https://github.com/arve-research/lpj_futuretropics

Postprocessing code and model output from the LPJ-LMfire tropical forest restoration project

https://github.com/arve-research/lpj_futuretropics

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Postprocessing code and model output from the LPJ-LMfire tropical forest restoration project

https://github.com/ARVE-Research/LPJ_futuretropics/blob/main/

# LPJ-LMfire model output and postprocessing code for tropical forest restoration experiments

This repository contains output from simulations performed with the LPJ-LMfire dynamic global vegetation model to investigate tropical forest restoration under different climate change and CO2 fertilization scenarios. This output and included code were used to create time series plots and various maps shown in Koch and Kaplan (2022).

Koch, A., & Kaplan, J. O. (2022). Tropical forest restoration under future climate change. _Nature Climate Change_, 12, 279-283. [doi:10.1038/s41558-022-01289-6](https://doi.org/10.1038/s41558-022-01289-6)

The raw model output in this repository amounts to **more than 56GB of data**. Data are stored using the Git Large File Storage (Git LFS) extension. Cloning the repository requires the user to have `git lfs` installed.

The model processing scripts are packaged as `bash` shell scripts calling [NCO](http://nco.sourceforge.net) and [CDO](https://code.mpimet.mpg.de/projects/cdo) routines. All that is (theoretically) needed is to set `$DATADIR` (location of the downloaded data) and `$WORKDIR` (location where the output should be stored). Both can also be set to the same directory. The `R` scripts require the following libraries: `ncdf4`, `ggplot2`, and `reshape`.  

The general workflow is as follows:
1) extract the carbon in the regrowing tile for each time step/scenario for the restoration areas: `ensemble_ts_t3C_restor.sh`
2) calculate the amount of carbon that could be stored after prioritizing for carbon uptake potential (based on current climate), minimizing restoration cost, or both - `climate_cost_optimization.sh`
3) same as 2) but after factoring in climate change impacts at the end of the century - `climate_cost_optimization_CC.sh`
4) calculate most suitable locations for restoration based on cost and climate - `permanence_map2.sh`
5) create the underlying data structure used for plotting the box-whisker plots - `plot_baseline_Cuptake.R`
6) create the box-whisker plot for Fig. 1 - `plot_t3_drivers_bp.R`
7) create the box-whisker plots for Fig. 2 - `plot_bp_t3C_scenarios.R`
8) plot time series for the extended data figure 1 - `plot_carbon_TS_individual.R`

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