scholarly journals Evaluation of AMSR-E Thin Ice Thickness Algorithm from a Mooring-Based Observation: How Can the Satellite Observe a Sea Ice Field with Nonuniform Thickness Distribution?

2019 ◽  
Vol 36 (8) ◽  
pp. 1623-1641 ◽  
Author(s):  
Haruhiko Kashiwase ◽  
Kay I. Ohshima ◽  
Yasushi Fukamachi ◽  
Sohey Nihashi ◽  
Takeshi Tamura
Keyword(s):  
Sea Ice ◽  
Global Climate ◽  
Thickness Distribution ◽  
Global Ocean ◽  
Small Scale ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Coastal Polynya ◽  
Ice Floes ◽  
Ice Production

AbstractThe quantification of sea ice production in coastal polynyas is a key issue to understand the global climate system. In this study, we directly compared Advanced Microwave Scanning Radiometer-EOS (AMSR-E) data with the sea ice thickness distribution obtained from a mooring observation during the winter of 2003 off Sakhalin in the Sea of Okhotsk to evaluate the algorithm for estimation of sea ice thickness in coastal polynyas. By using thermal ice thickness as a target physical quantity, we found that the obtained relationship between the polarization ratio (PR) and ice thickness can provide an appropriate AMSR-E algorithm to estimate thin ice thickness, irrespective of the uniform or nonuniform ice thickness field. The relationship between the PR value and thermal ice thickness is likewise consistent with the local PR–thickness relationship that is observed at individual ice floes. This is because both the PR value and thermal ice thickness are more sensitive to thinner ice. Furthermore, we evaluated the method for detection of active frazil in a coastal polynya by comparing with the mooring data, and subsequently modified it to classify the coastal polynya into three thin ice types, namely, active frazil, thin solid ice, and mixed ice (mixture of active frazil and thin solid ice). The improved algorithm successfully represents the thermal ice thickness even for a relatively small-scale polynya off Sakhalin and is expected to be useful for better quantification of sea ice production in the global ocean owing to its high versatility.

scholarly journals On the influence of model physics on simulations of Arctic and Antarctic sea ice

2011 ◽  
Vol 5 (3) ◽  
pp. 687-699 ◽  
Author(s):  
F. Massonnet ◽  
T. Fichefet ◽  
H. Goosse ◽  
M. Vancoppenolle ◽  
P. Mathiot ◽  
...  
Keyword(s):  
Sea Ice ◽  
Interannual Variability ◽  
Initial Conditions ◽  
Thickness Distribution ◽  
Arctic Sea Ice ◽  
Global Ocean ◽  
Small Scale ◽  
Ice Thickness ◽  
Model Skill ◽  
Antarctic Sea Ice

Abstract. Two hindcast (1983–2007) simulations are performed with the global, ocean-sea ice models NEMO-LIM2 and NEMO-LIM3 driven by atmospheric reanalyses and climatologies. The two simulations differ only in their sea ice component, while all other elements of experimental design (resolution, initial conditions, atmospheric forcing) are kept identical. The main differences in the sea ice models lie in the formulation of the subgrid-scale ice thickness distribution, of the thermodynamic processes, of the sea ice salinity and of the sea ice rheology. To assess the differences in model skill over the period of investigation, we develop a set of metrics for both hemispheres, comparing the main sea ice variables (concentration, thickness and drift) to available observations and focusing on both mean state and seasonal to interannual variability. Based upon these metrics, we discuss the physical processes potentially responsible for the differences in model skill. In particular, we suggest that (i) a detailed representation of the ice thickness distribution increases the seasonal to interannual variability of ice extent, with spectacular improvement for the simulation of the recent observed summer Arctic sea ice retreats, (ii) the elastic-viscous-plastic rheology enhances the response of ice to wind stress, compared to the classical viscous-plastic approach, (iii) the grid formulation and the air-sea ice drag coefficient affect the simulated ice export through Fram Strait and the ice accumulation along the Canadian Archipelago, and (iv) both models show less skill in the Southern Ocean, probably due to the low quality of the reanalyses in this region and to the absence of important small-scale oceanic processes at the models' resolution (~1°).

scholarly journals On the influence of model physics on simulations of Arctic and Antarctic sea ice

2011 ◽  
Vol 5 (2) ◽  
pp. 1167-1200 ◽  
Author(s):  
F. Massonnet ◽  
T. Fichefet ◽  
H. Goosse ◽  
M. Vancoppenolle ◽  
P. Mathiot ◽  
...  
Keyword(s):  
Sea Ice ◽  
Interannual Variability ◽  
Initial Conditions ◽  
Thickness Distribution ◽  
Arctic Sea Ice ◽  
Global Ocean ◽  
Small Scale ◽  
Ice Thickness ◽  
Model Skill ◽  
Antarctic Sea Ice

