Consistent Comparison of Remotely Sensed Sea Ice Concentration Products with ERA-Interim Reanalysis Data in Polar Regions

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.

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Dynamics and drivers of extreme seasons in the Arctic region

10.5194/egusphere-egu21-1521 ◽

2021 ◽

Author(s):

Katharina Hartmuth ◽

Lukas Papritz ◽

Maxi Boettcher ◽

Heini Wernli

Keyword(s):

Climate Change ◽

Sea Ice ◽

Surface Temperature ◽

Sea Surface ◽

The Arctic ◽

Polar Regions ◽

Sea Surface Temperatures ◽

Sea Ice Concentration ◽

Surface Temperatures ◽

Ice Concentration

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.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.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.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.

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Surface Freshwater Fluxes in the Arctic and Subarctic Seas during Contrasting Years of High and Low Summer Sea Ice Extent

Remote Sensing ◽

10.3390/rs13081570 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1570

Author(s):

Sarah B. Hall ◽

Bulusu Subrahmanyam ◽

Ebenezer S. Nyadjro ◽

Annette Samuelsen

Keyword(s):

Sea Ice ◽

Global Climate ◽

Sea Surface ◽

Sea Surface Salinity ◽

The Arctic ◽

Spatial And Temporal Variability ◽

Polar Regions ◽

Surface Salinity ◽

Sea Ice Concentration ◽

Sea Ice Extent

Freshwater (FW) flux between the Arctic Ocean and adjacent waterways, predominantly driven by wind and oceanic currents, influences halocline stability and annual sea ice variability which further impacts global circulation and climate. The Arctic recently experienced anomalous years of high and low sea ice extent in the summers of 2013/2014 and 2012/2016, respectively. Here we investigate the interannual variability of oceanic surface FW flux in relation to spatial and temporal variability in sea ice concentration (SIC), sea surface salinity (SSS), and sea surface temperature (SST), focusing on years with summer sea–ice extremes. Our analysis between 2010–2018 illustrate high parameter variability, especially within the Laptev, Kara, and Barents seas, as well as an overall decreasing trend of FW flux through the Fram Strait. We find that in 2012, a maximum average FW flux of 0.32 × 103 ms−1 in October passed over a large portion of the Northeast Atlantic Ocean at 53°N. This study highlights recent changes in the Arctic and Subarctic Seas and the importance of continued monitoring of key variables through remote sensing to understand the dynamics behind these ongoing changes. Observations of FW fluxes through major Arctic routes will be increasingly important as the polar regions become more susceptible to warming, with major impacts on global climate.

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

Remote Sensing ◽

10.3390/rs13061139 ◽

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.

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Ice Concentration Retrieval from the Analysis of Microwaves: A New Methodology Designed for the Copernicus Imaging Microwave Radiometer

Remote Sensing ◽

10.3390/rs12071060 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1060 ◽

Cited By ~ 1

Author(s):

Lise Kilic ◽

Catherine Prigent ◽

Filipe Aires ◽

Georg Heygster ◽

Victor Pellet ◽

...

Keyword(s):

Sea Ice ◽

Spatial Resolution ◽

Microwave Radiometer ◽

Optimal Estimation ◽

Arctic Sea Ice ◽

The Arctic ◽

Special Focus ◽

Polar Regions ◽

Sea Ice Concentration ◽

Ice Concentration

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.

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Changes of the Arctic marginal ice zone during the satellite era

The Cryosphere ◽

10.5194/tc-14-1971-2020 ◽

2020 ◽

Vol 14 (6) ◽

pp. 1971-1984 ◽

Cited By ~ 1

Author(s):

Rebecca J. Rolph ◽

Daniel L. Feltham ◽

David Schröder

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

Satellite Observations ◽

The Arctic ◽

Upper And Lower Bounds ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Ice Concentration ◽

Marginal Ice Zone ◽

Definition Of

Abstract. Many studies have shown a decrease in Arctic sea ice extent. It does not logically follow, however, that the extent of the marginal ice zone (MIZ), here defined as the area of the ocean with ice concentrations from 15 % to 80 %, is also changing. Changes in the MIZ extent has implications for the level of atmospheric and ocean heat and gas exchange in the area of partially ice-covered ocean and for the extent of habitat for organisms that rely on the MIZ, from primary producers like sea ice algae to seals and birds. Here, we present, for the first time, an analysis of satellite observations of pan-Arctic averaged MIZ extent. We find no trend in the MIZ extent over the last 40 years from observations. Our results indicate that the constancy of the MIZ extent is the result of an observed increase in width of the MIZ being compensated for by a decrease in the perimeter of the MIZ as it moves further north. We present simulations from a coupled sea ice–ocean mixed layer model using a prognostic floe size distribution, which we find is consistent with, but poorly constrained by, existing satellite observations of pan-Arctic MIZ extent. We provide seasonal upper and lower bounds on MIZ extent based on the four satellite-derived sea ice concentration datasets used. We find a large and significant increase (>50 %) in the August and September MIZ fraction (MIZ extent divided by sea ice extent) for the Bootstrap and OSI-450 observational datasets, which can be attributed to the reduction in total sea ice extent. Given the results of this study, we suggest that references to “rapid changes” in the MIZ should remain cautious and provide a specific and clear definition of both the MIZ itself and also the property of the MIZ that is changing.

