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Amos Tai

Massachusetts Institute of Technology (MIT), Civil and Environmental Engineering, Post-Doc

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I specialize in atmospheric chemistry and physics, ecological climatology, and biosphere-atmosphere interactions. My research addresses the important issues of air pollution, food security and ecosystem health under global climate change, via a better understanding of the complex interactions between climate, atmospheric composition and terrestrial ecosystems.
Supervisors: Daniel Jacob and Colette Heald

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Estonian University of Life Sciences

The University of Hong Kong

Win Trivitayanurak

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Papers by Amos Tai

Author Responses to Referees' Comments

A PDF version of the author responses to referees' comments with proper formatting to clearly ind... more A PDF version of the author responses to referees' comments with proper formatting to clearly indicate referees' comments, our responses, cited revised texts and specialized symbols is included as a supplement. Although the full responses are also copied and pasted below, some formatting is lost, and we highly recommend the Editor to use the PDF version for a more convenient perusal.

The newly developed offline land ecosystem model Terrestrial Ecosystem Model in R (TEMIR) version... more The newly developed offline land ecosystem model Terrestrial Ecosystem Model in R (TEMIR) version 1.0 is described here. This version of the model simulates plant ecophysiological (e.g., photosynthetic, stomatal) responses to varying meteorological conditions and concentrations of CO2 and ground-level ozone (O3) based on prescribed meteorological and atmospheric chemical inputs from various sources. Driven by the same meteorological data used in the GEOS-Chem chemical transport model, this allows asynchronously coupled experiments with GEOS-Chem simulations with unique coherency for investigating biosphere-atmosphere chemical interactions. TEMIR agrees well with FLUXNET site-level gross primary productivity (GPP) in terms of both the diurnal and monthly cycles (correlation coefficients R 2 > 0.85 and R 2 > 0.8, respectively) for most plant functional types (PFTs). Grass and shrub PFTs have larger biases due to generic model representations. The model performs best when driven by local site-level meteorology rather than reanalyzed gridded meteorology. Simulation using gridded meteorology agrees well for annual GPP in seasonality and spatial distribution with a global average of 134 Pg C yr-1. Application of Monin-Obukhov similarity theory to infer canopy conditions from gridded meteorology does not improve model performance, predicting a uniform increase of +21% for global GPP. Present-day O3 concentrations simulated by GEOS-Chem and an O3 damage scheme at high sensitivity show a 2% reduction in global GPP with prominent reductions by up to 15% in eastern China and the eastern US. Regional correlations are generally unchanged when O3 is present, and biases are reduced especially for regions with high O3 damages. An increase in atmospheric CO2 concentration by 20 ppmv from year-2000 to year-2010 level modestly decreases O3 damage due to reduced stomatal uptake, consistent with ecophysiological understanding. Our work showcases the utility of this version of TEMIR for evaluating biogeophysical responses of vegetation to changes in atmospheric composition and meteorological conditions.

Impacts of ozone-vegetation coupling and feedbacks on global air quality, ecosystems and food security

Quantifying PM2.5-meteorology sensitivities in a global climate model

Atmospheric Environment, Oct 1, 2016

h i g h l i g h t s Climate-driven global PM 2.5 concentrations increase modestly due to climate ... more

Meteorological modes of variability for fine particulate matter (PM2.5) air quality in the United States: implications for PM2.5 sensitivity to climate change

AGU Fall Meeting Abstracts, Dec 1, 2011

We applied a multiple linear regression model to understand the relationships of PM 2.5 with mete... more We applied a multiple linear regression model to understand the relationships of PM 2.5 with meteorological variables in the contiguous US and from there to infer the sensitivity of PM 2.5 to climate change. We used 2004-2008 PM 2.5 observations from ∼1000 sites (∼200 sites for PM 2.5 components) and compared to results from the GEOS-Chem chemical transport model (CTM). All data were deseasonalized to focus on synoptic-scale correlations. We find strong positive correlations of PM 2.5 components with temperature in most of the US, except for nitrate in the Southeast where the correlation is negative. Relative humidity (RH) is generally positively correlated with sulfate and nitrate but negatively correlated with organic carbon. GEOS-Chem results indicate that most of the correlations of PM 2.5 with temperature and RH do not arise from direct dependence but from covariation with synoptic transport. We applied principal component analysis and regression to identify the dominant meteorological modes controlling PM 2.5 variability, and show that 20-40 % of the observed PM 2.5 dayto-day variability can be explained by a single dominant meteorological mode: cold frontal passages in the eastern US and maritime inflow in the West. These and other synoptic transport modes drive most of the overall correlations of PM 2.5 with temperature and RH except in the Southeast. We show that interannual variability of PM 2.5 in the US Midwest is strongly correlated with cyclone frequency as diagnosed from a spectral-autoregressive analysis of the dominant meteorological mode. An ensemble of five realizations of 1996-2050 climate change with the GISS general circulation model (GCM) using the same climate forcings shows inconsistent trends in cyclone frequency over the Midwest (including in sign), with a likely decrease in cyclone frequency implying an increase in PM 2.5. Our results demonstrate the need for multiple GCM realizations (because of climate chaos) when diagnosing the effect of climate change on PM 2.5 , and suggest that analysis of meteorological modes of variability provides a computationally more affordable approach for this purpose than coupled GCM-CTM studies.

