Search NASA⌕ Search

SEARCH · Search NASA

Results for “Ice”

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.

At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Modeling Sea Ice Effects for Wave Energy Resource Assessments

Wave-generated power has potential as a valuable coastal resource, but the wave climate needs to be mapped for feasibility before wave energy converters are installed. Numerical models are used for wave resource assessments to quantify the amount of available power and its seasonality. Alaska is the U.S. state with the longest coastline and has extensive wave resources, but it is affected by seasonal sea ice that dampens the wave energy and the full extent of this dampening is unknown. To accurately characterize the wave resource in regions that experience seasonal sea ice, coastal wave models must account for these effects. The aim of this study is to determine how the dampening effects of sea ice change wave energy resource assessments in the nearshore. Here, we show that by combining high-resolution sea ice imagery with a sea ice/wave dampening parameterization in an unstructured grid, the Simulating Waves Nearshore (SWAN) model improves wave height predictions and demonstrates the extent to which wave power decreases when sea ice is present. The sea ice parametrization decreases the bias and root mean square errors of wave height comparisons with two wave buoys and predicts a decrease in the wave power of up to 100 kW/m in areas around Prince William Sound, Alaska. The magnitude of the improvement of the model/buoy comparison depends on the coefficients used to parameterize the wave–ice interaction.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Arctic Mixed-Phase Cloud Base Ice Precipitation Properties Over the NSA Site

Cloud-climate feedbacks are still the greatest source of uncertainty in current climate projections. Arctic clouds, which are predominantly stratiform and supercooled, often long-lived, and nearly continuously precipitate ice particles, contribute roughly 10% of the uncertainty attributed to the global cloud feedback. This arctic cloud uncertainty is driven by incomplete observational and theoretical knowledge required to estimate and explain the state and active processes occurring in those clouds. A focus on ice precipitation properties at arctic cloud base rather than the surface deconfounds the product of cloud condensate sink processes from the influence of the atmospheric thermodynamic state below cloud base, rendering cloud-base properties a more appealing target for inference and evaluation of model simulations. This data set provides more than 1800 samples of cloud-base ice precipitation properties over Utqiagvik, North Slope of Alaska, all of which were retrieved using the synthesis of ARM radar and lidar measurements. The retrieved ice precipitation variables in this data set include, among others, the ice number concentration, water content, PSD parameters, precipitation rate, mass-weighted fall velocity, vertical air motion, and effective radius, all of which are highly valuable for model evaluation and a general understanding of polar cloud sink processes. Each variable sample includes its mean value and associated uncertainty. Additional variables based on ARM measurements (liquid layer statistics, etc.) are included in this data set. The retrieval algorithm and analysis of this data set are described in Silber (JGR, 2023, https://doi.org/10.1029/2022JD038202).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Beyond the Stokes approximation: shallow visco-elastic ice-sheet models

Abstract The hypothesis that ice-sheet evolution is only controlled by the long-term non-Newtonian viscous behavior of ice has been challenged by observations indicating that effects like brittle failure, stick-slip sliding, tides and wave action may affect ice-sheet evolution on sub-daily timescales. Over these timescales, the quasi-static-creep approximation is no longer appropriate and elastic effects become important. Simulating elastic effects in ice-sheet models over relevant timescales, however, remains challenging. Here, we show that by including a visco-elastic rheology and reintroducing the oft neglected acceleration term back into the ice-sheet stress balance, we can create a visco-elastic system where the velocity is locally determined and information propagates at the elastic wave speed. Crucially, the elastic wave speed can be treated like an adjustable parameter and set to any value to reproduce a range of phenomena, provided the wave speed is large compared to the viscous velocity. We illustrate the system using three examples. The first two examples demonstrate that the system converges to the steady-state viscous and elastic limits. The third example examines ice-shelf rifting and iceberg calving. This final example hints at the utility of the visco-elastic formulation in treating both long-term evolution and short-term environmental effects.

