/
Hist_Line_Graph_Pr_Monthly.py
1100 lines (955 loc) · 59 KB
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Hist_Line_Graph_Pr_Monthly.py
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"""
Created on Wednesday August 2 2017
@author: s0899345
"""
import matplotlib.pyplot as plt
import iris
import iris.coord_categorisation as iriscc
import iris.plot as iplt
import iris.quickplot as qplt
import iris.analysis.cartography
import cf_units
import datetime
import numpy as np
import calendar
#this file is split into parts as follows:
#PART 1: load and format all models
#PART 2: load and format observed data
#PART 3: format files to be plot specific
#PART 4: plot data
def main():
#-------------------------------------------------------------------------
#PART 1: LOAD and FORMAT ALL MODELS
#bring in all the ERAINT models we need and give them a name
CCCma = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/ERAINT/1979-2012/pr_AFR-44_ECMWF-ERAINT_evaluation_r1i1p1_CCCma-CanRCM4_r2_mon_198901-200912.nc'
CLMcom = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/ERAINT/1979-2012/pr_AFR-44_ECMWF-ERAINT_evaluation_r1i1p1_CLMcom-CCLM4-8-17_v1_mon_198901-200812.nc'
DMI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/ERAINT/1979-2012/pr_AFR-44_ECMWF-ERAINT_evaluation_r1i1p1_DMI-HIRHAM5_v2_mon_198901-201012.nc'
KNMI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/ERAINT/1979-2012/pr_AFR-44_ECMWF-ERAINT_evaluation_r1i1p1_KNMI-RACMO22T_v1_mon_197901-201212.nc'
MPIE = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/ERAINT/1979-2012/pr_AFR-44_ECMWF-ERAINT_evaluation_r1i1p1_MPI-CSC-REMO2009_v1_mon_198902-200812.nc'
SMHI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/ERAINT/1979-2012/pr_AFR-44_ECMWF-ERAINT_evaluation_r1i1p1_SMHI-RCA4_v1_mon_198001-201012.nc'
#bring in all the CORDEX RCM models we need and give them a name
CCCmaCanRCM= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_CCCma-CanESM2_historical_r1i1p1_CCCma-CanRCM4_r2_mon_195101-200512.nc'
CCCmaSMHI= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_CCCma-CanESM2_historical_r1i1p1_SMHI-RCA4_v1_mon_195101-200512.nc'
CNRM= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_CNRM-CERFACS-CNRM-CM5_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_mon_195001-200512.nc'
CNRMSMHI= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_CNRM-CERFACS-CNRM-CM5_historical_r1i1p1_SMHI-RCA4_v1_mon_195101-200512.nc'
CSIRO = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_CSIRO-QCCCE-CSIRO-Mk3-6-0_historical_r1i1p1_SMHI-RCA4_v1_mon_195101-200512.nc'
ICHECDMI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_ICHEC-EC-EARTH_historical_r3i1p1_DMI-HIRHAM5_v2_mon_195101-200512.nc'
ICHECCCLM = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_ICHEC-EC-EARTH_historical_r12i1p1_CLMcom-CCLM4-8-17_v1_mon_194912-200512.nc'
ICHECKNMI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_ICHEC-EC-EARTH_historical_r12i1p1_KNMI-RACMO22T_v1_mon_195001-200512.nc'
ICHECMPI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_ICHEC-EC-EARTH_historical_r12i1p1_MPI-CSC-REMO2009_v1_mon_195001-200512.nc'
ICHECSMHI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_ICHEC-EC-EARTH_historical_r12i1p1_SMHI-RCA4_v1_mon_195101-200512.nc'
IPSL = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_IPSL-IPSL-CM5A-MR_historical_r1i1p1_SMHI-RCA4_v1_mon_195101-200512.nc'
MIROC = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_MIROC-MIROC5_historical_r1i1p1_SMHI-RCA4_v1_mon_195101-200512.nc'
MOHCCCLM = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_MOHC-HadGEM2-ES_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_mon_194912-200512.nc'
MOHCKNMI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_MOHC-HadGEM2-ES_historical_r1i1p1_KNMI-RACMO22T_v2_mon_195001-200512.nc'
MOHCSMHI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_MOHC-HadGEM2-ES_historical_r1i1p1_SMHI-RCA4_v1_mon_195101-200512.nc'
MPICCLM = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_CLMcom-CCLM4-8-17_v1_mon_194912-200512.nc'
MPIREMO = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_MPI-CSC-REMO2009_v1_mon_195001-200512.nc'
MPISMHI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_SMHI-RCA4_v1_mon_195101-200512.nc'
NCCDMI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_NCC-NorESM1-M_historical_r1i1p1_DMI-HIRHAM5_v1_mon_195101-200512.nc'
NCCSMHI = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_NCC-NorESM1-M_historical_r1i1p1_SMHI-RCA4_v1_mon_195101-200512.nc'
NOAA = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/AFR_44_pr/Historical/1950-2005/pr_AFR-44_NOAA-GFDL-GFDL-ESM2M_historical_r1i1p1_SMHI-RCA4_v1_mon_195101-200512.nc'
#bring in all the GCM models we need and give them a name
CanESM2= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_CanESM2_historical_r1i1p1_185001-200512.nc'
CNRMG= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_CNRM-CM5-2_historical_r1i1p1_195001-200512.nc'
MK3 = '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_CSIRO-Mk3-6-0_historical_r1i1p1_185001-200512.nc'
EARTH= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_EC-EARTH_historical_r12i1p1_195001-201212.nc'
EARTH3= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_EC-EARTH_historical_r3i1p1_1850-2009.nc'
GFDL='/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_GFDL-ESM2M_historical_r1i1p1_194601-200512.nc'
HadGEM2='/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_HadGEM2-ES_historical_r1i1p1_193412-200511.nc'
IPSLG='/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_IPSL-CM5B-LR_historical_r1i1p1_185001-200512.nc'
