Search and Load CMIP6 Data via ESGF / OPeNDAP
Contents
Search and Load CMIP6 Data via ESGF / OPeNDAP#
This notebooks shows how to search and load data via Earth System Grid Federation infrastructure. This infrastructure works great and is the foundation of the CMIP6 distribution system.
The main technologies used here are the ESGF search API, used to figure out what data we want, and OPeNDAP, a remote data access protocol over HTTP.
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import xarray as xr
import cftime
import cmaps
import cartopy.crs as ccrs
xr.set_options(display_style='html')
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
Search using ESGF API#
#!/usr/bin/env python
from __future__ import print_function
import requests
import xml.etree.ElementTree as ET
import numpy
# Author: Unknown
# I got the original version from a word document published by ESGF
# https://docs.google.com/document/d/1pxz1Kd3JHfFp8vR2JCVBfApbsHmbUQQstifhGNdc6U0/edit?usp=sharing
# API AT: https://github.com/ESGF/esgf.github.io/wiki/ESGF_Search_REST_API#results-pagination
def esgf_search(server="https://esgf-node.llnl.gov/esg-search/search",
files_type="OPENDAP", local_node=True, project="CMIP6",
verbose=False, format="application%2Fsolr%2Bjson",
use_csrf=False, **search):
client = requests.session()
payload = search
payload["project"] = project
payload["type"]= "File"
if local_node:
payload["distrib"] = "false"
if use_csrf:
client.get(server)
if 'csrftoken' in client.cookies:
# Django 1.6 and up
csrftoken = client.cookies['csrftoken']
else:
# older versions
csrftoken = client.cookies['csrf']
payload["csrfmiddlewaretoken"] = csrftoken
payload["format"] = format
offset = 0
numFound = 10000
all_files = []
files_type = files_type.upper()
while offset < numFound:
payload["offset"] = offset
url_keys = []
for k in payload:
url_keys += ["{}={}".format(k, payload[k])]
url = "{}/?{}".format(server, "&".join(url_keys))
print(url)
r = client.get(url)
r.raise_for_status()
resp = r.json()["response"]
numFound = int(resp["numFound"])
resp = resp["docs"]
offset += len(resp)
for d in resp:
if verbose:
for k in d:
print("{}: {}".format(k,d[k]))
url = d["url"]
for f in d["url"]:
sp = f.split("|")
if sp[-1] == files_type:
all_files.append(sp[0].split(".html")[0])
return sorted(all_files)
result = esgf_search(activity_id='CMIP', variable_id='mmroa',
institution_id="NCC", source_id='NorESM2-LM', experiment_id='abrupt-4xCO2')
result
https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&variable_id=mmroa&institution_id=NCC&source_id=NorESM2-LM&experiment_id=abrupt-4xCO2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=0
https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&variable_id=mmroa&institution_id=NCC&source_id=NorESM2-LM&experiment_id=abrupt-4xCO2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=10
https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&variable_id=mmroa&institution_id=NCC&source_id=NorESM2-LM&experiment_id=abrupt-4xCO2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=20
https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&variable_id=mmroa&institution_id=NCC&source_id=NorESM2-LM&experiment_id=abrupt-4xCO2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=30
https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&variable_id=mmroa&institution_id=NCC&source_id=NorESM2-LM&experiment_id=abrupt-4xCO2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=40
https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&variable_id=mmroa&institution_id=NCC&source_id=NorESM2-LM&experiment_id=abrupt-4xCO2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=50
https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&variable_id=mmroa&institution_id=NCC&source_id=NorESM2-LM&experiment_id=abrupt-4xCO2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=60
https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&variable_id=mmroa&institution_id=NCC&source_id=NorESM2-LM&experiment_id=abrupt-4xCO2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=70
https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&variable_id=mmroa&institution_id=NCC&source_id=NorESM2-LM&experiment_id=abrupt-4xCO2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=80
https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&variable_id=mmroa&institution_id=NCC&source_id=NorESM2-LM&experiment_id=abrupt-4xCO2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=90
