Identifying large-scale patterns of unpredictability and response to insolation in atmospheric data
Understanding the complex dynamics of the atmosphere is of paramount interest due to its impact in the entire climate system and in human society. Here we focus on identifying, from data, the geographical regions which have similar atmospheric properties. We study surface air temperature (SAT) time...
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| Other Authors: | , |
| Format: | article |
| Language: | English |
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2017
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| Online Access: | https://hdl.handle.net/20.500.12008/22014 |
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| _version_ | 1868890219613257728 |
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| author | Arizmendi, Fernando |
| author2 | Barreiro, Marcelo Masoller, Cristina |
| author2_role | author author |
| author_browse | Arizmendi, Fernando Barreiro, Marcelo Masoller, Cristina |
| author_facet | Arizmendi, Fernando Barreiro, Marcelo Masoller, Cristina |
| author_role | author |
| collection | COLIBRI |
| dc.contributor.none.fl_str_mv | Barreiro, Marcelo. Universidad de la República (Uruguay). Facultad de Ciencias. Instituto de Física. |
| dc.creator.none.fl_str_mv | Arizmendi, Fernando Barreiro, Marcelo Masoller, Cristina |
| dc.date.none.fl_str_mv | 2017 2019-10-02T22:08:27Z 2019-10-02T22:08:27Z 20190930 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Arizmendi, F., Barreiro, M., Masoller, C.Identifying large-scale patterns of unpredictability and response to insolation in atmospheric data. Scientific Reports, 2017, 7, art. nro. 45676. doi: 10.1038/srep45676 2045-2322 https://hdl.handle.net/20.500.12008/22014 10.1038/srep45676 |
| dc.language.none.fl_str_mv | en eng |
| dc.publisher.none.fl_str_mv | Nature Publishing Group |
| dc.relation.none.fl_str_mv | Scientific Reports, 2017, 7, art. no. 45676 |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess Licencia Creative Commons Atribución (CC –BY 4.0) |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.none.fl_str_mv | Air temperature Entropy Solar radiation Temperature sensitivity Time series analysis |
| dc.title.none.fl_str_mv | Identifying large-scale patterns of unpredictability and response to insolation in atmospheric data |
| dc.type.none.fl_str_mv | Artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| description | Understanding the complex dynamics of the atmosphere is of paramount interest due to its impact in the entire climate system and in human society. Here we focus on identifying, from data, the geographical regions which have similar atmospheric properties. We study surface air temperature (SAT) time series with monthly resolution, recorded at a regular grid covering the Earth surface. We consider two datasets: NCEP CDAS1 and ERA Interim reanalysis. We show that two surprisingly simple measures are able to extract meaningful information: i) the distance between the lagged SAT and the incoming solar radiation and ii) the Shannon entropy of SAT and SAT anomalies. The distance uncovers well-defined spatial patterns formed by regions with similar SAT response to solar forcing while the entropy uncovers regions with similar degree of SAT unpredictability. The entropy analysis also allows identifying regions in which SAT has extreme values. Importantly, we uncover differences between the two datasets which are due to the presence of extreme values in one dataset but not in the other. Our results indicate that the distance and entropy measures can be valuable tools for the study of other climatological variables, for anomaly detection and for performing model inter-comparisons. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | anni_e62ec0b8f2bd25b8b1f4bff7cf267a7e |
| identifier_str_mv | Arizmendi, F., Barreiro, M., Masoller, C.Identifying large-scale patterns of unpredictability and response to insolation in atmospheric data. Scientific Reports, 2017, 7, art. nro. 45676. doi: 10.1038/srep45676 2045-2322 10.1038/srep45676 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | anni |
| network_name_str | oai-lr-anni |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/22014 |
| publishDate | 2017 |
| publishDateSort | 2017 |
| publisher.none.fl_str_mv | Nature Publishing Group |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | Licencia Creative Commons Atribución (CC –BY 4.0) |
| spelling | Identifying large-scale patterns of unpredictability and response to insolation in atmospheric dataArizmendi, FernandoBarreiro, MarceloMasoller, CristinaAir temperatureEntropySolar radiationTemperature sensitivityTime series analysisUnderstanding the complex dynamics of the atmosphere is of paramount interest due to its impact in the entire climate system and in human society. Here we focus on identifying, from data, the geographical regions which have similar atmospheric properties. We study surface air temperature (SAT) time series with monthly resolution, recorded at a regular grid covering the Earth surface. We consider two datasets: NCEP CDAS1 and ERA Interim reanalysis. We show that two surprisingly simple measures are able to extract meaningful information: i) the distance between the lagged SAT and the incoming solar radiation and ii) the Shannon entropy of SAT and SAT anomalies. The distance uncovers well-defined spatial patterns formed by regions with similar SAT response to solar forcing while the entropy uncovers regions with similar degree of SAT unpredictability. The entropy analysis also allows identifying regions in which SAT has extreme values. Importantly, we uncover differences between the two datasets which are due to the presence of extreme values in one dataset but not in the other. Our results indicate that the distance and entropy measures can be valuable tools for the study of other climatological variables, for anomaly detection and for performing model inter-comparisons.Nature Publishing GroupBarreiro, Marcelo. Universidad de la República (Uruguay). Facultad de Ciencias. Instituto de Física.2019-10-02T22:08:27Z2019-10-02T22:08:27Z201720190930Artículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfArizmendi, F., Barreiro, M., Masoller, C.Identifying large-scale patterns of unpredictability and response to insolation in atmospheric data. Scientific Reports, 2017, 7, art. nro. 45676. doi: 10.1038/srep456762045-2322https://hdl.handle.net/20.500.12008/2201410.1038/srep45676reponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaenengScientific Reports, 2017, 7, art. no. 45676Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad De La República. (Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución (CC –BY 4.0)oai:colibri.udelar.edu.uy:20.500.12008/220142026-04-14T10:09:15Z |
| spellingShingle | Identifying large-scale patterns of unpredictability and response to insolation in atmospheric data Arizmendi, Fernando Air temperature Entropy Solar radiation Temperature sensitivity Time series analysis |
| status_str | publishedVersion |
| title | Identifying large-scale patterns of unpredictability and response to insolation in atmospheric data |
| title_full | Identifying large-scale patterns of unpredictability and response to insolation in atmospheric data |
| title_fullStr | Identifying large-scale patterns of unpredictability and response to insolation in atmospheric data |
| title_full_unstemmed | Identifying large-scale patterns of unpredictability and response to insolation in atmospheric data |
| title_short | Identifying large-scale patterns of unpredictability and response to insolation in atmospheric data |
| title_sort | Identifying large-scale patterns of unpredictability and response to insolation in atmospheric data |
| topic | Air temperature Entropy Solar radiation Temperature sensitivity Time series analysis |
| url | https://hdl.handle.net/20.500.12008/22014 |