Abstract
The efficacy of vegetation dynamics simulations in offline land surface models (LSMs) largely depends on the quality and spatial resolution of meteorological forcing data. In this study, the Princeton Global Meteorological Forcing Data (PMFD) and the high spatial resolution and upscaled China Meteorological Forcing Data (CMFD) were used to drive the Simplified Simple Biosphere model version 4/Top-down Representation of Interactive Foliage and Flora Including Dynamics (SSiB4/TRIFFID) and investigate how meteorological forcing datasets with different spatial resolutions affect simulations over the Tibetan Plateau (TP), a region with complex topography and sparse observations. By comparing the monthly Leaf Area Index (LAI) and Gross Primary Production (GPP) against observations, we found that SSiB4/TRIFFID driven by upscaled CMFD improved the performance in simulating the spatial distributions of LAI and GPP over the TP, reducing RMSEs by 24.3% and 20.5%, respectively. The multi-year averaged GPP decreased from 364.68 gC m–2 yr–1 to 241.21 gC m–2 yr–1 with the percentage bias dropping from 50.2% to –1.7%. When using the high spatial resolution CMFD, the RMSEs of the spatial distributions of LAI and GPP simulations were further reduced by 7.5% and 9.5%, respectively. This study highlights the importance of more realistic and high-resolution forcing data in simulating vegetation growth and carbon exchange between the atmosphere and biosphere over the TP.
摘要
陆面模式中植被动态的模拟效果与大气驱动数据的准确性以及模式空间分辨率密切相关, 特别是在地形复杂、 观测资料稀少的青藏高原地区, 模式的模拟效果对不同的大气驱动数据和空间分辨率更加敏感. 本研究分别使用普林斯顿全球大气驱动数据(PMFD)和中国区域高分辨率气象驱动数据集(CMFD)驱动耦合了动态植被过程的陆面模式SSiB4/TRIFFID, 探究不同的大气驱动数据和空间分辨率对青藏高原植被动态模拟的影响. 通过比较模拟和观测的月平均叶面积指数(LAI)和总初级生产力(GPP), 发现相较于使用PMFD气象驱动数据的模式模拟结果, CMFD气象驱动数据显著提高了模式对于青藏高原地区LAI和GPP空间分布的模拟能力, 均方根误差(RMSE)分别降低了24.3%和20.5%. 研究发现, CMFD气象驱动数据对GPP的模拟改进尤其明显, 年平均GPP从364.68 gC m-2 yr-1下降到241.21 gC m-2 yr-1, 模拟偏差从50.2%下降到-1.7%. 当使用更高分辨率的CMFD驱动SSiB4/TRIFFID时, 模拟的LAI和GPP空间分布 的RMSE分别进一步降低了7.5%和9.5%. 本研究强调了利用更真实和更高分辨率的大气驱动场在模拟青藏高原植被生长以及陆地生态系统碳通量中的重要作用.
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References
Alemohammad, S. H., and Coauthors, 2017: Water, Energy, and Carbon with Artificial Neural Networks (WECANN): A statistically-based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence. Biogeosciences, 14, 4101–4124, https://doi.org/10.5194/bg-14-4101-2017.
Bastrikov, V., N. MacBean, C. Bacour, D. Santaren, S. Kuppel, and P. Peylin, 2018: Land surface model parameter optimisation using in situ flux data: Comparison of gradient-based versus random search algorithms (a case study using ORCHIDEE v1.9.5.2). Geoscientific Model Development, 11, 4739–4754, https://doi.org/10.5194/gmd-11-4739-2018.
Chen, A. P., L. Huang, Q. Liu, and S. Piao, 2021: Optimal temperature of vegetation productivity and its linkage with climate and elevation on the Tibetan Plateau. Global Change Biology, 27, 1942–1951, https://doi.org/10.1111/gcb.15542.
