Analysis on long-term variability of sea ice albedo and its relationship with sea ice concentration over Antarctica

2016 ◽  
Author(s):  
Minji Seo ◽  
Hyun-cheol Kim ◽  
Noh-hun Seong ◽  
Chaeyoung Kwon ◽  
Honghee Kim ◽  
...  
Keyword(s):  
Sea Ice ◽  
Sea Ice Concentration ◽  
Ice Concentration

scholarly journals Employing Satellite-Derived Sea Ice Concentration to Constrain Upper-Ocean Temperature in a Global Ocean GCM

2008 ◽  
Vol 21 (17) ◽  
pp. 4498-4513 ◽  
Author(s):  
Achim Stössel
Keyword(s):  
Sea Ice ◽  
Deep Ocean ◽  
Global Ocean ◽  
Upper Ocean ◽  
Ocean Temperature ◽  
Freshwater Input ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Ocean Properties

Abstract The quality of Southern Ocean sea ice simulations in a global ocean general circulation model (GCM) depends decisively on the simulated upper-ocean temperature. This is confirmed by assimilating satellite-derived sea ice concentration to constrain the upper-layer temperature of a sea ice–ocean GCM. The resolution of the model’s sea ice component is about 22 km and thus comparable to the pixel resolution of the satellite data. The ocean component is coarse resolution to afford long-term integrations for investigations of the deep-ocean equilibrium response. Besides improving the sea ice simulation considerably, the simulations with constrained upper-ocean temperature yield much more realistic global deep-ocean properties, in particular when combined with glacial freshwater input. Both outcomes are relatively insensitive to the passive-microwave algorithm used to retrieve the ice concentration being assimilated. The sensitivity of the long-term global deep-ocean properties and circulation to the possible freshwater input from ice shelves and to the parameterization of vertical mixing in the Southern Ocean is reevaluated under the new constraint.

scholarly journals A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring

2013 ◽  
Vol 6 (1) ◽  
pp. 95-117
Author(s):  
G. Peng ◽  
W. N. Meier ◽  
D. J. Scott ◽  
M. H. Savoie
Keyword(s):  
Sea Ice ◽  
Passive Microwave ◽  
Climate Data ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Data Record ◽  
Climate Data Record ◽  
Climate Studies

Abstract. A long-term, consistent, and reproducible satellite-based passive microwave sea ice concentration climate data record (CDR) is available for climate studies, monitoring, and model validation with an initial operation capability (IOC). The daily and monthly sea ice concentration data are on the National Snow and Ice Data Center (NSIDC) polar stereographic grid with nominal 25 × 25 km grid cells in both the Southern and Northern Hemisphere Polar Regions from 9 July 1987 to 31 December 2007 with an update through 2011 underway. The data files are available in the NetCDF data format at http://nsidc.org/data/g02202.html and archived by the National Oceanic and Atmospheric Administration (NOAA)'s National Climatic Data Center (NCDC) under the satellite climate data record program (http://www.ncdc.noaa.gov/cdr/operationalcdrs.html). The description and basic characteristics of the NOAA/NSIDC passive microwave sea ice concentration CDR are presented here. The CDR provides similar spatial and temporal variability as the heritage products to the user communities with the additional documentation, traceability, and reproducibility that meet current standards and guidelines for climate data records. The dataset along with detailed data processing steps and error source information can be found at: doi:10.7265/N5B56GN3.

scholarly journals A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring

2013 ◽  
Vol 5 (2) ◽  
pp. 311-318 ◽  
Author(s):  
G. Peng ◽  
W. N. Meier ◽  
D. J. Scott ◽  
M. H. Savoie
Keyword(s):  
Sea Ice ◽  
Passive Microwave ◽  
Climate Data ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Data Record ◽  
Climate Data Record ◽  
Climate Studies

Abstract. A long-term, consistent, and reproducible satellite-based passive microwave sea ice concentration climate data record (CDR) is available for climate studies, monitoring, and model validation with an initial operation capability (IOC). The daily and monthly sea ice concentration data are on the National Snow and Ice Data Center (NSIDC) polar stereographic grid with nominal 25 km × 25 km grid cells in both the Southern and Northern Hemisphere polar regions from 9 July 1987 to 31 December 2007. The data files are available in the NetCDF data format at http://nsidc.org/data/g02202.html and archived by the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA) under the satellite climate data record program (http://www.ncdc.noaa.gov/cdr/operationalcdrs.html). The description and basic characteristics of the NOAA/NSIDC passive microwave sea ice concentration CDR are presented here. The CDR provides similar spatial and temporal variability as the heritage products to the user communities with the additional documentation, traceability, and reproducibility that meet current standards and guidelines for climate data records. The data set, along with detailed data processing steps and error source information, can be found at http://dx.doi.org/10.7265/N55M63M1.

