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Keywords:

  • future;
  • air pollution;
  • climate change

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model Description
  5. 3. Experimental Setup
  6. 4. Results
  7. 5. Radiative Forcing
  8. 6. Conclusions
  9. Acknowledgments
  10. References
  11. Supporting Information

[1] We apply the Goddard Institute for Space Studies composition-climate model to an assessment of tropospheric O3, CH4, and sulfate at 2030. We compare four different anthropogenic emissions forecasts: A1B and B1 from the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios and Current Legislation (CLE) and Maximum Feasible Reduction (MFR) from the International Institute for Applied Systems Analysis. The projections encompass a wide range of possible man-made emissions changes. The A1B, B1, and CLE forecasts all suggest large increases in surface O3 and sulfate baseline pollution at tropical and subtropical latitudes, especially over the Indian subcontinent, where the pollution increases may be as large as 100%. The ranges of annual mean regional ground level O3 and sulfate changes across all scenarios are −10 to +30 ppbv and −1200 to +3000 pptv, respectively. Physical climate changes reduce future surface O3, but tend to increase ground level sulfate locally over North Africa because of an enhancement of aqueous-phase SO2 oxidation. For all examined future scenarios the combined sum of the CH4, O3, and sulfate radiative forcings is positive, even for the MFR scenario, because of the large reduction in sulfate. For A1B the forcings are as much as half of that of the preindustrial to present-day forcing for each species. For MFR the sign of the forcing for each species is reversed with respect to the other scenarios. At 2030, global changes in climate-sensitive natural emissions of CH4 from wetlands, NOx from lightning, and dimethyl sulfide from the ocean appear to be small (<5%).

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model Description
  5. 3. Experimental Setup
  6. 4. Results
  7. 5. Radiative Forcing
  8. 6. Conclusions
  9. Acknowledgments
  10. References
  11. Supporting Information

[2] Two of the major global environmental problems of our time, human-induced climate change and air pollution, are coupled by several trace species in the troposphere including ozone (O3), methane (CH4) and sulfate aerosol. CH4 is emitted directly, but O3 and sulfate are secondary pollutants formed in the troposphere from the photooxidation of precursor emissions. Human activities and population growth since the preindustrial era have increased emissions of precursor gases and resulted directly in significantly enhanced burdens of O3, CH4, and sulfate in the troposphere. Methane is the second most important greenhouse gas forcing with an estimated value of 0.5 Wm−2 since the preindustrial [Ramaswamy et al., 2001]. O3 is a potent greenhouse gas with a direct forcing of +0.35 ± 0.15 Wm−2 since the preindustrial [Ramaswamy et al., 2001]. The direct radiative forcing of sulfate may be −0.2 to −0.9 Wm−2 since the preindustrial [Penner et al., 2001], the indirect effects (the impact of sulfate aerosols on cloud cover and lifetime) are uncertain but likely to be negative in sign. O3 pollution has known adverse effects on human health, agriculture and ecosystems (for example, http://www.healtheffects.org and Emberson et al. [2003]). Sulfate aerosol is implicated in damage to human health [e.g., Pope, 2004] and the environment through acid deposition and visibility reduction. Tropospheric O3 and sulfate are relatively short-lived (days to weeks) and have heterogeneous spatial distributions. CH4 has a reasonably long atmospheric lifetime (9–10 years) and a relatively homogeneous tropospheric distribution, thus permitting inclusion in the Kyoto Protocol.

[3] O3 is formed via the photochemical oxidation of carbon monoxide (CO), methane (CH4) or non-methane volatile organic compounds (NMVOCs) in the presence of nitrogen oxides (NOx, NOx = NO + NO2). Sulfate is formed in the troposphere from the photooxidation of sulfur dioxide (SO2) emissions. In the gas phase, the SO2 oxidation is initiated by the hydroxyl radical (OH) and this process leads to the formation of new sulfate particles. Inside clouds, in preexisting water droplets, SO2 may be oxidized by dissolved hydrogen peroxide (H2O2), a faster process than the gas-phase oxidation.

[4] Fossil fuel combustion processes are the major man-made sources of both O3 and sulfate precursors. Transportation is a major source of both NOx and CO emissions, power generation is also important for NOx emissions while domestic biofuel burning is important for CO emissions. The primary man-made source of SO2 emissions is the burning of coal and oil for electric power generation. Coal burning accounts for nearly 50% of global SO2 emissions. SO2 is also emitted during industrial and manufacturing processes such as metal smelting and pulp and paper manufacturing. The man-made sources of NMVOCs are more diversified than CO and NOx and include fossil fuel combustion (especially vehicles), industrial chemicals production, oil products handling and solvent evaporation. About 60% of global CH4 emissions are related to human activities, including fossil fuel production, animal husbandry, rice cultivation, biomass burning, and waste management.

[5] In addition to being coupled through co-location of precursor emissions, strong couplings exist between O3, sulfate and CH4 through tropospheric photochemistry. CH4 oxidation is a major source of background O3. Meanwhile, O3 photolysis is the major source of OH, which is the main tropospheric sink for CH4. The formation rate of sulfate depends critically on the availability of tropospheric oxidants OH and H2O2. The formation of H2O2 is intimately connected to the presence of OH. Sulfate feeds back on the oxidant chemistry by providing a surface for the heterogeneous conversion of NOx into nitric acid (HNO3), which is readily deposited from the system, thereby limiting O3 production. Hence tropospheric perturbations in either CH4, O3, or sulfate have the potential to affect each other. Because of the importance of CH4 as both a greenhouse gas and a major source of background O3, man-made CH4 emissions have been identified as an attractive target for reduction due to the prospective concurrent mitigation of climate forcing and air pollution [Hansen et al., 2000; Fiore et al., 2002; Shindell et al., 2004; Dentener et al., 2004; Hansen and Sato, 2004]. In addition, recent work indicates that CH4 emissions reductions are viable from a cost perspective [West and Fiore, 2005]. The development of climate policy for O3 and sulfate is complicated because the resultant climate forcings are driven by the emissions of precursor gases in a nonlinear way that is dependent on the location of the emissions [Rypdal et al., 2005]. Regional O3 production responds strongly to NOx but reducing NOx is neutral or possibly the wrong direction for climate [Fuglestvedt et al., 1999; Wild et al., 2001; Shindell et al., 2005; Naik et al., 2005]. Reduction of SO2 emissions leads to less sulfate and improved public health, but incurs a positive forcing.

