Using ANN's to reveal changes in extreme events and internal variability in climate models
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Updated
Feb 11, 2021 - Python
Using ANN's to reveal changes in extreme events and internal variability in climate models
ClimWIP allows to calculate & apply performance and independence weights to CMIP models
A lightweight and extensible framework for execution scripts written in YAML.
Assessing the influence of the QBO in Arctic sea ice perturbation experiments
Evaluating anomalously early spring onsets in the 21st century
ANN detecting signal from internal variability
Using a neural network to predict changes in the rate of global mean surface temperature warming
ScriptEngine tasks for the EC-Earth model
(Non)linear atmospheric response to Arctic Amplification forcing
Timing of emergence of CONUS summertime temperatures
Climate drivers of the springtime North America cooling pattern
Using explainable to identify regional climate signals to stratospheric aerosol injection
Set of functions used for aerosols in climate models
New geocarb model
Investigation of model biases in historical internal variability using explainable AI
This repository is for storing the code I have written for an internship started during the summer of 2021 at Université du Québec à Montréal (UQÀM) under the direction of professor Alejandro Di Luca. The internship concerns the evaluation of the Canadian Regional Climate Model 6 - Global Environmental Multiscale 5 (CRCM6-GEM5)'s energy balance.
Climate models
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