Articles | Volume 15, issue 2
https://doi.org/10.5194/gmd-15-787-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-787-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
27 Jan 2022
Model description paper |
| 27 Jan 2022
| 27 Jan 2022
Improvement of stomatal resistance and photosynthesis mechanism of Noah-MP-WDDM (v1.42) in simulation of NO2 dry deposition velocity in forests
Ming Chang
Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
Jiachen Cao
Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
Qi Zhang
School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China
Tianjin Academy of Eco-environmental Science, Tijian, China
Weihua Chen
Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
Guotong Wu
Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
Liping Wu
Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
Weiwen Wang
Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
Xuemei Wang
CORRESPONDING AUTHOR
Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
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Cited articles
Aber, J. D. and Federer, C. A.: A generalized, lumped-parameter model of photosynthesis, evapotranspiration and net primary production in temperate and boreal forest ecosystems, Oecologia, 92, 463–474, 1992. a
Adon, M., Galy-Lacaux, C., Delon, C., Yoboue, V., Solmon, F., and Kaptue Tchuente, A. T.: Dry deposition of nitrogen compounds (NO2, HNO3, NH3), sulfur dioxide and ozone in west and central African ecosystems using the inferential method, Atmos. Chem. Phys., 13, 11351–11374, https://doi.org/10.5194/acp-13-11351-2013, 2013. a
Ball, J. T., Woodrow, I. E., and Berry, J. A.: A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions, in: Progress in photosynthesis research, edited by: Biggins, J., Springer, Dordrecht, 221–224, https://doi.org/10.1007/978-94-017-0519-6_48, 1987. a, b, c
Bernhard, A.: The nitrogen cycle: Processes, players, and human impact, Nature Education Knowledge, 3, 25, 2012. a
Bonan, G. B., Patton, E. G., Finnigan, J. J., Baldocchi, D. D., and Harman, I. N.: Moving beyond the incorrect but useful paradigm: reevaluating big-leaf and multilayer plant canopies to model biosphere-atmosphere fluxes – a review, Agr. Forest Meteorol., 306, 108435, 2021. a
Braghiere, R. K., Wang, Y., Doughty, R., Sousa, D., Magney, T., Widlowski, J.-L., Longo, M., Bloom, A. A., Worden, J., Gentine, P., and Frankenberg, C.: Accounting for canopy structure improves hyperspectral radiative transfer and sun-induced chlorophyll fluorescence representations in a new generation Earth System model, Remote Sens. Environ., 261, 112497, https://doi.org/10.1016/j.rse.2021.112497, 2021. a
Chang, M.: Noah-MP-WDDMv1.42 code, Version 1.42, Zenodo [code], https://doi.org/10.5281/zenodo.4756246, 2021a (code available at: https://zenodo.org/record/4756246, last access: 13 May 2021). a
Chang, M.: The result data of Noah-MP-WDDMv1.42, Version 1, Zenodo [data set], https://doi.org/10.5281/zenodo.4756316, 2021b. a
Chang, M., Cao, J., Ma, M., Liu, Y., Liu, Y., Chen, W., Fan, Q., Liao, W., Jia, S., and Wang, X.: Dry deposition of reactive nitrogen to different ecosystems across eastern China: A comparison of three community models, Sci. Total Environ., 720, 137548, https://doi.org/10.1016/j.scitotenv.2020.137548 , 2020a. a, b
