Published January 24, 2025 | Version v1.0.0
Dataset Open

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

  • 1. ROR icon Lawrence Berkeley National Laboratory
  • 2. USDA Forest Service
  • 3. ROR icon University of California, Berkeley
  • 4. ROR icon University of California, Los Angeles
  • 5. ROR icon Jet Propulsion Laboratory
  • 6. ROR icon Wake Forest University
  • 7. University of California Los Angeles Institute of the Environment and Sustainability
  • 8. ROR icon Harvard University
  • 9. ROR icon National Institute for Space Research
  • 10. ROR icon Universidade Federal de Minas Gerais
  • 11. ROR icon Cornell University
  • 12. ROR icon Universidade Federal dos Vales do Jequitinhonha e Mucuri
  • 13. UrbanFootprint
  • 14. UCLA Life Sciences
  • 15. EcoAct - Atos
  • 16. ROR icon Oregon State University
  • 17. Pachama, Inc.

Description

This data set contains the configurations for the ED2 simulations presented in the following manuscript:

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. In review.

The configurations are organised into five compressed folders, corresponding to the simulation steps.

  • R001_BrAmaz_s1c1t1l0f0.tgz. This is the spin up step of simulation R006_BrAmaz_s1c0t1l0f0 (Recovery 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 R006_BrAmaz_s1c0t1l0f0 (Recovery), but otherwise this simulation was not analysed in the manuscript.
  • R003_BrAmaz_s1c1t1l1f0.tgz. This is the spin up step of simulation R005_BrAmaz_s1c0t1l1f0 (Degradation 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 R005_BrAmaz_s1c0t1l1f0 (Degradation), but otherwise this simulation was not analysed in the manuscript.
  • R004_BrAmaz_s1c0t0l0f0.tgz. This is the simulation Control 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.
  • R005_BrAmaz_s1c0t1l1f0.tgz. This is the simulation Degradation in the manuscript. This simulation was initialised with the last time step of simulation R003_BrAmaz_s1c1t1l1f0, and run with vegetation dynamics disabled. The results of this simulation were presented in the manuscript.
  • R006_BrAmaz_s1c0t1l0f0.tgz. This is the simulation Recovery in the manuscript. This simulation was initialised with the last time step of simulation R001_BrAmaz_s1c1t1l0f0, and run with vegetation dynamics disabled. The results of this simulation were presented in the manuscript.

The initial conditions and boundary conditions are provided in the linked archives. In addition, in each directory, there are 350 sub-directories with a name structure that follows this example:  ta0006_lon-60.50_lat-12.50_ifire00. In this example, ta0006 is the grid cell ID 0006, and lon-60.50 and lat-12.50 are the coordinates of the grid cell centre (60.5°W; 12.5°S, respectively). The key ifire00 is always zero, as a reminder that fires were disabled in all runs. . In addition, each dire

  • ED2IN. 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.
  • read_monthly.r. 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 Rsc (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. The one obsolete package that is required is R package hdf5, which is also provided (see below).
  • histo. This directory contains the first and last history (restart) files generated by the simulation. 

In addition, the following files are provided outside the sub-folder structure

  • 01_regional_gridded.r. 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 Rsc (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
  • Rsc.tgz. A suite of R scripts that may be called by read_monthly.r or 01_regional_gridded.r. Make sure the correct path is given in read_monthly.r or 01_regional_gridded.r scripts, and these scripts should be automatically loaded.
  • hdf5_1.6.12.tar.gz. This is the source code of the now obsolete hdf5 R package. To install it, start an R session, set the working directory to be the same path where hdf5_1.6.12.tar.gz is located, and run the following command: install.packages(“hdf5_1.6.12.tar.gz”,repos=NULL). Additional configuration may be needed if the C compiler and/or the hdf5 libraries are not in default locations. 

 

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