Sea Ice MURI

Submitted/In preparation:

• Buckley, E. M., Cañuelas, L., Timmermans, M.-L., and Wilhelmus, M. M. (2024) “Seasonal Evolution of the Sea Ice Floe Size Distribution from Two Decades of MODIS Data”, submitted to The Cryosphere. EGUsphere [preprint].

• Giannakis, D., M. J. Latifi Jebelli (2024). Kernel smoothing operators on thick open domains. submitted. [preprint].

• Watkins, Buckley, Kim, and Wilhelmus (2024) “Observing floe-scale sea ice motion in the Greenland Sea marginal ice zone during summer,” submitted to Annals of Glaciology. [preprint].

• Montemuro, B. P. and Manucharyan, G. E. (2024). The Role of Islands in Sea Ice Transport Through Nares Strait. submitted [preprint].

2025

• Gupta, M., Regan, H., Koo, Y., Chua, S. M. T., Li, X., & Heil, P. (2025). Inferring the seasonality of sea ice floes in the Weddell Sea using ICESat-2. The Cryosphere, 19(3), 1241-1257. [link]

• Covington, J., Chen, N., Lunasin, E. and Wiggins, S. (2025). Probabilistic Eddy Identification with Uncertainty Quantification, Physica D: Nonlinear Phenomena, 473. [link]

• Deng, Q., N. Chen, S.N. Stechmann, J. Hu (2025). LEMDA: A Lagrangian-Eulerian Multiscale Data Assimilation Framework, Journal of Advances in Modeling Earth Systems, 17. [link].

2024

• Moncada, R., M. Gupta, A.F. Thompson, and J. Andrade (2024). Comparing the role of floe breakage and melt on summer sea ice loss. J. Geophys. Res. In press.

• Chen, N.,Evelyn, L., Wiggins, S. (2024). Lagrangian Descriptors with Uncertainty, Physica D: Nonlinear Phenomena, 467. [link].

• Froyland, G., D. Giannakis, E. Luna, J. Slawinska (2024). Revealing trends and persistent cycles of non-autonomous systems with operator-theoretic techniques: Applications to past and present climate dynamics. Nature Communications 15, Article number: 4268. [link].

• De Diego, G., Gupta, M., Gering, S., Haris, R., and Stadler, G. (2024) Modelling sea ice in the marginal ice zone as a dense granular flow with rheology inferred from discrete element model data. Journal of Fluid Mechanics, 1000:A22 [link].

• Chen, N., Lunasin, E. and Wiggins, S. (2024). Launching drifter observations in the presence of uncertainty. Physica D: Nonlinear Phenomena, p.134086.

• Deng, Quanling, Samuel N. Stechmann, and Nan Chen. (2024). Particle-continuum multiscale modeling of sea ice floes. Multiscale Modeling & Simulation 22.1: 230-255. [link].

• Gupta, M., Gürcan, E., & Thompson, A. F. (2024). Eddy-induced dispersion of sea ice floes at the marginal ice zone. Geophysical Research Letters, 51, e2023GL105656. [link].

• Freeman, D., D. Giannakis, J. Slawinska (2024). Quantum mechanics for closure of dynamical systems. Multiscale Modeling and Simulation, 22(1), 283-333. [link].

2023

• Dinh, H., Giannakis, D., Slawinska, J., and Stadler, G. (2023). Phase-field models of floe fracture in sea ice. The Cryosphere. 17, 3883–3893. [link].

• Watkins, D. M., Bliss, A. C., Hutchings, J., Wilhelmus, M.M. (2023). Evidence of abrupt transitions between sea ice dynamical regimes in the East Greenland marginal ice zone. Geophysical Research Letters , 50(15), e2023GL103558. [link].

• Montemuro, B. P. and Manucharyan, G. E. (2023). SubZero: a discrete element sea ice model that simulates floes as evolving concave polygons. Journal of Open Source Software, 8(88), 5039. [link].

• Davis, A. D., D. Giannakis (2023). Graph-theoretic algorithms for Kolmogorov operators: Approximating solutions and their gradients in elliptic and parabolic problems on manifolds. Calcolo, 60, 5. [link].

• Freeman, D., D. Giannakis, B. Mintz, A. Ourmazd, J. Slawinska (2023). Data Assimilation in Operator Algebras. Proceedings of the National Academy of Sciences, 120(8), e2211115120. [link].

• Moncada, R., Gupta, M., Thompson, A. F., Andrade, J. E. (2023). Level Set Discrete Element Method for modeling sea ice floes, Computer Methods in Applied Mechanics and Engineering, 406. [link].

