The freeze-thaw cycle of near-surface soils significantly affects energy and water exchanges between the atmosphere and land surface. Passive microwave remote sensing is commonly used to observe the freeze-thaw state. However, existing algorithms face challenges in accurately monitoring near-surface soil freeze/thaw in alpine zones. This article proposes a framework for enhancing freeze/thaw detection capability in alpine zones, focusing on band combination selection and parameterization. The proposed framework was tested in the three river source region (TRSR) of the Qinghai-Tibetan Plateau. Results indicate that the framework effectively monitors the freeze/thaw state, identifying horizontal polarization brightness temperature at 18.7 GHz (TB18.7H) and 23.8 GHz (TB23.8H) as the optimal band combinations for freeze/thaw discrimination in the TRSR. The framework enhances the accuracy of the freeze/thaw discrimination for both 0 and 5-cm soil depths. In particular, the monitoring accuracy for 0-cm soil shows a more significant improvement, with an overall discrimination accuracy of 90.02%, and discrimination accuracies of 93.52% for frozen soil and 84.68% for thawed soil, respectively. Furthermore, the framework outperformed traditional methods in monitoring the freeze-thaw cycle, reducing root mean square errors for the number of freezing days, initial freezing date, and thawing date by 16.75, 6.35, and 12.56 days, respectively. The estimated frozen days correlate well with both the permafrost distribution map and the annual mean ground temperature distribution map. This study offers a practical solution for monitoring the freeze/thaw cycle in alpine zones, providing crucial technical support for studies on regional climate change and land surface processes.

Permafrost degradation is a growing direct impact of climate change. Detecting permafrost shrinkage, in terms of extension, depth reduction and active layer shift is fundamental to capture the magnitude of trends and address actions and warnings. Temperature profiles in permafrost allow direct understanding of the status of the frozen ground layer and its evolution in time. The Sommeiller Pass permafrost monitoring station, at about 3000 m of elevation, is the key site of the regional network installed in 2009 during the European Project PermaNET in the Piedmont Alps (NW Italy). The station consists of three vertical boreholes with different characteristics, equipped with a total of 36 thermistors distributed in three different chains. The collected raw data shows a degradation of the permafrost base at approximately 60 m of depth since 2014, corresponding to about 0.03 degrees C/yr. In order to verify and better quantify this potential degradation, three on-site sensor calibration campaigns were carried out to understand the reliability of these measurements. By repeating calibrations in different years, two key results have been achieved: the profiles have been corrected for errors and the re-calibration allowed to distinguish the effective change of permafrost temperatures during the years, from possible drifts of the sensors, which can be of the same order of magnitude of the investigated thermal change. The warming of permafrost base at a depth of similar to 60 m has been confirmed, with a rate of (4.2 +/- 0.5)center dot 10(-2) degrees C/yr. This paper reports the implementation and installation of the on-site metrology laboratory, the dedicated calibration procedure adopted, the calibration results and the resulting adjusted data, profiles and their evolution with time. It is intended as a further contribution to the ongoing studies and definition of best practices, to improve data traceability and comparability, as prescribed by the World Meteorological Organization Global Cryosphere Watch programme.

Freeze-thaw cycles (FTC) alter soil function through changes to physical organization of the soil matrix and biogeochemical processes. Understanding how dynamic climate and soil properties influence FTC may enable better prediction of ecosystem response to changing climate patterns. In this study, we quantified FTC occurrence and frequency across 40 National Ecological Observatory Network (NEON) sites. We used site mean annual precipitation (MAP) and mean annual temperature (MAT) to define warm and wet, warm and dry, and cold and dry climate groupings. Site and soil properties, including MAT, MAP, maximum-minimum temperature difference, aridity index, precipitation as snow (PAS), and organic mat thickness, were used to characterize climate groups and investigate relationships between site properties and FTC occurrence and frequency. Ecosystem-specific drivers of FTC provided insight into potential changes to FTC dynamics with climate warming. Warm and dry sites had the most FTC, driven by rapid diurnal FTC close to the soil surface in winter. Cold and dry sites were characterized by fewer, but longer-duration FTC, which mainly occurred in spring and increased in number with higher organic mat thickness (Spearman's rho = 0.97, p < 0.01). The influence of PAS and MAT on the occurrence of FTC depended on climate group (binomial model interaction p (chi(2)) < 0.05), highlighting the role of a persistent snowpack in buffering soil temperature fluctuations. Integrating ecosystem type and season-specific FTC patterns identified here into predictive models may increase predictive accuracy for dynamic system response to climate change.

