diff --git a/da/preprocessing/category_info.py b/da/preprocessing/category_info.py
index 6df8ff9c5b52720f983e31f53ee4f9741dde1412..31528958fbf8757b690579c71685938bcc5d8a86 100755
--- a/da/preprocessing/category_info.py
+++ b/da/preprocessing/category_info.py
@@ -3,38 +3,38 @@ categories = OrderedDict({
 'A_Public_power': {
   'gnfr': 'A',
   'model': 1,
-  'description': 'CO2 emitted by the generation of public power, including biofuel portion'},
+  'description': 'CO2 emitted by the generation of public power (GNFR A), including biofuel portion. Based on ENTSO-E'},
 'B_Industry': {
   'gnfr': 'B',
   'model': 1,
-  'description': 'CO2 emitted by the industry sector, including biofuel portion'},
+  'description': 'CO2 emitted by the industry sector (GNFR B), including biofuel portion. Based on CAMS data, extrapolated by Key Economic Indicators from Eurostat'},
 'C_Other_stationary_combustion_consumer': {
   'gnfr': 'C',
   'model': 1,
-  'description': 'CO2 emitted by other stationary combustion, including residential and commercial heating, including biofuel portion'},
+  'description': 'CO2 emitted by other stationary combustion (GNFR C), including residential and commercial heating, including biofuel portion. Based on CAMS data, extrapolated by heating degree days from ERA5'},
 'F_On-road': {
   'gnfr': 'F',
   'model': 1,
   'different_profiles_weekends': True,
-  'description': 'CO2 emitted by on-road traffic, including cars and high- and low duty vehicles'},
+  'description': 'CO2 emitted by on-road traffic (GNFR F), including cars and high- and low duty vehicles. Based on CAMS dtata, extrapolated by Eurostat fuel demand'},
 'H_Aviation': {
   'gnfr': 'H',
   'model': 1,
-  'description': 'CO2 emitted for Landing and TakeOff of aviation'},
+  'description': 'CO2 emitted for Landing and TakeOff of aviation (GNFR H). Based on CAMS, with extrapolation based on kerosine demand, taken from Eurostat'},
 'I_Off-road': {
   'gnfr': 'I',
   'model': 1,
-  'description': 'CO2 emitted by off-road vehicles, including construction work and lawn mowers'},
-'L_Agri_other': {
-  'gnfr': 'L',
-  'model': 1,
-  'description': 'CO2 emissions from agriculture, excluding livestock'},
-'K_Agri_livestock': {
-  'gnfr': 'K',
-  'model': 1,
-  'description': 'CO2 emissions from agriculture livestock'},
+  'description': 'CO2 emitted by off-road vehicles (GNFR I, including construction work and lawn mowers). Based on CAMS emissions, without modification'},
+#'L_Agri_other': {
+#  'gnfr': 'L',
+#  'model': 1,
+#  'description': 'CO2 emissions from agriculture, excluding livestock'},
+#'K_Agri_livestock': {
+#  'gnfr': 'K',
+#  'model': 1,
+#  'description': 'CO2 emissions from agriculture livestock'},
 'G_Shipping': {
   'gnfr': 'G',
   'model': 1,
-  'description': 'CO2 emitted ships, including international shipping'},
+  'description': 'CO2 emitted ships, including international shipping (GNFR G). Based on CAMS data, extrapolated by Eurostat fuel demand'},
 })
diff --git a/da/preprocessing/emissionmodel.py b/da/preprocessing/emissionmodel.py
index c13d05a954bcf35f5c8cee8e3887b1fd5a78eecf..e498507b83c2c2466df4773e003ca2ccede43583 100755
--- a/da/preprocessing/emissionmodel.py
+++ b/da/preprocessing/emissionmodel.py
@@ -13,7 +13,7 @@ to create pseudo-data
 """
 
 import shutil, os, logging, sys, pickle
-import pytz
+import pytz, glob
 import datetime as dtm
 import numpy as np
 import pandas as pd
@@ -39,7 +39,7 @@ class EmisModel(Regional):
 
     def __init__(self, dacycle=None):
         super(EmisModel, self).__init__(dacycle)
-        self.name = 'fossil'
+        self.name = 'anthropogenic'
 
     def setup(self):
         """Set up the emission model.
@@ -858,13 +858,16 @@ class EmisModel(Regional):
         emissions = self.get_emissions(time_profiles)
 
         # Also write the sum of all sectors
-        filename = self.write(emissions[:, :-1, :, :].sum(axis=1))
+        varname = 'combustion'
+        filename = self.write(emissions[:, :-1, :, :].sum(axis=1), varname=varname)
         # Add cement as a sector
         with nc.Dataset(filename, 'r+') as ds:
+            ds[varname].comment = 'Surface CO2 emissions due to anthropogenic  combustion of GNFR sectors A, B, C, F, G, H, I'
             ds.createVariable('cement', 'f4', ('time', 'latitude', 'longitude'))
             ds['cement'][:] = emissions[:, -1, :, :]
-            ds['cement'].long_name = 'Emissions from cement production'
+            ds['cement'].long_name = 'Emissions from the calination of cement'
             ds['cement'].units = 'umol m-2 s-1'
+            ds['cement'].comment = 'CO2 emissions from the calcination of cement. Taken from GridFED v2021.1 (https://doi.org/10.6084/m9.figshare.13333643) from the last available year)'
         return filename, self.name
         
     def get_yearly_emissions(self, species):
@@ -1182,9 +1185,10 @@ class EmisModel(Regional):
     def add_non_ff_anthrop(self):
         """Get bunker and cement fuels"""
         year = self.dacycle['time.start'].year
-        if year > 2019: year = 2019
-        filename = self.dacycle.dasystem['dir.ff.global'] + '/orig/GCP_Global_{}.nc'
-        filename = filename.format(year -1) # Get fluxes from last year
+        available_files = glob.glob(self.dacycle.dasystem['dir.GridFED'] + '/GCP_Global_*')
+        available_years = np.array([f.split('_')[-1].strip('.nc') for f in available_files], dtype=int)
+        closest_year = available_years[np.argmin(np.abs(available_years - year))]
+        filename = self.dacycle.dasystem['dir.GridFED'] + '/GCP_Global_{}.nc'.format(closest_year)
         # Select the correct spatial domain
         with nc.Dataset(filename) as ds:
             monthind = self.startdate.month -1
@@ -1234,22 +1238,22 @@ class EmisModel(Regional):
                 savedict['dtype'] = 'float'
                 for i, cat in enumerate(list(self.categories.keys())):
                     savedict['name'] = cat
-                    savedict['long_name'] = '{} CO2 emissions'.format(cat)
+                    savedict['long_name'] = 'Surface CO2 emissions from GNFR sector {}'.format(cat)
                     savedict['values'] = values[:, i, :, :]
                     savedict['comment'] = self.categories[cat]['description']
                     f.add_data(savedict)
                 # Add cement (non FF)
-                savedict['name'] = 'Cement'
+                savedict['name'] = 'cement'
                 savedict['long_name'] = 'CO2 emissions due to calcination of cement'
                 savedict['values'] = values[:, -1, :, :]
-                savedict['comment'] = 'CO2 emissions due to calcination of cement'
+                savedict['comment'] = 'CO2 emissions from the calcination of cement. Taken from GridFED v2021.1 (https://doi.org/10.6084/m9.figshare.13333643) from the last available year)'
                 f.add_data(savedict)
 
                 # Add the sum of all FF sectors
-                savedict['name'] = 'fossil'
-                savedict['long_name'] = 'Sum of all fossil fuel emissions of CO2'
+                savedict['name'] = 'combustion'
+                savedict['long_name'] = 'Sum of CO2 emissions from anthropogenic combustion emissions of GNFR sectors A, B, C, F, G, H, I.'
                 savedict['values'] = values[:, :-1, :, :].sum(axis=1)
-                savedict['comment'] = 'CO2 emissions due to anthropogenic emissions of fossil fuels and biofuels'
+                savedict['comment'] = 'CO2 emissions from anthropogenic combustion from GNFR sectors A, B, C, F, G, H, I, includion biofuel portion'
                 f.add_data(savedict)
 
                 # Add the sum of all FF sectors