https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&variable_id=mmroa&institution_id=NCC&source_id=NorESM2-LM&experiment_id=abrupt-4xCO2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=100
https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&variable_id=mmroa&institution_id=NCC&source_id=NorESM2-LM&experiment_id=abrupt-4xCO2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=110
https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&variable_id=mmroa&institution_id=NCC&source_id=NorESM2-LM&experiment_id=abrupt-4xCO2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=120
https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&variable_id=mmroa&institution_id=NCC&source_id=NorESM2-LM&experiment_id=abrupt-4xCO2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=130
https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&variable_id=mmroa&institution_id=NCC&source_id=NorESM2-LM&experiment_id=abrupt-4xCO2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=140
https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&variable_id=mmroa&institution_id=NCC&source_id=NorESM2-LM&experiment_id=abrupt-4xCO2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=150
https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&variable_id=mmroa&institution_id=NCC&source_id=NorESM2-LM&experiment_id=abrupt-4xCO2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=160
https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&variable_id=mmroa&institution_id=NCC&source_id=NorESM2-LM&experiment_id=abrupt-4xCO2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=170
https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&variable_id=mmroa&institution_id=NCC&source_id=NorESM2-LM&experiment_id=abrupt-4xCO2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=180
https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&variable_id=mmroa&institution_id=NCC&source_id=NorESM2-LM&experiment_id=abrupt-4xCO2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=190
https://esgf-node.llnl.gov/esg-search/search/?activity_id=CMIP&variable_id=mmroa&institution_id=NCC&source_id=NorESM2-LM&experiment_id=abrupt-4xCO2&project=CMIP6&type=File&distrib=false&format=application%2Fsolr%2Bjson&offset=200
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'http://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20191108/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_007101-008012.nc',
'http://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20191108/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_008101-009012.nc',
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# pick one file for testing
files_to_open = [result[-1]]
Load Data with Xarray#
These are OPeNDAP endpoints. Xarray, together with the netCDF4 python library, allow lazy loading.
ds = xr.open_mfdataset(files_to_open, combine='by_coords')
ds
<xarray.Dataset>
Dimensions: (bnds: 2, lat: 96, lev: 32, lon: 144, time: 120)
Coordinates:
* time (time) object 0491-01-16 12:00:00 ... 0500-12-16 12:00:00
* lev (lev) float64 0.9926 0.9763 0.9575 ... 0.01436 0.007595 0.003643
* lat (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 86.21 88.11 90.0
* lon (lon) float64 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5
Dimensions without coordinates: bnds
Data variables:
time_bnds (time, bnds) object dask.array<chunksize=(120, 2), meta=np.ndarray>
lev_bnds (lev, bnds) float64 dask.array<chunksize=(32, 2), meta=np.ndarray>
p0 float64 ...
a (lev) float64 dask.array<chunksize=(32,), meta=np.ndarray>
b (lev) float64 dask.array<chunksize=(32,), meta=np.ndarray>
a_bnds (lev, bnds) float64 dask.array<chunksize=(32, 2), meta=np.ndarray>
b_bnds (lev, bnds) float64 dask.array<chunksize=(32, 2), meta=np.ndarray>
lat_bnds (lat, bnds) float64 dask.array<chunksize=(96, 2), meta=np.ndarray>
lon_bnds (lon, bnds) float64 dask.array<chunksize=(144, 2), meta=np.ndarray>
ps (time, lat, lon) float32 dask.array<chunksize=(120, 96, 144), meta=np.ndarray>
mmroa (time, lev, lat, lon) float32 dask.array<chunksize=(120, 32, 96, 144), meta=np.ndarray>
Attributes: (12/50)
Conventions: CF-1.7 CMIP-6.2
activity_id: CMIP
branch_method: Hybrid-restart from year 1600-01-01 of p...
branch_time: 0.0
branch_time_in_child: 0.0
branch_time_in_parent: 430335.0
... ...
variable_id: mmroa
variant_label: r1i1p1f1
license: CMIP6 model data produced by NCC is lice...
cmor_version: 3.5.0
tracking_id: hdl:21.14100/7e00d8c9-0c7f-40fc-911b-6f7...
DODS_EXTRA.Unlimited_Dimension: timexarray.Dataset
- bnds: 2
- lat: 96
- lev: 32
- lon: 144
- time: 120
- time(time)object0491-01-16 12:00:00 ... 0500-12-...
