scholarly journals Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data

2021 ◽  
Vol 13 (11) ◽  
pp. 2174
Author(s):  
Lijian Shi ◽  
Sen Liu ◽  
Yingni Shi ◽  
Xue Ao ◽  
Bin Zou ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Ocean Circulation ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Atmospheric Effects ◽  
Observation Data ◽  
Long Time Series ◽  
Ice Concentration ◽  
Long Time

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.

scholarly journals Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

2021 ◽  
Vol 13 (12) ◽  
pp. 2283
Author(s):  
Hyangsun Han ◽  
Sungjae Lee ◽  
Hyun-Cheol Kim ◽  
Miae Kim
Keyword(s):  
Random Forest ◽  
Sea Ice ◽  
Arctic Ocean ◽  
Open Water ◽  
The Arctic ◽  
Atmospheric Effects ◽  
Sea Ice Concentration ◽  
Air Temperatures ◽  
The Pacific ◽  
Ice Concentration

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

scholarly journals On the assimilation of ice velocity and concentration data into large-scale sea ice models

2007 ◽  
Vol 4 (2) ◽  
pp. 265-301 ◽  
Author(s):  
V. Dulière ◽  
T. Fichefet
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Ocean Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Observation Data ◽  
Concentration Data ◽  
Ice Concentration ◽  
Ice Velocity

Abstract. Data assimilation into sea ice models designed for climate studies has started about 15 years ago. In most of the studies conducted so far, it is assumed that the improvement brought by the assimilation is straightforward. However, some studies suggest this might not be true. In order to elucidate this question and to find an appropriate way to further assimilate sea ice concentration and velocity observations into a global sea ice-ocean model, we analyze here results from a number of twin experiments (i.e. experiments in which the assimilated data are model outputs) carried out with a simplified model of the Arctic sea ice pack. Our objective is to determine to what degree the assimilation of ice velocity and/or concentration data improves the global performance of the model and, more specifically, reduces the error in the computed ice thickness. A simple optimal interpolation scheme is used, and outputs from a control run and from perturbed experiments without and with data assimilation are thoroughly compared. Our results indicate that, under certain conditions depending on the assimilation weights and the type of model error, the assimilation of ice velocity data enhances the model performance. The assimilation of ice concentration data can also help in improving the model behavior, but it has to be handled with care because of the strong connection between ice concentration and ice thickness. This study is preliminary study towards real observation data assimilation into NEMOLIM, a global sea ice-ocean model.

Long term observation of crustal deformation in NE-Italy

2020 ◽  
Author(s):  
Carla Braitenberg ◽  
Barbara Grillo ◽  
Alberto Pastorutti ◽  
Tommaso Pivetta
Keyword(s):  
Time Series ◽  
Crustal Deformation ◽  
Surface Deformation ◽  
Earth Tide ◽  
Atmospheric Effects ◽  
Transfer Of Knowledge ◽  
Long Time Series ◽  
Ne Italy ◽  
Long Time

<p>The long term monitoring of crustal deformation in NE-Italy derives from tilt and strainmeter observations since 1960. The stations have been maintained by three generations of scientists starting with the geodesist Antonio Marussi, keeping the instrumentation active and up to date. The decade-long time series have given observations of rare events, as the free oscillations recorded by the largest earthquakes ever recorded (Chile 1960, Sumatra 2004, Tohoku 2011) and climatic extreme events leading to extremely intense rainfalls that generate underground flooding and surface deformation (Braitenberg et al., 2019; Braitenberg, 2018). The stations have the characteristic of being representative of geodetic monitoring in karst geologic formation, that they are placed in a seismically active area which has experienced a magnitude M 6.4 earthquake in the past (1976 Gemona), and that they are influenced by the ocean loading deformation of the Adriatic Sea. The seismic area implies that the strain accumulation is an ongoing process, presently activating the elastic energy of the next earthquake. We show some relevant observations, which could hardly have been caught without such a long time series. Between 1973 and 1976 the long base horizontal pendulums of the Grotta Gigante cave gave episodic disturbances, that seized 6 months after the Gemona main shock. The hydrology of the karst is made of an underground channel system that is completely flooded during extreme rainfall and is pressurized close to simultaneously over a distance of 30 km, leading to an observable uplift and deformation of the surface (Braitenberg et al., 2019). It has been possible to extract and model this type of deformation.</p><p>The tilt and strainmeters have high accuracies and precision in the detection of crustal deformation, with the drawback to be point measurements. InSAR acquisitions cover thousands of points on the surface, but with coarser accuracy. One major problem is in the correction of atmospheric effects in the InSAR signal, which produces apparent movement in the direction of Line of Sight, uncorrelated to the real soil movement. Our present research objective is the transfer of knowledge from the signals known in the tilt and strainmeter observations to the detection of these signals with InSAR. </p><p> </p><p>Braitenberg C. (2018). The deforming and rotating Earth - A review of the 18th International Symposium on Geodynamics and Earth Tide, Trieste 2016 , Geodesy and Geodynamics, 187-196, doi::10.1016/j.geog.2018.03.003 .</p><p>Braitenberg C., Pivetta T., Barbolla D. F., Gabrovsek F., Devoti R., Nagy I. (2019). Terrain uplift due to natural hydrologic overpressure in karstic conduits. Scientific Reports, 9:3934, 1-10, doi.:10.1038/s41598-019-38814-1.</p>

