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.

Climate change impacts water supply dynamics in the Upper Rio Grande (URG) watersheds of the US Southwest, where declining snowpack and altered snowmelt patterns have been observed. While temperature and precipitation effects on streamflow often receive the primary focus, other hydroclimate variables may provide more specific insight into runoff processes, especially at regional scales and in mountainous terrain where snowpack is a dominant water storage. The study addresses the gap by examining the mechanisms of generating streamflow through multi-modal inferences, coupling the Bayesian Information Criterion (BIC) and Bayesian Model Averaging (BMA) techniques. We identified significant streamflow predictors, exploring their relative influences over time and space across the URG watersheds. Additionally, the study compared the BIC-BMA-based regression model with Random Forest Regression (RFR), an ensemble Machine Learning (RFML) model, and validated them against unseen data. The study analyzed seasonal and long-term changes in streamflow generation mechanisms and identified emergent variables that influence streamflow. Moreover, monthly time series simulations assessed the overall prediction accuracy of the models. We evaluated the significance of the predictor variables in the proposed model and used the Gini feature importance within RFML to understand better the factors driving the influences. Results revealed that the hydroclimate drivers of streamflow exhibited temporal and spatial variability with significant lag effects. The findings also highlighted the diminishing influence of snow parameters (i. e., snow cover, snow depth, snow albedo) on streamflow while increasing soil moisture influence, particularly in downstream areas moving towards upstream or elevated watersheds. The evolving dynamics of snowmelt-runoff hydrology in this mountainous environment suggest a potential shift in streamflow generation pathways. The study contributes to the broader effort to elucidate the complex interplay between hydroclimate variables and streamflow dynamics, aiding in informed water resource management decisions.

The Tibetan Plateau (TP) covers the largest regions under low- and mid-latitude permafrost. The evolution of permafrost has significantly affected the hydrology, biogeochemistry, and infrastructure of Asia. However, model reconstructions of long-term permafrost evolution with high accuracy and reliability are insufficient. Here, spatial changes in mean annual ground temperature at the depth where the annual amplitude is zero (MAGT) on the TP since 1981 were modeled and validated based on temperature records from 155 boreholes, and future changes were predicted under scenarios from the Climate Model Intercomparison Project 6 (CMIP6). The results indicated that the MAGT on the TP was approximately 1.5 degrees C (2010 - 2018), and the corresponding permafrost extent on the TP is estimated to be approximately 1.03 x 106 km2, which is projected to decrease to 0.77 x 106, 0.50 x 106, 0.30 x 106, and 0.17 x 106 km2 under the scenarios of shared socioeconomic pathway (SSP)126, SSP245, SSP370, and SSP585, respectively, by 2100. As predicted in the SSP585 scenario, permafrost is predicted to largely disappear from many basins of major Asian rivers, such as the Yarlung Zangpo-Brahmaputra, NuSalween, and Lancang-Mekong Rivers, between 2041 and 2060, followed by the Yellow and Yangtze Rivers between 2061 and 2080. Moreover, the original stable permafrost in the West Kunlun Mountains will change to transitional and unstable conditions. Our study offers comprehensive datasets of year-to-year ground temperatures and permafrost extent maps for the TP, which can serve as a fundamental resource for further investigations on the hydrogeology, engineering geology, ecology, and geochemistry of the TP.

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.

Reanalysis is a valuable potential data source for permafrost studies. The latest-generation reanalysis of the Japanese Reanalysis for three quarters of a century (JRA-3Q) benefits from improved snow and soil schemes and demonstrates encouraging performance for soil temperature in permafrost regions compared to its predecessor, JRA-55, and other state-of-the-art reanalyses. We find JRA-3Q to have an overall mean annual air temperature bias of-0.17 degrees C, with-0.55 degrees C in permafrost regions. The snow depth was underestimated by-5.5 cm. In permafrost regions, the mean annual ground temperature bias was about-0.09 degrees C. The estimated permafrost area from JRA-3Q is between 10.8 and 15.8 x 106 km2. The active layer thickness is substantially overestimated by about 0.65 m. The JRA-3Q soil temperature exhibits a pronounced warm bias in Alaska, which is very likely due to the overestimated snow insulation and simplified soil organic content. The decoupled energy conservation parameterization (DECP) method employed in the JRA-3Q soil scheme restricts its suitability for the interpretation of detailed permafrost phenomena, such as zero-curtain effects. This DECP method is used in many stateof-the-art land surface models; our results demonstrate the need for additional contributions to improve the representation of permafrost-specific processes.

