Ozone air quality in 2030:
a multi model assessment of risks for health and
vegetation.
K. Ellingsen1 , R. Van Dingenen2, F.J. Dentener2, L. Emberson22, A. Fiore14, M. Schultz4,
D.S. Stevenson3, M. Gauss1, M. Amann7, C.S. Atherton8, N. Bell9, D.J. Bergmann8, I.
Bey10, T. Butler11, J. Cofala7, W.J. Collins12, R.G. Derwent13, R.M. Doherty1, J. Drevet10,
H. Eskes5, D. Hauglustaine15, I. Isaksen1, L. Horowitz14, M. Krol2, J.F. Lamarque16, M.
Lawrence11, V. Montanaro17, J.F. Müller18, T. van Noije5, G. Pitari17, M.J. Prather19, J.
Pyle6, S. Rast3, J. Rodriguez20, M. Sanderson12, N. Savage6, D. Shindell9, S. Strahan20, K.
Sudo21, S. Szopa15, O. Wild21, G. Zeng6
NB Addresses currently don’t match up with numbers next to names.
1. University of Edinburgh, School of Geosciences, Edinburgh, United Kingdom.
2. Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy.
3. Max Planck Institute for Meteorology, Hamburg, Germany.
4. University of Oslo, Department of Geosciences, Oslo, Norway.
5. Royal Netherlands Meteorological Institute (KNMI), Atmospheric Composition
Research, De Bilt, the Netherlands.
6. Frontier Research Center for Global Change, JAMSTEC, Yokohama, Japan.
7. University of Cambridge, Centre of Atmospheric Science, United Kingdom.
8. IIASA, International Institute for Applied Systems Analysis, Laxenburg, Austria.
9. Lawrence Livermore National Laboratory, Atmospheric Science Division,
Livermore, USA.
10. NASA-Goddard Institute for Space Studies, New York, USA.
11. Ecole Polytechnique Fédéral de Lausanne (EPFL), Switzerland.
12. Max Planck Institute for Chemistry, Mainz, Germany.
13. Met Office, Exeter, United Kingdom.
14. rdscientific, Newbury, UK.
15. NOAA GFDL, Princeton, NJ, USA.
16. Laboratoire des Sciences du Climat et de l'Environnement, Gif-sur-Yvette,
France.
1
17. National Center of Atmospheric Research, Atmospheric Chemistry Division,
Boulder, CO, USA.
18. Università L'Aquila, Dipartimento dUniversity of Oslo, Department of
Geosciences, Oslo, Norway.
19. Belgian Institute for Space Aeronomy, Brussels, Belgium.
20. Department of Earth System Science, University of California, Irvine, USA
21. Goddard Earth Science & Technology Center (GEST), Maryland, Washington,
DC, USA.
22. Stockholm Environment Institute, University of York, Heslington, England.
Abstract
1. Introduction
High concentrations of the air pollutant ozone (O3) are toxic to many parts of the Earth’s
biosphere, and can damage human health, crops and natural ecosystems. WHO [2003]
reported that exposure to high ozone levels is linked to respiratory problems, such as
asthma and inflammation of lung cells. Ozone may also aggravate chronic illnesses such
as emphysema and bronchitis and weaken the immune system. Eventually, ozone may
cause permanent lung damage. These effects can be aggravated in children and exercising
adults [USEPA, 1999]. Elevated ground level ozone reduces agricultural and commercial
forest yields, and increases plant vulnerability to disease, pests, insects, other pollutants
and harsh weather [USEPA, 1999; Aunan et al., 2000; Mauzerall and Wang, 2001;
Emberson et al., 2003; Wang and Mauzerall, 2004]. Model studies indicate that much of
the world’s population and food production areas are currently exposed to dangerously
high levels of ozone [West and Fiore, 2005], and that this situation is likely to
significantly worsen over the coming century [e.g., Prather et al., 2003]. Despite these
major concerns, there has been little research focused on quantifying the risks of future
ozone exposure of the global biosphere.
Ozone is formed when carbon monoxide (CO) and hydrocarbons are photo-oxidised in
the presence of nitrogen oxides and sunlight. Elevated surface ozone levels are therefore
2
closely linked to emissions of CO, NOx, CH4 and other hydrocarbons from the industrial,
power generation and transportation sectors. O3 has been considered to be an urban or
regional problem connected to local pollution and short-term episodes of peak ozone
concentrations. However, there are increasing concerns about transboundary and
intercontinental transport of air pollution [e.g., Berntsen et al., 1999; Bey et al., 2001; Li
et al., 2002]. The atmospheric lifetime of tropospheric O3 is long enough (1-2 weeks in
summer to 1-2 months in winter) to be transported from a polluted region in one
continent to another. Long-range transport can elevate the background level of ozone and
add to locally or regionally produced ozone, sometimes leading to persistent exceedance
of critical levels and air quality standards [e.g., Fiore et al., 2003]. Regional efforts to
reduce emissions of ozone precursors could be counteracted by non-regulated processes
on a global scale [e.g., Derwent et al., 2004]. Furthermore, recent epidemiological studies
have revealed damage to human health not only during high-concentration ozone
episodes, but also at much lower concentrations, even at typical present-day Northern
Hemisphere background levels (i.e. 30-40 ppbv)[WHO, 2003].
The number of peak-level ozone episodes is currently stable or decreasing in Europe and
the U.S. [USEPA, 2004, 2005; EMEP, 2004, 2005; Jonson et al., 2005]. However, in
2002 (or do you mean 2003, with the European heat-wave?) the EU threshold for
informing the public (90 ppb) was exceeded in 17 of the 27 reporting countries [EMEP,
2004 (reports on 2002; or 2005 reports on 2003)]. France, Spain and Italy regularly
reported hourly peak concentrations in excess of 120 ppb, levels which can cause serious
health problems and damage to plants (EMEP). The critical level for agricultural crops (is
this 40 ppbv? – based on AOT40 – or is it a flux (was exceeded in 2001) is regularly
exceeded at most EMEP-stations in central Europe. The critical level for forest was
exceeded in larger parts of central and Eastern Europe (EMEP). Simultaneously, there are
strong indications that Northern Hemispheric background ozone levels are increasing
[e.g., Staehelin et al., 1994; Simmonds et al., 2004], driven upwards by increases in
global emissions of ozone precursors, mainly centered on rapid development in Asia
(Martin: maybe a RETRO emissions reference?).
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Future surface ozone levels will be mainly controlled by the development of emissions of
ozone precursors. The implications of the IPCC SRES scenarios [Nakicenovic et al.,
2000] on future surface ozone levels were discussed by Prather et al. [2003] indicating
that surface ozone in the Northern Hemisphere was likely to increase by about 5 ppbv by
2030 (the range across all the SRES scenarios was 2-7 ppbv) and, under the most
pessimistic scenarios, by over 20 ppbv in 2100. Climate change will also contribute to
future surface ozone levels through chemistry-climate and emission-climate feedbacks
[e.g., Murazaki and Hess, 2005; Sanderson et al., 2003]. Changes in temperature and
water vapor will affect chemical conversion rates, and changes in global circulation
dynamics may affect transport, mixing and deposition rates, altering important processes
that govern the tropospheric distribution of ozone [e.g., Sudo et al., 2003; Zeng and Pyle,
2003; Stevenson et al., 2005].
This study is a part of a wider global model intercomparison ‘PHOTOCOMP-2030’
(Dentener et al, in preparation +submitted?, 2005; Stevenson et al accepted, van Nojie et
al in preparation, Shindell D. et al in preparation) coordinated under Integrated Activity 3
of ACCENT (‘Atmospheric Composition Change: the European NeTwork of
excellence’).
‘PHOTOCOMP-2030’ focuses on the global atmospheric environment
between 2000 and 2030 using over 20 different state-of the-art global atmospheric
chemistry models and three different emission scenarios. This paper presents surface
ozone results and discusses the development of ozone air quality between 2000 and 2030
using a range of current air quality (AQ) standards with respect to vegetation and human
health: AOT40 [Fuhrer et al., 1997], SUM06 [Mauzerall and Wang, 2001], W126
[Lefohn and Runeckles, 1987], the U.S. EPA [USEPA, 1997] and European [WHO,
2000] health standards as well as the recent WHO recommendation SOMO35 [UNECE,
2004]. This is the first study to comprehensively evaluate the possible development of
global ozone AQ indices.
In the following section, we describe the ozone air quality standards. In section 3, we
describe the model simulations that were carried out, before briefly describing some of
the participating models in section 4. In section 5 annual mean surface ozone levels are
4
discussed for all scenarios and surface ozone levels for 2000 are compared to
observations. We then consider results for the calculated AQ indices for all scenarios,
followed by a summary and conclusions in section 6.
2. O3 Air Quality indices
2.1. Vegetation Air Quality indices
Ozone concentrations show characteristic diurnal variations (which may differ between
e.g. urban, rural, coastal and mountainous locations) and variations over growing seasons,
which are related to local and regional climatic conditions. Given these variations, there
has been considerable discussion over the last two decades in North America and Europe
as to how to summarize the effects on crop yield and forest growth of seasonal ozone
exposure in a single index [e.g., Lefohn and Runeckles, 1987; Fuhrer et al., 1997;
Mauzerall and Wang, 2001]. Initial analyses of the economic impacts of ozone in the US
based on the NCLAN study in open-top chambers used 7h or 12h seasonal mean
concentrations to derive exposure-yield relationships for a range of crops, using nonlinear Weibull (what are these?) weighting functions. There is a considerable body of
evidence that ozone exposure indices should give greater weight to the higher ozone
concentration, with the rationale that the plant’s natural detoxification capacity would
negate the effect of lower concentrations. Within this study, three guidelines that account
for this phenomenon have been applied at the global scale to provide an indication of risk
resulting from ozone exposure to agriculture and forestry. They have been shown to
perform well in terms of explaining variation in growth and yield (i.e. AOT40, SUM06
and W126). It is stressed that, since these indices have been developed under US and
European conditions, for only a limited number of crop/forest species and crop cultivars
they should not be considered to provide definitive risk assessments, neither in their
region of origination nor in other regions of the world where different species and
cultivars, climate and pollution patterns and management regimes occur.
