pwd

'/Users/ud4/repos/GitHub/FATESFACE'

from IPython.display import Image, display
display(Image(filename='/Users/ud4/Downloads/PIDs.png'))

Added features

• for a cell, you can choose which variables you are interested in for a single simulation.
• some plots from postprocessor

import os,glob
import xarray as xr
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pyreadr # to read .rds files

path_in = "/Users/ud4/FATESMDS_analysis/outputs/runs/tests_alp/230309/"

# RD only

fnames={}
fnames["DUK_PIDA_Conly"] = f"{path_in}Bharat_AW_Nalloc_api25e3sm_mf0df100_r0615_Base_PIDA_AgBgW_processed/Bharat_AW_Nalloc_api25e3sm_mf0df100_r0615_Base_PIDA_AgBgW_US-DUK_trans.nc"
fnames["ORN_PIDA_Conly"] = f"{path_in}Bharat_AW_Nalloc_api25e3sm_mf0df100_r0615_Base_PIDA_AgBgW_processed/Bharat_AW_Nalloc_api25e3sm_mf0df100_r0615_Base_PIDA_AgBgW_US-ORN_trans.nc"

fnames["DUK_PIDA_RD"] = f"{path_in}Bharat_AW_Nalloc_api25e3sm_mf0df100_r0615_Base_PIDA_AgBgW_RD_processed/Bharat_AW_Nalloc_api25e3sm_mf0df100_r0615_Base_PIDA_AgBgW_RD_US-DUK_trans.nc"
fnames["ORN_PIDA_RD"] = f"{path_in}Bharat_AW_Nalloc_api25e3sm_mf0df100_r0615_Base_PIDA_AgBgW_RD_processed/Bharat_AW_Nalloc_api25e3sm_mf0df100_r0615_Base_PIDA_AgBgW_RD_US-ORN_trans.nc"

fnames["DUK_PIDB_RD"] = f"{path_in}Bharat_AW_Nalloc_api25e3sm_mf0df100_r0614_Base_PIDB_AgBgW_RD_processed/Bharat_AW_Nalloc_api25e3sm_mf0df100_r0614_Base_PIDB_AgBgW_RD_US-DUK_trans.nc"
fnames["ORN_PIDB_RD"] = f"{path_in}Bharat_AW_Nalloc_api25e3sm_mf0df100_r0614_Base_PIDB_AgBgW_RD_processed/Bharat_AW_Nalloc_api25e3sm_mf0df100_r0614_Base_PIDB_AgBgW_RD_US-ORN_trans.nc"

#"ORN_PIDC_RD" : Simulation missing *Running
fnames["DUK_PIDC_RD"] = f"{path_in}Bharat_AW_Nalloc_api25e3sm_mf0df100_r0614_Base_PIDC_AgBgW_RD_processed/Bharat_AW_Nalloc_api25e3sm_mf0df100_r0614_Base_PIDC_AgBgW_RD_US-DUK_trans.nc"
fnames["ORN_PIDC_RD"] = f"{path_in}Bharat_AW_Nalloc_api25e3sm_mf0df100_r0614_Base_PIDC_AgBgW_RD_processed/Bharat_AW_Nalloc_api25e3sm_mf0df100_r0614_Base_PIDC_AgBgW_RD_US-DUK_trans.nc"

fnames["DUK_PIDD_RD"] = f"{path_in}Bharat_AW_Nalloc_api25e3sm_mf0df100_r0614_Base_PIDD_AgBgW_RD_processed/Bharat_AW_Nalloc_api25e3sm_mf0df100_r0614_Base_PIDD_AgBgW_RD_US-DUK_trans.nc"
fnames["ORN_PIDD_RD"] = f"{path_in}Bharat_AW_Nalloc_api25e3sm_mf0df100_r0614_Base_PIDD_AgBgW_RD_processed/Bharat_AW_Nalloc_api25e3sm_mf0df100_r0614_Base_PIDD_AgBgW_RD_US-ORN_trans.nc"

