Uncertainty in land model projections remains high, yet the distinct roles of parametric errors versus structural model choices are difficult to disentangle. We compared parametric sensitivities in the Community Land Model (CLM) version 6.0 and the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) operating in satellite phenology mode to isolate canopy biophysics. Using a one-at-a-time (OAAT) perturbation strategy, we modified over 300 parameters to their physical limits to quantify their effects on biophysical fluxes globally and across biomes. The resulting dataset contains global, zonal, and biome-level aggregations of these ensembles, alongside annual spatial maps of model output for each parameter perturbation. We found that while most parameters had minimal impact, the models exhibited contrasting sensitivities; CLM-FATES showed a larger spread in gross primary productivity (GPP) driven strongly by carboxylation rate , whereas CLM sensitivities were more distributed among vegetation and hydrology parameters. Additionally, CLM-FATES displayed higher water use efficiency and a dampened response to soil hydrology parameters compared to CLM. These divergence points underscore how model structure fundamentally alters parametric sensitivity. This dataset provides a comprehensive resource for identifying influential parameters to guide future calibration efforts and for investigating the mechanistic drivers of uncertainty in global land surface models. This dataset contains post-processed model output from two global parameter perturbation ensembles (PPEs) generated using the Community Land Model (CLM) version 6.0. The ensembles are designed to compare parametric sensitivity and structural uncertainty between two vegetation model configurations: 1) the default CLM vegetation module, and 2) the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). Both models were run in "satellite phenology" (SP) mode, where vegetation structure (LAI, PFT distribution) is prescribed via remote sensing data to isolate canopy biophysics and hydrological processes.We employed a one-at-a-time (OAAT) perturbation strategy, modifying parameters independently to their estimated minimum and maximum physical bounds. Simulations were conducted on a 400-member "sparse grid" representative of global climatological and ecological heterogeneity, driven by GSWP3v2 reanalysis climate forcing cycled from 2000 to 2014.File formats include several netcdf files that include global annual averages, by-biome annual averages, zonal (by-latitude) annual averages, and global monthly averages for several different experiments. These data were generated and processed using Python (3.11) and the xarray (2024.1.1), pandas (2.2.0), numpy (1.24.3), and dask (2024.1.0) libraries. The workflow used to transform raw model history files into this post-processed dataset is documented in the associated GitHub repository: https://github.com/adrifoster/fates_calibration_library/. The Jupyter Notebooks provided in the GitHub repository (specifically under notebooks/JAMES_2025_OAAT_Manuscript) contain the exact logic used to generate the figures and tables in the accompanying JAMES manuscript from the data files provided in this repository.

Uncertainty in land model projections remains high, yet the distinct roles of parametric errors versus structural model choices are difficult to disentangle. We compared parametric sensitivities in the Community Land Model (CLM) version 6.0 and the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) operating in satellite phenology mode to isolate canopy biophysics. Using a one-at-a-time (OAAT) perturbation strategy, we modified over 300 parameters to their physical limits to quantify their effects on biophysical fluxes globally and across biomes. The resulting dataset contains global, zonal, and biome-level aggregations of these ensembles, alongside annual spatial maps of model output for each parameter perturbation. We found that while most parameters had minimal impact, the models exhibited contrasting sensitivities; CLM-FATES showed a larger spread in gross primary productivity (GPP) driven strongly by carboxylation rate , whereas CLM sensitivities were more distributed among vegetation and hydrology parameters. Additionally, CLM-FATES displayed higher water use efficiency and a dampened response to soil hydrology parameters compared to CLM. These divergence points underscore how model structure fundamentally alters parametric sensitivity. This dataset provides a comprehensive resource for identifying influential parameters to guide future calibration efforts and for investigating the mechanistic drivers of uncertainty in global land surface models. This dataset contains post-processed model output from two global parameter perturbation ensembles (PPEs) generated using the Community Land Model (CLM) version 6.0. The ensembles are designed to compare parametric sensitivity and structural uncertainty between two vegetation model configurations: 1) the default CLM vegetation module, and 2) the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). Both models were run in "satellite phenology" (SP) mode, where vegetation structure (LAI, PFT distribution) is prescribed via remote sensing data to isolate canopy biophysics and hydrological processes.We employed a one-at-a-time (OAAT) perturbation strategy, modifying parameters independently to their estimated minimum and maximum physical bounds. Simulations were conducted on a 400-member "sparse grid" representative of global climatological and ecological heterogeneity, driven by GSWP3v2 reanalysis climate forcing cycled from 2000 to 2014.File formats include several netcdf files that include global annual averages, by-biome annual averages, zonal (by-latitude) annual averages, and global monthly averages for several different experiments. These data were generated and processed using Python (3.11) and the xarray (2024.1.1), pandas (2.2.0), numpy (1.24.3), and dask (2024.1.0) libraries. The workflow used to transform raw model history files into this post-processed dataset is documented in the associated GitHub repository: https://github.com/adrifoster/fates_calibration_library/. The Jupyter Notebooks provided in the GitHub repository (specifically under notebooks/JAMES_2025_OAAT_Manuscript) contain the exact logic used to generate the figures and tables in the accompanying JAMES manuscript from the data files provided in this repository.

