Aboveground biomass (AGB) serves as a crucial measure of ecosystem productivity and carbon storage in alpine grasslands, playing a pivotal role in understanding the dynamics of the carbon cycle and the impacts of climate change on the Qinghai-Xizang Plateau. This study utilized Google Earth Engine to amalgamate Landsat 8 and Sentinel-2 satellite imagery and applied the Random Forest algorithm to estimate the spatial distribution of AGB in the alpine grasslands of the Beiliu River Basin in the Qinghai-Xizang Plateau permafrost zone during the 2022 growing season. Additionally, the geodetector technique was employed to identify the primary drivers of AGB distribution. The results indicated that the random forest model, which incorporated the normalized vegetation index (NDVI), the enhanced vegetation index (EVI), the soil-adjusted vegetation index (SAVI), and the normalized burn ratio index (NBR2), demonstrated robust performance in regards to AGB estimation, achieving an average coefficient of determination (R2) of 0.76 and a root mean square error (RMSE) of 70 g/m2. The average AGB for alpine meadows was determined to be 285 g/m2, while for alpine steppes, it was 204 g/m2, both surpassing the regional averages in the Qinghai-Xizang Plateau. The spatial pattern of AGB was primarily driven by grassland type and soil moisture, with q-values of 0.63 and 0.52, and the active layer thickness (ALT) also played a important role in AGB change, with a q-value of 0.38, demonstrating that the influences of ALT should not be neglected in regards to grassland change.

Extensive, detailed information on the spatial distribution of active layer thickness (ALT) in northern Alaska and how it evolves over time could greatly aid efforts to assess the effects of climate change on the region and also help to quantify greenhouse gas emissions generated due to permafrost thaw. For this reason, we have been developing high-resolution maps of ALT throughout northern Alaska. The maps are produced by upscaling from high-resolution swaths of estimated ALT retrieved from airborne P-band synthetic aperture radar (SAR) images collected for three different years. The upscaling was accomplished by using hundreds of thousands of randomly selected samples from the SAR-derived swaths of ALT to train a machine learning regression algorithm supported by numerous spatial data layers. In order to validate the maps, thousands of randomly selected samples of SAR-derived ALT were excluded from the training in order to serve as validation pixels; error performance calculations relative to these samples yielded root-mean-square errors (RMSEs) of 7.5-9.1 cm, with bias errors of magnitude under 0.1 cm. The maps were also compared to ALT measurements collected at a number of in situ test sites; error performance relative to the site measurements yielded RMSEs of approximately 11-12 cm and bias of 2.7-6.5 cm. These data are being used to investigate regional patterns and underlying physical controls affecting permafrost degradation in the tundra biome.

Global warming has caused changes in the area and thickness of permafrost on the Qinghai-Tibet Plateau and prompted the transition from permafrost to seasonally frozen soil, which has affected the soil moisture, soil temperature, and distribution of plant roots. This, in turn, affects grassland vegetation productivity and aboveground/belowground biomass. In this study, we took Qinghai Province in the northeastern Qinghai-Tibet Plateau as the research area to model the spatial pattern of grassland biomass and then evaluated the potential influence of frozen soil type information on aboveground and belowground biomass. Our research shows that there are significantly more biomass observations in seasonally frozen soil regions than in permafrost regions. However, when we ignore the type of frozen soil, the model does not show more accurate simulation in seasonally frozen soil regions, mainly because the stronger correlation between permafrost biomass and environmental factors, such as precipitation, compensates for the lack of observational data. In addition, we found that the biomass estimation error can be reduced significantly by building different models for each type of frozen soil, which implies that the type of frozen soil has an important impact on grassland biomass. Therefore, in considering the effects of future climate warming, more attention should be given to the impact of changes in frozen soil type on regional vegetation productivity. In addition, our investigation contributes a benchmark dataset of above- and belowground vegetation carbon storage in different frozen soil types, which provides the research community with useful information for optimizing process-based carbon cycle models.

Knowledge of the spatial and temporal distribution of active layer thickness (ALT) throughout northern Alaska would help to understand the effects of climate change in the region, as well as to quantify how much the permafrost degradation manifestly in progress there is contributing to the accumulation of greenhouse gases in the atmosphere. For this reason, we are developing extensive high-resolution maps of ALT in northern Alaska. We use machine learning along with an extensive set of spatial data layers to upscale ALT from thousands of training pixels taken from high resolution swaths of estimated ALT derived from airborne polarimetric P-band synthetic aperture radar (SAR). The resulting maps of up-scaled ALT have been compared to thousands of validation samples set aside from the PolSAR-derived swaths and to in situ ALT measurements. The maps have achieved root-mean-square errors (RMSEs) of 5-7 cm relative to validation samples, and RMSEs of approximately 10-12 cm relative to in situ ALT measurements.

