A53C-0392
Data-driven Analysis and Prediction of Arctic Sea Ice
Friday, 18 December 2015
Poster Hall (Moscone South)
Dmitri A Kondrashov1, Mickael Chekroun2, Michael Ghil2, Xiaojun Yuan3 and Mingfang Ting4, (1)University of California Los Angeles, Atmos. Sci, Los Angeles, CA, United States, (2)University of California Los Angeles, Los Angeles, CA, United States, (3)Lamont -Doherty Earth Observatory, Palisades, NY, United States, (4)Lamont Doherty Earth Observ, Palisades, NY, United States
Abstract:
We present results of data-driven predictive analyses of sea ice over the main Arctic regions. Our approach relies on the Multilayer Stochastic Modeling (MSM) framework of Kondrashov, Chekroun and Ghil [Physica D, 2015] and it leads to prognostic models of sea ice concentration (SIC) anomalies on seasonal time scales.This approach is applied to monthly time series of leading principal components from the multivariate Empirical Orthogonal Function decomposition of SIC and selected climate variables over the Arctic. We evaluate the predictive skill of MSM models by performing retrospective forecasts with “no-look ahead” forup to 6-months ahead. It will be shown in particular that the memory effects included in our non-Markovian linear MSM models improve predictions of large-amplitude SIC anomalies in certain Arctic regions. Furtherimprovements allowed by the MSM framework will adopt a nonlinear formulation, as well as alternative data-adaptive decompositions.
