1. Zong-Liang Yang Guo-Yue Niu, Enrique Rosero, Xiaoyan Jiang, and Lindsey Gulden http://www.geo.utexas.edu/climate/ Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin Prepared for NCAR Noah Meeting July 25-26, 2007 Noah Development at UT-Austin

2. 2 Towards a physically complete model Water Space Time

3. 3 Improving Hydrological Representation in the Community Noah Land Surface Model for Intraseasonal to Interannual Prediction Studies PI: Zong-Liang Yang Co-PIs: Guo-Yue Niu, Fei Chen, David Gochis Collaborator: Ken Mitchell Funded by NOAA CPPA Summer 2007 – Summer 2010

4. 4 New Developments include:  A 3-Layer physically-based snow model  A simple TOPMODEL-based runoff model  A simple groundwater model  Modifications on frozen soil permeability  Evaluation against snow and runoff data over grassland  A interactive vegetation canopy model (LAI is a predicted variable)

5. 5 Model Development at UT-Austin (http://www.geo.utexas.edu/climate/Research/publications.htm) Improved TOPMODEL runoff (Yang and Niu, 2003, GPC; Niu and Yang, 2003, GPC; Niu et al., 2005, JGR) Improved frozen soil scheme (Niu and Yang, 2006, JHM) Multi-layer snow (Yang and Niu, 2003, GPC) Snow and vegetation canopy interaction (Niu and Yang, 2004, JGR) Snow cover fraction (Niu and Yang, 2007, JGR) Global unconfined aquifer/groundwater component (Niu et al., 2007, JGR) Comparison of stochastic and physically-based subgrid snow cover fraction for snow assimilation (Su et al., 2007; Yang et al., 2007) These physical parameterizations are expected to work for both climate and weather models.

6. 6 Snow layer number and depth The total no. of layers can be up to 3 layers depending on total snow depth: Δz(-2): 0.025 ~ 0.05m Snow Soil Δz(-1): 0.05 ~ 0.10m Δz(0): 0.10 ~ (snowh–Δz(-1)-Δz(-2)) T(-2) T(-1) T(-0) T(4) T(3) T(2) T(1) 0.1m 0.3m 0.6m 1.0m Tg Aquifer ice(-2), liq(-2), ρs(-2) ice(-1), liq(-1), ρs(-1) ice(0), liq(0), ρs(0)

7. 7 Solving snow temperature B(-2) C(-2) 0 0 0 0 0 T(-2) R(-2) A(-1) B(-1) C(-1) 0 0 0 0 T(-1) R(-1) 0 A(0) B(0) C(0) 0 0 0 T(0) R(0) 0 0 A(1) B(1) C(1) 0 0 X T(1) = R(1) 0 0 0 A(2) B(2) C(2) D(2) T(2) R(2) 0 0 0 0 A(3) B(3) C(3) T(3) R(3) 0 0 0 0 0 A(4) C(4) T(4) R(4) A(i), B(i), C(i), R(i) are functions of λ(i) - thermal conductivity C(i) - heat capacity z(i) - layer-bottom depth from the snow/soil surface (neg.) R(-nsn+1) is a function of G: G = λ(1) ( T12 – T(-nsn+1) )/ ( 0.5*dz(-nsn+1) ) T12 ~ skin temperature? T12 = F (Ta + T12A + T12B)

8. 8 Available Energy for melting/freezing The energy excess or deficit needed to change snow/soil temperature to melting/freezing point: H fm (i) = C (i) * dz(i) * (T frz - T(i) ) / dt where i = -nsn+1, nsoil (for snow and soil) When ice(i) > 0 and T(i) > T frz, melting occurs, When liq(i) > 0 and T(i) < T frz, freezing occurs T(i) = T frz For soil, only when liq(i) – supercool(i) > 0 and T(i) < T frz, freezing occurs (because of absorptive and capillary forces by soil particles) Supercool(i) has two options: Koren et al (1999) Niu and Yang (2006) Water flow through snowpack: holding capacity = 0.03 m3/m3 T frz T

9. 9 Results - snow

10. 10 Results – surface albedo Α = α v + (1-f veg )*f snow (α snow –α v ) Α = α v + (1-(1-f b )*f veg )*f snow (α snow –α v ) where f b is the buried fraction of the canopy Snow aging – grain size, soot, leaf litter

11. 11 Results – surface albedo Melting Energy is too low – T12 is the forcing of snow/soil system Α = α v + (1-(1-f b )*f veg )*f snow (α snow –α v ) where f b is the buried fraction of the canopy

12. 12 Snow Skin Temperature How T12 performs compared to observations (A France grassland dataset) ?

