Introduction to the NOAH Land Surface
Model
N:
O:
A:
H:
National Centers for Environmental Prediction (NCEP)
Oregon State University (Dept of Atmospheric Sciences)
Air Force (both AFWA and AFRL - formerly AFGL, PL)
Hydrologic Research Lab - NWS (now Office of Hydrologic Dev –
OHD)
Outline
1.
2.
3.
What is an LSM?
What is the NOAH LSM?
NOAH @ UT
Zong-Liang Yang
Prepared for Surface Water Hydrology
February 24, 2005
Outline
1. What is an LSM? (29 slides)
2. What is the NOAH LSM? (13
slides)
3. NOAH @ UT (14 slides)
Computer Code to Model Land Surface
Processes
• Exchanges with the atmosphere
–
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Momentum
Energy
Vapor
Trace gases/dust/aerosols
• Exchanges with the oceans
– Runoff
– Sediments / nitrogen
• Land-memory processes
–
–
–
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Topography
Snow cover
Soil moisture
Vegetation
• Human activities
– Land use (agriculture, deforestation, urbanization…)
– Inland water pollution
Earth’s Radiation Budget
Shortwave: Earth reflects 30% directly back to space, absorbs about
20% in the atmosphere, and absorbs about 50% at the surface.
Longwave: Greenhouse effects keep the surface warm.
Turbulent heat fluxes: sensible and latent heat keeps the surface cool.
Land-atmosphere coupling strength (JJA)
Koster et al.,
2004
(Science)
Hot spots: initial soil moisture impacts
rainfall forecasts
Locations for the routine monitoring of
soil moisture
Interpretations
Rainfall in a region depends on atmospheric moisture transport
from upwind areas and local evapotranspiration.

Moisture transport depends on large-scale circulations

Evapotranspiration depends on vegetation, soil, and
weather conditions
L
Qa In
Qa Out
Sa
Pm Pa
E
Ql
Do Land Surface Processes Matter to
Climate Prediction?
What Are Land Surface Processes
• Land surface consists of
– urban areas, soil, vegetation, snow, topography, inland water (lake, river) …
• Land surface processes describe
– exchanges of momentum, energy, water vapor, and other trace gases between
land surface and the overlying atmosphere
– states of land surface (e.g., soil moisture, soil temperature, canopy
temperature, snow water equivalent)
– characteristics of land surface (e.g., roughness, albedo, emissivity, soil
texture, vegetation type, cover extent, leaf area index, and seasonality)
What Are Land Surface Processes
• Land surface processes function as
– lower boundary condition in Atmospheric Models
• Atmospheric Boundary Layer Simulation
• Numerical Weather Prediction
Climate Simulation
4-D Data Assimilation
– upper boundary condition in Hydrological Models
• Water Resources Estimation; Crop Water Use; Runoff Simulation
– interface for coupled Atmospheric/Hydrological/Ecological Models
Land Surface Models (LSMs)
• Computer code describing land surface processes (also called
LSSs, LSPs, SVATs)
– FORTRAN, C, C++, PASCAL, … ...
– Tens to thousands of lines
• There are a huge number of LSMs (100+ examples in literature)
– many are just “research models’’, local-scale oriented, with specific
process emphasis
– up to ~100 canopy, ~100 soil, ~100 snow, even ~100 atmosphere layers!