Abstract. Two hindcast (1983–2007) simulations are performed with the global, ocean-sea ice models NEMO-LIM2 and NEMO-LIM3 driven by atmospheric reanalyses and climatologies. The two simulations differ only in their sea ice component, while all other elements of experimental design (resolution, initial conditions, atmospheric forcing) are kept identical. The main differences in the sea ice models lie in the formulation of the subgrid-scale ice thickness distribution, of the thermodynamic processes, of the sea ice salinity and of the sea ice rheology. To assess the differences in model skill over the period of investigation, we develop a set of metrics for both hemispheres, comparing the main sea ice variables (concentration, thickness and drift) to available observations and focusing on both mean state and seasonal to interannual variability. Based upon these metrics, we discuss the physical processes potentially responsible for the differences in model skill. In particular, we suggest that (i) a detailed representation of the ice thickness distribution increases the seasonal to interannual variability of ice extent, with spectacular improvement for the simulation of the recent observed summer Arctic sea ice retreats, (ii) the elastic-viscous-plastic rheology enhances the response of ice to wind stress, compared to the classical viscous-plastic approach, (iii) the grid formulation and the air-sea ice drag coefficient affect the simulated ice export through Fram Strait and the ice accumulation along the Canadian Archipelago, and (iv) both models show less skill in the Southern Ocean, probably due to the low quality of the reanalyses in this region and to the absence of important small-scale oceanic processes at the models' resolution (~1°).

scholarly journals Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6–LIM3 global ocean–sea ice model

2020 ◽  
Vol 13 (10) ◽  
pp. 4773-4787
Author(s):  
Eduardo Moreno-Chamarro ◽  
Pablo Ortega ◽  
François Massonnet
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
The Arctic ◽  
Global Ocean ◽  
Ice Thickness ◽  
Model Data ◽  
Atmospheric Variability ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Concentration

Abstract. This study assesses the impact of different sea ice thickness distribution (ITD) discretizations on the sea ice concentration (SIC) variability in ocean stand-alone NEMO3.6–LIM3 simulations. Three ITD discretizations with different numbers of sea ice thickness categories and boundaries are evaluated against three different satellite products (hereafter referred to as “data”). Typical model and data interannual SIC variability is characterized by K-means clustering both in the Arctic and Antarctica between 1979 and 2014. We focus on two seasons, winter (January–March) and summer (August–October), in which correlation coefficients across clusters in individual months are largest. In the Arctic, clusters are computed before and after detrending the series with a second-degree polynomial to separate interannual from longer-term variability. The analysis shows that, before detrending, winter clusters reflect the SIC response to large-scale atmospheric variability at both poles, while summer clusters capture the negative and positive trends in Arctic and Antarctic SIC, respectively. After detrending, Arctic clusters reflect the SIC response to interannual atmospheric variability predominantly. The cluster analysis is complemented with a model–data comparison of the sea ice extent and SIC anomaly patterns. The single-category discretization shows the worst model–data agreement in the Arctic summer before detrending, related to a misrepresentation of the long-term melting trend. Similarly, increasing the number of thin categories reduces model–data agreement in the Arctic, due to a poor representation of the summer melting trend and an overly large winter sea ice volume associated with a net increase in basal ice growth. In contrast, more thin categories improve model realism in Antarctica, and more thick ones improve it in central Arctic regions with very thick ice. In all the analyses we nonetheless identify no optimal discretization. Our results thus suggest that no clear benefit in the representation of SIC variability is obtained from increasing the number of sea ice thickness categories beyond the current standard with five categories in NEMO3.6–LIM3.

scholarly journals On the discretization of the ice thickness distribution in the NEMO3.6-LIM3 global ocean–sea ice model

2019 ◽  
Author(s):  
François Massonnet ◽  
Antoine Barthélemy ◽  
Koffi Worou ◽  
Thierry Fichefet ◽  
Martin Vancoppenolle ◽  
...  
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Thickness Distribution ◽  
Circulation Model ◽  
Grid Cell ◽  
The Arctic ◽  
Global Ocean ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Mean State

