Downloading the data

All data files used in the lecture are available on this webserver.

CERES Data

For Week 02, we will use climatological data from the CERES (Clouds and the Earth’s Radiant Energy System) mission. We are going to use the EBAF-TOA and the EBAF-Surface data products (both freely available on this webpage) as climatologies (i.e. monthly averages 2005-2015).

The data quality summary of these data (PDF) can be found here, and more accessible publications can be found here for TOA and here for Surface.

Download:

• TOA file: CERES_EBAF-TOA_Ed4.1_Clim-2005-2015.nc

• Surface file: CERES_EBAF-Surface_Ed4.1_Clim-2005-2015.nc

ERA5 data

ERA5 is an atmospheric reanalysis product.

Invariant:

• ERA5_LowRes_Invariant.nc

Monthly surface (3D) data:

• ERA5_LowRes_Monthly_t2m.nc: 2m temperature

• ERA5_LowRes_Monthly_tp.nc: total precipitation

• ERA5_LowRes_Monthly_seaice.nc: sea ice cover

• ERA5_LowRes_Monthly_snow.nc: snow cover

• ERA5_LowRes_Monthly_uvslp.nc: u, v, slp

• ERA5_LowRes_Monthly_sst.nc: sea surface temperatures

• ERA5_LowRes_Monthly_eb.nc: energy balance terms

Reading data from an url

You can also open files without downloading them locally. This is somehow inefficient (it will download all data in memory each time you run the notebook), but might be useful e.g. on MyBinder where you can’t store files. You will need the h5netcdf library to be installed for the following to work:

import xarray as xr
import urllib, io

url = 'https://cluster.klima.uni-bremen.de/~fmaussion/teaching/climate/ERA5_LowRes_Invariant.nc'

req = urllib.request.Request(url)
with urllib.request.urlopen(req) as resp:
    ds = xr.open_dataset(io.BytesIO(resp.read()))

ds

<xarray.Dataset>
Dimensions:    (longitude: 480, latitude: 241, time: 1)
Coordinates:
  * longitude  (longitude) float32 -179.6 -178.9 -178.1 ... 178.1 178.9 179.6
  * latitude   (latitude) float32 90.0 89.25 88.5 87.75 ... -88.5 -89.25 -90.0
  * time       (time) datetime64[ns] 1979-01-01
Data variables:
    lsm        (time, latitude, longitude) float32 ...
    wmb        (time, latitude, longitude) float32 ...
    z          (time, latitude, longitude) float32 ...
Attributes:
    Conventions:  CF-1.6
    history:      2019-11-18 09:24:36 GMT by grib_to_netcdf-2.14.0: /opt/ecmw...

xarray.Dataset

• Dimensions:
  • longitude: 480
  • latitude: 241
  • time: 1
• Coordinates: (3)
  • longitude
    (longitude)
    float32
    -179.6 -178.9 ... 178.9 179.6

    units :

    degrees_east

    long_name :

    longitude

    array([-179.625, -178.875, -178.125, ...,  178.125,  178.875,  179.625],
          dtype=float32)

  • latitude
    (latitude)
    float32
    90.0 89.25 88.5 ... -89.25 -90.0

    units :

    degrees_north

    long_name :

    latitude

    array([ 90.  ,  89.25,  88.5 , ..., -88.5 , -89.25, -90.  ], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1979-01-01

    long_name :

    time

    array(['1979-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (3)
  • lsm
    (time, latitude, longitude)
    float32
    ...

    units :

    (0 - 1)

    long_name :

    Land-sea mask

    standard_name :

    land_binary_mask

    [115680 values with dtype=float32]

  • wmb
    (time, latitude, longitude)
    float32
    ...

    units :

    m

    long_name :

    Model bathymetry

    [115680 values with dtype=float32]

  • z
    (time, latitude, longitude)
    float32
    ...

    units :

    m**2 s**-2

    long_name :

    Geopotential

    standard_name :

    geopotential

    [115680 values with dtype=float32]

• Attributes: (2)

  Conventions :

  CF-1.6

  history :

  2019-11-18 09:24:36 GMT by grib_to_netcdf-2.14.0: /opt/ecmwf/eccodes/bin/grib_to_netcdf -o /cache/data7/adaptor.mars.internal-1574069068.3461654-30525-1-50125e5a-c7c5-4a85-b6c0-71d1026d7cc4.nc /cache/tmp/50125e5a-c7c5-4a85-b6c0-71d1026d7cc4-adaptor.mars.internal-1574069068.349032-30525-1-tmp.grib