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Development of the Temperate Shrub Submodel for the Community Land Model-Dynamic Global Vegetation Model (CLM-DGVM)

1. 2. Motivation. Development of the Temperate Shrub Submodel for the Community Land Model-Dynamic Global Vegetation Model (CLM-DGVM). Xubin Zeng Xiaodong Zeng Mike Barlage Department of Atmospheric Sciences University of Arizona Tucson, AZ 85721 xubin@atmo.arizona.edu.

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Development of the Temperate Shrub Submodel for the Community Land Model-Dynamic Global Vegetation Model (CLM-DGVM)

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  1. 1 2 Motivation Development of the Temperate Shrub Submodel for the Community Land Model-Dynamic Global Vegetation Model (CLM-DGVM) Xubin Zeng Xiaodong Zeng Mike Barlage Department of Atmospheric Sciences University of Arizona Tucson, AZ 85721 xubin@atmo.arizona.edu • Dry regions represent a large fraction of the global land; • Most of the existing Dynamic Global Vegetation Model • (DGVMs) do not include shrubs or do not effectively • distinguish shrubs from grasses; • Exclusion of DGVMs and associated carbon cycle is • recognized as one of the main deficiencies of • IPCC AR4 model simulations 3 4 5 6 solid line: new dotted: control Shrubs can exist when grasses or trees cannot in the default DGVM Shrubs occupies the bare area and slightly reduces grass area in default DGVM Figure 1 Shrub Submodel Shrubs can not be established in the default DGVM due to too small photosynthesis Default DGVM photosynthesis over SW U.S. MODIS-based photosynthesis (from Running et al.) • drought-tolerance in the photosynthesis computation – use of different soil moisture stress function for shrubs • appropriate phenology type – raingreen for shrubs; no air temperature limitation for establishment • appropriate morphology parameters • consistent treatment of fractional vegetation coverage [in default DGVM, photosynthesis over plant crown area (PCA) while plant maintenance respiration over foliar projective cover (FPC) are used; FPC < PCA] • tree/grass/shrub hierarchy for light competition Without shrub components, the NCAR CLM-DGVM is deficient in simulating the global distribution of tree-grass-shrub distributions compared with the MODIS data Figure 2 BDT: broadleaf deciduous tree NET: needleleaf evergreen tree Without Figure 3 8 7 9 10 Figure 6 New -- Control 400-Yr Simulation using DGVM with shrub submodel Figure 7 solid line: with shrub dotted line: DGVM Figure 4 Shrubs cover the correct regions but quantitative comparisons are difficult. Why? (see Fig. 8) Shrubs occupies shrubs do not exist the bare area Panels (a), (b) and (d): surprising that tree/grass competition and soil moisture are affected over NH high latitudes Panel (c): shrubs cover part of the bare over arid regions Figure 5 11 12 13 14 Figure 9 Summary Figure 8 OLD NEW • Developed a shrub submodel for the DGVM for the global competition of trees, grass, and shrubs • Shrubs grow primarily by reducing the bare soil coverage and to a lesser degree, by decreasing the grass coverage • Shrub coverage reaches its peak around annual precipitation (Pann) of 300 mm, the grass coverage reaches its peak over a broad range of Pann (from 400-1100 mm), and the tree coverage reaches its peak for Pann = 1500 mm or higher (Fig. 9) • Use of MODIS land cover data alone is not sufficient for the DGVM model evaluation (particularly for shrubs) (Fig. 9) Figure 10 Remaining issues: a) MODIS shows a significant boreal shrub coverage which is not covered in this work; b) as mentioned in Fig. 6, high latitude tree/grass competition is affected Current work: develop the boreal shrub submodel MODIS Land cover only MODIS Land cover + FVC Both MODIS land cover and fractional vegetation cover (FVC) data are needed for DGVM evaluations 15 16 17 18 Additional conclusions from Figs. 11 & 12 Figure 11 Vegetation Pattern and Diversity Response of Ecosystem to Perturbations Figure 12 Relevant publications Moisture index = 0.25 (very dry) can not fully recover after removal Moisture index = 0.26 (dry) can largely recover after removal • Developed a 3-variable ecosystem model for dry regions to simulate the bifurcation and spatial patterns and study the effect of grazing and climate variability on ecosystem • When spatial interactions are included, vegetation can exist even under the environmental condition in which uniform vegetation cannot exist • None of the current DGVMs or land models considers spatial interactions • These modeling results need to be confirmed using high-resolution satellite and insitu data • X.D. Zeng, X. Zeng, and M. Barlage, 2008: Growing temperate shrubs over arid and semiarid regions in the NCAR Dynamic Global Vegetation Model (CLM-DGVM). Global Biogeochemical Cycles, in press. • X.D. Zeng, and X. Zeng, 2007: Transition and pattern diversity in arid and semiarid grassland: A modeling study. J. Geophys. Res.-Biogeosciences, 112, G04008, doi:10.1029/2007JG000411. • Miller, J., M. Barlage, X. Zeng, H. Wei, K. Mitchell, and D. Tarpley, 2006: Sensitivity of the NCEP Noah land model to the MODIS green vegetation fraction dataset. Geophys. Res. Lett., 33, L13404, doi:10.1029/2006GL026636.   (1) (2) (3) (4) (5) (6) (7)

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