MIROCG='/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_MIROC5_historical_r1i1p1_185001-201212.nc'
MPI='/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_MPI-ESM-MR_historical_r1i1p1_185001-200512.nc'
NorESM1='/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/GCM_data/pr_Amon_NorESM1-M_historical_r1i1p1_185001-200512.nc'
#Load exactly one cube from given file
CCCma = iris.load_cube(CCCma)
CLMcom = iris.load_cube(CLMcom)
DMI = iris.load_cube(DMI, 'precipitation_flux')
KNMI = iris.load_cube(KNMI)
MPIE = iris.load_cube(MPIE)
SMHI = iris.load_cube(SMHI)
CCCmaCanRCM = iris.load_cube(CCCmaCanRCM)
CCCmaSMHI = iris.load_cube(CCCmaSMHI)
CNRM = iris.load_cube(CNRM)
CNRMSMHI = iris.load_cube(CNRMSMHI)
CSIRO = iris.load_cube(CSIRO)
ICHECDMI = iris.load_cube(ICHECDMI, 'precipitation_flux')
ICHECCCLM = iris.load_cube(ICHECCCLM)
ICHECKNMI = iris.load_cube(ICHECKNMI)
ICHECMPI = iris.load_cube(ICHECMPI)
ICHECSMHI = iris.load_cube(ICHECSMHI)
IPSL = iris.load_cube(IPSL)
MIROC = iris.load_cube(MIROC)
MOHCCCLM = iris.load_cube(MOHCCCLM)
MOHCKNMI = iris.load_cube(MOHCKNMI)
MOHCSMHI = iris.load_cube(MOHCSMHI)
MPICCLM = iris.load_cube(MPICCLM)
MPIREMO = iris.load_cube(MPIREMO)
MPISMHI = iris.load_cube(MPISMHI)
NCCDMI = iris.load_cube(NCCDMI, 'precipitation_flux')
NCCSMHI = iris.load_cube(NCCSMHI)
NOAA = iris.load_cube(NOAA)
CanESM2 = iris.load_cube(CanESM2)
CNRMG = iris.load_cube(CNRMG)
MK3 = iris.load_cube(MK3)
EARTH = iris.load_cube(EARTH)
EARTH3 = iris.load_cube(EARTH3)
GFDL = iris.load_cube(GFDL, 'precipitation_flux')
HadGEM2 = iris.load_cube(HadGEM2)
IPSLG = iris.load_cube(IPSLG)
MIROCG = iris.load_cube(MIROCG)
MPI = iris.load_cube(MPI)
NorESM1 = iris.load_cube(NorESM1)
#remove flat latitude and longitude and only use grid latitude and grid longitude to make consistent with the observed data, also make sure all of the longitudes are monotonic. This is only applicable to the ERAINT and CORDEX Models as the Observed data and GCMs are already in standard linear format.
lats = iris.coords.DimCoord(CCCma.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = CCCma.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
CCCma.remove_coord('latitude')
CCCma.remove_coord('longitude')
CCCma.remove_coord('grid_latitude')
CCCma.remove_coord('grid_longitude')
CCCma.add_dim_coord(lats, 1)
CCCma.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(CLMcom.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = CLMcom.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
CLMcom.remove_coord('latitude')
CLMcom.remove_coord('longitude')
CLMcom.remove_coord('grid_latitude')
CLMcom.remove_coord('grid_longitude')
CLMcom.add_dim_coord(lats, 1)
CLMcom.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(DMI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = DMI.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
DMI.remove_coord('latitude')
DMI.remove_coord('longitude')
DMI.remove_coord('grid_latitude')
DMI.remove_coord('grid_longitude')
DMI.add_dim_coord(lats, 1)
DMI.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(KNMI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = KNMI.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
KNMI.remove_coord('latitude')
KNMI.remove_coord('longitude')
KNMI.remove_coord('grid_latitude')
KNMI.remove_coord('grid_longitude')
KNMI.add_dim_coord(lats, 1)
KNMI.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(MPIE.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = MPIE.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
MPIE.remove_coord('latitude')
MPIE.remove_coord('longitude')
MPIE.remove_coord('grid_latitude')
MPIE.remove_coord('grid_longitude')
MPIE.add_dim_coord(lats, 1)
MPIE.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(SMHI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = SMHI.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
SMHI.remove_coord('latitude')
SMHI.remove_coord('longitude')
SMHI.remove_coord('grid_latitude')
SMHI.remove_coord('grid_longitude')
SMHI.add_dim_coord(lats, 1)
SMHI.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(CCCmaCanRCM.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = CCCmaCanRCM.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
CCCmaCanRCM.remove_coord('latitude')
CCCmaCanRCM.remove_coord('longitude')
CCCmaCanRCM.remove_coord('grid_latitude')
CCCmaCanRCM.remove_coord('grid_longitude')
CCCmaCanRCM.add_dim_coord(lats, 1)
CCCmaCanRCM.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(CCCmaSMHI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = CCCmaSMHI.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
CCCmaSMHI.remove_coord('latitude')
CCCmaSMHI.remove_coord('longitude')
CCCmaSMHI.remove_coord('grid_latitude')
CCCmaSMHI.remove_coord('grid_longitude')
CCCmaSMHI.add_dim_coord(lats, 1)
CCCmaSMHI.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(CNRM.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = CNRM.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
CNRM.remove_coord('latitude')
CNRM.remove_coord('longitude')
CNRM.remove_coord('grid_latitude')
CNRM.remove_coord('grid_longitude')
CNRM.add_dim_coord(lats, 1)
CNRM.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(CNRMSMHI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = CNRMSMHI.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
CNRMSMHI.remove_coord('latitude')
CNRMSMHI.remove_coord('longitude')
CNRMSMHI.remove_coord('grid_latitude')
CNRMSMHI.remove_coord('grid_longitude')
CNRMSMHI.add_dim_coord(lats, 1)
