# Atlas notebooks
***
> This notebook reproduces and extends parts of the figures and products of the AR6-WGI Atlas. It is part of a notebook collection available at https://github.com/IPCC-WG1/Atlas for reproducibility and reusability purposes. This work is licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0).
>
> 
Selecting reference regions used for every CORDEX domain#
12/07/2021
J. Diez-Sierra (Santander Meteorology Group, Instituto de FĂsica de Cantabria, CSIC-UC, Santander, Spain)
For the different CORDEX domains, aggregated results are calculated only in those reference regions (Iturbide et al. 2020) with overlap larger than 80%. Reference overlapped regions resulting for every CORDEX domain are calculated with the present script. Regular geographic grids for every CORDEX domain are obtained from Table 2 in the CORDEX specification archive: [Christensen et al.].
Load packages#
This script requires regionmask version 0.6.1 or later and xarray version 0.15.1 or later. Check the documentation of regionmask for details.
import regionmask
import xarray as xr
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
Loading the reference regions#
The regions are available at regionmask.defined_regions.ar6. The whole set of 58 regions is available under (ar6.all). In addition the land (ar6.land) and ocean (ar6.ocean) regions are given separately. The numbering is kept consistent between the categories. Note that some regions are in the land and in the ocean categories (e.g. the Mediterranean).
ar6_all = regionmask.defined_regions.ar6.all
ar6_all
Show code cell output
<regionmask.Regions>
Name: AR6 reference regions
Source: Iturbide et al., 2020 (Earth Syst. Sci. Data)
Regions:
0 GIC Greenland/Iceland
1 NWN N.W.North-America
2 NEN N.E.North-America
3 WNA W.North-America
4 CNA C.North-America
.. .. ...
53 ARS Arabian-Sea
54 BOB Bay-of-Bengal
55 EIO Equatorial.Indic-Ocean
56 SIO S.Indic-Ocean
57 SOO Southern-Ocean
[58 regions]
ar6_all.plot()
<GeoAxesSubplot:>
Loading reference grids (0.5 degree)#
Global longitude and latitude, at the same resoluction as aggregated results for CORDEX domain (0.5 degree), is obtained from the WFDE5 dataset in order to compare the total area covered for every reference region with those overlapped with each CORDEX domains.
Bellow we calculate the number of grid boxes that fall in every reference regions. We use cos(lat) as proxy of the grid area. This works well for the rectangular grid in our example. In general, it is better to the model’s own grid area.
ds = xr.open_dataset("../reference-grids/land_sea_mask_05degree.nc4")
XX_WORLD, YY_WORLD = np.meshgrid(ds.lon, ds.lat)
mask_2d_WORLD = ar6_all.mask(ds.lon, ds.lat)
weights_WORLD = np.cos(np.deg2rad(YY_WORLD))
ds.close()
Defining CORDEX domains#
Regular geographic grids for every CORDEX domain are obtained from Table 2 in the CORDEX specification archive [Christensen et al.]. Note that CORDEX corner definition can present some differences among simulations. Note also that native rotated-pole projections occupy a smaller area than regular grids.
