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VOOZH | about |
Open-source Python package for Collective and Systematic Evaluation of Climate and Earth System Models
🌀 Weather and climate graphics for science communication 🌀
📈 Series of workshops and lectures using Python in the climate/atmospheric sciences 📉
A 2-layer quasi-geostrophic atmospheric model in Python. Can be coupled to a simple land or shallow-water ocean component.
Code used to identify and analyze drought clusters from gridded data.
Given a global mean temperature pathway, generate random global climate fields consistent with it and with spatial and temporal correlation derived from an ESM
Assessing the influence of the QBO in Arctic sea ice perturbation experiments
Using neural networks to detect effects of rapid climate mitigation
(Non)linear atmospheric response to Arctic Amplification forcing
Large ensemble to explore robustness of stratospheric response to Arctic sea-ice loss
Climate generator using Generative Adversarial Networks
ANN detecting signal from internal variability
Using a neural network to predict changes in the rate of global mean surface temperature warming
Timing of emergence of CONUS summertime temperatures
Using explainable to identify regional climate signals to stratospheric aerosol injection
Evaluating anomalously early spring onsets in the 21st century
Large-scale atmospheric response to Antarctic sea ice loss
Coupled Natural Human Systems at the Ridge-2-Reef Climate and Life program at UC Irvine, August 2018
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