CNRMSMHI.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(CSIRO.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = CSIRO.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
CSIRO.remove_coord('latitude')
CSIRO.remove_coord('longitude')
CSIRO.remove_coord('grid_latitude')
CSIRO.remove_coord('grid_longitude')
CSIRO.add_dim_coord(lats, 1)
CSIRO.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(ICHECDMI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = ICHECDMI.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
ICHECDMI.remove_coord('latitude')
ICHECDMI.remove_coord('longitude')
ICHECDMI.remove_coord('grid_latitude')
ICHECDMI.remove_coord('grid_longitude')
ICHECDMI.add_dim_coord(lats, 1)
ICHECDMI.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(ICHECCCLM.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = ICHECCCLM.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
ICHECCCLM.remove_coord('latitude')
ICHECCCLM.remove_coord('longitude')
ICHECCCLM.remove_coord('grid_latitude')
ICHECCCLM.remove_coord('grid_longitude')
ICHECCCLM.add_dim_coord(lats, 1)
ICHECCCLM.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(ICHECKNMI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = ICHECKNMI.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
ICHECKNMI.remove_coord('latitude')
ICHECKNMI.remove_coord('longitude')
ICHECKNMI.remove_coord('grid_latitude')
ICHECKNMI.remove_coord('grid_longitude')
ICHECKNMI.add_dim_coord(lats, 1)
ICHECKNMI.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(ICHECMPI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = ICHECMPI.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
ICHECMPI.remove_coord('latitude')
ICHECMPI.remove_coord('longitude')
ICHECMPI.remove_coord('grid_latitude')
ICHECMPI.remove_coord('grid_longitude')
ICHECMPI.add_dim_coord(lats, 1)
ICHECMPI.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(ICHECSMHI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = ICHECSMHI.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
ICHECSMHI.remove_coord('latitude')
ICHECSMHI.remove_coord('longitude')
ICHECSMHI.remove_coord('grid_latitude')
ICHECSMHI.remove_coord('grid_longitude')
ICHECSMHI.add_dim_coord(lats, 1)
ICHECSMHI.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(IPSL.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = IPSL.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
IPSL.remove_coord('latitude')
IPSL.remove_coord('longitude')
IPSL.remove_coord('grid_latitude')
IPSL.remove_coord('grid_longitude')
IPSL.add_dim_coord(lats, 1)
IPSL.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(MIROC.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = MIROC.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
MIROC.remove_coord('latitude')
MIROC.remove_coord('longitude')
MIROC.remove_coord('grid_latitude')
MIROC.remove_coord('grid_longitude')
MIROC.add_dim_coord(lats, 1)
MIROC.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(MOHCCCLM.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = MOHCCCLM.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
MOHCCCLM.remove_coord('latitude')
MOHCCCLM.remove_coord('longitude')
MOHCCCLM.remove_coord('grid_latitude')
MOHCCCLM.remove_coord('grid_longitude')
MOHCCCLM.add_dim_coord(lats, 1)
MOHCCCLM.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(MOHCKNMI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = MOHCKNMI.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
MOHCKNMI.remove_coord('latitude')
MOHCKNMI.remove_coord('longitude')
MOHCKNMI.remove_coord('grid_latitude')
MOHCKNMI.remove_coord('grid_longitude')
MOHCKNMI.add_dim_coord(lats, 1)
MOHCKNMI.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(MOHCSMHI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = MOHCSMHI.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
MOHCSMHI.remove_coord('latitude')
MOHCSMHI.remove_coord('longitude')
MOHCSMHI.remove_coord('grid_latitude')
MOHCSMHI.remove_coord('grid_longitude')
MOHCSMHI.add_dim_coord(lats, 1)
MOHCSMHI.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(MPICCLM.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = MPICCLM.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
MPICCLM.remove_coord('latitude')
MPICCLM.remove_coord('longitude')
MPICCLM.remove_coord('grid_latitude')
MPICCLM.remove_coord('grid_longitude')
MPICCLM.add_dim_coord(lats, 1)
MPICCLM.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(MPIREMO.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = MPIREMO.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
MPIREMO.remove_coord('latitude')
MPIREMO.remove_coord('longitude')
MPIREMO.remove_coord('grid_latitude')
MPIREMO.remove_coord('grid_longitude')
MPIREMO.add_dim_coord(lats, 1)
MPIREMO.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(MPISMHI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = MPISMHI.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
MPISMHI.remove_coord('latitude')
MPISMHI.remove_coord('longitude')
MPISMHI.remove_coord('grid_latitude')
MPISMHI.remove_coord('grid_longitude')
MPISMHI.add_dim_coord(lats, 1)
MPISMHI.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(NCCDMI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = NCCDMI.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
NCCDMI.remove_coord('latitude')
NCCDMI.remove_coord('longitude')
NCCDMI.remove_coord('grid_latitude')
NCCDMI.remove_coord('grid_longitude')
NCCDMI.add_dim_coord(lats, 1)
NCCDMI.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(NCCSMHI.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = NCCSMHI.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
