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 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 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 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.

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).

Estimating the thickness of ridged sea ice from ship observations in the Ross Sea

2003 ◽  
Vol 15 (1) ◽  
pp. 47-54 ◽  
Author(s):  
TINA TIN ◽  
MARTIN O. JEFFRIES ◽  
MIKKO LENSU ◽  
JUKKA TUHKURI
Keyword(s):  
Sea Ice ◽  
Ross Sea ◽  
Low Cost ◽  
Thickness Distribution ◽  
Ice Thickness ◽  
Equivalent Thickness ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice ◽  
Using Data ◽  
Original Formula

Ship-based observations of sea ice thickness using the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol provide information on ice thickness distribution at relatively low cost. This protocol uses a simple formula to calculate the mass of ice in ridges based on surface observations. We present two new formulae and compare these with results from the “Original” formula using data obtained in the Ross Sea in autumn and winter. The new “r-star” formula uses a more realistic ratio of sail and keel areas to transform dimensions of sails to estimates of mean keel areas. As a result, estimates of “equivalent thickness” (i.e. mean thickness of ice in ridged areas) increased by over 200%. The new “Probability” formula goes one step further, by incorporating the probability that a sail is associated with a keel underwater, and the probability that keels may be found under level surfaces. This resulted in estimates of equivalent thickness comparable with the Original formula. Estimates of equivalent thickness at one or two degree latitude resolution are sufficiently accurate for validating sea ice models. Although ridges are small features in the Ross Sea, we have shown that they constitute a significant fraction of the total ice mass.

Advances in Sea Ice Mechanics in the USA

1987 ◽  
Vol 40 (9) ◽  
pp. 1232-1242 ◽  
Author(s):  
Devinder S. Sodhi ◽  
Gordon F. N. Cox
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
Failure Process ◽  
Field Measurements ◽  
Small Scale ◽  
Ice Thickness ◽  
Ice Drift ◽  
Sea Ice Drift ◽  
Ice Failure ◽  
Ice Forces

A brief review of significant advances in the field of sea ice mechanics in the United States is presented in this paper. Emphasis is on ice forces on structures, as the subject relates to development of oil and gas resources in the southern Beaufort Sea. The main topics discussed here are mechanical properties, ice–structure interaction, modeling of sea ice drift, and oil industry research activities. Significant advances in the determination of ice properties are the development of testing procedures to obtain consistent results. Using stiff testing machines, researchers have been able to identify the dependence of tensile and compressive strengths on different parameters, eg, strain rate, temperature, grain size, c-axis orientation, porosity, and state of stress (uniaxial or multiaxial). Now reliable data exist on the tensile and compressive strengths of first-year and multi-year sea ice. Compressive strengths obtained from field testing of large specimens (6 × 3 × 2 m thick) were found to be within 30% of the strengths obtained from small samples tested in laboratory at the same temperature and strain rate as found in the field. Recent advances in the development of constitutive relations and yield criteria have incorporated the concept of damage mechanics to include the effect of microfracturing during the ice failure process. Ice forces generated during an ice–structure interaction are related to ice thickness and properties by conducting analytical or small-scale experimental studies, or both. Field measurements of ice forces have been made to assess the validity of theoretical and small-scale experimental results. There is good agreement between theoretical and small-scale experimental results for ice forces on conical structures. Theoretical elastic buckling loads also agree with the results of small-scale experiments. Though considerable insight has been achieved for ice crushing failure, estimation of ice forces for this mode is based on empirical relations developed from small-scale experiments. A good understanding of the ice failure process has been achieved when ice fails in a single failure mode, but our understanding of multi-modal ice failure still remains poor. Field measurements of effective pressure indicate that it decreases with increasing contact area. Research in fracture mechanics and nonsimultaneous failure is underway to explain this observed trend. Ice ridge formation and pile-up have been modeled, and the forces associated with these processes are estimated to be low. The modeling of sea ice drift has progressed to a point where it is able to determine the extent, thickness distribution, and drift velocity field of sea ice over the entire arctic basin. Components of this model relate to momentum balance, thermodynamic processes, ice thickness distribution, ice strength, and ice rheology.