The three guideline indices used here are summarized in Table 1 and Figure 1 [Figure to
be included] For all three indices, the hourly values are accumulated over a defined
5
growing season to obtain the exposure index. The AOT40 index (accumulated ozone
concentration over a threshold of 40 ppb) is favored in Europe where analysis of
experimental data for crops and young trees led to the adoption of this index by the
UNECE (2004). A limit AOT40 value of 3 ppm.h (6000 µg/m3.h) for the protection of
sensitive crops calculated from May to July during daylight hours is recommended. For
forests the limit value is 5 ppm.h (10000 µg/m3.h) accumulated from April to September.
In the US, the most widely used indices for risk assessment include SUM06 and W126.
SUM06 only considers concentrations above 60ppb and then accumulates the total
concentration. The W126 index uses a continuous rather than a step-weighting function,
with a sigmoidal distribution, i.e. weights of 0.03, 0.11, 0.30, 0.61 and 0.84 at ozone
volume mixing ratios of 40, 50, 60, 70, and 80 ppbv, respectively [cut this and show it in
Figure 1?].. Hence, out of the indices discussed here, the AOT40 index implies the lowest
threshold for significant effects (strictly speaking, W126 does register non-zero values
below 40 ppbv) (Figure 1).
For all indices there are some difficulties in applying these guidelines on a global scale.
Perhaps the most problematic of these is the definition of the growing season over which
the index should be applied. The AOT40 index should be applied over a three month
period for agricultural crops and over a six month period for forest trees, respectively.
This complicates the application of the index in multi-cropping areas where a number of
different crops may be exposed sequentially to ozone episodes which could be causing
damage throughout the year; or e.g. an evergreen tropical forest. In this work we assume
a worst case scenario by estimating the maximum AOT40 over a consecutive three or six
month period as appropriate for the receptor. However, it should be noted that this
method will ignore risk to rotation crops grown outside this growth period that may still
be subject to substantial ozone exposures. Only daytime ozone contributes to AOT. It
should be noted that there is currently some debate both in the US and Europe as to
whether elevated ozone levels during the night-time can damage plants both due to
observed night-time stomatal opening (ref) in many species as well as to reduce
detoxification levels (ref). Therefore, in this work we use the SUM06 index calculated in
two ways: over 24 hours and over daylight-only hours (is that what you mean? Table 5
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definition indicates 24 hours, should be clarified there are 2 ways to calculate); W126 is
calculated over 24 hours. Finally, as discussed above, the ozone concentrations making
up the indices should be at canopy height, whereas effectively models provided ozone
concentrations at various heights. In this respect, the AQ indices resulting from this work
may be somewhat overestimated since above-canopy ozone concentrations are probably
higher than those at the canopy level.
In addition to problems connected with the spatial scale, there are additional uncertainties
related to the indices’ ability to represent actual risk for damage by ozone. They can
largely be attributed to issues in extrapolation of chamber based experimentally derived
dose-response relationships to field conditions. Key to these uncertainties are the
modified environmental conditions experienced in chambers, resulting in i)
environmental conditions that may enhance ozone uptake (e.g. via reduced atmospheric,
boundary layer and stomatal resistance to pollutant transfer) and ii) enhanced ozone
concentrations occurring under environmental conditions different to those that might be
expected in the field. To address this problem a new “flux-based” approach has recently
been adopted in Europe by the UNECE which characterizes ozone in terms of an
absorbed dose rather than an ambient concentration, hence incorporating key factors (e.g.
species, phenology and environmental conditions) that affect ozone uptake and
subsequently modify plant sensitivity to ambient ozone concentrations.
These new
insights have not yet been adopted for this work.
2.2. Health Air Quality indices
Currently, the European Ozone Directive 2002/3/EC EU requires Member States to
inform the public when hourly average ozone mixing ratios (concentrations) (this is a bit
pedantic, but I got asked to say mixing ratio rather than concentration when I used ppb in
the JGR paper) exceed the ‘information’ threshold (i.e. the threshold when the public
must be informed?) of 90 ppb (180 μg/m3), whereas an hourly average mixing ratio
(concentration) in excess of 120 ppb (240 μg/m3), measured over three consecutive hours,
is an ‘alert’ threshold.
As a long term objective, the European Ozone Directive has introduced, next to
information thresholds, a target value for the protection of human health, defined as a
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maximum daily eight-hour mean value of 60 ppb (120 µg/m3) not to be exceeded on
more than 25 days per year averaged over three years. In the following we will use the
abbreviation EU60 as an AQ index indicating the number of days per year exceeding the
eight-hour mean 60 ppb threshold.
The European target is in line with WHO guidelines [WHO 2000]: “a guideline value for
ambient air of 60 ppb (120 µg/m3) for a maximum period of 8 hours per day is
established as a level at which acute effects on public health are likely to be small”. It
was further considered that the 8-hour guideline would also protect against acute elevated
1-hour exposures.
The WHO/CLRTAP Task Force on Health Aspects of Air Pollution has recently
recommended a different metric for assessment of policy options [ESC-ECE, 2004],
SOMO35 (annual Sum Of daily maximum 8-h Means Over 35 ppb; Table 5). This
proposed exposure parameter is defined as the average excess of daily maximum eighthour means over a cut-off level of 35 ppb (70 μg/m3) calculated for all days in a year and
is based on the newest insights of epidemiological studies on health effects.
In the US, standards have been governed by the consecutive revisions of the Clean Air
Act. In 1997, EPA revised and strengthened the ozone NAAQS to change from a
standard measured over a 1-hour period (1-hour standard) to a standard measured over an
8-hour period (8-hour standard) [USEPA, 1997]. Previously, the 1-hour standard was 120
ppb. (see e.g. EPA, 2005)
http://www.epa.gov/ttn/naaqs/ozone/o3imp8hr/documents/finalrule/NFR_NSR.pdf).
If ozone levels from continuous monitoring averaged 120-125 ppb or more over one
hour, the threshold is exceeded. A region earned non-attainment status if it exceeded the
standard on three days over? three consecutive years. As a consequence, a county with
generally good air quality could be labeled a non-attainment area because of one hot
summer or some rare meteorological event that caused poor air quality for a brief time.
The new U.S. standard lowers the acceptable ozone level to 80 ppb. To smooth out rapid
variations, ozone concentrations are averaged over 8-hours. Whether or not the new
standard is met is determined by taking the fourth highest 8-hour ozone levels of each
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year for three consecutive years and averaging these three levels, equivalent to a
maximum value of 3 exceedence days per year. A region is designated as a nonattainment area when this three years average exceeds 85 ppbv (80 ppb in Table 5?). In
the following we will evaluate the latter US standard with index USEPA80, being the
number of days per year the eight-hour-average limit value of 80 ppbv is exceeded. In our
work, however, we generally only use one year to calculate the USEPA80 index.
Although all based on 8 hourly averages (in contrast to the 1 hourly averages for
vegetation) the three health indices focus on different aspects of trends and fluctuations in
O3 concentrations: SOMO35 accumulates ozone levels exceeding a ‘background’ level of
35 ppbv. Consequently, this index will be sensitive to regional scale changes in
background levels. On the other hand, EU60, and even more so USEPA80, are indicative
of high peak levels during O3 episodes.
3. Participating models
20 different global atmospheric chemistry models have participated in the comparison.
Four models were set up to run in different configurations giving a total of 25 model
configurations described in detail in Table A.1. Thirteen of these models were chemistrytransport models (CTMs) driven by meteorological analyses. Most models used analyses
from ECMWF (European Centre for Medium range Weather Forecasting). Twelve
models had an underlying global circulation model (GCM) driving the CTMs (eleven
different GCMs for twelve GCM-CTMs).
The horizontal resolution ranged from 10°x22.5° to 1.8°x1.8°, with one model using a
two-way nested grid of 1°x1° over Europe, North-America and Asia. The vertical
resolution is highly variable among the models and by altitude. The thickness of the
surface layer varies between 18 and 800 meters.
The surface ozone levels produced by the models are compared to observations in section
5.a. Comparison of free tropospheric ozone to ozone soundings by Stevenson et al
[2005], showed that the model ensemble mean agree well with the observations. A
comparison of modeled NO2 columns with the results of three GOME retrieved NO2
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columns will be given in van Noije et al, in preparation, 2005; nitrate deposition is
evaluated by Dentener et al., manuscript in preparation 2005.
4. Experimental setup
Five different simulations were performed (Table 1). S1 is the year 2000 reference
simulation, whereas simulations S2-S4 use the same meteorological data as S1 and three
different 2030 emission cases. The CTMs used meteorological data from year 2000, the
GCMs performed 5-10 year simulations with meteorological data for the 1990s.
Simulation S5 was performed by GCM-CTM models only, using the emission case of S2
and meteorology for the 2020s. All GCMs were configured as atmosphere-only models
with prescribed sea-surface temperature (SSTs) and sea-ice distributions. Most GCMs
used values from a simulation of HadCM3 (Hadley Centre Coupled Model, version 3
[Johns et al., 2003]) forced by the IS92a scenario [Leggett el al 1992; Cox et al 2000] for
the 2030 climate. Some GCMs used their own climate simulations. Spin-ups of at least 3
months were used for all experiments. Table 2 gives a summary of the simulations
performed by the individual models.
Gridded anthropogenic emissions on 1˚x1˚ of NOx, CO, NMHC, SO2 and NH3 were
specified. In order to reduce the spinning up time, global CH4 mixing ratios were
prescribed across the model domain (Table 3). The choice of CH4 values were based on
two earlier studies {Dentener, 2005 #1567; Stevenson, 2005 #1588}, together with IPCC
recommendations for SRES-A2 [IPCC, 2001- Table II.2.2].
