Purpose
Health damage from ambient fine particulate matter (PM
2.5
) shows large regional variations and can have an impact on a global scale due to its transboundary movement. However, existing ...damage factors (DFs) for human health in life cycle assessments (LCA) are calculated only for a few limited regions based on various regional chemical transport models (CTMs). The aim of this research is to estimate the human health DFs of PM
2.5
originating from ten different regions of the world by using one global CTM.
Methods
The DFs express changes in worldwide disability-adjusted life years (DALYs) due to unit emission of black carbon and organic carbon (BCOC), nitrogen oxides (NO
x
), and sulfur dioxide (SO
2
). DFs for ten regions were calculated as follows. Firstly, we divided the whole world into ten regions. With a global CTM (MIROC-ESM-CHEM), we estimated the concentration change of PM
2.5
on the world caused by changes in the emission of a targeted precursor substance from a specific region. Secondly, we used population data and epidemiological concentration response functions (CRFs) of mortality and morbidity to estimate changes in the word’s DALYs occurring due to changes in the concentration of PM
2.5
. Finally, the above calculations were done for all ten regions.
Results and discussion
DFs of BCOC, NO
x
, and SO
2
for ten regions were estimated. The range of DFs could be up to one order of magnitude among the ten regions in each of the target substances. While population density was an important parameter, variation in transport of PM
2.5
on a continental level occurring due to different emission regions was found to have a significant influence on DFs. Especially for regions of Europe, Russia, and the Middle East, the amount of damage which occurred outside of the emitted region was estimated at a quarter, a quarter, and a third of their DFs, respectively. It was disclosed that the DFs will be underestimated if the transboundary of PM
2.5
is not taken into account in those regions.
Conclusions
The human health damage factors of PM
2.5
produced by BCOC, NO
x
, and SO
2
are estimated for ten regions by using one global chemical transport model. It became clear that the variation of transport for PM
2.5
on a continental level greatly influences the regionality in DFs. For further research to quantify regional differences, it is important to consider the regional values of concentration response function (CRF) and DALY loss per case of disease or death.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The sixth version of the Model for Interdisciplinary Research on Climate
(MIROC), called MIROC6, was cooperatively developed by a Japanese modeling
community. In the present paper, simulated mean ...climate, internal
climate variability, and climate sensitivity in MIROC6 are evaluated and
briefly summarized in comparison with the previous version of our climate
model (MIROC5) and observations. The results show that the overall
reproducibility of mean climate and internal climate variability in MIROC6
is better than that in MIROC5. The tropical climate systems (e.g.,
summertime precipitation in the western Pacific and the eastward-propagating
Madden–Julian oscillation) and the midlatitude atmospheric circulation
(e.g., the westerlies, the polar night jet, and troposphere–stratosphere
interactions) are significantly improved in MIROC6. These improvements can
be attributed to the newly implemented parameterization for shallow
convective processes and to the inclusion of the stratosphere. While there
are significant differences in climates and variabilities between the two
models, the effective climate sensitivity of 2.6 K remains the same because
the differences in radiative forcing and climate feedback tend to offset
each other. With an aim towards contributing to the sixth phase of the
Coupled Model Intercomparison Project, designated simulations tackling a
wide range of climate science issues, as well as seasonal to decadal climate
predictions and future climate projections, are currently ongoing using
MIROC6.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
We introduce a Multi-mOdel Multi-cOnstituent Chemical data assimilation (MOMO-Chem) framework that directly accounts for model error in transport and chemistry, and we integrate a portfolio of data ...assimilation analyses obtained using multiple forward chemical transport models in a state-of-the-art ensemble Kalman filter data assimilation system. The data assimilation simultaneously optimizes both concentrations and emissions of multiple species through ingestion of a suite of measurements (ozone, NO2, CO, HNO3) from multiple satellite sensors. In spite of substantial model differences, the observational density and accuracy was sufficient for the assimilation to reduce the multi-model spread by 20 %–85 % for ozone and annual mean bias by 39 %–97 % for ozone in the middle troposphere, while simultaneously reducing the tropospheric NO2 column biases by more than 40 % and the negative biases of surface CO in the Northern Hemisphere by 41 %–94 %. For tropospheric mean OH, the multi-model mean meridional hemispheric gradient was reduced from 1.32±0.03 to 1.19±0.03, while the multi-model spread was reduced by 24 %–58 % over polluted areas. The uncertainty ranges in the a posteriori emissions due to model errors were quantified in 4 %–31 % for NOx and 13 %–35 % for CO regional emissions. Harnessing assimilation increments in both NOx and ozone, we show that the sensitivity of ozone and NO2 surface concentrations to NOx emissions varied by a factor of 2 for end-member models, revealing fundamental differences in the representation of fast chemical and dynamical processes. A systematic investigation of model ozone response and analysis increment in MOMO-Chem could benefit evaluation of future prediction of the chemistry–climate system as a hierarchical emergent constraint.
