scholarly journals Erroneous sea-ice concentration retrieval in the East Antarctic

2018 ◽  
Vol 59 (76pt2) ◽  
pp. 201-212 ◽  
Author(s):  
Hoi Ming Lam ◽  
Gunnar Spreen ◽  
Georg Heygster ◽  
Christian Melsheimer ◽  
Neal W. Young
Keyword(s):  
Sea Ice ◽  
Microwave Remote Sensing ◽  
Atmospheric Effects ◽  
Sea Ice Concentration ◽  
Microwave Brightness Temperature ◽  
Antarctic Sea Ice ◽  
Optical Images ◽  
Ice Concentration ◽  
Retrieval Algorithms ◽  
Landfast Sea Ice

ABSTRACTLarge discrepancies have been observed between satellite-derived sea-ice concentrations(IC) from passive microwave remote sensing and those derived from optical images at several locations in the East Antarctic, between February and April 2014. These artefacts, that resemble polynyas in the IC maps, appear in areas where optical satellite data show that there is landfast sea ice. The IC datasets and the corresponding retrieval algorithms are investigated together with microwave brightness temperature, air temperature, snowfall and bathymetry to understand the failure of the IC retrieval. The artefacts are the result of the application of weather filters in retrieval algorithms. These filters use the 37 and 19 GHz channels to correct for atmospheric effects on the retrieval. These channels show significant departures from typical ranges when the artefacts occur. A melt–refreeze cycle with associated snow metamorphism is proposed as the most likely cause. Together, the areas of the artefacts account for up to 0.5% of the Antarctic sea-ice area and thus cause a bias in sea-IC time series. In addition, erroneous sea ICs can adversely affect shipping operations.

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 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 Comparison between AMSR2 Sea Ice Concentration Products and Pseudo-Ship Observations of the Arctic and Antarctic Sea Ice Edge on Cloud-Free Days

2018 ◽  
Vol 10 (2) ◽  
pp. 317 ◽  
Author(s):  
Xiaoping Pang ◽  
Jian Pu ◽  
Xi Zhao ◽  
Qing Ji ◽  
Meng Qu ◽  
...  
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Ice Edge ◽  
Sea Ice Edge

scholarly journals Comparison between AMSR-E ASI sea-ice concentration product, MODIS and pseudo-ship observations of the Antarctic sea-ice edge

2015 ◽  
Vol 56 (69) ◽  
pp. 45-52 ◽  
Author(s):  
Xi Zhao ◽  
Haoyue Su ◽  
Alfred Stein ◽  
Xiaoping Pang
Keyword(s):  
Sea Ice ◽  
Average Distance ◽  
Sea Ice Concentration ◽  
Earth Observing System ◽  
Antarctic Sea Ice ◽  
Spatial Coverage ◽  
Ice Concentration ◽  
Ice Edge ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Antarctic

AbstractThe performance of passive microwave sea-ice concentration products in the marginal ice zone and at the ice edge draws much attention in accuracy assessments. In this study, we generated 917 pseudo-ship observations from four Moderate Resolution Imaging Spectroradiometer (MODIS) images based on the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol to assess the quality of the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) ARTIST (Arctic Radiation and Turbulence Interaction STudy) Sea Ice (ASI) concentrations at the ice edge in Antarctica. The results indicate that the ASI pixels in the pseudo-ASPeCt observations have a mean ice concentration of 13% and are significantly different from the well-established 15% threshold. The average distance between the pseudo-ice edge and the 15% threshold contour is ~10 km. The correlation between the sea-ice concentration (SIC), SICASI and SICMODIS values at the ice edge was considerably lower than the high coefficients obtained from a transect analysis. Underestimation of SICASI occurred in summer, whereas no clear bias was observed in winter. The proposed method provides an opportunity to generate a new source of reference data in which the spatial coverage is wider and more flexible than in traditional in situ observations.

scholarly journals The impact of melt ponds on summertime microwave brightness temperatures and sea ice concentrations

