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The HTAP (GAINS/ECLIPSE v5) Scenarios
Terry Keating based on presentations from
Markus Amann, Zig Klimont, and the IIASA teamRita Van Dingenen, Frank Dentener (JRC)Oliver Wild (Lancaster University)
HTAP Workshop on Global Emission ScenariosLaxenburg, February 11-13, 2015
Activity projections for HTAP scenarios
• Energy and industrial production derived from IEA’s ETP model
• Published in 2012
• 16 world regions
• Detailed representation of the energy sector
• 6˚/4˚/2˚ scenarios
Energy consumption by fuel (EJ/yr)
Coal Gas Oil
0
100
200
300
400
2010 2020 2030 2040 2050
RCP range
ETP 6°
ETP 2°
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100
200
300
400
2010 2020 2030 2040 2050
RCP range
ETP 6°
ETP 2°
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100
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400
2010 2020 2030 2040 2050
RCP range
ETP 6°
ETP 2°
Global CO2 emissions
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20
40
60
80
2010 2020 2030 2040 2050
GtCO2/yr RCP range
ETP 6°
ETP 2°
Key features• Driven by HTAP objectives and the need to improve representation of aerosol
emissions in the global long‐term IAM (Integrated Assessment Models) scenarios
• Developed with IIASA’s GAINS modelThe emissions of all species are estimated using the same primary activity data set within one common framework, which assures internal consistency (also with the CO2 calculation), including multi‐pollutant character of several control technologies
• 165 regions, period 1990‐2050
• Considered pollutants: SO2, NOx, PM (PM1, PM2.5, PM10, BC, OC, OM), NMVOC, CO, NH3, CH4
• Improved spatial (0.5ox0.5o) distribution – updated proxies
• Annual and monthly distribution of emissions
• A number of ‘new’ sources included: shale gas, gas flaring, wick lamps, diesel generators, superemitters
• Several scenarios: CLE, NFC, MTFR, Climate mitigation, SLCP mitigation
Three air pollution control scenarios
• Current legislation (CLE)– Full implementation of national legislations as of 2013– Including known implementation failures– Europe: baseline for 2014 NEC review, China: 11th Five years plan– International shipping: IMO agreement, low sulfur fuels
• No Further Controls (NFC)– No change in emission factors after 2015
• Maximum technically feasible reductions (MTFR)– All currently available control technologies– Subject to site‐specific application limits– No premature scrapping of existing capital stock– Disregarding implementation barriers, costs, institutional issues, etc.
Future air pollutant emissions –the role for AQ policies
0
20
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180
1990 2000 2010 2020 2030 2040 2050
Million tons
RCP
GAINS CLE
GAINS NFC
GAINS MTFR
NOx
0
1
2
3
4
5
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7
8
1990 2000 2010 2020 2030 2040 2050
Million tons
RCP
GAINS CLE
GAINS NFC
GAINS MTFR
BC
0
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100
120
140
160
1990 2000 2010 2020 2030 2040 2050
Million tons
RCP
GAINS CLE
GAINS NFC
GAINS MTFR
SO2
Future air pollutant emissions –the role for climate policies
0
20
40
60
80
100
120
140
160
1990 2000 2010 2020 2030 2040 2050
Million tons
RCP
GAINS CLE
GAINS NFC
GAINS MTFR
GAINS 2° CLE
SO2
0
20
40
60
80
100
120
140
160
180
1990 2000 2010 2020 2030 2040 2050
Million tons
RCP
GAINS CLEGAINS NFCGAINS MTFRGAINS 2° CLE
NOx
0
1
2
3
4
5
6
7
8
1990 2000 2010 2020 2030 2040 2050
Million tons
RCP
GAINS CLEGAINS NFCGAINS MTFRGAINS 2° CLE
BC
Future air pollutant emissions –Non‐energy‐related emissions
0
20
40
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120
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160
180
1990 2000 2010 2020 2030 2040 2050
