Benchmark seasonal prediction skill estimates based on regional indices
Basic statistical metrics such as autocorrelations and across-region lag correlations of sea ice variations provide benchmarks for the assessments of forecast skill achieved by other methods such as more sophisticated statistical formulations, numerical models, and heuristic approaches. In this stud...
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ftdoajarticles:oai:doaj.org/article:07d00a267b9947a1b07309707cb7dcdb 2023-05-15T14:56:56+02:00 Benchmark seasonal prediction skill estimates based on regional indices J. E. Walsh J. S. Stewart F. Fetterer 2019-04-01T00:00:00Z https://doi.org/10.5194/tc-13-1073-2019 https://doaj.org/article/07d00a267b9947a1b07309707cb7dcdb EN eng Copernicus Publications https://www.the-cryosphere.net/13/1073/2019/tc-13-1073-2019.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-13-1073-2019 1994-0416 1994-0424 https://doaj.org/article/07d00a267b9947a1b07309707cb7dcdb The Cryosphere, Vol 13, Pp 1073-1088 (2019) Environmental sciences GE1-350 Geology QE1-996.5 article 2019 ftdoajarticles https://doi.org/10.5194/tc-13-1073-2019 2022-12-31T01:20:25Z Basic statistical metrics such as autocorrelations and across-region lag correlations of sea ice variations provide benchmarks for the assessments of forecast skill achieved by other methods such as more sophisticated statistical formulations, numerical models, and heuristic approaches. In this study we use observational data to evaluate the contribution of the trend to the skill of persistence-based statistical forecasts of monthly and seasonal ice extent on the pan-Arctic and regional scales. We focus on the Beaufort Sea for which the Barnett Severity Index provides a metric of historical variations in ice conditions over the summer shipping season. The variance about the trend line differs little among various methods of detrending (piecewise linear, quadratic, cubic, exponential). Application of the piecewise linear trend calculation indicates an acceleration of the winter and summer trends during the 1990s. Persistence-based statistical forecasts of the Barnett Severity Index as well as September pan-Arctic ice extent show significant statistical skill out to several seasons when the data include the trend. However, this apparent skill largely vanishes when the data are detrended. In only a few regions does September ice extent correlate significantly with antecedent ice anomalies in the same region more than 2 months earlier. The springtime <q>predictability barrier</q> in regional forecasts based on persistence of ice extent anomalies is not reduced by the inclusion of several decades of pre-satellite data. No region shows significant correlation with the detrended September pan-Arctic ice extent at lead times greater than a month or two; the concurrent correlations are strongest with the East Siberian Sea. The Beaufort Sea's ice extent as far back as July explains about 20 % of the variance of the Barnett Severity Index, which is primarily a September metric. The Chukchi Sea is the only other region showing a significant association with the Barnett Severity Index, although only at a lead ... Article in Journal/Newspaper Arctic Beaufort Sea Chukchi Chukchi Sea East Siberian Sea Sea ice The Cryosphere Directory of Open Access Journals: DOAJ Articles Arctic Chukchi Sea East Siberian Sea ENVELOPE(166.000,166.000,74.000,74.000) The Cryosphere 13 4 1073 1088 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
spellingShingle |
Environmental sciences GE1-350 Geology QE1-996.5 J. E. Walsh J. S. Stewart F. Fetterer Benchmark seasonal prediction skill estimates based on regional indices |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
description |
Basic statistical metrics such as autocorrelations and across-region lag correlations of sea ice variations provide benchmarks for the assessments of forecast skill achieved by other methods such as more sophisticated statistical formulations, numerical models, and heuristic approaches. In this study we use observational data to evaluate the contribution of the trend to the skill of persistence-based statistical forecasts of monthly and seasonal ice extent on the pan-Arctic and regional scales. We focus on the Beaufort Sea for which the Barnett Severity Index provides a metric of historical variations in ice conditions over the summer shipping season. The variance about the trend line differs little among various methods of detrending (piecewise linear, quadratic, cubic, exponential). Application of the piecewise linear trend calculation indicates an acceleration of the winter and summer trends during the 1990s. Persistence-based statistical forecasts of the Barnett Severity Index as well as September pan-Arctic ice extent show significant statistical skill out to several seasons when the data include the trend. However, this apparent skill largely vanishes when the data are detrended. In only a few regions does September ice extent correlate significantly with antecedent ice anomalies in the same region more than 2 months earlier. The springtime <q>predictability barrier</q> in regional forecasts based on persistence of ice extent anomalies is not reduced by the inclusion of several decades of pre-satellite data. No region shows significant correlation with the detrended September pan-Arctic ice extent at lead times greater than a month or two; the concurrent correlations are strongest with the East Siberian Sea. The Beaufort Sea's ice extent as far back as July explains about 20 % of the variance of the Barnett Severity Index, which is primarily a September metric. The Chukchi Sea is the only other region showing a significant association with the Barnett Severity Index, although only at a lead ... |
format |
Article in Journal/Newspaper |
author |
J. E. Walsh J. S. Stewart F. Fetterer |
author_facet |
J. E. Walsh J. S. Stewart F. Fetterer |
author_sort |
J. E. Walsh |
title |
Benchmark seasonal prediction skill estimates based on regional indices |
title_short |
Benchmark seasonal prediction skill estimates based on regional indices |
title_full |
Benchmark seasonal prediction skill estimates based on regional indices |
title_fullStr |
Benchmark seasonal prediction skill estimates based on regional indices |
title_full_unstemmed |
Benchmark seasonal prediction skill estimates based on regional indices |
title_sort |
benchmark seasonal prediction skill estimates based on regional indices |
publisher |
Copernicus Publications |
publishDate |
2019 |
url |
https://doi.org/10.5194/tc-13-1073-2019 https://doaj.org/article/07d00a267b9947a1b07309707cb7dcdb |
long_lat |
ENVELOPE(166.000,166.000,74.000,74.000) |
geographic |
Arctic Chukchi Sea East Siberian Sea |
geographic_facet |
Arctic Chukchi Sea East Siberian Sea |
genre |
Arctic Beaufort Sea Chukchi Chukchi Sea East Siberian Sea Sea ice The Cryosphere |
genre_facet |
Arctic Beaufort Sea Chukchi Chukchi Sea East Siberian Sea Sea ice The Cryosphere |
op_source |
The Cryosphere, Vol 13, Pp 1073-1088 (2019) |
op_relation |
https://www.the-cryosphere.net/13/1073/2019/tc-13-1073-2019.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-13-1073-2019 1994-0416 1994-0424 https://doaj.org/article/07d00a267b9947a1b07309707cb7dcdb |
op_doi |
https://doi.org/10.5194/tc-13-1073-2019 |
container_title |
The Cryosphere |
container_volume |
13 |
container_issue |
4 |
container_start_page |
1073 |
op_container_end_page |
1088 |
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1766328989873340416 |