scholarly journals A Bayesian approach towards daily pan-Arctic sea ice freeboard estimates from combined CryoSat-2 and Sentinel-3 satellite observations

2021 ◽  
Author(s):  
William Gregory ◽  
Isobel R. Lawrence ◽  
Michel Tsamados
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Gaussian Process Regression ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Uncertainty Estimates ◽  
Spatio Temporal ◽  
Satellite Radar

Abstract. Observations of sea ice freeboard from satellite radar altimeters are crucial in the derivation of sea ice thickness estimates, which in turn inform on sea ice forecasts, volume budgets, and productivity rates. Current spatio-temporal resolution of radar freeboard is limited as 30 days are required in order to generate pan-Arctic coverage from CryoSat-2, or 27 days from Sentinel-3 satellites. This therefore hinders our ability to understand physical processes that drive sea ice thickness variability on sub-monthly time scales. In this study we exploit the consistency between CryoSat-2, Sentinel-3A and Sentinel-3B radar freeboards in order to produce daily gridded pan-Arctic freeboard estimates between December 2018 and April 2019. We use the Bayesian inference approach of Gaussian Process Regression to learn functional mappings between radar freeboard observations in space and time, and to subsequently retrieve pan-Arctic freeboard, as well as uncertainty estimates. The estimated daily fields are, on average across the 2018–2019 season, equivalent to CryoSat-2 and Sentinel-3 freeboards to within 2 mm (standard deviations < 5 cm), and cross-validation experiments show that errors in predictions are, on average, within 3 mm across the same period. We also demonstrate the improved temporal variability of a pan-Arctic daily product by comparing time series of the predicted freeboards, with time series from CryoSat-2 and Sentinel-3 freeboards, across 9 sectors of the Arctic. The mean of predicted and CryoSat-2 or Sentinel-3 time series are generally consistent to within 3 mm, except for the Canadian Archipelago and Greenland, Iceland and Norwegian Seas, which show discrepancies greater than 1 cm due, in part, to biases between CryoSat-2 and Sentinel-3 observations in these locations.

scholarly journals A Bayesian approach towards daily pan-Arctic sea ice freeboard estimates from combined CryoSat-2 and Sentinel-3 satellite observations

2021 ◽  
Vol 15 (6) ◽  
pp. 2857-2871
Author(s):  
William Gregory ◽  
Isobel R. Lawrence ◽  
Michel Tsamados
Keyword(s):  
Sea Ice ◽  
Bayesian Approach ◽  
Gaussian Process Regression ◽  
Temporal Correlation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Uncertainty Estimates ◽  
Spatio Temporal

Abstract. Observations of sea ice freeboard from satellite radar altimeters are crucial in the derivation of sea ice thickness estimates, which in turn provide information on sea ice forecasts, volume budgets, and productivity rates. Current spatio-temporal resolution of radar freeboard is limited as 30 d are required in order to generate pan-Arctic coverage from CryoSat-2 and 27 d are required from Sentinel-3 satellites. This therefore hinders our ability to understand physical processes that drive sea ice thickness variability on sub-monthly timescales. In this study we exploit the consistency between CryoSat-2, Sentinel-3A, and Sentinel-3B radar freeboards in order to produce daily gridded pan-Arctic freeboard estimates between December 2018 and April 2019. We use the Bayesian inference approach of Gaussian process regression to learn functional mappings between radar freeboard observations in space and time and to subsequently retrieve pan-Arctic freeboard as well as uncertainty estimates. We also employ an empirical Bayesian approach towards learning the free (hyper)parameters of the model, which allows us to derive daily estimates related to radar freeboard spatial and temporal correlation length scales. The estimated daily radar freeboard predictions are, on average across the 2018–2019 season, equivalent to CryoSat-2 and Sentinel-3 freeboards to within 1 mm (standard deviations <6 cm), and cross-validation experiments show that errors in predictions are, on average, ≤ 4 mm across the same period. We also demonstrate the improved temporal variability of a pan-Arctic daily product by comparing time series of the predicted freeboards, with 31 d running means from CryoSat-2 and Sentinel-3 freeboards, across nine sectors of the Arctic, as well as making comparisons with daily ERA5 snowfall data. Pearson correlations between daily radar freeboard anomalies and snowfall are as high as +0.52 over first-year ice and +0.41 over multi-year ice, suggesting that the estimated daily fields are able to capture real physical radar freeboard variability at sub-weekly timescales.

scholarly journals A means-corrected estimate for the Arctic sea-ice volume in 1990–2019

2021 ◽  
Author(s):  
Petteri Uotila ◽  
Joula Siponen ◽  
Eero Rinne ◽  
Steffen Tietsche
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Satellite Altimetry ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Volume ◽  
Basin Scale ◽  
Arctic Sea

