Lagrangian moisture sources for an arid region in northeast Greenland

study region: 79.5° N - 81° N, 22.5° W - 21° W, 460 months (February 1979 - May 2017), gridpoints of longitude and latitude in 1.0° resolution

Dataset name: traj_ym_evapo_final.nc

evapo: monthly accumulated evaporation per grid point of all 15-day backward trajectories contributing to precipitation in NE Greenland (mm per month),

  • used variable for the analysis done for the manuscript with the title "Lagrangian detection of precipitation moisture sources for an arid region in northeast Greenland: relations to the North Atlantic Oscillation, sea ice cover and temporal trends from 1979 to 2017" submitted to the Weather and Climate dynamics journal
  • computed from backward trajectories (LAGRANTO) and the lagrangian moisture source diagnostic (Sodemann et al., 2008)

additional information along the trajectories:

AREA: area corresponding to grid points (square kilometres)

TIME: time before arrival (h)

RH: relative humidity (%)

p: mean pressure height of trajectories (hPa)

BLH: 1.5 scaled boundary layer height (hPa)

(dataset computed by Lukas Langhamer)

License

This dataset is licensed under a Creative Commons Attribution 4.0 International License (CC-BY).

How to cite the data?

When using this dataset, please refer to the original publication in addition to this Zenodo repository:

  • Schuster, L., Maussion, F., Langhamer, L., Moseley, G.E. : "Lagrangian detection of precipitation moisture sources for an arid region in northeast Greenland: relations to the North Atlantic Oscillation, sea ice cover and temporal trends from 1979 to 2017", 2020 (submitted to Weather and Climate Dynamics)

For questions please write to lilian.schuster@student.uibk.ac.at

How to open the dataset and example plot:

In [6]:
# open dataset
import xarray as xr  
d_traj =xr.open_dataset('traj_ym_evapo_final.nc')
 
d_traj
Out[6]:
<xarray.Dataset>
Dimensions:    (latitude: 180, longitude: 360, time: 460)
Coordinates:
  * latitude   (latitude) float32 -90.0 -89.0 -88.0 -87.0 ... 87.0 88.0 89.0
  * time       (time) datetime64[ns] 1979-02-01 1979-03-01 ... 2017-05-01
  * longitude  (longitude) float32 -180.0 -179.0 -178.0 ... 177.0 178.0 179.0
Data variables:
    evapo      (time, latitude, longitude) float64 ...
    AREA       (time, latitude, longitude) float32 ...
    TIME       (time, latitude, longitude) float32 ...
    RH         (time, latitude, longitude) float32 ...
    p          (time, latitude, longitude) float32 ...
    BLH        (time, latitude, longitude) float32 ...
In [7]:
# used variable for the analysis: 
d_traj['evapo']
Out[7]:
<xarray.DataArray 'evapo' (time: 460, latitude: 180, longitude: 360)>
[29808000 values with dtype=float64]
Coordinates:
  * latitude   (latitude) float32 -90.0 -89.0 -88.0 -87.0 ... 87.0 88.0 89.0
  * time       (time) datetime64[ns] 1979-02-01 1979-03-01 ... 2017-05-01
  * longitude  (longitude) float32 -180.0 -179.0 -178.0 ... 177.0 178.0 179.0
Attributes:
    long_name:  contributing evaporative moisture sources of the study region
    units:      mm per month

example plot of the contributing evaporative moisture sources (evapo) :

In [8]:
import numpy as np
import matplotlib
import matplotlib.ticker as mticker
import matplotlib.pyplot as plt 
from matplotlib.pyplot import text
from matplotlib.colors import LinearSegmentedColormap
import matplotlib.path as mpath
%matplotlib inline

# Some defaults:
import cartopy.io.shapereader as shpreader
import cartopy  # Map projections libary
import cartopy.crs as ccrs  # Projections list

d_evapo_year = d_traj['evapo'].groupby('time.year').sum(dim='time')[1:-1,:,:].mean(dim='year')
fig = plt.figure(figsize=(14,12))
ax1 = plt.axes(projection=ccrs.Orthographic(-10., 70., globe = None))  
theta = np.linspace(0, 2*np.pi, 100)
map_circle = mpath.Path(np.vstack([np.sin(theta), np.cos(theta)]).T * 0.5 + [0.5, 0.5])
ax1.set_boundary(map_circle, transform=ax1.transAxes)
ax1.coastlines();
ax1.gridlines();
figure = d_evapo_year.plot(ax=ax1, #levels = np.arange(0,0.0006,0.00005),vmax = 0.023/31,
                             transform=ccrs.PlateCarree(), 
                    cmap='gnuplot2_r', rasterized=True);
cbar = figure.colorbar
cbar.ax.tick_params(labelsize=18)
cbar.set_label('mm year$^{-1}$ gridpoint$^{-1}$',fontsize=20)
plt.title('contributing evaporative moisture sources for the study region\n yearly mean\n summed up moisture sources: {} mm per year '.format(d_evapo_year.sum().values.round(1)),
         fontsize=20);