Understanding eddy field in the Arctic Ocean from high-resolution satellite observations

2020 ◽  
Author(s):  
Igor Kozlov ◽  
Anastasia Artamonova ◽  
Larisa Petrenko ◽  
Evgeny Plotnikov ◽  
Georgy Manucharyan ◽  
...  
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Arctic Ocean ◽  
Ocean Circulation ◽  
The Arctic ◽  
Special Focus ◽  
Deep Basin ◽  
Eddy Characteristics ◽  
Ice Concentration ◽  
The Arctic Ocean

<p>The Arctic Ocean is a host to major ocean circulation systems, many of which generate eddies that can transport water masses and corresponding tracers over long distances from their formation sites. However, comprehensive observations of critical eddy characteristics are currently not available and are limited to spatially and temporally sparse in situ observations.</p><p>Here we use multi-mission high‐resolution spaceborne synthetic aperture radar (SAR) measurements to detect eddies over open ocean and marginal ice zones (MIZ) of Fram Strait and Beaufort Gyre regions. We provide the first estimate of eddy properties, including their locations, size, vorticity sign and monthly distribution during summer period (from June to October). The results of historical Envisat ASAR observations for 2007 and 2011 are then compared to Sentinel-1 and ALOS-2 PALSAR-2 measurements acquired in 2016 and 2018, to infer the possible changes in the intensity and locations of eddy generation over the last decade.</p><p>The most prominent feature of the obtained results is that cyclonic eddies strongly dominate over anticyclones. Eddies range in size between 0.5 and 100 km and are frequently found over the shelf and near continental slopes but also present in the deep basin. For MIZ eddies, the number of eddies clearly depends on sea ice concentration with more eddies detected at the ice edge and over low ice concentration regions. The obtained results clearly show that eddies are ubiquitous in the Arctic Ocean even in the presence of sea ice and emphasize the need for improved ocean observations and modeling at eddy scales.</p><p>A special focus is also given to infer eddy dynamics over the Arctic marginal ice zones. The use of sequential Sentinel-1 SAR images enables to retrieve high-resolution velocity field over MIZ on a daily basis and observe eddy-driven MIZ dynamics down to submesoscales. The obtained eddy orbital velocities are in agreement with historical observations and may reach up to 0.5-0.7 m/s. We believe that this information is critical for better understanding of the key dynamical processes governing the MIZ state, as well as for improving and validation of sea ice and coupled ice-ocean models.</p><p>The analysis of eddies in this work was supported by RFBR grant 18‐35‐20078. Processing and analysis of Sentinel‐1 and ALOS‐2 Palsar‐2 data were done within RSF grant 18‐77‐00082. Software development for data analysis in this work was made under the Ministry of Science and Higher Education of the Russian Federation contract 0555‐2019‐0001.</p>

scholarly journals Tracing Atlantic Water Signature in the Arctic Sea Ice Cover East of Svalbard

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Vladimir V. Ivanov ◽  
Vladimir A. Alexeev ◽  
Irina Repina ◽  
Nikolay V. Koldunov ◽  
Alexander Smirnov
Keyword(s):  
Heat Flux ◽  
Time Series ◽  
Sea Ice ◽  
Ice Cover ◽  
Atlantic Water ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Ice Concentration ◽  
Ice Conditions

