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ang
Land Surface Modeling:
Past, Present and Future
Zong-Liang Yang
512-471-3824
liang@jsg.utexas.edu
http://www.geo.utexas.edu/climate
Department of Geological Sciences
Jackson School of Geosciences
1/3/2011
ang
Outline
• Introduction
• Land Surface Processes
• Land Surface Modeling
• Example 1: NCAR Community Land Model (CLM)
• Example 2: Noah LSM
• Summary
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Why Land
• Land research has direct societal relevance.
• Land provides us food, clothing, shelter, and infrastructure.
• Land is at the central stage for extreme weather and climate events.
• Land research is cross-disciplinary, and land processes cover all time and space scales.
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-Biogeochemistry
-Genetic bank
-Water
-Air
-Institutions
-Culture
-Technology
-Population
-Economic
LANDCOVER [Biophysically controlled]
Ecosystem goods & services
-clean air/water
-waste recycling
-food/fibre/fuel
-recreation
Ecological Problems
-pollution
-diseases
-food/fibre/fuel shortages
-overcrowding
DYNAMIC GLOBAL LAND TRANSITIONS
HUMAN DECISION MAKING
political/economic choices
Human
Systems
Ecological
Systems
LANDUSE [Human control]
Economic Problems
-poverty
-unequal wealth
-war
-globalization
Running 2006 4
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How do Human Activities Contribute to Climate Change and How do
They Compare with Natural Influences?
Foley et al. 2005
IPCC 2007
5
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BATS, SiB, …
6
PILPS
CLM
Land is an important component in weather and
climate models
Modified from IPCC 2007
Bucket
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What Are Land Surface Processes?
• Exchange processes with the atmosphere – Momentum
– Energy (reflected shortwave, emitted longwave, latent/sensible heat)
– Water (precipitation, evapotranspiration)
– Trace gases (CO2)/dusts/aerosols/pollutants
• Exchange processes with the ocean – Fresh water
– Sediments/nutrients
– Salinity
• Land-memory processes – Topography
– Snow/ice cover
– Soil moisture
– Vegetation
• Human activities – Land use (agriculture, afforestation, deforestation, urbanization,…)
– Land air/water pollution
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Do Land Surface Processes Matter to Climate Prediction?
Observed transient soil
moisture anomalies
can be more important
to accurately predict
mid-continental
summertime extreme
rainfalls (in USA) than
sea surface
temperatures
(Entekhabi et al.,
1999).
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Land–Atmosphere Coupling Strength
The greatest land–
atmosphere coupling appears
to lie in arid-to-humid
transition zones, where soil
moisture anomalies strongly
influence precipitation
anomalies (Koster et al.,
Science, 2004).
The profile of soil moisture can be determined by the water table position (e.g., Levine and Salvucci, 1999).
Shallow groundwater table sustains surface vegetation, especially during drought (e.g., York et al., 2002).
Kim and Wang (2007) found
that soil moisture-induced
precipitation increase is
enhanced under wet summer
when vegetation phenology is
included in their model,
consistent with the findings of
others
(e.g., Dickinson and Henderson-Sellers, 1988; Hoffmann et al,. 2000; Matsui et al., 2005; Xue et al. 2006).
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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., soil texture, surface roughness, albedo,
emissivity, vegetation type, cover extent, leaf area index, and seasonality)
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What Are Land Surface Processes
• Land surface processes function as
– lower boundary condition in Atmospheric Models
• Atmospheric Boundary Layer Simulation Climate Simulation
• Numerical Weather Prediction 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
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Land Surface Models (LSMs)
• Computer code describing land surface processes (also called
LSSs, LSPs, SVATs)
– FORTRAN, C, C++, … ...
