Francis X. Diebold, Maximilian Göbel, Philippe Goulet Coulombe, Glenn D. Rudebusch, Boyuan Zhang
arXiv 31 Mar 2020 · Statistics — Applications · publishedInternational Journal of Forecasting (2020) · 14 citations (OpenAlex)
arXiv:2003.14276 · PDF · DOI · OpenAlex · Extracted main text
The diminishing extent of Arctic sea ice is a key indicator of climate change as well as 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 the raw satellite data. We propose and estimate a dynamic factor model that combines four of these measures in an optimal way that 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Comiso, Meier, and Gersten (2017) Variability and trends in the Arctic Sea ice cover: Results from different techniques, Journal of Geophysical Research: Oceans\/… | 0.874 | 5 | 2 | 100% |
| 2 | Fetterer, Knowles, Meier, Savoie, and Windnagel (2017) Sea Ice Index, Version 3, https://doi.org/10.7265/N5K072F8, accessed: 2019-08-29 | 0.811 | 4 | 2 | 100% |
| 3 | Cavalieri, Parkinson, Gloersen, and Zwally (1996) Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1, https://doi.org/10.5067/8GQ8LZ… | 0.644 | 4 | 1 | 100% |
| 4 | Diebold and Rudebusch (2019) Probability Assessments of an Ice-Free Arctic: Comparing Statistical and Climate Model Projections, Working Paper, https://arxiv… | 0.644 | 2 | 2 | 100% |
| 5 | Goldstein, Lynch, Zsom, Arbetter, Chang, and Fetterer (2018) The Step-Like Evolution of Arctic Open Water, Nature Scientific Reports\/, 8, 16902 | 0.585 | 3 | 1 | 100% |
| 6 | Comiso, Gersten, Stock, Turner, and Cho (2017) Positive Trend in the Antarctic Sea Ice Cover and Associated Changes in Surface Temperature, Journal of Climate\/, 30, 2251–2267 | 0.511 | 2 | 1 | 100% |
| 7 | Meier and Stewart (2019) Assessing Uncertainties in Sea Ice Extent Climate Indicators, Environmental Research Letters\/, 14, 035005 | 0.511 | 2 | 1 | 100% |
| 8 | Maslanik and Stroeve (1999) Near-Real-Time DMSP SSMIS Daily Polar Gridded Sea Ice Concentrations, Version 1, https://doi.org/10.5067/U8C09DWVX9LM, accessed:… | 0.511 | 2 | 1 | 100% |
| 9 | Aruoba, Diebold, Nalewaik, Schorfheide, and Song (2016) Improving GDP Measurement: A Measurement-Error Perspective, Journal of Econometrics\/, 191, 384–397 | 0.405 | 1 | 1 | 100% |
| 10 | Bunzel, Notz, Baehr, Müller, and Fröhlich (2016) Seasonal Climate Forecasts Significantly Affected by Observational Uncertainty of Arctic Sea Ice Concentration, Geophysical Rese… | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 27 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Nowcasting R&D Expenditures: A Machine Learning Approach | 0.405 | 1 | 1 |