• LSMs in GCMs and Hydrological Models are less diverse
– one dimensional, with 1-2 canopy, 1-10 soil, 1-10 snow layers
– three general classes
• “Bucket” Models (no vegetation canopy)
• “Micrometeorological” Models (detailed soil/snow/canopy
processes)
• “Intermediate” Models (some soil/snow/canopy features)
Land Surface Models (LSMs)
• Four basic requirements
– frequently-sampled (hourly or sub-hourly) weather “data” to
“drive” LSMs
•
•
•
•
•
•
precipitation (rate; coverage, large-scale/convective in GCMs)
radiation (shortwave, longwave)
temperature
wind components (u, v)
specific humidity
surface pressure
– initialization of state variables
• soil moisture (liquid, frozen)
deep soil temperature
– specification of surface characteristics
• albedo
roughness
vegetation cover
– validation of simulations of state variables and fluxes
• soil moisture
sensible/latent heat fluxes
Land Surface Models (LSMs)
•
Minimum Requirement is to describe:
– bulk momentum exchange (roughness length, zero displacement height)
– exchange of radiation for:
• solar radiation (0-3 micrometers): albedo
• long-wave (3-100 micrometers): surface emissivity
– partition of radiant energy between soil heat, latent heat and sensible heat, along with its
relationship to water availability
•
Micrometeorological Models typically
– have 20-50+ parameters
– require to prescribe or predefine them before running the models
•
Research Issues
– Obtaining and applying relevant “pure biome” data to test or calibrate LSMs
– Dealing with spatial/temporal heterogeneity
• defining area-average parameters; scaling up surface parameters from local to regional
• defining space-time structure of atmospheric inputs
– Making best use of remote sensing data for initialization, specification and validation
– Improving key processes
• Snow/Frozen soil
• Runoff generation/routing
• “Greening” of LSMs (carbon balance and vegetation dynamics)
Land Surface Models (LSMs)
•
Project for Intercomparison of Land-surface Parameterization Schemes (PILPS)
– is helping define needs, understanding processes, from sensitivity tests and
field data to coupled modeling
– ongoing project (4 phases, starting from 1992, 20-30 LSMs participating)
• Phase 1: GCM-generated forcing data for three “biomes”:
– models’ response time to initialization of soil moisture
– sensitivity experiments with albedo, canopy/soil water storage,
aerodynamic resistances
• Phase 2 : observed data
– 2(a) Cabauw, Netherlands (grass; 1-year)
– 2(b) HAPEX-MOBILHY, France (crop; 1-year)
– 2(c ) Red-Arkansas River Basin, USA (mixed land covers; 10-year)
– 2(d) Valdai, Russia (snow/frozen soil; 18-year)
– 2(e) Sweden Torne/Kalix Basins, 58000 sq. km, 218 grids, 1979-98
• Phase 3: joint with Atmospheric Model Intercomparison Project -AMIP
• Phase 4: selected LSMs coupled to one host model
– Regional Model
Global Model
Coupling Strategy
Simple “Bucket” LSMs
•
“Bucket” Models” first in hydrological models, later in GCMs
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–
•
In original “bare soil” form, e.g., Manabe (1969) include no vegetation so
specify (fixed) parameter values for
–
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–
–
–
–
•
“Bucket” is filled by precipitation, and emptied by evaporation
Runoff occurs when “Bucket” is full (soil water level exceeds the field capacity)
Evaporation is based on soil-water limited “Potential Evaporation”
The limiting factor is a linear function of soil moisture
albedo
(Solar radiation)
surface emissivity
(Longwave radiation)
roughness length
(Momentum exchange)
soil thermal properties
(Soil heat storage)
field capacity (15 cm) for all land points
(Soil hydrology)
snow areal coverage (covers 100% gridbox) (Snow energy/water budgets)
Some variants of “Bucket” Models
– allowing leakage by implicit transpiration or gravitational drainage
– including surface resistance in evaporation formula
– extending to 2 connected “Buckets”: Deardorff (1977) includes a thin surface layer
and a deep bulk layer
Micrometeorological Model-Based LSMs
•
Micrometeorological Model-Based LSMs are
– more complex than bucket models
– less complex than “multi-layer” or “second order closure” canopy models
•
Best known examples:
– “Biosphere-Atmosphere Transfer Scheme (BATS)”
– “Simple Biosphere Model (SiB)”
– SiB more complex (not at run time) some whole-canopy parameters from “preprocessing”, one calculation, prior to actual model run
•
Five components of radiation exchange considered in BATS and SiB
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Direct beam photosynthetically active radiation (PAR) (<0.72 micrometers)
Diffuse solar photosynthetically active radiation (PAR) (<0.72 micrometers)
Direct beam near infrared (NIR) (0.72-4.0 micrometers)
Diffuse solar near infrared (NIR) (0.72-4.0 micrometers)
Thermal infrared (>4.0 micrometers)
Micrometeorological Model-Based LSMs
Biosphere-Atmosphere
Transfer Scheme
BATS
Micrometeorological Model-Based LSMs
•
“Two-Stream Approximation” are used to calculate solar radiation exchange
in SiB for both PAR and NIR, diffuse and direct beam components from
dI 
 [1  (1  )  ]I       I       K  0  e  KL
dL
dI 

 [1  (1  )  ]I       I       K  (1  0 )  e  KL
dL
divergence forward scatter back scatter direct beam scatter

