Emission data sets and methodologies for estimating

REanalysis of the TROpospheric chemical composition over the past 40 years A long-term global modeling study of tropospheric chemistry funded under the 5th EU framework programme EU-Contract No. EVK2-CT-2002-00170

Emission data sets and methodologies for estimating emissions Work Package 1, Deliverable D1-6

Editor: Authors:

Martin Schultz, Sebastian Rast, MPI for Meteorology, Hamburg Maarten van het Bolscher, Tinus Pulles, Roel Brand, TNO, Apeldoorn Jose Pereira, Bernardo Mota, IICT, Lisbon Allan Spessa, MPI for Biogeochemistry, Jena Stig Dalsøren, University of Oslo Twan van Nojie, KNMI, De Bilt Sophie Szopa, LSCE, Gif-sur-Yvette

Document prepared on 26 February 2007 UPDATED VERSION 28 March 2008 (modified Annex 4)

RETRO deliverable D1-6: Report on emissions / 2


1. Introduction ......................................................................................................................................... 5 2. Emitted compounds and their use in the RETRO models ................................................................... 6 2.1 Classification of emissions............................................................................................................ 6 2.2 Parameterisation of emissions in the five RETRO models ........................................................... 6 3. Anthropogenic emissions .................................................................................................................. 10 3.1 Definition of emission sectors ..................................................................................................... 10 3.2 TNO Emission Assessment Model (TEAM)............................................................................... 10 3.2.1 Structure of TEAM............................................................................................................... 13 3.2.2 Data input ............................................................................................................................. 13 3.2.3 Post-processing..................................................................................................................... 14 3.2.4 The smoothing procedure..................................................................................................... 15 3.3 Emissions from International ship traffic .................................................................................... 18 3.4 Aircraft emissions........................................................................................................................ 19 4. Wildfire emissions............................................................................................................................. 21 4.1 The RETRO wildfire inventory................................................................................................... 21 4.2 The Reg-FIRM Model................................................................................................................. 26 5. Natural emissions .............................................................................................................................. 29 5.1. Ocean emissions ......................................................................................................................... 29 5.2 Biogenic emissions...................................................................................................................... 29 5.3 Lightning NOx production .......................................................................................................... 31 6. Methane emissions ............................................................................................................................ 34 7. Comparison with other emission data bases...................................................................................... 35 7.1 Anthropogenic land surface emissions........................................................................................ 35 7.2 Ship emissions............................................................................................................................. 39 7.3 Aircraft emissions........................................................................................................................ 39 7.4 Comparison of NOx emission trends in China with satellite observations of tropospheric column NO2 .................................................................................................................................................... 40 8. Analysis of emission trends and characteristic patterns .................................................................... 41 8.1 Spatial distribution of RETRO emissions ................................................................................... 41 8.2 Temporal evolution of emissions in the different sectors............................................................ 42 9. Conclusions and implications for emission control strategies........................................................... 49 10. Known problems with the RETRO emission data sets.................................................................... 50 11. References ....................................................................................................................................... 53 11.1 Scientific literature .................................................................................................................... 53 11.2 Relevant web links: ................................................................................................................... 57 Annex 1: Sector definition of the UNFCCC Common reporting format .............................................. 59 Annex 2: RETRO emissions: tabulated global annual totals for selected species ................................ 64 NOx (Tg(NO2)) ................................................................................................................................. 64 CO (Tg(CO)) ..................................................................................................................................... 65 Methanol (Tg(CH3OH)) .................................................................................................................... 66 Ethane (Tg(C2H6)) ............................................................................................................................. 67


Propane (Tg(C3H6)) ........................................................................................................................... 68 Ethene (Tg(C2H4)) ............................................................................................................................. 69 Propene (Tg(C3H6)) ........................................................................................................................... 70 Formaldehyde (Tg(CH2O)) ............................................................................................................... 71 Annex 3: Pulles et al report on TEAM.................................................................................................. 72 Annex 4: Schultz et al . paper on RETRO wildfires ............................................................................. 73 Annex 5: On the use of AVHRR data for estimating trends and variability of burnt area.................... 74 Annex 6: IMAGE 2.2 regions definition............................................................................................... 77


1. Introduction The RETRO project aimed at analyzing the long-term changes in the atmospheric budget of trace gases and aerosols over the time period from 1960 to 2000. Five global-scale numerical models of atmospheric transport and chemistry were employed to achieve a statistically robust and temporally consistent estimate of atmospheric composition changes during this period. Such changes are driven to a large extent by changes in the emissions of primary pollutants and pollutant precursors. It was therefore a fundamental prerequisite to the project to obtain consistent long-term data sets of trace compound emissions, which could then be used in the modelling work. A key objective of the project activities in work package 1 was the quantification of emission changes and the interannual and seasonal variability of emissions, which are described in this report. In recent decades, human activities (in particular combustion processes) have begun to exert a noticeable influence on the atmospheric chemical composition and as a consequence also on the physical climate system (IPCC, 2001). The RETRO project aimed at an understanding of the factors controlling the budgets of ozone and ozone precursor species and at identifying the anthropogenic influence on the abundance of these compounds. The main focus of the emissions work within the RETRO project was on gas-phase species and anthropogenic as well as wildfire emissions. The main deliverable from work package 1 are a large number of new comprehensive global gridded data sets for anthropogenic and wildfire emissions over the past 40 years. These data sets comprise unprecedented level of detail in the speciation of NMVOC compounds, and improved seasonality and grid resolution (0.5°×0.5° instead of the common 1°×1°). Emissions from international ship traffic and aircraft as well as natural sources were adapted from other state-of-the-art data bases and interpolated in space and time in order to be consistent with the new RETRO data sets. In support of these main activities, partners IICT/ISA investigated the usefulness of a 20-year time series of NOAA AVHRR GAC data for the estimation of burnt areas and their interannual variability and improved the quality of ATSR hot spot data delivered through the ESA/ESRIN World Fire Atlas through the development of a scientifically based filtering procedure. This document describes all emission data sets used in the long-term model calculations of the tropospheric chemical composition and evaluates emissions computed interactively by the models (e.g. NOx from lightning or biogenic NMVOC emissions from the terrestrial vegetation). While the scientific understanding of emissions is a very ambitious goal on its own, the RETRO project also wanted to generate information which is useful for policy makers, i.e. past emission control regulations and future options were assessed in targeted sensitivity studies. For this purpose, specific emission scenarios targeting the traffic and power generation sectors were developed at TNO and employed by the various RETRO models. These emission scenarios are described in the report D5-5 together with the ensuing model results.


2. Emitted compounds and their use in the RETRO models 2.1 Classification of emissions For technical and organisational reasons we adopted the following categorization of emissions for the RETRO project: •

anthropogenic emissions, comprising of stationary and mobile combustion sources (including international ship traffic and aircraft) and emissions from the manufacturing industry,

•

wildfire emissions, e.g. controlled and uncontrolled burns of natural or anthropogenic origin in the open vegetation,

•

natural emissions from the terrestrial or oceanic biosphere and lightning emissions of NOx.

This classification allows for a distinction between controllable and uncontrollable emissions, which is important for evaluating the success or failure of past policy measures and facilitates the ex-post analysis (RETRO WP5).

2.2 Parameterisation of emissions in the five RETRO models Emission data sets were compiled specifically for CO, NOx, and a range of NMVOC compounds and compound groups. Emissions for other species which were simulated by the RETRO models were either generated by linear scaling to one of the above (e.g. H2 emissions were derived from CO emissions), or they were adopted from other data sources. Table 1 gives an overview about all compounds for which the RETRO project generated emissions data sets and summarizes their use in the five models participating in the project. Detailed descriptions of the models and their set-up for performing the RETRO simulations can be found in the reports D4-1 and D4-4. Except for aircraft, lightning NOx, and wildfire emissions, all emissions are released at the surface level of the models. Therefore, we did not distinguish between ground-level and high stack emissions.


Table 1: Overview on chemical compounds for which surface emission data sets were generated in the RETRO project and used by the models. Unless otherwise indicated, all anthropogenic emissions (labelled “A”) were from the TNO TEAM inventory and include the UiO-VERITAS inventory of international ship traffic emissions. All biomass burning emissions (labelled “B”) stem from the RETRO fire emissions inventory. Natural emissions from soils and the terrestrial vegetation (labelled “N”) are either from the 1985 GEIA inventory (Guenther et al., 1995), from Lathiere et al. (2004), or from the interactive Model of Emissions of Gases and Aerosols from Nature (MEGAN) by Guenther et al. (2006). Other natural emissions include those from the ocean waters (taken from the GEIA data base and not listed here). Emissions from other sources than those specified above are marked by an “X” and described in the footnotes. In addition to the surface emissions listed here, all models used NOx emissions from aircraft by Grewe et al. (2001) and from lightning. The latter were parameterized in relation to cloud top heights or convective mass fluxes. Besides the compounds listed here, the TNO inventory also contains emission estimates for chlorinated HCs and some higher alkanals, ketones, etc.

Compound Chemical name formula Inorganic compounds Nitrogen NO, NO2 1 oxides (NOx) Carbon CO monoxide Hydrogen H2 Ammonia NH3 Sulfur dioxide SO2 Methane CH4 2 Alcohols Methanol3 CH3OH Ethanol3 C2H5OH Alkanes Ethane C2H6 Propane C3H8 Butanes C4H10 Pentanes C5H12 Hexanes and higher alkanes Alkadienes and Alkynes Ethene C2H4 Propene C3H6 Isoprene C5H8 Monoterpenes C10H16 Ethyne C2H2 (acetylene) Other alkadienes and alkynes

LMDzINCA

MOZECH

TM4

Oslo-CTM

pTOMCAT

A, B, N

A, B, NM

A, B, N

A, B, N

A, B, N

A, B, N

A, B, N

A, B, N

A, B, N

A, B, N

A, B, N

A, B, N

A, B, N A, B AO AO AO

A, B, N A, BP AP AP AP

A, B, NM X, B, N X, B A, B

A, B, NM

A, B, N A

A, B, N

A, B, N A, B, N AL AL AL

A, B, N A, B AM AM AM

A, B, NT A, BT AT AT AT

A, B, N A, B, N B, N B, N A, B, N

A, B, N A, B, N B, N B, N

A, B, N A, BT B, NT B, NT

(A)T

A

N N


Table 1 (continued) Compound Chemical name formula Aromatics Benzene C6H6 Toluene C7H8 Xylenes C8H10 Trimethylbenzene Other aromatics Esters Ethers Alkanals Formaldehyde CH2O (methanal) Acetaldehyde CH3CHO (ethanal) Other alkanals Ketones Acetone CH3COCH3 Other ketones Acids Formic acid CH3OOH Acetic acid C2H5OOH Other acids

LMDzINCA

MOZECH

A L, B A L, B A L, B AL

AM AM AM

AL

AM

TM4

Oslo-CTM

A, BT AT

AO AO AO AO

AT

AO

pTOMCAT

AT AT B

A, B, N

B

A, B, N

N

A, B, NT

N

AT B, N

A, B, N

A, B, NT AT

N

AT A, NT AT

1

Actual emissions largely consist of NO, which is then rapidly converted into NO2 by reacting with ozone. For reporting purposes, NOx emissions are generally referenced as NO2 emissions (e.g. base units are kgNO2). 2 Methane concentrations were prescribed at the model surface level. Some models used methane emissions anyhow and employed a relaxation technique to constrain the methane concentrations with the prescribed surface fields 3 The TNO inventory contains all alcohols as one compound group LMDz-INCA comments (L): Biogenic emissions from Lathiere et al. (2004) Oceanic emissions from Erickson and Taylor (1992) Alkanes C4, C5, C6 and higher lumped into ALKAN group species Alkenes C3 and higher, dienes and alkynes (>2C) lumped into ALKEN group species All aromatics lumped into AROM group species MOZECH comments (M): NOx emissions split into 95% NO and 5% NO2 emissions H2 scaled to CO emissions, factors from Novelli et al., 1999 CH4 from Bergamaschi and Dentener (pers. communication, 2005) with linear trends applied All alcohol emissions treated as methanol Alkanes C4, C5, C6 and higher lumped with aromatics and treated as butanes Ketones treated as acetone Biogenic (natural) emissions from the MEGAN model TM4 comments (T):


NMVOC lumping according to the CBM-4 scheme (Houweling, 1998) Monoterpenes are included as isoprene (1 molecule/molecule) Anthropogenic NH3 and SO2 emissions from EDGAR-HYDE version 1.3 (van Aardenne et al., 2001) Non-eruptive volcanic SO2 emissions from GEIA (Andres and Kasgnoc, 1998). Biogenic emissions from Lathiere et al. (2004); NH3, CO, C2H6 and C2H4 from GEIA Oslo CTM2 comments (O): Butanes and pentanes lumped and emitted as butane Hexanes and higher emitted as hexane All aromatics lumped and emitted as xylene Other alkanals (VOC_22) assumed to be CH3CHO Ketones (VOC_23) assumed to be acetone p-TOMCAT comments (P): C3, C4, C5, and C6 alkanes lumped as propane weighted by number of C atoms Other alkanals (VOC_22) assumed to be CH3CHO Ketones (VOC_23) assumed to be acetone Biogenic (natural) emissions from GEIA


3. Anthropogenic emissions 3.1 Definition of emission sectors To avoid duplication of previous work and to produce information that is useful for scientific and policy purposes, the RETRO consortium decided to focus on the categorisation schemes adopted by the United Nations Framework Convention on Climate Change (UNFCCC) / International Panel on Climate Change (IPCC) common reporting format (IPCC, 1997), and the Nomenclature for Reporting (EMEP/LRTAP, 2005) applicable for the Convention on Long-Range Transboundary Air Pollution (CLRTAP). However, for modelling purposes to be practical, this detailed categorisation of source sectors is used in an aggregated manner. For anthropogenic emissions, we adopted the LOTOS categorisation scheme (Schaap et al., 2005) with 10 sectors (Table 2). A listing of the Common reporting format (CRF) categories is provided in Annex A. Table 2: LOTOS source categories LOTOS Group

LOTOS Group Description

CRF classification

1

Power generation

1.A.1.a; 1.A.1.b; 1.A.1.c

2

1.A.4.a; 1.A.4.b; 1.A.4.c

3

Residential, commercial and other combustion Industrial combustion

4

Industrial processes

1.A.2.a; 1.A.2.b; 1.A.2.c; 1.A.2.d; 1.A.2.e; 1.A.2.f 2

5

Extraction distribution of fossil fuels

1.B.2.a.ii; 1.B.2.a.iii; 1.B.2.a.iv; 1.B.2.a.v

6

Solvent use

3.A; 3.B; 3.C; 3.D

7

Road transport

1.A.3.b; 1.A.3.b.v

8

Other mobile sources

1.A.3.a; 1.A.3.c; 1.A.3.d; 1.A.3.e

9

Waste treatment and disposal

6.C; 6.D

10

Agriculture and Landuse change

4.E; 5.A

3.2 TNO Emission Assessment Model (TEAM) An important goal for WP1 – Emissions was to produce values of emissions to air by various sources for various pollutants over a period of 4 decades and worldwide. The results were meant to serve as input for analysis of tropospheric chemistry by means of chemistry climate models and chemistry transport models.


