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A ML project aimed at removing the natural variability from forced surface temperature trends.

License: MIT License

Jupyter Notebook 99.31% Python 0.69%

surfacetrendlearing's Introduction

SurfaceTrendLearing

Observations of tropical land (10S-10N) show surface temperature trends from 1996-2014 lower than predictions from the recent Coupled Model Intercomparison Project Phases 6 (CMIP6). Recent studies have shown that a potential source of discrepancies between model predicted and observed temperature trends is the natural variability of the climate system (Po-Chedley et al., 2022). In this GitHub, we use a machine learning method to show that the tropical land trend from 1996-2014 is significantly impacted by natural variability. After removing this natural variability from the observational record, observed trends align much better with model simulations of surface temperature trends.

Observed Trends Before (red) and After (blue) Removing Natural Variability Compared to CMIP6 Multi-Model Mean.

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