[6] Since O3, CH4, and sulfate play such important roles in determining the quality of our environment, it is instructive to understand how their distributions are likely to change in the near future. In the coming decades, man-made emissions of the precursor gases (NOx, CO, CH4, NMVOCs, and SO2) are expected to change as more nations industrialize, other nations implement emissions control strategies, and world population grows. The changes in man-made emissions will alter the distributions of O3, CH4, and sulfate in the troposphere. At the same time, changes in climate variables, such as temperature, humidity, precipitation, clouds, climate-sensitive natural emissions, circulation and convection, will also affect the tropospheric distributions and lifetimes of O3, CH4, and sulfate. For example, Feichter et al. [2004] found that the sulfate aerosol load is considerably reduced in a warmer climate relative to the present-day for the same SO2 source strength because of feedbacks between temperature changes and the hydrological cycle leading to increased wet removal of sulfate. Similarly, O3 concentrations have been found to decrease in a warmer, wetter climate predominantly because of increased water vapor, which is a chemical sink for O3 but source of OH [Stevenson et al., 2000; Johnson et al., 2001; Stevenson et al., 2005]. The increased OH concentrations and temperature both contribute to a faster CH4 oxidation rate in a future warmer climate.

[7] We investigate how the tropospheric distributions of O3, CH4, and sulfate will change because of changes in man-made emissions and physical climate changes at 2030. Future changes in human activities are difficult to predict, therefore we explore four different man-made emissions scenarios that encompass a wide range of potential changes in activity. We employ the A1B and B1 storylines from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) [Nakicenovic et al., 2000] and the Current Legislation (CLE) and Maximum Feasible Reduction (MFR) scenarios developed at the International Institute for Applied Systems Analysis (IIASA) [Amann et al., 1999; Dentener et al., 2004]. The CLE and MFR forecasts have recently been used in multimodel assessments of changes to O3 radiative forcing [Stevenson et al., 2006]. We improve and extend these previous studies of changes to tropospheric composition at 2030 in several ways: (1) use a broad set of future man-made emissions scenarios, (2) examine the relative influences of man-made emissions changes and physical climate changes, and (3) simulate changes to O3, CH4, and sulfate aerosol simultaneously in a fully coupled atmospheric composition-climate model.

[8] The Goddard Institute for Space Studies (GISS) composition-climate model is described in section 2. Our experimental setup is presented in section 3 with descriptions of the man-made emissions scenarios (section 3.1), natural emissions included in the current study (section 3.2) and the set of simulations (section 3.3). In section 4 the results of changes at 2030 relative to the present-day are presented, including climate-sensitive natural emissions (section 4.1), surface O3 and sulfate air pollution (section 4.2) and O3, sulfate and CH4 global budgets (section 4.3). The radiative forcing consequences of each future scenario are presented in section 5. Conclusions are presented in section 6.

2. Model Description

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model Description
  5. 3. Experimental Setup
  6. 4. Results
  7. 5. Radiative Forcing
  8. 6. Conclusions
  9. Acknowledgments
  10. References
  11. Supporting Information

[9] We employ the Goddard Institute for Space Studies (GISS) general circulation model (GCM) version model III with fully interactive tropospheric chemistry and sulfate aerosol modules. Model III is a new reprogrammed and documented version of the GISS GCM, which includes improved representations of several physical processes and produces better climate simulations than any prior GISS GCM based on comparison with a wide suite of observations [Schmidt et al., 2006]. We use 23 vertical layers (model top in the mesosphere at 0.01 mb) and 4° × 5° horizontal resolution.

[10] The tropospheric chemistry and sulfate aerosol modules have been described in detail and evaluated elsewhere [Shindell et al., 2003; Koch et al., 2006]. The tropospheric gas-phase mechanism represents background HOx-NOx-Ox-CO-CH4 chemistry as well as peroxyacetylnitrates, hydrocarbon families, and isoprene based on 32 species and 77 reactions. The sulfate module includes gas-phase and in-cloud formation of sulfate aerosol [Koch et al., 1999]. In the present study, the tropospheric chemistry and sulfate aerosol modules are coupled such that instantaneous oxidant concentrations (OH, NO3 and H2O2) are available to the sulfate module and instantaneous sulfate aerosol mass (SO4) and sulfur species concentrations (SO2 and dimethyl sulfide (DMS)) are available to the chemistry module yielding a total of 19 transported tracers (15 from the tropospheric chemistry and four sulfur species) [Bell et al., 2005]. The two-way coupling allows assessment of future feedbacks (either climate or emissions-driven) between the oxidant and sulfate cycles.

[11] Chemical calculations are performed only in the troposphere in the present version of the model. We use a thermal tropopause defined by the meteorological lapse rate. Stratospheric values of O3, NOx, and CH4 are prescribed according to satellite observations with seasonally varying abundances [Shindell et al., 2003].

[12] Our present focus is to quantify the response of the O3, CH4, and sulfate tropospheric composition to global changes including emissions and climate. We do not feed back the model generated O3, CH4, and sulfate aerosol to the radiation scheme and therefore do not quantify the feedback of the tropospheric chemical changes on the climate system, although we do provide a measure of the climate impacts using the concept of radiative forcing, which has been found to be a robust and useful metric of the potential climatic impact of trace species [Fuglestvedt et al., 2003].

3. Experimental Setup

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model Description
  5. 3. Experimental Setup
  6. 4. Results
  7. 5. Radiative Forcing
  8. 6. Conclusions
  9. Acknowledgments
  10. References
  11. Supporting Information

3.1. Man-Made Emissions

[13] We compare four different future man-made trace gas emissions scenarios. A1B and B1 are based on the IPCC SRES reference models [Nakicenovic et al., 2000] and were generated using regional growth factors for each emission source from the IMAGE socioeconomic model [IMAGE Team, 2001]. The CLE and MFR scenarios were developed at IIASA more recently than the IPCC projections using the global version of the Regional Air Pollution Information and Simulation (RAINS) model [Amann et al., 1999]. Further details of the RAINS model and IIASA emissions projections are available at http://www.iiasa.ac.at/rains/global_emiss/global_emiss.html and presented by Dentener et al. [2004]. A1B and B1 are from the IPCC storylines that emphasize future sustainable development. The A1B scenario features rapid economic growth with a balance between fossil fuel intensive and renewable energy sources whereas the more optimistic B1 scenario envisages the use of clean and resource efficient technologies. The CLE scenario is based on present emissions control legislation and national expectations of economic growth and takes into account air quality management legislation that was initiated in Asia and Latin America after the IPCC storylines were constructed. The MFR scenario is an optimistic future vision based on world-wide implementation of the available advanced emissions control technologies for trace gases.

[14] Two present-day control trace gas emissions inventories are used in the study. CONT1, for comparison with the IPCC projections (A1B and B1), is based on anthropogenic emissions for 1995 from the Emissions Database for Global Atmospheric Research (EDGAR3.2) representative of the year 1995 [Olivier and Berdowski, 2001]. CONT2, for comparison with the IIASA projections, is based on the present-day Current Legislation emissions inventory [Dentener et al., 2004].