Modeling ammonia-aerosol-climate feedback mechanisms using an earth system model: Implications for future food security and air quality

AGU Fall Meeting Abstracts, Dec 1, 2019

Parameterizing the impacts of ozone-vegetation coupling and feedbacks on ozone air quality in a chemical transport model

AGUFM, Dec 1, 2016

Improvement in terrestrial nitrogen cycle modeling for estimating ammonia emission from food production using current practices and sustainable alternatives

AGUFM, Dec 1, 2018

Responses of Surface Ozone Air Quality to Anthropogenic Nitrogen Deposition

AGU Fall Meeting Abstracts, Dec 1, 2017

Human activities have substantially increased atmospheric deposition of reactive nitrogen to the ... more Human activities have substantially increased atmospheric deposition of reactive nitrogen to the Earth's surface, inducing unintentional effects on ecosystems with complex environmental and climate consequences. One consequence remaining unexplored is how surface air quality might respond to the enhanced nitrogen deposition through surface-atmosphere exchange. Here we combine a chemical transport model (GEOS-Chem) and a global land model (Community Land Model, CLM) to address this issue with a focus on ozone pollution in the Northern Hemisphere. We consider three processes that are important for surface ozone and can be perturbed by the addition of atmospheric deposited nitrogen-namely, emissions of biogenic volatile organic compounds (VOCs), ozone dry deposition, and soil nitrogen oxide (NO x) emissions. We find that present-day anthropogenic nitrogen deposition (65 Tg N a −1 to the land), through enhancing plant growth (represented as increases in vegetation leaf area index, LAI, in the model), could increase surface ozone from increased biogenic VOC emissions (e.g., a 6.6 Tg increase in isoprene emission), but it could also decrease ozone due to higher ozone dry deposition velocities (up to 0.02-0.04 cm s −1 increases). Meanwhile, deposited anthropogenic nitrogen to soil enhances soil NO x emissions. The overall effect on summer mean surface ozone concentrations shows general increases over the globe (up to 1.5-2.3 ppbv over the western US and South Asia), except for some regions with high anthropogenic NO x emissions (0.5-1.0 ppbv decreases over the eastern US, western Europe, and North China). We compare the surface ozone changes with those driven by the past 20-year climate and historical land use changes. We find that the impacts from anthropogenic nitrogen deposition can be comparable to the climate-and land-use-driven surface ozone changes at regional scales and partly offset the surface ozone reductions due to land use changes reported in previous studies. Our study emphasizes the complexity of biosphere-atmosphere interactions, which can have important implications for future air quality prediction.

Sustainable, alternative farming practices as a means to simultaneously secure food production and reduce air pollution in East Asia

2015 AGU Fall Meeting, Dec 17, 2015

Impacts of Ozone-vegetation Interactions and Biogeochemical Feedbacks on Atmospheric Composition and Air Quality Under Climate Change

2015 AGU Fall Meeting, Dec 17, 2015

Ozone-vegetation interaction in the Earth system: implications for air quality, ecosystems and agriculture

2015 AGU Fall Meeting, Dec 17, 2015

Satellite-derived constraints on the effect of drought stress on biogenic isoprene emissions in the southeastern US

Atmospheric Chemistry and Physics, Nov 7, 2022

While substantial progress has been made to improve our understanding of biogenic isoprene emissi... more While substantial progress has been made to improve our understanding of biogenic isoprene emissions under unstressed conditions, large uncertainties remain with respect to isoprene emissions under stressed conditions. Here, we use the US Drought Monitor (USDM) as a weekly drought severity index and tropospheric columns of formaldehyde (HCHO), the key product of isoprene oxidation, retrieved from the Ozone Monitoring Instrument (OMI) to derive top-down constraints on the response of summertime isoprene emissions to drought stress in the southeastern United States (SE US), a region of high isoprene emissions that is also prone to drought. OMI HCHO column density is found to be 6.7 % (mild drought) to 23.3 % (severe drought) higher than that under non-drought conditions. A global chemical transport model, GEOS-Chem, with version 2.1 of the Model of Emissions of Gases and Aerosols from Nature (MEGAN2.1) emission algorithm can simulate this direction of change, but the simulated increases at the corresponding drought levels are 1.1-1.5 times that of OMI HCHO, suggesting the need for a drought-stress algorithm in the model. By minimizing the model-OMI differences in HCHO to temperature sensitivity under different drought levels, we derived a top-down drought stress factor (γ d_OMI) in GEOS-Chem that parameterizes using water stress and temperature. The algorithm led to an 8.6 % (mild drought) to 20.7 % (severe drought) reduction in isoprene emissions in the SE US relative to the simulation without it. With γ d_OMI the model predicts a nonlinear increasing trend in isoprene emissions with drought severity that is consistent with OMI HCHO and a single site's isoprene flux measurements. Compared with a previous drought stress algorithm derived from the latter, the satellite-based drought stress factor performs better with respect to capturing the regional-scale drought-isoprene responses, as indicated by the near-zero mean bias between OMI and simulated HCHO columns under different drought conditions. The drought stress algorithm also reduces the model's high bias in organic aerosol (OA) simulations by 6.60 % (mild drought) to 11.71 % (severe drought) over the SE US compared to the no-stress simulation. The simulated ozone response to the drought stress factor displays a spatial disparity due to the isoprene-suppressing effect on oxidants, with an <1 ppb increase in O 3 in high-isoprene regions and a 1-3 ppbv decrease in O 3 in low-isoprene regions. This study demonstrates the unique value of exploiting long-term satellite observations to develop empirical stress algorithms on biogenic emissions where in situ flux measurements are limited.

