Optimal combination of Arctic sea ice extent measures: A dynamic factor modeling approach

Francis Diebold, Maximilian Göbel, Philippe Goulet Coulombe, Glenn Rudebusch and Boyuan Zhang ()

International Journal of Forecasting, 2021, vol. 37, issue 4, 1509-1519

Abstract: The diminishing extent of Arctic sea ice is a key indicator of climate change as well as being an accelerant for future global warming. Since 1978, Arctic sea ice has been measured using satellite-based microwave sensing; however, different measures of Arctic sea ice extent have been made available based on differing algorithmic transformations of raw satellite data. We propose and estimate a dynamic factor model that combines four of these measures in an optimal way and accounts for their differing volatility and cross-correlations. We then use the Kalman smoother to extract an optimal combined measure of Arctic sea ice extent. It turns out that almost all weight is put on the NSIDC Sea Ice Index, confirming and enhancing confidence in the Sea Ice Index and the NASA Team algorithm on which it is based.

Keywords: Climate modeling; Nowcasting; Model averaging; Ensemble averaging; Denoising (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (11)

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Related works:
Working Paper: Optimal Combination of Arctic Sea Ice Extent Measures: A Dynamic Factor Modeling Approach (2020)
Working Paper: Optimal Combination of Arctic Sea Ice Extent Measures: A Dynamic Factor Modeling Approach (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:37:y:2021:i:4:p:1509-1519

DOI: 10.1016/j.ijforecast.2020.10.006

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