[15] The global annual total man-made trace gas emissions for each run (present-day controls and future scenarios) are given in Table 1. The IPCC A1B and B1 projections include changes to biomass burning emissions as described by Streets et al. [2004]. The CLE and MFR scenarios use biomass burning emissions from the Global Fire Emissions Data Set (G. V. D. Werf, personal communication, 2005) fixed to present-day values. In addition to the surface sources, the model includes aircraft emissions of NOx (0.6 Tg N/yr) and SO2 (0.1 Tg SO2/yr) [Baughcum et al., 1996]. Since none of the scenarios provide aircraft emissions changes at 2030, we estimate future values by applying a 2.3 growth factor to each projection [Henderson and Wickrama, 1999].

Table 1. Total Anthropogenic and Biomass Burning Trace Gas Emissions Inventories for the Control Simulations and Future Projectionsa
 CO, Tg CO/yrNOx, Tg N/yrNMVOC, Tg C/yrCH4, Tg CH4/yrSO2, Tg SO2/yr
  • a

    Total anthropogenic (TA) emissions include fossil fuel, industrial, biofuel, and waste sources. CONT1 and CONT2 are the control simulations, and A1B, B1, CLE, and MFR are the future projections. BB, biomass burning.

CONT1
   TA531.029.0112.3289.4143.9
   BB314.94.220.413.22.6
A1B
   TA665.052.5184.4513.2192.0
   BB344.33.922.820.32.8
B1
   TA463.428.5127.2409.7114.2
   BB237.33.015.117.91.9
CONT2
   TA470.027.896.1300.6108.3
   BB507.010.225.923.62.8
CLE
   TA397.132.894.7428.8114.6
   BB507.010.225.923.62.8
MFR
   TA221.713.160.7290.035.8
   BB507.010.225.923.62.8

[16] The emissions scenarios encompass a broad range of potential future changes and each has a unique character. The percentage change in total annual global anthropogenic emissions of key trace gases, relative to present-day control (CONT1 for A1B and B1 or CONT2 for CLE and MFR), is given in Figure 1. A1B projects significant global increases in all trace gas emissions while MFR projects significant global decreases in all trace gas emissions. The CH4 emissions reduction looks somewhat meager for the MFR scenario (about 5%), but should be compared to the CLE CH4 emission change at 2030. CLE and B1 are generally intermediate between A1B and MFR. CLE and B1 predict similar magnitude global changes in the CO and CH4 emissions, both scenarios suggest ∼−15% decrease in CO emissions and a +40% increase in CH4 emissions. There are significant differences in the NOx and SO2 emissions changes between CLE and B1. CLE predicts an increase of +18% in NOx emissions compared to −2% decrease in the B1 scenario. SO2 emissions increase by +5% in CLE but decrease by −20% in B1.

image

Figure 1. Percentage change in total anthropogenic precursor emissions: CO, NOx, NMVOCs, CH4, and SO2 at 2030 for each scenario relative to the present-day control simulations (CONT1 for A1B and B1; CONT2 for CLE and MFR).

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[17] In addition to changes in the magnitude of the short-lived precursor emissions (NOx, CO, NMVOCs, SO2), changes in the geographical location of the emissions may impact future air pollution and radiative forcing. For example, the resultant climate impacts of a sustained NOx perturbation through O3 and CH4 changes have been found to be different for a perturbation localized in the southeast Asia versus Europe [Berntsen et al., 2005]. In the case of sulfate aerosol, an equal reduction in SO2 emissions has a larger effect on concentrations in Europe compared to China because of different regional rates of oxidation and removal [Berglen et al., 2004]. In the present study, the A1B, B1, and CLE futures all envisage a regional shift in precursor emissions by 2030 with decreases at NH midlatitudes and increases at the more photochemically active subtropical and tropical latitudes. Over the United States and Europe, SO2 emissions decrease by up to −80% in A1B and B1 and −20% in CLE. Large increases in SO2 emissions occur over India: 400% in A1B and ∼100% in B1 and CLE. CLE has almost no change in SO2 emissions over China, while B1 has decreases (−30%) and A1B has increases (20%). B1 has the largest decreases in NOx emissions at the NH midlatitudes (−60% over Europe and the United States). A1B predicts NOx emissions decreases of −30% over the United States and western Europe, but increases in eastern Europe (∼100%). Fossil fuel NOx emissions increase by 500% over India in A1B and about 100% in B1 and CLE. B1 has almost no change in NOx emissions over China in contrast to the increases in A1B (+100%) and CLE (+30%). The precursor emissions changes in MFR are more regionally homogeneous with decreases in SO2 emissions up to −80–90% and decreases in NOx emissions up to −50%.

3.2. Natural Emissions

[18] The model includes additional trace gas emissions from natural sources detailed in Table 2. CH4 emissions from wetlands are the largest single source to the atmosphere representing about 20–45% of the total emission [e.g., Hein et al., 1997; Houweling et al., 1999; Matthews, 2000]. We include climate-sensitive CH4 emissions from wetlands using a linear parameterization that was derived from a detailed process model such that the emissions are dependent on the climate model's soil temperature and precipitation anomalies [Shindell et al., 2004]. We do not allow the geographic distribution of wetlands to respond to climate for this study. Emissions of NOx from lightning are climate-sensitive and dependent on the model's convection scheme [Price et al., 1997]. DMS emissions from the oceans are interactive with the model's surface wind speed [Koch et al., 2006]. In the present model formulation, climatological monthly mean emissions of isoprene from vegetation are used from the GEIA data set [Guenther et al., 1995].

Table 2. Summary of Natural Trace Gas Emissions in the Present Study
SpeciesEmission SourcePresent-Day Annual Total
CH4wetlands and tundra247 Tg CH4/yr
CH4termites20 Tg CH4/yr
CH4ocean13 Tg CH4/yr
CH4lake6 Tg CH4/yr
CH4ground7 Tg CH4/yr
NOxsoils5.83 Tg N/yr
NOxlightning6.36 Tg N/yr
Isoprenevegetation550 Tg C/yr
NMVOCvegetation30 Tg C/yr
SO2volcano10.5 Tg S/yr
DMSocean21.2 Tg S/yr

3.3. Simulations

[19] A description of the simulations is given in Table 3. Two simulations are performed for each future man-made emissions scenario: (1) with a present-day climate representative of the 1990s (the simulations are annotated with “(e)” to signify emissions changes only) and (2) with a future 2030s climate (the simulations are annotated with “(e+c)” to signify emissions and climate changes), yielding a total of eight future projections. The present-day control simulations (CONT1 and CONT2) are run with the 1990s climate.

