<?xml version="1.0" encoding="utf-8" standalone="no"?>
<!DOCTYPE article SYSTEM "http://www.nonlin-processes-geophys.net/inc/npg/copernicus.dtd">
<article language="en">
	<journal>
		<journal_title>Nonlinear Processes  in Geophysics</journal_title>
		<journal_url>www.nonlin-processes-geophys.net</journal_url>
		<issn>1023-5809</issn>
		<eissn>1607-7946</eissn>
		<volume_number>12</volume_number>
		<issue_number>4</issue_number>
		<publication_year>2005</publication_year>
	</journal>
	<doi>10.5194/npg-12-451-2005</doi>
	<article_url>http://www.nonlin-processes-geophys.net/12/451/2005/</article_url>
	<abstract_html>http://www.nonlin-processes-geophys.net/12/451/2005/npg-12-451-2005.html</abstract_html>
	<fulltext_pdf>http://www.nonlin-processes-geophys.net/12/451/2005/npg-12-451-2005.pdf</fulltext_pdf>
	<start_page>451</start_page>
	<end_page>460</end_page>
	<publication_date>2005-04-21</publication_date>
	<article_title content_type="html">Continuous partial trends and low-frequency oscillations of time series</article_title>
	<authors>
		<author numeration="1" affiliations="1">
			<name>A. R. Tomé</name>
		</author>
		<author numeration="2" affiliations="2">
			<name>P. M. A. Miranda</name>
		</author>
	</authors>
	<affiliations>
		<affiliation numeration="1" content_type="html">Universidade da Beira Interior, Department of Physics, and Centro de Geofísica da Universidade de Lisboa, Portugal</affiliation>
		<affiliation numeration="2" content_type="html">University of Lisbon, Faculdade de Ciências, Centro de Geofísica, Portugal</affiliation>
	</affiliations>
	<abstract content_type="html">This paper presents a recent methodology developed for the analysis of the
slow evolution of geophysical time series. The method is based on
least-squares fitting of continuous line segments to the data, subject to
flexible conditions, and is able to objectively locate the times of
significant change in the series tendencies. The time distribution of these
breakpoints may be an important set of parameters for the analysis of the
long term evolution of some geophysical data, simplifying the intercomparison
between datasets and offering a new way for the analysis of time varying
spatially distributed data. Several application examples, using data that is
important in the context of global warming studies, are presented and briefly
discussed.</abstract>
	<references>
	</references>
</article>

