Sea-ice prediction across timescales and the role of model complexity

• Main Author: Zampieri, Lorenzo
• Other Authors: Jung, Thomas, Haas, Christian
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: Universität Bremen 2021
• Subjects: Sea ice; Sea ice prediction; Sea ice modelling; Geoengineering; Model complexity; Subseasonal-to-seasonal timescale; Polar regions; 530; 530 Physics; ddc:530; Antarctic; Arctic; Arctic Ocean; Antarc*; Global warming
• Online Access: https://media.suub.uni-bremen.de/handle/elib/4649; https://doi.org/10.26092/elib/446; https://nbn-resolving.org/urn:nbn:de:gbv:46-elib46499

In addition to observations and lab experiments, the scientific investigation of the Arctic and Antarctic sea ice is conducted through the employment of geophysical models. These models describe in a numerical framework the physical behavior of sea ice and its interactions with the atmosphere, ocean, and polar biogeochemical systems. Sea-ice models find application in the quantification of the past, present, and future sea-ice evolution, which becomes particularly relevant in the context of a warming climate system that causes the reduction of the Arctic sea ice cover. Because of the sea-ice decline, the navigation in the Arctic ocean increased substantially in the recent past, a trend that is expected to continue in the next decades and that requires the formulation of reliable sea-ice predictions at various timescales. Sea-ice predictions can be delivered by modern forecast systems that feature dynamical sea-ice models. The simulation of sea ice is at the center of this thesis: A coupled climate model with a simple sea-ice component is used to quantify potential impacts of a geoengineering approach termed "Arctic Ice Management"; the skill of current operational subseasonal-to-seasonal sea-ice forecasts, based on global models with a varying degree of sea-ice model complexity, is evaluated; and, lastly, an unstructured-grid ocean model is equipped with state-of-the-art sea-ice thermodynamics to study the impact of sea-ice model complexity on model performance. In chapter 2, I examine the potential of a geoengineering strategy to restore the Arctic sea ice and to mitigate the warming of the Arctic and global climate throughout the 21st century. The results, obtained with a fully coupled climate model, indicate that it is theoretically possible to delay the melting of the Arctic sea ice by ~60 years, but that this does not reduce global warming. In chapters 3 and 4, I assess the skill of global operational ensemble prediction systems in forecasting the evolution of the Arctic and Antarctic sea-ice edge position ...