Table 3. Description of Simulations
Scenario Family, SSimulation NameEmissionsEmissions YearClimate-MeteorologyMethane
Control IPCCCONT1Edgar3.219951990scalculated
A1BA1B(e)IPCC A1B20301990scalculated
A1BA1B(e+c)IPCC A1B20302030scalculated
A1BA1B_CH4IPCC A1B20302030sprescribed to present-day values from CONT1
B1B1(e)IPCC B120301990scalculated
B1B1(e+c)IPCC B120302030scalculated
Control IIASACONT2IIASA CLE20001990sprescribed
CLECLE(e)IIASA CLE20301990sprescribed
CLECLE(e+c)IIASA CLE20302030sprescribed
MFRMFR(e)IIASA MFR20301990sprescribed
MFRMFR(e+c)IIASA MFR20302030sprescribed

[20] Prescribed decadal average (1990–1999 and 2030–2039) sea surface temperatures and sea ice that were generated in a previous simulation of the GISS atmosphere-ocean model (AOM) [Russell et al., 2000] provide the lower boundary conditions over the oceans. The AOM predictions of Northern Hemisphere regional climate trends show good agreement and high positive spatial correlation with NCEP (National Centers for Environmental Prediction) reanalysis data for 1960 to 2000 [Lucarini and Russell, 2002] implying that the model may be reliable in forecasting future climate change. For this study we select data from an AOM simulation that used observed greenhouse gases until 1990 and compounded 0.5% annual increases of CO2 after 1990. At this rate, CO2 abundance changes from 360 ppmv at 1995 to 429 ppmv at 2030. To be consistent, the forecast of future climate should be based on the greenhouse gas projections associated with each individual scenario. However, no data are available for CLE and MFR. The CO2 abundance increases to 454 ppmv and 437 ppmv for the A1B and B1 scenarios respectively [Intergovernmental Panel on Climate Change, 2001]. Hence the climate change scenario that we employ represents about three fourths of the climate change from A1B and is about the same as B1. In view of other uncertainties and climatic inertia, the use of the GISS AOM simulation provides a realistic, appropriate “middle-of-the-road” representation of potential future climate change and suits our present purposes in evaluating the relative roles of physical climate changes and anthropogenic emissions changes on tropospheric composition.

[21] The climate change forecast that we employ predicts a global annual mean surface air temperature increase of 0.68°C by the 2030s (Figure 2). Largest Northern Hemisphere warming of up to +2–3°C occurs in Central Asia, North America, and the Barents Sea regions. Over most other continental land areas, the temperature increase is in the range 0.3–1°C. Cooling of about −0.5°C occurs in the high-latitude North Atlantic Ocean and Bering Sea regions. Relative to the Northern Hemisphere, large surface warming occurs over the Antarctic region. As observed in similar models, the AOM simulation that provided the sea surface temperature and sea ice boundary conditions showed poor correlations with NCEP reanalysis data in the Southern Hemisphere, due in part to the model's unrealistic interannual variability in southern sea ice cover [Russell et al., 2000]. Changes in precipitation impact tropospheric chemistry through wet processing. Annual mean precipitation increases by a global average of 0.06 mm/day (∼2%), but there are considerable regional differences (Figure 2). Largest increases in precipitation occur in the tropical Atlantic and western Indian Ocean (1.5–2.5 mm/day, ∼10–20%). Largest decreases occur over the Indian subcontinent, Arabian Sea and Bay of Bengal (1–2 mm/day, ∼10–20%). The temperature increases extend throughout the troposphere (Figure 3) with largest warming in the SH and upper tropical troposphere for this particular model. The lower stratosphere shows some cooling. As a result of the warmer temperatures, zonal mean water vapor mixing ratios increase at 2030 throughout the troposphere by 5–10%. The largest absolute increases are in the tropics and subtropics.

image

Figure 2. Difference in annual mean (top) surface air temperature (degrees C) and (bottom) precipitation (mm/day), between the 2030s and 1990s climates.

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image

Figure 3. Difference in annual zonal mean (left) temperature (degrees C) and (right) specific humidity (102 ppmv), between the 2030s and 1990s climates.

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[22] The IPCC simulations (A1B, B1) include a full calculation of CH4. First, the present-day CH4 budget source and sink terms were balanced (assuming a growth rate of +14 Tg/yr [Prather and Ehhalt, 2001]) by adjusting the stratospheric exchange term. For the future emissions scenarios, an initial CH4 concentration was estimated according to the emissions increase and then the initial CH4 trend was extrapolated exponentially using the model's CH4 adjustment time (12.6 years) to infer the actual CH4 concentration change at 2030 [Shindell et al., 2005]. However, for the IIASA simulations (CLE and MFR), CH4 concentration is prescribed according to values generated in previous transient simulations using the STOCHEM model [Dentener et al., 2004]. An additional sensitivity simulation is performed (A1B_CH4) based on A1B(e+c), but with CH4 concentrations fixed to present-day values taken from CONT1.

[23] The A1B and B1 family simulations were run for 15 years. The first 5 years of the simulation are discarded as spin-up and the remaining 10 are averaged for analysis. The MFR and CLE family simulations were run for 12 years and the first 2 years are discarded as spin-up with the remaining 10 years averaged for analysis. We use 10-year averages to reduce interference from natural interannual climatic variability with the perturbation signal.

4. Results

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model Description
  5. 3. Experimental Setup
  6. 4. Results
  7. 5. Radiative Forcing
  8. 6. Conclusions
  9. Acknowledgments
  10. References
  11. Supporting Information

[24] The difference in a specific diagnostic variable (for example, O3 mixing ratio) between 2030 and the present-day is calculated for each projection within a scenario family, S, using the appropriate present-day control simulation, C. We derive the emissions-only impacts using the difference between simulations S(e) − C, the climate-only impacts using S(e+c) − S(e) and the combined emissions and climate change impacts using S(e+c) − C. Percentage changes are calculated relative to the appropriate present-day control simulation. Hence the climate-only impacts are determined as the difference between the combined emissions and climate change simulation and the emissions-only simulation and are therefore based on a 2030 background emissions state. Differences between CONT1 and CONT2 are relatively small compared to differences between the future projections and the appropriate present-day control simulation.

4.1. Changes in Climate-Sensitive Natural Emissions at 2030

[25] Physical climate changes drive changes to natural emissions (Figure 4). The climate-sensitive trace gas global total emissions included in this study increase in response to the temperature increase at 2030. Global annual mean CH4 emissions from wetlands and tundra increase by +16 Tg CH4/yr from 247 to 263 Tg CH4/yr at 2030 (∼5%). A projection based on doubled CO2 (representative of 2100 conditions), using the same emission algorithm, gave an increase in the CH4 wetlands emissions of +78% for a global mean annual average surface temperature increase of 3.4°C [Shindell et al., 2004]. NOx generated from lightning increases by 0.3 Tg N/yr from 6.2 to 6.5 Tg N/yr in the present study. Stevenson et al. [2005], using a similar formulation in a different GCM, found no trend in NOx from lightning between the 1990s and 2020s, however they did find important spatial changes. Oceanic DMS emissions increase modestly from 40.9 to 41.3 Tg S/yr with the majority of the increase localized in the western Southern Ocean at high latitudes. Our present study does not include climate-driven changes to isoprene emissions from vegetation. Stevenson et al. [2005] found a global increase of 9% in isoprene emissions by 2020 due to increased surface temperatures with most of the increase localized over South America. On short-term future timescales, it seems likely that the most significant changes to isoprene emissions will be a result of land-use changes rather than physical climatic factors.

image

Figure 4. Simulated changes in climate-sensitive natural trace gas emissions between 2030 and the present-day: (top) CH4 from wetlands and tundra (10−12 kg m−2 s−1), (middle) NOx from lightning (10−14 kg m−2 s−1), and (bottom) DMS from the ocean (10−13 kg m−2 s−1).

