In [1]:
import numpy as np
from matplotlib import pyplot as plt
import matplotlib as mpl
import datetime as dt
mpl.rc('xtick', labelsize=14)
mpl.rc('ytick', labelsize=14)
mpl.rc('legend', fontsize=16)
mpl.rc('axes', titlesize=16)
mpl.rc('figure', titlesize=16)
mpl.rc('axes', labelsize=16)
mpl.rc('font', size=16)
mpl.rc('legend', numpoints= 1)
mpl.rc('lines', markersize= 8)
%matplotlib inline
/home/eolson/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
/home/eolson/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
Temperature Response¶
Q10¶
In [2]:
#EXP( 0.07 * (tsn(:,:,:,jp_tem)-20.0_wp) )
T=np.arange(-5,100,.1)
f=np.exp(0.07*(T-20))
plt.plot(T,f)
np.exp(0.07*(30-20))/np.exp(0.07*(20-20)),np.exp(0.07*(35-20))/np.exp(0.07*(25-20)),np.exp(0.07*(10-20))/np.exp(0.07*(0-20))
Out[2]:
(2.0137527074704766, 2.0137527074704766, 2.0137527074704766)
phyto growth¶
In [3]:
T=np.arange(-5,100,.1)
In [4]:
R1=1.0*np.exp(0.07 * (T - 20))
In [5]:
plt.plot(T,R1)
Out[5]:
[<matplotlib.lines.Line2D at 0x7f438d69e400>]
In [6]:
R=[1.0*np.exp(0.07 * (TT - 20)) * min(max((35 - TT), 0.0),5) / (5 + 1e-10) for TT in T]
In [7]:
plt.plot(T,R)
plt.plot((35,35),(0,2.5),'r--')
plt.plot((35-5,35-5),(0,2.5),'r--')
plt.ylabel('Temperature Factor')
plt.xlabel('Temperature (Celsius)')
Out[7]:
<matplotlib.text.Text at 0x7f438d81d780>
In [8]:
#R3: match Khangaonkar max temps for diatoms (12 C) and dinoflagellates (18 C):
tr1=14 # 12 C
tr2=13 # 18 C
tm1=26
tm2=31
R3_1=[1.0*np.exp(0.07 * (TT - 20)) * min(max((tm1 - TT), 0.0),tr1) / (tr1 + 1e-10) for TT in T]
R3_2=[1.0*np.exp(0.07 * (TT - 20)) * min(max((tm2 - TT), 0.0),tr2) / (tr2 + 1e-10) for TT in T]
plt.plot(T,R,'k-',label='current model')
plt.plot(T,R3_1,'r--',label='new diatoms')
plt.plot(T,R3_2,'c--',label='new flags')
plt.plot((tm1-tr1,tm1-tr1),(0,2.5),'r--')
plt.plot((tm2-tr2,tm2-tr2),(0,2.5),'r--')
plt.legend()
ii1=np.argmax(R3_1)
ii2=np.argmax(R3_2)
print(T[ii1],T[ii2])
plt.ylabel('Temperature Factor')
plt.xlabel('Temperature (Celsius)')
12.0 18.0
Out[8]:
<matplotlib.text.Text at 0x7f438d6ac9e8>
In [8]:
#R3: match Khangaonkar max temps for diatoms (12 C) and dinoflagellates (18 C):
# simultaneously nudge down the flagellate max growth rate? not in first test
R1=0.600E-04
R2=0.250E-04
R2b=0.20E-04
tr1=14 # 12 C
tr2=13 # 18 C
tm1=26
tm2=31
R3_1=[R1*1.0*np.exp(0.07 * (TT - 20)) * min(max((tm1 - TT), 0.0),tr1) / (tr1 + 1e-10) for TT in T]
R3_2=[R2*1.0*np.exp(0.07 * (TT - 20)) * min(max((tm2 - TT), 0.0),tr2) / (tr2 + 1e-10) for TT in T]
plt.plot(T,R1*np.array(R)*24*3600,'m--',label='old diatoms')
plt.plot(T,R2*np.array(R)*24*3600,'c--',label='old sm. phyt.')
plt.plot(T,np.array(R3_1)*24*3600,'r-',label='new diatoms')
plt.plot(T,np.array(R3_2)*24*3600,'b-',label='new sm. phyt.')
