if i == 0:
mld = np.load(datpath+"model_output/"+ "stoch_output_%s.npz"%(expid),allow_pickle=True)['hclim']
# Calculate Autocorrelation for each cast
kmonth = mld.argmax()
acall = {}
for i,applyfac in enumerate(applyfacs):
ac = {}
for m in range(4):
sst = ssts[i][m]
sst = np.roll(sst,-1) # Roll since first month is feb (use jan, no-entrain 1d)
tsmodel = proc.year2mon(sst) # mon x year
# Deseason
tsmodel2 = tsmodel - np.mean(tsmodel,1)[:,None]
# Compute autocorrelation and save data for region
ac[m] = proc.calc_lagcovar(tsmodel2,tsmodel2,lags,kmonth+1,0)
acall[i] = ac.copy()
#%% Plot autocorrelation (Plot by Model)
applyfaclab = ["Forcing Only","Incl. Seasonal MLD","Incl. Seasonal MLD and Integration Factor"]
modelname = ("MLD Fixed","MLD Max", "MLD Seasonal", "MLD Entrain")
mons3=('Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec')
randts,
beta[o, a, :],
hclim[o, a, :],
kprev[o, a, :],
FAC[3][o, a, :],
multFAC=multFAC,
debug=True)
# Calculate autocorrelation
kmonth = hclim[o, a, :].argmax() # kmonth is the INDEX of the mongth
autocorr = {}
for model in range(4):
# Get the data
tsmodel = sst[model]
tsmodel = proc.year2mon(tsmodel) # mon x year
# Deseason (No Seasonal Cycle to Remove)
tsmodel2 = tsmodel - np.mean(tsmodel, 1)[:, None]
# Plot
autocorr[model] = proc.calc_lagcovar(tsmodel2, tsmodel2, lags, kmonth + 1,
0)
#%% Make Test Plot
fig, ax = plt.subplots(1, 1)
#ax.plot(lags,AVG0,color='b',label='CESM1 (YO)')
ax.plot(lags, AVG1, color='c', label='No-Entrain (YO)')
ax.plot(lags, AVG2, color='g', label='Entrain (YO)')
def scm_synt(hclim,
dampingr,
NAO1,
randts,
lags,
applyfac,
T0=0,
returncomponents=False):
t_end = randts.shape[0] # Get simulation length
# ----------------------------
# % Set-up damping parameters and kprev
# ----------------------------
# Converts damping parameters from raw form (Watts/m2) to (deg/sec)
# Also calculates beta and FAC
# Note: Consider combining with NAO Forcing conversion?
# Find Kprev
kpreva, _ = scm.find_kprev(hclim)
viz.viz_kprev(hclim, kpreva)
lbd, lbd_entr, FAC, beta = scm.set_stochparams(hclim,
dampingr,
dt,
ND=False,
rho=rho,
cp0=cp0,
hfix=hfix)
"""
Out Format:
lbd -> Dict (keys 0-3) representing each mode, damping parameter
lbd_entr -> array of entrainment damping
FAC -> Dict (keys 0-3) representing each model, integration factor
beta ->array [Mon]
kprev -> array [Mon]
"""
# ----------------------------
# % Set Up Forcing ------------------------------------------------
# ----------------------------
# Convert NAO from W/m2 to degC/sec. Returns dict with keys 0-2 (same as scm.convert_NAO)
NAOF = {}
conversionfac = dt / cp0 / rho
if applyfac == 0: # Dont apply MLD to NAOF
for hi in range(3):
NAOF[hi] = NAO1 * conversionfac
else: # Apply each MLD case to NAOF
NAOF[0] = NAO1 * conversionfac / hfix
NAOF[1] = NAO1 * conversionfac / hclim.max()
NAOF[2] = NAO1 * conversionfac / hclim
# Use random time series to scale the forcing pattern
F = {}
tilecount = int(12 / NAOF[0].shape[0] * nyr)
for hi in range(3):
F[hi] = np.tile(NAOF[hi].squeeze(), tilecount) * randts * fscale
# Save Forcing if option is set
if saveforcing == 1:
np.save(output_path + "stoch_output_%s_Forcing.npy" % (runid), F)
"""
Output:
F - dict (keys = 0-2, representing each MLD treatment) [time (simulation length)]
Fseas - dict (keys = 0-2, representing each MLD treatment) [ month]
"""
# ----------
# %RUN MODELS -----------------------------------------------------------------
# ----------
# Set mulFAC condition based on applyfac
if applyfac == 2:
multFAC = 1 # Don't apply integrationreduction factor if applyfac is set to 0 or 1
else:
multFAC = 0
#% Run Models <SET>
"""
Inputs:
FAC - Dict of Integration Factors (0-3)
lbd - Dict of Damping Parameters (0-3)
F - Dict of Forcings (0-2)
kprev - Array/Vector of Entraining Months
beta - Array/Vector of Entrainment velocities
hclima - Array/Vector of MLD cycle
Fixed Params: T0 (Initial SST), t_end (end timestep), multFAC (0 or 1)
Output
sst - Dict of model output (0-3)
"""
# Preallocate dictionary to store results (Use tuple instead?)
sst = {}
# Run Model Without Entrainment
# Loop for each Mixed Layer Depth Treatment
for hi in range(3):
start = time.time()
# Select damping and FAC based on MLD
FACh = FAC[hi]
lbdh = lbd[hi]
# Select Forcing
Fh = F[hi]
# Run Point Model
start = time.time()
sst[hi], _, _ = scm.noentrain(t_end,
lbdh,
T0,
Fh,
FACh,
multFAC=multFAC)
elapsed = time.time() - start
tprint = "\nNo Entrain Model, hvarmode %i, ran in %.2fs" % (hi,
elapsed)
print(tprint)
# Run Model With Entrainment
start = time.time()
Fh = F[2]
FACh = FAC[3]
sst[3] = scm.entrain(t_end,
lbd[3],
T0,
Fh,
beta,
hclim,
kpreva,
FACh,
multFAC=multFAC)
elapsed = time.time() - start
tprint = "\nEntrain Model ran in %.2fs" % (elapsed)
print(tprint)
# Calculate Autocorrelation
kmonth = hclim.argmax() # kmonth is the INDEX of the mongth
autocorr = {}
for model in range(4):
# Get the data
tsmodel = sst[model]
tsmodel = proc.year2mon(tsmodel) # mon x year
# Deseason (No Seasonal Cycle to Remove)
tsmodel2 = tsmodel - np.mean(tsmodel, 1)[:, None]
# Plot
autocorr[model] = proc.calc_lagcovar(tsmodel2, tsmodel2, lags,
kmonth + 1, 0)
if returncomponents:
return sst, autocorr, lbd, FAC, beta, kpreva, F
return sst, autocorr