Updates to intercomparison figures

common_grid.py

@@ -284,12 +284,12 @@ def common_grid (roms_file, cice_file, out_file):
         # First calculate the two derivatives
         dsvstr_dx = ma.empty(shape(svstr_common))
         # Forward difference approximation
-        dsvstr_dx[:,:-1] = (svstr_common[:,1:] - svstr_common[:,:-1])/(2*dx[:,:-1])
+        dsvstr_dx[:,:-1] = (svstr_common[:,1:] - svstr_common[:,:-1])/dx[:,:-1]
         # Backward difference for the last row
-        dsvstr_dx[:,-1] = (svstr_common[:,-1] - svstr_common[:,-2])/(2*dx[:,-1])
+        dsvstr_dx[:,-1] = (svstr_common[:,-1] - svstr_common[:,-2])/dx[:,-1]
         dsustr_dy = ma.empty(shape(sustr_common))
-        dsustr_dy[:-1,:] = (sustr_common[1:,:] - sustr_common[:-1,:])/(2*dy[:-1,:])
-        dsustr_dy[-1,:] = (sustr_common[-1,:] - sustr_common[-2,:])/(2*dy[-1,:])
+        dsustr_dy[:-1,:] = (sustr_common[1:,:] - sustr_common[:-1,:])/dy[:-1,:]
+        dsustr_dy[-1,:] = (sustr_common[-1,:] - sustr_common[-2,:])/dy[-1,:]
         curl_str = dsvstr_dx - dsustr_dy
         curl_str = ma.masked_where(mask_common==0, curl_str)
         # Write to file

depoorter_data

@@ -1,21 +1,22 @@
-Name		Mass loss	Melt rate
-Amery		39 +/- 21	0.65 +/- 0.35
-West		26 +/- 13	1.65 +/- 0.82
-Shackleton	76 +/- 23	2.20 +/- 0.67
+Name		Mass loss	Melt rate		Rignot mass loss
+Amery		39 +/- 21	0.65 +/- 0.35		35.5 +/- 23
+West		26 +/- 13	1.65 +/- 0.82		27.2 +/- 10
+Shackleton	76 +/- 23	2.20 +/- 0.67		72.6 +/- 15
 Totten		64 +/- 12	9.89 +/- 1.92
 Moscow Uni	28 +/- 7	4.93 +/- 1.32
-Mertz		5 +/- 4		0.87 +/- 0.79
-Ross		34 +/- 25	0.07 +/- 0.05
-Sulzberger	28 +/- 6	2.16 +/- 0.44
-Getz		136 +/- 23	4.09 +/- 0.68
-Thwaites	69 +/- 18	15.22 +/- 3.87
-Pine Island	95 +/- 14	15.96 +/- 2.38
-Abbot		86 +/- 22	2.72 +/- 0.7
-George VI	144 +/- 42	2.88 +/- 0.83
-Filchner-Ronne	50 +/- 40	0.12 +/- 0.09
-Brunt-RL	26 +/- 16	0.33 +/- 0.20
-Jelbart-Fimbul	24 +/- 13	0.52 +/- 0.27
-Total		1454 +/- 174	0.94 +/- 0.11
+Totten & MU	92 +/- 19				90.6 +/- 8
+Mertz		5 +/- 4		0.87 +/- 0.79		7.9 +/- 3
+Ross		34 +/- 25	0.07 +/- 0.05		47.7 +/- 34
+Sulzberger	28 +/- 6	2.16 +/- 0.44		18.2 +/- 3
+Getz		136 +/- 23	4.09 +/- 0.68		144.9 +/- 14
+Thwaites	69 +/- 18	15.22 +/- 3.87		97.5 +/- 7
+Pine Island	95 +/- 14	15.96 +/- 2.38		101.2 +/- 8
+Abbot		86 +/- 22	2.72 +/- 0.7		51.8 +/- 19
+George VI	144 +/- 42	2.88 +/- 0.83		135.4 +/- 40 (with Wilkins & Stange)
+Filchner-Ronne	50 +/- 40	0.12 +/- 0.09		155.4 +/- 45
+Brunt-RL	26 +/- 16	0.33 +/- 0.20		9.7 +/- 16
+Jelbart-Fimbul	24 +/- 13	0.52 +/- 0.27		26.8 +/- 14
+Total		1454 +/- 174	0.94 +/- 0.11		
 