I , I  normalized upward / downward diffuse beam components
, 0 diffuse / direct " on scatter" parameters for beam
•
•


K
scattering coefficients for " photoeleme nts"
average inverse optical depth / unit leaf area
direct beam optical depth / unit leaf area
Can obtain single-scattering albedo, diffuse fluxes within canopy (for stomatal
resistances) and at soil surface, and total albedo of the vegetated surface
Vegetation albedo depends on specifications of
– leaf area index
scattering coefficients of leaves and soil
– leaf angle distribution function
angle of the direct-beam incident radiation
– proportion of different components of the incident radiation
•
•
Not suitable for application over heterogeneous or ‘clumpy’ canopies
(coniferous forests); require three-dimensional radiation models
BATS prescribes vegetation albedo; 4-layer model for stomata
Micrometeorological Model-Based LSMs
•
Thermal Radiation from surface is computed from “Stefan-Boltzmann” law:
L out    Ts4
where Ts is an " appropriately defined" surface temperatu re
•
Whole-canopy turbulent transfer is important for exchange of momentum,
heat and mass (water vapor, trace gases)
– is a complex process
– requires higher-order closure models, Markov-chain numerical simulations, or eddy
simulation techniques
– simpler treatments in LSMs because of
• computational expense and storage requirements
• aerodynamic resistance roughly an order of magnitude smaller than aerodynamic
resistance
– z 0 and d : prescribed for BATS (and most other models); aerodynamic resistances
from curve-fitting functions, iteration for nonneutral corrections
– z 0 and d : calculated in SiB: pre-processed from canopy morphology using a “KTheory” equivalent of Shaw-Pereira (1982); aerodynamic resistances from canopy
morphology using “K-Theory”, iteration for nonneutral corrections
Micrometeorological Model-Based LSMs
•
•
Canopy interactions described by a “big leaf” model in BATS and SiB and
others [SiB allows an “understorey”; SiB2 allows only one layer]
Dry canopy parameterized through surface resistance based on the model of
Jarvis (1976). For a single leaf:
1
 gs  g0 gC g R g D gT g M
rs
rs - leaf stomatal resistance, sm -1
g s - leaf stomatal conductanc e, ms -1
g 0 - constant depending on vegetat ion type
g C - canopy cover factor
g R - radiation " stress function"
g D - vapor pressure deficit " stress function"
g T - temperatu re " stress function"
g M - soil moisture " stress function"
•
Canopy surface resistance is obtained by integrating above equation over leaf
angle and canopy depth. This integration and those dependencies are “coded”
in different forms in BATS and SiB and many other models.
Micrometeorological Model-Based LSMs
•
Different forms of coding for the integration:
– In SiB, an analytical integration is performed by using the Goudriaan formulation
for some regular leaf angle distribution
– In BATS, the integration is done by using a 4-layer numerical scheme for canopy
•
Different forms of coding for the stress factors:
– primarily determined from curves fits to data (e.g. Jarvis, 1976), but
– g M in SiB was a linear function of the leaf water potential, which, in turn, was
determined by a catenary model of water flow from root zone to leaf
– g M in BATS, was introduced only when the atmospheric demand exceeds the water
supply from the soil through roots
– g T in SiB taken as a smooth curve varying from zero at some minimum
temperature around freezing to unity at an optimum temperature between 25
degree C and 35 degree C, depending on species, and dropping sharply down to zero
at 45 degree C to 55 degree C
– g T in BATS parameterized as a quadratic function of canopy temperature,
varying from 0 at freezing point to unity at 25 degree C, and remaining at unity
when temperature is greater than 25 degree C
Micrometeorological Model-Based LSMs
•
•
•
Calibration of stomatal resistance largely speculative: guessed from single leaf
data, but occasional “Calibration Data Sets” available e.g. for forest,
Shuttleworth (1989); Gash et al. (1996)
Wet Canopy Description is a simplified “Rutter” Model: S, proportional to
Leaf Area Index ~[(0.1-0.25) x LAI] (mm)
Both BATS and SiB allow partially wet canopy
E
Total


E

Transpiration
ST  finite
[1  f (C / S)] E rPM

dC
ST  0
 g(F, LAI ) P  f (C / S) E rPM
D
dt
D  0, when C  S
D  P, when C  S
a c p D
 (R n  G ) 
ra
E 
  r 
   1   ST 
  ra 
E
Intercepte d Rain
ST  0
f(C/S) E rPM
canopy water balance equation
where D is canopy drainage rate
where P is precipitation rate
(whole - canopy) Penman - Monteith Equation
Micrometeorological Model-Based LSMs
•
•
•
“Plot-scale” calibration of interception models adequate, given tuning of S
against LAI
BATS/SiB have simple “vertical hydrology”: soil water diffuses vertically
through 2-3 layers, but is ultimately inaccessible by draining from lowest layer
Global Distributions of different land classes required, BATS has 15, SiB has
12, with parameters defined for each class:
Calibration of these Parameters Remains Speculative
“Intermediate” LSMs
•
•
“Intermediate” LSMs trade process detail for simplicity, justified by still
speculative need for more complete process models, and their poor calibration
Often have distributed vegetation class, but simpler physics: e.g.
