Heat waves and air pollution episodes pose a serious threat to human health and may worsen under future climate change. In this paper, we use 15 years (1999–2013) of commensurately gridded (1° x 1°) ...surface observations of extended summer (April–September) surface ozone (O₃), fine particulate matter (PM2.5), and maximum temperature (TX) over the eastern United States and Canada to construct a climatology of the coincidence, overlap, and lag in space and time of their extremes. Extremes of each quantity are defined climatologically at each grid cell as the 50 d with the highest values in three 5-y windows (∼95th percentile). Any two extremes occur on the same day in the same grid cell more than 50% of the time in the northeastern United States, but on a domain average, co-occurrence is approximately 30%. Although not exactly co-occurring, many of these extremes show connectedness with consistent offsets in space and in time, which often defy traditional mechanistic explanations. All three extremes occur primarily in large-scale, multiday, spatially connected episodes with scales of >1,000 km and clearly coincide with large-scale meteorological features. The largest, longest-lived episodes have the highest incidence of co-occurrence and contain extreme values well above their local 95th percentile threshold, by +7 ppb for O₃, +6 μg m−3 for PM2.5, and +1.7 °C for TX. Our results demonstrate the need to evaluate these extremes as synergistic costressors to accurately quantify their impacts on human health.
Episodes of high near-surface ozone concentrations tend to cover large areas for several days. They are strongly dependent on meteorology, precursor emissions, and the ambient photochemical ...conditions. This study introduces a new pseudo-Lagrangian algorithm that identifies the spatiotemporal patterns of episodes, allowing for a good characterization of their areal extent and an assessment of their drivers. The algorithm has been used to identify ozone episodes in Europe from April to September over the last twenty years (2003–2022) in the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis as well as in the historical simulation (1950–2014) and four shared socio-economic pathways (SSPs, spanning 2015–2100) of three Earth system models (UKESM1-0-LL, EC-Earth3-AerChem and GFDL-ESM4). While the total number of episodes has increased in recent years, the frequency of large episodes has decreased following European precursor emission reductions. The analysis of the 100 largest episodes shows that they tend to occur in Northern Europe during spring and in the center and south of the continent from June onwards. Most of the top 10 episodes occurred in the first years of the century and were associated with high temperatures, enhanced solar radiation, and anticyclonic conditions. Despite the decrease in large episodes in recent years, there is uncertainty regarding future European episodes. Episodes of reduced size are found for SSPs with weak greenhouse forcing and low precursor emissions, whereas episode sizes increase in scenarios with high methane concentrations and enhanced radiative forcing, even exceeding the maximum historical size. However, the three models project episodes of different sizes for any given scenario, probably associated with their differing warming trends and the varying level of complexity in the implementation of processes. These results point to the need to implement both effective climate and air quality policies to address the ozone air pollution problem in Europe in a warming climate.
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•A new algorithm for the identification of large-scale ozone episodes is presented.•Largest episodes located in northern (southern-central) Europe in Apr–May (Jun–Sep).•Large ozone episodes decreased from 2003 to 2022 in the CAMS reanalysis.•Diverging changes are found for the sizes of ozone episodes in future projections.•Emissions and climate change will determine the areal extent of future episodes.
Over the past few decades, the geographical distribution of emissions of substances that alter the atmospheric energy balance has changed due to economic growth and air pollution regulations. Here, ...we show the resulting changes to aerosol and ozone abundances and their radiative forcing using recently updated emission data for the period 1990-2015, as simulated by seven global atmospheric composition models. The models broadly reproduce large-scale changes in surface aerosol and ozone based on observations (e.g. 1 to 3 percent per year in aerosols over the USA and Europe). The global mean radiative forcing due to ozone and aerosol changes over the 1990-2015 period increased by 0.17 plus or minus 0.08 watts per square meter, with approximately one-third due to ozone. This increase is more strongly positive than that reported in IPCC AR5 (Intergovernmental Panel on Climate Change Fifth Assessment Report). The main reasons for the increased positive radiative forcing of aerosols over this period are the substantial reduction of global mean SO2 emissions, which is stronger in the new emission inventory compared to that used in the IPCC analysis, and higher black carbon emissions.
