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 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 Changes of the Arctic marginal ice zone during the satellite era

2020 ◽  
Vol 14 (6) ◽  
pp. 1971-1984 ◽  
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.

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

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.

scholarly journals Antarctic summer sea ice concentration and extent: comparison of ODEN 2006 ship observations, satellite passive microwave and NIC sea ice charts

2008 ◽  
Vol 2 (4) ◽  
pp. 623-647 ◽  
Author(s):  
B. Ozsoy-Cicek ◽  
H. Xie ◽  
S. F. Ackley ◽  
K. Ye
Keyword(s):  
Sea Ice ◽  
Linear Trend ◽  
Passive Microwave ◽  
The Arctic ◽  
Poor Correlation ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Ice Edge

Abstract. Antarctic sea ice cover has shown a slight increase in overall observed ice extent as derived from satellite mapping from 1979 to 2008, contrary to the decline observed in the Arctic regions. Spatial and temporal variations of the Antarctic sea ice however remain a significant problem to monitor and understand, primarily due to the vastness and remoteness of the region. While satellite remote sensing has provided and has great future potential to monitor the variations and changes of sea ice, uncertainties remain unresolved. In this study, the National Ice Center (NIC) ice edge and the AMSR-E (Advanced Microwave Scanning Radiometer – Earth Observing System) ice extent are examined, while the ASPeCt (Antarctic Sea Ice Process and Climate) ship observations from the Oden expedition in December 2006 are used as ground truth to verify the two products during Antarctic summer. While there is a general linear trend between ASPeCt and AMSR-E ice concentration estimates, there is poor correlation (R2=0.41) and AMSR-E tends to underestimate the low ice concentrations. We also found that the NIC sea ice edge agrees well with ship observations, while the AMSR-E shows the ice edge further south, consistent with its poorer detection of low ice concentrations. The northward extent of the ice edge at the time of observation (NIC) had mean values varying from 38 km to 102 km greater on different days for the area as compared with the AMSR-E sea ice extent. For the circumpolar area as a whole in the December period examined, AMSR-E therefore underestimates the area inside the ice edge at this time by up to 14% or, 1.5 million km2 less area, compared to the NIC ice charts. These differences alone can account for more than half of the purported sea ice loss between the pre 1960s and the satellite era suggested earlier from comparative analysis of whale catch data with satellite derived data. Preliminary comparison of satellite scatterometer data suggests better resolution of low concentrations than passive microwave, and therefore better fidelity with ship observations and NIC charts of the area inside the ice edge during Antarctic summer.

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

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

2020 ◽  
Vol 12 (18) ◽  
pp. 2880
Author(s):  
Shuang Liang ◽  
Jiangyuan Zeng ◽  
Zhen Li ◽  
Dejing Qiao ◽  
Ping Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
Sea Ice ◽  
Global Climate ◽  
Remotely Sensed ◽  
The Arctic ◽  
Polar Regions ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Climate Change Research ◽  
Ice Concentration

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.

scholarly journals Perspectives on future sea ice and navigability in the Arctic

2021 ◽  
Author(s):  
Jinlei Chen ◽  
Shichang Kang ◽  
Wentao Du ◽  
Junming Guo ◽  
Min Xu ◽  
...  
Keyword(s):  
Sea Ice ◽  
Coupled Model ◽  
Open Water ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Northwest Passage ◽  
Ice Concentration ◽  
Transportation Accessibility

Abstract. The retreat of sea ice is very significant in the Arctic under global warming. It is projected to continue and have great impacts on navigation. In this investigation, decadal changes in sea ice parameters were evaluated by multimodel from the Coupled Model Intercomparison Project Phase 6, and Arctic navigability was assessed under two shared socioeconomic pathways (SSPs) and two vessel classes within the Arctic transportation accessibility model. The sea ice extent is expected to decrease along the SSP5-8.5 scenario with a high possibility under current emissions and climate change. The decadal decreasing rate will increase in March but decrease in September until 2060 when the oldest ice completely disappears and sea ice changes reach an irreversible tipping point. The sea ice thickness will decrease and transit in parts of the Arctic and will decline overall by −0.22 m per decade after September 2060. Both the sea ice concentration and volume will thoroughly decline with decreasing decadal rates, while the decrease in volume will be higher in March than in September. Open water ships will be able to cross the Northeast Passage and Northwest Passage in August–October 2045–2055, with a maximum navigable area in September. The opportunistic crossing time for polar class 6 (PC6) ships will advance to October–December in 2021–2030, while the maximum navigable area will be seen in October. In addition, the Central Passage will also open for PC6 ships during September–October in 2021–2030.

scholarly journals Changes of the Arctic marginal ice zone

2019 ◽  
Author(s):  
Rebecca J. Rolph ◽  
Daniel L. Feltham ◽  
David Schroeder
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 ◽  
First Time

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. 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 during the last 40 years from observations. 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 4 satellite-derived sea ice concentration datasets used. An extrapolation of the observations shows the MIZ extent as remaining relatively constant in the coming decades, at least until the Arctic is completely covered by seasonal ice. We find a small increase in the summer MIZ fraction (MIZ extent divided by sea ice extent), which can be attributed to the reduction in total sea ice extent. The MIZ location is trending northwards, consistent with other studies. Given the results of this study, we suggest that future studies need to remain cautious and provide a specific and clear definition when stating the MIZ is ‘rapidly changing’.

scholarly journals Predicting Summer Arctic Sea Ice Concentration Intraseasonal Variability Using a Vector Autoregressive Model*

2016 ◽  
Vol 29 (4) ◽  
pp. 1529-1543 ◽  
Author(s):  
Lei Wang ◽  
Xiaojun Yuan ◽  
Mingfang Ting ◽  
Cuihua Li
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Forecast Skill ◽  
The Arctic ◽  
Var Model ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Vector Autoregressive ◽  
Arctic Sea ◽  
Ice Concentration

Abstract Recent Arctic sea ice changes have important societal and economic impacts and may lead to adverse effects on the Arctic ecosystem, weather, and climate. Understanding the predictability of Arctic sea ice melting is thus an important task. A vector autoregressive (VAR) model is evaluated for predicting the summertime (May–September) daily Arctic sea ice concentration on the intraseasonal time scale, using only the daily sea ice data and without direct information of the atmosphere and ocean. The intraseasonal forecast skill of Arctic sea ice is assessed using the 1979–2012 satellite data. The cross-validated forecast skill of the VAR model is found to be superior to both the anomaly persistence and damped anomaly persistence at lead times of ~20–60 days, especially over northern Eurasian marginal seas and the Beaufort Sea. The daily forecast of ice concentration also leads to predictions of ice-free dates and September mean sea ice extent. In addition to capturing the general seasonal melt of sea ice, the model is also able to capture the interannual variability of the melting, from partial melt of the marginal sea ice in the beginning of the period to almost a complete melt in the later years. While the detailed mechanism leading to the high predictability of intraseasonal sea ice concentration needs to be further examined, the study reveals for the first time that Arctic sea ice can be predicted statistically with reasonable skill at the intraseasonal time scales given the small signal-to-noise ratio of daily data.

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.

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