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Zong-Liang Yang Guo-Yue Niu Hua Su The University of Texas at Austin Modeling Frozen Soil and Subgrid Snow Cover in CLM CCSM LWGM March 28, 2006

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Presentation on theme: "Zong-Liang Yang Guo-Yue Niu Hua Su The University of Texas at Austin Modeling Frozen Soil and Subgrid Snow Cover in CLM CCSM LWGM March 28, 2006"— Presentation transcript:

1 Zong-Liang Yang Guo-Yue Niu Hua Su The University of Texas at Austin Modeling Frozen Soil and Subgrid Snow Cover in CLM CCSM LWGM March 28, 2006

2 NCAR Community Land Model (CLM) 1)a 10-layer soil sub-model 2)a 5-layer snow sub-model 3)a topography-based runoff scheme 4)an explicit solution of the freezing and thawing of soil water 5)sub-grid landunits, soil columns, and plant functional types New developments at University of Texas at Austin 1)Improved TOPMODEL (Yang and Niu, 2003; Niu and Yang, 2003; SIMTOP: Niu et al., 2005) 2)Improved frozen soil scheme (Niu and Yang, 2006) 3)Snow-vegetation canopy interaction (Niu and Yang, 2004) 4)Global unconfined aquifer/groundwater component (SIMGM: Niu et al., 2006, Yang et al., 2006a) 5)Stochastic subgrid snow cover in CLM (Yang et al., 2006b) Frozen Soil | Subgrid Snow

3 Topography-based Runoff Scheme (SIMTOP) Infiltration Excess Water Table Depth Saturation Excess Super-saturationTopographyBottom 1)Surface runoff R s = F sat Q wat +(1–F sat ) max(0, Q wat – I max ) 2) Subsurface runoff R sb = R sb,max exp (-f z w ) simplified from R sb = [ α K sat (0) / f ] exp(- λ m ) exp(- f z w ) α= anisotropic factor for K sat in v. and h. directions λ m = grid-cell averaged topographic index z w = grid-cell mean water table depth 3) K sat (0) = k sat exp (f D c ) K sat (z) = K sat (0) exp(–f z ) k sat is determined by Cosby et al. (1984). Allowing macropores. 4) F sat = ∫ λ ≥ (λm + f*zw) pdf(λ) dλ 5) The water table is diagnosed from an equilibrium relationship ψ(z) – z = ψ sat – z w (i.e., the total head is equal across the soil column layers) Yang and Niu (2003), Niu and Yang (2003), Niu and Yang et al. (2005, JGR-Atmospheres) Frozen Soil | Subgrid Snow

4 Radiative Transfer within the Vegetation Canopy: Two- Stream Model Accounting for the 3-D Canopy Structure (Niu and Yang, 2004, JGR-Atmos) ~100km Frozen Soil | Subgrid Snow

5 Canopy Water and Ice Balance Frozen Soil | Subgrid Snow (Niu and Yang, 2004, JGR-Atmos)

6 Frozen Soil Affects Climate  Thermal effects: increases the inertia of the climate system by enhancing the soil heat capacity through diurnal and seasonal freezing- thawing cycles.  Hydrological effects: affects snowmelt runoff and soil hydrology by reducing soil permeability. In turn, runoff from Arctic river systems affects ocean salinity and thermohaline circulation.  Ecological effects: affects ecosystem diversity and productivity and carbon decomposition and release. Frozen Soil | Subgrid Snow

7  When soil water freezes, the water closest to soil particles remains in liquid form due to the absorptive and capillary forces exerted by the soil particles.  The supercooled liquid water at subfreezing point is equivalent to a depression of the freezing- point (0˚C).  However, CLM does not account for these properly. Supercooled Liquid Water Exists in Frozen Soil Frozen Soil | Subgrid Snow

8 Frozen Soil Is Permeable? Early Russian literature and recent works showed that frozen soil has very weak or no effects on runoff  Russian laboratory and field experiments in 1960s and 1970s (Koren, 1980).  Shanley and Chalmers (1999) in Sleepers River, USA.  Lindstrom et al. (2002) in a 0.59 km 2 watershed in North Sweden.  Stahli et al. (2004): Dye tracer techniques revealed that water can infiltrate into deep soil through preferential pathways which are air-filled pores at the time of freezing. Frozen Soil | Subgrid Snow

9 The Frozen Soil Scheme in the NCAR CLM T > T frz T ≤ T frz Frozen Soil | Subgrid Snow

10 The Frozen Soil Scheme in the NCAR CLM The freezing and thawing processes are analogous to those in snow. It has three main flaws:  Matrix potential discontinuous at the freezing point.  High ice fraction: the ice content is solely determined by the heat content. Thus, the ice fraction of a soil layer can reach 100% when the heat content is sufficient to freeze all the water.  Low permeability: The hydraulic conductivity and the matrix potential are a function of liquid water only. Thus, when there is no or little liquid water in the soil, the soil permeability becomes too low. Frozen Soil | Subgrid Snow