#fnames["DUK_PIDzero_RD"] = : Simulation missing *Running
#fnames["ORN_PIDzero_RD"] = f"{path_in}Bharat_AW_Nalloc_api25e3sm_mf0df100_r06122_Base_PIDzero_RD_processed/Bharat_AW_Nalloc_api25e3sm_mf0df100_r06122_Base_PIDzero_RD_US-ORN_trans.nc"

# Other global attributes for plots

logging_year= 1855

ds = {}

for idx, key in enumerate(fnames.keys()):
    print (key)
    ds[key] = xr.open_mfdataset(fnames[key],decode_times=True)
for idx, key in enumerate(ds.keys()):
    ds[key]['time'] =  pd.to_datetime(ds[key].time.values.astype(str))

DUK_PIDA_Conly
ORN_PIDA_Conly
DUK_PIDA_RD
ORN_PIDA_RD
DUK_PIDB_RD
ORN_PIDB_RD
DUK_PIDC_RD
ORN_PIDC_RD
DUK_PIDD_RD
ORN_PIDD_RD

# For multiple variables on a same plot

sims = "DUK_PIDD_RD"
key=sims

vars_plot = (
"""
FATES_AUTORESP
FATES_GPP
FATES_NPP
"""
).split('\n')
vars_plot = vars_plot[1:-1]



ymin = 9e20
ymax = -9e20
sum_ts = 0
for i_var,var in enumerate(vars_plot):
    ts_data = ds[key][var].groupby("time.year").mean('time')
    sum_ts= sum_ts + ts_data
    ts_data.plot(figsize=(20,3))
    if np.min(ts_data.values) < ymin:
        ymin = np.min(ts_data.values)
    if np.max(ts_data.values) > ymax:
        ymax = np.max(ts_data.values)
    
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    #plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    #plt.ylim(.2e-6,1.5e-6)
    if i_var != len(vars_plot)-1 : 
        plt.xticks([])
        plt.xlabel(None)
        
fig = plt.figure(figsize=(20,9))
plt.title (f"Common Plot for simulation {key}", fontsize=15)
for i_var,var in enumerate(vars_plot):
    ts_data = ds[key][var].groupby("time.year").mean('time')
    plt.plot(ts_data, label = var)
    plt.ylim(ymin*.95,ymax*1.05)
    plt.legend(fontsize=14)
#plt.close(fig)
    
fig1 = plt.figure(figsize=(20,9))
plt.title (f"Fractional Plot for simulation {key}", fontsize=15)
for i_var,var in enumerate(vars_plot):
    ts_data = ds[key][var].groupby("time.year").mean('time')
    plt.plot(ts_data/sum_ts, label = var)
    #plt.ylim(ymin*.95,ymax*1.05)
    plt.legend(fontsize=14)
#plt.close(fig1)
#plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    
        

# For multiple variables on a same plot

sims = "ORN_PIDA_RD"
key=sims

'''
vars_plot = [
"FATES_CROOT_ALLOC",
"FATES_FROOT_ALLOC",
"FATES_FROOT_ALLOC",
"FATES_SEED_ALLOC",
"FATES_STEM_ALLOC",
"FATES_STORE_ALLOC"
]

'''

vars_plot = (
"""
FATES_CROOT_ALLOC
FATES_FROOT_ALLOC
FATES_FROOT_ALLOC
FATES_SEED_ALLOC
FATES_STEM_ALLOC
FATES_STORE_ALLOC
"""
).split('\n')
vars_plot = vars_plot[1:-1]



ymin = 9e20
ymax = -9e20
sum_ts = 0
for i_var,var in enumerate(vars_plot):
    ts_data = ds[key][var].groupby("time.year").mean('time')
    sum_ts= sum_ts + ts_data
    ts_data.plot(figsize=(20,3))
    if np.min(ts_data.values) < ymin:
        ymin = np.min(ts_data.values)
    if np.max(ts_data.values) > ymax:
        ymax = np.max(ts_data.values)
    
