Search Results (60)

Tree-ring width chronologies of Pinus sibirica Du Tour from near the upper treeline in the Western Sayan, Southern Siberia are found to have an exceptional (below mean–3SD) multi-year drop near 1700 CE, highlighted by the seven narrowest-ring years in a 1524–2022 regional chronology occurring in the short span of one decade. Tree rings are sometimes applied to reconstruct seasonal air temperatures; therefore, it is important to identify other factors that may have contributed to the growth suppression. The spatiotemporal scope of the “nosedive” in tree growth is investigated with a large network of P. sibirica (14 sites) and Larix sibirica Ledeb. (61 sites) chronologies, as well as with existing climatic reconstructions, natural archives, documentary evidence (e.g., earthquake records), and climate maps based on 20th-century reanalysis data. We conclude that stress from low summer temperatures in the Little Ice Age was likely exacerbated by tree damage associated with weather extremes, including infamous Mongolian “dzuds”, over 1695–1704. A tropical volcanic eruption in 1695 is proposed as the root cause of these disturbances through atmospheric circulation changes, possibly an amplified Scandinavia Northern Hemisphere teleconnection pattern. Conifer tree rings and forest productivity recorded this event across all of Altai–Sayan region. Full article

Surface albedo plays a pivotal role in the Earth’s energy balance and climate. This study conducted an analysis of the spatial distribution patterns and temporal evolution of albedo, normalized difference vegetation index (NDVI), normalized difference snow index snow cover (NSC), and land surface temperature (LST) within the Qilian Mountains (QLMs) from 2001 to 2022. This study evaluated the spatiotemporal correlations of albedo with NSC, NDVI, and LST at various temporal scales. Additionally, the study quantified the driving forces and relative contributions of topographic and natural factors to the albedo variation of the QLMs using geographic detectors. The findings revealed the following insights: (1) Approximately 22.8% of the QLMs exhibited significant changes in albedo. The annual average albedo and NSC exhibited a minor decline with rates of −0.00037 and −0.05083 (Sen’s slope), respectively. Conversely, LST displayed a marginal increase at a rate of 0.00564, while NDVI experienced a notable increase at a rate of 0.00178. (2) The seasonal fluctuations of NSC, LST, and vegetation collectively influenced the overall albedo changes in the Qilian Mountains. Notably, the highly similar trends and significant correlations between albedo and NSC, whether in intra-annual monthly variations, multi-year monthly anomalies, or regional multi-year mean trends, indicate that the changes in snow albedo reflected by NSC played a major role. Additionally, the area proportion and corresponding average elevation of PSI (permanent snow and ice regions) slightly increased, potentially suggesting a slow upward shift of the high mountain snowline in the QLMs. (3) NDVI, land cover type (LCT), and the Digital Elevation Model (DEM, which means elevation) played key roles in shaping the spatial pattern of albedo. Additionally, the spatial distribution of albedo was most significantly influenced by the interaction between slope and NDVI. Full article

The surface velocity of the Amery Ice Shelf (AIS) is vital to assessing its stability and mass balance. Previous studies have shown that the AIS basin has a stable multi-year average surface velocity. However, spatiotemporal variations in the surface velocity of the AIS and the underlying physical mechanism remain poorly understood. This study combined offset tracking and DInSAR methods to extract the monthly surface velocity of the AIS and obtained the inter-annual surface velocity from the ITS_LIVE product. An uneven spatial distribution in inter-annual variation in the surface velocity was observed between 2000 and 2022, although the magnitude of variation was small at less than 20.5 m/yr. The increase and decrease in surface velocity on the eastern and western-central sides of the AIS, respectively, could be attributed to the change in the thickness of the AIS. There was clear seasonal variation in monthly average surface velocity at the eastern side of the AIS between 2017 and 2021, which could be attributed to variations in the area and thickness of fast-ice and also to variations in ocean temperature. This study suggested that changes in fast-ice and ocean temperature are the main factors driving spatiotemporal variation in the surface velocity of the AIS. Full article