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4.2. Surface Pollution at 2030

[26] O3 and sulfate pollution levels at a particular location are determined by three factors: (1) the local precursor emissions and meteorology, (2) the synoptic or regional-scale meteorological conditions, and (3) the baseline levels of O3 and sulfate and their precursors present in ambient air, which depend on large-scale processes such as intercontinental transport of pollutants and precursors [e.g., Wild and Akimoto, 2001; Park et al., 2004]. Our global-scale model predictions represent the baseline levels of O3 and sulfate pollution upon which regional and urban pollution builds and is ideally suited to capturing large-scale influences on O3 and sulfate. Therefore the changes at 2030 that we forecast are to be considered in the context of changing baseline levels.

4.2.1. Surface Ozone

[27] The change in annual global mean surface O3 predicted for each scenario is shown in Table 4. The emissions-only changes in CLE and MFR agree well with results from similar experiments in a multimodel study using 25 different models (+1.7 ± 0.3 ppbv for CLE; −1.8 ± 0.5 ppbv for MFR) [Dentener et al., 2006]. Climate changes reduce global mean surface O3 for all scenarios. In the case of CLE and B1, the climate change response represents about 60% of the emissions change. Indeed, in remote regions over the oceans, climate changes dominate the total change in CLE, B1 and MFR scenarios. The spatial distribution of the climate change-only impacts on surface O3 is shown in Figure 5 for the B1 scenario (a similar pattern occurs for the other scenarios). Generally, the O3 reduction is about −1 to −1.5 ppbv but reaches −4 ppbv over the North Atlantic Ocean. Both the production and loss rates of O3 are changed in a warmer, wetter climate. Increased water vapor increases the rate of reaction of O1D with water vapor, an effective loss for O3 (but source for OH). Increased temperatures increase the rate of the photochemical production and loss reactions. In our analysis, the water vapor effect and subsequent O3 loss appears to dominate in most regions, except India and the north Pacific, resulting in reduced O3 concentrations due to climate change. The dominant reduction in O3 mixing ratio due to future physical climate change has been observed in several other studies [Stevenson et al., 2000, 2005; Johnson et al., 2001].

image

Figure 5. Impact of physical climate changes on surface O3 (ppbv) at 2030 relative to the present-day calculated using the B1 scenario family.

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Table 4. Relative Roles of Emissions and Climate Changes on the Global Annual Mean Surface O3 Change at 2030 Relative to the Present-Daya
Emissions FamilyEmissions-Only ResponseClimate-Only ResponseTotal of Emissions and Climate Response
  • a

    Units are ppbv.

A1B4.66−0.813.85
B11.17−0.700.47
CLE1.42−0.880.54
MFR−1.65−0.81−2.46

[28] Figure 6 shows the annual mean surface ozone change, including the impacts from emissions and climate changes, for each scenario relative to the control. Consistent across each scenario is that the largest absolute changes occur in subtropical and tropical regions. The surface O3 changes range from −10 to +30 ppbv depending on the scenario. Maximum surface O3 increases are forecast over India by three of the scenarios (AlB, B1, and CLE) because of the large regional increases in NOx precursor emissions there. The largest absolute increases occur for the A1B scenario, which predicts surface O3 increases everywhere, except for a small decrease in southwest Africa due to a reduction in biomass burning there. In A1B, surface O3 increases by 25–30 ppbv (60–80%) over the Indian subcontinent and by 10–15 ppbv (30–40%) over North Africa, Central America, the Middle East, and East Asia. A1B forecasts smaller increases of 2–5 ppbv (5–10%) over Europe and the United States, despite reductions in NOx and CO precursor emissions in those regions. The increases in surface O3 at NH midlatitudes in A1B appear to be driven by the global increase in CH4 emissions. Figure 7 shows the impact of holding CH4 to present-day levels on the A1B(e+c) simulation surface O3 forecast (A1B_CH4 – CONT1). Surface O3 is reduced everywhere relative to the A1B(e+c) forecast (global mean change is reduced to +1.77 ppbv compared to 3.85 ppbv). Hence future global CH4 emissions increases in an A1B world make a significant contribution to the surface O3 change at NH midlatitudes and remote regions and potentially compromise the effectiveness of NOx and CO precursor reductions in those regions. The MFR scenario predicts decreased surface O3 everywhere with a spatial pattern that mirrors in reverse the A1B scenario (Figure 6). The largest decreases of about 7–10 ppbv (20%) occur across Central America, Asia, and North Africa. Smaller decreases in surface O3 of about 1–2 ppbv (5%) occur over Northern Hemisphere high-latitude regions. Both the B1 and CLE scenarios predict maximum surface O3 increases across the Indian subcontinent of ∼10 ppbv (30–40%). B1 has similar increases (5–10ppbv, ∼30%) over East Africa and the Middle East, not forecast by the CLE. CLE has increases over Southeast Asia (10%), not present in B1. Both scenarios forecast negligible changes over Europe (< 1ppbv). A difference occurs over the eastern United States, where B1 indicates surface O3 decreases of up to 4–5ppbv (5–10%) compared to increases of 1–2 ppbv (<5%) in CLE. This difference reflects the different predictions of NOx emissions over the United States: B1 anticipates reductions of up to −60% whereas CLE envisages increases of around 10%. B1 predicts decreases in surface O3 over South West Africa due to the reduction in biomass burning whereas CLE did not include future changes to biomass burning emissions.

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Figure 6. Total change (emissions and climate) in surface O3 (ppbv) for each scenario family at 2030 relative to the present-day.

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Figure 7. Change in surface O3 (ppbv) for sensitivity simulation A1B_CH4 (based on A1B(e+c) with CH4 held to present-day concentrations) relative to the present-day.

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[29] The surface O3 increases at 2030 in the subtropical and tropical regions, (India, Africa, Central America, and South East Asia) do not have a strong seasonal cycle (not shown), which is different from the preindustrial to present-day O3 change predominantly at Northern Hemisphere midlatitudes, which demonstrates a significant seasonality. Plentiful sunlight is always available at the lower latitudes to drive the photochemistry, whereas at higher latitudes the photochemistry is limited by the availability of sunlight. Hence future increases in O3 air pollution at lower latitudes persist throughout the year in contrast to O3 pollution at midlatitudes, which is a summertime phenomenon.

