scholarly journals Implementation of Yale Interactive terrestrial Biosphere model version 1.0 into GEOS-Chem version 12.0.0: a tool for biosphere-chemistry interactions

2019 ◽  
Author(s):  
Yadong Lei ◽  
Xu Yue ◽  
Hong Liao ◽  
Cheng Gong ◽  
Lin Zhang
Keyword(s):  
Stomatal Conductance ◽  
Atmospheric Chemistry ◽  
Dry Deposition ◽  
Transport Model ◽  
Surface Ozone ◽  
Eastern China ◽  
Deposition Velocity ◽  
Chemical Transport Model ◽  
Terrestrial Biosphere ◽  
Area Index

Abstract. The terrestrial biosphere and atmospheric chemistry interact through multiple feedbacks, but the models of vegetation and chemistry are developed separately. In this study, the Yale Interactive terrestrial Biosphere (YIBs) model, a dynamic vegetation model with biogeochemical processes, is implemented into the Chemical Transport Model GEOS-Chem version 12.0.0. Within the GC-YIBs framework, leaf area index (LAI) and canopy stomatal conductance dynamically predicted by YIBs are used for dry deposition calculation in GEOS-Chem. In turn, the simulated surface ozone (O3) by GEOS-Chem affect plant photosynthesis and biophysics in YIBs. The updated stomatal conductance and LAI improve the simulated daytime O3 dry deposition velocity for major tree species. Compared with the GEOS-Chem model, the model-to-observation correlation for dry deposition velocities increases from 0.76 to 0.85 while the normalized mean error decreases from 35 % to 27 % using the GC-YIBs model. Furthermore, we quantify O3 vegetation damaging effects and find a global reduction of annual gross primary productivity by 2–5 %, with regional extremes of 11–15 % in the eastern U.S. and eastern China. The online GC-YIBs model provides a useful tool for discerning the complex feedbacks between atmospheric chemistry and terrestrial biosphere under global change.

scholarly journals Implementation of Yale Interactive terrestrial Biosphere model v1.0 into GEOS-Chem v12.0.0: a tool for biosphere–chemistry interactions

2020 ◽  
Vol 13 (3) ◽  
pp. 1137-1153 ◽  
Author(s):  
Yadong Lei ◽  
Xu Yue ◽  
Hong Liao ◽  
Cheng Gong ◽  
Lin Zhang
Keyword(s):  
Stomatal Conductance ◽  
Atmospheric Chemistry ◽  
Dry Deposition ◽  
Transport Model ◽  
Surface Ozone ◽  
Eastern China ◽  
Deposition Velocity ◽  
Chemical Transport Model ◽  
Terrestrial Biosphere ◽  
Area Index

Abstract. The terrestrial biosphere and atmospheric chemistry interact through multiple feedbacks, but the models of vegetation and chemistry are developed separately. In this study, the Yale Interactive terrestrial Biosphere (YIBs) model, a dynamic vegetation model with biogeochemical processes, is implemented into the Chemical Transport Model GEOS-Chem (GC) version 12.0.0. Within this GC-YIBs framework, leaf area index (LAI) and canopy stomatal conductance dynamically predicted by YIBs are used for dry deposition calculation in GEOS-Chem. In turn, the simulated surface ozone (O3) by GEOS-Chem affect plant photosynthesis and biophysics in YIBs. The updated stomatal conductance and LAI improve the simulated O3 dry deposition velocity and its temporal variability for major tree species. For daytime dry deposition velocities, the model-to-observation correlation increases from 0.69 to 0.76, while the normalized mean error (NME) decreases from 30.5 % to 26.9 % using the GC-YIBs model. For the diurnal cycle, the NMEs decrease by 9.1 % for Amazon forests, 6.8 % for coniferous forests, and 7.9 % for deciduous forests using the GC-YIBs model. Furthermore, we quantify the damaging effects of O3 on vegetation and find a global reduction of annual gross primary productivity by 1.5 %–3.6 %, with regional extremes of 10.9 %–14.1 % in the eastern USA and eastern China. The online GC-YIBs model provides a useful tool for discerning the complex feedbacks between atmospheric chemistry and the terrestrial biosphere under global change.

scholarly journals Importance of Dry Deposition Parameterization Choice in Global Simulations of Surface Ozone