– 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)
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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
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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/Groundwater (cold region hydrology)
• Runoff generation/River routing (landscape to coasts)
• “Greening” of LSMs (carbon/nitrogen balance and vegetation dynamics)
• Dusts/BVOCs/Secondary organic aerosols
• Human activities (agriculture, irrigation, urbanization, LULC)
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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
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Simple “Bucket” LSMs • “Bucket” Models” first in hydrological models, later in GCMs
– “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
• In original “bare soil” form, e.g., Manabe (1969) include no vegetation so
specify (fixed) parameter values for
– 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
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a.k.a.1st Generation LSMs
Pitman (2003);
also Figs. 18.12,
25.1 in Bonan
(2008)
The lowest
model level
in the host
atmospheric
model,
typically ~50
m above the
surface.
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Water and energy balance of a vegetated, soil-
covered land surface
• For some Δt (e.g day or month) on a unit area of land, the mass balance equation for water is
• SM is the water content of the soil
• Units are [m3/(m2xt)] or
depth/time (e.g., m/mo)
Soil
Recharge
Delayed flow
Quickflow
P
E Rnet
Advection of sensible heat (H)
Ground water
t
SMeargchReQuickflowEP
Dozer and Dunn (2007)
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Soil texture
affects water
holding capacity
Difference between
field capacity and
wilting point is the
“available water-
holding capacity”
(AWC)
Dozer and Dunn (2007)
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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 “pre-
processing”, one calculation, prior to actual model run
• Five components of radiation exchange considered in BATS and SiB
– 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)
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a.k.a. 2nd Generation LSMs
Pitman (2003); also
Figs. 18.12, 25.2 in
Bonan (2008); note rb
is defined differently
from that in Bonan,
p. 231
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Micrometeorological Model-Based LSMs • “Two-Stream Approximation” are used to calculate solar radiation exchange
in SiB, CLM for both PAR and NIR, diffuse and direct beam components from
• 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 or modified two-stream by Yang and Friedl (2003) and Niu and Yang (2004)
• BATS prescribes vegetation albedo; 4-layer model for stomata
area leafunit depth / optical beamdirect K
area leafunit depth / optical inverse average
nts"photoeleme"for tscoefficien scattering
beamfor parametersscatter"on "direct / diffuse ,
components beam diffuse downward / upward normalized I ,I
scatter beamdirect scatter back scatter forward divergence
e)1(K I I])1(1[ dL
dI
eK I I])1(1[ dL
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0
KL0
KL0
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Micrometeorological Model-Based LSMs
• Thermal Radiation from surface is computed from “Stefan-Boltzmann” law:
• 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
– prescribed for BATS (and most other models); aerodynamic resistances
from curve-fitting functions, iteration for nonneutral corrections
– calculated in SiB: pre-processed from canopy morphology using a “K-
Theory” equivalent of Shaw-Pereira (1982); aerodynamic resistances from canopy
morphology using “K-Theory”, iteration for nonneutral corrections
re temperatusurface defined"ely appropriat"an is T where
T L
s
4sout
:d and z0
:d and z0
Courtesy of W.J. Shuttleworth (1998)
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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:
• 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.
function" stress" moisture soil - g
function" stress" re temperatu- g
function" stress"deficit pressure vapor - g
function" stress" radiation - g
factorcover canopy - g
ion typeon vegetat dependingconstant - g
ms e,conductanc stomatal leaf - g
sm ,resistance stomatal leaf - r
g g g g gg gr
1
M
T
D
R
C
0
1-s
1-s
M TDRC 0s
s
Courtesy of W.J. Shuttleworth (1998)
ang
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
– 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
– in BATS, was introduced only when the atmospheric demand exceeds the water
supply from the soil through roots
– 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
– 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
Mg
Mg
Tg
Tg
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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
EquationMonteith -Penman canopy)-(whole
r
r1
r
Dc)GR(
E
rate ionprecipitat is P where SC when,PD
rate drainagecanopy is D re whe SC when,0D
equation balanceer canopy wat DE )S/C(fP )LAI,F(gdt
dC
E f(C/S) E )]S/C(f1[
Rain dIntercepteE
ionTranspiratE
TotalE
a
ST
a
pa
n
0rPM
0rPM
finiterPM
ST
STST
Courtesy of W.J. Shuttleworth (1998)
ang
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
Courtesy of W.J. Shuttleworth (1998)
ang
“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.