Chen, W., Chen, J., and Cihlar, J.: An integrated terrestrial ecosystem carbon-budget model based on changes in disturbance, climate, and atmospheric chemistry, Ecol. Model., 135, 55–79, 2000. a
Coe, H. and Gallagher, M.: Measurements of Dry Deposition of NO2 to A Dutch Heathland Using the Eddy-Correlation Technique, Q. J. Roy. Meteor. Soc., 118, 767–786, 1992. a
Cui, J., Zhou, J., and Yang, H.: Atmospheric inorganic nitrogen in dry deposition to a typical red soil agro-ecosystem in southeastern China, J. Environ. Monitor., 12, 1287–1294, 2010. a
Dai, Y., Dickinson, R. E., and Wang, Y.-P.: A two-big-leaf model for canopy temperature, photosynthesis, and stomatal conductance, J. Climate, 17, 2281–2299, 2004. a
De Kauwe, M. G., Kala, J., Lin, Y.-S., Pitman, A. J., Medlyn, B. E., Duursma, R. A., Abramowitz, G., Wang, Y.-P., and Miralles, D. G.: A test of an optimal stomatal conductance scheme within the CABLE land surface model, Geosci. Model Dev., 8, 431–452, https://doi.org/10.5194/gmd-8-431-2015, 2015. a
De Vries, W., Posch, M., Reinds, G. J., and Hettelingh, J.-P.: Quantifying relationships between N deposition and impacts on forest ecosystem services, Progress in the modelling of critical thresholds, impacts to plant species diversity and ecosystem services in Europe, Coordination Centre for Effects, Bilthoven, The Netherlands, Status Report, 43–53, 2009. a
Delaria, E. R. and Cohen, R. C.: A model-based analysis of foliar NOx deposition, Atmos. Chem. Phys., 20, 2123–2141, https://doi.org/10.5194/acp-20-2123-2020, 2020. a, b, c
Delaria, E. R., Vieira, M., Cremieux, J., and Cohen, R. C.: Measurements of NO and NO2 exchange between the atmosphere and Quercus agrifolia, Atmos. Chem. Phys., 18, 14161–14173, https://doi.org/10.5194/acp-18-14161-2018, 2018. a, b
Dentener, F., Vet, R., Dennis, R. L., Du, E., Kulshrestha, U. C., and Galy-Lacaux, C.: Progress in Monitoring and Modelling Estimates of Nitrogen Deposition at Local, Regional and Global Scales, in: Nitrogen Deposition, Critical Loads and Biodiversity, Springer, 7–22, https://doi.org/10.1007/978-94-007-7939-6_2, 2014. a
Durand, M., Murchie, E. H., Lindfors, A. V., Urban, O., Aphalo, P. J., and Robson, T. M.: Diffuse solar radiation and canopy photosynthesis in a changing environment, Agr. Forest Meteorol., 311, 108684, https://doi.org/10.1016/j.agrformet.2021.108684, 2021. a
Erisman, J., van Elzakker, B., Mennen, M., Hogenkamp, J., Zwart, E., van den Beld, L., Römer, F., Bobbink, R., Heil, G., Raessen, M., Duyzer, J. H., Verhage, H., Wyers, G. P., Otjes, R. P., and Möls, J. J.: The Elspeetsche Veld experiment on surface exchange of trace gases: summary of results, Atmos. Environ., 28, 487–496, 1994. a
Erisman, J. W., Leach, A., Adams, M., Agboola, J. I., Ahmetaj, L., Alard, D., Austin, A., Awodun, M. A., Bareham, S., Bird, T. L., Bleeker, A., Bull, K., Cornell, S. E., Davidson, E., de Vries, W., Dias, T., Emmett, B., Goodale, C., Greaver, T., Haeuber, R., Harmens, H., Hicks, W. K., Hogbom, L., Jarvis, P., Johansson, M., Russell, Z., McClean, C., Paton, B., Perez, T., Plesnik, J., Rao, N., Schmidt, S., Sharma, Y. B., Tokuchi, N., and Whitfield, C. P.: Nitrogen deposition effects on ecosystem services and interactions with other pollutants and climate change, in: Nitrogen deposition, critical loads and biodiversity, edited by: Sutton, M., Mason, K., Sheppard, L., Sverdrup, H., Haeuber, R., and Hicks, W., Springer, Dordrecht, 493–505, https://doi.org/10.1007/978-94-007-7939-6_51, 2014. a, b
