Tuesday, November 24, 2020, 10:00AM - 12:00PM
Improving the evaluation of seasonal Arctic sea ice transitions in climate models
Seasonal sea ice transitions dates are under-utilized in evaluating climate model projections of Arctic sea ice loss despite long, pan-Arctic satellite-based observational records of seasonal transition dates. In this thesis, we show how the limitations that have prevented their widespread use can be overcome through the use of large ensembles and a novel sea ice satellite simulator, and how seasonal sea ice transitions dates can benefit model evaluations.
In particular, we quantify the uncertainty related to definition differences and internal variability, allowing us to use seasonal sea ice transitions as process-based metrics to understand model biases in sea ice simulations. In addition, we demonstrate that a sea ice satellite simulator can take model evaluation a step further, providing novel and direct comparisons between satellite observations and model simulations as well as insights into the physical processes captured by sea ice remote sensing algorithms. These direct comparisons enable more accurate climate model assessment and improve the evaluation of seasonal Arctic sea ice transitions in models.