4.2.2. Surface Sulfate

[30] On a global scale, the largest annual mean surface sulfate increase occurs for the A1B scenario (+69 pptv) (Table 5). MFR predicts a similar magnitude change but in the opposite direction (−53 pptv). Climate changes cause an increase in surface sulfate for all scenarios. The climate change impact is small compared to the emissions changes for A1B and MFR, but, at least in a global context, appears significant compared to the B1 and CLE emissions changes. The annual mean surface sulfate changes for B1 and CLE are small (+5–6pptv). However, the small global changes belie large regional differences. Indeed, the global changes for A1B, CLE and B1 represent the difference between large decreases at NH midlatitudes and large increases at lower subtropical latitudes, a marked regional redistribution masked by the global change values.

Table 5. Relative Roles of Emissions and Climate Changes on the Global Annual Mean Surface Sulfate Change at 2030 Relative to the Present-Daya
Emissions FamilyEmissions-Only ResponseClimate-Only ResponseTotal of Emissions and Climate Response
  • a

    Units are pptv.

A1B64.724.3069.02
B13.392.936.32
CLE3.831.555.38
MFR−54.280.62−53.7

[31] The change in surface sulfate due to climate is localized over North Africa (Figure 8) and appears to be a result of increased aqueous phase oxidation in that region. The region is hot and dry; as such sulfate production is predominantly gas-phase (new particles) there. However, in the future 2030 climate both H2O2 and cloud cover increase in that region (Figure 8), driving an increase in aqueous phase production. The H2O2 increases are driven by increased production rate due to enhanced OH and water vapor. Figure 8 shows the results for the B1 scenario, and a similar result is obtained for the A1B scenario. Both A1B and B1 predict substantial SO2 emissions at 2030 along the North African coastline. CLE and MFR do not have substantial SO2 emissions in the North African region and therefore the climate effect is less pronounced for those scenarios.

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Figure 8. Impact of physical climate change on (top) surface sulfate (pptv), (middle) surface H2O2 (pptv), and (bottom) total cloud cover (%), at 2030 relative to the present-day calculated using the B1 scenario family.

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[32] Both the IPCC and IIASA scenarios suggest significant decreases in surface sulfate in the NH midlatitude region across Europe and the United States, except CLE, which has almost no change to sulfate over the United States (Figure 9). The surface sulfate decreases over Europe and the United States are about −0.5 ppbv (−50%) in A1B and −60–80% in B1. CLE predicts a −50% reduction in surface sulfate over Europe. MFR has −60–80% decreases over most of the continental Northern Hemisphere and southern Africa. A1B, CLE and B1 forecast large increases in surface sulfate over India: about 3ppbv or 200% in A1B, 100% in B1 and 150% in CLE. The A1B and B1 futures predict large increases in surface sulfate over north and east Africa and the Middle East (200% in A1B, 150% in B1). A1B has substantial increases in surface sulfate across Central and South America. The CLE future includes penetration of emissions controls on the African and South American continents and as such results in negligible changes or even decreases in surface sulfate over those regions, despite the economic development there. A1B has a significant increase in surface sulfate over China compared to a significant decrease in B1 for that region.

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Figure 9. Total combined change (emissions and climate) in surface sulfate (102 pptv) for each scenario family at 2030 relative to the present-day.

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4.3. Changes to Global Budgets at 2030

4.3.1. Ozone

[33] The global O3 budget is determined by three processes: net chemical production (the difference between chemical production rate and chemical loss rate), stratosphere-troposphere exchange and dry deposition. In the present simulations the stratospheric exchange term is inferred from a balance of the other two process terms. The A1B future would cause the largest increases in net chemical production (45%) and O3 burden (9%) (Table 6). The B1 and CLE scenarios both forecast a modest increase in net chemical production (6–8%), which is compensated for by a small increase in dry deposition. Both scenarios suggest almost no change in the global O3 burden despite different global NOx emissions changes (Figure 1). MFR forecasts a −22% decrease in O3 net chemical production, but only a −6% decrease in global O3 burden, despite the substantial precursor emissions reductions (Figure 1). In all scenarios, physical climate changes serve to reduce the emissions-driven changes in net chemical production, dry deposition and the global O3 burden. In the MFR scenario, the global O3 burden is reduced by only −3% because of the emissions-only changes. The present results agree well with similar recent analyses of the 2030 O3 budget changes [Stevenson et al., 2005, 2006].

Table 6. Relative Roles of Emissions and Climate Changes on the Global O3 Budget for Each Scenarioa
 IPCC SRESIIASA
CONT1A1BB1CONT2CLEMFR
  • a

    The value shown in each cell is for the total combined emissions and climate change simulation (i.e., S(e+c)). The number in parentheses is for the emissions-only simulation (i.e., S(e)).

Chemistry, Tg O3 yr−1478692 (721)508 (533)547589 (617)428 (452)
Stratosphere, Tg O3 yr−1634620 (616)632 (629)632623 (626)634 (638)
Dry deposition, Tg O3 yr−1−1112−1311 (−1337)−1140 −(1162)−1180−1212 −(1243)−1062 −(1090)
Burden, Tg O3451491 (502)455 (465)464466 (479)436 (448)
4.3.2. Sulfate

[34] The RAINS model estimates much lower SO2 emissions over Europe than the Edgar 1995 inventory (114.6 Tg SO2/yr for CONT2 versus 143.9 Tg SO2/yr for CONT1, see Table 1). In the present study, we are concerned with the relative changes between each 2030 scenario and the present-day control. The A1B and MFR futures impact the global sulfate budgets dramatically (Table 7). In A1B, the global production rates of sulfate increase by about 50%, the global dry deposition increases by about 50% and wet deposition by about 40%, leading to a change in the global sulfate burden of about +43%, with no change in the lifetime. Conversely, in the MFR future, global production rates of sulfate decrease by about 35%, the global dry deposition decreases by about 45% and wet deposition by about 33%, leading to a change in the global sulfate burden of about −35%. The CLE future predicts about a 7% increase in global sulfate burden, with about a 10% increase in gas-phase and aqueous-phase production rates. The B1 future has a global decrease in SO2 emissions (for example, Figure 1) but a small global increase in the sulfate burden (Table 7), owing to the regional shift in SO2 emissions to more subtropical regions characterized by high aseasonal oxidation rates and low wet deposition, (in contrast to NH midlatitudes, which are characterized by seasonal oxidant limitation and high wet deposition rates). The B1 scenario demonstrates the importance of the spatial location of the emissions in determining the climate and air pollution response. In general, the global sulfate burden change does not respond linearly to the global SO2 emissions change (compare Tables 1 and 7). In A1B the sulfate burden increase (∼42%) is amplified relative to the SO2 emissions increase (∼33%), while in MFR the sulfate burden decrease (∼−35%) is dampened relative to the SO2 emissions decrease (∼−65%).

Table 7. Relative Roles of Emissions and Climate Changes on Global Sulfate Budget for Each Scenarioa
 IPCC SRESIIASA
CONT1A1BB1CONT2CLEMFR
  • a

    The value shown in each cell is for the total combined emissions and climate change simulation (i.e., S(e+c)). The number in parentheses is for the emissions-only simulation (i.e., S(e)).

