Arlene M. Fiore’s research while affiliated with Massachusetts Institute of Technology and other places

The atmospheric chemistry of volatile organic compounds (VOC) has a major influence on atmospheric pollutants and particle formation. Accurate modeling of this chemistry is essential for air quality models. Complete representations of VOC oxidation chemistry are far too large for spatiotemporal simulations of the atmosphere, necessitating reduced mechanisms. We present Automated MOdel REduction version 2.0 (AMORE 2.0), an algorithm for the reduction of any VOC oxidation mechanism to a desired size by removing, merging, and rerouting sections of the graph representation of the mechanism. We demonstrate the algorithm on isoprene (398 species) and camphene (100,000 species) chemistry. We remove up to 95% of isoprene species while improving upon prior reduced isoprene mechanisms by 53-67% using a multi-species metric. We remove 99% camphene species while accurately matching camphene secondary organic aerosol production. This algorithm will bridge the gap between large and reduced mechanisms, helping to improve air quality models.

The hydroxyl radical (OH) plays a central role in tropospheric chemistry, as well as influencing the lifetimes of some greenhouse gases. Because of limitations in our ability to observe OH, we have historically relied on indirect methods to constrain its concentrations, trends, and variations but only as annual global or annual semi-hemispheric averages. Recent methods demonstrated the feasibility of indirectly constraining tropospheric OH on finer spatio-temporal scales using satellite observations as proxies for the photochemical drivers of OH (e.g., nitrogen dioxide, formaldehyde, isoprene, water vapor, ozone). We found that there are currently reasonable satellite proxies to constrain up to about 75 % of the global sources of tropospheric OH and up to about 50 % of the global sinks. With additional research and investment in observing various volatile organic compounds, there is potential to constrain an additional 10 % of the global sources and 30 % of the global sinks. We propose steps forward for the development of a comprehensive space-based observing strategy, which will improve our ability to indirectly constrain OH on much finer spatio-temporal scales than previously achieved. We discuss the strengths and limitations of such an observing strategy and potential improvements to current satellite instrument observing capabilities that would enable better constraint of OH. Suborbital observations (i.e., data collected from non-satellite platforms such as aircraft, balloons, and buildings) are required to collect information difficult to obtain from space and for validation of satellite-based OH estimates; therefore, they should be an integral part of a comprehensive observing strategy.

Climate models exhibit an approximately invariant surface warming pattern in typical end-of-century projections. This observation has been used extensively in climate impact assessments for fast calculations of local temperature anomalies, with a linear procedure known as pattern scaling. At the same time, emerging research has also shown that time-varying warming patterns are necessary to explain the time evolution of effective climate sensitivity in coupled models, a mechanism that is known as the pattern effect and that seemingly challenges the pattern scaling understanding. Here we present a simple theory based on local energy balance arguments to reconcile this apparent contradiction. Specifically, we show that the pattern invariance is an inherent feature of exponential forcing, linear feedbacks, a constant forcing pattern and diffusive dynamics. These conditions are approximately met in most CMIP6 Shared Socioeconomic Pathways (SSP), except in the Arctic where nonlinear feedbacks are important and in regions where aerosols considerably alter the forcing pattern. In idealized experiments where concentrations of CO2 are abruptly increased, such as those used to study the pattern effect, the warming pattern can change considerably over time because of spatially inhomogeneous ocean heat uptake, even in the absence of nonlinear feedbacks. Our results illustrate why typical future projections are amenable to pattern scaling, and provide a plausible explanation of why more complicated approaches, such as nonlinear emulators, have only shown marginal improvements in accuracy over simple linear calculations.

Wildfire frequency is increasing as the climate changes, and the resulting air pollution poses health risks. Just as people routinely use weather forecasts to plan their activities around precipitation, reliable air quality forecasts could help individuals reduce their exposure to air pollution. In the present work, we evaluate several existing forecasts of fine particular matter (PM2.5) within the continental United States in the context of individual decision-making. Our comparison suggests there is meaningful room for improvement in air pollution forecasting, which might be realized by incorporating more data sources and using machine learning tools. To facilitate future machine learning development and benchmarking, we set up a framework to evaluate and compare air pollution forecasts for individual decision making. We introduce a new loss to capture decisions about when to use mitigation measures. We highlight the importance of visualizations when comparing forecasts. Finally, we provide code to download and compare archived forecast predictions.

Plain Language Summary Carbon dioxide (CO2) emissions impact surface temperature. It is well established that the global mean temperature change is proportional to the cumulative emissions of CO2. This has led to the creation of carbon budgets to reach temperature goals. We test this relationship at the spatio‐temporal scale, quantifying a simple approach that estimates the local temperature response to CO2 emissions alone. We use an approach built from the Climate Model Intercomparison Project Phase 6 (CMIP6) Earth System Models, based on the concept that an additional unit of CO2 can be scaled for larger emissions and summed over time to estimate cumulative impacts. We evaluate this with additional CMIP6 experiments, showing that this approach captures the temperature response in most locations with lower accuracy in the Arctic and Southern Ocean. This type of approach may be useful to evaluate many policy scenarios and to better understand earth system processes that are represented in the models, as it takes around one second to quantify 90 years' worth of temperature change on a local computer, while Earth System Models can require weeks of runtime on supercomputers.

