Sea-ice model validation using submarine measurements of ice draft

AbstractSea-ice thickness distributions from 12 submarine cruises under the North Pole are used to evaluate and enhance the results of sea-ice model simulations. The sea-ice models include versions with cavitating fluid and elastic-viscous-plastic rheologies, and versions with a single thickness and with multiple (5–27) thicknesses in each gridcell. A greater portion of the interannual variance of observed mean thickness at the Pole is captured by the multiple-thickness models than by the single-thickness models, although even the highest correlations are only about 0.6. After The observed thickness distributions are used to ˚tune" the model to capture the primary mode of the distribution, the largest model-data discrepancies are in the thin-ice tail of the distribution. In a 41 year simulation ending in 1998, the model results show a pronounced decrease of mean ice thickness at the Pole around 1990; the minimum simulated thickness occurs in summer 1998. The decrease coincides with a shift of the Arctic Oscillation to its positive phase. The smallest submarine-derived mean thickness occurs in 1990, but no submarine data were available after 1992. The submarine-derived thicknesses for 1991 and 1992 are only slightly smaller than the 12–case mean.

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A Multithickness Sea Ice Model Accounting for Sliding Friction

Journal of Physical Oceanography ◽

10.1175/jpo2937.1 ◽

2006 ◽

Vol 36 (9) ◽

pp. 1719-1738 ◽

Cited By ~ 11

Author(s):

Alexander V. Wilchinsky ◽

Daniel L. Feltham ◽

Paul A. Miller

Keyword(s):

Sea Ice ◽

Sliding Friction ◽

Kinematic Model ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Model Code ◽

Ice Strength ◽

Discrete Element Simulations ◽

Ice Model

Abstract A multithickness sea ice model explicitly accounting for the ridging and sliding friction contributions to sea ice stress is developed. Both ridging and sliding contributions depend on the deformation type through functions adopted from the Ukita and Moritz kinematic model of floe interaction. In contrast to most previous work, the ice strength of a uniform ice sheet of constant ice thickness is taken to be proportional to the ice thickness raised to the 3/2 power, as is revealed in discrete element simulations by Hopkins. The new multithickness sea ice model for sea ice stress has been implemented into the Los Alamos “CICE” sea ice model code and is shown to improve agreement between model predictions and observed spatial distribution of sea ice thickness in the Arctic.

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Using CloudSat snowfall rate observations to constrain and characterize the uncertainties of Arctic snow-on-sea-ice

10.5194/egusphere-egu2020-11436 ◽

2020 ◽

Author(s):

Alex Cabaj ◽

Paul Kushner ◽

Alek Petty ◽

Stephen Howell ◽

Christopher Fletcher

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Snow Depth ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

The Arctic Ocean ◽

Ice Model

Snow on Arctic sea ice plays multiple&#8212;and sometimes contrasting&#8212;roles in several feedbacks between sea ice and the global climate system. For example, the presence of snow on sea ice may mitigate sea ice melt by increasing the sea ice albedo and enhancing the ice-albedo feedback. Conversely, snow can inhibit sea ice growth by insulating the ice from the atmosphere during the sea ice growth season. In addition to its contribution to sea ice feedbacks, snow on sea ice also poses a challenge for sea ice observations. In particular, snow contributes to uncertainties in retrievals of sea ice thickness from satellite altimetry measurements, such as those from ICESat-2. Snow-on-sea-ice models can produce basin-wide snow depth estimates, but these models require snowfall input from reanalysis products. In-situ snowfall measurements are absent over most of the Arctic Ocean, so it can be difficult to determine which reanalysis snowfall product is best suited to be used as input for a snow-on-sea-ice model.In the absence of in-situ snowfall rate measurements, measurements from satellite instruments can be used to quantify snowfall over the Arctic Ocean. The CloudSat satellite, which is equipped with a 94 GHz Cloud Profiling Radar instrument, measures vertical radar reflectivity profiles from which snowfall rates can be retrieved. This instrument provides the most extensive high-latitude snowfall rate observation dataset currently available. CloudSat&#8217;s near-polar orbit enables it to make measurements at latitudes up to 82&#176;N, with a 16-day repeat cycle, over the time period from 2006-2016.We present a calibration of reanalysis snowfall to CloudSat observations over the Arctic Ocean, which we then apply to reanalysis snowfall input for the NASA Eulerian Snow On Sea Ice Model (NESOSIM). This calibration reduces the spread in snow depths produced by NESOSIM when different reanalysis inputs are used. In light of this calibration, we revise the NESOSIM parametrizations of wind-driven snow processes, and we characterize the uncertainties in NESOSIM-generated snow depths resulting from uncertainties in snowfall input. We then extend this analysis further to estimate the resulting uncertainties in sea ice thickness retrieved from ICESat-2 when snow depth estimates from NESOSIM are used as input for the retrieval.

