An official website of the United States government
Abstract. Polar sea ice is a critical component of Earth’s climate system. As a material, it is a multiscale composite of pure ice with temperature-dependent millimeter-scale brine inclusions, and centimeter-scale polycrystalline microstructure which is largely determined by how the ice was formed. The surface layer of the polar oceans can be viewed as a granular composite of ice floes in a sea water host, with floe sizes ranging from centimeters to tens of kilometers. A principal challenge in modeling sea ice and its role in climate is how to use information on smaller-scale structures to find the effective or homogenized properties on larger scales relevant to process studies and coarse-grained climate models. That is, how do you predict macroscopic behavior from microscopic laws, like in statistical mechanics and solid state physics? Also of great interest in climate science is the inverse problem of recovering parameters controlling small-scale processes from large-scale observations. Motivated by sea ice remote sensing, the analytic continuation method for obtaining rigorous bounds on the homogenized coefficients of two-phase composites was applied to the complex permittivity of sea ice, which is a Stieltjes function of the ratio of the permittivities of ice and brine. Integral representations for the effective parameters distill the complexities of the composite microgeometry into the spectral properties of a self-adjoint operator like the Hamiltonian in quantum physics. These techniques have been extended to polycrystalline materials, advection diffusion processes, and ocean waves in the sea ice cover. Here we discuss this powerful approach in homogenization, highlighting the spectral representations and resolvent structure of the fields that are shared by the two-component theory and its extensions. Spectral analysis of sea ice structures leads to a random matrix theory picture of percolation processes in composites, establishing parallels to Anderson localization and semiconductor physics and providing new insights into the physics of sea ice.
more »
« less
This content will become publicly available on June 2, 2026
Numerical models for monitoring and forecasting sea ice: a short description of present status
The severe changes in climate resulting in the polar oceans getting warmer – with drastic consequences to their physical, biogeochemical, and biological state – require forecasting systems that can accurately simulate and skilfully predict the state of the ice cover and its temporal evolution. Sea-ice processes significantly impact ocean circulation, water mass formation and modifications, and air–sea fluxes. They comprise vertical processes, mainly related to thermodynamics, and horizontal ones, due to internal sea-ice mechanics and motion. We provide an overview on how these processes can be modelled and how operational systems work, in combination with data assimilation techniques, to enhance accuracy and reliability. We also emphasise the need for advancing research on improving such numerical techniques by highlighting current limits and ways forward.
more »
« less
- Award ID(s):
- 2103942
- PAR ID:
- 10635542
- Publisher / Repository:
- Copernicus
- Date Published:
- Journal Name:
- State of the planet
- ISSN:
- 2752-0706
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Important natural resources in the Arctic rely heavily on sea ice, making it important to forecast Arctic sea ice changes. Arctic sea ice forecasting often involves two connected tasks: sea ice concentration at each pixel and overall sea ice extent. Instead of having two separate models for two forecasting tasks, in this report, we study how to use multi-task learning techniques and leverage the connections between ice concentration and ice extent to improve accuracy for both prediction tasks. Because of the spatiotemporal nature of the data, we designed two novel multi-task learning models based on CNNs and ConvLSTMs, respectively. We also developed a custom loss function which trains the models to ignore land pixels when making predictions. Our experiments show our models can have better accuracies than separate models that predict sea ice extent and concentration separately, and that our accuracies are better than or comparable with results in the state-of-the-art studies.more » « less
-
Abstract. The melting of sea ice floes from the edges (lateral melting) results in open-water formation and subsequently increases absorption of solar shortwave energy. However, lateral melt plays a small role in the sea ice mass budget in both hemispheres in most climate models. This is likely influenced by the simple parameterization of lateral melting in sea ice models that are constrained by limited observations. Here we use a coupled climate model (CESM2.0) to assess the sensitivity of modeled sea ice state to the lateral melt parameterization in preindustrial and 2×CO2 runs. The runs explore the implications of how lateral melting is parameterized and structural changes in how it is applied. The results show that sea ice is sensitive both to the parameters determining the effective lateral melt rate and the nuances in how lateral melting is applied to the ice pack. Increasing the lateral melt rate is largely compensated for by decreases in the basal melt rate but still results in a significant decrease in sea ice concentration and thickness, particularly in the marginal ice zone. Our analysis suggests that this is tied to the increased efficiency of lateral melting at forming open water during the summer melt season, which drives the majority of the ice–albedo feedback. The more seasonal Southern Hemisphere ice cover undergoes larger relative reductions in sea ice concentration and thickness for the same relative increase in lateral melt rate, likely due to the hemispheric differences in the role of the sea-ice–upper-ocean coupling.Additionally, increasing the lateral melt rate under a 2×CO2 forcing, where sea ice is thinner, results in a smaller relative change in sea ice mean state but suggests that open-water-formation feedbacks are likely to steepen the decline to ice-free summer conditions.Overall, melt processes are more efficient at forming open water in thinner ice scenarios (as we are likely to see in the future), suggesting the importance of accurately representing thermodynamic evolution. Revisiting model parameterizations of lateral melting with observations will require finding new ways to represent salient physical processes.more » « less
-
Abstract Multiple stable equilibria are intrinsic to many complex dynamical systems, and have been identified in a hierarchy of climate models. Motivated by the idea that the Quaternary glacial–interglacial cycles could have resulted from orbitally forced transitions between multiple stable states mediated by internal feedbacks, this study investigates the existence and mechanisms of multiple equilibria in an idealized, energy-conserving atmosphere–ocean–sea ice general circulation model with a fully coupled carbon cycle. Four stable climates are found for identical insolation and global carbon inventory: an ice-free Warm climate, two intermediate climates (Cold and Waterbelt), and a fully ice-covered Snowball climate. A fifth state, a small ice cap state between Warm and Cold, is found to be barely unstable. Using custom radiative kernels and a thorough sampling of the model’s internal variability, three equilibria are investigated through the state dependence of radiative feedback processes. For fast feedbacks, the systematic decrease in surface albedo feedback from Cold to Warm states is offset by a similar increase in longwave water vapor feedback. At longer time scales, the key role of the carbon cycle is a dramatic lengthening of the adjustment time comparable to orbital forcings near the Warm state. The dynamics of the coupled climate–carbon system are thus not well separated in time from orbital forcings, raising interesting possibilities for nonlinear triggers for large climate changes. Significance Statement How do carbon cycle and other physical processes affect the physical and mathematical properties of the climate system? We use a complex climate model coupled with a carbon cycle to simulate the climate evolution under different initial conditions. Four stable climate states are possible, from the Snowball Earth, in which ice covers the whole planet, to the Warm state, an ice-free world. The carbon cycle drives the global climate change at an extremely slower pace after sea ice retreats. Sea ice and water vapor, on the other hand, constitute the major contributing factors that accelerate faster climate change.more » « less
-
Abstract. Abrupt and irreversible winter Arctic sea ice loss may occur under anthropogenic warming due to the disappearance of a sea ice equilibrium at athreshold value of CO2, commonly referred to as a tipping point. Previous work has been unable to conclusively identify whether a tippingpoint in winter Arctic sea ice exists because fully coupled climate models are too computationally expensive to run to equilibrium for manyCO2 values. Here, we explore the deviation of sea ice from its equilibrium state under realistic rates of CO2 increase todemonstrate for the first time how a few time-dependent CO2 experiments can be used to predict the existence and timing of sea ice tippingpoints without running the model to steady state. This study highlights the inefficacy of using a single experiment with slow-changing CO2to discover changes in the sea ice steady state and provides a novel alternate method that can be developed for the identification of tippingpoints in realistic climate models.more » « less
