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PV-gradient (PVG) tropopause: Time series 1980--2017 in four reanalyses
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/).
potential vorticity, utls, pv, tracer, atmospheric, stratosphere-troposphere exchange, tropics, pv-gradient, troposphere, tropopause, stratosphere, atmosphere, meteorology, subtropics, climate, ste
potential vorticity, utls, pv, tracer, atmospheric, stratosphere-troposphere exchange, tropics, pv-gradient, troposphere, tropopause, stratosphere, atmosphere, meteorology, subtropics, climate, ste
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