 Notes:
 Mertz a fair bit lower

mip_aice_minmax_nsidc.py

@@ -360,14 +360,14 @@ def mip_aice_minmax_nsidc (cice_file, cice_log, fesom_mesh_path, fesom_output_di
     ax.set_yticks([])
     # Add a colourbar at the bottom
     cbaxes = fig.add_axes([0.12, 0.04, 0.3, 0.04])
-    cbar = colorbar(img, orientation='horizontal', ticks=arange(0,1+0.25,0.25), cax=cbaxes)
+    cbar = colorbar(img, orientation='horizontal', ticks=arange(0,1+0.25,0.25), cax=cbaxes, extend='min')
     cbar.ax.tick_params(labelsize=20)
     # Add extent timeseries on rightmost column, with more space for labels
     gs2 = GridSpec(2, 1)
     gs2.update(left=0.73, right=0.95, wspace=0.1, hspace=0.15)
     # February
     ax = subplot(gs2[0, 0])
-    ax.plot(time_axis, nsidc_feb_extent, color='red', linewidth=1.5)
+    ax.plot(time_axis, nsidc_feb_extent, color='black', linewidth=2, linestyle='dashed')
     ax.plot(time_axis, cice_feb_extent, color='blue', linewidth=1.5)
     ax.plot(time_axis, fesom_feb_extent, color='green', linewidth=1.5)
     xlim([start_year, end_year])
@@ -379,7 +379,7 @@ def mip_aice_minmax_nsidc (cice_file, cice_log, fesom_mesh_path, fesom_output_di
     title('b) Sea ice extent\n'+r'(million km$^2$)', fontsize=26)
     # Extent timeseries, September
     ax = subplot(gs2[1, 0])
-    ax.plot(time_axis, nsidc_sep_extent, color='red', label='NSIDC', linewidth=1.5)
+    ax.plot(time_axis, nsidc_sep_extent, color='black', label='NSIDC', linewidth=2, linestyle='dashed')
     ax.plot(time_axis, cice_sep_extent, color='blue', label='MetROMS', linewidth=1.5)
     ax.plot(time_axis, fesom_sep_extent, color='green', label='FESOM (high-res)', linewidth=1.5)
     xlim([start_year, end_year])

mip_dpt_calc.py

@@ -71,11 +71,11 @@ def mip_dpt_calc (roms_log, fesom_log_low, fesom_log_high):
     fesom_dpt_low = fesom_dpt_low[t_start_fesom:t_end_fesom]
     fesom_dpt_high = fesom_dpt_high[t_start_fesom:t_end_fesom]
 
-    # Calculate and print averages
+    # Calculate and print averages and standard deviations
     print 'Average Drake Passage Transport'
-    print 'MetROMS: ' + str(mean(roms_dpt))
-    print 'FESOM low-res: ' + str(mean(fesom_dpt_low))
-    print 'FESOM high-res: ' + str(mean(fesom_dpt_high))
+    print 'MetROMS: ' + str(mean(roms_dpt)) + ' +/- ' + str(std(roms_dpt))
+    print 'FESOM low-res: ' + str(mean(fesom_dpt_low)) + ' +/- ' + str(std(fesom_dpt_low))
+    print 'FESOM high-res: ' + str(mean(fesom_dpt_high)) + ' +/- ' + str(std(fesom_dpt_high))
 
     # Calculate and print trends
     # Also print p-values so we can see if it's statistically significant