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) (Jouvet and Cordonnier 2023).

5 Incoming

6 References

Finn, Geppert, and Ament. 2021. “Ensemble-Based Data Assimilation of Atmospheric Boundary Layerobservations Improves the Soil Moisture Analysis.” Preprint.
Jouvet, and Cordonnier. 2023. “Ice-Flow Model Emulator Based on Physics-Informed Deep Learning.” Journal of Glaciology.
Kitsios, Frederiksen, and O’Kane. 2023. “Subgrid Parameterization of Eddy, Meanfield and Topographic Interactions in Simulations of an Idealized Antarctic Circumpolar Current.” Journal of Advances in Modeling Earth Systems.
O’Kane, Sandery, Kitsios, Sakov, Chamberlain, Collier, et al. 2021. “CAFE60v1: A 60-Year Large Ensemble Climate Reanalysis. Part I: System Design, Model Configuration and Data Assimilation.” Journal of Climate.
O’Kane, Sandery, Kitsios, Sakov, Chamberlain, Squire, et al. 2021. “CAFE60v1: A 60-Year Large Ensemble Climate Reanalysis. Part II: Evaluation.” Journal of Climate.
Sandery, O’Kane, Kitsios, et al. 2020. “Climate Model State Estimation Using Variants of EnKF Coupled Data Assimilation.” Monthly Weather Review.
Schiermeier. 2018. “Droughts, Heatwaves and Floods: How to Tell When Climate Change Is to Blame.” Nature.
Zammit-Mangion, Bertolacci, Fisher, et al. 2021. “WOMBAT v1.0: A fully Bayesian global flux-inversion framework.” Geoscientific Model Development Discussions.