scholarly journals Extreme Warming in the Kara Sea and Barents Sea during the Winter Period 2000–16

2017 ◽  
Vol 30 (22) ◽  
pp. 8913-8927 ◽  
Author(s):  
Svenja H. E. Kohnemann ◽  
Günther Heinemann ◽  
David H. Bromwich ◽  
Oliver Gutjahr
Keyword(s):  
Sea Ice ◽  
Air Temperature ◽  
Barents Sea ◽  
Kara Sea ◽  
The Arctic ◽  
Winter Period ◽  
Limited Area ◽  
Spatial And Temporal Variability ◽  
Large Variability ◽  
Air Temperatures

The regional climate model COSMO in Climate Limited-Area Mode (COSMO-CLM or CCLM) is used with a high resolution of 15 km for the entire Arctic for all winters 2002/03–2014/15. The simulations show a high spatial and temporal variability of the recent 2-m air temperature increase in the Arctic. The maximum warming occurs north of Novaya Zemlya in the Kara Sea and Barents Sea between March 2003 and 2012 and is responsible for up to a 20°C increase. Land-based observations confirm the increase but do not cover the maximum regions that are located over the ocean and sea ice. Also, the 30-km version of the Arctic System Reanalysis (ASR) is used to verify the CCLM for the overlapping time period 2002/03–2011/12. The differences between CCLM and ASR 2-m air temperatures vary slightly within 1°C for the ocean and sea ice area. Thus, ASR captures the extreme warming as well. The monthly 2-m air temperatures of observations and ERA-Interim data show a large variability for the winters 1979–2016. Nevertheless, the air temperature rise since the beginning of the twenty-first century is up to 8 times higher than in the decades before. The sea ice decrease is identified as the likely reason for the warming. The vertical temperature profiles show that the warming has a maximum near the surface, but a 0.5°C yr−1 increase is found up to 2 km. CCLM, ASR, and also the coarser resolved ERA-Interim data show that February and March are the months with the highest 2-m air temperature increases, averaged over the ocean and sea ice area north of 70°N; for CCLM the warming amounts to an average of almost 5°C for 2002/03–2011/12.

scholarly journals On the accuracy of thin-ice thickness retrieval using MODIS thermal imagery over Arctic first-year ice

2013 ◽  
Vol 54 (62) ◽  
pp. 87-96 ◽  
Author(s):  
Marko Mäkynen ◽  
Bin Cheng ◽  
Markku Similä
Keyword(s):  
Wind Speed ◽  
Air Temperature ◽  
Barents Sea ◽  
Weather Prediction ◽  
Weather Conditions ◽  
The Arctic ◽  
Ice Thickness ◽  
Limited Area ◽  
Night Time ◽  
Modis Data

AbstractWe have studied the accuracy of ice thickness (hi) retrieval based on night-time MODIS (Moderate Resolution Imaging Spectroradiometer) ice surface temperature (Ts) images and HIRLAM (High Resolution Limited Area Model) weather forcing data from the Arctic. The study area is the Kara Sea and eastern part of the Barents Sea, and the study period spans November-April 2008–11 with 199 hi charts. For cloud masking of the MODIS data we had to use manual methods in order to improve detection of thin clouds and ice fog. The accuracy analysis of the retrieved hi was conducted with different methods, taking into account the inaccuracy of the HIRLAM weather forcing data. Maximum reliable hi under different air-temperature and wind-speed ranges was 35–50 cm under typical weather conditions (air temperature <–20cC, wind speed <5ms–1) present in the MODIS data. The accuracy is best for the 15–30 cm thickness range, ∼38%. The largest hi uncertainty comes from air temperature data. Our ice-thickness limits are more conservative than those in previous studies where numerical weather prediction model data were not used in the hi retrieval. Our study gives new detailed insight into the capability of Ts-based hi retrieval in the Arctic marginal seas during freeze-up and wintertime, and should also benefit work where MODIS hi charts are used.

