/
russell18jgr-fig6a.ncl
713 lines (647 loc) · 29 KB
/
russell18jgr-fig6a.ncl
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
; #############################################################################
;
; russell18jgr_fig6a.ncl
;
; Based on Figure 6a - Russell, J.L.,et al., 2018, J. Geophysical Research –
; Oceans, 123, 3120-3143. https://doi.org/10.1002/2017JC013461 (figure 6a)
;
; Author: Pandde Amarjiit (University of Arizona, USA)
; Russell Joellen (University of Arizona, USA)
; Goodman Paul (University of Arizona, USA)
;
; #############################################################################
; Description
;
; - Calculates the time average of thetao, so and vo
; - Regrids temperature and salinity onto the vo grid
; - Calculate the potential density of all cells relative to
; 0, 2000, 4000 decibars (or 1977m and 3948m)
; - extracts the volcello of the lat closest to 30S
; - divide volcello by latitudinal distance between two data points on
; same lon, this gives us north-south cross-sectional area of each grid
; - volume transport(m^3/s) in each cell is calculated by vo(m/s)
; * cross sectional area(m^2)
; - make lxx variables that have 1 for specific density layer and
; missing values in rest
; - multiply lxx with volume transport per cell to get volume transport
; of cells in that layer (lvxx)
; - total sum of lvxx gives the net volume transported in that density layer
; - plots the volume transport per layer as a bar chart
;
; Density layers defined as per : (Talley, L.D., 2003. Shallow, intermediate
; and deep overturning components of the global heat budget.
; Journal of Physical Oceanography 33, 530–560)
;
; Required Preprocessor attributes ( no_preprocessor )
; - None (no preprocessing required)
; in the recipe do not keep a preprocessor in variable section
;
; Required diag_script_info attributes
;
; styleset = "CMIP5" - default
; ncdf = "default"
;
; Optional diag_script_info attributes (diagnostic specific)
;
; Caveats
;
; - MIROC-ESM and BNU-ESM do not work as depth variable is not called lev
; - MRI models does not work as the data has 0 as fillvalue instead of 1e+20
; - CCSM4 and CESM1-CAM5 dont work as the units for so is 1,
; which is not accepted by ESMValTool
; - Transport is very small in case of NorESM1-M and ME as volcello
; values look incorrect(very small)
;
;
; Modification history
; 20190510 - russell_joellen, pandde_amarjiit - written and
; implemented for ESMValTool v2.
;
; #############################################################################
load "$diag_scripts/../interface_scripts/interface.ncl" ; load metadata
load "$diag_scripts/shared/plot/style.ncl" ; load plot style functions
load "$diag_scripts/shared/plot/aux_plotting.ncl"
begin
enter_msg(DIAG_SCRIPT, "")
vo_items = select_metadata_by_name(input_file_info, "vo")
so_items = select_metadata_by_name(input_file_info, "so")
thetao_items = select_metadata_by_name(input_file_info, "thetao")
volcello_items = select_metadata_by_name(input_file_info, "volcello")
vo_datasets = metadata_att_as_array(vo_items, "dataset")
start_years_data = metadata_att_as_array(vo_items, "start_year")
end_years_data = metadata_att_as_array(vo_items, "end_year")
vo_inputfile_paths = metadata_att_as_array(vo_items, "filename")
thetao_inputfile_paths = metadata_att_as_array(thetao_items, "filename")
so_inputfile_paths = metadata_att_as_array(so_items, "filename")
nDatasets = ListCount(vo_items)
nVolcello = ListCount(volcello_items)
nVariables = ListCount(variable_info)
dim_models = dimsizes(vo_datasets)
if (nVolcello .ne. nDatasets) then
error_msg("f", "russell18jgr-fig6a.ncl", " ", "volcello files " + \
"for russell18jgr-fig6a.ncl do not match with vo datasets." + \
" Please do not add additional variables in variable groups ")
end if
end
begin
plotpath = config_user_info@plot_dir + "Russell_figure-6a_" \
+ sprinti("%0.4i", min(toint(start_years_data))) + "-" \
+ sprinti("%0.4i", max(toint(end_years_data)))
system("mkdir -p " + config_user_info@work_dir)
system("mkdir -p " + config_user_info@plot_dir)
wks = gsn_open_wks(output_type(), plotpath)
plots = new(nDatasets, graphic)
plot_1 = new(nDatasets, graphic)
plot_talley = new(nDatasets, graphic)
y_val = (/-0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5/)
y1_val = fspan(0.125, 10.875, 44)
yaxis_labels = (/-1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11/)
talley_SO_Zonal_all_Levels = (/0.0, -7.34, -1.9, 9.71, 2.37, -5.86, -10.02, \
-11.11, -2.84, 9.96, 16.49, 0.51 /)
res = True ; resources for plotting
res@gsnXYBarChart = True
res@gsnDraw = False
res@gsnFrame = False
res@vpXF = 0.1
res@vpYF = 0.75
res@vpHeightF = 0.5
res@vpWidthF = 0.4
res@gsnMaximize = True
res@trYReverse = True
res@tmXBTickStartF = -20.
