Changes in atmospheric methane abundance have implications for both chemistry and climate as methane is both a strong greenhouse gas and an important precursor for tropospheric ozone. A better ...understanding of the drivers of trends and variability in methane abundance over the recent past is therefore critical for building confidence in projections of future methane levels. In this work, the representation of methane in the atmospheric chemistry model AM4.1 is improved by optimizing total methane emissions (to an annual mean of 580±34 Tg yr−1) to match surface observations over 1980–2017. The simulations with optimized global emissions are in general able to capture the observed trend, variability, seasonal cycle, and latitudinal gradient of methane. Simulations with different emission adjustments suggest that increases in methane emissions (mainly from agriculture, energy, and waste sectors) balanced by increases in methane sinks (mainly due to increases in OH levels) lead to methane stabilization (with an imbalance of 5 Tg yr−1) during 1999–2006 and that increases in methane emissions (mainly from agriculture, energy, and waste sectors) combined with little change in sinks (despite small decreases in OH levels) during 2007–2012 lead to renewed growth in methane (with an imbalance of 14 Tg yr−1 for 2007–2017). Compared to 1999–2006, both methane emissions and sinks are greater (by 31 and 22 Tg yr−1, respectively) during 2007–2017. Our tagged tracer analysis indicates that anthropogenic sources (such as agriculture, energy, and waste sectors) are more likely major contributors to the renewed growth in methane after 2006. A sharp increase in wetland emissions (a likely scenario) with a concomitant sharp decrease in anthropogenic emissions (a less likely scenario), would be required starting in 2006 to drive the methane growth by wetland tracer. Simulations with varying OH levels indicate that a 1 % change in OH levels could lead to an annual mean difference of ∼4 Tg yr−1 in the optimized emissions and a 0.08-year difference in the estimated tropospheric methane lifetime. Continued increases in methane emissions along with decreases in tropospheric OH concentrations during 2008–2015 prolong methane's lifetime and therefore amplify the response of methane concentrations to emission changes. Uncertainties still exist in the partitioning of emissions among individual sources and regions.
US surface O3 responds to varying global-to-regional precursor emissions, climate, and extreme weather, with implications for designing effective air quality control policies. We examine these ...conjoined processes with observations and global chemistry-climate model (GFDL-AM3) hindcasts over 1980–2014. The model captures the salient features of observed trends in daily maximum 8 h average O3: (1) increases over East Asia (up to 2 ppb yr−1), (2) springtime increases at western US (WUS) rural sites (0.2–0.5 ppb yr−1) with a baseline sampling approach, and (3) summertime decreases, largest at the 95th percentile, and wintertime increases in the 50th to 5th percentiles over the eastern US (EUS). Asian NOx emissions have tripled since 1990, contributing as much as 65 % to modeled springtime background O3 increases (0.3–0.5 ppb yr−1) over the WUS, outpacing O3 decreases attained via 50 % US NOx emission controls. Methane increases over this period contribute only 15 % of the WUS background O3 increase. Springtime O3 observed in Denver has increased at a rate similar to remote rural sites. During summer, increasing Asian emissions approximately offset the benefits of US emission reductions, leading to weak or insignificant observed O3 trends at WUS rural sites. Mean springtime WUS O3 is projected to increase by ∼ 10 ppb from 2010 to 2030 under the RCP8.5 global change scenario. While historical wildfire emissions can enhance summertime monthly mean O3 at individual sites by 2–8 ppb, high temperatures and the associated buildup of O3 produced from regional anthropogenic emissions contribute most to elevating observed summertime O3 throughout the USA. GFDL-AM3 captures the observed interannual variability of summertime EUS O3. However, O3 deposition sink to vegetation must be reduced by 35 % for the model to accurately simulate observed high-O3 anomalies during the severe drought of 1988. Regional NOx reductions alleviated the O3 buildup during the recent heat waves of 2011 and 2012 relative to earlier heat waves (e.g., 1988, 1999). The O3 decreases driven by NOx controls were more pronounced in the southeastern US, where the seasonal onset of biogenic isoprene emissions and NOx-sensitive O3 production occurs earlier than in the northeast. Without emission controls, the 95th percentile summertime O3 in the EUS would have increased by 0.2–0.4 ppb yr−1 over 1988–2014 due to more frequent hot extremes and rising biogenic isoprene emissions.