– UKMO
– French ISBA
– Simple SiB
– “Super Bucket”
•
simple canopy model
simple soil model
Simplified empirical parameterization fitted to time
consuming, SiB-calculated functions
basic bucket, with “reference crop” estimates instead of
“potential” rate(+Several others)
Permissible, practical alternative, pending evidence of need for more complex
micrometeorological models
LSMs Calibration Issues
•
•
Primary LSM short-coming is lack of reliable calibration: 3 needs
– Obtaining and applying relevant “pure biome” data
– Understanding how to define area-average parameters
– Making best use of Remote Sensing Data
Single Biome Calibration
– Needs:
• sets of continuous “driving variables” for whole years
(frequentlysampled, near-surface, equivalent to GCM)
• some substantial periods with measured exchanged fluxes
(preferably for several seasons, all exchanges, time scales)
– Some biomes done or in-hand
• Rain forest (ARME, ABRACOS)
• Prairie grass (FIFE)
• Boreal forest (BOREAS)
• Sonora Desert, Tucson, Arizona (Example of multi-objective
calibration)
LSMs Calibration Issues: Area-average
•
•
Defining Area-average LSM parameters is active research which involves data collection,
analysis and coupled models for mixed land covers, and for selected regions
– mixed vegetation cover and/or soils
uneven precipitation
sloping terrain
Examples:
–
–
–
–
–
–
Hydrologic-Atmospheric Pilot Experiment (HAPEX) - S.W. France, 1986, (mixed temperate;
agriculture, forest)
First ISLSCP Field Experiment (FIFE) - Kansas, 1987,9 (tall prairie grass)
HAPEX-Sahel - Niger, 1992, (semi-arid Sahelian savannah-across rainfall gradient)
BOREAS - Canada, 1993,4, Boreal Forest
AMAZONIA (LBA) - S. America, 1997,8 - Amazonian land covers
GEWEX
LSMs Calibration Issues: Atmospheric Data
•
•
Calibration or model development depends on atmospheric input data
Runoff estimation error is determined by the accuracy of the precipitation
forcing as measured by the density of rain gauge
Micrometeorological Model-Based LSMs
•
New developments: The Greening of LSMs (improved canopy physiology)
– enzyme kinetics-electron transport model (Farquhar et al., 1980), relating leaf
photosynthetic rate to PAR, leaf temperature, leaf internal carbon dioxide
concentration, and leaf carboxylase concentration
– photosynthesis modeled as the minimum of three potential capacities to fix carbon
A g  min (J e , J c , J s )
A g - gross photosynth esis rate per unit leaf, mol CO 2 m - 2 s -1
J e - light - limited rate of photosynth esis per unit leaf, mol CO 2 m - 2 s -1
J c - Rubisco - limited rate of photosynth esis per unit leaf, mol CO 2 m - 2 s -1
J s - triose phosphate utilizatio n - limited rate of photosynth esis per unit leaf, mol CO 2 m - 2 s -1
A n  A g  R leaf  (C a  Cs ) g b, CO 2  (Cs  Ci )
g s, H 2 O
1.6
A n - net leaf assimilation rate, mol CO 2 m - 2 s -1
R leaf - leaf maintenanc e respiration, mol CO 2 m - 2 s -1
C a - CO 2 concentrat ion (mol/mol) in the atmosphere
Cs - CO 2 concentrat ion (mol/mol) at the leaf surface
Ci - CO 2 concentrat ion (mol/mol) in the intercellular air spaces of the leaf
g b, CO 2 - boundary layer conductanc e for CO 2 (mol CO 2 m - 2 s -1 )
1.6 - factor to account for different diffusivities of CO 2 and H 2 O
Micrometeorological Model-Based LSMs
•
New developments: The Greening of LSMs (improved canopy physiology)
– Ball-Berry model of stomatal conductance (Ball et al., 1986): robust semi-empirical
method, relating leaf stomatal conductance to photosynthetic rate and
environmental parameters
gs 
m An
hs  b
Cs
