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An empirical formulation of soil ice fraction based on in situ observations

An empirical formulation of soil ice fraction based on in situ observations. Mark Decker, Xubin Zeng Department of Atmospheric Sciences, the University of Arizona. CCSM Land/BGC, March 28, 2006, Boulder. (GRL 33, L05402, doi:10.1029/2005GL024914, 2006). Outline. Introduction

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An empirical formulation of soil ice fraction based on in situ observations

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  1. An empirical formulation of soil ice fraction based on in situ observations Mark Decker, Xubin Zeng Department of Atmospheric Sciences, the University of Arizona CCSM Land/BGC, March 28, 2006, Boulder (GRL 33, L05402, doi:10.1029/2005GL024914, 2006)

  2. Outline • Introduction • Purpose, Motivation, Fundamentals, General Model Description • Dataset Overview • Methodology • Calculation of ice fraction • Parameterization Comparison • ECMWF, NCEP Noah, Proposed • Offline Simulation Results

  3. Purpose • In situ soil water and temperature observations • Derive a relation between soil ice and temperature for use in climate and weather prediction • Compare new and current parameterizations • Test the sensitivity of CLM

  4. Motivation • Why be concerned with frozen soil water? • Water ice transition alters various time scales • Diurnal • Latent Heat release • Seasonal • Infiltration of Spring Runoff

  5. Fundamentals • Soil water doesn’t freeze at 0o Celsius? • Dissolved salts • Capillary forces • Forces between minerals and soil water • Heterogeneous Composition

  6. Datasets • Various sources • CEOP CAMP • GEWEX GAME • National Snow Ice Data Center • Data from various climate regimes

  7. Datasets

  8. Observations

  9. Methodology • Assume total soil moisture constant • qi = qt – ql • Define • qs the Saturated Volumetric Moisture • 10 km soil composition data • CLM formulation

  10. Observed fi

  11. Current Formulations • ECMWF T > Tfrz + 1 Tfrz - 3 < T < Tfrz +1 T < Tfrz - 3 qcap = 0.323 m3/m3

  12. Current Formulations • Noah • If divergent ck=0 then solved explicitly • CLM3 T < Tfrz T >Tfrz T < Tfrz

  13. Modeled fi vs. Observations

  14. Proposed Formulation • Derived to capture observed trends • rapid increase of qi/qt to a value greater than 0.8 as T drops below Tfrz when qt/qs is greater than 0.8 • qi/qt increases more slowly as T decreases for small qt/qs • Partially based on Noah formulation • a and b are adjustable parameters • Chosen as 2 and 4 respectively

  15. Comparison Noah b = 4.5 Noah b = 5.5 Noah b = 4.5 ck=0 Noah b = 5.5 ck=0 ECMWF Proposed

  16. Observations vs Proposed

  17. Sensitivity of CLM • Offline NCEP Reanalysis Forcing • T-42 Resolution • 20 Year run cycling 1998 • Model Defined Initial Condition • Only Soil Ice Calculation Was Altered

  18. Results ECMWF-Control Sensible Heat Flux Latent Heat Flux Ground Temperature

  19. Results Noah-Control Sensible Heat Flux Latent Heat Flux Ground Temperature

  20. Results Proposed-Control Sensible Heat Flux Latent Heat Flux Ground Temperature

  21. Results Proposed Fi Proposed Fi Difference Proposed-Control

  22. Summary of Results • All Three: • Showed a reduction in ground temperature • Drying of the soil column • Reduction in sensible heat flux • Increase in ground heat flux to balance the change in sensible • Reduction in latent heat flux • The proposed formulation had a larger magnitude and extent of all these changes

  23. Summary • In situ data used to • Calculate ratio of volumetric ice content to total moisture content versus temperature • Evaluate current model formulations • Derive a new empirical formulation • Sensitivity of CLM tested • Reduction in ground temperature • Lowering of ice fraction

  24. Derivation of a New Maximum Snow Albedo Dataset Using MODIS DataM.Barlage, X.Zeng, H.Wei, K.Mitchell; GRL 2005

  25. Motivation • Maximum snow albedo is used as an end member of the interpolation from snow- to non-snow covered grids • Current dataset is based on 1-year of DMSP observations from 1979 • Current resolution of 1° • Create new dataset using 4+ years of MODIS data with much higher resolution

  26. Raw MODIS Albedo Data • Tucson: little variation; no snow • Minnesota: cropland; obvious annual cycle • Canada: annual snow cycle; little summer variation • Moscow: some cloud complications

  27. How can you be sure it’s snow? • NDSI: Exploiting the differences in spectral signature between visible and NIR albedo.

  28. NDSI and NDVI

  29. Final 0.05° Maximum Snow Albedo

  30. Comparison with RK 0.05deg MODIS RK Figure 5

  31. High-resolution Improvements

  32. Application of MODIS Maximum Snow Albedo to WRF-NMM/NOAH • WRF-NMM Model: 10min(0.144°) input dataset converted from 0.05° by simple average; model run at 12km; initialized with Eta output; • Winter simulation: 24hr simulation beginning 12Z 31 Jan 2006

  33. Comparison of MODIS Maximum Snow Albedo with CCSM • Structure of CCSM maximum albedo is similar to MODIS maximum snow albedo • Albedo of boreal regions is high compared to MODIS • Albedo of high latitude open shrub/tundra is low compared to MODIS

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