Variation of global compound heatwaves and their associations with climate variability

Recent widespread heatwaves have broken local temperature records over the world. Large and inten... more Recent widespread heatwaves have broken local temperature records over the world. Large and intense heatwaves not only change the land surface biophysical environment in terms of temperature rise and water shortage, but also endanger our natural and human ecosystems by increasing health risks. Compared to the independent daytime or nighttime heatwave, the compound heatwave often yield higher hot extremes and even pose greater hazard to human and ecosystem health, as it prevents humans or ecosystems recovering from previous hot temperatures if the extreme hot occur in both day and night. However, factors shaping their spatiotemporal patterns on a global scale remain poorly understood, as do their links with large-scale interannual climatic variability. Here, with the air temperatures from multiple global datasets (e.g., ERA5L, CPC, MERRA2, and JRA55), we quantified the frequency and intensity patterns of compound heatwaves over 1980–2019 and analyzed their associations with modes of climate variability. Our results show a significant increasing trend of compound heatwave occurrences on a global scale, with the global average frequency and intensity increased by 90% and 32%, respectively, in 2010–2019 relative to 1980–1989. Specifically, Arctic and mid-latitudes of the Northern Hemisphere have seen the greatest increases in heatwave frequency and intensity over the last four decades, which may be connected to the amplifying influence of Arctic warming as well as human activity. The interannual variability of tropical compound heatwaves is dominated by ENSO occurrences from the previous year to the present year. And, the PDO and AMO modes dominate the interannual variability of extremely high temperatures in the majority of the mid-latitudes. In contrast, the interannual variability of compound heatwaves in the Arctic is most strongly tied to the AO and NAO in the winter of the present year, but it is most directly associated with the AAO and SAM in the Antarctic, which has an 8–9-month lag impact. This work will increase our knowledge of the global patterns and mechanisms of compound heatwaves in a multi-decadal context, hence enhancing our ability to predict hot extremes.

Incorporating stomatal ozone uptake into a biophysical crop model to examine ozone-induced yield loss

AGU Fall Meeting Abstracts, Dec 1, 2018

Satellite-derived Constraints on the Effect of Drought Stress on Biogenic Isoprene Emissions in the Southeast US

While substantial progress has been made to improve our understanding of biogenic isoprene emissi... more While substantial progress has been made to improve our understanding of biogenic isoprene emissions under unstressed conditions, there remain large uncertainties in isoprene emissions under stressed conditions. Here we use the US Drought Monitor (USDM) as a weekly drought severity index and tropospheric columns of formaldehyde (HCHO), the key product of isoprene oxidation, retrieved from the Ozone Monitoring Instrument (OMI) to derive top-down constraints on the response of summertime isoprene emissions to drought stress in the Southeast U.S. (SE US), a region of high isoprene emissions and prone to drought. OMI HCHO column density is found to be 5.3% (mild drought)-19.8% (severe drought) higher than that in no-drought conditions. A global chemical transport model, GEOS-Chem, with the MEGAN2.1 emission algorithm can simulate this direction of change, but the simulated increases at the corresponding drought levels are 1.4-2.0 times of OMI HCHO, suggesting the need for a droughtstress algorithm in the model. By minimizing the model-to-OMI differences in HCHO to temperature sensitivity under different drought levels, we derived a top-down drought stress factor (γd_OMI) in GEOS-Chem that parameterizes using water stress and temperature. The algorithm led to an 8.6% (mild drought)-20.7% (severe drought) reduction in isoprene emissions in the SE US relative to the simulation without it. With γd_OMI the model predicts a non-uniform trend of increase in isoprene emissions with drought severity that is consistent with OMI HCHO and a single site's isoprene flux measurements. Compared with a previous drought stress algorithm derived from the latter, the satellitebased drought stress factor performs better in capturing the regional scale drought-isoprene responses as indicated by the close-to-zero mean bias between OMI and simulated HCHO columns under different drought conditions. The drought stress algorithm also reduces the model's high bias in organic aerosols (OA) simulations by 6.60% (mild drought) to 11.71% (severe drought) over the SE US compared to the no-stress simulation. The simulated ozone response to the drought stress factor displays a spatial disparity due to the isoprene suppressing effect on oxidants, with an <1 ppb increase in O3 in high-isoprene regions and a 1-3 ppbv decrease in O3 in low-isoprene regions. This