Falge, E., Reth, S., Brüggemann, N., Butterbach-Bahl, K., Goldberg, V., Oltchev, A., Schaaf, S., Spindler, G., Stiller, B., Queck, R., Köstner, B., and Bernhofer, C.: Comparison of surface energy exchange models with eddy flux data in forest and grassland ecosystems of Germany, Ecol. Model., 188, 174–216, 2005. a
Farquhar, G., von Caemmerer, S., and Berry, J.: A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species, Planta, 149, 78–90, 1980. a
Finnigan, J.: Turbulence in plant canopies, Annu. Rev. Fluid Mech., 32, 519–571, 2000. a
Fisher, R. A. and Koven, C. D.: Perspectives on the future of land surface models and the challenges of representing complex terrestrial systems, J. Adv. Model. Earth Sy., 12, e2018MS001453, https://doi.org/10.1029/2018MS001453, 2020. a, b
Flechard, C. R., Nemitz, E., Smith, R. I., Fowler, D., Vermeulen, A. T., Bleeker, A., Erisman, J. W., Simpson, D., Zhang, L., Tang, Y. S., and Sutton, M. A.: Dry deposition of reactive nitrogen to European ecosystems: a comparison of inferential models across the NitroEurope network, Atmos. Chem. Phys., 11, 2703–2728, https://doi.org/10.5194/acp-11-2703-2011, 2011. a, b
Flechard, C. R., Massad, R.-S., Loubet, B., Personne, E., Simpson, D., Bash, J. O., Cooter, E. J., Nemitz, E., and Sutton, M. A.: Advances in understanding, models and parameterizations of biosphere-atmosphere ammonia exchange, Biogeosciences, 10, 5183–5225, https://doi.org/10.5194/bg-10-5183-2013, 2013. a, b
Friedlingstein, P., Joel, G., Field, C. B., and Fung, I. Y.: Toward an allocation scheme for global terrestrial carbon models, Glob. Change Biol., 5, 755–770, 1999. a
Galloway, J. N., Hiram Levy, I., and Kasibhatla, P. S.: Year 2020: Consequences of population growth and development on deposition of oxidized nitrogen, Ambio, 23, 120–123, 1994. a
Ganzeveld, L., Lelieveld, J., Dentener, F., Krol, M., and Roelofs, G.-J.: Atmosphere-biosphere trace gas exchanges simulated with a single-column model, J. Geophys. Res., 107, ACH 8-1–ACH 8-21, 2002. a
Gruber, N. and Galloway, J. N.: An Earth-system perspective of the global nitrogen cycle, Nature, 451, 293–296, 2008. a
Guerrieri, R., Lecha, L., Mattana, S., Cáliz, J., Casamayor, E. O., Barceló, A., Michalski, G., Peñuelas, J., Avila, A., and Mencuccini, M.: Partitioning between atmospheric deposition and canopy microbial nitrification into throughfall nitrate fluxes in a Mediterranean forest, J. Ecol., 108, 626–640, 2020. a, b
Han, X., Zhang, M., Skorokhod, A., and Kou, X.: Modeling dry deposition of reactive nitrogen in China with RAMS-CMAQ, Atmos. Environ., 166, 47–61, 2017. a
Horii, C. V., Munger, J. W., Wofsy, S. C., Zahniser, M., Nelson, D., and McManus, J. B.: Atmospheric reactive nitrogen concentration and flux budgets at a Northeastern US forest site, Agr. Forest Meteorol., 133, 210–225, 2005. a
Jarvis, P.: The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field, Philos. T. Roy. Soc. B, 273, 593–610, 1976. a
Jefferies, R. L. and Maron, J. L.: The embarrassment of riches: atmospheric deposition of nitrogen and community and ecosystem processes, Trends. Ecol. Evol., 12, 74–78, 1997. a
Ji, J.: A climate-vegetation interaction model: Simulating physical and biological processes at the surface, J. Biogeogr., 22, 445–451, 1995. a