scholarly journals MOSAiC drift expedition from October 2019 to July 2020: sea ice conditions from space and comparison with previous years

2021 ◽  
Vol 15 (8) ◽  
pp. 3897-3920 ◽  
Author(s):  
Thomas Krumpen ◽  
Luisa von Albedyll ◽  
Helge F. Goessling ◽  
Stefan Hendricks ◽  
Bennet Juhls ◽  
...  
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Melt Ponds ◽  
Ice Concentration ◽  
Ice Conditions ◽  
Snow Thickness ◽  
Lead Activity

Abstract. We combine satellite data products to provide a first and general overview of the physical sea ice conditions along the drift of the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition and a comparison with previous years (2005–2006 to 2018–2019). We find that the MOSAiC drift was around 20 % faster than the climatological mean drift, as a consequence of large-scale low-pressure anomalies prevailing around the Barents–Kara–Laptev sea region between January and March. In winter (October–April), satellite observations show that the sea ice in the vicinity of the Central Observatory (CO; 50 km radius) was rather thin compared to the previous years along the same trajectory. Unlike ice thickness, satellite-derived sea ice concentration, lead frequency and snow thickness during winter months were close to the long-term mean with little variability. With the onset of spring and decreasing distance to the Fram Strait, variability in ice concentration and lead activity increased. In addition, the frequency and strength of deformation events (divergence, convergence and shear) were higher during summer than during winter. Overall, we find that sea ice conditions observed within 5 km distance of the CO are representative for the wider (50 and 100 km) surroundings. An exception is the ice thickness; here we find that sea ice within 50 km radius of the CO was thinner than sea ice within a 100 km radius by a small but consistent factor (4 %) for successive monthly averages. Moreover, satellite acquisitions indicate that the formation of large melt ponds began earlier on the MOSAiC floe than on neighbouring floes.

scholarly journals The MOSAiC Drift: Ice conditions from space and comparison with previous years

2021 ◽  
Author(s):  
Thomas Krumpen ◽  
Luisa von Albedyll ◽  
Helge F. Goessling ◽  
Stefan Hendricks ◽  
Bennet Juhls ◽  
...  
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Melt Ponds ◽  
Ice Concentration ◽  
Ice Conditions ◽  
Snow Thickness ◽  
Lead Activity

Abstract. We combine satellite data products to provide a first and general overview of the sea-ice conditions along the MOSAiC drift and a comparison with previous years. We find that the MOSAiC drift was around 25 % faster than the climatological mean drift, as a consequence of large-scale low-pressure anomalies prevailing around the Barents-Kara-Laptev Sea region between January and March. In winter (October–April), satellite observations show that the sea-ice in the vicinity of the Central Observatory (CO) was rather thin compared to the previous years along the same trajectory. Unlike ice thickness, satellite-derived sea-ice concentration, lead frequency, and snow thickness during winter month were close to the long-term mean with little variability. With the onset of spring and decreasing distance to Fram Strait, variability in ice concentration and lead activity increased. In addition, frequency and strength of deformation events (divergence and shear) were higher during summer than during winter. Overall, we find that sea-ice conditions observed close (~ 5 km) to the CO are representative for the wider (50 km and 100 km) surroundings. An exception is the ice thickness: Here we find that sea-ice near the CO (50 km radius) was 4 % thinner than sea-ice within a 100 km radius. Moreover, satellite acquisitions indicate that the formation of large melt ponds began earlier on the MOSAiC floe than on neighbouring floes.