The anthropogenic emissions form a substantial part of the overall emissions to consider. At the same time, the place, time and origin of emission values should be provided at a high level of detail. In order to achieve this goal, the data were reported using the TNO Emission Assessment Model (TEAM). The following figures provide some examples of the output from and the possibilities of the TNO TEAM model of emissions. A description of the TEAM approach and methodology is given in the following sub sections and in the paper by Pulles et al. (2006) (see Annex 4).

80

60

40

20

0

-20

-40

-60 -180

t

-160

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

0 to 0.005 0.005 to 0.05 0.05 to 0.2 0.2 to 1 1 to 5 5 to 50 50 to 500 500 to 10000

Figure 1: Gridded TNO TEAM emissions of carbon monoxide for the year 1971 (all categories)

140

160

180


55

55

50

50

45

45

40

40

35

35

30

30

25

25

20

20

15

15

10

10

5

5

0

0

-5

-5 95

100

105

110

115

120

125

130

t

135 140 145 150 Emission density (kton/cell) 100 to 1000 25 to 100 5 to 25 1 to 5 0.2 to 1 0.05 to 0.2 0.001 to 0.05 0 to 0.001

95

100

105

110

115

120

125

130

t

135 140 145 150 Emission density (kton/cell) 100 to 1000 25 to 100 5 to 25 1 to 5 0.2 to 1 0.05 to 0.2 0.001 to 0.05 0 to 0.001

Figure 2: TNO TEAM NOx emissions in East Asia for the year 2000. Left: stationary sources, right: mobile sources. Note the missing data for Mongolia where no reports were available. Emissions for Mongolia were estimated in the post-processing step (see below).

Cummulative NMVOC emission by country groups

Cummulative NOx emission by country groups

200 000

90 000 Africa

160 000

Australia and New Zealand

Emission (Gg/year)

140 000

Other Asia (except USSR & Middle East)

120 000

Middle East 100 000 Central and South America

80 000 60 000

North America

40 000

former USSR

Africa 80 000 Australia and New Zealand 70 000 Emission (Gg/year)

180 000

Other Asia (except USSR & Middle East)

60 000

Middle East

50 000 40 000

Central and South America

30 000

North America

20 000

former USSR

10 000

20 000

Eastern Europe

Eastern Europe 0

0

2000

1995

1990

1985

1980

1975

1970

1965

1960

2000

1995

1990

1985

1980

1975

1970

1965

1960

Western Europe

Western Europe

Figure 3: Contribution by country group to annual anthropogenic emissions over the period 1960 – 2000 for NMVOC (left) and NOx (right)


3.2.1 Structure of TEAM The structure used in this project for the TNO emission database is based on the TNO Emission Assessment Model (TEAM). The approach of TEAM is described in all details in Pulles et al. (2006). In our approach we explicitly model the time dependent introduction of alternative technologies into the emission inventory by applying the following equation:

E pollutant (t ) =

∑

activities

∀ activities, ∀ t :

⎛ ⎞ ⎜ ∑ (ARactivity (t ) × Pactivity ,technology (t ) × EFtechnology , pollutant )⎟ ⎜ ⎟ ⎝ technologies ⎠

∑P

activity ,technology technologies

(t ) = 100%

with: Epollutant(t)

The emission of a pollutant at a time interval t

ARactivity(t)

The activity rate for a certain activity at time interval t

Pactivity,technology(t)

The penetration: the fraction (at time interval t) of the activity performed using a specific technology

EFtechnology, pollutant

The emission factor, an attribute of the technology selected determining the linear relation between the activity rate and the resulting emission of a certain pollutant, using a specific technology

The three main aspects of TEAM are: •

Economy: the economic aspect represented by a table with activity rates.

•

Technology: the technological aspect, represented by a table of all relevant technologies that can be used to perform specific activities accompanied by the emission factors for each relevant pollutant.

•

Behaviour: the behavioural aspect, linking technologies to each activity based on country and year (in this project, the link has been based on the link of activities and emission factors from the TROTREP project).

3.2.2 Data input Activity rates: •

Data for a selection of fuels have been obtained from the International Energy Agency (IEA). These data cover the period of 1960-2000 for the OECD countries and of 1970-2000 for the non-OECD countries. By using the IEA conversion factors, all activity data have been converted to TJ.


•

Solvent use emission data and residential biomass burning activity data have been obtained from a global inventory for anthropogenic NMVOC emissions that has been developed at TNO as input for EDGAR 2.0 and GEIA: http://www.mnp.nl/geia/data/NMVOC_Groups/

•

Other information was obtained from the TROTREP project: http://atmos.chem.le.ac.uk/trotrep/

Technologies & Emission factors: •

Emission factors have been obtained from the TROTREP project. The emission factors in the TROTREP project are developed, mainly as expert judgements by the TROTREP emissions team at TNO. In this development several international emission factor collections were used and were interpreted towards average values for a country or a group of countries in specific years. Technologies in our approach were identified from the TROTREP emission factors as unique combinations of emission factors for CO, NOx and NMVOC for a specific year for specific country groups, mostly OECD and non-OECD for stationary combustion and ‘Western’ and ‘Non-western’ for road transport.

Behaviour: The link between technologies and activities in the emission database created for the RETRO project WP1 is mainly based on the link between activities and emission factors that already existed in the TROTREP project. For the different scenarios with the use of the behavioural aspect of the TEAM, check the report on scenarios (D5-5).

3.2.3 Post-processing After the compilation of all national statistics and the set-up of the technology penetration and emission factor data bases, a number of postprocessing steps were necessary in order to generate the final gridded inventory product. These were: •

No data are available in the IEA Statistics for countries Mongolia and Western Sahara. Using the emission data of nearest countries the emission data of Mongolia and Western Sahara have been calculated by scaling on population.

•

The total emission of NMVOC has gone through a process of country specific speciation resulting in an NMVOC profile1 also used in the TROTREP project.

•

Since the activity data of the non-OECD countries were only available starting from 1970, the gap from 1960 up to 1970 has been calculated using linear extrapolation as back casting tool in Excel.

•

Gridded population data (CIESIN) were obtained to calculate a spatial distribution of the country totals, allocating the emissions over the globe.

•

Based on monthly patterns from LOTOS, the annual totals have been split up into monthly totals for the different LOTOS groups.

1 For NMVOC profile see Appendix II


After these postprocesing steps at TNO the TEAM data were delivered as Excel csv files to partner MPG-IMET where they were converted into the netcdf format in which they are made publically available (http://retro.enes.org/emissions). MPG-IMET also applied a smoothing procedure in order to harmonize the time series in various world regions (described below) and they merged the TNO TEAM data with the other RETRO emission data sets so that a complete description of emissions for NOx, CO, and NMVOC compounds could be made available.

3.2.4 The smoothing procedure As a result of analysing the final emissions and time series of emissions in various geographical regions, some smoothing of the emissions data delivered by TNO was necessary (see Figure 4 and Table 3). In order to accelerate the process and avoid another regridding and reformatting step of the data at TNO, a simple linear scaling procedure was designed which operates on rectangular regions. While this is not a perfect solution, it nevertheless produces reasonable results and data sets which are suitable for global modelling purposes. Table 3 lists the individual correction factors that were applied to the TNO data sets. Note that no correction was performed for other compounds as these were seen as of secondary importance for the atmospheric chemistry of ozone and its precursors.


Figure 4: Example for the smoothing process in the anthropogenic emissions data set. Left: sectorized emissions for CO (top) and C2H6 (bottom) in Eastern Asia as derived directly from the TNO data base; right: the same after the smoothing procedure has been applied.

CO

1.33 1.16 3.05 -

time period

1960-1969 1970 1980-2000 1960-1979 1960-1978 1986-1996

0.77 1.82 2.22 -

NOx -

alcohols 0.95 1.85 1.3 4.57 -

Ethane 0.92 2.84 1.48 3.13 3.45

propane 0.88 3.8 1.55 2.35 1.77

butanes

1970

1960-1970 1960-1977 1997-2000

1970 1990 1991 1992-2000 1992-2000 1991

res

res exf was

res tra tra tra pow inc

North America res 1960-1969 exf 1960-1977

ethene

propene

1.06 1.02 0.6 -

1.07 1.03 -

0.96 1.36 1.17 4.55 -

ethyne

1.02 0.73 -

1.24 1.12 3.14 -

benzene

1.6 0.81 0.55 0.75 -

-

-

-

-

South America 1.1 -

-

-

8.2 0.89 0.69 0.77 0.8 -

1.14 0.74 0.69

2.47 0.83 0.56 0.77 0.81 -

Former USSR 4.35 6.25 0.82 0.81 0.57 0.55 0.74 0.77 0.84 -

2.48 0.83 0.58 0.75 -

1.57 0.82 0.57 0.76 -

1.75 0.83 0.56 0.77 -

Europe 1.6 1.24 1.3 1.28 1.25 2.65 1.64 0.65 0.73 0.86 0.69 0.66 0.66 Not corrected: CO, propene, ethyne, benzene, xylene res 1960-1970 (increase over more than 1 year)

2.41

1.48 0.82 0.56 0.77 -

2.63 0.65

-

1.54 1.71 2.14 1.3 1.18 1.19 1.65 1.61 1.57 2.76 1.33 not corrected: solvent use (rather wiggly curves; toluene, xylene, alcohols and ketones), inc 1967, 1970 (xylene)

1.13 1.06 1.47 -

0.94 1.81 1.3 1.31 1.15 4.55 Mean value of 1985 and 1997

South Asia res 1970 1.06 1.26 1.15 1.27 1.5 res 1980-2000 1.03 1.02 1.07 1.13 1.2 tra 1960-1970 0.66 0.54 inc 1960-1979 1.85 1.75 not corrected : pow before 1970 and exf before 1970 (butanes), waste before 1970 (propane, propene)

sector East Asia res res res inc exf agr

Table 3: Correction factors applied to TNO emissions in order to produce homogeneous time series

1.27 0.81 0.55 0.77 0.82 -

1.3 3.3 0.78

-

1.69 -

0.65 2.03

1.11 1.06 3.62 2.73

toluene

1.71 0.81 0.55 0.76 0.81 -

3.6 -

-

-

1.04 0.86 2.1

0.96 1.34 1.16 4.38 2.88

xylene

3.3 Emissions from International ship traffic The TNO TEAM data covers only land surface based emissions. These had to be augmented by emissions data for international ship traffic as these emissions can have a strong influence on the pollutant background concentrations (cf. Corbet et al., 2001; Endresen et al., 2003). A detailed data set of international ship traffic emissions of the year 2001 had been developed by VERITAS and the University of Oslo group (Endresen et al., 2003). This data set comprises of annual values and we assume that there is no significant seasonal variation for these emissions. Ship emissions were provided among other compounds for NOx, CO, and total NMVOC. We applied the same NMVOC compound split as described in Endresen et al., 2003 (tables 12 and 13). No detailed information on emissions from ship traffic is available for other years within the RETRO period, so we had to develop a methodology to obtain a reasonable historical evolution of these emissions. It was decided to apply a globally uniform scaling factor derived from bunker fuel sales statistics, which should yield a good description of the CO2 emission trend. The quality of this scaling approach is less certain for other trace gases and aerosol precursors, because the emission factors for these compounds have very likely changed over time due to developments in engine technology. For example, in the 1960s there were still many steam boats operating on transatlantic routes, while they are virtually absent on today’s international waters. The globally uniform scaling also neglects the shifts in transportation routes which occurred in particular during the 1990s when more and more traffic was routed to East and South East Asia.

normalized global bunker fuel sales

1.2

1 0.8

0.6

0.4 0.2

19 60 19 62 19 64 19 66 19 68 19 70 19 72 19 74 19 76 19 78 19 80 19 82 19 84 19 86 19 88 19 90 19 92 19 94 19 96 19 98 20 00

0

Year

Figure 4: Normalized trend of international ship emissions derived from statistics of bunker fuel sales. For lack of more detailed information, these trends will be applied uniformly across the globe.


Figure 5 shows a comparison of satellite observations of the enhancement of the tropospheric NO2 column with the ship traffic emissions data from the Endresen et al. (2003) - RETRO inventory. While this figure cannot provide further constrains on the absolute magnitude of these emissions, it nevertheless shows the very realistic description of the ship traffic locations in the Endresen et al., (2003) inventory. Both data sets show enhanced emissions in particular between India and Southeast Asia along a very narrow route.

Figure 5: NOx signature of shipping in the Indian Ocean (a) Tropospheric NO2 columns derived from SCIAMACHY data from August 2002 to April 2004 using the Differential Optical Absorption Spectroscopy (DOAS) technique and the reference sector method for the region of the Red Sea (5°N to 35°N and 30°E to 60°E). (b) Estimated distribution of ship traffic NOx emissions from Endresen et al. (2003) in the same region.

3.4 Aircraft emissions Aircraft emissions were adopted from the AEROCHEM project (Lee et al., 2002; Grewe et al., 2002). These were data sets with monthly time resolution from 1960 to 2030 on a spatial grid of 3.75°x3.75° and in 7 altitude bins. Figure 6 displays the temporal evolution of the global accumulated NOx emissions from this data set. Due to lack of more specific data, changes in the spatial distribution of aircraft emissions over time were not taken into account. Thus, the RETRO model simulations neglect in particular the recent increase in flight traffic to and from Asia and within Europe.


Aircraft emissions [Tg(NO2)/year]

2.5

2.0

1.5

1.0

0.5

0.0 1960

1965

1970

1975

1980

1985

1990

1995

2000

Figure 6: Temporal trend of NOx emissions from aircraft as provided in the AEROCHEM data set

One model, LMDzINCA, used a different geographical distribution of the aircraft emissions obtained from the TRADEOFF project. The data sets from TRADEOFF were rescaled to the RETRO global annual flux to account the interannual variability.


4. Wildfire emissions Interannual variability in emissions from biomass burning can have important implications for the global budgets of various trace compounds [e.g. Schultz, 2002; Duncan et al., 2003]. However, trend estimates are notoriously difficult, because of the complex interactions between terrestrial vegetation, the climate system, and socio-economic factors. Up to this day, even the estimation of the global amount of trace substances emitted from biomass burning for a specific period is associated with large uncertainties, and on the continental scale, various estimates in the literature may disagree by factors of 2 or more (see Schultz et al., submitted manuscript; Annex 4). In spite of these difficulties the RETRO project had to come up with a consistent long-term data set of wildfire emissions, because they can be a significant source of the observed interannual variability of trace compound concentrations. The original idea was to base this effort on the derivation of burnt areas from a 20-year satellite data set from the Advanced Very High Resolution Radiaometer (AVHRR) and to extent this time series backward in time via some rough estimate of the interannual variability based on meteorological parameters. Unfortunately, however, it turned out that there were fundamental problems associated with the AVHRR data record (see Annex 5), and an alternative solution needed to be developed. The approach taken was a regional composite which includes information on annual burnt area statistics, output from a newly developed regional fire model (Reg-FIRM, see section 4.2), and some satellite data of the 1990s for the geographical and seasonal distribution of fires. The RETRO wildfire inventory limits itself to the open combustion of biomass since emissions from the closed combustion of waste wood and fuel wood are included in the anthropogenic TNO TEAM data base (see section 3).