Jia, Y., Yu, G., Gao, Y., He, N., Wang, Q., Jiao, C., and Zuo, Y.: Global inorganic nitrogen dry deposition inferred from ground-and space-based measurements, Sci. Rep., 6, 19810, https://doi.org/10.1038/srep19810, 2016. a
Katul, G. G., Oren, R., Manzoni, S., Higgins, C., and Parlange, M. B.: Evapotranspiration: a process driving mass transport and energy exchange in the soil-plant-atmosphere-climate system, Rev. Geophys., 50, RG3002, https://doi.org/10.1029/2011RG000366, 2012. a
Kavassalis, S. C. and Murphy, J. G.: Understanding ozone-meteorology correlations: A role for dry deposition, Geophys. Res. Lett., 44, 2922–2931, https://doi.org/10.1002/2016GL071791, 2017. a
Ke, P., Yu, Q., Luo, Y., Kang, R., and Duan, L.: Fluxes of nitrogen oxides above a subtropical forest canopy in China, Sci. Total Environ., 715, 136993, https://doi.org/10.1016/j.scitotenv.2020.136993, 2020. a, b
Landsberg, J. and Waring, R.: A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning, Forest Ecol. Manag., 95, 209–228, 1997. a
Li, L., Huang, M., Gu, F., and Zhang, L.: The Modeling Algorithms for the Effects of Nitrogen on Terrestrial Vegetation Carbon Cycle Process, Journal of Natural Resources, 28, 2012–2022, 2013. a
Li, Y., Schichtel, B. A., Walker, J. T., Schwede, D. B., Chen, X., Lehmann, C. M., Puchalski, M. A., Gay, D. A., and Collett, J. L.: Increasing importance of deposition of reduced nitrogen in the United States, P. Natl. Acad. Sci. USA, 113, 5874–5879, 2016. a
Liu, J., Chen, J., Cihlar, J., and Chen, W.: Net primary productivity distribution in the BOREAS region from a process model using satellite and surface data, J. Geophys. Res.-Atmos., 104, 27735–27754, 1999. a
Liu, J., Price, D. T., and Chen, J. M.: Nitrogen controls on ecosystem carbon sequestration: a model implementation and application to Saskatchewan, Canada, Ecol. Model., 186, 178–195, 2005. a
Liu, L., Zhang, X., Xu, W., Liu, X., Lu, X., Wei, J., Li, Y., Yang, Y., Wang, Z., and Wong, A. Y. H.: Reviewing global estimates of surface reactive nitrogen concentration and deposition using satellite retrievals, Atmos. Chem. Phys., 20, 8641–8658, https://doi.org/10.5194/acp-20-8641-2020, 2020. a
Liu, X., Zhang, Y., Han, W., Tang, A., Shen, J., Cui, Z., Vitousek, P., Erisman, J. W., Goulding, K., Christie, P., Fangmeier, A., and Zhang, F.: Enhanced nitrogen deposition over China, Nature, 494, 459–462, 2013. a
Lohammar, T., Larsson, S., Linder, S., and Falk, S.: FAST: simulation models of gaseous exchange in Scots pine, Ecol. Bull., 32, 505–523, 1980. a
Marner, B. and Harrison, R. M.: A spatially refined monitoring based study of atmospheric nitrogen deposition, Atmos. Environ., 38, 5045–5056, 2004. a
Massad, R. S., Tuzet, A., Personne, E., Bedos, C., Beekmann, M., Coll, I., Drouet, J.-L., Fortems-Cheiney, A., Génermont, S., Loubet, B., and Saint-Jean, S.: Modelling Exchanges: From the Process Scale to the Regional Scale, in: Agriculture and Air Quality, Springer, 159–207, https://doi.org/10.1007/978-94-024-2058-6_7, 2020. a, b, c, d
McGuire, A. D., Melillo, J. M., Kicklighter, D. W., Pan, Y., Xiao, X., Helfrich, J., Moore, B., Vorosmarty, C. J., and Schloss, A. L.: Equilibrium responses of global net primary production and carbon storage to doubled atmospheric carbon dioxide: Sensitivity to changes in vegetation nitrogen concentration, Global Biogeochem. Cy., 11, 173–189, 1997. a
Monteith, J.: Solar radiation and productivity in tropical ecosystems, J. Appl. Ecol., 9, 747–766, 1972. a