regular_CORDEX_grids = pd.read_csv('auxiliary-material/regular-CORDEX-grids.csv', index_col = 1)
regular_CORDEX_grids
Show code cell output
| Area | deg | Nlon | Nlat | West | East | South | North | |
|---|---|---|---|---|---|---|---|---|
| Name | ||||||||
| NAM-44i | North America | 0.500 | 300 | 129 | -171.7500 | -22.2500 | 12.2500 | 76.2500 |
| CAM-44i | Central America | 0.500 | 207 | 111 | -124.7500 | -21.7500 | -19.7500 | 35.2500 |
| SAM-44i | South America | 0.500 | 181 | 155 | -106.2500 | -16.2500 | -58.2500 | 18.7500 |
| ARC-44i | Arctic | 0.500 | 720 | 83 | -179.7500 | 179.7500 | 48.7500 | 89.7500 |
| AFR-44i | Africa | 0.500 | 173 | 179 | -25.2500 | 60.7500 | -46.2500 | 42.7500 |
| EUR-44i | Europe | 0.500 | 221 | 103 | -44.7500 | 65.2500 | 21.7500 | 72.7500 |
| MED-44i | Mediterranean | 0.500 | 144 | 65 | -20.7500 | 51.7500 | 25.2500 | 57.2500 |
| MNA-44i | Middle East and North Africa | 0.500 | 206 | 106 | -26.7500 | 75.7500 | -7.2500 | 45.2500 |
| EAS-44i | East Asia | 0.500 | 227 | 157 | 62.7500 | 175.7500 | -18.7500 | 59.2500 |
| WAS-44i | South Asia | 0.500 | 195 | 124 | 19.2500 | 116.2500 | -15.7500 | 45.7500 |
| CAS-44i | Central Asia | 0.500 | 260 | 133 | 10.7500 | 140.2500 | 17.7500 | 69.7500 |
| ANT-44i | Antarctica | 0.500 | 720 | 70 | -179.7500 | 179.7500 | -89.7500 | -55.2500 |
| AUS-44i | Australasia | 0.500 | 238 | 133 | 88.7500 | 207.2500 | -53.2500 | 12.7500 |
| MNA-22i | Middle East and North Africa | 0.250 | 410 | 209 | -26.6250 | 75.6250 | -6.8750 | 45.1250 |
| SEA-22i | South East Asia | 0.250 | 233 | 172 | 89.1250 | 147.1250 | -15.3750 | 27.3750 |
| EUR-11i | Europe high res. | 0.125 | 881 | 408 | -44.8125 | 65.1875 | 21.8125 | 72.6875 |
Selecting CORDEX domains
CORDEX_doms = ['NAM-44i','CAM-44i','SAM-44i','ARC-44i','AFR-44i','EUR-44i','MED-44i',
'MNA-44i','SEA-22i','EAS-44i','WAS-44i','CAS-44i','ANT-44i','AUS-44i']
Calculating overlaps#
Empty dataframe to add the results on the percentage of overlapping area:
Overlaps_CORDEX_ReferenceRegions = pd.DataFrame(index = CORDEX_doms, columns = np.arange(len(ar6_all)))
Overlap calculation comparing the number of cells that fall inside every reference region, using the coordinates lon and lat from the file land_sea_mask_05degree.nc4, with those that fall inside the regular grid for every CORDEX domain.
for dom in CORDEX_doms:
lon = np.arange(regular_CORDEX_grids.loc[dom]['West'], regular_CORDEX_grids.loc[dom]['East']+0.5, 0.5)
lat = np.arange(regular_CORDEX_grids.loc[dom]['South'], regular_CORDEX_grids.loc[dom]['North']+0.5, 0.5)
XX, YY = np.meshgrid(lon, lat)
mask_2d_domain = ar6_all.mask(lon, lat)
weights_domain = np.cos(np.deg2rad(YY))
for reg in np.unique(mask_2d_domain.values):
if not np.isnan(reg):
pos = np.where(mask_2d_domain.values == reg)
pos_w = np.where(mask_2d_WORLD.values == reg)
Overlaps_CORDEX_ReferenceRegions[reg].loc[dom] = np.round(100*np.sum(np.ones_like(pos[0])*weights_domain[pos])/np.sum(np.ones_like(pos_w[0])*weights_WORLD[pos_w]), 2)
Set the name for the reference regions, convert nan to 0 and transpose matrix
Overlaps_CORDEX_ReferenceRegions.columns = [ar6_all.regions[n].abbrev for n in Overlaps_CORDEX_ReferenceRegions.columns]
Overlaps_CORDEX_ReferenceRegions = Overlaps_CORDEX_ReferenceRegions.fillna(0).transpose()
Overlap results (as %)
Overlaps_CORDEX_ReferenceRegions
Show code cell output