4.1 The RETRO wildfire inventory Due to the fact that there are no satellite observations available that cover the complete RETRO period, and the few attempts to generate longer-term time series of global burned areas or fire hot spots are still being evaluated, the RETRO inventory had to rely on statistical methods and modeling techniques. We performed an extensive survey of the recent literature and created a composite inventory based on what we think is the best available information in each continental-scale region. The articles that formed our information base describe fire inventories which were constructed with different satellite products, different algorithms and/or models, and are valid for different years. Different authors place their focus on different fire quantities and compounds (e.g. total carbon, black carbon, or carbon monoxide). This makes a comparison of inventories rather difficult and leads to the dreadful conclusion that even the uncertainties of current inventories are almost unknown. For the RETRO wildfire inventory we adopted a highly aggregated approach, which allows for a first systematic intercomparison of different existing fire inventories and highlights the problem areas. In general, three main factors contribute to the uncertainties of fire emissions estimates: •

burned area quantification: there are only few regions with accurate long-term monitoring of burned areas. Existing satellite products can give a reasonable qualitative description of fire occurrence and seasonality, but their quantitative use still suffers from retrieval problems such as improper orbital characteristics, cloud and smoke obscurence, and varying detection efficiency for different ecosystems,


•

the amount of biomass actually combusted depends on the available fuel load, fuel moisture, the type of vegetation, the organic soil content and soil moisture, and the rate of spreading (driven by wind speed and moisture as well as the orography). Many of these parameters are highly variable and poorly determined on larger scales,

•

the emission factors of chemically active trace species and greenhouse gases depend on the fuel type and the burning characteristics and are quite uncertain in many cases. While fires can often be considered as a mix of flaming and smoldering combustion processes if one is interested in larger scales, there may still be large variability in emission factors for example if the amount of soil organic matter that is exposed to burning varies strongly as in Indonesia or Siberia.

Each region has different major uncertainties, and the study undertaken in the context of this work (Schultz et al., submitted manuscript; Annex 4) contributes to a better understanding of what these uncertainties are although more detailed studies are necessary to reveal all of the uncertainties. The general approach for estimating fire emissions is: (1)

E(i) = A × FL × CE × EF(i)

where E(i) is the emission flux (in kg m-2 s-1) of compound i, A denotes the burned area, FL the fuel load (dry biomass available for burning), CE the combustion efficiency (fraction of available biomass that is consumed by fire), and EF(i) is the emission factor (g species per kg dry matter burned). There is considerable confusion in the literature about the values of FL and CE for different ecosystems, and generally not enough information is made available to actually determine the cause of different emissions estimates in different regions. We therefore decided to base our inventory on aggregated estimates of total carbon emissions (CO2 and CO form about 90-95% of total carbon emitted), and we thus simplify equation (1) to: (2)

E(i) = A × Enet(C) × ER(i, C)

Here, Enet(C) is an aggregated quantity of net carbon emissions (in tonsC/ha), and ER(i, C) denotes the emission ratio of compound i relative to total carbon. The regional burnt area is distributed among three broad ecosystem classes (forest, wooded, and grasslands), and separate emission factors are assigned to each of these (varying by continent as well). The variability of emissions in the RETRO inventory is entirely driven by variations in the burned area, which are either prescribed from forest service observations (Canada, US, and Siberia), or they are derived from a newly developed prognostic fire model (Reg-FIRM) and scaled to a representative year for which a detailed analysis exists in the literature. Siberian burned areas were also scaled, because it is known that only a fraction of the total area is monitored and there may be underreporting even in areas that are routinely surveyed. Tables 1 and 2 in Annex 4 summarizes the main input parameters for the thirteen geographical regions defined in this study. Figure 7 shows the 41-year time series of total carbon emissions from vegetation fires as derived from the RETRO wildfire inventory. The global annual totals range from 1410 to 3140 TgC/year with only a small overall trend. The largest contributions are generally coming from Africa and South America, but the contribution from South East Asia is increasing over time. Some of the extreme fire events (e.g. Indonesia 1997, Siberia and Central America 1998) are clearly visible in the time series, others may be missed or underestimated (e.g. Mongolia 1987). The methodology paper for this inventory (see Annex 4) contains a detailed evaluation for total carbon, CO, and black carbon emissions.


Generally, the emission estimates from the RETRO inventory are rather consistent with the values from the Global Fire Emission Database (GFED) version 2 (van der Werf et al., 2006) as shown in Figure 8 for one particular year. 3500

Total direcxt carbon emissions [TgC/year]

Australia Indonesia

3000

SE Asia India

2500

SH Africa NH Africa+trop.forest

2000

South America

1500

Central America Europe

1000

contiguous US Russian Federation+Mongolia

500

Canada Alaska

0 1960

1965

1970

1975

1980

1985

1990

1995

2000

Figure 7: Estimated total direct carbon emissions from wildland fires (i.e. excluding carbon release after degradation of remaining organic matter) for the 40-year period of RETRO.

RETRO

GFED




Figure 8: Comparison of monthly mean total direct carbon emissions from open fires for the year 1997 in the RETRO inventory (left) and in the GFED2 inventory (right). The data were resampled on 2°×2° for plotting.

4.2 The Reg-FIRM Model As described in Annex 4, in many world regions information is lacking about the interannual variability of burnt area on longer time scales. Therefore within RETRO a modelling approach was chosen based on a fire disturbance model developed by the consortium of the Lund-Potsdam-Jena dynamic vegetation model (LPJ). With funding and with the interest from the RETRO project, the first version of this model (Thonicke et al., 2001; Venevsky et al., 2002) was extended to a regional fire model (Reg-FIRM) which contains more mechanistic parameterisations of fire ignition and fire


spread. A particular focus in the development was placed on the following aspects of Reg-FIRM: simulation of fire frequency as a function of population density and land use; probability of successful ignition in relation to fuel moisture characteristics; modelling the spread of fires both among surface fuels and tree crowns- the latter under conditions of high fire intensity; simulating plant mortality in relation to fire intensity; and modelling combustion of dead and live fuels. Reg-FIRM requires the following input data: •

Monthly climatology (min. 30 years); temperature, precipitation, number of rain days & cloudiness (Reg-FIRM: diurnal temperature range)

•

0.5° climatology, Climate Research Unit (CRU), Norwich, UK

•

Atmospheric CO2 concentration

•

FAO-Data derived soil texture code

•

Field capacity for 2 soil layers (0-50 cm & 50-200 cm)

•

Water percolation (depending on soil moisture)

•

Thermal conductivity (depending on soil moisture)

•

Vegetation Structure – Plant functional types (PFTs)

•

Classification of plants is based on functional differences, depending on the focus of research. Based on functional traits. Set of characteristics of plants, independent of their taxonomic unit. According to bioclimatic limits, physiology, physiognomy, phenology -

•

10 Plant functional types: 3 boreal, 3 temperate, 2 tropical, C3/C4 grasses

•

Additional parameter define functionality of PFTs in model processes.

The model contains PFT-specific moisture of extinction and parameterises PFT-specific fire resistance (severity & intensity). Fire occurrence is calculated as a function of the daily litter moisture m:

p (m ) = e

⎛ m ⎞ −π ⎜ ⎟ ⎝ me ⎠

2

, with me – moisture of extinction

The longer the burning conditions persist, the larger the fire size can grow and result in area burnt. Reg-FIRM was evaluated for several regions. Three publications appeared on these evaluation efforts: •

Iberian peninsula; spatial distribution & inter-annual climate variability (Venevsky et al. 2003)

•

Brandenburg/Germany; influence of vegetation composition on fire regime (Thonicke et al. 2003, in rev.)

•

Australian Kimberleys; inter-annual variability of human-caused fires adapting to environmental conditions (Spessa et al. 2003, in prep.)

Figure 9 gives an example for the performance of Reg-FIRM over the Iberian peninsula.


Figure 9: Time series of simulated fire danger index (top panel), simulated versus observed number of fires (second panel), and simulated versus observed area burnt (bottom panel) for peninsula Spain (1974-2000).

Problems were experienced in simulating oberved fire activity in some regions due to the poor performance of the ignition potential function in the model. Specifically, the simulation of the number of human-caused ignitions as a function of population density does not always reflect the highly complex causes behind deliberately and accidental fire ignitions, which tend to have more do to with infrastructure, land use patterns and socio-economic factors than demography per se.


5. Natural emissions For the purposes of the RETRO project, natural emissions consist of biogenic emissions from the oceans and the terrestrial vegetation (sections 5.1 and 5.2, respectively) and of the production of NOx from lightning thunderstorms (section 5.3). For the investigation of aerosols, other natural sources of relevance are dust and seasalt lifting and the emissions from degassing and eruptive volcanoes. Due to the limitation of resources and the unexpected delays in generating the gas-phase emission inventories, the planned work on aerosols in the RETRO project had to be cancelled. Some activity on aerosol emissions in conjunction with RETRO occurred in the AEROCOM initiative, and a paper on the emissions from AEROCOM has been published (Dentener et al., 2006). This section of this report focuses exclusively on the natural emissions of CO, NOx, and NMVOC compounds.

5.1. Ocean emissions Measurements from various ship and aircraft field campaigns have shown that the oceans act as a significant source of several trace gases. Exchange of trace gases between the oceans and the atmosphere affects the atmospheric content and cycling of a range of chemical species, which are related to climate change, ozone layer depletion, acid deposition, eutrophication, atmospheric particle formation, photo oxidants, trace metals, and persistent organic compounds (e.g. Pacyna et al., 1998). In many cases, the specific source mechanisms are not well understood (c.f. Frost and UpstillGoddard, 1999), but there is general agreement that the dominant fraction of marine emissions originates from the marine biosphere. While oceanic emissions are generally small compared to present-day terrestrial sources, they may have a sizeable impact on the global budgets of a number of ozone and aerosol precursors, such as DMS, C2H4, C3H6, acetone, and organic halogens. In addition, local and regional impacts can be significant, in particular when considering coastal regions, which often emit species in higher quantities than the open oceans (Pacyna and Hov, 2003). Current inventories of oceanic emissions are largely focusing on global budgets and are based on individual scientific studies analysing the small body of available measurements. In a few cases, ocean biogeochemistry models have been used in the attempt to formulate a consistent approach for modelling. Given the relatively minor importance of most oceanic emissions on the global scale, their small interannual variability, and the scarcity of new data sets to improve the knowledge about these processes, the RETRO consortium has decided not to dedicate any efforts to improving the existing inventories. Most of the models adopted the GEIA emission data set for their long-term simulations except the LMDz-INCA model which was forced with the distribution of Erickson and Taylor 1992.

5.2 Biogenic emissions The current standard for modelling emissions of isoprene, terpenes, and other NMVOC (e.g. methanol, acetone, acetaldehyde, ethane, ethanol) from living vegetation is described by Guenther et al. (1995), who recently developed a parametrisation suitable for use in global chemistry transport or chemistry climate models (see section 5.2.1). Another recent data set which was used in the project is from Lathiere et al., 2006 using the ORCHIDEE model at LSCE. The release of biogenic NMVOC


compounds is very much dependent on the plant species and can be parameterised as a function of the ambient temperature and light intensity. The MEGAN model of Gunether et al. uses near surface air temperature, solar radiation (including information on cloud cover), foliar density or leaf area index and vegetation type as input parameters. It calculates the net exchange (emission and deposition) rate of a chemical species between a terrestrial ecosystem and the atmosphere at a specific location and time as: ER= AEF * MEA * DEA * HEA

(equation 1)

where ER (10-3 moles compound km-2 h-1) is the net exchange rate, AEF (10-3 moles compound km2 h-1) is an annual emission factor, MEA (normalized ratio) is a monthly exchange activity factor, DEA (normalized ratio) is a daily exchange activity factor, and HEA (normalized ratio) is an hourly exchange activity factor. AEF, MEA and HEA are supplied as input data sets on the NCAR community data portal together with a users’ manual. Emissions of nitrogen oxides and other species from soils are of microbial origin, and account for significant fractions of the total nitrogen oxide emissions in particular in less industrialised regions. Currently, no coherent description of soil emissions is available on the global scale, which would allow for a reliable assessment of changes in these emissions over the past 40 years. The emission rate depends on meteorological parameters and on the environmental conditions of the soils. It is estimated that currently about 50% of all soil NOx emissions are due to fertilizer use. Unfortunately, data on historical and geographical trends of fertilizer usage were not available to the project and it was therefore impossible to assess the possible implications of changes in soil NOx emissions. Average emissions from the MEGAN model implemented in MOZECH are listed in Table 4. The interannual variability of global emissions computed by MEGAN is typically around ± 5 % (Figure 10). Exceptions are the years 1974-1976 when isoprene (monoterpene) emissions were 7-9 % (around 6%) lower than on average and 1998 when they were 14 % (10%) higher. Table 4: Average global annual emission fluxes of various organic substances emitted from the terrestrial vegetation as computed by the MEGAN model in MOZECH Compound Isoprene Monoterpenes CO Ethene Ethane Propene Formaldehyde Methanol Acetaldehyde Acetone

Average Emission Flux [Tg/year] 539.07 194.66 96.01 3.84 4.12 15.66 33.76 269.72 37.44 30.26


Biogenic Emissions (MOZECH-BMEGAN)

emissions anomaly

1.2

1.1

1.0

0.9 Isoprene Monoterpenes 0.8 1960

1965

1970

1975

1980

1985

1990

1995

2000

Figure 10: Time series of the emissions anomaly (relative to the 41-year mean emissions) for isoprene and monoterpene emissions computed by MEGAN in MOZECH. Note that all other volatile organic compounds follow the same pattern as the monoterpenes in the MEGAN parameterisation.

5.3 Lightning NOx production The production of NOx from lightning flashes is still a major uncertainty in the global NOx budget. Current estimates range from 2 to 20 TgN/year (Lawrence et al., 1995; Price et al., 1997), and model studies suggest a sensitivity to climate variability of 5-14%/K (Price and Rind, 1994; Stenke and Grewe, 2002). Most models participating in RETRO use the parameterisation by Price and Rind (1993) to estimate the column lightning NOx production, which is then distributed vertically according to standard profiles developed by Pickering et al. (1998). The MOZECH model uses a parameterisation based on the convective mass flux (Grewe et al., 2002). TM4 uses the parameterisation of Meijer et al. (2001), which is based on convective precipitation and LMDz-INCA employs the parameterisation of Price et al. (1997), which scales the flash frequency to cloud top height. Generally, all of these parameterisations yield a reasonable distribution of lightning flashes, but the absolute magnitude of NOx produced depends on the physical parameterisations of the model and its grid resolution. Therefore, in most modelling studies, a tuning parameter is introduced, which scales the computed column NOx production to yield a global annual total between 3 and 7 TgN/yr. For the purposes of the RETRO project, we have adopted a target value of 3 to 5 TgN/yr for the year 1997. The so-derived tuning parameter was held constant throughout the long-term simulations so that the interannual variability of the lightning NOx production can be assessed.


