This paper documents the biogeochemistry configuration of the Energy Exascale Earth
System Model (E3SM), E3SMv1. 1‐BGC. The model simulates historical carbon cycle …

Sea ice thickness is a key parameter in the polar climate and ecosystem. Thermodynamic
and dynamic processes alter the sea ice thickness. The Multidisciplinary drifting Observatory …

The surface roughness of sea ice is highly variable because of the diversity of discrete
obstacles to the flow present on the sea ice surface. These obstacles result in form drag, an …

Melt ponds play a crucial role in the melting of Arctic sea ice. Studying the evolution of melt
ponds is essential for understanding changes in Arctic sea ice. In this study, we used a …

Ensemble Kalman filters are an efficient class of algorithms for large-scale ensemble data
assimilation, but their performance is limited by their underlying Gaussian approximation. A …

We developed a new version of the Alfred Wegener Institute Climate Model (AWI-CM3),
which has higher skills in representing the observed climatology and better computational …

Accurate simulation of sea ice is critical for predictions of future Arctic sea ice loss, looming
climate change impacts, and more. A key feature in Arctic sea ice is the formation of melt …

The intensity and spectrum of light under Arctic sea ice, key to the energy budget and
primary productivity of the Arctic Ocean, are tedious to observe. Earth System Models …

Abstract Models struggle to accurately simulate observed sea ice thickness changes, which
could be partially due to inadequate representation of thermodynamic processes. We …

Freezing temperature parameterization significantly impacts the heat balance at sea‐ice
bottom and, consequently, the simulated sea‐ice thickness. Here, the single‐column model …