m, b - constants 9 and 0.01, respectively
h s - relative humidity on leaf surface
A n - net leaf assimilation rate, mol CO 2 m - 2 s -1
Cs - CO 2 concentrat ion (mol/mol) at the leaf surface, or
p CO 2 [Pa]
p [Pa]
– Other forms: Leuning (1995), Foley et al. (1996), Dickinson et al. (1998): using
vapor pressure deficit instead of relative humidity
gs 
m An
D
(Cs  * )(1  s )
Do
b
D s - water vapor mole fraction difference between th e leaf and the air (mol/mol)
D o - reference value (mol/mol)
D s / D o - written as (1  0.05 VPD) as in Dickinson et al. (1998); VPD in Pa
Micrometeorological Model-Based LSMs
• New developments: The Greening of LSMs (improved
canopy physiology)
– C3 (all trees and many herbaceous) and C4 (warm
grasses) plants are similarly modeled as the minimum
of three potential capacities to fix carbon, but
– The three potential capacities are defined differently for
C3 and C4 plants
– The parameters are different for both types of plants
– The Ball-Berry model of stomatal conductance replaces
all the terms in the Jarvis model except for the soil
water stress term
Micrometeorological Model-Based LSMs
•
New developments: The Greening of LSMs (improved vegetation phenology)
– Traditionally, annual cycle in areal vegetation coverage and leaf area index
prescribed as a quadratic function of deep soil temperature
– Some recent attempts: relating vegetation to climate using empirical rules
involving monthly or annual mean temperature and precipitation
– New developments:
• simple rule-based formulation to describe behavior of winter-deciduous
and drought-deciduous plants (Foley et al., 1996), using
– daily average temperature, yearly carbon balance
– competition between trees and grasses (through allocation/shading)
• interactive canopy model (Dickinson et al., 1998)
– short time-scale leaf dynamics
– consistent treatment of stomatal conductance and assimilation
– assimilated carbon allocated into leaf, fine root and wood
– soil carbon model
» fast pool: leaf/root turnover (senescence, herbivory or
mechanical), leaf loss (cold and drought stress), soil respiration
» slow pool: inert for seasonal variations
Recent LSM Developments
• Improved understanding in physical hydrology
– Snow hydrology
– Topographic controls on soil moisture/runoff
• Enhanced linkage with biogeochemistry/ecology
– Carbon and nitrogen
– Vegetation growth/competition
• Closed water cycle through river routing
• Advanced techniques for using remotely-sensed
land parameters
• Extended field datasets and model calibration
Outline
1. What is an LSM? (29 slides)
2. What is the NOAH LSM? (13
slides)
3. NOAH @ UT (14 slides)
Land-Atmosphere Interaction
NOAH LSM Development Team
NCEP/EMC:
NCEP Environmental Modeling Center (EMC)
(Mitchell, Chen, Ek, Lin, Marshall, Janjic, Manikin,
Lohmann, Grunmann, Pan)
OSU:
Oregon State University
(Mahrt, Pan, Ek, Kim, Rusher)
HL:
NWS Hydrology Lab - formerly Office of Hydrology
(Schaake, Koren, Duan)
AFWA:
Air Force Weather Agency - formerly AFGWC
(Moore, Mitchell, Gayno)
AFRL:
Air Force Research Lab - formerly AFGL and PL
(Mitchell, Hahn, Chang, Yang)
NOAH LSM Evaluation Team
E.H. Berbery and Rasmusson:
U. Maryland
(ARM/CART)
C. Marshall and Crawford
U. Oklahoma
(OU Mesonet)
I. Yucel and Shuttleworth:
U. Arizona
A.K. Betts:
Atmospheric Res Inc
(ARM/CART,
AZNET)
(ISLSCP/FIFE)
C.D. Peters-Lidard, Wood
Princeton U.
L. Hinkelman and Ackerman:
Penn State U.
(TOPLATS
extensions)
(ARM/CART)
T.H. Chen, W. Qu,
Henderson-Sellers, et al.
RMIT
(PILPS-2a)
E. Wood, Lettenmaier,
Liang, Lohmann:
Princeton U.
