Transcript No Slide Title

The Structural Design and Operational Behavior
of a Specific SVAT* Model
The particular land surface model (LSM) examined here is the “Mosaic LSM”.
Although this model has some unique features, its description should nevertheless
give a sense for how typical SVAT models work.
References:
Koster, R., and M. Suarez, Modeling the land surface boundary in climate models as a composite
of independent vegetation stands, J. Geophys. Res., 97, 2697-2715, 1992.
Koster, R. and M. Suarez, Water and Energy Balance Calculations in the Mosaic LSM, NASA Tech.
Memo. 104606, Vol. 9., 1996.
*SVAT
stands for “soil-vegetation-atmosphere transfer”. SVAT models include
SiB and BATS.
MOSAIC LSM: OVERALL STRUCTURE
Mosaic Strategy: Using vegetation maps, the heterogeneous vegetation cover within
a grid cell is subdivided into a “mosaic” of “tiles”. Separate energy and water budgets
are computed over each (relatively homogeneous) tile. The GCM atmosphere responds
to the areally-weighted fluxes.
Bare soil (9%)
Deciduous
Trees (35%)
Needleleaf
Trees (24%)
Grassland (32%)
TYPICAL TILE BREAKDOWN FOR A GCM LAND SURFACE GRID CELL
Percent coverage of vegetation type
within grid cell
Structure of a Mosaic LSM tile: Water Balance
precipitation
evaporation +
transpiration
INTERCEPTION RESERVOIR
throughfall
infiltration
surface runoff
SURFACE LAYER
ROOT ZONE LAYER
soil moisture
diffusion
RECHARGE LAYER
drainage
Structure of a Mosaic LSM tile: resistance network
Evaporation
network
Not shown on this
diagram is the “zero
resistance” associated
with evaporation from
the canopy interception
reservoir.
Sensible
heat
network
Operations performed at each time step:
time step n
Update
SeasonallyVarying
Parameters
Turbulence
subroutine
computes Eo,
Ho (and tendencies) from Tsold
and eaold
preprocessing
Reflectances
computed =>
Net shortwave,
PAR flux
LSM
computes
energy and
water balances
LSM call
Seasonally-varying parameters include:
“greenness fraction”: the fraction of vegetation leaves that are alive
LAI: the leaf area index
roughness length and other boundary layer parameters
root length
We prescribe values to these parameters, using one of two approaches:
1. Assign values based on vegetation (or soil) type and time of year.
(This is a necessary approach for many parameters.)
2. Assign geographically (and seasonally) varying parameter values
from maps derived, e.g., from remote sensing data.
LAI from tables (=f(veg type))
LAI from satellite data
Preliminary Turbulent Flux Calculation
Without considering any energy or water balance requirement, we can compute
“preliminary” values of evaporation (Eo) and sensible heat flux (Ho) based on the
values of surface temperature (Ts-old) and canopy air vapor pressure (ea-old) determined
in the previous time step.
A function of Ts-old, ea-old,
Tr, er, roughness, etc.
Tr
Ho
ra-old
The surface temperature, Ts,
is assumed to apply to the
canopy air, as well.
Ts-old
ea is the vapor pressure in
the canopy air. We treat ea
as a prognostic variable,
keeping track of its value
between time steps.
er
Eo
ra-old
ea-old
rc-eff
es(Ts-old)
Under this framework, compute Eo, Ho, and their tendencies. As will be seen later,
these will be necessary for the energy balance calculation.
Eo =
.622r
ps
ea-old - er
ra-old
Ho =
Cpr
E
=
T e ,T
a-old s-old
-.622r
ps
ea-old - er
ra-old2
ra
T e ,T
a-old s-old
E
=
ea e ,T
a-old s-old
-.622r
ps
ea-old - er
ra-old2
ra
ea e ,T
a-old s-old
-Cpr
Ts-old - Tr
ra-old2
ra
T e ,T
a-old s-old
-Cpr
Ts-old - Tr
ra-old2
ra
ea e ,T
a-old s-old
H
=
T e ,T
a-old s-old
H
=
ea e ,T
a-old s-old
Ts-old - Tr
ra-old
-.622r
ps ra-old
+
+
Cpr
ra-old
Preliminary Reflectance Calculation
Reflectances (and thus net shortwave radiation and photosynthetically active
radiation [PAR]) are assumed not to be affected by the energy and water balance
calculations, which means we can compute them ahead of time.