Assessing trends in Arctic sea-ice distribution in the Barents and Kara seas using the Kosmos–Okean satellite series

Polar Record ◽  
1995 ◽  
Vol 31 (177) ◽  
pp. 129-134 ◽  
Author(s):  
Gennady I. Belchansky ◽  
Ilia N. Mordvintsev ◽  
Gregory K. Ovchinnikov ◽  
David C. Douglas
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Satellite Image ◽  
Kara Sea ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Published Data ◽  
Sea Ice Extent ◽  
The Barents Sea ◽  
Barents And Kara Seas

AbstractTrends in the annual minimum sea-ice extent, determined by three criteria (absolute annual minimum, minimum monthly mean, and the extent at the end of August), were investigated for the Barents and western Kara seas and adjacent parts of the Arctic Ocean during 1984–1993. Four definitions of ice extent were examined, based on thresholds of ice concentration: >90%, >70%, >40%, and >10% (El, E2, E3, and E4, respectively). Trends were studied using ice maps produced by the Russian Hydro-Meteorological Service, Kosmos and Okean satellite imagery, and data extracted from published literature. During 1984–1993, an increasing trend in the extent of minimum sea-ice cover was observed in the Barents, Kara, and combined Barents–Kara seas, for all ice-extent definitions. Root-mean-square differences between hydro-meteorological ice maps and satellite-image ice classifications for coincident areas and dates were 15.5%, 19.3%, 18.8%, and 11.5%, for ice extensions El–E4, respectively. The differences were subjected to Monte Carlo analyses to construct confidence intervals for the 10-year ice-map trends. With probability p = 0.8, the average 10-year increase in the minimum monthly mean sea-ice extent (followed in brackets by the average increase in the absolute annual minimum ice extent) was 12–46% [26–96%], 31–71% [55–140%], 30–69% [26–94%], and 48–94% [35–108%] in the Barents Sea; 20–60% [32–120%], 10–45% [20–92%], 2–36% [13–78%], and 10–47% [8–69%] in the Kara Sea; and 9–43% [26–59%], 9–41% [30–63%], 8–41% [22–52%] and 15–51% [21–51%] in the combined Barents–Kara seas, for ice concentrations El–E4, respectively. Including published data from 1966–1983, the trend in minimum monthly mean sea-ice extent for the combined 28-year period showed an average reduction of 8% in the Barents Sea and a 55% reduction in the western Kara Sea; ice extent at the end of August showed an average reduction of 33% in the Barents Sea.

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 Polar cod in jeopardy under the retreating Arctic sea ice

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Mats Brockstedt Olsen Huserbråten ◽  
Elena Eriksen ◽  
Harald Gjøsæter ◽  
Frode Vikebø
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Keystone Species ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Biophysical Model ◽  
The Arctic ◽  
Polar Cod ◽  
Arctic Sea ◽  
Shelf Sea

Abstract The Arctic amplification of global warming is causing the Arctic-Atlantic ice edge to retreat at unprecedented rates. Here we show how variability and change in sea ice cover in the Barents Sea, the largest shelf sea of the Arctic, affect the population dynamics of a keystone species of the ice-associated food web, the polar cod (Boreogadus saida). The data-driven biophysical model of polar cod early life stages assembled here predicts a strong mechanistic link between survival and variation in ice cover and temperature, suggesting imminent recruitment collapse should the observed ice-reduction and heating continue. Backtracking of drifting eggs and larvae from observations also demonstrates a northward retreat of one of two clearly defined spawning assemblages, possibly in response to warming. With annual to decadal ice-predictions under development the mechanistic physical-biological links presented here represent a powerful tool for making long-term predictions for the propagation of polar cod stocks.

Arctic Freshwater in CMIP6: Declining Sea Ice, Increasing Ocean Storage and Export

2021 ◽  
Author(s):  
Hannah Zanowski ◽  
Alexandra Jahn ◽  
Marika Holland
Keyword(s):  
Sea Ice ◽  
21St Century ◽  
Barents Sea ◽  
The Arctic ◽  
Historical Period ◽  
Freshwater Flux ◽  
Bering Strait ◽  
The Barents Sea ◽  
Late 21St Century ◽  
Freshwater Export

