Seasonal Arctic sea ice predictability and prediction

• Main Author: Cruz García, Rubén
• Other Authors: Universitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental, Ortega Montilla, Pablo, Guemas, Virginie
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: Universitat Politècnica de Catalunya 2020
• Subjects: Àrees temàtiques de la UPC::Enginyeria civil i ambiental; Arctic; Baffin Bay; Baffin; Chukchi; Labrador Sea; Sea ice
• Online Access: http://hdl.handle.net/2117/334685; http://hdl.handle.net/10803/670223; https://doi.org/10.5821/dissertation-2117-334685

Arctic sea ice plays a central role in the Earth’s climate. Changes in the sea ice on seasonal-to-interannual timescales impact ecosystems, populations and a growing number of stakeholders. A prerequisite for achieving better sea ice predictions is a better understanding of the underlying mechanisms of sea ice predictability. In the first part, we investigate the seasonal-to-interannual Arctic sea ice predictability in perfect-model experiments performed with six different climate models. Similar pan-Arctic winter sea ice extent (SIE) reemergence is found for HadGEM1.2, GFDL-CM3 and E6F, while a sea ice volume (SIV) persistence from 1 to 3 years is confirmed for all models. Similarities in winter SIE predictability remergence in the GIN seas and Baffin Bay are found even though models have distinct sea ice states. A summer SIV skill reemergence is also found in the Barents, Kara and Chukchi seas. A regional analysis in EC-Earth2.3 suggests that Arctic basins can be classified according to three distinct regimes. The central Arctic drives most of the pan-Arctic SIV persistence. In peripheral seas, predictability for the SIE in winter is associated with ocean thermal anomalies persistence. The Labrador Sea exhibits the longest predictability (up to 1.5 years), the reemergence of predictability in winter being driven by the advection of heat content anomalies along the subpolar gyre. In real predictions, forecast errors appear due to inconsistencies between the initial states of the different model components and to the development of the inherent model biases. We identify and quantify the contribution of initial condition (IC) inconsistencies and systematic model errors to the forecast model errors in two sets of seasonal forecasts (May and November initialized) produced with EC-Earth3.2 during the first forecast month. After 24 (19) days, the inherent model biases become the largest contributor to the forecast error for the May (November) initialized forecasts, while the initial inconsistency dominates in the ...