Abstract. The Ice Thickness Distribution (ITD) is one of the core constituents of modern sea ice models. The ITD accounts for the unresolved spatial variability of sea ice thickness within each model grid cell. While there is a general consensus on the added physical realism brought by the ITD, how to implement it remains an open question. Here, we use the ocean--sea ice general circulation model NEMO3.6-LIM3 forced by atmospheric reanalyses to test how the ITD discretization (number of ice thickness categories, positions of the category boundaries) impacts the simulated mean Arctic and Antarctic sea ice states. We find that winter ice volumes in both hemispheres increase with the number of categories, and attribute that increase to a net enhancement of basal ice growth rates. The range of simulated mean winter volumes in the various experiments amounts to ~ 30 % and ~ 10 % of the reference values (run with 5 categories) in the Arctic and Antarctic, respectively. This suggests that the way the ITD is discretized has a significant influence on the model mean state, all other things being equal. We also find that the existence of a thick category with lower bounds at ~ 4 m and ~ 2 m for the Arctic and Antarctic, respectively, is a prerequisite for allowing the storage of deformed ice, and therefore for fostering thermodynamic growth in thinner categories. Our analysis finally suggests that increasing the resolution of the ITD without changing the lower limit of the upper category results in small but not negligible variations of ice volume and extent. Our study proposes for the first time a bi-polar process-based explanation of the origin of mean state changes when the ITD discretization is modified. The sensitivity experiments conducted in this study, based on one model, emphasize that the choice of category positions, especially of thickest categories, has a primary influence on the simulated mean sea ice states while the number of categories and resolution have only a secondary influence.

scholarly journals Long-term coastal-polynya dynamics in the southern Weddell Sea from MODIS thermal-infrared imagery

2015 ◽  
Vol 9 (6) ◽  
pp. 2027-2041 ◽  
Author(s):  
S. Paul ◽  
S. Willmes ◽  
G. Heinemann
Keyword(s):  
Sea Ice ◽  
Satellite Imagery ◽  
Cloud Cover ◽  
Thickness Distribution ◽  
Thermal Infrared ◽  
Weddell Sea ◽  
Ice Thickness ◽  
Coastal Polynya ◽  
Ice Production

Abstract. Based upon thermal-infrared satellite imagery in combination with ERA-Interim atmospheric reanalysis data, we derive long-term polynya characteristics such as polynya area, thin-ice thickness distribution, and ice-production rates for a 13-year investigation period (2002–2014) for the austral winter (1 April to 30 September) in the Antarctic southern Weddell Sea. All polynya parameters are derived from daily cloud-cover corrected thin-ice thickness composites. The focus lies on coastal polynyas which are important hot spots for new-ice formation, bottom-water formation, and heat/moisture release into the atmosphere. MODIS has the capability to resolve even very narrow coastal polynyas. Its major disadvantage is the sensor limitation due to cloud cover. We make use of a newly developed and adapted spatial feature reconstruction scheme to account for cloud-covered areas. We find the sea-ice areas in front of the Ronne and Brunt ice shelves to be the most active with an annual average polynya area of 3018 ± 1298 and 3516 ± 1420 km2 as well as an accumulated volume ice production of 31 ± 13 and 31 ± 12 km3, respectively. For the remaining four regions, estimates amount to 421 ± 294 km2 and 4 ± 3 km3 (Antarctic Peninsula), 1148 ± 432 km2 and 12 ± 5 km3 (iceberg A23A), 901 ± 703 km2 and 10 ± 8 km3 (Filchner Ice Shelf), as well as 499 ± 277 km2 and 5 ± 2 km3 (Coats Land). Our findings are discussed in comparison to recent studies based on coupled sea-ice/ocean models and passive-microwave satellite imagery, each investigating different parts of the southern Weddell Sea.

scholarly journals Interannual sea ice thickness variability in the Bay of Bothnia

2018 ◽  
Vol 12 (11) ◽  
pp. 3459-3476 ◽  
Author(s):  
Iina Ronkainen ◽  
Jonni Lehtiranta ◽  
Mikko Lensu ◽  
Eero Rinne ◽  
Jari Haapala ◽  
...  
Keyword(s):  
Sea Ice ◽  
Interannual Variability ◽  
Thickness Distribution ◽  
Ice Thickness ◽  
Data Sets ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Thickness Variability ◽  
Fast Ice ◽  
Ridged Ice