res@tmXBTickSpacingF = 4.
res@tmXBTickEndF = 20.
res@tmXBMode = "Manual"
res@tmXBMinorPerMajor = 1
res@trXMinF = -20.
res@trXMaxF = 20.
res@trYMinF = -1.
res@trYMaxF = 11.
res@gsnXRefLine = 0.0
res@gsnXYBarChartColors2 = ("red")
res@xyLineColors = (/"black", "black"/)
res@tmYLMode = "Explicit"
res@tmYLLabelsOn = True
res@tmYLValues = yaxis_labels
res@tmYLLabelFont = 5
res@tmYLLabelFontHeightF = 0.008
res@tmYLLabels = (/"~F0~Net~F~", "~F0~Surface~F~", "26.10s~B~0", \
"26.40s~B~0", "26.90s~B~0", "27.10s~B~0", "27.40s~B~0", \
"36.8s~B~2", "45.8s~B~4", "45.86s~B~4", "45.92s~B~4", \
"46.0s~B~4", "~F0~Bottom~F~" /)
res@gsnRightStringFontHeightF = 0.009
res@gsnLeftStringFontHeightF = 0.009
do iii = 0, dim_models - 1
fx_var = read_data(volcello_items[iii])
if (all(ismissing(fx_var))) then
; to give fatal error if volcello is missing
fx_variable = "volcello"
error_msg("f", "russell18jgr-fig6.ncl", " ", "volcello file for " \
+ vo_datasets(iii) \
+ " not found in the metadata file, please add "\
+ "'fx_files: [volcello]' to the variable dictionary in the " \
+ "recipe or add the location of file to input directory " \
+ "in config-user.yml ")
end if
dataset_so_time = read_data(so_items[iii])
dataset_so = dim_avg_n_Wrap(dataset_so_time, 0)
delete(dataset_so_time)
dataset_thetao_time = read_data(thetao_items[iii])
dataset_thetao = dim_avg_n_Wrap(dataset_thetao_time, 0)
delete(dataset_thetao_time)
if (max(dataset_thetao) .gt. 250) then ; making sure temperature is in K
dataset_thetao = dataset_thetao - 273.15
end if
rho0 = new(2, double)
assignFillValue(dataset_thetao, rho0)
vo_file = addfile(vo_inputfile_paths(iii), "r")
thetao_file = addfile(thetao_inputfile_paths(iii), "r")
var_test_lat = vo_file->lat ; extracting lat array of vo
var_test_lon = vo_file->lon ; extracting lon array of vo
if((iscoord(dataset_so, "lat"))) then
a = closest_val(-30.0, var_test_lat)
; getting index for lat closest to 30S
exact_lat = var_test_lat(a) ; lat value closest to -30
; interpolate thetao and so onto vo grid
theta_inter = linint1_n_Wrap(dataset_thetao&lat, dataset_thetao, \
False, exact_lat, 0, 1)
theta_new = linint1_n_Wrap(dataset_thetao&lon, theta_inter, \
True, var_test_lon, 0, 2)
delete(theta_inter)
delete(dataset_thetao)
so_inter = linint1_n_Wrap(dataset_so&lat, dataset_so, False, \
exact_lat, 0, 1)
so_new = linint1_n_Wrap(dataset_so&lon, so_inter, True, \
var_test_lon, 0, 2)
delete(dataset_so)
delete(so_inter)
; potential density calculation
rho0_30s = rho_mwjf(theta_new(:, 0, :), so_new(:, 0, :), 0.0)
rho2_30s = rho_mwjf(theta_new(:, 0, :), so_new(:, 0, :), 1977.0)
rho4_30s = rho_mwjf(theta_new(:, 0, :), so_new(:, 0, :), 3948.0)
delete(theta_new)
delete(so_new)
elseif (iscoord(dataset_so, "rlat")) then
theta_lat = thetao_file->lat ; extracting lat array of thetao
theta_lon = thetao_file->lon ; extracting lon arrays of thetao
lat1 = closest_val(-15.0, var_test_lat(:, 0))
