Dataset and Code for CIA to GMST estimates

# CIA–GMST Reconstruction This repository contains the data and code used to reconstruct **Phanerozoic global mean surface temperature (GMST)** from chemical weathering indices (CIA) and paleoclimate model outputs. --- ## Overview Understanding long-term temperature evolution is critical for constraining Earth system feedbacks. This project presents a data-driven reconstruction of GMST based on: - A global compilation of **siliciclastic sediment CIA data** - Calibration between **CIA and temperature** - Data assimilation using **HadCM3 paleoclimate simulations** The workflow enables reconstruction of GMST across the Phanerozoic and provides reproducible datasets for further analysis. --- ## Repository Structure├── CIA2GMST_Main.py├── Data S1. Modern river sediments CIA and local temperature.xlsx├── Data S2. SGP2 dataset and GMST estimates.xlsx├── Data S3. GMST at all periods.xlsx└── README.md --- ## File Description - **CIA2GMST_Main.py** Main script to reproduce GMST estimates using the SGP dataset and climate model outputs. - **Data S1** Modern river sediment dataset used for CIA–temperature calibration. - **Data S2 (SGP_GMST dataset)** Core dataset including: - SGP Phase 2 compilation - CIA values - Reconstructed paleocoordinates - Local temperature estimates - GMST estimates This dataset is sufficient to reproduce all GMST results in the paper. - **Data S3** Final GMST estimates across all geological periods. --- ## Reproducibility All GMST estimates presented in the paper can be reproduced using: - `Data S2. SGP2 dataset and GMST estimates.xlsx` - `CIA2GMST_Main.py` ### Basic workflow 1. Load SGP dataset (Data S2) 2. Apply data filtering (e.g., lithology, geochemistry, outliers) 3. Convert CIA to local temperature 4. Match local temperature to paleoclimate simulations 5. Extract corresponding GMST --- ## Requirements - Python ≥ 3.8 - numpy - pandas - matplotlib - cartopy