58 GEOSCIENCES↗
DOE OSTI2023

A hybrid deep neural operator/finite element method for ice-sheet modeling

One of the most challenging and consequential problems in climate modeling is to provide probabilistic projections of sea level rise. A large part of the uncertainty of sea level projections is due to uncertainty in ice sheet dynamics. At the moment, accurate quantification of the uncertainty is hindered by the cost of ice sheet computational models. In this work we develop a hybrid approach to approximate existing ice sheet models at a fraction of their cost. Our approach consists of replacing the finite element model for the momentum equations for the ice velocity, the most expensive part of an ice sheet model, with a Deep Operator Network, while we retain a classic finite element discretization for the evolution of the ice thickness. We show that the resulting hybrid model is very accurate and it is an order of magnitude faster than the traditional finite element model. Further, a distinctive feature of the proposed model, compared to other neural network approaches, is that it can handle high-dimensional parameter spaces (parameter fields) such as the basal friction at the bed of the glacier and can therefore be used for generating samples for uncertainty quantification. Further, we study the impact of hyper-parameters, number of unknowns and correlation length of the parameter distribution on the training and accuracy of the Deep Operator Network on a synthetic ice sheet model. We then target the evolution of the Humboldt glacier in Greenland and show that our hybrid model can provide accurate statistics of the glacier mass loss and can be effectively used to accelerate the quantification of uncertainty.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Near-surface seismic anisotropy in Antarctic glacial snow and ice revealed by high-frequency ambient noise

Ambient seismic recordings taken at broad locations across Ross Ice Shelf and a dense array near West Antarctic Ice Sheet (WAIS) Divide, Antarctica, show pervasive temporally variable resonance peaks associated with trapped seismic waves in near-surface firn layers. These resonance peaks feature splitting on the horizontal components, here interpreted as frequency-dependent anisotropy in the firn and underlying ice due to several overlapping mechanisms driven by ice flow. Frequency peak splitting magnitudes and fast/slow axes were systematically estimated at single stations using a novel algorithm and compared with good agreement with active source anisotropy measurements at WAIS Divide determined via active sources recorded on a 1 km circular array. The approach was further applied to the broad Ross Ice Shelf (RIS) array, where anisotropy axes were directly compared with visible surface features and ice shelf flow lines. The near-surface firn, depicted by anisotropy above 30 Hz, was shown to exhibit a novel plastic stretching mechanism of anisotropy, whereby the fast direction in snow aligns with accelerating ice shelf flow.

58 GEOSCIENCES↗
DOE OSTI2026

Engineering Antifreeze Proteins to Optimally Resist Engulfment by Ice

Antifreeze proteins (AFPs) facilitate the survival of organisms in cold climates by inhibiting the growth and/or recrystallization of ice. To function, AFPs must first bind to ice crystals; bound AFPs must then resist engulfment by using their nonbinding side (NBS) to pin the ice–water interface. Here, we seek to understand how the molecular characteristics of an NBS, such as its ice-phobicity or shape, influence its ability to resist engulfment. By characterizing the free energy barriers that impede the engulfment of model AFPs, we find that the critical supercooling ΔT*, above which an AFP is engulfed, is dictated by an optimal pinning site on the NBS. We further find that the optimal pinning site is determined by an interplay between the contact line perimeter P and a pinning efficiency η, with ΔT* ∝ ηP at the optimal pinning site. For a hemispherical AFP, which displays progressively inward tapering, we find that η increases during engulfment, whereas P decreases; conversely, an NBS with outward tapering can achieve high P, but it suffers from low η. Because the product of η and P determines ΔT*, the inverse correlation between them limits ΔT. To circumvent such limiting behavior, we propose an NBS shape with an outward bulge; by initially tapering outward, a bulged NBS permits higher P, and by subsequently tapering inward, it promotes high η as well. Importantly, we find that ΔT* is enhanced by more than a factor of 2 with an outward bulge of only 1 nm. We also find that the more ice-phobic an NBS is, the more efficiently it pins the ice–water interface, resulting in a higher ΔT. Furthermore, our findings shed light on how the NBS molecular characteristics influence ΔT* and suggest strategies for engineering the NBS to optimally resist engulfment by ice.