We consider future changes in tropospheric ozone based on the Representative Concentration Pathways (RCPs), which are new emission and concentration scenarios for the 5th coupled model ...intercomparison project. In contrast to the SRES scenarios, all the RCP scenarios assume an emission reduction of NOx by the late 21st Century that has the potential to achieve tropospheric ozone reduction. However, increasing radiative forcing (RF) due to greenhouse gases and changes in CH4 concentration also contribute to differences in the tropospheric ozone distribution among RCP scenarios. In the RCP4.5 and RCP6.0, assuming the stabilization of RF, the increase in tropospheric ozone due to enhanced residual circulation is cancelled out by the ozone reduction due to ozone precursor reductions. In contrast, in the RCP8.5, assuming increasing RF even after 2100, further enhanced residual circulation and significant increase in CH4 cause a dramatic increase in tropospheric ozone.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
The relative contributions of various source regions to the long-term (1980–2005) increasing trend in surface ozone (O3) over Japan were estimated by a series of tracer-tagging simulations using a ...global chemical transport model. The model simulated the observed increasing trend in surface O3, including its seasonal variation and geographical features, in Japan well and demonstrated the relative roles of different source regions in forming this trend. Most of the increasing trend in surface O3 over Japan ( ∼ 97 %) that was simulated was explained as the sum of trends in contributions of different regions to photochemical O3 production. The increasing trend in O3 produced in China accounted for 36 % of the total increasing trend and those in the other northeast Asian regions (the Korean Peninsula, coastal regions in East Asia, and Japan) each accounted for about 12–15 %. Furthermore, the contributions of O3 created in the entire free troposphere and in western, southern, and southeastern Asian regions also increased, and their increasing trends accounted for 16 and 7 % of the total trend, respectively. The impact of interannual variations in climate, in methane concentration, and in emission of O3 precursors from different source regions on the relative contributions of O3 created in each region estimated above was also investigated. The variation of climate and the increase in methane concentration together caused the increase of photochemical O3 production in several regions, and represented about 19 % of the total increasing trend in surface O3 over Japan. The increase in emission of O3 precursors in China caused an increase of photochemical O3 production not only in China itself but also in the other northeast Asian regions and accounted for about 46 % of the total increase in surface O3 over Japan. Similarly, the relative impact of O3 precursor emission changes in the Korean Peninsula and Japan were estimated as about 16 and 4 % of the total increasing trend, respectively. The O3 precursor emission change in regions other than northeast Asia caused increases in surface O3 over Japan mainly through increasing photochemical O3 production in western, southern, and southeast Asia and the free troposphere and accounted for about 16 % of the total.
We present an overview of state-of-the-art chemistry-climate and chemistry transport models that are used within phase 1 of the Chemistry-Climate Model Initiative (CCMI-1). The CCMI aims to conduct a ...detailed evaluation of participating models using process-oriented diagnostics derived from observations in order to gain confidence in the models' projections of the stratospheric ozone layer, tropospheric composition, air quality, where applicable global climate change, and the interactions between them. Interpretation of these diagnostics requires detailed knowledge of the radiative, chemical, dynamical, and physical processes incorporated in the models. Also an understanding of the degree to which CCMI-1 recommendations for simulations have been followed is necessary to understand model responses to anthropogenic and natural forcing and also to explain inter-model differences. This becomes even more important given the ongoing development and the ever-growing complexity of these models. This paper also provides an overview of the available CCMI-1 simulations with the aim of informing CCMI data users.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
>We analyse simulations performed for the Chemistry-Climate Model Initiative (CCMI) to estimate the return dates of the stratospheric ozone layer from depletion caused by anthropogenic stratospheric ...chlorine and bromine. We consider a total of 155 simulations from 20 models, including a range of sensitivity studies which examine the impact of climate change on ozone recovery. For the control simulations (unconstrained by nudging towards analysed meteorology) there is a large spread (±20 DU in the global average) in the predictions of the absolute ozone column. Therefore, the model results need to be adjusted for biases against historical data. Also, the interannual variability in the model results need to be smoothed in order to provide a reasonably narrow estimate of the range of ozone return dates. Consistent with previous studies, but here for a Representative Concentration Pathway (RCP) of 6.0, these new CCMI simulations project that global total column ozone will return to 1980 values in 2049 (with a 1σ uncertainty of 2043–2055). At Southern Hemisphere mid-latitudes column ozone is projected to return to 1980 values in 2045 (2039–2050), and at Northern Hemisphere mid-latitudes in 2032 (2020–2044). In the polar regions, the return dates are 2060 (2055–2066) in the Antarctic in October and 2034 (2025–2043) in the Arctic in March. The earlier return dates in the Northern Hemisphere reflect the larger sensitivity to dynamical changes. Our estimates of return dates are later than those presented in the 2014 Ozone Assessment by approximately 5–17 years, depending on the region, with the previous best estimates often falling outside of our uncertainty range. In the tropics only around half the models predict a return of ozone to 1980 values, around 2040, while the other half do not reach the 1980 value. All models show a negative trend in tropical total column ozone towards the end of the 21st century. The CCMI models generally agree in their simulation of the time evolution of stratospheric chlorine and bromine, which are the