An official website of the United States government
Sea ice surface patterns encode more information than can be represented solely by the ice fraction. The aim of this paper is thus to establish the importance of using a broader set of surface characterization metrics, and to identify a minimal set of such metrics that may be useful for representing sea-ice in Earth System Models. Large-eddy simulations of the atmospheric boundary layer over various idealized sea ice surface patterns, with equivalent ice fraction and average floe area, demonstrate that the spatial organization of ice and water can play a crucial role in determining boundary-layer structure. Thus, different methods to quantify heterogeneity in categorical lattice spatial data, such as those done in landscape ecology and Geographic Information System (GIS) studies, are used here on a set of high-resolution, recently-declassified sea ice surface images. It is found that, in conjunction with ice fraction, the patch density (representing the fragmentation of the surface), the splitting index (representing the variability in patch size), and perimeter-area fractal dimension (representing the tortuosity of the interface) are all required to describe the two-dimensional pattern exhibited by a sea ice surface. Furthermore, for surfaces with anisotropic patterns, the orientation of the surface relative to the mean wind is needed. Furthermore, scaling laws are derived for these relevant landscape metrics to estimate them from aggregated spatial sea ice surface data at any resolution. The methods used and results gained from this study are a first step towards further development of methods to quantify the variability of non-binary surfaces, and for parameterizing mixed ice-water surfaces in coarse geophysical models.
more »
« less
Spatiotemporal Analysis of Sea Ice Leads in the Arctic Ocean Retrieved from IceBridge Laxon Line Data 2012–2018
The ocean and atmosphere exert stresses on sea ice that create elongated cracks and leads which dominate the vertical exchange of energy, especially in cold seasons, despite covering only a small fraction of the surface. Motivated by the need of a spatiotemporal analysis of sea ice lead distribution, a practical workflow was developed to classify the high spatial resolution aerial images DMS (Digital Mapping System) along the Laxon Line in the NASA IceBridge Mission. Four sea ice types (thick ice, thin ice, open water, and shadow) were identified, and relevant sea ice lead parameters were derived for the period of 2012–2018. The spatiotemporal variations of lead fraction along the Laxon Line were verified by ATM (Airborne Topographic Mapper) surface height data and correlated with coarse spatial resolution sea ice motion, air temperature, and wind data through multiple regression models. We found that the freeboard data derived from sea ice leads were compatible with other products. The temperature and ice motion vorticity were the leading factors of the formation of sea ice leads, followed by wind vorticity and kinetic moments of ice motion.
more »
« less
- PAR ID:
- 10300675
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 13
- Issue:
- 20
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 4177
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The ship-based experiment MOSAiC 2019/2020 was carried out during a full year in the Arctic and yielded an excellent data set to test the parameterizations of ocean/sea-ice/atmosphere interaction processes in regional climate models (RCMs). In the present paper, near-surface data during MOSAiC are used for the verification of the RCM COnsortium for Small-scale MOdel–Climate Limited area Mode (COSMO-CLM or CCLM). CCLM is used in a forecast mode (nested in ERA5) for the whole Arctic with 15 km resolution and is run with different configurations of sea ice data. These include the standard sea ice concentration taken from passive microwave data with around 6 km resolution, sea ice concentration from Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data and MODIS sea ice lead fraction data for the winter period. CCLM simulations show a good agreement with the measurements. Relatively large negative biases for temperature occur for November and December, which are likely associated with a too large ice thickness used by CCLM. The consideration of sea ice leads in the sub-grid parameterization in CCLM yields improved results for the near-surface temperature. ERA5 data show a large warm bias of about 2.5°C and an underestimation of the temperature variability.more » « less
-
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.more » « less
-
Surface waves are becoming more prevalent in the Arctic as sea ice cover reduces. Here we use 3D turbulence-resolving simulations to explore how surface waves affect upper ocean dynamics, and hence surface conditions, as they propagate along summertime leads (narrow regions of open ocean between melting sea-ice cover). We separate the ocean dynamics into turbulent motions which dominate vertical kinetic energy, and a mean cross-lead circulation which drives near-surface downwelling within the lead. Without waves, along-lead winds create weak mixing and an asymmetric circulation where a sinking plume within the lead is balanced by upwelling that extends under the ice to the right of the wind vector. The presence of waves enhances both mixing and circulation by localizing, strengthening and deepening the downwelling plume and turbulent vertical velocities, increasing vertical buoyancy fluxes, and creating an upwelling cell to the left of the wind which significantly alters surface conditions beneath the left lead edge. Waves also drive a sharp front and convection within the lead. Physically-based scalings are proposed for the mixing and circulation changes to capture the effects of various system parameters including lead width, which has a leading-order impact on both turbulence and circulation. The wave-driven changes to turbulence and circulation are present even for relatively weak (developing) waves, although the biggest changes are seen for strong (equilibrium) waves.more » « less
-
Upper ocean mixing governs the vertical transport of heat, momentum, and tracers in the ocean surface boundary layer (OSBL), yet large-scale climate models often misrepresent its underlying processes, leading to significant uncertainty in sea ice and ocean predictions. Langmuir turbulence (LT) is one of the primary mechanisms of mixing in the open ocean and is generated by the interaction of wind stress and wave-induced Stokes drift. Observations have confirmed LT activity in leads, polynyas, and the marginal ice zone (MIZ), however its spatial and seasonal variability remains poorly constrained. In this study, we conduct the first Arctic-wide assessment of LT potential using a coupled sea ice–wave model that integrates neXtSIM and WAVEWATCH III. We analyze the spatiotemporal variability of LT by examining model-resolved turbulent dissipation and vertical kinetic energy within the OSBL. Our analysis reveals that LT potential is higher in the MIZ during melt and freeze-up, when partial sea ice cover allows intermittent wave propagation. Under these conditions, LT commonly coexists with wind-driven shear, forming a mixed-forcing regime that shapes upper-ocean energetics in response to evolving sea ice and wave states. Sea ice concentration and wind–wave alignment strongly influence the intensity and distribution of LT-driven mixing. On average, LT contributes roughly 15 % of the total upper-ocean dissipation in the Arctic MIZ, with episodic wave-driven events during transitional ice periods doubling local mixing rates compared to wind-only conditions. This analysis highlights the energetic role of wave-induced mixing in the upper ocean, with potential implications for vertical momentum transport, mixed layer structure, and sea ice–ocean interactions in the Arctic.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.3390/rs13204177