Abstract. While variations of Baltic Sea ice extent and thickness have been extensively studied, there is little information about drift ice thickness, distribution, and its variability. In our study, we quantify the interannual variability of sea ice thickness in the Bay of Bothnia during the years 2003–2016. We use various different data sets: official ice charts, drilling data from the regular monitoring stations in the coastal fast ice zone, and helicopter and shipborne electromagnetic soundings. We analyze the different data sets and compare them to each other to characterize the interannual variability, to discuss the ratio of level and deformed ice, and to derive ice thickness distributions in the drift ice zone. In the fast ice zone the average ice thickness is 0.58±0.13 m. Deformed ice increases the variability of ice conditions in the drift ice zone, where the average ice thickness is 0.92±0.33 m. On average, the fraction of deformed ice is 50 % to 70 % of the total volume. In heavily ridged ice regions near the coast, mean ice thickness is approximately half a meter thicker than that of pure thermodynamically grown fast ice. Drift ice exhibits larger interannual variability than fast ice.

scholarly journals East Antarctic sea ice: observations and modelling

1998 ◽  
Vol 27 ◽  
pp. 427-432 ◽  
Author(s):  
Anthony P. Worby ◽  
Xingren Wu
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Numerical Models ◽  
Global Climate ◽  
Thickness Distribution ◽  
Ice Thickness ◽  
Antarctic Sea Ice ◽  
Pack Ice ◽  
Concentration Sensitivity ◽  
Sensitivity Studies

The importance of monitoring sea ice for studies of global climate has been well noted for several decades. Observations have shown that sea ice exhibits large seasonal variability in extent, concentration and thickness. These changes have a significant impact on climate, and the potential nature of many of these connections has been revealed in studies with numerical models. An accurate representation of the sea-ice distribution (including ice extent, concentration and thickness) in climate models is therefore important for modelling global climate change. This work presents an overview of the observed sea-ice characteristics in the East Antarctic pack ice (60-150° E) and outlines possible improvements to the simulation of sea ice over this region by modifying the ice-thickness parameterisation in a coupled sea-ice-atmosphere model, using observational data of ice thickness and concentration. Sensitivity studies indicate that the simulation of East Antarctic sea ice can be improved by modifying both the “lead parameterisation” and “rafting scheme” to be ice-thickness dependent. The modelled results are currently out of phase with the observed data, and the addition of a multilevel ice-thickness distribution would improve the simulation significantly.

scholarly journals New insight from CryoSat-2 sea ice thickness for sea ice modelling

2018 ◽  
Author(s):  
David Schröder ◽  
Danny L. Feltham ◽  
Michel Tsamados ◽  
Andy Ridout ◽  
Rachel Tilling
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Atmospheric Conditions ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Melt Pond ◽  
The Impact

Abstract. Estimates of Arctic sea ice thickness are available from the CryoSat-2 (CS2) radar altimetry mission during ice growth seasons since 2010. We derive the sub-grid scale ice thickness distribution (ITD) with respect to 5 ice thickness categories used in a sea ice component (CICE) of climate simulations. This allows us to initialize the ITD in stand-alone simulations with CICE and to verify the simulated cycle of ice thickness. We find that a default CICE simulation strongly underestimates ice thickness, despite reproducing the inter-annual variability of summer sea ice extent. We can identify the underestimation of winter ice growth as being responsible and show that increasing the ice conductive flux for lower temperatures (bubbly brine scheme) and accounting for the loss of drifting snow results in the simulated sea ice growth being more realistic. Sensitivity studies provide insight into the impact of initial and atmospheric conditions and, thus, on the role of positive and negative feedback processes. During summer, atmospheric conditions are responsible for 50 % of September sea ice thickness variability through the positive sea ice and melt pond albedo feedback. However, atmospheric winter conditions have little impact on winter ice growth due to the dominating negative conductive feedback process: the thinner the ice and snow in autumn, the stronger the ice growth in winter. We conclude that the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season rather than by winter temperature. Our optimal model configuration does not only improve the simulated sea ice thickness, but also summer sea ice concentration, melt pond fraction, and length of the melt season. It is the first time CS2 sea ice thickness data have been applied successfully to improve sea ice model physics.

Sea ice deformation and thickness in the Western Ross Sea

2021 ◽  
Author(s):  
Wolfgang Rack ◽  
Daniel Price ◽  
Christian Haas ◽  
Patricia J. Langhorne ◽  
Greg H. Leonard
Keyword(s):  
Sea Ice ◽  
Satellite Images ◽  
Ross Sea ◽  
Thickness Distribution ◽  
Ice Thickness ◽  
Ice Dynamics ◽  
Sea Ice Thickness ◽  
Ice Volume ◽  
Terra Nova Bay ◽  
Western Ross Sea

