scholarly journals Seasonal Trends in Clouds and Radiation over the Arctic Seas from Satellite Observations during 1982 to 2019

2021 ◽  
Vol 13 (16) ◽  
pp. 3201
Author(s):  
Xi Wang ◽  
Jian Liu ◽  
Bingyun Yang ◽  
Yansong Bao ◽  
George P. Petropoulos ◽  
...  
Keyword(s):  
Sea Ice ◽  
Cloud Fraction ◽  
Satellite Observations ◽  
The Arctic ◽  
Arctic Seas ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Ice Retreat ◽  
Sea Ice Retreat ◽  
Longwave Flux

A long-term dataset of 38 years (1982–2019) from the Advanced Very High Resolution Radiometer (AVHRR) satellite observations is applied to investigate the spatio-temporal seasonal trends in cloud fraction, surface downwelling longwave flux, and surface upwelling longwave flux over the Arctic seas (60~90°N) by the non-parametric methods. The results presented here provide a further contribution to understand the cloud cover and longwave surface radiation trends over the Arctic seas, and their correlations to the shrinking sea ice. Our results suggest that the cloud fraction shows a positive trend for all seasons since 2008. Both surface downwelling and upwelling longwave fluxes present significant positive trends since 1982 with higher magnitudes in autumn and winter. The spatial distribution of the trends is nearly consistent between the cloud fraction and the surface longwave radiation, except for spring over the Chukchi and Beaufort Seas. We further obtained a significant negative correlation between cloud fraction (surface downwelling/upwelling longwave fluxes) and sea-ice concentration during autumn, which is largest in magnitude for regions with substantial sea ice retreat. We found that the negative correlation between cloud fraction and sea-ice concentration is not as strong as that for the surface downwelling longwave flux. It indicates the increase in cloudiness may result in positive anomalies in surface downwelling longwave flux which is highly correlated with the sea-ice retreat in autumn.

scholarly journals Sea-Ice Indicators of Polar Bear Habitat

2016 ◽  
Author(s):  
Harry L. Stern ◽  
Kristin L. Laidre
Keyword(s):  
Sea Ice ◽  
Polar Bear ◽  
Barents Sea ◽  
Arctic Basin ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
The Barents Sea ◽  
Ice Concentration ◽  
Ice Retreat ◽  
Sea Ice Retreat

Abstract. Abstract. Nineteen distinct subpopulations of polar bears (Ursus maritimus) are found throughout the Arctic, and in all regions they depend on sea ice as a platform for traveling, hunting, and breeding. Therefore polar bear phenology – the cycle of biological events – is tied to the timing of sea-ice retreat in spring and advance in fall. We analyzed the dates of sea-ice retreat and advance in all 19 polar bear subpopulation regions from 1979 to 2014, using daily sea-ice concentration data from satellite passive microwave instruments. We define the dates of sea-ice retreat and advance in a region as the dates when the area of sea ice drops below a certain threshold (retreat) on its way to the summer minimum, or rises above the threshold (advance) on its way to the winter maximum. The threshold is chosen to be halfway between the historical (1979–2014) mean September and mean March sea-ice areas. In all 19 regions there is a trend toward earlier sea-ice retreat and later sea-ice advance. Trends generally range from −3 to −9 days decade−1 in spring, and from +3 to +9 days decade−1 in fall, with larger trends in the Barents Sea and central Arctic Basin. The trends are not sensitive to the threshold. We also calculated the number of days per year that the sea-ice area exceeded the threshold (termed ice-covered days), and the average sea-ice concentration from 1 June through 31 October. The number of ice-covered days is declining in all regions at the rate of −7 to −19 days decade−1, with larger trends in the Barents Sea and central Arctic Basin. The June–October sea-ice concentration is declining in all regions at rates ranging from −1 to −9 percent decade−1. These sea-ice metrics (or indicators of change in marine mammal habitat) were designed to be useful for management agencies. We recommend that the National Climate Assessment include the timing of sea-ice retreat and advance in future reports.

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.