; index value of vo-lat closest to -10
lat2 = closest_val(-45.0, var_test_lat(:, 0))
; index value of vo-lat closest to -50
a = closest_val(-30.0, var_test_lat(:, 0))
; index value of vo-lat closest to -30
exact_lat = var_test_lat(a, 0) ; lat value closest to -30
; interpolate thetao and so onto vo grid
theta_inter = linint1_n_Wrap(theta_lat(lat2:lat1, 1), \
dataset_thetao(:, lat2:lat1, :), False, \
exact_lat, 0, 1)
delete(dataset_thetao)
so_inter = linint1_n_Wrap(theta_lat(lat2:lat1, 1), \
dataset_so(:, lat2:lat1, :), False, \
exact_lat, 0, 1)
delete(dataset_so)
; checking for monotonic nature of lon arrays, as NCL
; needs monotonically increasing lon for interpolations.
if ((isMonotonic(theta_lon(a, :)) .eq. 1) .and. \
(isMonotonic(var_test_lon(a, :)) .eq. 1)) then
theta_new = linint1_n_Wrap(theta_lon(a, :), theta_inter, \
True, var_test_lon(a, :), 0, 2)
else
theta_new = theta_inter
end if
delete(theta_inter)
if ((isMonotonic(theta_lon(a, :)) .eq. 1) .and. \
(isMonotonic(var_test_lon(a, :)) .eq. 1)) then
so_new = linint1_n_Wrap(theta_lon(a, :), so_inter, True, \
var_test_lon(a, :), 0, 2)
else
so_new = so_inter
end if
delete(theta_lat)
delete(theta_lon)
delete(so_inter)
; potential density calculation
rho0_30s = rho_mwjf(theta_new(:, 0, :), so_new(:, 0, :), 0.)
rho2_30s = rho_mwjf(theta_new(:, 0, :), so_new(:, 0, :), 1977.0)
rho4_30s = rho_mwjf(theta_new(:, 0, :), so_new(:, 0, :), 3948.0)
delete(theta_new)
delete(so_new)
else
theta_lat = thetao_file->lat ; extracting lat array of thetao
theta_lon = thetao_file->lon ; extracting lon arrays of thetao
lat1 = closest_val(-15.0, var_test_lat(:, 0))
; index value of vo-lat closest to -10
lat2 = closest_val(-45.0, var_test_lat(:, 0))
; index value of vo-lat closest to -50
a = closest_val(-30.0, var_test_lat(:, 0))
; index value of vo-lat closest to -30
exact_lat = var_test_lat(a, 0) ; lat value closest to -30
; interpolate thetao and so onto vo grid
theta_inter = linint1_n_Wrap(theta_lat(lat2:lat1, 1), \
dataset_thetao(:, lat2:lat1, :), False, \
exact_lat, 0, 1)
delete(dataset_thetao)
so_inter = linint1_n_Wrap(theta_lat(lat2:lat1, 1), \
dataset_so(:, lat2:lat1, :), False, \
exact_lat, 0, 1)
delete(dataset_so)
; checking for monotonic nature of lon arrays, as NCL needs
; monotonically increasing lon for interpolations.
if ((isMonotonic(theta_lon(a, :)) .eq. 1) .and. \
(isMonotonic(var_test_lon(a, :)) .eq. 1)) then
theta_new = linint1_n_Wrap(theta_lon(a, :), theta_inter, True, \
var_test_lon(a, :), 0, 2)
else
theta_new = theta_inter
end if
if ((isMonotonic(theta_lon(a, :)) .eq. 1) .and. \
(isMonotonic(var_test_lon(a, :)) .eq. 1)) then
so_new = linint1_n_Wrap(theta_lon(a, :), so_inter, True, \
var_test_lon(a, :), 0, 2)
else
so_new = so_inter
end if
delete(theta_inter)
delete(so_inter)
delete(theta_lat)
delete(theta_lon)
; potential density calculation
rho0_30s = rho_mwjf(theta_new(:, 0, :), so_new(:, 0, :), 0.)
