One hundred years of climate change in Mexico

ANUSPLIN produces a list of the stations with the largest data residuals that denote fitting errors. With this list, Cuervo-Robayo et al. [17] corrected the geographic position for 100 erro- neous stations by using online gazetteers and Google Earth; however, for the two later periods, we eliminated about 20 stations that maintained high residual values (< 1) during the fitting process, without increasing error in the diagnostic statistics. This residual error may have resulted from the lack of a homogenization process in the stations’ data. We assessed the accu- racy of the fitted surfaces by examining ANUSPLIN diagnostic measures [42]. The signal indi- cates the degrees of freedom associated with the surfaces, which reflects the complexity of the surface and varies between a small positive integer and the number of stations used to generate the surface [46,48]. Hutchinson & Gessler [46] suggested that the signal should not be greater than about half the number of data points. Models with a signal below this threshold tend to be more robust and reliable in regions where data are scarce [49]. We also examined the root mean square error (RTMSE) and the square root of the GCV (RTGCV). RTMSE is an optimis- tic assessment of predictive error because some interpolation error is removed, therefore the true error is somewhere between the RTMSE and the RTGCV error [42]. Gridded climate Fig 1. Location of weather stations used to generate climate surfaces of precipitation, maximum and minimum temperature for three periods: a. t1-1940 (1910–1949), b.

t2-1970 (1950–1979) and c. t3-2000 (1980–2009). Shaded areas represent mountainous regions above 1500 masl. Stations used were not corrected for inhomogeneities and densities vary between periods and climate variables. For t1-1940 we used 803 stations for precipitation and 500 for temperature, for t2-1970, we used 3411 and 3670 and in t3-2000, 3870 and 4200 for precipitation and temperature, respectively. https://doi.org/10.1371/journal.pone.0209808.g001 PLOS ONE Historical climate change in Mexico PLOS ONE | https://doi.org/10.1371/journal.pone.0209808 July 16, 2020 4 / 19