http://www.cnr.it/ontology/cnr/individuo/prodotto/ID196272
The Indian Ocean: the region of highest skill worldwide in decadal climate prediction (Articolo in rivista)
- Type
- Label
- The Indian Ocean: the region of highest skill worldwide in decadal climate prediction (Articolo in rivista) (literal)
- Anno
- 2013-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1175/JCLI-D-12-00049.1 (literal)
- Alternative label
Guemas, V., Corti S., Garcìa-Serrano J., Doblas-Reyes F., Balmaseda M., Magnusson L (2013)
The Indian Ocean: the region of highest skill worldwide in decadal climate prediction
in Journal of climate
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Guemas, V., Corti S., Garcìa-Serrano J., Doblas-Reyes F., Balmaseda M., Magnusson L (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://adsabs.harvard.edu/abs/2013JCli...26..726G (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Note
- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Virginie Guemas *
Institut Catal`a de Ci`encies del Clima, Barcelona, Spain
Susanna Corti
European Centre for Medium-Range Weather Forecasts, Reading, England
and Istituto di Scienze dell'Atmosfera e del Clima - Consiglio Nazionale delle Riceche, Bologna Italy
J. Garc´?a-Serrano
Institut Catal`a de Ci`encies del Clima, Barcelona, Spain
F. J. Doblas-Reyes
Institut Catal`a de Ci`encies del Clima and Instituci`o Catalana de Recerca i Estudis Avancats, Barcelona, Spain
Magdalena Balmaseda and Linus Magnusson
European Centre for Medium-Range Weather Forecasts, Reading, England
? (literal)
- Titolo
- The Indian Ocean: the region of highest skill worldwide in decadal climate prediction (literal)
- Abstract
- The Indian Ocean stands out as the region where the state-of-the-art decadal climate pre10
dictions of sea surface temperature (SST) perform the best worldwide for forecast times
ranging from the second to the ninth year, according to correlation and RMSE (Root Mean
Square Error) scores. This paper investigates the reasons for this high skill, by assessing
the contributions from the initial conditions, greenhouse gases, solar activity and volcanic
aerosols. The comparison between the SST correlation skill in uninitialized historical simu15
lations and hindcasts initialized from estimates of the observed climate state shows that the
high Indian Ocean skill is largely explained by the varying radiative forcings, the latter find17
ing being supported by a set of additional sensitivity experiments. The long-term warming
trend is the primary contributor to the high skill, though not the only one. Volcanic aerosols
bring additional skill in this region as shown by the comparison between initialized hindcasts
taking into account or not the effect of volcanic stratospheric aerosols and by the drop in
skill when filtering out their effect in hindcasts which take them into account. Indeed, the
Indian Ocean is shown to be the region where the ratio of the internally-generated over the
externally-forced variability is the lowest, where the amplitude of the internal variability has
been estimated by removing the effect of long-term warming trend and volcanic aerosols by
a multiple least-square linear regression on observed SSTs. (literal)
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