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>

scholarly journals Sea-ice indicators of polar bear habitat

2016 ◽  
Vol 10 (5) ◽  
pp. 2027-2041 ◽  
Author(s):  
Harry L. Stern ◽  
Kristin L. Laidre
Keyword(s):  
Sea Ice ◽  
Polar Bear ◽  
Barents Sea ◽  
Arctic Basin ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
The Barents Sea ◽  
Ice Concentration ◽  
Ice Retreat ◽  
Sea Ice Retreat

Abstract. Nineteen subpopulations of polar bears (Ursus maritimus) are found throughout the circumpolar Arctic, and in all regions they depend on sea ice as a platform for traveling, hunting, and breeding. Therefore polar bear phenology – the cycle of biological events – is linked to the timing of sea-ice retreat in spring and advance in fall. We analyzed the dates of sea-ice retreat and advance in all 19 polar bear subpopulation regions from 1979 to 2014, using daily sea-ice concentration data from satellite passive microwave instruments. We define the dates of sea-ice retreat and advance in a region as the dates when the area of sea ice drops below a certain threshold (retreat) on its way to the summer minimum or rises above the threshold (advance) on its way to the winter maximum. The threshold is chosen to be halfway between the historical (1979–2014) mean September and mean March sea-ice areas. In all 19 regions there is a trend toward earlier sea-ice retreat and later sea-ice advance. Trends generally range from −3 to −9 days decade−1 in spring and from +3 to +9 days decade−1 in fall, with larger trends in the Barents Sea and central Arctic Basin. The trends are not sensitive to the threshold. We also calculated the number of days per year that the sea-ice area exceeded the threshold (termed ice-covered days) and the average sea-ice concentration from 1 June through 31 October. The number of ice-covered days is declining in all regions at the rate of −7 to −19 days decade−1, with larger trends in the Barents Sea and central Arctic Basin. The June–October sea-ice concentration is declining in all regions at rates ranging from −1 to −9 percent decade−1. These sea-ice metrics (or indicators of habitat change) were designed to be useful for management agencies and for comparative purposes among subpopulations. We recommend that the National Climate Assessment include the timing of sea-ice retreat and advance in future reports.

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’.

Influences of Summertime Arctic-Dipole Atmospheric Circulation on Sea Ice Concentration Variations in the Pacific Sector of the Arctic During Different Pacific Decadal Oscillation Phases

2021 ◽  
pp. 1-43
Author(s):  
Haibo Bi ◽  
Yunhe Wang ◽  
Yu Liang ◽  
Weifu Sun ◽  
Xi Liang ◽  
...  
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
Air Temperature ◽  
Pacific Decadal Oscillation ◽  
Satellite Observations ◽  
Specific Humidity ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
The Pacific ◽  
Ice Concentration

AbstractAtmospheric circulation associated with the Arctic Dipole (AD) pattern plays a crucial role in modulating the variations of summertime sea ice concentration (SIC) within the Pacific Arctic sector (PAS). Based on reanalysis data and satellite observations, we found that the impacts of atmospheric circulation associated with AD+ on SIC change over different regions of the PAS (including East Siberian Sea (ESS), Beaufort and Chukchi Seas (BCS), and Canadian Arctic Archipelago (CAA)), are dependent on the phase shifts of Pacific Decadal Oscillation (PDO). Satellite observations reveal that SIC anomalies, influenced by AD+ during PDO- relative to that during PDO+, varies significantly in summer by 4.9%, -7.3%, and -6.4% over ESS, BCS, and CAA, respectively. Overall, the atmospheric anomalies over CAA and BCS in terms of specific humidity, air temperature, and thereby downward longwave radiation (DLR), are enhanced (weakened) in the atmospheric conditions associated with AD+ during PDO- (PDO+). In these two regions, the larger (smaller) increases in specific humidity and air temperature, associated with AD+ during PDO- (PDO+), are connected to the increased (decreased) poleward moisture flux, strengthened (weakened) convergence of moisture and heat flux, and partly to adiabatic heating. As a consequence, the DLR and surface net energy flux anomalies over the two regions are reinforced in the atmospheric scenarios associated with AD+ during PDO- compared with that during PDO+. Therefore, smaller SIC anomalies are identified over CAA and BCS in the cases related to AD+ during PDO- than during PDO+. Essentially, the changes of the DLR anomaly in CAA and BCS are in alignment with geopotential height anomalies, which are modulated by the anticyclonic circulation pattern in association with AD+ during varying PDO phases. In contrast, the SIC changes over ESS is primarily attributed to the variations in mechnical wind focring and sea surface temperature (SST) anomalies. The cloud fraction anomalies associated with AD+ during different PDO phases are found not to be a significant contributor to the variations of sea ice anomaly in the studied regions. Given the oscillatory nature of PDO, we speculate that the recent shift to the PDO+ phase may temporarily slow the observed significant decline trend of the summertime SIC within PAS of the Arctic.

scholarly journals Increasing cloudiness in Arctic damps the increase in phytoplankton primary production due to sea ice receding