NCCSMHI.remove_coord('latitude')
NCCSMHI.remove_coord('longitude')
NCCSMHI.remove_coord('grid_latitude')
NCCSMHI.remove_coord('grid_longitude')
NCCSMHI.add_dim_coord(lats, 1)
NCCSMHI.add_dim_coord(lons, 2)
lats = iris.coords.DimCoord(NOAA.coord('latitude').points[:,0], standard_name='latitude', units='degrees')
lons = NOAA.coord('longitude').points[0]
for i in range(len(lons)):
if lons[i]>100.:
lons[i] = lons[i]-360.
lons = iris.coords.DimCoord(lons, standard_name='longitude', units='degrees')
NOAA.remove_coord('latitude')
NOAA.remove_coord('longitude')
NOAA.remove_coord('grid_latitude')
NOAA.remove_coord('grid_longitude')
NOAA.add_dim_coord(lats, 1)
NOAA.add_dim_coord(lons, 2)
#-------------------------------------------------------------------------
#PART 2: OBSERVED DATA
#bring in all the files we need and give them a name
CRU= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/Actual_Data/cru_ts4.00.1901.2015.pre.dat.nc'
UDel= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/Actual_Data/UDel_precip.mon.total.v401.nc'
GPCC= '/exports/csce/datastore/geos/users/s0899345/Climate_Modelling/Actual_Data/ESRL_PSD_GPCC_precip.mon.combined.total.v7.nc'
#Load exactly one cube from given file
CRU = iris.load_cube(CRU, 'precipitation')
UDel = iris.load_cube(UDel)
GPCC = iris.load_cube(GPCC)
#-------------------------------------------------------------------------
#PART 3: FORMAT DATA TO BE PLOT SPECIFIC
#fix EARTH3 time units as they differ from all other models
t_coord=EARTH3.coord('time')
t_unit = t_coord.attributes['invalid_units']
timestep, _, t_fmt_str = t_unit.split(' ')
new_t_unit_str= '{} since 1850-01-01 00:00:00'.format(timestep)
new_t_unit = cf_units.Unit(new_t_unit_str, calendar=cf_units.CALENDAR_STANDARD)
new_datetimes = [datetime.datetime.strptime(str(dt), t_fmt_str) for dt in t_coord.points]
new_dt_points = [new_t_unit.date2num(new_dt) for new_dt in new_datetimes]
new_t_coord = iris.coords.DimCoord(new_dt_points, standard_name='time', units=new_t_unit)
t_coord_dim = EARTH3.coord_dims('time')
EARTH3.remove_coord('time')
EARTH3.add_dim_coord(new_t_coord,t_coord_dim)
#regrid all models to have same latitude and longitude system, all regridded to model with lowest resolution
CCCma = CCCma.regrid(CanESM2, iris.analysis.Linear())
CLMcom =CLMcom.regrid(CanESM2, iris.analysis.Linear())
DMI=DMI.regrid(CanESM2, iris.analysis.Linear())
KNMI=KNMI.regrid(CanESM2, iris.analysis.Linear())
MPIE=MPIE.regrid(CanESM2, iris.analysis.Linear())
SMHI=SMHI.regrid(CanESM2, iris.analysis.Linear())
CCCmaCanRCM = CCCmaCanRCM.regrid(CanESM2, iris.analysis.Linear())
CCCmaSMHI = CCCmaSMHI.regrid(CanESM2, iris.analysis.Linear())
CNRM =CNRM.regrid(CanESM2, iris.analysis.Linear())
CNRMSMHI =CNRMSMHI.regrid(CanESM2, iris.analysis.Linear())
CSIRO=CSIRO.regrid(CanESM2, iris.analysis.Linear())
ICHECDMI=ICHECDMI.regrid(CanESM2, iris.analysis.Linear())
ICHECCCLM=ICHECCCLM.regrid(CanESM2, iris.analysis.Linear())
ICHECKNMI=ICHECKNMI.regrid(CanESM2, iris.analysis.Linear())
ICHECMPI=ICHECMPI.regrid(CanESM2, iris.analysis.Linear())
ICHECSMHI=ICHECSMHI.regrid(CanESM2, iris.analysis.Linear())
IPSL=IPSL.regrid(CanESM2, iris.analysis.Linear())
MIROC=MIROC.regrid(CanESM2, iris.analysis.Linear())
MOHCCCLM=MOHCCCLM.regrid(CanESM2, iris.analysis.Linear())
MOHCKNMI=MOHCKNMI.regrid(CanESM2, iris.analysis.Linear())
MOHCSMHI=MOHCSMHI.regrid(CanESM2, iris.analysis.Linear())
MPICCLM=MPICCLM.regrid(CanESM2, iris.analysis.Linear())
MPIREMO=MPIREMO.regrid(CanESM2, iris.analysis.Linear())
MPISMHI=MPISMHI.regrid(CanESM2, iris.analysis.Linear())
NCCDMI=NCCDMI.regrid(CanESM2, iris.analysis.Linear())
NCCSMHI=NCCSMHI.regrid(CanESM2, iris.analysis.Linear())
NOAA=NOAA.regrid(CanESM2, iris.analysis.Linear())
#CanESM2 = CanESM2.regrid(CanESM2, iris.analysis.Linear())
CNRMG =CNRMG.regrid(CanESM2, iris.analysis.Linear())
MK3 =MK3.regrid(CanESM2, iris.analysis.Linear())
EARTH =EARTH.regrid(CanESM2, iris.analysis.Linear())
EARTH3 =EARTH3.regrid(CanESM2, iris.analysis.Linear())
GFDL=GFDL.regrid(CanESM2, iris.analysis.Linear())
HadGEM2=HadGEM2.regrid(CanESM2, iris.analysis.Linear())
IPSLG=IPSLG.regrid(CanESM2, iris.analysis.Linear())
MIROCG=MIROCG.regrid(CanESM2, iris.analysis.Linear())
MPI=MPI.regrid(CanESM2, iris.analysis.Linear())
NorESM1=NorESM1.regrid(CanESM2, iris.analysis.Linear())
CRU = CRU.regrid(CanESM2, iris.analysis.Linear())
UDel = UDel.regrid(CanESM2, iris.analysis.Linear())
GPCC = GPCC.regrid(CanESM2, iris.analysis.Linear())
#we are only interested in the latitude and longitude relevant to Malawi (has to be slightly larger than country boundary to take into account resolution of GCMs)
Malawi = iris.Constraint(longitude=lambda v: 32.0 <= v <= 36., latitude=lambda v: -17. <= v <= -8.)
CCCma = CCCma.extract(Malawi)
CLMcom =CLMcom.extract(Malawi)
DMI=DMI.extract(Malawi)
KNMI=KNMI.extract(Malawi)
MPIE=MPIE.extract(Malawi)
SMHI=SMHI.extract(Malawi)
CCCmaCanRCM = CCCmaCanRCM.extract(Malawi)
CCCmaSMHI = CCCmaSMHI.extract(Malawi)
CNRM =CNRM.extract(Malawi)
CNRMSMHI =CNRMSMHI.extract(Malawi)
CSIRO=CSIRO.extract(Malawi)
ICHECDMI=ICHECDMI.extract(Malawi)
ICHECCCLM=ICHECCCLM.extract(Malawi)
ICHECKNMI=ICHECKNMI.extract(Malawi)
ICHECMPI=ICHECMPI.extract(Malawi)
ICHECSMHI=ICHECSMHI.extract(Malawi)
IPSL=IPSL.extract(Malawi)
MIROC=MIROC.extract(Malawi)
MOHCCCLM=MOHCCCLM.extract(Malawi)
MOHCKNMI=MOHCKNMI.extract(Malawi)
MOHCSMHI=MOHCSMHI.extract(Malawi)
MPICCLM=MPICCLM.extract(Malawi)
MPIREMO=MPIREMO.extract(Malawi)
MPISMHI=MPISMHI.extract(Malawi)
NCCDMI=NCCDMI.extract(Malawi)
NCCSMHI=NCCSMHI.extract(Malawi)
NOAA=NOAA.extract(Malawi)