- bounds :
- time_bnds
- axis :
- T
- long_name :
- time
- standard_name :
- time
- _ChunkSizes :
- 1
array([cftime.DatetimeNoLeap(491, 1, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(491, 2, 15, 0, 0, 0, 0), cftime.DatetimeNoLeap(491, 3, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(491, 4, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(491, 5, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(491, 6, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(491, 7, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(491, 8, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(491, 9, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(491, 10, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(491, 11, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(491, 12, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(492, 1, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(492, 2, 15, 0, 0, 0, 0), cftime.DatetimeNoLeap(492, 3, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(492, 4, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(492, 5, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(492, 6, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(492, 7, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(492, 8, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(492, 9, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(492, 10, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(492, 11, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(492, 12, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(493, 1, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(493, 2, 15, 0, 0, 0, 0), cftime.DatetimeNoLeap(493, 3, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(493, 4, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(493, 5, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(493, 6, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(493, 7, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(493, 8, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(493, 9, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(493, 10, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(493, 11, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(493, 12, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(494, 1, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(494, 2, 15, 0, 0, 0, 0), cftime.DatetimeNoLeap(494, 3, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(494, 4, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(494, 5, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(494, 6, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(494, 7, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(494, 8, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(494, 9, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(494, 10, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(494, 11, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(494, 12, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(495, 1, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(495, 2, 15, 0, 0, 0, 0), cftime.DatetimeNoLeap(495, 3, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(495, 4, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(495, 5, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(495, 6, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(495, 7, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(495, 8, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(495, 9, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(495, 10, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(495, 11, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(495, 12, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(496, 1, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(496, 2, 15, 0, 0, 0, 0), cftime.DatetimeNoLeap(496, 3, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(496, 4, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(496, 5, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(496, 6, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(496, 7, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(496, 8, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(496, 9, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(496, 10, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(496, 11, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(496, 12, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(497, 1, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(497, 2, 15, 0, 0, 0, 0), cftime.DatetimeNoLeap(497, 3, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(497, 4, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(497, 5, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(497, 6, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(497, 7, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(497, 8, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(497, 9, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(497, 10, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(497, 11, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(497, 12, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(498, 1, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(498, 2, 15, 0, 0, 0, 0), cftime.DatetimeNoLeap(498, 3, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(498, 4, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(498, 5, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(498, 6, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(498, 7, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(498, 8, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(498, 9, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(498, 10, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(498, 11, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(498, 12, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(499, 1, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(499, 2, 15, 0, 0, 0, 0), cftime.DatetimeNoLeap(499, 3, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(499, 4, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(499, 5, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(499, 6, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(499, 7, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(499, 8, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(499, 9, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(499, 10, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(499, 11, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(499, 12, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(500, 1, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(500, 2, 15, 0, 0, 0, 0), cftime.DatetimeNoLeap(500, 3, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(500, 4, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(500, 5, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(500, 6, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(500, 7, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(500, 8, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(500, 9, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(500, 10, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(500, 11, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(500, 12, 16, 12, 0, 0, 0)], dtype=object) - lev(lev)float640.9926 0.9763 ... 0.007595 0.003643
- bounds :
- lev_bnds
- units :
- 1
- axis :
- Z
- positive :
- down
- long_name :
- hybrid sigma pressure coordinate
- standard_name :
- atmosphere_hybrid_sigma_pressure_coordinate
- formula :
- p = a*p0 + b*ps
- formula_terms :
- p0: p0 a: a b: b ps: ps
array([0.992556, 0.976325, 0.957485, 0.936198, 0.912645, 0.88702 , 0.859535, 0.820858, 0.763404, 0.691389, 0.609779, 0.524687, 0.445993, 0.379101, 0.322242, 0.273911, 0.232829, 0.197908, 0.168225, 0.142994, 0.121547, 0.103317, 0.087821, 0.073751, 0.06152 , 0.051677, 0.043194, 0.035923, 0.024612, 0.014357, 0.007595, 0.003643]) - lat(lat)float64-90.0 -88.11 -86.21 ... 88.11 90.0
- bounds :
- lat_bnds
- units :
- degrees_north
- axis :
- Y
- long_name :
- Latitude
- standard_name :
- latitude
array([-90. , -88.105263, -86.210526, -84.315789, -82.421053, -80.526316, -78.631579, -76.736842, -74.842105, -72.947368, -71.052632, -69.157895, -67.263158, -65.368421, -63.473684, -61.578947, -59.684211, -57.789474, -55.894737, -54. , -52.105263, -50.210526, -48.315789, -46.421053, -44.526316, -42.631579, -40.736842, -38.842105, -36.947368, -35.052632, -33.157895, -31.263158, -29.368421, -27.473684, -25.578947, -23.684211, -21.789474, -19.894737, -18. , -16.105263, -14.210526, -12.315789, -10.421053, -8.526316, -6.631579, -4.736842, -2.842105, -0.947368, 0.947368, 2.842105, 4.736842, 6.631579, 8.526316, 10.421053, 12.315789, 14.210526, 16.105263, 18. , 19.894737, 21.789474, 23.684211, 25.578947, 27.473684, 29.368421, 31.263158, 33.157895, 35.052632, 36.947368, 38.842105, 40.736842, 42.631579, 44.526316, 46.421053, 48.315789, 50.210526, 52.105263, 54. , 55.894737, 57.789474, 59.684211, 61.578947, 63.473684, 65.368421, 67.263158, 69.157895, 71.052632, 72.947368, 74.842105, 76.736842, 78.631579, 80.526316, 82.421053, 84.315789, 86.210526, 88.105263, 90. ]) - lon(lon)float640.0 2.5 5.0 ... 352.5 355.0 357.5
- bounds :
- lon_bnds
- units :
- degrees_east
- axis :
- X
- long_name :
- Longitude
- standard_name :
- longitude
array([ 0. , 2.5, 5. , 7.5, 10. , 12.5, 15. , 17.5, 20. , 22.5, 25. , 27.5, 30. , 32.5, 35. , 37.5, 40. , 42.5, 45. , 47.5, 50. , 52.5, 55. , 57.5, 60. , 62.5, 65. , 67.5, 70. , 72.5, 75. , 77.5, 80. , 82.5, 85. , 87.5, 90. , 92.5, 95. , 97.5, 100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5, 120. , 122.5, 125. , 127.5, 130. , 132.5, 135. , 137.5, 140. , 142.5, 145. , 147.5, 150. , 152.5, 155. , 157.5, 160. , 162.5, 165. , 167.5, 170. , 172.5, 175. , 177.5, 180. , 182.5, 185. , 187.5, 190. , 192.5, 195. , 197.5, 200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5, 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5, 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5, 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5, 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5, 325. , 327.5, 330. , 332.5, 335. , 337.5, 340. , 342.5, 345. , 347.5, 350. , 352.5, 355. , 357.5])
- time_bnds(time, bnds)objectdask.array<chunksize=(120, 2), meta=np.ndarray>
- _ChunkSizes :
- [1 2]
Array Chunk Bytes 1.92 kB 1.92 kB Shape (120, 2) (120, 2) Count 2 Tasks 1 Chunks Type object numpy.ndarray - lev_bnds(lev, bnds)float64dask.array<chunksize=(32, 2), meta=np.ndarray>
- formula :
- p = a*p0 + b*ps
- standard_name :
- atmosphere_hybrid_sigma_pressure_coordinate
- units :
- 1
- formula_terms :
- p0: p0 a: a_bnds b: b_bnds ps: ps
- _ChunkSizes :
- [32 2]
Array Chunk Bytes 512 B 512 B Shape (32, 2) (32, 2) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - p0()float64...
- long_name :
- vertical coordinate formula term: reference pressure
- units :
- Pa
array(100000.)
