scholarly journals Spatial and temporal variability of Arctic ice thickness

1995 ◽  
Vol 21 ◽  
pp. 323-329 ◽  
Author(s):  
Gregory M. Flato
Keyword(s):  
Temporal Variability ◽  
The Arctic ◽  
Ice Thickness ◽  
Spatial And Temporal Variability ◽  
Ice Volume ◽  
Auto Correlation ◽  
Air Temperatures ◽  
East Siberian Sea ◽  
Arctic Ice ◽  
The Laptev Sea

A numerical model is used to study the spatial and temporal variability of ice thickness in the Arctic. The model is run to cyclo-stationary equilibrium, forced with daily varying geostrophic winds and monthly varying surface air temperatures from 1951 to 1990. Decadal-average winter ice-thickness fields exhibit a trend of increasing ice thickness in the East Siberian Sea and somewhat thicker ice overall in the 1980s. Inter-annual variability was largest in the Beaufort and East Siberian Seas and generally lower in the Laptev Sea and central Arctic. Spatial correlation patterns show that ice thickness at a point is correlated with ice thickness over a surrounding area of roughly 4 × 105 km2 with a correlation coefficient of 0.7. Temporal auto-correlation functions indicate that the total ice volume has a correlation time-scale of roughly 7 years, while the volume of ice between 2 and 5 m thick and the volume of ice less than 1 m thick have correlation times of roughly 2 years and 2 months, respectively.

scholarly journals Spatial and temporal variability of Arctic ice thickness

1995 ◽  
Vol 21 ◽  
pp. 323-329 ◽  
Author(s):  
Gregory M. Flato
Keyword(s):  
Temporal Variability ◽  
The Arctic ◽  
Ice Thickness ◽  
Spatial And Temporal Variability ◽  
Ice Volume ◽  
Auto Correlation ◽  
Air Temperatures ◽  
East Siberian Sea ◽  
Arctic Ice ◽  
The Laptev Sea

A numerical model is used to study the spatial and temporal variability of ice thickness in the Arctic. The model is run to cyclo-stationary equilibrium, forced with daily varying geostrophic winds and monthly varying surface air temperatures from 1951 to 1990. Decadal-average winter ice-thickness fields exhibit a trend of increasing ice thickness in the East Siberian Sea and somewhat thicker ice overall in the 1980s. Inter-annual variability was largest in the Beaufort and East Siberian Seas and generally lower in the Laptev Sea and central Arctic. Spatial correlation patterns show that ice thickness at a point is correlated with ice thickness over a surrounding area of roughly 4 × 105 km2 with a correlation coefficient of 0.7. Temporal auto-correlation functions indicate that the total ice volume has a correlation time-scale of roughly 7 years, while the volume of ice between 2 and 5 m thick and the volume of ice less than 1 m thick have correlation times of roughly 2 years and 2 months, respectively.

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 Open Northeast Passage Record - NGC 40 New Global Warming Energy Source

2019 ◽  
Vol 4 (5) ◽  
Keyword(s):  
Energy Source ◽  
Global Warming ◽  
Heat Source ◽  
Planetary Nebula ◽  
The Arctic ◽  
Impact Point ◽  
Ice Volume ◽  
Measles Outbreak ◽  
Arctic Ice ◽  
Loss Of Strength

In 2018 and 2019, the Arctic ice volume was increasing due to the reduction of SN1006 and V606 Aquilae heat delivering incoming debris stream particles or a decrease in strength. When the volume of ice on our planet was increasing in 2018-19, the planet was impacted by the new heat source of planetary nebula, PN, NGC 40. Currently the strength of PN NGC 40 is overcoming the loss of strength of the SN 1006 and V606 Aquilae and the Arctic ice volume started decreasing in March 2019. Particular longitude locations moving eastward from the initial impact point of PN NGC 40 show the effects of the PN NGC 40 hotspot passing over their locations. Shipping time through the Northeast Passage will increase for 2019 and for years thereafter. The ten-year measles outbreak that occurred from 1981 to 1991 will repeat for the period 2019 to 2029.

scholarly journals Satellite-based sea ice thickness changes in the Laptev Sea from 2002 to 2017: comparison to mooring observations

2020 ◽  
Vol 14 (7) ◽  
pp. 2189-2203
Author(s):  
H. Jakob Belter ◽  
Thomas Krumpen ◽  
Stefan Hendricks ◽  
Jens Hoelemann ◽  
Markus A. Janout ◽  
...  
Keyword(s):  
Sea Ice ◽  
Source Region ◽  
European Space Agency ◽  
The Arctic ◽  
Ice Thickness ◽  
Laptev Sea ◽  
Validation Data ◽  
Data Set ◽  
Sea Ice Thickness ◽  
The Laptev Sea