– Noah LSM simple canopy model
– French ISBA simple soil model
– Simple SiB (SSiB) Simplified empirical parameterization fitted to time
consuming, SiB-calculated functions
– “Super Bucket” basic bucket, with “reference crop” estimates instead of
“potential” rate(+Several others)
• Permissible, practical alternative, pending evidence of need for more complex
micrometeorological models
Courtesy of W.J. Shuttleworth (1998)
ang
The Greening of LSMs (1) • New developments: improved plant 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
OH and CO of iesdiffusivitdifferent for account factor to - 1.6
)s m CO (mol COfor econductanclayer boundary - g
leaf theof spacesair lar intercellu in the (mol/mol)ion concentrat CO - C
surface leaf at the (mol/mol)ion concentrat CO - C
atmosphere in the (mol/mol)ion concentrat CO - C
s m CO mol n,respiratio emaintenanc leaf -R
s m CO mol rate,on assimilati leafnet - A
6.1
g)CC(g )CC(RA A
s m CO mol leaf,unit per esisphotosynth of rate limited-nutilizatio phosphate triose- J
s m CO mol leaf,unit per esisphotosynth of rate limited-Rubisco - J
s m CO mol leaf,unit per esisphotosynth of rate limited-light - J
s m CO mol leaf,unit per rate esisphotosynth gross - A
)J ,J ,J( minA
22
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ang
• New developments: improved plant 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
– Other forms: Leuning (1995), Foley et al. (1996), Dickinson (1998): using vapor
pressure deficit instead of relative humidity
[Pa] p
[Pa] por surface, leaf at the (mol/mol)ion concentrat CO - C
s m CO mol rate,on assimilati leafnet - A
surface leafon humidity relative - h
lyrespective 0.01, and 9 constants - b,m
bhC
A m g
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Pain VPD (1998); al.et Dickinson in as VPD) 0.05(1 as written - D/D
(mol/mol) valuereference - D
(mol/mol)air theand leaf ebetween th differencefraction moleor water vap- D
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The Greening of LSMs (2)
ang
• New developments: improved plant 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
The Greening of LSMs (3)
ang
• New developments: 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 (or dynamic phenology) 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
The Greening of LSMs (4)
ang
a.k.a. 3rd Generation LSMs
Pitman (2003); also
Figs. 16.2, Eqn
(17.2), Section 17.8,
Section 25.2.3 in
Bonan (2008); note
rb is defined
differently from that
in Bonan, p. 231
ang
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 (land cover types, LAI, albedo,
PAR, roughness length, snow cover fraction, soil moisture, terrestrial
water storage change, …)
• Single Biome Calibration
– Needs:
• sets of continuous “driving variables” for whole years
(frequently-sampled, 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, LBA)
• Prairie grass (FIFE)
• Boreal forest (BOREAS)
• Sonora Desert, Tucson, Arizona (Example of multi-objective
calibration)
• … …
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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 Phase I (1990-2002), Phase II (2003-2013) and CEOP
– Surface observations in China
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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
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Two schools of thoughts in LSM development and evaluation
Atmospheric Forcing
Model Structure
Augments (gw, dv, …)
Model Evaluation Pyramid
Land Surface Model (CLM,
Noah, Vic, …)
LSM developers consider
1. Increasing realism in representing key processes
2. Understanding feedbacks and interactions
3. Maintaining synergism between LSM and other modules in the host GCM
4. Aiming for past, present, and future climate applications
5. Generalizing parameterizations across sites
LSM evaluators consider
1. Uncertainty in many subsurface parameters and other non-measurable parameters
2. Uncertainty in atmospheric forcing and observations used for evaluation
3. Calibration of the parameters for the augmented part only or for the entire LSM
4. Evaluation in all dimensions
5. Equifinality?
LSM developers do not use automated, sophisticated
evaluation tools.
