Dry deposition of ozone is an important sink of ozone in near‐surface air. When dry deposition occurs through plant stomata, ozone can injure the plant, altering water and carbon cycling and reducing crop yields. Quantifying both stomatal and nonstomatal uptake accurately is relevant for understanding ozone's impact on human health as an air pollutant and on climate as a potent short‐lived greenhouse gas and primary control on the removal of several reactive greenhouse gases and air pollutants. Robust ozone dry deposition estimates require knowledge of the relative importance of individual deposition pathways, but spatiotemporal variability in nonstomatal deposition is poorly understood. Here we integrate understanding of ozone deposition processes by synthesizing research from fields such as atmospheric chemistry, ecology, and meteorology. We critically review methods for measurements and modeling, highlighting the empiricism that underpins modeling and thus the interpretation of observations. Our unprecedented synthesis of knowledge on deposition pathways, particularly soil and leaf cuticles, reveals process understanding not yet included in widely used models. If coordinated with short‐term field intensives, laboratory studies, and mechanistic modeling, measurements from a few long‐term sites would bridge the molecular to ecosystem scales necessary to establish the relative importance of individual deposition pathways and the extent to which they vary in space and time. Our recommended approaches seek to close knowledge gaps that currently limit quantifying the impact of ozone dry deposition on air quality, ecosystems, and climate.

Atmospheric ozone loss through dry deposition to vegetation is a critically important process for both air quality and ecosystem health. The majority of atmospheric chemistry models calculate dry deposition using a resistance-in-series parameterization by Wesely [1989], which is dependent on many environmental variables and look up table values. The uncertainties contained within this parameterization have not been fully explored, ultimately challenging our ability to understand global scale biosphere-atmosphere interactions. In this work, we evaluate the GEOS-Chem model simulation of ozone dry deposition using a globally distributed suite of observations. We find that simulated daytime deposition velocities generally reproduce the magnitude of observations to within a factor of 1.4. When correctly accounting for differences in land class between the observations and model, these biases improve, most substantially over the grasses and shrubs land class. These biases do not impact the global ozone burden substantially, however they do lead to local absolute changes of up to 4 ppbv and relative changes of 15% in summer surface concentrations. We use MERRA meteorology from 1979-2008 to assess that the interannual variability in simulated annual mean ozone dry deposition due to model input meteorology is small (generally less than 5% over vegetated surfaces). Sensitivity experiments indicate that the simulation is most sensitive to the stomatal and ground surface resistances, as well as leaf area index. To improve ozone dry deposition models, more measurements are necessary over rainforests and various crop types, alongside constraints on individual depositional pathways and other in-canopy ozone loss processes.

We present in this technical note the research protocol for phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII4). This research initiative is divided into two activities, collectively having three goals: (i) to define the current state of the science with respect to representations of wet and especially dry deposition in regional models, (ii) to quantify the extent to which different dry deposition parameterizations influence retrospective air pollutant concentration and flux predictions, and (iii) to identify, through the use of a common set of detailed diagnostics, sensitivity simulations, model evaluation, and reduction of input uncertainty, the specific causes for the current range of these predictions. Activity 1 is dedicated to the diagnostic evaluation of wet and dry deposition processes in regional air quality models (described in this paper), and Activity 2 to the evaluation of dry deposition point models against ozone flux measurements at multiple towers with multiyear observations (to be described in future submissions as part of the special issue on AQMEII4). The scope of this paper is to present the scientific protocols for Activity 1, as well as to summarize the technical information associated with the different dry deposition approaches used by the participating research groups of AQMEII4. In addition to describing all common aspects and data used for this multi-model evaluation activity, most importantly, we present the strategy devised to allow a common process-level comparison of dry deposition obtained from models using sometimes very different dry deposition schemes. The strategy is based on adding detailed diagnostics to the algorithms used in the dry deposition modules of existing regional air quality models, in particular archiving diagnostics specific to land use–land cover (LULC) and creating standardized LULC categories to facilitate cross-comparison of LULC-specific dry deposition parameters and processes, as well as archiving effective conductance and effective flux as means for comparing the relative influence of different pathways towards the net or total dry deposition. This new approach, along with an analysis of precipitation and wet deposition fields, will provide an unprecedented process-oriented comparison of deposition in regional air quality models. Examples of how specific dry deposition schemes used in participating models have been reduced to the common set of comparable diagnostics defined for AQMEII4 are also presented.

