def main(cfg):
"""Execute the program.
Argument cfg, containing directory paths, preprocessed input dataset
filenames and user-defined options, is passed by ESMValTool preprocessor.
"""
provlog = ProvenanceLogger(cfg)
lorenz = lorenz_cycle
comp = computations
logger.info('Entering the diagnostic tool')
# Load paths
wdir_up = cfg['work_dir']
pdir_up = cfg['plot_dir']
input_data = cfg['input_data'].values()
logger.info('Work directory: %s \n', wdir_up)
logger.info('Plot directory: %s \n', pdir_up)
plotsmod = plot_script
data = e.Datasets(cfg)
logger.debug(data)
models = data.get_info_list('dataset')
model_names = list(set(models))
model_names.sort()
logger.info(model_names)
varnames = data.get_info_list('short_name')
curr_vars = list(set(varnames))
logger.debug(curr_vars)
# load user-defined options
lsm = str(cfg['lsm'])
wat = str(cfg['wat'])
lec = str(cfg['lec'])
entr = str(cfg['entr'])
met = str(cfg['met'])
flags = [wat, lec, entr, met]
# Initialize multi-model arrays
modnum = len(model_names)
te_all = np.zeros(modnum)
toab_all = np.zeros([modnum, 2])
toab_oc_all = np.zeros(modnum)
toab_la_all = np.zeros(modnum)
atmb_all = np.zeros([modnum, 2])
atmb_oc_all = np.zeros(modnum)
atmb_la_all = np.zeros(modnum)
surb_all = np.zeros([modnum, 2])
surb_oc_all = np.zeros(modnum)
surb_la_all = np.zeros(modnum)
wmb_all = np.zeros([modnum, 2])
wmb_oc_all = np.zeros(modnum)
wmb_la_all = np.zeros(modnum)
latent_all = np.zeros([modnum, 2])
latent_oc_all = np.zeros(modnum)
latent_la_all = np.zeros(modnum)
baroc_eff_all = np.zeros(modnum)
lec_all = np.zeros([modnum, 2])
horzentr_all = np.zeros([modnum, 2])
vertentr_all = np.zeros([modnum, 2])
matentr_all = np.zeros([modnum, 2])
irrevers_all = np.zeros(modnum)
diffentr_all = np.zeros([modnum, 2])
logger.info("Entering main loop\n")
i_m = 0
for model in model_names:
# Load paths to individual models output and plotting directories
wdir = os.path.join(wdir_up, model)
pdir = os.path.join(pdir_up, model)
os.makedirs(wdir)
os.makedirs(pdir)
aux_file = wdir + '/aux.nc'
te_ymm_file, te_gmean_constant, te_file = mkthe.init_mkthe_te(
model, wdir, input_data)
te_all[i_m] = te_gmean_constant
logger.info('Computing energy budgets\n')
in_list, eb_gmean, eb_file, toab_ymm_file = comp.budgets(
model, wdir, aux_file, input_data)
prov_rec = provenance_meta.get_prov_map(
['TOA energy budgets', model],
[in_list[4], in_list[6], in_list[7]])
provlog.log(eb_file[0], prov_rec)
prov_rec = provenance_meta.get_prov_map(
['atmospheric energy budgets', model], [
in_list[0], in_list[1], in_list[2], in_list[3], in_list[4],
in_list[5], in_list[6], in_list[7], in_list[8]
])
provlog.log(eb_file[1], prov_rec)
prov_rec = provenance_meta.get_prov_map(
['surface energy budgets', model], [
in_list[0], in_list[1], in_list[2], in_list[3], in_list[5],
in_list[7]
])
provlog.log(eb_file[2], prov_rec)
toab_all[i_m, 0] = np.nanmean(eb_gmean[0])
toab_all[i_m, 1] = np.nanstd(eb_gmean[0])
atmb_all[i_m, 0] = np.nanmean(eb_gmean[1])
atmb_all[i_m, 1] = np.nanstd(eb_gmean[1])