Supplementary material to "Satellite-derived Constraints on the Effect of Drought Stress on Biogenic Isoprene Emissions in the Southeast US

Terrestrial Ecosystem Model in R (TEMIR) version 1.0: Simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes

. The newly developed offline land ecosystem model Terrestrial Ecosystem Model in R (TEMIR) versi... more . The newly developed offline land ecosystem model Terrestrial Ecosystem Model in R (TEMIR) version 1.0 is described here. This version of the model simulates plant ecophysiological (e.g., photosynthetic, stomatal) responses to varying meteorological conditions and concentrations of CO2 and ground-level ozone (O3) based on prescribed meteorological and atmospheric chemical inputs from various sources. Driven by the same meteorological data used in the GEOS-Chem chemical transport model, this allows asynchronously coupled experiments with GEOS-Chem simulations with unique coherency for investigating biosphere-atmosphere chemical interactions. TEMIR agrees well with FLUXNET site-level gross primary productivity (GPP) in terms of both the diurnal and monthly cycles (correlation coefficients R2 &gt; 0.85 and R2 &gt; 0.8, respectively) for most plant functional types (PFTs). Grass and shrub PFTs have larger biases due to generic model representations. The model performs best when driven by local site-level meteorology rather than reanalyzed gridded meteorology. Simulation using gridded meteorology agrees well for annual GPP in seasonality and spatial distribution with a global average of 134 Pg C yr–1. Application of Monin-Obukhov similarity theory to infer canopy conditions from gridded meteorology does not improve model performance, predicting a uniform increase of +21 % for global GPP. Present-day O3 concentrations simulated by GEOS-Chem and an O3 damage scheme at high sensitivity show a 2 % reduction in global GPP with prominent reductions by up to 15 % in eastern China and the eastern US. Regional correlations are generally unchanged when O3 is present, and biases are reduced especially for regions with high O3 damages. An increase in atmospheric CO2 concentration by 20 ppmv from year-2000 to year-2010 level modestly decreases O3 damage due to reduced stomatal uptake, consistent with ecophysiological understanding. Our work showcases the utility of this version of TEMIR for evaluating biogeophysical responses of vegetation to changes in atmospheric composition and meteorological conditions.

Multidecadal ozone trends in China and implications for human health and crop yields: A hybrid approach combining chemical transport model and machine learning

. Surface ozone (O3) is well known to pose significant threats to both human health and crop prod... more . Surface ozone (O3) is well known to pose significant threats to both human health and crop production worldwide. However, a multi-decadal assessment of O3 impacts on public health and crop yields in China is lacking due to insufficient long-term continuous O3 observations. In this study, we used a machine learning (ML) algorithm to correct the biases of O3 concentrations simulated by the chemical transport model from 1981–2019 by integrating multi-source datasets. The ML-enabled bias correction offers improved performance in reproducing observed O3 concentrations, and thus further improves our estimates of O3 impacts on human health and crop yields. Our results show that a warm-season increasing trend of O3 in Beijing-Tianjin-Hebei and its surroundings (BTHs), Yangtze River Delta (YRD), Sichuan Basin (SCB) and Pearl River Delta (PRD) regions are 0.32 μg m–3 yr–1, 0.63 μg m–3 yr–1, 0.84 μg m–3 yr–1, and 0.81 μg m–3 yr–1 from 1981 to 2019, respectively. In more recent years, O3 concentrations experience more fluctuations in the four major regions. Our results show that only BTHs have a perceptible increasing trend of 0.81 μg m–3 yr–1 during 2013–2019. Meteorological factors play important roles in modulating the interannual variability of surface O3, wherein synoptic systems (e.g., high-pressure system, Western Pacific subtropical high, tropical cyclone) are closely related to the spatiotemporal distribution of regional O3 via influencing regional weather conditions and transport processes. The estimated annual all-cause premature deaths induced by O3 increased from ~55,900 in 1981 to ~162,000 in 2019 with an increasing trend of ~2,980 deaths yr–1. The annual premature deaths related to respiratory and cardiovascular disease are ~34,200 and ~40,300 in 1998, and ~26,500 and ~79,000 in 2019, having a rate of change of –546 and +1,770 deaths yr–1 during 1998–2019, respectively. The estimated annual crop relative yield loss (RYL) for wheat, rice, soybean, and maize is 3.3 %, 5.5 %, 10.6 %, and 2.6 % in 1981, and increases to 5.9 %, 6.8 %, 15.1 %, and 4.1 % in 2019, respectively. The average annual crop RYL from 1981 to 2019 for wheat, rice, soybean, and maize range between 1.1–7.2 %, 2.7–9.4 %, 6.3–24.8 %, and 0.8–7.4 %, respectively, using different concentration-based metrics. Our study, for the first time, used ML to provide a robust dataset of O3 concentrations over the past four decades in China, enabling a long-term evaluation of O3-induced crop losses and health impacts. These findings are expected to fill the gap in the long-term O3 trend and impact assessment in China.

