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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Sea Ice Rheology Experiment (SIREx): 2. Evaluating Linear Kinematic Features in High-Resolution Sea Ice Simulations

Simulating sea ice drift and deformation in the Arctic Ocean is still a challenge because of the multiscale interaction of sea ice floes that compose the Arctic Sea ice cover. The Sea Ice Rheology Experiment (SIREx) is a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). In SIREx, skill metrics are designed to evaluate different recently suggested approaches for modeling linear kinematic features (LKFs) to provide guidance for modeling small-scale deformation. These LKFs are narrow bands of localized deformation that can be observed in satellite images and also form in high resolution sea ice simulations. In this contribution, spatial and temporal properties of LKFs are assessed in 36 simulations of state-of-the-art sea ice models and compared to deformation features derived from the RADARSAT Geophysical Processor System. All simulations produce LKFs, but only very few models realistically simulate at least some statistics of LKF properties such as densities, lengths, or growth rates. All SIREx models overestimate the angle of fracture between conjugate pairs of LKFs and LKF lifetimes pointing to inaccurate model physics. The temporal and spatial resolution of a simulation and the spatial resolution of atmospheric boundary condition affect simulated LKFs as much as the model's sea ice rheology and numerics. Only in very high resolution simulations (≤2 km) the concentration and thickness anomalies along LKFs are large enough to affect air-ice-ocean interaction processes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Implementation of Interactive Sea Ice Radiative Transfer into the GISS GCM and Its Impact on Sea Ice Simulation

To improve the representation of solar radiation in sea ice in the Goddard Institute for Space Studies (GISS) climate model (ModelE), we have implemented an advanced radiative transfer (RT) scheme to interactively calculate the surface albedo and transmittance of the ice layer and the solar absorption within. The ice physical properties (temperature, salinity, and density), which are simulated by the thermodynamic module in ModelE, are directly used as the input of the RT scheme to determine the inherent optical properties (IOPs) of brine pockets and air bubbles in the ice layers. This provides a more sophisticated, accurate, and complete treatment of sea ice RT for ModelE. The sea ice RT model is also validated with measured spectral albedo and transmittance for different ice types. Compared with ModelE simulations with current radiative parameterization for sea ice, the new RT implementation will slightly increase the ice surface albedo in the summer in the Arctic and greatly increase the radiative transmittance. Consequently, the ice surface melting is reduced and the base melting is increased but the difference in the simulated ice area is small. The effect of this new RT implementation on the Antarctic ice is negligible. The observed decrease in Arctic sea ice extent is well captured by the ModelE simulation.

Sea ice↗
NASA NTRS2016

Arctic Sea Ice Simulation in the PlioMIP Ensemble

Eight general circulation models have simulated the mid-Pliocene warm period (mid-Pliocene, 3.264 to 3.025 Ma) as part of the Pliocene Modelling Intercomparison Project (PlioMIP). Here, we analyse and compare their simulation of Arctic sea ice for both the pre-industrial period and the mid-Pliocene. Mid-Pliocene sea ice thickness and extent is reduced, and the model spread of extent is more than twice the pre-industrial spread in some summer months. Half of the PlioMIP models simulate ice-free conditions in the mid-Pliocene. This spread amongst the ensemble is in line with the uncertainties amongst proxy reconstructions for mid-Pliocene sea ice extent. Correlations between mid-Pliocene Arctic temperatures and sea ice extents are almost twice as strong as the equivalent correlations for the pre-industrial simulations. The need for more comprehensive sea ice proxy data is highlighted, in order to better compare model performances.

simulation↗
NASA NTRS2003

The Effects of Snow Depth Forcing on Southern Ocean Sea Ice Simulations

The spatial and temporal distribution of snow on sea ice is an important factor for sea ice and climate models. First, it acts as an efficient insulator between the ocean and the atmosphere, and second, snow is a source of fresh water for altering the already weak Southern Ocean stratification. For the Antarctic, where the ice thickness is relatively thin, snow can impact the ice thickness in two ways: a) As mentioned above snow on sea ice reduces the ocean-atmosphere heat flux and thus reduces freezing at the base of the ice flows; b) a heavy snow load can suppress the ice below sea level which causes flooding and, with subsequent freezing, a thickening of the sea ice (snow-to-ice conversion). In this paper, we compare different snow fall paramterizations (incl. the incorporation of satellite-derived snow depth) and study the effect on the sea ice using a sea ice model.