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Comparison of sea ice concentrations from ASI, BT and NT2 algorithms with ERA-Interim dataset in the Arctic and Antarctic regions

10.5194/egusphere-egu2020-4531 ◽

2020 ◽

Author(s):

Shuang Liang ◽

Jiangyuan Zeng ◽

Zhen Li

Keyword(s):

Sea Ice ◽

Temporal Distribution ◽

Arctic Region ◽

The Arctic ◽

Polar Regions ◽

Significant Discrepancy ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Long Time ◽

Mean Square Difference

Evaluating the performance and consistency of passive microwave (PM) sea ice concentration (SIC) products derived from different algorithms is critical since a good knowledge of the quality of the satellite SIC products is essential for their application and improvement. To comprehensively evaluate the performance of satellite SIC in long time series and the whole polar regions (both Arctic and Antarctic), in the study we examined the spatial and temporal distribution of the discrepancy between four PM satellite SIC products with the ERA-Interim sea ice fraction dataset (ERA SIC) during the period of 2015-2018. The four PM SIC products include the DMSP SSMIS with Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) algorithm (SSMIS/ASI), the GCOM-W AMSR2 with NASA Bootstrap (BT) algorithm (AMSR2/BT), the Chinese Feng Yun-3B with enhanced NASA Team (NT2) sea ice algorithm (FY3B/NT2), and the Chinese Feng Yun-3C with NT2 (FY3C/NT2) at a spatial resolution of 12.5 km.The results show the spatial patterns of PM SIC products are generally in good agreement with ERA SIC. The comparison of monthly and annual SIC shows that the largest bias and root mean square difference (RMSD) for the PM SIC products mainly occur in summer and the marginal ice zone, indicating that there are still many uncertainties in PM SIC products in such period and region. Meanwhile, the daily sea ice extent (SIE) and sea ice area (SIA) derived from the four PM SIC products can generally well reflect the variation trend of SIE and SIA in Arctic and Antarctic. The largest bias of SIE and SIA are above 4&#215;106 km2 when the sea ice reaches the maximum and minimum value, and the daily bias of SIE and SIA vary seasonally and regionally, which is mainly concentrated from June to October in Arctic. In general, among the four PM SIC products, the SSMIS/ASI product performs the best compared with ERA SIC though it usually underestimates SIC with a negative bias. The FY3B/NT2 and FY3C/NT2 products show more significant discrepancy with higher RMSD and bias in Arctic and Antarctic compared with the SSMIS/ASI and AMSR2/BT. The AMSR2/BT product performs much better in Antarctic than in Arctic and it always overestimates ERA SIC with a positive bias. The consistency of the four PM products concerning ERA SIC in the Antarctic region is generally superior to that in Arctic region.

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September Arctic Sea Ice minimum prediction – a new skillful statistical approach

10.5194/esd-2018-61 ◽

2018 ◽

Author(s):

Monica Ionita ◽

Klaus Grosfeld ◽

Patrick Scholz ◽

Renate Treffeisen ◽

Gerrit Lohmann

Keyword(s):

Sea Ice ◽

Statistical Model ◽

Climate Models ◽

Global Climate ◽

Arctic Sea Ice ◽

The Arctic ◽

Polar Regions ◽

Sea Ice Extent ◽

Important Indicator ◽

Predictive Skill

Abstract. Sea ice in both Polar Regions is an important indicator for the expression of global climate change and its polar amplification. Consequently, a broad interest exists on sea ice coverage, variability and long term change. However, its predictability is complex and it depends on various atmospheric and oceanic parameters. In order to provide insights into the potential development of a monthly/seasonal signal of sea ice evolution, we developed a robust statistical model based on oceanic and different atmospheric variables to calculate an estimate of the September sea ice extent (SSIE) on monthly time scale. Although previous statistical attempts of monthly/seasonal SSIE forecasts show a relatively reduced skill, when the trend is removed, we show here that the September sea ice extent has a high predictive skill, up to 4 months ahead, based on previous months' atmospheric and oceanic conditions. Our statistical model skillfully captures the interannual variability of the SSIE and could provide a valuable tool for identifying relevant regions and atmospheric parameters that are important for the sea ice development in the Arctic and for detecting sensitive and critical regions in global coupled climate models with focus on sea ice formation.

