.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "generated/gallery/meteorology/plot_COP_1d.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_generated_gallery_meteorology_plot_COP_1d.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_generated_gallery_meteorology_plot_COP_1d.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. 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`.

.. GENERATED FROM PYTHON SOURCE LINES 30-113



.. image-sg:: /generated/gallery/meteorology/images/sphx_glr_plot_COP_1d_001.png
   :alt: North American mean air temperature
   :srcset: /generated/gallery/meteorology/images/sphx_glr_plot_COP_1d_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    import iris
    import iris.analysis.cartography
    import iris.plot as iplt
    import iris.quickplot as qplt


    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 reuse. 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()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.381 seconds)


.. _sphx_glr_download_generated_gallery_meteorology_plot_COP_1d.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_COP_1d.ipynb <plot_COP_1d.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_COP_1d.py <plot_COP_1d.py>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_