Antifreeze↗
DOE OSTI2021

Global Importance of Secondary Ice Production

Measured ice crystal number concentrations are often orders of magnitude higher than the number concentrations of ice nucleating particles, indicating the existence of secondary ice production (SIP) in clouds. Here, we present the first study to examine the global importance of SIP through the droplet shattering during freezing of rain, ice-ice collision fragmentation, and rime splintering, using a global climate model. Our results show that SIP happens quite uniformly in the two hemispheres and dominates the ice formation in the moderately cold clouds with temperatures warmer than –15°C. SIP decreases the global annual average liquid water path by –14.6 g m –2 (–22%), increases the ice water path by 8.7 g m –2 (23%), improving the model agreement with observations. SIP changes the global annual average shortwave, longwave, and net cloud forcing by 2.1, –1.0, and 1.1 W m –2 , respectively, highlighting the importance of SIP on cloud properties on the global scale.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Impact of Ural Blocking on Early Winter Climate Variability Under Different Barents‐Kara Sea Ice Conditions

Abstract Ural blocking (UB) is a prominent mode of variability of the Northern Hemisphere atmospheric circulation, particularly in fall. It can persist for several days and exert a lagged influence on the wintertime NH circulation, providing predictability at the subseasonal time scale. Using two atmospheric models, we explore how the early winter atmospheric circulation responds to a 2‐week persistent UB anomaly imposed in early November. Experiments are carried out with two different configurations of Barents‐Kara (BK) sea‐ice concentration to examine whether it plays a role in how UB impacts atmospheric variability. In both models, the UB anomaly is followed by a weakening of the stratospheric polar vortex and a negative phase of the North Atlantic Oscillation (NAO), which lasts up to 2 months after the forcing is released. Interestingly, the response is more persistent under low BK sea‐ice conditions, that is, BK sea ice modulates the atmospheric response to UB. Additional experiments with prescribed sea ice concentration anomalies alone suggest that BK sea ice exerts a limited influence on early winter NH atmospheric variability. The response to UB involves a weakening of the polar vortex that persists longer under low BK sea ice, which explains the more persistent response in that configuration. Our study highlights that UB variability in November is a robust precursor for early winter NAO/polar stratosphere anomalies, and this may be more relevant in the context of declining Arctic sea‐ice extent. Provided that climate models accurately capture this teleconnection, it has the potential to improve subseasonal predictions of the NH wintertime climate.

Meteorology & Atmospheric Sciences↗
DOE OSTI2024

Modeling the Winter Heat Conduction Through the Sea Ice System During MOSAiC

Abstract Models struggle to accurately simulate observed sea ice thickness changes, which could be partially due to inadequate representation of thermodynamic processes. We analyzed co‐located winter observations of the Arctic sea ice from the Multidisciplinary Drifting Observatory for the Study of the Arctic Climate for evaluating and improving thermodynamic processes in sea ice models, aiming to enable more accurate predictions of the warming climate system. We model the sea ice and snow heat conduction for observed transects forced by realistic boundary conditions to understand the impact of the non‐resolved meter‐scale snow and sea ice thickness heterogeneity on horizontal heat conduction. Neglecting horizontal processes causes underestimating the conductive heat flux of 10% or more. Furthermore, comparing model results to independent temperature observations reveals a ∼5 K surface temperature overestimation over ice thinner than 1 m, attributed to shortcomings in parameterizing surface turbulent and radiative fluxes rather than the conduction. Assessing the model deficiencies and parameterizing these unresolved processes is required for improved sea ice representation.