Supplementary material to "Multidecadal ozone trends in China and implications for human health and crop yields: A hybrid approach combining chemical transport model and machine learning

Author Responses to Referees' Comments

A PDF version of the author responses to referees' comments with proper formatting to clearly ind... more A PDF version of the author responses to referees' comments with proper formatting to clearly indicate referees' comments, our responses, cited revised texts and specialized symbols is included as a supplement. Although the full responses are also copied and pasted below, some formatting is lost, and we highly recommend the Editor to use the PDF version for a more convenient perusal.

The newly developed offline land ecosystem model Terrestrial Ecosystem Model in R (TEMIR) version... more The newly developed offline land ecosystem model Terrestrial Ecosystem Model in R (TEMIR) version 1.0 is described here. This version of the model simulates plant ecophysiological (e.g., photosynthetic, stomatal) responses to varying meteorological conditions and concentrations of CO2 and ground-level ozone (O3) based on prescribed meteorological and atmospheric chemical inputs from various sources. Driven by the same meteorological data used in the GEOS-Chem chemical transport model, this allows asynchronously coupled experiments with GEOS-Chem simulations with unique coherency for investigating biosphere-atmosphere chemical interactions. TEMIR agrees well with FLUXNET site-level gross primary productivity (GPP) in terms of both the diurnal and monthly cycles (correlation coefficients R 2 > 0.85 and R 2 > 0.8, respectively) for most plant functional types (PFTs). Grass and shrub PFTs have larger biases due to generic model representations. The model performs best when driven by local site-level meteorology rather than reanalyzed gridded meteorology. Simulation using gridded meteorology agrees well for annual GPP in seasonality and spatial distribution with a global average of 134 Pg C yr-1. Application of Monin-Obukhov similarity theory to infer canopy conditions from gridded meteorology does not improve model performance, predicting a uniform increase of +21% for global GPP. Present-day O3 concentrations simulated by GEOS-Chem and an O3 damage scheme at high sensitivity show a 2% reduction in global GPP with prominent reductions by up to 15% in eastern China and the eastern US. Regional correlations are generally unchanged when O3 is present, and biases are reduced especially for regions with high O3 damages. An increase in atmospheric CO2 concentration by 20 ppmv from year-2000 to year-2010 level modestly decreases O3 damage due to reduced stomatal uptake, consistent with ecophysiological understanding. Our work showcases the utility of this version of TEMIR for evaluating biogeophysical responses of vegetation to changes in atmospheric composition and meteorological conditions.

Impacts of ozone-vegetation coupling and feedbacks on global air quality, ecosystems and food security

Quantifying PM2.5-meteorology sensitivities in a global climate model

Atmospheric Environment, Oct 1, 2016

h i g h l i g h t s Climate-driven global PM 2.5 concentrations increase modestly due to climate ... more

Meteorological modes of variability for fine particulate matter (PM2.5) air quality in the United States: implications for PM2.5 sensitivity to climate change

AGU Fall Meeting Abstracts, Dec 1, 2011

We applied a multiple linear regression model to understand the relationships of PM 2.5 with mete... more We applied a multiple linear regression model to understand the relationships of PM 2.5 with meteorological variables in the contiguous US and from there to infer the sensitivity of PM 2.5 to climate change. We used 2004-2008 PM 2.5 observations from ∼1000 sites (∼200 sites for PM 2.5 components) and compared to results from the GEOS-Chem chemical transport model (CTM). All data were deseasonalized to focus on synoptic-scale correlations. We find strong positive correlations of PM 2.5 components with temperature in most of the US, except for nitrate in the Southeast where the correlation is negative. Relative humidity (RH) is generally positively correlated with sulfate and nitrate but negatively correlated with organic carbon. GEOS-Chem results indicate that most of the correlations of PM 2.5 with temperature and RH do not arise from direct dependence but from covariation with synoptic transport. We applied principal component analysis and regression to identify the dominant meteorological modes controlling PM 2.5 variability, and show that 20-40 % of the observed PM 2.5 dayto-day variability can be explained by a single dominant meteorological mode: cold frontal passages in the eastern US and maritime inflow in the West. These and other synoptic transport modes drive most of the overall correlations of PM 2.5 with temperature and RH except in the Southeast. We show that interannual variability of PM 2.5 in the US Midwest is strongly correlated with cyclone frequency as diagnosed from a spectral-autoregressive analysis of the dominant meteorological mode. An ensemble of five realizations of 1996-2050 climate change with the GISS general circulation model (GCM) using the same climate forcings shows inconsistent trends in cyclone frequency over the Midwest (including in sign), with a likely decrease in cyclone frequency implying an increase in PM 2.5. Our results demonstrate the need for multiple GCM realizations (because of climate chaos) when diagnosing the effect of climate change on PM 2.5 , and suggest that analysis of meteorological modes of variability provides a computationally more affordable approach for this purpose than coupled GCM-CTM studies.