- a(lev)float64dask.array<chunksize=(32,), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: a(k)
- _ChunkSizes :
- 32
Array Chunk Bytes 256 B 256 B Shape (32,) (32,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - b(lev)float64dask.array<chunksize=(32,), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: b(k)
- _ChunkSizes :
- 32
Array Chunk Bytes 256 B 256 B Shape (32,) (32,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - a_bnds(lev, bnds)float64dask.array<chunksize=(32, 2), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: a(k+1/2)
Array Chunk Bytes 512 B 512 B Shape (32, 2) (32, 2) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - b_bnds(lev, bnds)float64dask.array<chunksize=(32, 2), meta=np.ndarray>
- long_name :
- vertical coordinate formula term: b(k+1/2)
Array Chunk Bytes 512 B 512 B Shape (32, 2) (32, 2) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - lat_bnds(lat, bnds)float64dask.array<chunksize=(96, 2), meta=np.ndarray>
- _ChunkSizes :
- [96 2]
Array Chunk Bytes 1.54 kB 1.54 kB Shape (96, 2) (96, 2) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - lon_bnds(lon, bnds)float64dask.array<chunksize=(144, 2), meta=np.ndarray>
- _ChunkSizes :
- [144 2]
Array Chunk Bytes 2.30 kB 2.30 kB Shape (144, 2) (144, 2) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - ps(time, lat, lon)float32dask.array<chunksize=(120, 96, 144), meta=np.ndarray>
- long_name :
- Surface Air Pressure
- units :
- Pa
- _ChunkSizes :
- [ 1 96 144]
Array Chunk Bytes 6.64 MB 6.64 MB Shape (120, 96, 144) (120, 96, 144) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - mmroa(time, lev, lat, lon)float32dask.array<chunksize=(120, 32, 96, 144), meta=np.ndarray>
- standard_name :
- mass_fraction_of_particulate_organic_matter_dry_aerosol_particles_in_air
- long_name :
- Total Organic Aerosol Mass Mixing Ratio
- comment :
- We recommend a scale factor of POM=1.4*OC, unless your model has more detailed info available.
- units :
- kg kg-1
- original_name :
- mmr_OM
- original_units :
- kg/kg
- history :
- 2019-12-11T13:14:22Z altered by CMOR: Converted units from 'kg/kg' to 'kg kg-1'. 2019-12-11T13:14:25Z altered by CMOR: Converted type from 'd' to 'f'. 2019-12-11T13:14:25Z altered by CMOR: Inverted axis: lev.
- cell_methods :
- area: time: mean
- cell_measures :
- area: areacella
- _ChunkSizes :
- [ 1 32 96 144]
Array Chunk Bytes 212.34 MB 212.34 MB Shape (120, 32, 96, 144) (120, 32, 96, 144) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray
- Conventions :
- CF-1.7 CMIP-6.2
- activity_id :
- CMIP
- branch_method :
- Hybrid-restart from year 1600-01-01 of piControl
- branch_time :
- 0.0
- branch_time_in_child :
- 0.0
- branch_time_in_parent :
- 430335.0
- contact :
- Please send any requests or bug reports to noresm-ncc@met.no.
- creation_date :
- 2019-12-11T13:14:25Z
- data_specs_version :
- 01.00.31
- experiment :
- abrupt quadrupling of CO2
- experiment_id :
- abrupt-4xCO2
- external_variables :
- areacella
- forcing_index :
- 1
- frequency :
- mon
- further_info_url :
- https://furtherinfo.es-doc.org/CMIP6.NCC.NorESM2-LM.abrupt-4xCO2.none.r1i1p1f1
- grid :
- finite-volume grid with 1.9x2.5 degree lat/lon resolution
- grid_label :
- gn
- history :
- 2019-12-11T13:14:25Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.
- initialization_index :
- 1
- institution :
- NorESM Climate modeling Consortium consisting of CICERO (Center for International Climate and Environmental Research, Oslo 0349), MET-Norway (Norwegian Meteorological Institute, Oslo 0313), NERSC (Nansen Environmental and Remote Sensing Center, Bergen 5006), NILU (Norwegian Institute for Air Research, Kjeller 2027), UiB (University of Bergen, Bergen 5007), UiO (University of Oslo, Oslo 0313) and UNI (Uni Research, Bergen 5008), Norway. Mailing address: NCC, c/o MET-Norway, Henrik Mohns plass 1, Oslo 0313, Norway
- institution_id :
- NCC
- mip_era :
- CMIP6
- model_id :
- NorESM2-LM
- nominal_resolution :
- 250 km
- parent_activity_id :
- CMIP
- parent_experiment_id :
- piControl
- parent_mip_era :
- CMIP6
- parent_source_id :
- NorESM2-LM
- parent_sub_experiment_id :
- none
- parent_time_units :
- days since 0421-01-01
- parent_variant_label :
- r1i1p1f1
- physics_index :
- 1
- product :
- model-output
- realization_index :
- 1
- realm :
- aerosol
- run_variant :
- N/A
- source :
- NorESM2-LM (2017): aerosol: OsloAero atmos: CAM-OSLO (2 degree resolution; 144 x 96; 32 levels; top level 3 mb) atmosChem: OsloChemSimp land: CLM landIce: CISM ocean: MICOM (1 degree resolution; 360 x 384; 70 levels; top grid cell minimum 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]) ocnBgchem: HAMOCC seaIce: CICE
- source_id :
- NorESM2-LM
- source_type :
- AOGCM AER BGC
- sub_experiment :
- none
- sub_experiment_id :
- none
- table_id :
- AERmon
- table_info :
- Creation Date:(24 July 2019) MD5:0bb394a356ef9d214d027f1aca45853e
- title :
- NorESM2-LM output prepared for CMIP6
- variable_id :
- mmroa
- variant_label :
- r1i1p1f1
- license :
- CMIP6 model data produced by NCC is licensed under a Creative Commons Attribution ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and at https:///pcmdi.llnl.gov/. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.
