Changes In Snow Depth Research Articles

Application of HY-2B Satellite Data to Retrieve Snow Depth on Antarctic Sea Ice

Sea ice and its surface snow are crucial components of the energy cycle and mass balance between the atmosphere and ocean, serving as sensitive indicators of climate change. Observing and understanding changes in snow depth on Antarctic sea ice are essential for sea ice research and global climate change studies. This study explores the feasibility of retrieving snow depth on Antarctic sea ice using data from the Chinese marine satellite HY-2B. Using generic retrieval algorithms, snow depth on Antarctic sea ice was retrieved from HY-2B Scanning Microwave Radiometer (SMR) data, and compared with existing snow depth products derived from other microwave radiometer data. A comparison against ship-based snow depth measurements from the Chinese 35th Antarctic Scientific Expedition shows that snow depth derived from HY-2B SMR data using the Comiso03 retrieval algorithm exhibits the lowest RMSD, with a deviation of −1.9 cm compared to the Markus98 and Shen22 models. The snow depth derived using the Comiso03 model from HY-2B SMR shows agreement with the GCOM-W1 AMSR-2 snow depth product released by the National Snow and Ice Data Center (NSIDC). Differences between the two primarily occur during the sea ice ablation and in the Bellingshausen Sea, Amundsen Sea, and the southern Pacific Ocean. In 2019, the monthly average snow depth on Antarctic sea ice reached its maximum in January (36.2 cm) and decreased to its minimum in May (15.3 cm). Thicker snow cover was observed in the Weddell Sea, Ross Sea, and Bellingshausen and Amundsen seas, primarily due to the presence of multi-year ice, while thinner snow cover was found in the southern Indian Ocean and the southern Pacific Ocean. The derived snow depth product from HY-2B SMR data demonstrates high accuracy in retrieving snow depth on Antarctic sea ice, highlighting its potential as a reliable alternative for snow depth measurements. This product significantly contributes to observing and understanding changes in snow depth on Antarctic sea ice and its relationship with climate change.

The Varied Role of Atmospheric Rivers in Arctic Snow Depth Variations

AbstractThe state and fate of snow on sea ice are crucial in the mass and energy balance of sea ice. The function of atmospheric rivers (ARs) on snow depth over sea ice has not been measured thus far, limiting the understanding of the mechanism of snow depth changes. Here, the effect of ARs on snow depth changes was explored. We found that increased AR frequency is responsible for winter‐autumn snow accumulation and spring‐summer snow melting. The 2 m air temperature (T2m), rainfall, snowfall, mean net longwave radiation (NLR), mean net shortwave radiation (NSR) and cloud radiative effect (CRE) during ARs explain the changes in snow depth triggered by AR occurrence. This work helps us understand how ARs affect snow depth changes through related physical processes, promotes an understanding of climate systems and provides a theoretical basis for snow treatment in sea ice models.

Observed changes in the climate and snow dynamics of the Third Pole

The Third Pole (TP) is the world’s largest highland and has one of the biggest reservoirs of glacier ice mass and snow cover on the Earth. Three major Asian rivers (the Indus, Ganga and Brahmaputra) are nourished by the melting of glaciers and snow in Central Himalaya, which are inevitable for the socioeconomic sustainability and water security of South Asia. Here, we investigate the long-term (1980–2020) changes in snow depth and precipitation in TP, where major precipitation occurs in the form of rainfall in summer, and snowfall in winter and spring. The seasonal mean snow depth is deep (≥1 m) in winter and shallow (≤0.2 m) in summer. The average snowmelt and snow water equivalent are higher in the central and western Himalaya and Karakoram ranges in spring, which are the regions with most glaciers in TP. There is a significant positive trend in total precipitation, about 0.01–0.03 mm d−1 yr−1 in the central and eastern TP during the South Asian Summer Monsoon for the 1980–2020 period. Snowmelt is also increasing (>0.5 × 10−3 mm yr−1) in the western Himalaya during spring, which is consistent with the temperature rise (0.04–0.06 °C yr−1) there. In addition, there is a notable increase in the annual mean glacier melt (here, the water equivalent thickness) in TP (−1 to −5 cm w.e. yr−1), with its highest values in the eastern and central Himalaya (−3 to −5 cm w.e. yr−1), as estimated for the period 2003–2020. On top of these, by the end of the 21st century, the Coupled Model Intercomparison Project Phase 6 (CMIP6) projections show that there would be a significant decrease in snow depth and an increase in temperature of TP in all shared socioeconomic pathways (SSPs). Henceforth, the increasing trend in temperature and melting of snow/glaciers in TP would be a serious threat to the regional climate, water security and livelihood of the people of South Asia.