Monteith, J. L. and Moss, C.: Climate and the efficiency of crop production in Britain [and discussion], Philos. T. Roy. Soc. B, 281, 277–294, 1977. a
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011. a
Niyogi, D., Alapaty, K., Raman, S., and Chen, F.: Development and evaluation of a coupled photosynthesis-based gas exchange evapotranspiration model (GEM) for mesoscale weather forecasting applications, J. Appl. Meteorol. Clim., 48, 349–368, 2009. a
Oleson, K. W., Lawrence, D. M., Gordon, B., Flanner, M. G., Kluzek, E., Peter, J., Levis, S., Swenson, S. C., Thornton, E., Feddema, J.,
Heald, C. L., Hoffman, F., Lamarque, J.-F., Mahowald, N., Niu, G.-Y., Qian, T., Randerson, J., Running, S., Sakaguchi, K., Slater, A., Stockli, R., Wang, A., Yang, Z.-L., Zeng, X., and Zeng, X.: Technical description of version 4.0 of the Community Land Model (CLM), University Corporation for Atmospheric Research, 2010. a
Pan, Y. P., Wang, Y. S., Tang, G. Q., and Wu, D.: Wet and dry deposition of atmospheric nitrogen at ten sites in Northern China, Atmos. Chem. Phys., 12, 6515–6535, https://doi.org/10.5194/acp-12-6515-2012, 2012. a
Peng, C., Liu, J., Dang, Q., Apps, M. J., and Jiang, H.: TRIPLEX: a generic hybrid model for predicting forest growth and carbon and nitrogen dynamics, Ecol. Model., 153, 109–130, 2002. a
Phillips, S. B., Aneja, V. P., Kang, D., and Arya, S. P.: Modelling and analysis of the atmospheric nitrogen deposition in North Carolina, International Journal of Global Environmental Issues, 6, 231–252, 2006. a
Place, B. K., Delaria, E. R., Liu, A. X., and Cohen, R. C.: Leaf Stomatal Control over Acyl Peroxynitrate Dry Deposition to Trees, ACS Earth and Space Chemistry, 4, 2162–2170, 2020. a
Schulz, M., Prospero, J. M., Baker, A. R., Dentener, F., Ickes, L., Liss, P. S., Mahowald, N. M., Nickovic, S., García-Pando, C. P., Rodríguez, S., Sarin, M., Tegen, I., and Duce, R. A.: Atmospheric transport and deposition of mineral dust to the ocean: implications for research needs, Environ. Sci. Technol., 46, 10390–10404, 2012. a
Schwede, D., Zhang, L., Vet, R., and Lear, G.: An intercomparison of the deposition models used in the CASTNET and CAPMoN networks, Atmos. Environ., 45, 1337–1346, 2011. a
Seinfeld, J. H. and Pandis, S. N.: Atmospheric chemistry and physics: from air pollution to climate change, John Wiley & Sons, ISBN: 978-1-118-94740-1, 2012. a
Simpson, D., Benedictow, A., Berge, H., Bergström, R., Emberson, L. D., Fagerli, H., Flechard, C. R., Hayman, G. D., Gauss, M., Jonson, J. E., Jenkin, M. E., Nyíri, A., Richter, C., Semeena, V. S., Tsyro, S., Tuovinen, J.-P., Valdebenito, Á., and Wind, P.: The EMEP MSC-W chemical transport model – technical description, Atmos. Chem. Phys., 12, 7825–7865, https://doi.org/10.5194/acp-12-7825-2012, 2012. a
Stella, P., Kortner, M., Ammann, C., Foken, T., Meixner, F. X., and Trebs, I.: Measurements of nitrogen oxides and ozone fluxes by eddy covariance at a meadow: evidence for an internal leaf resistance to NO2, Biogeosciences, 10, 5997–6017, https://doi.org/10.5194/bg-10-5997-2013, 2013. a
Stevens, C. J., Dise, N. B., Mountford, J. O., and Gowing, D. J.: Impact of nitrogen deposition on the species richness of grasslands, Science, 303, 1876–1879, 2004. a
Szinyei, D.: Modelling and evaluation of ozone dry deposition, PhD thesis, Freie Universität Berlin, 2015. a