- cmor_version :
- 3.5.0
- tracking_id :
- hdl:21.14100/7e00d8c9-0c7f-40fc-911b-6f7e57646f2e
- DODS_EXTRA.Unlimited_Dimension :
- time
ds.mmroa
<xarray.DataArray 'mmroa' (time: 120, lev: 32, lat: 96, lon: 144)>
dask.array<open_dataset-d0504567ed5e2e669614a36f6263cfeammroa, shape=(120, 32, 96, 144), dtype=float32, chunksize=(120, 32, 96, 144), chunktype=numpy.ndarray>
Coordinates:
* time (time) object 0491-01-16 12:00:00 ... 0500-12-16 12:00:00
* lev (lev) float64 0.9926 0.9763 0.9575 ... 0.01436 0.007595 0.003643
* lat (lat) float64 -90.0 -88.11 -86.21 -84.32 ... 84.32 86.21 88.11 90.0
* lon (lon) float64 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5
Attributes:
standard_name: mass_fraction_of_particulate_organic_matter_dry_aerosol_...
long_name: Total Organic Aerosol Mass Mixing Ratio
comment: We recommend a scale factor of POM=1.4*OC, unless your m...
units: kg kg-1
original_name: mmr_OM
original_units: kg/kg
history: 2019-12-11T13:14:22Z altered by CMOR: Converted units fr...
cell_methods: area: time: mean
cell_measures: area: areacella
_ChunkSizes: [ 1 32 96 144]xarray.DataArray
'mmroa'
- time: 120
- lev: 32
- lat: 96
- lon: 144
- dask.array<chunksize=(120, 32, 96, 144), meta=np.ndarray>
Array Chunk Bytes 212.34 MB 212.34 MB Shape (120, 32, 96, 144) (120, 32, 96, 144) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - time(time)object0491-01-16 12:00:00 ... 0500-12-...
- bounds :
- time_bnds
- axis :
- T
- long_name :
- time
- standard_name :
- time
- _ChunkSizes :
- 1
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0, 0), cftime.DatetimeNoLeap(499, 9, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(499, 10, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(499, 11, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(499, 12, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(500, 1, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(500, 2, 15, 0, 0, 0, 0), cftime.DatetimeNoLeap(500, 3, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(500, 4, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(500, 5, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(500, 6, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(500, 7, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(500, 8, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(500, 9, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(500, 10, 16, 12, 0, 0, 0), cftime.DatetimeNoLeap(500, 11, 16, 0, 0, 0, 0), cftime.DatetimeNoLeap(500, 12, 16, 12, 0, 0, 0)], dtype=object) - lev(lev)float640.9926 0.9763 ... 0.007595 0.003643
- bounds :
- lev_bnds
- units :
- 1
- axis :
- Z
- positive :
- down
- long_name :
- hybrid sigma pressure coordinate
- standard_name :
- atmosphere_hybrid_sigma_pressure_coordinate
- formula :
- p = a*p0 + b*ps
- formula_terms :
- p0: p0 a: a b: b ps: ps
array([0.992556, 0.976325, 0.957485, 0.936198, 0.912645, 0.88702 , 0.859535, 0.820858, 0.763404, 0.691389, 0.609779, 0.524687, 0.445993, 0.379101, 0.322242, 0.273911, 0.232829, 0.197908, 0.168225, 0.142994, 0.121547, 0.103317, 0.087821, 0.073751, 0.06152 , 0.051677, 0.043194, 0.035923, 0.024612, 0.014357, 0.007595, 0.003643]) - lat(lat)float64-90.0 -88.11 -86.21 ... 88.11 90.0
- bounds :
- lat_bnds
- units :
- degrees_north
