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An update of high-resolution monthly climate surfaces for Mexico

Authors

  • Angela P. Cuervo-Robayo,

    1. Laboratorio de Biología Evolutiva, CIRB, Facultad de Ciencias, Universidad Autónoma del Estado de México, Toluca, Estado de México, Mexico
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  • Oswaldo Téllez-Valdés,

    Corresponding author
    1. Laboratorio de Recursos Naturales, UBRIPO, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla de Baz, Estado de México, Mexico
    • Correspondence to: O. Téllez-Valdés, Laboratorio de Recursos Naturales, UBRIPO, Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Av. de los Barrios 1, Los Reyes Iztacala, Tlalnepantla de Baz, Estado de México C.P. 54090, México. E-mail: tellez@servidor.unam.mx

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  • Miguel A. Gómez-Albores,

    1. Laboratorio de Hidrogeomática, Centro Interamericano de Recursos del Agua, Universidad Autónoma del Estado de México, Toluca, Estado de México, Mexico
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  • Crystian S. Venegas-Barrera,

    1. Division de Estudios de Posgrado e Investigación, Instituto Tecnológico de Ciudad Victoria, Tamaulipas, Mexico
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  • Javier Manjarrez,

    1. Laboratorio de Biología Evolutiva, CIRB, Facultad de Ciencias, Universidad Autónoma del Estado de México, Toluca, Estado de México, Mexico
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  • Enrique Martínez-Meyer

    1. Laboratorio de Análisis Espaciales, Instituto de Biología, Universidad Nacional Autónoma de México, Mexico city, Mexico
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ABSTRACT

Climate surfaces are digital representations of climatic variables from a region in the planet estimated via geographical interpolation techniques. Climate surfaces have multiple applications in research planning, experimental design, and technology transfer. Although high-resolution climatologies have been developed worldwide, Mexico is one of the few countries that have developed several climatic surfaces. Here, we present an updated high-resolution (30 arc sec) climatic surfaces for Mexico for the average monthly climate period 1910–2009, corresponding to monthly values of precipitation, daily maximum, and minimum temperature, as well as 19 bioclimatic variables derived from the monthly precipitation and temperature values. To produce these surfaces we applied the thin-plate smoothing spline interpolation algorithm implemented in the ANUSPLIN software to nearly 5000 climate weather stations countrywide. As an additional product and unlike the previous efforts, we generated monthly standard error surfaces for the three climate parameters, which can be used for error assessment when using these climate surfaces. Our climate surface predicted slightly drier and cooler conditions than the previous ones. ANUSPLIN diagnostic statistics indicated that model fit was adequate. We implemented a more recent error assessment, a set of withheld stations to perform an independent evaluation of the model surfaces. We estimate the mean absolute error and mean error, with the withheld data and all the available data. Average RTGCV for monthly temperatures was of 1.26–1.12 °C and 24.67% for monthly precipitation, and a RTMSE of 0.48–0.56 °C and 11.11%. The main advantage of the surfaces presented here regarding the other three developed for the country is that ours cover practically the entire 20th century and almost the entire first decade of the 21st century. It is the most up to date high-resolution climatology for the country.

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