Analysis of tidal sea-ice movement using a drifting ice beacon array in the Barents Sea

2020 ◽  
Author(s):  
Amey Vasulkar ◽  
Lars Kaleschke ◽  
Martin Verlaan ◽  
Cornelis Slobbe
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Global Ocean ◽  
Series Data ◽  
Small Scale ◽  
Ice Thickness ◽  
Drag Coefficients ◽  
The Barents Sea ◽  
Ice Drift ◽  
Drifting Ice

<p>In an experiment to validate an ice forecast and route optimization system, an array of 15 ice drift beacons/buoys were deployed between Edgeøya and Kong Karls Land in the east of Svalbard to measure the sea ice movement. These beacons recorded data at a sampling frequency of 15 minutes in the duration from March 2014 to May 2014 with different start and end dates based on their life. The particularly short time step captures the small scale effect of tides on the drifting ice. In this region of the Barents Sea, the frequency of the inertial motion is very close to the M2 tidal frequency. Hence, it is not possible to extract the tidal motion from the time series data of the buoys by using a Fourier analysis. It is also likely that these effects will interact. Instead, we develop a physics-based <em>free drift</em> ice model that can simulate the drift at all tidal and other frequencies.</p><p>The model is forced by winds obtained from the ERA5 Reanalysis dataset of ECMWF and ocean currents obtained from the Global Ocean Analysis product of CMEMS. Due to the effect of tides, the model is also forced by the tides obtained from the Global Tide and Surge Model (GTSM v3.0) which is built upon Delft3D-FM unstructured mesh code. This free drift model is validated against 8 of the 15 beacon trajectories. The model along with the observed data can be then be used to obtain insights on the relationship between the sea ice velocities and the tides. This will be particularly useful to obtain the effect of ice drift on tides in tidal models.</p><p>The model uncertainty is mainly due to oceanic and atmospheric drag coefficients, C<sub>dw</sub> and C<sub>da</sub>, respectively, and the sea ice thickness, h<sub>i</sub>. This study also focuses on optimizing the ratio of drag coefficients (C<sub>dw</sub>/C<sub>da</sub>) for the different beacon trajectories while varying the ice thickness between 0.1 m - 1.5 m and the ice-air drag coefficient between (0.5-2.5)x10<sup>-3</sup>. This ratio facilitates the evaluation of the frictional drag between the ice-water interface and thus, helps in determining the effect of ice on tides in tidal models.</p>

Impact of Winds and Southern Ocean SSTs on Antarctic Sea Ice Trends and Variability

2021 ◽  
Vol 34 (3) ◽  
pp. 949-965
Author(s):  
Edward Blanchard-Wrigglesworth ◽  
Lettie A. Roach ◽  
Aaron Donohoe ◽  
Qinghua Ding
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Interannual Variability ◽  
Initial Conditions ◽  
Regional Scale ◽  
Earth System ◽  
Anthropogenic Forcing ◽  
Antarctic Sea Ice ◽  
Long Term Trends

AbstractAntarctic sea ice extent (SIE) has slightly increased over the satellite observational period (1979 to the present) despite global warming. Several mechanisms have been invoked to explain this trend, such as changes in winds, precipitation, or ocean stratification, yet there is no widespread consensus. Additionally, fully coupled Earth system models run under historic and anthropogenic forcing generally fail to simulate positive SIE trends over this time period. In this work, we quantify the role of winds and Southern Ocean SSTs on sea ice trends and variability with an Earth system model run under historic and anthropogenic forcing that nudges winds over the polar regions and Southern Ocean SSTs north of the sea ice to observations from 1979 to 2018. Simulations with nudged winds alone capture the observed interannual variability in SIE and the observed long-term trends from the early 1990s onward, yet for the longer 1979–2018 period they simulate a negative SIE trend, in part due to faster-than-observed warming at the global and hemispheric scale in the model. Simulations with both nudged winds and SSTs show no significant SIE trends over 1979–2018, in agreement with observations. At the regional scale, simulated sea ice shows higher skill compared to the pan-Antarctic scale both in capturing trends and interannual variability in all nudged simulations. We additionally find negligible impact of the initial conditions in 1979 on long-term trends.

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