CanESM2 = CanESM2.extract(Malawi)
CNRMG =CNRMG.extract(Malawi)
MK3 =MK3.extract(Malawi)
EARTH =EARTH.extract(Malawi)
EARTH3 =EARTH3.extract(Malawi)
GFDL=GFDL.extract(Malawi)
HadGEM2=HadGEM2.extract(Malawi)
IPSLG=IPSLG.extract(Malawi)
MIROCG=MIROCG.extract(Malawi)
MPI=MPI.extract(Malawi)
NorESM1=NorESM1.extract(Malawi)
CRU = CRU.extract(Malawi)
UDel = UDel.extract(Malawi)
GPCC = GPCC.extract(Malawi)
#time constraignt to make all series the same, for ERAINT this is 1990-2008 and for RCMs and GCMs this is 1961-2005
iris.FUTURE.cell_datetime_objects = True
t_constraint_ERAINT = iris.Constraint(time=lambda cell: 1990 <= cell.point.year <= 2008)
CCCma = CCCma.extract(t_constraint_ERAINT)
CLMcom =CLMcom.extract(t_constraint_ERAINT)
DMI=DMI.extract(t_constraint_ERAINT)
KNMI=KNMI.extract(t_constraint_ERAINT)
MPIE=MPIE.extract(t_constraint_ERAINT)
SMHI=SMHI.extract(t_constraint_ERAINT)
CRUE = CRU.extract(t_constraint_ERAINT)
UDelE = UDel.extract(t_constraint_ERAINT)
GPCCE = GPCC.extract(t_constraint_ERAINT)
t_constraint = iris.Constraint(time=lambda cell: 1961 <= cell.point.year <= 2005)
CCCmaCanRCM = CCCmaCanRCM.extract(t_constraint)
CCCmaSMHI = CCCmaSMHI.extract(t_constraint)
CNRM =CNRM.extract(t_constraint)
CNRMSMHI =CNRMSMHI.extract(t_constraint)
CSIRO=CSIRO.extract(t_constraint)
ICHECDMI=ICHECDMI.extract(t_constraint)
ICHECCCLM=ICHECCCLM.extract(t_constraint)
ICHECKNMI=ICHECKNMI.extract(t_constraint)
ICHECMPI=ICHECMPI.extract(t_constraint)
ICHECSMHI=ICHECSMHI.extract(t_constraint)
IPSL=IPSL.extract(t_constraint)
MIROC=MIROC.extract(t_constraint)
MOHCCCLM=MOHCCCLM.extract(t_constraint)
MOHCKNMI=MOHCKNMI.extract(t_constraint)
MOHCSMHI=MOHCSMHI.extract(t_constraint)
MPICCLM=MPICCLM.extract(t_constraint)
MPIREMO=MPIREMO.extract(t_constraint)
MPISMHI=MPISMHI.extract(t_constraint)
NCCDMI=NCCDMI.extract(t_constraint)
NCCSMHI=NCCSMHI.extract(t_constraint)
NOAA=NOAA.extract(t_constraint)
CanESM2 = CanESM2.extract(t_constraint)
CNRMG =CNRMG.extract(t_constraint)
MK3 =MK3.extract(t_constraint)
EARTH =EARTH.extract(t_constraint)
EARTH3 =EARTH3.extract(t_constraint)
GFDL=GFDL.extract(t_constraint)
HadGEM2=HadGEM2.extract(t_constraint)
IPSLG=IPSLG.extract(t_constraint)
MIROCG=MIROCG.extract(t_constraint)
MPI=MPI.extract(t_constraint)
NorESM1=NorESM1.extract(t_constraint)
CRU = CRU.extract(t_constraint)
UDel = UDel.extract(t_constraint)
GPCC = GPCC.extract(t_constraint)
#Convert units to match, CORDEX data in precipitation_flux (kg m-2 s-1) but want all data in precipitation rate (mm month-1).
#Since 1 kg of rain spread over 1 m of surface is 1mm in thickness, and there are 60*60*24*365.25=31557600 seconds in a year and 12 months in the year, the conversion is:
Convert=31557600/12
CCCma = CCCma*Convert
CLMcom=CLMcom*Convert
DMI=DMI*Convert
KNMI=KNMI*Convert
MPIE=MPIE*Convert
SMHI=SMHI*Convert
CCCmaCanRCM = CCCmaCanRCM*Convert
CCCmaSMHI = CCCmaSMHI*Convert
CNRM = CNRM*Convert
CNRMSMHI = CNRMSMHI*Convert
CSIRO = CSIRO*Convert
ICHECDMI = ICHECDMI*Convert
ICHECCCLM = ICHECCCLM*Convert
ICHECKNMI = ICHECKNMI*Convert
ICHECMPI = ICHECMPI*Convert
ICHECSMHI = ICHECSMHI*Convert
IPSL = IPSL*Convert
MIROC = MIROC*Convert
MOHCCCLM = MOHCCCLM*Convert
MOHCKNMI = MOHCKNMI*Convert
MOHCSMHI = MOHCSMHI*Convert
MPICCLM = MPICCLM*Convert
MPIREMO = MPIREMO*Convert
MPISMHI = MPISMHI*Convert
NCCDMI = NCCDMI*Convert
NCCSMHI = NCCSMHI*Convert
NOAA = NOAA*Convert
CanESM2 = CanESM2*Convert
CNRMG = CNRMG*Convert
MK3=MK3*Convert
EARTH=EARTH*Convert
EARTH3=EARTH3*Convert
GFDL=GFDL*Convert
HadGEM2=HadGEM2*Convert
IPSLG = IPSLG*Convert
MIROCG = MIROCG*Convert
MPI=MPI*Convert
NorESM1=NorESM1*Convert
#Convert units to match, UDel data in cm per month but want precipitation rate in mm per month.
#Since there are 10mm in a cm, the conversion is:
Convert=10
UDelE =UDelE*Convert
UDel =UDel*Convert
#add month numbers to data
iriscc.add_month_number(CCCma, 'time')
iriscc.add_month_number(CLMcom, 'time')
iriscc.add_month_number(DMI, 'time')
iriscc.add_month_number(KNMI, 'time')
iriscc.add_month_number(MPIE, 'time')
iriscc.add_month_number(SMHI, 'time')
iriscc.add_month_number(CCCmaCanRCM, 'time')
iriscc.add_month_number(CCCmaSMHI, 'time')
iriscc.add_month_number(CNRM, 'time')
iriscc.add_month_number(CNRMSMHI, 'time')
iriscc.add_month_number(CSIRO, 'time')
iriscc.add_month_number(ICHECDMI, 'time')
iriscc.add_month_number(ICHECCCLM, 'time')
iriscc.add_month_number(ICHECKNMI, 'time')
iriscc.add_month_number(ICHECMPI, 'time')
iriscc.add_month_number(ICHECSMHI, 'time')
iriscc.add_month_number(IPSL, 'time')
iriscc.add_month_number(MIROC, 'time')
iriscc.add_month_number(MOHCCCLM, 'time')
iriscc.add_month_number(MOHCKNMI, 'time')
iriscc.add_month_number(MOHCSMHI, 'time')
iriscc.add_month_number(MPICCLM, 'time')
iriscc.add_month_number(MPIREMO, 'time')
iriscc.add_month_number(MPISMHI, 'time')
iriscc.add_month_number(NCCDMI, 'time')
iriscc.add_month_number(NCCSMHI, 'time')
iriscc.add_month_number(NOAA, 'time')
iriscc.add_month_number(CanESM2, 'time')
iriscc.add_month_number(CNRMG, 'time')
iriscc.add_month_number(MK3, 'time')
iriscc.add_month_number(EARTH, 'time')
iriscc.add_month_number(EARTH3, 'time')
iriscc.add_month_number(GFDL, 'time')
iriscc.add_month_number(HadGEM2, 'time')
iriscc.add_month_number(IPSLG, 'time')
iriscc.add_month_number(MIROCG, 'time')
iriscc.add_month_number(MPI, 'time')
iriscc.add_month_number(NorESM1, 'time')
iriscc.add_month_number(CRUE, 'time')
iriscc.add_month_number(CRU, 'time')
iriscc.add_month_number(UDelE, 'time')
iriscc.add_month_number(UDel, 'time')
iriscc.add_month_number(GPCCE, 'time')
iriscc.add_month_number(GPCC, 'time')
