Articles | Volume 11, issue 9
https://doi.org/10.5194/gmd-11-3781-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-11-3781-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| Highlight paper
| 
18 Sep 2018
Model description paper | Highlight paper |
| 18 Sep 2018
| 18 Sep 2018
sympl (v. 0.4.0) and climt (v. 0.15.3) – towards a flexible framework for building model hierarchies in Python
Department of Meteorology, Stockholm University, 106 91 Stockholm, Sweden
Jeremy McGibbon
Atmospheric Sciences–Geophysics (ATG) Building, University of Washington, Seattle, Washington 98195-1640, USA
Rodrigo Caballero
Department of Meteorology, Stockholm University, 106 91 Stockholm, Sweden
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15 citations as recorded by crossref.
- Feedbacks and eddy diffusivity in an energy balance model of tropical rainfall shifts H. Peterson & W. Boos 10.1038/s41612-020-0114-4
- fv3gfs-wrapper: a Python wrapper of the FV3GFS atmospheric model J. McGibbon et al. 10.5194/gmd-14-4401-2021
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- A Python-enhanced urban land surface model SuPy (SUEWS in Python, v2019.2): development, deployment and demonstration T. Sun & S. Grimmond 10.5194/gmd-12-2781-2019
- Atmospheric Bistability and Abrupt Transitions to Superrotation: Wave–Jet Resonance and Hadley Cell Feedbacks C. Herbert et al. 10.1175/JAS-D-19-0089.1
- Pace v0.2: a Python-based performance-portable atmospheric model J. Dahm et al. 10.5194/gmd-16-2719-2023
- Tropical Free‐Tropospheric Humidity Differences and Their Effect on the Clear‐Sky Radiation Budget in Global Storm‐Resolving Models T. Lang et al. 10.1029/2021MS002514
- Correcting a 200 km Resolution Climate Model in Multiple Climates by Machine Learning From 25 km Resolution Simulations S. Clark et al. 10.1029/2022MS003219
- Earth’s long-term climate stabilized by clouds C. Goldblatt et al. 10.1038/s41561-021-00691-7
- RadNet 1.0: exploring deep learning architectures for longwave radiative transfer Y. Liu et al. 10.5194/gmd-13-4399-2020
- A Numerical Analysis of Six Physics‐Dynamics Coupling Schemes for Atmospheric Models S. Ubbiali et al. 10.1029/2020MS002377
- A 1D RCE Study of Factors Affecting the Tropical Tropopause Layer and Surface Climate S. Dacie et al. 10.1175/JCLI-D-18-0778.1
- A Python interface to the Dutch Atmospheric Large-Eddy Simulation G. van den Oord et al. 10.1016/j.softx.2020.100608
5 citations as recorded by crossref.
- Harsanyi Solutions in Line-Graph Games R. van den Brink et al. 10.2139/ssrn.455100
- Solving Discrete Zero Point Problems G. van der Laan et al. 10.2139/ssrn.610861
- Stability of Steady States in a Model of Pleasant Monetarist Arithmetic M. Espinosa-Vega & S. Russell 10.2139/ssrn.291027
- The New Neoclassical Synthesis and the Role of Monetary Policy M. Goodfriend & R. King 10.2139/ssrn.2123683
- The Case for Price Stability M. Goodfriend & R. King 10.2139/ssrn.2182231
Discussed (preprint)
Latest update: 08 Nov 2024
Short summary
In the same way that the fruit fly or the yeast cell serve as model systems in biology, climate scientists use a range of computer models to gain a fundamental understanding of our climate system. These models range from extremely simple models that can run on your phone to those that require supercomputers. Sympl and climt are packages that make it easy for climate scientists to build a hierarchy of such models using Python, which facilitates easy to read and self-documenting models.
In the same way that the fruit fly or the yeast cell serve as model systems in biology, climate...
