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 Near Real Time Arctic sea ice thickness and volume from CryoSat-2

2016 ◽  
Author(s):  
R. L. Tilling ◽  
A. Ridout ◽  
A. Shepherd
Keyword(s):  
Sea Ice ◽  
Real Time ◽  
Arctic Sea Ice ◽  
Arctic Climate ◽  
The Arctic ◽  
Ice Thickness ◽  
Satellite Orbits ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Arctic Sea Ice Thickness

Abstract. Timely observations of sea ice thickness help us to understand Arctic climate, and can support maritime activities in the Polar Regions. Although it is possible to calculate Arctic sea ice thickness using measurements acquired by CryoSat-2, the latency of the final release dataset is typically one month, due to the time required to determine precise satellite orbits. We use a new fast delivery CryoSat-2 dataset based on preliminary orbits to compute Arctic sea ice thickness in near real time (NRT), and analyse this data for one sea ice growth season from October 2014 to April 2015. We show that this NRT sea ice thickness product is of comparable accuracy to that produced using the final release CryoSat-2 data, with an average thickness difference of 5 cm, demonstrating that the satellite orbit is not a critical factor in determining sea ice freeboard. In addition, the CryoSat-2 fast delivery product also provides measurements of Arctic sea ice thickness within three days of acquisition by the satellite, and a measurement is delivered, on average, within 10, 7 and 6 km of each location in the Arctic every 2, 14 and 28 days respectively. The CryoSat-2 NRT sea ice thickness dataset provides an additional constraint for seasonal predictions of Arctic climate change, and will allow industries such as tourism and transport to navigate the polar oceans with safety and care.

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 Estimating Arctic Sea Ice Thickness with CryoSat-2 Altimetry Data Using the Least Squares Adjustment Method

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7011
Author(s):  
Feng Xiao ◽  
Fei Li ◽  
Shengkai Zhang ◽  
Jiaxing Li ◽  
Tong Geng ◽  
...  
Keyword(s):  
Sea Ice ◽  
Least Squares ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Altimetry Data ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Least Squares Adjustment ◽  
Arctic Sea Ice Thickness

Satellite altimeters can be used to derive long-term and large-scale sea ice thickness changes. Sea ice thickness retrieval is based on measurements of freeboard, and the conversion of freeboard to thickness requires knowledge of the snow depth and snow, sea ice, and sea water densities. However, these parameters are difficult to be observed concurrently with altimeter measurements. The uncertainties in these parameters inevitably cause uncertainties in sea ice thickness estimations. This paper introduces a new method based on least squares adjustment (LSA) to estimate Arctic sea ice thickness with CryoSat-2 measurements. A model between the sea ice freeboard and thickness is established within a 5 km × 5 km grid, and the model coefficients and sea ice thickness are calculated using the LSA method. Based on the newly developed method, we are able to derive estimates of the Arctic sea ice thickness for 2010 through 2019 using CryoSat-2 altimetry data. Spatial and temporal variations of the Arctic sea ice thickness are analyzed, and comparisons between sea ice thickness estimates using the LSA method and three CryoSat-2 sea ice thickness products (Alfred Wegener Institute (AWI), Centre for Polar Observation and Modelling (CPOM), and NASA Goddard Space Flight Centre (GSFC)) are performed for the 2018–2019 Arctic sea ice growth season. The overall differences of sea ice thickness estimated in this study between AWI, CPOM, and GSFC are 0.025 ± 0.640 m, 0.143 ± 0.640 m, and −0.274 ± 0.628 m, respectively. Large differences between the LSA and three products tend to appear in areas covered with thin ice due to the limited accuracy of CryoSat-2 over thin ice. Spatiotemporally coincident Operation IceBridge (OIB) thickness values are also used for validation. Good agreement with a difference of 0.065 ± 0.187 m is found between our estimates and the OIB results.

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 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.

scholarly journals Arctic sea-ice area and volume production:1996/97 versus 1997/98

2002 ◽  
Vol 34 ◽  
pp. 447-453 ◽  
Author(s):  
Ron Kwok
Keyword(s):  
Arctic Ocean ◽  
Large Scale ◽  
Heat Budget ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
The Arctic Ocean ◽  
Volume Production ◽  
Area And Volume

AbstractThe RADARSAT geophysical processor system (RGPS) produces measurements of ice motion and estimates of ice thickness using repeat synthetic aperture radar maps of the Arctic Ocean. From the RGPS products, we compute the net deformation and advection of the winter ice cover using the motion observations, and the seasonal ice area and volume production using the estimates of ice thickness. The results from the winters of 1996/97 and 1997/98 are compared. The second winter is of particular interest because it coincides with the Surface Heat Budget of the Arctic Ocean (SHEBA) field program. The character of the deformation of the ice cover from the two years is very different. Over a domain covering a large part of the western Arctic Ocean (~2.5 × 106 km2), the net divergence of that area during the 6 months of the first winter was 2.7% and for the second winter was 49.3%. In a subregion where the SHEBA camp was located, the net divergence was almost 38% compared to a net divergence of the same subregion of ~9% in 1996/97. The resulting deformation created a much larger volume of seasonal ice than in the earlier year. The net seasonal ice-volume production is 1.6 times (0.38 m vs 0.62 m) that of the first year. In addition to the larger divergence, this part of the ice cover advected a longer distance toward the Chukchi Sea over the same time-span. The total coverage of multi-year ice remained almost identical at ~2.08 × 106 km2, or 83% of the initial area of the domain. In this paper, we compare the behavior of the ice cover over the two winters and discuss these observations in the context of large-scale ice motion and atmospheric-pressure pattern.

