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carbchem_cube_revelle.py
421 lines (354 loc) · 13.1 KB
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carbchem_cube_revelle.py
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'''
Input should be:
Temperature in K
S in PSU (I think)
DIC and ALK in MOL? values about 2.0
'''
'''
NOTE - this is currently designed to work with a single time-interval (i.e. cube without a time dimension)
'''
import numpy as np
import numpy.ma as ma
import scipy.stats
import keyword
import warnings
import matplotlib.pyplot as plt
warnings.filterwarnings("ignore", category=RuntimeWarning)
print' ops= 0 ; output is iteration count'
print' 1 ; pCO2'
print' 2 ; pH'
print' 3 ; [H2CO3]'
print' 4 ; [HCO3]'
print' 5 ; [CO3]'
print' 6 ; satn [co3] : calcite'
print' 7 ; saturation state: calcite'
print' 8 ; satn [CO3] : aragonite'
print' 9 ; saturation state: aragonite'
print' 10; Ravelle factor (DIC) calculated from Egleston et al. 2010'
print' 11; Alkalinity buffer factor calculated from Egleston et al. 2010'
print 'inputs: op_swtch,mdi,T,S,TCO2,TALK'
#Supply these as .data (arrays)
#salinity needs to be converted into psu *1000+35
#TCO2 and TALK must be in mol/kg /(1026.*1000.)
#the ones below here are not needed
def pressure_fun(a,b,c,d,e,T):
del_vol = np.ones(T.shape, dtype='f')
del_com = np.ones(T.shape, dtype='f')
pf = np.ones(T.shape, dtype='f')
del_vol = a + b *T + c * np.power(T,2.0)
del_com = 1.0e-3*( d + e*T )
pf = np.exp( ( 0.5*del_com*Pr - del_vol )*Pr / ( 83.131*TK ) )
return pf
def carbiter(T, TCO2, TALK, TB, msk, tol, mxiter, K1, K2, KB, KW):
aH = np.empty_like(T, dtype='f')
aH.fill(1.0e-8)
count = np.zeros_like(T)
tol_swtch = np.zeros_like(T)
#MB -
# AB = np.ones(T.shape)
# AC = np.ones(T.shape)
# AW = np.ones(T.shape)
#MB+
TBKB = TB * KB
K2_K1x4 = 4.0 * K2 / K1
K2_2 = 0.5 * K1
#
iter = 0
test = 2.0
while test > 0.5 and iter < mxiter:
# Compute alkalinity guesses for Boron, Silicon, Phosphorus and Water
#MB- AB = TB * KB / (aH + KB)
#AB = TBKB / (aH + KB)
AB = np.divide(TBKB,(aH + KB))
# ASi = TSi*KSi/( aH $
# + KSi )
# AP = TP*( 1.0/( 1.0 + KP2/aH $
# + KP2*KP3/(aH^2.0) ) + 2.0/( 1.0 $
# + aH/KP2 + KP3/aH ) $
# + 3.0/( 1.0 + aH/KP3 $
# + (aH^2.0)/(KP2*KP3) ) )
AW = (KW / aH) - aH
# using the guessed alkalinities and total alkalinity, calculate the
# alkalinity due to carbon
# AC = TALK - ( AB + ASi $
# + AP + AW )
AC = TALK - (AB + AW)
# and recalculate aH with the new As
#MB+
TCO2_AC = TCO2 - AC
#
old_aH = np.copy(aH)
#MB- aH = (0.5 * K1 / AC) * ((TCO2 - AC) + np.sqrt((TCO2 - AC) * (TCO2 - AC) + 4.0 * (AC * K2 / K1) * (2.0 * TCO2 - AC)))
temp = TCO2_AC*TCO2_AC + (AC * K2_K1x4) * (2.0 * TCO2 - AC)
aH = (K2_2 / AC) * (TCO2_AC + np.sqrt(temp))
tol_swtch = abs((aH - old_aH) / old_aH) > tol
count = count + tol_swtch
test = np.sum(tol_swtch)
iter += 1
#aH[~msk] = 1.0
#count[~msk] = 0
return AC, AW, AB, aH, count
def carbchem_revelle(op_swtch,mdi,T_cube,S_cube,TCO2_cube,TALK_cube,Pr=0.0,TB=0.0,Ni=100.0,Tl=1.0e-5):
# This function calculates the inorganic carbon chemistry balance
# according to the method of Peng et al 1987
# The parameters are set in the first few lines
#salinity needs to be converted into psu
