Models and Datasets for "Extracting Paleoweather from Paleoclimate: A Deep Learning Reconstruction of Northern Hemisphere Summertime Atmospheric Blocking over the Last Millennium"

Associated publication: Karamperidou, C., Extracting Paleoweather from Paleoclimate: A Deep Learning Reconstruction of Northern Hemisphere Summertime Atmospheric Blocking over the Last Millennium, Nature Communications Earth & Environment, (2024)

This repository contains:

the architecture and weights of PaleoBlockNet v1.0

the following ensemble DL reconstructions of JJA frequency of blocked days inferred by PaleoBlockNet:

the 10-member NTREND-based DL reconstruction; uses as input the NTREND DA N.Hemisphere MJJA surface temperature anomaly by King et al. (2021)

the 100-member PHYDA-based DL reconstruction; uses as input the PHYDA JJA surface temperature anomaly by Steiger et al. (2018)

the 12-member LME-based DL reconstruction; uses as input the CESM-LME surface temperature anomaly; this is a sensitivity experiment (see publication for details).

Integrated Gradients that assign importance to the input features for PaleoblockNet's blocking inferences 

train-validate-test samples to use with sample scripts from the Gituhub repo github/ckaramp-research/paleoblocknet

If you use this dataset, please cite the associated publication and the present repository.

To interactively explore the datasets, a web interface has been developed and can be accessed at https://www2.hawaii.edu/~ckaramp/paleoblocknet

Contact the author Christina Karamperidou (https://www2.hawaii.edu/~ckaramp) for more information about the details of these datasets.