The emissions for the reference year 2000 were based on recent inventories developed by
the International Institute for Applied System Analysis (IIASA). The global totals of the
future scenarios ‘Current Legislation’ (CLE) and ‘Maximum Feasible Reduction’ (MFR)
were developed by IIASA and distributed spatially according to EDGAR3.2 [Olivier et al
2001] as described in [Dentener et al 2005]. To evaluate a high emission case we also
used the IPCC SRES A2 (A2) scenario [Nakicenovic et al, 2000]. EDGAR3.2 ship
emissions for 1995 [Olivier et al 2001] were added to all scenarios, assuming a 1.5%
growth from 1995 to 2030. Satellite-derived monthly-varying biomass-burning 1˚x1˚
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gridded distributions [van der Werf et al, 2004] were specified, scaling NOx emissions to
those of CO. The values are averages for the period 1997-2002 and were used for all
future scenarios as we felt that any other assumption would be highly speculative. The
global totals for both the reference year and the future scenarios are reported in Table 3.
In addition, aircraft emission totals of 0.8 Tg N/year for the year 2000 and 1.7 Tg N/year
(all 2030 cases) and NASA {Isaksen, 1999 #1463} or ANCAT distributions [Henderson
et al 1999] were recommended. Recommendations given on natural emissions are
reported in Table 4.
Lightning and soil NOx emissions were requested to be
approximately 5 and 7 Tg (N)/year respectively. Modelers used values in the range of
3.7-7.0 Tg(N)/year for lightning and 5.5-8.0 Tg(N)/year for soil emissions. A total annual
emission of 512 Tg/year isoprene [Guenther et al, 1999] were recommended, values in
the range of 260-631 TgC/year were used.
The NMHC totals were recommended distributed among individual species using the
specification given by [Prather et al 2001, Table 4.7]. Species not included in the model
were either ignored or lumped into higher species.
Height distributions for biomass burning (boundaries at 0, 0.1, 0.5, 1.0, 2.0, 3.0 and 4.0
km) [van der Werf et al, 2004] and industrial emissions (height range 100-300 m) were
recommended. However, some models added emissions to the lowest model layer only,
whereas GMI, IASB, TM4, TM5 and OsloCTM2 used the recommendations for
industrial emissions. These models as well as GFDL-MOZ2, LLNL-IMPACT and
MOZECH also used the recommended height profile for biomass burning.
The modelers provided hourly O3 surface concentrations. This allows, for the first time, a
evaluation of the global distribution of various O3 air quality indices which are based on
the accumulation of hourly (or 8-hourly) averaged concentrations above a given threshold
(see section 5.3 below).
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This work focuses on the use of the mean of the ensemble, however also individual
model results were analysed. Individual model results were interpolated to 1°x1° and
averaged to give the model ensemble mean values. Use of an ensemble should improve
the robustness of model results, as individual model errors are likely to cancel, whereas
the real signal should reinforce [e.g., Cubasch et al., 2001]. The standard deviation of the
mean results gives an indication on the uncertainty. Stevenson et al (2005) tested the
robustness of the computation of an ensemble mean by removing “outliers” from the
model ensemble and found little influence on the mean. We assume the entire ensemble
to represent the most robust method to assess future levels of ozone and to quantitatively
assess uncertainties.
5. Model treatment of surface ozone
All models reported surface ozone values from the mid-point of the first layer. In stable
meteorological conditions the thickness of the model’s surface layer will strongly
influence the vertical ozone gradient; especially when simultaneous NO emissions from
surface sources are present. In this respect we note that probably the best reference level
for comparing model results with ozone measurements and for calculating AQ indices
would be around 5-10 m above the surface or at canopy level. Unfortunately, there is no
straight-forward method to correct ozone computed at the model’s mid-level to this
altitude.
6. Results
6.1. Annual mean surface ozone
Figure 1a shows ensemble means and standard deviations of annually-averaged surface
ozone. The model ensemble comprises all 25 models (Table 2). The calculated annual
average surface O3 varies between 40 and 50 ppb over industrialized parts of NorthAmerica, Southern Europe, and Asia, south of 40°N. The Northern Hemisphere average
12
is found to be 33.7±3.8 ppb. The Southern Hemisphere average is calculated to be
23.7±3.8 ppb, with background values of 15-25 ppb and somewhat higher values (35-40
ppb) in biomass burning areas in Africa and Latin America.
Figure 1b, 1c, 1d and 1e display the changes of annual-mean surface ozone for CLE (S2),
MFR (S3), SRES A2 (S4) and CLE2030c (S5), respectively. The CLE scenario (Figure
1b) would stabilize O3 approximately at its 2000 levels by the year 2030 in large parts of
North America, Europe and Asia. In areas where large increases in emissions from
transport and power generation are expected, we see an increase in surface O3 levels, e.g.
increases in the range of 5 ppb over South-East Asia and 10-15 ppb over the Indian subcontinent. Background O3 increases by 2-4 ppbv in the tropical and mid-latitude NH,
related to the interaction of the increasing concentrations of CH4 and the worldwide
increase of emissions of NOx, CO, and NMHCs.
The MFR scenario (Figure 1c) demonstrates the effects of maximal implementation and
use of currently available technologies to reduce emissions of ozone precursors. Ozone
surface levels are reduced by 5-10 ppb in industrial areas, e.g. in large parts of North
America, Southern Europe, the Middle East and Southern Asia.
As in earlier studies [Prather et al. 2001] the SRES A2 scenario (Figure 1d) leads to
worldwide increases in surface ozone levels with an average increase of 4.3 ppb. The
largest increases (5-20 ppb) are seen in Southern parts of USA, Latin-America, Africa
and South Asia.
The change in tropospheric ozone abundancies due to climate change was discussed in
Stevenson et al 2005. A slight decrease in tropospheric ozone was found in the lower
troposphere, whereas increased stratospheric influx of ozone lead to an increase in the
NH upper troposphere and reduced stratospheric influx lead to a decrease in SH the upper
troposphere. For surface ozone, the effect of climate change (Figure 1e) is a reduction of
0.5-2 ppb over the ocean. Continental surface ozone levels are reduced by up to 1 ppb in
remote regions. Increased temperature and water vapor leads to more OH and tend to
13
decrease ozone, especially over clean regions. Surface ozone levels over polluted areas in
the ensemble mean model are increased by up to 0.7 ppb.
6.2. Comparison to observed surface ozone levels for year 2000
Figure 2 compares observed and model ensemble monthly mean surface ozone levels in
selected regions [Figure xx] for year 2000. The model ensemble comprises all 25 models
(Table 2). Modelled surface ozone levels were averaged over the land area of the
respective regions (Table 7). The variation in the model ensemble mean is illustrated
including the model maximum and minimum values as well as standard deviations. The
observations are averages over data from several observational sites. The European data
are from EMEP (Co-operative programme for monitoring and evaluation of the longrange
transmissions
of
air
pollutants
in
Europe
(EMEP),
http://www.nilu.no/projects/ccc/emepdata.html ) and Airbase (European Topic Centre on
Air and Climate Change, Topic Centre of European Environment Agency, Airbase
dataset
through
AirView,
http://air-
climate.eionet.eu.int/databases/airbase/airview/index.html ), including 100 observational
sites for Central Europe and 22 stations for the Central Mediterranean. The US data
(Clean
Air
Status
and
Trends
Network
(CASTNET),
http://www.epa.gov/castnet/ozone.html) includes up to 13 different stations in each of the
three regions (South East USA, South West USA and Great Lakes). The Central-West
African data (Carmichael et al. 2003) includes 3 observational sites and the south-African
data (Zunckel et al. 2004) covers 6 observational sites. The Indian data (Lal et al. 2000,
Nair et al. 2002, Debaje et al. 2003, Naja and Lal 2002, Naja et al. 2003, Carmichael et
al. 2003) represent 6 (North-India) and 4 (South India) stations. Central-East Asian data
are
taken
from
Carmichael
et
al.,
2003
(3
sites).
Northern
China
(http://gaw.kishou.go.jp/wdcgg.html, Akimoto and Pochanart (personal communication,
2004), Wang and Mauzerall 2004, Carmichael et al., 2003) is represented by 8 sites and
the Middle East by a single station (Carmichael et al., 2003).
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The model ensemble is well representing the observed surface ozone levels in Northern
Europe, and the United States. The discrepancy is larger in other regions such as the
Middle East, Central West Africa and India.
{I would propose to
insert a figure with the
regions indicated by
rectangles,
for
the
clarity.RVD}
For
South-West
USA (30°N-40°N/125°W-110°W) the mean model closely resembles the observations
within one standard deviation, whereas the model ensemble overestimates the
observations by 5-15 ppb in South-East USA (25°N-35°N/90°W-80°W). The model
ensemble represents the winter time and early spring values in the Great Lakes area
(40°N-50°N/95°W-75°W) very well, but overestimates summertime ozone by about 10
ppbv.
In the Central Mediterranean (35°N-45°N/5E-30E), the model ensemble mean is <10
ppbv higher than the observed average. The model ensemble mean is within one standard
deviation of the observed monthly means, with except for the summertime maximum
which is overestimated by 15 ppb. For Central Europe (48°N-54°N/7°E-17°E) the mean
model closely resembles the winter and early spring observations, whereas the summer
and autumn observations are overestimated by about 6-13 ppb.
The model ensemble values for the African regions are generally too high. The Central
West African (5°N-15°N/5°W-15°E) model ensemble is about 15 ppbv too high in the
wet season and up to 20-30 ppb in the dry biomass burning season. The overestimation
15
of ozone in central Africa could be partly due to an overestimation of NOx biomass
burning emissions. Indeed Andreae (personal communication, 2005) has revised the
emission factor for NO emissions from savannah fires which was used in this study, with
approximately -30 %. In southern Africa, the model overestimation is 10-20 ppb with the
largest discrepancy around the SH spring ozone maximum; possibly the overestimation
of ozone is related to an underestimation of the NO2 column by a factor of 2-3 in this
region (Van Noije, in preparation, 2005); effectively leading to titration of ozone in high
NOx conditions.
