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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↗
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↗
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↗
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↗
DOE OSTI2022

Tethered balloon-borne profile measurements of atmospheric properties in the cloudy atmospheric boundary layer over the Arctic sea ice during MOSAiC: Overview and first results

The tethered balloon-borne measurement system BELUGA (Balloon-bornE moduLar Utility for profilinG the lower Atmosphere) was deployed over the Arctic sea ice for 4 weeks in summer 2020 as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition. Using BELUGA, vertical profiles of dynamic, thermodynamic, aerosol particle, cloud, radiation, and turbulence properties were measured from the ground up to a height of 1,500 m. BELUGA was operated during an anomalously warm period with frequent liquid water clouds and variable sea ice conditions. Three case studies of liquid water phase, single-layer clouds observed on 3 days (July 13, 23, and 24, 2020) are discussed to show the potential of the collected data set to comprehensively investigate cloud properties determining cloud evolution in the inner Arctic over sea ice. Simulated back-trajectories show that the observed clouds have evolved within 3 different air masses (“aged Arctic,” “advected over sea ice,” and “advected over open ocean”), which left distinct fingerprints in the cloud properties. Strong cloud top radiative cooling rates agree with simulated results of previous studies. The weak warming at cloud base is mostly driven by the vertical temperature profile between the surface and cloud base. In-cloud turbulence induced by the cloud top cooling was similar in strength compared to former studies. From the extent of the mixing layer, it is speculated that the overall cloud cooling is stronger and thus faster in the warm oceanic air mass. Larger aerosol particle number concentrations and larger sizes were observed in the air mass advected over the sea ice and in the air mass advected over the open ocean.

Arctic↗
DOE OSTI2026

Surface freshening contributes to weak Atlantic Meridional Overturning Circulation (AMOC) in non-eddying ocean simulations

To complete this study, researchers developed a suite of water mass diagnostics to be compatible with E3SM simulation data. These diagnostics were applied to two ocean-sea ice simulations run using E3SM Version 2.0 on non-eddy-resolving and eddy-resolving ocean meshes. The atmospheric component was forced using reanalysis data spanning 1948–2009. The simulations were analyzed over a 5-year period coinciding with surface freshening and AMOC decline in the non-eddying simulation. SWMF was calculated from the surface heat, salt, and freshwater fluxes, and transport across boundaries was calculated from the mesh edge velocities. Both SWMF and cross-boundary transport were binned by density, temperature, and salinity to produce diagnostics in density coordinates and in temperature–salinity coordinates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Mechanisms of heat flux across the Southern Greenland continental shelf in 1/10° and 1/12° ocean/sea ice simulations

The increased presence of warm Atlantic water on the Greenland continental shelf has been connected to the accelerated melting of the Greenland Ice Sheet, particularly in the southwest and southeast shelf regions. Results from two high-resolution coupled ocean-sea ice simulations that utilized either the 1/10-degree Parallel Ocean Program (POP) or the 1/12-degree HYbrid Coordinate Ocean Model (HYCOM) are used to understand the flux of heat on and off the southern Greenland shelf. The analysis reveals that the region of greatest heat flux onto the shelf is southeast Greenland. On the southwestern shelf, heat is mainly exported from the shelf to the interior basins. We identify differences in the shelf break current structure and on-shelf heat content between the two simulations. Just south of the Denmark strait, there is a seasonally persistent pattern of multi-day variability in the cross-shelf heat flux in both simulations. In the POP simulation, this high-frequency signal results in net on-shore heat flux. In the HYCOM simulation, the signal is weaker and results in net off-shelf heat flux. This variability is consistent with Denmark Strait Overflow eddies traveling along the shelf break.

58 GEOSCIENCES↗
DOE OSTI2021

A spatial evaluation of Arctic sea ice and regional limitations in CMIP6 historical simulations

The Arctic sea ice response to a warming climate is assessed in a subset of models participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6), using several metrics in comparison with satellite observations and results from the Pan-Arctic Ice Ocean Modeling and Assimilation System and the Regional Arctic System Model. Our study examines the historical representation of sea ice extent, volume, and thickness using spatial analysis metrics, such as the integrated ice-edge error, Brier score, and Spatial Probability Score. We find that the CMIP6 multi-model mean captures the mean annual cycle and 1979-2014 sea ice trends remarkably well. However, individual models experience a wide range of uncertainty in the spatial distribution of sea ice when compared against satellite measurements and reanalysis data. Our metrics expose common and individual regional model biases, which sea ice temporal analyses alone do not capture. We identify large ice edge and ice thickness errors in Arctic sub-regions, implying possible model specific limitations in or lack of representation of some key physical processes. We postulate that many of them could be related to the oceanic forcing, especially in the marginal and shelf seas, where seasonal sea ice changes are not adequately simulated. We therefore conclude that an individual model’s ability to represent the observed/reanalysis spatial distribution still remains a challenge. We propose the spatial analysis metrics as useful tools to diagnose model limitations, narrow down possible processes affecting them, and guide future model improvements critical to the representation and projections of Arctic climate change.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

The Imprint of Southern Ocean Storms on Modeled Surface Chlorophyll, Their Drivers and Satellite Biases

