Weather or not—Global climate databases: Reliable on tropical mountains?

vegetation [21,22], the existence of which could not be explained by rainfall data alone. Taking this into account, the bias of the modelled data would have been greater. We are only addressing the mean annual precipitation, the basic bioclimatic variable BIO12 [10]. However, we can assume that the other precipitation-related bioclimatic variables BIO13-BIO19 modelled by WC and C have a similar deviation on Kilimanjaro and very likely also on other tropical mountains. Conclusions In areas of the world with very good coverage by weather stations (e.g. North America, Western Europe), the accuracy of the interpolated climate databases is correspondingly high in most places. For all those parts of the world where there are no such stations, the currently available data sets will continue to be used extensively despite their disadvantages. The promising results in predicting extreme precipitation using a neural network [23] could point the way to modelling grid-based precipitation data. Until this is possible, exist- ing "high-resolution" databases in tropical regions must be used with caution and conclu- sions must take into account the risk of error, which can be drastic, as shown on Kilimanjaro. Tools such as WC and C must be treated as what they are, at least in the tropi- cal mountains: possible models that deviate more or less from reality. If such reanalysis products projecting into the past have such drawbacks, their failure in future projections is likely to be even greater and would require more data for better results [24]. It is clear from

this study that the conclusions of our research on biodiversity patterns and ecosystem func- tioning at Kilimanjaro [25,26] would have been significantly different if we had used global climate datasets instead of our own measurements (Fig 5). However, to obtain comprehen- sive data for the models, continuous long-term observations must be carried out, and this depends on the availability of long-term funding [27]. Supporting information S1 Table. Details of the stations used. TMA: Tanzania Meteorological Authority. (PDF) Acknowledgments We thank the Tanzanian Commission for Science and Technology, the Tanzania Wildlife Research Institute and the Kilimanjaro National Park authority for their support, and for granting us access to the Kilimanjaro National Park; all of the companies and private farmers who allowed us to use their rainfall data, in particular Bente Luther-Medoch, Machare Estate, the Tanganyika Plantation Company (TPC), the Kilimanjaro Plantation Ltd., furthermore the Tanzania Meteorological Authority enabling the acquisition of rainfall data and our field assis- tants for helping to collect the data. Author Contributions Conceptualization: Andreas Hemp. Data curation: Andreas Hemp, Judith Hemp. Formal analysis: Andreas Hemp. Funding acquisition: Andreas Hemp. Investigation: Andreas Hemp. PLOS ONE Global climate datasets on tropical mountains PLOS ONE | https://doi.org/10.1371/journal.pone.0299363 March 13, 2024 10 / 12