scholarly journals Reconstructing spring sea ice concentration in the Chukchi Sea over recent centuries: insights into the application of the PIP 25 index

2019 ◽  
Vol 14 (12) ◽  
pp. 125004 ◽  
Author(s):  
Jung-Hyun Kim ◽  
Jong-Ku Gal ◽  
Sang-Yoon Jun ◽  
Lukas Smik ◽  
Dahae Kim ◽  
...  
Keyword(s):  
Sea Ice ◽  
Chukchi Sea ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Spring Sea Ice

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>

scholarly journals Satellite-retrieved sea ice concentration uncertainty and its effect on modelling wave evolution in marginal ice zones

2020 ◽  
Vol 14 (6) ◽  
pp. 2029-2052 ◽  
Author(s):  
Takehiko Nose ◽  
Takuji Waseda ◽  
Tsubasa Kodaira ◽  
Jun Inoue
Keyword(s):  
Sea Ice ◽  
Wave Attenuation ◽  
Chukchi Sea ◽  
Cumulative Effect ◽  
Wave Model ◽  
Subgrid Scale ◽  
Sea Ice Concentration ◽  
Ocean Surface Waves ◽  
Modelling Uncertainty ◽  
Ice Concentration

Abstract. Ocean surface waves are known to decay when they interact with sea ice. Wave–ice models implemented in a spectral wave model, e.g. WAVEWATCH III® (WW3), derive the attenuation coefficient based on several different model ice types, i.e. how the model treats sea ice. In the marginal ice zone (MIZ) with sea ice concentration (SIC) < 1, the wave attenuation is moderated by SIC: we show that subgrid-scale processes relating to the SIC and sea ice type heterogeneity in the wave–ice models are missing and the accuracy of SIC plays an important role in the predictability. Satellite-retrieved SIC data (or a sea ice model that assimilates them) are often used to force wave–ice models, but these data are known to have uncertainty. To study the effect of SIC uncertainty ΔSIC on modelling MIZ waves during the 2018 R/V Mirai observational campaign in the refreezing Chukchi Sea, a WW3 hindcast experiment was conducted using six satellite-retrieved SIC products based on four algorithms applied to SSMIS and AMSR2 data. The results show that ΔSIC can cause considerable wave prediction discrepancies in ice cover. There is evidence that bivariate uncertainty data (model significant wave heights and SIC forcing) are correlated, although off-ice wave growth is more complicated due to the cumulative effect of ΔSIC along an MIZ fetch. The analysis revealed that the effect of ΔSIC can overwhelm the uncertainty arising from the choice of model ice types, i.e. wave–ice interaction parameterisations. Despite the missing subgrid-scale physics relating to the SIC and sea ice type heterogeneity in WW3 wave–ice models – which causes significant modelling uncertainty – this study found that the accuracy of satellite-retrieved SIC used as model forcing is the dominant error source of modelling MIZ waves in the refreezing ocean.

scholarly journals Satellite retrieved sea ice concentration uncertainty and its effect on modelling wave evolution in marginal ice zones

2019 ◽  
Author(s):  
Takehiko Nose ◽  
Takuji Waseda ◽  
Tsubasa Kodaira ◽  
Jun Inoue
Keyword(s):  
Sea Ice ◽  
Chukchi Sea ◽  
Cumulative Effect ◽  
Sea Ice Concentration ◽  
Wave Hindcast ◽  
Significant Wave ◽  
Ice Concentration ◽  
Wave Heights ◽  
Ice Field ◽  
Significant Wave Heights

