VIIRS Data and Data Access at the NASA National Snow and Ice Data Center Distributed Active Archive Center

[1] We examine the spatial trends in Arctic sea ice drift speed from satellite data and the role of wind forcing for the winter months of October through May. Between 1992 and 2009, the spatially averaged trend in drift speed within the Arctic Basin is 10.6% ± 0.9%/decade, and ranges between −4% and 16%/decade depending on the location. The mean trend is dominated by the second half of the period. In fact, for the five years after a clear break point in March 2004, the average trend increased to 46% ± 5%/decade. Over the 1992–2009 period, averaged trends of wind speed from four atmospheric reanalyses are only 1% to 2%/decade. Regionally, positive trends in wind speed (of up to 9%/decade) are seen over a large fraction of the Central Arctic, where the trends in drift speeds are highest. Spatial correlations between the basin-wide trends in wind and drift speeds are moderate (between 0.40 and 0.52). Our results suggest that changes in wind speed explain a fraction of the observed increase in drift speeds in the Central Arctic but not over the entire basin. In other regions thinning of the ice cover is a more likely cause of the increase in ice drift speed.

Trends in Arctic sea ice drift and role of wind forcing: 1992-2009

Abstract. We report on results of an intercomparison of 10 global sea-ice
concentration (SIC) data products at 12.5 to 50.0 km grid resolution from
satellite passive microwave (PMW) observations. For this we use SIC
estimated from >350 images acquired in the visible–near-infrared frequency range by the joint National Aeronautics and Space
Administration (NASA) and United States Geological Survey (USGS) Landsat sensor
during the years 2003–2011 and 2013–2015. Conditions covered are late winter/early spring in the Northern Hemisphere and from late winter through fall
freeze-up in the Southern Hemisphere. Among the products investigated are
the four products of the European Organisation for the Exploitation of
Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application
Facility (OSI SAF) and European Space Agency (ESA) Climate Change Initiative
(CCI) algorithms SICCI-2 and OSI-450. We stress the importance to consider
intercomparison results across the entire SIC range instead of focusing on
overall mean differences and to take into account known biases in PMW SIC
products, e.g., for thin ice. We find superior linear agreement between PMW
SIC and Landsat SIC for the 25 and the 50 km SICCI-2 products in both
hemispheres. We discuss quantitatively various uncertainty sources of the
evaluation carried out. First, depending on the number of mixed ocean–ice
Landsat pixels classified erroneously as ice only, our Landsat SIC is found
to be biased high. This applies to some of our Southern Hemisphere data,
promotes an overly large fraction of Landsat SIC underestimation by PMW SIC
products, and renders PMW SIC products overestimating Landsat SIC
particularly problematic. Secondly, our main results are based on SIC data
truncated to the range 0 % to 100 %. We demonstrate using
non-truncated SIC values, where possible, can considerably improve linear
agreement between PMW and Landsat SIC. Thirdly, we investigate the impact of
filters often used to clean up the final products from spurious SIC over
open water due to weather effects and along coastlines due to land
spillover. Benefiting from the possibility to switch on or off certain
filters in the SICCI-2 and OSI-450 products, we quantify the impact land
spillover filtering can have on evaluation results as shown in this paper.


/pdf/satellite-passive-microwave-sea-ice-concentration-data-set-laww63da.pdf

Satellite passive microwave sea-ice concentration data set intercomparison using Landsat data

[i] Airborne remote sensing observations in the vicinity of the Surface Heat Budget of the Arctic Ocean (SHEBA) camp in the Beaufort and Chukchi Seas during May 1998 are used to characterize leads. This characterization was accomplished using observations from a passive microwave radiometer, video camera, and infrared pyrometer. An automated methodology is presented to determine lead fraction, width, and orientation. Refrozen lead fraction decreased in the latter half of May, due in part to local convergence in the ice pack. Airborne passive microwave and video observations are combined to produce a lead width distribution for the SHEBA region. Narrow leads are most prevalent, with lead fraction decreasing exponentially with increasing lead width. Lead orientation showed a preference in a northwesterly direction, which is consistent with prevailing wind patterns and ocean circulation. The lead width distribution and lead orientation observed by the airborne sensors are similar to the characteristics derived from an advanced very high resolution radiometer (AVHRR) albedo image encompassing the Beaufort/Chukchi Seas during the same time period. The AVHRR albedos in the SHEBA vicinity are also comparable to the larger region. These similarities suggest that the area of study used by SHEBA investigators was representative of the Western Arctic during this time period for these surface characteristics.