Code related to the publication Eckes-Shephard et al 2025The code contains analysis code for the publication and processing code for the benchmark data used in the publication. The processed benchmark observations are also deposited as .csv in folder /observations/2_processed./model_outputs:contains all model outputs used in this publication, usually as .nc file, mostly in "D-BEN" format*. The subfolder structure can vary, but is kept to conform with the functions in DBEN-helper_functions_clean.R/observations:       /1_raw/README: contains the referenes to the dataset origins that were used by Process_observations.R, to create benchmarking files into /2_processed     /1_raw/temperature_data:contains scripts and data used for mean - temperature analysis (in supplementary materials)    /2_processed: benchmarking observations created by Process_observations.R/scripts (download from github):Eckes-Shephard_et_al2025.Rmd: Analysis script that creates the Figures and Supplementary figures for the publication Eckes-Shephard. et al2025.DBEN_helper_functions.R: set of demographic-benchmarking-analysis specific functions that is used by Eckes-Shephard_et_al2025.R.Process_observations.R: Script that creates the benchmark files (in 2_processed) used in Eckes-Shephard_et_al2025.RFormat_DBEN_Eckes-Shephard_et_al2025.R:  a Format, quantity, and layer definition of D-BEN outputs to be compatible for managing outputs and data in DGVM-Tools format.  For mort information, see https://github.com/MagicForrest/DGVMTools. Note that this Format is not the same as the D-BEN data-format described below.*D-BEN format:From the informal demographic benchmarking initiative, where the format has been discussed with members of the demographic vegetation modelling community. the format is not settled yet, but the table S3.1 in the simulation protocol reflects (Supplementary notes S1) the version that modellers tried to adhere to for this analysis. Get started:checkout the github repository https://github.com/teatree1212/DBEN_Eckes_Shephard_et_al; https://zenodo.org/records/15870478into the git repository, download and unzip model_outputs.zip and observations.zip from this zenodo repository.inside folder "scripts", render the file Eckes-Shephard_et_al2025.Rmd by doing Rscript -e "rmarkdown::render('Eckes-Shephard_et_al2025.Rmd')"

Code related to the publication Eckes-Shephard et al 2025The code contains analysis code for the publication and processing code for the benchmark data used in the publication. The processed benchmark observations are also deposited as .csv in folder /observations/2_processed./model_outputs:contains all model outputs used in this publication, usually as .nc file, mostly in "D-BEN" format*. The subfolder structure can vary, but is kept to conform with the functions in DBEN-helper_functions_clean.R/observations:       /1_raw/README: contains the referenes to the dataset origins that were used by Process_observations.R, to create benchmarking files into /2_processed     /1_raw/temperature_data:contains scripts and data used for mean - temperature analysis (in supplementary materials)    /2_processed: benchmarking observations created by Process_observations.R/scripts (download from github):Eckes-Shephard_et_al2025.Rmd: Analysis script that creates the Figures and Supplementary figures for the publication Eckes-Shephard. et al2025.DBEN_helper_functions.R: set of demographic-benchmarking-analysis specific functions that is used by Eckes-Shephard_et_al2025.R.Process_observations.R: Script that creates the benchmark files (in 2_processed) used in Eckes-Shephard_et_al2025.RFormat_DBEN_Eckes-Shephard_et_al2025.R:  a Format, quantity, and layer definition of D-BEN outputs to be compatible for managing outputs and data in DGVM-Tools format.  For mort information, see https://github.com/MagicForrest/DGVMTools. Note that this Format is not the same as the D-BEN data-format described below.*D-BEN format:From the informal demographic benchmarking initiative, where the format has been discussed with members of the demographic vegetation modelling community. the format is not settled yet, but the table S3.1 in the simulation protocol reflects (Supplementary notes S1) the version that modellers tried to adhere to for this analysis. Get started:checkout the github repository https://github.com/teatree1212/DBEN_Eckes_Shephard_et_al; https://zenodo.org/records/15870478into the git repository, download and unzip model_outputs.zip and observations.zip from this zenodo repository.inside folder "scripts", render the file Eckes-Shephard_et_al2025.Rmd by doing Rscript -e "rmarkdown::render('Eckes-Shephard_et_al2025.Rmd')"

SummaryThis 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 characteristicsSpatial 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: MonthlyMethodsStep 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 historyv1.0.1. 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.