Intensified warming suppressed the snowmelt in the Tibetan Plateau

Understanding how hydrological factors interrelate is crucial when examining the impact of climate warming on snowmelt. However, these connections are often overlooked, leading to an unclear relationship between temperature and snowmelt. This study investigates the complex interplay between temperature and snowmelt in the Tibetan Plateau from 1961 to 2020, focusing on how extreme high-temperature events affect the frequency of extreme snowmelt. Using a structural equation model, we detected three temperature-related factors that predominantly influenced snowmelt and extreme snowmelt. The annual average temperature was found to have a significant indirect impact on snowmelt, mediated by changes in snowfall, snow depth and snow cover. By contrast, high-temperature days (daily maximum temperatures exceeding the 90th percentile) and heat waves (at least three consecutive high-temperature days) negatively affected extreme snowmelt directly or indirectly. The direct effect of increasing extreme temperature events was associated with an earlier onset of high-temperature periods, which accelerated snowmelt and shortened the duration of extreme snowmelt periods. Additionally, the reduction in snow cover owing to warming emerged as a main factor suppressing snowmelt and extreme snowmelt frequencies. We also revealed spatiotemporal variations in the temperature‒snowmelt relationship that highly depended on changes in snowmelt patterns. The study elucidated why warming suppresses snowmelt and extreme snowmelt events in the Tibetan Plateau, highlighting the mediating roles of snow-related and phenological factors. The findings will provide scientific support for climate simulation and water management policymaking in alpine regions worldwide.

Estimating snow depth based on dual polarimetric radar index from Sentinel-1 GRD data: A case study in the Scandinavian Mountains

The sensitivity of synthetic aperture radar (SAR) polarization information to snow depth changes provides new opportunities for regional snow depth retrieval in mountains with thick snow cover. However, interference from soil signals can affect the accurate quantification of snow volume scattering signals. The aim of this study was to develop a dual-polarimetric radar snow depth estimation (DpRSE) methodological framework that utilizes Sentinel-1 data for snow depth retrieval in the Scandinavian Mountains. This framework is based on the dual-polarimetric radar vegetation index (DpRVIc), which enhances the snow volume scattering signal and reduces soil interference by integrating soil purity and a conditioning factor to fully exploit the advantages of polarimetric SAR information. The results revealed a strong correlation between the DpRVIc and snow depth, and the DpRVIc significantly outperformed the other polarimetric radar indices. The multitemporal retrieval results clearly reflect the heterogeneity of the snow depth distribution. This method is applicable to areas without tree cover. Meanwhile, the shrub-filled regions also affect the snow depth retrieval accuracy. Under the same environmental conditions and using the same validation dataset, a comparison between the DpRSE method and the cross-polarization ratio method was conducted. The results indicated that the DpRSE method surpasses similar existing methods in terms of snow depth retrieval accuracy. Specifically, the coefficient of determination (R2) for the DpRSE method reached 0.66, which represents a 26.9 % improvement over that of the cross-polarization ratio method. Moreover, the mean absolute error (MAE) decreased by 20 %. This study offers a novel perspective on SAR snow depth retrieval utilizing polarization information.