Abstract. Satellite retrieved Sea Ice Concentration (SIC) uncertainty is studied with respect to its effect on spectral wave modelling of ice-covered water. Eight SIC products based on four algorithms applied to SSMIS and AMSR2 data were analysed. They were compared with sea-truth images captured from a 12 day fixed Marginal Ice Zone (MIZ) transect observation during the November 2018 R/V Mirai expedition in the Chukchi Sea. The analysis shows the refreezing sea ice field is highly variable in time and space, and the uncertainty of SIC estimates is considerable. A wave hindcast experiment for the observation period using these SIC products as model forcing has shown that the SIC uncertainty translates to wave prediction discrepancies in ice cover. There is evidence that bivariate uncertainty data (model significant wave heights and SIC forcing) are correlated, although off-ice wave growth is more complicated due to the cumulative effect of SIC uncertainty and the model implementation of wave-ice interactions along an MIZ fetch. Analysis of significant wave height uncertainty distributions for SIC forcing and wave-ice interaction source terms reveals that they are both sizeable; however, the study concludes the more dominant uncertainty source of modelling wave-ice interactions is the accuracy of satellite retrieved SIC estimates that are used as model forcing.

scholarly journals INTER-COMPARISONS AMONG PASSIVE MICROWAVE SEA ICE CONCENTRATION PRODUCTS FROM FY-3D MWRI AND AMSR2

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 861-867
Author(s):  
Y. Chen ◽  
X. Zhao ◽  
M. Qu ◽  
Z. Cheng ◽  
X. Pang ◽  
...  
Keyword(s):  
Sea Ice ◽  
Chukchi Sea ◽  
Regional Analysis ◽  
Correlation Coefficients ◽  
Arctic Sea Ice ◽  
Passive Microwave ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Ice Concentration

Abstract. Passive microwave (PM) sensors on satellite can monitor sea ice distribution with their strengths of daylight- and weather-independent observations. Microwave Radiation Imager (MWRI) sensor aboard on the Chinese FengYun-3D (FY-3D) satellites was launched in 2017 and provides continuous observation for Arctic sea ice since then. In this study, sea ice concentration (SIC) product is derived from brightness temperature (TB) data of MWRI, based on an Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) dynamic tie points algorithm. Our product is inter-compared with a published MWRI SIC product by the Enhanced NASA Team (NT2) algorithm, and three Advanced Microwave Scanning Radiometer 2 (AMSR2) SIC products by the ASI, Bootstrap (BST) and NT2 algorithm. Results show that MWRI SIC are generally higher than AMSR2 SIC and the median of monthly SIC differences are larger in summer. Regional analysis indicates that the smaller differences between AMSR2 SIC and MWRI-ASI SIC occur in the higher SIC areas, and the biases are within ±5% in the Beaufort Sea, Chukchi Sea, East Siberian Sea, Canadian Archipelago Sea and Central Arctic Sea. There is the smallest SIC difference in the Central Arctic Sea with the biases of −0.77%, −0.60%, and 0.19% for AMSR2-ASI, AMSR2-BST and AMSR2-NT2, respectively. The trends of MWRI and AMSR2 sea ice extent and sea ice area are consistent with correlation coefficients all greater than 0.997. Besides, mean SIC, sea ice extent and sea ice area of MWRI-ASI are closer to those of AMSR2 than those of MWRI-NT2.

scholarly journals DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations

2021 ◽  
pp. 1-6
Author(s):  
Hao Luo ◽  
Qinghua Yang ◽  
Longjiang Mu ◽  
Xiangshan Tian-Kunze ◽  
Lars Nerger ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Southern Ocean ◽  
Sea Ice ◽  
Coupled Model ◽  
Sea Ice Concentration ◽  
Model Free ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Data Assimilation System ◽  
Assimilation System

Abstract To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation.

scholarly journals Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks

2021 ◽  
Vol 13 (6) ◽  
pp. 1139
Author(s):  
David Llaveria ◽  
Juan Francesc Munoz-Martin ◽  
Christoph Herbert ◽  
Miriam Pablos ◽  
Hyuk Park ◽  
...  
Keyword(s):  
Sea Ice ◽  
Microwave Radiometer ◽  
Cost Effective ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Neural Network Approach ◽  
Ice Concentration ◽  
L Band ◽  
Moderate Cost

CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved.

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.

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