.. _graphics-COP_1d_plot:


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. 2010; Lowe et al. 2009).

References
----------

   Johns T.C., et al. (2010) Climate change under aggressive mitigation: The ENSEMBLES multi-model
   experiment. Climate Dynamics (submitted)

   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.

.. seealso::

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



.. plot:: /data/local/ecamp/iris/docs/iris/example_code/graphics/COP_1d_plot.py

::

    """
    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. 2010; Lowe et al. 2009).
    
    References
    ----------
    
       Johns T.C., et al. (2010) Climate change under aggressive mitigation: The ENSEMBLES multi-model
       experiment. Climate Dynamics (submitted)
    
       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.
    
    .. 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
    import matplotlib.dates as mdates
    
    
    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)
    
        # Show ticks 30 years apart
        plt.gca().xaxis.set_major_locator(mdates.YearLocator(30))
    
        # 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)
    
        # Establish where r and t have the same data, i.e. the observations
        common = np.where(a1b_mean.data == e1_mean.data)[0]
        observed = a1b_mean[common]
    
        # 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()