rho2_30s = rho_mwjf(theta_new(:, 0, :), so_new(:, 0, :), 1977.0)
rho4_30s = rho_mwjf(theta_new(:, 0, :), so_new(:, 0, :), 3948.0)
delete(theta_new)
delete(so_new)
end if
dataset_vo_time = read_data(vo_items[iii])
dataset_vo = dim_avg_n_Wrap(dataset_vo_time, 0)
delete(dataset_vo_time)
delete(var_test_lon)
volcello = fx_var
delete(fx_var)
; transport calculation
if(iscoord(dataset_vo, "lat")) then
var_tmp2 = dataset_vo(:, a, :) ; y velocity of lat closest to -30
dlat = tofloat(abs(dataset_vo&lat(a) - dataset_vo&lat(a + 1)) \
* 6.37e06 * 0.0174533)
; dlat is the north-south distance between 2 consecutive data latitudes
volumecello_3d = volcello/dlat
; north-south crossectional area is volcello / dlat
if(abs(volcello&lev(0)) .gt. abs(volcello&lev(1))) then
; extracting volume of cells closest to 30S
volcello_2d = volumecello_3d(::-1, a, :)
; reversing lev if it is in descending order.
else
volcello_2d = volumecello_3d(:, a, :)
end if
transportpercell = var_tmp2 * volcello_2d / (10 ^ 6)
; unit conversion from m^3/s to sverdrups
copy_VarCoords(var_tmp2, transportpercell)
elseif (iscoord(dataset_vo, "rlat")) then
var_tmp2 = dataset_vo(:, a, :) ; y transport of lat closest to -30
drlat = tofloat(abs(var_test_lat(a, 0) - var_test_lat(a+1, 0)) \
* 6.37e06 * 0.0174533)
; drlat is north-south distance between 2 consecutive data latitudes
volumecello_3d = volcello/drlat
; north-south crossectional area is volcello / drlat
if(abs(volcello&lev(0)) .gt. abs(volcello&lev(1))) then
; extracting volume of cells closest to 30S
volcello_2d = volumecello_3d(::-1, a, :)
; reversing lev if it is in descending order.
else
volcello_2d = volumecello_3d(:, a, :)
end if
transportpercell = var_tmp2 * volcello_2d / (10 ^ 6)
; unit conversion from m^3/s to sverdrups
copy_VarCoords(var_tmp2, transportpercell)
else
var_tmp2 = dataset_vo(:, a, :) ; y transport of lat closest to 30S
dlat = tofloat(abs(var_test_lat(a, 0) - var_test_lat(a + 1, 0)) \
* 6.37e06 * 0.0174533)
; dlat is the north-south distance between 2 consecutive data latitudes
volumecello_3d = volcello / dlat
; north-south crossectional area is volcello / dlat
if(abs(volcello&lev(1)) .gt. abs(volcello&lev(2))) then
; extracting volume of cells closest to 30S
volcello_2d = volumecello_3d(::-1, a, :)
; reversing lev if it is in descending order.
else
volcello_2d = volumecello_3d(:, a, :)
end if
transportpercell = var_tmp2 * volcello_2d / (10 ^ 6)
; unit conversion from m^3/s to sverdrups
copy_VarCoords(var_tmp2, transportpercell)
end if
delete(volcello)
delete(var_test_lat)
delete(dataset_vo)
delete(volcello_2d)
delete(volumecello_3d)
delete(var_tmp2)
rho0_30s = 1000. * (rho0_30s - 1.0)
rho2_30s = 1000. * (rho2_30s - 1.0)
rho4_30s = 1000. * (rho4_30s - 1.0)
; making masks for density levels based on : Talley, L.D., 2003.