#plt.plot((tm1-tr1,tm1-tr1),(0,.0001),'r--')
#plt.plot((tm2-tr2,tm2-tr2),(0,.0001),'b--')
#plt.plot((30,30),(0,.00013),'k--')
plt.ylabel('Max Growth Rate (day$^{-1}$)')
plt.xlabel('Temperature (Celsius)')
plt.legend(bbox_to_anchor=(1.6,1))
plt.xlim((-2,36))
ii1=np.argmax(R3_1)
ii2=np.argmax(R3_2)
print(T[ii1],T[ii2])
12.0 18.0
plt.plot(T,R)¶
plt.xlim(20,40) plt.plot(T,R1) plt.ylim(0,3)
In [9]:
plt.plot(T,[ min(max((35 - TT), 0.0),5) / (5 + 1e-10) for TT in T])
plt.xlim(20,40)
Out[9]:
(20, 40)
Light Response¶
In [12]:
I=np.arange(0,200)
Iopt=42
L42=(1-np.exp(-I/(.33*Iopt)))*np.exp(-I/(30*Iopt))*1.06
Iopt=37.000
L37=(1-np.exp(-I/(.33*Iopt)))*np.exp(-I/(30*Iopt))*1.06
Iopt=10.000
L10=(1-np.exp(-I/(.33*Iopt)))*np.exp(-I/(30*Iopt))*1.06
Iopt=47.000
L47=(1-np.exp(-I/(.33*Iopt)))*np.exp(-I/(30*Iopt))*1.06
In [13]:
plt.plot(I,L42,'b-',label='diatoms')
plt.plot(I,L37,'r-',label='M.rubrum')
plt.plot(I,L10,'c-',label='flags')
plt.plot(I,L47,'b--',label='47')
plt.legend()
plt.xlabel('W/m$^2$')
Out[13]:
<matplotlib.text.Text at 0x7f59618dfdd8>
Mesozooplankton¶
REAL(wp), dimension (1:3) :: zz_rate_mesozoo_sumpeakval !uM N magnitude of mesozooplankton summer concentration peaks
REAL(wp), dimension (1:3) :: zz_rate_mesozoo_sumpeakpos ! year-day times of mesozooplankton summer concentration peaks
REAL(wp), dimension (1:3) :: zz_rate_mesozoo_sumpeakwid ! year-days widths of mesozooplankton summer concentration peaks
In [83]:
#&nampismezo ! parameters for microzooplankton
zz_rate_mesozoo_winterconc = 0.369 #uM N mesozooplankton background concentration
zz_rate_mesozoo_winterconc2 = 0.38 #uM N mesozooplankton background concentration
zz_rate_mesozoo_summerconc = 1.0 # uM N mesozooplankton relative summer concentration
zz_rate_mesozoo_sumpeakval = np.expand_dims(np.array((0.339, 0.0, 0.0)) ,0) #uM N magnitude of mesozooplankton summer concentration peaks
zz_rate_mesozoo_sumpeakval2 = np.expand_dims(np.array((0.45, 0.3, 0.0)) ,0) #uM N magnitude of mesozooplankton summer concentration peaks
zz_rate_mesozoo_sumpeakpos = np.expand_dims(np.array((217.000, 120.000, 300.000)),0) # year-day times of mesozooplankton summer concentration peaks
zz_rate_mesozoo_sumpeakwid = np.expand_dims(np.array((114.0, 51.600, 40.000)),0) # year-days widths of mesozooplankton summer concentration peaks,0)
zz_rate_mesozoo_sumpeakwid2 = np.expand_dims(np.array((100.0, 10.00, 40.000)),0) # year-days widths of mesozooplankton summer concentration peaks,0)
In [84]:
zz_day=np.arange(1,367) # in model, nday_year starts at 1 on jan 1
