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 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 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.

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 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.

scholarly journals WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry-meteorology interactions

2021 ◽  
Author(s):  
Xu Feng ◽  
Haipeng Lin ◽  
Tzung-May Fu ◽  
Melissa P. Sulprizio ◽  
Jiawei Zhuang ◽  
...  
Keyword(s):  
Open Source ◽  
Spatial Correlation ◽  
Atmospheric Chemistry ◽  
Eastern China ◽  
Wrf Model ◽  
Temporal Correlation ◽  
Cloud Microphysics ◽  
Shortwave Radiation ◽  
Spatial Distributions ◽  
Meteorological Model

Abstract. We present the WRF-GC model v2.0, an online two-way coupling of the Weather Research and Forecasting (WRF) meteorological model (v3.9.1.1) and the GEOS-Chem chemical model (v12.7.2). WRF-GC v2.0 is built on the modular framework of WRF-GC v1.0 and further includes aerosol-radiation interactions (ARI) and aerosol-cloud interactions (ACI) based on bulk aerosol mass and composition, as well as the capability to nest multiple domains for high-resolution simulations. WRF-GC v2.0 is the first implementation of the GEOS-Chem model in an open-source dynamic model with chemical feedbacks to meteorology. We apply prescribed size distributions to the 10 aerosol types simulated by GEOS-Chem to diagnose aerosol optical properties and activated cloud droplet numbers; the results are passed to the WRF model for radiative and cloud microphysics calculations. We use WRF-GC v2.0 to conduct sensitivity simulations with different combinations of ARI and ACI over China during January 2015 and July 2016, with the goal of evaluating the simulated aerosol and cloud properties and the impacts of ARI and ACI on meteorology and air quality. WRF-GC reproduces the day-to-day variability of the aerosol optical depth (AOD) observed by the Aerosol Robotic Network (AERONET) project at four representative Chinese sites in January 2015, with temporal correlation coefficients of 0.56 to 0.85. The magnitudes and spatial distributions of the simulated liquid cloud effective radii, liquid cloud optical depths, surface downward shortwave radiation, and surface temperature over China for July 2016 are in good agreement with aircraft, satellite, and surface observations. WRF-GC simulations including both ARI and ACI reproduce the observed surface concentrations and spatial distributions of PM2.5 in January 2015 (normalized mean bias = −6.6 %, spatial correlation r = 0.74) and afternoon ozone in July 2016 (normalized mean bias = 19 %, spatial correlation r = 0.56) over Eastern China, respectively. Our sensitivity simulations show that including the ARI and ACI improved the model's performance in simulating ozone concentrations over China in July, 2016. WRF-GC v2.0 is open source and freely available from http://wrf.geos-chem.org.

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

2010 ◽  
Vol 10 (11) ◽  
pp. 27853-27891 ◽  
Author(s):  
Y. Wang ◽  
Y. Zhang ◽  
J. Hao ◽  
M. Luo
Keyword(s):  
Yangtze River ◽  
Transport Model ◽  
Surface Ozone ◽  
Eastern China ◽  
Chemical Transport ◽  
Anthropogenic Emissions ◽  
Spatial Gradient ◽  
Chemical Transport Model ◽  
Significant Drop ◽  
Background Ozone

Abstract. Both observations and a 3-D chemical transport model suggest that surface ozone over populated eastern China features a significant drop in mid-summer and that the peak month 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. Anthropogenic background (annual mean of 12.6 ppbv) shows distinct troughs in the summer and peaks 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 mid-summer drop in ozone over eastern China is driven by the decrease of background ozone (−15 ppbv). Tagged simulations suggest that this decrease is driven by reduced transport from Europe and North America, whereas ozone from Southeast Asia and Pacific Ocean 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.

scholarly journals Can a global model reproduce observed trends in summertime surface ozone levels?