#We are interested in plotting the data by month, so we need to take a mean of all the data by month
CCCma = CCCma.aggregated_by('month_number', iris.analysis.MEAN)
CLMcom = CLMcom.aggregated_by('month_number', iris.analysis.MEAN)
DMI = DMI.aggregated_by('month_number', iris.analysis.MEAN)
KNMI = KNMI.aggregated_by('month_number', iris.analysis.MEAN)
MPIE = MPI.aggregated_by('month_number', iris.analysis.MEAN)
SMHI = SMHI.aggregated_by('month_number', iris.analysis.MEAN)
CCCmaCanRCM = CCCmaCanRCM.aggregated_by('month_number', iris.analysis.MEAN)
CCCmaSMHI = CCCmaSMHI.aggregated_by('month_number', iris.analysis.MEAN)
CNRM = CNRM.aggregated_by('month_number', iris.analysis.MEAN)
CNRMSMHI = CNRMSMHI.aggregated_by('month_number', iris.analysis.MEAN)
CSIRO = CSIRO.aggregated_by('month_number', iris.analysis.MEAN)
ICHECDMI = ICHECDMI.aggregated_by('month_number', iris.analysis.MEAN)
ICHECCCLM = ICHECCCLM.aggregated_by('month_number', iris.analysis.MEAN)
ICHECKNMI = ICHECKNMI.aggregated_by('month_number', iris.analysis.MEAN)
ICHECMPI = ICHECMPI.aggregated_by('month_number', iris.analysis.MEAN)
ICHECSMHI = ICHECSMHI.aggregated_by('month_number', iris.analysis.MEAN)
IPSL = IPSL.aggregated_by('month_number', iris.analysis.MEAN)
MIROC = MIROC.aggregated_by('month_number', iris.analysis.MEAN)
MOHCCCLM = MOHCCCLM.aggregated_by('month_number', iris.analysis.MEAN)
MOHCKNMI = MOHCKNMI.aggregated_by('month_number', iris.analysis.MEAN)
MOHCSMHI = MOHCSMHI.aggregated_by('month_number', iris.analysis.MEAN)
MPICCLM = MPICCLM.aggregated_by('month_number', iris.analysis.MEAN)
MPIREMO = MPIREMO.aggregated_by('month_number', iris.analysis.MEAN)
MPISMHI = MPISMHI.aggregated_by('month_number', iris.analysis.MEAN)
NCCDMI = NCCDMI.aggregated_by('month_number', iris.analysis.MEAN)
NCCSMHI = NCCSMHI.aggregated_by('month_number', iris.analysis.MEAN)
NOAA = NOAA.aggregated_by('month_number', iris.analysis.MEAN)
CanESM2 = CanESM2.aggregated_by('month_number', iris.analysis.MEAN)
CNRMG = CNRMG.aggregated_by('month_number', iris.analysis.MEAN)
MK3 = MK3.aggregated_by('month_number', iris.analysis.MEAN)
EARTH = EARTH.aggregated_by('month_number', iris.analysis.MEAN)
EARTH3 = EARTH3.aggregated_by('month_number', iris.analysis.MEAN)
GFDL = GFDL.aggregated_by('month_number', iris.analysis.MEAN)
HadGEM2 = HadGEM2.aggregated_by('month_number', iris.analysis.MEAN)
IPSLG = IPSLG.aggregated_by('month_number', iris.analysis.MEAN)
MIROCG = MIROCG.aggregated_by('month_number', iris.analysis.MEAN)
MPI = MPI.aggregated_by('month_number', iris.analysis.MEAN)
NorESM1 = NorESM1.aggregated_by('month_number', iris.analysis.MEAN)
CRUE = CRUE.aggregated_by('month_number', iris.analysis.MEAN)
CRU = CRU.aggregated_by('month_number', iris.analysis.MEAN)
UDelE = UDelE.aggregated_by('month_number', iris.analysis.MEAN)
UDel = UDel.aggregated_by('month_number', iris.analysis.MEAN)
GPCCE = GPCCE.aggregated_by('month_number', iris.analysis.MEAN)
GPCC = GPCC.aggregated_by('month_number', iris.analysis.MEAN)
#Returns an array of area weights, with the same dimensions as the cube.
CCCma_grid_areas = iris.analysis.cartography.area_weights(CCCma)
CLMcom_grid_areas = iris.analysis.cartography.area_weights(CLMcom)
DMI_grid_areas = iris.analysis.cartography.area_weights(DMI)
KNMI_grid_areas = iris.analysis.cartography.area_weights(KNMI)
MPIE_grid_areas = iris.analysis.cartography.area_weights(MPIE)
SMHI_grid_areas = iris.analysis.cartography.area_weights(SMHI)
CCCmaCanRCM_grid_areas = iris.analysis.cartography.area_weights(CCCmaCanRCM)
CCCmaSMHI_grid_areas = iris.analysis.cartography.area_weights(CCCmaSMHI)
CNRM_grid_areas = iris.analysis.cartography.area_weights(CNRM)
CNRMSMHI_grid_areas = iris.analysis.cartography.area_weights(CNRMSMHI)
CSIRO_grid_areas = iris.analysis.cartography.area_weights(CSIRO)
ICHECDMI_grid_areas = iris.analysis.cartography.area_weights(ICHECDMI)
ICHECCCLM_grid_areas = iris.analysis.cartography.area_weights(ICHECCCLM)
ICHECKNMI_grid_areas = iris.analysis.cartography.area_weights(ICHECKNMI)
ICHECMPI_grid_areas = iris.analysis.cartography.area_weights(ICHECMPI)
ICHECSMHI_grid_areas = iris.analysis.cartography.area_weights(ICHECSMHI)
IPSL_grid_areas = iris.analysis.cartography.area_weights(IPSL)
MIROC_grid_areas = iris.analysis.cartography.area_weights(MIROC)
MOHCCCLM_grid_areas = iris.analysis.cartography.area_weights(MOHCCCLM)
MOHCKNMI_grid_areas = iris.analysis.cartography.area_weights(MOHCKNMI)
MOHCSMHI_grid_areas = iris.analysis.cartography.area_weights(MOHCSMHI)
MPICCLM_grid_areas = iris.analysis.cartography.area_weights(MPICCLM)
MPIREMO_grid_areas = iris.analysis.cartography.area_weights(MPIREMO)
MPISMHI_grid_areas = iris.analysis.cartography.area_weights(MPISMHI)
NCCDMI_grid_areas = iris.analysis.cartography.area_weights(NCCDMI)
NCCSMHI_grid_areas = iris.analysis.cartography.area_weights(NCCSMHI)
NOAA_grid_areas = iris.analysis.cartography.area_weights(NOAA)
CanESM2_grid_areas = iris.analysis.cartography.area_weights(CanESM2)
CNRMG_grid_areas = iris.analysis.cartography.area_weights(CNRMG)
MK3_grid_areas = iris.analysis.cartography.area_weights(MK3)
EARTH_grid_areas = iris.analysis.cartography.area_weights(EARTH)
EARTH3_grid_areas = iris.analysis.cartography.area_weights(EARTH3)
GFDL_grid_areas = iris.analysis.cartography.area_weights(GFDL)
HadGEM2_grid_areas = iris.analysis.cartography.area_weights(HadGEM2)
IPSLG_grid_areas = iris.analysis.cartography.area_weights(IPSLG)
MIROCG_grid_areas = iris.analysis.cartography.area_weights(MIROCG)
MPI_grid_areas = iris.analysis.cartography.area_weights(MPI)