SummaryThis 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 characteristicsSpatial 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: MonthlyMethodsStep 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 historyv1.0.1. 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.

These scripts uses the RData objects produced by the first set of post-processing workflows (available in this archive) to generate most figures 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.This data set contains the following scripts, each in its own compressed directory. All directories for plots and summaries require setting the path to RUtils, which is also provided in this data set too.02+05+S14_PlotGMeanCompare.tgz. This script combines individual plots of global averages into Figure 2, Figure 5 and Figure S14.03+S10_PlotDYDX.tgz. This script combines individual plots of sensitivities to environmental drivers into Figure 3 and Figure S10.04_PlotXYSummary.tgz. This script combines plots of ET and GPP responses to environmental drivers by patch AGB classes into Figure 4.06_PlotXYSimSumm.tgz. This script combines plots of ET and GPP responses to environmental drivers by  simulation to into Figure 6.S06_PlotGMeanEval_AGB+LAI.tgz. This script combines plots of global averages of AGB and LAI from ED2 and benchmarks into Figure S06.S08_PlotEMeanCorr_GPP+ET+SH.tgz. This script combines plots of correlations of GPP, ET and SH between ED2 and select benchmarks into Figure S08.S09_PlotGMeanFixed.tgz. This script combines global averages of edaphic conditions and mean annual precipitation into Figure S09. S11+S12_PlotXYbyPAGB.tgz. This script plots the ET and GPP response to environmental drivers by patch AGB classes and by Amazonian region into Figures S11 and S12.S13_PlotGStateCompare.tgz. This script plots the global averages of AGB and LAI, and the differences between simulations Recovery and Degradation and Control.S15+S16+S17_PlotXYbySimul.tgz. This script plots the plots of ET and GPP responses to environmental drivers by simulation and by Amazonian region into Figures S15, S16 and S17.SummCompGMean.tgz. Summaries of the global mean analyses by variable of interest and simulation.SummDYDXbyPAGB.tgz. Summaries of the sensitivities of ET and GPP to environmental drivers by patch AGB (simulation Control).SummDYDXbySimul.tgz. Summaries of the sensitivities of ET and GPP to environmental drivers by simulation.SummResponseByPAGB.tgz. Summaries of the responses of ET and GPP to environmental drivers by patch AGB (simulation Control).SummResponseBySimul.tgz. Summaries of the responses of ET and GPP to environmental drivers by simulation.RUtils.tgz. Folder containing additional R scripts that may be used for running all scripts above.Additional figures not listed here were either generated with QGIS (Figure 1, Figure S1 and Figure S3),  by manually combining panels from existing output from the first set of post-processing workflows (Figure S2, Figure S5, Figure S7), or because they are generated directly by the first set of post-processing workflows (Figure S4).

These scripts uses the RData objects produced by the first set of post-processing workflows (available in this archive) to generate most figures 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.This data set contains the following scripts, each in its own compressed directory. All directories for plots and summaries require setting the path to RUtils, which is also provided in this data set too.02+05+S14_PlotGMeanCompare.tgz. This script combines individual plots of global averages into Figure 2, Figure 5 and Figure S14.03+S10_PlotDYDX.tgz. This script combines individual plots of sensitivities to environmental drivers into Figure 3 and Figure S10.04_PlotXYSummary.tgz. This script combines plots of ET and GPP responses to environmental drivers by patch AGB classes into Figure 4.06_PlotXYSimSumm.tgz. This script combines plots of ET and GPP responses to environmental drivers by  simulation to into Figure 6.S06_PlotGMeanEval_AGB+LAI.tgz. This script combines plots of global averages of AGB and LAI from ED2 and benchmarks into Figure S06.S08_PlotEMeanCorr_GPP+ET+SH.tgz. This script combines plots of correlations of GPP, ET and SH between ED2 and select benchmarks into Figure S08.S09_PlotGMeanFixed.tgz. This script combines global averages of edaphic conditions and mean annual precipitation into Figure S09. S11+S12_PlotXYbyPAGB.tgz. This script plots the ET and GPP response to environmental drivers by patch AGB classes and by Amazonian region into Figures S11 and S12.S13_PlotGStateCompare.tgz. This script plots the global averages of AGB and LAI, and the differences between simulations Recovery and Degradation and Control.S15+S16+S17_PlotXYbySimul.tgz. This script plots the plots of ET and GPP responses to environmental drivers by simulation and by Amazonian region into Figures S15, S16 and S17.SummCompGMean.tgz. Summaries of the global mean analyses by variable of interest and simulation.SummDYDXbyPAGB.tgz. Summaries of the sensitivities of ET and GPP to environmental drivers by patch AGB (simulation Control).SummDYDXbySimul.tgz. Summaries of the sensitivities of ET and GPP to environmental drivers by simulation.SummResponseByPAGB.tgz. Summaries of the responses of ET and GPP to environmental drivers by patch AGB (simulation Control).SummResponseBySimul.tgz. Summaries of the responses of ET and GPP to environmental drivers by simulation.RUtils.tgz. Folder containing additional R scripts that may be used for running all scripts above.Additional figures not listed here were either generated with QGIS (Figure 1, Figure S1 and Figure S3),  by manually combining panels from existing output from the first set of post-processing workflows (Figure S2, Figure S5, Figure S7), or because they are generated directly by the first set of post-processing workflows (Figure S4).

