Repository for Dyer et al. 2021 PNAS
The seas are rising as the planet warms, and reconstructions of past sea level provide critical insight into the sensitivity of ice sheets to warmer temperatures. Past sea level is reconstructed from the geologic record by measuring the elevations of fossilized marine sediments and coral reefs. However, the elevations of these features also record local uplift or subsidence due to the growth and decay of ice sheets since the time of deposition. We compare new observations of paleo sea level across the Bahamian archipelago to a range of Earth deformation models to revise estimates of last interglacial global mean sea level. Our results suggest that polar ice sheets may be less sensitive to high latitude warming than previously thought.
Follow this link to explore field photos and outcrop locations associated with this project.
bahamas_lig/utils.py contains all of the helper functions defined for the analysis in this paper. These functions are used throughout the included notebooks. The PyMC3 GP Regression model is created in the inference_model() function. In the notebooks folder, there are three GP Regression notebooks that illustrate how to use these functions to estimate past sea level.
There are four archives that should be downloaded from the most recent release of this repository:
- The gridded GIA model outputs: get_GIA.zip
- extract archive contents into
/model_outputs/
- extract archive contents into
- Posterior traces for the LIG data: lig_inversion.zip
- extract archive contents into
/model_outputs/
- extract archive contents into
- Posterior traces for the synthetic data: synthetic_test.zip
- extract archive contents into
/model_outputs/
- extract archive contents into
- Posterior traces for the holocene data: holocene.zip
- extract archive contents into
/model_outputs/
- extract archive contents into
To run the CNN code using tensorflow follow the steps here, paying special attention to the steps to install CUDA for your machine if you want to use a CUDE enabled GPU for the calculations. https://www.tensorflow.org/install
Use the .yml files with anaconda to create an environment to run the contained code:
conda env create -f environment.yml
(or alternatively with pip)
pip install -r requirements.txt