surb_all[i_m, 0] = np.nanmean(eb_gmean[2])
surb_all[i_m, 1] = np.nanstd(eb_gmean[2])
logger.info('Global mean emission temperature: %s\n',
te_gmean_constant)
logger.info('TOA energy budget: %s\n', toab_all[i_m, 0])
logger.info('Atmospheric energy budget: %s\n', atmb_all[i_m, 0])
logger.info('Surface energy budget: %s\n', surb_all[i_m, 0])
logger.info('Done\n')
baroc_eff_all[i_m] = comp.baroceff(model, wdir, aux_file,
toab_ymm_file, te_ymm_file)
logger.info('Baroclinic efficiency (Lucarini et al., 2011): %s\n',
baroc_eff_all[i_m])
logger.info('Running the plotting module for the budgets\n')
plotsmod.balances(cfg, wdir_up, pdir,
[eb_file[0], eb_file[1], eb_file[2]],
['toab', 'atmb', 'surb'], model)
logger.info('Done\n')
# Water mass budget
if wat == 'True':
(wm_file,
wmb_all[i_m, 0],
wmb_all[i_m, 1],
latent_all[i_m, 0],
latent_all[i_m, 1]) = compute_water_mass_budget(
cfg, wdir_up, pdir, model, wdir, input_data, flags, aux_file)
if lsm == 'True':
sftlf_fx = e.select_metadata(input_data,
short_name='sftlf',
dataset=model)[0]['filename']
logger.info('Computing energy budgets over land and oceans\n')
toab_oc_all[i_m], toab_la_all[i_m] = compute_land_ocean(
model, wdir, eb_file[0], sftlf_fx, 'toab')
atmb_oc_all[i_m], atmb_la_all[i_m] = compute_land_ocean(
model, wdir, eb_file[1], sftlf_fx, 'atmb')
surb_oc_all[i_m], surb_la_all[i_m] = compute_land_ocean(
model, wdir, eb_file[2], sftlf_fx, 'surb')
if wat == 'True':
logger.info('Computing water mass and latent energy'
' budgets over land and oceans\n')
wmb_oc_all[i_m], wmb_la_all[i_m] = compute_land_ocean(
model, wdir, wm_file[0], sftlf_fx, 'wmb')
latent_oc_all[i_m], latent_la_all[i_m] = compute_land_ocean(
model, wdir, wm_file[1], sftlf_fx, 'latent')
logger.info('Done\n')
if lec == 'True':
logger.info('Computation of the Lorenz Energy '
'Cycle (year by year)\n')
_, _ = mkthe.init_mkthe_lec(model, wdir, input_data)
lect = lorenz.preproc_lec(model, wdir, pdir, input_data)
lec_all[i_m, 0] = np.nanmean(lect)
lec_all[i_m, 1] = np.nanstd(lect)
logger.info(
'Intensity of the annual mean Lorenz Energy '
'Cycle: %s\n', lec_all[i_m, 0])
logger.info('Done\n')
else:
lect = np.repeat(2.0, len(eb_gmean[0]))
lec_all[i_m, 0] = 2.0
lec_all[i_m, 1] = 0.2
if entr == 'True':
if met in {'1', '3'}:
logger.info('Computation of the material entropy production '
'with the indirect method\n')
indentr_list = [te_file, eb_file[0]]
horz_mn, vert_mn, horzentr_file, vertentr_file = comp.indentr(
model, wdir, indentr_list, input_data, aux_file,
eb_gmean[0])
listind = [horzentr_file, vertentr_file]
provenance_meta.meta_indentr(cfg, model, input_data, listind)
horzentr_all[i_m, 0] = np.nanmean(horz_mn)
horzentr_all[i_m, 1] = np.nanstd(horz_mn)
vertentr_all[i_m, 0] = np.nanmean(vert_mn)
vertentr_all[i_m, 1] = np.nanstd(vert_mn)
logger.info(
'Horizontal component of the material entropy '
'production: %s\n', horzentr_all[i_m, 0])
logger.info(
'Vertical component of the material entropy '
'production: %s\n', vertentr_all[i_m, 0])
logger.info('Done\n')