Shortwave radiation is divided into four components:
a) Visible direct radiation
b) Visible diffuse radiation
c) Near-infrared direct radiation
d) Near-infrared diffuse radiation.
We calculate a reflectance for each component
using a simple empirical formula that approximates the results of the full two-stream
calculation. For full details, see NASA Tech.
Memo. #104538 (1991). Note: in the Mosaic
LSM, albedo is not a function of surface water
content.
Now that the preliminary calculations are done, it’s time to call the Mosaic LSM
itself. The parameter list includes inputs, updates, and outputs:
INPUTS:
- Vegetation type and time step length
- GCM “weather”: rainfall, wind speed, vapor pressure in air, etc.)
- Seasonally-varying parameters
- Eo, Ho, and tendencies
- Radiation quantities (with shortwave reduced by pre-calculated albedo)
UPDATES (i.e., prognostic variables):
- Surface/canopy temperature Ts
- Deep soil temperature Td
- Canopy vapor pressure ea
- Water contents of three soil layers
- Water content of interception reservoir
- Snow water equivalent
OUTPUTS/DIAGNOSTICS:
- Evaporation E and sensible heat flux H
- Surface runoff and drainage out of the column
- Anything else that might me of interest
INSIDE THE MOSAIC LSM
First step: Compute rc-eff, a single surface resistance that accounts for all evaporation
pathways (transpiration, bare soil evaporation, interception loss, snow evaporation).
Assuming no snow, we assume that the tile is covered by a “wet fraction” (over which
the interception reservoir is full) and a “dry fraction” (over which it is empty).
Dry fraction
rdry-eff
ea
=
(
1
rc
+
1
rd + rsurf
)
-1
ea
subcanopy
aerodynamic
resistance
surface
resistance
rd
rsurf
es(Ts)
rc
es(Ts)
rdry-eff
es(Ts)
canopy
resistance
(a function
of environmental
stress)
Note similarity to electrical
resistance network calculation
Wet fraction + Dry fraction
We find the single effective resistance
reff (for the entire surface) that would give
the same evaporation as the dry area evaporation (computed with rdry-eff) added to
potential evaporation from the wet area.
The energy balance calculation has two unknowns: DTs and Dea. It thus needs
two equations. The first one has been seen before:
Sw + Lw = Sw + Lw + H + lE + G
basic energy balance
In the Mosaic LSM, we assume:
Snowmelt (M) is a special case, to be treated later.
Emissivity =1, so that the upward longwave radiation = sT4
The ground heating, G, is composed of two terms: heating of the
surface system (CpDTs/Dt) and a heat flux into the deep soil
(assumed proportional to Ts - Td).
Ho, Eo, and their “tendencies” are provided by the GCM as described
above, so that we can assume
H = Ho +
H
DTs
T e ,T
a-old s-old
E = Eo +
E
DTs
T e ,T
a-old s-old
+
+
H
ea e ,T
a-old s-old
E
ea e ,T
a-old s-old
Dea
Dea
Assuming Ts = Ts-old + DTs and ea = ea-old +Dea, we can show that the
energy balance equation reduces to:
Qo = [ 4sTs-old3 + dH/dT + ldE/dT + Cp/Dt + b] DTs + [dH/dea + ldE/dea]Dea,
where Qo = Sw - Sw + Lw - sTs-old4 - Ho - lEo - b(Ts-old -Td), and
b = deep soil heat flux proportionality constant.
How do we get the second equation?
Assume that evaporative flux
from canopy air to reference level...
er
E
ra
ea
...equals the flux from the saturated
surface (within stomates, etc.) to the
canopy air. (That is, the canopy air
can’t “build up” moisture.)