Figure 11 shows the time series of annual NOx emissions from lightning for the three models which performed the 40-year simulations. The mean nitrogen production over the complete RETRO period is 3.59 TgN/year in MOZECH, 4.39 TgN/year in TM4, and 5.71 TgN/year in LMDz-INCA (equivalent to 11.8, 14.4, and 18.7 Tg(NO2)/year, respectively). While TM4 shows a significant upward trend in lightning activity after 1980 (which is correlated with a trend of excessive tropical precipitation in ERA-40; Hagemann et al., 2006), no significant trend is observed in the MOZECH and LMDz-INCA simulations (and the tropical precipitation also exhibits no discernible trend). The apparently spurious trend in lightning NOx emissions in TM4 has important implications for the analysis of the oxidizing capacity from this model (see D4-4). In the parameterisation of most global models, the lightning N production is directly proportional to the flash frequency. The average flash frequency diagnosed by MOZECH is 60 flashes per second. This value is about 20% higher than recent estimates from the space-based instruments lightning imaging sensor LIS and optical transient detector OTD (Boccippio, 2002; Christian et al., 2003; data available at http://thunder.nsstc.nasa.gov/). This discrepancy is not very important since the flash frequency is merely used as a scaling parameter. More relevant is the geographical distribution of lightning flashes, which should exhibit a distinct maximum in tropical latitudes and about 3 times as many flashes over the continents than over the ocean waters. Figure 12 shows the decadal average geographical distribution of lightning flashes in the MOZECH model and the 1996-2005 climatology from satellite observations for comparison. The agreement is quite reasonable, although the model tends to produce too many lightning flashes outside the tropical rainforest region in Africa and also over the tropical Pacific and Indian ocean The four decades of the simulated lightning activity are rather consistent, but there appears to be slightly more lightning over the African continent in the 1980s and 1990s than in the earlier period. On average MOZECH produces about 3.7 flashes per square km and year in the latitude range between 60°S and 60°N, of these 2.9 flashes km-2 year-1 occur over land and 0.8 flashes km-2 year-1 over the ocean. The LIS-OTD climatology shows a total of 3.0 flashes km-2 year-1 with 2.2 flashes km2 year-1 over land and 0.8 flashes km-2 year-1 over the oceans.

N production from lightning [Tg/year]

7 6.5 6

MOZECH TM4 LMDz

5.5 5 4.5 4 3.5 3 2.5 2 1960

1965

1970

1975

1980

1985

1990

1995

2000

Figure 11: Temporal evolution of the global annual NOx production (as TgN/year) from lightning in the three models which performed the 40-year simulations


1960-1969

1970-1979

1980-1989

1990-1999

OTD/LIS climatology 1995-2006

Figure 12: Geographical distribution of decadal mean flash frequency from the MOZECH model (in flashes per grid box and year) and comparison with annual mean data from the OTD and LIS satellite instruments (in units of flashes per km2 per day)


6. Methane emissions Because of the long lifetime of methane in the atmosphere and the difficulties to achieve a balanced global budget of methane sources and sinks, which must also reflect the observed trends in the methane concentration, we did not attempt to improve current estimates of natural methane emissions within RETRO. Instead, interpolated measurements of near-surface concentrations were used as boundary condition in the models. Surface measurements from the CMDL (now NOAA/ESRL-GMD) station network were interpolated by partner MPG-IMET and extended in time through various fitting procedures. Data for 1800 and 1900 were taken from the IPCC TAR (2001). The homogenized time series was constructed by computing a global mean trend in three periods (Figure 13): 1800-1970 (exponential fit); 1970-1998 (second order polynomial fit); 1998-2100 (linear trend with 5 ppb/year increase). Secondly, the annual mean normalized latitudinal gradient derived from data for 1984-2001 was applied and then, for each latitude, the normalized seasonal cycle derived from the same data was applied. In LMDzINCA and MOZECH, a relaxation approach combining an emission inventory with the zonal mean surface concentrations is used in order to avoid drifts while still accounting for regional differences.

Figure 13: Temporal evolution of latitudinal averaged methane surface concentrations used as boundary conditions to constrain the chemistry models


7. Comparison with other emission data bases In this section the data from the RETRO inventory are compared with data from other inventories. The focus of this comparison lies on the anthropogenic emissions from the TNO TEAM inventory, but a short comparison of the inventories of ship and aircraft emissions is also given.

7.1 Anthropogenic land surface emissions Table 5 shows a comparison with EMEP data for the year 2000 obtained from WebDab (EMEP/LRTAP, 2005). These are the Expert Emissions used in the EMEP models. For NOx emissions there is generally good agreement for the EU region and the top emitting countries. The only exception is Germany, where the TEAM inventory lists about 30% larger emissions. Substantial differences are observed however for CO and NMVOC emissions. CO emissions agree well for Germany and the UK, but for France, Italy and Spain they are lower by almost a factor of 2 in the TEAM inventory. In contrast, the TEAM NMVOC emissions are larger by 30-50% than the EMEP expert estimates almost everywhere in Europe. Since the traffic sector is the dominant source of CO emissions in Europe, most of the differences for this species are related to the use of different assumptions on traffic activities and the related emission factors (see discussion on the comparison with the EDGAR inventory below). Table 5: Comparison of the RETRO anthropogenic emissions to EMEP data for the year 2000. Units: Tg(NO2) for NOx and Tg(species) for the other compounds.

EU25*

12.2

NOx RETRO gridded & smoothed 11.6

Germany United Kingdom France Italy Spain Poland

2.5 1.6

2.5 1.5

1.7 1.7

4.7 3.5

1.5 1.4 1.1 1.0

1.4 1.3 0.9 1.0

1.4 1.4 1.3 0.8

3.0 2.9 1.5 2.4

Country/ Region

RETRO TEAM

EMEP

RETRO TEAM

11.5

25.5

CO RETRO gridded & smoothed 24.4

EMEP

NMVOC RETRO EMEP TEAM

36.6

19.6

11.1

4.9 3.4

4.9 3.9

3.8 2.7

1.7 1.4

3.0 2.8 1.3 2.4

6.6 5.2 2.8 3.5

2.7 2.6 1.7 1.1

1.7 1.6 1.5 0.6

*Malta not included in EU25 EMEP data

Table 6 shows a comparison with the data from EDGAR3.2 (EDGAR, 2005). For NOx and CO, the EDGAR data are grouped into similar categories as in the LOTOS scheme**. EDGAR biomass

** For the LOTOS inc category, the following EDGAR sectors are summed up: B10 (Industry), F10 (Industrial sector), I10 (Iron and Steel), I30 (Chemicals), I41 (Cement), I50 (Pulp and Paper). Pow is assumed to be equivalent to B20 and F20 (Power generation) and for the CO comparison also includes B30 (charcoal production), res to F30 (OTS), F40 and B40 (Residential, commercial, and others). EDGAR tra emissions are


burning emissions and emissions from international ship traffic are left out of the comparison. For the NMVOC comparison, all emissions in EDGAR were used, since some of the biomass burning activities were included for the TEAM NMVOC data (these data were not used in the gridded RETRO emission fields, however. They were replaced by the RETRO wildfire inventory emissions – see section 4.2 and Annex 4 of this report). Also listed in Table 4 are the regional totals for NOx and CO emissions obtained from the gridded and smoothed data set in 0.5°×0.5° resolution. Small differences between the TEAM values and the gridded data are due to gridding artifacts such as imprecise country boundary outlines. Larger differences reflect the effects of the smoothing procedure (see description in section 3.2.4). EDGAR3.2 NOx and CO emissions are about 8% higher globally than the TEAM emissions. For NOx, the largest differences are found in the southern and eastern parts of Asia and in Africa while for CO the differences occur especially in OECD Europe and North America. In order to further investigate these differences, a more detailed analysis of individual emission sectors is necessary. As an example for this kind of analysis, Figure 14 presents the sectoral split of NOx emissions for China and India from both the RETRO and EDGAR 3.2 inventories for the year 1995 and the sectoral split of CO emissions for OECD Europe and North America. From this diagram it becomes apparent that the differences in NOx emissions are related to the industrial emissions. Here the RETRO inventory may lack some activities which are included in the EDGAR inventory (notably emissions from cement manufacturing). In particular for India the residential sector emissions in RETRO are also substantially lower than the residential emissions in the EDGAR inventory. The CO emissions from industrial processes are also very different (Figure 14). However, CO emissions in Europe and North America are dominated by the traffic sector (in the RETRO inventory the fraction of traffic emissions to total anthropogenic emissions is 0.87 and 0.89 for Europe and North America, respectively). Thus, the observed differences between EDGAR and RETRO are related to the different treatment of traffic emissions. The RETRO CO emission values for the United States are closer to reports from the US EPA and more in line with the results of Parrish et al. (2002), who report a decrease in CO emissions over the 1990s from 65 Tg to 38 Tg. The reasons for the discrepancies observed in industrial emissions are less obvious and probably related to the use of different emission factors for various specific activities. The differences between the NMVOC data used in TEAM and in EDGAR 3.2 (Table 6) can largely be explained by the differences between EDGAR 2.0 and 3.2 (see http://www.mnp.nl/edgar/ documentation/differences/index.jsp). A more detailed analysis of the NMVOC emissions and their differences will require a separate study and is beyond the scope of this report.

taken from F51 (Road transport) and F54 (Non-road land transport). Note that the EDGAR sectors B30 (charcoal production), B51 (Road transport, ETH), F57 (Air traffic), F58 (International ship traffic), F80 (Oil production), W40 (waste incineration) and L41-L44 (Biomass burning) are not included in the comparison of NOx and CO emissions. For NOx these sectors are negligible.


Table 6: Comparison with EDGAR 3.2 emissions data for the year 1995 (see footnote on previous page). Units: Tg(NO2) for NOx and Tg(species) for the other compounds. Country/Region

RETRO TEAM

Global OECD Europe North-America (USA + Canada) Latin America South Asia East Asia South East Asia Africa Oceania

79.2 11.2 20.6

NOx RETRO gridded & smoothed 78.1 10.5 18.8

5.4 4.8 12.1 3.2 3.5 1.3

5.2 4.7 11.5 2.8 3.4 1.1

EDGAR3.2

RETRO TEAM

85.5 11.1 19.9

496.6 29.8 77.0

CO RETRO gridded & smoothed 504.4 28.2 70.2

5.8 6.5 14.8 3.3 4.2 1.5

33.8 78.9 94.9 43.5 67.5 3.8

32.5 78.7 101.2 38.4 66.0 3.3

EDGAR3.2

Top United States 18.9 17.2 18.0 71.4 64.9 China 9.9 9.7 12.1 86.3 93.5 Russian Federation 4.7 6.3 (+) 5.0 19.9 25.7 (+) India 4.0 3.9 5.3 57.1 59.5 Japan 3.5 3.1 2.8 8.1 7.3 Indonesia 1.1 1.0 1.1 18.5 16.2 (+) the gridded data were totalled for the region of the former USSR instead of the Russian Federation

Country/Region Global OECD Europe North-America (USA + Canada) Latin America South Asia East Asia South East Asia Africa Oceania United States China India Brazil Russian Federation

RETRO TEAM 184.4 18.5 34.6

NMVOC EDGAR3.2 159.6 16.8 28.7

20.4 18.5 22.2 12.0 27.1 2.8

16.8 11.1 13.7 10.8 20.1 2.8

29.7 20.2 13.7 10.6 6.5

19.5 11.1 8.1 5.7 8.5

537.1 42.4 90.9 38.3 74.6 98.9 39.9 67.9 5.1 84.3 87.6 15.7 53.8 10.2 16.8


6 inc

NOx emissions 1995 [Tg(NO2)/year]

China

pow

5

res tra

4

3

India 2

1

0 RETRO

EDGAR

90

CO emissions 1995 [Tg(CO)/year]

80 70

RETRO

EDGAR

North America

inc*10 pow*10 res*10 tra

60 50

OECD Europe

40 30 20 10 0 RETRO

EDGAR

RETRO

EDGAR

Figure 14: Comparison of the sectoral split of NOx emissions from China and India for the year 1995 from the RETRO and EDGAR 3.2 inventories (top) and comparison of CO emissions for OECD Europe and North America (bottom). Note that for CO the values of the inc, pow and res sectors were scaled up by a factor of 10 for clarity.


7.2 Ship emissions Emissions from international ship traffic were recently evaluated in three scientific publications by Endresen et al. (2003) (the data used in the RETRO project), Corbett and Köhler (2003), and Eyring et al. (2005). Eyring et al. (2005) include a detailed discussion on the differences between these inventories, so we limit ourselves to reporting a few major points: •

For the years 2000 and 2001, NOx emissions from the Endresen et al. (2003) inventory are about a factor of 2 lower than Corbett and Köhler (2003) and Eyring et al. (2005). For CO the difference is about 50%, and for NMVOCs it is a factor of three

•

Eyring et al. (2005) dispute that the trend in bunker fuel sales reflects the actual trends in ship emissions (see their Figure 2). Their historic inventory is scaled by the number of sea going vessels and engine sizes, which has been continuously increasing since the 1950s.

Table 7 below lists the NOx emission estimates from Eyring et al. (2005) in comparison with the RETRO inventory data. Until further clarification is found on the absolute number of emissions from international ship traffic, we have to regard the differences between the inventories as an estimate of the uncertainty of these emissions. Concerning the historical emission trend, there are reasons to believe that the Eyring et al. (2005) trend is more realistic than the RETRO trend. However, that study neglects the major technology change from steam vessels to oil vessels which occurred during the 1960s and 1970s. Furthermore, none of the studies reported in the literature so far have taken into account the large shifts in the geographical distributions of ship vessels due to enhanced trade between East Asia and Europe and North America after about 1990. Table 7: Comparison of NOx emissions from international ship traffic between the RETRO inventory and two recent studies in the scientific literature. All values given in Tg(N)/year (multiply these by 46/14 to obtain numbers in Tg(NO2)/year). Year 1960 1970 1980 2000/2001

Corbett and Köhler 2003 ---6.87

Eyring et al. 2005 2.01 3.26 5.63 6.51

RETRO 1.44 2.57 3.12 3.63

7.3 Aircraft emissions Table 8 gives a comparison of emission estimates from the WMO 1998 Ozone assessment and the AEROCHEM data set. For the early period in RETRO, the AEROCHEM values are substantially lower than the WMO values, while they are within 12% for the second half of the 41-year RETRO period. Regarding future emission estimates, the WMO scenario is more pessimistic, while the AEROCHEM prediction takes into account possible improvements in engine technology.


Table 8: Comparison of aircraft NOx emission estimates from the 1998 WMO ozone assessment and the AEROCHEM data set used in the RETRO project. All values given in Tg(N)/year (multiply these by 46/14 to obtain numbers in Tg(NO2)/year). Year 1976 1984 1992 2015

WMO ozone assessment 1998 0.30 0.39 0.51 1.26

AEROCHEM emissions 0.15 0.32 0.57 1.12

7.4 Comparison of NOx emission trends in China with satellite observations of tropospheric column NO2 Trend analysis using GOME and SCIAMACHY NO2 data were done for different regions. The results show substantial reductions over some areas of Europe and the USA but a highly significant and accelerating increase of about 50% over the industrial areas of China from 1996 to 2004 (see Figure 15 and Richter et al., 2005). It should be noted, however, that most of this increase over China occurred after the year 2000 and is thus not included in the RETRO emission inventories or the RETRO model simulations.

Figure 15: NO2 trend above industrial regions in China.


8. Analysis of emission trends and characteristic patterns 8.1 Spatial distribution of RETRO emissions Figures 16 and 17 show maps of the aggregated RETRO emissions (all sectors including wild fires and natural emissions) for NOx, CO, propane and toluene for the years 1970 and 2000 as examples for the spatial distributions and level of detail afforded by this inventory. When comparing these graphs with similar ones from other inventories, it must be noted that the grid box size of the RETRO inventory is 0.5°×0.5° and thus smaller than the size of many other inventories.