Snow depth has greater influence on moss biocrusts' soil multifunctionality than the number of freeze-thaw cycles

Continued global warming and changes in precipitation patterns, especially regarding snow depth and freeze-thaw cycles (FTCs), have significantly affected the hydrothermal environment in winter at mid- and high-latitudes. In this study, we assessed the effects of changes in snow depth and FTCs, on the biogeochemical cycling of biological soil crusts. We collected moss crust soil samples from a temperate desert in northwest China, and measured the changes in the amount of snow (65 % snow removal, ambient snow, and 65 % snow increase) and FTCs (FT0, FT5, and FT15) under an artificial environment simulation, to determine the nutrient content and enzyme activities related to soil carbon, nitrogen, and phosphorus cycles. In terms of how snowfall and FTCs affect soil multifunctionality (SMF) of desert moss crusts, the research findings demonstrated that snow depth, FTCs, and their interplay have a significant influence on the carbon, nitrogen, phosphorus contents, enzyme activities, and SMF of moss crusts. Compared to snow removal, an increase in snow depth corresponded to a significant increase in the soil water content, and an improvement in soil carbon and nitrogen cycling and SMF. The number of FTCs increased, which led to enhancements in soil phosphorus cycling, soil total phosphorus, and available phosphorus content, together with a reduction in inorganic nitrogen content and the activities of certain enzymes involved in carbon and nitrogen cycling. The structural equation model indicated that snow depth had the greatest effect (total effect of 0.165) on SMF in moss crusts compared to FTCs (total effect of 0.048). Therefore, it is necessary to consider snow depth when examining the effects of climate change on nutrient cycling in the biological soil crusts of temperate deserts.

An Ensemble Learning Model Reveals Accelerated Reductions in Snow Depth Over Arctic Sea Ice Under High‐Emission Scenarios

AbstractThere are significant differences in snow depth predictions among different earth system models, and many models underestimate snow depth, restricting their application. Here, major factors influencing snow depth changes in the Coupled Model Intercomparison Project Phase 6 (CMIP6) were identified and evaluated. Based on satellite‐derived snow depth and CMIP6 data, an ensemble learning model based on multiple deep learning methods (hereafter referred to as the Multi‐DL model) was developed to predict future snow depth. According to satellite observations and two Operation IceBridge products, the Multi‐DL model yielded root mean square errors of 7.48, 6.20, and 6.17 cm. A continuous decrease in snow depth was observed from 2002 to 2100, and the rate of decrease accelerated with increasing emissions. Under the highest emission scenario, the first snow‐free year occurred in 2047, within the same decade as the first ice‐free year (2056). The predicted warm season snow depth was sensitive to sea ice velocity, sea ice concentration (siconc), precipitation, sea surface temperature (tos) and albedo, while the predicted cold season snow depth was sensitive to tos, air temperature, and siconc. The above parameters introduce some snow depth uncertainty. This method provides new ideas for predicting snow depth, and the generated snow depth records can provide data support for formulating Arctic‐related policies.

Spatiotemporal Changes of Snow Depth in Western Jilin, China from 1987 to 2018

Spatiotemporal Changes of Snow Depth in Western Jilin, China from 1987 to 2018

Spatiotemporal variations in frost cracking measures in two dimensions: A case study for rock walls in Jotunheimen, southern Norway

The ground thermal regime has a profound impact on geomorphological processes and has been suggested to be particularly important for weathering processes in periglacial environments. Several frost-related damage indices have hitherto been developed to link climate and frost weathering potential in bedrock, although only for individual points or grid cells. Here, we model ground temperature and frost weathering potential in steep rock walls in the Jotunheimen Mountains, southern Norway, along a two-dimensional profile line for the Younger Dryas Stadial-Preboreal transition (c. 11.5 ka), the Holocene Thermal Maximum (c. 7.5 ka), the Little Ice Age (1750), and the 2010s. We use an established heat flow model and frost-cracking index based on the ice segregation theory. A central innovation of our model treatment is the implementation of ensemble simulations using distributions of automatically mapped crack radii in a rock wall, whereas previous frost damage models considered only a single characteristic crack radius. Our results allowed for the identification of sites with enhanced frost weathering. Such sites are typically found between rock walls and retreating glaciers, as well as in areas where snow depth changes abruptly, resulting in large thermal gradients. Hence, frost weathering may be highly active during glacier retreat, enhancing the damage to rock walls during deglaciation by adding to the damage from stress release. The coldest climates of the Younger Dryas Stadial-Preboreal transition and the Little Ice Age were generally most favorable for frost cracking. Such timing compares well with the knowledge about the timing of rockfall accumulations in Norway.

