cnit 2.2.0

CNit (Carbon-Nitrogen Interactions in Terrestrial ecosystems) is a process-based terrestrial biogeochemistry model that simulates coupled carbon and nitrogen cycles in terrestrial ecosystems. It is the land component of MAGICC (Model for the Assessment of Greenhouse Gas Induced Climate Change).

Features

• Explicit carbon-nitrogen coupling with nitrogen limitation feedbacks
• Four nitrogen pools (plant, litter, soil, mineral) and three carbon pools
• Environmental modifiers: CO₂ fertilization, temperature, land use
• Separation of deforestation and afforestation for CDR scenarios
• Annual timestep with sub-annual process representation

Installation

For users:

pip install cnit

For developers:

To set up a development environment for this project, follow these steps:

1. Create a new conda environment (replace cnit with your preferred name if desired):

   conda create --name cnit python=3.9
   

2. Activate your environment:

   conda activate cnit
   

3. Install all dependencies and set up the package:

   make env
   

This will install all required dependencies (including notebook, documentation, testing, linting, and formatting tools) and install the package in editable mode.

Note:
The Makefile will check that you are not in the base environment and will refuse to run if you are.

TL;DR:

conda create --name cnit python=3.9
conda activate cnit
make env

Quick Start

import numpy as np
from cnit import CNitModel, CNitModelConfig, CNitExpConfig
from cnit import Q # Local pint.quantity class for units handling

# Create model with default parameters
config = CNitModelConfig()
model = CNitModel.from_config(config)

# Set up time axis and forcing
# We use the _s suffix to indicate series (time-dependent) inputs.
time_axis = Q(np.arange(1850, 2101), "yr")
n_steps = len(time_axis)

# Simple forcing (constant values for illustration)
dT_s = Q(np.linspace(0, 2, n_steps), "K")  # 2°C warming
CO2_s = Q(np.linspace(280, 560, n_steps), "ppm")  # CO2 doubling

# Land use emissions (zero for simplicity)
CemsLUnet_s = Q(np.zeros(n_steps), "GtC/yr")
CemsLUgrs_s = Q(np.zeros(n_steps), "GtC/yr")

# Nitrogen inputs
NflxAD_s = Q(np.linspace(0.05, 0.15, n_steps), "GtN/yr")  # Increasing deposition
NflxFT_s = Q(np.linspace(0, 0.1, n_steps), "GtN/yr")  # Increasing fertilizer

# Nitrogen land use emissions (zero for simplicity)
NemsLUnet_s = Q(np.zeros(n_steps), "GtN/yr")
NemsLUgrs_s = Q(np.zeros(n_steps), "GtN/yr")
NemsLUmin_s = Q(np.zeros(n_steps), "GtN/yr")

# Run the model
res = model.run(
    time_axis=time_axis,
    dT_s=dT_s,
    CO2_s=CO2_s,
    CemsLUnet_s=CemsLUnet_s,
    CemsLUgrs_s=CemsLUgrs_s,
    NflxAD_s=NflxAD_s,
    NflxFT_s=NflxFT_s,
    NemsLUnet_s=NemsLUnet_s,
    NemsLUgrs_s=NemsLUgrs_s,
    NemsLUmin_s=NemsLUmin_s,
)

# Access results
cveg, cveg_unit = res["CplsP"].data, res["CplsP"].units
nveg, nveg_unit = res["NplsP"].data, res["NplsP"].units
npp, npp_unit = res["CflxNPP"].data, res["CflxNPP"].units

print(f"Final plant carbon: {cveg[-1]} {cveg_unit}")
print(f"Final plant nitrogen: {nveg[-1]} {nveg_unit}")
print(f"NPP change: {npp[0]} → {npp[-1]} {npp_unit}")

Documentation

The full documentation for CNit is available at https://cnit.readthedocs.io.

License

BSD 3-Clause License. See LICENSE for details.

Copyright (c) 2026, Gang Tang and contributors.

Authors and Contributors

Author, Developer, and Maintainer:
Gang Tang

• gang.tang.au@gmail.com (primary)
• gang.tang.1@unimelb.edu.au
• tgang@bgc-jena.mpg.de

Contributors:

• Zebedee Nicholls (zebedee.nicholls@climate-energy-college.org)
• Alexander Norton (alex.norton@csiro.au)
• Sönke Zaehle (szaehle@bgc-jena.mpg.de)
• Malte Meinshausen (malte.meinshausen@unimelb.edu.au)

Citation

If you use CNit in your research, please cite:

• Tang, G., Nicholls, Z., Norton, A., Zaehle, S., and Meinshausen, M.: Synthesizing global carbon–nitrogen coupling effects – the MAGICC coupled carbon–nitrogen cycle model v1.0, Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025, 2025.

• Tang, G., Zaehle, S., Nicholls, Z., Norton, A., Ziehn, T., & Meinshausen, M. (2026). Understanding the drivers of carbon–nitrogen cycle variability in CMIP6 ESMs with MAGICC CNit v2.0: Model and calibration updates. Journal of Advances in Modeling Earth Systems, 18, e2025MS005270. https://doi.org/10.1029/2025MS005270

Related Projects

MAGICC - Model for the Assessment of Greenhouse Gas Induced Climate Change,

• https://magicc.org/
• https://gitlab.com/magicc/magicc