Chen, J., 1984: Uncoupled multi-layer model for the transfer of sensible and latent heat flux densities from vegetation. Bound.-Layer Meteorol., 28, 213–225, https://doi.org/10.1007/bf00121305.
Chen, Y. Y., K. Yang, J. He, J. Qin, J. C. Shi, J. Y. Du, and Q. He, 2011: Improving land surface temperature modeling for dry land of China. J. Geophys. Res.: Atmos., 116, D20104, https://doi.org/10.1029/2011jd015921.
Collatz, G. J., J. T. Ball, C. Grivet, and J. A. Berry, 1991: Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: A model that includes a laminar boundary layer. Agricultural And Forest Meteorology, 54, 107–136, https://doi.org/10.1016/0168-1923(91)90002-8.
Collatz, G. J., L. Bounoua, S. O. Los, D. A. Randall, I. Y. Fung, and P. J. Sellers, 2000: A mechanism for the influence of vegetation on the response of the diurnal temperature range to changing climate. Geophys. Res. Lett., 27, 3381–3384, https://doi.org/10.1029/1999gl010947.
Cox, P., and C. Jones, 2008: Illuminating the modern dance of climate and CO2. Science, 321, 1642–1644, https://doi.org/10.1126/science.1158907.
Cox, P. M., C. Huntingford, and R. J. Harding, 1998: A canopy conductance and photosynthesis model for use in a GCM land surface scheme. J. Hydrol., 212-213, 79–94, https://doi.org/10.1016/s0022-1694(98)00203-0.
Dan, L., X. J. Yang, F. Q. Yang, J. Peng, Y. Y. Li, D. D. Gao, J. J. Ji, and M. Huang, 2020: Integration of nitrogen dynamics into the land surface model AVIM. Part 2: Baseline data and variation of carbon and nitrogen fluxes in China. Atmospheric and Oceanic Science Letters, 13, 518–526, https://doi.org/10.1080/16742834.2020.1819145.
De Kauwe, M. G., and Coauthors, 2015: A test of an optimal stomatal conductance scheme within the CABLE land surface model. Geoscientific Model Development, 8, 431–452, https://doi.org/10.5194/gmd-8-431-2015.
Dong, Z. B., S. Y. Gao, and D. W. Fryrear, 2001: Drag coefficients, roughness length and zero-plane displacement height as disturbed by artificial standing vegetation. Journal of Arid Environments, 49, 485–505, https://doi.org/10.1006/jare.2001.0807.
Duan, Q., and Coauthors, 2006: Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops. J. Hydrol., 320, 3–17, https://doi.org/10.1016/j.jhydrol.2005.07.031.
Giambelluca, T. W., D. Hölscher, T. X. Bastos, R. R. Frazão, M. A. Nullet, and A. D. Ziegler, 1997: Observations of albedo and radiation balance over postforest land surfaces in the eastern Amazon Basin. J. Climate, 10, 919–928, https://doi.org/10.1175/1520-0442(1997)010<0919:Ooaarb>2.0.Co;2.
Goudriaan, J., and P. E. Waggoner, 1972: Simulating both aerial microclimate and soil temperature from observations above the foliar canopy. Netherlands Journal of Agricultural Science, 20, 104–124, https://doi.org/10.18174/njas.v20i2.17290.
Guo, D. L., and H. J. Wang, 2013: Simulation of permafrost and seasonally frozen ground conditions on the Tibetan Plateau, 1981–2010,. J. Geophys. Res.: Atmos., 118, 5216–5230, https://doi.org/10.1002/jgrd.50457.
He, J., K. Yang, W. J. Tang, H. Lu, J. Qin, Y. Y. Chen, and X. Li, 2020: The first high-resolution meteorological forcing dataset for land process studies over China. Scientific Data, 7, 25, https://doi.org/10.1038/s41597-020-0369-y.
Immerzeel, W. W., L. P. H. van Beek, and M. F. P. Bierkens, 2010: Climate change will affect the Asian water towers. Science, 328, 1382–1385, https://doi.org/10.1126/science.1183188.