11 Introduction of supercooled liquid water by using the freezing-point depression equation Most researchers Koren et al., 1991 Frozen Soil | Subgrid Snow

12 Relaxes the dependence of hydraulic properties on the soil ice content Fractional impermeable area Frozen Soil | Subgrid Snow

13 Model Results CTRLKorenNew Ice Fraction Infiltration Soil Moisture New scheme has less ice, higher infiltration, and greater soil water Frozen Soil | Subgrid Snow

14 Soil Moisture Profiles Total waterLiquid waterIce Fraction CTRL KorenNew New scheme has more total soil water in the upper 0.5 m soil Frozen Soil | Subgrid Snow

15 Effects on Runoff CTRL New The baseline CLM produces higher peaks and lower baseflow in recession period, while the NEW scheme improves the runoff simulation Frozen Soil | Subgrid Snow

16 Effects on Runoff in Six Large Rivers CLM produces higher peaks and lower baseflow in recession period, while the NEW scheme improves the runoff simulation CTRLGRDCNew Frozen Soil | Subgrid Snow

17 Modeled Snow Depth Earlier runoff does not result from earlier snowmelt Frozen Soil | Subgrid Snow

18 Change in Water Storage (Snow + Soil) The water storage of CLM reaches its maximum in March, while NEW in April Frozen Soil | Subgrid Snow

19 GRACE and CLM GRACE-derived terrestrial water storage anomalies compare well with those modeled by CLM augmented by soil freezing-thawing cycles and water table dynamics. Ob Amazon Frozen Soil | Subgrid Snow Yang et al., 2006, Niu and Yang, 2006, Niu et al., 2006)

20 1.Supercooled liquid water is improperly treated in the baseline CLM (easy to get 100% soil ice). 2.We made the following changes: i.implemented the supercooled liquid water by using the freezing-point depression equation. ii.introduced a concept of fractional unfrozen ground in CLM. iii.relaxed the dependence of hydraulic properties on ice content. 3.The resultant scheme produces better simulations of runoff (comparing with GRDC and ArcticNet) and soil water storage (comparing with GRACE). See Niu and Yang (2006), J. Hydromet. (in press). Summary Frozen Soil | Subgrid Snow

21 Subgrid Snow Cover and Surface Temperature Frozen Soil | Subgrid Snow

22 Winter Warm Bias in NCAR Simulations CCM3/CLM2 T42 - OBS CCSM3.0 T85 - OBS (Dickinson et al., 2006) (Bonan et al., 2002) Why? Excessive LW↓ due to excessive low clouds Anomalously southerly winds Frozen Soil | Subgrid Snow

23 Snow Cover Fraction and Air Temperature NEW – OBS OLD – OBS The new scheme reduces the warm bias in winter and spring in NCAR GCM (i.e. CAM2/CLM2). Smaller Snow Cover  Warmer Surface SnowVegetation Liston (2004) JCL Frozen Soil | Subgrid Snow

24 The new SCF scheme improves the simulations of snow depth in mid-latitudes in both Eurasia and North America. New Snow Cover Fraction Scheme Eurasia (55-70°N,60-90°E) North America (40-65°N, °W) Frozen Soil | Subgrid Snow

25 Representations of Snow Cover and SWE Nature Climate ModelingRemote Sensing 1.A land grid has multiple PFTs plus bare ground. 2.Energy and mass balances. 3.For each PFT-covered area, on the ground, one mean SWE, one SCF. Canopy interception and canopy snow cover. 1.Pixels. 2.Integrated signals from multi-sources (e.g., snow, soil, water, vegetation), depending on many factors (e.g., view angle, aerosols, cloud cover, etc). 3.Each pixel, MODIS provides one SCF. AMSR provides one SWE. PFT Ground SCF Interception SWE SCF Interception SWE Frozen Soil | Subgrid Snow

26 Theory of Sub-grid Snow Cover Liston (2004), “Representing Subgrid Snow Cover Heterogeneities in Regional and Global Models”. Journal of Climate. The snow distribution during the accumulation phase can be represented using a lognormal distribution function, with the mean of snow water equivalent and the coefficient of variation as two parameters. The snow distribution during the melting phase can be analyzed by assuming a spatially homogenous melting rate applied to the snow accumulation distribution. Liston (2004) JCL Frozen Soil | Subgrid Snow

27 CV values are assigned to 9 categories. Liston (2004) JCL The Coefficient of Variation (CV) Frozen Soil | Subgrid Snow

