Modeling sensitivities of BVOCs to different versions of MEGAN emission schemes in WRF-Chem (v3.6) and its impacts over eastern China

Abstract. Biogenic volatile organic compounds (BVOCs) simulated by current air quality and climate models still have large uncertainties, which can influence atmospheric chemistry and secondary pollutant formation. These modeling sensitivities are primarily due to two sources. One originates from different treatments in the physical and chemical processes associated with the emission rates of BVOCs. The other is errors in the specification of vegetation types and their distribution over a specific region. In this study, the version of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) updated by the University of Science and Technology of China (USTC version of WRF-Chem) from the public WRF-Chem(v3.6) is used. The modeling results over eastern China with different versions (v1.0, v2.0, v3.0) of the Model of Emissions of Gases and Aerosols from Nature (MEGAN) in WRF-Chem are examined or documented. Sensitivity experiments with these three versions of MEGAN and two vegetation datasets are conducted to investigate the difference of three MEGAN versions in modeling BVOCs and its dependence on the vegetation distributions. The experiments are also conducted for spring (April) and summer (July) to examine the seasonality of the modeling results. The results indicate that MEGAN v3.0 simulates the largest amount of biogenic isoprene emissions over eastern China. The different performance among MEGAN versions is primarily due to their different treatments of applying emission factors and vegetation types. In particular, the results highlight the importance of considering the sub-grid vegetation fraction in estimating BVOC emissions over eastern China, which has a large area of urbanization. Among all activity factors, the temperature-dependent factor dominates the seasonal change of activity factor in all three versions of MEGAN, while the different response to the leaf area index (LAI) change determines the difference among the three versions in seasonal variation of BVOC emissions. The simulated surface ozone concentration due to BVOCs can be significantly different (ranging from 1 to more than 10 ppbv in some regions) among the experiments with three versions of MEGAN, which is mainly due to their impacts on surface VOCs and NOx concentrations. Theoretically MEGAN v3.0 that is coupled with the land surface scheme and considers the sub-grid vegetation effect should overcome previous versions of MEGAN in WRF-Chem. However, considering uncertainties of retrievals and anthropogenic emissions over eastern China, it is still difficult to apply satellite retrievals of formaldehyde and/or limited sparse in situ observations to constrain the uncertain parameters or functions in BVOC emission schemes and their impacts on photochemistry and ozone production. More accurate vegetation distribution and measurements of biogenic emission fluxes and species concentrations are still needed to better evaluate and optimize models.

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Sensitivity of different BVOC emission schemes in WRF-Chem(v3.6) to vegetation distributions and its impacts over East China

10.5194/gmd-2021-29 ◽

2021 ◽

Author(s):

Mingshuai Zhang ◽

Chun Zhao ◽

Yuhan Yang ◽

Qiuyan Du ◽

Yonglin Shen ◽

...

Keyword(s):

Climate Models ◽

Surface Ozone ◽

Model Performance ◽

Ozone Production ◽

East China ◽

Vegetation Types ◽

Area Index ◽

Biogenic Emission ◽

Vegetation Fraction ◽

The Difference

Abstract. Biogenic volatile organic compounds (BVOCs) simulated by current air quality and climate models still have large uncertainties, which can influence atmosphere chemistry and secondary pollutant formation over East China. These uncertainties are generally resulted from two sources. One is from different biogenic emission schemes coupled in model, representing for different treatments of physical and chemistry progresses during the emissions of BVOCs. The other is from the biased distribution of vegetation types over a specific region. In this study, the version of WRF-Chem updated by the University of Science and Technology of China (USTC version of WRF-Chem) from the public WRF-Chem(v3.6) is used. The modeling results over East China with different versions (v1.0, v2.0, v3.0) of Model of Emissions of Gases and Aerosols from Nature (MEGAN) in WRF-Chem are examined and documented. Sensitivity experiments with these three versions of MEGAN and two vegetation datasets are conducted to investigate the difference of three MEGAN versions in modeling biogenic VOCs and its dependence on the vegetation distributions. The experiments are also conducted for spring (April) and summer (July) to examine the seasonality of the modeling results. The results indicate that MEGANv3.0 simulates the largest amount of biogenic isoprene emissions over East China. The different performance among MEGAN versions is primarily due to their different treatments of applying emission factors and vegetation types. In particular, the results highlight the importance of considering sub-grid vegetation fraction in estimating BVOCs emissions. Among all activity factors, temperature-dependent factor dominates the seasonal change of activity factor in all three versions of MEGAN, while the different response to the leaf area index (LAI) change determines the difference among the three versions in seasonal variation of BVOC emissions. The simulated surface ozone concentration due to BVOCs can be significantly different among the experiments with three versions of MEGAN, which is mainly due to their impacts on surface VOCs and NOx concentrations. This study suggests that there is still large uncertain range in modeling BVOCs and their impacts on photochemistry and ozone production. More accurate vegetation distribution and measurements of biogenic emission flux and species concentration are needed to evaluate the model performance and reduce the uncertainties.