2012 ◽  
Vol 12 (15) ◽  
pp. 6983-6998 ◽  
Author(s):  
S. Koumoutsaris ◽  
I. Bey
Keyword(s):  
Transport Model ◽  
Control Strategies ◽  
Surface Ozone ◽  
The United States ◽  
Daily Maximum ◽  
Chemical Transport Model ◽  
Ozone Concentrations ◽  
Western Us ◽  
Ozone Trends ◽  
Rural Sites

Abstract. Quantifying trends in surface ozone concentrations is critical for assessing pollution control strategies. Here we use observations and results from a global chemical transport model to examine the trends (1991–2005) in daily maximum 8-h average concentrations in summertime surface ozone at rural sites in Europe and the United States (US). We find a decrease in observed ozone concentrations at the high end of the probability distribution at many of the sites in both regions. The model attributes these trends to a decrease in local anthropogenic ozone precursors, although simulated decreasing trends are overestimated in comparison with observed ones. The low end of observed distribution show small upward trends over Europe and the western US and downward trends in Eastern US. The model cannot reproduce these observed trends, especially over Europe and the western US. In particular, simulated changes between the low and high end of the distributions in these two regions are not significant. Sensitivity simulations indicate that emissions from far away source regions do not affect significantly summer ozone trends at both ends of the distribution in both Europe and US. Possible reasons for discrepancies between observed and simulated trends are discussed.

scholarly journals Anthropogenic drivers of 2013–2017 trends in summer surface ozone in China

2018 ◽  
Vol 116 (2) ◽  
pp. 422-427 ◽  
Author(s):  
Ke Li ◽  
Daniel J. Jacob ◽  
Hong Liao ◽  
Lu Shen ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Transport Model ◽  
Southern China ◽  
Surface Ozone ◽  
Fine Particulate Matter ◽  
Eastern China ◽  
Multiple Linear Regression Model ◽  
Urban China ◽  
Ozone Production ◽  
Chemical Transport Model ◽  
Ozone Trends

Observations of surface ozone available from ∼1,000 sites across China for the past 5 years (2013–2017) show severe summertime pollution and regionally variable trends. We resolve the effect of meteorological variability on the ozone trends by using a multiple linear regression model. The residual of this regression shows increasing ozone trends of 1–3 ppbv a−1 in megacity clusters of eastern China that we attribute to changes in anthropogenic emissions. By contrast, ozone decreased in some areas of southern China. Anthropogenic NOx emissions in China are estimated to have decreased by 21% during 2013–2017, whereas volatile organic compounds (VOCs) emissions changed little. Decreasing NOx would increase ozone under the VOC-limited conditions thought to prevail in urban China while decreasing ozone under rural NOx-limited conditions. However, simulations with the Goddard Earth Observing System Chemical Transport Model (GEOS-Chem) indicate that a more important factor for ozone trends in the North China Plain is the ∼40% decrease of fine particulate matter (PM2.5) over the 2013–2017 period, slowing down the aerosol sink of hydroperoxy (HO2) radicals and thus stimulating ozone production.

scholarly journals WRF-GC (v1.0): online coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.2.1) for regional atmospheric chemistry modeling – Part 1: Description of the one-way model

2020 ◽  
Vol 13 (7) ◽  
pp. 3241-3265 ◽  
Author(s):  
Haipeng Lin ◽  
Xu Feng ◽  
Tzung-May Fu ◽  
Heng Tian ◽  
Yaping Ma ◽  
...  
Keyword(s):  
Open Source ◽  
Atmospheric Chemistry ◽  
Message Passing ◽  
Message Passing Interface ◽  
Eastern China ◽  
State Variables ◽  
Meteorological Model ◽  
Chemistry Modeling ◽  
Meteorological Observations ◽  
Upper Level

