Ice Edge Verification -- Measuring the skill in our forecasts and disagreement in our observations

• Main Author: Niraula, Bimochan
• Other Authors: Jung, Thomas, Goessling, Helge, Spreen, Gunnar
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: Universität Bremen 2023
• Subjects: Sea ice; Sea ice prediction; Observations; 530; 530 Physics; ddc:530
• Online Access: https://media.suub.uni-bremen.de/handle/elib/6977; https://doi.org/10.26092/elib/2298; https://nbn-resolving.org/urn:nbn:de:gbv:46-elib69773

Sea ice is one of the most crucial components of the polar system. Alongside its effects on the climate and weather, it also impacts human lives and operations in these regions. Climate scientists, as well as other stakeholders (such as marine industries, mining operations, governmental authorities as well as local/aboriginal population), have a high interest in getting accurate observation and prediction of sea ice presence, and its effective border – the ice edge. In this context, this thesis is focused on the verification of ice presence and ice edge, across different datasets. We do this by describing a new method of generating reference forecasts of the ice edge (as benchmark for predictions at sub-seasonal to seasonal timescales), analysing initial state errors in forecasts and analysis from ECMWF, and comparing the ice presence and ice edge difference between several observational and analysis datasets. Operational systems generating sea ice forecasts at sub-seasonal timescales are mostly compared against simple references based solely on climatological or initial states, which can lead to a potential overestimation of their prediction skill. In chapter 1, we describe the Spatial Damped Anomaly Persistence (SDAP) method, which combines historical sea ice probability with the initial ice edge anomalies to generate probabilistic reference forecasts of the ice edge. The SDAP forecasts outperform both traditional references, as well as most dynamical forecast models from the Sub-seasonal to Seasonal (S2S) project at long lead times, establishing a more challenging benchmark for operational forecast systems. Within the S2S database, forecasts from the European Centre for Medium- Range Weather Forecasts (ECMWF) have the highest prediction skill compared to both traditional references and SDAP forecasts, but show significant initial error. In chapter 2, we analyse this initial state issue in the ECMWF forecasts, as well as corresponding analysis from ORAS5, by measuring the errors against observations from ...