&lt;p&gt;Decadal changes in sea-ice thickness are one of the most visible signs of climate variability and change. To gain a comprehensive understanding of mechanisms involved, long time series, preferably with good uncertainty estimates, are needed. Importantly, the development of accurate predictions of sea ice in the Arctic requires good observational products. To assist this, a new sea-ice thickness product by ESA Climate Change Initiative (CCI) is compared to a set of five ocean reanalysis (ECCO-V4r4, GLORYS12V1, ORAS5 and PIOMAS).&lt;/p&gt;&lt;p&gt;The CCI product is based on two satellite altimetry missions, CryoSat-2 and ENVISAT, which are combined to the longest continuous satellite altimetry time series of Arctic-wide sea-ice thickness, 2002&amp;#8211;2017. The CCI product performs well in the validation of the reanalyses: overall root-mean-square difference (RMSD) between monthly sea-ice thickness from CCI and the reanalyses ranges from 0.4&amp;#8211;1.2 m. The differences are a sum of reanalysis biases, such as incorrect physics or forcing, as well as uncertainties in satellite altimetry, such as the snow climatology used in the thickness retrieval.&lt;/p&gt;&lt;p&gt;The CCI and reanalysis basin-scale sea-ice volumes have a good match in terms of year-to-year variability and long-term trends but rather different monthly mean climatologies. These findings provide a rationale to construct a multi-decadal sea-ice volume time series for the Arctic Ocean and its sub-basins from 1990&amp;#8211;2019 by adjusting the ocean reanalyses ensemble toward CCI observations. Such a time series, including its uncertainty estimate, provides new insights to the evolution of the Arctic sea-ice volume during the past 30 years.&lt;/p&gt;

scholarly journals Interannual variability in Transpolar Drift summer sea ice thickness and potential impact of Atlantification

2021 ◽  
Vol 15 (6) ◽  
pp. 2575-2591
Author(s):  
H. Jakob Belter ◽  
Thomas Krumpen ◽  
Luisa von Albedyll ◽  
Tatiana A. Alekseeva ◽  
Gerit Birnbaum ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Fram Strait ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Arctic Sea ◽  
Transpolar Drift

Abstract. Changes in Arctic sea ice thickness are the result of complex interactions of the dynamic and variable ice cover with atmosphere and ocean. Most of the sea ice exiting the Arctic Ocean does so through Fram Strait, which is why long-term measurements of ice thickness at the end of the Transpolar Drift provide insight into the integrated signals of thermodynamic and dynamic influences along the pathways of Arctic sea ice. We present an updated summer (July–August) time series of extensive ice thickness surveys carried out at the end of the Transpolar Drift between 2001 and 2020. Overall, we see a more than 20 % thinning of modal ice thickness since 2001. A comparison of this time series with first preliminary results from the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) shows that the modal summer thickness of the MOSAiC floe and its wider vicinity are consistent with measurements from previous years at the end of the Transpolar Drift. By combining this unique time series with the Lagrangian sea ice tracking tool, ICETrack, and a simple thermodynamic sea ice growth model, we link the observed interannual ice thickness variability north of Fram Strait to increased drift speeds along the Transpolar Drift and the consequential variations in sea ice age. We also show that the increased influence of upward-directed ocean heat flux in the eastern marginal ice zones, termed Atlantification, is not only responsible for sea ice thinning in and around the Laptev Sea but also that the induced thickness anomalies persist beyond the Russian shelves and are potentially still measurable at the end of the Transpolar Drift after more than a year. With a tendency towards an even faster Transpolar Drift, winter sea ice growth will have less time to compensate for the impact processes, such as Atlantification, have on sea ice thickness in the eastern marginal ice zone, which will increasingly be felt in other parts of the sea-ice-covered Arctic.

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

&lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite&amp;#8208; 2 (ICESat&amp;#8208;2) mission was launched in September 2018 and is now providing routine, very high&amp;#8208;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&amp;#8217;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).&lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt;

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.

Sea Ice Enhancements to Polar WRF*

2015 ◽  
Vol 143 (6) ◽  
pp. 2363-2385 ◽  
Author(s):  
Keith M. Hines ◽  
David H. Bromwich ◽  
Lesheng Bai ◽  
Cecilia M. Bitz ◽  
Jordan G. Powers ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Surface Model ◽  
Sea Ice Thickness ◽  
Near Surface ◽  
The Arctic Ocean

Abstract The Polar Weather Research and Forecasting Model (Polar WRF), a polar-optimized version of the WRF Model, is developed and made available to the community by Ohio State University’s Polar Meteorology Group (PMG) as a code supplement to the WRF release from the National Center for Atmospheric Research (NCAR). While annual NCAR official releases contain polar modifications, the PMG provides very recent updates to users. PMG supplement versions up to WRF version 3.4 include modified Noah land surface model sea ice representation, allowing the specification of variable sea ice thickness and snow depth over sea ice rather than the default 3-m thickness and 0.05-m snow depth. Starting with WRF V3.5, these options are implemented by NCAR into the standard WRF release. Gridded distributions of Arctic ice thickness and snow depth over sea ice have recently become available. Their impacts are tested with PMG’s WRF V3.5-based Polar WRF in two case studies. First, 20-km-resolution model results for January 1998 are compared with observations during the Surface Heat Budget of the Arctic Ocean project. Polar WRF using analyzed thickness and snow depth fields appears to simulate January 1998 slightly better than WRF without polar settings selected. Sensitivity tests show that the simulated impacts of realistic variability in sea ice thickness and snow depth on near-surface temperature is several degrees. The 40-km resolution simulations of a second case study covering Europe and the Arctic Ocean demonstrate remote impacts of Arctic sea ice thickness on midlatitude synoptic meteorology that develop within 2 weeks during a winter 2012 blocking event.

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