dts=[dt.datetime(2014,12,31)+dt.timedelta(days=float(ii)) for ii in zz_day]
zz_day=np.expand_dims(zz_day,1)
zz_MesZoBar = zz_rate_mesozoo_winterconc + \
zz_rate_mesozoo_summerconc*(np.sum ( zz_rate_mesozoo_sumpeakval * \
np.exp(-(zz_day-zz_rate_mesozoo_sumpeakpos)**2/zz_rate_mesozoo_sumpeakwid**2),1) \
+ np.sum ( zz_rate_mesozoo_sumpeakval * \
np.exp( -(zz_day-zz_rate_mesozoo_sumpeakpos-365.25)**2/zz_rate_mesozoo_sumpeakwid**2),1) \
+ np.sum ( zz_rate_mesozoo_sumpeakval * \
np.exp( -(zz_day-zz_rate_mesozoo_sumpeakpos+365.25)**2/zz_rate_mesozoo_sumpeakwid**2),1) )
zz_MesZoBar2 = zz_rate_mesozoo_winterconc + \
zz_rate_mesozoo_summerconc*(np.sum ( 2*zz_rate_mesozoo_sumpeakval * \
np.exp(-(zz_day-zz_rate_mesozoo_sumpeakpos)**2/zz_rate_mesozoo_sumpeakwid**2),1) \
+ np.sum ( 2*zz_rate_mesozoo_sumpeakval * \
np.exp( -(zz_day-zz_rate_mesozoo_sumpeakpos-365.25)**2/zz_rate_mesozoo_sumpeakwid**2),1) \
+ np.sum ( 2*zz_rate_mesozoo_sumpeakval * \
np.exp( -(zz_day-zz_rate_mesozoo_sumpeakpos+365.25)**2/zz_rate_mesozoo_sumpeakwid**2),1) )
zz_MesZoBar3 = zz_rate_mesozoo_winterconc2 + \
zz_rate_mesozoo_summerconc*(np.sum ( zz_rate_mesozoo_sumpeakval2 * \
np.exp(-(zz_day-zz_rate_mesozoo_sumpeakpos)**2/zz_rate_mesozoo_sumpeakwid2**2),1) \
+ np.sum ( zz_rate_mesozoo_sumpeakval2 * \
np.exp( -(zz_day-zz_rate_mesozoo_sumpeakpos-365.25)**2/zz_rate_mesozoo_sumpeakwid2**2),1) \
+ np.sum ( zz_rate_mesozoo_sumpeakval2 * \
np.exp( -(zz_day-zz_rate_mesozoo_sumpeakpos+365.25)**2/zz_rate_mesozoo_sumpeakwid2**2),1) )
In [86]:
fig,ax=plt.subplots(1,1,figsize=(20,6))
ax.plot(dts,zz_MesZoBar,'b-')
ax.plot(dts,zz_MesZoBar2,'r-')
ax.plot(dts,zz_MesZoBar3,'c--')
Out[86]:
[<matplotlib.lines.Line2D at 0x7f36170a4cf8>]
In [20]:
zz_MesZoBar
Out[20]:
array([ 0.43942235, 0.43834659, 0.43730266, 0.43629065, 0.43531065,
0.43436275, 0.43344703, 0.43256358, 0.43171247, 0.43089376,
0.43010753, 0.42935383, 0.42863271, 0.42794425, 0.42728847,
0.42666543, 0.42607518, 0.42551775, 0.42499317, 0.42450149,
0.42404273, 0.42361693, 0.4232241 , 0.42286428, 0.42253747,
0.42224372, 0.42198301, 0.42175539, 0.42156085, 0.4213994 ,
0.42127107, 0.42117584, 0.42111374, 0.42108476, 0.4210889 ,
0.42112616, 0.42119655, 0.42130005, 0.42143666, 0.42160638,
0.42180919, 0.42204509, 0.42231406, 0.42261608, 0.42295114,
0.42331922, 0.42372029, 0.42415434, 0.42462133, 0.42512123,
0.42565403, 0.42621967, 0.42681812, 0.42744935, 0.4281133 ,
0.42880993, 0.4295392 , 0.43030104, 0.4310954 , 0.43192221,
0.43278142, 0.43367294, 0.43459671, 0.43555264, 0.43654065,