main drivers of ozone loss and recovery. However, there are a few outliers which show that the multi-model mean results for ozone recovery are not as tightly constrained as possible. Throughout the stratosphere the spread of ozone return dates to 1980 values between models tends to correlate with the spread of the return of inorganic chlorine to 1980 values. In the upper stratosphere, greenhouse gas-induced cooling speeds up the return by about 10–20 years. In the lower stratosphere, and for the column, there is a more direct link in the timing of the return dates of ozone and chlorine, especially for the large Antarctic depletion. Comparisons of total column ozone between the models is affected by different predictions of the evolution of tropospheric ozone within the same scenario, presumably due to differing treatment of tropospheric chemistry. Therefore, for many scenarios, clear conclusions can only be drawn for stratospheric ozone columns rather than the total column. As noted by previous studies, the timing of ozone recovery is affected by the evolution of N2O and CH4. However, quantifying the effect in the simulations analysed here is limited by the few realisations available for these experiments compared to internal model variability. The large increase in N2O given in RCP 6.0 extends the ozone return globally by ∼ 15 years relative to N2O fixed at 1960 abundances, mainly because it allows tropical column ozone to be depleted. The effect in extratropical latitudes is much smaller. The large increase in CH4 given in the RCP 8.5 scenario compared to RCP 6.0 also lengthens ozone return by ∼ 15 years, again mainly through its impact in the tropics. Overall, our estimates of ozone return dates are uncertain due to both uncertainties in future scenarios, in particular those of greenhouse gases, and uncertainties in models. The scenario uncertainty is small in the short term but increases with time, and becomes large by the end of the century. There are still some model–model differences related to well-known processes which affect ozone recovery. Efforts need to continue to ensure that models used for assessment purposes accurately represent stratospheric chemistry and the prescribed scenarios of ozone-depleting substances, and only those models are used to calculate return dates. For future assessments of single forcing or combined effects of CO2, CH4, and N2O on the stratospheric column ozone return dates, this work suggests that it is more important to have multi-member (at least three) ensembles for each scenario from every established participating model, rather than a large number of individual models.
We investigate the contributions of emission changes from 10 world regions, as well as the global methane concentration change, on the global tropospheric ozone burden change from 1980 to 2010. The ...modeled global tropospheric ozone burden has increased by 28.1 Tg, with 26.7% (7.5 Tg) of this change attributed to the global methane increase. Southeast Asia (5.6 Tg) and South Asia (4.0) contribute comparably to the global ozone burden change as East Asia (5.6), even though NOx emission increases in each region are less than one‐third of those in East Asia, highlighting the greater sensitivity of global ozone to these regions. Emission decreases from North America, Europe, and Former Soviet Union have led to ozone burden decreases of 2.8, 1.0, and 0.3 Tg. The greater sensitivity of the global ozone burden to emission changes in tropical and subtropical regions emphasizes the importance of controlling emissions in these regions for global ozone.
Plain Language Summary
The global tropospheric ozone burden is highly sensitive to emission changes in tropical and subtropical regions, due to high temperature, strong sunlight, and convection which are favorable for ozone production and accumulation. Through model sensitivity simulations, we show that emission increases in Southeast Asia, South Asia, and East Asia contribute over half of the global tropospheric ozone burden increase from 1980 to 2010. Southeast Asia and South Asia contribute about as much to the ozone increase as East Asia, even though emission increases were much smaller from these regions, showing the high ozone sensitivity in these regions.
Key Points
Tropospheric ozone burden increased from 1980 to 2010, driven mainly by increases in emissions from Southeast Asia, East Asia, and South Asia, as well as global methane concentration increases
Among regions, the greatest ozone burden influence came from Southeast Asia despite smaller emission increases, highlighting the much greater sensitivity for this region
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Interannual variation in global tropospheric ozone associated with meteorological variability is characterized in this study using a global chemical transport model CHASER. We focus on five ...meteorological variability: El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Arctic Oscillation (AO), Hadley, and monsoon circulations. Results show that the anomaly in tropospheric column ozone (TCO) is positive (1–1.5 DU) in the western Pacific including Indonesia and negative (−2.5 DU) in the eastern Pacific in October–November–December (OND) during positive phase of ENSO. The model exhibits TCO increase (0.5–1.5 DU) in the central to eastern Pacific over the subtropics. During the positive phase of IOD, the model shows TCO increase (1.5–2 DU) in the west of 90°E with a decrease (1–1.5 DU) in the east. Intensified Hadley circulation causes TCO increase (0.8 DU) in North America in DJF. Intensified monsoon circulation enhances TCO (1.2 DU) in the western Indian Ocean. During positive phase of AO, TCO is decreased (1 DU) in the high northern latitudes. The contribution of meteorological variability to total interannual variation in global and regional TCO is also quantified in this study. The results suggest that interannual variation of TCO in the tropics and high northern latitudes are generally explainable by ENSO, IOD, AO, and interannual variation in Hadley and monsoon circulations. ENSO explains 79% of the variance in the tropical eastern Pacific in OND. AO explains 72% of the variance in the high northern latitudes in DJF.
Key Points
Impact of meteorological variability on tropospheric ozone is investigated
The impacts are dominated by interannual variation in ozone transport
Meteorological variability affects tropospheric ozone in the tropics and Arctic
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