| NAM-44i | CAM-44i | SAM-44i | ARC-44i | AFR-44i | EUR-44i | MED-44i | MNA-44i | SEA-22i | EAS-44i | WAS-44i | CAS-44i | ANT-44i | AUS-44i | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GIC | 60.45 | 0.00 | 0.00 | 100.00 | 0.00 | 41.04 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| NWN | 98.06 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| NEN | 94.44 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| WNA | 100.00 | 8.77 | 0.00 | 9.64 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| CNA | 100.00 | 32.76 | 0.00 | 6.20 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| ENA | 100.00 | 32.30 | 0.00 | 8.27 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| NCA | 100.00 | 100.00 | 4.27 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| SCA | 63.87 | 100.00 | 85.09 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| CAR | 100.00 | 100.00 | 54.81 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| NWS | 0.00 | 100.00 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| NSA | 0.00 | 100.00 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| NES | 0.00 | 100.00 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| SAM | 0.00 | 100.00 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| SWS | 0.00 | 17.32 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| SES | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| SSA | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 6.38 | 0.00 |
| NEU | 0.00 | 0.00 | 0.00 | 99.92 | 0.00 | 100.00 | 24.67 | 0.00 | 0.00 | 0.00 | 0.00 | 41.41 | 0.00 | 0.00 |
| WCE | 0.00 | 0.00 | 0.00 | 60.87 | 0.00 | 100.00 | 95.42 | 5.76 | 0.00 | 0.00 | 4.82 | 74.33 | 0.00 | 0.00 |
| EEU | 0.00 | 0.00 | 0.00 | 78.99 | 0.00 | 100.00 | 41.05 | 3.08 | 0.00 | 0.00 | 6.14 | 100.00 | 0.00 | 0.00 |
| MED | 0.00 | 0.00 | 0.00 | 0.00 | 87.88 | 100.00 | 100.00 | 100.00 | 0.00 | 0.00 | 42.00 | 59.00 | 0.00 | 0.00 |
| SAH | 0.00 | 0.00 | 2.09 | 0.00 | 100.00 | 51.49 | 29.27 | 100.00 | 0.00 | 0.00 | 32.39 | 36.35 | 0.00 | 0.00 |
| WAF | 0.00 | 0.00 | 8.46 | 0.00 | 100.00 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| CAF | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 88.61 | 0.00 | 0.00 | 47.16 | 0.00 | 0.00 | 0.00 |
| NEAF | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 |
| SEAF | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 80.15 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 |
| WSAF | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 8.69 | 0.00 | 0.00 | 0.00 |
| ESAF | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 34.82 | 0.00 | 0.00 | 0.00 |
| MDG | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 26.20 | 0.00 | 0.00 | 0.00 |
| RAR | 1.89 | 0.00 | 0.00 | 100.00 | 0.00 | 12.27 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 32.36 | 0.00 | 0.00 |
| WSB | 0.00 | 0.00 | 0.00 | 78.99 | 0.00 | 18.33 | 0.00 | 1.64 | 0.00 | 71.11 | 6.14 | 100.00 | 0.00 | 0.00 |
| ESB | 0.00 | 0.00 | 0.00 | 78.99 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 77.57 | 4.07 | 100.00 | 0.00 | 0.00 |
| RFE | 0.00 | 0.00 | 0.00 | 86.21 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 69.09 | 0.00 | 28.74 | 0.00 | 0.00 |
| WCA | 0.00 | 0.00 | 0.00 | 0.00 | 56.46 | 75.00 | 32.55 | 100.00 | 0.00 | 32.90 | 100.00 | 100.00 | 0.00 | 0.00 |