E
rc-eff
es(Ts)
Equation #1
In other words,
Eo + (dE/dT) DTs + (dE/dea) Dea = (0.622r/ps) (es(Ts) - ea) / reff
Flux from canopy air to reference level
Flux from surface to canopy air
Expanding, and neglecting 2nd order terms, gives
Eo - (0.622r/ps) (es(Ts-old) - ea-old) / reff-old
= (1/reff-old) [(0.622r/ps) des/dT - (dE/dT) reff-old - Eo(dreff/dT) ] DTs
+ (1/ reff-old ) [(-0.622r/ps) - (dE/dea) reff-old - Eo(dreff/dea) ] Dea
Equation #2
The two equations are solved for DTs and Dea. Afterward, snowmelt is
accounted for, if necessary. Note that the equations simplify in cases
of dewfall or snow evaporation, for which we assume reff = 0.
Next: compute water fluxes (i.e., solve the various water balance equations).
1. Evaporation. With DTs and Dea computed, the evaporation rate for the time
step is known. We remove this moisture from the interception reservoir, the surface
soil layer, and the root zone soil layer in amounts consistent with the resistances.
2. Moisture transport between soil layers. We use
a discretized version of Darcy’s law for unsaturated
flow. (See water balance lecture. Some features differ;
e.g., we use an “upstream” hydraulic conductivity.)
Moisture flow from surface layer to root zone layer
accounts slightly for subgrid heterogeneity.
3. Assign precipitation water to reservoirs.
Assume a uniform precipitation depth within a
prescribed fractional wetted area, and allow a
fraction of this storm area to consist of previously
wetted leaves. Surface runoff and infiltration are
computed fromresulting throughfall.
The Mosaic LSM, like any other SVAT model, has a drawback -- it requires
“realistic” values for numerous parameters:
Soil:
Layer capacities
Porosity
Saturated soil matric potential
Saturated soil hydraulic conductivity
Soil pore size distribution index
Bedrock slope
Surface heat capacity
Vegetation:
Other:
Surface “type” (one of 10 generalized types)
Leaf area index
Greenness fraction
Roughness height
Vegetation height
Unstressed canopy resistance parameters (6)
Vapor pressure deficit stress parameter
Temperature stress parameters (3)
Leaf water potential stress parameters (5)
Subcanoy aerodynamic resistance parameters (2)
Storm fractional area
The vegetation type
assigned to the tile
defines the values
used for most of
these parameters
Note that some of the
parameters cannot
be directly measured
The Mosaic LSM’s structure allows
the breakdown of total evaporation
by vegetation tile...
… and the breakdown of each tile’s
evaporation by component.
How do we evaluate the performance of such an LSM?
“Online” approach: test GCM output against observations.
GCM
P, radiation,
Tair, etc.
E, H, upward
longwave
Advantage: The coupling effects can be
studied, and various sensitivity tests can
be performed.
Disadvantage: The model forcing
(precipitation, radiation, etc.) can be wrong,
so validating the land surface model can be
very difficult. (“Garbage in -- Garbage out”)
LSM
Example from GISS
GCM/LSM: The Amazon
river is poorly simulated,
but we can’t tell if this is
due to a bad LSM or poor
precipitation from the GCM.
Better approach: Offline forcing (one-way coupling)
Forcing
Data
P, radiation,
Tair, etc.
Output
File
E, H, Rlw ,
diagnostics
LSM
Advantage: Land surface model can be
driven with realistic atmospheric forcing, so
that the impact of the LSM’s formulations
on the surface fluxes can be isolated.
Disadvantage: Deficient behavior of the LSM
may seem small in offline tests but may grow
(through feedback) in a coupled system.
Thus, offline tests can’t get at all of the
important aspects of a land surface model’s
behavior.
PILPS model intercomparisons (to be discussed in a later lecture) have
largely focused on such offline evaluations.
Mosaic LSM’s behavior in PILPS 2c (a study based in the Red-Arkansas
River Basin).