Figure 16: Spatial distribution of NOx (left) and CO (right) emissions from the RETRO inventory for the years 1970 (top) and 2000 (bottom). These figures contain all RETRO surface emissions (including wild fires and natural sources, but without aircraft and lightning NOx emissions)


Figure 17: as Figure 16 but for propane (left) and toluene (right)

8.2 Temporal evolution of emissions in the different sectors Figures 18 and 19 display the relative contribution of different emission sectors to the global surface sources of NOx and CO. While total NOx emissions increased by about 50% between the 1960s and the 1990s, the sector split remained relatively constant. As the RETRO inventory assumes that biogenic NOx emissions remained more or less constant throughout the period, the relative share of biogenic emissions decreased from 33% in the 1960s to 22% in the 1990s (Figure 18). Wildfires contribute between 12% and 14%, stationary sources between 26% and 33%, and mobile sources between 27% and 33% to the global total. The most significant changes occurred between the 1960s and the 1970s during the period of strongest economic growth in Europe and North America. During the 1980s and 1990s, NOx emissions started to decline in these regions due to emission control policies, but these decreases were compensated by emission increases in other world regions, notably in Asia. For carbon monoxide, the RETRO inventory diagnoses a slight decrease in the relative contribution of natural sources (soil and ocean) from 23% during the 1960s to 17% in the 1990s. Wildland fire emissions show a generally increasing trend with contributions of 31% in the 1960s and 1970s and 38% in the 1990s. Of all stationary sources, the residential sector is the most prominent one and contributes between 24% and 26% to the total CO surface emissions with little relative change over


time. CO emissions from the traffic sector had the strongest contribution in the 1970s (26%) and their share decreased to 19% in the 1990s, which is a smaller contribution than during the 1960s (21%). Note, however, that absolute emissions from the traffic sector were still larger by 33% during the 1990s compared to the 1960s (214 Tg vs. 160 Tg).

1970-1979

1960-1969

26%

25%

20% 33% 7% 8%

7%

12%

12% 7%

8%

14%

13%

8% total: 117.3 Tg

total: 89.6 Tg

1980-1989

1990-1999

22%

25%

24%

25%

8%

7%

13%

12% 8%

9% 15%

8%

17% total: 127.8 Tg

7%

biogenic wildfires inc pow res ships tra

total: 137.1 Tg

Figure 18: Contribution of different emission sectors to total surface NOx emissions as decadal means. Units are teragram NO2 per year.


1970-1979

1960-1969

26%

21%

17%

20%

2%

3%

25%

31%

24%

31% total: 787.3 Tg

total: 941.7 Tg

1990-1999

1980-1989

23%

19%

16%

15% 2%

2%

26%

25%

34%

total: 1033.6 Tg

38%

soil ocean wildfires res tra

total: 1108.6 Tg

Figure 19: Contribution of different emission sectors to total surface carbon monoxide emissions as decadal means. Units are teragram CO per year.

In Figure 20, temporal trends of sectorized NOx and CO emissions are displayed for selected countries and world regions. These results were calculated from the gridded data sets in 0.5°×0.5° resolution and include the emissions from wildfires, but not those of aircraft take off and landings. Emissions from international ship traffic are formally included, but since the regions were selected with binary masks of the country outlines, the ship emissions displayed here are marginal. At first sight of Figure 20 one can distinguish three groups of countries or regions depending on the temporal trend of their emissions and the dominant contributions from individual sectors. In OECD


Europe, North America and Japan, emissions of NOx and CO are dominated by anthropogenic activities. These emissions have increased during the 1960s, reached a maximum somewhere during the 1970s and show a tendency to decline afterwards to various degrees. The decreasing trend during the 1990s is much more pronounced for CO than for NOx in all three regions. The second group of countries or regions (China, India, and the Middle East in Figure 20) shows a continuous increase in both the NOx and CO emissions, and again the dominant contributions come from anthropogenic activities. In China and India, the majority of CO emissions stems from the residential sector. NOx emissions were also dominated by the residential sector in the early period (1960s to 1970s), but are outweighed by emissions from power generation since the 1980s. In China, the industrial sector also plays a major role. In contrast to these countries, the emission trends in the Middle East (both for NOx and CO) are driven by the strong increase in traffic emissions. The third group of countries exhibits no steady trend but the emissions rather show a very large inter-annual variability. Here, biomass burning emissions make a substantial contribution and control the temporal pattern. Of the countries and regions shown in Figure 20, this pattern is reflected in Indonesia, Latin America and Africa, and to some extent also in the former USSR (at least for CO).

NOx

CO NOx emissions, OECD Europe

CO emissions, OECD Europe

10

70

biogenic wildfires inc pow res ships tra total


60 CO Emissions [Tg(CO)/year]

NOx Emissions [Tg(NO2)/year]

15

5

50 40 30 20 10

0 1960

1970

1980

1990

0 1960

2000

1970

NOx emissions, USA

2000

1990

2000

150

20

CO Emissions [Tg(CO)/year]


1990

CO emissions, USA

25

15

10

5

0 1960

1980

1970

1980

1990

2000

100

50

0 1960

1970

1980


NOx emissions, former USSR

CO emissions, USSR 100 90 CO Emissions [Tg(CO)/year]


15

10

5

80 70 60 50 40 30 20 10

0 1960

1970

1980

1990

0 1960

2000

1970

NOx emissions, Japan

2000

1990

2000

1990

2000

15



1990

CO emissions, Japan

4

3

2

1

0 1960

1970

1980

1990

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5

0 1960

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NOx emissions, China 150 biogenic wildfires inc pow res ships tra total


10

5

0 1960

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CO emissions, China

15


1980

1970

1980

1990

2000

100


50

0 1960

1970

1980


NOx emissions, India

CO emissions, India 100 90 CO Emissions [Tg(CO)/year]


10

5

80 70 60 50 40 30 20 10

0 1960

1970

1980

1990

0 1960

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1970

NOx emissions, Indonesia


CO Emissions [Tg(CO)/year] 1970

1980

1990

150

100


50

0 1960

2000

1970

NOx emissions, Middle East

1990

2000

30 biogenic wildfires inc pow res ships tra total

1970



1980

CO emissions, Middle East

10

0 1960

2000

200

5

5

1990

CO emissions, Indonesia

10

0 1960

1980

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0 1960

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1990

2000


NOx emissions, Latin America

CO emissions, Latin America 300



20

15

10

5

0 1960

1970

1980

1990

250 200 150 100 50 0 1960

2000

1970

Anthropogenic NOx emissions, Africa

2000

1990

2000

400

20



1990

CO emissions, Africa

25

15

10

5

0 1960

1980

1970

1980

1990

2000

300

200

100

0 1960

1970

1980

Figure 20: Temporal trends of sectorized NOx and CO emissions in selected countries and world regions. The region definitions are adapted from the IMAGE 2.2 model and identical to those used in the description of the EDGAR3 inventory (see http://www.mnp.nl/image/background_info/regions/; Figure reproduced in Annex 5)


9. Conclusions and implications for emission control strategies The distinction of countries and regions with respect to their emission patterns and temporal emission trends is very important for the design of control strategies. Many concepts for emission control regulations were developed in Europe and the US where the situation is markedly different from that of other world regions. Depending on the economic development status and potential country-specific behavioural patterns, different emission sectors should be targeted in different regions for the most effective strategy. Also, the notion of “controllable emissions” needs to be carefully re-defined if one wants to formulate recommendations which are suitable also for countries with a large contribution from biomass burning sources. Since probably more than 90% of biomass burning emissions are caused by anthropogenic activities, it must be theoretically feasible to control these emissions just as other anthropogenic emissions from industrial or residential activities, for example. In practice, biomass burning activities are quite distinct from these other activities, however, and new methods will have to be developed in order to control such emissions effectively. The data presented in Figure 20 clearly demonstrates that emission controls in Europe, North America and Japan have worked to some degree, so that after 1990 there is practically no further increase in NOx emissions and a steep decline in CO emissions in spite of the maintained economic growth in these regions. On the other hand, it is also clear from these graphs that European, North American and Japanese NOx emissions are a factor of 2-3 larger in the year 2000 compared to the year 1960, which marks the beginning of the RETRO period. In contrast, CO emissions have often reached similar levels in the year 2000 as they had in 1960. As a consequence, the RETRO emissions inventories indicates a pronounced shift in the NOx/CO emission ratio between 1960 and 2000, and this might have implications on the regional budgets of secondary air pollutants such as tropospheric ozone.


10. Known problems with the RETRO emission data sets The following table lists the known errors, inconsistencies or major uncertainties associated with the RETRO inventory as they are known at the time of writing of this report. This information is valid for version 2 of the inventory, which has been released in July 2006 and is available at ftp://ftp.retro.enes.org/pub/emissions/retro. Additional problems should be reported to [email protected] so that they can be corrected in future versions of this inventory.

Table 9: Errors, inconsistencies and major uncertainties related to version 2 of the RETRO set of emission inventories Problem No. 1

2

3

4

Description The RETRO TEAM inventory neglects a few sectors which may be of regional significance. In particular, no emissions are reported for railway and national ship traffic. For CO and NOx no emissions are provided for waste treatment and disposal (category 9), this implies an approximate underestimate of 23 TgCO and 0.22 TgN, respectively (Yevich and Logan, GBC 2003). Furthermore, no emissions are reported from cement manufacturing. In TNO’s original data files, the agricultural sector emissions (LOTOS category 10) for NMVOC compounds include savanna burning and deforestation. However, as these data do not reflect the spatial and temporal patterns of burning as observed from satellites or predicted in detailed fire emission models, we have chosen to ignore these emissions entirely (the fire emissions are contained in the dedicated RETRO fire inventory anyhow). This may lead to underestimates in NMVOC emissions from agricultural processes other than burning. The statistics reported to UNFCCC by individual countries are often inconsistent in time so that some post-treatment is needed to generate a homogeneous time-series of emissions from these. In this version of the inventory we applied a rather simple smoothing procedure over larger regions. This will lead to some inconsistencies between or within individual countries. Furthermore, significant uncertainty is introduced, as the choice of correction factors is sometimes arbitrary. Industrial combustion emissions for CO are reported much lower in the RETRO inventory compared to EDGAR version 3.2. It has been found that some emission factors are too low in the RETRO inventory (see report by Pulles et al. in the appendix).

Suggested solution If funding becomes available, the TNO TEAM inventory should be updated to incorporate these emissions

A major effort is needed to identify the problems in the TNO sector 10 data set. The sector split should be re-defined in order to distinguish more clearly between agricultural and wildfire burning and avoid overlap with the dedicated wildfire inventories. The use of biofuels and the associated emissions should be assessed in an interdisciplinary approach. Ideally, a major revision of all UNFCCC statistics should be undertaken with the objective to harmonize historical data and resolve inconsistencies between individual countries’ reports. As an interim solution, the next version of the smoothing procedure should be based on national instead of regional statistics and apply some more objective criteria from statistical time-series analyses. If funding becomes available, the TNO TEAM model should be rerun with updated emission factors.


5

6

7

We note a factor of 2 discrepancy for NOx emissions from international ship traffic compared to other recent literature. Other inventories also report a different temporal trend of emissions. The known shifts in the geographical distribution of international ship traffic and aircraft emissions between 1960 and the year 2000 are not taken into account in the RETRO inventory. Major uncertainties persist with regard to the amount of compounds emitted from wildfires. The RETRO fire inventory has adopted an oversimplified approach by basing all inter-annual variability on the variability of burned area.

8

The time series of burned area are very uncertain for many world regions, and this may cause unrealistic variability in fire emissions – in particular, individual fire episodes may be missed or false episodes may be contained in the RETRO inventory.

9

Weekday and hourly time profiles are not included in the RETRO inventory The seasonal profile for anthropogenic emissions has been developed for Europe and may not be suitable for other areas, in particular in the tropics and sub tropics.

10

For a future version of the RETRO inventory, it should be considered to use the Eyring et al. (2005) inventory instead of the Endresen et al. (2003) inventory. New (economic or technological) data sets need to be identified which could be used to quantify the pattern shifts and construct maps for earlier years. It is unclear how large the errors are which result from this simplified approach. Some estimates for such effects were given by Ito et al. (IGAC conference, 2006) for South Africa, but clearly more work is needed to quantify these uncertainties on the global scale. We believe that the ETRO fire inventory gives a good representation of the statistical variability of fire emissions. Where the modelling or analysis concentrates on specific years, more detailed information on fire occurrence should be sought. --We have reduced the seasonal amplitude towards the equator, but this remains a crude fix. There is only very little information available on seasonal emission changes and some thorough research should be carried out in this area.



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Houweling, S., F. Dentener, and J. Lelieveld, The impact of nonmethane hydrocarbon compounds on tropospheric photochemistry, J. Geophys. Res., 103(D9), 10,673-10,696, 1998. Hurst, D.F., Griffith, D.W.T., Cook, G.D., Trace gas emissions from biomass burning in tropical Australian savannas, J. Geophys. Res., 99 (D8), 16441-16456, 1994. IEA, Energy Statistics for OECD and non-OECD member, 2003 version, International Energy Agency, Paris, 2003. Ito, A., Penner, J.E., Global estimates of biomass burning emissions based on satellite imagery for the year 2000, J. Geophys. Res., 109 (D14S05), doi:10.1029/2003JD004423, 2004. Korovin, G.N., and E.N. Romanovich, The 1994 Forest fire season (in the Russian Federation), International Forest Fire News, 14, 1996. Lathière, J., D. A. Hauglustaine, A. D. Friend, N. De Noblet-Ducoudré, N. Viovy, and G. A. Folberth, Impact of climate variability and land use changes on global biogenic volatile organic compound emissions, Atmos. Chem. Phys., 6, 2129–2146, 2006. Lawrence, M. G., W. L. Chameides, P. S. Kasibhatla, H. Levy II, and W. Moxim, Lightning and atmospheric chemistry: The rate of atmospheric NO production, volume I, pages 189-202., CRC Press, Inc., 1995. Lee, D.S., B. Brunner, A. Döpelheuer, R.S. Falk, R.M. Ardner, M. Lecht, M. Leech, D.L. Lister, and P.J. Newton, Aviation emsisions: present-day and future, Meteorologische Zeitschrift, 11 (3), 141-150, 2002. Meijer, E.W., P. van Velthoven, D.W. Brunner, Improvement and evaluation of the parameterisation of nitrogen oxide production by lightning, Phys. and Chem. of the Earth, Part C, 26 (8), 577-583, 2001. Monahan, E. C., D. E. Spiel, and K. L. Davidson, A model of marine aerosol generation via whitecaps and wave disruption, in Oceanic Whitecaps and Their Role in Air-Sea Exchange, E. C. Monahan and G. Mac Niocaill. Eds., D. Reidel, 167-174, 1986. Olivier, J.G.J., and J.J.M. Berdowski, Global emissions sourcesand sinks, in: Berdowski, J., R. Guicherit, and B.J. Heij (eds.), The Climate System, A.A. Balkema Publishers/Swets&Zeitlinger Publishers, Lisse, The Netherlands, 33-78, 2001. Pacyna J.M., Broman D., Lipiatou E. (1998): Sea-air exchange: modeling and processes. European Commission, Marine Science and Technology Programme, EUR 17660 monograph, Brussels, Belgium Pacyna, J.M. and O. Hov, Sea to air transport of trace gases in the coastal zone: A literature review, IGBP LOICZ programme report, 2003 Page, S.E., F. Siegert, J.O. Rieley, H.-D. v. Boehm, A. Jaya, and S. Limin, The amount of carbon released from peat and forest fires in Indonesia during 1997, Nature 420, 61-65, 2002. Palacios-Orueta, A., Parra, A., Chuvieco, E., Carmona-Moreno, C., Remote sensing and geographic information systems methods for global spatio-temporal modeling of biomass burning emissions: Assessment in the African continent, J. Geophys. Res., 109 (D14S09), doi:10.1029/2003JD004734, 2004. Parrish, D.D., M. Trainer, D. Hereid, E. J. Williams, K. J. Olszyna, R. A. Harley, J. F. Meagher, and F. C. Fehsenfeld, Decadal change in carbon monoxide to nitrogen oxide ratio in U.S. vehicular emissions, J. Geophys. Res., 107(D12) 10.1029/2001JD000720, 2002..