Powel, Dylan C.↗
NASA NTRS1980

Sea ice simulations based on fields generated by the GLAS GCM

The GLAS General Circulation Model (GCM) was applied to the four-month simulation of the thermodynamic part of the Parkinson-Washington sea ice model using atmospheric boundary conditions. The sea ice thickness and distribution were predicted for the Jan. 1-Apr. 30 period using the GCM-fields of solar and infrared radiation, specific humidity and air temperature at the surface, and snow accumulation; the sensible heat and evaporative surface fluxes were consistent with the ground temperatures produced by the ice model and the air temperatures determined by the atmospheric concept. It was concluded that the Parkinson-Washington sea ice model results in acceptable ice concentrations and thicknesses when used with GLAS GCM for the Jan.-Apr. period suggesting the feasibility of fully coupled ice-atmosphere simulations with these two approaches.

Parkinson, C. L.↗
NASA NTRS2011

Evaluation of Arctic Sea Ice Thickness Simulated by AOMIP Models

We compare results from six AOMIP model simulations with estimates of sea ice thickness obtained from ICESat, moored and submarine-based upward looking sensors, airborne electromagnetic measurements and drill holes. Our goal is to find patterns of model performance to guide model improvement. The satellite data is pan-arctic from 2004-2008, ice-draft data is from moored instruments in Fram Strait, the Greenland Sea and the Beaufort Sea from 1992-2008 and from submarines from 1975-2000. The drill hole data are from the Laptev and East Siberian marginal seas from 1982-1986 and from coastal stations from 1998-2009. While there are important caveats when comparing modeled results with measurements from different platforms and time periods such as these, the models agree well with moored ULS data. In general, the AOMIP models underestimate the thickness of measured ice thicker than about 2 m and overestimate thickness of ice thinner than 2 m. The simulated results are poor over the fast ice and marginal seas of the Siberian shelves. Averaging over all observational data sets, the better correlations and smaller differences from observed thickness are from the ECCO2 and UW models.

Johnson, Mark↗
DOE OSTI2023

Simulating Sea-Ice Deformation in Viscous-Plastic Sea-Ice Models With CD-Grids

Linear kinematic features (LKFs) are found everywhere in the Arctic sea-ice cover. They are strongly localized deformations often associated with the formation of leads and pressure ridges. In viscous-plastic (VP) sea-ice models, the simulation of LKFs depends on several factors such as the grid resolution, the numerical solver convergence, and the placement of the variables on the mesh. In this study, we compare two recently proposed discretization with a CD-grid placement with respect to their ability to reproduce LKFs. The first (CD1) is based on a nonconforming finite element discretization, whereas the second (CD2) uses a conforming subgrid discretization. To analyze their resolution properties, we evaluate runs from different models (e.g., FESOM, MPAS) on a benchmark problem using quadrilateral, hexagonal and triangular meshes. Our findings show that the CD1 setup simulates more deformation structure than the CD2 setup. This highlights the importance of the type of spatial discretization for the simulation of LKFs. Due to the higher number of degrees of freedom, both CD-grids resolve more LKFs than traditional A, B, and C-grids at fixed mesh level. This is an advantage of the CD-grid approach, as high spatial mesh resolution is needed in VP sea-ice models to simulate LKFs.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2014

Evaluating the Dominant Components of Warming in Pliocene Climate Simulations

The Pliocene Model Intercomparison Project (PlioMIP) is the first coordinated climate model comparison for a warmer palaeoclimate with atmospheric CO2 significantly higher than pre-industrial concentrations. The simulations of the mid-Pliocene warm period show global warming of between 1.8 and 3.6 C above pre-industrial surface air temperatures, with significant polar amplification. Here we perform energy balance calculations on all eight of the coupled ocean-atmosphere simulations within PlioMIP Experiment 2 to evaluate the causes of the increased temperatures and differences between the models. In the tropics simulated warming is dominated by greenhouse gas increases, with the cloud component of planetary albedo enhancing the warming in most of the models, but by widely varying amounts. The responses to mid-Pliocene climate forcing in the Northern Hemisphere midlatitudes are substantially different between the climate models, with the only consistent response being a warming due to increased greenhouse gases. In the high latitudes all the energy balance components become important, but the dominant warming influence comes from the clear sky albedo, only partially offset by the increases in the cooling impact of cloud albedo. This demonstrates the importance of specified ice sheet and high latitude vegetation boundary conditions and simulated sea ice and snow albedo feedbacks. The largest components in the overall uncertainty are associated with clouds in the tropics and polar clear sky albedo, particularly in sea ice regions. These simulations show that albedo feedbacks, particularly those of sea ice and ice sheets, provide the most significant enhancements to high latitude warming in the Pliocene.