- axis :
- Y
- long_name :
- Latitude
- standard_name :
- latitude
array([-90. , -88.105263, -86.210526, -84.315789, -82.421053, -80.526316, -78.631579, -76.736842, -74.842105, -72.947368, -71.052632, -69.157895, -67.263158, -65.368421, -63.473684, -61.578947, -59.684211, -57.789474, -55.894737, -54. , -52.105263, -50.210526, -48.315789, -46.421053, -44.526316, -42.631579, -40.736842, -38.842105, -36.947368, -35.052632, -33.157895, -31.263158, -29.368421, -27.473684, -25.578947, -23.684211, -21.789474, -19.894737, -18. , -16.105263, -14.210526, -12.315789, -10.421053, -8.526316, -6.631579, -4.736842, -2.842105, -0.947368, 0.947368, 2.842105, 4.736842, 6.631579, 8.526316, 10.421053, 12.315789, 14.210526, 16.105263, 18. , 19.894737, 21.789474, 23.684211, 25.578947, 27.473684, 29.368421, 31.263158, 33.157895, 35.052632, 36.947368, 38.842105, 40.736842, 42.631579, 44.526316, 46.421053, 48.315789, 50.210526, 52.105263, 54. , 55.894737, 57.789474, 59.684211, 61.578947, 63.473684, 65.368421, 67.263158, 69.157895, 71.052632, 72.947368, 74.842105, 76.736842, 78.631579, 80.526316, 82.421053, 84.315789, 86.210526, 88.105263, 90. ]) - lon(lon)float640.0 2.5 5.0 ... 352.5 355.0 357.5
- bounds :
- lon_bnds
- units :
- degrees_east
- axis :
- X
- long_name :
- Longitude
- standard_name :
- longitude
array([ 0. , 2.5, 5. , 7.5, 10. , 12.5, 15. , 17.5, 20. , 22.5, 25. , 27.5, 30. , 32.5, 35. , 37.5, 40. , 42.5, 45. , 47.5, 50. , 52.5, 55. , 57.5, 60. , 62.5, 65. , 67.5, 70. , 72.5, 75. , 77.5, 80. , 82.5, 85. , 87.5, 90. , 92.5, 95. , 97.5, 100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5, 120. , 122.5, 125. , 127.5, 130. , 132.5, 135. , 137.5, 140. , 142.5, 145. , 147.5, 150. , 152.5, 155. , 157.5, 160. , 162.5, 165. , 167.5, 170. , 172.5, 175. , 177.5, 180. , 182.5, 185. , 187.5, 190. , 192.5, 195. , 197.5, 200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5, 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5, 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5, 275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5, 300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5, 325. , 327.5, 330. , 332.5, 335. , 337.5, 340. , 342.5, 345. , 347.5, 350. , 352.5, 355. , 357.5])
- standard_name :
- mass_fraction_of_particulate_organic_matter_dry_aerosol_particles_in_air
- long_name :
- Total Organic Aerosol Mass Mixing Ratio
- comment :
- We recommend a scale factor of POM=1.4*OC, unless your model has more detailed info available.
- units :
- kg kg-1
- original_name :
- mmr_OM
- original_units :
- kg/kg
- history :
- 2019-12-11T13:14:22Z altered by CMOR: Converted units from 'kg/kg' to 'kg kg-1'. 2019-12-11T13:14:25Z altered by CMOR: Converted type from 'd' to 'f'. 2019-12-11T13:14:25Z altered by CMOR: Inverted axis: lev.
- cell_methods :
- area: time: mean
- cell_measures :
- area: areacella
- _ChunkSizes :
- [ 1 32 96 144]
Plot a map from a specific date.
fig = plt.figure(1, figsize=[30,13])
# Set the projection to use for plotting
ax = plt.subplot(1, 1, 1, projection=ccrs.PlateCarree())
ax.coastlines()
ds.mmroa.isel(lev=0).sel(time=cftime.DatetimeNoLeap(500, 10, 16), method='nearest').plot(ax=ax, cmap=cmaps.BlueYellowRed)
<matplotlib.collections.QuadMesh at 0x7f3878824460>
