Transcript 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,

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
– Purpose, Motivation, Fundamentals, General
Model Description
Dataset Overview
Methodology
– Calculation of ice fraction
Parameterization Comparison
– ECMWF, NCEP Noah, Proposed
Offline Simulation Results
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
Motivation
Why be concerned with frozen soil water?
– Water ice transition alters various time scales
Diurnal
– Latent Heat release
Seasonal
– Infiltration of Spring Runoff
Fundamentals
Soil water doesn’t freeze at 0o Celsius?
– Dissolved salts
– Capillary forces
– Forces between minerals and soil water
– Heterogeneous Composition
Datasets
Various sources
– CEOP CAMP
– GEWEX GAME
– National Snow Ice Data Center
Data from various climate regimes
Datasets
Site
Lon
Lat
Depths (cm)
Dates
AK Shrub
64.9 N
164.7 W
16,26
9/2000-9/2001
AK Woodland
64.9 N
163.7 W
16,43
9/2000-9/2001
AK Forest
64.9 N
163.7 W
15,31
9/2000-9/2001
AK Tundra
70.4 N
148.5 W
23
9/1999-8/2001
Mongolia Grass
46 N
107 E
15
10/2002-10/2003
Siberia Tundra
71.6 N
128.9 E
10
10/2000-10/2001
Observations
Methodology
Assume total soil moisture constant
– qi = qt – ql
Define
–
qi
fi 
qt
qt  qi  ql
qs the Saturated Volumetric Moisture
– 10 km soil composition data
– CLM formulation
Observed fi
qi  qcap
Current Formulations
ECMWF
q 0
T > Tfrz + 1
i
qi 
qcap
2
 {1  sin[
  (T  Tfrz  2)
qi  qcap
qcap = 0.323 m3/m3
4
]}
Tfrz - 3 < T < Tfrz +1
T < Tfrz - 3
Current Formulations
•Noah
gs
qt  qi b T  273 .16
 (1  ck  qi ) 2  (
) 
0
L
qs
T
T < Tfrz
•If divergent ck=0 then solved explicitly
•CLM3
qi  0
qi  qt
T >Tfrz
T < Tfrz
Modeled fi vs. Observations
Site
qt/qs
obs
ECMWF
Noah
AK Shrub 16
cm
0.966
0.762
0.708
0.821
AK Shrub 26
cm
0.822
0.868
0.832
0.801
AK Wood 16
cm
0.882
1.000
0.776
0.810
AK Wood 43
cm
0.902
0.912
0.757
0.813
AK Forest 15
cm
0.944
0.838
0.776
0.818
AK Forest 31
cm
0.902
0.795
0.757
0.813
AK Tundra
1999
0.955
0.752
0.717
0.820
AK Tundra
2000
0.964
0.743
0.710
0.821
AK Tundra
2001
0.972
0.795
0.704
0.822
Mongolia
Grass
0.244
0.150
1.000
0.527
Proposed Formulation
qt 
qi 1  exp{  (qs )  (T  Tfrz )}

qt
qt
exp(1  )
qs
• 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
•  and  are adjustable parameters
•Chosen as 2 and 4 respectively
Comparison
Noah b = 4.5
Noah b = 5.5
Noah b = 4.5 ck=0
Noah b = 5.5 ck=0
ECMWF
Proposed
Observations vs Proposed
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
Results ECMWF-Control
Sensible
Heat Flux
Latent Heat
Flux
Ground
Temperature
Results Noah-Control
Sensible
Heat Flux
Latent Heat
Flux
Ground
Temperature
Results Proposed-Control
Sensible
Heat Flux
Latent Heat
Flux
Ground
Temperature
Results Proposed
Fi Proposed
Fi Difference
Proposed-Control
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
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
Derivation of a New Maximum Snow
Albedo Dataset Using MODIS Data
M.Barlage, X.Zeng, H.Wei, K.Mitchell; GRL 2005
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
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
How can you be sure it’s snow?
NDSI: Exploiting the differences in
spectral signature between visible and
NIR albedo.
NDSI 
 4(0.55)   6(1.64)
 4(0.55)   6(1.64)
NDSI and NDVI
Final 0.05° Maximum Snow Albedo
Comparison with RK
0.05deg MODIS
RK Figure 5
High-resolution Improvements
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
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