Global average annual temperature plot

Produces a time-series plot of North American temperature forecasts for 2 different emission scenarios. Constraining data to a limited spatial area also features in this example.

The data used comes from the HadGEM2-AO model simulations for the A1B and E1 scenarios, both of which were derived using the IMAGE Integrated Assessment Model (Johns et al. 2011; Lowe et al. 2009).

References

Johns T.C., et al. (2011) Climate change under aggressive mitigation: the ENSEMBLES multi-model experiment. Climate Dynamics, Vol 37, No. 9-10, doi:10.1007/s00382-011-1005-5.

Lowe J.A., C.D. Hewitt, D.P. Van Vuuren, T.C. Johns, E. Stehfest, J-F. Royer, and P. van der Linden, 2009. New Study For Climate Modeling, Analyses, and Scenarios. Eos Trans. AGU, Vol 90, No. 21, doi:10.1029/2009EO210001.

See also

Further details on the aggregation functionality being used in this example can be found in Cube statistics.

"""
Global average annual temperature plot
======================================

Produces a time-series plot of North American temperature forecasts for 2
different emission scenarios. Constraining data to a limited spatial area also
features in this example.

The data used comes from the HadGEM2-AO model simulations for the A1B and E1
scenarios, both of which were derived using the IMAGE Integrated Assessment
Model (Johns et al. 2011; Lowe et al. 2009).

References
----------

   Johns T.C., et al. (2011) Climate change under aggressive mitigation: the
   ENSEMBLES multi-model experiment. Climate Dynamics, Vol 37, No. 9-10,
   doi:10.1007/s00382-011-1005-5.

   Lowe J.A., C.D. Hewitt, D.P. Van Vuuren, T.C. Johns, E. Stehfest, J-F.
   Royer, and P. van der Linden, 2009. New Study For Climate Modeling,
   Analyses, and Scenarios. Eos Trans. AGU, Vol 90, No. 21,
   doi:10.1029/2009EO210001.

.. seealso::

    Further details on the aggregation functionality being used in this example
    can be found in :ref:`cube-statistics`.

"""
import numpy as np
import matplotlib.pyplot as plt
import iris
import iris.plot as iplt
import iris.quickplot as qplt

import iris.analysis.cartography


def main():
    # Load data into three Cubes, one for each set of NetCDF files.
    e1 = iris.load_cube(iris.sample_data_path('E1_north_america.nc'))

    a1b = iris.load_cube(iris.sample_data_path('A1B_north_america.nc'))

    # load in the global pre-industrial mean temperature, and limit the domain
    # to the same North American region that e1 and a1b are at.
    north_america = iris.Constraint(longitude=lambda v: 225 <= v <= 315,
                                    latitude=lambda v: 15 <= v <= 60)
    pre_industrial = iris.load_cube(iris.sample_data_path('pre-industrial.pp'),
                                    north_america)

    # Generate area-weights array. As e1 and a1b are on the same grid we can
    # do this just once and re-use. This method requires bounds on lat/lon
    # coords, so let's add some in sensible locations using the "guess_bounds"
    # method.
    e1.coord('latitude').guess_bounds()
    e1.coord('longitude').guess_bounds()
    e1_grid_areas = iris.analysis.cartography.area_weights(e1)
    pre_industrial.coord('latitude').guess_bounds()
    pre_industrial.coord('longitude').guess_bounds()
    pre_grid_areas = iris.analysis.cartography.area_weights(pre_industrial)

    # Perform the area-weighted mean for each of the datasets using the
    # computed grid-box areas.
    pre_industrial_mean = pre_industrial.collapsed(['latitude', 'longitude'],
                                                   iris.analysis.MEAN,
                                                   weights=pre_grid_areas)
    e1_mean = e1.collapsed(['latitude', 'longitude'],
                           iris.analysis.MEAN,
                           weights=e1_grid_areas)
    a1b_mean = a1b.collapsed(['latitude', 'longitude'],
                             iris.analysis.MEAN,
                             weights=e1_grid_areas)

    # Plot the datasets
    qplt.plot(e1_mean, label='E1 scenario', lw=1.5, color='blue')
    qplt.plot(a1b_mean, label='A1B-Image scenario', lw=1.5, color='red')

    # Draw a horizontal line showing the pre-industrial mean
    plt.axhline(y=pre_industrial_mean.data, color='gray', linestyle='dashed',
                label='pre-industrial', lw=1.5)

    # Constrain the period 1860-1999 and extract the observed data from a1b
    constraint = iris.Constraint(time=lambda
                                 cell: 1860 <= cell.point.year <= 1999)
    observed = a1b_mean.extract(constraint)
    # Assert that this data set is the same as the e1 scenario:
    # they share data up to the 1999 cut off.
    assert np.all(np.isclose(observed.data,
                             e1_mean.extract(constraint).data))

    # Plot the observed data
    qplt.plot(observed, label='observed', color='black', lw=1.5)

    # Add a legend and title
    plt.legend(loc="upper left")
    plt.title('North American mean air temperature', fontsize=18)

    plt.xlabel('Time / year')

    plt.grid()

    iplt.show()


if __name__ == '__main__':
    main()

(Source code, png)

../../_images/COP_1d_plot.png