l10 = where((rho0_30s .lt. 26.10), 1, rho0@_FillValue)
l11 = where((rho0_30s .lt. 24.900), 1, rho0@_FillValue)
l12 = where((rho0_30s .ge. 24.900) .and. (rho0_30s .lt. 25.300), 1, \
rho0@_FillValue)
l13 = where((rho0_30s .ge. 25.300) .and. (rho0_30s .lt. 25.700), 1, \
rho0@_FillValue)
l14 = where((rho0_30s .ge. 25.700) .and. (rho0_30s .lt. 26.100), 1, \
rho0@_FillValue)
l20 = where((rho0_30s .ge. 26.100) .and. (rho0_30s .lt. 26.400), 1, \
rho0@_FillValue)
l21 = where((rho0_30s .ge. 26.100) .and. (rho0_30s .lt. 26.175), 1, \
rho0@_FillValue)
l22 = where((rho0_30s .ge. 26.175) .and. (rho0_30s .lt. 26.250), 1, \
rho0@_FillValue)
l23 = where((rho0_30s .ge. 26.250) .and. (rho0_30s .lt. 26.325), 1, \
rho0@_FillValue)
l24 = where((rho0_30s .ge. 26.325) .and. (rho0_30s .lt. 26.400), 1, \
rho0@_FillValue)
l30 = where((rho0_30s .ge. 26.400) .and. (rho0_30s .lt. 26.900), 1, \
rho0@_FillValue)
l31 = where((rho0_30s .ge. 26.400) .and. (rho0_30s .lt. 26.525), 1, \
rho0@_FillValue)
l32 = where((rho0_30s .ge. 26.525) .and. (rho0_30s .lt. 26.650), 1, \
rho0@_FillValue)
l33 = where((rho0_30s .ge. 26.650) .and. (rho0_30s .lt. 26.775), 1, \
rho0@_FillValue)
l34 = where((rho0_30s .ge. 26.775) .and. (rho0_30s .lt. 26.900), 1, \
rho0@_FillValue)
l40 = where((rho0_30s .ge. 26.900) .and. (rho0_30s .lt. 27.100), 1, \
rho0@_FillValue)
l41 = where((rho0_30s .ge. 26.900) .and. (rho0_30s .lt. 26.950), 1, \
rho0@_FillValue)
l42 = where((rho0_30s .ge. 26.950) .and. (rho0_30s .lt. 27.000), 1, \
rho0@_FillValue)
l43 = where((rho0_30s .ge. 27.000) .and. (rho0_30s .lt. 27.050), 1, \
rho0@_FillValue)
l44 = where((rho0_30s .ge. 27.050) .and. (rho0_30s .lt. 27.100), 1, \
rho0@_FillValue)
l50 = where((rho0_30s .ge. 27.100) .and. (rho0_30s .lt. 27.400), 1, \
rho0@_FillValue)
l51 = where((rho0_30s .ge. 27.100) .and. (rho0_30s .lt. 27.175), 1, \
rho0@_FillValue)
l52 = where((rho0_30s .ge. 27.175) .and. (rho0_30s .lt. 27.250), 1, \
rho0@_FillValue)
l53 = where((rho0_30s .ge. 27.250) .and. (rho0_30s .lt. 27.325), 1, \
rho0@_FillValue)
l54 = where((rho0_30s .ge. 27.325) .and. (rho0_30s .lt. 27.400), 1, \
rho0@_FillValue)
l60 = where((rho0_30s .ge. 27.400) .and. (rho2_30s .lt. 36.800), 1, \
rho0@_FillValue)
l61 = where((rho0_30s .ge. 27.400) .and. (rho0_30s .lt. 27.500), 1, \
rho0@_FillValue)
l62 = where((rho0_30s .ge. 27.500) .and. (rho2_30s .lt. 36.700), 1, \
rho0@_FillValue)
l63 = where((rho2_30s .ge. 36.700) .and. (rho2_30s .lt. 36.750), 1, \
rho0@_FillValue)
l64 = where((rho2_30s .ge. 36.750) .and. (rho2_30s .lt. 36.800), 1, \
rho0@_FillValue)
l70 = where((rho2_30s .ge. 36.800) .and. (rho4_30s .lt. 45.800), 1, \
rho0@_FillValue)
l71 = where((rho2_30s .ge. 36.800) .and. (rho2_30s .lt. 36.850), 1, \
rho0@_FillValue)
l72 = where((rho2_30s .ge. 36.850) .and. (rho2_30s .lt. 36.900), 1, \
rho0@_FillValue)
l73 = where((rho2_30s .ge. 36.900) .and. (rho2_30s .lt. 36.950), 1, \
rho0@_FillValue)
l74 = where((rho2_30s .ge. 36.950) .and. (rho4_30s .lt. 45.800), 1, \