Snow water equivalent retrieval over Idaho – Part 2: Using L-band UAVSAR repeat-pass interferometry

Abstract. This study evaluates using interferometry on low-frequency synthetic aperture radar (SAR) images to monitor snow water equivalent (SWE) over seasonal and synoptic scales. We retrieved SWE changes from nine pairs of SAR images, mean 8 d temporal baseline, captured by an L-band aerial platform, NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), over central Idaho as part of the NASA SnowEx 2020 and 2021 campaigns. The retrieved SWE changes were compared against coincident in situ measurements (SNOTEL and snow pits from the SnowEx field campaign) and to 100 m gridded SnowModel modeled SWE changes. The comparison of in situ to retrieved measurements shows a strong Pearson correlation (R=0.80) and low RMSE (0.1 m, n=64) for snow depth change and similar results for SWE change (RMSE = 0.04 m, R=0.52, n=57). The comparison between retrieved SWE changes to SnowModel SWE change also showed good correlation (R=0.60, RMSD = 0.023 m, n=3.2×106) and especially high correlation for a subset of pixels with no modeled melt and low tree coverage (R=0.72, RMSD = 0.013 m, n=6.5×104). Finally, we bin the retrievals for a variety of factors and show decreasing correlation between the modeled and retrieved values for lower elevations, higher incidence angles, higher tree percentages and heights, and greater cumulative melt. This study builds on previous interferometry work by using a full winter season time series of L-band SAR images over a large spatial extent to evaluate the accuracy of SWE change retrievals against both in situ and modeled results and the controlling factors of the retrieval accuracy.

A 0.01° daily improved snow depth dataset for the Tibetan Plateau

Snowpack is highly sensitive to global warming, and an accurate understanding of the changes in snow depth (SD) is essential in analyzing the impacts of SD on both regional and global climate change. However, the application of SD datasets is limited owing to their coarse spatial resolution, especially at the basin scale. As a result, it is difficult to obtain high-quality, long-term gridded SD datasets at the kilometer scale, especially in cold and high-altitude regions. To address this issue, we combine an improved spatiotemporal downscaling algorithm and an efficient snow depletion curve method to develop a composite long-term daily 0.01° SD dataset over the Tibetan Plateau (TP) by integrating an enhanced 0.05° SD dataset and a cloud-gap-filled fractional snow cover (FSC) product. The new 0.01° SD product is evaluated against the ground-observed SD data from 90 meteorological stations during 2001–2010, indicating that the new 0.01° SD product (with a root mean square error of 1.27 cm d-1 and a mean absolute error of 0.31 cm d-1) performs better than its 0.05° old version, as well as five other widely used SD products that cover the TP region. Thus, the new SD product is used to analyze the trends in the spatial SD pattern during 2000–2018. The results indicate that the annual SD is experiencing a decreasing trend over the inner and edge regions of the TP but an increasing trend over the areas between the inner and edge regions of the TP, e.g., the northern Himalayas, and upstream of the Yellow River, Yangtze River, and Mekong River. A negative correlation between the SD changes and the air temperature changes and a positive correlation between the SD changes and the snowfall changes are found in snow-dominated regions. Notably, the correlation between the SD changes and the air temperature changes is stronger than that between the SD changes and the snowfall changes, indicating that the SD is more sensitive to changes in air temperature. The new high-quality product will provide a more accurate means for understanding the SD changes in this region and a more accurate source of SD data for use in scientific studies that relate to hydrology, meteorology, and disaster evaluation.