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

Geoscientific Model Development ◽

10.5194/gmd-13-1137-2020 ◽

2020 ◽

Vol 13 (3) ◽

pp. 1137-1153 ◽

Cited By ~ 2

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.

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

10.5194/gmd-2019-281 ◽

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.

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Impacts of Green Vegetation Fraction Derivation Methods on Regional Climate Simulations

Atmosphere ◽

10.3390/atmos10050281 ◽

2019 ◽

Vol 10 (5) ◽

pp. 281 ◽

Cited By ~ 1

Author(s):

Jose Manuel Jiménez-Gutiérrez ◽

Francisco Valero ◽

Sonia Jerez ◽

Juan Pedro Montávez

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Spatial Average ◽

Area Index ◽

Near Surface ◽

Vegetation Fraction ◽

Selection Of

The representation of vegetation in land surface models (LSM) is crucial for modeling atmospheric processes in regional climate models (RCMs). Vegetation is characterized by the green fractional vegetation cover (FVC) and/or the leaf area index (LAI) that are obtained from nearest difference vegetation index (NDVI) data. Most regional climate models use a constant FVC for each month and grid cell. In this work, three FVC datasets have been constructed using three methods: ZENG, WETZEL and GUTMAN. These datasets have been implemented in a RCM to explore, through sensitivity experiments over the Iberian Peninsula (IP), the effects of the differences among the FVC data-sets on the near surface temperature (T2m). Firstly, we noted that the selection of the NDVI database is of crucial importance, because there are important bias in mean and variability among them. The comparison between the three methods extracted from the same NDVI database, the global inventory modeling and mapping studies (GIMMS), reveals important differences reaching up to 12% in spatial average and and 35% locally. Such differences depend on the FVC magnitude and type of biome. The methods that use the frequency distribution of NDVI (ZENG and GUTMAN) are more similar, and the differences mainly depends on the land type. The comparison of the RCM experiments exhibits a not negligible effect of the FVC uncertainty on the monthly T2m values. Differences of 30% in FVC can produce bias of 1 ∘ C in monthly T2m, although they depend on the time of the year. Therefore, the selection of a certain FVC dataset will introduce bias in T2m and will affect the annual cycle. On the other hand, fixing a FVC database, the use of synchronized FVC instead of climatological values produces differences up to 1 ∘ C, that will modify the T2m interannual variability.

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Effects of Forest Changes on Summer Surface Temperature in Changbai Mountain, China

Forests ◽

10.3390/f12111551 ◽

2021 ◽

Vol 12 (11) ◽

pp. 1551

Author(s):

Jiaqi Zhang ◽

Xiangjin Shen ◽

Yanji Wang ◽

Ming Jiang ◽

Xianguo Lu

Keyword(s):

Climate Change ◽

Surface Temperature ◽

Land Surface ◽

Climate Models ◽

Regional Climate ◽

Changbai Mountain ◽

Vegetation Coverage ◽

Area Index ◽

Forest Coverage ◽

Cooling Effects

The area and vegetation coverage of forests in Changbai Mountain of China have changed significantly during the past decades. Understanding the effects of forests and forest coverage change on regional climate is important for predicting climate change in Changbai Mountain. Based on the satellite-derived land surface temperature (LST), albedo, evapotranspiration, leaf area index, and land-use data, this study analyzed the influences of forests and forest coverage changes on summer LST in Changbai Mountain. Results showed that the area and vegetation coverage of forests increased in Changbai Mountain from 2003 to 2017. Compared with open land, forests could decrease the summer daytime LST (LSTD) and nighttime LST (LSTN) by 1.10 °C and 0.07 °C, respectively. The increase in forest coverage could decrease the summer LSTD and LSTN by 0.66 °C and 0.04 °C, respectively. The forests and increasing forest coverage had cooling effects on summer temperature, mainly by decreasing daytime temperature in Changbai Mountain. The daytime cooling effect is mainly related to the increased latent heat flux caused by increasing evapotranspiration. Our results suggest that the effects of forest coverage change on climate should be considered in climate models for accurately simulating regional climate change in Changbai Mountain of China.

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Comparison of surface ozone simulation among selected regional models in MICS-Asia III – effects of chemistry and vertical transport for the causes of difference

Atmospheric Chemistry and Physics ◽

10.5194/acp-19-603-2019 ◽

2019 ◽

Vol 19 (1) ◽

pp. 603-615 ◽

Cited By ~ 6

Author(s):

Hajime Akimoto ◽

Tatsuya Nagashima ◽

Jie Li ◽

Joshua S. Fu ◽

Dongsheng Ji ◽

...

