scholarly journals The polar sea ice topography reconstruction system

2020 ◽  
Vol 61 (82) ◽  
pp. 127-138
Author(s):  
Scott Sorensen ◽  
Vinit Veerendraveer ◽  
Wayne Treible ◽  
Andrew R. Mahoney ◽  
Chandra Kambhamettu

AbstractThe Polar Sea Ice Topography REconstruction System, or PSITRES, is a 3D camera system designed to continuously monitor an area of ice and water adjacent to an ice-going vessel. Camera systems aboard ships in the polar regions are common; however, the application of computer vision techniques to extract high-level information from the imagery is infrequent. Many of the existing systems are built for human involvement throughout the process and lack automation necessary for round the clock use. The PSITRES was designed with computer vision in mind. It can capture images continuously for days on end with limited oversight. We have applied the system in different ice observing scenarios. The PSITRES was deployed on three research expeditions in the Arctic and Subarctic, and we present applications in measuring ice concentration, melt pond fraction and presence of algae. Systems like PSITRES and the computer vision algorithms applied represent steps toward automatically observing, evaluating and analyzing ice and the environment around ships in ice-covered waters.

2020 ◽  
Vol 12 (7) ◽  
pp. 1060 ◽  
Author(s):  
Lise Kilic ◽  
Catherine Prigent ◽  
Filipe Aires ◽  
Georg Heygster ◽  
Victor Pellet ◽  
...  

Over the last 25 years, the Arctic sea ice has seen its extent decline dramatically. Passive microwave observations, with their ability to penetrate clouds and their independency to sunlight, have been used to provide sea ice concentration (SIC) measurements since the 1970s. The Copernicus Imaging Microwave Radiometer (CIMR) is a high priority candidate mission within the European Copernicus Expansion program, with a special focus on the observation of the polar regions. It will observe at 6.9 and 10.65 GHz with 15 km spatial resolution, and at 18.7 and 36.5 GHz with 5 km spatial resolution. SIC algorithms are based on empirical methods, using the difference in radiometric signatures between the ocean and sea ice. Up to now, the existing algorithms have been limited in the number of channels they use. In this study, we proposed a new SIC algorithm called Ice Concentration REtrieval from the Analysis of Microwaves (IceCREAM). It can accommodate a large range of channels, and it is based on the optimal estimation. Linear relationships between the satellite measurements and the SIC are derived from the Round Robin Data Package of the sea ice Climate Change Initiative. The 6 and 10 GHz channels are very sensitive to the sea ice presence, whereas the 18 and 36 GHz channels have a better spatial resolution. A data fusion method is proposed to combine these two estimations. Therefore, IceCREAM will provide SIC estimates with the good accuracy of the 6+10GHz combination, and the high spatial resolution of the 18+36GHz combination.


2021 ◽  
Author(s):  
Katharina Hartmuth ◽  
Lukas Papritz ◽  
Maxi Boettcher ◽  
Heini Wernli

<p>Single extreme weather events such as intense storms or blocks can have a major impact on polar surface temperatures, the formation and melting rates of sea-ice, and, thus, on minimum and maximum sea-ice extent within a particular year. Anomalous weather conditions on the time scale of an entire season, for example resulting from an unusual sequence of storms, can affect the polar energy budget and sea-ice coverage even more. Here, we introduce the concept of an extreme season in a distinct region using an EOF analysis in the phase space spanned by anomalies of a set of surface parameters (surface temperature, precipitation, surface solar and thermal radiation and surface heat fluxes). To focus on dynamical instead of climate change aspects, we define anomalies as departures of the seasonal mean from a transient climatology. The goal of this work is to study the dynamical processes leading to such anomalous seasons in the polar regions, which have not yet been analysed. Specifically, we focus here on a detailed analysis of Arctic extreme seasons and their underlying atmospheric dynamics in the ERA5 reanalysis data set.</p><p>We find that in regions covered predominantly by sea ice, extreme seasons are mostly determined by anomalies of atmospheric dynamical features such as cyclones and blocking. In contrast, in regions including large areas of open water the formation of extreme seasons can also be partially due to preconditioning during previous seasons, leading to strong anomalies in the sea ice concentration and/or sea surface temperatures at the beginning of the extreme season.</p><p>Two particular extreme season case studies in the Kara-Barents Seas are discussed in more detail. In this region, the winter of 2011/12 shows the largest positive departure of surface temperature from the background warming trend together with a negative anomaly in the sea ice concentration. An analysis of the synoptic situation shows that the strongly reduced frequency of cold air outbreaks compared to climatology combined with several blocking events and the frequent occurrence of cyclones transporting warm air into the region favored the continuous anomalies of both parameters. In contrast, the winter of 2016/17, which shows a positive precipitation anomaly and negative anomaly in the surface energy balance, was favored by a strong surface preconditioning. An extremely warm summer and autumn in 2016 caused strongly reduced sea ice concentrations and increased sea surface temperatures in the Kara-Barents Seas at the beginning of the winter, favoring increased air-sea fluxes and precipitation during the following months.</p><p>Our results reveal a high degree of variability of the processes involved in the formation of extreme seasons in the Arctic. Quantifying and understanding these processes will also be important when considering climate change effects in polar regions and the ability of climate models in reproducing extreme seasons in the Arctic and Antarctica.</p>


2020 ◽  
Vol 12 (18) ◽  
pp. 2880
Author(s):  
Shuang Liang ◽  
Jiangyuan Zeng ◽  
Zhen Li ◽  
Dejing Qiao ◽  
Ping Zhang ◽  
...  