climate models↗
NASA NTRS2012

Evaluation of Arctic Sea Ice Thickness Simulated by Arctic Ocean Model Intercomparison Project Models

Six Arctic Ocean Model Intercomparison Project model simulations are compared with estimates of sea ice thickness derived from pan-Arctic satellite freeboard measurements (2004-2008); airborne electromagnetic measurements (2001-2009); ice draft data from moored instruments in Fram Strait, the Greenland Sea, and the Beaufort Sea (1992-2008) and from submarines (1975-2000); and drill hole data from the Arctic basin, Laptev, and East Siberian marginal seas (1982-1986) and coastal stations (1998-2009). Despite an assessment of six models that differ in numerical methods, resolution, domain, forcing, and boundary conditions, the models generally overestimate the thickness of measured ice thinner than approximately 2 mand underestimate the thickness of ice measured thicker than about approximately 2m. In the regions of flat immobile landfast ice (shallow Siberian Seas with depths less than 25-30 m), the models generally overestimate both the total observed sea ice thickness and rates of September and October ice growth from observations by more than 4 times and more than one standard deviation, respectively. The models do not reproduce conditions of fast ice formation and growth. Instead, the modeled fast ice is replaced with pack ice which drifts, generating ridges of increasing ice thickness, in addition to thermodynamic ice growth. Considering all observational data sets, the better correlations and smaller differences from observations are from the Estimating the Circulation and Climate of the Ocean, Phase II and Pan-Arctic Ice Ocean Modeling and Assimilation System models.

sea ice↗
NASA NTRS2006

Simulations of Sea-Ice Dynamics Using the Material-Point Method

In recent years, the availability of large volumes of recorded ice motion derived from high-resolution SAR data has provided an amazingly detailed look at the deformation of the ice cover. The deformation is dominated by the appearance of linear kinematic features that have been associated with the presence of leads. These remarkable data put us in a position to begin detailed evaluation of current coupled mechanical and thermodynamic models of sea ice. This presentation will describe the material point method (MPM) for solving these model equations. MPM is a numerical method for continuum mechanics that combines the best aspects of Lagrangian and Eulerian discretizations. The material points provide a Lagrangian description of the ice that models convection naturally. Thus, properties such as ice thickness and compactness are computed in a Lagrangian frame and do not suffer from errors associated with Eulerian advection schemes, such as artificial diffusion, dispersion, or oscillations near discontinuities. This desirable property is illustrated by solving transport of ice in uniform, rotational and convergent velocity fields. Moreover, the ice geometry is represented by unconnected material points rather than a grid. This representation facilitates modeling the large deformations observed in the Arctic, as well as localized deformation along leads, and admits a sharp representation of the ice edge. MPM also easily allows the use of any ice constitutive model. The versatility of MPM is demonstrated by using two constitutive models for simulations of wind-driven ice. The first model is a standard viscous-plastic model with two thickness categories. The MPM solution to the viscous-plastic model agrees with previously published results using finite elements. The second model is a new elastic-decohesive model that explicitly represents leads. The model includes a mechanism to initiate leads, and to predict their orientation and width. The elastic-decohesion model can provide similar overall deformation as the viscous-plastic model; however, explicit regions of opening and shear are predicted. Furthermore, the efficiency of MPM with the elastic-decohesive model is competitive with the current best methods for sea ice dynamics. Simulations will also be presented for an area of the Beaufort Sea, where predictions can be validated against satellite observations of the Arctic.

