def read_textfile(datadir='/home/mw/project/', filename='Global_Net_Anomaly_Timeseries_12monthMean.txt', skip=1): f = open(datadir + filename, "r") lines = f.readlines() header = lines[skip-1] keys = header.split() f.close() ncols = len(lines[-1].split()) # Get number of columns from the last line.. nrows = len(lines) - skip # Get number of data lines in text file data = np.zeros([nrows, ncols]) # Convention of rows then columns for Numpy arrays (?) for jj in range(nrows): for ii in range(ncols): data[jj, ii] = float(lines[jj + skip].split()[ii]) data_dict = dict.fromkeys(keys) for kk, key in enumerate(keys): data_dict[key] = data[:, kk] return data_dict
# Include full model names as specified in Loeb et al (2020): # https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019GL086705 Net_dict['EC-Earth3-Veg'] = Net_dict['EC-Earth3'] SW_dict['EC-Earth3-Veg'] = SW_dict['EC-Earth3'] LW_dict['EC-Earth3-Veg'] = LW_dict['EC-Earth3']
c_obs = 'black'# Line color for CERES observations c_mean = 'darkred'# Line color for multi-model mean lw_model = 1.0 # Linewidth for individual CMIP models
text1 = """The ensemble mean of available CMIP6 climate models tracks the observed energy budget changes when forced with observed sea surface temperatures"""
xmin, xmax = 2000., 2018 ymin, ymax = -1.3, 1.3
plt.figure(1) f = plt.gcf() #f.set_size_inches(6.,8.) matplotlib.rcParams['font.size']=9 matplotlib.rcParams['axes.linewidth']=0.5 # set the value globally
yr = Net_dict['yr'] # In alphabetical order.. models = ['CanESM5','CESM2', 'EC-Earth3-Veg', 'ECHAM6.3', 'GFDL-AM4', 'HadGEM3', 'IPSL-CM6A']
# Panel A = SW radiation anomaly comparison ax = plt.subplot(3, 1, 1) plt.plot([xmin,xmax], [0., 0.], 'grey', linewidth=0.75) # Plot zero line for model in models: color = RGB_dict[model] # Adopt Chapter 7 convention of +ve downwards plt.plot(yr, -SW_dict[model], label=None, color=color, linewidth=lw_model) plt.plot(yr, -SW_dict['multimodel'], label='Model mean', color=c_mean, linewidth=3.0) plt.plot(yr, -SW_dict['CERES'], label='CERES observations', color=c_obs, linewidth=3.0) plt.xlim(xmin, xmax) plt.ylim(ymin, ymax) #ax.set_xticklabels('') ax.yaxis.set_ticks_position('both') ax.tick_params(width=0.5) plt.ylabel('W m$^{-2}$') #plt.title('a) Global mean reflected solar flux anomaly') plt.title('a) Global mean solar flux anomaly') plt.legend(loc='upper left', frameon=False, fontsize=8, ncol=4)
# Panel B = LW radiation anomaly comparison ax = plt.subplot(3, 1, 2) plt.plot([xmin,xmax], [0., 0.], 'grey', linewidth=0.75) # Plot zero line for model in models: color = RGB_dict[model] # Adopt Chapter 7 convention of +ve downwards plt.plot(yr, -LW_dict[model], label=model, color=color, linewidth=lw_model) plt.plot(yr, -LW_dict['multimodel'], label=None, color=c_mean, linewidth=3.0) plt.plot(yr, -LW_dict['CERES'], label=None, color=c_obs, linewidth=3.0) plt.xlim(xmin, xmax) plt.ylim(ymin, ymax) #ax.set_xticklabels('') ax.yaxis.set_ticks_position('both') ax.tick_params(width=0.5) plt.ylabel('W m$^{-2}$') #plt.title('b) Global mean emitted thermal flux anomaly') plt.title('b) Global mean thermal flux anomaly') plt.legend(loc='upper left', frameon=False, fontsize=8, ncol=4) #plt.legend(loc='lower left', frameon=False, fontsize=8, ncol=4)
# Panel C = Net radiation anomaly comparison ax = plt.subplot(3, 1, 3) plt.plot([xmin,xmax], [0., 0.], 'grey', linewidth=0.75) # Plot zero line for model in models: color = RGB_dict[model] plt.plot(yr, Net_dict[model], label=None, color=color, linewidth=lw_model) plt.plot(yr, Net_dict['multimodel'], label=None, color=c_mean, linewidth=3.0) plt.plot(yr, Net_dict['CERES'], label=None, color=c_obs, linewidth=3.0) plt.xlim(xmin, xmax) plt.ylim(ymin, ymax) plt.text(2000.5, 1.22, text1, fontsize=8, va='top') #plt.text(2000.5, 1.1, text2, fontsize=8) #ax.set_xticklabels('') ax.yaxis.set_ticks_position('both') ax.tick_params(width=0.5) plt.ylabel('W m$^{-2}$') plt.title('c) Global mean net flux anomaly') #plt.legend(loc='upper left', frameon=False, fontsize=8, ncol=4) plt.tight_layout()
