand and forecast treeline dynamics in response to global climate change.

明确不同野火数据产品的一致性与不确定性是开展产品分析应用的前提与基础。FireCCI51和MOSEV是国际上广泛使用的两套野火数据产品，泛北极多年冻土区是全球野火发生的集中区与重要碳库，分析该区野火数据产品的一致性可对未来提升野火产品的数据精度、降低泛北极碳通量估算等具有重要意义。本研究运用空间分析方法，从燃烧面积与燃烧区空间位置等方面，识别了FireCCI51和MOSEV两种数据产品在泛北极多年冻土区的一致性，进而分析了两者一致区与不一致区的地理环境特征。结果表明：(1)2001～2019年FireCCI51产品的燃烧面积均大于MOSEV，且两者燃烧面积的百分比差异在7%～60%无规律波动；(2)两种产品识别的燃烧区的空间分布一致性介于27.68%～47.14%，一致区主要分布在燃烧区集中分布的地区，例如加拿大中部地区、俄罗斯中、东西伯利亚地区及其南部延伸的大兴安岭地区，不一致区除了分布在这些地区之外，还在俄罗斯南部和西西伯利亚地区分布较多；(3)高程、气候类型和植被类型均对两套数据产品的一致区分布造成一定影响。在较低海拔和常湿冷温气候地区两种产品的一致区面积占比较多，在相对较高海拔...

The Net Ecosystem Carbon Balance (NECB) is a crucial metric for understanding integrated carbon dynamics in Arctic and boreal regions, which are vital to the global carbon cycle. These areas are associated with significant uncertainties and rapid climate change, potentially leading to unpredictable alterations in carbon dynamics. This mini-review examines key components of NECB, including carbon sequestration, methane emissions, lateral carbon transport, herbivore interactions, and disturbances, while integrating insights from recent permafrost region greenhouse gas budget syntheses. We emphasize the need for a holistic approach to quantify the NECB, incorporating all components and their uncertainties. The review highlights recent methodological advances in flux measurements, including improvements in eddy covariance and automatic chamber techniques, as well as progress in modeling approaches and data assimilation. Key research priorities are identified, such as improving the representation of inland waters in process-based models, expanding monitoring networks, and enhancing integration of long-term field observations with modeling approaches. These efforts are essential for accurately quantifying current and future greenhouse gas budgets in rapidly changing northern landscapes, ultimately informing more effective climate change mitigation strategies and ecosystem management practices. The review aligns with the goals of the Arctic Monitoring and Assessment Program (AMAP) and Conservation of Arctic Flora and Fauna (CAFF), providing important insights for policymakers, researchers, and stakeholders working to understand and protect these sensitive ecosystems.

Multi-source precipitation products (MSPs) are critical for hydrologic modeling, but their spatial and temporal heterogeneity and uncertainty present challenges to simulation accuracy that need to be addressed urgently. This study assessed the impact of different precipitation data sources on hydrologic modeling in an arid basin. There were seven precipitation products and meteorological station interpolated data that were used to drive the hydrological model, and we evaluated their performance by fusing the six precipitation products through the dynamic bayesian averaging algorithm. Ultimately, the runoff simulation uncertainty was quantified based on the DREAM algorithm, and the information transfer entropy was used to quantify the differences in hydrologic simulation processes driven by different precipitation data. The results showed that CMFD and ERA5 weights were higher, and the DBMA fused precipitation annual mean value was about 309.83 mm with good simulation accuracy (RMSE of 1.46 and R-2 of 0.75). The simulation was satisfactory (NSE >0.80) after parameter calibration and data assimilation for all driving data, with CHIRPS and TRMM performed better in the common mode, and HRLT and CMFD performed excellently in the glacier mode. The DREAM algorithm indicated less uncertainty for DBMA, CHIRPS and HRLT data. The entropy of information transfer revealed that precipitation occupied a significant position in information transfer, especially affecting evapotranspiration and surface soil moisture. CMFD and TPS CMADS were highest in snow water equivalent information entropy, and CHIRPS and TPS CMADS were highest in evapotranspiration information entropy. CDR, CHIRPS, ERA5-Land and IDW STATION had the highest snow water equivalent information entropy, DBMA and CMORPH had the highest runoff information entropy, CHIRPS and TRMM had the highest soil moisture information entropy, whereas ERA5, HRLT, and TPS CMADS had the highest evapotranspiration information entropy in glacial mode. This study reveals significant differences between different precipitation data sources in hydrological modeling of arid basin, which is an important reference for future water resources management and climate change adaptation strategies.

The global cryosphere is retreating under ongoing climate change. The Third Pole (TP) of the Earth, which serves as a critical water source for two billion people, is also experiencing this decline. However, the interplay between rising temperatures and increasing precipitation in the TP results in complex cryospheric responses, introducing uncertainties in the future budget of TP cryospheric water (including glacier and snow water equivalents and frozen soil moisture). Using a calibrated model that integrated multiple cryospheric-hydrological components and processes, we projected the TP cryospheric water budgets under both low and high climatic forcing scenarios for the period 2021-2100 and assessed the relative impact of temperature and precipitation. Results showed (1) that despite both scenarios involving simultaneous warming and wetting, under low climatic forcing, the total cryospheric budget exhibited positive dynamics (0.017 mm yr-1 with an average of 1.77 mm), primarily driven by increased precipitation. Glacier mass loss gradually declined with the rate of retreat slowing, accompanied by negligible declines in the budget of snow water equivalent and frozen soil moisture. (2) By contrast, high climatic forcing led to negative dynamics in the total cryospheric budget (-0.056 mm yr-1 with an average of -1.08 mm) dominated by warming, with accelerated decreases in the budget of all cryospheric components. These variations were most pronounced in higher-altitude regions, indicating elevation-dependent cryospheric budget dynamics. Overall, our findings present alternative futures for the TP cryosphere, and highlight novel evidence that optimistic cryospheric outcomes may be possible under specific climate scenarios.