Jeong, S.-J., C.-H. Ho, H.-J. Gim, and M. E. Brown, 2011: Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982–2008,. Global Change Biology, 17, 2385–2399, https://doi.org/10.1111/j.1365-2486.2011.02397.x.
Jung, M., and Coauthors, 2007: Uncertainties of modeling gross primary productivity over Europe: A systematic study on the effects of using different drivers and terrestrial biosphere models. Global Biogeochemical Cycles, 21, GB4021, https://doi.org/10.1029/2006gb002915.
Jung, M., and Coauthors, 2017: Compensatory water effects link yearly global land CO2 sink changes to temperature. Nature, 541, 516–520, https://doi.org/10.1038/nature20780.
Lan, X., Y. Li, R. Shao, X. H. Chen, K. R. Lin, L. Y. Cheng, H. K. Gao, and Z. Y. Liu, 2021: Vegetation controls on surface energy partitioning and water budget over China. J. Hydrol., 600, 125646, https://doi.org/10.1016/j.jhydrol.2020.125646.
Li, C. W., and Coauthors, 2018: The evaluation of SMAP enhanced soil moisture products using high-resolution model simulations and in-situ observations on the Tibetan Plateau. Remote Sensing, 10, 535, https://doi.org/10.3390/rs10040535.
Li, H. Q., W. D. Guo, G. D. Sun, Y. C. Zhang, and C. B. Fu, 2011: A new approach for parameter optimization in land surface model. Adv. Atmos. Sci., 28, 1056–1066, https://doi.org/10.1007/s00376-010-0050-z.
Liang, J. J., Z.-L. Yang, X. T. Cai, P. R. Lin, H. Zheng, and Q. Y. Bian, 2020: Modeling the impacts of nitrogen dynamics on regional terrestrial carbon and water cycles over China with Noah-MP-CN. Adv. Atmos. Sci., 37, 679–695, https://doi.org/10.1007/s00376-020-9231-6.
Liu, J. G., C. X. Shi, S. Sun, J. J. Liang, and Z.-L. Yang, 2019a: Improving land surface hydrological simulations in China using CLDAS meteorological forcing data. J. Meteor. Res., 33, 1194–1206, https://doi.org/10.1007/s13351-019-9067-0.
Liu, L. B., Y. Wang, Z. Wang, D. L. Li, Y. T. Zhang, D. H. Qin, and S. C. Li, 2019b: Elevation-dependent decline in vegetation greening rate driven by increasing dryness based on three satellite NDVI datasets on the Tibetan Plateau. Ecological Indicators, 107, 105569, https://doi.org/10.1016/j.ecolind.2019.105569.
Long, B., B. Q. Zhang, C. S. He, R. Shao, and W. Tian, 2018: Is there a change from a warm-dry to a warm-wet climate in the Inland River area of China? Interpretation and analysis through surface water balance J. Geophys. Res.: Atmos., 123, 7114–7131, https://doi.org/10.1029/2018jd028436.
Lü, J. H., and J. J. Ji, 2002a: A simulation study of atmospherevegetation interactions over the Tibetan Plateau. Part II: Physical fluxes and parameters. Chinese Journal of Atmospheric Sciences, 26, 111–126, https://doi.org/10.3878/j.issn.1006-9895.2002.01.11. (in Chinese with English abstract)
Lü, J. H., and J. J. Ji, 2002b: A simulation study of atmospherevegetation interaction over the Tibetan Plateau. Part II: Net primary productivity and leaf area index. Chinese Journal of Atmospheric Sciences, 26(2), 255–262, https://doi.org/10.3878/j.issn.1006-9895.2002.02.11. (in Chinese with English abstract)
Meng, X., and Coauthors, 2018: Simulated cold bias being improved by using MODIS time-varying albedo in the Tibetan Plateau in WRF model. Environmental Research Letters, 13, 044028, https://doi.org/10.1088/1748-9326/aab44a.