Modeling ammonia-aerosol-climate feedback mechanisms using an earth system model: Implications for future food security and air quality

AGU Fall Meeting Abstracts, Dec 1, 2019

Parameterizing the impacts of ozone-vegetation coupling and feedbacks on ozone air quality in a chemical transport model

AGUFM, Dec 1, 2016

Improvement in terrestrial nitrogen cycle modeling for estimating ammonia emission from food production using current practices and sustainable alternatives

AGUFM, Dec 1, 2018

Responses of Surface Ozone Air Quality to Anthropogenic Nitrogen Deposition

AGU Fall Meeting Abstracts, Dec 1, 2017

Human activities have substantially increased atmospheric deposition of reactive nitrogen to the ... more Human activities have substantially increased atmospheric deposition of reactive nitrogen to the Earth's surface, inducing unintentional effects on ecosystems with complex environmental and climate consequences. One consequence remaining unexplored is how surface air quality might respond to the enhanced nitrogen deposition through surface-atmosphere exchange. Here we combine a chemical transport model (GEOS-Chem) and a global land model (Community Land Model, CLM) to address this issue with a focus on ozone pollution in the Northern Hemisphere. We consider three processes that are important for surface ozone and can be perturbed by the addition of atmospheric deposited nitrogen-namely, emissions of biogenic volatile organic compounds (VOCs), ozone dry deposition, and soil nitrogen oxide (NO x) emissions. We find that present-day anthropogenic nitrogen deposition (65 Tg N a −1 to the land), through enhancing plant growth (represented as increases in vegetation leaf area index, LAI, in the model), could increase surface ozone from increased biogenic VOC emissions (e.g., a 6.6 Tg increase in isoprene emission), but it could also decrease ozone due to higher ozone dry deposition velocities (up to 0.02-0.04 cm s −1 increases). Meanwhile, deposited anthropogenic nitrogen to soil enhances soil NO x emissions. The overall effect on summer mean surface ozone concentrations shows general increases over the globe (up to 1.5-2.3 ppbv over the western US and South Asia), except for some regions with high anthropogenic NO x emissions (0.5-1.0 ppbv decreases over the eastern US, western Europe, and North China). We compare the surface ozone changes with those driven by the past 20-year climate and historical land use changes. We find that the impacts from anthropogenic nitrogen deposition can be comparable to the climate-and land-use-driven surface ozone changes at regional scales and partly offset the surface ozone reductions due to land use changes reported in previous studies. Our study emphasizes the complexity of biosphere-atmosphere interactions, which can have important implications for future air quality prediction.

Sustainable, alternative farming practices as a means to simultaneously secure food production and reduce air pollution in East Asia

2015 AGU Fall Meeting, Dec 17, 2015

Impacts of Ozone-vegetation Interactions and Biogeochemical Feedbacks on Atmospheric Composition and Air Quality Under Climate Change

2015 AGU Fall Meeting, Dec 17, 2015

Ozone-vegetation interaction in the Earth system: implications for air quality, ecosystems and agriculture

2015 AGU Fall Meeting, Dec 17, 2015

Satellite-derived constraints on the effect of drought stress on biogenic isoprene emissions in the southeastern US

Atmospheric Chemistry and Physics, Nov 7, 2022

While substantial progress has been made to improve our understanding of biogenic isoprene emissi... more While substantial progress has been made to improve our understanding of biogenic isoprene emissions under unstressed conditions, large uncertainties remain with respect to isoprene emissions under stressed conditions. Here, we use the US Drought Monitor (USDM) as a weekly drought severity index and tropospheric columns of formaldehyde (HCHO), the key product of isoprene oxidation, retrieved from the Ozone Monitoring Instrument (OMI) to derive top-down constraints on the response of summertime isoprene emissions to drought stress in the southeastern United States (SE US), a region of high isoprene emissions that is also prone to drought. OMI HCHO column density is found to be 6.7 % (mild drought) to 23.3 % (severe drought) higher than that under non-drought conditions. A global chemical transport model, GEOS-Chem, with version 2.1 of the Model of Emissions of Gases and Aerosols from Nature (MEGAN2.1) emission algorithm can simulate this direction of change, but the simulated increases at the corresponding drought levels are 1.1-1.5 times that of OMI HCHO, suggesting the need for a drought-stress algorithm in the model. By minimizing the model-OMI differences in HCHO to temperature sensitivity under different drought levels, we derived a top-down drought stress factor (γ d_OMI) in GEOS-Chem that parameterizes using water stress and temperature. The algorithm led to an 8.6 % (mild drought) to 20.7 % (severe drought) reduction in isoprene emissions in the SE US relative to the simulation without it. With γ d_OMI the model predicts a nonlinear increasing trend in isoprene emissions with drought severity that is consistent with OMI HCHO and a single site's isoprene flux measurements. Compared with a previous drought stress algorithm derived from the latter, the satellite-based drought stress factor performs better with respect to capturing the regional-scale drought-isoprene responses, as indicated by the near-zero mean bias between OMI and simulated HCHO columns under different drought conditions. The drought stress algorithm also reduces the model's high bias in organic aerosol (OA) simulations by 6.60 % (mild drought) to 11.71 % (severe drought) over the SE US compared to the no-stress simulation. The simulated ozone response to the drought stress factor displays a spatial disparity due to the isoprene-suppressing effect on oxidants, with an <1 ppb increase in O 3 in high-isoprene regions and a 1-3 ppbv decrease in O 3 in low-isoprene regions. This study demonstrates the unique value of exploiting long-term satellite observations to develop empirical stress algorithms on biogenic emissions where in situ flux measurements are limited.

