Climatology and Interannual Variability of Cloudiness in the Atlantic Arctic from Surface Observations since the Late Nineteenth Century

2017 ◽  
Vol 30 (6) ◽  
pp. 2103-2120 ◽  
Author(s):  
Alexander V. Chernokulsky ◽  
Igor Esau ◽  
Olga N. Bulygina ◽  
Richard Davy ◽  
Igor I. Mokhov ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Barents Sea ◽  
Open Water ◽  
Data Uncertainty ◽  
Late Nineteenth Century ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Barents And Kara Seas ◽  
Surface Observations

A long-term climatology of cloudiness over the Norwegian, Barents, and Kara Seas (NBK) based on visual surface observations is presented. Annual mean total cloud cover (TCC) is almost equal over solid-ice (SI) and open-water (OW) regions of the NBK (73% ± 3% and 76% ± 2%, respectively). In general, TCC has higher intra- and interannual variability over SI than over OW. A decrease of TCC in the middle of the twentieth century and an increase in the last few decades was found at individual stations and for the NBK as a whole. In most cases these changes are statistically significant with magnitudes exceeding the data uncertainty that is associated with the surface observations. The most pronounced trends are observed in autumn when the largest changes to the sea ice concentration (SIC) occur. TCC over SI correlates significantly with SIC in the Barents Sea, with a statistically significant correlation coefficient between annual TCC and SIC of −0.38 for the period 1936–2013. Cloudiness over OW shows nonsignificant correlation with SIC. An overall increase in the frequency of broken and scattered cloud conditions and a decrease in the frequency of overcast and cloudless conditions were found over OW. These changes are statistically significant and likely to be connected with the long-term changes of morphological types (an increase of convective and a decrease of stratiform cloud amounts).

scholarly journals Review of methods to retrieve sea ice parameters from satellite microwave radiometer data

2019 ◽  
Vol 55 (1) ◽  
pp. 128-151
Author(s):  
E. V. Zabolotskikh
Keyword(s):  
Sea Ice ◽  
Microwave Radiometer ◽  
Open Water ◽  
Sea Ice Concentration ◽  
Internet Resources ◽  
Ice Concentration ◽  
Satellite Microwave ◽  
Microwave Imager ◽  
Microwave Radiometers

Sea ice monitoring using long-term data of satellite passive microwave instruments enables climate change estimates. These numerical estimates depend on the methods used for sea ice parameter retrievals. This work presents a review of methods to retrieve sea ice parameters from the data of satellite microwave radiometers. Physical modeling of the sea ice–ocean–atmosphere microwave radiation provides the means to identify the general sources of the retrieval errors and to classify the methods by used approach. The basics of the algorithms are formulated along with assumptions and approximations as well as the data used for the algorithm verification. Weather filters are considered to identify the areas of open water. A comparative analysis of method advantages and limitations is given related to sea ice concentration retrievals from such satellite instruments as the series of Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer (AMSR). A review of the basic satellite sea ice products based on SSM/I, AMSR-E and AMSR2 data is complemented by the list of the essential internet resources for operational and historical sea ice data.

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.

The Role of Moist Intrusions in Winter Arctic Warming and Sea Ice Decline

2016 ◽  
Vol 29 (12) ◽  
pp. 4473-4485 ◽  
Author(s):  
Cian Woods ◽  
Rodrigo Caballero
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Surface Air Temperature ◽  
The Arctic ◽  
Positive Trend ◽  
Sea Ice Concentration ◽  
Local Conditions ◽  
The Barents Sea ◽  
Strong Inversion ◽  
Ice Concentration

Abstract This paper examines the trajectories followed by intense intrusions of moist air into the Arctic polar region during autumn and winter and their impact on local temperature and sea ice concentration. It is found that the vertical structure of the warming associated with moist intrusions is bottom amplified, corresponding to a transition of local conditions from a “cold clear” state with a strong inversion to a “warm opaque” state with a weaker inversion. In the marginal sea ice zone of the Barents Sea, the passage of an intrusion also causes a retreat of the ice margin, which persists for many days after the intrusion has passed. The authors find that there is a positive trend in the number of intrusion events crossing 70°N during December and January that can explain roughly 45% of the surface air temperature and 30% of the sea ice concentration trends observed in the Barents Sea during the past two decades.

