presentation figs

Dark_Scripts/plot_MSFigure_1_DARK.py

@@ -124,6 +124,8 @@ def read_obs_dataset(variq,dataset_obs,numOfEns,lensalso,randomalso,ravelyearsbi
 yearstrend_obsh,linetrend_obsh,indexslopeNegative_obsh,classes_obsh = HA.calc_HiatusAcc(obsm,trendlength,yearsobs,AGWstart,SLOPEthreshh_o,'hiatus',diff_o)
 yearstrend_mh,linetrend_mh,indexslopeNegative_mh,classes_mh = HA.calc_HiatusAcc(modelsm,trendlength,yearsall,AGWstart,SLOPEthreshh_m,'hiatus',diff_o)
 
+
+sys.exit()
 ##############################################################################
 ##############################################################################
 ##############################################################################

Dark_Scripts/plot_Observations_FutureHiatus_DARK.py

@@ -0,0 +1,180 @@
+"""
+Plots a time series of monthly temperatures in the Arctic
+
+Author : Zachary Labe
+Date : 20 January 2020
+"""
+
+import matplotlib.pyplot as plt
+from mpl_toolkits.basemap import Basemap, addcyclic, shiftgrid
+import numpy as np
+import cmocean
+import cmasher as cmr
+import palettable.cubehelix as cm
+from netCDF4 import Dataset
+import nclcmaps as ncm
+import scipy.stats as sts
+import calc_Utilities as UT
+
+### Define constants
+directorydata = '/Users/zlabe/Data/ERA5/monthly/'       
+directorydataoutput = '/Users/zlabe/Documents/SciComm/ERA5_ClimateMaps/Data/'            
+directoryfigure = '/Users/zlabe/Documents/Projects/predictGMSTrate/Dark_Figures/'
+allyears = np.arange(1950,2021+1,1)
+monq = [r'JAN',r'FEB',r'MAR',r'APR',r'MAY',r'JUN',r'JUL',r'AUG',r'SEP',r'OCT',r'NOV',r'DEC']
+
+### Read in data
+years = np.arange(1979,2021+1,1)
+data = Dataset(directorydata + 'T2M_1979-2021.nc')
+lat = data.variables['latitude'][:]
+lon = data.variables['longitude'][:]
+tempq = np.nanmean(data.variables['T2M'][:],axis=1) # for ERA5T
+data.close()
+
+### Selecting 2020 and 2021 data to add to 1979-2019 file
+empty = np.empty((years.shape[0]*12-len(tempq),lat.shape[0],lon.shape[0]))
+empty[:] = np.nan
+yr2021 = np.append(tempq[-12+len(empty):,:,:],empty,axis=0)
+temp = np.reshape(tempq[:-12+len(empty)],(tempq.shape[0]//12,12,lat.shape[0],lon.shape[0]))
+tempqq = np.append(temp,yr2021[np.newaxis,:,:,:],axis=0)
+recent20202021 = tempqq[-2:,:,:,:]
+
+### Read in data
+years50 = np.arange(1950,2019+1,1)
+data = Dataset(directorydata + 'T2M_1950-2019.nc')
+lats = data.variables['latitude'][:]
+lons = data.variables['longitude'][:]
+tempold = data.variables['T2M'][:]
+data.close()
+tempshape = np.reshape(tempold,(years50.shape[0],12,lats.shape[0],lons.shape[0]))
+
+### Combine all data
+alldata = np.append(tempshape,recent20202021,axis=0) - 273.15
+alldata = np.asarray(alldata)
+
+### Calculate anomalies
+base = np.where((allyears >= 1951) & (allyears <= 1980))[0]
+climo = np.nanmean(alldata[base,:,:,:],axis=0)
+anom = alldata - climo
+
+### Calculate annual mean
+anomyr = np.nanmean(anom[:,:,:,:],axis=1)
+
+### Calculate GMST
+lon2,lat2 = np.meshgrid(lon,lat)
+ave = UT.calc_weightedAve(anomyr,lat2)
+
+### Select 1990
+AGWyr = 1990
+yr90 = np.where((allyears >= 1990))[0]
+ave90 = ave[yr90][:-1]
+years90 = allyears[yr90]
+
+### Final points
+finaltwo = ave[-2:]
+
+###############################################################################
+###############################################################################
+###############################################################################
+### Read in data for observations
+directorydata = '/Users/zlabe/Documents/Research/GmstTrendPrediction/Data/'
+savename = 'ANNv2_OHC100_hiatus_relu_L2_0.5_LR_0.001_Batch128_Iters500_2x30_SegSeed24120_NetSeed87750_EnsembleMeanRemoved'