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. Full article

The Ku band microwave scatterometer (SCA) and scanning microwave radiometer (SMR) onboard HaiYang-2B (HY-2B) can simultaneously supply active and passive microwave observations over the polar region. In this paper, a polar ice water discrimination model and Arctic sea-ice-type classification model based on the support vector machine (SVM) method were established and used to produce a daily sea ice extent dataset from 2019 to 2021 with data from SCA and SMR. First, suitable scattering and radiation parameters are chosen as input data for the discriminant model. Then, the sea ice extent was obtained based on the monthly ice water discrimination model, and finally, the ice over the Arctic was classified into multiyear ice (MYI) and first-year ice (FYI). The 3-year ice extent and MYI extent products were consistent with the similar results of the National Snow and Ice Data Center (NSIDC) and Ocean and Sea Ice Satellite Application Facility (OSISAF). Using the OSISAF similar product as validation data, the overall accuracies (OAs) of ice/water discrimination and FYI/MYI discrimination are 99% and 97%, respectively. Compared with the high spatial resolution classification results of the Moderate Resolution Imaging Spectroradiometer (MODIS) and SAR, the OAs of ice/water discrimination and FYI/MYI discrimination are 96% and 86%, respectively. In conclusion, the SAC and SMR of HY-2B have been verified for monitoring polar sea ice, and the sea ice extent and sea-ice-type products are promising for integration into long-term sea ice records. Full article

Polar mesospheric cloud (PMC) data obtained from the Aeronomy of Ice in the Mesosphere (AIM)/Cloud Imaging and Particle Size (CIPS) experiment and Himawari-8/Advanced Himawari Imager (AHI) observations are analyzed for multi-year climatology and interannual variations. Linkages between PMCs, mesospheric temperature, and water vapor (H₂O) are further investigated with data from the Microwave Limb Sounder (MLS). Our analysis shows that PMC onset date and occurrence rate are strongly dependent on the atmospheric environment, i.e., the underlying seasonal behavior of temperature and water vapor. Upper-mesospheric dehydration by PMCs is evident in the MLS water vapor observations. The spatial patterns of the depleted water vapor correspond to the PMC occurrence region over the Arctic and Antarctic during the days after the summer solstice. The year-to-year variabilities in PMC occurrence rates and onset dates are highly correlated with mesospheric temperature and H₂O. They show quasi-quadrennial oscillation (QQO) with 4–5-year periods, particularly in the southern hemisphere (SH). The combined influence of mesospheric cooling and the mesospheric H₂O increase provides favorable conditions for PMC formation. The global increase in mesospheric H₂O during the last decade may explain the increased PMC occurrence in the northern hemisphere (NH). Although mesospheric temperature and H₂O exhibit a strong 11-year variation, little solar cycle signatures are found in the PMC occurrence during 2007–2021. Full article

In order to realize the goal of ice-free water conveyance in the winter for water conveyance projects in cold regions, the operation principle of ice-free water conveyance through channels is described based on the two ice-melting measures of a solar heating gallery and heated storage tank. Based on the multi-year meteorological data and the theory of a product probability event, the concept of a “comprehensive satisfaction rate” was proposed, and then the joint optimal regulating model under two ice-melting measures was established, and the genetic algorithm was used to solve the problem, which solved the important limitations of the economic and efficiency optimization of different ice-melting measures. This paper applies this model to the Zhanghe control gate–Mangniuhe control gate section of the middle route of the South-to-North Water Transfer Project. According to the optimization analysis of a large number of operating conditions, the operating costs of the ice-melting measures have also increased with the increase in the comprehensive satisfy rate. In the operation process, the water temperature along the lines presents a “ladder-like” shape. The average hourly flow and average hourly water temperature of the heated water storage tank have the characteristics of overall unity and local complementarity. With the increase in the water flow and downstream depth before the gate, its operating cost also increases. The increase in the flow velocity at the same time can increase the heat transfer efficiency, reducing the operating costs. In addition, the water temperature of the channel with a solar heating gallery decreased more slowly than that without a solar heating gallery due to its good thermal insulation effect. Full article

Snow depth is a crucial factor in the formation of snow, and its fluctuations play a significant role in the Earth’s climate system. The existing snow depth algorithms currently lack systematic quantitative evaluation, and most of them are not suitable for direct application to Chinese satellites. Therefore, a quantitative evaluation of four existing snow depth algorithms from the Advanced Microwave Scanning Radiometer 2 (AMSR2) was conducted by comparing their estimates with the measured dataset from the Operation IceBridge project (OIB). The study found that the algorithm developed by Rostosky et al. outperforms the other three algorithms in terms of correlation. However, it is unable to accurately retrieve both high and low snow depths. On the other hand, the algorithms developed by Comiso et al. and Li et al. demonstrated strong performance in correlation and statistical characteristics. Based on these results, these two algorithms were fused to enhance the accuracy of the final algorithm. The algorithm was applied to FengYun-3D/Microwave Radiation Imager (FY-3D/MWRI) data after calibration to develop a snow depth retrieval algorithm suitable for MWRI. Validation using the 2019 OIB data indicated that the algorithm had a bias and RMSE of 1 cm and 9 cm, respectively, for first-year ice (FYI) and 3 cm and 9 cm, respectively, for multi-year ice (MYI). Full article