The model ensemble fails to describe the observed seasonal cycle for the Middle East
(30°N-40°N/30°E-55°E), and generally overestimates ozone by up to 25 ppb. It should be
noted however that the Middle East is represented by a single observational site at high
altitude (Camkoru, Turkey, 1350 m asl), whereas the model ensemble regional mean is
dominated by high emissions related to oil-activity. For the Indian sub-continent (North
India, 20N-30N/70E-90E, and South India, 10N-20N/75N-85N) the mean model
ensemble are generally 15-20 ppb higher than the observational average. The discrepancy
is highest (25 ppb) for the summer hemisphere in South-India. The observational average
includes three coastal stations, which may have a higher content of clean air from the
ocean compared to the regional average. The South-East Asian (20N-35N/110E-125E)
model values are generally within one standard deviation of the observational average
with exception for the monsoon season (June to August) where the model overestimates
ozone by 20-25 ppb compared to the observations of one single station available for that
period. The strong decrease in measured surface ozone levels during the monsoon
transports ozone poor air into the region is a feature which is not resolved by the models.
Finally, for Northern China and Japan (35N-45N/110E-145E), the model ensemble mean
represents the observational average well being within one standard deviation. The model
ensemble mean generally underestimates the observational average by 5-7 ppb, but
overestimates ozone in July and August by 7 ppb.
16
We will further discuss possible reasons for the discrepancies and consequences for this
work in section xx.
5.3. O3 Air Quality indices
The 18 model configurations which provided hourly surface ozone values are shown in
bold face in Table 2. AQ indices defined in Table 6 and 7 were calculated based on local
time values. We focus on the analysis of the AQ indices for 14 selected regions given in
Table 7 or Figure xx. These regions were chosen as a convolution of regions with high
population density, expected high surface ozone and NO2 columns. The regional values
are calculated as land-only averages.
5.3.1 Health Air Quality indices
S1, Year 2000
Elevated values for SOMO35, EU60 and USEPA80 are found in industrialized areas, e.g.
Southern USA, Southern Europe and South-East Asia due to emissions of ozone
precursors from transport, industry and power generation exposing the population to
health risk. Since the three indices are based on different thresholds, a somewhat different
geographical pattern shows for the three indices. SOMO35 has high values in the
southern parts of USA, Southern Europe, Central-West Africa and southern parts of the
Asian continent. The EU and USEPA80 indices display high values in the same areas but
with larger variations/gradients. The white line in Figure 3a and 3b shows the EU60 and
USEPA80 thresholds for health risk, 25 days exceeding 60, and 3 days 80 ppb
respectively. Figure 3b-c shows that the EU60 ozone standard is exceeded over larger
areas than the USEPA80 standard, e.g. the EU60 threshold for health risk is exceeded in
central Europe, whereas the USEPA80 index is within recommended thresholds.
As illustrated in Figure 5a, the EU60 and USEPA80 limit values are surpassed in most of
the 14 selected regions. According to our ensemble model calculations, the EU60 index
is exceeded in South-Eastern USA during 105±58 days and 19±18 days for the USEPA80
17
limit. Other regions at risk are southern-India (89±58 days for EU60 and 9±22 for
EPA80) and the Mediterranean (75±46 days for EU60 and 12±18 days for USEPA80).
Of the 14 selected regions, only Australia has EU60 and USEPA80 indices below the
recommended thresholds. The southern hemisphere average is 17±8 days (EU60) and
5±4 days (USEPA). The northern-hemisphere values are 32±16 days (EU60) and 4±3
days (USEPA). The higher SH USEPA80 index reflects that we find the highest most
values in Africa and South-America due to biomass burning (Figure 3c).
The highest SOMO35 index of 6822 ppb.days is found in south-eastern USA. The central
Mediterranean, North-India, southern-India and the Middle-East all have values
exceeding 5000 ppb.days. Relatively, high values are also found in S.W. USA, S.E. Asia
and Northern China. The global average is 2619±910 ppb.days, the SH average is
1446±589 ppb.days and the NH average is 3172±1125 ppb.days. Australia and LatinAmerica are the only regions with SOMO35 values lower than 2500 ppb.days (Figure
5a). Australia and Latin-America are the only analysed regions with SOMO35 values
lower than 2500 ppb.days (Figure 5a).
Figure 6 shows the correlation of SOMO35 with EU60 and EPA80 based on the
regionally averaged of air quality indices in Figure 5. SOMO35 and EU60 are well
correlated for the regions considered (R² = 0.95). Although for SOMO35 no limit level is
established, the good correlation leads to a correspondence of the EU60 threshold of 25
days with SOMO35 ~2500 ppb.days, whereas the number of days exceeding 60 ppb
becomes zero at a SOMO35 value of 900 ppb.days. The correlation between SOMO35
and EPA80 is less strong (R² = 0.56); but we tentatively estimate that the 3 day threshold
of EPA80 corresponds roughly with SOMO35~2000-2500 ppb.days.
As most of the 14 regions are found not to be in compliance with the limit values of
EU60 (25 days), EPA80 (3 days) or SOMO35 (≈2500 ppb days), we explored to which
extent these regions would be compliant with somewhat less stringent regulations. Figure
5 indicates where limit values are exceeded by a factor of 3 (i.e. EU60>75 days,
18
EPA80>9 days, and SOMO35>5500 ppb.days, the latter value being consistent with the
EU60 limit). Two regions would be exposed to ozone surpassing the 3xSOMO35 limit,
whereas 5 and 7 regions would be non-conform with respect to 3xEU60 and 3xUSEPA80
limits respectively.
The standard deviation for the computed EU60 and especially USEPA80 are much larger
than for SOMO35 (Figure 5a). This reflects the higher consistency amongst the models in
prediction of average ozone levels exceeding background values and the larger
uncertainty connected with prediction of peak ozone levels. Therefore SOMO35 seems to
be the most robust indictor for ozone air quality for our ensemble of global model
calculations.
Ozone health indices in 2030: scenario S2-S4
Figure 5b-d give the changes in the three air quality indices for cases S2 to S4 compared
to S1 for the 14 selected regions.
S2, Current Legislation (CLE) scenario
CLE leads to a general increase in the computed health indices, with highest increases on
the northern hemisphere and in particular over the Indian sub-continent. The SOMO35
index increases with 23% on the NH and with 15% on the SH. The increase over India is
60% or approximately 3400 additional ppb.days.
The increase of background ozone (as indicated by SOMO35) is in general paired with
increased peak ozone values (indicated by EU60 and USEPA80). Averaged over the
continental northern hemisphere, the EU60 index augments with 47 % while the southern
hemisphere is subject to a smaller change of 12%. The change is not uniformly
distributed: we find the largest increase in 2030 over India with approximately 100
additional days of exceedence, a doubling compared to S1, which means that EU60 is
exceeded during 215 and 180 days in North and South India, respectively. Opposed to the
19
general trend, there is a small decrease over the central Mediterranean (-3%) and central
Europe (-15%) giving Central European values below the threshold for health risk.
Considering the USEPA80 index, we find again dramatic increases over the Indian subcontinent, amounting to 320% and 400% over the northern- and southern India
respectively giving a total of 34 and 27 days of excess. European values are decreased,
giving 9 days of exceedence for the Central Mediterranean and central European values
below the threshold for health risk.
Europe is an interesting policy relevant case: according to the conventional EU60 and
USEPA80 indices, air quality is improved in the CLE scenario, explained by imposed
reductions in anthropogenic emissions. However, the European SOMO35 index is found
to increase, caused by a continued increase in European background ozone values due to
the increased global emissions of ozone precursors and intercontinental transport. This is
contradictory to the general SOMO35 – EU60 correlation established before, showing
that the change of indices is not necessarily coupled in all cases.
Following our previous analysis, in 2030 CLE the “3x threshold” limits are exceeded in
7; 6 and 9 regions, for SOMO35, EU60 and EPA80, respectively (Figure 5). The
relatively “clean” regions are mostly limited to the extra-tropics, where photochemistry is
less intense. In the analysis above we have to consider the potentially large discrepancy
with measurements which will be further discussed in section ??
S3, maximum feasible reduction (MFR) scenario
The MFR scenario leads to a worldwide improvement of ozone air pollution. The global
distribution for SOMO35 and EU60 are shown in Figure 4. Regional averages are
represented in Figure 5c. The largest reductions appear in polluted areas, i.e. southern
parts of USA, southern Europe, the Middle East, India and South-east Asia in response to
the reduced anthropogenic emissions of ozone precursors. Reductions in SOMO35 are
more than 40% over S.W. and S.E. USA, Central Mediterranean, the Middle East and
S.E. Asia. SOMO35 in the most polluted regions is generally not exceeding 4000
20
ppb.days. The highest values are found over Northern-India (4541 ppb.days) and S.E.
USA (3639 ppb.days). Also for the EU60 and USEPA80 indices (not shown), there is a
clear reduction in the area surpassing the recommended threshold of health risk. Figure 5
shows a reduction in all of the 14 averaged regions. The reduction in the USEPA80 index
is largest for the North-American continent, S.E. Asia and North-China. These regions
also display the largest reductions in EU60, along with the European regions and the
Middle East.
Figure 5 also shows that ozone in most regions is now below the respective limit levels.
Exceptions are the South-Eastern USA where the EU60 and USEPA80 are exceeded by
25 and 0 days respectively, and Central Europe where we find 3 (EU60) and 0 (EPA80)
days of exceedance. Also over the Indian subcontinent the EU60 and EPA80 health risk
thresholds remain exceeded by 31 (EU60) and 5 (USEPA) days for South-India and 48
(EU60) and 7 (USEPA) for Northern-India. In Central-West Africa during 41 (EU60) and
12 (USEPA) days the thresholds are exceeded. We note however, that in our study we did
not assume that biomass burning emissions would decrease. Thus in general, a reduction
of emissions as indicated by the MFR scenario has a positive effect on future surface
ozone levels; the general compliance to all three health indices clearly demonstrates the
scope of this emission reduction scenario to reduce undesirable health effects.