Keyword(s):

Surface Ozone ◽

Early Morning ◽

Photochemical Activity ◽

Vertical Transport ◽

Ozone Production ◽

Phase Iii ◽

Regional Models ◽

Mixing Ratios ◽

The Difference ◽

Intercomparison Study

Abstract. In order to clarify the causes of variability among the model outputs for surface ozone in the Model Intercomparison Study Asia Phase III (MICS-Asia III), three regional models, CMAQ v.5.0.2, CMAQ v.4.7.1, and NAQPMS (abbreviated as NAQM in this paper), have been selected. Detailed analyses of monthly averaged diurnal variation have been performed for selected grids covering the metropolitan areas of Beijing and Tokyo and at a remote oceanic site, Oki. The chemical reaction mechanism, SAPRC99, used in the CMAQ models tended to give a higher net chemical ozone production than CBM-Z used in NAQM, agreeing with previous studies. Inclusion of the heterogeneous “renoxification” reaction of HNO3 (on soot surface)→NO+NO2 only in NAQM would give a higher NO concentration resulting in a better agreement with observational data for NO and nighttime O3 mixing ratios. In addition to chemical processes, the difference in the vertical transport of O3 was found to affect the simulated results significantly. Particularly, the increase in downward O3 flux from the upper layer to the surface after dawn was found to be substantially different among the models. Larger early morning vertical transport of O3 simulated by CMAQ 5.0.2 is thought to be the reason for higher daytime O3 in July in this model. All three models overestimated the daytime ozone by ca. 20 ppbv at the remote site Oki in July, where in situ photochemical activity is minimal.

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The influence of the land surface on hydrometeorology and ecology: new advances from modeling and satellite remote sensing

Hydrology Research ◽

10.2166/nh.2011.071 ◽

2011 ◽

Vol 42 (2-3) ◽

pp. 95-112 ◽

Cited By ~ 31

Author(s):

Venkat Lakshmi ◽

Seungbum Hong ◽

Eric E. Small ◽

Fei Chen

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Wrf Model ◽

Land Surface Model ◽

Surface Processes ◽

Surface Model ◽

Land Surface Processes ◽

Area Index ◽

Vegetation Fraction ◽

Moisture Variation

The importance of land surface processes has long been recognized in hydrometeorology and ecology for they play a key role in climate and weather modeling. However, their quantification has been challenging due to the complex nature of the land surface amongst other reasons. One of the difficult parts in the quantification is the effect of vegetation that are related to land surface processes such as soil moisture variation and to atmospheric conditions such as radiation. This study addresses various relational investigations among vegetation properties such as Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), surface temperature (TSK), and vegetation water content (VegWC) derived from satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) and EOS Advanced Microwave Scanning Radiometer (AMSR-E). The study provides general information about a physiological behavior of vegetation for various environmental conditions. Second, using a coupled mesoscale/land surface model, we examine the effects of vegetation and its relationship with soil moisture on the simulated land–atmospheric interactions through the model sensitivity tests. The Weather Research and Forecasting (WRF) model was selected for this study, and the Noah land surface model (Noah LSM) implemented in the WRF model was used for the model coupled system. This coupled model was tested through two parameterization methods for vegetation fraction using MODIS data and through model initialization of soil moisture from High Resolution Land Data Assimilation System (HRLDAS). Finally, this study evaluates the model improvements for each simulation method.

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Impact of GVF Derivation Methods on Noah Land Surface Model Simulations and WRF Model Forecasts

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0075.1 ◽

2018 ◽

Vol 19 (12) ◽

pp. 1917-1933 ◽

Cited By ~ 1

Author(s):

Li Fang ◽

Xiwu Zhan ◽

Christopher R. Hain ◽

Jifu Yin ◽

Jicheng Liu

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Weather Prediction ◽

Wrf Model ◽

Land Surface Model ◽

Surface Model ◽

Area Index ◽

Vegetation Fraction ◽

Data Products ◽

Noah Land Surface Model

Abstract Green vegetation fraction (GVF) plays a crucial role in the atmosphere–land water and energy exchanges. It is one of the essential parameters in the Noah land surface model (LSM) that serves as the land component of a number of operational numerical weather prediction models at the National Centers for Environmental Prediction (NCEP) of NOAA. The satellite GVF products used in NCEP models are derived from a simple linear conversion of either the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) currently or the enhanced vegetation index (EVI) from the Visible Infrared Imaging Radiometer Suite (VIIRS) planned for the near future. Since the NDVI or EVI is a simple spectral index of vegetation cover, GVFs derived from them may lack the biophysical meaning required in the Noah LSM. Moreover, the NDVI- or EVI-based GVF data products may be systematically biased over densely vegetated regions resulting from the saturation issue associated with spectral vegetation indices. On the other hand, the GVF is physically related to the leaf area index (LAI), and thus it could be beneficial to derive GVF from LAI data products. In this paper, the EVI-based and the LAI-based GVF derivation methods are mathematically analyzed and are found to be significantly different from each other. Impacts of GVF differences on the Noah LSM simulations and on weather forecasts of the Weather Research and Forecasting (WRF) Model are further assessed. Results indicate that LAI-based GVF outperforms the EVI-based one when used in both the offline Noah LSM and WRF Model.