2016 ◽  
Author(s):  
S. Kern ◽  
A. Rösel ◽  
L. T. Pedersen ◽  
N. Ivanova ◽  
R. Saldo ◽  
...  
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Water Fraction ◽  
Microwave Brightness Temperature ◽  
Ice Melt ◽  
Melt Ponds ◽  
Retrieval Algorithms ◽  
The Impact

Abstract. The sea ice concentration (SIC) derived from satellite microwave brightness temperature (TB) data are known to be less accurate during summer melt conditions – in the Arctic Ocean primarily because of the impact of melt ponds on sea ice. Using data from June to August 2009, we investigate how TBs and SICs vary as a function of the ice surface fraction (ISF) computed from open water fraction and melt pond fraction both derived from satellite optical reflectance data. SIC is computed from TBs using a set of eight different retrieval algorithms and applying a consistent set of tie points. We find that TB values change during sea ice melt non-linearly and not monotonically as a function of ISF for ISF of 50 to 100 %. For derived parameters such as the polarization ratio at 19 GHz the change is monotonic but substantially smaller than theoretically expected. Changes in ice/snow radiometric properties during melt also contribute to the TB changes observed; these contributions are functions of frequency and polarization and have the potential to partly counter-balance the impact of changing ISF on the observed TBs. All investigated SIC retrieval algorithms overestimate ISF when using winter tie points. The overestimation varies among the algorithms as a function of ISF such that the SIC retrieval algorithms could be categorized into two different classes. These reveal a different degree of ISF overestimation at high ISF and an opposite development of ISF over-estimation as ISF decreases. For one class, correlations between SIC and ISF are ≥ 0.85 and the associated linear regression lines suggest an exploitable relationship between SIC and ISF if reliable summer sea ice tie points can be established. This study shows that melt ponds are interpreted as open water by the SIC algorithms, while the concentration of ice between the melt ponds is in general being overestimated. These two effects may cancel each other out and thus produce seemingly correct SIC for the wrong reasons. This cancelling effect will in general only be "correct" at one specific value of MPF. Based on our findings we recommend to not correct SIC algorithms for the impact of melt ponds as this seems to violate physical principles. Users should be aware that the SIC algorithms available at the moment retrieve a combined parameter presented by SIC in winter and ISF in summer.

scholarly journals Variability in Antarctic sea ice from 1998-2017

2018 ◽  
Author(s):  
Zhankai Wu ◽  
Xingdong Wang
Keyword(s):  
Sea Ice ◽  
First Year ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ice Melt ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
The Antarctic ◽  
Melt Duration ◽  
Multiyear Ice

This study was based on the daily sea ice concentration data from the National Snow and Ice Data Center (Cooperative Institute for Research in Environmental Sciences, Boulder, CO, USA) from 1998 to 2017. The Antarctic sea ice was analysed from the total sea ice area (SIA), first year ice area, first year ice melt duration, and multiyear ice area. On a temporal scale, the changes in sea ice parameters were studied over the whole 20 years and for two 10-year periods. The results showed that the total SIA increased by 0.0083×106 km2 yr-1 (+2.07% dec-1) between 1998 and 2017. However, the total SIA in the two 10-year periods showed opposite trends, in which the total SIA increased by 0.026×106 km2 yr-1 between 1998 and 2007 and decreased by 0.0707×106 km2 yr-1 from 2008 to 2017. The first year ice area increased by 0.0059×106 km2 yr-1 and the melt duration decreased by 0.0908 days yr-1 between 1998 and 2017. The multiyear ice area increased by 0.0154×106 km2 yr-1 from 1998 to 2017, and the increase in the last 10 years was about 12.1% more than that in the first 10 years. On a spatial scale, the Entire Antarctica was divided into two areas, namely West Antarctica (WA) and East Antarctica (EA), according to the spatial change rate of sea ice concentration. The results showed that WA had clear warming in recent years; the total sea ice and multiyear ice areas showed a decreasing trend; multiyear ice area sharply decreased and reached the lowest value in 2017, and accounted for only about 10.1% of the 20-year average. However, the total SIA and multiyear ice area all showed an increased trend in EA, in which the multiyear ice area increased by 0.0478×106 km2 yr-1. Therefore, Antarctic sea ice presented an increasing trend, but there were different trends in WA and EA. Different sea ice parameters in WA and EA showed an opposite trend from 1998 to 2007. However, the total SIA, first year ice area, and multiyear ice area all showed a decreasing trend from 2008-2017, especially the total sea ice and first year ice, which changed almost the same in 2014-2017. In summary, although the Antarctic sea ice has increased slightly over time, it has shown a decreasing trend in recent years.