&lt;p&gt;Recently, the Arctic has undergone substantial changes in sea ice cover and the hydrologic cycle, both of which strongly impact the freshwater storage in, and export from, the Arctic Ocean. Here we analyze Arctic freshwater storage and fluxes in 7 climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6) and assess their agreement over the historical period (1980-2000) and in two future emissions scenarios, SSP1-2.6 and SSP5-8.5. In the historical simulation, few models agree closely with observations over 1980-2000. In both future scenarios the models show an increase in liquid (ocean) freshwater storage in conjunction with a reduction in solid storage and fluxes through the major Arctic gateways (Bering Strait, Fram Strait, Davis Strait, and the Barents Sea Opening) that is typically larger for SSP5-8.5 than SSP1-2.6. The liquid fluxes through the gateways exhibit a more complex pattern, with models exhibiting a change in sign of the freshwater flux through the Barents Sea Opening and little change in the flux through the Bering Strait in addition to increased export from the remaining straits by the end of the 21st century. A decomposition of the liquid fluxes into their salinity and volume contributions shows that the Barents Sea flux changes are driven by salinity changes, while the Bering Strait flux changes are driven by compensating salinity and volume changes. In the straits west of Greenland (Nares, Barrow, and Davis straits), the models disagree on whether there will be a decrease, increase, or steady liquid freshwater export in the early to mid 21st century, although they mostly show increased liquid freshwater export in the late 21st century. The underlying cause of this is a difference in the magnitude and timing of a simulated decrease in the volume flux through these straits. Although the models broadly agree on the sign of late 21st century storage and flux changes, substantial differences exist between the magnitude of these changes and the models&amp;#8217; Arctic mean states, which shows no fundamental improvement in the models compared to CMIP5.&lt;/p&gt;

scholarly journals Climate change impacts on sea–air fluxes of CO<sub>2</sub> in three Arctic seas: a sensitivity study using Earth observation

2013 ◽  
Vol 10 (12) ◽  
pp. 8109-8128 ◽  
Author(s):  
P. E. Land ◽  
J. D. Shutler ◽  
R. D. Cowling ◽  
D. K. Woolf ◽  
P. Walker ◽  
...  
Keyword(s):  
Climate Change ◽  
Sea Ice ◽  
Barents Sea ◽  
Earth Observation ◽  
Kara Sea ◽  
Sink Strength ◽  
Arctic Seas ◽  
The Barents Sea ◽  
Co2 Sink ◽  
Positive Effect

Abstract. We applied coincident Earth observation data collected during 2008 and 2009 from multiple sensors (RA2, AATSR and MERIS, mounted on the European Space Agency satellite Envisat) to characterise environmental conditions and integrated sea–air fluxes of CO2 in three Arctic seas (Greenland, Barents, Kara). We assessed net CO2 sink sensitivity due to changes in temperature, salinity and sea ice duration arising from future climate scenarios. During the study period the Greenland and Barents seas were net sinks for atmospheric CO2, with integrated sea–air fluxes of −36 ± 14 and −11 ± 5 Tg C yr−1, respectively, and the Kara Sea was a weak net CO2 source with an integrated sea–air flux of +2.2 ± 1.4 Tg C yr−1. The combined integrated CO2 sea–air flux from all three was −45 ± 18 Tg C yr−1. In a sensitivity analysis we varied temperature, salinity and sea ice duration. Variations in temperature and salinity led to modification of the transfer velocity, solubility and partial pressure of CO2 taking into account the resultant variations in alkalinity and dissolved organic carbon (DOC). Our results showed that warming had a strong positive effect on the annual integrated sea–air flux of CO2 (i.e. reducing the sink), freshening had a strong negative effect and reduced sea ice duration had a small but measurable positive effect. In the climate change scenario examined, the effects of warming in just over a decade of climate change up to 2020 outweighed the combined effects of freshening and reduced sea ice duration. Collectively these effects gave an integrated sea–air flux change of +4.0 Tg C in the Greenland Sea, +6.0 Tg C in the Barents Sea and +1.7 Tg C in the Kara Sea, reducing the Greenland and Barents sinks by 11% and 53%, respectively, and increasing the weak Kara Sea source by 81%. Overall, the regional integrated flux changed by +11.7 Tg C, which is a 26% reduction in the regional sink. In terms of CO2 sink strength, we conclude that the Barents Sea is the most susceptible of the three regions to the climate changes examined. Our results imply that the region will cease to be a net CO2 sink in the 2050s.

scholarly journals Mechanism of Seasonal Arctic Sea Ice Evolution and Arctic Amplification

2016 ◽  
Author(s):  
Kwang-Yul Kim ◽  
Benjamin D. Hamlington ◽  
Hanna Na ◽  
Jinju Kim
Keyword(s):  
Heat Flux ◽  
Sea Ice ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Longwave Radiation ◽  
The Arctic ◽  
Turbulent Heat ◽  
Arctic Amplification ◽  
Ice Melting ◽  
Downward Longwave Radiation