2019 ◽  
Author(s):  
Anthony Y. H. Wong ◽  
Jeffrey A. Geddes ◽  
Amos P. K. Tai ◽  
Sam J. Silva
Keyword(s):  
Land Cover ◽  
Dry Deposition ◽  
Transport Model ◽  
Surface Ozone ◽  
Deposition Velocity ◽  
Chemical Transport Model ◽  
Area Index ◽  
Ozone Sensitivity ◽  
Dry Deposition Velocity ◽  
The Impact

Abstract. Dry deposition is the second largest 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, multi-decade modelling and evaluation of ozone dry deposition velocity (vd) using multiple ozone dry deposition parameterizations. We use consistent assimilated meteorology and satellite-derived leaf area index (LAI) to simulate vd over 1982–2011 driven by four sets of ozone dry deposition parametrization that are representative of the current approaches of global ozone dry deposition modelling, such that the differences in simulated vd are entirely due to differences in deposition model structures. In addition, we use the surface ozone sensitivity to vd predicted by a chemical transport model to estimate the impact of mean and variability of ozone dry deposition velocity on surface ozone. Our estimated vd 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 vd 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 rainforest 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 vd in all parameterizations, driven by warming and drying (southern Amazonia, southern African savannah and Mongolia) or greening (high latitudes). The trends in July daytime vd 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 vd 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 are more sensitive to LAI while others are 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 vd on surface ozone, thus making a strong case for further measurement, evaluation and model-data integration of ozone dry deposition on different spatiotemporal scales.

scholarly journals Coupling between surface ozone and leaf area index in a chemical transport model: strength of feedback and implications for ozone air quality and vegetation health

2018 ◽  
Vol 18 (19) ◽  
pp. 14133-14148 ◽  
Author(s):  
Shan S. Zhou ◽  
Amos P. K. Tai ◽  
Shihan Sun ◽  
Mehliyar Sadiq ◽  
Colette L. Heald ◽  
...  
Keyword(s):  
Air Quality ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Dry Deposition ◽  
Transport Model ◽  
Surface Ozone ◽  
Chemical Transport ◽  
Chemical Transport Model ◽  
Area Index ◽  
Ozone Levels

Abstract. Tropospheric ozone is an air pollutant that substantially harms vegetation and is also strongly dependent on various vegetation-mediated processes. The interdependence between ozone and vegetation may constitute feedback mechanisms that can alter ozone concentration itself but have not been considered in most studies to date. In this study we examine the importance of dynamic coupling between surface ozone and leaf area index (LAI) in shaping ozone air quality and vegetation. We first implement an empirical scheme for ozone damage on vegetation in the Community Land Model (CLM) and simulate the steady-state responses of LAI to long-term exposure to a range of prescribed ozone levels (from 0 to 100 ppb). We find that most plant functional types suffer a substantial decline in LAI as ozone level increases. Based on the CLM-simulated results, we develop and implement in the GEOS-Chem chemical transport model a parameterization that computes fractional changes in monthly LAI as a function of local mean ozone levels. By forcing LAI to respond to ozone concentrations on a monthly timescale, the model simulates ozone–LAI coupling dynamically via biogeochemical processes including biogenic volatile organic compound (VOC) emissions and dry deposition, without the complication from meteorological changes. We find that ozone-induced damage on LAI can lead to changes in ozone concentrations by −1.8 to +3 ppb in boreal summer, with a corresponding ozone feedback factor of −0.1 to +0.6 that represents an overall self-amplifying effect from ozone–LAI coupling. Substantially higher simulated ozone due to strong positive feedbacks is found in most tropical forests, mainly due to the ozone-induced reductions in LAI and dry deposition velocity, whereas reduced isoprene emission plays a lesser role in these low-NOx environments. In high-NOx regions such as the eastern US, Europe, and China, however, the feedback effect is much weaker and even negative in some regions, reflecting the compensating effects of reduced dry deposition and reduced isoprene emission (which reduces ozone in high-NOx environments). In remote, low-LAI regions, including most of the Southern Hemisphere, the ozone feedback is generally slightly negative due to the reduced transport of NOx–VOC reaction products that serve as NOx reservoirs. This study represents the first step to accounting for dynamic ozone–vegetation coupling in a chemical transport model with ramifications for a more realistic joint assessment of ozone air quality and ecosystem health.