Sources, Tg S yr1
Direct emission2.22.81.81.71.70.7
Gas phase13.620.0 (19.7)14.5 (14.3)12.714.0 (13.9)8.4 (8.4)
Aqueous phase13.719.1 (19.1)14.1 (14.1)12.413.4 (13.5)8.4 (8.4)
 
Sinks, Tg S yr1
Dry deposition−3.8−5.7 (−5.5)−4.0 (−3.9)−3.4−3.6 (−3.5)−1.9 (−1.9)
Wet deposition−25.6−36.1 (−36.0)−26.3 (−26.3)−23.3−25.6 (−25.6)−15.6 (−15.7)
Burden, Tg S0.490.70 (0.70)0.52 (0.53)0.460.49 (0.50)0.30 (0.30)
Lifetime, days6.16.1 (6.2)6.3 (6.4)6.36.1 (6.3)6.3 (6.2)

[35] Physical climate changes do not exert a large impact relative to the emissions-driven changes for sulfate, at least on global scales. The most significant climate influence on the global sulfate budget appears to be an enhancement of gas-phase sulfate production by about 3–4%, reflecting increased oxidant levels in the future climate. In addition, regional precipitation increases in the future climate (Figure 2) lead to increased wet deposition of sulfate over China and the Southern Ocean, which reduces the global burden slightly.

4.3.3. Methane

[36] Table 8 summarizes the important CH4 budget terms for present-day simulations and future projections. A1B forecasts a large increase in global CH4 burden by 2030 (43%). B1 also has a large increase in CH4 burden (22%). In the CLE and MFR simulations, CH4 concentrations were fixed to values previously generated with the STOCHEM model [Dentener et al., 2004], yielding CH4 global burden changes in our model of 14% and −4%, respectively. B1 and CLE feature similar global man-made CH4 emissions changes (+20%), but B1 has a substantially higher CH4 burden and surface CH4 concentration at 2030 than CLE. The enhanced burden in B1 relative to CLE may reflect the difference in NOx emissions changes for those scenarios, which increase in CLE but decrease in B1, an idea supported by the shortened CH4 lifetime in CLE compared with B1. However, it is difficult to compare since in the B1 family simulations included a full calculation of CH4 whereas in CLE, CH4 was prescribed.

Table 8. Relative Roles of Emissions and Climate Changes on CH4 Concentrations and Budget Termsa
 IPCCIIASA
CONT1A1BB1CONT2CLEMFR
  • a

    The value shown in each cell is for the total combined emissions and climate change simulation (i.e., S(e+c)). The number in parentheses is for the emissions-only simulation (i.e., S(e)).

  • b

    Includes soil sink of 30 Tg CH4 yr−1.

Global mean surface concentration, ppbv17292478 (2522)2117 (2141)17602012 (2088)1696 (1760)
Burden, Tg47006723 (6849)5749 (5820)48455539 (5748)4669 (4845)
Chemical loss rate, Tg CH4 yr−1−505−714 (−698)−628 (−611)−528−621 (−615)−531 (−527)
Lifetime,b yrs8.89.0 (9.4)8.7 (9.1)8.78.5 (8.9)8.3 (8.7)

5. Radiative Forcing

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model Description
  5. 3. Experimental Setup
  6. 4. Results
  7. 5. Radiative Forcing
  8. 6. Conclusions
  9. Acknowledgments
  10. References
  11. Supporting Information

[37] We use instantaneous direct radiative forcing as a tool to assess the impact of future changes in the tropospheric burdens of O3, CH4, and sulfate on the radiative balance of the Earth system. The adjusted forcing would provide a better indication of the climate response in the case of O3 and this value would be slightly less [e.g., Hansen et al., 1997]. The tropopause radiative forcings of O3 and sulfate aerosol are calculated within the GISS climate model's internal radiative transfer scheme, which incorporates relative humidity dependence [Schmidt et al., 2006]. Tropospheric O3 and sulfate aerosol have extremely inhomogeneous spatial distributions, leading to similar inhomogeneity in the radiative forcings. Previous CTM studies have assumed a constant forcing per unit ozone change, thus neglecting the influence of spatial changes in the O3 distribution [Wild et al., 2001; Fiore et al., 2002]. The CH4 radiative forcing is calculated using a standard simplified expression based on concentration change, appropriate for small changes in concentration [Ramaswamy et al., 2001].

[38] The global mean annual average direct radiative forcings of O3, CH4, and sulfate due to composition changes at 2030 relative to present-day for each future projection are presented in Table 9. For O3 the sum of shortwave and longwave radiative forcing is given, whereas for sulfate the shortwave radiative forcing is shown. For comparison, the estimated radiative forcings between present-day and the preindustrial era are included [Ramaswamy et al., 2001].

Table 9. O3, CH4, and Sulfate Radiative Forcing in Units of mW m−2 at 2030 Relative to Present-Day for Each Scenario Due to Emissions Only and Emissions and Climate Changesa
Scenario FamilyGlobal ChangeO3SulfateCH4Total
  • a

    The pre-industrial to present-day change (PI to PD) is indicated for comparison.

PI to PD 350.0−400.0480.0430.0
A1Bemissions-only190.0−240.0264.1214.1
A1Btotal140.0−220.0250.1170.1
B1emissions-only50.0−30.0143.7163.7
B1total20.0−10.0135.2145.2
CLEemissions-only59.4−43.7115.7131.4
CLEtotal12.3−38.089.864.1
MFRemissions-only−51.9174.00.0122.1
MFRtotal−96.6178.3−23.857.9

[39] The resultant forcings span a wide range across the scenarios: +260 to −20.0 mW/m2 for CH4, +190 to −97 mW/m2 for O3, and −240.0 to +180.0 mW/m2 for sulfate aerosol. The A1B, B1, and CLE projections all show positive forcing for CH4 and O3 and negative forcing for sulfate aerosol between 2030 and the present-day. A1B features the largest absolute forcings, each component exerts a forcing approximately half that of the PI to PD change. In contrast, in the MFR scenario, the signs of the forcings for each component are reversed; that is, negative forcing for O3 and CH4 and positive forcing for sulfate aerosol. The reversal in sulfate forcing in the MFR scenario leads to an overall positive forcing for this scenario, despite negative forcings from O3 and CH4. Previous studies examining the climate impacts of the MFR scenario have considered only O3 and CH4 [Dentener et al., 2004; Stevenson et al., 2005]. Our studies reveal that including the impact of sulfate changes presents a significantly different picture with important implications for future climate change under the MFR scenario.