In the context of global climate change, changes in unfrozen water content in permafrost significantly impact regional terrestrial plant ecology and engineering stability. Through Differential Scanning Calorimetry (DSC) experiments, this study analyzed the thermal characteristic indicators, including supercooling temperature, freezing temperature, thawing temperature, critical temperature, and phase-transition temperature ranges, for silt loam with varying starting moisture levels throughout the freezing and thawing cycles. With varying starting moisture levels throughout the freezing and thawing cycles, a model describing the connection between soil temperature and variations in unfrozen water content during freeze-thaw cycles was established and corroborated with experimental data. The findings suggest that while freezing, the freezing and supercooling temperatures of unsaturated clay increased with the soil's starting moisture level, while those of saturated clay were less affected by water content. During thawing, the initial thawing temperature of clay was generally below 0 degrees C, and the thawing temperature exhibited a power function relationship with total water content. Model analysis revealed hysteresis effects in the unfrozen water content curve during freeze-thaw cycles. Both the phase-transition temperature range and model parameters were sensitive to temperature changes, indicating that the processes of permafrost freezing and thawing are mainly controlled by ambient temperature changes. The study highlights the stability of the difference between freezing temperature and supercooling temperature in clay during freezing. These results offer a conceptual framework for comprehending the thawing mechanisms of permafrost and analyzing the variations in mechanical properties and terrestrial ecosystems caused by temperature-dependent moisture changes in permafrost.

Northeastern China (NEC) is the largest grain base in China. Improving understanding of the effect of climate change on grain production over NEC is conducive to providing immediate response strategies for grain production. In this study, the relationships of the maize production with the dry state during the different maize growth stage have been investigated using the year-to-year increment method. Results showed that the severe drought that occurred from the jointing to maturity period have exerted severe effects on the maize growth. Further analysis indicated that the sea surface temperature (SST) anomalies over North Atlantic and Maritime Continent in later spring are the important factors affecting the summer droughts over NEC. The late spring SST anomaly over North Atlantic can excite the Rossby waves from the western North Atlantic and propagate eastward to NEC. The snow anomaly over western Siberia in late spring and the soil moisture anomaly over NEC in summer are key factors linking the SST anomaly to drought over the NEC. On the other hand, the Maritime Continent SST anomaly in late spring can modulate the activity of the East Asian jet stream via the East AsiaPacific (EAP) teleconnection, which can provide the favorable conditions for the soil moisture reduction over NEC. Eventually, a predictive model for maize yield over NEC is successfully developed by using the predictive indices of the North Atlantic and the Maritime Continental SST during late spring. Both the cross-validation and independent sample tests show that the calibrated prediction model is robust and exhibits high skill in predicting maize yield over NEC.

Carbonaceous aerosol, including organic carbon (OC) and elemental carbon (EC), has significant influence on human health, air quality and climate change. Accurate measurement of carbonaceous aerosol is essential to reduce the uncertainty of radiative forcing estimation and source apportionment. The accurate separation of OC and EC is controversial due to the charring of OC. Therefore, the development of reference materials (RM) for the validation of OC/EC separation is an important basis for further study. Previous RMs were mainly based on ambient air sampling, which could not provide traceability of OC and EC concentration. To develop traceable RMs with known OC/EC contents, our study applied an improved aerosol generation and mixing technique, providing uniform deposition of particles on quartz filters. To generate OC aerosol with similar pyrolytic property of ambient aerosol, both water soluble organic carbon (WSOC) and water insoluble organic carbon (WIOC) were used, and amorphous carbon was selected for EC surrogate. The RMs were analyzed using different protocols. The homogeneity within the filter was validated, reaching below 2%. The long -term stability of RMs has been validated with RSD ranged from 1.7%-3.2%. Good correlation was observed between nominal concentration of RMs with measured concentration by two protocols, while the difference of EC concentration was within 20%. The results indicated that the newly developed RMs were acceptable for the calibration of OC and EC, which could improve the accuracy of carbonaceous aerosol measurement. Moreover, the laboratory-generated EC-RMs could be suitable for the calibration of equivalent BC concentration by Aethalometers. (c) 2024 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

Understanding the dynamics of soil respiration (Rs) in response to freeze-thaw cycles is crucial due to permafrost degradation on the Qinghai-Tibet Plateau (QTP). We conducted continuous in situ observations of Rs using an Li-8150 automated soil CO2 flux system, categorizing the freeze-thaw cycle into four stages: completely thawed (CT), autumn freeze-thaw (AFT), completely frozen (CF), and spring freeze-thaw (SFT). Our results revealed distinct differences in Rs magnitudes, diurnal patterns, and controlling factors across these stages, attributed to varying thermal regimes. The mean Rs values were as follows: 2.51 (1.10) mu mol center dot m(-2)center dot s(-1) (CT), 0.37 (0.04) mu mol center dot m(-2)center dot s(-1) (AFT), 0.19 (0.06) mu mol center dot m(-2)center dot s(-1) (CF), and 0.68 (0.19) mu mol center dot m(-2)center dot s(-1) (SFT). Cumulatively, the Rs contributions to annual totals were 89.32% (CT), 0.79% (AFT), 5.01% (CF), and 4.88% (SFT). Notably, the temperature sensitivity (Q10) value during SFT was 2.79 times greater than that in CT (4.63), underscoring the significance of CO2 emissions during spring warming. Soil temperature was the primary driver of Rs in the CT stage, while soil moisture at 5 cm depth and solar radiation significantly influenced Rs during SFT. Our findings suggest that global warming will alter seasonal Rs patterns as freeze-thaw phases evolve, emphasizing the need to monitor CO2 emissions from alpine meadow ecosystems during spring.