A close up of daily temperature and moisture in two Mexican high-elevation forests

The scarcity of meteorological stations and the strong need for climatic information in alpine forests require the use of large-scale climatic algorithms but the lack of in situ information produces high uncertainty on their suitability. In this study, we used linear mixed models to study the topographic effect (elevation and aspect) and time variations (from hourly to monthly) on temperature (T) and relative humidity (RH) with a 5-year instrumental database. Furthermore, we compared climatic information from a geographical algorithm and our in-situ data. Our data covered two mountains (Tlaloc-TLA and Jocotitlan-JOC, State of Mexico), four elevation belts (from 3500 m to 3900 m a.s.l.), and two aspects (Northwest and Southwest). We found differences for average temperature (TLA = 7.56 degrees C +/- 0.03 degrees C and JOC = 6.98 degrees C +/- 0.02 degrees C), and relative humidity between mountains (TLA = 69.3% +/- 0.12% and JOC = 72.5% +/- 0.13%,). The most significant variables explaining T were the elevation (Delta = -0.36 degrees C by 100 m) and aspect, while the aspect was relevant for RH. May was the warmest month (9.50 degrees C +/- 0.10 degrees C for average temperature) while September the wettest for both mountains (85.1% +/- 0.30% and 87.4% +/- 0.25 % RH, respectively). Despite the higher correlations between climatic sources (up to r = 0.83), the geographical algorithm overestimates T and underestimates RH. We propose that when climatic information from geographical algorithms is used in alpine forests, calibrations are needed whenever possible with in situ information.