Comparison of sea ice classification methods based on satellite scatterometer and radiometer data in the Weddell Sea, Antarctica

2019 ◽  
Vol 31 (3) ◽  
pp. 150-164
Author(s):  
Xiaoping Pang ◽  
Xiang Gao ◽  
Qing Ji
Keyword(s):  
Sea Ice ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Weddell Sea ◽  
First Year ◽  
Sea Ice Concentration ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Type Information

AbstractInformation on sea ice type is an important factor for deriving sea ice parameters from satellite remote sensing data, such as sea ice concentration, extent and thickness. In this study, sea ice in the Weddell Sea was classified by the histogram threshold (HT) method, the Spreen model (SM) method from satellite scatterometer data and the strong contrast (SC) method from radiometer data, and this information was compared with Antarctic Sea Ice Processes and Climate (ASPeCt) sea ice-type ship-based observations. The results show that all three methods can distinguish the multi-year (MY) ice and first-year (FY) ice using Ku-band scatterometer data and radiometer data during the ice growth season, while C-band scatterometer data are not suitable for MY ice and FY ice discrimination using HT and SM methods. The SM model has a smaller MY ice classification extent than the HT method from scatterometer data. The classification accuracy of the SM method is the higher compared to ship-based observations. It can be concluded that the SM method is a promising method for discriminating MY ice from FY ice. These results provide a reference for further retrieval of long-term sea ice-type information for the whole of Antarctica.

scholarly journals Influence of Melt Ponds on the SSMIS-Based Summer Sea Ice Concentrations in the Arctic

2021 ◽  
Vol 13 (19) ◽  
pp. 3882
Author(s):  
Jiechen Zhao ◽  
Yining Yu ◽  
Jingjing Cheng ◽  
Honglin Guo ◽  
Chunhua Li ◽  
...  
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
High Concentration ◽  
Sea Ice Concentration ◽  
Melt Pond ◽  
Ice Surface ◽  
Melt Ponds ◽  
Ice Concentration ◽  
Microwave Imager

As a long-term, near real-time, and widely used satellite derived product, the summer performance of the Special Sensor Microwave Imager/Sounder (SSMIS)-based sea ice concentration (SIC) is commonly doubted when extensive melt ponds exist on the ice surface. In this study, three SSMIS-based SIC products were assessed using ship-based SIC and melt pond fraction (MPF) observations from 60 Arctic cruises conducted by the Ice Watch Program and the Chinese Icebreaker Xuelong I/II. The results indicate that the product using the NASA Team (SSMIS-NT) algorithm and the product released by the Ocean and Sea Ice Satellite Application Facility (SSMIS-OS) underestimated the SIC by 15% and 7–9%, respectively, which mainly occurred in the high concentration rages, such as 80–100%, while the product using the Bootstrap (SSMIS-BT) algorithm overestimated the SIC by 3–4%, usually misestimating 80% < SIC < 100% as 100%. The MPF affected the SIC biases. For the high MPF case (e.g., 50%), the estimated biases for the three products increased to 20% (SSMIS-NT), 7% (SSMIS-BT), and 20% (SSMIS-OS) due to the influence of MPF. The relationship between the SIC biases and the MPF observations established in this study was demonstrated to greatly improve the accuracy of the 2D SIC distributions, which are useful references for model assimilation, algorithm improvement, and error analysis.

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

&lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;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.&lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt;

scholarly journals Review of methods to retrieve sea ice parameters from satellite microwave radiometer data

2019 ◽  
Vol 55 (1) ◽  
pp. 128-151
Author(s):  
E. V. Zabolotskikh
Keyword(s):  
Sea Ice ◽  
Microwave Radiometer ◽  
Open Water ◽  
Sea Ice Concentration ◽  
Internet Resources ◽  
Ice Concentration ◽  
Satellite Microwave ◽  
Microwave Imager ◽  
Microwave Radiometers

Sea ice monitoring using long-term data of satellite passive microwave instruments enables climate change estimates. These numerical estimates depend on the methods used for sea ice parameter retrievals. This work presents a review of methods to retrieve sea ice parameters from the data of satellite microwave radiometers. Physical modeling of the sea ice–ocean–atmosphere microwave radiation provides the means to identify the general sources of the retrieval errors and to classify the methods by used approach. The basics of the algorithms are formulated along with assumptions and approximations as well as the data used for the algorithm verification. Weather filters are considered to identify the areas of open water. A comparative analysis of method advantages and limitations is given related to sea ice concentration retrievals from such satellite instruments as the series of Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer (AMSR). A review of the basic satellite sea ice products based on SSM/I, AMSR-E and AMSR2 data is complemented by the list of the essential internet resources for operational and historical sea ice data.

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