SEARCH · Search NASA
Results for “ice models”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Ice Accretion Test Results for Three Large-Scale Swept-Wing Models in the NASA Icing Research Tunnel
The design and certification of modern transport airplanes for flight in icing conditions increasing relies on three-dimensional numerical simulation tools for ice accretion prediction. There is currently no publically available, high-quality, ice accretion database upon which to evaluate the performance of icing simulation tools for large-scale swept wings that are representative of modern commercial transport airplanes. The purpose of this presentation is to present the results of a series of icing wind tunnel test campaigns whose aim was to provide an ice accretion database for large-scale, swept wings.
Inverse Electromagnetic Scattering Models for Sea Ice
Inverse scattering algorithms for reconstructing the physical properties of sea ice from scattered electromagnetic field data are developed.
Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under conditions of high ice-shelf basal melt
Abstract. Antarctica's Lambert Glacier drains about one-sixth of the ice from the East Antarctic Ice Sheet and is considered stable due to the strong buttressing provided by the Amery Ice Shelf. While previous projections of the sea-level contribution from this sector of the ice sheet have predicted significant mass loss only with near-complete removal of the ice shelf, the ocean warming necessary for this was deemed unlikely. Recent climate projections through 2300 indicate that sufficient ocean warming is a distinct possibility after 2100. This work explores the impact of parametric uncertainty on projections of the response of the Lambert–Amery system (hereafter “the Amery sector”) to abrupt ocean warming through Bayesian calibration of a perturbed-parameter ice-sheet model ensemble. We address the computational cost of uncertainty quantification for ice-sheet model projections via statistical emulation, which employs surrogate models for fast and inexpensive parameter space exploration while retaining critical features of the high-fidelity simulations. To this end, we build Gaussian process (GP) emulators from simulations of the Amery sector at a medium resolution (4–20 km mesh) using the Model for Prediction Across Scales (MPAS)-Albany Land Ice (MALI) model. We consider six input parameters that control basal friction, ice stiffness, calving, and ice-shelf basal melting. From these, we generate 200 perturbed input parameter initializations using space filling Sobol sampling. For our end-to-end probabilistic modeling workflow, we first train emulators on the simulation ensemble and then calibrate the input parameters using observations of the mass balance, grounding line movement, and calving front movement with priors assigned via expert knowledge. Next, we use MALI to project a subset of simulations to 2300 using ocean and atmosphere forcings from a climate model for both low- and high-greenhouse-gas-emission scenarios. From these simulation outputs, we build multivariate emulators by combining GP regression with principal component dimension reduction to emulate multivariate sea-level contribution time series data from the MALI simulations. We then use these emulators to propagate uncertainty from model input parameters to predictions of glacier mass loss through 2300, demonstrating that the calibrated posterior distributions have both greater mass loss and reduced variance compared to the uncalibrated prior distributions. Parametric uncertainty is large enough through about 2130 that the two projections under different emission scenarios are indistinguishable from one another. However, after rapid ocean warming in the first half of the 22nd century, the projections become statistically distinct within decades. Overall, this study demonstrates an efficient Bayesian calibration and uncertainty propagation workflow for ice-sheet model projections and identifies the potential for large sea-level rise contributions from the Amery sector of the Antarctic Ice Sheet after 2100 under high-greenhouse-gas-emission scenarios.
Computational Modeling Approaches to Multiscale Design of Icephobic Surfaces
To aid in the design of surfaces that prevent icing, a model and computational simulation of impact ice formation at the single droplet scale was implemented. The nucleation of a single supercooled droplet impacting on a substrate, in rime ice conditions, was simulated. Open source computational fluid dynamics (CFD) software was used for the simulation. To aid in the design of surfaces that prevent icing, a model of impact ice formation at the single droplet scale was proposed•No existing model simulates simultaneous impact and freezing of a single super-cooled water droplet•For the 10-week project, a low-fidelity feasibility study was the goal.
Polarimetric Signatures of Sea Ice Part I: Theoretical Model
Physical, structural, and electromagnetic properties and interrelating processes in sea ice are used to develop a composite model for polarimetric backscattering signatures of sea ice.
Modeling South Pacific Ice-Ocean Interactions in the Global Climate System
The objective of this project has been to improve the modeling of interactions between large Antarctic ice shelves and adjacent regions of the Southern Ocean. Our larger goal is to gain a better understanding of the extent to which the ocean controls ice shelf attrition, thereby influencing the size and dynamics of the Antarctic Ice Sheet. Melting and freezing under ice shelves also impacts seawater properties, regional upwelling and sinking and the larger-scale ocean circulation. Modifying an isopycnal coordinate general circulation model for use in sub-ice shelf cavities, we found that the abrupt change in water column thickness at an ice shelf front does not form a strong barrier to buoyancy-driven circulation across the front. Outflow along the ice shelf base, driven by melting of the thickest ice, is balanced by deep inflow. Substantial effort was focused on the Filchner-Ronne cavity, where other models have been applied and time-series records are available from instruments suspended beneath the ice. A model comparison indicated that observed changes in the production of High Salinity Shelf Water could have a major impact on circulation within the cavity. This water propagates into the cavity with an asymmetric seasonal signal that has similar phasing and shape in the model and observations, and can be related to winter production at the sea surface. Even remote parts of the sub-ice shelf cavity are impacted by external forcing on sub-annual time scales. This shows that cavity circulations and products, and therefore cavity shape, will respond to interannual variability in sea ice production and longer-term climate change. The isopycnal model gives generally lower net melt rates than have been obtained from other models and oceanographic data, perhaps due to its boundary layer formulation, or the lack of tidal forcing. Work continues on a manuscript describing the Ross cavity results.
The Effect of Roughness Model on Scattering Properties of Ice Crystals.