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Sea Ice in the Greenland Polynya in 2018 - A Study with CryoSat-2 and SMOS

10.5194/egusphere-egu2020-5495 ◽

2020 ◽

Author(s):

Weixin Zhu ◽

Lu Zhou ◽

Shiming Xu

Keyword(s):

Sea Ice ◽

Global Climate ◽

Arctic Sea Ice ◽

Climate System ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

The North ◽

Ice Surface ◽

Snow Conditions

AbstractArctic sea ice is a critical component in the global climate system. It affects the climate system by radiating incident heat back into space and regulating ocean-atmosphere heat and momentum. Satellite altimetry such as CryoSat-2 serves as the primary approach for observing sea ice thickness. Nevertheless, the thickness retrieval with CryoSat-2 mainly depends on the height of the ice surface above the sea level, which leads to significant uncertainties over thin ice regimes. The sea ice at the north of Greenland is considered one of the oldest and thickest in the Arctic. However, during late February - early March 2018, a polynya formed north to Greenland due to extra strong southern winds. We focus on the retrieval of sea ice thickness and snow conditions with CryoSat-2 and SMOS during the formation of the polynya. Specifically, we investigate the uncertainty of CryoSat-2 and carry out inter- comparison of sea ice thickness retrieval with SMOS and CryoSat-2/SMOS synergy. Besides, further discussion of retrieval with CryoSat-2 is provided for such scenarios where the m&#233;lange of thick ice and newly formed thin ice is present.

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First results of the Sea-Ice Model Intercomparison Project (SIMIP)

Annals of Glaciology ◽

10.3189/s0260305500013719 ◽

1997 ◽

Vol 25 ◽

pp. 8-11 ◽

Cited By ~ 14

Author(s):

Martin Kreyscher ◽

Markus Harder ◽

Peter Lemke

Keyword(s):

Sea Ice ◽

Fluid Model ◽

North Pole ◽

The Arctic ◽

Fram Strait ◽

Model Intercomparison ◽

The North ◽

Intercomparison Project ◽

Cavitating Fluid ◽

Ice Model

The Sea-Ice Model Intercomparison Project (SIMIP) is part of the activities of the Sea Ice-Ocean Modeling Panel (SIOM) of the Arctic Climate System Study (WMO) (ACSYS) that aims to determine the optimal sea-ice model for climate simulations. This investigation is focused on the dynamics of sea ice. A hierarchy of four sea-ice rheologies is applied, including a viscous-plastic rheology, a cavitating-fluid model, a compressible Newtonian fluid, and a simple scheme with a step-function stoppage for ice drift.For comparison, the same grid, land boundaries and forcing fields are applied to all models. Atmospheric forcing for a 7 year period is obtained from the European Centre for Medium-Range Weather Forecasts (UK) (ECMWF analyses), while occanic forcing consists of annual mean geostrophic currents and heal fluxes into a fixed mixed layer. Daily buoy-drift data monitored by the International Arctic Buoy Program (IABP) and ice thicknesses at the North Pole from submarine upward-looking sonar are available as verification data. The daily drift statistics for separate regions and seasons contribute to an error function showing significant differences between the models. Additionally, Fram Strait ice exports predicted by the different models are investigated. The ice export of the viscous-plastic model amounts to 0.11 Sv. when it is optimized to the mean daily buoy velocities and the observed North Pole ice thicknesses. The cavitating-fluid model yields a very similar Fram Strait outflow, but underestimates the North Pole ice thickness. The two other dynamic schemes predict unrealistically large ice thicknesses in the central Arctic region, while Fram Strait ice exports are too low.

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On the influence of sea-ice physics in multi-decadal ocean-ice hindcasts

10.5194/gmd-2016-187 ◽

2016 ◽

Author(s):

Petteri Uotila ◽

Dorotea Iovino ◽

Martin Vancoppenolle ◽

Mikko Lensu ◽

Clement Rousset

Keyword(s):