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    #plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    #plt.ylim(.2e-6,1.5e-6)
    if i_var != len(vars_plot)-1 : 
        plt.xticks([])
        plt.xlabel(None)
        
fig = plt.figure(figsize=(20,9))
plt.title (f"Common Plot for simulation {key}", fontsize=15)
for i_var,var in enumerate(vars_plot):
    ts_data = ds[key][var].groupby("time.year").mean('time')
    plt.plot(ts_data, label = var)
    plt.ylim(ymin*.95,ymax*1.05)
    plt.legend(fontsize=14)
#plt.close(fig)
    
fig1 = plt.figure(figsize=(20,9))
plt.title (f"Fractional Plot for simulation {key}", fontsize=15)
for i_var,var in enumerate(vars_plot):
    ts_data = ds[key][var].groupby("time.year").mean('time')
    plt.plot(ts_data/sum_ts, label = var)
    #plt.ylim(ymin*.95,ymax*1.05)
    plt.legend(fontsize=14)
#plt.close(fig1)
#plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    
        

# For multiple variables on a same plot

sims = "ORN_PIDA_RD"
key=sims

vars_plot = (
"""
FATES_FROOTC
FATES_LEAFC
FATES_NONSTRUCTC
FATES_REPROC
FATES_SAPWOODC
FATES_STOREC
FATES_STRUCTC
FATES_VEGC
"""
).split('\n')
vars_plot = vars_plot[1:-1]



ymin = 9e20
ymax = -9e20
sum_ts = 0
for i_var,var in enumerate(vars_plot):
    print(var)
    ts_data = ds[key][var].groupby("time.year").mean('time')
    sum_ts= sum_ts + ts_data
    ts_data.plot(figsize=(20,3))
    if np.min(ts_data.values) < ymin:
        ymin = np.min(ts_data.values)
    if np.max(ts_data.values) > ymax:
        ymax = np.max(ts_data.values)
    
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    #plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    #plt.ylim(.2e-6,1.5e-6)
    if i_var != len(vars_plot)-1 : 
        plt.xticks([])
        plt.xlabel(None)
        
fig = plt.figure(figsize=(20,9))
plt.title (f"Common Plot for simulation {key}", fontsize=15)
for i_var,var in enumerate(vars_plot):
    ts_data = ds[key][var].groupby("time.year").mean('time')
    plt.plot(ts_data, label = var)
    plt.ylim(ymin*.95,ymax*1.05)
    plt.legend(fontsize=14)
#plt.close(fig)
    
fig1 = plt.figure(figsize=(20,9))
plt.title (f"Fractional Plot for simulation {key}", fontsize=15)
for i_var,var in enumerate(vars_plot):
    ts_data = ds[key][var].groupby("time.year").mean('time')
    plt.plot(ts_data/sum_ts, label = var)
    #plt.ylim(ymin*.95,ymax*1.05)
    plt.legend(fontsize=14)
#plt.close(fig1)
#plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    
        

FATES_FROOTC
FATES_LEAFC
FATES_NONSTRUCTC
FATES_REPROC
FATES_SAPWOODC
FATES_STOREC
FATES_STRUCTC
FATES_VEGC

# For multiple variables on a same plot

sims = "ORN_PIDA_RD"
key=sims

vars_plot = (
"""
FATES_STOREC
FATES_STOREC_TF
"""
).split('\n')
vars_plot = vars_plot[1:-1]



ymin = 9e20
ymax = -9e20
sum_ts = 0
for i_var,var in enumerate(vars_plot):
    ts_data = ds[key][var].groupby("time.year").mean('time')
    sum_ts= sum_ts + ts_data
    ts_data.plot(figsize=(20,3))
    if np.min(ts_data.values) < ymin:
        ymin = np.min(ts_data.values)
    if np.max(ts_data.values) > ymax:
        ymax = np.max(ts_data.values)
    
