Meeting
2016 SIPN Workshop
Presentation Type
plenary
Presentation Theme
Statistical Predictions and Methods Session
Abstract Authors

Xiaojun Yuan, Lamont-Doherty Earth Observatory of Columbia University, xyuan [at] ldeo.columbia.edu
Cuihua Li, Lamont-Doherty Earth Observatory of Columbia University, cli [at] ldeo.columbia.edu
Lei Wang, Lamont-Doherty Earth Observatory of Columbia University, lwang.cu [at] gmail.com

Abstract

This study examines the predictability of Arctic sea ice concentration at the intra-seasonal time scale through developing a stochastic model using weekly time series of sea ice concentration (SIC), oceanic and atmospheric variables from 1979 to 2012. The first order selection of predictors was achieved through anomaly correlations (AC) between SIC and oceanic and atmospheric variables. Sea surface temperature (SST), surface air temperature (SAT), 300mb geopotential height (GH300) and downward long-wave radiation (DLR) stands out than other variables. A linear Markov model was then developed in a multi-variate space of SIC, SST, SAT, GH300 and DLR. Model variables were further selected through prediction skill measured by AC and root-mean-squared error (RMSE) between predictions and observations. The SIC and SST combination is sufficient to capture predictable variability in such a model. Due to different ocean-sea ice coupled relationships in the Pacific and Atlantic sectors of the Arctic, the final model was built in two regions' multi-variate spaces for capturing regional co-variability. Weekly predictions of SIC were made at grid points up to 12-week lead-time, initialized by each week of the time series. The model skill was evaluated by the cross-validated model experiments. Because SIC has relatively high persistence, the model usually cannot beat persistence in first two-week predictions in most study areas but posses predictable skill for longer lead time predictions. The model can out-performs persistence for the predictions with lead-time longer than 2-week in the Chukchi Sea and East Siberian Sea but only beats persistence with lead time longer than 6-week in the Baffin Bay and Bering Sea. The model has predictive skill (AC>0.6) with RMSE less than 20% for predictions up to 6-week lead-time in the most areas in the Arctic Basin during the warm season (May to September).

Time
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