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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. NCAR Community Land Model (CLM). a 10-layer soil sub-model a 5-layer snow sub-model - PowerPoint PPT Presentation
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Zong-Liang YangGuo-Yue Niu
Hua Su
The University of Texas at Austin
Modeling Frozen Soil and Subgrid Modeling Frozen Soil and Subgrid Snow Cover in CLMSnow Cover in CLM
CCSM LWGMMarch 28, 2006
www.geo.utexas.edu/climate
NCAR Community Land Model (CLM)1) a 10-layer soil sub-model
2) a 5-layer snow sub-model3) a topography-based runoff scheme4) an explicit solution of the freezing and thawing of soil
water5) 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
Topography-based Runoff Scheme (SIMTOP)
Infiltration Excess
Wa
ter
Ta
ble
De
pth
Saturation Excess
Super-saturationTopography Bottom
1) Surface runoff
Rs = FsatQwat+(1–Fsat) max(0, Qwat – Imax)
2) Subsurface runoff Rsb = Rsb,max exp (-f zw) simplified from
Rsb = [ α Ksat (0) / f ] exp(- λm) exp(- f zw)
α= anisotropic factor for Ksat in v. and h. directionsλm= grid-cell averaged topographic indexzw= grid-cell mean water table depth3) Ksat (0) = ksat exp (f Dc) Ksat (z) = Ksat(0) exp(–f z )
ksat is determined by Cosby et al. (1984).Allowing macropores.
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)
Yang and Niu (2003), Niu and Yang (2003), Niu and Yang et al. (2005, JGR-Atmospheres)
Frozen Soil | Subgrid Snow
Radiative Transfer within the Vegetation Canopy: Radiative Transfer within the Vegetation Canopy: Two-Stream Model Accounting for the 3-D Canopy Two-Stream Model Accounting for the 3-D Canopy
StructureStructure(Niu and Yang, 2004, JGR-Atmos)(Niu and Yang, 2004, JGR-Atmos)
~100km
Frozen Soil | Subgrid Snow
Canopy Water and Ice BalanceCanopy Water and Ice Balance
Frozen Soil | Subgrid Snow
(Niu and Yang, 2004, JGR-Atmos)(Niu and Yang, 2004, JGR-Atmos)
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
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
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
b
sat
liqsatliq
)(
gT
TTLT frzf )(10
)(3
The Frozen Soil Scheme in the NCAR CLM
T > Tfrz
T ≤ Tfrz
Frozen Soil | Subgrid Snow
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
Introduction of supercooled liquid water by using the freezing-point depression equation
Most researchers
b
sat
frzfsatliq gT
TTL/13
max,
)(10
gT
TTL frzf
b
sat
liqsatice
)(10)81(
3max,2
Koren et al., 1991
Frozen Soil | Subgrid Snow
Relaxes the dependence of hydraulic properties on the soil ice content
Fractional impermeable area
frzfrzufrz qFqFq )1(
Frozen Soil | Subgrid Snow
Model Results CTRL KorenNew
Ice
Fra
ctio
nIn
filtr
atio
nS
oil M
oist
ure
New scheme has less ice, higher infiltration, and greater
soil water
Frozen Soil | Subgrid Snow
Soil Moisture Profiles
Total water Liquid water Ice Fraction
CTRL
KorenNew
New scheme has more total soil water in the upper
0.5 m soil
Frozen Soil | Subgrid Snow
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
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
CTRL GRDC New
Frozen Soil | Subgrid Snow
Modeled Snow Depth
Earlier runoff does not result from earlier snowmelt
Frozen Soil | Subgrid Snow
Change in Water Storage (Snow + Soil)
The water storage of CLM reaches its maximum in March, while NEW in April
Frozen Soil | Subgrid Snow
GRACE and CLMGRACE-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)
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
Subgrid Snow Cover and Surface Subgrid Snow Cover and Surface TemperatureTemperature
Frozen Soil | Subgrid Snow
Winter Warm Bias in NCAR Winter Warm Bias in NCAR SimulationsSimulationsCCM3/CLM2 T42 - OBS CCM3/CLM2 T42 - OBS CCSM3.0 T85 - OBS CCSM3.0 T85 - OBS
(Dickinson et al., 2006)(Dickinson et al., 2006)
(Bonan et al., 2002)(Bonan et al., 2002)
Why?
Excessive LW↓ due to excessive low clouds
Anomalously southerly winds Frozen Soil | Subgrid Snow
Snow Cover Fraction and Air Snow Cover Fraction and Air TemperatureTemperature
])/(5.2
tanh[0
newsnog
sno
z
hSCF
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
Snow Vegetation
Liston (2004) JCL
Frozen Soil | Subgrid Snow
• 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,115-130°W)
Frozen Soil | Subgrid Snow
Representations of Snow Cover and Representations of Snow Cover and SWESWENatureClimate Modeling Remote 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
GroundSCF
Interception
SWE
SCF
Interception
SWE
Frozen Soil | Subgrid Snow
Theory of Sub-grid Snow CoverListon (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
CV values are assigned to 9 categories.
Liston (2004) JCL
Liston (2004) JCL
The Coefficient of Variation (CV)
Frozen Soil | Subgrid Snow
Relationship Between Snow Cover & SWEAccumulation 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(
*5.0)(
)(
2)(
)()2
(*5.0)(
)2
(*5.0)(
22
2
2
CVLn
uLn
DLnz
dtexerfc
DDz
erfcuDD
zerfcD
mDm
x
t
mmDm
ma
Dmm
(1) Snow Cover Fraction
(2) SWE
Liston (2004) JCL
Frozen Soil | Subgrid Snow
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
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
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
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, Dm
Frozen Soil | Subgrid Snow
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.
)()2
(*5.0)( mmDm
ma DDz
erfcuDD
to calculate Dm, then use to calculate SCF)2
(*5.0)( Dmm
zerfcD
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
(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 Dm
Frozen Soil | Subgrid Snow
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
Sn
ow
Wat
er E
qu
ival
ent
(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
Modeling SWE at Sleeper’s River, Vermont Using CLM with a Stochastic Representation of Sub-grid Snow Variability
CV=1.38 CV=0.8Blue: Simulated Red: Observed
Frozen Soil | Subgrid Snow
Values of CV in CLM
Barren Land
Vegetated Land
Frozen Soil | Subgrid Snow
PFT Type1 PFT Type2
PFT Type3 PFT Type4
Geographic Distribution of CV in CLM
Frozen Soil | Subgrid Snow
CV
Baseline
Tanh
AMSR Obs
Snow Density
Monthly SWE from 2002 to 2004
Frozen Soil | Subgrid Snow
Daily SCF for Northwest U.S. 2002-2004
CV
Baseline
Tanh
MODIS Obs
Snow Density
Frozen Soil | Subgrid Snow
CV
Baseline
Tanh
MODIS Obs
Snow Density
Daily SCF for High-latitude Regions 2002-2004
Frozen Soil | Subgrid Snow
CV - Baseline
Snow density - Baseline
Tanh - Baseline
Daily Trad for Northwest U.S. 2002-2004
Frozen Soil | Subgrid Snow
CV - Baseline
Snow density - Baseline
Tanh - Baseline
Daily Trad for High-latitude Regions 2002-2004
Frozen Soil | Subgrid Snow
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