Million tons
RCP
GAINS CLEGAINS NFCGAINS MTFRGAINS 2° CLE
VOC
0
10
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30
40
50
60
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90
1990 2000 2010 2020 2030 2040 2050
Million tons
RCP
NH3
0
100
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400
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600
700
800
1990 2000 2010 2020 2030 2040 2050
Million tons
RCP
GAINS CLEGAINS NFCGAINS MTFRGAINS 2° CLE
CH4
‘Current legislation’ emissionsby UNEP world region [million tons]
Source: GAINS model; ECLIPSE V5 scenario
0
20
40
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120
140
160
1990 2000 2010 2020 2030 2040 2050
Million tons
ShippingN. America, EuropeLatin AmericaEast AsiaAfricaSWC Asia
SO2
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180
1990 2000 2010 2020 2030 2040 2050
Million tons
ShippingNorth AmericaLatin AmericaEast AsiaAfricaSWC Asia
NOx
0
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2
3
4
5
6
7
8
1990 2000 2010 2020 2030 2040 2050Million tons
ShippingNorth AmericaLatin AmericaEast AsiaAfricaSWC Asia
BC
‘Current legislation’ emissions by key sectors [million tons]
Source: GAINS model; ECLIPSE V5 scenario
SO2 NOx PM2.5 BC
Global CH4 emissions and mitigation potentials
Source: GAINS model; ECLIPSE V5 scenario
0
100
200
300
400
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600
1990
1995
2000
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
Baselin
e em
ission
s Mt C
H4
Baseline
Livestock Rice cultivation Coal miningOil production Gas production Gas transmissionGas distrib networks Solid waste WastewaterCombustion
0
100
200
300
400
500
600
1990
1995
2000
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
Emission
s with
max te
chnical red
uctio
n Mt
CH4
Max technical mitigation
Global BC emissions in 2000, Tg BC (excluding forest and grassland fires)Source: GAINS model – ECLIPSE results (Klimont et al., in preparation)
Range of global estimates shown in Bond et al., 2013
GAINS; excluding ‘new/re-estimated’ sources GAINS; all sources
Bottom Line
• The ECLIPSE scenarios show a wider range of potential outcomes for aerosol emissions than projected in the RCP (Regional Concentration Pathway) scenarios
– The scenarios highlight the importance of enforcement of existing policies in the mid‐term as they can contribute to significant reductions or at least stabilization of aerosol emissions, especially in Europe, North America and East Asia
– In the long term, however, current policies do not guarantee that emissions would not be raising again and therefore call for more action, even in the developed world.
HTAP 2010 Findings
Reconstruction of Historical Ozone Trends
O. Wild et al., ACPD, 2011
How will regional boundary conditions change?European Inflow based on HTAP1
Quantify changes in European ozone under a range of likely future precursor emissions: ‐5 to +6 ppb by the 2030s (based on HTAP1)
Extend/refine approach using HTAP2 results…
Provision of future boundary conditions for regional models…Oliver Wild
From emissions to impacts: the FAst Scenario Screening Tool: TM5‐FASST
•Emissions considered: ‐ SO2, NOx, NMVOC, NH3, CO ; CH4, Elemental Carbon, Primary Organic Matter, other primary PM
•Examples of impacts considered:‐ PM2.5 and O3 surface concentration and population exposure‐ O3 metrics for crops and vegetation exposure + impact on yield loss‐ Radiative forcing and CO2eq of SLCFs (GWP and GTP based)‐ Temperature trend for selected time horizons and emission trajectories of pollutants and CO2‐Deposition of BC, nitrogen and impacts on sensitive ecosystems
•Global Source ‐ Receptor model for air pollutants, radiative forcing and deposition
•Simplified linear emission‐concentration/forcing/deposition relations between regions