rho0@_FillValue)
l80 = where((rho4_30s .ge. 45.800) .and. (rho4_30s .lt. 45.860), 1, \
rho0@_FillValue)
l81 = where((rho4_30s .ge. 45.800) .and. (rho4_30s .lt. 45.815), 1, \
rho0@_FillValue)
l82 = where((rho4_30s .ge. 45.815) .and. (rho4_30s .lt. 45.830), 1, \
rho0@_FillValue)
l83 = where((rho4_30s .ge. 45.830) .and. (rho4_30s .lt. 45.845), 1, \
rho0@_FillValue)
l84 = where((rho4_30s .ge. 45.845) .and. (rho4_30s .lt. 45.860), 1, \
rho0@_FillValue)
l90 = where((rho4_30s .ge. 45.860) .and. (rho4_30s .lt. 45.920), 1, \
rho0@_FillValue)
l91 = where((rho4_30s .ge. 45.860) .and. (rho4_30s .lt. 45.875), 1, \
rho0@_FillValue)
l92 = where((rho4_30s .ge. 45.875) .and. (rho4_30s .lt. 45.890), 1, \
rho0@_FillValue)
l93 = where((rho4_30s .ge. 45.890) .and. (rho4_30s .lt. 45.905), 1, \
rho0@_FillValue)
l94 = where((rho4_30s .ge. 45.905) .and. (rho4_30s .lt. 45.920), 1, \
rho0@_FillValue)
l100 = where((rho4_30s .ge. 45.920) .and. (rho4_30s .lt. 46.000), 1, \
rho0@_FillValue)
l101 = where((rho4_30s .ge. 45.920) .and. (rho4_30s .lt. 45.940), 1, \
rho0@_FillValue)
l102 = where((rho4_30s .ge. 45.940) .and. (rho4_30s .lt. 45.960), 1, \
rho0@_FillValue)
l103 = where((rho4_30s .ge. 45.960) .and. (rho4_30s .lt. 45.980), 1, \
rho0@_FillValue)
l104 = where((rho4_30s .ge. 45.980) .and. (rho4_30s .lt. 46.000), 1, \
rho0@_FillValue)
l110 = where((rho4_30s .ge. 46.000), 1, rho0@_FillValue)
l111 = where((rho4_30s .ge. 46.000) .and. (rho4_30s .lt. 46.050), 1, \
rho0@_FillValue)
l112 = where((rho4_30s .ge. 46.050) .and. (rho4_30s .lt. 46.100), 1, \
rho0@_FillValue)
l113 = where((rho4_30s .ge. 46.100) .and. (rho4_30s .lt. 46.150), 1, \
rho0@_FillValue)
l114 = where((rho4_30s .ge. 46.150), 1, rho0@_FillValue)
delete(rho0_30s)
delete(rho2_30s)
delete(rho4_30s)
; assignning filling values from rho0 to masked layers
assignFillValue(rho0, l10)
assignFillValue(rho0, l20)
assignFillValue(rho0, l30)
assignFillValue(rho0, l40)
assignFillValue(rho0, l50)
assignFillValue(rho0, l60)
assignFillValue(rho0, l70)
assignFillValue(rho0, l80)
assignFillValue(rho0, l90)
assignFillValue(rho0, l100)
assignFillValue(rho0, l110)
assignFillValue(rho0, l11)
assignFillValue(rho0, l21)
assignFillValue(rho0, l31)
assignFillValue(rho0, l41)
assignFillValue(rho0, l51)
assignFillValue(rho0, l61)
assignFillValue(rho0, l71)
assignFillValue(rho0, l81)
assignFillValue(rho0, l91)
assignFillValue(rho0, l101)
assignFillValue(rho0, l111)
assignFillValue(rho0, l12)
assignFillValue(rho0, l22)
assignFillValue(rho0, l32)
assignFillValue(rho0, l42)
assignFillValue(rho0, l52)
assignFillValue(rho0, l62)
assignFillValue(rho0, l72)
assignFillValue(rho0, l82)
assignFillValue(rho0, l92)
assignFillValue(rho0, l102)
assignFillValue(rho0, l112)
assignFillValue(rho0, l13)
assignFillValue(rho0, l23)
assignFillValue(rho0, l33)
assignFillValue(rho0, l43)
assignFillValue(rho0, l53)
assignFillValue(rho0, l63)
assignFillValue(rho0, l73)