Forcing data covering several years
for each of 61 1o X 1o grid cells in the
Red Arkansas Basin were provided to
participants.
model
obs
The resulting mean seasonal cycle
of runoff was compared to observations. In this particular test, the
Mosaic LSM did quite well.
Parameter values make a difference!
In a recent study, it was found that the apparently poor behavior of the
Mosaic LSM in an offline study using Oklahoma measurements was
associated with an inaccurate setting of the ground heat capacity.
Ground heating rates improve when
the right heat capacity is used.
Ground
heating rates
for mosaic
are way off,
throwing off
the other
fluxes.
Robock et al., JGR, 108, D22, 8846, doi:10.1029/2002JD003245, 2003.
Coupled System Analysis
Analysis, using Mosaic LSM, of what makes a SVAT model act differently
from the standard Bucket model...
Sensitivity test: Addition of vapor pressure deficit stress
Precipitation differences:
with VPD stress minus w/o VPD stress
Reduced humidity leads
to higher VPD stress
Inclusion of vapor pressure
deficit stress leads to large
decreases in rainfall in some
regions. Why? “Stomatal
suicide” -- a serious positive
feedback in the coupled
system:
Higher VPD stress leads
to reduced evaporation
Reduced evaporation
leads to reduced humidity
Reduced evaporation
leads to precipitation
Sensitivity tests:
Removal of temperature stress;
removal of interception loss
mechanism
Precipitation differences:
“with temperature stress” minus
“w/o temperature stress”
Precipitation (top) and evaporation
(bottom) differences:
“with interception loss allowed” minus
“w/o interception loss allowed”
Sensitivity tests:
Coupling strategy
A simulation was performed with
a “pseudo-bucket”, one that used
a “bucket-style” coupling to the
atmosphere but was carefully
controlled to reproduce the
Mosaic LSM’s long-term surface
energy budget in offline
simulations. In the plots, large
differences are seen in simulated
evaporation and precipitation
rates. These differences result
strictly from feedbacks between
the land and the atmosphere.
Precipitation (top) and evaporation
(bottom) differences:
“standard formulation” minus “buckettype formulation”
Main conclusions from coupled sensitivity analysis
(Koster and Suarez, Advances in Water Resources, 17, 61-78, 1994.)
1. Of the environmental stresses that increase canopy resistance,
-- temperature stress is not significant
-- vapor pressure deficit stress is significant, partly due to feedback.
2. Of the main differences between the two model types, the presence of the
interception reservoir in the SVAT model has the largest effect on
evaporation rates.
3. The incorporation of a bucket model structure appears to have an effect on
precipitation rates in the tropics and subtropics, perhaps due to the
damping of diunal and synoptic-scale variability in land surface control.
The differences, in any case, reflect land-atmosphere feedback.
(In a later study, with the same LSM but a modified AGCM, the impact
on the general circulation was found to be reduced.)
COMPUTER LAB: RUNNING A LAND SURFACE MODEL
This model is designed to simulate a tropical forest’s response to prescribed atmospheric
forcing over a repeated full seasonal cycle. The relevant files are:
Model: gm_model.f (Includes driver; written in FORTRAN.)
Forcing file: TRF.DAT.30 (Includes rainfall rates, radiation forcing, etc., at a 30 minute time
step over a full annual cycle. Model automatically interpolates to a 5 minute time step.)
Initialization file: input/lsm_input.dat (Includes parameter values to change for class
experiments.)
How to run the model:
1. Create input and output directories below the current directory. (This assumes a UNIX
system.)
2. Place lsm_input.dat in the input directory.
3. Find a directory that can comfortably hold trf.dat.30.diur (1.4 Mb)
4. Compile the program gm_model.f
5. Modify the model parameters in lsm_input.dat as appropriate.
6. Run the program.
7. Four output files will be produced in the output directory:
mosaic.trf.mon.xxxx (4.5 Kb)
mosaic.trf.dat.xxxx (388 Kb)
mosaic.trf.tra.xxxx (12.9 Kb for 3-year run)
mosaic.trf.123.xxxx (291 Kb)
where xxxx is the label for the particular experiment.