The satellite-based techniques for the monitoring of extreme ice features (EIFs) in the Canadian Arctic were investigated and demonstrated using synthetic aperture radar (SAR) and electro-optical data sources. The main EIF types include large ice islands and ice-island fragments, multiyear hummock fields (MYHF) and other EIFs, such as fragments of MYHF and large, newly formed hummock fields. The main objectives for the paper included demonstration of various satellite capabilities over specific regions in the Canadian Arctic to assess their utility to detect and characterize EIFs. Stereo pairs of very-high-resolution (VHR) imagery provided detailed measurements of sea ice topography and were used as validation information for evaluation of the applied techniques. Single-pass interferometric SAR (InSAR) data were used to extract ice topography including hummocks and ice islands. Shape from shading and height from shadow techniques enable us to extract ice topography relying on a single image. A new method for identification of EIFs in sea ice based on the thermal infrared band of Landsat 8 was introduced. The performance of the methods for ice feature height estimation was evaluated by comparing with a stereo or InSAR digital elevation models (DEMs). Full polarimetric RADARSAT-2 data were demonstrated to be useful for identification of ice islands. Full article

The aim of the study is to determine the scale of differentiation and variability of ice phenomena on the lakes in the south-eastern part of the Baltic Sea catchment area. The analysis was performed based on data from the period 1961–2020 from 15 lakes located in Poland (10) and Belarus (5). The characteristics of ice phenomena were characterized, i.e., the length of their occurrence and ice cover, the thickness of ice cover and the number of breaks occurring in the ice cover in the given years were characterized. The analysis of the course of ice phenomena made it possible to distinguish three regions with an increasing length of ice phenomenon occurrence from west to east. The zones were the west of the Vistula, the east of it and the eastern part of the Belarusian Lake District. In the analyzed multi-year period, a shortening of the duration of ice phenomena and ice cover, a decrease in the maximum thickness of the ice and an increasing number of breaks in ice cover were observed. These data correlate with the upward trend in air temperature. Full article

With the goal of automatic sea ice mapping during the summer sea ice melt cycle, this study involved designing a fully automatic sea ice segmentation method based on a deep learning semantic segmentation network applicable to summer SAR images, which achieved high accuracy and the fully automatic extraction of sea ice segmentation during the summer ice melt cycle by optimizing the process, improving the pixel-level semantic segmentation network, and introducing high-resolution sea ice concentration features. Firstly, a convolution-based, high-resolution sea ice concentration calculation method is proposed and was applied to the deep learning task. Secondly, the proposed DF-UHRNet network was improved upon by designing high- and low-level fusion modules, introducing an attention mechanism, and reducing the number of convolution layers and other operations, and it can effectively fuse high- and low-scale semantic features and global contextual information based on reducing the overall number of network parameters, enabling it to achieve pixel-level classification. The results show that this method meets the needs associated with the automatic mapping and high-precision classification of thin ice, one-year ice, open water, and multi-year ice and effectively reduces the model size. Full article

Satellite altimeters have been used to monitor Arctic sea ice (ASI) thickness for several decades, but whether the different altimeter missions (such as radar and laser altimeters) are in agreement with each other and suitable for long-term research needs to be investigated. To analyze the spatiotemporal characteristics of ASI, continuous long-term first-year ice, and multi-year ice of ASI freeboard, thickness, and volume from 2002 to 2021 using the gridded nadirization method from Envisat, CryoSat-2, and ICESat-2, altimeter data are comprehensively constructed and assessed. The influences of sea surface temperature (SST) and sea surface wind field (SSW) on ASI are also discussed. The freeboard/thickness and extent/area of ASI all varied seasonally and reached their maximum and minimum in April and October, March and September, respectively. From 2002 to 2021, the freeboard, thickness, extent, and area of ASI all consistently showed downward trends, and sea ice volume decreased by 5437 km³/month. SST in the Arctic rose by 0.003 degrees C/month, and the sea ice changes lagged behind this temperature variation by one month between 2002 and 2021. The meridional winds blowing from the central Arctic region along the eastern coast of Greenland to the North Atlantic each month are consistent with changes in the freeboard and thickness of ASI. SST and SSW are two of the most critical factors driving sea ice changes. This study provides new data and technical support for monitoring ASI and exploring its response mechanisms to climate change. Full article