(PILPS-2c)
A. Schlosser, A.G. Slater,
A. Robock, et al.
U. Maryland
(PILPS-2d)
R. Angevine
NOAA/AL
(Flatland Exp)
References
1. User’s Guide, Public Release Version 2.2, Last Updated 25 June 2001,
filename README_2.2.doc at
ftp://ftp.ncep.noaa.gov/pub/gcp/ldas/noahlsm/ver_2.2
2. Author / Point of Contact: Kenneth.Mitchell@noaa.gov (NCEP/EMC), phone
301-763-8000
3.
PROGRAM HISTORY LOG
01 Mar 99:
08 Mar 99:
27 Jul 99:
23 Oct 00:
25 Mar 01:
Ver_1.0
Ver_1.1
Ver_2.0
Ver_2.1
Ver_2.2
Timeline of NOAH LSM Evolution
Atmospheric Forcing Variables
Air temperature
Air humidity
Surface pressure
Wind speed
Surface downward longwave radiation
Surface downward solar radiation
Precipitation
at height Z above ground
at height Z above ground
at height Z above ground
at height Z above ground
Initial Conditions
1.
SMC:
total volumetric soil moisture (liquid & frozen) in each soil
layer
2.
SH2O:
liquid volumetric soil moisture in each soil layer
3.
STC:
temperature in each soil layer
4.
T1:
skin temperature
5.
CMC:
canopy water content
6.
SNOWH:
snow depth
7.
SNEQV:
water-equivalent snow depth
Model Parameters
There are five kinds of land-surface parameters, reviewed in order below.
a)
single universal values
b)
values dependent on the soil class index (default categories are 1–9)
c)
values dependent on the vegetation class index (default categories are 1–
13)
d)
values dependent on the surface slope index (default categories are 1–7)
e)
parameters specifying the numbers of vegetation, soil, and slope classes
Model Soil Parameters
SMCMAX:
maximum volumetric soil moisture (porosity)
SMCREF:
soil moisture threshold for onset of some transpiration stress
SMCWLT:
soil moisture wilting point at which transpiration ceases
SMCDRY:
top layer soil moisture threshold at which direct evaporation
from soil ceases
DKSAT:
saturated soil hydraulic conductivity
PSISAT:
saturated soil matric potential
B:
the “b” parameter in hydraulic functions
DWSAT:
saturated soil water diffusivity
QUARTZ:
quartz content, used to compute soil thermal diffusivity
FRZFACT:
a parameter used with FRZK to compute the value of
parameter FRZX
Model Vegetation Parameters
Z0 (m):
RCMIN (s/m) :
roughness length
minimal stomatal resistance used in canopy resistance
of routine CANRES
RGL:
radiation stress parameter used in F1 term in canopy
resistance of routine CANRES
HS:
coefficient used in vapor pressure deficit term F2 in
canopy resistance of routine CANRES
LAI:
presently set to universal value of 3.0 across all
vegetation classes
Note: seasonality of vegetation greenness carried by fraction of green
vegetation (SHDFAC)
NROOT:
SNUP:
number of soil layers from top down reached by roots
the water-equivalent snowdepth upper threshold at
which
1) 100 percent snow cover is achieved for given veg class
2) maximum snow albedo is achieved for given veg class
Forecasts of Dew Point Temperature
Old version
New version
Forecasts of Dew Point Temperature
Outline
1. What is an LSM? (29 slides)
2. What is the NOAH LSM? (13
slides)
3. NOAH @ UT (14 slides)
NOAH @ UT
• To put a dynamic vegetation component in the NOAH
LSM
– understand the feedback mechanisms between precipitation,
soil moisture, and vegetation dynamics
– To improve weather and climate forecasts on time scales
from hourly to seasonal
– To interpret and transfer weather and climate forecasts for
water resources management applications
• To put a biogenic VOC emission component in the
NOAH LSM
– understand the impacts of climate change and land use/land
cover change on BVOC emissions and the formation of near
ground ozone
The Modeling Domains: Multiple interactive nesting grids:
Weather Research and Forecast
(WRF) Model, version 2.02
(http://www.wrf-model.org/)
The model is developed by NCAR,
NCEP and Universities.
30 km grid; 31 Levels
Temperature, wind, humidity,
pressure predicted
3-10 km grid; 31
Levels
90 km grid; 31 Levels
Temperature, wind, humidity, pressure prescribed
from large-scale analyses
Temperature, wind,
humidity, pressure
predicted
Vegetation Distribution in the Model
Other Land Data:
Vegetation
LAI
Albedo
Roughness length
Stomatal parameters
Soil
Topography
Validation:
Weather stations
Hydrologic stations
Remote sensing
Field data
Stomatal Conductance in NOAH
•
Transpiration
parameterized through
surface conductance
based on the model of
Jarvis (1976). For a
single leaf:
1
 gs  g0 gC g R g D gT g M
rs
rs - leaf stomatal resistance, sm -1
g s - leaf stomatal conductanc e, ms -1
g 0 - constant depending on vegetat ion type
g C - canopy cover factor
g R - radiation " stress function"
g D - vapor pressure deficit " stress function"
g T - temperatu re " stress function"
g M - soil moisture " stress function"
NOAH with Modifications (1)
The ability of roots to uptake soil water depends on the availability
of soil water.
In NOAH, this is a linear function, which may work if soil is near
saturated.
We make it a step-function, guided by observations.
We call the experiment NOAH SWF (soil water factor).
Role of Land Surface Processes in Modulating NAMS Rainfall
OBS
WRF/SLAB
WRF/NOAH
WRF/NOAH SWF
JUN
JUL
AUG
WRF/SLAB fails to
produce rainfall for
June thru August.
WRF/NOAH fails to
capture rainfall in
August.
WRF/NOAH-SWF
captures rainfall in
August.
NOAH with Modifications (2)
NOAH prescribes fractional green vegetation.
In the real world, vegetation growth depends on
precipitation, temperature, nutrients, and others.
This can be modeled by
1) Relating stomatal conductance to photosynthesis &
environmental conditions, and
2) Allocating assimilated carbon to leaves, stem, wood,
and roots.
Leaf Anatomy
Stomate
(pl. stomata)
Carbon and Water
• Plants eat CO2 for
a living
• They open their
stomata to let
CO2 in
• Water gets out as
an (unfortunate?)
consequence
• For every CO2
molecule fixed
about 400 H2O
molecules are lost
Photosynthesis and Conductance
Stomatal conductance is linearly related to photosynthesis:
(The “Ball-Berry-Collatz” parameterization)
photosynthesis
stomatal
conductance
RH at leaf sfc
An hs
gs  m
pb
cs
CO2 at leaf sfc
Photosynthesis is controlled by three limitations
(The Farquahar-Berry model):
An  min( AC , AL , AS )  Rd
Enzyme kinetics
(“rubisco”)
Light
Starch
Photosynthesis and Carbon Allocation
Comparison of Observed & Simulated Rainfall
OBS
JUN
JUL
AUG
WRF/NOAH SWF
WRF/NOAH DV
Conclusions
1) Strong sensitivity to land surface processes
1) SLAB (without vegetation) fails to simulate the monsoon
rainfall in all months
2) NOAH (with prescribed vegetation) is better, but underestimates the August rainfall
3) NOAH (with improved root water uptake and response) is even
better
4) NOAH (with improved carbon and water coupling and leaf’s
dynamic response to rainfall) gives the best overall simulation
of the warm season rainfall
1) In the southern monsoon regions
2) In the southern Great Plains.
2) Dynamic vegetation has the largest impacts on the simulation of
rainfall in the SGP, which is the area that shows the largest
memory to soil moisture conditions.
PHYSICAL CLIMATOLOGY
GEO 387H
TTh 9:30-11:00 Fall 2005
www.geo.utexas.edu/courses/
387h/indexPHY.htm
Taught by
Dr. Liang Yang
Department of Geological Sciences
This course will cover the basics of a broad range of physical processes having an impact on precipitation and
evapotranspiration at the earth's surface. Local processes include thermodynamics, cloud microphysics, net
radiation at the surface, fluxes of heat and moisture into the soil and the atmospheric boundary layer, and the
effects of a vegetation canopy. Large-scale interactions between atmosphere, ocean, soil moisture, snow, ice,
biosphere, and lithosphere are reviewed in the context of potential local/large-scale transports. Methods for
measuring and estimating precipitation and evapotranspiration are described, including the sophisticated
models known as GCMs (General Circulation Models) and SVATS (Soil-Vegetation-Atmosphere Transfer
Schemes). Some basic programming/modeling techniques will be covered.
For both Geoscience and non-Geoscience graduate students.