Petron, G., Granier, C., Khattatov, B., Yudin, V., Lamarque, J.F., Emmons, L., Gille, J., Edwards, D.P., Monthly CO surface sources inventory based on the 2000-2001 MOPITT satellite data, Geophys. Res. Lett., 31 (L21107), doi:10.1029/2004GL020560, 2004. Pickering, K.E., Y. Wang, W.K. Tao, C. Price, and J.F. Muller, Vertical distributions of lightning NOx for use in regional and global chemical transport models, J. Geophys. Res., 103, 31203-31216, 1998. Price, C., and D. Rind, Possible implications of global climate change on global lightning distributions and frequencies, J. Geophys. Res., 99, 10823-10831, 1994. Price, C., J. Penner, and M. Prather, NOx from lightning: 1. Global distribution based on lightning physics, J. Geophys. Res., 102, 5929-5941, 1997. Pyne S, Andrews P and Laven R (1996), Introduction to Wildland Fire: 2nd Edition John Wiley & Sons. Russel-Smith, J., Edwards, A.C., Cook, G.D., Reliability of biomass burning estimates from savanna fires: Biomass burning in northern Australia during the 1999 Biomass Burning and Lightning Experiment B field campaign, J. Geophys. Res., 108 (D3), 8405, doi:10.1029/2001JD000787, 2003. Schaap. M., Roemer, M., Sauter, F., Boersen, G., Timmermans, R., Builtjes, P.J.H., Vermeulen, A.T. LOTOS-EUROS: Documentation, TNO-report: B&O-A R 2005/297, 2005. Schultz, M., On the use of ATSR fire count data to estimate thew seasonal and interannual variability of vegetation fire emissions, Atmos. Chem. Phys., 2, 387-395, 2002. Schultz, M.G., A. Heil, J.J. Hoelzemann, A. Spessa, K. Thonicke, J. Goldammer, A.C. Held, J.M. Pereira, Global Emissions from Wildland Fires from 1960 to 2000, Global Biogeochemical Cycles, 22, GB2002, doi:2007GB003031, 2008. Schulz M., G. de Leeuw and Y. Balkanski, Sea salt aerosol source functions and emissions, . In: Emission Of Atmospheric Trace Gases And Aerosols, ed : C. Granier, Ed. Kluwer. 2004. Sitch, S., Smith, B., Prentice, I.C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J.O., Levis, S., Lucht, W., Sykes, M.T., Thonicke, K. & Venevsky, S. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ Dynamic Global Vegetation Model, Global Change Biology 9, 161-185, 2003. Smith, M. H., P. M. Park, and I. E. Consterdine, Marine aerosol concentrations and estimated fluxes over the sea, Q. J. R. Meteorol. Soc., 119, 809-824, 1993. Smith, M. H., and N. M. Harrison, The sea spray generation function, J. Aerosol Sci., 29, Suppl. 1, S189-S190, 1998. Takemura, T., H. Okamoto, Y. Marujama, A. Numaguti, A. Higurashi, and T. Nakajima, Global threedimensional simulation of aerosol optical thickness distribution of various origins, J. Geophys. Res., 105, 17853-17873, 2000. Tegen, I., P. Hollrig, M. Chin, I. Fung, D. Jacob, and J. Penner, Contribution of different aerosol species to the global aerosol extinction optical thickness: Estimates from model results, J. Geophys. Res., 102, 23895-23915, 1997. Thonicke, K., S. Venevsky, S. Sitch, and W. Cramer, The role of fire disturbance for global vegetation dynamics: coupling fire into a Dynamic Global Vegetation Model, Global Ecology & Biogeography, 10, 661–677, 2001.


van Aardenne, J.A., F.J. Dentener, J.G.J. Olivier, C.G.M. Klein Goldewijk, and J. Lelieveld, A 1x1 degree resolution data set of historical anthropogenic trace gas emissions for the period 18901990, Global Biogeochemical Cycles, 15 (4), 909-928, 2001. van der Werf, G., Randerson, J.T., Collatz, G.J., Giglio, L., Carbon emissions from fires in tropical and subtropical ecosystems, Global Change Biology, 9, 547-562, 2003. Venevsky S, Thonicke K, Sitch S, Cramer W, Simulating fire regimes in human-dominated ecosystems: Iberian Peninsula case study. Global Change Biology, 8, 984-998, 2002.

11.2 Relevant web links: RETRO project home page: http:/retro.enes.org CIESIN, Center for International Earth Science Information Network, Colombia University, USA. http://www.ciesin.org/ EDGAR (2005), http://www.mnp.nl/edgar/ EMEP / LRTAP (2005), http://webdab.emep.int/ GEIA (2006), http://www.geiacenter.org IPCC (1997), Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories, IPCC/OECD/IEA, Paris; http://www.ipcc-nggip.iges.or.jp/public/gl/invs1.htm TROTREP project: http://atmos.chem.le.ac.uk/trotrep/



Annex 1: Sector definition of the UNFCCC Common reporting format Sector code 0 1 1A1 1A1a 1A1b 1A1c 1A2 1A2a 1A2b 1A2c 1A2d 1A2e 1A2f 1A3 1A3a 1A3b 1A3c 1A3d 1A3e 1A4 1A4a 1A4b 1A4c 1A5 1A5a 1A5b 1A-R 1A-S 1B 1B1 1B1a 1B1b 1B1c 1B2 1B2a 1B2b 1B2c 1B2c(i) 1B2c(ii) 1B2d 2

Parent sector code NULL 0 1A 1A1 1A1 1A1 1A 1A2 1A2 1A2 1A2 1A2 1A2 1A 1A3 1A3 1A3 1A3 1A3 1A 1A4 1A4 1A4 1A 1A5 1A5 1 1 1 1B 1B1 1B1 1B1 1B 1B2 1B2 1B2 1B2c 1B2c 1B2 0

Sector name Total National Emissions and Removals Energy Energy Industries Public Electricity and Heat Production Petroleum Refining Manufacture of Solid Fuels and Other Energy Industries Manufacturing Industries and Construction Iron and Steel Non-Ferrous Metals Chemicals Pulp, Paper and Print Food Processing, Beverages and Tobacco Other Transport Civil Aviation Road Transportation Railways Navigation Other Transportation Other Sectors Commercial/Institutional Residential Agriculture/Forestry/Fisheries Other Stationary Mobile Fuel Combustion - Reference Approach Fuel Combustion - Sectoral Approach Fugitive Emissions from Fuels Solid Fuels Coal Mining Solid Fuel Transformation Other Oil and Natural Gas Oil Natural Gas Venting and Flaring Venting Flaring Other Industrial Processes


2A 2A1 2A2 2A3 2A4 2A5 2A6 2A7 2B 2B1 2B2 2B3 2B4 2B5 2C 2C1 2C2 2C3 2C4 2C5 2D 2D1 2D2 2E 2E1 2E1(i) 2E1(ii) 2E2 2E3 2F 2F1 2F2 2F3 2F4 2F5 2F6 2F7 2F8 2G 3 3A 3B 3C 3D 4 4A 4A1 4A1(i) 4A1(ii) 4A10

2 2A 2A 2A 2A 2A 2A 2A 2 2B 2B 2B 2B 2B 2 2C 2C 2C 2C 2C 2 2D 2D 2 2E 20 20 2E 2E 2 2F 2F 2F 2F 2F 2F 2F 2F 2 0 3 3 3 3 0 4 4A 4A1 4A1 4A

Mineral Products Cement Production Lime Production Limestone and Dolomite Use Soda Ash Production and Use Asphalt Roofing Road Paving with Asphalt Other Chemical Industry Ammonia Production Nitric Acid Production Adipic Acid Production Carbide Production Other (please specify) Metal Production Iron and Steel Production Ferroalloys Production Aluminium Production SF6 Used in Aluminium and Magnesium Foundries Other Other Production Pulp and Paper Food and Drink Production of Halocarbons and SF6 By-product Emissions Production of HCFC-22 Other Fugitive Emissions Other Consumption of Halocarbons and SF6 Refrigeration and Air Conditioning Equipment Foam Blowing Fire Extinguishers Aerosols/ Metered Dose Inhalers Solvents Semiconductor Manufacture Electrical Equipment Other Other Solvent and Other Product Use Paint Application Degreasing and Dry Cleaning Chemical Products, Manufacture and Processing Other Agriculture Enteric Fermentation Cattle Dairy Cattle Non-Dairy Cattle Other


4A2 4A3 4A4 4A5 4A6 4A7 4A8 4A9 4B 4B1 4B1(i) 4B1(ii) 4B10 4B11 4B12 4B13 4B2 4B3 4B4 4B5 4B6 4B7 4B8 4B9 4C 4C1 4C2 4C3 4C4 4D 4D1 4D2 4D3 4D4 4E 4F 4F1 4F2 4F3 4F4 4F5 4G 5 5A 5A1 5A2 5A3 5A4 5A5 5B

4A 4A 4A 4A 4A 4A 4A 4A 4 4B 4B1 4B1 4B 4B 4B 4B 4B 4B 4B 4B 4B 4B 4B 4B 4 4C 4C 4C 4C 4 4D 4D 4D 4D 4 4 4F 4F 4F 4F 4F 4 0 5 5A 5A 5A 5A 5A 5

Buffalo Sheep Goats Camels and Llamas Horses Mules and Asses Swine Poultry Manure Management Cattle Dairy Cattle Non-Dairy Cattle Anaerobic Lagoons Liquid Systems Solid Storage and Dry Lot Other Buffalo Sheep Goats Camels and Llamas Horses Mules and Asses Swine Poultry Rice Cultivation Irrigated Rainfed Deep Water Other Agricultural Soils Direct Soil Emissions Animal Production Indirect Emissions Other Prescribed Burning of Savannas Field Burning of Agricultural Residues Cereals Pulse Tuber and Root Sugar Cane Other Other Land-Use Change and Forestry Changes in Forest and Other Woody Biomass Stocks Tropical Forests Temperate Forests Boreal Forests Grasslands/Tundra Other Forest and Grassland Conversion


5B1 5B2 5B3 5B4 5B5 5C 5C1 5C2 5C3 5C4 5C5 5D 5D1 5D2 5D3 5D4 5D5 5E 6 6A 6A1 6A2 6A3 6B 6B1 6B2 6B3 6C 6D 7 AB BIO IB MB MO

5B 5B 5B 5B 5B 5 5C 5C 5C 5C 5C 5 5D 5D 5D 5D 5D 5 0 6 6A 6A 6A 6 6B 6B 6B 6 6 0 IB 0 0 IB 0

Tropical Forests Temperate Forests Boreal Forests Grasslands/Tundra Other Abandonment of Managed Lands Tropical Forests Temperate Forests Boreal Forests Grasslands/Tundra Other CO2 Emissions and Removals from Soil Cultivation of Mineral Soils Cultivation of Organic Soils Liming of Agricultural Soils Forest Soils Other Other Waste Solid Waste Disposal on Land Managed Waste Disposal on Land Unmanaged Waste Disposal Sites Other Wastewater Handling Industrial Wastewater Domestic and Commercial Wastewater Other Waste Incineration Other Other Aviation CO2 Emissions from Biomass International Bunkers Marine Multilateral Operations

This information was copied from http://dataservice.eea.eu.int



Annex 2: RETRO emissions: tabulated global annual totals for selected species NOx (Tg(NO2)) year 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

biogenic 30.8 30.7 30.7 30.7 30.8 30.7 30.7 30.7 30.8 30.7 30.7 30.7 30.8 30.7 30.7 30.7 30.8 30.7 30.7 30.7 30.8 30.7 30.7 30.7 30.8 30.7 30.7 30.7 30.8 30.7 30.7 30.7 30.8 30.7 30.7 30.7 30.8 30.7 30.7 30.7 30.8

wildfires 16.0 10.9 10.9 10.7 12.9 14.0 10.8 11.1 12.2 13.9 18.5 10.6 14.7 12.3 10.3 12.9 14.6 15.0 14.0 18.5 19.4 14.2 20.6 16.8 11.8 12.8 13.7 18.5 12.4 14.0 19.8 17.7 22.6 16.6 16.7 16.0 16.1 21.0 22.4 15.8 16.6

inc 5.5 5.5 5.5 5.8 6.0 6.1 6.4 7.0 7.2 7.4 8.0 8.0 8.3 8.9 9.0 8.6 9.0 9.3 9.6 10.1 10.5 10.0 9.7 9.7 10.0 9.9 9.8 10.1 10.4 10.4 10.3 10.2 9.3 9.3 9.4 9.8 9.8 9.6 9.6 9.1 9.3

pow 8.5 8.8 9.4 9.9 10.5 10.9 11.5 11.7 12.3 13.1 14.2 14.2 14.5 15.1 14.9 14.7 15.8 16.3 16.7 17.2 17.8 17.6 17.7 18.1 18.5 19.0 19.3 20.2 20.7 21.4 21.4 21.9 22.0 22.5 22.6 23.0 24.2 24.7 25.4 25.7 26.7

res 4.8 4.9 5.2 5.5 5.7 6.0 6.3 6.7 7.0 7.4 7.0 8.1 8.5 8.8 8.8 9.4 9.8 10.1 10.4 10.7 10.7 10.5 10.8 10.9 11.1 11.3 11.4 11.6 11.9 11.9 12.1 12.1 11.2 11.0 11.0 11.2 11.2 11.2 10.9 10.9 10.9

ships 4.7 4.9 5.0 5.3 5.8 6.3 6.8 7.0 7.2 7.7 8.5 8.9 9.3 9.8 9.4 9.4 9.6 9.6 10.0 10.3 10.2 9.6 8.4 7.6 7.5 7.6 8.0 8.0 8.2 8.6 9.8 10.3 10.6 10.5 10.4 10.6 10.6 10.8 11.0 11.3 11.6

tra 13.9 14.5 15.2 16.1 17.0 17.7 18.7 19.6 21.2 22.4 23.9 25.0 26.9 28.9 28.9 30.2 31.6 32.5 33.7 33.4 32.8 32.5 32.3 32.4 32.3 32.3 33.1 33.7 34.2 34.4 34.4 34.4 34.1 33.9 34.0 34.2 33.2 31.7 30.2 31.0 31.8

total 84.1 80.3 82.0 83.9 88.6 91.8 91.2 93.7 97.9 102.7 110.7 105.6 113.0 114.7 112.1 115.9 121.1 123.6 125.1 131.0 132.1 125.1 130.1 126.2 121.9 123.6 126.0 132.7 128.6 131.3 138.6 137.4 140.6 134.4 134.7 135.4 135.9 139.6 140.1 134.5 137.6