Geology↗
DOE OSTI2023

Investigating Future Arctic Sea Ice Loss and Near–Surface Wind Speed Changes Related to Surface Roughness Using the Community Earth System Model

The Arctic is undergoing a pronounced and rapid transformation in response to changing greenhouse gasses, including reduction in sea ice extent and thickness. There are also projected increases in near-surface Arctic wind. This study addresses how the winds trends may be driven by changing surface roughness and/or stability in different Arctic regions and seasons, something that has not yet been thoroughly investigated. We analyze 50 experiments from the Community Earth System Model Version 2 (CESM2) Large Ensemble and five experiments using CESM2 with an artificially decreased sea ice roughness to match that of the open ocean. We find that with a smoother surface there are higher mean wind speeds and slower mean ice speeds in the autumn, winter, and spring. The artificially reduced surface roughness also strongly impacts the wind speed trends in autumn and winter, and we find that atmospheric stability changes are also important contributors to driving wind trends in both experiments. In contrast to the clear impacts on winds, the sea ice mean state and trends are statistically indistinguishable, suggesting that near-surface winds are not major drivers of Arctic sea ice loss. Two major results of this work are: (a) the near-surface wind trends are driven by changes in both surface roughness and near-surface atmospheric stability that are themselves changing from sea ice loss, and (b) the sea ice mean state and trends are driven by the overall warming trend due to increasing greenhouse gas emissions and not significantly impacted by coupled feedbacks with the surface winds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Ice Supersaturation Variability in Cirrus Clouds: Role of Vertical Wind Speeds and Deposition Coefficients

Abstract Aircraft measurements reveal ice supersaturation statistics in cirrus (ISSs) with broad maxima around ice saturation and pronounced variance. In this study, processes shaping ISSs in midlatitude and tropical upper tropospheric conditions are systematically investigated. Water vapor deposition and sublimation of size‐resolved ice crystal populations are simulated in an air parcel framework. Mesoscale temperature fluctuations (MTFs) due to gravity waves force the temporal evolution of supersaturation. Various levels of background wave forcing and cirrus thickness are distinguished in stochastic ensemble simulations. Kinetic limitations to ice mass growth are brought about by supersaturation‐dependent deposition coefficients that represent efficient and inefficient growth modes as a function of ice crystal size. The simulations identify a wide range of deposition coefficients in cirrus, but most values stay above 0.01 such that kinetic limitations to water uptake remain moderate. Supersaturation quenching times are long, typically 0.5–2 hr. The wave forcing thus causes a remarkably large variability in ISSs and cirrus microphysical properties except in the thickest cirrus, producing ensemble‐mean ISSs in line with in‐situ measurements. ISS variance is controlled by MTFs and increases with decreasing cirrus integral radii. In comparison, the impact of ice crystal growth rates on ISSs is small. These results contribute to efforts directed at identifying and solving issues associated with ice‐supersaturated areas and non‐equilibrium cirrus physics in global models.

Meteorology & Atmospheric Sciences↗
DOE OSTI2023

Stratospheric Aerosol Injection Can Reduce Risks to Antarctic Ice Loss Depending on Injection Location and Amount

Owing to increasing greenhouse gas emissions, the Antarctic Ice Sheet is vulnerable to rapid ice loss in the upcoming decades and centuries. Here this study examines the effectiveness of using stratospheric aerosol injection (SAI) that minimizes global mean temperature (GMT) change to slow projected 21st century Antarctic ice loss. We simulate 11 different SAI cases which vary by the latitudinal location(s) and the amount(s) of the injection(s) to examine the climatic response near Antarctica in each case as compared to the reference climate at the turn of the last century. We demonstrate that injecting at a single latitude in the northern hemisphere or at the Equator increases Antarctic shelf ocean temperatures pertinent to ice shelf basal melt, while injecting only in the southern hemisphere minimizes this temperature change. We use these results to analyze the results of more complex multi-latitude injection strategies that maintain GMT at or below 1.5°C above the pre-industrial. All these multi-latitude cases will slow Antarctic ice loss relative to the mid-to-late 21st century SSP2-4.5 emissions pathway. Yet, to avoid a GMT threshold estimated by previous studies pertaining to rapid West Antarctic ice loss (1.5°C above the pre-industrial GMT, though large uncertainty), our study suggests SAI would need to cool about 1.0°C below this threshold and predominately inject at low southern hemisphere latitudes (~15°S - 30°S). These results highlight the complexity of factors impacting the Antarctic response to SAI and the critical role of the injection strategy in preventing future ice loss.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The Nonlinear and Distinct Responses of Ocean Heat Content and Anthropogenic Carbon to Ice Sheet Freshwater Discharge in a Warming Climate

