We describe the baseline model configuration and simulation characteristics of the Geophysical Fluid Dynamics Laboratory (GFDL)'s Atmosphere Model version 4.1 (AM4.1), which builds on developments at GFDL over 2013–2018 for coupled carbon‐chemistry‐climate simulation as part of the sixth phase of the Coupled Model Intercomparison Project. In contrast with GFDL's AM4.0 development effort, which focused on physical and aerosol interactions and which is used as the atmospheric component of CM4.0, AM4.1 focuses on comprehensiveness of Earth system interactions. Key features of this model include doubled horizontal resolution of the atmosphere (~200 to ~100 km) with revised dynamics and physics from GFDL's previous‐generation AM3 atmospheric chemistry‐climate model. AM4.1 features improved representation of atmospheric chemical composition, including aerosol and aerosol precursor emissions, key land‐atmosphere interactions, comprehensive land‐atmosphere‐ocean cycling of dust and iron, and interactive ocean‐atmosphere cycling of reactive nitrogen. AM4.1 provides vast improvements in fidelity over AM3, captures most of AM4.0's baseline simulations characteristics, and notably improves on AM4.0 in the representation of aerosols over the Southern Ocean, India, and China—even with its interactive chemistry representation—and in its manifestation of sudden stratospheric warmings in the coldest months. Distributions of reactive nitrogen and sulfur species, carbon monoxide, and ozone are all substantially improved over AM3. Fidelity concerns include degradation of upper atmosphere equatorial winds and of aerosols in some regions.
Peters, Daniel R., Jordan L Schnell, Patrick L Kinney, Vaishali Naik, and Daniel E Horton, October 2020: Public Health and Climate Benefits and Tradeoffs of U.S. Vehicle Electrification. GeoHealth, 4(10), DOI:10.1029/2020GH000275. Abstract
Vehicle electrification is a common climate change mitigation strategy, with policymakers invoking co‐beneficial reductions in carbon dioxide (CO2) and air pollutant emissions. However, while previous studies of U.S. electric vehicle (EV) adoption consistently predict CO2 mitigation benefits, air quality outcomes are equivocal and depend on policies assessed and experimental parameters. We analyze climate and health co‐benefits and trade‐offs of six U.S. EV adoption scenarios: 25% or 75% replacement of conventional internal combustion engine vehicles, each under three different EV‐charging energy generation scenarios. We transfer emissions from tailpipe to power generation plant, simulate interactions of atmospheric chemistry and meteorology using the GFDL‐AM4 chemistry climate model, and assess health consequences and uncertainties using the U.S. Environmental Protection Agency Benefits Mapping Analysis Program Community Edition (BenMAP‐CE). We find that 25% U.S. EV adoption, with added energy demand sourced from the present‐day electric grid, annually results in a ~242 M ton reduction in CO2 emissions, 437 deaths avoided due to PM2.5 reductions (95% CI: 295, 578), and 98 deaths avoided due to lesser ozone formation (95% CI: 33, 162). Despite some regions experiencing adverse health outcomes, ~$16.8B in damages avoided are predicted. Peak CO2 reductions and health benefits occur with 75% EV adoption and increased emission‐free energy sources (~$70B in damages avoided). When charging‐electricity from aggressive EV adoption is combustion‐only, adverse health outcomes increase substantially, highlighting the importance of low‐to‐zero emission power generation for greater realization of health co‐benefits. Our results provide a more nuanced understanding of the transportation sector's climate change mitigation‐health impact relationship.
A central strategy in achieving greenhouse gas mitigation targets is the transition of vehicles from internal combustion engines to electric power. However, due to complex emission sources and nonlinear chemistry, it is unclear how such a shift might impact air quality. Here we apply a prototype version of the new-generation NOAA GFDL global Atmospheric Model, version 4 (GFDL AM4) to investigate the impact on U.S. air quality from an aggressive conversion of internal combustion vehicles to battery-powered electric vehicles (EVs). We examine a suite of scenarios designed to quantify the effect of both the magnitude of EV market penetration and the source of electricity generation used to power them. We find that summer surface ozone (O3) decreases in most locations due to widespread reductions of traffic NOx emissions. Summer fine particulate matter (PM2.5) increases on average and largest in areas with increased coal-fired power generation demands. Winter O3 increases due to reduced loss via traffic NOx while PM2.5 decreases since larger ammonium nitrate reductions offset increases in ammonium sulfate. The largest magnitude changes are simulated at the extremes of the probability distribution. Increasing the fraction of vehicles converted to EVs further decreases summer O3, while increasing the fraction of electricity generated by “emission-free” sources largely eliminates the increases in summer PM2.5 at high EV adoption fractions. Ultimately, the number of conventional vehicles replaced by EVs has a larger effect on O3 than PM2.5, while the source of the electricity for those EVs exhibit greater control on PM2.5.
