DOI: 10.5281/zenodo.3953131
title: Sato and Ise (submitted) Open Data

description: ______________________________________________________Sato and Ise (2021) Open DataAuthor: Hisashi SATO (JAMSTEC) hsatoscb_(at)_gmail.comDate: 22 Feb 2022URLs:https://ebcrpa.jamstec.go.jp/~hsato/Sato_and_Ise_2020

Corresponding publication: H. Sato and T. Ise (2021) Predicting global terrestrial biomes with the LeNet convolutional neural network. Geoscientific Model Development 2022 Vol. 15 Issue 7 Pages 3121-3132. DOI: 10.5194/gmd-15-3121-2022______________________________________________________1. Folder "1.VCDs"It contains Visualized Climate Environments (VCEs) for training and testing the Convolutional-Neural-Network (CNN) model. Names of compressed files (*.tar.gz) correspond to experiments. Each compressed file contains 4 to 16 folders.Naming rule of folders:The strings "CRU," "NCEP," "HadGEM," and "Miroc" stand for that the files are made from CRU_TS4.0, NCEP/NCAR reanalysis, Had2GEM-ESM, and Miroc-ESM climate datasets, respectively. The strings "AMeans" and "MMeans", respectively, stand for the annual-mean and monthly-mean climates, which are represented by VCEs in the compressed file.The string "EachYear" means that the compressed files contains VCEs of each year climate from 1971 to 1980, otherwise the compressed files contains VCEs of averaged climate over 10 years. The strings "hist", "RCP26", and "RCP85" mean that the compressed files contain VCEs of climate averaged over 1971-1980, 2091-2100@RCP2.6, and 2091-2100@RCP8.5, respectively.MainSimulation_training.tar.gzVCEs for training the CNN model for the main simulation and dependency test of climatic datasets for training and reconstructing performances.MainSimulation.tar.gzVCEs for testing the CNN model for the main simulation.CombinationSelection_Amean.tar.gzVCEs for an experiment to find optimal combination of climatic variables (annual means) represented by the VCEs. In the VCE of the RGB color tile, up to three climate variables can be represented by RGB channels. To find the optimal combination of climatic variables, we systematically evaluated the model performance of 14 combinations of climatic variable experiments for annual means. It's result is presented in the Table S3.Extracting this compressed fire results in "CRU", "NCEP", "HadGEM", and "Miroc" folders. Each folder contains 14 subfolders named with sequential numbers, which corresponds to model numbers in the Table S3.CombinationSelection_Mmean.tar.gzSame as the CombinationSelection_Amean.tar.gz except made with monthly mean climates, and related information is available in the Table S4.ScalerSelection.tar.gzVCEs for evaluating the influences of different transformations of climatic variables on the resulting accuracy. It's result is presented in the Table S5.Extracting this compressed fire results in "CRU", "NCEP", "HadGEM", and "Miroc" folders. Each folder contains 4 subfolders named with sequential numbers. "Scaler1" stands for log transformed, "Scaler2" stands for no transformed (linear), "Scaler3" stands for Sigmoid(gain=5) transformed, "Scaler4" stands for Sigmoid(gain=10) transformed.ColorAssignExperiment.tar.gzVCEs for evaluate the influences of assignment patterns of air temperature and precipitation to RGB color channels of the VCE. It's result is presented in the Table S6. Extracting this compressed fire results in "CRU", "NCEP", "HadGEM", and "Miroc" folders. Each folder contains 6 subfolders named with sequential numbers, which indicates model number in the Table S6.AverageExtentExperiment.tar.gzVCEs for sensitivity test, where training and test accuracies are compared among models that are trained by monthly climate averaged over three different periods: 10 years (1971-1980; Control), 20 years (1961-1980), and 30 years (1951-1980).Individual VCE shows climatic condition of each half degree grid cell. According to the ISLSCP2 data, VCEs are classified by their their potential vegetation type of the grid. Number of deepest folder names correspond vegetation code of the ISLSCP2.Following is the vegetation code.  01: Tropical Evergreen Forest/Woodland  02: Tropical Deciduous Forest/Woodland  03: Temperate Broadleaf Evergreen Forest/ Woodland  04: Temperate Needleleaf Evergreen Forest/Woodland  05: Temperate Deciduous Forest/Woodland  06: Boreal Evergreen Forest/Woodland  07: Boreal Deciduous Forest/Woodland  08: Evergreen/Deciduous Mixed Forest  09: Savanna  10: Grassland/Steppe  11: Dense Shrubland  12: Open Shrubland  13: Tundra  14: Desert  15: Polar Desert/Rock/IceThe naming rule for VCEs of 10 years average climate is following:Latitude number + "_" + Longitude number + ".png"The naming rule for VCEs of each year's climate is following:Latitude number + "_" + Longitude number + "_" + Year of climate + ".png"Here, latitude number ranges from 001 to 360, starting from north-latitude-90 to southward with half degree interval. The longitude number ranges from 001 to 720, starting from west-longitude-180 to eastward with half degree interval. On top of this subfolder, there is PicList.zip, which is a compressed file of PicList.txt. This text file is required to classify VCEs with the trained model._____________________________________________2. Folder "2.Result"It contains copies of the image classification results on the Digits screen. Subfolder names correspond to experiment names. Each subfolder contains sub sub-folders "DigitsOutput," "ConfusionMatrix," and "ReconstructedBiomeMap." Naming rules for files are basically same as those of VCEs._____________________________________________3. Folder "3.LearningCurves"It contains screen capture of Digits output, showing learning curve of CNN models. CRU climate data. This folder contains 5 subfolders. Naming rules of these subfolders are same as those of VCEs._____________________________________________4. Folder "5.FigureMaterials" It contains data and codes (in R) for drawing the figures in the manuscript."_____________________________________________________________