Abstract. The gridded sea ice thickness (SIT) climate data record (CDR) produced by the European Space Agency (ESA) Sea Ice Climate Change Initiative Phase 2 (CCI-2) is the longest available, Arctic-wide SIT record covering the period from 2002 to 2017. SIT data are based on radar altimetry measurements of sea ice freeboard from the Environmental Satellite (ENVISAT) and CryoSat-2 (CS2). The CCI-2 SIT has previously been validated with in situ observations from drilling, airborne remote sensing, electromagnetic (EM) measurements and upward-looking sonars (ULSs) from multiple ice-covered regions of the Arctic. Here we present the Laptev Sea CCI-2 SIT record from 2002 to 2017 and use newly acquired ULS and upward-looking acoustic Doppler current profiler (ADCP) sea ice draft (VAL) data for validation of the gridded CCI-2 and additional satellite SIT products. The ULS and ADCP time series provide the first long-term satellite SIT validation data set from this important source region of sea ice in the Transpolar Drift. The comparison of VAL sea ice draft data with gridded monthly mean and orbit trajectory CCI-2 data, as well as merged CryoSat-2–SMOS (CS2SMOS) sea ice draft, shows that the agreement between the satellite and VAL draft data strongly depends on the thickness of the sampled ice. Rather than providing mean sea ice draft, the considered satellite products provide modal sea ice draft in the Laptev Sea. Ice drafts thinner than 0.7 m are overestimated, while drafts thicker than approximately 1.3 m are increasingly underestimated by all satellite products investigated for this study. The tendency of the satellite SIT products to better agree with modal sea ice draft and underestimate thicker ice needs to be considered for all past and future investigations into SIT changes in this important region. The performance of the CCI-2 SIT CDR is considered stable over time; however, observed trends in gridded CCI-2 SIT are strongly influenced by the uncertainties of ENVISAT and CS2 and the comparably short investigation period.

Winter Arctic sea ice freeboard, snow depth and thickness variability from ICESat-2 and NESOSIM

2021 ◽  
Author(s):  
Alek Petty ◽  
Nicole Keeney ◽  
Alex Cabaj ◽  
Paul Kushner ◽  
Nathan Kurtz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Cloud ◽  
Ice Drift ◽  
Thickness Variability ◽  
Arctic Ice

<div> <div> <div> <div> <p>National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite‐ 2 (ICESat‐2) mission was launched in September 2018 and is now providing routine, very high‐resolution estimates of surface height/type (the ATL07 product) and freeboard (the ATL10 product) across the Arctic and Southern Oceans. In recent work we used snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM) together with ATL10 freeboard data to estimate sea ice thickness across the entire Arctic Ocean. Here we provide an overview of updates made to both the underlying ATL10 freeboard product and the NESOSIM model, and the subsequent impacts on our estimates of sea ice thickness including updated comparisons to the original ICESat mission and ESA’s CryoSat-2. Finally we compare our Arctic ice thickness estimates from the 2018-2019 and 2019-2020 winters and discuss possible causes of these differences based on an analysis of atmospheric data (ERA5), ice drift (NSIDC) and ice type (OSI SAF).</p> </div> </div> </div> </div>

scholarly journals Multidecadal Arctic sea ice thickness and volume derived from ice age

2020 ◽  
Vol 14 (4) ◽  
pp. 1325-1345 ◽  
Author(s):  
Yinghui Liu ◽  
Jeffrey R. Key ◽  
Xuanji Wang ◽  
Mark Tschudi
Keyword(s):  
Sea Ice ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Ice Age ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Volume ◽  
Arctic Sea ◽  
Arctic Sea Ice Thickness

Abstract. Sea ice is a key component of the Arctic climate system, and has impacts on global climate. Ice concentration, thickness, and volume are among the most important Arctic sea ice parameters. This study presents a new record of Arctic sea ice thickness and volume from 1984 to 2018 based on an existing satellite-derived ice age product. The relationship between ice age and ice thickness is first established for every month based on collocated ice age and ice thickness from submarine sonar data (1984–2000) and ICESat (2003–2008) and an empirical ice growth model. Based on this relationship, ice thickness is derived for the entire time period from the weekly ice age product, and the Arctic monthly sea ice volume is then calculated. The ice-age-based thickness and volume show good agreement in terms of bias and root-mean-square error with submarine, ICESat, and CryoSat-2 ice thickness, as well as ICESat and CryoSat-2 ice volume, in February–March and October–November. More detailed comparisons with independent data from Envisat for 2003 to 2010 and CryoSat-2 from CPOM, AWI, and NASA GSFC (Goddard Space Flight Center) for 2011 to 2018 show low bias in ice-age-based thickness. The ratios of the ice volume uncertainties to the mean range from 21 % to 29 %. Analysis of the derived data shows that the ice-age-based sea ice volume exhibits a decreasing trend of −411 km3 yr−1 from 1984 to 2018, stronger than the trends from other datasets. Of the factors affecting the sea ice volume trends, changes in sea ice thickness contribute more than changes in sea ice area, with a contribution of at least 80 % from changes in sea ice thickness from November to May and nearly 50 % in August and September, while less than 30 % is from changes in sea ice area in all months.

scholarly journals Variability of Arctic sea ice thickness and volume from CryoSat-2