#TCO2 and TALK must be in mol/kg
#the ones below here are not needed
# This procedure calculates the inorganic carbon chemistry balance
# according to the method of Peng et al 1987
# The parameters are set in the first few lines
#
# ops= 0 ; output is iteration count
# 1 ; pCO2
# 2 ; pH
# 3 ; [H2CO3]
# 4 ; [HCO3]
# 5 ; [CO3]
# 6 ; satn [co3] : calcite
# 7 ; saturation state: calcite
# 8 ; satn [CO3] : aragonite
# 9 ; saturation state: aragonite
# 10; Ravelle factor (DIC) calculated from Egleston et al. 2010
# 11; Alkalinity buffer factor calculated from Egleston et al. 2010
#make sure grids are same size
#make sure rthey years are the same
#extarct the data from the cubes
# from iris import *
# from iris.analysis import *
# import iris.analysis
# from numpy import *
# from matplotlib.pyplot import *
# from scipy.stats.mstats import *
# import iris.plot as iplt
# import seawater
# import numpy
# import iris.quickplot as quickplot
# import iris.analysis.stats as istats
# temp = iris.load_cube('/home/ph290/tmp/hadgem2es_potential_temperature_historical_regridded.nc').extract(Constraint(depth = 0))
# sal = iris.load_cube('/home/ph290/tmp/hadgem2es_salinity_historical_regridded.nc').extract(Constraint(depth = 0))
# carb = iris.load_cube('/home/ph290/tmp/hadgem2es_dissolved_inorganic_carbon_historical_regridded.nc').extract(Constraint(depth = 0))
# alk = iris.load_cube('/home/ph290/tmp/hadgem2es_total_alkalinity_historical_regridded.nc').extract(Constraint(depth = 0))
# import carbchem
# co2 = carbchem.carbchem(1,temp.data.fill_value,temp,sal,carb,alk)
# T_cube = temp
# S_cube = sal
# TCO2_cube = carb
# TALK_cube = alk
# mdi = temp.data.fill_value
t_lat = np.size(T_cube.coord('latitude').points)
s_lat = np.size(S_cube.coord('latitude').points)
c_lat = np.size(TCO2_cube.coord('latitude').points)
a_lat = np.size(TALK_cube.coord('latitude').points)
lat_test = t_lat == s_lat == c_lat == a_lat
t_lon = np.size(T_cube.coord('longitude').points)
s_lon = np.size(S_cube.coord('longitude').points)
c_lon = np.size(TCO2_cube.coord('longitude').points)
a_lon = np.size(TALK_cube.coord('longitude').points)
lon_test = t_lon == s_lon == c_lon == a_lon
if lat_test and lon_test:
output_cube = T_cube.copy()
T_cube = T_cube-273.15
T = T_cube.data.copy()
S = S_cube.data.copy()
TCO2_cube = TCO2_cube/1026.0
TCO2 = np.roll(ma.swapaxes(TCO2_cube.data.copy(),0,1),180)
#NOTE - this is only required here 'cos glodap and WOA are differently ordered - not necessary for other stuff
TALK_cube = TALK_cube/1026.0
TALK = np.roll(ma.swapaxes(TALK_cube.data.copy(),0,1),180)
print np.mean(T)
print np.mean(S)
print np.mean(TCO2)
print np.mean(TALK)
msk1=ma.masked_greater_equal(T,mdi-1.0,copy=True)
msk2=ma.masked_greater_equal(S,mdi-1.0,copy=True)
msk3=ma.masked_greater_equal(TCO2,mdi-1.0,copy=True)
msk4=ma.masked_greater_equal(TALK,mdi-1.0,copy=True)
msk=msk1.mask | msk2.mask | msk3.mask | msk4.mask
T[msk]=np.nan
S[msk]=np.nan
TALK[msk]=np.nan
TCO2[msk]=np.nan