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    #plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    #plt.ylim(.2e-6,1.5e-6)
    if i_var != len(vars_plot)-1 : 
        plt.xticks([])
        plt.xlabel(None)
        
fig = plt.figure(figsize=(20,9))
plt.title (f"Common Plot for simulation {key}", fontsize=15)
for i_var,var in enumerate(vars_plot):
    if var == "FATES_L2FR" : continue
    ts_data = ds[key][var].groupby("time.year").mean('time')
    plt.plot(ts_data, label = var)
    plt.ylim(ymin*.95,ymax*1.05)
    plt.legend(fontsize=14)
#plt.close(fig)
    
fig1 = plt.figure(figsize=(20,9))
plt.title (f"Fractional Plot for simulation {key}", fontsize=15)
for i_var,var in enumerate(vars_plot):
    ts_data = ds[key][var].groupby("time.year").mean('time')
    plt.plot(ts_data/sum_ts, label = var)
    #plt.ylim(ymin*.95,ymax*1.05)
    plt.legend(fontsize=14)
    
fig2 = plt.figure(figsize=(20,9))
plt.title (f"Fractional Plot for simulation {key}", fontsize=15)
for i_var,var in enumerate(vars_plot):
    ts_data = (ds[key]["FATES_STOREC"]/ds[key]["FATES_STOREC_TF"]).groupby("time.year").mean('time')
    plt.plot(ts_data/sum_ts, label = "FATES_STOREC/FATES_STOREC_TF")
    #plt.ylim(ymin*.95,ymax*1.05)
    plt.legend(fontsize=14)
    plt.title(f"{key}: FATES_STOREC/FATES_STOREC_TF")
    
        

# For multiple variables on a same plot

sims = "ORN_PIDA_RD"
key=sims

vars_plot = (
"""
FATES_L2FR
FATES_LEAFC
FATES_FROOTC
"""
).split('\n')
vars_plot = vars_plot[1:-1]



ymin = 9e20
ymax = -9e20
sum_ts = 0
for i_var,var in enumerate(vars_plot):
    ts_data = ds[key][var].groupby("time.year").mean('time')
    sum_ts= sum_ts + ts_data
    ts_data.plot(figsize=(20,3))
    if np.min(ts_data.values) < ymin:
        ymin = np.min(ts_data.values)
    if np.max(ts_data.values) > ymax:
        ymax = np.max(ts_data.values)
    
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    #plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    #plt.ylim(.2e-6,1.5e-6)
    if i_var != len(vars_plot)-1 : 
        plt.xticks([])
        plt.xlabel(None)
        
fig = plt.figure(figsize=(20,9))
plt.title (f"Common Plot for simulation {key}", fontsize=15)
for i_var,var in enumerate(vars_plot):
    if var == "FATES_L2FR" : continue
    ts_data = ds[key][var].groupby("time.year").mean('time')
    plt.plot(ts_data, label = var)
    plt.ylim(ymin*.95,ymax*1.05)
    plt.legend(fontsize=14)
#plt.close(fig)
    
fig1 = plt.figure(figsize=(20,9))
plt.title (f"Fractional Plot for simulation {key}", fontsize=15)
for i_var,var in enumerate(vars_plot):
    ts_data = ds[key][var].groupby("time.year").mean('time')
    plt.plot(ts_data/sum_ts, label = var)
    #plt.ylim(ymin*.95,ymax*1.05)
    plt.legend(fontsize=14)
    
fig2 = plt.figure(figsize=(20,9))
plt.title (f"Fractional Plot for simulation {key}", fontsize=15)
#for i_var,var in enumerate(vars_plot):
if True:
    ts_data = (ds[key]["FATES_LEAFC"]/ds[key]["FATES_FROOTC"]).groupby("time.year").mean('time')
    plt.plot(ts_data/sum_ts, label = "FATES_LEAFC/FATES_FROOTC")
    ts_data = ds[key]["FATES_L2FR"].groupby("time.year").mean('time')
    plt.plot(ts_data/sum_ts, label = "FATES_L2FR")
    #plt.ylim(ymin*.95,ymax*1.05)
    plt.legend(fontsize=14)
    plt.title(f"{key}: FATES_LEAFC/FATES_FROOTC")
    
