![]() ![]() The data were manipulated, worked, standardized, converted using the software: Climate Data Operators (cdo) version 1.7.0, Grid Analysis and Display System (Grads) ( Documentation of GrADS ) version 2.0.2, and RStudio Desktop version (R Core Team, 2020), through command lines and several scripts developed for this purpose. ![]() This archive contains only the data description from the simulations (outputs) by the DGVMs. The complete description of the data, including the climate forcing, LUC, the validation datasets, methodology, simulations, discussion and conclusion is in Rezende et al. PNV constant CO 2 (Rezende et al., 2022). All combinations of CO2 and land use change resulted in four sets of simulation experiments per climate input: 1. We ran both CO2 experiments under Land Use Change ( LUC) and Potential Natural Vegetation ( PNV) conditions. ![]() ![]() We conducted two sets of simulation experiments with different values of CO2: 1) increasing CO2 from the pre-industrial period to 2010 named historical CO 2 ( hist CO 2) 2) constant concentration of 278 ppm of (pre-industrial) atmospheric CO2 named constant CO 2 (const CO 2 ). We used three forcings with climate data (GLDAS, GSWP3, and WATCH+WFDEI), Land Use Change (LUC) data and validation data (FLUXCOM (Remote sensor+meteorological data+artificial neural network approach), FLUXCOM (eddy covariance), MODIS (Light Use Efficiency), GLEAM, and TerraClimate (Rezende et al., 2022). 2001, Hickler et al., 2012), and Organising Carbon and Hydrology In Dynamic Ecosystems model (ORCHIDEE) (Krinner et al., 2005). We used four models that are classified as Dynamic Global Vegetation Models (DGVMs) (Prentice et al., 2007 Rezende et al., 2015): Integrated Model of Land Surface Processes (INLAND) (Tourigny, 2014) Lund-Potsdam-Jena managed Land model version 4 (LPJmL4) (Schaphoff et al., 2018), Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) (Smith et al. Special focus is given to an improved understanding of the effects of land use changes from the Amazon to the subtropics and their impact on climate. The objectives of CLIMAX include better understanding the combined role of remote and local drivers on South American climate variability from sub-seasonal to decadal timescales, and its impact on the occurrence and intensity of extreme events. More than 200 million people live in the study region, which is also one of the largest agricultural production regions of the world and home to the world’s second largest hydroelectric power plant. The project is sponsored by the Collaborative Research Action (CRA) on “Climate Predictability and Inter-Regional Linkages” of the Belmont Forum, launched in 2015.Ĭlimate variability patterns linking the South American Monsoon region, including Amazonia, with southeastern South America influence climate extremes and impact several societal sectors. The project consortium includes the following institutions: Centre National de la Recherche Scientifique CNRS/Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos (UMI-IFAECI) (Argentina-France) General Coordination of Earth Sciences /National Institute for Space Research (INPE) (Brazil) Institut de Recherche pour le Développement (IRD)/ Unité Mixte de Recherche (UMR 245) (France) Le Laboratoire des Sciences du Climat et de l'Environnement (LSCE) (France) Potsdam Institute for Climate Impact Research (PIK) (Germany) Technical University of Munich (TUM) (Germany) and Wageningen University and Research (WUR) Netherlands). This work was carried out in the scope and with the support of the project: Climate Services Through Knowledge Co-Production: A Euro-South American Initiative for Strengthening Societal Adaptation Response to Extreme Events (CLIMAX). ![]()
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