Exposure to elevated concentrations of surface ozone (O
3) causes substantial reductions in the agricultural yields of many crops. As emissions of O
3 precursors rise in many parts of the world over ...the next few decades, yield reductions from O
3 exposure appear likely to increase the challenges of feeding a global population projected to grow from 6 to 9 billion between 2000 and 2050. This study estimates year 2000 global yield reductions of three key staple crops (soybean, maize, and wheat) due to surface ozone exposure using hourly O
3 concentrations simulated by the Model for Ozone and Related Chemical Tracers version 2.4 (MOZART-2). We calculate crop losses according to two metrics of ozone exposure – seasonal daytime (08:00–19:59) mean O
3 (M12) and accumulated O
3 above a threshold of 40 ppbv (AOT40) – and predict crop yield losses using crop-specific O
3 concentration:response functions established by field studies. Our results indicate that year 2000 O
3-induced global yield reductions ranged, depending on the metric used, from 8.5–14% for soybean, 3.9–15% for wheat, and 2.2–5.5% for maize. Global crop production losses totaled 79–121 million metric tons, worth $11–18 billion annually (USD
2000). Our calculated yield reductions agree well with previous estimates, providing further evidence that yields of major crops across the globe are already being substantially reduced by exposure to surface ozone – a risk that will grow unless O
3-precursor emissions are curbed in the future or crop cultivars are developed and utilized that are resistant to O
3.
► Surface O
3 is having a substantial impact on the yields of key crops. ► Yields of wheat, soybean, and maize were reduced by up to 15% globally in 2000. ► Global year 2000 crop production losses totaled 79–121 million metric tons. ► Agricultural losses are estimated to be worth $11–18 billion USD
2000 annually.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
We analyze the relationship between fine particulate matter (PM2.5) and meteorology in winter in the Indo‐Gangetic Plain (IGP). We find that the concentration of PM2.5 exhibits similar increase with ...decreasing surface wind speed in 15 out of 18 cities considered. Using this observed relationship, we estimate that the reduction of surface wind speed with increasing CO2 simulated by models participating in the Coupled Model Intercomparison Project Phase 6 will result in higher average wintertime PM2.5 concentrations (1% per degree K of global warming) and more frequent high‐pollution events. This observation‐based estimate is qualitatively consistent with the simulated response of black carbon to global warming inferred from the AerChemMIP ssp370SST and ssp370pdSST experiments. We hypothesize that a reduction in the frequency and intensity of western disturbances with increasing CO2 may contribute to the reduction in the surface wind in the IGP.
Plain Language Summary
The Indo‐Gangetic Plain, home to over 800 million people, experiences among the most elevated concentrations of fine particulate matter in the world. Such high levels of air pollution are estimated to reduce average life expectancy by several years. Air quality is especially poor in wintertime, in part due to meteorological conditions such as slow wind speeds that favor the accumulation of air pollutants near the surface. CMIP6 models project that increasing CO2 will cause a reduction in surface wind speed in the Indo‐Gangetic Plain. We show that this reduction in wind speed will result in higher wintertime PM2.5 concentration (1%/K) and more frequent high‐PM2.5 days. This CO2 penalty highlights the need for stringent air pollution controls in this region.
Key Points
In winter, slow wind speeds are often accompanied by high concentrations of PM2.5 in the Indo‐Gangetic Plain
Wind speeds are projected to decrease with increasing CO2 worsening PM2.5
Slower wind speeds may be caused by less frequent and less intense western disturbances
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Background: Ground-level concentrations of ozone (O₃) and fine particulate matter ≤ 2.5 μm in aerodynamic diameter (PM2.5) have increased since preindustrial times in urban and rural regions and are ...associated with cardiovascular and respiratory mortality. Objectives: We estimated the global burden of mortality due to O₃ and PM2.5 from anthropogenic emissions using global atmospheric chemical transport model simulations of preindustrial and present-day (2000) concentrations to derive exposure estimates. Methods: Attributable mortalities were estimated using health impact functions based on long-term relative risk estimates for O₃ and PM2.5 from the epidemiology literature. Using simulated concentrations rather than previous methods based on measurements allows the inclusion of rural areas where measurements are often unavailable and avoids making assumptions for background air pollution. Results: Anthropogenic O₃ was associated with an estimated 0.7 ± 0.3 million respiratory mortalities (6.3 ± 3.0 million years of life lost) annually. Anthropogenic PM2.5 was associated with 3.5 ± 0.9 million cardiopulmonary and 220,000 ± 80,000 lung cancer mortalities (30 ± 7.6 million years of life lost) annually. Mortality estimates were reduced approximately 30% when we assumed low-concentration thresholds of 33.3 ppb for O₃ and 5.8 μg/m³ for PM2.5. These estimates were sensitive to concentration thresholds and concentration—mortality relationships, often by > 50%. Conclusions: Anthropogenic O₃ and PM2.5 contribute substantially to global premature mortality. PM2.5 mortality estimates are about 50% higher than previous measurement-based estimates based on common assumptions, mainly because of methodologic differences. Specifically, we included rural populations, suggesting higher estimates; however, the coarse resolution of the global atmospheric model may underestimate urban PM2.5 exposures.