We focus on the Arctic Ocean between Svalbard and Franz Joseph Land in order to elucidate the possible role of Atlantic water (AW) inflow in shaping ice conditions. Ice conditions substantially affect the temperature regime of the Spitsbergen archipelago, particularly in winter. We test the hypothesis that intensive vertical mixing at the upper AW boundary releases substantial heat upwards that eventually reaches the under-ice water layer, thinning the ice cover. We examine spatial and temporal variation of ice concentration against time series of wind, air temperature, and AW temperature. Analysis of 1979–2011 ice properties revealed a general tendency of decreasing ice concentration that commenced after the mid-1990s. AW temperature time series in Fram Strait feature a monotonic increase after the mid-1990s, consistent with shrinking ice cover. Ice thins due to increased sensible heat flux from AW; ice erosion from below allows wind and local currents to more effectively break ice. The winter spatial pattern of sea ice concentration is collocated with patterns of surface heat flux anomalies. Winter minimum sea ice thickness occurs in the ice pack interior above the AW path, clearly indicating AW influence on ice thickness. Our study indicates that in the AW inflow region heat flux from the ocean reduces the ice thickness.

scholarly journals Amderma open water period characteristics based on satellite observation of the sea ice concentration, archives for the 1979-2018 period

2020 ◽  
pp. 12-22
Author(s):  
Natalia Shabanova ◽  
Pavel Shabanov
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Satellite Observation ◽  
Water Area ◽  
The Arctic ◽  
Western Coast ◽  
Observation Data ◽  
Sea Ice Concentration ◽  
Adjacent Water ◽  
Ice Concentration

The aim of this study is to estimate the Amderma station (the Kara Sea) ice-free period using sea ice concentration satellite datasets in comparison to observation data. The work follows the research performed for the Western Coast of the Yamal (Marresalya) being the part of the Arctic coastal dynamics study. The OSISAF, JAXA and NSIDC (resolution 25 km, 1 day, 1979-2018) sea ice concentration datasets were used to characterize the water area adjacent to the station within the 30-50 km radius. The threshold (15%-concentration) and the author's "sliding window" methods were used to detect open water start and end dates. According to the satellite data, the ice-free period in the 30-50-kilometer water area along the Amderma coast is shifted by 2-3 weeks closer to December if compared to observations. At Amderma station (in the contrast to Marresalya), there are no significant trends in ice-free period start dates. On the adjacent water area, the destruction of the ice cover occurs earlier by 3-6 weeks if compared to the 1980’s, and the end date by 3-6 weeks later. The duration of the open water period over 40 years has increased by 32-36 days at the station and by 52-120 days (40-100%) in the adjacent water area.

Sea ice changes in the Chukchi Sea over the Industrial Era based on biomarkers

2021 ◽  
Author(s):  
Youcheng Bai ◽  
Marie-Alexandrine Sicre ◽  
Jian Ren ◽  
Bassem Jalali ◽  
Hongliang Li ◽  
...  
Keyword(s):  
Sea Ice ◽  
Ocean Circulation ◽  
Chukchi Sea ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Decadal Scale ◽  
Ice Concentration ◽  
Spring Sea Ice ◽  
Shelf Region

<p>High-resolution palaeo-climate records documenting sea ice extent over the Industrial Era is an important source of information to fully understand the emergence and magnitude of on-going changes and better predict future climate evolution of the Arctic Ocean. In this study, source-specific highly branched isoprenoids (HBIs) and phytosterols were measured in multicores retrieved from the Chukchi shelf region to document the history of seasonal sea ice in this area since the beginning of the Industrial Era. HBIs at the end of the 19th century (AD 1865-1875) point to a retreat of the sea ice edge and rapid return to colder conditions. After 1920-1930 AD, proxy records indicate a steady sea ice retreat reaching a maximum in the 1990s. Sympagic biomarker IP<sub>25</sub> and HBI II were generally low during negative Arctic Oscillation (AO) (i.e., before 1920s) while higher values were found during positive AO, in particular in the 1990s. Our data also suggest a role of remote ocean circulation features.</p><p>Among existing indices for semi-quantitative of sea ice concentration, the H-Print % sea ice index seems to generally perform less than so-called phytoplankton marker-IP<sub>25</sub> (PIP<sub>25</sub>) index to estimate spring sea ice concentration (SpSIC). However, P<sub>B</sub>IP<sub>25</sub>-derived SpSIC better reproduce decadal scale variability and the long-term sea ice decline since the mid-20th century. Our results also highlight the lack of data for improving the PIP<sub>25</sub> and their relationship to sea ice.</p>

Sub-seasonal prediction of Arctic sea ice concentration using time series forecasting technique

2020 ◽  
Author(s):  
Baek-Min Kim ◽  
Ha-Rim Kim ◽  
Yong-Sang Choi ◽  
Yejin Lee ◽  
Gun-Hwan Yang
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Time Scales ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Ncep Cfs