assignFillValue(rho0, l83)
assignFillValue(rho0, l93)
assignFillValue(rho0, l103)
assignFillValue(rho0, l113)
assignFillValue(rho0, l14)
assignFillValue(rho0, l24)
assignFillValue(rho0, l34)
assignFillValue(rho0, l44)
assignFillValue(rho0, l54)
assignFillValue(rho0, l64)
assignFillValue(rho0, l74)
assignFillValue(rho0, l84)
assignFillValue(rho0, l94)
assignFillValue(rho0, l104)
assignFillValue(rho0, l114)
delete(rho0)
lv = new((/12/), double) ; lv is array of big blue bars
assignFillValue(l10, lv)
lv(1) = sum(l10 * transportpercell)
lv(2) = sum(l20 * transportpercell)
lv(3) = sum(l30 * transportpercell)
lv(4) = sum(l40 * transportpercell)
lv(5) = sum(l50 * transportpercell)
lv(6) = sum(l60 * transportpercell)
lv(7) = sum(l70 * transportpercell)
lv(8) = sum(l80 * transportpercell)
lv(9) = sum(l90 * transportpercell)
lv(10) = sum(l100 * transportpercell)
lv(11) = sum(l110 * transportpercell)
lv(0) = 0
lv(0) = sum(lv)
lv0 = new((/44/), double) ; lv0 is array of small red bars
assignFillValue(l104, lv0)
lv0(0) = sum(l11 * transportpercell)
lv0(1) = sum(l12 * transportpercell)
lv0(2) = sum(l13 * transportpercell)
lv0(3) = sum(l14 * transportpercell)
lv0(4) = sum(l21 * transportpercell)
lv0(5) = sum(l22 * transportpercell)
lv0(6) = sum(l23 * transportpercell)
lv0(7) = sum(l24 * transportpercell)
lv0(8) = sum(l31 * transportpercell)
lv0(9) = sum(l32 * transportpercell)
lv0(10) = sum(l33 * transportpercell)
lv0(11) = sum(l34 * transportpercell)
lv0(12) = sum(l41 * transportpercell)
lv0(13) = sum(l42 * transportpercell)
lv0(14) = sum(l43 * transportpercell)
lv0(15) = sum(l44 * transportpercell)
lv0(16) = sum(l51 * transportpercell)
lv0(17) = sum(l52 * transportpercell)
lv0(18) = sum(l53 * transportpercell)
lv0(19) = sum(l54 * transportpercell)
lv0(20) = sum(l61 * transportpercell)
lv0(21) = sum(l62 * transportpercell)
lv0(22) = sum(l63 * transportpercell)
lv0(23) = sum(l64 * transportpercell)
lv0(24) = sum(l71 * transportpercell)
lv0(25) = sum(l72 * transportpercell)
lv0(26) = sum(l73 * transportpercell)
lv0(27) = sum(l74 * transportpercell)
lv0(28) = sum(l81 * transportpercell)
lv0(29) = sum(l82 * transportpercell)
lv0(30) = sum(l83 * transportpercell)
lv0(31) = sum(l84 * transportpercell)
lv0(32) = sum(l91 * transportpercell)
lv0(33) = sum(l92 * transportpercell)
lv0(34) = sum(l93 * transportpercell)
lv0(35) = sum(l94 * transportpercell)
lv0(36) = sum(l101 * transportpercell)
lv0(37) = sum(l102 * transportpercell)
lv0(38) = sum(l103 * transportpercell)
lv0(39) = sum(l104 * transportpercell)
lv0(40) = sum(l111 * transportpercell)
lv0(41) = sum(l112 * transportpercell)
lv0(42) = sum(l113 * transportpercell)
lv0(43) = sum(l114 * transportpercell)
if(any(ismissing(lv))) then ; if all cells are missing then print 0
aa = ind(ismissing(lv))
lv(aa) = 0
delete(aa)
end if
delete([/ l10, l11, l12, l13, l14, l20, l21, l22, l23, l24, l30, l31 /])
delete([/ l32, l33, l34, l40, l41, l42, l43, l44, l50, l51, l52, l53 /])
