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

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

Geoscientific Model Development ◽

10.5194/gmd-14-6155-2021 ◽

2021 ◽

Vol 14 (10) ◽

pp. 6155-6175

Author(s):

Mingshuai Zhang ◽

Chun Zhao ◽

Yuhan Yang ◽

Qiuyan Du ◽

Yonglin Shen ◽

...

Keyword(s):

Atmospheric Chemistry ◽

Land Surface ◽

Climate Models ◽

Surface Ozone ◽

Eastern China ◽

Ozone Production ◽

Vegetation Types ◽

Area Index ◽

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 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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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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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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Low modeled ozone production suggests underestimation of precursor emissions (especially NO<sub><i>x</i></sub>) in Europe

Atmospheric Chemistry and Physics ◽

10.5194/acp-18-2175-2018 ◽

2018 ◽

Vol 18 (3) ◽

pp. 2175-2198 ◽

Cited By ~ 10

Author(s):

Emmanouil Oikonomakis ◽

Sebnem Aksoyoglu ◽

Giancarlo Ciarelli ◽

Urs Baltensperger ◽

André Stephan Henry Prévôt

Keyword(s):

Air Quality ◽

Surface Ozone ◽

Model Performance ◽

Ozone Production ◽

Nox Emissions ◽

Air Quality Model ◽

Quality Model ◽

Voc Emissions ◽

Mixing Ratios ◽

The Impact

Abstract. High surface ozone concentrations, which usually occur when photochemical ozone production takes place, pose a great risk to human health and vegetation. Air quality models are often used by policy makers as tools for the development of ozone mitigation strategies. However, the modeled ozone production is often not or not enough evaluated in many ozone modeling studies. The focus of this work is to evaluate the modeled ozone production in Europe indirectly, with the use of the ozone–temperature correlation for the summer of 2010 and to analyze its sensitivity to precursor emissions and meteorology by using the regional air quality model, the Comprehensive Air Quality Model with Extensions (CAMx). The results show that the model significantly underestimates the observed high afternoon surface ozone mixing ratios (≥ 60 ppb) by 10–20 ppb and overestimates the lower ones (< 40 ppb) by 5–15 ppb, resulting in a misleading good agreement with the observations for average ozone. The model also underestimates the ozone–temperature regression slope by about a factor of 2 for most of the measurement stations. To investigate the impact of emissions, four scenarios were tested: (i) increased volatile organic compound (VOC) emissions by a factor of 1.5 and 2 for the anthropogenic and biogenic VOC emissions, respectively, (ii) increased nitrogen oxide (NOx) emissions by a factor of 2, (iii) a combination of the first two scenarios and (iv) increased traffic-only NOx emissions by a factor of 4. For southern, eastern, and central (except the Benelux area) Europe, doubling NOx emissions seems to be the most efficient scenario to reduce the underestimation of the observed high ozone mixing ratios without significant degradation of the model performance for the lower ozone mixing ratios. The model performance for ozone–temperature correlation is also better when NOx emissions are doubled. In the Benelux area, however, the third scenario (where both NOx and VOC emissions are increased) leads to a better model performance. Although increasing only the traffic NOx emissions by a factor of 4 gave very similar results to the doubling of all NOx emissions, the first scenario is more consistent with the uncertainties reported by other studies than the latter, suggesting that high uncertainties in NOx emissions might originate mainly from the road-transport sector rather than from other sectors. The impact of meteorology was examined with three sensitivity tests: (i) increased surface temperature by 4 ∘C, (ii) reduced wind speed by 50 % and (iii) doubled wind speed. The first two scenarios led to a consistent increase in all surface ozone mixing ratios, thus improving the model performance for the high ozone values but significantly degrading it for the low ozone values, while the third scenario had exactly the opposite effects. Overall, the modeled ozone is predicted to be more sensitive to its precursor emissions (especially traffic NOx) and therefore their uncertainties, which seem to be responsible for the model underestimation of the observed high ozone mixing ratios and ozone production.

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Parameterization of the snow-covered surface albedo in the Noah-MP Version 1.0 by implementing vegetation effects

Geoscientific Model Development ◽

10.5194/gmd-9-1073-2016 ◽

2016 ◽

Vol 9 (3) ◽

pp. 1073-1085 ◽

Cited By ~ 11

Author(s):

Sojung Park ◽

Seon Ki Park

Keyword(s):

Land Surface ◽

Surface Albedo ◽

Surface Condition ◽

Model Performance ◽

Land Surface Model ◽

Surface Model ◽

Vegetation Types ◽

Area Index ◽

Almost All ◽

Vegetation Effect

Abstract. Snow-covered surface albedo varies depending on many factors, including snow grain size, snow cover thickness, snow age, forest shading factor, etc., and its parameterization is still under great uncertainty. For the snow-covered surface condition, albedo of forest is typically lower than that of short vegetation; thus snow albedo is dependent on the spatial distributions of characteristic land cover and on the canopy density and structure. In the Noah land surface model with multiple physics options (Noah-MP), almost all vegetation types in East Asia during winter have the minimum values of leaf area index (LAI) and stem area index (SAI), which are too low and do not consider the vegetation types. Because LAI and SAI are represented in terms of photosynthetic activeness, stem and trunk in winter are not well represented with only these parameters. We found that such inadequate representation of the vegetation effect is mainly responsible for the large positive bias in calculating the winter surface albedo in the Noah-MP. In this study, we investigated the vegetation effect on the snow-covered surface albedo from observations and improved the model performance by implementing a new parameterization scheme. We developed new parameters, called leaf index (LI) and stem index (SI), which properly manage the effect of vegetation structure on the snow-covered surface albedo. As a result, the Noah-MP's performance in the winter surface albedo has significantly improved – the root mean square error is reduced by approximately 69 %.