Ran, Y. H., X. Li, L. Lu, and Z. Y. Li, 2012: Large-scale land cover mapping with the integration of multi-source information based on the Dempster-Shafer theory. International Journal of Geographical Information Science, 26, 169–191, https://doi.org/10.1080/13658816.2011.577745.
Ren, Y. H., K. Yang, H. Wang, L. Zhao, Y. Y. Chen, X. Zhou, and Z. La, 2021: The South Asia monsoon break promotes grass growth on the Tibetan Plateau. J. Geophys. Res.: Biogeosci., 126, e2020JG005951, https://doi.org/10.1029/2020jg005951.
Sellers, P. J., Y. Mintz, Y. C. Sud, and A. Dalcher, 1986: A simple biosphere model (SIB) for use within general circulation models. J. Atmos. Sci., 43, 505–531, https://doi.org/10.1175/1520-0469(1986)043<0505:Asbmfu>2.0.Co;2.
Sellers, P. J., and Coauthors, 1996: A revised land surface parameterization (SiB2) for atmospheric GCMS. Part I: Model formulation. J. Climate, 9, 676–705, https://doi.org/10.1175/1520-0442(1996)009<0676:Arlspf>2.0.Co;2.
Sheffield, J., G. Goteti, and E. F. Wood, 2006: Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Climate, 19, 3088–3111, https://doi.org/10.1175/jcli3790.1.
Shen, M. G., and Coauthors, 2015: Evaporative cooling over the Tibetan Plateau induced by vegetation growth. Proceedings of the National Academy of Sciences of the United States of America, 112, 9299–9304, https://doi.org/10.1073/pnas.1504418112.
Su, F. G., X. L. Duan, D. L. Chen, Z. C. Hao, and L. Cuo, 2013: Evaluation of the global climate models in the CMIP5 over the Tibetan Plateau. J. Climate, 26, 3187–3208, https://doi.org/10.1175/jcli-d-12-00321.1.
Sun, S. B., and Coauthors, 2016: Improving soil organic carbon parameterization of land surface model for cold regions in the Northeastern Tibetan Plateau, China. Ecological Modelling, 330, 1–15, https://doi.org/10.1016/j.ecolmodel.2016.03.014.
Wu, G. X., and Coauthors, 2007: The influence of mechanical and thermal forcing by the Tibetan Plateau on Asian climate. Journal of Hydrometeorology, 8, 770–789, https://doi.org/10.1175/jhm609.1.
Xiao, Z. Q., S. L. Liang, J. D. Wang, P. Chen, X. J. Yin, L. Q. Zhang, and J. L. Song, 2014: Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance. IEEE Trans. Geosci. Remote Sens., 52, 209–223, https://doi.org/10.1109/tgrs.2013.2237780.
Xue, Y., P. J. Sellers, J. L. Kinter, and J. Shukla, 1991: A simplified biosphere model for global climate studies. J. Climate, 4, 345–364, https://doi.org/10.1175/1520-0442(1991)004<0345:Asbmfg>2.0.Co;2.
Yang, K., J. He, W. J. Tang, J. Qin, and C. C. K. Cheng, 2010: On downward shortwave and longwave radiations over high altitude regions: Observation and modeling in the Tibetan Plateau. Agricultural and Forest Meteorology, 150, 38–46, https://doi.org/10.1016/j.agrformet.2009.08.004.
Yang, K., Y. Y. Chen, J. He, L. Zhao, H. Lu, J. Qin, D. H. Zheng, and X. Li, 2020: Development of a daily soil moisture product for the period of 2002–2011 in Chinese mainland. Science China Earth Sciences, 63, 1113–1125, https://doi.org/10.1007/s11430-019-9588-5.
