Elemental carbon (EC), also known as black carbon, plays an important role in climate change. Accurately assessing EC concentration in aerosols remains challenging due to the overestimations caused by carbonates and organic carbon (OC) during thermal-optical measurement in the Tibetan Plateau (TP). This study evaluates the extent of EC overestimated by carbonates and OC at four remote sites (Nyalamu, Lulang, Everest and Ngari) in southern and western of the TP using different treatments. The average overestimation of EC concentration due to acid treatment was consistent across all sites (25.5 f 2.4 %). After correction, the proportion of EC overestimated by carbonates were approximately 8.5 f 7.3 %, 12.3 f 6.9 %, 18.1 f 11.8 % and 22.7 f 13.3 %, respectively, revealing an increasing trend from humid to arid regions. Methanol-soluble OC (MSOC) concentrations were significantly correlated with the reduction of EC concentrations, indicating that the methanol extraction effectively mitigates EC overestimation. Seasonal variation of carbonaceous aerosol concentrations was significantly affected by sources from South Asia. Despite the variations in climate and aerosol sources, the average overestimations of measured EC concentration by carbonates and OC were similar at Nyalamu (49.4 f 14.0 %), Lulang (47.8 f 8.4 %), Everest (48.7 f 15.9 %) and Ngari (49.3 f 13.7 %) sites. Therefore, the actual EC concentrations were only about 51.2 f 13.1 % of the original values. This estimation will significantly enhance the contribution of brown carbon (BrC) to radiative forcing relative to EC, highlighting a critical area for future research. Investigating the actual concentrations of EC in the TP provides critical data to support model simulation and validate model accuracy, further enhancing our understanding of EC's impacts on climate warming and glacier melting.

Quantification of active-layer thickness (ALT) over seasonally frozen terrains is critical to understand the impacts of climate warming on permafrost ecosystems in cold regions. Current large-scale process-based models cannot characterize the heterogeneous response of local landscapes to homogeneous climatic forcing. Here we linked a climate-permafrost model with a machine learning solution to indirectly quantify soil conditions reflected in the edaphic factor using high resolution remote sensor products, and then effectively estimated ALT across space and time down to local scales. Our nine-year field measurements during 2014-2022 and coincident high resolution airborne hyperspectral, lidar, and spaceborne sensor products provided a unique opportunity to test the developed protocol across two permafrost experiment stations in lowland terrains of Interior Alaska. Our developed model could explain over 60% of the variance of the field measured ALT for estimating the shallowest and deepest ALT in 2015 and 2019, suggesting the potential of the designed procedure for projecting local varying terrain response to long-term climate warming scenarios. This work will enhance the National Aeronautics and Space Administration's Arctic-Boreal Vulnerability Experiment's mission of combining field, airborne, and spaceborne sensor products to understand the coupling of permafrost ecosystems and climate change.

Almost 2 billion people depend on freshwater provided by the Asian water towers, yet long-term runoff estimation is challenging in this high-mountain region with a harsh environment and scarce observations. Most hydrologic models rely on observed runoff for calibration, and have limited applicability in the poorly gauged Asian water towers. To overcome such limitations, here we propose a novel data-driven model, SM2R (Soil Moisture to Runoff), to simulate monthly runoff based on soil moisture dynamics using reanalysis forcing data. The SM2R model was applied and examined in 20 drainage basins across seven Asian water towers during the past four decades of 1981-2020. Without invoking any observations for calibration, the overall good performance of SM2R-derived runoff (correlation coefficient & GE;0.74 and normalized root mean square error & LE;0.22 compared to observed runoff at 20 gauges) suggests considerable potential for runoff simulation in poorly gauged basins. Even though the SM2R model is forced by ERA5-Land (ERA5L) reanalysis data, it largely outperforms the ERA5L-estimated runoff across the seven Asian water towers, particularly in basins with widely distributed glaciers and frozen soil. The SM2R approach is highly promising for constraining hydrologic variables from soil moisture information. Our results provide valuable insights for not only long-term runoff estimation over key Asian basins, but also understanding hydrologic processes across poorly gauged regions globally.

Black carbon plays an important role in climate change. Whereas, accurate measurement of black carbon (also known as elemental carbon (EC)) is still a challenging issue because portion of the pyrolytic carbon produced from the organic carbon (OC) can cause the overestimation of EC when measured by thermal-optical method. As one of the remote regions in the world, the Tibetan Plateau (TP) is characterized as high OC/EC ratio in its atmosphere. In this study, potential influence of relative high OC concentration to EC were investigated at three remote sites (Yaze, Everest and Nam Co) in the TP. The results showed that carbonaceous aerosols from different sources can affect the fraction of OC extracted by methanol. Concentration of OC extracted by methanol had a significantly positive correlation with the reduction of pyrolytic carbon and EC concentrations, indicating that part of OC extracted by methanol can decrease the production of pyrolytic carbon and then reduce the over-estimation of EC. After considering this effect, it is shown in this study that actual EC concentration at Yaze, Everest and Nam Co were overestimated by approximately 40.0 +/- 12.6%, 28.8 +/- 9.1% and 24.8 +/- 4.7%, respectively. Accordingly, combined with the overestimation of EC concentration by carbonates, actual ratios of solar energy absorbed by organic carbon to EC were 1.67, 2.33 and 2.78 times those of original ones at Yaze, Everest and Nam Co, respectively. Therefore, warming effect caused by EC on the TP should be lower than that previously estimated. This phenomenon needs to be considered for both in situ study and model simulation in the future.