Anthropogenic climate change will drive extensive mass loss across both the Antarctic (AIS) and Greenland Ice Sheets (GrIS), with the potential for global climate system feedbacks, especially in polar regions. Historically, the high-latitude North Atlantic and Southern Ocean have been critical regions for anthropogenic heat and carbon uptake, but our understanding of how this uptake will be altered by future freshwater discharge is incomplete. We assess each ice sheet's impact on global ocean anthropogenic heat and carbon storage for a high-emission scenario over the 21 st -century using a coupled Earth system model. We explore the impact of contemporaneous mass loss from both ice sheets on anthropogenic heat and carbon storage and quantify their linear and nonlinear contributions. Notably, added freshwater reduces ocean heat and carbon storage by 2,100, and the sum of individual freshwater effects differ from those induced by simultaneous freshwater discharge from both ice sheets. Combined AIS and GrIS freshwater engenders distinct anthropogenic storage anomalies—particularly in the high-latitude Southern Ocean and North Atlantic. From 2080 to 2100, GrIS freshwater exerts primary control on the temporal evolution of global ocean heat storage, while global ocean carbon storage is modulated by the linear AIS and GrIS freshwater impacts. Nonlinear impacts of simultaneous ice sheet discharge have a non-negligible contribution to the evolution of global ocean heat storage. Further, anthropogenic heat changes are realized more quickly in response to ice sheet discharge than anthropogenic carbon. Our results highlight the need to incorporate both ice sheets actively in climate models to accurately project future global climate.

Gorte, Tessa↗
DOE OSTI2025

A Machine Learning Framework for Predicting Microphysical Properties of Ice Crystals From Cloud Particle Imagery

The microphysical properties of ice crystals are important because they significantly alter the radiative properties and spatiotemporal distributions of clouds, which in turn strongly affect Earth's climate. However, it is challenging to measure key properties of ice crystals, such as mass or morphological features. Here, we present a proof-of-concept framework for predicting three-dimensional (3D) microphysical properties of ice crystals from in situ two-dimensional (2D) imagery. First, we computationally generated synthetic ice crystals using 3D modeling software along with geometric parameters estimated from the 2021 Ice Cryo-Encapsulation Balloon (ICEBall) field campaign. Then, we used synthetic crystals to train machine learning (ML) models to predict effective density ($ρ_e$), effective surface area ($A_e$), and number of bullets ($N_b$) from synthetic rosette imagery. On unseen synthetic images, our ML models accurately predicted ice crystal properties. ResNet-18 performed best, achieving $R^2$ values of 0.99 and 0.98 for $ρ_e$ and $A_e$, respectively, and MAE of 0.10 for mathematical equation in single view tasks. Stereo view ResNet-18 further reduced RMSE by 40% for $ρ_e$ and $A_e$ and reduced MAE by 0.08 for $N_b$. This work provides a novel ML-driven framework for estimating ice microphysical properties from in situ imagery, which will allow for downstream constraints on microphysical parameterizations, such as the mass-size relationship.