Testing the Suitability of Snow Depth in Winter Recreation Areas in Cities in the Republican Period with InSAR Technique: Abalı Ski Center Example

The Republican period is a period that coincides with the modernization process of Turkey, and the lifestyle in the cities has also changed significantly during this period. Winter recreation areas are the areas that people prefer to have fun, do sports and relax in the cold months. The Republic of Türkiye has a great importance in the tourism sector. In addition, the alternative tourism sector is developing rapidly and leading to different investments. Increasing winter recreation areas and facility investments in order to keep four seasons tourism alive with the advantage of climatic diversity is of great importance for alternative tourism. Different criteria such as slope, aspect, height, transportation, and sunshine duration are used to determine suitable areas for these investments. However, the most important suitability criterion is the snow factor. In order for the facilities to be established, the snow must be permanent and of a certain thickness in the winter season. In this study, the winter season (December-January-February) was examined temporally (2020-21-22), in the example of the Abalı Ski Center established in the republican period, and the average snow depth change in the area was determined by the InSAR (Interferometric Artificial Aperture Radar) technique. In addition, the accuracy of the results was tested with the real measurements of the 2021-22 winter season in the study area. The analysis conducted to assess the accuracy of Sentinel-1A C-band RADAR images by comparing snow depth data has yielded a high correlation value of 0.79, indicating a strong and consistent result. As a result, the importance of determining the snow factor both as a suitability criterion in determining the suitability of land use and the suitability of snow permanence in temporal change for establishment has been revealed.

Performance of precipitation phase partitioning methods and their impact on snowpack evolution in a humid continental climate

AbstractAccurate estimations of the precipitation phase at the surface are critical for hydrological and snowpack modelling in cold regions. Precipitation phase partitioning methods (PPMs) vary in their ability to estimate the precipitation phase at around 0°C and can significantly impact simulations of snowpack accumulation and melt. The goal of this study is to evaluate PPMs of varying complexity using high‐quality observations of precipitation phase and to assess the impact on snowpack simulations. We used meteorological data collected in Edmundston, New Brunswick, Canada, during the 2021 Saint John River Experiment on Cold Season Storms (SAJESS). These data were combined with manual observations of snow depth. Five PPMs commonly used in hydrological models were tested against observations from a laser‐optical disdrometer and a Micro Rain Radar. Most PPMs produced similar accuracy in estimating only rainfall and snowfall. Mixed precipitation was the most difficult phase to predict. The multi‐physics model Crocus was then used to simulate snowpack evolution and to diagnose model sensitivity to snowpack accumulation processes (PPM, snowfall density, and snowpack compaction). Sixteen snowpack accumulation periods, including nine warm accumulation events (average temperatures above −2°C) were observed during the study period. When considering all accumulation events, simulated changes in snow water equivalent (SWE) were more sensitive to the type of PPM used, whereas simulated changes in snow depth were more sensitive to uncertainties in snowfall density. Choice of PPM was the main source of model sensitivity for changes in SWE and snow depth when only considering warm events. Overall, this study highlights the impact of precipitation phase estimations on snowpack accumulation at the surface during near‐0°C conditions.

Flowering shift due to climate warming in cool temperate Japan

Biological processes can be disrupted by global climate warming, causing phenological mismatching and mistiming. On Hokkaido Island (Japan), which has a cool temperate climate, warmer and shorter winters affect plant flowering phenology. Rapid changes in flowering phenology suggest that shorter winters will result in earlier flowering. Flowering phenology patterns were recorded for nine years (2013–2021), and nine species were selected for analysis. I analyzed climate variables that affect plant flowering phenology, including annual changes in aerial temperature, snow depth, and snowmelt timing. The results indicated that winters are getting shorter and that the accumulated degree hours of aerial temperature have not significantly changed over nine years for these plant species except one species. Plant responses to these changes differed between species, with the first flowering day occurring earlier (1.5–1.8 days per year) for two species, although that of the other seven not. If climate warming continues, it will have diverse, complex, and unpredictable effects on various individual organisms and biological relationships among species. Detailed studies are needed to link climate change predictions to the predicted degree of mismatch in species interactions and networks.