S4, IPCC SRES A2 scenario
The SRES A2 scenario health indices increase the health indices worldwide and
especially over industrialized regions (Figure 4 and 5). The area with elevated values is
extending into larger parts of northern Europe, northern Asia and Africa. Both
background ozone and peak ozone episodes increase, but with a relatively stronger
increase in peak ozone episodes, giving NH values of 5548 ppb.days for SOMO35 (75 %
increase), 81 days for EU60 (153% increase) and 16 days for USEPA (300% increase). In
the Indian subcontinent we find air quality indices higher than anywhere else in the
world: 12797 ppb.days (SOMO35), 177 days (EU60) and 103 days of exceedance
(USEPA80) for Northern India. Very high values are also found over North America, e.g.
9434 ppb.days (SOMO35), 164 days of exc. (EU60) and 48 days of exc. (USEPA) over
S.E. USA. Also in the Central Mediterranean, the Middle East and S.E. Asia we find
21
high values, i.e. SOMO35 values of approximately 8000 ppb.days, EU60 values around
100 days of exc. and USEPA values surpassing 30 days of exc. Figure 5 demonstrates the
large increase in peak ozone episodes (EU60 and USEPA80 indices) compared to the
increase in background ozone (SOMO35), due to the impact of large increases in regional
emission?
In particular, the increase in number of days exceeding 80 ppb (USEPA index) over the
Indian sub-continent and the Middle East is prominent. The large increase in the number
of days exceeding 60 ppbv over Australia is probably due to transport of pollution from
the southern part of Asia. The increase in SOMO35 is largest over Latin-America, the
African regions and India. The EU60 and USEPA thresholds for health risk are largely
surpassed in all regions and on a global average, demonstrating an alarming development
if emissions are augmented as anticipated in the A2 scenario.
Vegetation indices
S1, Year 2000
Figure 6 shows the model ensemble mean AOT40 considering a 3 months (AOT403m)
and a 6 months (AOT406m) growing season for crops and forests respectively), SUM06
accumulated both over 24 hours (SUM0624h) and daylight hours only (SUM06day), and
W126 indices for the baseline scenario (S1). Figure 8 gives the ensemble mean weighted
averages and standard deviations of these indices for the 14 selected regions.
As for the health indices, ‘hot spots’ with elevated values in the vegetation indices are
found in industrialized areas of Europe, the US and Asia as well as in biomass burning
areas in Latin-America and Africa. Note, that the Latin-American and African regions in
Figure 7, do not include these biomass burning areas. The 5 vegetation indices in the 14
selected regions show high mutual correlation coefficients, between R2 = 0.85 (AOT406m
vs. SUMO624h) and R2 = 0.99 (SUM0624h vs. W126). The AOT40 ensemble means show
lower standard deviations than SUM06 and W126, confirming the higher consistency
amongst the models when predicting exceedance of background ozone levels compared
to exceedance of high ozone levels.
22
AOT403m indices larger than 20000 ppb.h are found over the Central Mediterranean,
South-East USA, the Middle East and North-India. The lowest values are found over
Australia and Latin-America. Only in Australia AOT403m is below the threshold for yield
reduction (3000 ppb.h) of agricultural crops. When we calculate the same AOT403m
solely based on the set of observations given in section xxx, we calculate for Central
Europe and the Central Mediterranean AOT40 of 9350±2830 ppb.h and 12970±6530
ppb.h, respectively (ref EMEP). The corresponding model ensemble values of
10285±5893 ppb.h are consistent for Central Europe .However, the computed
23230±10680 ppb.h for the Central Mediterranean is a factor of two higher. The larger
model ensemble values for the Central Mediterranean can partially be explained by the
fact that the growing season is defined as the 3 consecutive months with highest AOT40
index. Using this definition of growing season produces ‘worst case scenario’ values,
however for the European data the difference between the two approaches is never larger
than 15%. (Don’t understand fd) Furthermore, the surface ozone summer maximum is
overestimated by the model ensemble mean (Figure 2).
For AOT406m (6 months) values are exceeding 30000 ppb.h in the US regions, the
Central Mediterranean and the Middle East. Of the 14 regions, only Australia does not
exceed the threshold for reduction of forest growth (5000 ppb.h.).
The SUM06 indices exceed 50000 ppb.h over South-Eastern USA, the Central
Mediterranean, North-India and the Middle East and have the lowest value over
Australia. Night time ozone levels have only a minor contribution to SUM0624h.
SUM06day values increase with less than 3%, except in Latin America (13%), CentralWest (9%) and Southern (8%) Africa, North (4.6%) and Southern (7.2%) India and a
remarkable 75% increase in Australia, the latter on top of a very low SUM06day value.
As opposed to AOT40 and SUM06, the W126 index uses a continuous rather than a
discontinuous weighting function (Figure 1). However, W126 correlates very well with
AOT403m and SUM06day. The highest W126 values are found in the Central
Mediterranean, South-East US, India and the Middle East (exceeding 40000 ppb.h)
23
whereas the lowest-most values are found over Australia, Latin-America and Central
Europe.
Limit levels have not been established for the SUM06 and W126 indices, but empirical
(non-linear) exposure-yield response equations are available for SUM06day and W126
(Wang and Mauzerall, 2004). From these relations, we obtain a 5% yield loss ‘limit
values’ of 13000 ppb.hr and 9000 ppb.hr for SUM06day and W126 respectively. In the S1
case, in all regions except Australia these limit levels are exceeded.
KE: SJEKK W&M SUM06day ???????
KE: How do we treat the model overestimation ? Do we consider 3xthreshold as for
health? This would be based on 5 % damage of wheat; 3x13000 and 3x9000????
[I’m not sure if it makes sense to apply a 3x less stringent level analogous to the health
indices as the dose-response curves are not linear for W126 and SUM06, but linear for
AOT40; it would make more sense to look at levels corresponding to yield reduction of
10% or 15%]
KE: What would the corresponding 15 % levels be?
It is probably defendable that at 15 % levels- there is at least some damage due to ozone.
RITA for you to answer!
Vegetation indices in 2030: scenario S2-S4
Figure 7 illustrates the changes in the vegetation AQ indices over the relevant areas
(shown are AOT403m and SUM06day for crops, AOT406m for forests). Figure 8b-d give
the changes of all vegetation air quality indices for CLE, MFR and A2 compared to S1
for 14 selected regions. General trends and geographical distributions are similar to the
health AQ indices; hence we limit the discussion to some salient features.
S2, current legislation (CLE) scenario
24
As expected, CLE leads to a general increase in the vegetation indices in the year 2030.
The increase over the northern hemisphere for the vegetation indices ranges between 21 –
38%. On the Indian subcontinent, where no restrictive measures on emissions of O3
precursors are implemented, the AOT40 indices increase 50-63%, whereas the SUM06
and W126 indices increase with 75-97%. This indicates a larger increase in episodic peak
levels compared to the increase in O3 background levels in this region. Large increases
are found also in South-East Asia where increases in emissions from transport and power
generation are anticipated, i.e. 20-30 % for AOT40 and 30-40 % for SUM06 and W126.
Increases of the same order are also found over the African regions due to increased
biomass burning. Despite the implementation of policies for air quality improvement, the
selected US regions show an increase between 20 and 30% for SUM06 and W126 (i.e.
the US favoured indices) and between 12 and 16% for AOT40. In contrast, currently
implemented European legislation stabilize or slightly decrease the vegetation indices
over Europe by 2030. In particular the Mediterranean area is the only region out of the 14
selected ones where all indices show a decreasing trend. In agreement with the findings
for the health AQ indices, we find larger reductions over Europe for indices based on
higher threshold values (SUM06 and W126) than for the lower threshold AOT40 indices.
S3, maximum feasible reduction (MFR) scenario
The MFR scenario leads to a worldwide decrease or stabilization of the vegetation
indices. The reductions appear in polluted areas with the largest reductions in southern
parts of USA, Europe, the Middle East, N. China and S.E. Asia, i.e. AOT40 decrease by
50-70 %, SUM06 and W126 by 70 -200 % in these regions
The Indian sub-continent shows the largest reductions, however, the highest absolute
values are still found over India (e.g. in North-India AOT403m is 12757 ppb.h, W126 is
23784 ppb.h and SUM06day is 22642 ppb.h.) The analysis of health indices demonstrated
how the MFR scenario reduced the health indices below critical levels in polluted
regions. Although strongly reduced, the MFR emission reductions do not bring AOT40
values below the threshold for yield reduction of agricultural crops (3000 ppb.h) or forest
growth (5000 ppb.h). In the US regions and C. Mediterranean, W126 and SUM06 values
are in the range 10000-15000 ppb.h close to the 5% reduction limit (9000 and 13000
25
ppb.h). In Central Europe and South-East Asia the values are well below the 5%
reduction limit, i.e. SUM06 in the range of 3500-4000 ppb.h and W126 in the range of
6000-7000 ppb.h. The differences between Northern and Southern Hemisphere averages
are strongly reduced, caused by large reductions on the Northern Hemisphere and a
stabilization on the Southern Hemisphere. e.g. SUM06day is 5980 ppb.h on the Northern
Hemisphere and 5652 on the Southern Hemisphere
S4, IPCC SRES A2 scenario
As for the health indices the SRES A2 scenario leads to large worldwide increases in the
vegetation indices and in particular over industrialized regions (Figure 6 and 7). The
largest increases are found in South-East Asia and India. where AOT40 increases by 80100 %, SUM06 and W126 by 90-160 %. Very high levels are found over India, the
Middle East, S.E. Asia, USA, C. Med. and N. China/Japan, e.g AOT40 values in USA
and the Central Mediterranean exceed 40000 ppb.h, SUM06 and W126 are in the range
60-95000 ppb.h.
5. Discussion
5.1 Can we understand the discrepancy of models and measurements?
The description of emissions, chemical and meteorological processes (e.g. deposition and
mixing in the boundary layer) will affect the model ensemble results.
