.. _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.



.. plot:: /net/home/h06/ecamp/nounpack_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.
    
    """
    import os.path
    
    import numpy
    import matplotlib.pyplot as plt
    import iris
    import iris.coords as coords
    import iris.plot as iplt
    import iris.quickplot as qplt
    
    import iris.analysis.calculus
    import matplotlib.dates as mdates
    
    
    def main():
        # Load data into three Cubes, one for each set of PP files
        e1 = iris.load_strict(iris.sample_data_path('E1_north_america.nc'))
    
        a1b = iris.load_strict(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_strict(iris.sample_data_path('pre-industrial.pp'),
                                      north_america
                                      )
    
        pre_industrial_mean = pre_industrial.collapsed(['latitude', 'longitude'], iris.analysis.MEAN)
        e1_mean = e1.collapsed(['latitude', 'longitude'], iris.analysis.MEAN)
        a1b_mean = a1b.collapsed(['latitude', 'longitude'], iris.analysis.MEAN)
    
        # Show ticks 30 years apart
        plt.gca().xaxis.set_major_locator(mdates.YearLocator(30))
    
        # Label the ticks with year data
        plt.gca().format_xdata = mdates.DateFormatter('%Y')
    
        # Plot the datasets
        qplt.plot(e1_mean, coords=['time'], label='E1 scenario', lw=1.5, color='blue')
        qplt.plot(a1b_mean, coords=['time'], 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 = numpy.where(a1b_mean.data == e1_mean.data)[0]
        observed = a1b_mean[common]
    
        # Plot the observed data
        qplt.plot(observed, coords=['time'], 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()