Arctic and boreal ecosystems play an important role in the global carbon (C) budget, and whether they act as a future net C sink or source depends on climate and environmental change. Here, we used complementary in situ measurements, model simulations, and satellite observations to investigate the net carbon dioxide (CO2) seasonal cycle and its climatic and environmental controls across Alaska and northwestern Canada during the anomalously warm winter to spring conditions of 2015 and 2016 (relative to 2010-2014). In the warm spring, we found that photosynthesis was enhanced more than respiration, leading to greater CO2 uptake. However, photosynthetic enhancement from spring warming was partially offset by greater ecosystem respiration during the preceding anomalously warm winter, resulting in nearly neutral effects on the annual net CO2 balance. Eddy covariance CO2 flux measurements showed that air temperature has a primary influence on net CO2 exchange in winter and spring, while soil moisture has a primary control on net CO2 exchange in the fall. The net CO2 exchange was generally more moisture limited in the boreal region than in the Arctic tundra. Our analysis indicates complex seasonal interactions of underlying C cycle processes in response to changing climate and hydrology that may not manifest in changes in net annual CO2 exchange. Therefore, a better understanding of the seasonal response of C cycle processes may provide important insights for predicting future carbon-climate feedbacks and their consequences on atmospheric CO2 dynamics in the northern high latitudes.

The freezing-thawing cycle is a basic feature of a frozen soil ecosystem, and it affects the growth of alpine vegetation both directly and indirectly. As the climate changes, the freezing-thawing mode, along with its impact on frozen soil ecosystems, also changes. In this research, the freezing-thawing cycle of the Nagqu River Basin in the Qinghai-Tibet Plateau was studied. Vegetation growth characteristics and microbial abundance were analyzed under different freezing-thawing modes. The direct and indirect effects of the freezing-thawing cycle mode on alpine vegetation in the Nagqu River Basin are presented, and the changing trends and hazards of the freezing-thawing cycle mode due to climate change are discussed. The results highlight two major findings. First, the freezing-thawing cycle in the Nagqu River Basin has a high-frequency mode (HFM) and a low-frequency mode (LFM). With the influence of climate change, the LFM is gradually shifting to the HFM. Second, the alpine vegetation biomass in the HFM is lower than that in the LFM. Frequent freezing-thawing cycles reduce root cell activity and can even lead to root cell death. On the other hand, frequent freezing-thawing cycles increase microbial (Bradyrhizobium, Mesorhizobium, and Pseudomonas) death, weaken symbiotic nitrogen fixation and the disease resistance of vegetation, accelerate soil nutrient loss, reduce the soil water holding capacity and soil moisture, and hinder root growth. This study provides a complete response mechanism of alpine vegetation to the freezing-thawing cycle frequency while providing a theoretical basis for studying the change direction and impact on the frozen soil ecosystem due to climate change.

NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) integrates field and airborne data into modeling and synthesis activities for understanding Arctic and Boreal ecosystem dynamics. The ABoVE Benchmarking System (ABS) is an operational software package to evaluate terrestrial biosphere models against key indicators of Arctic and Boreal ecosystem dynamics, i.e.: carbon biogeochemistry, vegetation, permafrost, hydrology, and disturbance. The ABS utilizes satellite remote sensing data, airborne data, and field data from ABoVE as well as collaborating research networks in the region, e.g.: the Permafrost Carbon Network, the International Soil Carbon Network, the Northern Circumpolar Soil Carbon Database, AmeriFlux sites, the Moderate Resolution Imaging Spectroradiometer, the Orbiting Carbon Observatory 2, and the Soil Moisture Active Passive mission. The ABS is designed to be interactive for researchers interested in having their models accurately represent observations of key Arctic indicators: a user submits model results to the system, the system evaluates the model results against a set of Arctic-Boreal benchmarks outlined in the ABoVE Concise Experiment Plan, and the user then receives a quantitative scoring of model strengths and deficiencies through a web interface. This interactivity allows model developers to iteratively improve their model for the Arctic-Boreal Region by evaluating results from successive model versions. Weshow here, for illustration, the improvement of the Lund-Potsdam-Jena-Wald Schnee und Landschaft (LPJwsl) version model through the ABoVE ABS as a new permafrost module is coupled to the existing model framework. The ABS will continue to incorporate new benchmarks that address indicators of Arctic-Boreal ecosystem dynamics as they become available.