<p>Sea ice cover is arguably the longest and best observed climate variable from space, with over four decades of highly reliable daily records of extent in both hemispheres. In Antarctica, a slight positive decadal trend in sea ice cover is driven by changes in the western Ross Sea, where a variation in weather patterns over the wider region forced a change in meridional winds. The distinguishing wind driven sea ice process in the western Ross Sea is the regular occurrence of the Ross Sea, McMurdo Sound, and Terra Nova Bay polynyas. Trends in sea ice volume and mass in this area unknown, because ice thickness and dynamics are particularly hard to measure.</p><p>Here we present the first comprehensive and direct assessment of large-scale sea-ice thickness distribution in the western Ross Sea. Using an airborne electromagnetic induction (AEM) ice thickness sensor towed by a fixed wing aircraft (Basler BT-67), we observed in November 2017 over a distance of 800 km significantly thicker ice than expected from thermodynamic growth alone. By means of time series of satellite images and wind data we relate the observed thickness distribution to satellite derived ice dynamics and wind data. Strong southerly winds with speeds of up to 25 ms<sup>-1</sup> in early October deformed the pack ice, which was surveyed more than a month later.</p><p>We found strongly deformed ice with a mean and maximum thickness of 2.0 and 15.6 m, respectively. Sea-ice thickness gradients are highest within 100-200 km of polynyas, where the mean thickness of the thickest 10% of ice is 7.6 m. From comparison with aerial photographs and satellite images we conclude that ice preferentially grows in deformational ridges; about 43% of the sea ice volume in the area between McMurdo Sound and Terra Nova Bay is concentrated in more than 3 m thick ridges which cover about 15% of the surveyed area. Overall, 80% of the ice was found to be heavily deformed and concentrated in ridges up to 11.8 m thick.</p><p>Our observations hold a link between wind driven ice dynamics and the ice mass exported from the western Ross Sea. The sea ice statistics highlighted in this contribution forms a basis for improved satellite derived mass balance assessments and the evaluation of sea ice simulations.</p>

scholarly journals On the discretization of the ice thickness distribution in the NEMO3.6-LIM3 global ocean–sea ice model

2019 ◽  
Vol 12 (8) ◽  
pp. 3745-3758 ◽  
Author(s):  
François Massonnet ◽  
Antoine Barthélemy ◽  
Koffi Worou ◽  
Thierry Fichefet ◽  
Martin Vancoppenolle ◽  
...  
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Large Scale ◽  
Thickness Distribution ◽  
Circulation Model ◽  
Grid Cell ◽  
The Arctic ◽  
Global Ocean ◽  
Ice Thickness ◽  
Ice Model

Abstract. The ice thickness distribution (ITD) is one of the core constituents of modern sea ice models. The ITD accounts for the unresolved spatial variability of sea ice thickness within each model grid cell. While there is a general consensus on the added physical realism brought by the ITD, how to discretize it remains an open question. Here, we use the ocean–sea ice general circulation model, Nucleus for European Modelling of the Ocean (NEMO) version 3.6 and Louvain-la-Neuve sea Ice Model (LIM) version 3 (NEMO3.6-LIM3), forced by atmospheric reanalyses to test how the ITD discretization (number of ice thickness categories, positions of the category boundaries) impacts the simulated mean Arctic and Antarctic sea ice states. We find that winter ice volumes in both hemispheres increase with the number of categories and attribute that increase to a net enhancement of basal ice growth rates. The range of simulated mean winter volumes in the various experiments amounts to ∼30 % and ∼10 % of the reference values (run with five categories) in the Arctic and Antarctic, respectively. This suggests that the way the ITD is discretized has a significant influence on the model mean state, all other things being equal. We also find that the existence of a thick category with lower bounds at ∼4 and ∼2 m for the Arctic and Antarctic, respectively, is a prerequisite for allowing the storage of deformed ice and therefore for fostering thermodynamic growth in thinner categories. Our analysis finally suggests that increasing the resolution of the ITD without changing the lower limit of the upper category results in small but not negligible variations of ice volume and extent. Our study proposes for the first time a bi-polar process-based explanation of the origin of mean sea ice state changes when the ITD discretization is modified. The sensitivity experiments conducted in this study, based on one model, emphasize that the choice of category positions, especially of thickest categories, has a primary influence on the simulated mean sea ice states while the number of categories and resolution have only a secondary influence. It is also found that the current default discretization of the NEMO3.6-LIM3 model is sufficient for large-scale present-day climate applications. In all cases, the role of the ITD discretization on the simulated mean sea ice state has to be appreciated relative to other influences (parameter uncertainty, forcing uncertainty, internal climate variability).

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