Estimation of Arctic Basin-Scale Sea Ice Thickness From Satellite Passive Microwave Measurements

2020 ◽  
pp. 1-10
Author(s):  
Sang-Moo Lee ◽  
Walter N. Meier ◽  
Byung-Ju Sohn ◽  
Hoyeon Shi ◽  
Albin J. Gasiewski
Keyword(s):  
Sea Ice ◽  
Arctic Basin ◽  
Passive Microwave ◽  
Ice Thickness ◽  
Microwave Measurements ◽  
Sea Ice Thickness ◽  
Basin Scale

scholarly journals Optimization of a Sea Ice Model Using Basinwide Observations of Arctic Sea Ice Thickness, Extent, and Velocity

2006 ◽  
Vol 19 (7) ◽  
pp. 1089-1108 ◽  
Author(s):  
Paul A. Miller ◽  
Seymour W. Laxon ◽  
Daniel L. Feltham ◽  
Douglas J. Cresswell
Keyword(s):  
Sea Ice ◽  
Parameter Space ◽  
National Laboratory ◽  
Arctic Sea Ice ◽  
Strength Parameter ◽  
Ice Thickness ◽  
Dimensional Parameter ◽  
Sea Ice Thickness ◽  
Basin Scale ◽  
Ice Model

Abstract A stand-alone sea ice model is tuned and validated using satellite-derived, basinwide observations of sea ice thickness, extent, and velocity from the years 1993 to 2001. This is the first time that basin-scale measurements of sea ice thickness have been used for this purpose. The model is based on the CICE sea ice model code developed at the Los Alamos National Laboratory, with some minor modifications, and forcing consists of 40-yr ECMWF Re-Analysis (ERA-40) and Polar Exchange at the Sea Surface (POLES) data. Three parameters are varied in the tuning process: Ca, the air–ice drag coefficient; P*, the ice strength parameter; and α, the broadband albedo of cold bare ice, with the aim being to determine the subset of this three-dimensional parameter space that gives the best simultaneous agreement with observations with this forcing set. It is found that observations of sea ice extent and velocity alone are not sufficient to unambiguously tune the model, and that sea ice thickness measurements are necessary to locate a unique subset of parameter space in which simultaneous agreement is achieved with all three observational datasets.

scholarly journals A method for sea ice thickness and concentration analysis based on SAR data and a thermodynamic model

2012 ◽  
Vol 6 (6) ◽  
pp. 1507-1526 ◽  
Author(s):  
J. Karvonen ◽  
B. Cheng ◽  
T. Vihma ◽  
M. Arkett ◽  
T. Carrieres
Keyword(s):  
Spatial Distribution ◽  
Sea Ice ◽  
Thickness Distribution ◽  
Added Value ◽  
Ice Thickness ◽  
Motion Field ◽  
Sea Ice Thickness ◽  
Automated Method ◽  
Basin Scale ◽  
Sar Data

Abstract. An analysis of ice thickness distribution is a challenge, particularly in a seasonal sea ice zone with a strongly dynamic ice motion field, such as the Gulf of St. Lawrence off Canada. We present a novel automated method for ice concentration and thickness analysis combining modeling of sea ice thermodynamics and detection of ice motion on the basis of space-borne Synthetic Aperture Radar (SAR) data. Thermodynamic evolution of sea ice thickness in the Gulf of St. Lawrence was simulated for two winters, 2002–2003 and 2008–2009. The basin-scale ice thickness was controlled by atmospheric forcing, but the spatial distribution of ice thickness and concentration could not be explained by thermodynamics only. SAR data were applied to detect ice motion and ice surface structure during these two winters. The SAR analysis is based on estimation of ice motion between SAR image pairs and analysis of the local SAR texture statistics. Including SAR data analysis brought a significant added value to the results based on thermodynamics only. Our novel method combining the thermodynamic modeling and SAR yielded results that well match with the distribution of observations based on airborne Electromagnetic Induction (EM) method. Compared to the present operational method of producing ice charts for the Gulf of St. Lawrence, which is based on visual interpretation of SAR data, the new method reveals much more detailed and physically based information on spatial distribution of ice thickness. The algorithms can be run automatically, and the final products can then be used by ice analysts for operational ice service. The method is globally applicable to all seas where SAR data are available.

