Skip to main content

Advertisement

SpringerLink
Log in

Uncertainty of multi-source vegetation products on regional climate simulation in China

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

Vegetation is one of the most important parts of the Earth’s land surface system. To investigate the impact and uncertainty of multi-source vegetation data on climate simulations in East Asia, we have compared two Fractional Vegetation Cover (FVC) and five Leaf Area Index (LAI) products and then performed seven experiments from 1999 to 2018 with the WRF model. The results suggest that the sensitivity of vegetation climatology is large especially in south East Asia, with a maximum difference of 4 m2/m2 in LAI and 16% in FVC. For the mean temperature, the responses in East Asia are between ± 0.4 K, and the uncertainty over the Chinese Mainland gradually enhances with the rapid vegetation growth. The feedback of precipitation is highly heterogeneous with sensitivities ranging between ± 1 mm. For interannual variability, vegetation has a greater impact in summer and autumn. The uncertainty of temperature and precipitation in summer over the Chinese Mainland is 118 and 1754 times that in winter. In addition, the linear trend results also indicate that the maximum trend difference in temperature is around 0.04 K/a in eastern China, while the precipitation has a larger range of 0.04–0.10 mm/a in southern East Asia. Besides, the biophysical mechanism analysis suggests that vegetation impact on surface energy budget is mainly a competition between albedo feedback and moisture feedback, and the large differences in multi-source FVC and LAI are sufficient to simulate different or even opposite feedbacks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Availability of data and material

The datasets and material generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Alkama R, Cescatti A (2016) Biophysical climate impacts of recent changes in global forest cover. Science 351:600–604

    Google Scholar 

  • Baret F, Hagolle O, Geiger B et al (2007) LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: principles of the algorithm. Remote Sens Environ 110:275–286

    Google Scholar 

  • Baret F, Weiss M, Lacaze R et al (2013) GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: principles of development and production. Remote Sens Environ 137:299–309

    Google Scholar 

  • Cao Q, Yu D, Georgescu M et al (2018) Substantial impacts of landscape changes on summer climate with major regional differences: the case of China. Sci Total Environ 625:416–427

    Google Scholar 

  • Chen J, Jönsson P, Tamura M et al (2004) A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sens Environ 91:332–344

    Google Scholar 

  • Christidis N, Stott PA, Hegerl GC et al (2013) The role of land use change in the recent warming of daily extreme temperatures. Geophys Res Lett 40:589–594

    Google Scholar 

  • Churkina G, Schimel D, Braswell BH et al (2005) Spatial analysis of growing season length control over net ecosystem exchange. Glob Change Biol 11:1777–1787

    Google Scholar 

  • Claverie M, Matthews JL, Vermote EF et al (2016) A 30+ year AVHRR LAI and FAPAR climate data record: algorithm description and validation. Remote Sens 8:263

    Google Scholar 

  • Cohen WB, Maiersperger TK, Yang Z et al (2003) Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products. Remote Sens Environ 88:233–255

    Google Scholar 

  • de Noblet-Ducoudré N, Boisier J-P, Pitman A et al (2012) Determining robust impacts of land-use-induced land cover changes on surface climate over North America and Eurasia: results from the first set of LUCID experiments. J Clim 25:3261–3281

    Google Scholar 

  • Deng F, Chen JM, Plummer S et al (2006) Algorithm for global leaf area index retrieval using satellite imagery. IEEE Trans Geosci Remote Sens 44:2219–2229

    Google Scholar 

  • Duveiller G, Hooker J, Cescatti A (2018) The mark of vegetation change on Earth’s surface energy balance. Nat Commun 9:679

    Google Scholar 

  • Fang H, Wei S, Jiang C et al (2012) Theoretical uncertainty analysis of global MODIS, CYCLOPES, and GLOBCARBON LAI products using a triple collocation method. Remote Sens Environ 124:610–621

    Google Scholar 

  • Fang H, Jiang C, Li W et al (2013) Characterization and intercomparison of global moderate resolution leaf area index (LAI) products: analysis of climatologies and theoretical uncertainties. J Geophys Res Biogeosci 118:529–548

    Google Scholar 

  • Gálos B, Hagemann S, Hänsler A et al (2013) Case study for the assessment of the biogeophysical effects of a potential afforestation in Europe. Carbon Balance Manag 8:3

    Google Scholar 

  • Ge Q, Zhang X, Zheng J (2014) Simulated effects of vegetation increase/decrease on temperature changes from 1982 to 2000 across the Eastern China. Int J Climatol 34:187–196

    Google Scholar 

  • Georgievski G, Hagemann S (2019) Characterizing uncertainties in the ESA-CCI land cover map of the epoch 2010 and their impacts on MPI-ESM climate simulations. Theoret Appl Climatol 137:1587–1603

    Google Scholar 

  • Glotfelty T, Alapaty K, He J et al (2019) The weather research and forecasting model with aerosol-cloud interactions (WRF-ACI): development, evaluation, and initial application. Mon Weather Rev 147:1491–1511

    Google Scholar 

  • Gutman GG (1991) Vegetation indices from AVHRR: an update and future prospects. Remote Sens Environ 35:121–136