NorESM1_grid_areas = iris.analysis.cartography.area_weights(NorESM1)
CRUE_grid_areas = iris.analysis.cartography.area_weights(CRUE)
CRU_grid_areas = iris.analysis.cartography.area_weights(CRU)
UDelE_grid_areas = iris.analysis.cartography.area_weights(UDelE)
UDel_grid_areas = iris.analysis.cartography.area_weights(UDel)
GPCCE_grid_areas = iris.analysis.cartography.area_weights(GPCCE)
GPCC_grid_areas = iris.analysis.cartography.area_weights(GPCC)
#We want to plot the mean for the whole region so we need a mean of all the lats and lons
CCCma_mean = CCCma.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CCCma_grid_areas)
CLMcom_mean = CLMcom.collapsed(['latitude', 'longitude'],iris.analysis.MEAN, weights=CLMcom_grid_areas)
DMI_mean = DMI.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=DMI_grid_areas)
KNMI_mean = KNMI.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=KNMI_grid_areas)
MPIE_mean = MPIE.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPIE_grid_areas)
SMHI_mean = SMHI.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=SMHI_grid_areas)
CCCmaCanRCM_mean = CCCmaCanRCM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CCCmaCanRCM_grid_areas)
CCCmaSMHI_mean = CCCmaSMHI.collapsed(['latitude', 'longitude'],iris.analysis.MEAN, weights=CCCmaSMHI_grid_areas)
CNRM_mean = CNRM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CNRM_grid_areas)
CNRMSMHI_mean = CNRMSMHI.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CNRMSMHI_grid_areas)
CSIRO_mean = CSIRO.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CSIRO_grid_areas)
ICHECDMI_mean = ICHECDMI.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECDMI_grid_areas)
ICHECCCLM_mean = ICHECCCLM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECCCLM_grid_areas)
ICHECKNMI_mean = ICHECKNMI.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECKNMI_grid_areas)
ICHECMPI_mean = ICHECMPI.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECMPI_grid_areas)
ICHECSMHI_mean = ICHECSMHI.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=ICHECSMHI_grid_areas)
IPSL_mean = IPSL.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=IPSL_grid_areas)
MIROC_mean = MIROC.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MIROC_grid_areas)
MOHCCCLM_mean = MOHCCCLM.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MOHCCCLM_grid_areas)
MOHCKNMI_mean = MOHCKNMI.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MOHCKNMI_grid_areas)
MOHCSMHI_mean = MOHCSMHI.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MOHCSMHI_grid_areas)
MPICCLM_mean = MPICCLM.collapsed(['latitude', 'longitude'],iris.analysis.MEAN, weights=MPICCLM_grid_areas)
MPIREMO_mean = MPIREMO.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPIREMO_grid_areas)
MPISMHI_mean = MPISMHI.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPISMHI_grid_areas)
NCCDMI_mean = NCCDMI.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NCCDMI_grid_areas)
NCCSMHI_mean = NCCSMHI.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NCCSMHI_grid_areas)
NOAA_mean = NOAA.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NOAA_grid_areas)
CanESM2_mean = CanESM2.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CanESM2_grid_areas)
CNRMG_mean = CNRMG.collapsed(['latitude', 'longitude'],iris.analysis.MEAN, weights=CNRMG_grid_areas)
MK3_mean = MK3.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MK3_grid_areas)
EARTH_mean = EARTH.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=EARTH_grid_areas)
EARTH3_mean = EARTH3.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=EARTH3_grid_areas)
GFDL_mean = GFDL.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=GFDL_grid_areas)
HadGEM2_mean = HadGEM2.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=HadGEM2_grid_areas)
IPSLG_mean = IPSLG.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=IPSLG_grid_areas)
MIROCG_mean = MIROCG.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MIROCG_grid_areas)
MPI_mean = MPI.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=MPI_grid_areas)
NorESM1_mean = NorESM1.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=NorESM1_grid_areas)
CRUE_mean = CRUE.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CRUE_grid_areas)
CRU_mean = CRU.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CRU_grid_areas)
UDelE_mean = UDelE.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=UDelE_grid_areas)
UDel_mean = UDel.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=UDel_grid_areas)
GPCCE_mean = GPCCE.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=GPCCE_grid_areas)
GPCC_mean = GPCC.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=GPCC_grid_areas)
#Create averages
AverageEY = (CCCma_mean.data + CLMcom_mean.data + DMI_mean.data + KNMI_mean.data + MPIE_mean.data + SMHI_mean.data)/6.
AverageRY = (CCCmaCanRCM_mean.data + CCCmaSMHI_mean.data + CNRM_mean.data + CNRMSMHI_mean.data + CSIRO_mean.data + ICHECDMI_mean.data + ICHECCCLM_mean.data + ICHECKNMI_mean.data + ICHECMPI_mean.data + ICHECSMHI_mean.data + IPSL_mean.data + MIROC_mean.data + MOHCCCLM_mean.data + MOHCKNMI_mean.data + MOHCSMHI_mean.data + MPICCLM_mean.data + MPIREMO_mean.data + MPISMHI_mean.data + NCCDMI_mean.data + NCCSMHI_mean.data + NOAA_mean.data)/21.
AverageGY = (CanESM2_mean.data + CNRMG_mean.data + MK3_mean.data + EARTH_mean.data + EARTH3_mean.data + GFDL_mean.data + HadGEM2_mean.data + IPSLG_mean.data + MIROCG_mean.data + MPI_mean.data + NorESM1_mean.data )/11.
ObsEY = (CRUE_mean.data + UDelE_mean.data + GPCCE_mean.data)/3.
ObsY = (CRU_mean.data + UDel_mean.data + GPCC_mean.data)/3.