delete([/ l54, l60, l61, l62, l63, l64, l70, l71, l72, l73, l74, l80 /])
delete([/ l81, l82, l83, l84, l90, l91, l92, l93, l94, l100, l101 /])
delete([/ l102, l103, l104, l110, l111, l112, l113, l114 /])
delete(transportpercell)
exact_lat = exact_lat * -1
strUnits = ""
res@gsnLeftString = "(" + start_years_data(iii) + " - " + \
end_years_data(iii) + ") at (" \
+ sprintf("%4.2f", exact_lat) + "S)"
res@gsnRightString = "Net Transport out of Southern ocean = " + \
sprintf("%4.2f", lv(0)) + "Sv"
res@tiMainString = vo_datasets(iii)
res@xyLineColors = (/"black", "black"/)
res@gsnXYBarChartColors2 = ("red")
plot_1(iii) = gsn_csm_xy(wks, lv0, y1_val, res) ; plotting of red bars
res@gsnXYBarChartColors2 = ("blue4")
res@xyLineColors = (/"blue4", "blue4"/)
plots(iii) = gsn_csm_xy(wks, lv, y_val, res) ; plotting of blue bars
txres = True ; to print volume transport of blue bars
txres@gsnFrame = False
txres@txFontHeightF = 0.009
gsn_text_ndc(wks, sprintf("%4.3f", lv(0)), 0.27, 0.845, txres)
gsn_text_ndc(wks, sprintf("%4.3f", lv(1)), 0.27, 0.78, txres)
gsn_text_ndc(wks, sprintf("%4.3f", lv(2)), 0.27, 0.715, txres)
gsn_text_ndc(wks, sprintf("%4.3f", lv(3)), 0.27, 0.655, txres)
gsn_text_ndc(wks, sprintf("%4.3f", lv(4)), 0.27, 0.58, txres)
gsn_text_ndc(wks, sprintf("%4.3f", lv(5)), 0.27, 0.51, txres)
gsn_text_ndc(wks, sprintf("%4.3f", lv(6)), 0.27, 0.435, txres)
gsn_text_ndc(wks, sprintf("%4.3f", lv(7)), 0.27, 0.375, txres)
gsn_text_ndc(wks, sprintf("%4.3f", lv(8)), 0.27, 0.31, txres)
gsn_text_ndc(wks, sprintf("%4.3f", lv(9)), 0.27, 0.2475, txres)
gsn_text_ndc(wks, sprintf("%4.3f", lv(10)), 0.27, 0.175, txres)
gsn_text_ndc(wks, sprintf("%4.3f", lv(11)), 0.27, 0.11, txres)
res@gsnXYBarChartColors2 = ("White")
res@xyLineColors = (/"magenta2", "magenta2"/)
plot_talley(iii) = gsn_csm_xy(wks, talley_SO_Zonal_all_Levels, y_val, res)
; to plot the magenta for talley values of transport
overlay(plots(iii), plot_1(iii))
overlay(plot_talley(iii), plots(iii))
draw(plot_talley(iii))
frame(wks)
out_var = new((/56/), double)
out_var(0:11) = (/lv/)
out_var(12:55) = (/lv0/)
out_var!0 = "i"
out_var&i = ispan(0, 55, 1)
out_var@var = "transport_per_layer"
out_var@diag_script = "russell18jgr_fig6a.ncl"
out_var@description = "Transport in main layers(blue bars) in i(0-11)" + \
" and transport in sub layers (red bars) in i(12-55) at " + \
exact_lat + "S of model " + vo_datasets(iii)
delete(lv)
delete(lv0)
delete(exact_lat)
nc_filename = config_user_info@work_dir + "russell18jgr-figure6a_" \
+ vo_datasets(iii) + "_" + (start_years_data(iii)) + "-" + \
(end_years_data(iii)) + ".nc"
ncdf_outfile = ncdf_write(out_var, nc_filename)
log_provenance(ncdf_outfile, \
plotpath + "." + output_type(), \
"Russell et al 2018 figure 6 part a", \
"mean", \
"sh", \
(/"bar", "vert"/), \
"russell_joellen", \
"russell18jgr", \
(/ vo_inputfile_paths(iii), thetao_inputfile_paths(iii), \
so_inputfile_paths(iii)/))
end do
end