Mean

30.7

15.1

8.7

17.3

9.4

8.7

28.4

118.4


CO (Tg(CO)) year 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

soil 160.3 160.0 160.0 160.0 160.3 160.0 160.0 160.0 160.3 160.0 160.0 160.0 160.3 160.0 160.0 160.0 160.3 160.0 160.0 160.0 160.3 160.0 160.0 160.0 160.3 160.0 160.0 160.0 160.3 160.0 160.0 160.0 160.3 160.0 160.0 160.0 160.3 160.0 160.0 160.0 160.3

ocean 20.0 19.9 19.9 19.9 20.0 19.9 19.9 19.9 20.0 19.9 19.9 19.9 20.0 19.9 19.9 19.9 20.0 19.9 19.9 19.9 20.0 19.9 19.9 19.9 20.0 19.9 19.9 19.9 20.0 19.9 19.9 19.9 20.0 19.9 19.9 19.9 20.0 19.9 19.9 19.9 20.0

wildfires 316.1 220.5 221.2 216.7 258.2 283.6 220.1 224.4 247.6 291.3 363.9 220.4 317.2 259.2 216.5 266.0 302.5 327.4 296.0 387.5 405.4 307.6 488.4 373.0 267.3 284.5 319.8 428.4 284.4 311.7 425.2 419.2 493.1 370.8 399.6 351.1 355.3 555.3 514.0 342.4 358.9

inc 1.9 1.9 1.9 2.0 2.0 2.0 2.0 2.3 2.3 2.3 2.3 2.2 2.3 2.3 2.3 2.3 2.3 2.4 2.5 2.6 2.8 2.8 2.7 2.7 2.8 2.8 2.8 2.8 2.9 2.9 2.8 2.7 2.6 2.5 2.5 2.5 2.5 2.3 2.3 2.2 2.2

pow 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 1.0 1.1 1.1 1.1 1.2 1.2 1.2 1.3 1.4 1.4 1.5 1.5 1.5 1.5 1.6 1.6 1.7 1.7 1.8 1.8 1.9 1.9 1.9 2.0 2.0 2.0 2.0 2.1 2.2 2.2 2.2 2.3

res 185.3 186.3 189.4 192.0 192.3 193.9 194.9 196.2 198.4 201.0 201.3 209.7 212.6 215.3 218.4 223.9 227.0 231.3 235.0 238.6 241.1 244.6 248.3 252.3 256.8 262.4 267.1 272.9 278.9 281.4 283.8 285.5 285.9 288.0 286.9 289.8 289.5 285.7 280.7 280.1 279.6

ships 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.7 0.7 0.8 0.8 0.9 0.9 0.9 0.9 0.9 0.9 0.9 1.0 1.0 0.9 0.8 0.7 0.7 0.7 0.7 0.7 0.8 0.8 0.9 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.1 1.1

tra 131.0 135.2 141.3 148.0 155.3 161.4 170.1 177.8 189.7 200.1 212.1 217.2 229.2 240.4 235.7 242.3 250.5 255.1 262.7 257.5 249.0 245.1 241.1 238.6 235.7 232.6 236.2 239.9 243.9 244.9 244.6 241.8 232.6 223.9 214.9 210.1 204.6 197.0 188.8 191.2 191.9

total 815.7 725.0 734.8 739.7 789.4 822.1 768.5 782.0 819.9 876.3 961.3 831.3 943.6 899.2 854.9 916.3 964.9 998.4 978.4 1068.5 1081.1 982.3 1162.7 1048.7 945.3 964.6 1008.1 1126.4 992.9 1023.5 1139.0 1132.0 1197.4 1068.0 1086.7 1036.4 1035.2 1223.4 1169.0 999.0 1016.3

Mean

160.1

19.9

329.6

2.4

1.5

241.1

0.8

213.7

969.0


Methanol (Tg(CH3OH)) year 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

biogenic 274.940 272.720 262.980 273.120 274.980 274.330 279.300 265.830 267.210 275.730 266.820 259.180 269.930 268.840 254.830 256.130 253.140 265.360 261.180 267.210 273.960 271.530 270.240 280.850 263.550 262.870 263.870 273.900 271.440 263.730 271.300 273.360 271.940 266.180 271.160 274.520 267.400 278.710 296.950 274.400 273.260

wildfires 7.427 5.122 5.175 5.092 6.062 6.624 5.172 5.251 5.791 6.877 8.534 5.151 7.437 6.041 5.043 6.222 7.119 7.710 6.922 9.086 9.537 7.207 11.555 8.721 6.298 6.666 7.598 10.195 6.647 7.284 10.020 9.933 11.571 8.790 9.565 8.260 8.270 13.286 12.007 8.005 8.387

inc 0.004 0.004 0.004 0.005 0.005 0.005 0.005 0.006 0.006 0.006 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.008 0.008 0.007 0.006 0.006 0.005 0.005 0.005 0.005 0.005 0.005 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.003 0.003 0.004

pow 0.003 0.003 0.003 0.003 0.003 0.004 0.004 0.004 0.005 0.005 0.007 0.007 0.008 0.009 0.009 0.009 0.010 0.011 0.011 0.011 0.011 0.010 0.010 0.009 0.009 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.007 0.007 0.007 0.007 0.007 0.007

res 1.739 1.761 1.786 1.811 1.838 1.861 1.888 1.916 1.947 1.975 2.043 2.068 2.106 2.142 2.177 2.213 2.243 2.269 2.297 2.324 2.353 2.385 2.419 2.454 2.492 2.528 2.579 2.631 2.686 2.736 2.788 2.827 2.868 2.904 2.942 2.980 2.982 2.980 2.980 2.980 2.982

sol 1.892 1.947 2.003 2.062 2.122 2.187 2.252 2.321 2.391 2.466 2.414 2.486 2.571 2.962 3.031 2.960 3.167 3.262 3.381 3.490 3.240 3.612 3.637 3.736 3.864 3.876 4.071 4.181 4.317 4.439 4.160 4.473 4.452 4.431 4.416 4.410 4.410 4.403 4.326 4.248 4.167

was 1.021 1.037 1.053 1.070 1.087 1.104 1.122 1.140 1.159 1.178 1.185 1.221 1.226 1.269 1.290 1.295 1.330 1.278 1.327 1.333 1.360 1.374 1.393 1.396 1.433 1.455 1.473 1.469 1.493 1.521 1.534 1.518 1.530 1.557 1.567 1.574 1.601 1.617 1.617 1.617 1.617

total 287.025 282.593 273.005 283.163 286.097 286.115 289.744 276.469 278.508 288.238 281.009 270.119 283.285 281.271 266.387 268.835 267.016 279.897 275.124 283.462 290.468 286.124 289.260 297.171 277.652 277.408 279.604 292.390 286.595 279.722 289.814 292.123 292.373 283.873 289.662 291.755 284.673 301.007 317.890 291.260 290.423

Mean

269.729

7.748

0.005

0.007

2.387

3.372

1.352

284.600


Ethane (Tg(C2H6)) year 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

biogenic 4.202 4.169 4.020 4.175 4.203 4.193 4.269 4.063 4.084 4.215 4.078 3.962 4.126 4.109 3.896 3.915 3.869 4.056 3.992 4.084 4.188 4.150 4.131 4.293 4.028 4.018 4.033 4.187 4.149 4.031 4.147 4.179 4.157 4.069 4.145 4.196 4.087 4.260 4.539 4.194 4.177

ocean 0.980 0.977 0.977 0.977 0.980 0.977 0.977 0.977 0.980 0.977 0.977 0.977 0.980 0.977 0.977 0.977 0.980 0.977 0.977 0.977 0.980 0.977 0.977 0.977 0.980 0.977 0.977 0.977 0.980 0.977 0.977 0.977 0.980 0.977 0.977 0.977 0.980 0.977 0.977 0.977 0.980

wildfires 1.856 1.371 1.374 1.362 1.592 1.850 1.444 1.446 1.610 1.899 2.233 1.482 2.190 1.776 1.512 1.778 2.025 2.332 2.061 2.609 2.710 2.166 3.892 2.790 2.002 2.091 2.419 3.244 2.124 2.250 2.901 3.267 3.623 2.669 3.037 2.474 2.487 4.907 3.744 2.410 2.499

inc 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.005 0.005 0.005 0.005 0.006 0.005 0.005 0.006 0.006 0.006 0.006 0.006 0.006 0.006 0.006 0.006 0.006 0.006 0.006 0.005 0.005

pow 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003

res 1.283 1.285 1.301 1.311 1.307 1.311 1.310 1.311 1.319 1.329 1.373 1.358 1.378 1.395 1.416 1.465 1.484 1.523 1.555 1.582 1.530 1.553 1.576 1.602 1.630 1.663 1.690 1.726 1.763 1.774 1.789 1.788 1.758 1.770 1.761 1.775 1.770 1.740 1.707 1.702 1.698

exf 0.321 0.337 0.358 0.378 0.400 0.420 0.449 0.493 0.531 0.563 0.604 0.643 0.678 0.713 0.739 0.750 0.800 0.833 0.976 1.039 1.062 1.067 1.082 1.111 1.197 1.247 1.288 1.355 1.418 1.467 1.491 1.538 1.581 1.590 1.575 1.608 1.662 1.675 1.704 1.761 1.837

ships 0.038 0.040 0.041 0.042 0.047 0.051 0.055 0.056 0.058 0.062 0.068 0.071 0.075 0.079 0.076 0.076 0.077 0.078 0.080 0.083 0.083 0.077 0.067 0.061 0.060 0.061 0.064 0.064 0.066 0.069 0.079 0.083 0.086 0.084 0.083 0.085 0.086 0.087 0.089 0.091 0.093

tra 0.431 0.440 0.454 0.470 0.488 0.503 0.526 0.546 0.576 0.603 0.634 0.631 0.663 0.692 0.678 0.696 0.708 0.709 0.718 0.693 0.660 0.635 0.610 0.589 0.567 0.544 0.535 0.526 0.515 0.498 0.478 0.461 0.434 0.406 0.378 0.355 0.330 0.301 0.270 0.275 0.276

was 0.730 0.742 0.754 0.766 0.778 0.791 0.804 0.817 0.830 0.844 0.851 0.873 0.881 0.908 0.923 0.930 0.952 0.933 0.962 0.971 0.989 1.001 1.016 1.022 1.046 1.061 1.076 1.080 1.097 1.116 1.129 1.126 1.137 1.156 1.166 1.174 1.195 1.209 1.209 1.209 1.209

total 9.847 9.365 9.283 9.487 9.801 10.101 9.838 9.713 9.993 10.497 10.823 10.003 10.976 10.654 10.220 10.592 10.900 11.446 11.327 12.044 12.208 11.632 13.359 12.452 11.517 11.669 12.090 13.166 12.121 12.191 12.998 13.427 13.764 12.729 13.131 12.654 12.605 15.165 14.248 12.627 12.778

Mean

4.123

0.978

2.329

0.005

0.002

1.545

1.033

0.070

0.524

0.987

11.596


Propane (Tg(C3H6)) year 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

ocean 1.294 1.290 1.290 1.290 1.294 1.290 1.290 1.290 1.294 1.290 1.290 1.290 1.294 1.290 1.290 1.290 1.294 1.290 1.290 1.290 1.294 1.290 1.290 1.290 1.294 1.290 1.290 1.290 1.294 1.290 1.290 1.290 1.294 1.290 1.290 1.290 1.294 1.290 1.290 1.290 1.294

wildfires 0.754 0.615 0.622 0.641 0.721 0.928 0.735 0.712 0.802 0.951 1.010 0.775 1.185 0.953 0.836 0.927 1.064 1.331 1.138 1.373 1.414 1.208 2.543 1.687 1.215 1.233 1.488 2.009 1.266 1.284 1.555 2.077 2.158 1.547 1.884 1.382 1.348 3.545 2.141 1.338 1.363

inc 0.007 0.007 0.007 0.008 0.008 0.008 0.008 0.009 0.010 0.010 0.010 0.009 0.010 0.010 0.010 0.010 0.011 0.011 0.011 0.012 0.013 0.013 0.013 0.013 0.014 0.014 0.014 0.014 0.015 0.015 0.015 0.014 0.013 0.013 0.013 0.014 0.014 0.014 0.014 0.013 0.014

pow 0.004 0.004 0.004 0.005 0.005 0.005 0.005 0.005 0.006 0.006 0.007 0.007 0.007 0.007 0.007 0.007 0.008 0.008 0.008 0.009 0.009 0.009 0.009 0.009 0.010 0.011 0.011 0.012 0.012 0.013 0.014 0.015 0.014 0.014 0.015 0.015 0.016 0.016 0.017 0.018 0.018

res 0.550 0.546 0.551 0.553 0.545 0.542 0.535 0.530 0.529 0.530 0.517 0.521 0.528 0.533 0.540 0.566 0.574 0.595 0.611 0.625 0.575 0.585 0.593 0.605 0.617 0.633 0.642 0.656 0.672 0.669 0.669 0.661 0.633 0.635 0.620 0.623 0.619 0.597 0.573 0.570 0.567

exf 0.422 0.436 0.457 0.478 0.500 0.520 0.550 0.594 0.633 0.664 0.706 0.681 0.718 0.758 0.780 0.776 0.824 0.851 0.924 0.963 0.949 0.900 0.890 0.898 0.940 0.953 0.969 1.004 1.046 1.073 1.075 1.076 1.299 1.276 1.247 1.257 1.276 1.297 1.306 1.330 1.374

ships 0.155 0.162 0.165 0.172 0.190 0.207 0.222 0.228 0.235 0.253 0.277 0.290 0.305 0.322 0.307 0.308 0.313 0.316 0.327 0.337 0.335 0.313 0.274 0.249 0.244 0.249 0.261 0.261 0.268 0.280 0.322 0.338 0.348 0.343 0.339 0.347 0.347 0.352 0.360 0.369 0.379

tra 0.251 0.257 0.266 0.276 0.291 0.303 0.318 0.331 0.353 0.373 0.391 0.389 0.408 0.427 0.420 0.432 0.443 0.446 0.451 0.434 0.421 0.408 0.402 0.403 0.401 0.385 0.381 0.375 0.370 0.362 0.350 0.344 0.333 0.311 0.299 0.287 0.280 0.273 0.258 0.268 0.287

was 0.511 0.519 0.527 0.536 0.544 0.553 0.563 0.572 0.581 0.591 0.597 0.610 0.619 0.633 0.644 0.653 0.666 0.667 0.682 0.692 0.703 0.713 0.725 0.734 0.747 0.759 0.770 0.779 0.791 0.805 0.816 0.823 0.833 0.845 0.855 0.864 0.878 0.889 0.889 0.889 0.889

total 3.948 3.836 3.890 3.958 4.099 4.356 4.225 4.270 4.443 4.668 4.804 4.572 5.074 4.933 4.835 4.969 5.197 5.515 5.442 5.732 5.712 5.440 6.739 5.887 5.482 5.528 5.826 6.400 5.735 5.791 6.105 6.637 6.926 6.275 6.561 6.079 6.073 8.274 6.849 6.085 6.185