Full text
Available for:
BFBNIB, DOBA, IZUM, KILJ, NMLJ, NUK, OILJ, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK, VSZLJ
We present estimates of changes in the direct aerosol effects (DRE) and its
anthropogenic component (DRF) from 2001 to 2015 using the GFDL
chemistry–climate model AM3 driven by CMIP6 historical ...emissions. AM3 is
evaluated against observed changes in the clear-sky shortwave direct aerosol
effect (DREswclr) derived from the Clouds and
the Earth's Radiant Energy System (CERES) over polluted regions. From 2001 to
2015, observations suggest that DREclrsw
increases (i.e., less radiation is scattered to space by aerosols) over
western Europe (0.7–1 W m−2 decade−1) and the eastern US
(0.9–1.4 W m−2 decade−1), decreases over India (−1 to
−1.6 W m−2 decade−1), and does not change significantly over
eastern China. AM3 captures these observed regional changes in
DREclrsw well in the US and western Europe,
where they are dominated by the decline of sulfate aerosols, but not in Asia,
where the model overestimates the decrease of
DREclrsw. Over India, the model bias can be
partly attributed to a decrease of the dust optical depth, which is not
captured by our model and offsets some of the increase of anthropogenic
aerosols. Over China, we find that the decline of SO2 emissions
after 2007 is not represented in the CMIP6 emission inventory. Accounting for
this decline, using the Modular Emission Inventory for China, and for the
heterogeneous oxidation of SO2 significantly reduces the model
bias. For both India and China, our simulations indicate that nitrate and
black carbon contribute more to changes in
DREclrsw than in the US and Europe. Indeed,
our model suggests that black carbon (+0.12 W m−2) dominates the
relatively weak change in DRF from 2001 to 2015 (+0.03 W m−2). Over
this period, the changes in the forcing from nitrate and sulfate are both
small and of the same magnitude (−0.03 W m−2 each). This is in sharp
contrast to the forcing from 1850 to 2001 in which forcings by sulfate and
black carbon largely cancel each other out, with minor contributions from
nitrate. The differences between these time periods can be well understood
from changes in emissions alone for black carbon but not for nitrate and
sulfate; this reflects non-linear changes in the photochemical production of
nitrate and sulfate associated with changes in both the magnitude and spatial
distribution of anthropogenic emissions.
Using observations and model simulations (ESM4.1) during 1988–2018, we show large year‐to‐year variability in western U.S. PM2.5 pollution caused by regional and distant fires. Widespread wildfires, ...combined with stagnation, caused summer PM2.5 pollution in 2017 and 2018 to exceed 2 standard deviations over long‐term averages. ESM4.1 with a fire emission inventory constrained by satellite‐derived fire radiative energy and aerosol optical depth captures the observed surface PM2.5 means and extremes above the 35 μg/m3 U.S. air quality standard. However, aerosol emissions from the widely used Global Fire Emissions Database (GFED) must be increased by 5 times for ESM4.1 to match observations. On days when observed PM2.5 reached 35–175 μg/m3, wildfire emissions can explain 90% of total PM2.5, with smoke transported from Canada contributing 25–50% in northern states, according to model sensitivity simulations. Fire emission uncertainties pose challenges to accurately assessing the impacts of fire smoke on air quality, radiation, and climate.
Plain Language Summary
Frequent and intense wildfires harm public health over the western United States. In order to understand how wildfires affect fine particulate air quality, we analyze surface and satellite measurements and computer model simulations of weather and atmospheric chemistry over the past 30 years. We show that widespread fires and regional transport of fire smoke are the main causes of year‐to‐year changes in summertime particle pollution measured at western U.S. surface sites. The U.S. Environmental Protection Agency defines daily particle concentration above 35 μg/m3 as unhealthy. In the summers of 2017–2018, record‐breaking wildfires, combined with stable weather conditions, resulted in daily particle concentration of 35 to 175 μg/m3 across western U.S. sites. These particle pollution extremes are twice as severe as long‐term average conditions. Wildfire emissions contributed 90% of particle levels on these periods. Notably, transport of fire smoke from southwestern Canada can explain 25% to 50% of particle pollution in northern states such as Washington. Our model successfully simulates these pollution extremes when applying a fire emission data set constrained by satellite observations of total particle abundances. Our results indicate fourfold to fivefold underestimates of particle emissions from the widely used Global Fire Emissions Database not constrained by satellite observations.
Key Points
Large interannual variations of western U.S. fine particulate pollution in summer were driven by regional and distant fires
Widespread wildfires and stagnation in 2017–2018 caused fine particulate extremes to exceed 2 standard deviations over long‐term averages
Observations and model analyses indicate fourfold to fivefold underestimate of aerosol emissions from the widely used Global Fire Emissions Database
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
PDF
