Skillful spring forecasts of September Arctic sea ice extent using passive microwave sea ice observations

by Alek Petty, David Schroeder, Julienne Stroeve, Thorsten Markus, Jeff Miller, Nathan Kurtz, Daniel Feltham, Daniella Flocco (2017)

Info

Citation

Petty, A. A., D. Schroeder, J. C. Stroeve, T. Markus, J. Miller, N. T. Kurtz, D. L. Feltham, D. Flocco (2017), Skillful spring forecasts of September Arctic sea ice extent using passive microwave sea ice observations, Earth's Future, 5, doi:10.1002/2016EF000495.

Abstract

In this study we demonstrate skillful spring forecasts of detrended September Arctic sea ice extent using passive microwave observations of sea ice concentration (SIC) and melt onset (MO). We compare these to forecasts produced using data from a sophisticated melt pond model, and find similar to higher skill values, where the forecast skill is calculated relative to linear trend persistence. The MO forecasts shows the highest skill in March-May, while the SIC forecasts produce the highest skill in June-August, especially when the forecasts are evaluated over recent years (since 2008). The high MO forecast skill in early spring appears to be driven primarily by the presence and timing of open water anomalies, while the high SIC forecast skill appears to be driven by both open water and surface melt processes. Spatial maps of detrended anomalies highlight the drivers of the different forecasts, and enable us to understand regions of predictive importance.Correctly capturing sea ice state anomalies, along with changes in open water coverage appear to be key processes in skillfully forecasting summer Arctic sea ice.

Bibtex

@article {petty2017,
    author = {Petty, A. A. and Schröder, D. and Stroeve, J. C. and Markus, T. and Miller, J. and Kurtz, N. T. and Feltham, D. L. and Flocco, D.},
    title = {Skillful spring forecasts of September Arctic sea ice extent using passive microwave sea ice observations},
    journal = {Earth's Future},
    publisher = {Wiley Periodicals, Inc.},
    issn = {2328-4277},
    url = {http://dx.doi.org/10.1002/2016EF000495},
    doi = {10.1002/2016EF000495},
    pages = {n/a--n/a},
    keywords = {Sea ice, Remote sensing, Probabilistic forecasting, Arctic, sea ice, prediction},
    year = {2017},
}