• Shih, Y, C. Mehlmann, M. Losch, G. Stadler (2023). Robust and efficient primal-dual Newton-Krylov solvers for viscous-plastic sea-ice models, Journal of Computational Physics, 474, 111802. [link].

• Taylor, J.R., A.F. Thompson (2023) Submesoscale Dynamics in the Upper Ocean. Ann. Rev. Fluid Mech, 55, 103-27. [link].

• Timmermans, M.-L., & Toole, J. M. (2023). The Arctic Ocean’s Beaufort Gyre. Annual Review of Marine Science, 15, 223-248. [link].

2022

• Chen, N., Q. Deng, S.N. Stechmann (2022). Superfloe Parameterization with Physics Constraints for Uncertainty Quantification of Sea Ice Floes, SIAM/ASA Journal on Uncertainty Quantification, Vol. 10, pgs 1384-1409. [link][ArXiv]

• Chen, N., Fu, S., Manucharyan, G. E. (2022). An efficient and statistically accurate Lagrangian data assimilation algorithm with applications to discrete element sea ice models, Journal of Computational Physics 455 (2022): 111000. [link][ArXiv]

• Covington, J., N. Chen, M.M. Wilhelmus (2022). Bridging Gaps in the Climate Observation Network: A Physics-based Nonlinear Dynamical Interpolation of Lagrangian Ice Floe Measurements via Data-Driven Stochastic Models. Journal of Advances in Modeling Earth Systems, 14(9), e2022MS003218. [link].

• Denton, A. A., & Timmermans, M. L. (2022). Characterizing the sea-ice floe size distribution in the Canada Basin from high-resolution optical satellite imagery. The Cryosphere, 16(5), 1563-1578. [link]

• Gupta, M., and Thompson, A. F. (2022). Regimes of sea-ice floe melt: Ice-ocean coupling at the submesoscales. Journal of Geophysical Research: Oceans, 127, e2022JC018894. [link].

• Manucharyan, G. E., Lopez-Acosta, R., Wilhelmus, M. M. (2022). Spinning ice floes reveal intensification of mesoscale eddies in the western Arctic Ocean. Scientific Reports, 12, 7070. [link].

• Manucharyan, G. E., and Montemuro, B. P. (2022). SubZero: A sea ice model with an explicit representation of the floe life cycle. Journal of Advances in Modeling Earth Systems, 14, e2022MS003247. [link].

• Manucharyan, G. E., A. F. Thompson (2022). Heavy footprints of upper-ocean eddies on weakened Arctic sea ice in marginal ice zones. Nature Communications 13 (2147). [link].

• Moon, W., Manucharyan, G. E., & Dijkstra, H. A. (2022). Baroclinic instability and large‐scale wave propagation on planetary‐scale atmosphere. Quarterly Journal of the Royal Meteorological Society. [link].

2021

• Chen, N., S. Fu, G. E. Manucharyan (2021). Lagrangian data assimilation and parameter estimation of a simple sea ice discrete element model. Journal of Advances in Modeling Earth Systems 13(10), e2021MS002513. [link].

• Giddy, I., S. Swart, A. F. Thompson, M. duPlessis, S.A. Nicholson (2021). Stirring of sea ice meltwater enhances submesoscale fronts in the Southern Ocean. Journal of Geophysical Research, 126, e2020JC016814. [link].

• Manucharyan, G. E., L. Siegelman, P. Klein (2020). A deep learning approach to spatiotemporal SSH interpolation and estimation of deep currents in geostrophic ocean turbulence. Journal of Advances in Modeling Earth Systems, 13, e2019MS001965. [link].

• Moon, W., G. E. Manucharyan, H. A. Dijkstra (2021). Eddy memory as an explanation of intra‐seasonal periodic behavior in baroclinic eddies. Quarterly Journal of the Royal Meteorological Society, 147(737), 2395-2408. [link].

2020

• Armitage, T. W. K., G. E. Manucharyan, A.A. Petty, et al. (2020). Enhanced eddy activity in the Beaufort Gyre in response to sea ice loss, Nature Communications, 11, 761. [link].

• Swart, S., M. D. du Plessis, A. F. Thompson, L. C. Biddle, I. Giddy, T. Linders, M. Mohrmann, S.-A. Nicholson, (2020). Submesoscale fronts in the Antarctic marginal ice zone and their response to wind forcing. Geophysical Research Letters, 47(6), e2019GL086649. [link].