Tropospheric ozone (O3) is a strong greenhouse gas, particularly in the upper troposphere (UT). Limited observations point to a continuous increase in UT O3 in recent decades, but the attribution of UT O3 changes is complicated by large internal climate variability. We show that the anthropogenic signal (“fingerprint”) in the patterns of UT O3 increases is distinguishable from the background noise of internal variability. The time-invariant fingerprint of human-caused UT O3 changes is derived from a 16-member initial-condition ensemble performed with a chemistry-climate model (CESM2-WACCM6). The fingerprint is largest between 30°S and 40°N, especially near 30°N. In contrast, the noise pattern in UT O3 is mainly associated with the El Niño–Southern Oscillation (ENSO). The UT O3 fingerprint pattern can be discerned with high confidence within only 13 years of the 2005 start of the OMI/MLS satellite record. Unlike the UT O3 fingerprint, the lower tropospheric (LT) O3 fingerprint varies significantly over time and space in response to large-scale changes in anthropogenic precursor emissions, with the highest signal-to-noise ratios near 40°N in Asia and Europe. Our analysis reveals a significant human effect on Earth’s atmospheric chemistry in the UT and indicates promise for identifying fingerprints of specific sources of ozone precursors.

The hydroxyl radical (OH) plays a central role in tropospheric chemistry as well as influencing the lifetimes of some climate gases, such as methane. Because of limitations in our ability to observe OH, we have historically relied on indirect methods to constrain its concentrations, trends, and variations, but only as annual global or semi-hemispheric averages. Recent methods demonstrated the feasibility of indirectly constraining tropospheric OH on finer spatio-temporal scales (e.g., seasonal, 1° latitude x 1° longitude), using satellite observations as proxies of the photochemical drivers of OH (e.g., nitrogen dioxide, formaldehyde, isoprene, water vapor, ozone). We found that there are currently reasonable satellite proxies to constrain about 75 % of the global source of tropospheric OH and about 50 % of the global sink. With additional research and investment in observing various volatile organic compounds, there is the potential to constrain an additional 10 % of the global source and 30 % of the global sink. In addition, these novel methods could be refined and made more robust by improvements in the capabilities of satellite instruments (e.g., signal-to-noise, spatial resolution) and retrieval algorithms that are used to develop data products. Another benefit of more robust data products is that they may be used to better constrain the chemical and dynamical processes in atmospheric chemical transport models that simulate the spatio-temporal variations of OH and OH drivers. Therefore, we propose steps forward for the development of a strategic and comprehensive space-based and suborbital observing strategy, which will improve our ability to indirectly constrain OH on much finer spatio-temporal scales than previously achieved. We discuss the strengths and limitations of such an observing strategy and potential improvements to current satellite instrument observing capabilities that would enable better constraint of OH. These improvements include ones that are obtainable with current technologies (e.g., more observations, co-located observations) as well as ones requiring additional technology development (e.g., to obtain vertically-resolved observations). Suborbital observations, which are required for information difficult to obtain from space and for validation of satellite-based OH estimates, will be an integral part of a comprehensive observing strategy.

The hydroxyl radical (OH) lies at the nexus of climate and air quality as the primary oxidant for both reactive greenhouse gases and many hazardous air pollutants. To better understand the role of climate variability on spatiotemporal patterns of OH, we utilize a 13-member ensemble of the Community Earth System Model version 2-Whole Atmosphere Community Climate Model version 6 (CESM2-WACCM6), a fully coupled chemistry-climate model, spanning the years 1950–2014. Ensemble members vary only in their initial conditions of the climate state in 1950. We focus on the final decade of the simulation, 2005–2014, when prior studies disagree on the signs of the global OH trends. The ensemble mean global airmass-weighted mean tropospheric column OH ( ΩTOH ), which is an estimate of the forced signal, increases by 0.06%/year between 2005 and 2014 while regional ΩTOH trends range from −0.56%/year over Southern Europe to +0.64%/year over South America. We show that ten-year ΩTOH trends are strongly affected by internal climate variability, as the spread of ΩTOH trends across the ensemble varies between 0.23%/year in Asia and 1.53%/year in South America. We train a fully connected neural network to emulate the ΩTOH simulated by the CESM2-WACCM6 model and combine it with satellite observations to interpret the role of OH chemical proxies. While the OH chemical proxies are subject to internal variability, the impact of internal variability on ΩTOH trends is primarily due to the meteorological parameters except for South America. Forced trends in global mean ΩTOH do not unambiguously emerge from trends driven by internal variability over the 2005–2014 period. The observation-constrained ΩTOH presents opposite trends due to climate variability, resulting in varying conclusions on the attribution of OH to CH4 trends.