We showed in section 5.2 that in North America, Europe and Northern China computed
monthly average surface ozone concentrations are relatively well in agreement by
measurements. However, in other regions such as Central West Africa, Southern Africa,
the Middle East, North and South India all models systematically showed overestimates
of the limited amount of measurements to our disposal. Several possible reasons may
explain these discrepancies.
1. Incorrect emissions of NOx, and/or NMVOC
2. Incorrect description of meteorological processes in the models
3. Incorrect description of chemical processes in the models
26
4. Inaccuracies of measurements and non-representativity of these measurements for
the large scale model calculations.
In our study we used estimates by IIASA for the year 2000 for anthropogenic NOx,
NMVOC and CO emissions [Cofala et al., 2005]. Whereas relatively much knowledge
on emissions and technology levels is available in Europe and North America, the
uncertainties are much larger in other continents. Especially the emissions from biofuels,
which are important fuel types in Asia and Africa, are poorly quantified. Nevertheless,
analysis of GOME satellite data [van Noije et al., in preparation, 2005] indicates a too
low NOx column (by a factor of two) over Southern Africa, and relatively good
agreement over India [TWAN VAN NOIJE CHECK]. Also the analysis of nitrate wet
deposition reveals no systematic deviations over Africa and India [Dentener et al., in
preparation, 2005]. The open-fire NOx emissions were calculated from the GFED [Van
der Werf et al., 2003] database using the emission factors of [Andreae and Merlot, 2001].
A recent revision of these emission factors revealed an approximately 30 % lower
emission factor for NO emissions from grass-land (savannah) fires. Sensitivity
calculations with the TM4 model [Van Noije et al.., in preparation, 2005] show that the
use of these lower emission factors would lead to lower mixing ratios by 10-15 % and 610% in Central West Africa and Southern African dry-season, respectively, explaining a
small part of the discrepancy.
An incorrect description of meteorological processes in the models could also explain
some discrepancies. Most models are driven by meteorological parent models that are
better tested and constrained in middle latitudes than in tropical regions. For instance in
South East Asia it important to have a correct description of the monsoon circulation,
which can not done in the coarse resolution models. It is notoriously difficult to give a
correct description of mixing by turbulent and convective mixing [Dentener et al., 1999;
Jacob et al., 1997]; the effect of different mixing parameterizations on surface ozone is
not easy to predict.
The variation in horizontal resolution and the thickness of the surface layers from model
to model will also lead to variations in the model ensemble. Higher resolution models
27
tend to produce less ozone from the same levels of precursor emissions, due to less forced
‘mixing’ when emissions are added to large grid boxes [e.g., Esler, 2003].
Dry deposition schemes in models are generally based the well-known [Wesely, 1989]
scheme; however the over-all effect on e.g. ozone removal and vertical ozone profiles in
the model surface layer is strongly dependent on the assumptions on surface properties,
boundary layer turbulence and surface layer thickness [Ganzeveld and Lelieveld, 1995].
Indeed our preliminary analysis of ozone deposition distributions from the models
suggest that the schemes generate quite variable deposition velocities over different
terrains [Stevenson et al., 2005].
All models included NMVOC degradation chemistry schemes in various degrees of
detail; Stevenson et al.. [Stevenson et al., 2005] show that the gross tropospheric ozone
production terms is rather similar between models. However, the description of
heterogeneous chemistry is in most models relatively simplified. Earlier studies
[Dentener et al., 1996.; Grassian, 2001; Jacob, 2000] suggested an important role for
ozone destruction on mineral aerosol; however there is no consensus on the overall
impact. The atmosphere in e.g. Southern Africa, India and China in the dry season can be
loaded with dust and pollution aerosol- and if heterogeneous chemistry is important these
are place were the impact is likely to be largest.
Finally, inaccuracies of measurements and non-representativity of these measurements
for large scale model calculations may also contribute to discrepancies of models and
measurements. Whereas there are a substantial amount of measurements used for North
America and Europe; the number of measurements in other regions is rather small; and
they are not necessarily representative for the whole region. E.g. for the Middle East we
used only one measurement taken in Turkey. This can however not explain the systematic
overestimate of the models in other regions. A significant fraction of the measurements in
Africa and Asia was taken from the passive sampler network of [Carmichael et al.,
2003]. There have been discussions in the past about the accuracy of passive samplers to
measure O3 at high concentration levels, and possible interference with humidity.
28
Comparison with in-situ analyzers on a limited amount of locations did however not
reveal systematic differences.
Another important issue is the fact that we compare the ozone concentrations of models
averaged for their lowest gridbox; which may be not representative for the O3
concentrations at measurement height (often at 10 m). Especially at polluted places
relatively much O3 is titrated by fresh NO emissions in first 10-20 meters above the
ground. An indication for the importance of this effect is the much better agreement of
‘well mixed’ afternoon ozone concentrations on sites in China and India (e.g. analysis
with the TM5 and FRSG model).
Since the air quality indices assess elevated
concentrations we expect that these can be better represented by models than 24 hours
averaged concentrations. For future model studies, as well as for measurement, we
strongly recommend to also evaluate Ox=NO2+O3; which parameter should be less
dependent on local conditions.
RITA can we give one or two examples; e.g. based on Chinese measurements?
Therefore we think that even in regions with apparent discrepancies of models and
measurements we are able to do a meaningful analysis for exceedances of air quality and
vegetation indices. To give further credibility to our analysis of regions at risk for health
and vegetation damage by ozone, we have further analyzed our results in terms regions
where the thresholds are exceeded by a factor of 3. The results were relatively consistent
for the three different AQ indices.
5.2 Robustness of AQ indices
We showed before the interesting correspondence of the health related AQ indices
SOMO35; EU60 and EPA80.
Then discuss the correlation between AOT40; SUM06; W126. KE: Is discussed in the
Veg. indices part as for health.
How is SOMO35 correlated with the vegetation indices?
It is worth noting that the vegetation indices show a good correlation with the health
indices SOMO35 and EU60 (R2 > 0.80 for all mutual correlations between the latter two
health indices and all vegetation indices, except for AOT406m vs. EU60 where R2 = 0.69),
29
whereas the USEPA80 index has an R2 < 0.6 with any of the vegetation indices. More in
particular, the European indices AOT403m and EU60 have a R2 = 0.84 and are related
through AOT403m = 1300xEU600.62. This implies that the limit levels of these indices are
in fact not consistent with each other: 25 allowed exceedence days under EU60
corresponds with AOT403m near 10000 ppb.hr, more than 3 times the limit level for the
latter index.
KE: It is not very clear to me what we try to say here. Is it a big point that the limit levels
are not consistent between health and veg. indices ? As the receptors are so different
(humans and plants) the limits are expected to be different. The limit values are worked
out using completely different considerations (exposure-effect relations). What we see is
that vegetation is damaged at lower ozone levels that human health. Is the point to see if
there is an index appropriate to human health and veg ?
6. Summary and conclusions
We compute current and future ozone air quality indices for health and vegetation using a
xx member ensemble of models. The models are driven by 3 different emission scenarios
for the year 2030. The first (CLE) reflects worldwide currently decided air quality
legislation, whilst the second (MFR) represents an optimistic case assuming that that all
technology currently available would be used to achieve all possible emissions
reductions. It however does not consider progressive energy use scenarios, or for instance
a substantial fuel-shift towards hydrogen, which would have similar effects on emissions
as MFR. We contrast these scenarios with the pessimistic IPCC SRES A2 scenario. The
three scenarios reflect current insight regarding the possible range of future air pollution
and emission developments. This study represents, to our knowledge, the most extensive
evaluation of surface ozone calculated by global chemistry transport models. Comparison
with a host of measurements in North America, Europe, and a limited amount of
measurements in Northern China are in relatively good agreement. However, large
30
overestimates of computed ozone are found for almost all models in Africa, the Middle
East and India. Problems with emissions, chemical and meteorological descriptions of the
coarse resolution global models may contribute to the discrepancy, but we can not
exclude that the few measurements available in these regions were not representative for
the larger model scales. Until these measurements become available model improvement
and testing of reasons for discrepancies is precluded.
To evaluate current and future ozone air quality xx models generated hourly surface
ozone fields; since typically ozone AQ indices are derived from the correlation of results
of epidemiological studies with measured hourly surface ozone concentrations [WHO,
2003]. We evaluated three health related AQ indices, the recently recommended
SOMO35, evaluating ozone concentrations above a threshold of 35 ppbv; EU60 using a
threshold of 60 ppbv; and the USEPA80 evaluating the exceedance of 80 ppbv.
Correlation EU60 and EPA80 with SOMO35 shows that a SOMO35 threshold of ca.
2000-2500 ppb days is consistent with 25 days of exceedance of EU60 and 3 days of
exceedance of EPA80. The variation of SOMO35 calculated by the individual model
ensemble members is substantial less than for EU60 and EPA80. Thus SOMO35 is
probably the most robust AQ index when evaluating results from individual models. This
result is probably independent from the models’ ability to accurately calculate worldwide
surface ozone. In which regions is the population currently and in future at risk to ozone
related health problems? Air quality standards in the European Union and in the United
States state that the EU60 and EPA80 should not be exceeded by 25 and 3 days,
respectively. According to our calculations presently in 10/12 out 14 selected regions the
standards are exceeded for SOMO35, EU60 and EPA80. Even if we choose less stringent
criteria of 6000 ppb days, 75 and 9 days for SOMO35, EU60 and EPA80, respectively, 5
to 6 out of 12 world regions are above the limits.
The currently decided legislation is not enough change this situation; only in Europe the
ozone air quality somewhat improves; but regional emission reductions measures are
counteracted by an increase in global background O3. Large increase of ozone and in
31
exceedance of air quality standards are predicted for India and too a lesser extent for all
world regions, 6-7 world regions will not conform to any O3 AQ standard.
In contrast, we show that implementation of all currently possible technical measures can
dramatically improve O3 AQ; using the MFR scenario almost all regions comply with
currently regulations on Air quality.
We show that considering the pessimistic SRES A2 scenario leads to a dramatic
detoriation of global airquality in almost all polluted regions of the world.
