Energy Balance Model

The code in this repository is used to study the sensitivity of the nocturnal and polar atmospheric boundary layer to transient phenomena. The analysis is performed with a model for near-surface temperature inversion transitions which was originally defined by van de Wiel et. al. [1] but has been modified to include different types of perturbations.

The corresponding publication is: Kaiser, A., Vercauteren, N., and Krumscheid, S.: Sensitivity of the nocturnal and polar boundary layer to transient phenomena, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1519, 2023.

If comments in the code include references to figures or equations they refer to the ones in this publication.

How to set up and run the model

1. Clone repository bash git clone git@github.com:am-kaiser/energy_balance_model.git cd energy_balance_model

2. Create and activate environment using a virtual environment: bash module load Python/3.9.5-GCCcore-10.3.0 # depending on system set up pip install virtualenv # if you don't have virtualenv installed virtualenv ebm_venv source ebm_venv/bin/activate pip install -r docker/requirements.txt alternatively using docker: bash cd docker docker build . -t energy_balance_model cd .. docker run -ti -v "$PWD:/home/" energy_balance_model cd home

3. Run the model bash python main.py # when using virtualenv python3 main.py # docker Output files can be found in output/ directory with current data and time.

Change model parameters

All model parameters can be set in the file. For a description of most of the model parameters see section 2.1.

Command-line options for running the model

```bash usage: main.py [-h] [-pl] [-V] [-odeu] [-f] [-u] [-uf] [-sf] [-sfu] [-ss] [-a]

Run the energy balance model.

optional arguments:
-h, --help show this help message and exit
-pl, --plot Make potential, bifurcation, ... plots.
-V, --version show program's version number and exit
-odeu, --odewithvaru
Run ODE model with time dependent u.
-f, --function Randomize the model function itself.
-u, --u Randomize the model parameter u. -uf, --uandfunction Randomize the model parameter u and the model itself. -sf, --stabfunction Randomize the stability function. -sfu, --stabfunctionandtimedependentu Randomize the stability function and u is time dependent. -ss, --sensitivitystudy Perform a sensitivity study. -a, --all Run model with all randomizations. ```

Contributors

• Amandine Kaiser
• Nikki Vercauteren (support)

Disclaimer

This code has been set up to run on Ubuntu 20.04. Only the python versions 3.8 and 3.9 have been used to run the code.

There is no guarantee that the code runs on any other operating system or with a different python version.

References

[1] van de Wiel, B. J. H., Vignon, E., Baas, P., van Hooijdonk, I. G. S., van der Linden, S. J. A., Antoon van Hooft, J., Bosveld, F. C., de Roode, S. R., Moene, A. F., and Genthon, C.: Regime Transitions in Near-Surface Temperature Inversions: A Conceptual Model, Journal of Atmospheric Sciences, 74, 1057–1073, https://doi.org/10.1175/JAS-D-16-0180.1, 2017.