Summary

This dataset provides output results from three ED2 model simulations that used a combination of forest structure derived from a regional airborne lidar survey across the Brazilian Amazon carried out in 2016, which was led by the Brazilian National Institute for Space Research (INPE), and two forest structure change scenarios. These results are 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. 2025. Degradation and deforestation increase the sensitivity of the Amazon Forest to climate extremes. In review.

For all simulations, we used bias-corrected hourly reanalyses (WFDE5) for most meteorological drivers, except for precipitation, which was obtained from CHIRPS. The meteorological drivers used in the study span 38 years (Jan 1981–Dec 2018). The output results correspond to the last 38 years of simulation (one full cycle of meteorological drivers), in which ED2 simulations used static stand structure (i.e., the forest structure was held constant). The following files are provided:

• ED2_emean_Global_R004_BrAmaz_s1c0t0l0f0.nc. This corresponds to the Control simulation. The forest structure was obtained from the airborne lidar.
• ED2_emean_Global_R005_BrAmaz_s1c0t1l1f0.nc. This corresponds to the Degraded simulation. The forest structure was obtained from a spin-up simulation initialized with airborne lidar and a scenario that expanded deforestation and selective logging across the Amazon.
• ED2_emean_Global_R006_BrAmaz_s1c0t1l0f0.nc. This corresponds to the Recovery simulation. The forest structure was obtained from a spin-up simulation initialized with airborne lidar and a scenario that completely halted deforestation and degradation, allowing degraded forests to recover for 38 years.
• ED2_zones_R004_BrAmaz_s1c0t0l0f0.nc. This file classifies each grid cell into zones used in the reference manuscript: 1: Southeast. 2: South. 3: West. 4: Central. 5: Northeast. 6: North. 7: Northwest". Index 0 corresponds to grid cells excluded from sub-region analyses because they were dominated by flooded forests, deforestation, and naturally non-forest vegetation.

The variables included in the NetCDF files contain metadata describing the quantity and the units. Important: For biomass-related variables, units shown in kg actually correspond to kg C (~50% of oven-dry biomass).

Data characteristics

• Spatial coverage: Brazilian Amazon Biome (74°W–45°W; 14°S—5°N)
• Spatial resolution: 1×1°, with sub-grid information available for several variables. Sub-grid information include data aggregated by plant functional type, by plant size, by disturbance history, and by edaphic characteristics (soil texture or soil depth).
• Temporal coverage: Jan 1981–Dec 2018 (based on meteorological drivers)
• Temporal resolution: Monthly

Methods

Step 1. To carry out the ED2 simulations, we used ED2 initialization files generated following the algorithm described in Longo et al. (2020) and available on Zenodo.
Step 2. We carried out ED2 simulations using the version tag v.2.2.1-BrAmazALS2, which is available both on GitHub and on a permanent archive. Using the boundary conditions archived on Zenodo, we carried out 5 sets of simulations using the configuration settings (archived here), and used the initial post-processing R scripts available on the same archive.
Step 3. The consolidated R objects were converted to NetCDF files using the R Markdown notebooks available on Zenodo, and defining the output for NetCDF files to span from 1981 to 2018.

Version history

• v1.0.1. This fixes a previous upload that only had part of the monthly averages in the NetCDF files. For this version, we deleted the multiple domain and zone files, because they did not provide any unique information.
• v1.0.0. First submission.