Julia is an increasingly established language for Earth and climate science due to interactivity, productivity, performance and a growing software stack for high-performance computing, big data analysis and visualisation. It empowers developers to build performant Earth-System model components in a composable way, thus allowing users to easily become developers themselves and fostering collaborative work. In this minisymposium speakers will present software projects used for and in Earth system science.
Traditionally, Earth system science relied on Fortran/C/C++ for performance-critical software and, more recently, on Python for data analysis and visualisation. This dichotomy slows down the development process, which relies on iterating between source code modification, simulations requiring high-performance computing and data visualisation. It puts a divide between domain scientists, developers, performance software engineers and users, preventing an easy inclusion of new contributors and a more mutually beneficial co-development.
Julia offers an appealing option to develop new Earth-System models that are performant, accessible and interactive. "Come for the performance, stay for multiple dispatch" might be best illustrated by Earth-System model components, which rely on a vast number of choices that can be elegantly solved via this paradigm. Thus, Julia allows writing composable code, which can be easily extended by users, seamlessly turning them into developers. This translates into a simplified development process and an accelerated time to first paper, stressing that Julia's technical advantages have a direct impact on the scientific output.
We invite users and developers and those bridging these roles, to present Earth system science software. Talks can focus on software that solves equations, analyses or visualises data, processes measurements and especially software with any combination of the above. In particular this includes numerical and machine learning-based Earth system models and their components. For this minisymposium, we accept abstracts on every field in and adjacent to Earth and climate sciences, including but not limited to
- Atmospheric sciences, including atmospheric dynamics, physics, chemistry and climatology
- Oceanography, including sea ice and ocean biogeochemistry
- Earth science, including land surface processes, hydrology and glaciology
- Land vegetation and interactions of ecosystems with the climate
- Geodynamics, seismology, geodesy and geochemistry
- Human and physical geography
- Climate policy and economics, including integrated assessment models
- Planetary sciences
Conveners
- Milan Klöwer (Oxford, UK)
- Jan Swierczek-Jereczek (Complutense University of Madrid, Spain)
- Lazaro Alonso (MPI-BGC Jena, Germany)
- Brian Groenke (Potsdam Insitute for Climate Impact Research, Germany)
Milan Klöwer is a NERC Independent Research Fellow at the University of Oxford. He did his postdoc at the Massachusetts Institute of Technology (MIT) working on climate model development in Julia. He started SpeedyWeather.jl, a global atmospheric model designed as a research playground to develop prototype ideas on machine-learned representations of climate processes and computationally efficient climate models. He also works on low precision computing, data compression and information theory, predictability of weather and climate, and software engineering.
A scientist at the Max Planck Institute for Biogeochemistry, advancing earth system models through hybrid modeling, integrating process-based models with machine learning. Through open, reproducible research and compelling visualizations, I bridge the gap between cutting-edge research and societal impact.
I am a postdoctoral researcher at the Potsdam Institute for Climate Impact Research in Potsdam, Germany. My primary research interests are in applications of differentiable and probabilistic programming, uncertainty quantification, and scientific machine learning to geophysical modeling of Earth systems.
In my PhD, I worked on probabilistic inverse modeling of subsurface heat transfer in terrestrial permafrost. Prior to that, I worked on the application generative deep learning to statistical downscaling of climate and weather variables from coarse scale model outputs.
My industry background consists primarily of software engineering, both front-end and back-end development, with a wide range of frameworks and languages.
I study the stability of ice sheets in the past, present and future, focusing on their interaction with the solid Earth and the sea level. To this end, I use and develop numerical models of ice-sheet evolution and glacial isostatic adjustment.