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Dryad

Data and code from: Long-term climate impacts of large stratospheric water vapor perturbations (Part 1)

Cite this dataset

Jucker, Martin; Lucas, Chris; Dutta, Deepashree (2024). Data and code from: Long-term climate impacts of large stratospheric water vapor perturbations (Part 1) [Dataset]. Dryad. https://doi.org/10.5061/dryad.0zpc8675t

Abstract

The amount of water vapor injected into the stratosphere after the eruption of Hunga Tonga-Hunga Ha’apai (HTHH) was unprecedented, and it is therefore unclear what it might mean for surface climate. We use chemistry climate model simulations to assess the long-term surface impacts of stratospheric water vapor (SWV) anomalies similar to those caused by HTHH, but neglect the relatively minor aerosol loading from the eruption. The simulations show that the SWV anomalies lead to strong and persistent warming of Northern Hemisphere landmasses in boreal winter, and austral winter cooling over Australia, years after eruption, demonstrating that large SWV forcing can have surface impacts on a decadal timescale. We also emphasize that the surface response to SWV anomalies is more complex than simple warming due to greenhouse forcing and is influenced by factors such as regional circulation patterns and cloud feedbacks. Further research is needed to fully understand the multi-year effects of SWV anomalies and their relationship with climate phenomena like El Nino Southern Oscillation.

README: Long-term climate impacts of large stratospheric water vapor perturbations

https://doi.org/10.5061/dryad.0zpc8675t

This data is used for published article Jucker et al. Journal of Climate (2024).

Description of the data and file structure

This collection contains observational data, all necessary code, plus hungatonga.yml which contains information about the conda environment which allows running of all code. The code is also stored on GitHub and Zenodo, and additional links are given below.

The dataset is large, and therefore split into two further dryad repositories, also given below. In order to run the code, all data needs to be in the working directory. Then, plot_paper_figures_published.sh (contained within hungatonga-v1.0.zip) will produce all figures from the paper. Note that there is much more data than what is plotted in the figures, but users are expected to use netCDF metadata to find particular variables.

Files

File Purpose
hungatonga-v1.0.zip  collection of python code used for the analysis and plotting of all figures
hungatonga.yml  Contains information on conda environment used to run all diagnostics scripts.
MLS_data.nc Observational total water mass data as a function of time and latitude.
MLS_data_h2o.nc Observational water vapor data as a function of time, latitude, and pressure.
MLS_data_lev.nc Observational total water mass data as a function of time and latitude above different pressure levels.
atmos_daily.nc  Required to plot MLS data on vertical model grid

Sharing/Access information

The published article is

Jucker, M., C. Lucas, and D. Dutta, 2024: Long-term climate impacts of large stratospheric water vapor perturbations. J. Climate, doi 10.1175/JCLI-D-23-0437.1.

Additional data is stored at

Code/Software

All code is in python and provided within this dataset (hungatonga-v1.0.zip), except the external package aostools which can be obtained on GitHub.

The recommended conda environment is stored in hungatonga.yml.

Methods

This data contains selected variables from WACCM and MiMA simulations related to the associated article.

The relevant figures from associated paper can be reproduced using the code at https://https://zenodo.org/doi/10.5281/zenodo.11471857.

In addition to the code, the data from WACCM and MiMA will also be needed.

Funding

Australian Research Council, Award: CE170100023

Australian Research Council, Award: LE200100040