The development and application of an algorithm to compute K€ oppen-Geiger climate classifications from the Coupled Model Intercomparison Project (CMIP) and Paleo Model Intercomparison Project (PMIP) climate model simulation data is described in this study. The classification algorithm was applied to data from the PMIP III paleoclimate experiments for the Last Glacial Maximum, 21k years before present (yBP), Mid-Holocene (6k yBP) and the Pre-Industrial (0k yBP, control run) time slices. To infer detailed classification maps, the simulation datasets were interpolated to a higher resolution. The classification method presented is based on the application of Open Source Software, and the implementation is described with attention to detail. The source code and the exact input data sets as well as the resulting data sets are provided to enable the application of the presented approach. an easily reproducible method for deriving K€ oppen-Geiger climate classifications from CMIP/ PMIP climate model simulation outputs using accessible open source tools, the computations and single GIS processing steps are described in detail, to facilitate the reapplication of the presented method discussed. Thus, the aim of this work is not to compare the results of different climate models, for which the presented method would be suitable as well, but to present a reproducible method, application, or tool, to derive the K€ oppen-Geiger classifications as basis or input for further applications and research. Because there is clearly an increasing momentum towards open science (Hey and Payne 2015) in which the authors of this study want to participate, close attention is paid to the reproducibility of the presented method and its application. To support this, in addition to using open source software for the computations, all resulting and source datasets are provided by the authors.This study proceeds by first looking at related research of applying climate classifications to climate model simulations in Section 2. As described and applied in some of these studies, the here presented classification method can also be used to cross-validate and evaluate climate models. In the following sections of this article, we describe the data and methods applied in this study in Section 3. The CMIP/PMIP models and experiments are introduced in Section 3.1, the input variables for the classifications are described in Section 3.1.2 and the exact data sources for the classification maps presented in this article are given in Section 3.1.3. This is followed by a description of the process of creating the land masks needed for time slices with different sea levels in Section 3.2 and the interpolation procedure applied to the input data in Section 3.3. In Section 3.4, we describe the updated K€ oppen-Geiger classification scheme after Peel et al. (2007) and Kottek et al. (2006), which we implemented in this contribution. Section 4 concerns the actual practical implementation details of how the classifications are computed. The resulting classifications...

Abstract:The overall objective of this work is to apply GIS-based cost distance modeling (CDM) to site catchment modeling and analysis of prehistoric (Solutrean) sites in Andalusia. The implementation of a GIS-method for slope-based CDM was explained in detail, so that it can be replicated easily in future studies. Additional cost components, vegetation and stream networks, were included in the method. The presented CDM approach uses slope rasters as input data, which were derived from digital elevation models (DEMs). Various DEMs that differ in cell size, accuracy and other characteristics can be applied to this method. Thus, a major goal of this work is to investigate the influence different publicly available DEMs (SRTM, ASTER GDEM, EU-DEM, official 5-m/10-m cell size DEMs) have on the results of GIS-based CDM. While the investigation was conducted on sites from different chronocultural periods, a case study was performed on Solutrean sites in order to test the CDM approach by producing actual results and then comparing and interpreting them from an archaeological perspective. The results of the DEM evaluation with resampled horizontal resolutions show a clear influence of the DEM cell size on the modeled catchment area sizes. The investigation also indicates that this influence can be superimposed by other factors, such as noise and residuals of filtered anthropogenic features, when using DEMs of different origins.

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