8. For new experiments, start at instruction 5.
INPUT FILE:
/land/koster/pilps/TRF.DAT.30
This is the forcing data: modify path as necessary.
VEGETATION IDENTIFIER:
trf
Leave as is
EXPERIMENT IDENTIFIER:
gp7 By changing this according to your own system of codes, you control the labeling of the output files of different experiments.
TIME STEPS T.S. LENGTH DIAGS 1ST FORCING ALAT
534529
300.
2880
0
-3.
534529 = (365x3 + 31) x 24 x 12 + 1
= # of time steps in 3 years + 1 January + 1 time step.
300 = number of seconds in the 5 minute time step.
DIAGS, 1ST FORCING, ALAT do not need to be changed.
NUMBER OF TILES:
1
TYPE FRACTION
1
1.0
Type 1 = tropical forest
Fraction = 1 means a homogeneous cover
INITIALIZATION:
TC TD TA TM
300.0 300.0 300.0 300.0
TC = Initial canopy temperature
TD = Initial deep soil temperature
TA = Initial near-surface atmospheric temperature
TM = Initial assumed first forcing temperature
WWW(1) WWW(2) WWW(3) CAPAC SNOW
0.5000
0.5000
0.5000
0.5
0.
WWW(i) = Initial degree of saturation in soil layer i
CAPAC = Initial fraction of interception reservoir filled
SNOW = Initial snow amount
EXPERIMENT 1
HEAT CAPACITY WATER CAPACITY FACTOR TURBULENCE FLAG
70000.
1.
0
Heat capacity is in J/oK.
If water capacity factor is 0.5, then the default capacity is halved; if it is 2, then the default capacity is doubled, etc.
Turbulence flag: you won’t need this.
EXPERIMENT 2
INTERCEPTION PARAMETER PRECIP. FACTOR
1.
1.
Interception parameter: you won’t need this.
Precip. factor: factor by which to multiply all precipitation forcing.
EXPERIMENT 3
ALBFIX RGHFIX STOFIX
0
0
0
ALBFIX: If this is 1, you are using tropical forest albedo.
RGHFIX: If this is 1, you are using tropical forest roughness heights
STOFIX: If this is 1, you are using tropical forest water holding capacities.
EXPERIMENT 4
FRAC. WET PRCP CORRELATION
0.3
0.
FRAC. WET: The assumed fractional coverage of a storm; equivalent here to the assumed probability that a rainfall
event will be applied to the land surface model.
PRCP CORRELATION: Imposed time-step-to-time-step autocorrelation of precipitation events.
EXPERIMENT 1: CHANGE IN MODEL PARAMETERS
Background:
The heat capacity of the soil surface has an important effect on the land surface model’s surface
energy budget calculations. Presumably, the higher the heat capacity, the more slowly the surface
temperature will change under a given forcing, leading to a smaller amplitude of the diurnal
temperature cycle. This could have profound effects on the annual energy balance.
The water holding capacity of the soil has an important effect on the annual water
balance and thus on the annual energy balance. A larger water holding capacity, for example,
means that high precipitation rates in the spring can more easily lead to high evaporation rates
during a subsequent dry summer.
Possible experiments:
.Modify the heat capacity. You may have to modify it by an order of magnitude or so
to see significant effect on the energy budget terms.
.Modify the water capacity factor. For starters, try 0.5 and 2.
Questions to answer (choose 1)
1. How does varying the heat capacity affect the diurnal energy balance, in particular
the amplitude of the diurnal temperature cycle? How large does the change have to be
to see an effect? Is the effect in the expected direction?
2. How does varying the heat capacity affect the annual energy balance?
3. How does varying the water holding capacity affect the diurnal and annual energy
and water budgets? Does a higher capacity imply a larger annual evaporation?
EXPERIMENT 2: CHANGE IN MODEL INITIALIZATION
Background:
All models require a “spin-up” period to remove the effects of initialization. In other words, the initial conditions
imposed in a model may be inconsistent with the preferred model state, and this inconsistency may lead to energy
and water budget terms that are unrealistic – they reflect the inappropriate initial conditions imposed rather than the
model parameterizations or the atmospheric forcing. The length of the spin-up period is a function of the model (in
particular its heat and moisture capacities) and the forcing.