Yang, X. J., L. Dan, F. Q. Yang, J. Peng, Y. Y. Li, D. D. Gao, J. J. Ji, and M. Huang, 2019: The integration of nitrogen dynamics into a land surface model. Part 1: Model description and site-scale validation. Atmospheric and Oceanic Science Letters, 12, 50–57, https://doi.org/10.1080/16742834.2019.1548246.
Yu, M., H. S. Chen, and Z. B. Sun, 2011: Seasonal and interannual variations of boreal vegetation simulated by an improved interactive canopy model (ICM). Chinese Journal of Atmospheric Sciences, 35, 571–588, https://doi.org/10.3878/j.issn.1006-9895.2011.03.16. (in Chinese with English abstract)
Zhan, X. W., Y. K. Xue, and G. J. Collatz, 2003: An analytical approach for estimating CO2 and heat fluxes over the Amazonian region. Ecological Modelling, 162, 97–117, https://doi.org/10.1016/s0304-3800(02)00405-2.
Zhang, T., and Coauthors, 2018: Water availability is more important than temperature in driving the carbon fluxes of an alpine meadow on the Tibetan Plateau. Agricultural and Forest Meteorology, 256-257, 22–31, https://doi.org/10.1016/j.agrformet.2018.02.027.
Zhang, Y. Q., and Coauthors, 2016: Multi-decadal trends in global terrestrial evapotranspiration and its components. Scientific Reports, 6, 19124, https://doi.org/10.1038/srep19124.
Zhang, Z. Q., Y. K. Xue, G. MacDonald, P. M. Cox, and G. J. Collatz, 2015: Investigation of North American vegetation variability under recent climate: A study using the SSiB4/TRIFFID biophysical/dynamic vegetation model. J. Geophys. Res.: Atmos., 120, 1300–1321, https://doi.org/10.1002/2014jd021963.
Zhao, W., and A. N. Li, 2015: A review on land surface processes modelling over complex terrain. Advances in Meteorology, 2015, 607181, https://doi.org/10.1155/2015/607181.
Zhong, L., Y. M. Ma, Y. K. Xue, and S. Piao, 2019: Climate change trends and impacts on vegetation greening over the Tibetan Plateau. J. Geophys. Res.: Atmos., 124, 7540–7552, https://doi.org/10.1029/2019jd030481.
Zhu, Z. C., and Coauthors, 2016: Greening of the Earth and its drivers. Nature Climate Change, 6, 791–795, https://doi.org/10.1038/nclimate3004.
Acknowledgements
This work was jointly supported by the National Natural Science Foundation of China (Grant Nos. 42130602, 42175136) and the Collaborative Innovation Center for Climate Change, Jiangsu Province, China. The authors thank the Terrestrial Hydrology Research Group, Princeton University, for providing the Global Meteorological Forcing Dataset, which can be found at http://hydrology.princeton.edu/data/pgf/1.0degree/3hourly/. The authors acknowledge the Big Earth Data Platform for Three Poles for providing the China Meteorological Forcing Dataset (1979–2018) and plant functional types map in China.
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Article Highlights
• Vegetation dynamics over the Tibetan Plateau (TP) were simulated and analyzed with a dynamic vegetation model.
• Atmospheric forcing data significantly affected the simulations of Leaf Area Index (LAI) and Gross Primary Production (GPP) over the TP.
• China Meteorological Forcing Data (CMFD) largely improved vegetation growth and carbon exchange simulations over the TP.
This paper is a contribution to the special issue on Third Pole Atmospheric Physics, Chemistry, and Hydrology.
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Improving Simulations of Vegetation Dynamics over the Tibetan Plateau: Role of Atmospheric Forcing Data and Spatial Resolution
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Kang, Z., Qiu, B., Xiang, Z. et al. Improving Simulations of Vegetation Dynamics over the Tibetan Plateau: Role of Atmospheric Forcing Data and Spatial Resolution. Adv. Atmos. Sci. 39, 1115–1132 (2022). https://doi.org/10.1007/s00376-022-1426-6
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DOI: https://doi.org/10.1007/s00376-022-1426-6