scholarly journals Exponentially decaying modes and long-term prediction of sea ice concentration using Koopman mode decomposition

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
James Hogg ◽  
Maria Fonoberova ◽  
Igor Mezić
Keyword(s):  
Sea Ice ◽  
Annual Variation ◽  
Temporal Dynamics ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Mode Decomposition ◽  
Spatial Modes ◽  
Ice Concentration ◽  
The Mean ◽  
Insight Into

Abstract Sea ice cover in the Arctic and Antarctic is an important indicator of changes in the climate, with important environmental, economic and security consequences. The complexity of the spatio-temporal dynamics of sea ice makes it difficult to assess the temporal nature of the changes—e.g. linear or exponential—and their precise geographical loci. In this study, Koopman Mode Decomposition (KMD) is applied to satellite data of sea ice concentration for the Northern and Southern hemispheres to gain insight into the temporal and spatial dynamics of the sea ice behavior and to predict future sea ice behavior. We observe spatial modes corresponding to the mean and annual variation of Arctic and Antarctic sea ice concentration and observe decreases in the mean sea ice concentration from early to later periods, as well as corresponding shifts in the locations that undergo significant annual variation in sea ice concentration. We discover exponentially decaying spatial modes in both hemispheres and discuss their precise spatial extent, and also perform predictions of future sea ice concentration. The Koopman operator-based, data-driven decomposition technique gives insight into spatial and temporal dynamics of sea ice concentration not apparent in traditional approaches.

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

2020 ◽  
Vol 12 (7) ◽  
pp. 1060 ◽  
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.

scholarly journals Cryospheric Impacts of Soviet River Diversion Schemes

1984 ◽  
Vol 5 ◽  
pp. 61-68 ◽  
Author(s):  
T. Holt ◽  
P. M. Kelly ◽  
B. S. G. Cherry
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
River Discharge ◽  
Large Scale ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
The Arctic Ocean ◽  
The Impact

Soviet plans to divert water from rivers flowing into the Arctic Ocean have led to research into the impact of a reduction in discharge on Arctic sea ice. We consider the mechanisms by which discharge reductions might affect sea-ice cover and then test various hypotheses related to these mechanisms. We find several large areas over which sea-ice concentration correlates significantly with variations in river discharge, supporting two particular hypotheses. The first hypothesis concerns the area where the initial impacts are likely to which is the Kara Sea. Reduced riverflow is associated occur, with decreased sea-ice concentration in October, at the time of ice formation. This is believed to be the result of decreased freshening of the surface layer. The second hypothesis concerns possible effects on the large-scale current system of the Arctic Ocean and, in particular, on the inflow of Atlantic and Pacific water. These effects occur as a result of changes in the strength of northward-flowing gradient currents associated with variations in river discharge. Although it is still not certain that substantial transfers of riverflow will take place, it is concluded that the possibility of significant cryospheric effects and, hence, large-scale climate impact should not be neglected.

scholarly journals Ice and ocean velocity in the Arctic marginal ice zone: Ice roughness and momentum transfer

Elem Sci Anth ◽  
2017 ◽  
Vol 5 ◽  
Author(s):  
Sylvia T. Cole ◽  
John M. Toole ◽  
Ratnaksha Lele ◽  
Mary-Louise Timmermans ◽  
Shawn G. Gallaher ◽  
...  
Keyword(s):  
Sea Ice ◽  
Drag Coefficient ◽  
Momentum Transfer ◽  
Mixed Layer ◽  
Drill Hole ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Ice Roughness ◽  
Ice Concentration ◽  
Decadal Variations