2015 ◽  
Vol 373 (2045) ◽  
pp. 20140157 ◽  
Author(s):  
R. Kwok ◽  
G. F. Cunningham
Keyword(s):  
Large Scale ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Notable Increase ◽  
Arctic Ice ◽  
Arctic Sea Ice Thickness ◽  
Volume Estimates

We present our estimates of the thickness and volume of the Arctic Ocean ice cover from CryoSat-2 data acquired between October 2010 and May 2014. Average ice thickness and draft differences are within 0.16 m of measurements from other sources (moorings, submarine, electromagnetic sensors, IceBridge). The choice of parameters that affect the conversion of ice freeboard to thickness is discussed. Estimates between 2011 and 2013 suggest moderate decreases in volume followed by a notable increase of more than 2500 km 3 (or 0.34 m of thickness over the basin) in 2014, which could be attributed to not only a cooler summer in 2013 but also to large-scale ice convergence just west of the Canadian Arctic Archipelago due to wind-driven onshore drift. Variability of volume and thickness in the multiyear ice zone underscores the importance of dynamics in maintaining the thickness of the Arctic ice cover. Volume estimates are compared with those from ICESat as well as the trends in ice thickness derived from submarine ice draft between 1980 and 2004. The combined ICESat and CryoSat-2 record yields reduced trends in volume loss compared with the 5 year ICESat record, which was weighted by the record-setting ice extent after the summer of 2007.

scholarly journals Dynamics and Thermodynamics of the Mean Transpolar Drift and Ice Thickness in the Arctic Ocean

2019 ◽  
Vol 32 (24) ◽  
pp. 8449-8463 ◽  
Author(s):  
Michael A. Spall
Keyword(s):  
Heat Loss ◽  
The Arctic ◽  
Ice Thickness ◽  
Fram Strait ◽  
Ice Volume ◽  
The Arctic Ocean ◽  
Western Arctic ◽  
Transpolar Drift ◽  
The Mean ◽  
Ice Regime

Abstract A theory for the mean ice thickness and the Transpolar Drift in the Arctic Ocean is developed. Asymptotic expansions of the ice momentum and thickness equations are used to derive analytic expressions for the leading-order ice thickness and velocity fields subject to wind stress forcing and heat loss to the atmosphere. The theory is most appropriate for the eastern and central Arctic, but not for the region of the Beaufort Gyre subject to anticyclonic wind stress curl. The scale analysis reveals two distinct regimes: a thin ice regime in the eastern Arctic and a thick ice regime in the western Arctic. In the eastern Arctic, the ice drift is controlled by a balance between wind and ocean drag, while the ice thickness is controlled by heat loss to the atmosphere. In contrast, in the western Arctic, the ice thickness is determined by a balance between wind and internal ice stress, while the drift is indirectly controlled by heat loss to the atmosphere. The southward flow toward Fram Strait is forced by the across-wind gradient in ice thickness. The basic predictions for ice thickness, heat loss, ice volume, and ice export from the theory compare well with an idealized, coupled ocean–ice numerical model over a wide range of parameter space. The theory indicates that increasing atmospheric temperatures or wind speed result in a decrease in maximum ice thickness and ice volume. Increasing temperatures also result in a decrease in heat loss to the atmosphere and ice export through Fram Strait, while increasing winds drive increased heat loss and ice export.

scholarly journals Arctic sea ice thickness loss determined using subsurface, aircraft, and satellite observations

2015 ◽  
Vol 9 (1) ◽  
pp. 269-283 ◽  
Author(s):  
R. Lindsay ◽  
A. Schweiger
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Spatial Scales ◽  
Arctic Basin ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Spatial And Temporal Variability ◽  
Sampling Errors ◽  
Sea Ice Thickness

Abstract. Sea ice thickness is a fundamental climate state variable that provides an integrated measure of changes in the high-latitude energy balance. However, observations of mean ice thickness have been sparse in time and space, making the construction of observation-based time series difficult. Moreover, different groups use a variety of methods and processing procedures to measure ice thickness, and each observational source likely has different and poorly characterized measurement and sampling errors. Observational sources used in this study include upward-looking sonars mounted on submarines or moorings, electromagnetic sensors on helicopters or aircraft, and lidar or radar altimeters on airplanes or satellites. Here we use a curve-fitting approach to determine the large-scale spatial and temporal variability of the ice thickness as well as the mean differences between the observation systems, using over 3000 estimates of the ice thickness. The thickness estimates are measured over spatial scales of approximately 50 km or time scales of 1 month, and the primary time period analyzed is 2000–2012 when the modern mix of observations is available. Good agreement is found between five of the systems, within 0.15 m, while systematic differences of up to 0.5 m are found for three others compared to the five. The trend in annual mean ice thickness over the Arctic Basin is −0.58 ± 0.07 m decade−1 over the period 2000–2012. Applying our method to the period 1975–2012 for the central Arctic Basin where we have sufficient data (the SCICEX box), we find that the annual mean ice thickness has decreased from 3.59 m in 1975 to 1.25 m in 2012, a 65% reduction. This is nearly double the 36% decline reported by an earlier study. These results provide additional direct observational evidence of substantial sea ice losses found in model analyses.

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