        

print (break)

  File "/var/folders/f1/01gxw8vn74q_x_rf_p5ztryjr405zq/T/ipykernel_26041/1454241351.py", line 1
    print (break)
           ^
SyntaxError: invalid syntax

Ignore the plots after this cell!

start = "1855-01-01"
end = "1885-01-01"
var = "BTRAN"
for idx, key in enumerate(ds.keys()):
    ds[key][var].plot(figsize=(20,3))
    plt.title(f"{var} {key} (DAILY)")
    plt.axvspan(start,end,alpha = .2, color = 'red')

var = "BTRAN"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3))
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,1)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
    

ds[key][var].long_name 
# Remarks: 

len(ds.keys())

BTRAN

Increased BTRAN shown increase water stress during after the logging event which hinder NPP growth, especially in the case of ECA and RD. For Carbon only simulations BTRAN recovered quickly. What could cause BTRAN to be so low in N-active model?

ds[key][var].groupby("time.year").mean('time')

start = "1855-12-01"
end = "2000-01-01"
var = "ACTUAL_IMMOB"
for idx, key in enumerate(ds.keys()):
    ds[key][var].plot(figsize=(20,3))
    plt.title(f"{var} {key} (DAILY)")
    plt.axvspan(start,end,alpha = .2, color = 'red')

#ACTUAL_IMMOB
var = "ACTUAL_IMMOB"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3))
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(.2e-6,1.5e-6)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
    
ds[key][var].long_name 
# Remarks: 
# Increase in Immobilization of 

ACTUAL_IMMOB

Increased ACTUAL_IMMOB shows less nutrient available for plants in ECA model.

#ACTUAL_IMMOB
var = "ACTUAL_IMMOB_P"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3))
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(.5e-8,4.4e-8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
        
ds[key][var].long_name 
# Remarks: 
    

var = "ADSORBTION_P"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3))
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
        
ds[key][var].long_name 
# Remarks: 

var = "CMASS_BALANCE_ERROR"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3))
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(.6e-6,1.5e-6)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
        
ds[key][var].long_name 
# Remarks: 

var = "DENIT"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(.6e-6,1.5e-6)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)

ds[key][var].long_name 
# Remarks: 

var = "DESORPTION_P"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3))
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(.6e-6,1.5e-6)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)

ds[key][var].long_name 
# Remarks: 

var = "ELAI"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(.6e-6,1.5e-6)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)

ds[key][var].long_name 
# Remarks: 

var = "ESAI"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(.6e-6,1.5e-6)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
        
ds[key][var].long_name 
# Remarks: 

var = "FATES_AREA_PLANTS"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(.6e-6,1.5e-6)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
        
ds[key][var].long_name 
# Remarks: 

var = "FATES_AREA_TREES"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,1)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
        
ds[key][var].long_name 
# Remarks: 

var = "FATES_AUTORESP"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(.6e-6,1.5e-6)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
        
ds[key][var].long_name 
# Remarks: 

var = "FATES_AUTORESP_CANOPY"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(.6e-6,1.5e-6)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
        
ds[key][var].long_name 
# Remarks: 
    

var = "FATES_AUTORESP_USTORY"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(.6e-6,1.5e-6)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
        
ds[key][var].long_name 
# Remarks: 

var = "FATES_CROOTMAINTAR"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,6e-9)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
        
ds[key][var].long_name 
# Remarks: 
    

var = "FATES_CROOT_ALLOC"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,6e-9)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)

ds[key][var].long_name 
# Remarks: 

var = "FATES_DISTURBANCE_RATE_LOGGING"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,1)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)

ds[key][var].long_name 
# Remarks: 

var = "FATES_EXCESS_RESP"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,6e-9)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)

ds[key][var].long_name 
# Remarks: 

var = "FATES_DROUGHT_STATUS"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: NA")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    #plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,6e-9)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
        
ds[key][var].long_name 
# Remarks: 
    

var = "FATES_FROOTMAINTAR"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,1.5e-9)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
    
ds[key][var].long_name 
# Remarks: 

var = "FATES_FROOT_ALLOC"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,1.8e-9)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)

ds[key][var].long_name 
# Remarks: 
    

var = "FATES_GDD"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 30, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,60)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)

ds[key][var].long_name 
# Remarks: 

var = "FATES_GPP"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,7e-8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
        
ds[key][var].long_name 
# Remarks: 

var = "FATES_HARVEST_CARBON_FLUX"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,7e-8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)

        
ds[key][var].long_name 
# Remarks:     

var = "FATES_HET_RESP"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,2.8e-8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 
# Remarks: Does this tells us that the microbes in soil live and flurish after logging (ECA). Slowly decline in aCO2 and eCO2. due to less litter?

var = "FATES_LBLAYER_COND"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,2.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "FATES_LEAF_ALLOC"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,4e-9)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "FATES_LITTER_IN"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,5e-7)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "FATES_LITTER_OUT"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,1e-7)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks:  

var = "FATES_NEP"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(-2.5e-8,2e-8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "FATES_NPP"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,5e-8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "FATES_STEM_ALLOC"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,2.8e-8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks:  

var = "FATES_STOMATAL_COND"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.04)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks:  

var = "FCEV"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,8.5)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "FCOV"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,8.5)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "FGEV"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,60)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 
# Remarks: High EVAP maybe causing water stress.

var = "FH2OSFC"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,.008)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "FSH_G"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(-6,30)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "F_DENIT"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(-6,30)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "F_N2O_DENIT"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(-6,30)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "F_NIT"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(-6,30)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "GROSS_NMIN"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(-6,30)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "H2OCAN"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,.1)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "HR"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "LITHR"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "NET_NMIN"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "NET_PMIN"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "PLANT_NDEMAND_COL"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 
# Remarks:

var = "PLANT_PDEMAND_COL"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: P recovers while N is limited in ECA.

var = "POTENTIAL_IMMOB"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,5e-4)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "POTENTIAL_IMMOB_P"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "POT_F_DENIT"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "POT_F_NIT"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "QDRAI_PERCH"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "QINFL"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 2e-5, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,3.5e-5)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "SMINN"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "SMINN_TO_PLANT"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "SMINP"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "TLAI"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)

ds[key][var].long_name 
# Remarks: 

var = "TOTCOLC"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    #plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "TOTECOSYSC"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "TOTECOSYSN"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks:  

var = "TOTECOSYSP"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "TOTLITC"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "TOTLITN"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks:  

var = "TOTLITP"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "TSAI"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,.8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

Looking at Biomass variables

The biomass components that remain close to zero for logging under ECA are:

• FATES_CANOPY_VEGC
• FATES_DEMOTION_CARBONFLUX
• FATES_LEAFC
• FATES_MORTALITY_CFLUX_CANOPY
• FATES_MORTALITY_CFLUX_USTORY
• FATES_NONSTRUCTC
• FATES_SAPWOODC
• FATES_STOREC
• FATES_STRUCTC (slow recovery for C only)
• FATES_USTORY_VEGC
• FATES_VEGC (slow recovery for C only)
• FATES_VEGC_ABOVEGROUND (slow recovery for C only)

Non-Zero are:

• FATES_FROOTC (full recovery)

Zero after logging (C and ECA):

• FATES_PROMOTION_CARBONFLUX

both the fluxes as well as carbon pools are low for ECA.

var = "FATES_CANOPY_VEGC"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,27)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
        
ds[key][var].long_name 
# Remarks: 

var = "FATES_DEMOTION_CARBONFLUX"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,2.7e-8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
        
ds[key][var].long_name 
# Remarks: 

var = "FATES_FROOTC"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,.1)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)

ds[key][var].long_name 
# Remarks: 

var = "FATES_LEAFC"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,.2)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "FATES_MORTALITY_CFLUX_CANOPY"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,1.3e-8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "FATES_MORTALITY_CFLUX_USTORY"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,1.4e-8)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "FATES_NONSTRUCTC"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,10)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "FATES_PROMOTION_CARBONFLUX"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,10)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "FATES_REPROC"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,10)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "FATES_SAPWOODC"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,10)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "FATES_STOREC"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,1.6)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks:  

var = "FATES_STRUCTC"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,28)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks:  

var = "FATES_USTORY_VEGC"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,28)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks:  

var = "FATES_VEGC"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,35)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
 
ds[key][var].long_name 
# Remarks: 

var = "FATES_VEGC_ABOVEGROUND"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    plt.ylim(0,35)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)

ds[key][var].long_name 
# Remarks: 

var = "LITTERC"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,35)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
ds[key][var].long_name
# Remarks: 

var = "LITHR"
for idx, key in enumerate(ds.keys()):
    ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
    plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
    plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
    plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
    plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
    plt.xlim(1850,2010)
    #plt.ylim(0,35)
    if idx != len(ds.keys())-1 : 
        plt.xticks([])
        plt.xlabel(None)
ds[key][var].long_name
# Remarks: 

var = "FATES_LITTER_AG_FINE_EL"
#ds[key][var][:,0].plot()
ds[key][var][:,1].plot()
#ds[key][var][:,2].plot()

var = "FATES_LITTER_AG_FINE_EL"
for idx, key in enumerate(ds.keys()):
    for Fi in range (len(ds[key][var].fates_levelem)):
        ds[key][var][:,Fi].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
        plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units} | {(ds[key][var][:,Fi].fates_levelem.values)}")
        plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
        plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
        plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
        plt.xlim(1850,2010)
        #plt.ylim(0,35)
        if idx != len(ds.keys())-1 : 
            plt.xticks([])
            plt.xlabel(None)
ds[key][var].long_name

var = "FATES_LITTER_AG_CWD_EL"
for idx, key in enumerate(ds.keys()):
    for Fi in range (len(ds[key][var].fates_levelem)):
        ds[key][var][:,Fi].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
        plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units} | {(ds[key][var][:,Fi].fates_levelem.values)}")
        plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
        plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
        plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
        plt.xlim(1850,2010)
        #plt.ylim(0,35)
        if idx != len(ds.keys())-1 : 
            plt.xticks([])
            plt.xlabel(None)
ds[key][var].long_name

var = "FATES_LITTER_BG_FINE_EL"
for idx, key in enumerate(ds.keys()):
    for Fi in range (len(ds[key][var].fates_levelem)):
        ds[key][var][:,Fi].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
        plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units} | {(ds[key][var][:,Fi].fates_levelem.values)}")
        plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
        plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
        plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
        plt.xlim(1850,2010)
        #plt.ylim(0,35)
        if idx != len(ds.keys())-1 : 
            plt.xticks([])
            plt.xlabel(None)
ds[key][var].long_name

var = "FATES_LITTER_BG_CWD_EL"
for idx, key in enumerate(ds.keys()):
    for Fi in range (len(ds[key][var].fates_levelem)):
        ds[key][var][:,Fi].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
        plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units} | {(ds[key][var][:,Fi].fates_levelem.values)}")
        plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
        plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
        plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
        plt.xlim(1850,2010)
        #plt.ylim(0,35)
        if idx != len(ds.keys())-1 : 
            plt.xticks([])
            plt.xlabel(None)
ds[key][var].long_name

var = "FATES_LITTER_IN_EL"
for idx, key in enumerate(ds.keys()):
    for Fi in range (len(ds[key][var].fates_levelem)):
        ds[key][var][:,Fi].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
        plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units} | {(ds[key][var][:,Fi].fates_levelem.values)}")
        plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
        plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
        plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
        plt.xlim(1850,2010)
        #plt.ylim(0,35)
        if idx != len(ds.keys())-1 : 
            plt.xticks([])
            plt.xlabel(None)
ds[key][var].long_name

var = "FATES_LITTER_OUT_EL"
for idx, key in enumerate(ds.keys()):
    for Fi in range (len(ds[key][var].fates_levelem)):
        ds[key][var][:,Fi].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
        plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units} | {(ds[key][var][:,Fi].fates_levelem.values)}")
        plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
        plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
        plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
        plt.xlim(1850,2010)
        #plt.ylim(0,35)
        if idx != len(ds.keys())-1 : 
            plt.xticks([])
            plt.xlabel(None)
ds[key][var].long_name

var = "LITR1C_vr"
for idx, key in enumerate(ds.keys()):
    for Fi in range (len(ds[key][var].levdcmp)):
        ds[key][var][:,Fi].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
        plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units} | {(ds[key][var][:,Fi].levdcmp.values)}")
        plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
        plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
        plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
        plt.xlim(1850,2010)
        #plt.ylim(0,35)
        if idx != len(ds.keys())-1 : 
            plt.xticks([])
            plt.xlabel(None)
ds[key][var].long_name

ds[key][var]

ds[key][var].levdcmp

#print(ds[key][var][:,Fi].fates_levelem.values)

var = "FATES_NO3UPTAKE"#"FATES_NH4UPTAKE"
for idx, key in enumerate(ds.keys()):
    try:
        ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
        plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
        plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
        plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
        plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
        plt.xlim(1850,2010)
    #plt.ylim(0,35)
        if idx != len(ds.keys())-1 : 
            plt.xticks([])
            plt.xlabel(None)
    except: 
        pass
ds[key][var].long_name
# Remarks: 

var = "FATES_NH4UPTAKE"
for idx, key in enumerate(ds.keys()):
    try:
        ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
        plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
        plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
        plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
        plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
        plt.xlim(1850,2010)
    #plt.ylim(0,35)
        if idx != len(ds.keys())-1 : 
            plt.xticks([])
            plt.xlabel(None)
    except: 
        pass
ds[key][var].long_name

var = "FATES_PUPTAKE"
for idx, key in enumerate(ds.keys()):
    try:
        ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
        plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
        plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
        plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
        plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
        plt.xlim(1850,2010)
    #plt.ylim(0,35)
        if idx != len(ds.keys())-1 : 
            plt.xticks([])
            plt.xlabel(None)
    except: 
        pass
ds[key][var].long_name

var = "FATES_STOREC_TF"
for idx, key in enumerate(ds.keys()):
    try:
        ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
        plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
        plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
        plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
        plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
        plt.xlim(1850,2010)
    #plt.ylim(0,35)
        if idx != len(ds.keys())-1 : 
            plt.xticks([])
            plt.xlabel(None)
    except: 
        pass
ds[key][var].long_name

var = "FATES_STOREN_TF"
for idx, key in enumerate(ds.keys()):
    try:
        ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
        plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
        plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
        plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
        plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
        plt.xlim(1850,2010)
    #plt.ylim(0,35)
        if idx != len(ds.keys())-1 : 
            plt.xticks([])
            plt.xlabel(None)
    except: 
        pass
ds[key][var].long_name

var = "FATES_STOREP_TF"
for idx, key in enumerate(ds.keys()):
    try:
        ds[key][var].groupby("time.year").mean('time').plot(figsize=(20,3),marker='+')
        plt.title(f"{ds[key][var].long_name} ({var}) - {key} - AnnualSUM | Units: {ds[key][var].units}")
        plt.axvline(x = logging_year, color = 'r',lw=5, label = 'logging year', alpha=.2)
        plt.axhline(y = 0, color = 'r',lw=2, label = 'Zero', alpha=.2)
        plt.axvline(x = 1996, color = 'g',lw=5, label = 'logging year', alpha=.2)
        plt.xlim(1850,2010)
    #plt.ylim(0,35)
        if idx != len(ds.keys())-1 : 
            plt.xticks([])
            plt.xlabel(None)
    except: 
        pass
ds[key][var].long_name

print ("Successful run")