[40] Results from a recent multimodel ensemble investigation, in which the present model was a participant, using the CLE and MFR scenarios indicate combined CH4 and O3 forcings of +180 mW/m2 (CLE) and −40 mW/m2 (MFR) [Stevenson et al., 2006]. The O3 forcings in the present study compare well with the mean values from the multi model study. For CLE emissions only, the present study O3 forcing is +59 mW/m2 versus 63 ± 16 mW/m2 in the multimodel study. For MFR emissions only, the present study O3 forcing is −52 mW/m2 versus −43 ± 15 mW/m2 in the multimodel study. However, inclusion of the sulfate and CH4 forcings in our study, leads to similar combined forcings for CLE and MFR (∼+60 mW/m2).

[41] The coupling of climate change effects to the emissions changes reduces the absolute magnitude of the forcing for all three components. In general, the impact of climate change on the CH4 forcing is small (about 5% for the IPCC scenarios, up to 20% for the IIASA scenarios which used prescribed CH4 concentrations). The O3 and sulfate forcings are more sensitive to the inclusion of climate change effects. For the B1 and CLE scenarios, inclusion of the tropospheric response to climate change dampens the O3 forcing by 60–80%. Hence impacts of climate changes are comparable to emissions changes for the CLE and B1 scenarios.

6. Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model Description
  5. 3. Experimental Setup
  6. 4. Results
  7. 5. Radiative Forcing
  8. 6. Conclusions
  9. Acknowledgments
  10. References
  11. Supporting Information

[42] We have applied the GISS composition-climate model to explore changes to O3, CH4, and sulfate aerosol at 2030 on the basis of four different future scenarios of man-made emissions. The A1B, B1, and CLE futures all suggest a spreading of global air pollution to lower subtropical and tropical latitudes as more nations industrialize. Existing air pollution at northern midlatitudes either increases (A1B), decreases (B1), or remains approximately the same (CLE). The range of regional average surface O3 changes spans −10 to +30 ppbv and the range of regional average surface sulfate changes spans −1200 to 3000 pptv dependent on the scenario. There is a temporal persistence of the surface O3 air pollution in subtropics versus midlatitudes. The Indian subcontinent appears to be a future hot spot for O3 and sulfate pollution because of the large man-made emissions increases projected in the A1B, B1, and CLE scenarios.

[43] We have found a range of projected radiative forcings dependent on the scenario: CH4 (260 to −20 mWm−2), O3 (+190 to −10 mWm−2), sulfate (−240 to +180 mWm−2). The forcings may be as much as half that of the preindustrial to present-day forcing (A1B). All the scenarios have a combined positive forcing, which in the case of the MFR scenario is due to a relatively large positive forcing from the reduced sulfate burden.

[44] In general, physical climate changes dampen climate and air pollution effects of increased man-made emissions, although do increase surface sulfate. For the CLE and B1 scenarios the impacts of physical climate changes on O3 and sulfate are of comparable magnitude to the emissions changes whereas A1B and MFR responses are dominated by the man-made emissions changes. Climate change reduced the radiative forcings, 5–20% for CH4 and up to 60–80% for O3 and sulfate (B1 and CLE).

[45] Despite aggressive (expensive) reductions in O3 precursor gases, the dramatic reductions in sulfate in the MFR future lead to an overall combined positive radiative forcing of similar magnitude to the CLE future. The positive forcing from sulfate reduction will have to be faced at some point, after 2030 if not before. Analysis over a longer time horizon would put the MFR scenario in a more favorable light relative to the other projections. The B1 future enjoys reductions in surface O3 and sulfate across the world's most polluted regions relative to the present-day, but results in a larger CH4 forcing (and therefore overall combined forcing) than the CLE future, which has similar or slightly larger surface O3 values by 2030 relative to the present-day in the NH polluted midlatitude belt. For the A1B future, man strongly negatively influences the quality of the environment through emissions-driven changes in O3, sulfate and CH4. In particular, global increases in CH4 emissions in A1B drive regional increases in surface O3 pollution in areas, especially the eastern United States, where other precursor emissions (NOx, CO) have decreased. However, the climate change scenario that we employ represents about 3/4 of the climate change from the A1B scenario. Use of the consistent A1B climate change scenario would most likely lead to lower surface O3, higher surface sulfate and lower CH4 concentrations than in the present study, although the man-made emissions changes would still dominate the overall changes for this scenario.

[46] The current study has some limitations. We do not consider the impacts of physical climate changes (for example, temperature, humidity, and precipitation) on important biogenic trace gas emissions including isoprene (a major natural O3 precursor), other NMVOCs from vegetation and NOx from soils. Furthermore, the vegetation distribution itself will change in the future because of climate changes and man-made activities such as deforestation, which will influence trace gas emissions and deposition. Neither do we consider future changes in stratospheric composition in the present study. Therefore changes in stratosphere-troposphere exchange are not fully treated. Heterogeneous reactions on mineral dust and interactions with other aerosol types (for example, carbonaceous and nitrate) have not been included in this study although other work suggests sizable forcings, which may impact the present results [e.g., Hansen, 2002, and references therein]. Our future modeling efforts will move us toward more realistic models incorporating these aforementioned processes, including interactive biogenic emissions, dynamic vegetation, stratospheric chemistry and heterogeneous aerosol-chemistry interactions. Nevertheless, the present results do provide effective limits for the magnitude of possible future changes to O3, sulfate and CH4 composition at 2030. We intend to investigate other relevant future time frames (for example, 2050 and 2100) and examine the influence of particular emission sectors (for example, biomass burning versus fossil fuel burning) on future air quality and radiative forcing.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model Description
  5. 3. Experimental Setup
  6. 4. Results
  7. 5. Radiative Forcing
  8. 6. Conclusions
  9. Acknowledgments
  10. References
  11. Supporting Information

[47] This research was supported by the NASA Atmospheric Chemistry Modeling and Analysis Program (ACMAP). We thank the NASA Center for Computational Sciences for computing support.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model Description
  5. 3. Experimental Setup
  6. 4. Results
  7. 5. Radiative Forcing
  8. 6. Conclusions
  9. Acknowledgments
  10. References
  11. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Model Description
  5. 3. Experimental Setup
  6. 4. Results
  7. 5. Radiative Forcing
  8. 6. Conclusions
  9. Acknowledgments
  10. References
  11. Supporting Information
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jgrd12544-sup-0001-t01.txtplain text document1KTab-delimited Table 1.
jgrd12544-sup-0002-t02.txtplain text document0KTab-delimited Table 2.
jgrd12544-sup-0003-t03.txtplain text document1KTab-delimited Table 3.
jgrd12544-sup-0004-t04.txtplain text document0KTab-delimited Table 4.
jgrd12544-sup-0005-t05.txtplain text document0KTab-delimited Table 5.
jgrd12544-sup-0006-t06.txtplain text document1KTab-delimited Table 6.
jgrd12544-sup-0007-t07.txtplain text document1KTab-delimited Table 7.
jgrd12544-sup-0008-t08.txtplain text document1KTab-delimited Table 8.
jgrd12544-sup-0009-t09.txtplain text document1KTab-delimited Table 9.

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