Thornton, P., Law, B., Gholz, H. L., Clark, K. L., Falge, E., Ellsworth, D., Goldstein, A., Monson, R., Hollinger, D., Falk, M., Chen, J., and Sparks, J. P.: Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests, Agr. Forest Meteorol., 113, 185–222, 2002. a, b
Tian, D., Du, E., Jiang, L., Ma, S., Zeng, W., Zou, A., Feng, C., Xu, L., Xing, A., Wang, W., Zheng, C., Ji, C., Shen, H., and Fang, J.: Responses of forest ecosystems to increasing N deposition in China: A critical review, Environ. Pollut., 243, 75–86, https://doi.org/10.1016/j.envpol.2018.08.010, 2018. a, b
Tian, H., Liu, M., Zhang, C., Ren, W., Xu, X., Chen, G., Lv, C., and Tao, B.: The Dynamic Land Ecosystem Model (DLEM) for simulating terrestrial processes and interactions in the context of multifactor global change, Acta Geographica Sinica, 65, 1027–1047, 2010. a
Wang, W., Ganzeveld, L., Rossabi, S., Hueber, J., and Helmig, D.: Measurement report: Leaf-scale gas exchange of atmospheric reactive trace species (NO2, NO, O3) at a northern hardwood forest in Michigan, Atmos. Chem. Phys., 20, 11287–11304, https://doi.org/10.5194/acp-20-11287-2020, 2020. a
Wang, Y., Köhler, P., He, L., Doughty, R., Braghiere, R. K., Wood, J. D., and Frankenberg, C.: Testing stomatal models at the stand level in deciduous angiosperm and evergreen gymnosperm forests using CliMA Land (v0.1), Geosci. Model Dev., 14, 6741–6763, https://doi.org/10.5194/gmd-14-6741-2021, 2021b. a
Weathers, K. C., Cadenasso, M. L., and Pickett, S. T.: Forest edges as nutrient and pollutant concentrators: potential synergisms between fragmentation, forest canopies, and the atmosphere, Conserv. Biol., 15, 1506–1514, 2001. a
Wesely, M.: Parameterization of surface resistances to gaseous dry deposition in regional-scale numerical models, Atmos. Environ., 23, 1293–1304, 1989. a
Wolfe, G. M. and Thornton, J. A.: The Chemistry of Atmosphere-Forest Exchange (CAFE) Model – Part 1: Model description and characterization, Atmos. Chem. Phys., 11, 77–101, https://doi.org/10.5194/acp-11-77-2011, 2011. a
Woodward, F. I., Smith, T. M., and Emanuel, W. R.: A global land primary productivity and phytogeography model, Global Biogeochem. Cy., 9, 471–490, 1995. a
Wu, Z., Wang, X., Chen, F., Turnipseed, A. A., Guenther, A. B., Niyogi, D., Charusombat, U., Xia, B., Munger, J. W., and Alapaty, K.: Evaluating the calculated dry deposition velocities of reactive nitrogen oxides and ozone from two community models over a temperate deciduous forest, Atmos. Environ., 45, 2663–2674, 2011. a, b
Wu, Z., Wang, X., Turnipseed, A. A., Chen, F., Zhang, L., Guenther, A. B., Karl, T., Huey, L., Niyogi, D., Xia, B., and Alapaty, K.: Evaluation and improvements of two community models in simulating dry deposition velocities for peroxyacetyl nitrate (PAN) over a coniferous forest, J. Geophys. Res., 117, D04310, https://doi.org/10.1029/2011JD016751, 2012. a, b
Wu, Z., Schwede, D. B., Vet, R., Walker, J. T., Shaw, M., Staebler, R., and Zhang, L.: Evaluation and Intercomparison of Five North American Dry Deposition Algorithms at a Mixed Forest Site, J. Adv. Model. Earth Sy., 10, 1571–1586, https://doi.org/10.1029/2017MS001231, 2018. a
Xu, G.-L., Schleppi, P., Li, M.-H., and Fu, S.-L.: Negative responses of Collembola in a forest soil (Alptal, Switzerland) under experimentally increased N deposition, Environ. Pollut., 157, 2030–2036, 2009. a