| ECA | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 2.67 | 0.00 | 100.00 | 100.00 | 100.00 | 0.00 | 0.00 |
| TIB | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 3.06 | 1.75 | 100.00 | 100.00 | 100.00 | 0.00 | 0.00 |
| EAS | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 32.94 | 100.00 | 36.11 | 93.25 | 0.00 | 0.00 |
| ARP | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 55.01 | 26.63 | 100.00 | 0.00 | 0.00 | 100.00 | 82.35 | 0.00 | 0.00 |
| SAS | 0.00 | 0.00 | 0.00 | 0.00 | 1.36 | 7.49 | 0.00 | 37.02 | 19.30 | 96.60 | 100.00 | 78.74 | 0.00 | 0.00 |
| SEA | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 98.84 | 100.00 | 52.33 | 5.66 | 0.00 | 81.37 |
| NAU | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 46.30 | 90.23 | 8.75 | 0.00 | 0.00 | 100.00 |
| CAU | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 |
| EAU | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 |
| SAU | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 |
| NZ | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 |
| EAN | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 |
| WAN | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 100.00 | 0.00 |
| ARO | 9.21 | 0.00 | 0.00 | 100.00 | 0.00 | 1.48 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| NPO | 45.62 | 9.88 | 1.72 | 8.31 | 0.00 | 0.00 | 0.00 | 0.00 | 7.08 | 33.74 | 0.00 | 2.14 | 0.00 | 9.76 |
| EPO | 0.00 | 30.20 | 16.34 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 4.89 | 25.59 | 0.00 | 0.00 | 0.00 | 49.19 |
| SPO | 0.00 | 10.28 | 25.14 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 4.24 | 0.00 | 0.00 | 1.33 | 33.87 |
| NAO | 77.79 | 56.11 | 20.53 | 14.51 | 14.02 | 47.82 | 11.13 | 17.94 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| EAO | 0.00 | 45.10 | 57.68 | 0.00 | 64.21 | 0.00 | 0.00 | 56.15 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| SAO | 0.00 | 7.63 | 59.49 | 0.00 | 58.88 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.06 | 0.00 |
| ARS | 0.00 | 0.00 | 0.00 | 0.00 | 33.17 | 3.92 | 0.00 | 100.00 | 0.00 | 57.64 | 100.00 | 20.66 | 0.00 | 0.00 |
| BOB | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 39.88 | 100.00 | 100.00 | 10.31 | 0.00 | 21.81 |
| EIO | 0.00 | 0.00 | 0.00 | 0.00 | 29.83 | 0.00 | 0.00 | 53.28 | 8.77 | 66.88 | 100.00 | 0.00 | 0.00 | 9.87 |
| SIO | 0.00 | 0.00 | 0.00 | 0.00 | 22.54 | 0.00 | 0.00 | 0.00 | 7.50 | 27.54 | 22.19 | 0.00 | 0.00 | 33.99 |
| SOO | 0.00 | 0.00 | 6.65 | 0.00 | 13.15 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 47.89 | 15.37 |
Selecting those reference regions that overlap larger than 80% for every CORDEX domain#
Select those regions that overlap larger than 80%
threshold = 80
for dom in CORDEX_doms:
AR6_dom = Overlaps_CORDEX_ReferenceRegions.query(f'`{dom}`>80').index
print(dom + ": " + str(AR6_dom.tolist()))
Show code cell output
NAM-44i: ['NWN', 'NEN', 'WNA', 'CNA', 'ENA', 'NCA', 'CAR']
CAM-44i: ['NCA', 'SCA', 'CAR', 'NWS', 'NSA', 'NES', 'SAM']
SAM-44i: ['SCA', 'NWS', 'NSA', 'NES', 'SAM', 'SWS', 'SES', 'SSA']
ARC-44i: ['GIC', 'NWN', 'NEN', 'NEU', 'RAR', 'RFE', 'ARO']
AFR-44i: ['MED', 'SAH', 'WAF', 'CAF', 'NEAF', 'SEAF', 'WSAF', 'ESAF', 'MDG', 'ARP']
EUR-44i: ['NEU', 'WCE', 'EEU', 'MED']
MED-44i: ['WCE', 'MED']
MNA-44i: ['MED', 'SAH', 'WAF', 'CAF', 'NEAF', 'SEAF', 'WCA', 'ARP', 'ARS']
SEA-22i: ['SEA']
EAS-44i: ['ECA', 'TIB', 'EAS', 'SAS', 'SEA', 'NAU', 'BOB']