Динамика и оценка метеоусловий всесезонного туристско-рекреационного комплекса «Мамисон»

Introduction. By Decree of the Government of Russia dated September 25, 2019 No. 1248, a decision was made to create an all-season tourist and recreational complex “Mamison” on the territory of the Alagirsky district of the Republic of North Ossetia – Alania, in the upper reaches of the Ardon River, the valleys of the Mamikh-Don and Zemegon-Don rivers. One of the most favorable areas is the high-mountain gorge Mamison, where the Zemegon-Don and Kozydon-Don rivers merge, which, downstream, form the Mamikh-Don river. To date, the territory of the Mamison ATRC has been poorly studied in meteorological and hydrological terms. There are practically no studies of the physical properties of snow; the peculiarities of changes in the weight of the snow cover depending on the absolute height have not been identified, as, for example, at the Elbrus ATRC in Kabardino-Balkaria. The work is devoted to the analysis of the dynamics of the average seasonal values of outdoor air temperature, snow cover depth and the amount of precipitation during the cold period (October – May) from 1981 to 2022. On the territory of the Mamison All-Season Tourist and Recreational Complex. Research methods and materials. The work carried out a mathematical and statistical analysis of the meteorological parameters on which the snow avalanche regime depends to the greatest extent. To analyze the average seasonal values of snow depth, air temperatures and total (for the season) amount of precipitation, the work used weather data for the cold period (October – May) obtained at the snow avalanche station (SLS) Roki Pass (from 1981 to 2022). Research results. Mathematical analysis showed that the mid-season snow depth changes linearly, the trend is negative. Mid-season air temperature changes linearly, the trend is also positive. The trend in changes in the seasonal total values of the amount of solid precipitation is positive, the highest amount of precipitation occurs in the spring months – March, April, May (3396 mm, 4809 mm, 4910 mm, respectively). Discussion of research results. The observed changes were manifested in a decrease in the depth of snow cover, a change in precipitation patterns, and an increase in the average seasonal characteristics of the temperature regime. In view of these changes, we can say that the decrease in avalanche activity in the Roki Pass area is associated with interannual changes in the depth of snow cover, which is the main factor in avalanche formation. Conclusions. It was found that from 1981 to 2022, the average annual snow depth decreased by 0.56 cm per year. An expression for the time variation of snow cover height is obtained. Suggestions for practical applications and future research directions. The results obtained have practical significance in the development of mountainous territories and should be taken into account when designing infrastructure facilities of the Mamison ATRC.

Спутниковые оценки вариаций увлажненности внутреннего Тянь-Шаня в период 1982–2022 гг. на примере хребта Ат-Баши (Киргизия)

