Model output for a storyline analysis of hurricane Irma's precipitation under various levels of climate warming

Reed, Kevin1 ; Huprikar, Annika2; Stansfield, Alyssa3

Published Nov 14, 2023 on Dryad. https://doi.org/10.5061/dryad.m905qfv6b

Data files

Nov 14, 2023 version files 33.78 GB

irmadata.tar.gz

33.78 GB
README.md

2.82 KB

Abstract

Understanding how extreme weather, such as tropical cyclones, will change with future climate warming is an interesting computational challenge. Here, the hindcast approach is used to create different storylines of a particular tropical cyclone, Hurricane Irma (2017). Using the Community Atmosphere Model, we explore how Irma’s precipitation would change under various levels of climate warming. Analysis is focused on a 48-hour period where the simulated hurricane tracks reasonably represent Irma's observed track. Under future scenarios of 2 K, 3 K, and 4 K global average surface temperature increase above pre-industrial levels, the mean 3-hourly rainfall rates in the simulated storms increase by 3-7%/K compared to present. This change increases in magnitude for the 95th and 99th percentile 3-hourly rates, which intensify by 10-13%/K and 17-21%/K, respectively. Over Florida, the simulated mean rainfall accumulations increase by 16-26%/K, with local maxima increasing by 18-43%/K. All percent changes increase monotonically with warming level.

README: Model output for a storyline analysis of hurricane Irma's precipitation under various levels of climate warming

Available on Dryad: https://doi.org/10.5061/dryad.m905qfv6b

Description of the data and file structure

For each combination of initialization time and climate scenario, 20-member ensembles of 7-day long simulations are completed, resulting in four scenarios for each initialization time and 320 total simulations.

For each climate scenario there is a folder of track data, formatted as:
[scenario]_tracks

Then within these folders there are two forms of files:

radprof.txt.YYYY_hindcast_natlantic_30_x4_CAM5_L30.ENS-INITIALIZATION_TIME - provides radial profiles of wind at 6 hour increments for the 7 day forecasts

For each simulation there are the following data files in 6 hourly increments:

trajectories.txt.YYYY_hindcast_natlantic_30_x4_CAM5_L30.ENS-INITIALIZATION_TIME - provides location (latitude, longitude) and intensity at 6 hour increments for the 7 day forecasts

For each climate scenario there is a folder of track data, formatted as:
[scenario]

Where there are folders for each of the 20-member ensembles:

[scenario_year]_hindcast_natlantic_30_x4_CAM5_L30.ENS

Which contains 4 sub-folders:

INITIALIZATION_TIME

That then contain standard CESM netcdf output files at 6 hour increments:

[scenario_year]_hindcast_natlantic_30_x4_CAM5_L30.ENS.cam.h1.2017-MM-DD-SSSSS_filtered.nc

This files contain the 2D precipitation used for the analysis in the manuscript.

Note, [scenario] is either present, 2K, 3K or 4K as outlined in the manuscript and [scenario_year] is 2017, 2044, 2064, or 2082 as stated in the manuscript.

Some additional data archived:

There is a directory called "Fig2_files" this includes the exact netcdf files used for Figure 2 in the manuscript.
There are files "TCrain_distributions_[data_set].nc" that are the complied data that goes into Figure 3 in the manuscript.
"NWS_NEW.nc" is the observed NWS Stage IV data that has been restructure for the analysis in the paper. The raw NWS Stage IV precipitation analysis data is available for download at \url{https://water.weather.gov/precip/download.php}

Sharing/Access information

The data is only available here on Dryad to replicate the analysis in the manuscript. Additional model output, including the raw model output is available by contacting Kevin Reed (kevin.reed@stonybrook.edu).

The methodology for the modeling approach is described in the following papers:
https://doi.org/10.1126/sciadv.aaw9253
https://doi.org/10.1038/s41467-022-29379-1
https://doi.org/10.1088/2752-5295/acfd4e

And is available on GitHub here:
https://github.com/zarzycki/betacast

As is the CESM model code:
https://github.com/ESCOMP/CESM