scholarly journals Employing Satellite-Derived Sea Ice Concentration to Constrain Upper-Ocean Temperature in a Global Ocean GCM

2008 ◽  
Vol 21 (17) ◽  
pp. 4498-4513 ◽  
Author(s):  
Achim Stössel
Keyword(s):  
Sea Ice ◽  
Deep Ocean ◽  
Global Ocean ◽  
Upper Ocean ◽  
Ocean Temperature ◽  
Freshwater Input ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Ocean Properties

Abstract The quality of Southern Ocean sea ice simulations in a global ocean general circulation model (GCM) depends decisively on the simulated upper-ocean temperature. This is confirmed by assimilating satellite-derived sea ice concentration to constrain the upper-layer temperature of a sea ice–ocean GCM. The resolution of the model’s sea ice component is about 22 km and thus comparable to the pixel resolution of the satellite data. The ocean component is coarse resolution to afford long-term integrations for investigations of the deep-ocean equilibrium response. Besides improving the sea ice simulation considerably, the simulations with constrained upper-ocean temperature yield much more realistic global deep-ocean properties, in particular when combined with glacial freshwater input. Both outcomes are relatively insensitive to the passive-microwave algorithm used to retrieve the ice concentration being assimilated. The sensitivity of the long-term global deep-ocean properties and circulation to the possible freshwater input from ice shelves and to the parameterization of vertical mixing in the Southern Ocean is reevaluated under the new constraint.

scholarly journals Model-data comparison and data assimilation of mid-Holocene Arctic sea-ice concentration

2013 ◽  
Vol 9 (6) ◽  
pp. 6515-6549 ◽  
Author(s):  
F. Klein ◽  
H. Goosse ◽  
A. Mairesse ◽  
A. de Vernal
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Atmospheric Circulation ◽  
Barents Sea ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Model Data ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration

Abstract. The consistency between a new quantitative reconstruction of Arctic sea-ice concentration based on dinocyst assemblages and the results of climate models has been investigated for the mid-Holocene. The comparison shows that the simulated sea-ice changes are weaker and spatially more homogeneous than the recorded ones. Furthermore, although the model-data agreement is relatively good in some regions such as the Labrador Sea, the skill of the models at local scale is low. The response of the models follows mainly the increase in summer insolation at large scale. This is modulated by changes in atmospheric circulation leading to differences between regions in the models that are albeit smaller than in the reconstruction. Performing simulations with data assimilation using the model LOVECLIM amplifies those regional differences, mainly through a reduction of the southward winds in the Barents Sea and an increase in the westerly winds in the Canadian Basin of the Arctic. This leads to an increase in the ice concentration in the Barents and Chukchi Seas and a better agreement with the reconstructions. This underlines the potential role of atmospheric circulation to explain the reconstructed changes during the Holocene.

scholarly journals A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring

2013 ◽  
Vol 6 (1) ◽  
pp. 95-117
Author(s):  
G. Peng ◽  
W. N. Meier ◽  
D. J. Scott ◽  
M. H. Savoie
Keyword(s):  
Sea Ice ◽  
Passive Microwave ◽  
Climate Data ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Data Record ◽  
Climate Data Record ◽  
Climate Studies

