Predicting dominant terrestrial biomes at a global scale using machine learning algorithms, climate variable indices, and extreme climate indices

Abstract. Several methods have been proposed for modelling global biome distribution. Climate data are typically summarised in terms of a few climate indices. However, with the recent advancement of machine learning algorithms, such summarisation is no longer required. Extreme climate events such as intense droughts and very low temperatures cannot be captured by monthly mean climate data, which may limit the applicability of biome boundaries. In this study, I assessed the influences of machine learning algorithms, climate variable indices, and extreme climate indices on the accuracy and robustness of global biome modelling. I found that the random forest and convolutional neural network algorithms produced highly accurate models for reconstructing the global biome distribution. However, the convolutional neural network algorithm was preferable, because the random forest algorithm substantially overfit the training data relative to the other machine learning algorithms examined. Including indexed climate data slightly reduced model accuracy, whereas including extreme climate data slightly improved it. However, there were significant deviations in the distribution of values between the observed and predicted climate when extreme climate data was included; this fatally reduced the robustness of the models, which were evaluated in terms of prediction consistency. Therefore, I recommend that extreme climate data not be considered in global-scale biome prediction applications.