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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 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_016101-017012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_017101-018012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_018101-019012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_019101-020012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_020101-021012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_021101-022012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_022101-023012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_023101-024012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_024101-025012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_025101-026012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_026101-027012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_027101-028012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_028101-029012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_029101-030012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_030101-031012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_031101-032012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_032101-033012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_033101-034012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_034101-035012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_035101-036012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_036101-037012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_037101-038012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_038101-039012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_039101-040012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_040101-041012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_041101-042012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_042101-043012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_043101-044012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_044101-045012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_045101-046012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_046101-047012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_047101-048012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_048101-049012.nc',
 'https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/NCC/NorESM2-LM/abrupt-4xCO2/r1i1p1f1/AERmon/mmroa/gn/v20210118/mmroa_AERmon_NorESM2-LM_abrupt-4xCO2_r1i1p1f1_gn_049101-050012.nc']

# 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:  time

xarray.Dataset

Dimensions:
- bnds: 2
- lat: 96
- lev: 32
- lon: 144
- time: 120

Coordinates: (4)

time

(time)

object

0491-01-16 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)

float64

0.9926 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)

float64

0.0 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])

Data variables: (11)

time_bnds

(time, bnds)

object

dask.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)

float64

dask.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)

float64

dask.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)

float64

dask.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)

float64

dask.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)

float64

dask.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)

float64

dask.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)

float64

dask.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)

float32

dask.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)

float32

dask.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

Attributes: (50)
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

Coordinates: (4)

time

(time)

object

0491-01-16 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)

float64

0.9926 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)

float64

0.0 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])

Attributes: (10)
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>

eScience Tools in Climate Science - Linking Observations with Modelling

Search and Load CMIP6 Data via ESGF / OPeNDAP

Contents

Search and Load CMIP6 Data via ESGF / OPeNDAP¶

Search using ESGF API¶

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