Abstract. We developed the WRF-GC model, an online coupling of the Weather Research and Forecasting (WRF) mesoscale meteorological model and the GEOS-Chem atmospheric chemistry model, for regional atmospheric chemistry and air quality modeling. WRF and GEOS-Chem are both open-source community models. WRF-GC offers regional modellers access to the latest GEOS-Chem chemical module, which is state of the science, well documented, traceable, benchmarked, actively developed by a large international user base, and centrally managed by a dedicated support team. At the same time, WRF-GC enables GEOS-Chem users to perform high-resolution forecasts and hindcasts for any region and time of interest. WRF-GC uses unmodified copies of WRF and GEOS-Chem from their respective sources; the coupling structure allows future versions of either one of the two parent models to be integrated into WRF-GC with relative ease. Within WRF-GC, the physical and chemical state variables are managed in distributed memory and translated between WRF and GEOS-Chem by the WRF-GC coupler at runtime. We used the WRF-GC model to simulate surface PM2.5 concentrations over China during 22 to 27 January 2015 and compared the results to surface observations and the outcomes from a GEOS-Chem Classic nested-China simulation. Both models were able to reproduce the observed spatiotemporal variations of regional PM2.5, but the WRF-GC model (r=0.68, bias =29 %) reproduced the observed daily PM2.5 concentrations over eastern China better than the GEOS-Chem Classic model did (r=0.72, bias =55 %). This was because the WRF-GC simulation, nudged with surface and upper-level meteorological observations, was able to better represent the pollution meteorology during the study period. The WRF-GC model is parallelized across computational cores and scales well on massively parallel architectures. In our tests where the two models were similarly configured, the WRF-GC simulation was 3 times more efficient than the GEOS-Chem Classic nested-grid simulation due to the efficient transport algorithm and the Message Passing Interface (MPI)-based parallelization provided by the WRF software framework. WRF-GC v1.0 supports one-way coupling only, using WRF-simulated meteorological fields to drive GEOS-Chem with no chemical feedbacks. The development of two-way coupling capabilities, i.e., the ability to simulate radiative and microphysical feedbacks of chemistry to meteorology, is under way. The WRF-GC model is open source and freely available from http://wrf.geos-chem.org (last access: 10 July 2020).

scholarly journals WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactions

2021 ◽  
Vol 14 (6) ◽  
pp. 3741-3768
Author(s):  
Xu Feng ◽  
Haipeng Lin ◽  
Tzung-May Fu ◽  
Melissa P. Sulprizio ◽  
Jiawei Zhuang ◽  
...  
Keyword(s):  
Open Source ◽  
Spatial Correlation ◽  
Atmospheric Chemistry ◽  
Eastern China ◽  
Wrf Model ◽  
Chemical Species ◽  
Cloud Droplet ◽  
Cloud Microphysics ◽  
Meteorological Variables ◽  
Meteorological Model

Abstract. We present the WRF-GC model v2.0, an online two-way coupling of the Weather Research and Forecasting (WRF) meteorological model (v3.9.1.1) and the GEOS-Chem model (v12.7.2). WRF-GC v2.0 is built on the modular framework of WRF-GC v1.0 and further includes aerosol–radiation interaction (ARI) and aerosol–cloud interaction (ACI) based on bulk aerosol mass and composition, as well as the capability to nest multiple domains for high-resolution simulations. WRF-GC v2.0 is the first implementation of the GEOS-Chem model in an open-source dynamic model with chemical feedbacks to meteorology. In WRF-GC, meteorological and chemical calculations are performed on the exact same 3-D grid system; grid-scale advection of meteorological variables and chemical species uses the same transport scheme and time steps to ensure mass conservation. Prescribed size distributions are applied to the aerosol types simulated by GEOS-Chem to diagnose aerosol optical properties and activated cloud droplet numbers; the results are passed to the WRF model for radiative and cloud microphysics calculations. WRF-GC is computationally efficient and scalable to massively parallel architectures. We use WRF-GC v2.0 to conduct sensitivity simulations with different combinations of ARI and ACI over China during January 2015 and July 2016. Our sensitivity simulations show that including ARI and ACI improves the model's performance in simulating regional meteorology and air quality. WRF-GC generally reproduces the magnitudes and spatial variability of observed aerosol and cloud properties and surface meteorological variables over East Asia during January 2015 and July 2016, although WRF-GC consistently shows a low bias against observed aerosol optical depths over China. WRF-GC simulations including both ARI and ACI reproduce the observed surface concentrations of PM2.5 in January 2015 (normalized mean bias of −9.3 %, spatial correlation r of 0.77) and afternoon ozone in July 2016 (normalized mean bias of 25.6 %, spatial correlation r of 0.56) over eastern China. WRF-GC v2.0 is open source and freely available from http://wrf.geos-chem.org (last access: 20 June 2021).

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