DOI: 10.5281/zenodo.10739678
title: Models and Datasets for "Extracting Paleoweather from Paleoclimate: A Deep Learning Reconstruction of Northern Hemisphere Summertime Atmospheric Blocking over the Last Millennium"

description: 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.
description: Projected changes in atmospheric blocking and associated extreme weather events are marked by significant uncertainties due to climate model biases, the complex nature of the phenomenon, and its strong natural variability. Paleoclimate proxy records, suggesting shifts in blocking activity, extend observational records and may help reduce these uncertainties. However, extracting synoptic-scale signals from low-temporal-resolution paleoclimate records is challenging. This study introduces a Deep Learning (DL) model that infers summertime blocking frequencies from tree-ring-based gridded reconstructions of Northern Hemisphere surface temperatures over the Last Millennium. The model is shown to be constrained implicitly by the paleoclimate proxies, even without directly incorporating them or having any knowledge of their specific locations.  The DL blocking reconstructions highlight the significant influence of the tropical Pacific in driving strong blocking variability from interannual to centennial scales. A weakened tropical Pacific zonal temperature gradient during the Little Ice Age correlates with a hemispherically reduced -yet more variable at interannual scales- blocking frequency and altered regional patterns. Long-term reconstructions of blocking statistics are essential for understanding its response to external forcing; thus, this Deep Learning approach offers a new pathway for extracting paleoweather signals from paleoclimate records to constrain model projections of blocking under climate change scenarios.