ALL PID cases

PID Kp Kd
A 0.0005 0.1
B 0.0005 0.005
C 0.0001 0.01
D 0.001 0.1
E 0.001 0.5
F 0.005 0.1
G 0.005 0.5
H 0.001 1.0

Index

Figure Set 1: FATES_NPP (for C-only at that site and RD/ECA)
Figure Set 2: FATES_NPP
Figure Set 3: FATES_STOREC
Figure Set 3: FATES_STOREC_TF
Figure Set 4: FATES_STOREN
Figure Set 4: FATES_STOREN_TF
Figure Set 5: FATES_STOREC_TF / FATES_STOREN_TF
Figure Set 3: FATES_L2FR
Figure Set 6: FATES_FROOTC_ALLOC / FATES_LEAFC_ALLOC
Figure Set 7: FATES_FROOTC / FATES_LEAFC
Figure Set 7: FATES_FROOTC
Figure Set 7: FATES_LEAFC
Figure Set 8: FATES_NO3UPTAKE
Figure Set 8: FATES_NH4UPTAKE
Figure Set 9: NET_NMIN
Figure Set 9: GROSS_NMIN
Figure Set10: SMIN_NO3_LEACHED
Figure Set10: DENIT

Features of this Jupyter Notebook

Define FilePaths

Enter name of the single simulation that you want to investigate:

Select the timeperiod you are interested to plot for daily timeseries

Daily Plots

Working on the PID Variables

Hint:

'''

   cx_int = cx_int + cx_logratio

   ! Reset the integrator if its sign changes
   if( abs(cx_logratio)>nearzero .and. abs(cx0)>nearzero) then
      if( abs(cx_logratio/abs(cx_logratio) - cx0/abs(cx0)) > nearzero ) then
         cx_int = cx_logratio
      end if
   end if

   dcxdt_ratio = cx_logratio-cx0

   ema_dcxdt = pid_drv_wgt*dcxdt_ratio + (1._r8-pid_drv_wgt)*ema_dcxdt

   cx0 = cx_logratio

'''

Plots with L2FR calculated based on C/C' and N/N' from the model outputs

The variables that are calcuated based on the C/C' and N/N'

Other variables are directly ploted from model variables (except l2fr_delta_model, which is just the difference of FATES_L2FR one month apart

Leaf C C Store C/C' CN Ratio derivative/integral proportion of there terms multiple terms delta l2fr l2fr

Printing Variables that go in L2FR calculations

Printing the following lines of the routine CNPAdjustFRootTargets in the file parteh/PRTAllometricCNPMod.F90

852     print*, 'sinkhole',trim(dateTimeString) ,cx_logratio, cp_ratio, cn_ratio, cx_int, &
853     dcxdt_ratio, ema_dcxdt, cx0, l2fr_delta, l2fr, store_c_max, store_c_act, &
854     store_nut_max, store_nut_act, l2fr_min, store_N_max, store_N_act, &
855     store_P_max, store_P_act, dbh

Number of Cohorts over time

FATES_NPP just for reference

Plot of L2FR from print file

Plotting for multiple decades

Ploting only after logging

#plt.plot(ds_FATES_daily['FATES_L2FR']) #sum_l2fr['l2fr'].plot() fig1, ax = plt.subplots(figsize=(30, 6)) scatter = ax.scatter(x=range(len(ds_FATES_daily['FATES_L2FR'])), y = ds_FATES_daily['FATES_L2FR'], c=ds_FATES_daily['FATES_L2FR'], cmap='viridis', marker='o', label = 'ds_FATES_daily') plt.legend()

The next cell is for the carbon only simuations for the chosen site

Figure Set 1

Rest of the results are for the chosen sim!

Figure Set 2

Figure Set 3

Figure Set 4

Figure Set 5

Figure Set 6

Figure Set 7

Figure Set 8

Figure Set 10

FATES_NPP (for C-only at that site and RD/ECA) [done] FATES_NPP [done] FATES_STOREC FATES_STOREC_TF FATES_STOREN FATES_STOREN_TF FATES_STOREC_TF / FATES_STOREN_TF FATES_L2FR FATES_FROOTC_ALLOC / FATES_LEAFC_ALLOC FATES_FROOTC / FATES_LEAFC FATES_FROOTC FATES_LEAFC FATES_NO3UPTAKE FATES_NH4UPTAKE NET_NMIN GROSS_NMIN It's also be good to look at N leaching (NLEACH?) and denitrification (DENIT?). Not sure those are the variable names but the variables (however named) should be in the output files I hope. And let's take a look at PID-D for both Duke and ORNL, and PID-B for Duke.

Subplot 1: FATES_NPP (for C-only at that site and RD/ECA) Subplot 1: FATES_NPP Subplot 2: FATES_STOREC Subplot 2: FATES_STOREC_TF Subplot 3: FATES_STOREN Subplot 3: FATES_STOREN_TF Subplot 4: FATES_STOREC_TF / FATES_STOREN_TF Subplot 5: FATES_FROOTC_ALLOC / FATES_LEAFC_ALLOC Subplot 5: FATES_L2FR Subplot 6: FATES_FROOTC / FATES_LEAFC Subplot 6: FATES_FROOTC Subplot 6: FATES_LEAFC Subplot 7: FATES_NO3UPTAKE Subplot 7: FATES_NH4UPTAKE Subplot 8: NET_NMIN Subplot 8: GROSS_NMIN Subplot 9: SMIN_NO3_LEACHED Subplot 9: DENIT

Ignore the plots after this cell!