Sea Ice ◽

Thickness Distribution ◽

The Arctic ◽

Ice Thickness ◽

Polar Regions ◽

Sea Ice Thickness ◽

Atmospheric Data ◽

Single Category ◽

Ice Model ◽

Thermodynamic Processes

Abstract. A set of hindcast simulations with the new NEMO3.6 ocean-ice model in the ORCA1 grid and forced by the DFS5.2 atmospheric data was performed from 1958–2012. We focussed on simulations that differ only in their sea-ice component: the old standard version LIM2 and its successor LIM3. Main differences between these sea-ice models are the parameterisations of sub-grid-scale sea-ice thickness distribution, ice deformation, thermodynamic processes, and sea-ice salinity. Our main objective was to diagnose the ocean-ice sensitivity to the updated NEMO-LIM sea-ice physics. Results of such analysis have not been published for this new NEMO version. In the polar regions, NEMO-LIM3 compares better with observations, while NEMO-LIM2 deviates more, producing too much ice in the Arctic, for example. Differences between NEMO-LIM2 and NEMO-LIM3 do not change in simulations even when the freshwater adjustments are turned off. In the extra-polar regions, the oceanographic conditions of the two NEMO-LIM versions remain relatively similar, although they slowly drift apart over decades. A simplified NEMO-LIM3 configuration, having a virtual, single-category sea-ice thickness distribution, produced sea ice with a skill sufficient for ocean-ice hindcasts that target oceanographic studies. We conclude that NEMO3.6 is ready to be used as a stand-alone ocean-ice model and as a component of coupled atmosphere-ocean models.

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Influence of initial stratification, wind and sea ice on the modelled oceanic circulation in Nares Strait, northwest Greenland

10.5194/os-2018-122 ◽

2018 ◽

Author(s):

Lovisa Waldrop Bergman ◽

Céline Heuzé

Keyword(s):

High Resolution ◽

Sea Ice ◽

Global Climate ◽

The Arctic ◽

Ice Thickness ◽

Oceanic Circulation ◽

Regional Modelling ◽

Sea Ice Thickness ◽

Nares Strait ◽

The North

Abstract. Nares Strait in northwest Greenland is one of the main gateways for oceanic freshwater and heat exchanges between the Arctic and the North Atlantic. With a changing Arctic climate, understanding the processes that govern the oceanic circulation in Arctic straits has become crucial and urgent, but this cannot be done with current geographically and temporally sparse in-situ observations only. High resolution regional modelling is thus required, but costly. We here report on one-year sensitivity experiments performed with the coupled ice-ocean regional model MITgcm to determine the relative importance of wind forcing, initial stratification and sea ice thickness on the accuracy of the modelled oceanic circulation in Nares Strait. We find that the modelled basin's circulation is mainly driven by density gradients in the upper oceanic layer, making accurate initial fields of temperature and salinity essential for a realistic oceanic circulation. The influence of the wind and sea ice thickness is less important, potentially making such high resolution fields not necessary for accurate strait modelling, provided these results are valid for other sea ice models as well. Comparison with ship-based measurements collected in summer 2015 reveals the experiments to be too cold at the surface, probably because of a not-dynamic-enough sea ice cover. Although the modelled freshwater is rather accurate, large efforts need to be put into observing the ocean and the sources of freshwater continuously throughout the year to produce realistic and efficient model simulations of the Arctic Straits, key players in the entire Arctic system and global climate.

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Improving the WRF model's (version 3.6.1) simulation over sea ice surface through coupling with a complex thermodynamic sea ice model (HIGHTSI)

Geoscientific Model Development ◽

10.5194/gmd-9-2239-2016 ◽

2016 ◽

Vol 9 (6) ◽

pp. 2239-2254 ◽

Cited By ~ 3

Author(s):

Yao Yao ◽

Jianbin Huang ◽

Yong Luo ◽

Zongci Zhao

Keyword(s):

Sea Ice ◽

Heat Budget ◽

Wrf Model ◽

The Arctic ◽

Wave Radiation ◽

Ice Thickness ◽

Sea Ice Thickness ◽

The Impact ◽

Ice Model ◽

Snow And Ice

Abstract. Sea ice plays an important role in the air–ice–ocean interaction, but it is often represented simply in many regional atmospheric models. The Noah sea ice scheme, which is the only option in the current Weather Research and Forecasting (WRF) model (version 3.6.1), has a problem of energy imbalance due to its simplification in snow processes and lack of ablation and accretion processes in ice. Validated against the Surface Heat Budget of the Arctic Ocean (SHEBA) in situ observations, Noah underestimates the sea ice temperature which can reach −10 °C in winter. Sensitivity tests show that this bias is mainly attributed to the simulation within the ice when a time-dependent ice thickness is specified. Compared with the Noah sea ice model, the high-resolution thermodynamic snow and ice model (HIGHTSI) uses more realistic thermodynamics for snow and ice. Most importantly, HIGHTSI includes the ablation and accretion processes of sea ice and uses an interpolation method which can ensure the heat conservation during its integration. These allow the HIGHTSI to better resolve the energy balance in the sea ice, and the bias in sea ice temperature is reduced considerably. When HIGHTSI is coupled with the WRF model, the simulation of sea ice temperature by the original Polar WRF is greatly improved. Considering the bias with reference to SHEBA observations, WRF-HIGHTSI improves the simulation of surface temperature, 2 m air temperature and surface upward long-wave radiation flux in winter by 6, 5 °C and 20 W m−2, respectively. A discussion on the impact of specifying sea ice thickness in the WRF model is presented. Consistent with previous research, prescribing the sea ice thickness with observational information results in the best simulation among the available methods. If no observational information is available, we present a new method in which the sea ice thickness is initialized from empirical estimation and its further change is predicted by a complex thermodynamic sea ice model. The ice thickness simulated by this method depends much on the quality of the initial guess of the ice thickness and the role of the ice dynamic processes.