scholarly journals Aspects of intraseasonal variability of Antarctic sea ice in austral winter related to ENSO and SAM events

2017 ◽  
Vol 63 (241) ◽  
pp. 838-846 ◽  
Author(s):  
KENJI BABA ◽  
JAMES RENWICK
Keyword(s):  
Sea Ice ◽  
El Nino ◽  
Intraseasonal Variability ◽  
Southern Annular Mode ◽  
Wave Number ◽  
Austral Winter ◽  
Sea Ice Concentration ◽  
Annular Mode ◽  
Antarctic Sea Ice ◽  
Ice Concentration

ABSTRACTWe performed an Empirical Orthogonal Function (EOF) analysis to assess the intraseasonal variability of 5–60 day band-pass filtered Antarctic sea-ice concentration in austral winter using a 20-year daily dataset from 1995 to 2014. Zonal wave number 3 dominated in the Antarctic, especially so across the west Antarctic. Results showed the coexistence of stationary and propagating wave components. A spectral analysis of the first two principal components (PCs) showed a similar structure for periods up to 15 days but generally more power in PC1 at longer periods. Regression analysis upon atmospheric fields using the first two PCs of sea-ice concentration showed a coherent wave number 3 pattern. The spatial phase delay between the sea-ice and mean sea-level pressure patterns suggests that meridional flow and associated temperature advection are important for modulating the sea-ice field. EOF analyses carried out separately for El Niño, La Niña and neutral years, and for Southern Annular Mode positive, negative and neutral periods, suggest that the spatial patterns of wave number 3 shift between subsets. The results also indicate that El Niño-Southern Oscillation and Southern Annular Mode affect stationary wave interactions between sea-ice and atmospheric fields on intraseasonal timescales.

scholarly journals Assessing the Potential of Enhanced Resolution Gridded Passive Microwave Brightness Temperatures for Retrieval of Sea Ice Parameters

2020 ◽  
Vol 12 (16) ◽  
pp. 2552
Author(s):  
Walter N. Meier ◽  
J. Scott Stewart
Keyword(s):  
Sea Ice ◽  
Correlation Method ◽  
Open Water ◽  
Circulation Pattern ◽  
Time Of Day ◽  
Passive Microwave ◽  
Sea Ice Concentration ◽  
Microwave Brightness Temperature ◽  
Enhanced Resolution ◽  
Ice Concentration

A new enhanced resolution gridded passive microwave brightness temperature (TB) product is used to estimate sea ice concentration and motion. The effective resolution of the TBs is found to be roughly twice that of the standard 25 km resolution, though the gridded resolution of the distributed product is higher. Enhanced resolution sea ice concentrations from the Bootstrap algorithm show more detail in the sea ice, including relatively small open water regions within the ice pack. Sea ice motion estimates from the enhanced resolution TBs using a maximum cross-correlation method show a smoother motion circulation pattern; in comparison to buoys, RMS errors are 15–20% lower than motion estimates from the standard resolution fields and the magnitude of the bias is smaller as well. The enhanced resolution product includes other potentially beneficial characteristics, including twice-daily grids based on local time of day and a complete timeseries of data from nearly all multi-channel passive microwave radiometers since 1978. These enhanced resolution TBs are potential new source for long-term records of sea ice concentration, motion, age, melt, as well as salinity and ocean-atmosphere fluxes.

Sign in / Sign up

Export Citation Format

Share Document