Simulating climate

April 2, 2020 — August 13, 2024

calculus
climate
dynamical systems
geometry
how do science
machine learning
neural nets
PDEs
physics
regression
sciml
SDEs
signal processing
statistics
statmech
stochastic processes
surrogate
time series
uncertainty
wonk
Figure 1

1 ML-enhanced

See climate+ML.

2 Oceans

See oceanography.

3 Atmosphere

See atmospheric science.

Figure 2
Figure 3

4 Ice

jouvetg/igm: Instructed Glacier Model (IGM)(JouvetIceflow2023?).

5 Incoming

6 References

Australian Information Industry Association. 2023. “Tech and Sustainability.”
Guibas, Mardani, Li, et al. 2021. “Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators.” In.
Huang, Zhang, Lan, et al. 2023. “Adaptive Frequency Filters As Efficient Global Token Mixers.”
Kurth, Subramanian, Harrington, et al. 2023. “FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators.” In Proceedings of the Platform for Advanced Scientific Computing Conference. PASC ’23.
Nguyen, Brandstetter, Kapoor, et al. 2023. “ClimaX: A Foundation Model for Weather and Climate.”
Pandey, van Nistelrooij, Maasakkers, et al. 2023. “Daily Detection and Quantification of Methane Leaks Using Sentinel-3: A Tiered Satellite Observation Approach with Sentinel-2 and Sentinel-5p.” Remote Sensing of Environment.
Rolnick, Donti, Kaack, et al. 2019. “Tackling Climate Change with Machine Learning.” arXiv:1906.05433 [Cs, Stat].
Schiermeier. 2018. “Droughts, Heatwaves and Floods: How to Tell When Climate Change Is to Blame.” Nature.
Schuit, Maasakkers, Bijl, et al. 2023. “Automated detection and monitoring of methane super-emitters using satellite data.” Atmospheric Chemistry and Physics.