Possible experiments:
Initialize the soil moisture reservoirs to complete saturation: set WWW(1), WWW(2), and WWW(3) to 1.
Initialize the soil moisture reservoirs to be completely dry: set WWW(1), WWW(2), and WWW(3) to 0.0001.
Initialize the soil moisture reservoirs to be completely dry, and double the water holding capacity: set WWW(1),
WWW(2), and WWW(3) to 0.0001, and set the “water capacity factor” (from experiment 1) to 2. Complete
drydown. Set WWW(1), WWW(2), and WWW(3) to 1, and set the “precip. factor” to 0. (This turns off all
precipitation.)
Note: for these experiments, you may want to increase the number of time steps. (You won’t know if you need to
until you run them.) If n is the number of years you want the model to run, set the # of time steps to
[(365*n)+31)]*24*12+1.
Questions to answer (Choose 1):
1. How does the transient model response differ in the drydown and wet-up simulations (1 & 2)?
2. How does doubling the water holding capacity affect the wet-up period?
3. How long does complete drydown take (simulation 4)? Is equilibrium ever really achieved? Can you define a
time scale for the drydown?
EXPERIMENT 3: CHANGE IN MODEL BOUNDARY CONDITIONS
Background:
GCM deforestation experiments have examined how replacing the Amazon’s forest with grassland can affect the
regional climate. In a land surface model, forest and grassland are distinguished from each other only by the values
used for various parameters. The experiments below examine “deforestation” in an offline environment. (Of course,
deforestation effects in a fully coupled GCM environment may be different.)
Possible experiments:
.Perform a control simulation, using TYPE =1 (tropical forest).
.Replace the tropical forest with grassland: set TYPE=4.
.Replace the tropical forest with grassland, but maintain tropical forest albedo: set TYPE=4 and
ALBFIX=1.
.Replace the tropical forest with grassland, but maintain tropical forest roughness: set TYPE=4 and
RGHFIX=1.
.Replace the tropical forest with grassland, but maintain tropical forest water holding capacity: set
TYPE=4 and STOFIX=1.
.Replace the tropical forest with grassland, but maintain tropical forest albedo, surface roughness, and
water holding capacity: set TYPE=4, ALBFIX=1, RGHFIX=1, and STOFIX=1.
Questions to answer (choose 1)
1. What is the effect of deforestation on the annual energy and water budget? What effect does it have
on diurnal cycles?
2. How do albedo change, roughness change, and storage change contribute to the tropical forest /
grassland differences? Which effect is largest?
3. Are the impacts of albedo change, roughness change, and storage change linear? E.g., do the changes
induced by these three parameters alone add up to the changes seen in simulation 6?
EXPERIMENT 4: CHANGE IN MODEL FORCING
Background:
The precipitation forcing, which comes from a GCM, need not be assumed to fall uniformly within the GCM’s
grid cell area. If the typical areal storm coverage is, say, only half the grid cell’s area, then one can consider
an alternative interpretation: that whenever the GCM provides precipitation for a grid cell, the probability that
it occurs at a given point within the cell is ½, and when it does occur there, the GCM’s precipitation intensity
is doubled. A further consideration is the temporal autocorrelation of storm events, i.e., the probability that a
point gets wet during one time step given that it was wetted in the previous time step.
Possible experiments:
.Perform a control simulation.
.Perform simulations that assume a fractional storm coverage of ranging from .1 to .9 (i.e., set
FRAC. WET = x, where x ranges from .1 to .9).
.Perform simulations that assume a fractional storm coverage of .1 and a time step to time step
autocorrelation that ranges from .1 to .9. (i.e., set FRAC. WET=0.5 and PRCP
CORRELATION=x, where x ranges from .1 to .9).
Questions to answer (Choose 1):
1. How does runoff ratio (runoff / precipitation) change with the assumed fractional coverage?
2. How do runoff ratios change when temporal autocorrelations are included?