WAS-44i: ['NEAF', 'SEAF', 'WCA', 'ECA', 'TIB', 'ARP', 'SAS', 'ARS', 'BOB', 'EIO']
CAS-44i: ['EEU', 'WSB', 'ESB', 'WCA', 'ECA', 'TIB', 'EAS', 'ARP']
ANT-44i: ['EAN', 'WAN']
AUS-44i: ['SEA', 'NAU', 'CAU', 'EAU', 'SAU', 'NZ']
Session info#
!conda list
Show code cell output
# packages in environment at /home/phanaur/mambaforge/envs/tfg:
#
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_gnu conda-forge
_r-mutex 1.0.1 anacondar_1 conda-forge
affine 2.3.1 pyhd8ed1ab_0 conda-forge
alabaster 0.7.13 pyhd8ed1ab_0 conda-forge
alembic 1.9.1 pyhd8ed1ab_0 conda-forge
alsa-lib 1.2.3.2 h166bdaf_0 conda-forge
antlr-python-runtime 4.7.2 py37h89c1867_1003 conda-forge
anyio 3.6.2 pyhd8ed1ab_0 conda-forge
appdirs 1.4.4 pyh9f0ad1d_0 conda-forge
argon2-cffi 21.3.0 pyhd8ed1ab_0 conda-forge
argon2-cffi-bindings 21.2.0 py37h540881e_2 conda-forge
async_generator 1.10 py_0 conda-forge
attrs 21.4.0 pyhd8ed1ab_0 conda-forge
babel 2.11.0 pyhd8ed1ab_0 conda-forge
backcall 0.2.0 pyh9f0ad1d_0 conda-forge
backports 1.0 pyhd8ed1ab_3 conda-forge
backports.functools_lru_cache 1.6.4 pyhd8ed1ab_0 conda-forge
beautifulsoup4 4.11.1 pyha770c72_0 conda-forge
binutils_impl_linux-64 2.39 he00db2b_1 conda-forge
binutils_linux-64 2.39 h5fc0e48_11 conda-forge
bleach 5.0.1 pyhd8ed1ab_0 conda-forge
blinker 1.5 pyhd8ed1ab_0 conda-forge
bokeh 2.4.3 pyhd8ed1ab_3 conda-forge
boost-cpp 1.74.0 hc6e9bd1_3 conda-forge
bottleneck 1.3.5 py37hda87dfa_0 conda-forge
branca 0.6.0 pyhd8ed1ab_0 conda-forge
brotli 1.0.9 h166bdaf_8 conda-forge
brotli-bin 1.0.9 h166bdaf_8 conda-forge
brotlipy 0.7.0 py37h540881e_1004 conda-forge
bwidget 1.9.14 ha770c72_1 conda-forge
bzip2 1.0.8 h7f98852_4 conda-forge
c-ares 1.18.1 h7f98852_0 conda-forge
ca-certificates 2022.12.7 ha878542_0 conda-forge
cairo 1.16.0 h6cf1ce9_1008 conda-forge
cartopy 0.19.0.post1 py37h0c48da3_1 conda-forge
cdo 1.9.8 had776b3_14 conda-forge
certifi 2022.12.7 pyhd8ed1ab_0 conda-forge
certipy 0.1.3 py_0 conda-forge
cf-units 3.0.1 py37hb1e94ed_2 conda-forge
cffi 1.15.1 py37h43b0acd_1 conda-forge
cfitsio 3.470 hb418390_7 conda-forge
cftime 1.6.2 py37hc105733_0 conda-forge
charset-normalizer 2.1.1 pyhd8ed1ab_0 conda-forge
click 7.1.2 pyh9f0ad1d_0 conda-forge
click-completion 0.5.2 py37h89c1867_4 conda-forge
click-log 0.4.0 pyhd8ed1ab_0 conda-forge
click-plugins 1.1.1 py_0 conda-forge
cligj 0.7.2 pyhd8ed1ab_1 conda-forge
cloudpickle 2.2.0 pyhd8ed1ab_0 conda-forge
colorama 0.4.6 pyhd8ed1ab_0 conda-forge
configurable-http-proxy 4.5.4 h5556789_0 conda-forge
cryptography 38.0.2 py37h38fbfac_1 conda-forge
curl 7.76.1 h979ede3_1 conda-forge
cycler 0.11.0 pyhd8ed1ab_0 conda-forge
cytoolz 0.12.0 py37h540881e_0 conda-forge
dask 2021.10.0 pyhd8ed1ab_0 conda-forge
dask-core 2021.10.0 pyhd3eb1b0_0
dataclasses 0.8 pyhc8e2a94_3 conda-forge
dbus 1.13.6 h5008d03_3 conda-forge
debugpy 1.6.3 py37hd23a5d3_0 conda-forge
decorator 5.1.1 pyhd8ed1ab_0 conda-forge
defusedxml 0.7.1 pyhd8ed1ab_0 conda-forge
distributed 2021.10.0 py37h06a4308_0
docutils 0.17.1 py37h89c1867_2 conda-forge
eccodes 2.21.0 ha0e6eb6_0 conda-forge
entrypoints 0.4 pyhd8ed1ab_0 conda-forge
esmf 8.1.0 nompi_hed08645_0 conda-forge
expat 2.5.0 h27087fc_0 conda-forge
fftw 3.3.10 nompi_hf0379b8_106 conda-forge
fiona 1.8.18 py37h17d6ad9_1 conda-forge
flit-core 3.8.0 pyhd8ed1ab_0 conda-forge
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