Introduction. Western atmospheric jet streams carry moisture over the Northern Tian Shan ridges into the Inner Tian Shan. Long-term weather changes in the Inner Tian Shan diagnose climatic fluctuations in atmospheric moisture transport. This is of considerable interest for understanding the direction of modern climate change in Central Asia. Materials. The area of the At-Bashi Ridge (2700 км2) in Inner Tian Shan, including a large high-altitude Lake Chatyr-Kul (170 km2, 3530 m), were considered. The ridge, with a peak of 4786 m, has a length of 135 km and is characterized by an Alpine type of relief. The initial information related to the period 1982-2022. The list of data used included: FLDASSnowDepth_inst; JRC Monthly Water History, v1.4; ECMWF/ERA5_LAND/MONTHLY (precipitation); NOAA/NCEP_DOE_RE2 (cloud fraction). Also, to diagnose the mountain glaciation state of the At-Bashi Ridge, three satellite images of sub meter resolution dated 09/21/2002, 07/27/2013 and 09/13/2021 were used. The purpose of the work. The aim of the research was to study the humidity regime of the At-Bashi Ridge area during 1982-2022. Research methodology. The change in the average annual snow depth in the test area in the period 1982-2022 was analyzed, as well as the degree of correlation of this parameter with other weather characteristics.Fischer’s criterion acted as a quantitative basis for estimates. Satellite images of sub meter resolution were used to analyze long–term changes in the area of rock outcrops from ice on the slopes of glacial chambers, which diagnoses the glacier state (degradation – stability – growth). Satellite data recorded the process of mountain glaciation growth above the height of 4000 m in the period between 2002–2013. Research results. In order of increasing correlation (F-criterion), with the snowiness of the cold period (October-May) of the At-Bashi Ridge area, the parameters considered were arranged as follows (F=2.11; α=0.01): cloud fraction, F= 3.02; precipitation, F=12.42; the Lake Chatyr-Kul area, F= 12.53; the balance of snow deposits between the windward (north-western) and leeward (south-eastern) At-Bashi Ridge slopes, F= 22.01. According to satellite data, the growth of mountain glaciation in the zone with an altitude above 4000 m in the period 2002-2013 was recorded. Discussion. The At-Bashi Ridge area has three different periods in terms of humidity: dry in 1982-1999 with an average annual snow depth of 0.099± 0.051 m; wet in 2000-2012; 0.192± 0.074 m; and moderately humid in 2013-2022; 0.133± 0.058 m. Ultra-high-resolution satellite images recorded the growth of the mountain glaciation of the At-Bashi Ridge at altitudes of more than 4000 m in the period from 2002 to 2013. This confirms the actual existence of the wet period of 2001-2012. Conclusion. Thus, the current trend of humidity changes of the At-Bashi Ridge does not have an unambiguous direction, but in general, for the period 1982-2022, it is aimed at a certain increase in the snow content in mountains and the area of the Lake Chatyr-Kul. Apparently, the current climate change is accompanied by an increase in the ability of western atmospheric jet fluxes to transfer moisture over the front Northern Tian Shan ranges into the Inner and Central Tian Shan ridges, which may even be accompanied by an increase in the mountain glaciation of these territories in certain periods of time.

Snow cover duration in northern Finland and the influence of key variables through a conceptual framework based on observed variations in snow depth

Seasonal snow cover duration is the net result from many processes acting on snow fallen on the Earth's surface. Several of these processes feed back into the atmosphere-cryosphere system causing non-linear interactions. The timing of snow retreat is of essential importance, but the duration of snow cover has large spatiotemporal variabilities. However, from a large data set of observed snow depth changes in northern Finland, systematic similar evolutions are identified that allow for a considerable simplification and reduction of the complexity in snow depth changes. Here, a novel conceptual framework is designed based on dividing the season into two main periods (dark and bright period, based on solar irradiance), for which snow depth decrease is parameterized based on three variables, average temperature, incoming shortwave radiation, and light-absorbing particles (LAP) in the snow. The processes are simplified into two linear relations, and a new formulation for concentration enhancement of LAP, which is dependent on snow depth decrease, is given. The results show that the seasonal snow cover duration is shifted by about one day for every 10 mm snow water equivalent of precipitation. This effect is comparable in scale to that of doubling of the amount of LAP concentration in snow. We also found that the combined shift in snow cover duration from interannual variability in ambient temperature and shortwave radiation (warm and bright vs. cold and dark season) is large enough to explain the variability of a couple of weeks for a given precipitation amount in Northern Finland.

