py-simclimat: Teaching-purpose Earth energy balance model
py-simclimat is a Python package based on a numerical Energy Balance Model which can be run in integration mode (transient trajectories) and continuation mode (steady-state branch). The package also includes two notebooks to learn and apply concepts related to equilibrium climate sensitivity and transient climate response to anthropogenic forcing, the greenhouse effect, feedback loops, commitment, scenarios and remaining carbon budget. I base the first two tutorials of the course Climate Change & Energy Transition on these notebooks.
The model is based on the C++ software SimClimat developed by Camille Risi and Nicolas Gama.
Want to try the notebooks on Binder?
to do the notebook on the equilibrium climate sensitivity to greenhouse-gas emissions (may take a few minutes to launch). Click here to open the associated tutorial.
to do the notebook on the transient climate response to greenhouse-gas emissions (may take a few minutes to launch). Click here to open the associated tutorial.
I consider sharing the programs I develop as documented open-source software essential to promote the reproducibility of the scientific results I publish and facilitate knowledge transfer to researchers, students and organizations.
I have developed, documented and shared the following packages.
e4clim: The Energy for CLimate Integrated Model
e4clim is an open-source Python software developed to integrate multiscale flexibility needs associated with Variable Renewable Energy (VRE) in the development and evaluation of regional energy mix scenarios. The software is designed to provide a flexible and extensible tool to the research and engineering community, and for educational and outreach missions. It aims at (i) evaluating and optimizing energy deployment strategies with high shares of VRE; (ii) assessing the impact of new technologies and of climate variability on mixes; (iii) conducting sensitivity studies. Together, the following aspects are specific to e4clim. First, to limit the complexity of the algorithm, we avoid solving a cost-minimization problem for the full energy mix by conducting a mean-variance analysis whereby the mean and the variance of the renewable production-demand ratio are taken together as proxies to flexibility services needed to match the demand. Second, observations of VRE technologies being typically too short to integrate the impact of climate variability and change, or nonexistent for new technologies, the demand and production are estimated from climate time series and fitted to available observations. Moreover, daily-mean climate data can be used thanks to stochastic parameterizations of hourly fluctuations. The software includes the implementation of particular case studies and an extensive documentation. Contributions are welcomed.
ergoPack: Statistical analysis of dynamical and stochastic systems from time series and more
ergoPack is a C++ library for the analysis of the statistical properties of dynamical and stochastic systems developed by Alexis Tantet for research purpose.