scholarly journals Probabilistic Forecasts of Arctic Sea Ice Thickness

2021 ◽  
Author(s):  
Peter A. Gao ◽  
Hannah M. Director ◽  
Cecilia M. Bitz ◽  
Adrian E. Raftery
Keyword(s):  
Sea Ice ◽  
Dynamic Models ◽  
Gaussian Random Field ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Probabilistic Forecasts ◽  
Arctic Sea ◽  
Arctic Sea Ice Thickness

AbstractIn recent decades, warming temperatures have caused sharp reductions in the volume of sea ice in the Arctic Ocean. Predicting changes in Arctic sea ice thickness is vital in a changing Arctic for making decisions about shipping and resource management in the region. We propose a statistical spatio-temporal two-stage model for sea ice thickness and use it to generate probabilistic forecasts up to three months into the future. Our approach combines a contour model to predict the ice-covered region with a Gaussian random field to model ice thickness conditional on the ice-covered region. Using the most complete estimates of sea ice thickness currently available, we apply our method to forecast Arctic sea ice thickness. Point predictions and prediction intervals from our model offer comparable accuracy and improved calibration compared with existing forecasts. We show that existing forecasts produced by ensembles of deterministic dynamic models can have large errors and poor calibration. We also show that our statistical model can generate good forecasts of aggregate quantities such as overall and regional sea ice volume. Supplementary materials accompanying this paper appear on-line.

scholarly journals Brief communication: Arctic sea ice thickness internal variability and its changes under historical and anthropogenic forcing

2019 ◽  
Author(s):  
Guillian Van Achter ◽  
Leandro Ponsoni ◽  
François Massonnet ◽  
Thierry Fichefet ◽  
Vincent Legat
Keyword(s):  
Sea Ice ◽  
Internal Variability ◽  
Temporal Analysis ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Modes Of Variability ◽  
Arctic Sea ◽  
Arctic Sea Ice Thickness

Abstract. We use model simulations from the CESM1-CAM5-BGC-LE dataset to characterise the Arctic sea ice thickness internal variability both spatially and temporally. These properties, and their stationarity, are investigated in three different contexts: (1) constant, pre-industrial, (2) historical and (3) projected conditions. Spatial modes of variability show highly stationary patterns regardless of the forcing and mean state. A temporal analysis reveals two peaks of significant variability and despite a non-stationarity on short time-scales, they remain more or less stable until the first half of the 21st century, where they start to change once summer ice-free events occur, after 2050.

scholarly journals Variability and changes of Arctic sea ice thickness distribution under different AO/DA states

2011 ◽  
Vol 5 (1) ◽  
pp. 131-167
Author(s):  
A. Oikkonen ◽  
J. Haapala
Keyword(s):  
Sea Ice ◽  
Seasonal Variability ◽  
Large Scale ◽  
Thickness Distribution ◽  
Beaufort Sea ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Pack

Abstract. Changes of the mean sea ice thickness and concentration in the Arctic are well known. However, comparable little is known about the ice thickness distribution and the composition of ice pack in quantity. In this paper we determine the ice thickness distributions, mean and modal thicknesses, and their regional and seasonal variability in the Arctic under different large scale atmospheric circulation modes. We compare characteristics of the Arctic ice pack during the periods 1975–1987 and 1988–2000, which have a different distribution in the AO/DA space. The study is based on submarine measurements of sea ice draft. The prevalent feature is that the peak of sea ice thickness distributions has generally taken a narrower form and shifted toward thinner ice. Also, both mean and modal ice thickness have generally decreased. These noticeable changes result from a loss of thick, mostly deformed, ice. In the spring the loss of the volume of ice thicker than 5 m exceeds 35% in all regions except the Nansen Basin, and the reduction is as much as over 45% at the North Pole and in the Eastern Arctic. In the autumn the volume of thick, mostly deformed ice has decreased by more than 40% in the Canada Basin only, but the reduction is more than 30% also in the Beaufort Sea and in the Chukchi Sea. In the Beaufort Sea region the decrease of the modal draft has been so strong that the peak has shifted from multiyear ice to first-year type ice. Also, the regional and seasonal variability of the sea ice thickness has decreased, since the thinning has been the most pronounced in the regions with the thickest pack ice (the Western Arctic), and during the spring (0.6–0.8 m per decade).

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