Monthly Arctic Sea‐Ice Prediction With a Linear Inverse Model

We evaluate Linear Inverse Models (LIMs) trained on last millennium model data to predict Arctic sea‐ice concentration, thickness, and other atmospheric and oceanic variables on monthly timescales. We find that more than 500 years of training data and 100 years of validation data are needed to relia...

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Bibliographic Details
Published inGeophysical research letters Vol. 50; no. 7
Main Authors Brennan, M. Kathleen, Hakim, Gregory J., Blanchard‐Wrigglesworth, Edward
Format Journal Article
LanguageEnglish
Published Washington John Wiley & Sons, Inc 16.04.2023
Wiley
Subjects
Arctic sea ice
Atmospheric models
Autoregressive models
Datasets
Ice conditions
Ice cover
Ice edge
Lead time
Mathematical models
Modelling
Monthly
Navigation behavior
Polar environments
Sea ice
Statistical analysis
Statistical methods
Statistical models
Training
Arctic region
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Summary:We evaluate Linear Inverse Models (LIMs) trained on last millennium model data to predict Arctic sea‐ice concentration, thickness, and other atmospheric and oceanic variables on monthly timescales. We find that more than 500 years of training data and 100 years of validation data are needed to reliably estimate LIM forecast skill. The best LIM has skill up to 8 months lead time and outperforms an autoregressive model of order one (AR1) forecast at all locations, with particularly large outperformance near the ice edge. However, for out‐of‐sample validation tests using data from various different model simulations and reanalysis products, they underperform an AR1 model due to differences in the location of the sea‐ice edge from the training data. We present a metric for predicting LIM forecast skill, based on the spatial correlation of the variance in the training and validation data sets. Plain Language Summary Rapid changes in sea‐ice concentration in recent decades have produced new navigational challenges and hazards in the region, elevating the importance of seasonal sea‐ice forecasts. Arctic sea ice has been shown to contain inherent predictability on seasonal timescales, yet current predictions generally show poor skill. Here, we employ a statistical technique referred to as Linear Inverse Modeling, which uses linearized dynamical modes estimated from a training data set to predict sea‐ice conditions on monthly timescales. We find a Linear Inverse Model is able to outperform a baseline statistical model throughout the Arctic when initialized on the data derived from the same model. Key Points A Linear Inverse Model is evaluated using last millennium model simulations for Arctic climate prediction The Linear Inverse Model successfully predicts Arctic conditions when the same model simulation is used for training and validation Linear Inverse Model forecast skill is proportional to the spatial correlation of variance in the validation and training data
ISSN:0094-8276
1944-8007
DOI:10.1029/2022GL101656