0.43756066, 0.43861255, 0.43969625, 0.44081163, 0.44195859,
0.443137 , 0.44434675, 0.44558771, 0.44685972, 0.44816266,
0.44949636, 0.45086067, 0.45225542, 0.45368044, 0.45513554,
0.45662053, 0.4581352 , 0.45967936, 0.46125278, 0.46285524,
0.46448649, 0.4661463 , 0.46783441, 0.46955054, 0.47129443,
0.47306579, 0.47486432, 0.47668972, 0.47854166, 0.48041981,
0.48232384, 0.48425339, 0.4862081 , 0.48818759, 0.49019147,
0.49221935, 0.49427081, 0.49634543, 0.49844278, 0.5005624 ,
0.50270384, 0.50486662, 0.50705026, 0.50925426, 0.5114781 ,
0.51372127, 0.51598323, 0.51826343, 0.5205613 , 0.52287629,
0.52520779, 0.52755521, 0.52991794, 0.53229535, 0.53468681,
0.53709167, 0.53950927, 0.54193894, 0.54437999, 0.54683172,
0.54929344, 0.55176441, 0.55424392, 0.55673121, 0.55922554,
0.56172615, 0.56423226, 0.56674308, 0.56925784, 0.57177572,
0.57429592, 0.57681761, 0.57933997, 0.58186215, 0.58438333,
0.58690263, 0.58941922, 0.59193221, 0.59444073, 0.59694392,
0.59944088, 0.60193072, 0.60441256, 0.60688548, 0.60934861,
0.61180102, 0.6142418 , 0.61667006, 0.61908488, 0.62148533,
0.62387052, 0.62623952, 0.62859142, 0.63092531, 0.63324027,
0.63553539, 0.63780976, 0.64006248, 0.64229264, 0.64449935,
0.6466817 , 0.64883882, 0.65096982, 0.65307382, 0.65514994,
0.65719734, 0.65921514, 0.6612025 , 0.66315858, 0.66508256,
0.66697362, 0.66883094, 0.67065372, 0.67244119, 0.67419258,
0.6759071 , 0.67758403, 0.67922263, 0.68082217, 0.68238196,
0.68390129, 0.68537951, 0.68681595, 0.68820996, 0.68956094,
0.69086826, 0.69213134, 0.69334962, 0.69452253, 0.69564956,
0.69673018, 0.69776391, 0.69875027, 0.69968881, 0.70057911,
0.70142075, 0.70221335, 0.70295655, 0.70364999, 0.70429337,
0.70488637, 0.70542874, 0.70592022, 0.70636058, 0.70674961,
0.70708714, 0.707373 , 0.70760708, 0.70778925, 0.70791943,
0.70799756, 0.70802361, 0.70799756, 0.70791943, 0.70778925,
0.70760708, 0.707373 , 0.70708714, 0.70674961, 0.70636058,
0.70592022, 0.70542874, 0.70488637, 0.70429337, 0.70364999,
0.70295655, 0.70221335, 0.70142075, 0.70057911, 0.69968881,
0.69875027, 0.69776391, 0.69673018, 0.69564956, 0.69452253,
0.69334962, 0.69213134, 0.69086826, 0.68956094, 0.68820996,
0.68681595, 0.68537951, 0.68390129, 0.68238196, 0.68082217,
0.67922263, 0.67758403, 0.6759071 , 0.67419258, 0.67244119,
0.67065372, 0.66883094, 0.66697362, 0.66508256, 0.66315858,
0.6612025 , 0.65921514, 0.65719734, 0.65514994, 0.65307382,
0.65096982, 0.64883882, 0.6466817 , 0.64449935, 0.64229264,
0.64006248, 0.63780976, 0.63553539, 0.63324027, 0.63092531,