Sea ice concentration (SIC) plays a significant role in climate change research and ship’s navigation in polar regions. Satellite-based SIC products have become increasingly abundant in recent years; however, the uncertainty of these products still exists and needs to be further investigated. To comprehensively evaluate the consistency of the SIC derived from different SIC algorithms in long time series and the whole polar regions, we compared four passive microwave (PM) satellite SIC products with the ERA-Interim sea ice fraction dataset during the period of 2015–2018. The PM SIC products include the SSMIS/ASI, AMSR2/BT, the Chinese FY3B/NT2, and FY3C/NT2. The results show that the remotely sensed SIC products derived from different SIC algorithms are generally in good consistency. The spatial and temporal distribution of discrepancy among satellite SIC products for both Arctic and Antarctic regions are also observed. The most noticeable difference for all the four SIC products mostly occurs in summer and at the marginal ice zone, indicating that large uncertainties exist in satellite SIC products in such period and areas. The SSMIS/ASI and AMSR2/BT show relatively better consistency with ERA-Interim in the Arctic and Antarctic, respectively, but they exhibit opposite bias (dry/wet) relative to the ERA-Interim data. The sea ice extent (SIE) and sea ice area (SIA) derived from PM and ERA-Interim SIC were also compared. It is found that the difference of PM SIE and SIA varies seasonally, which is in line with that of PM SIC, and the discrepancy between PM and ERA-Interim data is larger in Arctic than in Antarctic. We also noticed that different algorithms have different performances in different regions and periods; therefore, the hybrid of multiple algorithms is a promising way to improve the accuracy of SIC retrievals. It is expected that our findings can contribute to improving the satellite SIC algorithms and thus promote the application of these useful products in global climate change studies.


2021 ◽  
Author(s):  
Charel Wohl ◽  
Anna E. Jones ◽  
William T. Sturges ◽  
Philip D. Nightingale ◽  
Brent Else ◽  
...  

Abstract. The marginal sea ice zone has been identified as a source of different climate active gases to the atmosphere due to its unique biogeochemistry. However, it remains highly undersampled and the impact of changes in sea ice concentration on the distributions of these gases is poorly understood. To address this, we present measurements of dissolved methanol, acetone, acetaldehyde, dimethyl sulfide and isoprene in the sea ice zone of the Canadian Arctic from the surface down to 60 m. The measurements were made using a Segmented Flow Coil Equilibrator coupled to a Proton Transfer Reaction Mass Spectrometer. These gases varied in concentrations with depth, with the highest concentrations generally observed near the surface. Underway (3–4 m) measurements showed broadly higher concentrations in partial sea ice cover compared to ice-free waters. The large number of depth profiles at different sea ice coverages enables proposition of the likely dominant production processes of these compounds in this area. Methanol concentrations appear to be controlled by specific biological consumption processes. Acetone and acetaldehyde concentrations are influenced by the penetration depth of light and the mixed layer depth, implying dominant photochemical sources in this area. Dimethyl sulfide and isoprene both display higher surface concentrations in partial sea ice coverage compared to ice-free waters due to ice edge blooms. Dimethyl sulfide concentrations sometimes display a subsurface maximum in ice -free conditions, while isoprene displays more reliably a subsurface maximum. Surface gas concentrations were used to estimate their air – sea fluxes. Despite obvious in situ production, we estimate that the sea ice zone is absorbing methanol and acetone from the atmosphere. In contrast, DMS and isoprene are consistently emitted from the ocean, with marked episodes of high emissions during ice-free conditions, suggesting that these gases are produced in ice-covered areas and emitted once the ice has melted. Our measurements show that the seawater concentrations and air-sea fluxes of these gases are clearly impacted by sea ice concentration. These novel measurements and insights will allow us to better constrain the cycling of these gases in the polar regions and their effect on the oxidative capacity and aerosol budget in the Arctic atmosphere.


2011 ◽  
Vol 52 (57) ◽  
pp. 192-196 ◽  
Author(s):  
D.K. Perovich ◽  
K.F. Jones ◽  
B. Light ◽  
H. Eicken ◽  
T. Markus ◽  
...  

