# Project: 2025_NHESS_Priesner_et_al

This workspace contains code, data, and auxiliary scripts used to prepare figures and run model experiments for the publication (2025). The layout below highlights the main folders and what you'll find in each.

Top-level structure

- `data_and_plotting_scripts/` — Code and data used to produce publication figures. Each figure has a dedicated subfolder with notebooks, plotting scripts, and minimal data required to reproduce the figure. See `data_and_plotting_scripts/README.md` for details about path fixes and running notebooks.

- `input_data/` — Input datasets used by the model and analysis scripts. These are copies of the original inputs (soil, elevation, meteorology/climate `.clm` files, landmask, and a CO2 text file). 

- `lpjmlfit_source_code/` — Source code for the LPJmlfit model version used for the publication

- `scripts_for_running_lpjmlfit/` — Helper and job-creation scripts used to run ensemble/model experiments (many are `createjobs_*.sh`). These scripts were used to generate the model output and may contain machine-specific absolute paths; update them before re-running.

Quick notes

- Absolute paths: Many scripts/notebooks were developed on a different host and may contain absolute paths (for example `/home/jamir/...`). Use the project-level mapping (see `data_and_plotting_scripts/README.md`) or update paths manually before running.

- Reproducing analysis: To reproduce figures, first ensure the input files in `input_data/` are present (or symlinked). Then open the relevant `FigureX/` notebook in `data_and_plotting_scripts/` and follow the notebook cells. Install required Python packages (commonly: numpy, pandas, xarray, matplotlib, cartopy, geopandas, scipy).

- Model output: If you plan to rerun or reprocess LPJ-mlfit outputs, look at `scripts_for_running_lpjmlfit/` for job-generation logic and `lpjmlfit_source_code/` for model sources.