Resumo

NIETO-BARAJAS, Luis E.. Interpolation of paleoclimatology datasets. Atmósfera [online]. 2018, vol.31, n.2, pp.125-141. ISSN 0187-6236.  https://doi.org/10.20937/atm.2018.31.02.02.

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.

Palavras-chave : Bayesian analysis; EPICA; Gaussian process; interpolation; pliocene; paleozoic.