Jet feature data from PAMIP model simulations

Author: Yvonne Anderson

Contact: ee22ya@leeds.ac.uk

Dataset created: 21/08/2023

Paper title: Minimal influence of future Arctic sea ice loss on North Atlantic jet stream morphology

Dataset information

CSV files contain arrays of daily jet feature data for all ensemble member winters for a given model.

Dimensions of the arrays are (number of ensemble members, 90 winter days).

Filename structure

Filenames of CSV files can be interpreted as: timeperiod_jetfeature_model.csv

Example filename structure

Jet feature description

Jet feature data are for the largest mass jet region found on each day of winter, where jet mass is the area weighted jet speed.

The jet features and corresponding units contained in the csv files are as follows:

Time periods

Time periods are present-day and future, which refer to simulations forced by present-day and future sea ice concentrations, from which the jet features have been extracted.

Models

Models are AWI-CM-1-1-MR, CanESM5, FGOALS-f3-L, HadGEM3-GC31-MM, IPSL-CM6A-LR and MIROC6 from the Polar Amplification Model Intercomparison Project (PAMIP; https://doi.org/10.5194/gmd-12-1139-2019)

Spatial and temporal information

Arrays contain daily jet feature data that has been constrained to the North Atlantic region (0-60 ° W, 15-75 ° N) and to the winter period (December, January and February)

Prior processing

• Original dataset: netcdf files of daily zonal wind data from Polar Amplification Model Intercomparison Project simulations forced by present-day and future sea ice concentrations
• 850 hPa wind speed data was extracted and regridded to 2.81 ° x 2.81 ° resolution
• Constrained to North Atlantic region and winter period
• Wind speed data was filtered using a 10-day Lanczos filter with a 61 day window
• Jet feature data was extracted for each day in ensemble member winters and saved to numpy arrays

Example code for loading jet variables from csv file

To generate a numpy array of jet variable arrays contained in the csv file:

loaded_jet_variable_arrays = np.genfromtxt((path_to_file/filename.csv'), delimiter=',')

To combine arrays for all ensemble member winters, which allows plotting of daily jet feature distributions:

jet_variable_array_all_winters = np.concatenate(loaded_jet_variable_arrays)