Ko, J. [Columbia Univ., New York, NY (United State↗
DOE OSTI2022

Thermodynamics of high-pressure ice phases explored with atomistic simulations

Most experimentally known high-pressure ice phases have a body-centred cubic (bcc) oxygen lattice. Our large-scale molecular-dynamics simulations with a machine-learning potential indicate that, amongst these bcc ice phases, ices VII, VII' and X are the same thermodynamic phase under different conditions, whereas superionic ice VII" has a first-order phase boundary with ice VII'. Moreover, at about 300 GPa, the transformation between ice X and the Pbcm phase has a sharp structural change but no apparent activation barrier, whilst at higher pressures the barrier gradually increases. Our study thus clarifies the phase behaviour of the high-pressure ices and reveals peculiar solid–solid transition mechanisms not known in other systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Summertime low clouds mediate the impact of the large-scale circulation on Arctic sea ice

The rapid Arctic sea ice retreat in the early 21 st century is believed to be driven by several dynamic and thermodynamic feedbacks, such as ice-albedo feedback and water vapor feedback. However, the role of clouds in these feedbacks remains unclear since the causality between clouds and these processes is complex. Here, we use NASA CERES satellite products and NCAR CESM model simulations to suggest that summertime low clouds have played an important role in driving sea ice melt by amplifying the adiabatic warming induced by a stronger anticyclonic circulation aloft. The upper-level high pressure regulates low clouds through stronger downward motion and increasing lower troposphere relative humidity. The increased low clouds favor more sea ice melt via emitting stronger longwave radiation. Then decreased surface albedo triggers a positive ice-albedo feedback, which further enhances sea ice melt. Considering the importance of summertime low clouds, accurate simulation of this process is a prerequisite for climate models to produce reliable future projections of Arctic sea ice.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Ice, glass, and solid phases in artificial spin systems with quenched disorder

We present a numerical study on a disordered artificial spin-ice system, which interpolates between the long-range ordered square ice and the fully degenerate shakti ice. Starting from the square-ice geometry, disorder is implemented by adding vertical/horizontal magnetic islands to the center of some randomly chosen square plaquettes of the array at different densities. When no island is added, we have ordered square ice. When all square plaquettes have been modified, we obtain shakti ice, which is disordered yet in a topological phase corresponding to the Rys F-model. In between, geometrical frustration due to these additional center spins disrupts the long-range Ising order of square ice, giving rise to a spin-glass regime at low temperatures. The artificial spin system proposed in our work provides an experimental platform to study the interplay between quenched disorder and geometrical frustration.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Medium-density amorphous ice unveils shear rate as a new dimension in water’s phase diagram

Recent experiments revealed a new amorphous ice phase, medium-density amorphous ice (MDA), formed by ball-milling ice I h at 77 K [Rosu-Finsen et al., Science 379, 474–478 (2023)]. MDA has density between that of low-density amorphous (LDA) and high-density amorphous (HDA) ices, adding to the complexity of water’s phase diagram, known for its glass polyamorphism and two-state thermodynamics. The nature of MDA and its relation to other amorphous ices and liquid water remain unsolved. Here, we use molecular simulations under controlled pressure and shear rate at 77 K to produce and investigate MDA. Here. we find that MDA formed at constant shear rate is a steady-state nonequilibrium shear-driven amorphous ice (SDA), that can be produced by shearing ice I h , LDA, or HDA. Our results suggest that MDA could be obtained by ball-milling water glasses without crystallization interference. Increasing the shear rate at ambient pressure produces SDAs with densities ranging from LDA to HDA, revealing shear rate as a new thermodynamic variable in the nonequilibrium phase diagram of water. Indeed, shearing provides access to amorphous states inaccessible by controlling pressure and temperature alone. SDAs produced with shearing rates as high as 10 6 s −1 sample the same region of the potential energy landscape than hyperquenched glasses with identical density, pressure, and temperature. Intriguingly, SDAs obtained by shearing at ~10 8 s −1 have density, enthalpy, and structure indistinguishable from those of water “instantaneously” quenched from room temperature to 77 K over 10 ps, making them good approximants for the “true glass” of ambient liquid water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