Abstract. Sea ice melting is proposed as a primary reason for the Artic amplification, although physical mechanism of the Arctic amplification and its connection with sea ice melting is still in debate. In the present study, monthly ERA-interim reanalysis data are analyzed via cyclostationary empirical orthogonal function analysis to understand the seasonal mechanism of sea ice melting in the Arctic Ocean and the Arctic amplification. While sea ice melting is widespread over much of the perimeter of the Arctic Ocean in summer, sea ice remains to be thin in winter only in the Barents-Kara Seas. Excessive turbulent heat flux through the sea surface exposed to air due to sea ice melting warms the atmospheric column. Warmer air increases the downward longwave radiation and subsequently surface air temperature, which facilitates sea surface remains to be ice free. A 1 % reduction in sea ice concentration in winter leads to ~ 0.76 W m−2 increase in upward heat flux, ~ 0.07 K increase in 850 hPa air temperature, ~ 0.97 W m−2 increase in downward longwave radiation, and ~ 0.26 K increase in surface air temperature. This positive feedback mechanism is not clearly observed in the Laptev, East Siberian, Chukchi, and Beaufort Seas, since sea ice refreezes in late fall (November) before excessive turbulent heat flux is available for warming the atmospheric column in winter. A detailed seasonal heat budget is presented in order to understand specific differences between the Barents-Kara Seas and Laptev, East Siberian, Chukchi, and Beaufort Seas.

scholarly journals Climate change impacts on sea-air fluxes of CO<sub>2</sub> in three Arctic seas: a sensitivity study using earth observation

2012 ◽  
Vol 9 (9) ◽  
pp. 12377-12432 ◽  
Author(s):  
P. E. Land ◽  
J. D. Shutler ◽  
R. D. Cowling ◽  
D. K. Woolf ◽  
P. Walker ◽  
...  
Keyword(s):  
Climate Change ◽  
Sea Ice ◽  
Barents Sea ◽  
Earth Observation ◽  
Kara Sea ◽  
Sink Strength ◽  
Arctic Seas ◽  
The Barents Sea ◽  
Co2 Sink ◽  
Positive Effect

Abstract. During 2008 and 2009 we applied coincident Earth observation data collected from multiple sensors (RA2, AATSR and MERIS, mounted on the European Space Agency satellite Envisat) to characterise environmental conditions and net sea-air fluxes of CO2 in three Arctic seas (Greenland, Barents, Kara) to assess net CO2 sink sensitivity due to changes in temperature, salinity and sea ice duration arising from future climate scenarios. During the study period the Greenland and Barents Seas were net sinks for atmospheric CO2, with sea-air fluxes of −34±13 and −13±6 Tg C yr−1, respectively and the Kara Sea was a weak net CO2 source with a sea-air flux of +1.5±1.1 Tg C yr−1. The combined net CO2 sea-air flux from all three was −45±18 Tg C yr−1. In a sensitivity analysis we varied temperature, salinity and sea ice duration. Variations in temperature and salinity led to modification of the transfer velocity, solubility and partial pressure of CO2 taking into account the resultant variations in alkalinity and dissolved organic carbon (DOC). Our results showed that warming had a strong positive effect on the annual net sea-air flux of CO2 (i.e. reducing the sink), freshening had a strong negative effect and reduced sea ice duration had a small but measurable positive effect. In the climate change scenario examined, the effects of warming in just over a decade of climate change up to 2020 outweighed the combined effects of freshening and reduced sea ice duration. Collectively these effects gave a net sea-air flux change of +3.5 Tg C in the Greenland Sea, +5.5 Tg C in the Barents Sea and +1.4 Tg C in the Kara Sea, reducing the Greenland and Barents sinks by 10% and 50% respectively, and increasing the weak Kara Sea source by 64%. Overall, the regional flux changed by +10.4 Tg C, reducing the regional sink by 23%. In terms of CO2 sink strength we conclude that the Barents Sea is the most susceptible of the three regions to the climate changes examined. Our results imply that the region will cease to be a net CO2 sink by 2060.

scholarly journals Frequency and distribution of winter melt events from passive microwave satellite data in the pan-Arctic, 1988–2013

2016 ◽  
Author(s):  
Libo Wang ◽  
Peter Toose ◽  
Ross Brown ◽  
Chris Derksen
Keyword(s):  
Snow Cover ◽  
Microwave Radiometer ◽  
Surface Air Temperature ◽  
Arctic Region ◽  
Temporal Variations ◽  
Passive Microwave ◽  
The Arctic ◽  
Winter Period ◽  
Strongly Correlated ◽  
Air Temperatures

