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Title: Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting

Abstract

Predicting Arctic sea ice extent is a notoriously difficult forecasting problem, even for lead times as short as one month. Motivated by Arctic intraannual variability phenomena such as reemergence of sea surface temperature and sea ice anomalies, we use a prediction approach for sea ice anomalies based on analog forecasting. Traditional analog forecasting relies on identifying a single analog in a historical record, usually by minimizing Euclidean distance, and forming a forecast from the analog’s historical trajectory. An ensemble of analogs is used to make forecasts, where the ensemble weights are determined by a dynamics-adapted similarity kernel, which takes into account the nonlinear geometry on the underlying data manifold. We apply this method for forecasting pan-Arctic and regional sea ice area and volume anomalies from multi-century climate model data, and in many cases find improvement over the benchmark damped persistence forecast. Examples of success include the 3–6 month lead time prediction of Arctic sea ice area, the winter sea ice area prediction of some marginal ice zone seas, and the 3–12 month lead time prediction of sea ice volume anomalies in many central Arctic basins. Finally, we discuss possible connections between KAF success and sea ice reemergence, and find KAFmore » to be successful in regions and seasons exhibiting high interannual variability.« less

Authors:
ORCiD logo [1];  [2];  [3];  [2]
  1. New York Univ. (NYU), NY (United States). Center for Atmosphere Ocean Science. Courant Inst. of Mathematical Sciences; Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. New York Univ. (NYU), NY (United States). Center for Atmosphere Ocean Science. Courant Inst. of Mathematical Sciences
  3. Univ. of Illinois, Urbana, IL (United States). Dept. of Electrical and Computer Engineering. Coordinated Science Lab.
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States); New York Univ. (NYU), NY (United States); Univ. of Illinois at Urbana-Champaign, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER); Office of Naval Research (ONR) (United States); National Science Foundation (NSF)
OSTI Identifier:
1480002
Report Number(s):
LA-UR-18-25886
Journal ID: ISSN 0930-7575
Grant/Contract Number:  
AC52-06NA25396; 25-74200-F7112; N00014-14-1-0150; DMS-1521775
Resource Type:
Accepted Manuscript
Journal Name:
Climate Dynamics
Additional Journal Information:
Journal Volume: 52; Journal Issue: 9; Journal ID: ISSN 0930-7575
Publisher:
Springer-Verlag
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Comeau, Darin, Giannakis, Dimitrios, Zhao, Zhizhen, and Majda, Andrew J. Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting. United States: N. p., 2018. Web. doi:10.1007/s00382-018-4459-x.
Comeau, Darin, Giannakis, Dimitrios, Zhao, Zhizhen, & Majda, Andrew J. Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting. United States. doi:10.1007/s00382-018-4459-x.
Comeau, Darin, Giannakis, Dimitrios, Zhao, Zhizhen, and Majda, Andrew J. Thu . "Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting". United States. doi:10.1007/s00382-018-4459-x. https://www.osti.gov/servlets/purl/1480002.
@article{osti_1480002,
title = {Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting},
author = {Comeau, Darin and Giannakis, Dimitrios and Zhao, Zhizhen and Majda, Andrew J.},
abstractNote = {Predicting Arctic sea ice extent is a notoriously difficult forecasting problem, even for lead times as short as one month. Motivated by Arctic intraannual variability phenomena such as reemergence of sea surface temperature and sea ice anomalies, we use a prediction approach for sea ice anomalies based on analog forecasting. Traditional analog forecasting relies on identifying a single analog in a historical record, usually by minimizing Euclidean distance, and forming a forecast from the analog’s historical trajectory. An ensemble of analogs is used to make forecasts, where the ensemble weights are determined by a dynamics-adapted similarity kernel, which takes into account the nonlinear geometry on the underlying data manifold. We apply this method for forecasting pan-Arctic and regional sea ice area and volume anomalies from multi-century climate model data, and in many cases find improvement over the benchmark damped persistence forecast. Examples of success include the 3–6 month lead time prediction of Arctic sea ice area, the winter sea ice area prediction of some marginal ice zone seas, and the 3–12 month lead time prediction of sea ice volume anomalies in many central Arctic basins. Finally, we discuss possible connections between KAF success and sea ice reemergence, and find KAF to be successful in regions and seasons exhibiting high interannual variability.},
doi = {10.1007/s00382-018-4459-x},
journal = {Climate Dynamics},
number = 9,
volume = 52,
place = {United States},
year = {2018},
month = {9}
}

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Cited by: 3 works
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Figures / Tables:

Fig. 1 Fig. 1: Standard deviation of monthly sea ice concentration (SIC) from the CCSM4 control run, with regions considered in our forecasting: pan-Arctic (45N–90N), Beaufort Sea (155W–125W, 65N–75N), Chukchi Sea (175E–155W, 65N–75N), East Siberian Sea (140E–175E, 65N–75N), Laptev Sea (105E–140E, 70N–80N), Kara Sea (60E–90E, 65N–80N), Barents Sea (30E–60E, 65N–80N), Greenland Seamore » (35W– 0E, 65N–80N), Baffin Bay (80W–50W, 70N–80N), Labrador Sea (70W–50W, 50–70N), Bering Sea (165E–160W, 55N–65N), and Sea of Okhotsk (135E–165E, 45N–65N).« less

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