# plt.contourf(T)
# plt.show()
# plt.contourf(TCO2)
# plt.show()
# T = np.array([13.74232016,25.0])
# S = np.array([33.74096661,35.0])
# TCO2 = np.array([0.0019863,2.0e-3])
# TALK = np.array([0.00226763,2.2e-3])
# msk = ma.masked_greater_equal(T,mdi-1.0,copy=True)
#create land-sea mask used by sea_msk.mask
salmin = 1.0
S2=np.copy(S)
S2[np.abs(S) < salmin]=salmin
tol = Tl
mxiter = Ni
op_fld = np.empty(T.shape)
op_fld.fill(np.NAN)
# TB = np.ones(T.shape)
# TB = 4.106e-4*S2/35.0
TB = np.empty_like(T)
TB = np.multiply(S2,4.106e-4/35.0, TB)
# this boron is from Peng
#convert to Kelvin
TK=np.copy(T[:])
TK += +273.15
alpha_s = np.ones(T.shape)
alpha_s = np.exp( ( -60.2409 + 9345.17/TK + 23.3585*np.log(TK/100.0) ) + ( 0.023517 - 0.023656*(TK/100.0) + 0.0047036*np.power((TK/100.0),2.0) )*S )
K1 = np.ones(T.shape)
K1 = np.exp( ( -2307.1266/TK + 2.83655 - 1.5529413*np.log(TK) ) - ( 4.0484/TK + 0.20760841 )*np.sqrt(S) + 0.08468345*S - 0.00654208*np.power(S,1.5) + np.log( 1.0 - 0.001005*S ) )
a = np.array([-25.50,-15.82,-29.48,-25.60,-48.76,-46.0])
b = np.array([0.1271,0.0219,0.2324,0.5304,0.5304])
c = np.array([0.0,0.0,0.0026080,0.0036246,0.0,0.0])
d = np.array([-3.08,1.13,(-2.84e-3)/(1.0e-3),-5.13,-11.76,-11.76])
e = np.array([0.0877,0.1475,0.0,0.0794,0.3692,0.3692])
if keyword.iskeyword(Pr):
instance = 0
pf = pressure_fun(a[instance],b[instance],c[instance],d[instance],e[instance],T)
K1 = K1*pf
K2 = np.ones(T.shape)
K2 = np.exp( ( -3351.6106/TK - 9.226508 - 0.2005743*np.log(TK) ) - ( 23.9722/TK + 0.106901773 )*np.power(S,0.5) + 0.1130822*S - 0.00846934*np.power(S,1.5) + np.log( 1.0 - 0.001005*S ) )
if keyword.iskeyword(Pr):
instance = 1
pf = pressure_fun(a[instance],b[instance],c[instance],d[instance],e[instance],T)
K2 = K2*pf
KB = np.ones(T.shape)
KB = np.exp( ( -8966.90 - 2890.53*np.power(S,0.5) - 77.942*S + 1.728*np.power(S,1.5)- 0.0996*np.power(S,2.0) )/TK + ( 148.0248 + 137.1942*np.power(S,0.5) + 1.62142*S ) - ( 24.4344 + 25.085*np.power(S,0.5) + 0.2474*S )*np.log(TK) + 0.053105*(np.power(S,0.5))*TK )
if keyword.iskeyword(Pr):
instance = 2
pf = pressure_fun(a[instance],b[instance],c[instance],d[instance],e[instance],T)
KB = KB*pf
KW = np.ones(T.shape)
KW = np.exp( ( -13847.26/TK + 148.96502 - 23.6521*np.log(TK) ) + ( 118.67/TK - 5.977 + 1.0495*np.log(TK) )*np.power(S,0.5) - 0.01615*S )
if keyword.iskeyword(Pr):
instance = 3
pf = pressure_fun(a[instance],b[instance],c[instance],d[instance],e[instance],T)
KW = KW*pf
if ( op_swtch >= 6 or op_swtch <= 9 ):
ca_conc = np.ones(T.shape)
ca_conc = 0.01028*S2/35.0
if ( op_swtch == 6 or op_swtch == 7 ):
K_SP_C = np.ones(T.shape)
K_SP_C = np.power(10.0,( ( -171.9065 - 0.077993*TK + 2839.319/TK + 71.595*np.log10(TK) ) + ( -0.77712 + 0.0028426*TK + 178.34/TK )*np.power(S,0.5) - 0.07711*S+ 0.0041249*np.power(S,1.5) ))
if keyword.iskeyword(Pr):
instance = 4
pf = pressure_fun(a[instance],b[instance],c[instance],d[instance],e[instance],T)
K_SP_C = K_SP_C*pf
if ( op_swtch == 8 or op_swtch == 9 ):
K_SP_A = np.ones(T.shape)
K_SP_A = np.power(10,( ( -171.945 - 0.077993*TK + 2903.293/TK + 71.595*np.log10(TK) ) + ( -0.068393 + 0.0017276*TK + 88.135/TK )*np.power(S,0.5) - 0.10018*S + 0.0059415*np.power(S,1.5) ))
if keyword.iskeyword(Pr):
instance = 5
pf = pressure_fun(a[instance],b[instance],c[instance],d[instance],e[instance],T)