HERE SOME CONCLUSION FROM THE VEGETATION WORK
Closing sentence.
Acknowledgements:
We thank Kobus Pienaar and Owen Pretorius (Sasol) for kindly providing their data.
Ackn.:Unpublished data were kindly provided by H. Akimoto and P. Pochanart
We thank Prof. Syam Lal for kindly providing us their ozone data.
Ackn.:Unpublished data were kindly provided by H. Akimoto and P. Pochanart
Data for XX and XX provided by mr Owen Pretorius of Sasol"
Greg Carmichael; Indian and Japanese groups.
Kobus Pienaar for discussions.
32
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37
Tables
Code Scenario
Meteorology
Emissions
S1
Y2000
2000 (CTMs)
2000
1995-2004 (GCMs) (EDGAR3.2)
Olivier et al. 2001
S2
CLE
2000 (CTMs)
2030 IIASA CLE
1995-2004 (GCMs)
Dentener et al. 2004
S3
MFR
2000 (CTMs)
2030 IIASA MFR
1995-2004 (GCMs)
Dentener et al. 2004
S4
A2
2000 (CTMs)
2030 SRES A2
1995-2004 (GCMs)
Nakicenovic et al. 2000
S5
CLE-2030c 2025-2034 (GCMs) 2030 IIASA CLE
Table 1
Reference
Stevenson et al. 2005
Specifications of the simulations.
38
CHASER_CTM
CHASER_GCM
FRSGC_UCI
GEOS-CHEM
GISS
GMI/CCM
GMI/DAO
GMI/GIS
IASB
LLNL-IMPACT
LMDz/INCA (CTM)
LMDz/INCAc (GCM)
MATCH-MPIC-ECMWF
MATCH-MPIC-NCEP
MOZ2-GFDL
MOZART4 (NCAR)
MOZECH
p-TOMCAT
STOCHEM-HadAM3
STOCHEM-HadGEM
TM4
TM5
OsloCTM2
ULAQ
UM_CAM
S1
S2
S3
S4
S5c
1
9
1
1
5
1
1
1
1
1
1
5
1
1
2
2
5
1
1(10)
5
1
1
1
10
1(10)
1
9
1
1
5
1
1
1
1
1
1
5
1
1
2
2
5
1
1(6)
5
1
1
1
10
1
1
-1
1
5
1
1
1
1
1
1
-1
1
2
2
-1
1
5
1
-1
10
1
1
-1
1
5
1
1
1
1
1
1
-1
1
2
2
-1
1
5
1
-1
10
1
-9
--5
------5
---2
5
-10
5
---10
10
Table 2
Number of simulated years (excluding spin-up) performed by individual models. Two
models (STOCHEM_HadAM3 and UM_CAM) performed multi-annual simulations to
compare with S5c, as well as single year simulations to compare with S3 and S4. Models
shown in bold face provided hourly surface ozone concentrations for calculation of ozone
indices.
39
NOx (Tg N)
CO (Tg)
NMVOC (Tg)
SO2 (Tg S)
NH3 (Tg N)
CH4 (ppbv)
Table 3
S1
S2
S3
S4
S5c
124.8
977
147.1
111.1
64.8
1760
141.1
904.1
145.5
117.6
84.8
2088
76.0
728.7
104.4
35.8
84.8
1760
206.7
1268.2
206.7
202.3
89.2
2163
141.1
904.1
145.5
117.6
84.8
2012
Specified global annual anthropogenic emission totals for each scenario.
40
NOx (Tg N)
CO (Tg)
NMHC (Tg)
SO2 (Tg S)
DMS (Tg S)
NH3 (Tg N)
Soils
Lightning
Oceans/vegetation
Vegetation isoprene
Vegetation terpenes
Volcanos
Oceans/terrestrial biosphere
Oceans
Soils
7
5
100
512
260
14.6
20
8.3
2.4
Table 4
Recommended natural emissions sources.
41
Vegetation
index
AOT40
Units ppb.h
Definition
Receptor
Sum of hourly daylight (> 50 W/m2 PAR) O3 Agricultural crops
volume mixing ratio (vmr) above 40 ppb.
Accumulated over 3 consecutive
n
months of the growing season.
[CO3  40]i for CO3 > 40 ppb
Threshold: 3, 000 ppb.h
i 1
Effect: Yield reduction
where CO3 is the hourly O3 vmr in ppb, i is the
index, and n is the number of hours with CO3
Forest trees
> 40 ppb.
Accumulated over 6 consecutive
months of the growing season.
Threshold: 5, 000 ppb.h
Effect: Growth reduction

SUM06
Units ppb.h
Sum of 24-hourly O3 vmr at or above 60 ppb.
W126
Units ppb.h
Weighted sum of 24-hourly O3 vmr.
Vegetation
Accumulated over 3 consecutive
[CO3]i for CO3  60 ppb
months of the growing season.
i 1
Threshold: No threshold defined
where CO3 is the hourly O3 vmr in ppb, i is the Effect: Yield reduction
index, and n is the number of hours with CO3
≥ 60 ppb.
n

n
C * w
i
i
i 1
where wi = 1/(1+4403 * exp(-0.126*Ci)) and Ci
is the hourly O3 vmr in ppb.
Use CO3 here, or use C above, for consistency.
Vegetation
Accumulated over 3 consecutive
months of the growing season.
Threshold: No threshold defined
Effect: Yield reduction
Table 5
Definition of vegetation indices used in the risk assessment.
42
Index
SOMO35
Units
ppb.days.
Definition
n
 [max_ 8h _ mean  35]
i 1
i
Threshold: No threshold defined
Effect: Health risk.
for max_8h_mean > 35 ppb.
Max_8h_mean is the daily maximum
8 hour average O3 vmr in ppb, n is the
number of days in a year.
EU60
Number of days with maximum 8-h Threshold: 25 days per year (during a
Units days of average ozone vmr exceeding 60 ppb
three years period)
exccedence
Effect: Health risk.
USEPA80
Number of days with maximum 8-h Threshold: the 3-year average of the
Units days of average ozone vmr exceeding 80 ppb. 4-th highest daily maximum at each
exceedence
location must not exceed 80 ppb
(equivalent to a maximum value of 3
days exceedance per year)
Effect: Health risk
Table 6
Definition of health indices used in the risk assessment.
43
region
Lat
Lon
# of stations
References
1. S.W. USA LA regions
30N-40N
125W-110W
11
A. Fiore, CASTNET
2. S.E. USA
25N-35N
90W-80W
12
A. Fiore, CASTNET
3.Great Lakes
40N-50N
95W-75W
13
A. Fiore, CASTNET
4. Latin America/Brazil
25S-15S
50W-30W
N.a.
5. Central-West Africa
5N-15N
5W-15E
3
Carmichael et al., 2003
6.Southern Africa/High Field 30S-20S
20E-35E
6
Zunckel et al., 2004
7. Central Mediterranean
35N-45N
5E-30E
21
EMEP, Airbase
8. Central Europe
48N-54N
7E-17E
101
EMEP, Airbase
9. Middle East
30N-40N
30E-55E
1
Carmichael et al., 2003
10. North India + Nepal
20N-30N
70E-90E
6
Naja et al., 2003
Lal et al., 2000
Carmichael et al., 2003
11. Southern India
10N-20N
75E-85E
4
Naja and Lal, 2002
Debaje et al., 2003
Nair et al., 2002
Carmichael et al., 2003
12. S.E. Asia
20N-35N
110E-125E
3
Carmichael et al., 2003
35N-45N
110E-145E
8
WMO-WDC-GG
Akimoto and Pochanart
Wang and Mauzerall, 2004
Carmichael et al., 2004
25S-40S
145E-155E
N.a.
(Guangzhou Hongkong)
13. Northern China
(Beijing)-Japan
14. Australia
Table 7: Fourteen regions selected in this study
44
Figures
a)
b)
45
c)
d)
e)
46
Figure 1:
Yearly mean surface ozone for model ensemble mean. Figure a displays year 2000 (S1)
mean and standard deviation. Figure b, c, and d displays differences between scenario
CLE (S2), MFR (S3) and SRES A4. Figure e) shows the change in yearly mean surface
ozone due to climate change. Ensemble differences are calculated taking the average of
the individual model simulation differences. Units are ppb.
47
3
4
5
6 7
month
8
9
10 11 12
1
2
3
4
5
6 7
month
8
9
4
5
6 7
month
8
9
SRFC O3 (ppb)
8
9
2
3
4
5
6 7
month
8
9
1
1
2
3
4
5
6 7
month
8
9
10 11 12
3
4
5
6 7
month
8
9
10 11 12
2
3
4
5
6 7
month
8
9
10 11 12
2
3
4
5
6 7
month
8
9
10 11 12
S. Africa - High Field
2
3
4
5
6 7
month
8
9
90
80
70
60
50
40
30
20
10
0
10 11 12
1
2
N. India + Nepal
2
3
4
5
6 7
month
8
9
90
80
70
60
50
40
30
20
10
0
10 11 12
1
SE Asia
90
80
70
60
50
40
30
20
10
0
10 11 12
90
80
70
60
50
40
30
20
10
0
10 11 12
SRFC O3 (ppb)
1
SRFC O3 (ppb)
SRFC O3 (ppb)
6 7
month
S. India
90
80
70
60
50
40
30
20
10
0
2
3
4
5
6 7
month
8
9
10 11 12
2
3
4
5
6 7
month
8
9
10 11 12
90
80
70
60
50
40
30
20
10
0
1
Australia
SRFC O3 (ppb)
90
80
70
60
50
40
30
20
10
0
1
5
90
80
70
60
50
40
30
20
10
0
10 11 12
N. China
SRFC O3 (ppb)
1
SRFC O3 (ppb)
3
Middle East
1
4
C. Europe
90
80
70
60
50
40
30
20
10
0
2
3
90
80
70
60
50
40
30
20
10
0
10 11 12
C. Mediterranean
1
2
CE Africa
90
80
70
60
50
40
30
20
10
0
SRFC O3 (ppb)
SRFC O3 (ppb)
Latin A./Brazil
1
SRFC O3 (ppb)
2
SRFC O3 (ppb)
1
US Great Lakes
SRFC O3 (ppb)
90
80
70
60
50
40
30
20
10
0
SRFC O3 (ppb)
SE US
90
80
70
60
50
40
30
20
10
0
SRFC O3 (ppb)
SW US
2
3
4
5
6 7
month
8
9
10 11 12
90
80
70
60
50
40
30
20
10
0
1
48
Figure 2
Comparison of observed surface ozone levels (black squares) and model ensemble mean
(blue line). The observations are averages over several sites, and the black line represents
the standard deviation. The blue shaded area gives the standard deviations of the model
ensemble. The green area indicates the variation among the models, the upper line of the
green shaded region gives the maxima of the model ensemble, the lower line gives the
model ensemble minima.
a)
b)
49
c)
Figure 3
Model ensemble mean health indices for year 2000 (S1).
