zong-liang yang guo-yue niu hua su the university of texas at austin

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)


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


Canopy Water and Ice BalanceCanopy Water and Ice Balance


(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.


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 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


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.


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


Relaxes the dependence of hydraulic properties on the soil ice content

Fractional impermeable area

frzfrzufrz qFqFq )1(


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


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


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


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


Modeled Snow Depth

Earlier runoff does not result from earlier snowmelt


Change in Water Storage (Snow + Soil)

The water storage of CLM reaches its maximum in March, while NEW in April


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


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


Subgrid Snow Cover and Surface Subgrid Snow Cover and Surface TemperatureTemperature


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


• 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)


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


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


CV values are assigned to 9 categories.

Liston (2004) JCL

Liston (2004) JCL

The Coefficient of Variation (CV)


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


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


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


A Flowchart for Deriving a Grid-scale SCF

Three records for each sub-grid:

snow cover fraction,

cloud cover fraction,

confidence index


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


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


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).


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


Values of CV in CLM

Barren Land

Vegetated Land


PFT Type1 PFT Type2

PFT Type3 PFT Type4

Geographic Distribution of CV in CLM


CV

Baseline

Tanh

AMSR Obs

Snow Density

Monthly SWE from 2002 to 2004


Daily SCF for Northwest U.S. 2002-2004

CV

Baseline

Tanh

MODIS Obs

Snow Density


CV

Baseline

Tanh

MODIS Obs

Snow Density

Daily SCF for High-latitude Regions 2002-2004


CV - Baseline

Snow density - Baseline

Tanh - Baseline

Daily Trad for Northwest U.S. 2002-2004


CV - Baseline

Snow density - Baseline

Tanh - Baseline

Daily Trad for High-latitude Regions 2002-2004


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

zong-liang yang guo-yue niu hua su the university of texas at austin

Documents

soil layer

soil permeability

soil particles

soil columns

soil hydrology

deep soil

austinmodeling frozen

soil heat capacity