Soil moisture is an important driver of growth in boreal Alaska, but estimating soil hydraulic parameters can be challenging in this data-sparse region. Parameter estimation is further complicated in regions with rapidly warming climate, where there is a need to minimize model error dependence on interannual climate variations. To better identify soil hydraulic parameters and quantify energy and water balance and soil moisture dynamics, we applied the physically based, one-dimensional ecohydrological Simultaneous Heat and Water (SHAW) model, loosely coupled with the Geophysical Institute of Permafrost Laboratory (GIPL) model, to an upland deciduous forest stand in interior Alaska over a 13-year period. Using a Generalized Likelihood Uncertainty Estimation parameterisation, SHAW reproduced interannual and vertical spatial variability of soil moisture during a five-year validation period quite well, with root mean squared error (RMSE) of volumetric water content at 0.5 m as low as 0.020 cm(3)/cm(3). Many parameter sets reproduced reasonable soil moisture dynamics, suggesting considerable equifinality. Model performance generally declined in the eight-year validation period, indicating some overfitting and demonstrating the importance of interannual variability in model evaluation. We compared the performance of parameter sets selected based on traditional performance measures such as the RMSE that minimize error in soil moisture simulation, with one that is designed to minimize the dependence of model error on interannual climate variability using a new diagnostic approach we call CSMP, which stands for Climate Sensitivity of Model Performance. Use of the CSMP approach moderately decreases traditional model performance but may be more suitable for climate change applications, for which it is important that model error is independent from climate variability. These findings illustrate (1) that the SHAW model, coupled with GIPL, can adequately simulate soil moisture dynamics in this boreal deciduous region, (2) the importance of interannual variability in model parameterisation, and (3) a novel objective function for parameter selection to improve applicability in non-stationary climates.

In this study, summer rainfall contributions to streamflow were quantified in the sub-arctic, 30% glacierized Tarfala (21.7km(2)) catchment in northern Sweden for two non-consecutive summer sampling seasons (2004 and 2011). We used two-component hydrograph separation along with isotope ratios (O-18 and D) of rainwater and daily streamwater samplings to estimate relative fraction and uncertainties (because of laboratory instrumentation, temporal variability and spatial gradients) of source water contributions. We hypothesized that the glacier influence on how rainfall becomes runoff is temporally variable and largely dependent on a combination of the timing of decreasing snow cover on glaciers and the relative moisture storage condition within the catchment. The results indicate that the majority of storm runoff was dominated by pre-event water. However, the average event water contribution during storm events differed slightly between both years with 11% reached in 2004 and 22% in 2011. Event water contributions to runoff generally increased over 2011 the sampling season in both the main stream of Tarfala catchment and in the two pro-glacial streams that drain Storglaciaren (the largest glacier in Tarfala catchment covering 2.9km(2)). We credit both the inter-annual and intra-annual differences in event water contributions to large rainfall events late in the summer melt season, low glacier snow cover and elevated soil moisture due to large antecedent precipitation. Together amplification of these two mechanisms under a warming climate might influence the timing and magnitude of floods, the sediment budget and nutrient cycling in glacierized catchments. Copyright (c) 2012 John Wiley & Sons, Ltd.

Black carbon (BC) is an important aerosol constituent in the atmosphere and climate forcer. A good understanding of the radiative forcing of BC and associated climate feedback and response is critical to minimize the uncertainty in predicting current and future climate influenced by anthropogenic aerosols. One reason for this uncertainty is that current emission inventories of BC are mostly obtained from the so-called bottom-up approach, an approach that derives emissions based on categorized emitting sources and emission factors used to convert burning mass to emissions. In this work, we provide a first global-scale top-down estimation of global BC emissions, as well as an estimated error range, by using a Kalman Filter. This method uses data of both column aerosol absorption optical depth and surface concentrations from global and regional networks to constrain our fully coupled climate-aerosol-urban model and thus to derive an optimized estimate of BC emissions as 17.85.6 Tg/yr, a factor of more than 2 higher than commonly used global BC emissions data sets. We further perform 22 additional optimization simulations that incorporate the known uncertain ranges of various important physical, model, and measurement parameters and still yield an optimized value within the above given range, from a low of 14.6 Tg/yr to a high of 22.2 Tg/yr. Furthermore, we show that the emissions difference between our optimized and a priori estimation is not uniform, with East Asia, Southeast Asia, and Eastern Europe underestimated, while North America is overestimated in the a priori inventory.

Springtime near-surface soil thaw event is important for understanding the near-surface earth system. Previous researches based on both active and passive microwave remote sensing technologies have paid scant attention, especially at middle latitudes where the near-surface earth system has been changed substantially by climate change and human activities, and are characterized by more complex climate and land surface conditions than the permafrost areas. SSM/I brightness temperature and QuikSCAT Ku-band backscatter were applied in this study at a case study area of northern China and Mongolia in springtime of 2004. The soil freeze-thaw algorithm was employed for SSM/I data, and a random sampling technique was applied to determine the brightness temperature threshold for 37 GHz vertically polarized radiation: 258.2 and 260.1 K for the morning and evening satellite passes, respectively. A multi-step method was proposed for QuikSCAT Ku-band backscatter based on both field observed soil thaw events and the typical signature of radar backscatter time series when soil thaw event occurred. The method is mainly focuses on the estimated boundary of thaw events and detection of primary thaw date. The passive microwave remote sensing (SSM/I) based result had a good relationship with the near-surface soil temperature, while the active microwave remote sensing (QuikSCAT) based result had both relationships with temperature and soil moisture conditions. And also, QuikSCAT result identifies the geographical boundary of water-drove thaw event, which is crucial for understanding the different types of springtime near-surface soil thaw at middle latitudes.