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Tropospheric ozone in CMIP6 Simulations

10.5194/acp-2019-1216 ◽

2020 ◽

Cited By ~ 2

Author(s):

Paul T. Griffiths ◽

Lee T. Murray ◽

Guang Zeng ◽

Alexander T. Archibald ◽

Louisa K. Emmons ◽

...

Keyword(s):

Tropospheric Ozone ◽

Climate Models ◽

Decadal Variability ◽

Stratospheric Ozone ◽

Coupled Model ◽

Mean Value ◽

Ozone Production ◽

Chemical Production ◽

The Difference ◽

Using Data

Abstract. The evolution of tropospheric ozone from 1850 to 2100 has been studied using data from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). We evaluate long-term changes using coupled atmosphere-ocean chemistry-climate models, focusing on the CMIP historical and ScenarioMIP ssp370 experiments, for which detailed tropospheric ozone diagnostics were archived. The model ensemble has been evaluated against a suite of surface, sonde, and satellite observations of the past several decades, and found to reproduce well the salient spatial, seasonal and decadal variability and trends. The tropospheric ozone burden increases from 244 ± 30 Tg in 1850 to a mean value of 348 ± 15 Tg for the period 2005–2014, an increase of 40 %. Modelled present day values agree well with previous determinations (ACCENT: 336 ± 27 Tg; ACCMIP: 337 ± 23 Tg and TOAR: 340 ± 34 Tg). In the ssp370 experiments, the ozone burden reaches a maximum of 402 ± 36 Tg in 2090, before declining slightly to 396 ± 32 Tg by 2100. The ozone budget has been examined over the same period using lumped ozone production (PO3) and loss (LO3) diagnostics. There are large differences (30 %) between models in the preindustrial period, with the difference narrowing to 15 % in the present day. Both ozone production and chemical loss terms increase steadily over the period 1850 to 2100, with net chemical production (PO3-LO3) reaching a maximum around the year 2000. The residual term, which contains contributions from stratosphere-troposphere transport reaches a minimum around the same time, while dry deposition increases steadily across the experiment. Differences between the model residual terms are explained in terms of variation in tropopause height and stratospheric ozone burden.

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Summertime surface ozone variation over East Asia in CCMI

10.5194/egusphere-egu2020-16374 ◽

2020 ◽

Author(s):

Jieun Wie ◽

Hyo-Jin Park ◽

Hyomee Lee ◽

Byung-Kwon Moon

Keyword(s):

East Asia ◽

Climate Model ◽

Southern Oscillation ◽

Surface Ozone ◽

Eastern China ◽

Empirical Orthogonal Functions ◽

Ozone Production ◽

Shortwave Radiation ◽

Horizontal Wind ◽

Second Mode

The concentration of surface ozone in East Asia is high due to strong solar radiation, but decreases in areas affected by summer monsoons. This study analyzes the summer surface ozone variations in East Asia using meteorological and atmospheric chemistry variables in 12 models participating in Chemistry-Climate Model Initiative (CCMI) for the period of 1979 to 2010. The concentration of 850 hPa ozone was identified two modes by Empirical Orthogonal Functions (EOF) analysis. The first mode is an increase in all regions over East Asia, mainly in eastern China. This mode was associated with downward wind, weak horizontal wind speed, increase in temperatures, decrease in precipitation. The second mode showed high ozone concentrations in eastern China and low in northern Japan. In eastern China, temperatures and precipitation are decreased, and shortwave radiation reaches the surface is increased. In addition, the concentration of nitrogen oxides and carbon monoxide and the net ozone production are increased. The second mode was highly correlated with El Nino-Southern Oscillation (ENSO) and western North Pacific subtropical high (WNPSH) indices and was found to be closely associated with East Asian summer monsoons.&#160;Acknowledgements: This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2019R1A2C1008549). We acknowledge the modeling groups for making their simulations available for this analysis, the joint WCRP SPARC/IGAC Chemistry&#8211;Climate Model Initiative (CCMI) for organizing and coordinating the model simulations and data analysis activity, and the British Atmospheric Data Centre (BADC) for collecting and archiving the CCMI model output.

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