Abstract. This study presents an algorithm for detecting winter melt events in seasonal snow cover based on temporal variations in the brightness temperature difference between 19 and 37 GHz from satellite passive microwave measurements. An advantage of the passive microwave approach is that it is based on the physical presence of liquid water in the snowpack, which may not be the case with melt events inferred from surface air temperature data. The algorithm is validated using in situ observations from weather stations, snowpit surveys, and a surface-based passive microwave radiometer. The results of running the algorithm over the pan-Arctic region (north of 50º N) for the 1988–2013 period show that winter melt days are relatively rare averaging less than 7 melt days per winter over most areas, with higher numbers of melt days (around two weeks per winter) occurring in more temperate regions of the Arctic (e.g. central Quebec and Labrador, southern Alaska, and Scandinavia). The observed spatial pattern was similar to winter melt events inferred with surface air temperatures from ERA-interim and MERRA reanalysis datasets. There was little evidence of trends in winter melt frequency except decreases over northern Europe attributed to a shortening of the duration of the winter period. The frequency of winter melt events is shown to be strongly correlated to the duration of winter period. This must be taken into account when analyzing trends to avoid generating false increasing trends from shifts in the timing of the snow cover season.

Aerosols and their impacts on future Arctic climate change under different emission projections in the GISS-E2.1 Earth system model

2021 ◽  
Author(s):  
Ulas Im ◽  
Kostas Tsigaridis ◽  
Gregory S. Faluvegi ◽  
Peter L. Langen ◽  
Joshua P. French ◽  
...  
Keyword(s):  
Sea Ice ◽  
Earth System Model ◽  
System Model ◽  
The Arctic ◽  
Earth System ◽  
Anthropogenic Aerosols ◽  
Emission Reductions ◽  
Air Temperatures ◽  
The Future ◽  
Emission Projections

&lt;p&gt;In order to study the future aerosol burdens and their radiative and climate impacts over the Arctic (&gt;60 &amp;#176;N), future (2015-2050) simulations have been carried out using the GISS-E2.1 Earth system model. Different future anthrpogenic emission projections have been used from the Eclipse V6b and the Coupled Model Intercomparison Project Phase 6 (CMIP6) databases. Results showed that&amp;#160;Arctic BC, OC and SO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;2-&lt;/sup&gt; burdens decrease significantly in all simulations following the emission projections, with the CMIP6 ensemble showing larger reductions in Arctic aerosol burdens compared to the Eclipse ensemble. For the 2030-2050 period, both the Eclipse Current Legislation (CLE) and the Maximum Feasible Reduction (MFR) ensembles simulated an aerosol top of the atmosphere (TOA) forcing of -0.39&amp;#177;0.01 W m&lt;sup&gt;-2&lt;/sup&gt;, of which -0.24&amp;#177;0.01 W m&lt;sup&gt;-2&lt;/sup&gt; were attributed to the anthropogenic aerosols. The CMIP6 SSP3-7.0 scenario simulated a TOA aerosol forcing of -0.35 W m&lt;sup&gt;-2&lt;/sup&gt; for the same period, while SSP1-2.6 and SSP2-4.5 scenarios simulated a slightly more negative TOA forcing (-0.40 W m&lt;sup&gt;-2&lt;/sup&gt;), of which the anthropogenic aerosols accounted for -0.26 W m&lt;sup&gt;-2&lt;/sup&gt;. The 2030-2050 mean surface air temperatures are projected to increase by 2.1 &amp;#176;C and 2.4 &amp;#176;C compared to the 1990-2010 mean temperature according to the Eclipse CLE and MFR ensembles, respectively, while the CMIP6 simulation calculated an increase of 1.9 &amp;#176;C (SSP1-2.6) to 2.2 &amp;#176;C (SSP3-7.0). Overall, results show that even the scenarios with largest emission reductions lead to similar impact on the future Arctic surface air temperatures compared to scenarios with smaller emission reductions, while scenarios with no or little mitigation leads to much larger sea-ice loss, implying that even though the magnitude of aerosol reductions lead to similar responses in surface air temperatures, high mitigation of aerosols are still necessary to limit sea-ice loss.&amp;#160;&lt;/p&gt;

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