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

1. Comparison of Two Approaches to Modeling Subgrid Snow Cover Variability Zong-Liang Yang Hua Su Guo-Yue Niu The University of Texas at Austin July 25, 2006 www.geo.utexas.edu/climate

2. Subgrid Snow Cover and Surface Temperature

3. 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

4. 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

5. 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)

6. 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.

7. 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

8. The Coefficient of Variation (CV) CV values are assigned to 9 categories. Liston (2004) JCL Liston (2004) JCL

9. 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

10. 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?

11. 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

12. A Flowchart for Deriving a Grid-scale SCF Three records for each sub-grid: snow cover fraction, cloud cover fraction, confidence index

13. 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

14. 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.

15. 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).

16. 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

17. Values of CV in CLM Vegetated Land Barren Land

18. Geographic Distribution of CV in CLM PFT Type1 PFT Type2 PFT Type3 PFT Type4

19. AMSR Obs CV Snow Density Baseline Tanh Monthly SWE from 2002 to 2004

20. MODIS Obs CV Snow Density Baseline Tanh Daily SCF for Northwest U.S. 2002-2004

21. MODIS Obs CV Snow Density Baseline Tanh Daily SCF for High-latitude Regions 2002-2004

22. CV - Baseline Snow density - Baseline Tanh - Baseline Daily Trad for Northwest U.S. 2002-2004

23. CV - Baseline Snow density - Baseline Tanh - Baseline Daily Trad for High-latitude Regions 2002-2004

24. 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.