Extracting common pulse-like signals from multiple ice core time series
International audience To understand the nature and cause of natural climate variability, it is important to possess an accurate estimate of past climate forcings. Direct measurements that are reliable only exist for the past few decades. Therefore knowledge of prior variations has to be established...
Published in: | Computational Statistics & Data Analysis |
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Main Authors: | , , , , , |
Other Authors: | , , , , , , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
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HAL CCSD
2013
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Subjects: | |
Online Access: | https://hal.science/hal-00922271 https://doi.org/10.1016/j.csda.2012.01.024 |
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ftunivnantes:oai:HAL:hal-00922271v1 |
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record_format |
openpolar |
institution |
Open Polar |
collection |
Université de Nantes: HAL-UNIV-NANTES |
op_collection_id |
ftunivnantes |
language |
English |
topic |
Signal extraction Multiprocess Kalman filter Volcanic eruptions Pulse-like signals Climate forcing [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology [SDE.MCG]Environmental Sciences/Global Changes [SDE]Environmental Sciences |
spellingShingle |
Signal extraction Multiprocess Kalman filter Volcanic eruptions Pulse-like signals Climate forcing [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology [SDE.MCG]Environmental Sciences/Global Changes [SDE]Environmental Sciences Gazeaux, Julien Batista, Deborah Ammann, Caspar M. Naveau, Philippe Jégat, Cyrille Gao, Chaochao Extracting common pulse-like signals from multiple ice core time series |
topic_facet |
Signal extraction Multiprocess Kalman filter Volcanic eruptions Pulse-like signals Climate forcing [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology [SDE.MCG]Environmental Sciences/Global Changes [SDE]Environmental Sciences |
description |
International audience To understand the nature and cause of natural climate variability, it is important to possess an accurate estimate of past climate forcings. Direct measurements that are reliable only exist for the past few decades. Therefore knowledge of prior variations has to be established based on indirect information derived from natural archives. The challenge has always been to find a strict objective method that can identify volcanic events and offer sound amplitude estimates in these noisy records. An automatic procedure is introduced here to estimate the magnitude of strong, but short-lived, volcanic signals from a suite of polar ice core series. Rather than treating records from individual ice cores separately and then averaging their respective magnitudes, our extraction algorithm jointly handles multiple time series to identify their common, but hidden, volcanic pulses. The statistical procedure is based on a multivariate multi-state space model. Exploiting the joint fluctuations, it provides an accurate estimator of the timing, peak magnitude and duration of individual pulse-like deposition events within a set of different series. This ensures a more effective separation of the real signals from spurious noise that can occur in any individual time series, and thus a higher sensitivity to identify smaller scale events. At the same time, it provides a measure of confidence through the posterior probability for each pulse-like event, indicating how well a pulse can be recognized against the background noise. The flexibility and robustness of our approach, as well as important underlying assumptions and remaining limitations, are discussed by applying our method to first simulated and then real world ice core time series. |
author2 |
TROPO - LATMOS Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS) Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS) Department of Mathematical Sciences Denver University of Colorado Denver Research Applications Laboratory Boulder (RAL) National Center for Atmospheric Research Boulder (NCAR) Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette (LSCE) Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS) École des Mines de Paris Department of Environmental Sciences New Brunswick School of Environmental and Biological Sciences New Brunswick Rutgers, The State University of New Jersey New Brunswick (RU) Rutgers University System (Rutgers)-Rutgers University System (Rutgers)-Rutgers, The State University of New Jersey New Brunswick (RU) Rutgers University System (Rutgers)-Rutgers University System (Rutgers) |
format |
Article in Journal/Newspaper |
author |
Gazeaux, Julien Batista, Deborah Ammann, Caspar M. Naveau, Philippe Jégat, Cyrille Gao, Chaochao |
author_facet |
Gazeaux, Julien Batista, Deborah Ammann, Caspar M. Naveau, Philippe Jégat, Cyrille Gao, Chaochao |
author_sort |
Gazeaux, Julien |
title |