K_SP_A = K_SP_A*pf
# Get first estimate for H+ concentration.
AC, AW, AB, aH, count = carbiter(T, TCO2, TALK, TB, msk, tol, mxiter, K1, K2, KB, KW)
# plt.contourf(aH)
# plt.show()
# plt.contourf(AC)
# plt.show()
# plt.contourf(AW)
# plt.show()
# plt.contourf(aH)
# plt.show()
# now we have aH we can calculate...
denom = np.zeros(T.shape)
H2CO3 = np.zeros(T.shape)
HCO3 = np.zeros(T.shape)
CO3 = np.zeros(T.shape)
pH = np.zeros(T.shape)
pCO2 = np.zeros(T.shape)
if ( op_swtch == 6 or op_swtch == 7 ):
sat_CO3_C = np.zeros(T.shape)
if ( op_swtch == 7 ):
sat_stat_C = np.zeros(T.shape)
if ( op_swtch == 8 or op_swtch == 9 ):
sat_CO3_A = np.zeros(T.shape)
if ( op_swtch == 9 ):
sat_stat_A = np.zeros(T.shape)
denom = np.power(aH,2.0) + K1*aH + K1*K2
H2CO3 = TCO2*np.power(aH,2.0)/denom
HCO3 = TCO2*K1*aH/denom
CO3 = TCO2*K1*K2/denom
# plt.contourf(K1)
# plt.show()
# plt.contourf(aH) -no
# plt.show()
# plt.contourf(denom) -no
# plt.show()
pH = -np.log10(aH)
pCO2 = H2CO3/alpha_s
if ( op_swtch == 6 or op_swtch == 7 ):
sat_CO3_C = K_SP_C/ca_conc
if ( op_swtch == 7 ):
sat_stat_C = CO3/sat_CO3_C
if ( op_swtch == 8 or op_swtch == 9 ):
sat_CO3_A = K_SP_A/ca_conc
if ( op_swtch == 9 ):
sat_stat_A = CO3/sat_CO3_A
TALKc=+HCO3+2*(CO3)
var1=HCO3
DIC_buffer=HCO3
ALK_buffer=HCO3
var1=HCO3+4*(CO3)+((aH*AB)/(KB+aH))-AW
DIC_buffer=TCO2-((TALKc*TALKc)/var1)
ALK_buffer=((TALKc*TALKc)-TCO2*var1)/TALKc
output_cube = output_cube*0.0+np.nan
if ( op_swtch == 0 ):
op_fld = np.zeros(T.shape)
op_fld = count
elif ( op_swtch == 1 ):
print np.mean(pCO2)
output_cube.data = pCO2*1.0e6
output_cube.standard_name = 'surface_partial_pressure_of_carbon_dioxide_in_sea_water'
output_cube.long_name = 'CO2 concentration'
output_cube.units = 'uatm'
elif ( op_swtch == 2 ):
output_cube.data = pH
output_cube.standard_name = 'sea_water_ph_reported_on_total_scale'
output_cube.long_name = 'pH'
output_cube.units = '1'
elif ( op_swtch == 3 ):
output_cube.data = H2CO3
elif ( op_swtch == 4 ):
output_cube.data = HCO3
elif ( op_swtch == 5 ):
output_cube.data = CO3
elif ( op_swtch == 6 ):
output_cube.data = sat_CO3_C
elif ( op_swtch == 7 ):
output_cube.data = sat_stat_C
elif ( op_swtch == 8 ):
output_cube.data = sat_CO3_A
elif ( op_swtch == 9 ):
output_cube.data = sat_stat_A
elif ( op_swtch == 10 ):
output_cube.data = TCO2/DIC_buffer
elif ( op_swtch == 11 ):
output_cube.data = ALK_buffer*1000.0
return output_cube
'''
test-data
'''
# def main():
# mdi=-999.0
# sizing=(500,500)
# T = np.empty(sizing)
# S = np.empty(sizing)
# TCO2 = np.empty(sizing)
# TALK = np.empty(sizing)
# T.fill(10.0)
# S.fill(35.0)
# TCO2.fill(0.0020)
# TALK.fill(0.0022)
# T[0,0]=mdi
# S[2,3]=mdi
# S[0,0]=0.5
# TALK[2,3]=mdi
# TCO2[2,3]=mdi
# print carbchem(1,mdi,T,S,TCO2,TALK)
# import cProfile
# if __name__ == '__main__':
# x=cProfile.run('main()')
#main()