<div> <div> <div> <p>Recently, many studies have highlighted the importance of the ability to predict the Arctic sea ice concentration in the sub-seasonal time scales. Notably, the Arctic sea ice concentration has a potential for skillful predictions through their long-term trend memory. Based on the long-term memory of Arctic sea ice concentration, we evaluate the predictability of Arctic sea ice concentration (SIC) by applying a time-series analysis technique of the Prophet model on sub-seasonal time scales. A Prophet is a recently introduced method as a statistical approach inspired by the nature of time series forecasted at Facebook and has not been applied to the prediction of Arctic SIC before. Sub-seasonal prediction skills of Arctic SIC in the Prophet model were compared with the NCEP Climate Forecast System Reforecast (CFS-Reforecast) model as a dynamical approach and verified with the satellite observation during wintertime from 2000 to 2018 for 1 to 8 weeks lead times. The result shows that the Prophet model exhibits much better skill than the NCEP CFS-Reforecast model in the climatology prediction except for the 1 to 3 weeks lead times, as the Prophet model has mainly the ability to capture the long-term trend. In the anomaly prediction, however, the NCEP CFS-Reforecast model is superior to the Prophet model in the prediction of sub-seasonal time scales, as the NCEP CFS-Reforecast captures more effectively the sub-seasonal transition of the underlying dynamical system. Therefore, even if the Prophet model has shown a useful skill in predicting the climatological Arctic SIC, there is still a need to improve the accuracy and robustness of the predictions in an anomalous Arctic SIC. Further, we suggest that the bias correction method is needed to improve the forecast skill of Arctic SIC using the time-series analysis technique, and it will be critical to advance the field of the Arctic SIC forecasting on the sub-seasonal time scales.</p> </div> </div> </div>

scholarly journals Summer sea ice in the recent Arctic: morphological properties in the Pacific sector from the CHINARE 2010 cruise

2012 ◽  
Vol 6 (3) ◽  
pp. 1963-2004
Author(s):  
H. Xie ◽  
R. Lei ◽  
C. Ke ◽  
H. Wang ◽  
Z. Li ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Cover ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
Morphological Properties ◽  
The Arctic ◽  
Ice Thickness ◽  
Observation Data ◽  
Concentration Data ◽  
Ice Concentration

Abstract. The Chinese National Arctic Research Expedition (CHINARE) in the summer 2010, primarily from 21 July to 28 August, in the ice zone of Arctic Pacific Sector, between 150° W to 180° W to 88.5° N, conducted comprehensive scientific studies on atmosphere-ice-ocean interactions, using icebreaker R.V. Xuelong. Measurements made included underway visual observations of snow and ice conditions at half-hourly time scale and EM31-measured ice thickness at one 12-day and eight short-term (3–4 h each) ice stations. The visual observation data are compared with AMSR-E ice concentration, ice thickness measured by a hanging EM31 from the vessel, and video-recorded image-derived ice concentration and pond coverage. A grid of 8 profiles of ice thickness measurements (four repeats) was conducted at the 12-day ice station (∼86°50' N–87°20' N) in the central Arctic and an average 2 cm day−1 melt rate, primarily bottom melt, was found, after surface melting had almost ceased. The high bottom melt rate, as compared with previous results from other studies, is consistent with the high floe speed (mean 0.17 m s−1) that is also larger than that previously reported. We also found that the daily AMSR-E ice concentration data can be used to map the marginal ice zone (MIZ) and its change. There are clear differences between the MIZ and pack ice zone in terms of ice concentration, thickness, ice type, floe size, as well as pond coverage. Results indicate that, as compared with the 2005 data from the Healy/Oden Trans-Arctic Expedition for the Arctic Pacific Sector (9 August to 10 September), the 2010 was first-year ice dominant (vs. the multiyear ice dominant in 2005), 70% or less in ice concentration (vs. 90% in 2005), 94–114 cm in ice thickness (vs. 150 cm in 2005). No snow cover was observed on the ice south of 78° N and 8–10 cm mean snow depth due to new snowfall events, which melted away after 17 August (vs. no snow cover south of 84.3° N or ~10 cm snow depth further north in 2005). Those changes indicate the continuation of ice thinning, less concentration, and younger ice after the 2007 shift, when a record minimum sea ice extent was observed. Overall, the measurements provided a valuable dataset of sea ice morphological properties over the Arctic Pacific Sector in summer 2010, which confirms, by comparison with previous data, that a "new normal" of Arctic sea ice is now present and is a benchmark for measurements of possible future changes.

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