Refractory black carbon (rBC) is an important climate-forcing agent emitted by biomass burning and fossil fuel combustion. Antarctica can receive rBC aerosols emitted in Southern Hemisphere (SH) and preserve the history of emissions and atmospheric transport. Here, we present a high-resolution record of rBC in an ice core (CA2016-75) acquired from the coastal Eastern Antarctica, which accumulated during the past 100 years (1915-2015). The rBC concentration (0.030 ng g(-1)) and flux (7.22 mu g m(-2) yr(-1)) are both among the lowest values in Antarctic snow and ice. The rBC concentration reaches higher values on average in the period aligned with the austral Winter. The rBC concentrations show a long-term descending trend during the period between 1950s and mid-1990s, followed by an ascending trend to 2015. Back trajectory analysis indicates that the emissions resulting from the biomass burning and anthropogenic biofuel consumption in Southern America and Australia were the main sources for the rBC deposition. Wavelet spectral analysis and temporal correlation analysis on rBC deposition and the atmospheric circulation indices (El Nino-Southern Oscillation, Southern Annular Mode and Antarctic Oscillation) confirmed that the atmospheric circulations have certain influences on the rBC deposition, likely by their direct effects on rBC transport and on weather conditions driving the occurrence of fires and subsequent emissions in source regions.

The Karakoram Anomaly has been intensively investigated, but the factors that control this anomaly, such as the glacier velocity, topography, and mass balance, remain poorly understood. To improve our understanding of the velocity, topography, and mass balance of the Karakoram Glacier, in this study, the spatiotemporal variability of four glacier velocities in the Hunza Basin of the Karakoram range were surveyed using co-registration of optically sensed images and correlation (COSI-Corr) on Landsat imagery from 1993-2019. The results show that the velocity of the Gulmit Glacier increases with a rising altitude from the glacier terminal. The three other glaciers initially display high velocity, followed by a decrease from the glacier terminal, with the maximum velocity attained in the middle of the glacier. In addition, the Karakoram glaciers produced a slight mass gain, with all mountain glaciers exhibiting clear regional acceleration from 1993-2019. The ice deformation velocity of the Batura Glacier diminished at an average rate of 8.49 %. However, the topography of the glacier base and physical factors require further analysis to determine their contribution to the observed changes in glacier velocity. In the present work, multi-temporal remote sensing image interpretations were carried out to determine glacier kinematics, which could enhance our understanding of glacier change mechanisms.

Snow cover changes can have important effects on ecosystems, especially where spatial variability in cover is high, influencing the biogeochemical conditions of the underlying soil as well as the vegetation. In this study, snow thickness and areal distribution were monitored using a time lapse camera over a grid of 15 x 20 m between 2009 and 2017 at Signy Island (60 degrees S, South Orkney Islands, maritime Antarctica). The data obtained confirmed high spatial and temporal variability in snow cover. Over the study period, the mean annual snow depth ranged between 5.6 cm (2017) and 11.1 cm (2012) while the maximum of the mean daily snow depth across the entire grid ranged between 17.1 cm (2017) and 50.1 cm (2015). No temporal trend was apparent but there was a strong correlation with mean annual air temperature, suggesting that possible future warming could decrease snow depth in the area. A negative correlation was identified between the winter Southern Oscillation Index (SOI) and mean annual snow depth, indicating an influence of El Nino-Southern Oscillation (ENSO) on snow cover in this part of Antarctica. There was considerable small-scale spatial variability in snow depth at each individual stake, with mean values between 3.9 and 25.3 cm and maximum values between 27 and 85 cm. Snow depth variability was influenced primarily by microtopography and wind direction, but also by the land cover type (vegetation). Our data highlight that spatial monitoring of snow accumulation is required at small physical scale to predict future effects of climatic changes on these sensitive maritime Antarctic terrestrial ecosystems.

Snow on sea ice is a sensitive indicator of climate change because it plays an important role regulating surface and near surface air temperatures. Given its high albedo and low thermal conductivity, snow cover is considered a key reason for amplified warming in polar regions. This study focuses on retrieving snow depth on sea ice from brightness temperatures recorded by the Microwave Radiation Imager (MWRI) on board the FengYun (FY)-3B satellite. After cross calibration with the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) Level 2A data from January 1 to May 31, 2011, MWRI brightness temperatures were used to calculate sea ice concentrations based on the Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) algorithm. Snow depths were derived according to the proportional relationship between snow depth and surface scattering at 18.7 and 36.5 GHz. To eliminate the influence of uncertainties in snow grain sizes and sporadic weather effects, seven-day averaged snow depths were calculated. These results were compared with snow depths from two external data sets, the IceBridge ICDIS4 and AMSR-E Level 3 Sea Ice products. The bias and standard deviation of the differences between the MWRI snow depth and IceBridge data were respectively 1.6 and 3.2 cm for a total of 52 comparisons. Differences between MWRI snow depths and AMSR-E Level 3 products showed biases ranging between -1.01 and -0.58 cm, standard deviations from 3.63 to 4.23 cm, and correlation coefficients from 0.61 to 0.79 for the different months.