0.62859142, 0.62623952, 0.62387052, 0.62148533, 0.61908488,
0.61667006, 0.6142418 , 0.61180102, 0.60934861, 0.60688548,
0.60441256, 0.60193072, 0.59944088, 0.59694392, 0.59444073,
0.59193221, 0.58941922, 0.58690263, 0.58438333, 0.58186215,
0.57933997, 0.57681761, 0.57429592, 0.57177572, 0.56925784,
0.56674308, 0.56423226, 0.56172615, 0.55922554, 0.55673121,
0.55424392, 0.55176441, 0.54929344, 0.54683172, 0.54437999,
0.54193894, 0.53950927, 0.53709167, 0.53468681, 0.53229535,
0.52991794, 0.52755521, 0.52520779, 0.52287629, 0.5205613 ,
0.51826343, 0.51598323, 0.51372127, 0.5114781 , 0.50925426,
0.50705026, 0.50486662, 0.50270384, 0.5005624 , 0.49844278,
0.49634543, 0.49427081, 0.49221935, 0.49019147, 0.48818759,
0.4862081 , 0.48425339, 0.48232384, 0.48041981, 0.47854166,
0.47668972, 0.47486432, 0.47306579, 0.47129443, 0.46955054,
0.46783441, 0.4661463 , 0.46448649, 0.46285524, 0.46125278,
0.45967936, 0.4581352 , 0.45662053, 0.45513554, 0.45368044,
0.45225542, 0.45086067, 0.44949636, 0.44816266, 0.44685972,
0.44558771, 0.44434675, 0.443137 , 0.44195859, 0.44081163,
0.43969625])
N
In [2]:
zz_rate_kapa_diat = 1.0
zz_rate_kapa_myri = 0.5
zz_rate_kapa_nano = 0.300
zz_rate_k_diat = 2.0
zz_rate_k_myri = 0.5
zz_rate_k_nano = 0.1
In [3]:
def OUP(zz_NO,zz_NH,zz_rate_kapa,zz_rate_k):
Oup_cell = zz_NO * zz_rate_kapa / (zz_rate_k + zz_NO * zz_rate_kapa + zz_NH)
return Oup_cell
In [4]:
def HUP(zz_NO,zz_NH,zz_rate_kapa,zz_rate_k):
Hup_cell = zz_NH / (zz_rate_k + zz_NO * zz_rate_kapa + zz_NH)
return Hup_cell
In [9]:
NH=np.arange(0,20,.1)
NO=5
plt.plot(NH,HUP(NO,NH,zz_rate_kapa_diat,zz_rate_k_diat),'b-')
plt.plot(NH,HUP(NO,NH,.9,zz_rate_k_diat),'-',color='purple')
plt.plot(NH,HUP(NO,NH,zz_rate_kapa_myri,zz_rate_k_myri),'m-')
plt.plot(NH,HUP(NO,NH,zz_rate_kapa_nano,zz_rate_k_nano),'r-')
plt.plot(NH,OUP(NO,NH,zz_rate_kapa_diat,zz_rate_k_diat),'b--')
plt.plot(NH,OUP(NO,NH,zz_rate_kapa_myri,zz_rate_k_myri),'m--')
plt.plot(NH,OUP(NO,NH,zz_rate_kapa_nano,zz_rate_k_nano),'r--')
Out[9]:
[<matplotlib.lines.Line2D at 0x7f22ba55fe48>]
In [8]:
NH=np.arange(0,20,.1)
NO=1
plt.plot(NH,HUP(NO,NH,zz_rate_kapa_diat,zz_rate_k_diat),'b-')
plt.plot(NH,HUP(NO,NH,zz_rate_kapa_myri,zz_rate_k_myri),'m-')
plt.plot(NH,HUP(NO,NH,zz_rate_kapa_nano,zz_rate_k_nano),'r-')
plt.plot(NH,OUP(NO,NH,zz_rate_kapa_diat,zz_rate_k_diat),'b--')
plt.plot(NH,OUP(NO,NH,zz_rate_kapa_myri,zz_rate_k_myri),'m--')
plt.plot(NH,OUP(NO,NH,zz_rate_kapa_nano,zz_rate_k_nano),'r--')
Out[8]:
[<matplotlib.lines.Line2D at 0x7f22ba56c160>]
In [ ]: