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scenario comparison/catalogues_comparisons/.ipynb_checkpoints/BC-checkpoint.ipynb
View file @ 1cdafd38
%% Cell type:markdown id:5050981c-360a-4278-914a-b3aea9785d7a tags:
 
# BC yealy emissions comparison across data catalogues
 
%% Cell type:code id:53b6a176-a6a2-4374-80aa-48e4f550b981 tags:
 
``` python
import xarray as xr
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import glob
```
 
%% Cell type:code id:b16cdca9-d355-47fc-b977-36d5e29e3e82 tags:
 
``` python
seconds_in_year = 31536000
area_earth = 5.1006447295*pow(10,14)
```
 
%% Cell type:code id:d66bf063-eed0-4885-a582-0acf69441edc tags:
 
``` python
specie = 'bc'
specie_title = 'BC'
```
 
%% Cell type:code id:ec16a33e-d2a7-4e1d-b0e0-fb362a3699e8 tags:
 
``` python
def make_emission_plottable(file_name, column_name):
emissions = pd.read_csv(file_name, sep = ',')
emissions.columns = ['Date', column_name]
return emissions.set_index(pd.to_datetime(emissions['Date'])).drop(columns = ['Date'])
```
 
%% Cell type:markdown id:7a73116c-0475-418a-935b-a4e25cb9232e tags:
 
### SSPs, CMIP6
 
%% Cell type:code id:8d21505e-f81b-4754-877f-0ae5ff679698 tags:
 
``` python
ssp126_road_file = '/work/bd1063/b309253/emissions/ssps-anthro-'+ specie +'-ssp126-ye.csv'
ssp245_road_file = '/work/bd1063/b309253/emissions/ssps-anthro-'+ specie +'-ssp245-ye.csv'
ssp370_road_file = '/work/bd1063/b309253/emissions/ssps-anthro-'+ specie +'-ssp370-ye.csv'
ssp460_road_file = '/work/bd1063/b309253/emissions/ssps-anthro-'+ specie +'-ssp460-ye.csv'
ssp585_road_file = '/work/bd1063/b309253/emissions/ssps-anthro-'+ specie +'-ssp585-ye.csv'
```
 
%% Cell type:code id:baf1dba3-aa3f-4af2-a02f-93b28185da06 tags:
 
``` python
ssp126_road = make_emission_plottable(ssp126_road_file, 'SSP126')
ssp245_road = make_emission_plottable(ssp245_road_file, 'SSP245')
ssp370_road = make_emission_plottable(ssp370_road_file, 'SSP370')
ssp460_road = make_emission_plottable(ssp460_road_file, 'SSP460')
ssp585_road = make_emission_plottable(ssp585_road_file, 'SSP585')
```
 
%% Cell type:code id:9c7e1926-1c98-42a5-b2fb-9afc17628eb0 tags:
 
``` python
ax = ssp126_road.plot()
ssp245_road.plot(ax=ax)
ssp370_road.plot(ax=ax)
ssp460_road.plot(ax=ax)
ssp585_road.plot(ax=ax)
 
ax.set_title('Yearly '+specie_title+' emissions in available SSPs')
ax.set_ylabel('Tg/a')
ax.legend(bbox_to_anchor=(1.0, 1.0))
```
 
%% Output
 
<matplotlib.legend.Legend at 0x7fffa95f5330>
 
 
%% Cell type:markdown id:5e3c1681-076c-4816-aba0-234aeab7fa43 tags:
 
### Historical
 
%% Cell type:code id:f9aff789-ddeb-41a4-a5fe-d09f872503f8 tags:
 
``` python
hist_emissions_file = '/work/bd1063/b309253/emissions/hist_road_'+ specie +'.csv'
hist_emissions = make_emission_plottable(hist_emissions_file, 'CMIP6v6')
```
 
%% Cell type:code id:ccffcfa7-d464-4963-8d3f-da9388858f37 tags:
 
``` python
ax = hist_emissions.plot()
 
ax.set_title('Historical CMIP6 '+specie_title+' emissions')
ax.set_ylabel('Tg/a')
```
 
%% Output
 
Text(0, 0.5, 'Tg/a')
 
 
%% Cell type:code id:6b97e590-2e3b-47ea-9d3b-45f6e556fd95 tags:
 
``` python
ax = ssp126_road.plot(figsize=(10, 5))
ssp245_road.plot(ax=ax)
ssp370_road.plot(ax=ax)
ssp460_road.plot(ax=ax)
ssp585_road.plot(ax=ax)
 
hist_emissions.plot(ax=ax)
 
ax.set_title('Yearly '+specie_title+' emissions, historical CMIP6 and future SSP projections')
ax.set_ylabel('Tg/a')
```
 
%% Output
 
Text(0, 0.5, 'Tg/a')
 
 
%% Cell type:code id:304b06ef-a046-453b-b775-2e5a994fc306 tags:
 
``` python
ax = ssp126_road.plot(figsize=(10, 5))
ssp245_road.plot(ax=ax)
ssp370_road.plot(ax=ax)
ssp460_road.plot(ax=ax)
ssp585_road.plot(ax=ax)
hist_emissions[200:].plot(ax=ax)
 
ax.set_title('Yearly '+specie_title+' emissions, (zoomed in) historical CMIP6 and future SSP projections')
ax.set_ylabel('Tg/a')
```
 
%% Output
 
Text(0, 0.5, 'Tg/a')
 
 
%% Cell type:markdown id:385dc639-2d50-4f1d-9ef5-1411ecf6bb42 tags:
 
# Adding all catalogues
 
%% Cell type:markdown id:9c85f12d-6e92-4960-8226-228515c926ad tags:
 
### CAMS v5.3
 
%% Cell type:markdown id:0e9cba2e-def9-484e-8f43-50f087643f95 tags:
 
To get land transport emissions in CAMS we sum the sectors **ROAD TRANSPORTATION** and **OFF ROAD TRANSPORTATION**
 
%% Cell type:code id:ccb73c83-3612-41a0-9f48-308caddab5c0 tags:
 
``` python
cams_file = '/work/bd1063/b309253/emissions/cams-glob-ant-anthro-'+ specie +'.csv'
```
 
%% Cell type:code id:f945eb5c-fca1-40e0-9e22-8fa9a5a0728d tags:
 
``` python
cams = pd.read_csv(cams_file, sep = ',')
cams.columns = ['Date', 'CAMS Off Road transportation', 'CAMS Road transportation']
cams = cams.set_index(pd.to_datetime(cams['Date'])).drop(columns = ['Date'])
```
 
%% Cell type:code id:5d082a5c-a440-4676-a47d-c96389ead155 tags:
 
``` python
cams['CAMS Land Transport'] = cams['CAMS Off Road transportation'] + cams['CAMS Road transportation']
cams[:3]
```
 
%% Output
 
CAMS Off Road transportation CAMS Road transportation
Date
2000-01-01 0.025978 0.522829 \
2001-01-01 0.025411 0.507231
2002-01-01 0.025943 0.497098
CAMS Land Transport
Date
2000-01-01 0.548808
2001-01-01 0.532642
2002-01-01 0.523041
 
%% Cell type:code id:5ee855c6-8dbb-413e-ae7b-bea90c3aa520 tags:
 
``` python
ax = ssp126_road.plot(figsize=(10, 5))
ssp245_road.plot(ax=ax)
ssp370_road.plot(ax=ax)
ssp460_road.plot(ax=ax)
ssp585_road.plot(ax=ax)
hist_emissions[200:].plot(ax=ax)
cams.plot(ax=ax)
 
ax.set_title('Yearly '+specie_title+' emissions in CMIP6 and CAMS transport sectors')
ax.set_ylabel('Tg/a')
ax.legend(bbox_to_anchor=(1.0, 1.0))
```
 
%% Output
 
<matplotlib.legend.Legend at 0x7fffa82dbfa0>
 
 
%% Cell type:markdown id:8b4d47d2-335b-496d-ab2e-584bf478d51a tags:
TODO: send this to Mattia and Binkorp to check with them if it makes sense. Double check units
%% Cell type:markdown id:5d794155-5768-4f9f-8411-b319d79b6387 tags:
 
### CEDS
 
%% Cell type:code id:e606dcd7-c425-49d9-ae92-ed9c0f15a975 tags:
 
``` python
ceds_file = '/work/bd1063/b309253/emissions/ceds-anthro-'+ specie +'-yearly.csv'
ceds = make_emission_plottable(ceds_file, 'CEDS Transportation')
```
 
%% Cell type:code id:38517403-a064-4203-b8c7-c60a44ce6d50 tags:
 
``` python
# Annual Total Anthropogenic Emissions prepared for input4CMIP from CEDS_GBD-MAPS
ceds_input4cmip_path = '/work/bd1063/b309253/emissions/ceds_input4cmip_'+ specie +'.csv'
ceds_input4cmip = make_emission_plottable(ceds_input4cmip_path, 'CEDS input4cmip Transportation')
```
 
%% Cell type:code id:34aa6161-952a-4231-9484-5468b9c7bca6 tags:
 
``` python
# Mystery experiment
ceds_v1_path = '/work/bd1063/b309253/emissions/ceds_v1_'+ specie +'.csv'
ceds_v1 = make_emission_plottable(ceds_v1_path, 'CEDS v1 Transportation')
```
 
%% Cell type:code id:2a934f7b-25e9-4db0-a4c7-6de795abb643 tags:
 
``` python
ax = ssp126_road.plot(figsize=(10, 5), linestyle='dashed', alpha = 0.5)
ssp245_road.plot(ax=ax)
ssp370_road.plot(ax=ax)
ssp460_road.plot(ax=ax)
ssp585_road.plot(ax=ax)
hist_emissions[200:].plot(ax=ax, linestyle='dashed', alpha = 0.5)
ceds.plot(ax=ax)
ceds_input4cmip.plot(ax=ax)
ceds_v1.plot(ax=ax)
 
ax.set_title('Yearly '+specie_title+' emissions in CMIP6 and CEDS transport sectors')
ax.set_ylabel('Tg/a')
ax.legend(bbox_to_anchor=(1.0, 1.0))
```
 
%% Output
 
<matplotlib.legend.Legend at 0x7fffa808a410>
 
 
%% Cell type:markdown id:0f8f5730-9b4b-47ee-a0ed-567a383038ce tags:
 
### ECLISPE
 
%% Cell type:code id:936fde76-2f5f-44fc-ab5d-c3b20d28c31f tags:
 
``` python
eclipse6_cle_file = '/work/bd1063/b309253/emissions/eclipse-v6-anthro-'+ specie +'-cle.csv'
eclipse5_cle_file = '/work/bd1063/b309253/emissions/eclipse-gains-v5a-anthro-cle-'+ specie +'.csv'
eclipse5_cle2degrees_file = '/work/bd1063/b309253/emissions/eclipse-gains-v5a-anthro-cle-2degrees-'+ specie +'.csv'
eclipse5_mfr_file = '/work/bd1063/b309253/emissions/eclipse-gains-v5a-anthro-mfr-'+ specie +'.csv'
eclipse5_slcp_file = '/work/bd1063/b309253/emissions/eclipse-gains-v5a-anthro-slcp-'+ specie +'.csv'
```
 
%% Cell type:code id:72f12921-3a1c-4b9f-8abc-7c05d18df37c tags:
 
``` python
eclipse6_cle = make_emission_plottable(eclipse6_cle_file, 'ECLIPSE V6 CLE Transportation')
eclipse5_cle = make_emission_plottable(eclipse5_cle_file, 'ECLIPSE V5 CLE Transportation')
eclipse5_cle2degrees = make_emission_plottable(eclipse5_cle2degrees_file, 'ECLIPSE CLE 2^{o} Transportation')
eclipse5_mfr = make_emission_plottable(eclipse5_mfr_file, 'ECLIPSE V5 MFR Transportation')
eclipse5_slcp = make_emission_plottable(eclipse5_slcp_file, 'ECLIPSE V5 SLCP Transportation')
```
 
%% Cell type:code id:25871212-02f2-41ac-bdb1-45e5dd8a31c1 tags:
 
``` python
ax = ssp126_road.plot(figsize=(10, 5), linestyle='dashed', alpha = 0.5)
ssp245_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
ssp370_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
ssp460_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
ssp585_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
hist_emissions[200:].plot(ax=ax, linestyle='dashed', alpha = 0.5)
eclipse5_cle.plot(ax=ax)
eclipse5_cle2degrees.plot(ax=ax)
eclipse5_mfr.plot(ax=ax)
eclipse5_slcp.plot(ax=ax)
eclipse6_cle.plot(ax=ax)
 
ax.set_title('Yearly '+specie_title+' emissions ECLIPSE scenarios')
ax.set_ylabel('Tg/a')
ax.legend(bbox_to_anchor=(1.0, 1.0))
```
 
%% Output
 
<matplotlib.legend.Legend at 0x7fffa371cfa0>
 
 
%% Cell type:markdown id:59220620-6a71-4411-832c-39d6cd3d1cb0 tags:
 
### EDGAR
 
%% Cell type:markdown id:4712f5aa-179d-49c5-88b8-ef325386d62e tags:
 
**Transport** contains emissions from the combustion of fuel for all transport activity, regardless of the sector, except for international marine bunkers and international aviation bunkers, which are not included in transport emissions at a national or regional level (except for World transport emissions). This includes domestic aviation, domestic navigation, road, rail and pipeline transport, and corresponds to IPCC Source/ Sink Category 1 A 3. The IEA data are not collected in a way that allows the autoproducer consumption to be split by specific end-use and therefore, this publication shows autoproducers as a separate item.
The procedures given for calculating emissions ensure that emissions from the use of fuels for international marine and air transport are excluded from national emissions totals.
 
 
**Road** contains the emissions arising from fuel use in road vehicles, including the use of agricultural vehicles on highways. This corresponds to the IPCC Source/Sink Category 1 A 3 b
 
%% Cell type:code id:a0bbe135-34d7-4c96-8668-2992a3d285ef tags:
 
``` python
edgar6_file = '/work/bd1063/b309253/emissions/edgarv6-anthro-'+ specie +'-yearly.csv'
edgar6 = pd.read_csv(edgar6_file, sep = ',')
edgar6[:3]
```
 
%% Output
 
Date Non-road ground transportation
0 1970-01-01 00:00:00 0.091566 \
1 1971-01-01 00:00:00 0.090448
2 1972-01-01 00:00:00 0.091963
Road transportation no resuspension
0 0.152304
1 0.161220
2 0.178024
 
%% Cell type:code id:884a4f0c-a5e1-4d8d-af15-c1182bb86a98 tags:
 
``` python
edgar6.columns = ['Date', 'EDGAR V6 Non-road ground transportation', 'EDGAR V6 Road transportation no resuspension']
edgar6 = edgar6.set_index(pd.to_datetime(edgar6['Date'])).drop(columns = ['Date'])
```
 
%% Cell type:code id:a2b0e91f-e5ba-4662-9c66-6f0006099d46 tags:
 
``` python
edgar6['EDGAR V6 Land Transport'] = edgar6['EDGAR V6 Non-road ground transportation'] + edgar6['EDGAR V6 Road transportation no resuspension']
edgar6[:3]
```
 
%% Output
 
EDGAR V6 Non-road ground transportation
Date
1970-01-01 0.091566 \
1971-01-01 0.090448
1972-01-01 0.091963
EDGAR V6 Road transportation no resuspension
Date
1970-01-01 0.152304 \
1971-01-01 0.161220
1972-01-01 0.178024
EDGAR V6 Land Transport
Date
1970-01-01 0.243871
1971-01-01 0.251668
1972-01-01 0.269987
 
%% Cell type:code id:697b6519-7541-4803-b861-f00d5ee734dc tags:
 
``` python
ax = ssp126_road.plot(figsize=(10, 5), linestyle='dashed', alpha = 0.5)
ssp245_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
ssp370_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
ssp460_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
ssp585_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
hist_emissions[200:].plot(ax=ax, linestyle='dashed', alpha = 0.5)
edgar6.plot(ax=ax)
 
ax.set_title('Yearly '+specie_title+' emissions in EDGAR v6')
ax.set_ylabel('Tg/a')
ax.legend(bbox_to_anchor=(1.0, 1.0))
```
 
%% Output
 
<matplotlib.legend.Legend at 0x7fffa8129cc0>
 
 
%% Cell type:code id:65f402e5-06ee-46e5-993e-cea7ed97c57e tags:
 
``` python
edgar4_file = '/work/bd1063/b309253/emissions/edgarv432-anthro-'+specie+'-year.csv'
edgar4 = pd.read_csv(edgar4_file, sep = ',')
edgar4.columns = ['Date', 'EDGAR V4 Non-road ground transportation', 'EDGAR V4 Road transportation no resuspension']
edgar4 = edgar4.set_index(pd.to_datetime(edgar4['Date'])).drop(columns = ['Date'])
edgar4['EDGAR V4 Land Transport'] = edgar4['EDGAR V4 Non-road ground transportation'] + edgar4['EDGAR V4 Road transportation no resuspension']
```
 
%% Cell type:code id:7e346451-f629-4c31-bc16-4e276b683646 tags:
 
``` python
edgar5_file = '/work/bd1063/b309253/emissions/edgarv5-anthro-'+specie+'-yearly.csv'
edgar5 = pd.read_csv(edgar5_file, sep = ',')
edgar5.columns = ['Date', 'EDGAR V5 Non-road ground transportation', 'EDGAR V5 Road transportation no resuspension']
edgar5 = edgar5.set_index(pd.to_datetime(edgar5['Date'])).drop(columns = ['Date'])
edgar5['EDGAR V5 Land Transport'] = edgar5['EDGAR V5 Non-road ground transportation'] + edgar5['EDGAR V5 Road transportation no resuspension']
```
 
%% Cell type:code id:c008da30-1301-4283-b6d6-adee8c9082ef tags:
 
``` python
ax = ssp126_road.plot(figsize=(10, 5), linestyle='dashed', alpha = 0.5)
ssp245_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
ssp370_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
ssp460_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
ssp585_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
hist_emissions[200:].plot(ax=ax, linestyle='dashed', alpha = 0.5)
edgar4['EDGAR V4 Land Transport'].plot(ax=ax)
edgar5['EDGAR V5 Land Transport'].plot(ax=ax)
edgar6['EDGAR V6 Land Transport'].plot(ax=ax)
 
ax.set_title('Yearly '+specie_title+' land transport emissions in EDGAR versions')
ax.set_ylabel('Tg/a')
ax.legend(bbox_to_anchor=(1.0, 1.0))
```
 
%% Output
 
<matplotlib.legend.Legend at 0x7fffa2d16f20>
 
 
%% Cell type:markdown id:ecbfa690-5923-4055-b843-f2657c122c4b tags:
 
# All together
 
%% Cell type:code id:82906576-a9b8-4e85-8aa3-454d9f373cdf tags:
 
``` python
ax = ssp126_road.plot(figsize=(10, 5), linestyle='dotted', alpha = 0.75)
ssp245_road.plot(ax=ax, linestyle='dashed', alpha = 0.75)
ssp370_road.plot(ax=ax, linestyle='dashed', alpha = 0.75)
ssp460_road.plot(ax=ax, linestyle='dotted', alpha = 0.75)
ssp585_road.plot(ax=ax, linestyle='dotted', alpha = 0.75)
hist_emissions[200:].plot(ax=ax, linestyle='dotted', alpha = 0.75)
ceds.plot(ax=ax)
#ceds_input4cmip.plot(ax=ax)
#ceds_v1.plot(ax=ax)
cams['CAMS Land Transport'].plot(ax=ax)
#edgar4['EDGAR V4 Land Transport'].plot(ax=ax, linestyle='dashed')
#edgar5['EDGAR V5 Land Transport'].plot(ax=ax, linestyle='dashed')
edgar6['EDGAR V6 Land Transport'].plot(ax=ax, linestyle='dashed')
eclipse5_cle.plot(ax=ax, linestyle='dashdot')
#eclipse5_cle2degrees.plot(ax=ax, linestyle='dashdot')
#eclipse5_mfr.plot(ax=ax, linestyle='dashdot')
#eclipse5_slcp.plot(ax=ax, linestyle='dashdot')
eclipse6_cle.plot(ax=ax, linestyle='dashdot')
 
ax.set_title('Yearly '+specie_title+' emission catalogue comparison')
ax.set_ylabel('Tg/a')
ax.legend(bbox_to_anchor=(1.0, 1.0))
```
 
%% Output
 
<matplotlib.legend.Legend at 0x7fffa8306470>
 
 
%% Cell type:code id:d2aff23a-b3ef-4096-9932-5f7e65b73360 tags:
 
``` python
ax = ssp126_road.plot(figsize=(10, 5), linestyle='dotted', alpha = 0.75)
ssp245_road.plot(ax=ax, linestyle='dashed', alpha = 0.75)
ssp370_road.plot(ax=ax, linestyle='dashed', alpha = 0.75)
ssp460_road.plot(ax=ax, linestyle='dotted', alpha = 0.75)
ssp585_road.plot(ax=ax, linestyle='dotted', alpha = 0.75)
hist_emissions[200:].plot(ax=ax, linestyle='dotted', alpha = 0.75)
ceds.plot(ax=ax)
ceds_input4cmip.plot(ax=ax)
ceds_v1.plot(ax=ax)
cams['CAMS Land Transport'].plot(ax=ax)
edgar4['EDGAR V4 Land Transport'].plot(ax=ax, linestyle='dashed')
edgar5['EDGAR V5 Land Transport'].plot(ax=ax, linestyle='dashed')
edgar6['EDGAR V6 Land Transport'].plot(ax=ax, linestyle='dashed')
eclipse5_cle.plot(ax=ax, linestyle='dashdot')
eclipse5_cle2degrees.plot(ax=ax, linestyle='dashdot')
eclipse5_mfr.plot(ax=ax, linestyle='dashdot')
eclipse5_slcp.plot(ax=ax, linestyle='dashdot')
eclipse6_cle.plot(ax=ax, linestyle='dashdot')
 
ax.set_title('Yearly '+specie_title+' ALL emission catalogue comparison')
ax.set_ylabel('Tg/a')
ax.legend(bbox_to_anchor=(1.0, 1.0))
```
 
%% Output
 
<matplotlib.legend.Legend at 0x7fffa8442350>
 
 
%% Cell type:markdown id:f36feb81-080d-4877-b0d7-db3718be5934 tags:
 
### Sectors definitions
 
%% Cell type:markdown id:9f63bc3e-6314-4111-b747-b589a7ffd69d tags:
 
**SSP & CMIP6 transport**:
 
%% Cell type:markdown id:f982dbee-6249-4d98-922b-f874e70504a9 tags:
 
Transportation sector for CMIP6 is the sum of **Road transportation** (proxy data source from EDGAR v4.3.2 ROAD)
and **Non-road transportation** (EDGAR v4.2 NRTR)
 
%% Cell type:markdown id:775cab11-a48f-4187-90e7-dfee6bc5d8d4 tags:
 
**CEDS Transportation**
 
%% Cell type:markdown id:ba8a3e5f-cb77-4832-8c5b-ebb5af514853 tags:
 
Since it's the set of emissions prepared for CMIP6, the definition of the sectors are the same
 
%% Cell type:markdown id:67d34f09-5a1d-4eb3-95dd-7df104bc6de9 tags:
 
**CAMS Land Transport**
 
%% Cell type:markdown id:f80a7e86-2b11-4b9f-8b23-5ca636536246 tags:
 
To get land transport emissions in CAMS we sum the sectors **ROAD TRANSPORTATION** and **OFF ROAD TRANSPORTATION**
 
%% Cell type:markdown id:753709f4-08da-4860-bb88-f0d96e05935a tags:
 
**EDGAR Transportation**
 
%% Cell type:markdown id:b015ec19-6121-4ef6-95e2-1ac40d761440 tags:
 
To get land transport in EDGAR, we sum the sub-sectors: **Non-road ground transportation** and **Road transportation no resuspension**
 
%% Cell type:markdown id:fcfd4191-9579-49d7-9d7a-725818da21a3 tags:
 
**Transport** contains emissions from the combustion of fuel for all transport activity, regardless of the sector, except for international marine bunkers and international aviation bunkers, which are not included in transport emissions at a national or regional level (except for World transport emissions). This includes domestic aviation, domestic navigation, road, rail and pipeline transport, and corresponds to IPCC Source/ Sink Category 1 A 3. The IEA data are not collected in a way that allows the autoproducer consumption to be split by specific end-use and therefore, this publication shows autoproducers as a separate item.
The procedures given for calculating emissions ensure that emissions from the use of fuels for international marine and air transport are excluded from national emissions totals.
 
 
**Road** contains the emissions arising from fuel use in road vehicles, including the use of agricultural vehicles on highways. This corresponds to the IPCC Source/Sink Category 1 A 3 b
 
%% Cell type:markdown id:38f8a303-f5ab-4c8c-a970-21c16376d749 tags:
 
**ECLIPSE Transportation**
 
%% Cell type:markdown id:ee99bca6-43f5-4394-b32d-8be7d6d8d6c5 tags:
 
**CLE** (Current legislation for air pollutants)
 
**MFR** (Maximum technically feasible reductions)
 
**CLE-2°** (Climate scenario (2 degrees, CLE))
 
**SLCP** (Short lived climate pollutants mitigation)
 
%% Cell type:markdown id:c4fef2a1-6606-4307-8345-be1711058635 tags:
 
Definition of **Transport** sector is consistent with CMIP6, EDGAR
 
%% Cell type:code id:4ea35b75-7834-4502-a97b-a815ba57ec73 tags:
 
``` python
```
 
%% Cell type:code id:ed433adf-fe64-46b1-a878-c364d5818775 tags:
 
``` python
```
 
%% Cell type:code id:c05f85f0-444a-4245-a6b9-6e6a14ba1d42 tags:
 
``` python
```
 
%% Cell type:code id:c725e850-ab02-495a-9f73-bddb1f0e2554 tags:
 
``` python
```
 
%% Cell type:code id:f74f60fd-a87d-42c9-a263-a5459d8af721 tags:
 
``` python
```
 
%% Cell type:code id:ff52f735-4b74-494c-9895-16b60d151e21 tags:
 
``` python
```
scenario comparison/catalogues_comparisons/BC.ipynb
View file @ 1cdafd38
%% Cell type:markdown id:5050981c-360a-4278-914a-b3aea9785d7a tags:
 
# BC yealy emissions comparison across data catalogues
 
%% Cell type:code id:53b6a176-a6a2-4374-80aa-48e4f550b981 tags:
 
``` python
import xarray as xr
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import glob
```
 
%% Cell type:code id:b16cdca9-d355-47fc-b977-36d5e29e3e82 tags:
``` python
seconds_in_year = 31536000
area_earth = 5.1006447295*pow(10,14)
```
%% Cell type:code id:d66bf063-eed0-4885-a582-0acf69441edc tags:
 
``` python
specie = 'bc'
specie_title = 'BC'
```
 
%% Cell type:code id:ec16a33e-d2a7-4e1d-b0e0-fb362a3699e8 tags:
 
``` python
def make_emission_plottable(file_name, column_name):
emissions = pd.read_csv(file_name, sep = ',')
emissions.columns = ['Date', column_name]
return emissions.set_index(pd.to_datetime(emissions['Date'])).drop(columns = ['Date'])
```
 
%% Cell type:markdown id:7a73116c-0475-418a-935b-a4e25cb9232e tags:
 
### SSPs, CMIP6
 
%% Cell type:code id:8d21505e-f81b-4754-877f-0ae5ff679698 tags:
 
``` python
ssp126_road_file = '/work/bd1063/b309253/emissions/ssps-anthro-'+ specie +'-ssp126-ye.csv'
ssp245_road_file = '/work/bd1063/b309253/emissions/ssps-anthro-'+ specie +'-ssp245-ye.csv'
ssp370_road_file = '/work/bd1063/b309253/emissions/ssps-anthro-'+ specie +'-ssp370-ye.csv'
ssp460_road_file = '/work/bd1063/b309253/emissions/ssps-anthro-'+ specie +'-ssp460-ye.csv'
ssp585_road_file = '/work/bd1063/b309253/emissions/ssps-anthro-'+ specie +'-ssp585-ye.csv'
```
 
%% Cell type:code id:baf1dba3-aa3f-4af2-a02f-93b28185da06 tags:
 
``` python
ssp126_road = make_emission_plottable(ssp126_road_file, 'SSP126')
ssp245_road = make_emission_plottable(ssp245_road_file, 'SSP245')
ssp370_road = make_emission_plottable(ssp370_road_file, 'SSP370')
ssp460_road = make_emission_plottable(ssp460_road_file, 'SSP460')
ssp585_road = make_emission_plottable(ssp585_road_file, 'SSP585')
```
 
%% Cell type:code id:9c7e1926-1c98-42a5-b2fb-9afc17628eb0 tags:
 
``` python
ax = ssp126_road.plot()
ssp245_road.plot(ax=ax)
ssp370_road.plot(ax=ax)
ssp460_road.plot(ax=ax)
ssp585_road.plot(ax=ax)
 
ax.set_title('Yearly '+specie_title+' emissions in available SSPs')
ax.set_ylabel('Tg/a')
ax.legend(bbox_to_anchor=(1.0, 1.0))
```
 
%% Output
 
<matplotlib.legend.Legend at 0x7fffa95f5330>
 
 
%% Cell type:markdown id:5e3c1681-076c-4816-aba0-234aeab7fa43 tags:
 
### Historical
 
%% Cell type:code id:f9aff789-ddeb-41a4-a5fe-d09f872503f8 tags:
 
``` python
hist_emissions_file = '/work/bd1063/b309253/emissions/hist_road_'+ specie +'.csv'
hist_emissions = make_emission_plottable(hist_emissions_file, 'CMIP6v6')
```
 
%% Cell type:code id:ccffcfa7-d464-4963-8d3f-da9388858f37 tags:
 
``` python
ax = hist_emissions.plot()
 
ax.set_title('Historical CMIP6 '+specie_title+' emissions')
ax.set_ylabel('Tg/a')
```
 
%% Output
 
Text(0, 0.5, 'Tg/a')
 
 
%% Cell type:code id:6b97e590-2e3b-47ea-9d3b-45f6e556fd95 tags:
 
``` python
ax = ssp126_road.plot(figsize=(10, 5))
ssp245_road.plot(ax=ax)
ssp370_road.plot(ax=ax)
ssp460_road.plot(ax=ax)
ssp585_road.plot(ax=ax)
 
hist_emissions.plot(ax=ax)
 
ax.set_title('Yearly '+specie_title+' emissions, historical CMIP6 and future SSP projections')
ax.set_ylabel('Tg/a')
```
 
%% Output
 
Text(0, 0.5, 'Tg/a')
 
 
%% Cell type:code id:304b06ef-a046-453b-b775-2e5a994fc306 tags:
 
``` python
ax = ssp126_road.plot(figsize=(10, 5))
ssp245_road.plot(ax=ax)
ssp370_road.plot(ax=ax)
ssp460_road.plot(ax=ax)
ssp585_road.plot(ax=ax)
hist_emissions[200:].plot(ax=ax)
 
ax.set_title('Yearly '+specie_title+' emissions, (zoomed in) historical CMIP6 and future SSP projections')
ax.set_ylabel('Tg/a')
```
 
%% Output
 
Text(0, 0.5, 'Tg/a')
 
 
%% Cell type:markdown id:385dc639-2d50-4f1d-9ef5-1411ecf6bb42 tags:
 
# Adding all catalogues
 
%% Cell type:markdown id:9c85f12d-6e92-4960-8226-228515c926ad tags:
 
### CAMS v5.3
 
%% Cell type:markdown id:0e9cba2e-def9-484e-8f43-50f087643f95 tags:
 
To get land transport emissions in CAMS we sum the sectors **ROAD TRANSPORTATION** and **OFF ROAD TRANSPORTATION**
 
%% Cell type:code id:ccb73c83-3612-41a0-9f48-308caddab5c0 tags:
 
``` python
cams_file = '/work/bd1063/b309253/emissions/cams-glob-ant-anthro-'+ specie +'.csv'
```
 
%% Cell type:code id:f945eb5c-fca1-40e0-9e22-8fa9a5a0728d tags:
 
``` python
cams = pd.read_csv(cams_file, sep = ',')
cams.columns = ['Date', 'CAMS Off Road transportation', 'CAMS Road transportation']
cams = cams.set_index(pd.to_datetime(cams['Date'])).drop(columns = ['Date'])
```
 
%% Cell type:code id:5d082a5c-a440-4676-a47d-c96389ead155 tags:
 
``` python
cams['CAMS Land Transport'] = cams['CAMS Off Road transportation'] + cams['CAMS Road transportation']
cams[:3]
```
 
%% Output
 
CAMS Off Road transportation CAMS Road transportation
Date
2000-01-01 0.025978 0.522829 \
2001-01-01 0.025411 0.507231
2002-01-01 0.025943 0.497098
CAMS Land Transport
Date
2000-01-01 0.548808
2001-01-01 0.532642
2002-01-01 0.523041
 
%% Cell type:code id:5ee855c6-8dbb-413e-ae7b-bea90c3aa520 tags:
 
``` python
ax = ssp126_road.plot(figsize=(10, 5))
ssp245_road.plot(ax=ax)
ssp370_road.plot(ax=ax)
ssp460_road.plot(ax=ax)
ssp585_road.plot(ax=ax)
hist_emissions[200:].plot(ax=ax)
cams.plot(ax=ax)
 
ax.set_title('Yearly '+specie_title+' emissions in CMIP6 and CAMS transport sectors')
ax.set_ylabel('Tg/a')
ax.legend(bbox_to_anchor=(1.0, 1.0))
```
 
%% Output
 
<matplotlib.legend.Legend at 0x7fffa82dbfa0>
 
 
%% Cell type:markdown id:8b4d47d2-335b-496d-ab2e-584bf478d51a tags:
TODO: send this to Mattia and Binkorp to check with them if it makes sense. Double check units
%% Cell type:markdown id:5d794155-5768-4f9f-8411-b319d79b6387 tags:
 
### CEDS
 
%% Cell type:code id:e606dcd7-c425-49d9-ae92-ed9c0f15a975 tags:
 
``` python
ceds_file = '/work/bd1063/b309253/emissions/ceds-anthro-'+ specie +'-yearly.csv'
ceds = make_emission_plottable(ceds_file, 'CEDS Transportation')
```
 
%% Cell type:code id:38517403-a064-4203-b8c7-c60a44ce6d50 tags:
 
``` python
# Annual Total Anthropogenic Emissions prepared for input4CMIP from CEDS_GBD-MAPS
ceds_input4cmip_path = '/work/bd1063/b309253/emissions/ceds_input4cmip_'+ specie +'.csv'
ceds_input4cmip = make_emission_plottable(ceds_input4cmip_path, 'CEDS input4cmip Transportation')
```
 
%% Cell type:code id:34aa6161-952a-4231-9484-5468b9c7bca6 tags:
 
``` python
# Mystery experiment
ceds_v1_path = '/work/bd1063/b309253/emissions/ceds_v1_'+ specie +'.csv'
ceds_v1 = make_emission_plottable(ceds_v1_path, 'CEDS v1 Transportation')
```
 
%% Cell type:code id:2a934f7b-25e9-4db0-a4c7-6de795abb643 tags:
 
``` python
ax = ssp126_road.plot(figsize=(10, 5), linestyle='dashed', alpha = 0.5)
ssp245_road.plot(ax=ax)
ssp370_road.plot(ax=ax)
ssp460_road.plot(ax=ax)
ssp585_road.plot(ax=ax)
hist_emissions[200:].plot(ax=ax, linestyle='dashed', alpha = 0.5)
ceds.plot(ax=ax)
ceds_input4cmip.plot(ax=ax)
ceds_v1.plot(ax=ax)
 
ax.set_title('Yearly '+specie_title+' emissions in CMIP6 and CEDS transport sectors')
ax.set_ylabel('Tg/a')
ax.legend(bbox_to_anchor=(1.0, 1.0))
```
 
%% Output
 
<matplotlib.legend.Legend at 0x7fffa808a410>
 
 
%% Cell type:markdown id:0f8f5730-9b4b-47ee-a0ed-567a383038ce tags:
 
### ECLISPE
 
%% Cell type:code id:936fde76-2f5f-44fc-ab5d-c3b20d28c31f tags:
 
``` python
eclipse6_cle_file = '/work/bd1063/b309253/emissions/eclipse-v6-anthro-'+ specie +'-cle.csv'
eclipse5_cle_file = '/work/bd1063/b309253/emissions/eclipse-gains-v5a-anthro-cle-'+ specie +'.csv'
eclipse5_cle2degrees_file = '/work/bd1063/b309253/emissions/eclipse-gains-v5a-anthro-cle-2degrees-'+ specie +'.csv'
eclipse5_mfr_file = '/work/bd1063/b309253/emissions/eclipse-gains-v5a-anthro-mfr-'+ specie +'.csv'
eclipse5_slcp_file = '/work/bd1063/b309253/emissions/eclipse-gains-v5a-anthro-slcp-'+ specie +'.csv'
```
 
%% Cell type:code id:72f12921-3a1c-4b9f-8abc-7c05d18df37c tags:
 
``` python
eclipse6_cle = make_emission_plottable(eclipse6_cle_file, 'ECLIPSE V6 CLE Transportation')
eclipse5_cle = make_emission_plottable(eclipse5_cle_file, 'ECLIPSE V5 CLE Transportation')
eclipse5_cle2degrees = make_emission_plottable(eclipse5_cle2degrees_file, 'ECLIPSE CLE 2^{o} Transportation')
eclipse5_mfr = make_emission_plottable(eclipse5_mfr_file, 'ECLIPSE V5 MFR Transportation')
eclipse5_slcp = make_emission_plottable(eclipse5_slcp_file, 'ECLIPSE V5 SLCP Transportation')
```
 
%% Cell type:code id:25871212-02f2-41ac-bdb1-45e5dd8a31c1 tags:
 
``` python
ax = ssp126_road.plot(figsize=(10, 5), linestyle='dashed', alpha = 0.5)
ssp245_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
ssp370_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
ssp460_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
ssp585_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
hist_emissions[200:].plot(ax=ax, linestyle='dashed', alpha = 0.5)
eclipse5_cle.plot(ax=ax)
eclipse5_cle2degrees.plot(ax=ax)
eclipse5_mfr.plot(ax=ax)
eclipse5_slcp.plot(ax=ax)
eclipse6_cle.plot(ax=ax)
 
ax.set_title('Yearly '+specie_title+' emissions ECLIPSE scenarios')
ax.set_ylabel('Tg/a')
ax.legend(bbox_to_anchor=(1.0, 1.0))
```
 
%% Output
 
<matplotlib.legend.Legend at 0x7fffa371cfa0>
 
 
%% Cell type:markdown id:59220620-6a71-4411-832c-39d6cd3d1cb0 tags:
 
### EDGAR
 
%% Cell type:markdown id:4712f5aa-179d-49c5-88b8-ef325386d62e tags:
 
**Transport** contains emissions from the combustion of fuel for all transport activity, regardless of the sector, except for international marine bunkers and international aviation bunkers, which are not included in transport emissions at a national or regional level (except for World transport emissions). This includes domestic aviation, domestic navigation, road, rail and pipeline transport, and corresponds to IPCC Source/ Sink Category 1 A 3. The IEA data are not collected in a way that allows the autoproducer consumption to be split by specific end-use and therefore, this publication shows autoproducers as a separate item.
The procedures given for calculating emissions ensure that emissions from the use of fuels for international marine and air transport are excluded from national emissions totals.
 
 
**Road** contains the emissions arising from fuel use in road vehicles, including the use of agricultural vehicles on highways. This corresponds to the IPCC Source/Sink Category 1 A 3 b
 
%% Cell type:code id:a0bbe135-34d7-4c96-8668-2992a3d285ef tags:
 
``` python
edgar6_file = '/work/bd1063/b309253/emissions/edgarv6-anthro-'+ specie +'-yearly.csv'
edgar6 = pd.read_csv(edgar6_file, sep = ',')
edgar6[:3]
```
 
%% Output
 
Date Non-road ground transportation
0 1970-01-01 00:00:00 0.091566 \
1 1971-01-01 00:00:00 0.090448
2 1972-01-01 00:00:00 0.091963
Road transportation no resuspension
0 0.152304
1 0.161220
2 0.178024
 
%% Cell type:code id:884a4f0c-a5e1-4d8d-af15-c1182bb86a98 tags:
 
``` python
edgar6.columns = ['Date', 'EDGAR V6 Non-road ground transportation', 'EDGAR V6 Road transportation no resuspension']
edgar6 = edgar6.set_index(pd.to_datetime(edgar6['Date'])).drop(columns = ['Date'])
```
 
%% Cell type:code id:a2b0e91f-e5ba-4662-9c66-6f0006099d46 tags:
 
``` python
edgar6['EDGAR V6 Land Transport'] = edgar6['EDGAR V6 Non-road ground transportation'] + edgar6['EDGAR V6 Road transportation no resuspension']
edgar6[:3]
```
 
%% Output
 
EDGAR V6 Non-road ground transportation
Date
1970-01-01 0.091566 \
1971-01-01 0.090448
1972-01-01 0.091963
EDGAR V6 Road transportation no resuspension
Date
1970-01-01 0.152304 \
1971-01-01 0.161220
1972-01-01 0.178024
EDGAR V6 Land Transport
Date
1970-01-01 0.243871
1971-01-01 0.251668
1972-01-01 0.269987
 
%% Cell type:code id:697b6519-7541-4803-b861-f00d5ee734dc tags:
 
``` python
ax = ssp126_road.plot(figsize=(10, 5), linestyle='dashed', alpha = 0.5)
ssp245_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
ssp370_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
ssp460_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
ssp585_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
hist_emissions[200:].plot(ax=ax, linestyle='dashed', alpha = 0.5)
edgar6.plot(ax=ax)
 
ax.set_title('Yearly '+specie_title+' emissions in EDGAR v6')
ax.set_ylabel('Tg/a')
ax.legend(bbox_to_anchor=(1.0, 1.0))
```
 
%% Output
 
<matplotlib.legend.Legend at 0x7fffa8129cc0>
 
 
%% Cell type:code id:65f402e5-06ee-46e5-993e-cea7ed97c57e tags:
 
``` python
edgar4_file = '/work/bd1063/b309253/emissions/edgarv432-anthro-'+specie+'-year.csv'
edgar4 = pd.read_csv(edgar4_file, sep = ',')
edgar4.columns = ['Date', 'EDGAR V4 Non-road ground transportation', 'EDGAR V4 Road transportation no resuspension']
edgar4 = edgar4.set_index(pd.to_datetime(edgar4['Date'])).drop(columns = ['Date'])
edgar4['EDGAR V4 Land Transport'] = edgar4['EDGAR V4 Non-road ground transportation'] + edgar4['EDGAR V4 Road transportation no resuspension']
```
 
%% Cell type:code id:7e346451-f629-4c31-bc16-4e276b683646 tags:
 
``` python
edgar5_file = '/work/bd1063/b309253/emissions/edgarv5-anthro-'+specie+'-yearly.csv'
edgar5 = pd.read_csv(edgar5_file, sep = ',')
edgar5.columns = ['Date', 'EDGAR V5 Non-road ground transportation', 'EDGAR V5 Road transportation no resuspension']
edgar5 = edgar5.set_index(pd.to_datetime(edgar5['Date'])).drop(columns = ['Date'])
edgar5['EDGAR V5 Land Transport'] = edgar5['EDGAR V5 Non-road ground transportation'] + edgar5['EDGAR V5 Road transportation no resuspension']
```
 
%% Cell type:code id:c008da30-1301-4283-b6d6-adee8c9082ef tags:
 
``` python
ax = ssp126_road.plot(figsize=(10, 5), linestyle='dashed', alpha = 0.5)
ssp245_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
ssp370_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
ssp460_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
ssp585_road.plot(ax=ax, linestyle='dashed', alpha = 0.5)
hist_emissions[200:].plot(ax=ax, linestyle='dashed', alpha = 0.5)
edgar4['EDGAR V4 Land Transport'].plot(ax=ax)
edgar5['EDGAR V5 Land Transport'].plot(ax=ax)
edgar6['EDGAR V6 Land Transport'].plot(ax=ax)
 
ax.set_title('Yearly '+specie_title+' land transport emissions in EDGAR versions')
ax.set_ylabel('Tg/a')
ax.legend(bbox_to_anchor=(1.0, 1.0))
```
 
%% Output
 
<matplotlib.legend.Legend at 0x7fffa2d16f20>
 
 
%% Cell type:markdown id:ecbfa690-5923-4055-b843-f2657c122c4b tags:
 
# All together
 
%% Cell type:code id:82906576-a9b8-4e85-8aa3-454d9f373cdf tags:
 
``` python
ax = ssp126_road.plot(figsize=(10, 5), linestyle='dotted', alpha = 0.75)
ssp245_road.plot(ax=ax, linestyle='dashed', alpha = 0.75)
ssp370_road.plot(ax=ax, linestyle='dashed', alpha = 0.75)
ssp460_road.plot(ax=ax, linestyle='dotted', alpha = 0.75)
ssp585_road.plot(ax=ax, linestyle='dotted', alpha = 0.75)
hist_emissions[200:].plot(ax=ax, linestyle='dotted', alpha = 0.75)
ceds.plot(ax=ax)
#ceds_input4cmip.plot(ax=ax)
#ceds_v1.plot(ax=ax)
cams['CAMS Land Transport'].plot(ax=ax)
#edgar4['EDGAR V4 Land Transport'].plot(ax=ax, linestyle='dashed')
#edgar5['EDGAR V5 Land Transport'].plot(ax=ax, linestyle='dashed')
edgar6['EDGAR V6 Land Transport'].plot(ax=ax, linestyle='dashed')
eclipse5_cle.plot(ax=ax, linestyle='dashdot')
#eclipse5_cle2degrees.plot(ax=ax, linestyle='dashdot')
#eclipse5_mfr.plot(ax=ax, linestyle='dashdot')
#eclipse5_slcp.plot(ax=ax, linestyle='dashdot')
eclipse6_cle.plot(ax=ax, linestyle='dashdot')
 
ax.set_title('Yearly '+specie_title+' emission catalogue comparison')
ax.set_ylabel('Tg/a')
ax.legend(bbox_to_anchor=(1.0, 1.0))
```
 
%% Output
 
<matplotlib.legend.Legend at 0x7fffa8306470>
 
 
%% Cell type:code id:d2aff23a-b3ef-4096-9932-5f7e65b73360 tags:
 
``` python
ax = ssp126_road.plot(figsize=(10, 5), linestyle='dotted', alpha = 0.75)
ssp245_road.plot(ax=ax, linestyle='dashed', alpha = 0.75)
ssp370_road.plot(ax=ax, linestyle='dashed', alpha = 0.75)
ssp460_road.plot(ax=ax, linestyle='dotted', alpha = 0.75)
ssp585_road.plot(ax=ax, linestyle='dotted', alpha = 0.75)
hist_emissions[200:].plot(ax=ax, linestyle='dotted', alpha = 0.75)
ceds.plot(ax=ax)
ceds_input4cmip.plot(ax=ax)
ceds_v1.plot(ax=ax)
cams['CAMS Land Transport'].plot(ax=ax)
edgar4['EDGAR V4 Land Transport'].plot(ax=ax, linestyle='dashed')
edgar5['EDGAR V5 Land Transport'].plot(ax=ax, linestyle='dashed')
edgar6['EDGAR V6 Land Transport'].plot(ax=ax, linestyle='dashed')
eclipse5_cle.plot(ax=ax, linestyle='dashdot')
eclipse5_cle2degrees.plot(ax=ax, linestyle='dashdot')
eclipse5_mfr.plot(ax=ax, linestyle='dashdot')
eclipse5_slcp.plot(ax=ax, linestyle='dashdot')
eclipse6_cle.plot(ax=ax, linestyle='dashdot')
 
ax.set_title('Yearly '+specie_title+' ALL emission catalogue comparison')
ax.set_ylabel('Tg/a')
ax.legend(bbox_to_anchor=(1.0, 1.0))
```
 
%% Output
 
<matplotlib.legend.Legend at 0x7fffa8442350>
 
 
%% Cell type:markdown id:f36feb81-080d-4877-b0d7-db3718be5934 tags:
 
### Sectors definitions
 
%% Cell type:markdown id:9f63bc3e-6314-4111-b747-b589a7ffd69d tags:
 
**SSP & CMIP6 transport**:
 
%% Cell type:markdown id:f982dbee-6249-4d98-922b-f874e70504a9 tags:
 
Transportation sector for CMIP6 is the sum of **Road transportation** (proxy data source from EDGAR v4.3.2 ROAD)
and **Non-road transportation** (EDGAR v4.2 NRTR)
 
%% Cell type:markdown id:775cab11-a48f-4187-90e7-dfee6bc5d8d4 tags:
 
**CEDS Transportation**
 
%% Cell type:markdown id:ba8a3e5f-cb77-4832-8c5b-ebb5af514853 tags:
 
Since it's the set of emissions prepared for CMIP6, the definition of the sectors are the same
 
%% Cell type:markdown id:67d34f09-5a1d-4eb3-95dd-7df104bc6de9 tags:
 
**CAMS Land Transport**
 
%% Cell type:markdown id:f80a7e86-2b11-4b9f-8b23-5ca636536246 tags:
 
To get land transport emissions in CAMS we sum the sectors **ROAD TRANSPORTATION** and **OFF ROAD TRANSPORTATION**
 
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**EDGAR Transportation**
 
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To get land transport in EDGAR, we sum the sub-sectors: **Non-road ground transportation** and **Road transportation no resuspension**
 
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**Transport** contains emissions from the combustion of fuel for all transport activity, regardless of the sector, except for international marine bunkers and international aviation bunkers, which are not included in transport emissions at a national or regional level (except for World transport emissions). This includes domestic aviation, domestic navigation, road, rail and pipeline transport, and corresponds to IPCC Source/ Sink Category 1 A 3. The IEA data are not collected in a way that allows the autoproducer consumption to be split by specific end-use and therefore, this publication shows autoproducers as a separate item.
The procedures given for calculating emissions ensure that emissions from the use of fuels for international marine and air transport are excluded from national emissions totals.
 
 
**Road** contains the emissions arising from fuel use in road vehicles, including the use of agricultural vehicles on highways. This corresponds to the IPCC Source/Sink Category 1 A 3 b
 
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**ECLIPSE Transportation**
 
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**CLE** (Current legislation for air pollutants)
 
**MFR** (Maximum technically feasible reductions)
 
**CLE-2°** (Climate scenario (2 degrees, CLE))
 
**SLCP** (Short lived climate pollutants mitigation)
 
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Definition of **Transport** sector is consistent with CMIP6, EDGAR
 
%% Cell type:code id:4ea35b75-7834-4502-a97b-a815ba57ec73 tags:
 
``` python
```
 
%% Cell type:code id:ed433adf-fe64-46b1-a878-c364d5818775 tags:
 
``` python
```
 
%% Cell type:code id:c05f85f0-444a-4245-a6b9-6e6a14ba1d42 tags:
 
``` python
```
 
%% Cell type:code id:c725e850-ab02-495a-9f73-bddb1f0e2554 tags:
 
``` python
```
 
%% Cell type:code id:f74f60fd-a87d-42c9-a263-a5459d8af721 tags:
 
``` python
```
 
%% Cell type:code id:ff52f735-4b74-494c-9895-16b60d151e21 tags:
 
``` python
```
scenario comparison/catalogues_comparisons/NH3.ipynb
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],
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"name": "python3_2023_01"
},
scenario comparison/regional_catalogues_comparison/.ipynb_checkpoints/regions-checkpoint.ipynb 0 → 100644
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