Abstract. A long-term, consistent, and reproducible satellite-based passive microwave sea ice concentration climate data record (CDR) is available for climate studies, monitoring, and model validation with an initial operation capability (IOC). The daily and monthly sea ice concentration data are on the National Snow and Ice Data Center (NSIDC) polar stereographic grid with nominal 25 × 25 km grid cells in both the Southern and Northern Hemisphere Polar Regions from 9 July 1987 to 31 December 2007 with an update through 2011 underway. The data files are available in the NetCDF data format at http://nsidc.org/data/g02202.html and archived by the National Oceanic and Atmospheric Administration (NOAA)'s National Climatic Data Center (NCDC) under the satellite climate data record program (http://www.ncdc.noaa.gov/cdr/operationalcdrs.html). The description and basic characteristics of the NOAA/NSIDC passive microwave sea ice concentration CDR are presented here. The CDR provides similar spatial and temporal variability as the heritage products to the user communities with the additional documentation, traceability, and reproducibility that meet current standards and guidelines for climate data records. The dataset along with detailed data processing steps and error source information can be found at: doi:10.7265/N5B56GN3.

scholarly journals The impact of melt ponds on summertime microwave brightness temperatures and sea-ice concentrations

2016 ◽  
Vol 10 (5) ◽  
pp. 2217-2239 ◽  
Author(s):  
Stefan Kern ◽  
Anja Rösel ◽  
Leif Toudal Pedersen ◽  
Natalia Ivanova ◽  
Roberto Saldo ◽  
...  
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Sea Ice Concentration ◽  
Melt Pond ◽  
Surface Fraction ◽  
Brightness Temperatures ◽  
Ice Surface ◽  
Melt Ponds ◽  
Ice Concentration ◽  
Ice Floes

Abstract. Sea-ice concentrations derived from satellite microwave brightness temperatures are less accurate during summer. In the Arctic Ocean the lack of accuracy is primarily caused by melt ponds, but also by changes in the properties of snow and the sea-ice surface itself. We investigate the sensitivity of eight sea-ice concentration retrieval algorithms to melt ponds by comparing sea-ice concentration with the melt-pond fraction. We derive gridded daily sea-ice concentrations from microwave brightness temperatures of summer 2009. We derive the daily fraction of melt ponds, open water between ice floes, and the ice-surface fraction from contemporary Moderate Resolution Spectroradiometer (MODIS) reflectance data. We only use grid cells where the MODIS sea-ice concentration, which is the melt-pond fraction plus the ice-surface fraction, exceeds 90 %. For one group of algorithms, e.g., Bristol and Comiso bootstrap frequency mode (Bootstrap_f), sea-ice concentrations are linearly related to the MODIS melt-pond fraction quite clearly after June. For other algorithms, e.g., Near90GHz and Comiso bootstrap polarization mode (Bootstrap_p), this relationship is weaker and develops later in summer. We attribute the variation of the sensitivity to the melt-pond fraction across the algorithms to a different sensitivity of the brightness temperatures to snow-property variations. We find an underestimation of the sea-ice concentration by between 14 % (Bootstrap_f) and 26 % (Bootstrap_p) for 100 % sea ice with a melt-pond fraction of 40 %. The underestimation reduces to 0 % for a melt-pond fraction of 20 %. In presence of real open water between ice floes, the sea-ice concentration is overestimated by between 26 % (Bootstrap_f) and 14 % (Bootstrap_p) at 60 % sea-ice concentration and by 20 % across all algorithms at 80 % sea-ice concentration. None of the algorithms investigated performs best based on our investigation of data from summer 2009. We suggest that those algorithms which are more sensitive to melt ponds could be optimized more easily because the influence of unknown snow and sea-ice surface property variations is less pronounced.

scholarly journals Comparison of Passive Microwave Data with Shipborne Photographic Observations of Summer Sea Ice Concentration along an Arctic Cruise Path

2019 ◽  
Vol 11 (17) ◽  
pp. 2009 ◽  
Author(s):  
Qingkai Wang ◽  
Peng Lu ◽  
Yongheng Zu ◽  
Zhijun Li ◽  
Matti Leppäranta ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Moving Average ◽  
Open Water ◽  
Arctic Sea Ice ◽  
Passive Microwave ◽  
Sea Ice Concentration ◽  
Melt Ponds ◽  
Ice Concentration ◽  
Arctic Navigation