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Comparison of Surface Ozone Simulation among Selected Regional Models in MICS-Asia III – Effect of Chemistry and Vertical Transport for the Causes of Difference –

10.5194/acp-2018-797 ◽

2018 ◽

Author(s):

Hajime Akimoto ◽

Tatsuya Nagashima ◽

Jie Li ◽

Joshua 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 cause 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. The detailed analyses have been made for monthly averaged diurnal variation for select grids covering metropolitan area of Beijing and Tokyo, and at a remote oceanic site, Oki. The chemical reaction mechanism, SAPRC99 used in the CMAQ models tends to give higher net chemical ozone production than CBM-Z used in NAQM agreeing with previous studies. Inclusion of heterogeneous “renoxification” reaction of HNO3 (on soot) → NO + NO2 only in NAQM is supposed to give higher NO concentration to give better agreement with observational data for NO and nighttime O3 mixing ratios. In addition to chemistry, the difference in vertical transport of O3 was found to affect the simulated results significantly. Particularly, the increase in downward flux of O3 from upper layer to the surface after the dawn is found to be substantially different among the models. Larger early morning vertical transport of O3 by CMAQ 5.0.2 would be the reason for higher daytime O3 by this model in July. All the three models overestimate 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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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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Evaluation of two isoprene emission models for use in a long-range air pollution model

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-12-9247-2012 ◽

2012 ◽

Vol 12 (4) ◽

pp. 9247-9281 ◽

Cited By ~ 2

Author(s):

A. Zare ◽

J. Brandt ◽

J. H. Christensen ◽

P. Irannejad

Keyword(s):

Model Performance ◽

Life Time ◽

Ozone Production ◽

Emission Model ◽

Direct Evaluation ◽

Biogenic Emission ◽

Isoprene Emission ◽

Ozone Concentrations ◽

Emission Models ◽

The Impact

Abstract. Knowledge about isoprene emissions and concentration distribution is important for chemistry transport models (CTMs), because isoprene acts as a precursor for tropospheric ozone and subsequently affects the atmospheric concentrations of many other atmospheric compounds. Isoprene has a short lifetime, and hence it is very difficult to evaluate its emission estimates against measurements. For this reason, we coupled two isoprene emission models with the Danish Eulerian Hemispheric Model (DEHM), and evaluated the simulated background ozone concentrations based on different models for isoprene emissions. In this research, results of using the two global biogenic emission models; GEIA (Global Emissions Inventory Activity) and MEGAN (the global Model of Emissions of Gases and Aerosols from Nature) are compared and evaluated. The total annual emissions of isoprene for the year 2006 estimated by using MEGAN is 732 Tg yr−1 for an extended area of the Northern Hemisphere, which is 50% higher than that estimated by using GEIA. The overall feature of the emissions from the two models are quite similar, but significant differences are found mainly in Africa's savannah and the rain forests of South America, and in some subtropical regions, such as the Middle East, India and the southern part of North America. Differences in spatial distribution of emission factors are found to be a key source of these discrepancies. In spite of the short life-time of isoprene, a direct evaluation of isoprene concentrations using the two biogenic emission models has been made against available measurements in Europe. Results show that the two models in general represent the measurements well and that the CTM is able to simulate isoprene concentrations. Additionally, investigation of ozone concentrations resulting from the two biogenic emission models show that isoprene simulated by MEGAN strongly affects the ozone production in the African savannah; the effect is up to 20% more than that obtained using GEIA. In contrast, the impact of using GEIA is higher in the Amazon region with more than 15% higher ozone concentrations compared to that of using MEGAN. Comparing the results for ozone concentrations for Europe obtained by using the two different models with measurements, show that the MEGAN emission model improves the model performance significantly in the Mediterranean area.

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Climate Drift in the CMIP5 Models*

Journal of Climate ◽

10.1175/jcli-d-12-00521.1 ◽

2013 ◽

Vol 26 (21) ◽

pp. 8597-8615 ◽

Cited By ~ 128

Author(s):

Alexander Sen Gupta ◽

Nicolas C. Jourdain ◽

Jaclyn N. Brown ◽

Didier Monselesan

Keyword(s):

Climate Models ◽

Coupled Model ◽

Deep Ocean ◽

Internal Variability ◽

Correction Method ◽

Physical Models ◽

Cmip5 Models ◽

Steric Sea Level ◽

Biogeochemical Models ◽

The Difference

Abstract Climate models often exhibit spurious long-term changes independent of either internal variability or changes to external forcing. Such changes, referred to as model “drift,” may distort the estimate of forced change in transient climate simulations. The importance of drift is examined in comparison to historical trends over recent decades in the Coupled Model Intercomparison Project (CMIP). Comparison based on a selection of metrics suggests a significant overall reduction in the magnitude of drift from phase 3 of CMIP (CMIP3) to phase 5 of CMIP (CMIP5). The direction of both ocean and atmospheric drift is systematically biased in some models introducing statistically significant drift in globally averaged metrics. Nevertheless, for most models globally averaged drift remains weak compared to the associated forced trends and is often smaller than the difference between trends derived from different ensemble members or the error introduced by the aliasing of natural variability. An exception to this is metrics that include the deep ocean (e.g., steric sea level) where drift can dominate in forced simulations. In such circumstances drift must be corrected for using information from concurrent control experiments. Many CMIP5 models now include ocean biogeochemistry. Like physical models, biogeochemical models generally undergo long spinup integrations to minimize drift. Nevertheless, based on a limited subset of models, it is found that drift is an important consideration and must be accounted for. For properties or regions where drift is important, the drift correction method must be carefully considered. The use of a drift estimate based on the full control time series is recommended to minimize the contamination of the drift estimate by internal variability.

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