The interplay between sea ice concentration, sea ice roughness, ocean stratification, and momentum transfer to the ice and ocean is subject to seasonal and decadal variations that are crucial to understanding the present and future air-ice-ocean system in the Arctic. In this study, continuous observations in the Canada Basin from March through December 2014 were used to investigate spatial differences and temporal changes in under-ice roughness and momentum transfer as the ice cover evolved seasonally. Observations of wind, ice, and ocean properties from four clusters of drifting instrument systems were complemented by direct drill-hole measurements and instrumented overhead flights by NASA operation IceBridge in March, as well as satellite remote sensing imagery about the instrument clusters. Spatially, directly estimated ice-ocean drag coefficients varied by a factor of three with rougher ice associated with smaller multi-year ice floe sizes embedded within the first-year-ice/multi-year-ice conglomerate. Temporal differences in the ice-ocean drag coefficient of 20–30% were observed prior to the mixed layer shoaling in summer and were associated with ice concentrations falling below 100%. The ice-ocean drag coefficient parameterization was found to be invalid in September with low ice concentrations and small ice floe sizes. Maximum momentum transfer to the ice occurred for moderate ice concentrations, and transfer to the ocean for the lowest ice concentrations and shallowest stratification. Wind work and ocean work on the ice were the dominant terms in the kinetic energy budget of the ice throughout the melt season, consistent with free drift conditions. Overall, ice topography, ice concentration, and the shallow summer mixed layer all influenced mixed layer currents and the transfer of momentum within the air-ice-ocean system. The observed changes in momentum transfer show that care must be taken to determine appropriate parameterizations of momentum transfer, and imply that the future Arctic system could become increasingly seasonal.

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.

Gridded Arctic sea ice concentration reconstruction for the first half of the 20th century based on different proxy data

2021 ◽  
Author(s):  
Vladimir Semenov ◽  
Tatiana Matveeva
Keyword(s):  
Sea Ice ◽  
20Th Century ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
The North ◽  
Arctic Sea ◽  
Ice Concentration

<p>Global warming in the recent decades has been accompanied by a rapid recline of the Arctic sea ice area most pronounced in summer (10% per decade). To understand the relative contribution of external forcing and natural variability to the modern and future sea ice area changes, it is necessary to evaluate a range of long-term variations of the Arctic sea ice area in the period before a significant increase in anthropogenic emissions of greenhouse gases into the atmosphere. Available observational data on the spatiotemporal dynamics of Arctic sea ice until 1950s are characterized by significant gaps and uncertainties. In the recent years, there have appeared several reconstructions of the early 20<sup>th</sup> century Arctic sea ice area that filled the gaps by analogue methods or utilized combined empirical data and climate model’s output. All of them resulted in a stronger that earlier believed negative sea ice area anomaly in the 1940s concurrent with the early 20<sup>th</sup> century warming (ETCW) peak. In this study, we reconstruct the monthly average gridded sea ice concentration (SIC) in the first half of the 20th century using the relationship between the spatiotemporal features of SIC variability, surface air temperature over the Northern Hemisphere extratropical continents, sea surface temperature in the North Atlantic and North Pacific, and sea level pressure. In agreement with a few previous results, our reconstructed data also show a significant negative anomaly of the Arctic sea ice area in the middle of the 20th century, however with some 15% to 30% stronger amplitude, about 1.5 million km<sup>2</sup> in September and 0.7 million km<sup>2</sup> in March. The reconstruction demonstrates a good agreement with regional Arctic sea ice area data when available and suggests that ETWC in the Arctic has been accompanied by a concurrent sea ice area decline of a magnitude that have been exceeded only in the beginning of the 21<sup>st</sup> century.</p>

Convolutional Neural Networks for Sea Ice Concentration Charting for Maritime Navigation in the Arctic

2021 ◽  
Author(s):  
Andreas Stokholm ◽  
Leif Pedersen ◽  
René Forsberg ◽  
Sine Hvidegaard
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Arctic Seas ◽  
Sea Ice Concentration ◽  
Data Set ◽  
Architecture Model ◽  
Radar Images ◽  
North East ◽  
Ice Concentration

<p>In recent years the Arctic has seen renewed political and economic interest, increased maritime traffic and desire for improved sea ice navigational tools. Despite a rise in digital technology, maps of sea ice concentration used for Arctic maritime operations are still today created by humans manually interpreting radar images. This process is slow with low map release frequency, uncertainties up to 20 % and discrepancies up to 60 %. Utilizing emerging AI Convolutional Neural Network (CNN) semantic image segmentation techniques to automate this process is drastically changing navigation in the Arctic seas, with better resolution, accuracy, release frequency and coverage. Automatic Arctic sea ice products may contribute to enabling the disruptive Northern Sea Route connecting North East Asia to Europe via the Arctic oceans.</p><p>The AI4Arctic/ASIP V2 data set, that combines 466 Sentinel-1 HH and HV SAR images from Greenland, Passive Microwave Radiometry from the AMSR2 instrument, and an equivalent sea ice concentration chart produced by ice analysts at the Danish Meteorological Institute, have been used to train a CNN U-Net Architecture model. The model shows robust capabilities in producing highly detailed sea ice concentration maps with open water, intermediate sea ice concentrations as well as full sea ice cover, which resemble those created by professional sea ice analysts. Often cited obstacles in automatic sea ice concentration models are wind-roughened sea ambiguities resembling sea ice. Final inference scenes show robustness towards such ambiguities.</p>

Influence of Nordic Seas dynamics on the Atlantic Water propagation and its impacts on sea ice concentration.