Arctic sea ice concentration (SIC) has been studied extensively using passive microwave (PM) remote sensing. This technology could be used to improve navigation along vessel cruise paths; however, investigations on this topic have been limited. In this study, shipborne photographic observation (P-OBS) of sea ice was conducted using oblique-oriented cameras during the Chinese National Arctic Research Expedition in the summer of 2016. SIC and the areal fractions of open water, melt ponds, and sea ice (Aw, Ap, and Ai, respectively) were determined along the cruise path. The distribution of SIC along the cruise path was U-shaped, and open water accounted for a large proportion of the path. The SIC derived from the commonly used PM algorithms was compared with the moving average (MA) P-OBS SIC, including Bootstrap and NASA Team (NT) algorithms based on Special Sensor Microwave Imager/Sounder (SSMIS) data; and ARTIST sea ice, Bootstrap, Sea Ice Climate Change Initiative, and NASA Team 2 (NT2) algorithms based on Advanced Microwave Scanning Radiometer 2 (AMSR2) data. P-OBS performed better than PM remote sensing at detecting low SIC (< 10%). Our results indicate that PM SIC overestimates MA P-OBS SIC at low SIC, but underestimates it when SIC exceeds a turnover point (TP). The presence of melt ponds affected the accuracy of the PM SIC; the PM SIC shifted from an overestimate to an underestimate with increasing Ap, compared with MA P-OBS SIC below the TP, while the underestimation increased above the TP. The PM algorithms were then ranked; SSMIS-NT and AMSR2-NT2 are the best and worst choices for Arctic navigation, respectively.

Climatic trends of sea surface temperature and sea ice concentration in the Barents Sea

2021 ◽  
Author(s):  
Bayoumy Mohamed ◽  
Frank Nilsen ◽  
Ragnheid Skogseth
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Seasonal Cycle ◽  
Barents Sea ◽  
Warming Trend ◽  
Sea Surface ◽  
Sea Ice Concentration ◽  
The Barents Sea ◽  
Ice Concentration

&lt;p&gt;Sea ice loss in the Arctic region is an important indicator for climate change. Especially in the Barents Sea, which is expected to be free of ice by the mid of this century (Onarheim et al., 2018). Here, we analyze 38 years (1982-2019) of daily gridded sea surface temperature (SST) and sea ice concentration (SIC) from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) project. These data sets have been used to investigate the seasonal cycle and linear trends of SST and SIC, and their spatial distribution in the Barents Sea. From the SST seasonal cycle analysis, we have found that most of the years that have temperatures above the climatic mean (1982-2019) were recorded after 2000. This confirms the warm transition that has taken place in the Barents Sea over the last two decades. The year 2016 was the warmest year in both winter and summer during the study period. &amp;#160;&amp;#160;&lt;/p&gt;&lt;p&gt;Results from the linear trend analysis reveal an overall statistically significant warming trend for the whole Barents Sea of about 0.33&amp;#177;0.03 &amp;#176;C/decade, associated with a sea ice reduction rate of about -4.9&amp;#177;0.6 %/decade. However, the SST trend show a high spatial variability over the Barents Sea. The highest SST trend was found over the eastern part of the Barents Sea and south of Svalbard (Storfjordrenna Trough), while the Northern Barents Sea shows less distinct and non-significant trends. The largest negative trend of sea ice was observed between Novaya Zemlya and Franz Josef Land. Over the last two decades (2000-2019), the data show an amplified warming trend in the Barents Sea where the SST warming trend has increased dramatically (0.46&amp;#177;0.09 &amp;#176;C/decade) and the SIC is here decreasing with rate of about -6.4&amp;#177;1.5 %/decade.&amp;#160; Considering the current development of SST, if this trend persists, the Barents Sea annual mean SST will rise by around 1.4 &amp;#176;C by the end of 2050, which will have a drastic impact on the loss of sea ice in the Barents Sea.&amp;#160; &amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Keywords: Sea surface temperature; Sea ice concentration; Trend analysis; Barents Sea&lt;/p&gt;

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