# Surface Skin Temperature, Soil Moisture, and Turbulent Fluxes in Land Models Xubin Zeng, Mike Barlage, Mark Decker, Jesse Miller, Cindy Wang, Jennifer.

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Surface Skin Temperature, Soil Moisture, and Turbulent Fluxes in Land Models Xubin Zeng, Mike Barlage, Mark Decker, Jesse Miller, Cindy Wang, Jennifer Wang Dept of Atmospheric Sciences University of Arizona Tucson, AZ 85721, USA (a) A revised form of Richards equation (b) CLM3 simulation versus MODIS skin T Consistent (c) Treatment of turbulence below and above canopy as well as snow burial of canopy (d) Vegetation and snow albedo data

Revised Richards Eq.

Decker and Zeng (2007)

CLM3 offline tests over Sahara, southwest US and Tibet For July 1-5, 2003.

C s = C s,soil W + C s,veg (1 – W) Zeng et al. (2005) W = exp(– LAI)

Dickinson et al. (2006)

Thought experiment: What would be the land zo and d If above-ground biomass disappears? CLM3 deficiency: z o and d depend on vegetation type only Solution: d e = d V + (1 – V) d g ln (z oc,e ) = V ln(z oc ) + (1 – V) ln (z og ) V = (1 – exp[-β min(L t, L cr )])/(1 – exp[- β L cr ])

Impact in CLM3

Figure C. 1 CLM3-simulated snow depth and surface fluxes from Jan. 11-13, 1996 over a boreal grassland site in Canada. Both simulation with new formulation of f v,sno and simulation with standard CLM3 are shown (52.16ºN, 106.13ºW ).

Figure C.2 The same simulation as in Fig. C. 1 but for averaged diurnal cycles of winter time (Dec. 1995, Jan. and Feb. 1996). Wang and Zeng (2007)

Figure C.4 (a) Ten-year averaged DJF differences of T g between CLM3 with Eq.(C. 3) and the standard CLM3 global offline simulations, and (b) ten-year averaged annual cycle of T g difference over Alaska (59-72ºN, 170-140ºW).

Zeng et al. (2000) NCAR/CLM3: FVC(x,y), LAI(x,y,t) NCEP/Noah: GVF(x,y,t),LAI=Const Validation: 1-3m spy sat data, 1-5m aircraft data, 30m Landsat data, Surface survey data

Data Impact Barlage and Zeng (2004)

NLDAS GVF Data Noah 1/8 degree monthlyMODIS 2km 16-day Miller et al. (2006)

NLDAS GVF Results crop grass Addition of new GVF dataset results in an increase of transpiration (up to 35W/m 2 ) and canopy evaporation (up to 8W/m 2 ) Balanced by a decrease in ground evaporation (up to 20W/m 2 ) Overall increase in LHF(up to 20W/m 2 ) is balanced by decreases in SHF(up to 10W/m 2 ) and Lwup(5W/m2) Miller et al. (2006)

Albedo NDSI NDVI Land cover Individual bands Red: NN filled Blue: LAT filled Green: > 0.84 Barlage et al. (2005) MODIS versus Noah maximum snow albedo data

Barlage et al. (2005) Impact on NLDAS Offline Noah Tests

Application of MODIS Maximum Snow Albedo to WRF-NMM/NOAH up to 0.5 C decreases in 2-m Tair in regions of significant albedo change > 0.5 C increase in 2-m Tair in several regions Barlage et al. (2007)

Skin temperature and turbulent fluxes are all strongly affected by the treatment of below and above-canpy turbulence and snow burial They are also affected by green vegetation cover data as well as maximum snow albedo data While Terra/Aqua MODIS provides 4 skin Ts measurements a day, its use without constraint from Tair requires additional efforts The revised Richards equation should be used for land models for improved simulations of soil moisture and fluxes Summary

Suggestions on LANDFLUX Try to reach some consensuses on the land boundary data to be used Identify flux tower sites with relatively comprehensive data over different climate regimes to set up minimum criteria for land models or model components to meet Try to use land-atmosphere constrained land and atmospheric forcing data

Model Run Model Alterations –New Richards equation Including new bottom boundary condition NO TUNABLE PARAMETERS –Soil texture constant with depth –Infiltration –Area of Saturated Fraction 1984-2004 with Qian/Dai forcing

Comparison of CAM/CLM3 with the Terra and Aqua MODIS data

Zeng et al. (2007)

NCAR/CLM3: FVC(x,y), LAI(x,y,t) NCEP/Noah: GVF(x,y,t),LAI=Const Validation: 1-3m spy sat data, 1-5m aircraft data, 30m Landsat data, Surface survey data Histogram of evergreen Broadleaf tree NDVI veg = 0.69 Fractional Vegetation Cover

Interannual variability and decadal trend of global fractional vegetation cover from 1982 to 2000 Zeng et al. (2003)

(a)Shading effect (b)Shadowing effect LAI is difficult to measure in winter! A = A sn f sn + A v (1-f sn ) Then the question is (1) what is satellite snow fraction? (2) What is A sn ? Maximum Snow Albedo in the NCEP Noah Land Model

Issue: Consistency of Cx below/within canopy Motivation: warm bias of 10 K in Tg in CCSM2 Below/within canopy in CLM H g ~ C s u * (T g – T va ) H f ~ C f LAI u * 0.5 (T v – T va ) C s = const in BATS, LSM, CLM2 Based on K-theory C s ≈ 0.13 b exp(-0.9b)/[1 – exp(-2b/3)] b = f(LAI, stability)

Surface Skin Temperature, Soil Moisture, and Turbulent Fluxes in Land Models Xubin Zeng Mike Barlage, Mark Decker, Jesse Miller, Cindy Wang, Jennifer Wang Dept of Atmospheric Sciences University of Arizona Tucson, AZ 85721, USA

Turbulence Energy Balance: R net + G + F t + F q ≈ 0 Water Balance: P ≈ F q + R Turbulent fluxes F x ~ C x U (Xa – Xs) C x = f(Z om, Z ot, stability) X: temperaure, humidity, wind, trace gas (a)Consistent treatment of turbulence below and above canopy as well as snow burial of canopy (b)Vegetation and snow albedo data (c)CAM3/CLM3 simulation versus MODIS skin T (d)A revised form of Richards equation

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