scholarly journals Coupling between surface ozone and leaf area index in a chemical transport model: Strength of feedback and implications for ozone air quality and vegetation health

2018 ◽  
Author(s):  
Shan S. Zhou ◽  
Amos P. K. Tai ◽  
Shihan Sun ◽  
Mehliyar Sadiq ◽  
Colette L. Heald ◽  
...  
Keyword(s):  
Air Quality ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Dry Deposition ◽  
Transport Model ◽  
Surface Ozone ◽  
Chemical Transport ◽  
Chemical Transport Model ◽  
Area Index ◽  
Ozone Levels

Abstract. Tropospheric ozone is a significant air pollutant with substantial harm on vegetation, but is also strongly dependent on various vegetation-mediated processes. The interdependence between ozone and vegetation may constitute feedback mechanisms that can alter ozone concentration itself but have not been considered in most studies to date. In this study we examine the importance of biogeochemical coupling between surface ozone and leaf area index (LAI) in shaping ozone air quality and foliage density. We first implement an empirical scheme for ozone damage on vegetation in the Community Land Model (CLM), and simulate the steady-state responses of LAI to long-term exposure to a range of prescribed ozone levels (from 0 ppb to 100 ppb). We find that most plant functional types (PFTs) suffer a substantial decline in LAI as ozone level increases. Based on the CLM-simulated results, we develop and implement in the GEOS-Chem chemical transport model a parameterization that correlates the fractional changes in monthly LAI to local mean ozone levels. By dynamically forcing LAI to respond to ozone concentrations on a monthly timescale, the model simulates ozone-vegetation coupling synchronously via biogeochemical processes including biogenic volatile organic compound (VOC) emissions and dry deposition. We find that ozone-induced damage on LAI can lead to an ozone feedback of −1.8 ppb to +3 ppb in northern summer, with a corresponding ozone feedback factor of −0.1 to +0.6. Significantly higher simulated ozone due to strong positive ozone-LAI feedback is found in most tropical forests, mainly due to the ozone-induced reductions in LAI and dry deposition velocity, whereas reduced isoprene emission plays a lesser role in these low-NOx environments. In high-NOx regions such as eastern US, Europe and China, however, the feedback effect is much weaker and even negative in some regions, reflecting the compensating effects of reduced dry deposition and reduced isoprene emission (which leads to lower ozone in the high-NOx regime). In remote, low-LAI regions including most of the Southern Hemisphere, the ozone feedback is generally slightly negative, likely due to reduced transport of NOx-VOC reaction products that serve as NOx reservoirs. This study represents the first step to account for dynamic ozone-vegetation coupling in a chemical transport model with important ramifications for more realistic assessment of ozone air quality and ecosystem health.

WRF-GC: online two-way coupling of WRF and GEOS-Chem for regional atmospheric chemistry modeling

2020 ◽  
Author(s):  
Xu Feng ◽  
Haipeng Lin ◽  
Tzung-May Fu
Keyword(s):  
Atmospheric Chemistry ◽  
Transport Model ◽  
Surface Ozone ◽  
Eastern China ◽  
Cloud Microphysics ◽  
Chemical Transport Model ◽  
Meteorological Model ◽  
Ozone Concentrations ◽  
Chemistry Modeling ◽  
Nested Grid

<p>We developed the two-way version of the WRF-GC model, which is an online coupling of the Weather Research and Forecasting (WRF) mesoscale meteorological model and the GEOS-Chem chemical transport model, for regional air quality and atmospheric chemistry modeling. WRF-GC allows the two parent models to be updated independently, such that WRF-GC can stay state-of-the-science. The meteorological fields and chemical variables are transferred between the two models in the coupler to simulate the feedback of gases and aerosols to meteorological processes via interactions with radiation and cloud microphysics. We used the WRF-GC model to simulate surface PM<sub>2.5</sub> concentrations over China during January 22 to 27, 2015 and compared the results to the outcomes from classic GEOS-Chem nested-grid simulations as well as the surface observations. For PM<sub>2.5</sub> simulations, both models were able to reproduce the spatiotemporal variations, but the WRF-GC (r = 0.68, bias = 29%) performing better than GEOS-Chem (r = 0.72, bias = 55%) especially over Eastern China. For ozone simulations, we found that including aerosol-chemistry-cloud-radiation interactions reduced the mean bias of simulated surface ozone concentrations from 34% to 29% compared to observed afternoon ozone concentrations. WRF-GC is computationally efficient, with the physical and chemical variables managed in distributed memory. At similar resolutions, WRF-GC simulations were three times faster than the classic GEOS-Chem nested-grid simulations, due to the more efficient transport algorithm and the MPI-based parallelization provided by the WRF software framework. We envision WRF-GC to become a powerful tool for advancing science, serving the public, and informing policy-making.</p>