AbstractThe summer extent of the Arctic sea-ice cover has decreased in recent decades and there have been alterations in the timing and duration of the summer melt season. These changes in ice conditions have affected the partitioning of solar radiation in the Arctic atmosphere–ice–ocean system. the impact of sea-ice changes on solar partitioning is examined on a pan-Arctic scale using a 25 km × 25 km Equal-Area Scalable Earth Grid for the years 1979–2007. Daily values of incident solar irradiance are obtained from NCEP reanalysis products adjusted by ERA-40, and ice concentrations are determined from passive microwave satellite data. the albedo of the ice is parameterized by a five-stage process that includes dry snow, melting snow, melt pond formation, melt pond evolution, and freeze-up. the timing of these stages is governed by the onset dates of summer melt and fall freeze-up, which are determined from satellite observations. Trends of solar heat input to the ice were mixed, with increases due to longer melt seasons and decreases due to reduced ice concentration. Results indicate a general trend of increasing solar heat input to the Arctic ice–ocean system due to declines in albedo induced by decreases in ice concentration and longer melt seasons. the evolution of sea-ice albedo, and hence the total solar heating of the ice–ocean system, is more sensitive to the date of melt onset than the date of fall freeze-up. the largest increases in total annual solar heat input from 1979 to 2007, averaging as much as 4%a–1, occurred in the Chukchi Sea region. the contribution of solar heat to the ocean is increasing faster than the contribution to the ice due to the loss of sea ice.


2021 ◽  
Vol 13 (19) ◽  
pp. 3882
Author(s):  
Jiechen Zhao ◽  
Yining Yu ◽  
Jingjing Cheng ◽  
Honglin Guo ◽  
Chunhua Li ◽  
...  

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.


2008 ◽  
Vol 25 (2) ◽  
pp. 327-334 ◽  
Author(s):  
Jun Inoue ◽  
Judith A. Curry ◽  
James A. Maslanik

Abstract Continuous observation of sea ice using a small robotic aircraft called the Aerosonde was made over the Arctic Ocean from Barrow, Alaska, on 20–21 July 2003. Over a region located 350 km off the coast of Barrow, images obtained from the aircraft were used to characterize the sea ice and to determine the fraction of melt ponds on both multiyear and first-year ice. Analysis of the data indicates that melt-pond fraction increased northward from 20% to 30% as the ice fraction increased. However, the fraction of ponded ice was over 30% in the multiyear ice zone while it was about 25% in the first-year ice zone. A comparison with a satellite microwave product showed that the ice concentration derived from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) has a negative bias of 7% due to melt ponds. These analyses demonstrate the utility of recent advances in unmanned aerial vehicle (UAV) technology for monitoring and interpreting the spatial variations in the sea ice with melt ponds.


2020 ◽  
Author(s):  
Sanggyun Lee ◽  
Julienne Stroeve ◽  
Michel Tsamados

&lt;p&gt;&amp;#160;Melt ponds are a dominant feature on the Arctic sea ice surface in summer, occupying up to about 50 &amp;#8211; 60% of the sea ice surface during advanced melt. Melt ponds normally begin to form around mid-May in the marginal ice zone and expand northwards as the summer melt season progresses. Once melt ponds emerge, the scattering characteristics of the ice surface changes, dramatically lowering the sea ice albedo. Since 96% of the total annual solar heat into the ocean through sea ice occurs between May and August, the presence of melt ponds plays a significant role in this transfer of solar heat, influencing not only the sea ice energy balance, but also the amount of light available under the sea ice and ocean primary productivity. Given the importance melt ponds play in the coupled Arctic climate-ecosystem, mapping and quantification of melt pond variability on a Pan-Arctic basin scale are needed. Satellite-based observations are the only way to map melt ponds and albedo changes on a pan-Arctic scale. R&amp;#246;sel et al. (2012) utilized a MODIS 8-day average product to map melt ponds on a pan-Arctic scale and over several years. In another approach, melt pond fraction and surface albedo were retrieved based on the physical and optical characteristics of sea ice and melt ponds without a priori information using MERIS.Here, we propose a novel machine learning-based methodology to map Arctic melt ponds from MODIS 500m resolution data. We provide a merging procedure to create the first pan-Arctic melt pond product spanning a 20-year period at a weekly temporal resolution. Specifically, we use MODIS data together with machine learning, including multi-layer neural network and logistic regression to test our ability to map melt ponds from the start to the end of the melt season. Since sea ice reflectance is strongly dependent on the viewing and solar geometry (i.e. sensor and solar zenith and azimuth angles), we attempt to minimize this dependence by using normalized band ratios in the machine learning algorithms. Each melt pond retrieval algorithm is different and validation ways are different as well producing somewhat dissimilar melt pond results. In this study, we inter-compare melt ponds products from different institutes, including university of Hamburg, university of Bremen, and university college London. The melt pond maps are compared with melt onset and freeze-up dates data and sea ice concentration. The melt pond maps are evaluated by melt pond fraction statistics from high resolution satellite (MEDEA) images that have not been used for the evaluation in melt pond products.&amp;#160;&lt;/p&gt;


2021 ◽  
Vol 13 (11) ◽  
pp. 2174
Author(s):  
Lijian Shi ◽  
Sen Liu ◽  
Yingni Shi ◽  
Xue Ao ◽  
Bin Zou ◽  
...  

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.


2021 ◽  
Vol 13 (6) ◽  
pp. 1139
Author(s):  
David Llaveria ◽  
Juan Francesc Munoz-Martin ◽  
Christoph Herbert ◽  
Miriam Pablos ◽  
Hyuk Park ◽  
...  

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.


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