    Google Scholar 

  • He J, Yu Y, Yu L et al (2017) Impacts of uncertainty in land surface information on simulated surface temperature and precipitation over China. Int J Climatol 37:829–847

    Google Scholar 

  • Hersbach H, Bell B, Berrisford P et al (2020) The ERA5 global reanalysis. Q J R Meteorol Soc 146:1999–2049

    Google Scholar 

  • Hong S-Y, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev 134:2318–2341

    Google Scholar 

  • Iacono MJ, Delamere JS, Mlawer EJ et al (2008) Radiative forcing by long-lived greenhouse gases: calculations with the AER radiative transfer models. J Geophys Res Atmos. https://doi.org/10.1029/2008JD009944

    Article  Google Scholar 

  • Jeong SJ, Ho CH, Gim HJ et al (2011) Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982–2008. Glob Change Biol 17:2385–2399

    Google Scholar 

  • Jia K, Liang S, Liu S et al (2015) Global land surface fractional vegetation cover estimation using general regression neural networks from MODIS surface reflectance. IEEE Trans Geosci Remote Sens 53:4787–4796

    Google Scholar 

  • Jia K, Yang L, Liang S et al (2018) Long-term Global Land Surface Satellite (GLASS) fractional vegetation cover product derived from MODIS and AVHRR Data. IEEE J Select Top Appl Earth Observ Remote Sens 12:508–518

    Google Scholar 

  • Jia G, Shevliakova E, Artaxo P et al (2019) Land–climate interactions. In: Special report on climate change and land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems, IPCC, pp 133–206

  • Jiang C, Ryu Y, Fang H et al (2017) Inconsistencies of interannual variability and trends in long-term satellite leaf area index products. Glob Change Biol 23:4133–4146

    Google Scholar 

  • Jiao F, Liu H, Xu X et al (2020) Trend evolution of vegetation phenology in China during the period of 1981–2016. Remote Sens 12:572

    Google Scholar 

  • Lian X, Jeong S, Park C-E et al (2022) Biophysical impacts of northern vegetation changes on seasonal warming patterns. Nat Commun 13:3925

    Google Scholar 

  • Liang S, Zhao X, Liu S et al (2013) A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies. Int J Digit Earth 6:5–33

    Google Scholar 

  • Liu Y, Liu R, Chen JM (2012) Retrospective retrieval of long-term consistent global leaf area index (1981–2011) from combined AVHRR and MODIS data. J Geophys Res Biogeosci. https://doi.org/10.1029/2012JG002084

    Article  Google Scholar 

  • Liu Y, Li Y, Li S et al (2015) Spatial and temporal patterns of global NDVI trends: correlations with climate and human factors. Remote Sens 7:13233–13250

    Google Scholar 

  • Mahowald N, Lo F, Zheng Y et al (2016) Projections of leaf area index in earth system models. Earth Syst Dyn 7:211–229

    Google Scholar 

  • Meng X, Evans J, McCabe M (2014) The impact of observed vegetation changes on land–atmosphere feedbacks during drought. J Hydrometeorol 15:759–776

    Google Scholar 

  • Meng X, Lyu S, Zhang T et al (2018) Simulated cold bias being improved by using MODIS time-varying albedo in the Tibetan Plateau in WRF model. Environ Res Lett 13:044028

    Google Scholar 

  • Myneni RB, Hoffman S, Knyazikhin Y et al (2002) Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens Environ 83:214–231

    Google Scholar 

  • Niu GY, Yang ZL, Mitchell KE et al (2011) The community Noah land surface model with multiparameterization options (Noah‐MP): 1. Model description and evaluation with local‐scale measurements. J Geophys Res Atmos 116

  • Notaro M, Liu Z, Williams JW (2006) Observed vegetation–climate feedbacks in the United States. J Clim 19:763–786

    Google Scholar 

  • Park T, Ganguly S, Tømmervik H et al (2016) Changes in growing season duration and productivity of northern vegetation inferred from long-term remote sensing data. Environ Res Lett 11:084001

    Google Scholar 

  • Piao S, Ciais P, Friedlingstein P et al (2008) Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature 451:49–52

    Google Scholar 

  • Piao S, Liu Q, Chen A et al (2019) Plant phenology and global climate change: current progresses and challenges. Glob Change Biol 25:1922–1940

    Google Scholar 

  • Piao S, Wang X, Park T et al (2020) Characteristics, drivers and feedbacks of global greening. Nat Rev Earth Environ 1:14–27

    Google Scholar 

  • Pitman AJ, de Noblet-Ducoudré N, Cruz F et al (2009) Uncertainties in climate responses to past land cover change: first results from the LUCID intercomparison study. Geophys Res Lett. https://doi.org/10.1029/2009GL039076

    Article  Google Scholar 

  • Reed BC, Brown JF, VanderZee D et al (1994) Measuring phenological variability from satellite imagery. J Veg Sci 5:703–714

    Google Scholar 

  • Shen X, Liu B, Henderson M et al (2022) Vegetation greening, extended growing seasons, and temperature feedbacks in warming temperate grasslands of China. J Clim 35:5103–5117