2021 ◽  
Author(s):  
Sourav Chatterjee ◽  
Roshin P Raj ◽  
Laurent Bertino ◽  
Nuncio Murukesh
Keyword(s):  
Sea Ice ◽  
Deep Water ◽  
Atlantic Water ◽  
The Arctic ◽  
Deep Water Formation ◽  
Sea Ice Concentration ◽  
Nordic Seas ◽  
Ice Concentration ◽  
Water Formation

<p>Enhanced intrusion of warm and saline Atlantic Water (AW) to the Arctic Ocean (AO) in recent years has drawn wide interest of the scientific community owing to its potential role in ‘Arctic Amplification’. Not only the AW has warmed over the last few decades , but its transfer efficiency have also undergone significant modifications due to changes in atmosphere and ocean dynamics at regional to large scales. The Nordic Seas (NS), in this regard, play a vital role as the major exchange of polar and sub-polar waters takes place in this region. Further, the AW and its significant modification on its way to AO via the Nordic Seas has large scale implications on e.g., deep water formation, air-sea heat fluxes. Previous studies have suggested that a change in the sub-polar gyre dynamics in the North Atlantic controls the AW anomalies that enter the NS and eventually end up in the AO. However, the role of NS dynamics in resulting in the modifications of these AW anomalies are not well studied. Here in this study, we show that the Nordic Seas are not only a passive conduit of AW anomalies but the ocean circulations in the Nordic Seas, particularly the Greenland Sea Gyre (GSG) circulation can significantly change the AW characteristics between the entry and exit point of AW in the NS. Further, it is shown that the change in GSG circulation can modify the AW heat distribution in the Nordic Seas and can potentially influence the sea ice concentration therein. Projected enhanced atmospheric forcing in the NS in a warming Arctic scenario and the warming trend of the AW can amplify the role of NS circulation in AW propagation and its impact on sea ice, freshwater budget and deep water formation.</p>

scholarly journals Brief communication: The challenge and benefit of using sea ice concentration satellite data products with uncertainty estimates in summer sea ice data assimilation

2016 ◽  
Vol 10 (2) ◽  
pp. 761-774 ◽  
Author(s):  
Qinghua Yang ◽  
Martin Losch ◽  
Svetlana N. Losa ◽  
Thomas Jung ◽  
Lars Nerger ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Satellite Data ◽  
General Circulation ◽  
The Arctic ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Uncertainty Estimates ◽  
Ice Concentration ◽  
Data Products

Abstract. Data assimilation experiments that aim at improving summer ice concentration and thickness forecasts in the Arctic are carried out. The data assimilation system used is based on the MIT general circulation model (MITgcm) and a local singular evolutive interpolated Kalman (LSEIK) filter. The effect of using sea ice concentration satellite data products with appropriate uncertainty estimates is assessed by three different experiments using sea ice concentration data of the European Space Agency Sea Ice Climate Change Initiative (ESA SICCI) which are provided with a per-grid-cell physically based sea ice concentration uncertainty estimate. The first experiment uses the constant uncertainty, the second one imposes the provided SICCI uncertainty estimate, while the third experiment employs an elevated minimum uncertainty to account for a representation error. Using the observation uncertainties that are provided with the data improves the ensemble mean forecast of ice concentration compared to using constant data errors, but the thickness forecast, based on the sparsely available data, appears to be degraded. Further investigating this lack of positive impact on the sea ice thicknesses leads us to a fundamental mismatch between the satellite-based radiometric concentration and the modeled physical ice concentration in summer: the passive microwave sensors used for deriving the vast majority of the sea ice concentration satellite-based observations cannot distinguish ocean water (in leads) from melt water (in ponds). New data assimilation methodologies that fully account or mitigate this mismatch must be designed for successful assimilation of sea ice concentration satellite data in summer melt conditions. In our study, thickness forecasts can be slightly improved by adopting the pragmatic solution of raising the minimum observation uncertainty to inflate the data error and ensemble spread.

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