28 Relationship Between Snow Cover & SWE Accumulation phase: SCF is constant =1; SWE is the cumulative value of snowfall. Melting phase: The SCF and SWE relationship can be described by equations (1) and (2), with the cumulative snowfall, snow distribution coefficient of variation (CV) and melting rate as the parameters. (1) Snow Cover Fraction (2) SWE Liston (2004) JCL Frozen Soil | Subgrid Snow

29 SCF-SWE in Different Methods Liston (2004) JCL Questions: Can we derive CV values from MODIS and AMSR? How is the CV method compared to “traditional” methods? Each curve represents a distinct SCF-SWE relationship in melting season Frozen Soil | Subgrid Snow

30 Datasets Daily SWE from AMSR Oct 2002–Dec 2004 Daily Snow Cover Fraction from MODIS Oct 2002–Dec 2004 (MOD10C1 CMG 0.05º × 0.05º) GLDAS 1˚×1˚ 3-hourly, near-surface meteorological data for 2002– 2004 Frozen Soil | Subgrid Snow

31 A Flowchart for Deriving a Grid-scale SCF Three records for each sub-grid: snow cover fraction, cloud cover fraction, confidence index Frozen Soil | Subgrid Snow

32 Upscale 0.05º snow cover data to a coarse grid (0.25º, 0.5º or 1º) using the upscaling algorithm described above; Average SWE to the same grid. Quality check the snow cover and SWE data for each analyzed grid and for each day to make sure there are no missing data or no cloud obscuring SCF data. Steps to Derive CV Compare MODIS SCF and AMSR SWE at the same grid Estimate snowfall at the same grid from other sources Optimize CV by calibrating the theory-derived SCF against the MODIS SCF through a Nonlinear-Discrete Genetic Algorithm Design a SCF retrieving algorithm from SWE, CV, µ, D m Frozen Soil | Subgrid Snow

33 Recursive method: If snowfall at day t is zero, use Snowmelt starts from the first day when SCF is less than 1. This criteria can be relaxed to a smaller value like 0.9 because the MODIS data may underestimate SCF in forest-covered areas. to calculate D m, then use to calculate SCF If snowfall µ t at day t is larger than zero, and D m is the cumulative melting rate at day t-1, then if µ t >D m, then the cumulative snowfall as the mean of snow distribution, μ, would be replaced by µ+µ t -D m, and follow the same method in (1) to calculate SCF; if µ t ≤D m, then directly follow the method in (1) to calculate SCF (1) (2) This SCF retrieving algorithm is used to derive grid- or PFT-specific CV based on SCF data and SWE data with Genetic Algorithm Optimization. Retrieving SCF from SWE, CV,μand D m Frozen Soil | Subgrid Snow

34 1°× 1° Grid (46–47°N, 107–108°W) Grassland in Great Plains 6 January – 23 March, 2003 Characterizing Sub-grid-scale Variability of Snow Water Equivalent Using MODIS and AMSR Satellite Datasets Snow Water Equivalent (mm) Days from November 1, 2002 AMSR Optimization RMSE = 16 mm Coefficient of Variation (CV) = 1.38 In the optimization, the relationship between snow cover fraction and SWE follows the stochastic scheme of Liston (2004). The optimized CV value is used in CLM (next slide). Frozen Soil | Subgrid Snow

35 Modeling SWE at Sleeper’s River, Vermont Using CLM with a Stochastic Representation of Sub-grid Snow Variability CV=1.38CV=0.8Blue: SimulatedRed: Observed Frozen Soil | Subgrid Snow

36 Values of CV in CLM Barren Land Vegetated Land Frozen Soil | Subgrid Snow

37 PFT Type1PFT Type2 PFT Type3 PFT Type4 Geographic Distribution of CV in CLM Frozen Soil | Subgrid Snow

38 CV Baseline Tanh AMSR Obs Snow Density Monthly SWE from 2002 to 2004 Frozen Soil | Subgrid Snow

39 Daily SCF for Northwest U.S CV Baseline Tanh MODIS Obs Snow Density Frozen Soil | Subgrid Snow

40 CV Baseline Tanh MODIS Obs Snow Density Daily SCF for High-latitude Regions Frozen Soil | Subgrid Snow

41 CV - Baseline Snow density - Baseline Tanh - Baseline Daily T rad for Northwest U.S Frozen Soil | Subgrid Snow

42 CV - Baseline Snow density - Baseline Tanh - Baseline Daily T rad for High-latitude Regions Frozen Soil | Subgrid Snow

43 Summary 1)The high latitude wintertime warm bias in NCAR climate model simulations can be caused by an improper parameterization of snow cover fraction. 2)A procedure is developed to estimate CV using MODIS and AMSR data. 3)The CV method (i.e. stochastic subgrid snow cover scheme) is implemented in CLM and the results are promising. 4)The density-dependent SCF scheme is sensitive to the parameters used. 5)We will look at coupled land-atmosphere simulations using CAM3. Frozen Soil | Subgrid Snow

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