scholarly journals Sea ice concentration impacts dissolved organic gases in the Canadian Arctic

2021 ◽  
Author(s):  
Charel Wohl ◽  
Anna E. Jones ◽  
William T. Sturges ◽  
Philip D. Nightingale ◽  
Brent Else ◽  
...  
Keyword(s):  
Sea Ice ◽  
Dimethyl Sulfide ◽  
Mixed Layer Depth ◽  
Canadian Arctic ◽  
The Arctic ◽  
Polar Regions ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Subsurface Maximum ◽  
The Impact

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.

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 Short-Term Daily Prediction of Sea Ice Concentration Based on Deep Learning of Gradient Loss Function

2021 ◽  
Vol 8 ◽  
Author(s):  
Quanhong Liu ◽  
Ren Zhang ◽  
Yangjun Wang ◽  
Hengqian Yan ◽  
Mei Hong
Keyword(s):  
Deep Learning ◽  
Sea Ice ◽  
Loss Function ◽  
Model Performance ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Short Term ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
The Impact

The navigability potential of the Northeast Passage has gradually emerged with the melting of Arctic sea ice. For the purpose of navigation safety in the Arctic area, a reliable daily sea ice concentration (SIC) prediction result is required. As the mature application of deep learning technique in short-term prediction of other fields (atmosphere, ocean, and hurricane, etc.), a new model was proposed for daily SIC prediction by selecting multiple factors, adopting gradient loss function (Grad-loss) and incorporating an improved predictive recurrent neural network (PredRNN++). Three control experiments are designed to test the impact of these three improvements for model performance with multiple indicators. Results show that the proposed model has best prediction skill in our experiments by taking physical process and local SIC variation into consideration, which can continuously predict daily SIC for up to 9 days.

Dynamics and drivers of extreme seasons in the Arctic region

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

<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>

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 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.

The impact of a warm moist air intrusion on dynamic and thermodynamic sea ice tendencies in the Arctic.

2020 ◽  
Author(s):  
Evelien Dekker
Keyword(s):  
Sea Ice ◽  
Radiative Forcing ◽  
Surface Air Temperature ◽  
Longwave Radiation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Moist Air ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
The Impact

<p>Atmospheric blocking events in the Northern Hemishpere have been related to regional Arctic sea ice decline. During blocking events, pulses of warm and moist air enhance the radiative forcing on the sea ice in winter due to the increased longwave radiation associated with clouds. Several studies have shown that such events are related to regional sea ice concentration decline. Daily sea ice output with the latest version of CICE from the coupled Regional Arctic System model is used to study sea ice tendencies during January-February 2014. In this period there was a follow-up of a Atlantic warm moist air insturion and a Pacific warm moist air intrusion associated with surface air temperature perturbations up to 20 degrees locally.</p><p>A decline in sea ice concentration during wintertime does not neccesarily mean that ice melt has occurred. The goal of this case study is to distinguish the sea ice response between a dynamic and a thermodynamic component. In this way, we learn how much of the sea ice is advected into another region during such an event and how much the sea ice is lost due to the enhanced forcing and temperature increase.</p><p> </p><p> </p><p> </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 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 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.

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