We reviewed various physical and biological manifestations of an unprecedented large-scale water temperature anomaly that emerged in the Northeast Pacific in late 2013. The anomaly dubbed “The Blob” persisted through 2014–2016, with some signs of its persistence through 2017–2018 and a possible reemergence in 2019. The tentative timeline of The Blob’s successive appearances around the Northeast Pacific is suggestive of its advection by currents around the Gulf of Alaska, along the Aleutians, into the Bering Sea, and eventually to the Bering Strait. During the initial phase of The Blob’s development in 2013–2014, advection along the Polar Front might have played a certain role. The extreme persistence and magnitude of The Blob resulted in numerous and sometimes dramatic ecosystem responses in the eastern Bering Sea. The multi-year duration of The Blob might have preconditioned the Bering Sea for the record low seasonal sea ice extent during the winter of 2017–2018 and the disappearance of the cold pool in 2016 and 2018 that profoundly affected zooplankton, invertebrates, fishes, seabirds, and marine mammals. A comparison of the time series of population responses across trophic levels suggests that The Blob lowered primary production during spring, increased production of small copepods and jellyfish, and reduced the efficiency of energy transfer to higher trophic levels. While the Bering Sea’s water temperature, seasonal sea ice, and cold pool seem to return to the long-term mean state in 2022, it remains to be seen if the Bering Sea ecosystem will completely recover. The two most likely alternative scenarios envision either irreversible changes or hysteresis recovery. Full article

Arctic climate change has already resulted in amplified and accelerated regional warming, or the Arctic amplification. Satellite observations have captured this climate phenomenon in its development and in sufficient spatial details. As such, these observations have been—and still are—indispensable for monitoring of the amplification in this remote and inhospitable region, which is sparsely covered with ground observations. This study synthesizes the key contributions of satellite observations into an understanding and characterization of the amplification. The study reveals that the satellites were able to capture a number of important environmental transitions in the region that both precede and follow the emergence of the apparent amplification. Among those transitions, we find a rapid decline in the multiyear sea ice and subsequent changes in the surface radiation balance. Satellites have witnessed the impact of the amplification on phytoplankton and vegetation productivity as well as on human activity and infrastructure. Satellite missions of the European Space Agency (ESA) are increasingly contributing to amplification monitoring and assessment. The ESA Climate Change Initiative has become an essential provider of long-term climatic-quality remote-sensing data products for essential climate variables. Still, such synthesis has found that additional efforts are needed to improve cross-sensor calibrations and retrieval algorithms and to reduce uncertainties. As the amplification is set to continue into the 21st century, a new generation of satellite instruments with improved revisiting time and spectral and spatial resolutions are in high demand in both research and stakeholders’ communities. Full article

The Ku-band scatterometer called CSCAT onboard the Chinese–French Oceanography Satellite (CFOSAT) is the first spaceborne rotating fan-beam scatterometer (RFSCAT). A new algorithm for classification of Arctic sea ice types on CSCAT measurement data using a random forest classifier is presented. The random forest classifier is trained on the National Snow and Ice Data Center (NSIDC) weekly sea ice age and sea ice concentration product. Five feature parameters, including the mean value of horizontal and vertical polarization backscatter coefficient, the standard deviation of horizontal and vertical polarization backscatter coefficient and the copol ratio, are innovatively extracted from orbital measurement for the first time to distinguish water, first-year ice (FYI) and multi-year ice (MYI). The overall accuracy and kappa coefficient of sea ice type model are 93.35% and 88.53%, respectively, and the precisions of water, FYI, and MYI are 99.67%, 86.60%, and 79.74%, respectively. Multi-source datasets, including daily sea ice type from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF), NSIDC weekly sea ice age, multi-year ice concentration (MYIC) provided by the University of Bremen, and SAR-based sea ice type released by Copernicus Marine Environment Monitoring Service (CMEMS) have been used for comparison and validation. It is shown that the most obvious difference in the distribution of sea ice types between the CSCAT results and OSI SAF sea ice type are mainly concentrated in the marginal zones of FYI and MYI. Furthermore, compared with OSI SAF sea ice type, the area of MYI derived from CSCAT is more homogeneous with less noise, especially in the case of younger multiyear ice. In the East Greenland region, CSCAT identifies more pixels as MYI with lower MYIC values, showing better accuracy in the identification of areas with obvious mobility of MYI. In conclusion, this research verifies the capability of CSCAT in monitoring Arctic sea ice classification, especially in the spatial homogeneity and detectable duration of sea ice classification. Given the high accuracy and processing speed, the random forest-based algorithm can offer good guidance for sea ice classification with FY-3E/RFSCAT, i.e., a dual-frequency (Ku and C band) scatterometer called WindRAD. Full article