Variation of global compound heatwaves and their associations with climate variability

Recent widespread heatwaves have broken local temperature records over the world. Large and inten... more Recent widespread heatwaves have broken local temperature records over the world. Large and intense heatwaves not only change the land surface biophysical environment in terms of temperature rise and water shortage, but also endanger our natural and human ecosystems by increasing health risks. Compared to the independent daytime or nighttime heatwave, the compound heatwave often yield higher hot extremes and even pose greater hazard to human and ecosystem health, as it prevents humans or ecosystems recovering from previous hot temperatures if the extreme hot occur in both day and night. However, factors shaping their spatiotemporal patterns on a global scale remain poorly understood, as do their links with large-scale interannual climatic variability. Here, with the air temperatures from multiple global datasets (e.g., ERA5L, CPC, MERRA2, and JRA55), we quantified the frequency and intensity patterns of compound heatwaves over 1980–2019 and analyzed their associations with modes of climate variability. Our results show a significant increasing trend of compound heatwave occurrences on a global scale, with the global average frequency and intensity increased by 90% and 32%, respectively, in 2010–2019 relative to 1980–1989. Specifically, Arctic and mid-latitudes of the Northern Hemisphere have seen the greatest increases in heatwave frequency and intensity over the last four decades, which may be connected to the amplifying influence of Arctic warming as well as human activity. The interannual variability of tropical compound heatwaves is dominated by ENSO occurrences from the previous year to the present year. And, the PDO and AMO modes dominate the interannual variability of extremely high temperatures in the majority of the mid-latitudes. In contrast, the interannual variability of compound heatwaves in the Arctic is most strongly tied to the AO and NAO in the winter of the present year, but it is most directly associated with the AAO and SAM in the Antarctic, which has an 8–9-month lag impact. This work will increase our knowledge of the global patterns and mechanisms of compound heatwaves in a multi-decadal context, hence enhancing our ability to predict hot extremes.

Incorporating stomatal ozone uptake into a biophysical crop model to examine ozone-induced yield loss

AGU Fall Meeting Abstracts, Dec 1, 2018

Satellite-derived Constraints on the Effect of Drought Stress on Biogenic Isoprene Emissions in the Southeast US

While substantial progress has been made to improve our understanding of biogenic isoprene emissi... more While substantial progress has been made to improve our understanding of biogenic isoprene emissions under unstressed conditions, there remain large uncertainties in isoprene emissions under stressed conditions. Here we use the US Drought Monitor (USDM) as a weekly drought severity index and tropospheric columns of formaldehyde (HCHO), the key product of isoprene oxidation, retrieved from the Ozone Monitoring Instrument (OMI) to derive top-down constraints on the response of summertime isoprene emissions to drought stress in the Southeast U.S. (SE US), a region of high isoprene emissions and prone to drought. OMI HCHO column density is found to be 5.3% (mild drought)-19.8% (severe drought) higher than that in no-drought conditions. A global chemical transport model, GEOS-Chem, with the MEGAN2.1 emission algorithm can simulate this direction of change, but the simulated increases at the corresponding drought levels are 1.4-2.0 times of OMI HCHO, suggesting the need for a droughtstress algorithm in the model. By minimizing the model-to-OMI differences in HCHO to temperature sensitivity under different drought levels, we derived a top-down drought stress factor (γd_OMI) in GEOS-Chem that parameterizes using water stress and temperature. The algorithm led to an 8.6% (mild drought)-20.7% (severe drought) reduction in isoprene emissions in the SE US relative to the simulation without it. With γd_OMI the model predicts a non-uniform trend of increase in isoprene emissions with drought severity that is consistent with OMI HCHO and a single site's isoprene flux measurements. Compared with a previous drought stress algorithm derived from the latter, the satellitebased drought stress factor performs better in capturing the regional scale drought-isoprene responses as indicated by the close-to-zero mean bias between OMI and simulated HCHO columns under different drought conditions. The drought stress algorithm also reduces the model's high bias in organic aerosols (OA) simulations by 6.60% (mild drought) to 11.71% (severe drought) over the SE US compared to the no-stress simulation. The simulated ozone response to the drought stress factor displays a spatial disparity due to the isoprene suppressing effect on oxidants, with an <1 ppb increase in O3 in high-isoprene regions and a 1-3 ppbv decrease in O3 in low-isoprene regions. This

Supplementary material to "Satellite-derived Constraints on the Effect of Drought Stress on Biogenic Isoprene Emissions in the Southeast US