Sulsky, D.↗
NASA NTRS1979

Arctic Sea ice decay simulated for a CO2-induced temperature rise

A large scale numerical time-dependent model of sea ice that takes into account the heat fluxes in and out of the ice, the seasonal occurrence of snow, and ice motions was used in an experiment to determine the response of the Arctic Ocean ice pack to a warming of the atmosphere. The degree of warming specified is that expected for a doubling of atmospheric carbon dioxide with its associated greenhouse effect, a condition that could occur before the middle of the next century. The results of three 5-year simulations with a warmer atmosphere and varied boundary conditions were: (1) that in the face of a 5 K surface atmospheric temperature increase the ice pack disappeared completely in August and September but reformed in the central Arctic Ocean in mid fall; (2) that the simulations were moderately dependence on assumptions concerning cloud cover; and (3) that even when atmospheric temperature increases of 6-9 K were combined with an order-of-magnitude increase in the upward heat flux from the ocean, the ice still appeared in winter. It should be noted that a year-round ice-free Arctic Ocean has apparently not existed for a million years or more.

Heat flux↗
DOE OSTI2023

Assessment of the Pan-Arctic Accelerated Rate of Sea Ice Decline in CMIP6 Historical Simulations

Abstract Arctic sea ice loss in response to a warming climate is assessed in 42 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Sea ice observations show a significant acceleration in the rate of decline commencing near the turn of the twenty-first century. It is our assertion that state-of-the-art climate models should qualitatively reflect this accelerated trend within the limitations of internal variability and observational uncertainty. Our analysis shows that individual CMIP6 simulations of sea ice depict a wide range of model spread on biases and anomaly trends both across models and among their ensemble members. While the CMIP6 multimodel mean captures the observed sea ice area (SIA) decline relatively well, an individual model’s ability to represent the acceleration in sea ice decline remains a challenge. Seventeen (40%) out of 42 CMIP6 models and 37 (13%) out of the total 286 ensemble members reasonably capture the observed trends and acceleration in SIA decline. In addition, a larger ensemble size appears to increase the odds for a model to include at least one ensemble member skillfully representing the accelerated SIA trends. Simulations of sea ice volume (SIV) show much larger spread and uncertainty than SIA; however, due to limited availability of sea ice thickness data, these are not as well constrained by observations. Finally, we find that models with more ocean heat transport simulate larger sea ice declines, which suggests an emergent constraint in CMIP6 ensembles. This relationship points to the need for better understanding and modeling of ice–ocean interactions, especially with respect to frazil ice growth.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Subpolar North Atlantic Water Mass Transformation and Overturning in Eddying and Non‐Eddying Simulations

Buoyancy forcing in the subpolar North Atlantic Ocean (SPNA) is an important driver of the Atlantic Meridional Overturning Circulation (AMOC). To advance understanding of the mechanisms connecting the two processes and their relative importance in sub-basins within the SPNA, we apply the Water Mass Transformation Framework to a matched-pair of forced, ocean-sea ice simulations configured at eddying and non-eddying resolution. Within the first decade of simulation, the non-eddying simulation produces a weak AMOC, ~11 Sv at 26.5 ° N, while the AMOC in the eddying simulation is more realistic and is thus analyzed for comparison. Surface water mass transformation and boundary transport are calculated in both density and temperature–salinity coordinates in three separate deep water formation regions, the Iceland Basin, the Irminger Sea and Labrador Sea, during the first decade of both simulations. We identify strong surface freshening in the Irminger and Labrador seas in the non-eddying simulation during the AMOC decline. This freshening significantly reduces the density of the surface outcrops where surface water mass formation occurs, essentially removing this contribution to deep water formation. Concurrently, boundary transports in these two regions are warmer and saltier in the non-eddying simulation compared to the eddying simulation where the density of surface water mass formation is stable. The warming and salinification of boundary transports in the non-eddying simulation is interpreted as an obstacle to deep water formation. Labrador Sea surface water mass transformation is important, despite its small contribution to buoyancy overturning.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

Improving the Simulation of Sea Ice Lead Conditions and Turbulent Fluxes Using RGPS Products and Merged RADARSAT, AVHRR and MODIS Data

The importance of sea ice leads in the ice-ocean-atmosphere system lies in the fact that each of the boxes in the 'surface processes' interface in this diagram is closely linked to lead conditions. For example, heat, moisture and salt exchange between the Ocean and atmosphere within the ice pack occur nearly entirely through leads. The shear, divergence and convergence associated with lead formation and closure alter surface and basal roughness and topography, which in turn affects momentum transfer in the atmosphere and ocean boundary layers, and modifies the accumulation of snow on the ice surface, which then affects heat conduction and summertime albedo. In addition to providing openings for loss of heat and moisture fluxes to the atmosphere, leads absorb solar energy, which is used to melt ice and is transmitting to the underlying ocean. Given that leads dominate the ice-ocean interface in this manner, then it stands to reason that focusing on lead treatments within models can identify performance limitations of models and yield routes for significant improvements.