A) SOMO35 (ppb.days)
B) EU60 (days of exceedence). The white line indicates the recommended threshold
not to be exceeded (25 days pr year).
C) USEPA60 (days of exceedence). The white line indicates the recommended
threshold not to be exceeded (3 days pr year).
a)
50
b)
Figure 4
Differences of ensemble mean health indices. Ensemble differences are calculated taking
the average of the individual model simulation differences.
a) SOMO35 (ppb.days), difference between CLE (S2) and year 2000 (S1).
b) EU60 (days of exceedence), difference between MFR (S3) and year 2000 (S1).
51
7000
6000
5000
4000
3000
2000
1000
0
S.W.
USA
S.E. USA
Great
Lakes
Latin
CentralCentral
Southern
America/
East
Mediterr
Africa
Brazil
Africa
anean
350
300
250
200
150
100
50
0
Central
Europe
North
India
Middle
East
Southern
Northern
S.E. Asia
Australia
India
China
5654
6822
3412
1869
3733
2895
5559
2795
6780
5874
5656
4564
4559
540
EEA
67
105
44
20
47
29
75
26
109
83
89
55
58
0.6
USEPA *10
84
188
88
29
139
26
121
4
128
51
93
60
93
0
SOMO35
days of exceedence
ppb.days
S1
7000
6000
5000
4000
3000
2000
1000
0
-1000
Latin
CentralCentral
Southern
America/
East
Mediterr
Africa
Brazil
Africa
anean
S.W.
USA
S.E. USA
Great
Lakes
SOMO35
756
896
372
169
823
1018
EEA
28
27
6
2
12
15
USEPA *5
19
36
12
1
14
7
350
300
250
200
150
100
50
0
-50
Central
Europe
North
India
234
341
3726
463
3453
743
479
50
-2
-4
106
12
89
20
11
0
-13
-1
208
2
179
18
20
0
days of exceedence
ppb.days
S2 - S1
Middle Southern
Northern
S.E. Asia
Australia
East
India
China
0
-1000
-2000
-3000
-4000
-5000
-6000
-7000
S.W.
USA
S.E. USA
Great
Lakes
Latin
CentralCentral
Southern
America/
East
Mediterr
Africa
Brazil
Africa
anean
0
-50
-100
-150
-200
-250
-300
-350
Central
Europe
North
India
Middle
East
Southern
Northern
S.E. Asia
Australia
India
China
-2394
-3183
-1464
-530
-542
-668
-2318
-935
-2239
-2549
-2214
-2612
-1854
-217
EEA
-49
-80
-31
-5
-6
-7
-55
-23
-61
-65
-58
-49
-44
0
USEPA *5
-30
-99
-45
-4
-9
-3
-35
-2
-31
-10
-19
-28
-38
0
SOMO35
days of exceedence
ppb.days
S3 - S1
52
7000
6000
5000
4000
3000
2000
1000
0
S.W.
USA
S.E. USA
Great
Lakes
Latin
CentralCentral
Southern
America/
East
Mediterr
Africa
Brazil
Africa
anean
350
300
250
200
150
100
50
0
Central
Europe
North
India
Middle
East
Southern
Northern
S.E. Asia
Australia
India
China
2480
2612
1403
2371
3130
2596
2136
1133
6017
3103
4737
3126
2048
410
EEA
60
59
58
60
63
62
64
64
68
57
51
40
32
27
USEPA *5
99
144
53
23
53
26
57
21
452
133
304
194
99
0
SOMO35
days of exceedence
ppb.days
S4 - S1
Figure5
Model ensemble mean regional averages of health indices. Averages for S1 have been
normalized to the respective values for S.E. US. The scenario differences are expressed in
relative changes compared to S1 absolute values. Standard deviations are given by black
lines. Regions with values exceeding 3x threshold for health risk are marked in red, and
regions with values below recommended thresholds in green.
53
a)
b)
c)
54
d)
e)
55
Figure 6
Model ensemble mean, vegetation indices for agricultural areas in ppb.h. The growing
season is in each gridbox defined as the three or six consecutive months with the highest
index.
a) AOT40 (3 months, daylight hours)
b) AOT40 (6 months, daylight hours)
c) SUM06 (3 months growing season, 24 hours)
d) SUM06 (3 months growing season, daylight hours)
e) W126 (3 months growing season, 24 hours)
a)
56
b)
c)
57
Figure 7
Differences of ensemble mean vegetation indices (ppb.h) Ensemble differences are
calculated taking the average of the individual model simulation differences.
a) AOT40 (3 months, daylight hours)
b) AOT40 (6 months, daylight hours)
c) SUM06 (3 months growing season, daylight hours)
S1, vegetation
100000
ppb.hr
80000
60000
40000
20000
0
S.W. USA
S.E. USA
LA regions
Great
Lakes
Latin
America/B
razil
CentralEast
Africa
Southern
Central
Africa/Hig Mediterran
h Field
ean
Central
Europe
North India
Middle
East
Southern
India
S.E. Asia
(Guangzh
ou
Northern
China
(Beijing)-
Australia
AOT40 (3m)
19000
22508
13132
8491
13125
11249
23285
10310
20103
21690
18419
13760
17590
920
AOT40 (6m)
30877
36825
19165
10048
14087
15085
35966
16326
25541
34541
21424
22211
26143
1802
SUM06 (d)
46179
55997
29383
15312
28879
24048
60007
16695
55917
57470
43513
27573
40384
247
SUM06 (24)
47305
56551
29925
17362
31449
26109
60858
16786
58383
58044
46493
28231
40581
433
W126
36705
44865
25024
15810
27370
22073
47105
15208
42903
41471
35146
23903
32997
1744
58
(S2-S1) vegetation
ppb.hr
70000
50000
30000
10000
-10000
S.W. USA
S.E. USA
LA regions
Great
Lakes
Latin
America/B
razil
CentralEast
Africa
Southern
Central
Africa/Hig Mediterran
h Field
ean
Central
Europe
North India
Middle
East
Southern
India
S.E. Asia
Northern
China
(Beijing)-
Australia
AOT40 (3m)
2476
3343
1535
618
2330
2008
-1202
117
9920
786
10252
3037
1957
92
AOT40 (6m)
4485
5993
2195
828
3073
4171
-991
508
16235
1826
13536
5304
2985
117
SUM06 (d)
10867
13078
5235
1403
6288
7539
-4813
-2477
40492
5327
41947
10462
7796
19
SUM06 (24)
13922
10869
6561
830
6647
10608
-5149
-1769
43717
5368
43652
11518
7455
-146
W126
7651
9419
3916
1292
5319
5834
-3488
-604
33761
3331
33720
7306
5442
126
Central
Europe
North India
Middle
East
Northern
China
(Beijing)-
Australia
(S3-S1) vegetation
0
ppb.hr
-20000
-40000
-60000
-80000
-100000
S.W. USA
S.E. USA
LA regions
Great
Lakes
Latin
America/B
razil
CentralEast
Africa
Southern
Central
Africa/Hig Mediterran
h Field
ean
S.E. Asia
Southern
(Guangzh
India
ou
AOT40 (3m)
-9265
-11932
-6662
-1560
-1795
-1449
-11032
-4488
-6330
-10298
-6985
-8893
-8299
-353
AOT40 (6m)
-17142
-21629
-10791
-2352
-2026
-2662
-19460
-8122
-9043
-18951
-8372
-15310
-13652
-711
SUM06 (d)
-31246
-31311
-14937
-357
-2081
-7117
-31248
-9148
-24451
-39321
-26220
-18069
-22974
-66
SUM06 (24)
-35066
-41775
-21462
-4024
-4206
-4770
-42218
-13513
-26910
-45396
-28325
-25020
-29137
-286
W126
-42194
-14630
-14979
4875
-7384
-14751
-18597
-5455
-84635
-42513
-73385
-14880
-30712
-3003
Central
Europe
North India
Middle
East
Southern
India
S.E. Asia
Northern
China
(Beijing)-
Australia
(S4-S1) vegetation
100000
ppb.hr
80000
60000
40000
20000
0
S.W. USA
S.E. USA
LA regions
Great
Lakes
Latin
America/B
razil
CentralEast
Africa
Southern
Central
Africa/Hig Mediterran
h Field
ean
AOT40 (3m)
8891
10259
5776
4939
8652
4687
7828
4733
15816
11353
14182
14061
9584
741
AOT40 (6m)
16205
17769
8963
9095
12302
11027
14401
8087
27323
20884
17859
24042
15725
1307
SUM06 (d)
37198
41010
17726
16618
31761
22432
36240
16665
65093
53993
61456
45887
35590
202
SUM06 (24)
37917
40313
17359
16474
32718
20554
35475
16766
60723
51014
61587
46204
35212
38
W126
29058
32364
14774
12540
22035
13804
26485
12049
53422
39054
50326
38530
28239
799
Figure 8
Model ensemble mean regional averages of vegetation indices. Averages for S1 have
been normalized to the respective values for S.E. US. The scenario differences are
59
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