LSM evaluators calibrate/evaluate LSMs
that already exist.
How Can We Use Sophisticated Evaluation Methods To
Guide LSM Development?
40
ang
A New Approach to Evaluating LSMs:
Ensemble Methods
1) Gulden, L.E., E. Rosero, Z.-L. Yang, et al., 2008: Model performance, model robustness, and model fitness scores: A new method for
identifying good land-surface models, Geophys. Res. Lett., 35, L11404, doi:10.1029/2008GL033721.
2) Rosero, E., Z.-L. Yang, et al., 2009: Evaluating enhanced hydrological representations in Noah-LSM over transition zones:
Implications for model development, J. Hydrometeorology, 10, 600-622. DOI:10.1175/2009JHM1029.1
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Summary of Recent Developments
(2000–2010) • Land in earth system modeling
• Improved understanding in physical hydrology – Snow albedo, snow cover, and snow thermal/hydrologic processes
– Topographic controls on soil moisture/runoff
– Groundwater hydrology
– Terrain routing and river flow hydraulics (flood modeling)
• Enhanced linkage with biogeochemistry/ecology – Carbon and nitrogen (soil chemistry, BVOC, secondary organic aerosols)
– Vegetation phenology/species competition
– Dust emissions/aerosols
• Land use and land cover change – Urban canopy
– Air quality
– Water quality
• Advanced techniques for using remotely-sensed land parameters from multiple sensors (AVHRR, MODIS, AMSR, GRACE…)
– Various assimilation methods (EnKF, …)
• Extended in situ field datasets and ensemble model calibration/evaluation
• Seasonal, interannual, and decadal predictions of climate and other environmental variables
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NCAR Community Land Model (CLM4) for
Climate Models in 2010
Co-Chairs: David Lawrence (NCAR), Zong-Liang Yang (Univ of Texas at Austin)
ang
44
CLM4 • Evolved from CLM3.5 (released in 2008). CLM3.5 improves over
CLM3 (released in 2004)
Surface runoff (Niu, Yang et al., 2005)
Groundwater (Niu, Yang, et al., 2007)
Frozen soil (Niu and Yang, 2006)
Canopy integration, canopy interception scaling, and pft-dependency of the soil stress function
• CLM4 (released in 2010) improves over CLM3.5 Prognostic in carbon and nitrogen (CN) as well as vegetation phenology; the dynamic
global vegetation model is merged with CN
Transient landcover and land use change capability
Urban component
BVOC component (MEGAN2)
Dust emissions
Updated hydrology and ground evaporation
New density-based snow cover fraction, snow burial fraction, snow compaction
Improved permafrost scheme: organic soils, 50-m depth (5 bedrock layers)
Conserving global energy by separating river discharge into liquid and ice water streams
Co-Chairs: David Lawrence (NCAR), Zong-Liang Yang (University of Texas at Austin)
ang
NCAR CLM 3.5/4.0
Niu, Yang, et al., 2007 Niu, Yang, et al., 2005
Yang et al., 1997, 1999
Niu & Yang, 2004, 2006
Yang & Niu, 2003
Collaborators: UT (Yang, Niu, Dickinson), NCAR (Bonan, Oleson, Lawrence) and others
2008 NCAR CCSM
Distinguished Achievement
Award
45
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Subgrid Land Features in CLM4
Oleson et al. (2010)
Land is highly heterogeneous.
Representing sub-grid-scale land
features has been a challenge.
Methods: a) mosaic or tiled
approaches (see left); b) fine-mesh
approaches (retaining geographic
positions); c) aggregated parameters;
d) statistical distributions of
parameters.
CLM4 uses a hierarchy of three sub-
grid levels. 1) A land grid cell has up to
five landunits. 2) A landunit has ≥ 1
columns (e.g., 5L snow/15L soil/rock
columns). 3) A column has up to 16
pfts + 1 bare ground.
