Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion

Abstract

We study the response of the Lagrangian sea ice model neXtSIM to the uncertainty in sea surface wind and sea ice cohesion. The ice mechanics in neXtSIM are based on a brittle-like rheological framework. The study considers short-term ensemble forecasts of Arctic sea ice from January to April 2008. Ensembles are generated by perturbing the wind inputs and ice cohesion field both separately and jointly. The resulting uncertainty in the probabilistic forecasts is evaluated statistically based on the analysis of Lagrangian sea ice trajectories as sampled by virtual drifters seeded in the model to cover the Arctic Ocean and using metrics borrowed from the search-and-rescue literature. The comparison among the different ensembles indicates that wind perturbations dominate the forecast uncertainty (i.e., the absolute spread of the ensemble), while the inhomogeneities in the ice cohesion field significantly increase the degree of anisotropy in the spread—i.e., trajectories drift divergently in different directions. We suggest that in order to obtain enough uncertainties in a sea ice model with brittle-like rheologies, to predict sea ice drift and trajectories, one should consider using ensemble-based simulations where at least wind forcing and sea ice cohesion are perturbed.

Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F

“…A framework to produce an ensemble forecast with neXtSIM-F is also being developed (Cheng et al, 2021, as a follow-up of the work of Rabatel et al, 2018), with the ultimate aim of using the ensemble Kalman filter (EnKF) assimilation method. Work on using EnKF with models running on adaptive meshes (like neXtSIM) is being developed in parallel at the Nansen Environmental and Remote Sensing Center (NERSC; Aydogdu et al, 2019).…”

Ensemble Kalman filter for nonconservative moving mesh solvers with a joint physics and mesh location update

“…If the remeshing rules for the AMM model are based on strict considerations of node distances and mesh geometries, a 2‐d or 3‐d analogue of the HR reference mesh should be attainable, enabling the application of both the HR and HRA schemes presented here. An example of such a model is the novel Lagrangian sea‐ice model neXtSIM (Rampal et al ., 2016; Cheng et al ., 2020). neXtSIM uses a finite‐element method based on a triangular nonconservative adaptive mesh, with strict rules on the distance between nodes and angles between edges, and was the motivation behind our exploration in 1‐d presented in this work.…”

“…In this work, we are interested in adaptive meshes that evolve with the flow of a physical system and are nonconservative. We will make use of the same 1-d adaptive mesh scheme developed in Aydogdu et al (2019) as a prototype of the 2-or 3-d nonconservative adaptive meshes used in some modern numerical models, including the Lagrangian neXt generation Sea Ice Model, neXtSIM (Rampal et al, 2016;Rabatel et al, 2018;Cheng et al, 2020). The mesh itself is a 1-d mesh defined on the domain…”

“…We will make use of the same 1‐d adaptive mesh scheme developed in Aydoğdu et al . (2019) as a prototype of the 2‐ or 3‐d nonconservative adaptive meshes used in some modern numerical models, including the Lagrangian neXt generation Sea Ice Model, neXtSIM (Rampal et al ., 2016; Rabatel et al ., 2018; Cheng et al ., 2020).…”

Towards improving short-term sea ice predictability using deformation observations

“…Sea ice in the Arctic is continuously drifting and deforming under the influence of atmospheric winds and ocean currents (Sverdrup, 1950;Colony and Thorndike, 1984;Rampal et al, 2009). In summer, when ice concentration is low and ice extent is small, the sea ice is mostly in free drift -the speed and direction of the drift are dominated by the atmospheric and ocean drag forces and by the Coriolis force.…”