13. 13 Snow Skin Temperature Newton-Raphson Iterative Method Based on energy balance - Sg + L(Tg) + H(Tg) + LE(Tg) + G(Tg) = 0. Iteration of all the fluxes and stability correction.

14. 14 Snow Skin Temperature How Tg performs in VISA (A France grassland dataset) ?

15. 15 Available Energy for Snowmelt Compare snowmelt energy between VISA and Noah-3L

16. 16 A Simple Groundwater Model Water storage in an unconfined aquifer: Recharge Rate: Gravitational Drainage Upward Flow under capillary forces Buffer Zone

17. 17 A Simple TOPMODEL Model Surface Runoff : R s = P fsat f sat = F max e – C f zwt (1 – f frz ) + f frz p = precipitation zwt = the depth to water table f = the runoff decay parameter that determines recession curve Subsurface Runoff : R sb = R sb,max e –f zwt R sb,max = the maximum subsurface runoff when the grid-mean water table is zero. It should be related to lateral hydraulic conductivity of an aquifer and local slopes (e -λ ). SIMTOP parameters: Two calibration parameters R sb,max (~10mm/day) and f (1.0~2.0) Two topographic parameters F max (~0.37) and C (~0.6)

18. 18 Runoff – Sleepers River

19. 19 Runoff – Sleepers River RUNOFF1 + RUNOFF2 RUNOFF1 RUNOFF2

20. 20 Water table depth – Sleepers River

21. 21 Soil Moisture – Sleepers River

22. 22 Soil Moisture – Champion, Illinois f = 1.0 f = 1.5

23. 23 Soil Moisture – Frozen Soil Impacts SH2O(4) SH2O(3) SH2O(2) SH20(1) In default Noah: Freezing = Drying Niu and Yang (2006): Fractional frozen area is used to modify soil hydraulic properties. K(i) = (1 – f frz ) K(θ) SH20 -> SMC

24. 24 Stomatal conductance is linearly related to photosynthesis: (The “Ball-Berry-Collatz” parameterization) Photosynthesis is controlled by three limitations (The Farquahar-Berry model) : Enzyme kinetics (“rubisco”) LightStarch stomatal conductance photosynthesis CO2 at leaf sfc RH at leaf sfc Photosynthesis and Conductance

25. 25 Photosynthesis and Carbon Allocation

26. 26 Simulated versus observed guaged precipitation over the Central U.S.

27. 27 MODIS NDVI-derived and model simulated greenness fraction over the Central U.S. (in August) Fg = (NDVIi - NDVImin) / (NDVImax - NDVImin) NDVImin= 0.04 and NDVImax= 0.52 (Gutman and Ignatov 1997)

28. 28 Greenness fraction differences for three experiments

29. 29 Water balance over the Central U.S. in JJA, 2002 VariablesPrecipitation (mm/day) Evapotranspiration (mm/day) Moisture Flux Convergence (mm/day) NARR2.3642*2.9907-0.4912 DEFAULT1.25752.3181-0.8660 DV1.72152.9624-1.0313 DVGW2.08253.1033-1.2663 GW1.46142.2931-1.4180 Note: * using CPC observed gauged precipitation

30. 30 Cal/Val Plan:  IHOP (9 sites); FluxNet (23 sites across the globe)  Noah-DV  Noah-GW  Noah-DVGW  Noah-STD  Noah-DVBB (Ball-Berry rc + LAI)  Noah-STDBB (Ball-Berry rc only)  Noah-DVGWBB  Noah-GWBB (Multi-objective optimization tool: MOSCEM on Lonestar)  LBA-MIP  Noah-distributed  SIMGM added  Will add FLDWAV

31. 31 Summary 3L snow model improves the snow simulations. Further work is needed for surface energy balance/skin temperature (snowmelt energy). SIMTOP and SIMGM are successfully coupled to Noah. Soil moisture variability warrants more analysis. Frozen soil impacts on soil moisture are refined. DV and variants are added. Cal/Val plans are defined. http://www.geo.utexas.edu/climate/