A novel fine-resolution snow depth retrieval model to reveal detailed spatiotemporal patterns of snow cover in Northeast China

ABSTRACT Seasonal snow cover is a key component of the global climate and hydrological system, it has drawn considerable attention under global warming conditions. Although several passive microwave (PMW) snow depth (SD) products have been developed since the 1970s, they inherit noticeable errors and uncertainties when representing spatial distributions and temporal changes of SD, especially in complex mountainous regions. In this paper, we developed a fine-resolution SD retrieval model (FSDM) using machine learning to improve SD estimation quality for Northeast China and produced a long-term, fine-resolution, daily SD dataset. The accuracies of the FSDM dataset were evaluated against in-situ SD data along with existing SD products. The results showed the FSDM dataset provided satisfactory inversion accuracy in spatiotemporal evaluation, with the root-mean-square error (RMSE), bias, and correlation coefficient (R) of 7.10 cm, −0.13 cm, and 0.60. Additionally, we analyzed the spatiotemporal variations of SD in Northeast China and found that snow cover was mainly distributed in the Greater Khingan Range, Lesser Khingan Mountains, and Changbai Mountain regions. The SD exhibited high-low distribution patterns with the increased latitude. The annual mean SD slightly increased at the rate of 0.029 cm/year during 1987-2018.

Adaptability analysis of snow in the Zhangjiakou competition zone of the Beijing Olympic Winter Games for the next 30 years

Study regionZhangjiakou competition zone (ZJK-CZ) of the Beijing Olympic Winter Games. Study focusUnder the extreme uncertainty of future climate change scenarios, a system solution capable of snow depth forecasting is urgently needed for the sustainable use of Olympic Winter Games venues and the regular organization of winter sports in the post-Winter-Olympic era. This study predicts the snow depth distribution and changes in the ZJK-CZ of the Beijing Olympic Winter Games for the next 30 years utilizing the GBEHM (Geomorphology-Based Ecohydrological Model) model, together with CMIP6 climate forecast model data, and analyzes the operation and profitability of the ZJK-CZ in the post-Winter-Olympic era. New hydrological insights for the regionThis study developed a new integrated simulation methodology for the climate risk assessment of Olympic Winter Games venues in the context of future climate change. The percent bias of the snow depth simulated based on the new integrated model was found to be only 3 %. In the next 30 years, the ZJK-CZ will need to be replenished with at least 190 mm of artificial snow annually to meet the minimum standards for suitable winter sports days and requires an average of at least 49 days of artificially assisted snowmaking per year to ensure proper operation and profitability. This study is expected to provide a climate risk assessment for the construction and operation of winter sports venues.

Historical regional climate changes in Japan in winter as assessed by a 5-km regional climate model with a land surface process

We investigate historical regional climate changes in Japan from 1959 to 2020, analyzing a high-resolution dynamical downscaling forced by the Japanese 55-year Reanalysis (JRA-55). One-year continuous simulations are conducted by the non-hydrostatic regional climate model with a land surface model that includes the snow accumulation process, which enables us to evaluate the seasonal variation of snow cover in all of Japan. Our simulation reproduces interannual variations of the annual mean surface air temperature and annual total precipitation, and it shows rapid warming since around 1980. The annual maximum snow depth and annual maximum daily snowfall show significant decreasing trends at lower elevations on the Japan Sea sides of eastern and western Japan. Areas at higher elevations in eastern Japan show no trend in the maximum snow depth and a significant increasing trend in the maximum daily snowfall. In northern Japan, altitudinal dependencies in snow depth and snowfall changes are smaller on the Japan Sea side than on the Pacific Ocean side: the Japan Sea side shows insignificant changes in snow depth at all elevations, while the Pacific Ocean side shows decreasing and significant increasing trends at lower and higher elevations, respectively. The total number of snow-covered days is decreasing at most elevations in all regions, while the rate of decrease is smaller at higher elevations and latitudes. Composite analyses of annual maximum daily snowfall events at one prefectural city facing the Japan Sea in central Japan indicate that heavy daily snowfall occurs when the Japan Polar air mass Convergence Zone appears over the Japan Sea and the snowfall amounts show increasing and decreasing trends over the mountainous and coastal areas, respectively, due to historical warming and moistening. Our results are basically consistent with previous studies that have focused on future changes in snow depth and snowfall.