Zong-Liang Yang Guo-Yue Niu Hua Su The University of Texas at Austin

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

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

3. Surface runoff Rs = FsatQwat+(1–Fsat) max(0, Qwat – Imax) Saturation Excess 2) Subsurface runoff Rsb = Rsb,max exp (-f zw) Infiltration Excess simplified from Rsb = [ αKsat (0) / f ] exp(- λm) exp(- f zw) α= anisotropic factor for Ksat in v. and h. directions λm= grid-cell averaged topographic index zw= grid-cell mean water table depth 3) Ksat (0) = ksat exp (f Dc) Ksat (z) = Ksat(0) exp(–f z ) ksat is determined by Cosby et al. (1984). Allowing macropores. Water Table Depth 4)Fsat = ∫λ≥ (λm + f*zw)pdf(λ) dλ 5) The water table is diagnosed from an equilibrium relationship ψ(z) – z = ψsat – zw (i.e., the total head is equal across the soil column layers) Topography Bottom Super-saturation Topography-based Runoff Scheme (SIMTOP) Yang and Niu (2003), Niu and Yang (2003), Niu and Yang et al. (2005, JGR-Atmospheres) Frozen Soil | Subgrid Snow

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

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

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. Supercooled Liquid Water Exists in Frozen Soil • 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. 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 km2 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 > Tfrz T ≤ Tfrz 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 CTRL New Koren New scheme has less ice, higher infiltration, and greater soil water Ice Fraction Infiltration Soil Moisture Frozen Soil | Subgrid Snow

14. Soil Moisture Profiles New Koren CTRL Total water Liquid water Ice Fraction 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. CTRL GRDC New 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 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 Yang et al., 2006, Niu and Yang, 2006, Niu et al., 2006) Frozen Soil | Subgrid Snow

20. Summary • Supercooled liquid water is improperly treated in the baseline CLM (easy to get 100% soil ice). • We made the following changes: • implemented the supercooled liquid water by using the freezing-point depression equation. • introduced a concept of fractional unfrozen ground in CLM. • relaxed the dependence of hydraulic properties on ice content. • 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). 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 (Bonan et al., 2002) Why? Excessive LW↓ due to excessive low clouds (Dickinson et al., 2006) Anomalously southerly winds Frozen Soil | Subgrid Snow

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

24. New Snow Cover Fraction Scheme • The new SCF scheme improves the simulations of snow depth in mid-latitudes in both Eurasia and North America. Eurasia (55-70°N,60-90°E) North America (40-65°N,115-130°W) Frozen Soil | Subgrid Snow

25. Interception Interception PFT SWE SWE Ground SCF SCF Representations of Snow Cover and SWE Climate Modeling Remote Sensing Nature • A land grid has multiple PFTs plus bare ground. • Energy and mass balances. • For each PFT-covered area, on the ground, one mean SWE, one SCF. Canopy interception and canopy snow cover. • Pixels. • Integrated signals from multi-sources (e.g., snow, soil, water, vegetation), depending on many factors (e.g., view angle, aerosols, cloud cover, etc). • Each pixel, MODIS provides one SCF. AMSR provides one 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 rateapplied to the snow accumulation distribution. Liston (2004) JCL Frozen Soil | Subgrid Snow

27. The Coefficient of Variation (CV) CV values are assigned to 9 categories. Liston (2004) JCL Liston (2004) JCL 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 Each curve represents a distinct SCF-SWE relationship in melting season Liston (2004) JCL Questions: Can we derive CV values from MODIS and AMSR? How is the CV method compared to “traditional” methods? Frozen Soil | Subgrid Snow

30. Datasets GLDAS 1˚×1˚ 3-hourly, near-surface meteorological data for 2002–2004 Daily Snow Cover Fraction from MODIS Oct 2002–Dec 2004 (MOD10C1 CMG 0.05º × 0.05º) Daily SWE from AMSR Oct 2002–Dec 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. Steps to Derive CV Compare MODIS SCF and AMSR SWE at the same grid 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 gridand for each dayto make sure there are no missing data or no cloud obscuring SCF data. Estimate snowfall at the same grid from other sources Design a SCF retrieving algorithm from SWE, CV, µ, Dm Optimize CV by calibrating the theory-derived SCF against the MODIS SCF through a Nonlinear-Discrete Genetic Algorithm Frozen Soil | Subgrid Snow

33. Retrieving SCF from SWE, CV,μand Dm 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. Recursive method: (1) If snowfall at day t is zero, use to calculate Dm, then useto calculate SCF (2) If snowfall µt at day t is larger than zero, and Dm is the cumulative melting rate at day t-1, then if µt>Dm, then the cumulative snowfall as the mean of snow distribution, μ, would be replaced by µ+µt-Dm, and follow the same method in (1) to calculate SCF; if µt≤Dm, then directly follow the method in (1) to calculate SCF This SCF retrieving algorithm is used to derive grid- or PFT-specific CV based on SCF data and SWE data with Genetic Algorithm Optimization. Frozen Soil | Subgrid Snow

34. AMSR RMSE = 16 mm Optimization Snow Water Equivalent (mm) Coefficient of Variation (CV) = 1.38 Days from November 1, 2002 Characterizing Sub-grid-scale Variability of Snow Water Equivalent Using MODIS and AMSR Satellite Datasets 1°× 1° Grid (46–47°N, 107–108°W)Grassland in Great Plains 6 January–23 March, 2003 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=1.38 Blue: Simulated Red: Observed CV=0.8 Frozen Soil | Subgrid Snow

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

37. Geographic Distribution of CV in CLM PFT Type1 PFT Type2 PFT Type3 PFT Type4 Frozen Soil | Subgrid Snow

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

39. MODIS Obs CV Snow Density Baseline Tanh Daily SCF for Northwest U.S. 2002-2004 Frozen Soil | Subgrid Snow

40. MODIS Obs CV Snow Density Baseline Tanh Daily SCF for High-latitude Regions 2002-2004 Frozen Soil | Subgrid Snow

41. CV - Baseline Snow density - Baseline Tanh - Baseline Daily Trad for Northwest U.S. 2002-2004 Frozen Soil | Subgrid Snow

42. CV - Baseline Snow density - Baseline Tanh - Baseline Daily Trad for High-latitude Regions 2002-2004 Frozen Soil | Subgrid Snow

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