Abstract
Climate models are globally employed for addressing climate and climate change questions across a variety of landscapes. However, on a smaller pixel and temporal scale these ‘climate-scapes’ are often less studied, are poorly understood and assessments are lacking. The accuracy of climate-scapes is often affected by local topography and wider couplings. The science of local climate-scapes is still in its infancy, so are the methods of inquiry and inference for ‘weather’. This chapter presents an example for southern Alaska on how differences between regionalized IPCC and other widely used climate models can be data mined, described, assessed and predicted for a better and more informative outcome of climate-scapes. The approach presented here is based on a TreeNet machine learning algorithm using Salford Predictive Modeler Suite 8 (SPM8). These methods allow to locate disagreements in models beyond ‘just’ the means. The models are primarily based on elevation and slope; however other covariates are important such as month (July/January), landcover (shrub and closed mixed forest), aspect, proximity to roads and rivers, and coastal islands. Based on these findings thorough but rapid assessment of ‘climate-scapes’ and their models’ performances and error patterns (explicit in space and time) are recommended for an improved understanding and repeatable and transparent science, its methodology and inference. These methods also enable the ability to address key questions of whether climate and local weather are affected by large-scale macro patterns or regionally driven (bottom-up).
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Appendices
Appendix A Example Map of Data Sets Used for this Machine Learning Assessment of Climate Models: Adaptwest in July. Raw Climate Surface and All GIS Maps are available from the Authors on Request
Appendix B Remaining Details of the TreeNet Model not shown in the text: (a) gains curve, and partial dependence plots for (b) proximity to road, (c) proximity to river
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Huettmann, F. (2018). Advanced Data Mining (Cloning) of Predicted Climate-Scapes and Their Variances Assessed with Machine Learning: An Example from Southern Alaska Shows Topographical Biases and Strong Differences. In: Humphries, G., Magness, D., Huettmann, F. (eds) Machine Learning for Ecology and Sustainable Natural Resource Management. Springer, Cham. https://doi.org/10.1007/978-3-319-96978-7_11
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DOI: https://doi.org/10.1007/978-3-319-96978-7_11
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