This study quantifies the state of the art in the rapidly growing field of seasonal Arctic sea ice
prediction. A novel multimodel dataset of retrospective seasonal predictions of September …

Data assimilation is often viewed as a framework for correcting short‐term error growth in
dynamical climate model forecasts. When viewed on the time scales of climate however …

Research over the past decade has demonstrated that dynamical forecast systems can
skillfully predict pan-Arctic sea ice extent (SIE) on the seasonal time scale; however, there …

In this study, we perform online sea ice bias correction within a Geophysical Fluid Dynamics
Laboratory global ice‐ocean model. For this, we use a convolutional neural network (CNN) …

Atmospheric rivers (ARs) are characterized by intense lower tropospheric plumes of
moisture transport that are frequently responsible for midlatitude wind and precipitation …

Given the role played by the historical and extensive coverage of sea ice concentration (SIC)
observations in reconstructing the long‐term variability of Antarctic sea ice, and the limited …

Accurate prediction of Arctic sea ice is essential for ship navigation. The numerical forecast
is an important method to predict sea ice. However, currently, it has significant bias from …

Because of a spring predictability barrier, the seasonal forecast skill of Arctic summer sea ice
is limited by the availability of melt‐season sea ice thickness (SIT) observations. The first …

Over the past four decades, Arctic sea ice coverage has steadily declined. This loss of sea
ice has amplified solar radiation and heat absorption from the ocean, exacerbating both …

Abstract The Unified Forecast System (UFS) is the next generation modeling infrastructure
under development for NOAA's operational numerical weather/climate predictions. This …