2012 ◽  
Vol 9 (10) ◽  
pp. 13987-14012 ◽  
Author(s):  
S. Bélanger ◽  
M. Babin ◽  
J.-E. Tremblay
Keyword(s):  
Sea Ice ◽  
Primary Production ◽  
Arctic Ocean ◽  
Sea Surface ◽  
The Arctic ◽  
Diagnostic Model ◽  
Arctic Seas ◽  
Sea Ice Concentration ◽  
Phytoplankton Primary Production ◽  
Ice Concentration

Abstract. The Arctic Ocean and its marginal seas are among the marine regions most affected by climate change. Here we present the results of a diagnostic model used to elucidate the main drivers of primary production (PP) trends over the 1998–2010 period at pan-Arctic and local (i.e. 9.28 km resolution) scales. Photosynthetically active radiation (PAR) above and below the sea surface was estimated using precomputed look-up tables of spectral irradiance and satellite-derived cloud optical thickness and cloud fraction parameters from the International Satellite Cloud Climatology Project (ISCCP) and sea ice concentration from passive microwaves data. A spectrally resolved PP model, designed for optically complex waters, was then used to produce maps of PP trends. Results show that incident PAR above the sea surface (PAR(0+)) has significantly decreased over the whole Arctic and sub-Arctic Seas, except over the perrennially sea ice covered waters of the Central Arctic Ocean. This fading of PAR(0+) (+8% decade–1) was caused by increasing cloudiness May and June. Meanwhile PAR penetrating the ocean (PAR(0–)) increased only along the sea ice margin over the large Arctic continental shelf where sea ice concentration declined sharply since 1998. Overall, PAR(0–) slightly increased in the Circum Arctic (+3.4% decade–1), while it decreased when considering both Arctic and sub-Arctic Seas (–3% decade–1). We showed that rising phytoplankton biomass (i.e. chlorophyll a) normalized by the diffuse attenuation of photosynthetically usable radiation (PUR) by phytoplankton accounted for a larger proportion of the rise in PP than did the increase in light availability due to sea-ice loss in several sectors and particularly in perrennially and seasonally open waters. Against a general backdrop of rising productivity over Arctic shelves, significant negative trends were observed in regions known for their great biological importance such as the coastal polynyas of Northern Greenland.

scholarly journals Increasing cloudiness in Arctic damps the increase in phytoplankton primary production due to sea ice receding

2013 ◽  
Vol 10 (6) ◽  
pp. 4087-4101 ◽  
Author(s):  
S. Bélanger ◽  
M. Babin ◽  
J.-É. Tremblay
Keyword(s):  
Sea Ice ◽  
Primary Production ◽  
Arctic Ocean ◽  
Sea Surface ◽  
The Arctic ◽  
Diagnostic Model ◽  
Arctic Seas ◽  
Sea Ice Concentration ◽  
Phytoplankton Primary Production ◽  
Ice Concentration

Abstract. The Arctic Ocean and its marginal seas are among the marine regions most affected by climate change. Here we present the results of a diagnostic model used to assess the primary production (PP) trends over the 1998–2010 period at pan-Arctic, regional and local (i.e. 9.28 km resolution) scales. Photosynthetically active radiation (PAR) above and below the sea surface was estimated using precomputed look-up tables of spectral irradiance, taking as input satellite-derived cloud optical thickness and cloud fraction parameters from the International Satellite Cloud Climatology Project (ISCCP) and sea ice concentration from passive microwaves data. A spectrally resolved PP model, designed for optically complex waters, was then used to assess the PP trends at high spatial resolution. Results show that PP is rising at a rate of +2.8 TgC yr−1 (or +14% decade−1) in the circum-Arctic and +5.1 TgC yr−1 when sub-Arctic seas are considered. In contrast, incident PAR above the sea surface (PAR(0+)) has significantly decreased over the whole Arctic and sub-Arctic Seas, except over the perennially sea-ice covered waters of the Central Arctic Ocean. This fading of PAR(0+) (−8% decade−1) was caused by increasing cloudiness during summer. Meanwhile, PAR penetrating the ocean (PAR(0−)) increased only along the sea ice margin over the large Arctic continental shelf where sea ice concentration declined sharply since 1998. Overall, PAR(0−) slightly increased in the circum-Arctic (+3.4% decade−1), while it decreased when considering both Arctic and sub-Arctic Seas (−3% decade−1). We showed that rising phytoplankton biomass (i.e. chlorophyll a) normalized by the diffuse attenuation of photosynthetically usable radiation (PUR), accounted for a larger proportion of the rise in PP than did the increase in light availability due to sea-ice loss in several sectors, and particularly in perennially and seasonally open waters. Against a general backdrop of rising productivity over Arctic shelves, significant negative PP trends and the timing of phytoplankton spring-summer bloom were observed in regions known for their great biological importance such as the coastal polynyas of northern Greenland.

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.

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