State-of-the-art chemistry–climate models (CCMs) still show biases compared to ground-level ozone observations, illustrating the difficulties and challenges remaining in the simulation of atmospheric ...processes governing ozone production and loss. Therefore, CCM output is frequently bias-corrected in studies seeking to explore the health or environmental impacts from changing air quality burdens. Here, we assess four statistical bias correction techniques of varying complexities and their application to surface ozone fields simulated with four CCMs and evaluate their performance against gridded observations in the EU and US. We focus on two time periods (2005–2009 and 2010–2014), where the first period is used for development and training and the second to evaluate the performance of techniques when applied to model projections. We find that all methods are capable of significantly reducing the model bias. However, biases are lowest when we apply more complex approaches such as quantile mapping and delta functions. We also highlight the sensitivity of the correction techniques to individual CCM skill at reproducing the observed distributional change in surface ozone. Ensemble simulations available for one CCM indicate that model ozone biases are likely more sensitive to the process representation embedded in chemical mechanisms than to meteorology.
The effect of future climate change on surface ozone over North America, Europe, and East Asia is evaluated using present‐day (2000s) and future (2100s) hourly surface ozone simulated by four global ...models. Future climate follows RCP8.5, while methane and anthropogenic ozone precursors are fixed at year 2000 levels. Climate change shifts the seasonal surface ozone peak to earlier in the year and increases the amplitude of the annual cycle. Increases in mean summertime and high‐percentile ozone are generally found in polluted environments, while decreases are found in clean environments. We propose that climate change augments the efficiency of precursor emissions to generate surface ozone in polluted regions, thus reducing precursor export to neighboring downwind locations. Even with constant biogenic emissions, climate change causes the largest ozone increases at high percentiles. In most cases, air quality extreme episodes become larger and contain higher ozone levels relative to the rest of the distribution.
Key Points
Climate change increases surface ozone in polluted regions and decreases surface ozone in nearby and cleaner regions
Surface ozone increases are largest at high percentiles even with constant biogenic emissions
Air quality extremes become more hazardous under future climate warming
Wildfires have become larger and more frequent because of climate change, increasing their impact on air pollution. Air quality forecasts and climate models do not currently account for changes in ...the composition of wildfire emissions during the commonly observed progression from more flaming to smoldering combustion. Laboratory measurements have consistently shown decreased nitrogen dioxide (NO2) relative to carbon monoxide (CO) over time, as they transitioned from more flaming to smoldering combustion, while formaldehyde (HCHO) relative to CO remained constant. Here, we show how daily ratios between column densities of NO2 versus those of CO and HCHO versus CO from the Tropospheric Monitoring Instrument (TROPOMI) changed for large wildfires in the Western United States. TROPOMI‐derived emission ratios were lower than those from the laboratory. We discuss reasons for the discrepancies, including how representative laboratory burns are of wildfires, the effect of aerosols on trace gas retrievals, and atmospheric chemistry in smoke plumes.
Plain Language Summary
Climate change has led to an increase in the frequency and size of wildfires in the Western United States. The gases and particles released from wildfires impact air quality and climate, so it is important to understand the chemical composition of these emissions. In current air quality forecasts and climate models, the composition of wildfire emissions is based on the dominant vegetation burned and is assumed to be constant over time. In contrast, measurements from laboratory burns indicate that the composition of emissions from fires changes over time, as fires progress from more flaming combustion to flameless burning dominated by smoke (smoldering). It is challenging to have daily field measurements of the emissions from long‐lived wildfires, but there are instruments in space that can make daily observations of wildfires globally. In this study, we show how the composition of emissions from wildfires in California, Oregon, and Washington changed over time, as they progressed from more flaming to more smoldering combustion, using observations from a satellite instrument called TROPOMI. The analysis of the composition of wildfire emissions and their evolution over time using TROPOMI could improve air quality forecasting and climate modeling globally.