There is no knowledge of winter plant phenology and its controlling factors on the Qinghai-Tibetan Plateau (QTP). Thus, we conducted a 4 year winter phenology and growth dynamics study in the alpine meadow on the eastern QTP. From November 2013 to March 2017, the phenology of the 'winter-growth' and 'winter-green' species was recorded every 5 d. In November-February from 2014 to 2015, the above-ground biomass (AGB) in random plots was calculated to distinguish different growth patterns among winter growing species. The percentage of winter abundance relative to the summer population for forbs and the percentage of absolute coverage for grasses (W/S) were calculated to describe the importance of the winter population to the summer population. The soil moisture (SM) and soil temperature (ST) were used to explore the controlling factors on the AGB. Pearson's correlation analysis between winter phenology data and environmental variables, including air temperature (T-air), snow cover fraction (SCF), SM and ST, was used to investigate the factors affecting winter phenology during November-February from 2014 to 2017. There were 107 species in total in the sites, including ten 'winter-growth' species and four 'winter-green' species. Among the 'winter-green' species, Festuca ovina and Deschampsia cespitosa were the dominant species in the sites. The 'winter-growth' species grew new leaves or ramets or transitioned to reproductive growth. Gentiana spathulifolia even flowered in winter. 'Winter-growth' made important contributions to the annual AGB, e.g. winter growth of G. spathulifolia accounted for 23.26 % of its annual AGB, while 14.74 % of the annual AGB of G. crassuloides was from winter growth. In addition, winter warming and snowfall reduction under global climate change on the eastern QTP may decrease the AGB increment of the 'winter-growth' and delay the green-up onset date of 'winter-green' species. Also, winter warming and snowfall reduction may advance the first flowering date of 'winter-growth' species. In contrast to previous views that plants on the QTP were generally considered to remain dormant in winter, our study revealed that alpine meadow plants had strong winter growth which suggested the importance of re-evaluating the dynamics of ecosystem function of alpine meadow, including its contribution to the global carbon balance. It was also shown that soil moisture availability is more important than warmer temperature in controlling the green-up onset of 'winter-green' species on the eastern QTP, which contrasts with the traditional view that warmer winters could advance green-up. As snowmelt is the only source of soil water in winter, the prediction of the green-up trend may be further complicated by snowfall variation in winter.

The Tibetan Plateau has the largest expanse of high-elevation permafrost in the world, and it is experiencing climate warming that may jeopardize the functioning of its alpine ecosystems. Many studies have focused on the effects of climate warming on vegetation production and diversity on the Plateau, but their disparate results have hindered a comprehensive, regional understanding. From a synthesis of twelve warming experiments across the Plateau, we found that warming increased aboveground net primary production (ANPP) and vegetation height at sites with permafrost, but ANPP decreased with warming at non-permafrost sites. Aboveground net primary production responded more negatively to warming under drier conditions, due to both annual drought conditions and warming-induced soil moisture loss. Decreases in species diversity with warming were also larger at sites with permafrost. These results support the emerging understanding that water plays a central role in the functioning of cold environments and suggest that as ecosystems cross a threshold from permafrost to non-permafrost systems, ANPP will decrease across a greater proportion of the Tibetan Plateau. This study also highlights the future convergence of challenges from permafrost degradation and grassland desertification, requiring new collaborations among these currently distinct research and stakeholder groups.

This study tested the hypothesis that soil organic carbon (SOC) and total nitrogen (TN) spatial distributions show clear relationships with soil properties and vegetation composition as well as climatic conditions. Further, this study aimed to find the corresponding controlling parameters of SOC and TN storage in high-altitude ecosystems. The study was based on soil, vegetation and climate data from 42 soil pits taken from 14 plots. The plots were investigated during the summers of 2009 and 2010 at the northeastern margin of the Qinghai-Tibetan Plateau. Relationships of SOC density with soil moisture, soil texture, biomass and climatic variables were analyzed. Further, storage and vertical patterns of SOC and TN of seven representative vegetation types were estimated. The results show that significant relationships of SOC density with belowground biomass (BGB) and soil moisture (SM) can be observed. BGB and SM may be the dominant factors influencing SOC density in the topsoil of the study area. The average densities of SOC and TN at a depth of 1 m were about 7.72 kg C m(2) and 0.93 kg N m 2. Both SOC and TN densities were concentrated in the topsoil (0-20 cm) and fell exponentially as soil depth increased. Additionally, the four typical vegetation types located in the northwest of the study area were selected to examine the relationship between SOC and environmental factors (temperature and precipitation). The results indicate that SOC density has a negative relationship with temperature and a positive relationship with precipitation diminishing with soil depth. It was concluded that SOC was concentrated in the topsoil, and that SOC density correlates well with BGB. SOC was predominantly influenced by SM, and to a much lower extent by temperature and precipitation. This study provided a new insight in understanding the control of SOC and TN density in the northeastern margin of the Qinghai-Tibetan Plateau.