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Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F

The Cryosphere ◽

10.5194/tc-15-3207-2021 ◽

2021 ◽

Vol 15 (7) ◽

pp. 3207-3227

Author(s):

Timothy Williams ◽

Anton Korosov ◽

Pierre Rampal ◽

Einar Ólason

Keyword(s):

Sea Ice ◽

Initial Conditions ◽

Thickness Distribution ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Ice Concentration ◽

Forecasting System ◽

Ice Model

Abstract. The neXtSIM-F (neXtSIM forecast) forecasting system consists of a stand-alone sea ice model, neXtSIM (neXt-generation Sea Ice Model), forced by the TOPAZ ocean forecast and the ECMWF atmospheric forecast, combined with daily data assimilation of sea ice concentration. It uses the novel brittle Bingham–Maxwell (BBM) sea ice rheology, making it the first forecast based on a continuum model not to use the viscous–plastic (VP) rheology. It was tested in the Arctic for the time period November 2018–June 2020 and was found to perform well, although there are some shortcomings. Despite drift not being assimilated in our system, the sea ice drift is good throughout the year, being relatively unbiased, even for longer lead times like 5 d. The RMSE in speed and the total RMSE are also good for the first 3 or so days, although they both increase steadily with lead time. The thickness distribution is relatively good, although there are some regions that experience excessive thickening with negative implications for the summertime sea ice extent, particularly in the Greenland Sea. The neXtSIM-F forecasting system assimilates OSI SAF sea ice concentration products (both SSMIS and AMSR2) by modifying the initial conditions daily and adding a compensating heat flux to prevent removed ice growing back too quickly. The assimilation greatly improves the sea ice extent for the forecast duration.

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Hemispheric sea ice extent dynamics as observed from MSMR

MAUSAM ◽

10.54302/mausam.v60i3.1101 ◽

2021 ◽

Vol 60 (3) ◽

pp. 295-308

Author(s):

NILAY SHARMA ◽

M. K. DASH ◽

P. C. PANDEY ◽

N. K. VYAS

Keyword(s):

Sea Ice ◽

Global Climate ◽

Ice Cover ◽

The Arctic ◽

Ice Formation ◽

Spatial And Temporal Variation ◽

Polar Regions ◽

Sea Ice Extent ◽

Year 2000 ◽

Formation Phase

The ice covered regions of the polar seas influence the global climate in several ways. Any perturbation in the polar oceanic cryosphere affects the local weather and the global climate through modulation of the radiative forcing, the bottom water formation and the mass & the momentum transfer between Atmosphere-Cryosphere-Ocean System. The cold, harsh and inhospitable conditions in the polar regions prohibit the collection of extensive in situ data with sufficient spatial and temporal variation. However, satellite remote sensing is an ideal technique for studying the areas like the polar regions with synoptic and repetitive coverage. This paper discusses the analysis of the data obtained over the polar oceanic regions during the period June 1999 – September 2001 through the use of Multi-channel Scanning Microwave Radiometer (MSMR), onboard India’s first oceanographic satellite Oceansat-1. The MSMR observation shows that all the sectors in the Antarctic behave differently to the melting and formation of the sea ice. Certain peculiar features like the increase in sea ice extent during the melt season of 1999 – 2000 in the Indian Ocean sector, 15 – 20% decrease in the sea ice extent in the western Pacific sector during the ice formation period for the year 2000, melting spell within the formation phase of sea ice in B & A sector in the year 2000 were observed. On the other hand the northern polar sea ice extent is seen to be more dominated by the land characteristics. The ice formation in Kara and the Barent Sea sector is dominated by the ocean currents, where as the ice covered in the Japan and the Okhotsk Sea is dominated by the land processes. The sea ice extent in the Arctic Ocean show fluctuations from July to October and remain almost steady over other months. The global sea ice cover shows a formation phase from March to June and melting phase from November to February. In other months, i.e., from July – October the global sea ice cover is dominated by the hemispheric asymmetry of the ice growth and retreat.

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Monitoring and Assessing Arctic Climate Change

Oxford Research Encyclopedia of Climate Science ◽

10.1093/acrefore/9780190228620.013.659 ◽

2020 ◽

Author(s):

Lars-Otto Reiersen ◽

Robert W. Corell

Keyword(s):

Climate Change ◽

Sea Ice ◽

Global Climate ◽

Arctic Region ◽

The Arctic ◽

Sea Ice Extent ◽

The North ◽

Meteorological Observations ◽

Arctic Climate Change ◽

Global Systems

This overview of climate observation, monitoring, and research for the Arctic region outlines the key elements essential to an enhanced understanding of the unprecedented climate change in the region and its global influences. The first recorded observation of sea ice extent around Svalbard date back to the whaling activities around 1600. Over the following 300 years there are periodic and inadequate observations of climate and sea ice from explorers seeking a northern sea route for sailing to Asia or reaching the North Pole. Around 1900 there were few fixed meteorological stations in the circumpolar North. During the Second World War and the following Cold War, the observation network increased significantly due to military interest. Since the 1970s the use of satellites has improved the climate and meteorological observations of Arctic areas, and advancements in marine observations (beneath the sea surface and within oceanic sediments) have contributed to a much improved network of climate and meteorological variables. Climate change in the Arctic and its possible effects within the Arctic and on global climate such as extreme weather and sea level rise were first reported in the ACIA 2005 report. Since then there has been a lot of climate-related assessments based on data from the Arctic and ongoing processes within the Arctic that are linked to global systems.

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