Key Points
Space‐based remote sensing instruments can be used to observe changes in the composition of wildfire emissions over time
Changes in wildfire emissions composition observed with TROPOMI were caused by evolving combustion conditions rather than aerosol shielding
TROPOMI observations can be used to help parametrize how modeled wildfire emissions should change with evolving combustion conditions
Northern India (23–31∘ N, 68–90∘ E) is one of the most densely populated and polluted regions in world. Accurately modeling pollution in the region is difficult due to the extreme conditions with ...respect to emissions, meteorology, and topography, but it is paramount in order to understand how future changes in emissions and climate may alter the region's pollution regime. We evaluate the ability of a developmental version of the new-generation NOAA GFDL Atmospheric Model, version 4 (AM4) to simulate observed wintertime fine particulate matter (PM2.5) and its relationship to meteorology over Northern India. We compare two simulations of GFDL-AM4 nudged to observed meteorology for the period 1980–2016 driven by pollutant emissions from two global inventories developed in support of the Coupled Model Intercomparison Project Phases 5 (CMIP5) and 6 (CMIP6), and compare results with ground-based observations from India's Central Pollution Control Board (CPCB) for the period 1 October 2015–31 March 2016. Overall, our results indicate that the simulation with CMIP6 emissions produces improved concentrations of pollutants over the region relative to the CMIP5-driven simulation.While the particulate concentrations simulated by AM4 are biased low overall, the model generally simulates the magnitude and daily variability of observed total PM2.5. Nitrate and organic matter are the primary components of PM2.5 over Northern India in the model. On the basis of correlations of the individual model components with total observed PM2.5 and correlations between the two simulations, meteorology is the primary driver of daily variability. The model correctly reproduces the shape and magnitude of the seasonal cycle of PM2.5, but the simulated diurnal cycle misses the early evening rise and secondary maximum found in the observations. Observed PM2.5 abundances are by far the highest within the densely populated Indo-Gangetic Plain, where they are closely related to boundary layer meteorology, specifically relative humidity, wind speed, boundary layer height, and inversion strength. The GFDL AM4 model reproduces the overall observed pollution gradient over Northern India as well as the strength of the meteorology–PM2.5 relationship in most locations.
Abstract
Storylines of atmospheric circulation change, or physically self-consistent narratives of plausible future events, have recently been proposed as a non-probabilistic means to represent ...uncertainties in climate change projections. Here, we apply the storyline approach to 21st century projections of summer air stagnation over Europe and the United States. We use a Climate Model Intercomparison Project Phase 6 (CMIP6) ensemble to generate stagnation storylines based on the forced response of three remote drivers of the Northern Hemisphere mid-latitude atmospheric circulation: North Atlantic warming, North Pacific warming, and tropical versus Arctic warming. Under a high radiative forcing scenario (SSP5-8.5), models consistently project increases in stagnation over Europe and the U.S., but the magnitude and spatial distribution of changes vary substantially across CMIP6 ensemble members, suggesting that future projections are not well-constrained when using the ensemble mean alone. We find that the diversity of projected stagnation changes depends on the forced response of remote drivers in individual models. This is especially true in Europe, where differences of ∼2 summer stagnant days per degree of global warming are found amongst the different storyline combinations. For example, the greatest projected increase in stagnation for most European regions leads to the smallest increase in stagnation for southwestern Europe; i.e. limited North Atlantic warming combined with near-equitable tropical and Arctic warming. In the U.S., only the atmosphere over the northern Rocky Mountain states demonstrates comparable stagnation projection uncertainty, due to opposite influences of remote drivers on the meteorological conditions that lead to stagnation.
Wildfires and meteorological conditions influence the co-occurrence of multiple harmful air pollutants including fine particulate matter (PM
) and ground-level ozone. We examine the spatiotemporal ...characteristics of PM
/ozone co-occurrences and associated population exposure in the western United States (US). The frequency, spatial extent, and temporal persistence of extreme PM
/ozone co-occurrences have increased significantly between 2001 and 2020, increasing annual population exposure to multiple harmful air pollutants by ~25 million person-days/year. Using a clustering methodology to characterize daily weather patterns, we identify significant increases in atmospheric ridging patterns conducive to widespread PM
/ozone co-occurrences and population exposure. We further link the spatial extent of co-occurrence to the extent of extreme heat and wildfires. Our results suggest an increasing potential for co-occurring air pollution episodes in the western US with continued climate change.
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