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Miaoling Liang Zhenghui xie Institute of Atmospheric Physics, Chinese Academy of Sciences

Improving the vegetation dynamic simulations in a land surface model by using a statistical-dynamic canopy interception scheme. Miaoling Liang Zhenghui xie Institute of Atmospheric Physics, Chinese Academy of Sciences E-mail: mlliang@mail.iap.ac.cn. Outline. Introduction.

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Miaoling Liang Zhenghui xie Institute of Atmospheric Physics, Chinese Academy of Sciences

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  1. Improving the vegetation dynamic simulations in a land surface model by using a statistical-dynamic canopy interception scheme Miaoling Liang Zhenghui xie Institute of Atmospheric Physics, Chinese Academy of Sciences E-mail: mlliang@mail.iap.ac.cn

  2. Outline • Introduction • effect of soil moisture on vegetation growth • effect of canopy interception on soil moisture • importance of canopy interception on soil moisture • Model description • original canopy interception scheme • statistical-dynamic scheme based on LAI • Climatic forcing data • Simulations • Conclusions

  3. Introduction • Importance of soil moisture and vegetation in climate model(determines the albedo and thermal capacity of land surface) • Interactions between soil moisture and vegetation • Soil moisture affects vegetation growth by controlling vegetation transpiration • Vegetation influences soil moisture via evapo-transpiration: canopy interception, throughfall, transpiration

  4. How does canopy interception influences soil water availability and thus control the soil moisture? Canopy interception accounts for about 10~30% of the annual precipitation Surface runoff

  5. Introduction of CLM-DGVM Community Land Model is enabled to simulate vegetation dynamics coupled with LPJ Dynamic Global Vegetation Model Previous work has observed that: CLM-DGVM underestimates the forest coverage and vegetation production in favor of grass coverage than LPJ does due to its lower predictions of soil moisture.

  6. Excessive canopy interception results in the lower soil moisture: In CLM-DGVM, the fraction of precipitation intercepted by canopy is presented as: here, LAI and SAI is leaf area index and stem area index respectively. Accordingly, the model allows more than 90% of precipitation to be intercepted by canopy when LAI and SAI is greater than 4.6m2 m-2

  7. Objective • Canopy interception scheme of CLM allows unreasonable interception amount of precipitation • Present a statistical-dynamical canopy interception scheme to improve the vegetation simulation performance of CLM-DGVM.

  8. Statistically dynamic interception scheme based on LAI and SAI is proposed: Where a is PFT-dependent parameter, obtained based on the statistical canopy interception amount.

  9. Interception fraction as functions of the sum of LAI and SAI based on different canopy interception mechanisms

  10. Data sets • Study domain: China • Data: 40-year (1961-2000) climatic forcing data with 3 –hour, 0.5°× 0.5°temporal - spatial resolution • NCEP reanalysis data (4-times a day, 2.5 lat x 2.5 lon ) was regridded to 0.5°grids and averaged over the 6-hour to 3-hour interval (including: surface pressure, temperature, solar radiation, humidity and wind) • Daily observed precipitation data from 676 normal meteorology stations are linearly interpolated to 0.5°× 0.5°and 3-hour frequency based on the diurnal variations of NCEP precipitation rate data

  11. Simulations Two sets of paired simulations : • Initialization A: 200-year initialized run with the standard CLM-DGVM forced with the climatic data from 1961-1990 repeatedly, followed with a 20-year simulation (1981-2000) with standard CLM-DGVM (SA1) and modified CLM-DGVM with new canopy interception scheme(SA2), respectively; • Initialization B: 200-year initialized run with the modified CLM-DGVM, and 20-year simulation SB1 and SB2.

  12. Equilibrium vegetation distribution

  13. Comparisons of grasses coverage and trees coverage

  14. Initialization B – Initialization A

  15. SA1 SA2 Vegetation dynamics of simulations SA1 & SA2

  16. SB1 SB2 Vegetation dynamics of simulations SB1 & SB2

  17. Area change of PFT (data are from the average of 20-year simulation)

  18. Percent coverage of trees (a) and grasses (b) as well as net primary production (c) estimated from different simulations

  19. Difference of soil moisture (%) in the top 50cm in summer and winter: (a) SA2-SA1 for summer, (b) SA2-SA1 for winter, (c) SB2-SB1 for summer, and (c) SB2-SB1 for winter. Data are averages from the 20-year simulations.

  20. Model predicted (a) interception loss and (b) soil moisture of the top 50cm for the transition zone.

  21. Conclusions • The new canopy interception scheme allows more water falling on the ground and subsequently increases soil water availability for vegetation growth which is especially the case in semi-arid vegetation transition zone; • The statistical-dynamic interception scheme help increase the predicted soil moisture and improve the vegetation simulation performance of the model.

  22. Thanks for your attention!

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