PV-gradient (PVG) tropopause: Time series 1980--2017 in four reanalyses

PV-gradient tropopause time series

General description

These datasets contain time series of the PV-gradient tropopause (PVG tropopause) introduced by A. Kunz (2011, doi:10.1029/2010JD014343) and calculated by K. Turhal (2024, paper " Variability and Trends in the PVG Tropopause", preprint in EGUsphere: https://doi.org/10.5194/egusphere-2024-471).

Data and methods

The PVG tropopause has been computed by means of the Eddy Tracking Toolkit (developed by J. Clemens and K. Turhal, to be published):

from four reanalyses: ERA5, ERA-Interim, MERRA-2 and JRA-55

for the time range 1980/01/01 -- 2017/12/31 in time steps of the according reanalyses, i.e. four times daily  at 00h, 06h, 12h and 18h

on each isentropic level, with potential temperatures (theta) ranging from 320 K to 380 K, in steps of 5 K for ERA5 and 10 K for the other reanalyses.

Contents

Datasets are provided for each year and isentropic level in NetCDF4 format, every file consisting of two groups for the northern and southern hemisphere. Each group contains the following variables, with time as dimension:

time in seconds since 2000/01/01 00:00 UTC

u_lim: Zonal wind speed at the PVG tropopause

vh_lim: Horizontal wind speed at the PVG tropopause

q_lim: Maximum of Q = vh * Grad PV

eqlat_lim: Location of the PVG tropopause in equivalent latitudes

latmean_lim: Location of the PVG tropopause in latitudes

pv_lim: PV value at the PVG tropopause

In this upload, the PVG tropopause time series are included as *.zip files:

ERA5 dataset: "pvg-tp_era5_ts.zip"

ERA-Interim dataset: "pvg-tp_eraint_ts.zip"

MERRA-2 dataset: "pvg-tp_merra2_ts.zip"

JRA-55 dataset: "pvg-tp_jra55_ts.zip"

Plots of time series for each reanalysis of the variables eqlat_lim, latmean_lim and pv_lim: "pvg_tropopause_timeseries_plots.zip".

How to use

The variables in these netCDF files are grouped by hemisphere. To read in the data, specify the group first ("NorthernHemisphere" or "SouthernHemisphere") and then the variable name (see list above). In Python, this can be done as follows:

import netCDF4 as nc

file=""
d = nc.Dataset(file)

# read in a variable. Syntax: d["group name"]["variable name"][:]. For example:
latmean_lim = d["NorthernHemisphere"]["latmean_lim"][:]

# test print
print(f"First value of latmean_lim in NH: {latmean_lim[0]}")

If you would like to read in all variables in both hemispheres, you can loop e.g. as follows:

import netCDF4 as nc

file = ""
d = nc.Dataset(file)

# iterate through both hemispheres
for hem in ["NorthernHemisphere", "SouthernHemisphere"]:

# select the group to each hemisphere in the netCDF file
g = d.groups[hem]

# iterate through variables in each hemisphere. "v" is the name of each variable in the group.
for v in g.variables:

# read in the data for variable 'v' in hemisphere 'hem' as an array
var = g[v][:]

# just a test print, optional
print(f"First value of {v} in {hem.replace('Hem', ' Hem')} is {var[0]}")

Funding

This project has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – TRR 301 – Project-ID 428312742, TPChange:  The Tropopause Region in a Changing Atmosphere (https://tpchange.de/).