We compare stochastic models of microscale surface roughness assuming uniform and Weibull distributions of crystal facet tilt angles to calculate scattering by roughened hexagonal ice crystals using the geometric optics (GO) approximation. Both distributions are determined by similar roughness parameters, while the Weibull model depends on the additional shape parameter. Calculations were performed for two visible wavelengths (864 nm and 410 nm) for roughness values between 0.2 and 0.7 and Weibull shape parameters between 0 and 1.0 for crystals with aspect ratios of 0.21, 1 and 4.8. For this range of parameters we find that, for a given roughness level, varying the Weibull shape parameter can change the asymmetry parameter by up to about 0.05. The largest effect of the shape parameter variation on the phase function is found in the backscattering region, while the degree of linear polarization is most affected at the side-scattering angles. For high roughness, scattering properties calculated using the uniform and Weibull models are in relatively close agreement for a given roughness parameter, especially when a Weibull shape parameter of 0.75 is used. For smaller roughness values, a shape parameter close to unity provides a better agreement. Notable differences are observed in the phase function over the scattering angle range from 5deg to 20deg, where the uniform roughness model produces a plateau while the Weibull model does not.
Potential Link Between Ice Nucleation and Climate Model Spread in Arctic Amplification
Arctic amplification (AA) is simulated by all global climate models, however the spread in the degree of projected warming is large and the underlying mechanisms driving it are poorly understood. The impact of the temperature dependence of immersion freezing on cloud feedbacks and AA is studied using NASA's GEOS-5 model. Parameterizations that exhibit low ice-nucleating particle (INP) concentrations in the high Arctic during summer are found to weaken the cloud-phase feedback. This allows sunlight to readily melt sea-ice in the summer, which decreases the stability of the lower troposphere, causing a decrease in wintertime cloud fraction over open ocean. Arctic amplification was found to span from ∼1.4 to >2.6, which spans 30% of the spread in AA in the coupled model intercomparison project models, depending on the temperature dependence of immersion freezing. These results suggest that summertime INP concentrations may provide an observational constraint on AA.
Sea Ice Motion from Wavelet Analysis of Satellite Data
Wavelet analysis of NASA scatterometer (NSCAT) backscatter and Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) radiance data can be used to obtain daily sea ice drift information for the Arctic region. This technique provides improved spatial coverage over the existing array of Arctic Ocean buoys and better temporal resolution over techniques utilizing data from satellite synthetic aperture radars. Comparisons with ice motion derived from ocean buoys give good quantitative agreement. Both comparison results from NSCAT and SSM/I are compatible, and the results from NSCAT can definitely complement that from SSM/I when there are cloud or surface effects. Then three sea-ice drift daily results from NSCAT, SSM/I, and buoy data can be merged as a composite map by some data fusion techniques. The ice flow streamlines are highly correlated with surface air pressure contours. Examples of derived ice-drift maps in December 1996 illustrate large-scale circulation reversals over a period of four days. A method for deriving divergence and shear at the large-scale has been developed and comparison between buoys and satellite results shows a good agreement. These calibrated/validated results indicate that NSCAT, SSM/I merged daily ice motion are suitably accurate to identify and closely locate sea ice processes, and to improve our current knowledge of sea ice drift and related processes through the data assimilation of ocean-ice numerical model. For demonstration purpose, the ice velocities derived from satellite data are compared with the ice velocities derived from a coupled ice-ocean interaction model. The comparison reveals that the general circulation patterns of the two are quite similar but the ice velocity differences between the two are quite significant. In order to quantify the wind effects on ice motion, empirical orthogonal functions (EOF) are used in the principal component analysis for both ice motion and pressure field. Some preliminary results of sea-ice motion from QuikScat will also be presented.
Ice banding as a response of the coupled ice-ocean system to temporally varying winds
This study models formation of ice bands in the marginal ice zones. A one-dimensional coupled ice-ocean model is used in which the ice model is coupled to a reduced gravity ocean model through interfacial stresses. The internal ice stresses are important only at high ice concentrations (90-100 percent); otherwise, the main balance for the ice motion is between the air-ice stress and the ice-water stress, i.e., free drift. The drag coefficients were chosen so that the air-ice momentum flux is 3 times greater than the air-ocean momentum flux. Thus the Ekman transport is larger under the ice than in the open water, so that winds parallel to the ice edge, with the ice on the right, produce upwelling. The upwelling simulation was extended to include temporally varying forcing, which was chosen to vary sinusoidally with a 4-day period. This forcing resembles successive cyclone passings perpendicular to the ice edge. When the oceanic upper layer was thin, which means that the dynamics are strongly nonlinear, the ice bands were formed. The up/downwelling signals do not disappear in wind reversals because of nonlinear advection. This leads to convergences and divergences in oceanic and ice velocities that manifest themselves as ice banding. At least one wind reversal is needed to produce one ice band.
Sea Ice Outlook for September 2017: June Report - NASA Global Modeling and Assimilation Office
The GMAO seasonal forecast is produced from coupled model integrations that are initialized every five days, with seven additional ensemble members generated by coupled model breeding and initialized on the date closest to the beginning of the month. The main components of the AOGCM are the GEOS-5 atmospheric model, the MOM4 ocean model, and CICE sea ice model. Forecast fields were re-gridded to the passive microwave grid for averaging.
Sea Ice Outlook for September 2017 July Report - NASA Global Modeling and Assimilation Office
The GMAO seasonal forecast is produced from coupled model integrations that are initialized every five days, with seven additional ensemble members generated by coupled model breeding and initialized on the date closest to the beginning of the month. The main components of the AOGCM are the GEOS-5 atmospheric model, the MOM4 ocean model, and CICE sea ice model. Forecast fields were re-gridded to the passive microwave grid for averaging.