X = np.arange(1,13,1)
#-------------------------------------------------------------------------
#PART 4: PLOT LINE GRAPH
#PART 4A: ERAINT Models Line Graph
#set x-axis ticks
plt.xticks(range(12), calendar.month_abbr[0:12])
#assign the line colours and set x axis to 'month' rather than 'time'
plt.plot(X,CRUE_mean.data, lw=1, color='grey')
plt.plot(X,UDelE_mean.data, lw=1, color='grey')
plt.plot(X,GPCCE_mean.data, lw=1, color='grey')
plt.fill_between(X,CRUE_mean.data,UDelE_mean.data, color='grey', alpha='0.5')
plt.fill_between(X,CRUE_mean.data,GPCCE_mean.data, color='grey', alpha='0.5')
plt.fill_between(X,GPCCE_mean.data,UDelE_mean.data, color='grey', alpha='0.5')
qplt.plot(CCCma_mean.coord('month_number'), CCCma_mean, label='CanRCM4_ERAINT', lw=1.5, color='blue')
qplt.plot(CLMcom_mean.coord('month_number'), CLMcom_mean, label='CCLM4-8-17_ERAINT', lw=1.5, color='green')
qplt.plot(DMI_mean.coord('month_number'), DMI_mean, label='HIRHAM5_ERAINT', lw=1.5, color='red')
qplt.plot(KNMI_mean.coord('month_number'), KNMI_mean, label='RACMO22T_ERAINT', lw=1.5, color='cyan')
qplt.plot(MPIE_mean.coord('month_number'), MPIE_mean, label='REMO2009_ERAINT', lw=1.5, color='magenta')
qplt.plot(SMHI_mean.coord('month_number'), SMHI_mean, label='RCA4_ERAINT', lw=1.5, color='yellow')
plt.plot(X, ObsEY, label='Observed', lw=3, color='black')
plt.plot(X, AverageEY, label='Average ERAINT', lw=3, color='grey')
#set a title for the y axis
plt.ylabel('Precipitation Rate (mm per month)')
#create a legend and set its location to under the graph
plt.legend(loc="upper center", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=2)
#create a title
plt.title('Pr for Malawi by Month 1990-2008', fontsize=11)
#add grid lines
plt.grid()
#save the image of the graph and include full legend
plt.savefig('Pr_ERAINT_LineGraph_Monthly', bbox_inches='tight')
#show the graph in the console
iplt.show()
#PART 4B: Regional Climate Models Line Graph
#set x-axis ticks
plt.xticks(range(12), calendar.month_abbr[0:12])
#assign the line colours and set x axis to 'month' rather than 'time'
plt.plot(X,CRU_mean.data, lw=1, color='grey')
plt.plot(X,UDel_mean.data, lw=1, color='grey')
plt.plot(X,GPCC_mean.data, lw=1, color='grey')
plt.fill_between(X,CRU_mean.data,UDel_mean.data, color='grey', alpha='0.5')
plt.fill_between(X,CRU_mean.data,GPCC_mean.data, color='grey', alpha='0.5')
plt.fill_between(X,GPCC_mean.data,UDel_mean.data, color='grey', alpha='0.5')
qplt.plot(CCCmaCanRCM_mean.coord('month_number'), CCCmaCanRCM_mean, label='CanRCM4_CanESM2', lw=1.5, color='blue')
qplt.plot(CCCmaSMHI_mean.coord('month_number'), CCCmaSMHI_mean, label='RCA4_CanESM2', lw=1.5, color='green')
qplt.plot(CNRM_mean.coord('month_number'), CNRM_mean, label='CCLM4-8-17_CNRM-CM5', lw=1.5, color='red')
qplt.plot(CNRMSMHI_mean.coord('month_number'), CNRMSMHI_mean, label='RCA4_CNRM-CM5', lw=1.5, color='cyan')
qplt.plot(CSIRO_mean.coord('month_number'), CSIRO_mean, label='RCA4_CSIRO-MK3', lw=1.5, color='magenta')
qplt.plot(ICHECDMI_mean.coord('month_number'), ICHECDMI_mean, label='HIRHAM5_EC-EARTH', lw=1.5, color='yellow')
qplt.plot(ICHECCCLM_mean.coord('month_number'), ICHECCCLM_mean, label='CCLM4-8-17_EC-EARTH ', lw=1.5, color='blue', linestyle='--')
qplt.plot(ICHECKNMI_mean.coord('month_number'), ICHECKNMI_mean, label='RACMO22T_EC-EARTH', lw=1.5, color='green', linestyle='--')
qplt.plot(ICHECMPI_mean.coord('month_number'), ICHECMPI_mean, label='REMO2009_EC-EARTH ', lw=1.5, color='red', linestyle='--')
qplt.plot(ICHECSMHI_mean.coord('month_number'), ICHECSMHI_mean, label='RCA4_EC-EARTH', lw=1.5, color='cyan', linestyle='--')
qplt.plot(IPSL_mean.coord('month_number'), IPSL_mean, label='RCA4_IPSL-CM5A-MR', lw=1.5, color='magenta', linestyle='--')
qplt.plot(MIROC_mean.coord('month_number'), MIROC_mean, label='RCA4_MIROC5', lw=1.5, color='yellow', linestyle='--')
qplt.plot(MOHCCCLM_mean.coord('month_number'), MOHCCCLM_mean, label='CCLM4-8-17_HadGEM2-ES', lw=1.5, color='blue', linestyle='-.')
qplt.plot(MOHCKNMI_mean.coord('month_number'), MOHCKNMI_mean, label='RACMO22T_HadGEM2-ES', lw=1.5, color='green', linestyle='-.')
qplt.plot(MOHCSMHI_mean.coord('month_number'), MOHCSMHI_mean, label='RCA4_HadGEM2-ES', lw=1.5, color='red', linestyle='-.')
qplt.plot(MPICCLM_mean.coord('month_number'), MPICCLM_mean, label='CCLM4-8-17_MPI-ESM-LR', lw=1.5, color='cyan', linestyle='-.')
qplt.plot(MPIREMO_mean.coord('month_number'), MPIREMO_mean, label='REMO2009_MPI-ESM-LR ', lw=1.5, color='magenta', linestyle='-.')
qplt.plot(MPISMHI_mean.coord('month_number'), MPISMHI_mean, label='RCA4_MPI-ESM-LR ', lw=1.5, color='yellow', linestyle='-.')
qplt.plot(NCCDMI_mean.coord('month_number'), NCCDMI_mean, label='HIRHAM5_NorESM1-M', lw=1.5, color='blue', linestyle=':')
qplt.plot(NCCSMHI_mean.coord('month_number'), NCCSMHI_mean, label='RCA4_NorESM1-M', lw=1.5, color='green', linestyle=':')