The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1
Recent decades have seen significant developments in climate prediction capabilities at seasonal-to-interannual timescales. However, until recently the potential of such systems to predict Arctic climate had rarely been assessed. This paper describes a multi-model predictability experiment which was...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-06-01
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| Series: | Geoscientific Model Development |
| Online Access: | http://www.geosci-model-dev.net/9/2255/2016/gmd-9-2255-2016.pdf |
| _version_ | 1818508853422063616 |
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| author | J. J. Day S. Tietsche M. Collins H. F. Goessling V. Guemas A. Guillory W. J. Hurlin M. Ishii S. P. E. Keeley D. Matei R. Msadek M. Sigmond H. Tatebe E. Hawkins |
| author_facet | J. J. Day S. Tietsche M. Collins H. F. Goessling V. Guemas A. Guillory W. J. Hurlin M. Ishii S. P. E. Keeley D. Matei R. Msadek M. Sigmond H. Tatebe E. Hawkins |
| author_sort | J. J. Day |
| collection | DOAJ |
| description | Recent decades have seen significant developments in climate prediction
capabilities at seasonal-to-interannual timescales. However, until recently
the potential of such systems to predict Arctic climate had rarely been
assessed. This paper describes a multi-model predictability experiment which
was run as part of the Arctic Predictability and Prediction On Seasonal to
Interannual Timescales (APPOSITE) project. The main goal of APPOSITE was to
quantify the timescales on which Arctic climate is predictable. In order to
achieve this, a coordinated set of idealised initial-value predictability
experiments, with seven general circulation models, was conducted. This was
the first model intercomparison project designed to quantify the
predictability of Arctic climate on seasonal to interannual timescales. Here
we present a description of the archived data set (which is available at the
British Atmospheric Data Centre), an assessment of Arctic sea ice extent and
volume predictability estimates in these models, and an investigation into to
what extent predictability is dependent on the initial state.<br><br>
The inclusion of additional models expands the range of sea ice volume and
extent predictability estimates, demonstrating that there is model diversity
in the potential to make seasonal-to-interannual timescale predictions. We
also investigate whether sea ice forecasts started from extreme high and low
sea ice initial states exhibit higher levels of potential predictability than
forecasts started from close to the models' mean state, and find that the
result depends on the metric.<br><br>
Although designed to address Arctic predictability, we describe the archived
data here so that others can use this data set to assess the predictability
of other regions and modes of climate variability on these timescales, such
as the El Niño–Southern Oscillation. |
| first_indexed | 2024-12-10T22:37:34Z |
| format | Article |
| id | doaj.art-30d84b101f6046718f86f1033d6bc155 |
| institution | Directory Open Access Journal |
| issn | 1991-959X 1991-9603 |
| language | English |
| last_indexed | 2024-12-10T22:37:34Z |
| publishDate | 2016-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj.art-30d84b101f6046718f86f1033d6bc1552022-12-22T01:30:48ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032016-06-01962255227010.5194/gmd-9-2255-2016The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1J. J. Day0S. Tietsche1M. Collins2H. F. Goessling3V. Guemas4A. Guillory5W. J. Hurlin6M. Ishii7S. P. E. Keeley8D. Matei9R. Msadek10M. Sigmond11H. Tatebe12E. Hawkins13NCAS-Climate, Department of Meteorology, University of Reading, Reading, UKEuropean Centre for Medium-Range Weather Forecasts, Reading, UKCollege of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UKAlfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, GermanyInstitut Català de Ciències del Clima, Barcelona, SpainBritish Atmospheric Data Centre, Rutherford Appleton Laboratory, Chilton, UKGeophysical Fluid Dynamics Laboratory, Princeton, NJ, USAMeteorological Research Institute, Tsukuba, JapanEuropean Centre for Medium-Range Weather Forecasts, Reading, UKMax Planck Institute for Meteorology, Hamburg, GermanyCNRM/GAME, Toulouse, FranceCanadian Centre for Climate Modelling and Analysis, Environment Canada, Victoria, CanadaJapan Agency for Marine-Earth Science and Technology, Yokohama, JapanNCAS-Climate, Department of Meteorology, University of Reading, Reading, UKRecent decades have seen significant developments in climate prediction capabilities at seasonal-to-interannual timescales. However, until recently the potential of such systems to predict Arctic climate had rarely been assessed. This paper describes a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Interannual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre), an assessment of Arctic sea ice extent and volume predictability estimates in these models, and an investigation into to what extent predictability is dependent on the initial state.<br><br> The inclusion of additional models expands the range of sea ice volume and extent predictability estimates, demonstrating that there is model diversity in the potential to make seasonal-to-interannual timescale predictions. We also investigate whether sea ice forecasts started from extreme high and low sea ice initial states exhibit higher levels of potential predictability than forecasts started from close to the models' mean state, and find that the result depends on the metric.<br><br> Although designed to address Arctic predictability, we describe the archived data here so that others can use this data set to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Niño–Southern Oscillation.http://www.geosci-model-dev.net/9/2255/2016/gmd-9-2255-2016.pdf |
| spellingShingle | J. J. Day S. Tietsche M. Collins H. F. Goessling V. Guemas A. Guillory W. J. Hurlin M. Ishii S. P. E. Keeley D. Matei R. Msadek M. Sigmond H. Tatebe E. Hawkins The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1 Geoscientific Model Development |
| title | The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1 |
| title_full | The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1 |
| title_fullStr | The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1 |
| title_full_unstemmed | The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1 |
| title_short | The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1 |
| title_sort | arctic predictability and prediction on seasonal to interannual timescales apposite data set version 1 |
| url | http://www.geosci-model-dev.net/9/2255/2016/gmd-9-2255-2016.pdf |
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