•Uses TM5‐CTM output (2‐way nested model, 1°x1° over multiple zoom regions)
Rita Van Dingenen, Frank Dentener
TM5‐FASST
Overlaps with a ranges of IAMs (e.g. IMAGE‐MESSAGE‐POLES)
The HTAP regional mask is fairly consistent
Function of atmospheric concentrations, population density and exposure‐response functions
China+ China+
India+
USA
Central Europe
Western Europe
ROW
Western Europe China+
India+
USA
Central Europe
Western Europe
ROW
USA China+
India+
USA
Central Europe
Western Europe
ROW
India+ China+
India+
USA
Central Europe
Western Europe
ROW
Where is it coming from?Change in Particulate Matter 2050 CLE ‐ 2050 MFR
China+ China+
India+
USA
Central Europe
Western Europe
ROW
Western Europe China+
India+
USA
Central Europe
Western Europe
ROW
USA China+
India+
USA
Central Europe
Western Europe
ROW
India+ China+
India+
USA
Central Europe
Western Europe
ROW
Where is it coming from?O3 change by emission source region: 2050 CLE ‐ 2050 MFR
(Change in annual mean surface ozone concentration)
Focusing Our Analyses to Provide Information Relevant to Air Quality Management
1. How well can current global and regional models quantify:a. Spatial patterns and temporal trends in
i. Surface ozone concentrationsii. Fine particle concentrationsiii. Nitrogen deposition
b. Elevated levels of ozone in urban and rural locations, including high elevation sites.
c. Contributions of local and regional anthropogenic sources as distinguished from i. Anthropogenic sources outside the region (e.g. North America or the United
States)ii. Stratospheric intrusioniii. Biogenic, wildfires, wind‐blown dust, and other uncontrollable emissions
2. Are robust and generally acceptable model performance standards established and routinely applied in the global and regional modeling communities?
3. Establishing Regional Boundary Conditionsa. In a nested system, how does global model performance affect regional model
performance?b. How should future year regional boundary conditions (e.g. for western U.S.
regional modeling) be determined or evaluated?
Data sources and references• Activity data, drivers:
– Energy use: IEA and EUROSTAT statistics; IEA/OECD projections until 2050 (Energy Technology Perspectives, 2012; PRIMES, 2013):
• 6oC scenario – consistent (until 2035) with the WEO 2011 • 2oC mitigation scenario – comparable to the WEO 450ppm
– Agriculture: Eurostat, FAO (2012)
– Shipping: QUANTIFY (EU FP6; Endresen et al., 2007; IMO, 2011)
– Gas flaring: Elvidge et al. (2009)
– Industrial production, waste, other: IEA, UN, national stats, …
• Papers: – Published: Klimont et al (2013; Global SO2), Stohl et al (2013; Arctic BC), Yttri et al. (2014; BC Europe),
Lund et al. (2014; transport BC), Safieddine et al. (2014; ozone), Gadahvi et al. (2015; BC India)– Submitted: Eckhard et al (aerosols, BC Arctic), Huneeus et al (Global SO2, inverse modelling)– In preparation: Klimont et al. (documentation of ECLIPSE),
• The gridded emission data has been accessible via several portals:– ECLIPSE (http://nilu.eclipse.no) – GEIA (http://www.geiacenter.org) and directly at ECCAD http://eccad.sedoo.fr– IIASA/GAINS (http://gains.iiasa.ac.at)
Role of ‘new’ sources in global emissionsGAINS Baseline, ECLISPE V5 (excl int shipping, savannah & forest fires)
NOx, Mt NO2 BC, Mt
TM5‐FASST:
y = 0.95x + 0.71R² = 0.84
0
5
10
0 5 10TM
5 ‐FAS
ST
TM5 full model
PM2.5, µg/m³
y = 0.98x + 0.01R² = 0.99
00.20.40.60.81
1.21.41.6
0 0.5 1 1.5
TM5 ‐FAS
ST
TM5 full model
BC, µg/m³
Testing against ‘full’ model results
How would the regional pollution change due emission developments within the region compared to those outside for
the given emission scenarios?
TM5‐FASST evaluation of premature mortalities and crop losses