scholarly journals Importance of dry deposition parameterization choice in global simulations of surface ozone

2019 ◽  
Vol 19 (22) ◽  
pp. 14365-14385 ◽  
Author(s):  
Anthony Y. H. Wong ◽  
Jeffrey A. Geddes ◽  
Amos P. K. Tai ◽  
Sam J. Silva
Keyword(s):  
Land Cover ◽  
Dry Deposition ◽  
Transport Model ◽  
Surface Ozone ◽  
Deposition Velocity ◽  
Tropical Rainforests ◽  
Area Index ◽  
Ozone Sensitivity ◽  
Dry Deposition Velocity ◽  
The Impact

Abstract. 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 (vd) 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 vd 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 vd predicted by a chemical transport model to estimate the impact of mean and variability of ozone dry deposition velocity on surface ozone. Our estimated vd 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 vd 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 vd in all parameterizations, driven by warming and drying (southern Amazonia, southern African savannah, and Mongolia) or greening (high latitudes). The trend in July daytime vd 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 vd 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 vd on surface ozone, thus making a strong case for further measurement, evaluation, and model–data integration of ozone dry deposition on different spatiotemporal scales.

Effects of Dry Deposition on Surface Ozone over South Asia Inferred from a Regional Chemical Transport Model

2020 ◽  
Vol 4 (2) ◽  
pp. 321-327 ◽  
Author(s):  
Amit Sharma ◽  
Narendra Ojha ◽  
Tabish U. Ansari ◽  
Som K. Sharma ◽  
Andrea Pozzer ◽  
...  
Keyword(s):  
South Asia ◽  
Dry Deposition ◽  
Transport Model ◽  
Surface Ozone ◽  
Chemical Transport ◽  
Chemical Transport Model

Exploring the trends and potential drivers of surface ozone in eastern China in 2015-2018

2020 ◽  
Author(s):  
Ning Yang ◽  
Yanru Bai ◽  
Yong Zhu ◽  
Nan Ma ◽  
Qiaoqiao Wang
Keyword(s):  
Air Quality ◽  
Transport Model ◽  
Surface Ozone ◽  
Eastern China ◽  
Yangtze River Delta ◽  
River Delta ◽  
Pearl River Delta Region ◽  
Chemical Transport Model ◽  
Delta Region ◽  
Almost All

<p>In the last six years, China has experienced significant improvement in air quality due to great emission reduction efforts. However, ozone concentrations are still slowly increasing in three major regions of eastern China, respectively Jing-Jin-Ji(JJJ), Yangtze River Delta region(YRD) and Pearl River Delta region(PRD). It is shown from the 2015-2018 national urban air quality real-time release platform that the surface ozone in JJJ, YRD and PRD has increased each year and reached the highest in 2018. The monthly ozone concentration peaked in June in almost all cities of JJJ, while it had multiple peaks in other two regions (summer and autumn in YRD - and February, May and September in PRD). Simulation with a chemical transport model(GEOS-Chem) indicates that the formation of ozone is affected by the optical properties of PM<sub>2.5</sub> and also the heterogeneous uptake of N<sub>2</sub>O<sub>5</sub> on sea salt aerosol.</p>

scholarly journals Impact of regional climate change and future emission scenarios on surface O<sub>3</sub> and PM<sub>2.5</sub> over India

2018 ◽  
Vol 18 (1) ◽  
pp. 103-127 ◽  
Author(s):  
Matthieu Pommier ◽  
Hilde Fagerli ◽  
Michael Gauss ◽  
David Simpson ◽  
Sumit Sharma ◽  
...  
Keyword(s):  
Climate Change ◽  
Transport Model ◽  
Surface Ozone ◽  
Deposition Velocity ◽  
Public Health Issue ◽  
Anthropogenic Emissions ◽  
Reference Scenario ◽  
Chemical Transport Model ◽  
Data Set ◽  
Gangetic Plain