Mean

1.291

1.311

0.012

0.010

0.586

0.894

0.285

0.353

0.706

5.448


Ethene (Tg(C2H4)) year 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

biogenic 3.913 3.882 3.743 3.887 3.914 3.905 3.975 3.784 3.803 3.924 3.797 3.689 3.842 3.826 3.627 3.645 3.603 3.777 3.717 3.803 3.899 3.865 3.846 3.997 3.751 3.741 3.755 3.898 3.863 3.754 3.861 3.891 3.871 3.788 3.859 3.907 3.806 3.967 4.227 3.906 3.889

ocean 1.400 1.396 1.396 1.396 1.400 1.396 1.396 1.396 1.400 1.396 1.396 1.396 1.400 1.396 1.396 1.396 1.400 1.396 1.396 1.396 1.400 1.396 1.396 1.396 1.400 1.396 1.396 1.396 1.400 1.396 1.396 1.396 1.400 1.396 1.396 1.396 1.400 1.396 1.396 1.396 1.400

wildfires 4.344 3.079 3.094 3.070 3.633 4.100 3.188 3.227 3.566 4.173 5.144 3.205 4.627 3.793 3.203 3.875 4.417 4.872 4.380 5.642 5.884 4.534 7.635 5.673 4.056 4.271 4.857 6.537 4.258 4.603 6.196 6.457 7.476 5.524 6.065 5.163 5.144 9.088 7.539 5.034 5.245

inc 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.002 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.002

pow 0.000 0.000 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.002 0.002 0.002 0.002

res 2.513 2.517 2.547 2.569 2.562 2.569 2.566 2.567 2.583 2.603 2.659 2.668 2.708 2.741 2.781 2.879 2.918 2.994 3.058 3.111 2.998 3.044 3.087 3.139 3.193 3.259 3.312 3.381 3.456 3.475 3.503 3.500 3.439 3.463 3.443 3.470 3.459 3.399 3.333 3.323 3.315

exf 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.009 0.009 0.008 0.008 0.009 0.009 0.009 0.010 0.010 0.027 0.028 0.027 0.025 0.024 0.024 0.024 0.024 0.024 0.025 0.025 0.026 0.026 0.025 0.036 0.034 0.033 0.033 0.033 0.034 0.033 0.034 0.034

ships 0.029 0.030 0.030 0.032 0.035 0.038 0.041 0.042 0.043 0.047 0.051 0.054 0.056 0.060 0.057 0.057 0.058 0.058 0.060 0.062 0.062 0.058 0.051 0.046 0.045 0.046 0.048 0.048 0.050 0.052 0.059 0.062 0.064 0.063 0.063 0.064 0.064 0.065 0.066 0.068 0.070

tra 1.774 1.816 1.880 1.951 2.030 2.095 2.192 2.277 2.412 2.530 2.665 2.600 2.736 2.868 2.819 2.896 2.966 2.989 3.045 2.963 2.848 2.748 2.647 2.570 2.483 2.390 2.364 2.323 2.280 2.204 2.112 2.034 1.914 1.790 1.670 1.565 1.454 1.321 1.178 1.202 1.215

was 1.143 1.161 1.179 1.198 1.217 1.237 1.257 1.278 1.298 1.320 1.329 1.367 1.376 1.421 1.444 1.453 1.490 1.446 1.497 1.507 1.536 1.553 1.576 1.582 1.621 1.646 1.667 1.668 1.695 1.725 1.742 1.731 1.746 1.776 1.789 1.799 1.831 1.851 1.851 1.851 1.851

total 15.125 13.890 13.879 14.113 14.801 15.350 14.626 14.581 15.116 16.004 17.052 14.988 16.756 16.116 15.339 16.212 16.864 17.545 17.183 18.514 18.658 17.225 20.265 18.430 16.577 16.777 17.427 19.280 17.032 17.239 18.900 19.100 19.950 17.839 18.322 17.402 17.195 21.125 19.628 16.817 17.024

Mean

3.839

1.397

4.875

0.002

0.001

3.027

0.020

0.053

2.239

1.529

16.982


Propene (Tg(C3H6)) year 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

biogenic 15.965 15.837 15.270 15.859 15.969 15.933 16.220 15.436 15.518 16.010 15.492 15.052 15.674 15.612 14.800 14.873 14.701 15.411 15.167 15.519 15.909 15.767 15.693 16.309 15.304 15.266 15.322 15.903 15.763 15.315 15.756 15.875 15.793 15.457 15.745 15.941 15.530 16.186 17.244 15.935 15.870

ocean 1.521 1.516 1.516 1.516 1.521 1.516 1.516 1.516 1.521 1.516 1.516 1.516 1.521 1.516 1.516 1.516 1.521 1.516 1.516 1.516 1.521 1.516 1.516 1.516 1.521 1.516 1.516 1.516 1.521 1.516 1.516 1.516 1.521 1.516 1.516 1.516 1.521 1.516 1.516 1.516 1.521

wildfires 2.087 1.504 1.533 1.526 1.778 2.031 1.608 1.594 1.776 2.156 2.472 1.639 2.456 1.954 1.658 1.964 2.264 2.625 2.276 2.906 3.042 2.422 4.418 3.090 2.258 2.337 2.783 3.734 2.380 2.517 3.281 3.732 4.029 3.058 3.556 2.797 2.775 5.678 4.221 2.686 2.786

inc 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.002

pow 0.000 0.000 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.002 0.002 0.002 0.002

res 0.915 0.923 0.935 0.945 0.953 0.961 0.970 0.979 0.990 1.002 1.036 1.047 1.065 1.081 1.098 1.125 1.140 1.160 1.179 1.196 1.190 1.207 1.224 1.243 1.263 1.284 1.308 1.335 1.363 1.381 1.401 1.412 1.414 1.429 1.437 1.453 1.452 1.442 1.432 1.431 1.430

exf 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.009 0.009 0.009 0.008 0.008 0.009 0.009 0.009 0.010 0.010 0.027 0.028 0.027 0.025 0.024 0.024 0.024 0.024 0.024 0.025 0.025 0.026 0.026 0.025 0.036 0.034 0.033 0.033 0.033 0.034 0.033 0.034 0.034

ships 0.034 0.036 0.036 0.038 0.042 0.046 0.049 0.050 0.052 0.056 0.061 0.064 0.068 0.071 0.068 0.068 0.069 0.070 0.072 0.075 0.074 0.069 0.061 0.055 0.054 0.055 0.058 0.058 0.059 0.062 0.071 0.075 0.077 0.076 0.075 0.077 0.077 0.078 0.080 0.082 0.084

tra 0.607 0.623 0.646 0.672 0.700 0.723 0.757 0.787 0.834 0.875 0.921 0.909 0.956 1.000 0.982 1.008 1.030 1.036 1.052 1.020 0.977 0.942 0.908 0.881 0.851 0.818 0.808 0.794 0.779 0.754 0.722 0.697 0.658 0.615 0.574 0.540 0.503 0.460 0.413 0.421 0.427

was 0.480 0.488 0.495 0.503 0.511 0.520 0.528 0.537 0.545 0.554 0.558 0.574 0.577 0.597 0.607 0.609 0.626 0.603 0.625 0.629 0.641 0.648 0.657 0.659 0.676 0.686 0.695 0.694 0.705 0.718 0.724 0.717 0.724 0.736 0.741 0.745 0.757 0.765 0.765 0.765 0.765

total 21.620 20.937 20.442 21.070 21.484 21.740 21.659 20.910 21.248 22.180 22.067 20.812 22.328 21.843 20.740 21.175 21.363 22.434 21.918 22.891 23.385 22.599 24.504 23.780 21.955 21.990 22.517 24.062 22.600 22.292 23.500 24.053 24.255 22.925 23.682 23.105 22.652 26.163 25.709 22.875 22.921

Mean

15.663

1.517

2.619

0.002

0.001

1.201

0.020

0.063

0.773

0.638

22.497


Formaldehyde (Tg(CH2O)) year 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

biogenic 34.414 34.138 32.917 34.188 34.421 34.340 34.961 33.274 33.446 34.512 33.398 32.442 33.787 33.650 31.900 32.058 31.687 33.216 32.693 33.449 34.289 33.985 33.823 35.154 32.988 32.903 33.028 34.283 33.975 33.012 33.959 34.217 34.041 33.317 33.941 34.362 33.473 34.887 37.172 34.349 34.206

wildfires 3.748 2.655 2.641 2.542 3.031 3.303 2.554 2.612 2.893 3.432 4.225 2.594 3.778 3.059 2.540 3.104 3.507 3.836 3.458 4.547 4.750 3.645 5.680 4.370 3.135 3.366 3.761 4.993 3.430 3.759 5.014 4.922 5.720 4.353 4.706 4.175 4.348 6.418 6.317 4.083 4.294

inc 0.011 0.011 0.012 0.013 0.013 0.014 0.015 0.016 0.017 0.017 0.018 0.019 0.019 0.021 0.021 0.020 0.021 0.022 0.022 0.023 0.026 0.026 0.025 0.024 0.025 0.025 0.025 0.026 0.027 0.027 0.027 0.026 0.023 0.023 0.023 0.024 0.024 0.024 0.024 0.024 0.025

pow 0.008 0.008 0.009 0.009 0.010 0.010 0.011 0.012 0.013 0.014 0.016 0.017 0.018 0.019 0.019 0.019 0.021 0.021 0.022 0.023 0.023 0.023 0.023 0.023 0.024 0.025 0.026 0.027 0.028 0.030 0.030 0.032 0.033 0.033 0.033 0.033 0.035 0.036 0.037 0.038 0.038

res 0.298 0.301 0.304 0.308 0.312 0.316 0.321 0.325 0.330 0.334 0.345 0.353 0.360 0.366 0.373 0.379 0.385 0.389 0.394 0.400 0.405 0.410 0.416 0.422 0.429 0.436 0.445 0.454 0.464 0.473 0.481 0.489 0.498 0.505 0.511 0.517 0.520 0.519 0.518 0.519 0.520

tra 0.292 0.302 0.316 0.331 0.348 0.361 0.380 0.396 0.424 0.448 0.475 0.482 0.510 0.540 0.536 0.552 0.574 0.588 0.607 0.603 0.591 0.574 0.555 0.545 0.531 0.516 0.515 0.507 0.498 0.482 0.466 0.453 0.423 0.394 0.368 0.344 0.318 0.287 0.253 0.259 0.265

was 0.187 0.190 0.193 0.196 0.200 0.203 0.206 0.209 0.213 0.216 0.218 0.224 0.225 0.233 0.237 0.238 0.244 0.247 0.256 0.257 0.262 0.264 0.269 0.269 0.276 0.279 0.283 0.282 0.286 0.291 0.293 0.290 0.292 0.297 0.298 0.300 0.308 0.311 0.311 0.311 0.311

total 38.958 37.606 36.392 37.588 38.335 38.547 38.447 36.845 37.335 38.974 38.696 36.130 38.698 37.888 35.625 36.371 36.439 38.318 37.452 39.301 40.346 38.927 40.790 40.807 37.409 37.550 38.082 40.572 38.707 38.073 40.270 40.429 41.029 38.922 39.880 39.756 39.026 42.481 44.632 39.583 39.660

Mean

33.762

3.885

0.021

0.023

0.411

0.444

0.256

38.802

RETRO deliverable D1-6: Report on emissions / Annex

Annex 3: Pulles et al report on TEAM The final formatted version of this document is available as TNO report 2007-A-R0132/B: “Assessment of global emissions from fuel combustion in the final decades of the 20th century – Application of the emission inventory model TEAM” http://www.tno.nl/downloads%5C2007-A-R0132-B_rapport_internemissie.pdf


Annex 4: Schultz et al . paper on RETRO wildfires

This paper has been published as Schultz, M.G., A. Heil, J.J. Hoelzemann, A. Spessa, K. Thonicke, J. Goldammer, A.C. Held, J.M. Pereira, M. van het Bolscher (2008), Global Wildland Fire Emissions from 1960 to 2000, Global Biogeochem. Cyc., 22, GB2002, doi:2007GB003031.


Annex 5: On the use of AVHRR data for estimating trends and variability of burnt area The objective of the group at IICT was to produce global, monthly burned area maps, at 8 km spatial resolution, from the Advanced Very High Resolution Radiometer (AVHRR) Pathfinder data set for the period 1982 – 2001. These data were then to be used in an emissions model (GWEM, Hoelzemann et al., 2004) in order to quantify of pyrogenic emissions for the latter half of the RETRO period. This task entailed the compilation and processing of a data set containing about 7300 satellite images, for a total of about 260 Gbytes of data. All five AVHRR channels were used, as well as data on solar illumination and satellite viewing geometry, and quality assessment data available in the Pathfinder dataset. The data were navigated to sub-pixel accuracy, calibrated to account for sensor degradation, inter-calibrated among satellites, and atmospherically corrected for Rayleigh scattering and O3 absorption. Data with high solar zenith angles (> 80º) were excluded, and clouds were flagged using the CLAVR algorithm. Visible channels data are available in reflectance units (%), and thermal data as brightness temperature. There is a gap in the data set, ranging from mid-September to the end of 1994. The daily data over the entire period of analysis were processed into monthly composite images, using a compositing approach specifically designed for burned area mapping. This compositing technique combines albedo and brightness temperature data, to emphasize the burnt surface signal. The composite images display good spatial and radiometric properties. The burned area maps (example presented in Figure 1) are obtained via supervised classification of the monthly composite images. Based on previous research, the following variable are being used in the classification: an albedo-like index, obtained as the arithmetic mean of the AVHRR channel 1 and channel 2 reflectance; split-window surface temperature; the Global Environmental Monitoring Index (GEMI); channel 3 reflectance; and the GEMI3, a modified GEMI, where channel 3 reflectance data take the place of channel 1 data (see Figure 2). An extensive collection of ancillary data was used to support the selection of training data for the supervised classification. These data include maps, satellite data, statistical records and field reports. Training data were selected globally, and for the entire period of analysis. Particular attention was given to exceptional situations, such as the 1982/83 and 1997/98 ENSO events, but the selection of training data also covered more common fire seasons, and aimed at being representative of fire activity in all major biomes.


Figure 1: Burnt areas (red) and active fires (yellow), along the Sudan /Central African Republic border, December 1998.

Figure 2: Global false color image for August 1998. Red-Green-Blue composite of surface temperature, GEMI, and albedo.


After establishing the first global maps of burnt area in year 2 of the project, it was found that the radiometric channel calibration of the original PAL data set from the AVHRR instrument is unsuitable for the purpose of this project. Recent efforts to re-calibrate the entire data set are yielding promising results (Figure 3), but unfortunately it was not possible to generate a homogenous time series of global burnt area in time for use in the project.

Figure 3: AVHRR channels time series (ch1, ch2, ch3, ch4, ch5 e SZA) original data (blue), linear regression model correction (black) and EMD corrected (red). Data are from a Sudanian savanna pixel, from the Central African Republic. The lifetimes of the 4 AVHRR satellites are shown in the background.

Partner IICT (ISA in year 3) also evaluated the European Space Agency World Fire Atlas and developed a filtering procedure which removes spurious fire detections from this data set (Mota et al. 2006). This improved dataset has been extensively used to assist the selection of training data for the AVHRR burnt area detection and it has been made available for analyses of global fire activity over the five-year period from 1997 to 2003.

Reference: Mota, B.W., J. M. C. Pereira, D. Oom, M. J. P. Vasconcelos, and M. Schultz, Screening the ESA ATSR-2 World Fire Atlas (1997–2002), Atmos. Chem. Phys., 6, 1409–1424, 2006.


Annex 6: IMAGE 2.2 regions definition

Figure 1: Depiction of world regions defined in the IMAGE 2.2 model and adopted for the purposes of this report. Figure taken from http://www.mnp.nl/image/background_info/regions/

Emission data sets and methodologies for estimating

Emission data sets and methodologies for estimating

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