These are the monthly averages of select ED2 output variables, in NetCDF, for the three simulations conducted as part of the following paper: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 file suffixes describe to which simulation each file corresponds:R004_BrAmaz_s1c0t0l0f0. These files are from the Control simulation. The prefix of the files describes the type of output:ED2_domain_R004_BrAmaz_s1c0t0l0f0.nc. This is the extent of the file domain.ED2_zones_R004_BrAmaz_s1c0t0l0f0.nc. This files contains the attribution of each grid cell to one of the sub-domain zones used in the manuscript.ED2_emean_Global_R004_BrAmaz_s1c0t0l0f0.nc. This file contains the monthly averages of select variables for the entire domain.R005_BrAmaz_s1c0t1l1f0. These files are from the Degraded simulation. The files are similar to the ones described for simulation R004_BrAmaz_s1c0t0l0f0 (Control).R006_BrAmaz_s1c0t1l0f0. These files are from the Recovery simulation. The files are similar to the ones described for simulation R004_BrAmaz_s1c0t0l0f0 (Control).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).

These scripts uses the RData objects produced by the first set of post-processing workflows (available in this archive) to generate most figures 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.This data set contains the following scripts, each in its own compressed directory. All directories for plots and summaries require setting the path to RUtils, which is also provided in this data set too.01+04+S14_PlotGMeanCompare.tgz. This script combines individual plots of global averages into Figure 1, Figure 4 and Figure S14.02+S10_PlotDYDX.tgz. This script combines individual plots of sensitivities to environmental drivers into Figure 2 and Figure S10.03_PlotXYSummary.tgz. This script combines plots of ET and GPP responses to environmental drivers by patch AGB classes into Figure 3.05_PlotXYSimSumm.tgz. This script combines plots of ET and GPP responses to environmental drivers by  simulation to into Figure 5.S06_PlotGMeanEval_AGB+LAI.tgz. This script combines plots of global averages of AGB and LAI from ED2 and benchmarks into Figure S06.S08_PlotEMeanCorr_GPP+ET+SH.tgz. This script combines plots of correlations of GPP, ET and SH between ED2 and select benchmarks into Figure S08.S09_PlotGMeanFixed.tgz. This script combines global averages of edaphic conditions and mean annual precipitation into Figure S09. S11+S12_PlotXYbyPAGB.tgz. This script plots the ET and GPP response to environmental drivers by patch AGB classes and by Amazonian region into Figures S11 and S12.S13_PlotGStateCompare.tgz. This script plots the global averages of AGB and LAI, and the differences between simulations Recovery and Degradation and Control.S15+S16+S17_PlotXYbySimul.tgz. This script plots the plots of ET and GPP responses to environmental drivers by simulation and by Amazonian region into Figures S15, S16 and S17.SummCompGMean.tgz. Summaries of the global mean analyses by variable of interest and simulation.SummDYDXbyPAGB.tgz. Summaries of the sensitivities of ET and GPP to environmental drivers by patch AGB (simulation Control).SummDYDXbySimul.tgz. Summaries of the sensitivities of ET and GPP to environmental drivers by simulation.SummResponseByPAGB.tgz. Summaries of the responses of ET and GPP to environmental drivers by patch AGB (simulation Control).SummResponseBySimul.tgz. Summaries of the responses of ET and GPP to environmental drivers by simulation.RUtils.tgz. Folder containing additional R scripts that may be used for running all scripts above.Additional figures not listed here were either generated with QGIS (Figures S1 and Figure S3),  by manually combining panels from existing output from the first set of post-processing workflows (Figure S2, Figure S5, Figure S7), or because they are generated directly by the first set of post-processing workflows (Figure S4).