Xu, W., Luo, X. S., Pan, Y. P., Zhang, L., Tang, A. H., Shen, J. L., Zhang, Y., Li, K. H., Wu, Q. H., Yang, D. W., Zhang, Y. Y., Xue, J., Li, W. Q., Li, Q. Q., Tang, L., Lu, S. H., Liang, T., Tong, Y. A., Liu, P., Zhang, Q., Xiong, Z. Q., Shi, X. J., Wu, L. H., Shi, W. Q., Tian, K., Zhong, X. H., Shi, K., Tang, Q. Y., Zhang, L. J., Huang, J. L., He, C. E., Kuang, F. H., Zhu, B., Liu, H., Jin, X., Xin, Y. J., Shi, X. K., Du, E. Z., Dore, A. J., Tang, S., Collett Jr., J. L., Goulding, K., Sun, Y. X., Ren, J., Zhang, F. S., and Liu, X. J.: Quantifying atmospheric nitrogen deposition through a nationwide monitoring network across China, Atmos. Chem. Phys., 15, 12345–12360, https://doi.org/10.5194/acp-15-12345-2015, 2015. a
Yang, R. and Friedl, M. A.: Modeling the effects of three-dimensional vegetation structure on surface radiation and energy balance in boreal forests, J. Geophys. Res., 108, 8615, https://doi.org/10.1029/2002JD003109, 2003. a
Yang, Z.-L., Niu, G.-Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Longuevergne, L., Manning, K., Niyogi, D., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins, J. Geophys. Res., 116, D12110, https://doi.org/10.1029/2010JD015140, 2011. a
Yongjiu, D. and Qingcun, Z.: A land surface model (IAP94) for climate studies part I: Formulation and validation in off-line experiments, Adv. Atmos. Sci., 14, 433–460, 1997. a
Yu, G., Jia, Y., He, N., Zhu, J., Chen, Z., Wang, Q., Piao, S., Liu, X., He, H., Guo, X., Wen, Z., Li, P., Ding, G., and Goulding, K.: Stabilization of atmospheric nitrogen deposition in China over the past decade, Nat. Geosci., 12, 424–429, 2019. a
Zhang, L., Brook, J. R., and Vet, R.: A revised parameterization for gaseous dry deposition in air-quality models, Atmos. Chem. Phys., 3, 2067–2082, https://doi.org/10.5194/acp-3-2067-2003, 2003. a
Zhang, L., Vet, R., O'Brien, J., Mihele, C., Liang, Z., and Wiebe, A.: Dry deposition of individual nitrogen species at eight Canadian rural sites, J. Geophys. Res., 114, D02301, https://doi.org/10.1029/2008JD010640, 2009. a
Zhao, Y., Zhang, L., Chen, Y., Liu, X., Xu, W., Pan, Y., and Duan, L.: Atmospheric nitrogen deposition to China: A model analysis on nitrogen budget and critical load exceedance, Atmos. Environ., 153, 32–40, 2017. a
Zheng, S. and Shangguan, Z.: Photosynthetic characteristics and their relationships with leaf nitrogen content and leaf mass per area in different plant functional types, Acta Ecologica Sinica, 1, 2134–2144, 2007. a
Zhong, B., Wang, X., Ye, L., Ma, M., Jia, S., Chen, W., Yan, F., Wen, Z., and Padmaja, K.: Meteorological variations impeded the benefits of recent NOx mitigation in reducing atmospheric nitrate deposition in the Pearl River Delta region, Southeast China, Environ. Pollut., 266, 115076, https://doi.org/10.1016/j.envpol.2020.115076, 2020. a
Short summary
Despite the importance of nitrogen deposition, its simulation is still insufficiently represented in current atmospheric chemistry models. In this study, the improvement of the canopy stomatal resistance mechanism and the nitrogen-limiting schemes in Noah-MP-WDDM v1.42 give new options for simulating nitrogen dry deposition velocity. This study finds that the combined BN-23 mechanism agrees better with the observed NO2 dry deposition velocity, with the mean bias reduced by 50.1 %.
Despite the importance of nitrogen deposition, its simulation is still insufficiently...