    Google Scholar 

  • Skamarock W, Klemp J, Dudhia J et al (2008) A description of the advanced research WRF Version 3. NCAR Tech. Note NCAR/TN-475+ STR

  • Tang J, Niu X, Wang S et al (2016) Statistical downscaling and dynamical downscaling of regional climate in China: present climate evaluations and future climate projections. J Geophys Res Atmos 121:2110–2129

    Google Scholar 

  • Thompson G, Field PR, Rasmussen RM et al (2008) Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: implementation of a new snow parameterization. Mon Weather Rev 136:5095–5115

    Google Scholar 

  • Verger A, Baret F, Weiss M (2014) Near real-time vegetation monitoring at global scale. IEEE J Select Top Appl Earth Observ Remote Sens 7:3473–3481

    Google Scholar 

  • Verger A, Filella I, Baret F et al (2016) Vegetation baseline phenology from kilometric global LAI satellite products. Remote Sens Environ 178:1–14

    Google Scholar 

  • White MA, de Beurs KM, Didan K et al (2009) Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006. Glob Change Biol 15:2335–2359

    Google Scholar 

  • Windisch MG, Davin EL, Seneviratne SI (2021) Prioritizing forestation based on biogeochemical and local biogeophysical impacts. Nat Clim Chang 11:867–871

    Google Scholar 

  • Wu J, Wang D, Li LZ et al (2022) Hydrological feedback from projected Earth greening in the 21st century. Sustain Horiz 1:100007

    Google Scholar 

  • Xiao Z, Liang S, Wang J et al (2013) 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

    Google Scholar 

  • Xiao Z, Liang S, Wang J et al (2016) Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Trans Geosci Remote Sens 54:5301–5318

    Google Scholar 

  • Yang ZL, Niu GY, Mitchell KE et al (2011) The community Noah land surface model with multiparameterization options (Noah‐MP): 2. Evaluation over global river basins. J Geophys Res Atmos 116

  • Yang T, Li Q, Chen X et al (2020) Improving snow simulation with more realistic vegetation parameters in a regional climate model in the Tianshan Mountains, Central Asia. J Hydrol 590:125525

    Google Scholar 

  • Yu L, Liu Y, Liu T et al (2020) Impact of recent vegetation greening on temperature and precipitation over China. Agric for Meteorol 295:108197

    Google Scholar 

  • Zeng Z, Piao S, Li LZ et al (2017) Climate mitigation from vegetation biophysical feedbacks during the past three decades. Nat Clim Chang 7:432–436

    Google Scholar 

  • Zeng Z, Wang D, Yang L et al (2021) Deforestation-induced warming over tropical mountain regions regulated by elevation. Nat Geosci 14:23–29

    Google Scholar 

  • Zhang Y, Peña-Arancibia JL, McVicar TR et al (2016) Multi-decadal trends in global terrestrial evapotranspiration and its components. Sci Rep 6:1–12

    Google Scholar 

  • Zheng Y, Alapaty K, Herwehe JA et al (2016) Improving high-resolution weather forecasts using the Weather Research and Forecasting (WRF) Model with an updated Kain-Fritsch scheme. Mon Weather Rev 144:833–860

    Google Scholar 

  • Zhu Z, Bi J, Pan Y et al (2013) Global data sets of vegetation leaf area index (LAI) 3g and fraction of photosynthetically active radiation (FPAR) 3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011. Remote Sens 5:927–948

    Google Scholar 

  • Zhu Z, Piao S, Myneni RB et al (2016) Greening of the Earth and its drivers. Nat Clim Chang 6:791–795

    Google Scholar 

Download references

Acknowledgements

The work is jointly funded by the National Natural Science Foundation of China (42130602), the National Key Research and Development Program of China (2016YFA0600303) and the National Natural Science Foundation of China (41875124). This work is also supported by the Chinese Jiangsu Collaborative Innovation Center for Climate Change.

Funding

The work is jointly funded by the National Natural Science Foundation of China (42130602), the National Key Research and Development Program of China (2016YFA0600303) and the National Natural Science Foundation of China (41875124).

Author information

Authors and Affiliations

  1. School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu, 610225, China

    Yan Yan

  2. Institute for Climate and Global Change Research, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China

    Jianping Tang & Shuyu Wang

  3. Key Laboratory of Mesoscale Severe Weather/Ministry of Education, Nanjing University, Nanjing, 210023, China

    Yan Yan & Jianping Tang

  4. School of Atmospheric Sciences, Nanjing University, 163 Xianlin Road, Nanjing, China

    Jianping Tang

Authors
  1. Yan Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianping Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuyu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by YY and JT. The first draft of the manuscript was written by YY and all authors commented on previous versions of the manuscript.

Corresponding author

Correspondence to Jianping Tang.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethics approval and consent to participate

Not applicable.

Consent for publication

All authors read and approved the final manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yan, Y., Tang, J. & Wang, S. Uncertainty of multi-source vegetation products on regional climate simulation in China. Clim Dyn 61, 2991–3008 (2023). https://doi.org/10.1007/s00382-023-06739-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-023-06739-1

Keywords

Search

Navigation