+predict_obs = np.genfromtxt(directorydata + 'obsLabels_' + savename + '.txt')
+actual_obs = np.genfromtxt(directorydata + 'obsActualLabels_' + savename + '.txt')
+confidence = np.genfromtxt(directorydata + 'obsConfid_' + savename + '.txt')
+
+where_hiatusq = np.where(predict_obs == 1)[0]
+where_hiatus = years90[where_hiatusq]
+
+trendlength = 10 
+hiatusSLOPE = 0.01
+typeOfTrend = 'hiatus'
+data = ave90
+yearsnew = np.arange(1990,2020+1,1)
+### Calculate trend periods
+yearstrend = np.empty((len(yearsnew)-trendlength+1,trendlength))
+datatrend = np.empty((len(yearsnew)-trendlength+1,trendlength))
+for hi in range(len(yearsnew)-(trendlength-1)):
+    yearstrend[hi,:] = np.arange(yearsnew[hi],yearsnew[hi]+trendlength,1)
+    datatrend[hi,:] = data[hi:hi+trendlength]
+
+### Calculate trend lines    
+linetrend = np.empty((len(yearsnew)-trendlength+1,2))
+for hi in range(len(yearsnew)-trendlength+1):         
+    linetrend[hi,:] = np.polyfit(yearstrend[hi],datatrend[hi],1)
+
+### Count number of hiatus or acceleration periods
+slope = linetrend[:,0]     
+indexslopeNegative = np.where((slope[:] <= hiatusSLOPE))[0]
+print('INDEX OF **%s**---->' % typeOfTrend,indexslopeNegative)
+
+###############################################################################
+###############################################################################
+###############################################################################               
+### Plot Figure
+plt.rc('text',usetex=True)
+plt.rc('font',**{'family':'sans-serif','sans-serif':['Avant Garde']}) 
+plt.rc('savefig',facecolor='black')
+plt.rc('axes',edgecolor='darkgrey')
+plt.rc('xtick',color='darkgrey')
+plt.rc('ytick',color='darkgrey')
+plt.rc('axes',labelcolor='darkgrey')
+plt.rc('axes',facecolor='black')
+
+### Adjust axes in time series plots 
+def adjust_spines(ax, spines):
+    for loc, spine in ax.spines.items():
+        if loc in spines:
+            spine.set_position(('outward', 5))
+        else:
+            spine.set_color('none')  
+    if 'left' in spines:
+        ax.yaxis.set_ticks_position('left')
+    else:
+        ax.yaxis.set_ticks([])
+
+    if 'bottom' in spines:
+        ax.xaxis.set_ticks_position('bottom')
+    else:
+        ax.xaxis.set_ticks([]) 
+
+fig = plt.figure(figsize=(9,6))
+ax = plt.subplot(211)
+
+adjust_spines(ax, ['left', 'bottom'])            
+ax.spines['top'].set_color('none')
+ax.spines['right'].set_color('none')
+ax.spines['bottom'].set_linewidth(2)
+ax.spines['left'].set_linewidth(2)
+ax.tick_params('both',length=5.5,width=2,which='major',labelsize=6)
+
+plt.fill_between(x=years90[-11:-1],y1=0,y2=1.1,facecolor='darkgrey',zorder=0,
+             alpha=0.3,edgecolor='none')
+
+plt.plot(years90[:-1],ave90,linewidth=5,color='crimson',zorder=1,clip_on=False)
+plt.plot(years90[-2:],finaltwo,linewidth=5,color='crimson',zorder=2,
+         clip_on=False,linestyle='--',dashes=(0.5,0.5))
+plt.scatter(years90[-1],finaltwo[-1],marker='o',s=50,zorder=3,
+            color='crimson',clip_on=False)
+for hi in range(len(linetrend)):
+    if linetrend[hi,0] < 0.01:
+        cc = 'deepskyblue'
+        ll = 1.5
+    else:
+        cc = 'w'
+        ll = 0.4
+    plt.plot(yearstrend[hi,:],linetrend[hi,0]*yearstrend[hi,:]+linetrend[hi,1],
+         color=cc,linewidth=ll)
+
+plt.xticks(np.arange(1950,2040,5),np.arange(1950,2040,5))
+plt.yticks(np.arange(-5,5.1,0.1),map(str,np.round(np.arange(-5,5.1,0.1),2))) 
+plt.xlim([1990,2029])
+plt.ylim([0.2,1.1])
+
+plt.ylabel(r'\textbf{GMST Anomaly ($\bf{^\circ}$C)}',fontsize=8,
+                      color='w')
+
+plt.tight_layout()
+plt.subplots_adjust(hspace=0.4)
+
+plt.savefig(directoryfigure + 'GMST_obs-future_hiatus.png',dpi=600)