Abstract. Eleven of the world's 20 most polluted cities are located in India and poor air quality is already a major public health issue. However, anthropogenic emissions are predicted to increase substantially in the short-term (2030) and medium-term (2050) futures in India, especially if no further policy efforts are made. In this study, the EMEP/MSC-W chemical transport model has been used to predict changes in surface ozone (O3) and fine particulate matter (PM2.5) for India in a world of changing emissions and climate. The reference scenario (for present-day) is evaluated against surface-based measurements, mainly at urban stations. The evaluation has also been extended to other data sets which are publicly available on the web but without quality assurance. The evaluation shows high temporal correlation for O3 (r =  0.9) and high spatial correlation for PM2.5 (r =  0.5 and r =  0.8 depending on the data set) between the model results and observations. While the overall bias in PM2.5 is small (lower than 6 %), the model overestimates O3 by 35 %. The underestimation in NOx titration is probably the main reason for the O3 overestimation in the model. However, the level of agreement can be considered satisfactory in this case of a regional model being evaluated against mainly urban measurements, and given the inevitable uncertainties in much of the input data.For the 2050s, the model predicts that climate change will have distinct effects in India in terms of O3 pollution, with a region in the north characterized by a statistically significant increase by up to 4 % (2 ppb) and one in the south by a decrease up to −3 % (−1.4 ppb). This variation in O3 is assumed to be partly related to changes in O3 deposition velocity caused by changes in soil moisture and, over a few areas, partly also by changes in biogenic non-methane volatile organic compounds.Our calculations suggest that PM2.5 will increase by up to 6.5 % over the Indo-Gangetic Plain by the 2050s. The increase over India is driven by increases in dust, particulate organic matter (OM) and secondary inorganic aerosols (SIAs), which are mainly affected by the change in precipitation, biogenic emissions and wind speed.The large increase in anthropogenic emissions has a larger impact than climate change, causing O3 and PM2.5 levels to increase by 13 and 67 % on average in the 2050s over the main part of India, respectively. By the 2030s, secondary inorganic aerosol is predicted to become the second largest contributor to PM2.5 in India, and the largest in the 2050s, exceeding OM and dust.

scholarly journals Seasonal and spatial variability of surface ozone over China: contributions from background and domestic pollution

2011 ◽  
Vol 11 (7) ◽  
pp. 3511-3525 ◽  
Author(s):  
Y. Wang ◽  
Y. Zhang ◽  
J. Hao ◽  
M. Luo
Keyword(s):  
Yangtze River ◽  
Transport Model ◽  
Surface Ozone ◽  
Eastern China ◽  
Long Range Transport ◽  
Anthropogenic Emissions ◽  
Spatial Gradient ◽  
Chemical Transport Model ◽  
Background Ozone ◽  
Range Transport

Abstract. Both observations and a 3-D chemical transport model suggest that surface ozone over populated eastern China features a summertime trough and that the month when surface ozone peaks differs by latitude and region. Source-receptor analysis is used to quantify the contributions of background ozone and Chinese anthropogenic emissions on this variability. Annual mean background ozone over China shows a spatial gradient from 55 ppbv in the northwest to 20 ppbv in the southeast, corresponding with changes in topography and ozone lifetime. Pollution background ozone (annual mean of 12.6 ppbv) shows a minimum in the summer and maximum in the spring. On the monthly-mean basis, Chinese pollution ozone (CPO) has a peak of 20–25 ppbv in June north of the Yangtze River and in October south of it, which explains the peaks of surface ozone in these months. The summertime trough in surface ozone over eastern China can be explained by the decrease of background ozone from spring to summer (by −15 ppbv regionally averaged over eastern China). Tagged simulations suggest that long-range transport of ozone from northern mid-latitude continents (including Europe and North America) reaches a minimum in the summer, whereas ozone from Southeast Asia exhibits a maximum in the summer over eastern China. This contrast in seasonality provides clear evidence that the seasonal switch in monsoonal wind patterns plays a significant role in determining the seasonality of background ozone over China.

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