Interpolation of paleoclimatology datasets
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| Format: | Artículo científico |
| Langue: | en |
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Universidad Nacional Autónoma de México
2018
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| _version_ | 1876449655627186176 |
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| author | Luis E. Nieto-Barajas |
| author_facet | Luis E. Nieto-Barajas |
| contents | Interpolation of paleoclimatology datasets Luis E. Nieto-Barajas Biología EPICA pliocene paleozoic interpolation Gaussian process Paleoclimatology data includes measures of the amount of carbon dioxide in the atmosphere and level and temperature of the oceans, among others. Recent records of climate change data were done at equidistant times; the different variables were typically measured at the same time to allow for association studies among them. However, there are no registered records of climate change data for thousands or millions of years ago. Scientists have had to device alternative ways of measuring these quantities. These methods are usually a result of indirect measurements, such as ice coring, where both the variable of interest and the time have to be estimated. As a result, paleoclimate data are a collection of time series where observations are unequally spaced. Here we review a Bayesian statistical method to produce equally spaced series and apply it to three paleoclimatology datasets that span from 300 million years ago to the present. 2018 artículo científico 0187-6236 https://www.redalyc.org/articulo.oa?id=56556529005 https://www.redalyc.org/journal/565/56556529005/ https://www.redalyc.org/journal/565/56556529005/html/ https://www.redalyc.org/journal/565/56556529005/56556529005.epub https://www.redalyc.org/journal/565/56556529005/movil https://doi.org/10.20937/ATM.2018.31.02.02 en http://www.redalyc.org/revista.oa?id=565 Atmósfera application/pdf Universidad Nacional Autónoma de México Atmósfera (México) Num.2 Vol.31 |
| format | Artículo científico |
| id | redalyc_56556529005 |
| institution | Redalyc |
| language | en |
| publishDate | 2018 |
| publisher | Universidad Nacional Autónoma de México |
| spellingShingle | Interpolation of paleoclimatology datasets Luis E. Nieto-Barajas Biología EPICA pliocene paleozoic interpolation Gaussian process Interpolation of paleoclimatology datasets Luis E. Nieto-Barajas Biología EPICA pliocene paleozoic interpolation Gaussian process Paleoclimatology data includes measures of the amount of carbon dioxide in the atmosphere and level and temperature of the oceans, among others. Recent records of climate change data were done at equidistant times; the different variables were typically measured at the same time to allow for association studies among them. However, there are no registered records of climate change data for thousands or millions of years ago. Scientists have had to device alternative ways of measuring these quantities. These methods are usually a result of indirect measurements, such as ice coring, where both the variable of interest and the time have to be estimated. As a result, paleoclimate data are a collection of time series where observations are unequally spaced. Here we review a Bayesian statistical method to produce equally spaced series and apply it to three paleoclimatology datasets that span from 300 million years ago to the present. 2018 artículo científico 0187-6236 https://www.redalyc.org/articulo.oa?id=56556529005 https://www.redalyc.org/journal/565/56556529005/ https://www.redalyc.org/journal/565/56556529005/html/ https://www.redalyc.org/journal/565/56556529005/56556529005.epub https://www.redalyc.org/journal/565/56556529005/movil https://doi.org/10.20937/ATM.2018.31.02.02 en http://www.redalyc.org/revista.oa?id=565 Atmósfera application/pdf Universidad Nacional Autónoma de México Atmósfera (México) Num.2 Vol.31 |
| title | Interpolation of paleoclimatology datasets |
| topic | Biología EPICA pliocene paleozoic interpolation Gaussian process |
| url | https://www.redalyc.org/articulo.oa?id=56556529005 https://www.redalyc.org/journal/565/56556529005/ https://www.redalyc.org/journal/565/56556529005/html/ https://www.redalyc.org/journal/565/56556529005/56556529005.epub https://www.redalyc.org/journal/565/56556529005/movil https://doi.org/10.20937/ATM.2018.31.02.02 |