Terrestrial Ecosystem Model in R (TEMIR) version 1.0: Simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes

. The newly developed offline land ecosystem model Terrestrial Ecosystem Model in R (TEMIR) versi... more . The newly developed offline land ecosystem model Terrestrial Ecosystem Model in R (TEMIR) version 1.0 is described here. This version of the model simulates plant ecophysiological (e.g., photosynthetic, stomatal) responses to varying meteorological conditions and concentrations of CO2 and ground-level ozone (O3) based on prescribed meteorological and atmospheric chemical inputs from various sources. Driven by the same meteorological data used in the GEOS-Chem chemical transport model, this allows asynchronously coupled experiments with GEOS-Chem simulations with unique coherency for investigating biosphere-atmosphere chemical interactions. TEMIR agrees well with FLUXNET site-level gross primary productivity (GPP) in terms of both the diurnal and monthly cycles (correlation coefficients R2 &gt; 0.85 and R2 &gt; 0.8, respectively) for most plant functional types (PFTs). Grass and shrub PFTs have larger biases due to generic model representations. The model performs best when driven by local site-level meteorology rather than reanalyzed gridded meteorology. Simulation using gridded meteorology agrees well for annual GPP in seasonality and spatial distribution with a global average of 134 Pg C yr–1. Application of Monin-Obukhov similarity theory to infer canopy conditions from gridded meteorology does not improve model performance, predicting a uniform increase of +21 % for global GPP. Present-day O3 concentrations simulated by GEOS-Chem and an O3 damage scheme at high sensitivity show a 2 % reduction in global GPP with prominent reductions by up to 15 % in eastern China and the eastern US. Regional correlations are generally unchanged when O3 is present, and biases are reduced especially for regions with high O3 damages. An increase in atmospheric CO2 concentration by 20 ppmv from year-2000 to year-2010 level modestly decreases O3 damage due to reduced stomatal uptake, consistent with ecophysiological understanding. Our work showcases the utility of this version of TEMIR for evaluating biogeophysical responses of vegetation to changes in atmospheric composition and meteorological conditions.

Multidecadal ozone trends in China and implications for human health and crop yields: A hybrid approach combining chemical transport model and machine learning

. Surface ozone (O3) is well known to pose significant threats to both human health and crop prod... more . Surface ozone (O3) is well known to pose significant threats to both human health and crop production worldwide. However, a multi-decadal assessment of O3 impacts on public health and crop yields in China is lacking due to insufficient long-term continuous O3 observations. In this study, we used a machine learning (ML) algorithm to correct the biases of O3 concentrations simulated by the chemical transport model from 1981–2019 by integrating multi-source datasets. The ML-enabled bias correction offers improved performance in reproducing observed O3 concentrations, and thus further improves our estimates of O3 impacts on human health and crop yields. Our results show that a warm-season increasing trend of O3 in Beijing-Tianjin-Hebei and its surroundings (BTHs), Yangtze River Delta (YRD), Sichuan Basin (SCB) and Pearl River Delta (PRD) regions are 0.32 μg m–3 yr–1, 0.63 μg m–3 yr–1, 0.84 μg m–3 yr–1, and 0.81 μg m–3 yr–1 from 1981 to 2019, respectively. In more recent years, O3 concentrations experience more fluctuations in the four major regions. Our results show that only BTHs have a perceptible increasing trend of 0.81 μg m–3 yr–1 during 2013–2019. Meteorological factors play important roles in modulating the interannual variability of surface O3, wherein synoptic systems (e.g., high-pressure system, Western Pacific subtropical high, tropical cyclone) are closely related to the spatiotemporal distribution of regional O3 via influencing regional weather conditions and transport processes. The estimated annual all-cause premature deaths induced by O3 increased from ~55,900 in 1981 to ~162,000 in 2019 with an increasing trend of ~2,980 deaths yr–1. The annual premature deaths related to respiratory and cardiovascular disease are ~34,200 and ~40,300 in 1998, and ~26,500 and ~79,000 in 2019, having a rate of change of –546 and +1,770 deaths yr–1 during 1998–2019, respectively. The estimated annual crop relative yield loss (RYL) for wheat, rice, soybean, and maize is 3.3 %, 5.5 %, 10.6 %, and 2.6 % in 1981, and increases to 5.9 %, 6.8 %, 15.1 %, and 4.1 % in 2019, respectively. The average annual crop RYL from 1981 to 2019 for wheat, rice, soybean, and maize range between 1.1–7.2 %, 2.7–9.4 %, 6.3–24.8 %, and 0.8–7.4 %, respectively, using different concentration-based metrics. Our study, for the first time, used ML to provide a robust dataset of O3 concentrations over the past four decades in China, enabling a long-term evaluation of O3-induced crop losses and health impacts. These findings are expected to fill the gap in the long-term O3 trend and impact assessment in China.

Supplementary material to "Multidecadal ozone trends in China and implications for human health and crop yields: A hybrid approach combining chemical transport model and machine learning