Characterization of springtime leads in the Beaufort/Chukchi Seas from airborne and satellite observations during FIRE/SHEBA : The surface heat budget of arctic ocen (SHEBA)

A ship navigation information service system for the Arctic Northeast Passage using 3D GIS based on big Earth data

Spring leads in the Beaufort Sea and its interannual trend using Terra/MODIS thermal imagery

The Microwave Radiation Imager (MWRI) sensors aboard on the Chinese FengYun-3 (FY-3) series satellites have a great potential for long-term study of sea ice distribution. This study corrected the newly released FY-3D MWRI brightness temperature (TB) data with the Advanced Microwave Scanning Radiometer 2 (AMSR2) TB data and used an Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) dynamic tie points algorithm to derive the sea ice concentration (SIC) from these corrected MWRI TB data. We assessed the accuracy of our MWRI-ASI SIC product by comparing with the published SIC products at different spatial resolution and by validating with the Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-1 data. We find that MWRI-ASI SIC has the smallest difference with AMSR2-ASI SIC with the mean absolute difference (MAD) of 6.8%, and the differences mainly occur at the marginal ice zone regions. The MWRI-ASI displays the highest difference with Sea Ice Index and OSI-430-b at 25 km with the mean MAD of 11.8%. MADs between MWRI-ASI SIC and SIC products from other sensors are below 10% except for late June to early October, when those are higher. Compared with the MODIS SIC, MWRI-ASI SIC outperforms other SIC products at the same resolution, with the mean bias of -2.1% and mean RMSD of 13.5%. Although MWRI-ASI tends to under-estimate high SIC, it can capture details of large leads, ice edge, and fragmented ice area. Our MWRI-ASI SIC product at 12.5 km is promising to integrate into long-term sea ice records.

Sea Ice Concentration Derived From FY-3D MWRI and Its Accuracy Assessment

A daily sea ice concentration (SIC) product in the Arctic, derived from the brightness temperature (TB) data of the Microwave Radiation Imager (MWRI) sensor aboard on the FY-3D satellite, is descri...

https://www.tandfonline.com/doi/pdf/10.1080/20964471.2020.1865623

Daily sea ice concentration product based on brightness temperature data of FY-3D MWRI in the Arctic

. 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.

/pdf/inter-comparisons-among-passive-microwave-sea-ice-3d580ku6im.pdf

Inter-comparisons among passive microwave sea ice concentration products from fy-3d mwri and amsr2

Sea ice plays a significant role in global climate change. Marginal ice zone (MIZ) is defined as the transition zone between open ocean and pack ice where intensive air–ice–ocean–wave​ interactions between the ocean and the atmosphere occur. This definition of MIZ is rather vague, which affects its mapping. Previous data-driven methods extracted MIZ from single source data and conveniently used a single classification threshold. In this study, we propose to apply a concept-driven top down extraction method. We use a fuzzy inference system (FIS) to integrate multiple ice properties based upon the MIZ definition, and use membership functions to quantify the uncertainty of the threshold for several ice characteristics. This concept-driven method is applied to mapping the Antarctic MIZ between 2010 and 2021. Results show that a FIS successfully combines the ice concentration and ice thickness, while it allowed us to map the MIZ as objects with vague boundaries, thus well-presenting its indeterminate nature in space. 

/pdf/concept-driven-extraction-of-the-antarctic-marginal-sea-ice-2gjhx2cm.pdf

Ying Chen

Papers

Spring leads in the Beaufort Sea and its interannual trend using Terra/MODIS thermal imagery

Sea Ice Concentration Derived From FY-3D MWRI and Its Accuracy Assessment

Daily sea ice concentration product based on brightness temperature data of FY-3D MWRI in the Arctic

Inter-comparisons among passive microwave sea ice concentration products from fy-3d mwri and amsr2

Concept-driven extraction of the Antarctic marginal sea ice zone from remote sensing image time series