<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0187-6236</journal-id>
<journal-title><![CDATA[Atmósfera]]></journal-title>
<abbrev-journal-title><![CDATA[Atmósfera]]></abbrev-journal-title>
<issn>0187-6236</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Instituto de Ciencias de la Atmósfera y Cambio Climático]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0187-62362017000300233</article-id>
<article-id pub-id-type="doi">10.20937/atm.2017.30.03.04</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Climatic analysis linked to land vegetation cover of Mexico by applying multivariate statistical and clustering analysis]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pineda-Martínez]]></surname>
<given-names><![CDATA[Luis F.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Carbajal]]></surname>
<given-names><![CDATA[Noel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de Zacatecas Unidad Académica de Ciencias de la Tierra-PEIDA ]]></institution>
<addr-line><![CDATA[Zacatecas Zacatecas]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Potosino de Investigación Científica y Tecnológica, A. C.  ]]></institution>
<addr-line><![CDATA[San Luis Potosí San Luis Potosí]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2017</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2017</year>
</pub-date>
<volume>30</volume>
<numero>3</numero>
<fpage>233</fpage>
<lpage>242</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0187-62362017000300233&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0187-62362017000300233&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0187-62362017000300233&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT The climate regions of Mexico are delimitated using hierarchical clustering analysis (HCA). The data used consists of monthly means of maximum and minimum temperatures and monthly-accumulated precipitation. The dataset was obtained from heterogeneously distributed climatic stations in Mexico for the period from 1961 to 2004. This cluster method assigns precipitation and temperature variables to groups of clusters based on similar statistical characteristics. We carried out a principal components analysis to obtain a standardized reduced matrix to be used in HCA. By applying two clustering criteria (K-means and Ward´s method) it was possible to define statistically groups of stations that delimit regions of similar climate. In addition, the applied methodology describes the dominant vegetation distribution for each climate region. This analysis may contribute to the generation of new climate scenarios, where the dynamics of land vegetation cover could be included as a biomarker of climate.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Se delimitan las regiones climáticas de México mediante el análisis jerárquico de agrupamiento. Los datos utilizados fueron medias mensuales de temperatura máxima y mínima y la precipitación mensual acumulada, obtenidas de estaciones climáticas en México para el periodo 1961-2004. Este método de agrupamiento asigna cada variable de precipitación y temperatura a grupos con base en características estadísticas similares. Se realizó un análisis de componentes principales para obtener una matriz estandarizada que se utilizó en el agrupamiento. Aplicando dos criterios de agrupamiento (K-means y Ward) fue posible definir estadísticamente los grupos de estaciones que delimitan regiones de clima similar. Además, la metodología empleada describe la distribución de la vegetación dominante para cada región climática. Este análisis puede contribuir a la generación de nuevos escenarios climáticos, donde puede incluirse la dinámica de la cobertura vegetal como bioindicador del clima.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Hierarchical clustering analysis]]></kwd>
<kwd lng="en"><![CDATA[principal component analysis]]></kwd>
<kwd lng="en"><![CDATA[climate of Mexico]]></kwd>
<kwd lng="en"><![CDATA[vegetation distribution]]></kwd>
</kwd-group>
</article-meta>
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