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 Sea ice inertial oscillations in the Arctic Basin

2012 ◽  
Vol 6 (5) ◽  
pp. 1187-1201 ◽  
Author(s):  
F. Gimbert ◽  
D. Marsan ◽  
J. Weiss ◽  
N. C. Jourdain ◽  
B. Barnier
Keyword(s):  
Sea Ice ◽  
Arctic Basin ◽  
Original Method ◽  
The Arctic ◽  
Ice Thickness ◽  
Inertial Motion ◽  
Inertial Oscillations ◽  
Dimensional Parameter ◽  
Sea Ice Extent ◽  
Sea Ice Thickness

Abstract. An original method to quantify the amplitude of inertial motion of oceanic and ice drifters, through the introduction of a non-dimensional parameter M defined from a spectral analysis, is presented. A strong seasonal dependence of the magnitude of sea ice inertial oscillations is revealed, in agreement with the corresponding annual cycles of sea ice extent, concentration, thickness, advection velocity, and deformation rates. The spatial pattern of the magnitude of the sea ice inertial oscillations over the Arctic Basin is also in agreement with the sea ice thickness and concentration patterns. This argues for a strong interaction between the magnitude of inertial motion on one hand, the dissipation of energy through mechanical processes, and the cohesiveness of the cover on the other hand. Finally, a significant multi-annual evolution towards greater magnitudes of inertial oscillations in recent years, in both summer and winter, is reported, thus concomitant with reduced sea ice thickness, concentration and spatial extent.

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

<p>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).</p><p>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, 2002–2017. 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 0.4–1.2 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.</p><p>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 1990–2019 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.</p>

Characteristics of Arctic Sea-Ice Thickness Variability in GCMs

2014 ◽  
Vol 27 (21) ◽  
pp. 8244-8258 ◽  
Author(s):  
Edward Blanchard-Wrigglesworth ◽  
Cecilia M. Bitz
Keyword(s):  
Sea Ice ◽  
Time Scale ◽  
Spatial Scales ◽  
Arctic Basin ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Thickness Variability ◽  
Arctic Sea Ice Thickness

Abstract Skillful Arctic sea ice forecasts may be possible for lead times of months or even years owing to the persistence of thickness anomalies. In this study sea ice thickness variability is characterized in fully coupled GCMs and sea ice–ocean-only models (IOMs) that are forced with an estimate of observations derived from atmospheric reanalysis and satellite measurements. Overall, variance in sea ice thickness is greatest along Arctic Ocean coastlines. Sea ice thickness anomalies have a typical time scale of about 6–20 months, a time scale that lengthens about a season when accounting for ice transport, and a typical length scale of about 500–1000 km. The range of these scales across GCMs implies that an estimate of the number of thickness monitoring locations needed to characterize the full Arctic basin sea ice thickness variability field is model dependent and would vary between 3 and 14. Models with a thinner mean ice state tend to have ice-thickness anomalies that are generally shorter lived and smaller in amplitude but have larger spatial scales. Additionally, sea ice thickness variability in IOMs is damped relative to GCMs in part due to strong negative coupling between the dynamic and thermodynamic processes that affect sea ice thickness. The significance for designing prediction systems is discussed.

scholarly journals Thin sea ice thickness as inferred from passive microwave and in situ observations

2008 ◽  
Vol 113 (C2) ◽  
Author(s):  
Kazuhiro Naoki ◽  
Jinro Ukita ◽  
Fumihiko Nishio ◽  
Masashige Nakayama ◽  
Josefino C. Comiso ◽  
...  
Keyword(s):  
Sea Ice ◽  
Passive Microwave ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
In Situ Observations

scholarly journals A method for sea ice thickness and concentration analysis based on SAR data and a thermodynamic model

2012 ◽  
Vol 6 (3) ◽  
pp. 1871-1914 ◽  
Author(s):  
J. Karvonen ◽  
B. Cheng ◽  
T. Vihma ◽  
M. Arkett ◽  
T. Carrieres
Keyword(s):  
Spatial Distribution ◽  
Sea Ice ◽  
Thickness Distribution ◽  
Added Value ◽  
Ice Thickness ◽  
Motion Field ◽  
Visual Interpretation ◽  
Sea Ice Thickness ◽  
Basin Scale ◽  
Sar Data

Abstract. An analysis of ice thickness distribution is a challenge, particularly in a seasonal sea ice zone with strongly dynamic ice motion field, such as the Gulf of St. Lawrence off Canada. We present a method for ice concentration and thickness analysis combining modelling of sea ice thermodynamics and detection of ice motion on the basis of space-borne Synthetic Aperture Radar (SAR) data. Thermodynamic evolution of sea ice thickness in the Gulf of St. Lawrence was simulated for two winters, 2002–2003 and 2008–2009. The basin-scale ice thickness was controlled by atmospheric forcing, but the spatial distribution of ice thickness and concentration could not be explained by thermodynamics only. SAR data were applied to detect ice motion and ice surface structure during these two winters. The SAR analysis is based on estimation of ice motion between SAR image pairs and analysis of the local SAR texture statistics. Including SAR data analysis brought a significant added value to the results based on thermodynamics only. Our novel method combining the thermodynamic modelling and SAR yielded results that well match with the distribution of observations based on airborne Electromagnetic Induction (EM) method. Compared to the present operational method of producing ice charts for the Gulf of St. Lawrence, which is based on visual interpretation of SAR data, the new method reveals much more detailed and physically based information on spatial distribution of ice thickness.

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