logger.info('Running the plotting module for the material '
'entropy production (indirect method)\n')
plotsmod.entropy(pdir, vertentr_file, 'sver',
'Vertical entropy production', model)
logger.info('Done\n')
if met in {'2', '3'}:
matentr, irrevers, entr_list = comp.direntr(
logger, model, wdir, input_data, aux_file, te_file, lect,
flags)
provenance_meta.meta_direntr(cfg, model, input_data, entr_list)
matentr_all[i_m, 0] = matentr
if met in {'3'}:
diffentr = (float(np.nanmean(vert_mn)) +
float(np.nanmean(horz_mn)) - matentr)
logger.info('Difference between the two '
'methods: %s\n', diffentr)
diffentr_all[i_m, 0] = diffentr
logger.info('Degree of irreversibility of the '
'system: %s\n', irrevers)
irrevers_all[i_m] = irrevers
logger.info('Running the plotting module for the material '
'entropy production (direct method)\n')
plotsmod.init_plotentr(model, pdir, entr_list)
logger.info('Done\n')
os.remove(te_file)
os.remove(te_ymm_file)
logger.info('Done for model: %s \n', model)
i_m = i_m + 1
logger.info('I will now start multi-model plots')
logger.info('Meridional heat transports\n')
plotsmod.plot_mm_transp(model_names, wdir_up, pdir_up)
logger.info('Scatter plots')
summary_varlist = [
atmb_all, baroc_eff_all, horzentr_all, lec_all, matentr_all, te_all,
toab_all, vertentr_all
]
plotsmod.plot_mm_summaryscat(pdir_up, summary_varlist)
logger.info('Scatter plots for inter-annual variability of'
' some quantities')
eb_list = [toab_all, atmb_all, surb_all]
plotsmod.plot_mm_ebscatter(pdir_up, eb_list)
logger.info("The diagnostic has finished. Now closing...\n")
def balances(cfg, wdir, plotpath, filena, name, model):
"""Plot everything related to energy and water mass budgets.
This method provides climatological annal mean maps of TOA, atmospheric
and surface energy budgets, time series of annual mean anomalies in the
two hemispheres and meridional sections of meridional enthalpy
transports. Scatter plots of oceanic vs. atmospheric meridional
enthalpy transports are also provided.
Arguments:
- wdir: the working directory;
- plotpath: the path where the plot has to be saved;
- filena: the files containing input fields;
- name: the name of the variable associated with the input field;
- model: the name of the model to be analysed;
"""
cdo = Cdo()
provlog = ProvenanceLogger(cfg)
nsub = len(filena)
pdir = plotpath
plotentname = pdir + '/{}_heat_transp.png'.format(model)
plotwmbname = pdir + '/{}_wmb_transp.png'.format(model)
plotlatname = pdir + '/{}_latent_transp.png'.format(model)
# timesery = np.zeros([nsub, 2])
dims, ndims, tmean, zmean, timeser = global_averages(nsub, filena, name)
transp_mean = np.zeros([nsub, ndims[1]])
lat_maxm = np.zeros([nsub, 2, len(dims[3])])
tr_maxm = np.zeros([nsub, 2, len(dims[3])])
lim = [55, 55, 25]
for i_f in np.arange(nsub):
transp = transport(zmean[i_f, :, :], timeser[i_f, :, 0], dims[1])
transp_mean[i_f, :], list_peak = transports_preproc(
dims[1], ndims[3], lim[i_f], transp)
lat_maxm[i_f, :, :] = list_peak[0]
tr_maxm[i_f, :, :] = list_peak[1]
if nsub == 3:
ext_name = [
'TOA Energy Budget', 'Atmospheric Energy Budget',
'Surface Energy Budget'
]
transpty = (-6E15, 6E15)
coords = [dims[0], dims[1]]
plot_climap_eb(model, pdir, coords, tmean, ext_name)
fig = plt.figure()
strings = ['Meridional heat transports', 'Latitude [deg]', '[W]']
lats = dims[1]
for i in np.arange(nsub):
filename = filena[i] + '.nc'
if name[i] == 'toab':
nameout = 'total'
elif name[i] == 'atmb':
nameout = 'atmos'
elif name[i] == 'surb':
nameout = 'ocean'
nc_f = wdir + '/{}_transp_mean_{}.nc'.format(nameout, model)
removeif(nc_f)
lat_model = 'lat_{}'.format(model)
pr_output(transp_mean[i, :], filename, nc_f, nameout, lat_model)
name_model = '{}_{}'.format(nameout, model)
cdo.chname('{},{}'.format(nameout, name_model),
input=nc_f,
output='aux.nc')
move('aux.nc', nc_f)
cdo.chname('lat,{}'.format(lat_model), input=nc_f, output='aux.nc')
move('aux.nc', nc_f)
attr = ['{} meridional enthalpy transports'.format(nameout), model]
provrec = provenance_meta.get_prov_transp(attr, filename,
plotentname)
provlog.log(nc_f, provrec)
plot_1m_transp(lats, transp_mean[i, :], transpty, strings)
plt.grid()
plt.savefig(plotentname)
plt.close(fig)
plot_1m_scatter(model, pdir, lat_maxm, tr_maxm)
elif nsub == 2:
ext_name = ['Water mass budget', 'Latent heat budget']
transpwy = (-2E9, 2E9)
transply = (-6E15, 6E15)
coords = [dims[0], dims[1]]
plot_climap_wm(model, pdir, coords, tmean, ext_name, name)
nc_f = wdir + '/{}_transp_mean_{}.nc'.format('wmb', model)
removeif(nc_f)
filena[0] = filena[0].split('.nc', 1)[0]
filename = filena[0] + '.nc'
pr_output(transp_mean[0, :], filename, nc_f, 'wmb', 'lat')
attr = ['water mass transport', model]
provrec = provenance_meta.get_prov_transp(attr, filename, plotwmbname)
provlog.log(nc_f, provrec)
nc_f = wdir + '/{}_transp_mean_{}.nc'.format('latent', model)
removeif(nc_f)
filena[1] = filena[1].split('.nc', 1)[0]
filename = filena[1] + '.nc'
pr_output(transp_mean[1, :], filename, nc_f, 'latent', 'lat')
attr = ['latent energy transport', model]
provrec = provenance_meta.get_prov_transp(attr, filename, plotlatname)
provlog.log(nc_f, provrec)
strings = ['Water mass transports', 'Latitude [deg]', '[kg*s-1]']
fig = plt.figure()
plot_1m_transp(dims[1], transp_mean[0, :], transpwy, strings)
plt.grid()
plt.savefig(plotwmbname)
plt.close(fig)
strings = ['Latent heat transports', 'Latitude [deg]', '[W]']
fig = plt.figure()
plot_1m_transp(dims[1], transp_mean[1, :], transply, strings)
plt.grid()
plt.savefig(plotlatname)
plt.close(fig)
for i_f in np.arange(nsub):
fig = plt.figure()
axi = plt.subplot(111)
axi.plot(dims[3], timeser[i_f, :, 0], 'k', label='Global')
axi.plot(dims[3], timeser[i_f, :, 1], 'r', label='SH')
axi.plot(dims[3], timeser[i_f, :, 2], 'b', label='NH')
plt.title('Annual mean {}'.format(ext_name[i_f]))
plt.xlabel('Years')
plt.ylabel('[W/m2]')
axi.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.07),
shadow=True,
ncol=3)
plt.tight_layout()
plt.grid()
plt.savefig(pdir + '/{}_{}_timeser.png'.format(model, name[i_f]))
plt.close(fig)