Southern Ocean (SO) phytoplankton chlorophyll is highly variable on sub-seasonal time scales. Although the SO is the windiest ocean basin globally, it is not conclusively understood how storms impact SO phytoplankton dynamics. Much of our existing knowledge stems from satellites, but biases due to data gaps from cloud cover and low solar angles remain unquantified. Here, we use ocean–sea-ice simulations with the Community Earth System Model to quantify the climatological 1997–2018 imprint of storms on chlorophyll and phytoplankton dynamics in the ice-free SO. Additionally, by comparing the full-field model output to synthetic satellite observations, we quantify sampling biases in satellite-derived estimates. We find that both the sign and the magnitude of the average surface chlorophyll imprint vary substantially across storms but last for at least 4 days after the storm passing. Based on our analysis, more than one third of the storms explain the majority of local non-seasonal chlorophyll variability, but satellite-derived storm imprints are often too large in magnitude. On the day of the storm passing, changes in vertical mixing predominantly cause surface chlorophyll anomalies, and reduced light availability due to enhanced cloud cover outweighs the enhanced nutrient availability due to entrainment. Interestingly, storms imprint differently on total net primary production than on surface chlorophyll, demonstrating the difficulty to derive carbon-cycle impacts from a surface-chlorophyll assessment. With SO future storm activity projected to increase, complementing satellite observations with other observing technologies, for example, profiling floats, is necessary to better constrain how storms impact biological carbon cycling in the SO.

Nissen, Cara [University of Colorado, Boulder, CO ↗
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 OSTI2020

Arctic Sea Ice in CMIP6

Abstract We examine CMIP6 simulations of Arctic sea‐ice area and volume. We find that CMIP6 models produce a wide spread of mean Arctic sea‐ice area, capturing the observational estimate within the multimodel ensemble spread. The CMIP6 multimodel ensemble mean provides a more realistic estimate of the sensitivity of September Arctic sea‐ice area to a given amount of anthropogenic CO 2 emissions and to a given amount of global warming, compared with earlier CMIP experiments. Still, most CMIP6 models fail to simulate at the same time a plausible evolution of sea‐ice area and of global mean surface temperature. In the vast majority of the available CMIP6 simulations, the Arctic Ocean becomes practically sea‐ice free (sea‐ice area <1 × 10 6 km 2 ) in September for the first time before the Year 2050 in each of the four emission scenarios SSP1‐1.9, SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5 examined here.

Notz, Dirk↗
DOE OSTI2022

Overestimated Eddy Kinetic Energy in the Eddy‐Rich Regions Simulated by Eddy‐Resolving Global Ocean–Sea Ice Models

Abstract The performance of eddy‐resolving global ocean–sea ice models in simulating mesoscale eddies is evaluated using six eddy‐resolving experiments forced by different atmospheric reanalysis products. Interestingly, eddy‐resolving ocean general circulation models (OGCMs) tend to simulate more (less) energetic eddy‐rich (eddy‐poor) regions with a smaller (larger) spatial extent than satellite observation, which finally shows that larger (smaller) mesoscale energy intensity (EI) is simulated in the eddy‐rich (eddy‐poor) regions. Quantitatively, there is an approximately 27%–60% overestimation of EI in the eddy‐rich regions, which are mainly located in the Kuroshio–Oyashio Extension, the Gulf Stream, and the Antarctic Circumpolar Currents regions, although the global mean EI is underestimated by 25%–45%. Apparently, the eddy kinetic energy in the eddy‐poor region is underestimated. Further analyses based on coherent mesoscale eddy properties show that the overestimation in the eddy‐rich regions is mainly attributed to mesoscale eddies’ intensity and is more prominent when mesoscale eddies are in their growth stage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill

Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea ice motion. The ML models are built to predict present-day sea ice velocity given present-day wind velocity and previous-day sea ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and a convolutional neural network (CNN). We quantify the spatiotemporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea ice velocity with a correlation up to 0.81 between predicted and observed sea ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally: lower values occur in shallow coastal regions and during times of minimum sea ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea ice velocity on 1-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Arctic sea-ice loss drives a strong regional atmospheric response over the North Pacific and North Atlantic on decadal scales

Previous studies have suggested that Arctic sea-ice loss can have a profound influence on atmospheric circulation far away from the Arctic. However, there is little scientific consensus on the features of these remote responses, with the opposite impacts reported. Here we present a multi-model analysis of the decadal climate response to Arctic sea-ice loss using state-of-the-art energy conserving methodologies to isolate the impacts of sea-ice decline. We observe weakening of the Aleutian Low and development of a geopotential ridge in the North Pacific, associated with drier winter conditions over the southwest United States. Over the Atlantic, a negative NAO-like response drives wetter winter conditions across the western Mediterranean. These decadal-scale impacts substantially differ from reported centennial-scale responses to Arctic sea-ice loss simulated using non-energy conserving methodologies. Factors such as the timescale of the response and methodologies used to isolate the impacts of disappearing sea-ice cover should be carefully considered when consolidating scientific understanding on the future impacts of changing Arctic.

54 ENVIRONMENTAL SCIENCES↗