Extracting common pulse-like signals from multiple ice core time series |
title_short |
Extracting common pulse-like signals from multiple ice core time series |
title_full |
Extracting common pulse-like signals from multiple ice core time series |
title_fullStr |
Extracting common pulse-like signals from multiple ice core time series |
title_full_unstemmed |
Extracting common pulse-like signals from multiple ice core time series |
title_sort |
extracting common pulse-like signals from multiple ice core time series |
publisher |
HAL CCSD |
publishDate |
2013 |
url |
https://hal.science/hal-00922271 https://doi.org/10.1016/j.csda.2012.01.024 |
genre |
ice core |
genre_facet |
ice core |
op_source |
ISSN: 0167-9473 Computational Statistics and Data Analysis https://hal.science/hal-00922271 Computational Statistics and Data Analysis, 2013, 58, pp.45-57. ⟨10.1016/j.csda.2012.01.024⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.csda.2012.01.024 hal-00922271 https://hal.science/hal-00922271 doi:10.1016/j.csda.2012.01.024 |
op_doi |
https://doi.org/10.1016/j.csda.2012.01.024 |
container_title |
Computational Statistics & Data Analysis |
container_volume |
58 |
container_start_page |
45 |
op_container_end_page |
57 |
_version_ |
1766029218514206720 |
spelling |
ftunivnantes:oai:HAL:hal-00922271v1 2023-05-15T16:38:52+02:00 Extracting common pulse-like signals from multiple ice core time series Gazeaux, Julien Batista, Deborah Ammann, Caspar M. Naveau, Philippe Jégat, Cyrille Gao, Chaochao TROPO - LATMOS Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS) Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS) Department of Mathematical Sciences Denver University of Colorado Denver Research Applications Laboratory Boulder (RAL) National Center for Atmospheric Research Boulder (NCAR) Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette (LSCE) Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS) École des Mines de Paris Department of Environmental Sciences New Brunswick School of Environmental and Biological Sciences New Brunswick Rutgers, The State University of New Jersey New Brunswick (RU) Rutgers University System (Rutgers)-Rutgers University System (Rutgers)-Rutgers, The State University of New Jersey New Brunswick (RU) Rutgers University System (Rutgers)-Rutgers University System (Rutgers) 2013 https://hal.science/hal-00922271 https://doi.org/10.1016/j.csda.2012.01.024 en eng HAL CCSD Elsevier info:eu-repo/semantics/altIdentifier/doi/10.1016/j.csda.2012.01.024 hal-00922271 https://hal.science/hal-00922271 doi:10.1016/j.csda.2012.01.024 ISSN: 0167-9473 Computational Statistics and Data Analysis https://hal.science/hal-00922271 Computational Statistics and Data Analysis, 2013, 58, pp.45-57. ⟨10.1016/j.csda.2012.01.024⟩ Signal extraction Multiprocess Kalman filter Volcanic eruptions Pulse-like signals Climate forcing [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology [SDE.MCG]Environmental Sciences/Global Changes [SDE]Environmental Sciences info:eu-repo/semantics/article Journal articles 2013 ftunivnantes https://doi.org/10.1016/j.csda.2012.01.024 2023-02-14T23:59:48Z International audience To understand the nature and cause of natural climate variability, it is important to possess an accurate estimate of past climate forcings. Direct measurements that are reliable only exist for the past few decades. Therefore knowledge of prior variations has to be established based on indirect information derived from natural archives. The challenge has always been to find a strict objective method that can identify volcanic events and offer sound amplitude estimates in these noisy records. An automatic procedure is introduced here to estimate the magnitude of strong, but short-lived, volcanic signals from a suite of polar ice core series. Rather than treating records from individual ice cores separately and then averaging their respective magnitudes, our extraction algorithm jointly handles multiple time series to identify their common, but hidden, volcanic pulses. The statistical procedure is based on a multivariate multi-state space model. Exploiting the joint fluctuations, it provides an accurate estimator of the timing, peak magnitude and duration of individual pulse-like deposition events within a set of different series. This ensures a more effective separation of the real signals from spurious noise that can occur in any individual time series, and thus a higher sensitivity to identify smaller scale events. At the same time, it provides a measure of confidence through the posterior probability for each pulse-like event, indicating how well a pulse can be recognized against the background noise. The flexibility and robustness of our approach, as well as important underlying assumptions and remaining limitations, are discussed by applying our method to first simulated and then real world ice core time series. Article in Journal/Newspaper ice core Université de Nantes: HAL-UNIV-NANTES Computational Statistics & Data Analysis 58 45 57 |