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Configuration files for the ED2 simulations for the Brazilian Amazon, initialised with airborne lidar

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posted on 2025-08-20, 02:50 authored by Marcos Longo, Michael Keller, Lara Kueppers, Kevin Bowman, Ovidiu Csillik, Antonio Ferraz, Paul Moorcroft, Jean Ometto, Britaldo Silveira Soares Filho, Xiangtao Xu, Mauro Assis, Eric Gorgens, Erik Larson, Jessica Needham, Elsa M. Ordway, Francisca Rocha de Souza Pereira, Ekena Rangel Pinagé, Luciane Sato, Liang Xu, Sassan Saatchi
<p>This data set contains the configurations for the ED2 simulations presented in the following manuscript:</p> <p>Longo, M., M. Keller, L. M. Kueppers, K. Bowman, O. Csillik, A. Ferraz, P. R. Moorcroft, J. P. Ometto, B. S. Soares-Filho, X. Xu, M. L. F. de Assis, E. B. Görgens, E. J. L. Larson, J. F. Needham, E. M. Ordway, F. R. S. Pereira, E. Rangel Pinagé, L. Sato, L. Xu and S. Saatchi. Degradation and deforestation increase the sensitivity of the Amazon Forest to climate extremes. <em>In review</em>.</p> <p>The configurations are organised into five compressed folders, corresponding to the simulation steps.</p> <ul> <li><strong>R001_BrAmaz_s1c1t1l0f0.tgz</strong>. This is the spin up step of simulation <em>R006_BrAmaz_s1c0t1l0f0</em> (<em>Recovery</em> in the manuscript). These runs were initialised with airborne lidar data and run with vegetation dynamics enabled and natural disturbance only. The final step of this result became the initial condition for <em>R006_BrAmaz_s1c0t1l0f0</em> (<em>Recovery</em>), but otherwise this simulation was not analysed in the manuscript.</li> <li><strong>R003_BrAmaz_s1c1t1l1f0.tgz</strong>. This is the spin up step of simulation <em>R005_BrAmaz_s1c0t1l1f0</em> (<em>Degradation</em> in the manuscript). These runs were initialised with airborne lidar data and run with vegetation dynamics enabled and natural disturbance and anthropogenic disturbances. The final step of this result became the initial condition for <em>R005_BrAmaz_s1c0t1l1f0</em> (<em>Degradation</em>), but otherwise this simulation was not analysed in the manuscript.</li> <li><strong>R004_BrAmaz_s1c0t0l0f0.tgz</strong>. This is the simulation <em>Control</em> in the manuscript. This simulation was initialised with airborne lidar forest structure and run with vegetation dynamics disabled. The results of this simulation were presented in the manuscript.</li> <li><strong>R005_BrAmaz_s1c0t1l1f0.tgz</strong>. This is the simulation <em>Degradation</em> in the manuscript. This simulation was initialised with the last time step of simulation <em>R003_BrAmaz_s1c1t1l1f0</em>, and run with vegetation dynamics disabled. The results of this simulation were presented in the manuscript.</li> <li><strong>R006_BrAmaz_s1c0t1l0f0.tgz</strong>. This is the simulation <em>Recovery</em> in the manuscript. This simulation was initialised with the last time step of simulation <em>R001_BrAmaz_s1c1t1l0f0</em>, and run with vegetation dynamics disabled. The results of this simulation were presented in the manuscript.</li> </ul> <p>The <a href="https://dx.doi.org/10.5281/zenodo.14768399" rel="noopener" target="_blank">initial conditions</a> and <a href="https://dx.doi.org/10.5281/zenodo.14773328" rel="noopener" target="_blank">boundary conditions</a> are provided in the linked archives. In addition, in each directory, there are 350 sub-directories with a name structure that follows this example:  <code>ta0006_lon-60.50_lat-12.50_ifire00</code>. In this example, <code>ta0006</code> is the grid cell ID 0006, and <code>lon-60.</code>50 and <code>lat-12.50</code> are the coordinates of the grid cell centre (60.5°W; 12.5°S, respectively). The key <code>ifire00</code> is always zero, as a reminder that fires were disabled in all runs. . In addition, each dire</p> <ul> <li><strong>ED2IN</strong>. This is the namelist used for the simulation of each individual grid cell. For additional information on the namelist variables, check the ED2 Wiki page.</li> <li><strong>read_monthly.r</strong>. This script reads in the analysis output files, carries out some minimal processing of the monthly averages (e.g., unit conversion, simple aggregations), and saves R objects. These scripts require the folder <code>Rsc</code> (also provided, see below), and multiple packages. The script is old, so in case packages are missing and cannot be installed, try commenting out the package in <code>Rsc/load.everything.r</code>, because they may not be needed. The one obsolete package that is required is R package <code>hdf5</code>, which is also provided (see below).</li> <li><strong>histo</strong>. This directory contains the first and last history (restart) files generated by the simulation. </li> </ul> <p>In addition, the following files are provided outside the sub-folder structure</p> <ul> <li><strong>01_regional_gridded.r</strong>. This script concatenates the RData objects from each individual run, and creates a single RData file for each simulation, with a subset of variables of interest. These scripts require the folder <code>Rsc</code> (also provided, see below), and multiple packages. The script is old, so in case packages are missing and cannot be installed, try commenting out the package in Rsc/load.everything.r, because they may not be needed</li> <li><strong>Rsc.tgz</strong>. A suite of R scripts that may be called by <code>read_monthly.r</code> or <code>01_regional_gridded.r</code>. Make sure the correct path is given in <code>read_monthly.r</code> or <code>01_regional_gridded.r</code> scripts, and these scripts should be automatically loaded.</li> <li><strong>hdf5_1.6.12.tar.gz</strong>. This is the source code of the now obsolete <code>hdf5</code> R package. To install it, start an R session, set the working directory to be the same path where <code>hdf5_1.6.12.tar.gz</code> is located, and run the following command: <code>install.packages(“hdf5_1.6.12.tar.gz”,repos=NULL)</code>. Additional configuration may be needed if the C compiler and/or the hdf5 libraries are not in default locations. </li> </ul> <p> </p>

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Zenodo

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ISO Topic Category

  • biota
  • farming

National Agricultural Library Thesaurus terms

lidar; models; data collection; climate; vegetation; Amazonia; forests; deforestation

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  • Public

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