Computational models of the Earth System are critical tools for modern scientific inquiry. Efforts toward evaluating and improving errors in representations of physical and chemical processes in these large computational systems are commonly stymied by highly nonlinear and complex error behavior. Recent work has shown that these errors can be effectively predicted using modern Artificial Intelligence (A.I.) techniques. In this work, we go beyond these previous studies to apply an explainable A.I. technique to not only predict model errors but also move toward understanding the underlying reasons for successful error prediction. We use XGBoost classification trees and SHapley Additive exPlanations analysis to explore the errors in the prediction of lightning occurrence in the NASA Goddard Earth Observing System model, a widely used Earth System Model. This explainable error prediction system can effectively predict the model error and indicates that the errors are strongly related to convective processes and the characteristics of the land surface.

Little is known of how low-income residents of urban heat islands engage their knowledge, attitudes, behaviors, and resources to mitigate the health impacts of heat waves. In this qualitative study, we conducted semi-structured interviews with 40 adults in two such neighborhoods in Los Angeles California to explore their adaptation resources and behaviors, the impacts of heat waves on physical and mental health, and threat assessments of future heat waves. Eighty percent of participants received advanced warning of heat waves from television news and social media. The most common resource was air conditioning (AC) units or fans. However, one-third of participants lacked AC, and many of those with AC engaged in limited use due primarily to the high cost of electricity. Adaptation behaviors include staying hydrated, remaining indoors or going to cooler locations, reducing energy usage, and consuming certain foods and drinks. Most of the participants reported some physical or mental health problem or symptom during heat waves, suggesting vulnerability to heat waves. Almost all participants asserted that heat waves were likely to increase in frequency and intensity with adverse health effects for vulnerable populations. Despite limited resources, low-income residents of urban heat islands utilize a wide range of behaviors to minimize the severity of health impacts, suggesting they are both vulnerable and resilient to heat waves.

Dry deposition is a major sink of tropospheric ozone. Increasing evidence has shown that ozone dry deposition actively links meteorology and hydrology with ozone air quality. However, there is little systematic investigation on the performance of different ozone dry deposition parameterizations at the global scale and how parameterization choice can impact surface ozone simulations. Here, we present the results of the first global, multidecadal modelling and evaluation of ozone dry deposition velocity (v_d) using multiple ozone dry deposition parameterizations. We model ozone dry deposition velocities over 1982–2011 using four ozone dry deposition parameterizations that are representative of current approaches in global ozone dry deposition modelling. We use consistent assimilated meteorology, land cover, and satellite-derived leaf area index (LAI) across all four, such that the differences in simulated v_d are entirely due to differences in deposition model structures or assumptions about how land types are treated in each. In addition, we use the surface ozone sensitivity to v_d predicted by a chemical transport model to estimate the impact of mean and variability of ozone dry deposition velocity on surface ozone. Our estimated v_d values from four different parameterizations are evaluated against field observations, and while performance varies considerably by land cover types, our results suggest that none of the parameterizations are universally better than the others. Discrepancy in simulated mean v_d among the parameterizations is estimated to cause 2 to 5 ppbv of discrepancy in surface ozone in the Northern Hemisphere (NH) and up to 8 ppbv in tropical rainforests in July, and up to 8 ppbv in tropical rainforests and seasonally dry tropical forests in Indochina in December. Parameterization-specific biases based on individual land cover type and hydroclimate are found to be the two main drivers of such discrepancies. We find statistically significant trends in the multiannual time series of simulated July daytime v_d in all parameterizations, driven by warming and drying (southern Amazonia, southern African savannah, and Mongolia) or greening (high latitudes). The trend in July daytime v_d is estimated to be 1 % yr⁻¹ and leads to up to 3 ppbv of surface ozone changes over 1982–2011. The interannual coefficient of variation (CV) of July daytime mean v_d in NH is found to be 5 %–15 %, with spatial distribution that varies with the dry deposition parameterization. Our sensitivity simulations suggest this can contribute between 0.5 to 2 ppbv to interannual variability (IAV) in surface ozone, but all models tend to underestimate interannual CV when compared to long-term ozone flux observations. We also find that IAV in some dry deposition parameterizations is more sensitive to LAI, while in others it is more sensitive to climate. Comparisons with other published estimates of the IAV of background ozone confirm that ozone dry deposition can be an important part of natural surface ozone variability. Our results demonstrate the importance of ozone dry deposition parameterization choice on surface ozone modelling and the impact of IAV of v_d on surface ozone, thus making a strong case for further measurement, evaluation, and model–data integration of ozone dry deposition on different spatiotemporal scales.