Maslanik, James A.↗
NASA NTRS2002

Simulating Snow Over Sea Ice In Climate Models

We have evaluated two methods of simulating the seasonal cycle of snow over sea ice in and around the Arctic: The NCAR global climate model CCM3, with its standard snow hydrology, and the snow pack model SNTHERM, forced with hourly atmospheric output from CCM3. A new dataset providing dates for the onset of snow melt over Arctic sea ice provides a means for assessing basin-wide how well the models simulate melt onset, but contains no information on how long it then takes for all the snow to melt. Use of data from the SHEBA site provides very detailed information on the behavior of the snow before and during the melt season, but only for a very limited area. Russian drift data provide climatological data on the seasonal cycle of snow water equivalent and snow density, over multi-year sea ice in the central Arctic basin. These datasets are used to compare the two modeling methods, and to see if use of the more physically-realistic SNTHERM provides any significant improvements. Conclusions obtained so far include: 1. Both CCM3 and CCM3/SNTHERM do a good job overall of matching the onset of snow melt dataset; although CCM3/SNTHERM consistently trends to underestimate the date and CCM3 to overestimate it. 2. SHEBA and ice drift data for the Arctic show that CCM3/ SNTHERM does a better job than CCM3 at simulating the total melt period. 3. Ice drift snow density and accumulation data suggest that while providing superior results, CCM3/SNTHERM may still suffer from overly vigorous melting. 4. Both the large-scale atmospheric forcing and snow pack physical processes are important in proper simulation of the snow seasonal cycle. Ongoing work includes further diagnosis of CCM3/SNTHERM, use of more observational datasets, especially from marginal seas in the pan-Arctic, and full coupling of SNTHERM into CCM3 (work to date has all been off-line simulations).

Arnold, James E.↗
DOE OSTI2024

A high-resolution pseudo-polygon discrete element model for regional sea ice

Here, this work presents a pseudo-polygon discrete element model for high-resolution sea ice simulations. A scale-invariant bonded particle contact model is proposed to model joints between sea ice floes based on the smeared fracture model and a lattice spring beam model, and the Mohr–Coulomb failure criterion is implemented to represent the shearing failure mechanism of sea ice packings under complex loadings. All mechanical parameters of the bond model can be directly determined from laboratory tests. Validations of the proposed model are made by investigations of mechanical response and failure criteria of field sea ice sheets. Compared with the field observations of sea ice from satellite radar and in situ stress sensors, the proposed model is capable of reproducing the typical constitutive behavior and the Coulomb friction envelope of field sea ice. Finally, the proposed discrete element sea ice model is used to study the effect of loading rates on mechanical behavior including failure strength of regional sea ice.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Geometric remapping of particle distributions in the Discrete Element Model for Sea Ice (DEMSI v0.0)

Abstract. A new sea ice dynamical core, the Discrete Element Model for Sea Ice (DEMSI), is under development for use in coupled Earth system models. DEMSI is based on the discrete element method, which models collections of ice floes as interacting Lagrangian particles. In basin-scale sea ice simulations the Lagrangian motion results in significant convergence and ridging, which requires periodic remapping of sea ice variables from a deformed particle configuration back to an undeformed initial distribution. At the resolution required for Earth system models we cannot resolve individual sea ice floes, so we adopt the sub-grid-scale thickness distribution used in continuum sea ice models. This choice leads to a series of hierarchical tracers depending on ice fractional area or concentration that must be remapped consistently. The circular discrete elements employed in DEMSI help improve the computational efficiency at the cost of increased complexity in the effective element area definitions for sea ice cover that are required for the accurate enforcement of conservation. An additional challenge is the accurate remapping of element values along the ice edge, the location of which varies due to the Lagrangian motion of the particles. In this paper we describe a particle-to-particle remapping approach based on well-established geometric remapping ideas that enforces conservation, bounds preservation, and compatibility between associated tracer quantities, while also robustly managing remapping at the ice edge. One element of the remapping algorithm is a novel optimization-based flux correction that enforces concentration bounds in the case of nonuniform motion. We demonstrate the accuracy and utility of the algorithm in a series of numerical test cases.

58 GEOSCIENCES↗