Ducker, J A., C D Holmes, Trevor F Keenan, S Fares, A H Goldstein, I Mammarella, J W Munger, and Jordan L Schnell, September 2018: Synthetic ozone deposition and stomatal uptake at flux tower sites. Biogeosciences, 15(17), DOI:10.5194/bg-15-5395-2018. Abstract
We develop and evaluate a method to estimate O3 deposition and stomatal O3 uptake across networks of eddy covariance flux tower sites where O3 concentrations and O3 fluxes have not been measured. The method combines standard micrometeorological flux measurements, which constrain O3 deposition velocity and stomatal conductance, with a gridded dataset of observed surface O3 concentrations. Measurement errors are propagated through all calculations to quantify O3 flux uncertainties. We evaluate the method at three sites with O3 flux measurements: Harvard Forest, Blodgett Forest, and Hyytiälä Forest. The method reproduces 83% or more of the variability in daily stomatal uptake at these sites with modest mean bias (21% or less). At least 95% of daily average values agree with measurements within a factor of 2 and, according to the error analysis, the residual differences from measured O3 fluxes are consistent with the uncertainty in the underlying measurements.
The product, called synthetic O3 flux or SynFlux, includes 43 FLUXNET sites in the United States and 60 sites in Europe, totaling 926 site years of data. This dataset, which is now public, dramatically expands the number and types of sites where O3 fluxes can be used for ecosystem impact studies and evaluation of air quality and climate models. Across these sites, the mean stomatal conductance and O3 deposition velocity is 0.03–1.0cms−1. The stomatal O3 flux during the growing season (typically April–September) is 0.5–11.0nmol O3m−2s−1 with a mean of 4.5nmol O3m−2s−1 and the largest fluxes generally occur where stomatal conductance is high, rather than where O3 concentrations are high. The conductance differences across sites can be explained by atmospheric humidity, soil moisture, vegetation type, irrigation, and land management. These stomatal fluxes suggest that ambient O3 degrades biomass production and CO2 sequestration by 20%–24% at crop sites, 6%–29% at deciduous broadleaf forests, and 4%–20% at evergreen needleleaf forests in the United States and Europe.
Guo, Jean J., Arlene M Fiore, Lee T Murray, D A Jaffe, and Jordan L Schnell, et al., August 2018: Average versus high surface ozone levels over the continental U.S.A.: Model bias, background influences, and interannual variability. Atmospheric Chemistry and Physics, 18(16), DOI:10.5194/acp-18-12123-2018. Abstract
U.S. background ozone (O3) includes O3 produced from anthropogenic O3 precursors emitted outside of the U.S.A., from global methane, and from any natural sources. Using a suite of sensitivity simulations in the GEOS-Chem global chemistry-transport model, we estimate the influence from individual background versus U.S. anthropogenic sources on total surface O3 over ten continental U.S. regions from 2004–2012. Evaluation with observations reveals model biases of +0–19 ppb in seasonal mean maximum daily 8-hour average (MDA8) O3, highest in summer over the eastern U.S.A. Simulated high-O3 events cluster too late in the season. We link these model biases to regional O3 production (e.g., U.S. anthropogenic, biogenic volatile organic compounds (BVOC), and soil NOx, emissions), or coincident missing sinks. On the ten highest observed O3 days during summer (O3_top10obs_JJA), U.S. anthropogenic emissions enhance O3 by 5–11 ppb and by less than 2 ppb in the eastern versus western U.S.A. The O3 enhancement from BVOC emissions during summer is 1–7 ppb higher on O3_top10obs_JJA days than on average days, while intercontinental pollution is up to 2 ppb higher on average vs. on O3_top10obs_JJA days. In the model, regional sources of O3 precursor emissions drive interannual variability in the highest observed O3 levels. During the summers of 2004–2012, monthly regional mean U.S. background O3 MDA8 levels vary by 10–20 ppb. Simulated summertime total surface O3 levels on O3_top10obs_JJA days decline by 3 ppb (averaged over all regions) from 2004–2006 to 2010–2012 in both the observations and the model, reflecting rising U.S. background (+2 ppb) and declining U.S. anthropogenic O3 emissions (−6 ppb). The model attributes interannual variability in U.S. background O3 on O3_top10obs days to natural sources, not international pollution transport. We find that a three-year averaging period is not long enough to eliminate interannual variability in background O3.
Northern India (23° N–31° N, 68° E–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.
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 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.
We test the current generation of global chemistry-climate models in their ability to simulate observed, present-day surface ozone. Models are evaluated against hourly surface ozone from 4217 stations in North America and Europe that are averaged over 1° × 1° grid cells, allowing commensurate model-measurement comparison. Models are generally biased high during all hours of the day and in all regions. Most models simulate the shape of regional summertime diurnal and annual cycles well, correctly matching the timing of hourly (~ 15:00) and monthly (mid-June) peak surface ozone abundance. The amplitude of these cycles is less successfully matched. The observed summertime diurnal range (~ 25 ppb) is underestimated in all regions by about 7 ppb, and the observed seasonal range (~ 21 ppb) is underestimated by about 5 ppb except in the most polluted regions where it is overestimated by about 5 ppb. The models generally match the pattern of the observed summertime ozone enhancement, but they overestimate its magnitude in most regions. Most models capture the observed distribution of extreme episode sizes, correctly showing that about 80% of individual extreme events occur in large-scale, multi-day episodes of more than 100 grid cells. The observed linear relationship showing increases in ozone by up to 6 ppb for larger-sized episodes is also matched.