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

Geoscientific Model Development ◽

10.5194/gmd-13-4773-2020 ◽

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.

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First results of the Sea-Ice Model Intercomparison Project (SIMIP)

Annals of Glaciology ◽

10.1017/s0260305500013719 ◽

1997 ◽

Vol 25 ◽

pp. 8-11 ◽

Cited By ~ 7

Author(s):

Martin Kreyscher ◽

Markus Harder ◽

Peter Lemke

Keyword(s):

Sea Ice ◽

Fluid Model ◽

North Pole ◽

The Arctic ◽

Fram Strait ◽

Model Intercomparison ◽

The North ◽

Intercomparison Project ◽

Cavitating Fluid ◽

Ice Model

The Sea-Ice Model Intercomparison Project (SIMIP) is part of the activities of the Sea Ice-Ocean Modeling Panel (SIOM) of the Arctic Climate System Study (WMO) (ACSYS) that aims to determine the optimal sea-ice model for climate simulations. This investigation is focused on the dynamics of sea ice. A hierarchy of four sea-ice rheologies is applied, including a viscous-plastic rheology, a cavitating-fluid model, a compressible Newtonian fluid, and a simple scheme with a step-function stoppage for ice drift. For comparison, the same grid, land boundaries and forcing fields are applied to all models. Atmospheric forcing for a 7 year period is obtained from the European Centre for Medium-Range Weather Forecasts (UK) (ECMWF analyses), while occanic forcing consists of annual mean geostrophic currents and heal fluxes into a fixed mixed layer. Daily buoy-drift data monitored by the International Arctic Buoy Program (IABP) and ice thicknesses at the North Pole from submarine upward-looking sonar are available as verification data. The daily drift statistics for separate regions and seasons contribute to an error function showing significant differences between the models. Additionally, Fram Strait ice exports predicted by the different models are investigated. The ice export of the viscous-plastic model amounts to 0.11 Sv. when it is optimized to the mean daily buoy velocities and the observed North Pole ice thicknesses. The cavitating-fluid model yields a very similar Fram Strait outflow, but underestimates the North Pole ice thickness. The two other dynamic schemes predict unrealistically large ice thicknesses in the central Arctic region, while Fram Strait ice exports are too low.

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

10.5194/gmd-2019-325 ◽

2019 ◽

Author(s):

Eduardo Moreno-Chamarro ◽

Pablo Ortega ◽

François Massonnet

Keyword(s):

Sea Ice ◽

Thickness Distribution ◽

The Arctic ◽

Ice Thickness ◽

Model Data ◽

Atmospheric Variability ◽

Sea Ice Concentration ◽

Sea Ice Thickness ◽

Ice Concentration ◽

Single Category

Abstract. This study assesses the impact of different sea ice thickness distribution (ITD) configurations on the sea ice concentration (SIC) variability in ocean-standalone NEMO3.6-LIM3 simulations. Three ITD configurations 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 in two seasons, January–March and August–October, which show the largest coherence across clusters in individual months. Analysis in the Arctic is done before and after detrending the series with a 2nd degree polynomial to separate interannual from longer-term variability. Before detrending, winter clusters capture SIC response to atmospheric variability at both poles and summer cluster a positive and negative trend in the Arctic and Antarctic SIC respectively. After detrending, Arctic clusters reflect SIC response to interannual atmospheric variability predominantly. Model–data cluster comparison suggests that no specific ITD configuration or category number increases realism of the simulated Arctic and Antarctic SIC variability in winter. In the Arctic summer, more thin-ice categories decrease model–data agreement without detrending but increase agreement after detrending. Overall, a single-category configuration agrees the worst with data. Direct model–data comparison of SIC anomaly fields shows that more thick-ice categories improve winter SIC variability realism in Central Arctic regions with very thick ice. By contrast, more thin-ice categories reduce model–data agreement in the Central Arctic in summer, due to an overly large simulated sea ice volume. In summary, whereas better resolving thin ice in NEMO3.6-LIM3 can hamper model realism in the Arctic but improve it in Antarctica, more thick-ice categories increase realism in the Arctic winter. And although the single-category configuration performs the worst overall, no optimal configuration is identified. Our results suggest that no clear benefit is obtained from increasing the number of sea ice thickness categories beyond the current usual standard of 5 categories in NEMO3.6-LIM3.

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