Landunits = glacier, lake, wetland,
vegetated (all having a single soil
column) and urban (5 columns).
Vegetated landunit = natural +
managed (irrigated, non-irrigated).
ang
Hydrology in CLM4
Oleson et al. (2010)
Soil hydrology: 10L solving Richards
equation + aquifer; soil temperature
10L + 5L bedrock solving soil heat
conduction equation;
Layer thickness exponentially
increases with depth, 0.018 m at the
surface to 13.9 m for layer 15
Soil properties:
Thermal capacity
Thermal conductivity
Porosity
Saturated hydraulic conductivity
Saturated soil matric potential
Clapp-Hornberger exponent B
Soil parameters (datasets):
%sand, %clay (5-minute, each layer)
Soil organic matter density (1-degree)
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48
Model Validation
• Local scale (e.g., comparison with flux tower data)
• Regional scale (e.g., comparison with gridded surface datasets from
stations and flux networks and satellite datasets)
• Global scale (comparison with satellite and other gridded datasets)
• Offline model evaluations (standalone, detached from the host
atmospheric model): useful to assess the realism of LSMs, improve
parameterizations, and develop new methods
• Coupled model evaluations (comprehensive): useful to study land–
atmosphere interactions and feedbacks, evaluate sensitivity to
perturbations (e.g., land use and land cover change), and sort out cause–
effects
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Lawrence et al., 2010
Stöckli, R., D. M. Lawrence, G.-Y. Niu, K. W. Oleson, P. E. Thornton, Z.-L. Yang, G. B. Bonan, A. S. Denning, and
S. W. Running, 2008: Use of FLUXNET in the Community Land Model development, J. Geophys. Res.,113,
G01025, doi:10.1029/2007JG000562.
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Tower flux statistics (15 sites, hourly)
Latent Heat Flux Sensible Heat Flux
r RMSE (W/m2)
r RMSE (W/m2)
CLM3 0.54 72 0.73 91
CLM3.5 0.80 50 0.79 65
CLM4SP 0.80 48 0.84 58
Lawrence et al., 2011
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Abracos tower site (Amazon)
CLM3
Latent Heat Flux
OBS
Model
CLM4SP CLM3
CLM4SP
Latent Heat Flux
OBS
Model
Total soil water
Lawrence et al., 2011
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River Discharge River flow at outlet
Top 50 rivers (km3 yr-1) Annual discharge into
Global ocean
Accum
ula
ted d
ischarg
e f
rom
90 o
N (
10
6 m
3 s
-1)
CLM3: r = 0.86 CLM3.5: r = 0.87 CLM4SP: r = 0.94 CLM4CN: r = 0.77
CLM4SP CLM4CN Obs CLM4CN CLM4SP CLM3.5
Lawrence et al., 2011
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Improved
hydrological
schemes in
CLM3.5
Oleson, K. W., G.-Y. Niu, Z.-L. Yang, D. M. Lawrence, P. E. Thornton, P. J. Lawrence, R. Stöckli, R. E. Dickinson, G. B. Bonan, S. Levis, A.
Dai, and T. Qian, 2008: Improvements to the Community Land Model and their impact on the hydrological cycle, J. Geophys. Res., 113,
G01021, doi:10.1029/2007JG000563.
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Terrestrial Water Storage Change
GRACE (MAM – SON)
CCSM4 (MAM – SON) (Fully coupled global land, atmosphere,
ocean, ice climate model)
CCSM3 (MAM – SON)
Gent et al., 2011
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A Simple Groundwater Model (SIMGM)
bot
botbota
zz
zzKQ
)(
Water storage in an unconfined aquifer:
)1(bot
bota
zzK
sba RQ
dt
dW ya SWz /
Recharge Rate:
Modified to consider macropore effects: Cmic * ψbot Cmic fraction of micropore content
0.0 – 1.0 (0.0 ~ free drainage)
Niu, Yang et al. (2011)
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Does including dynamic vegetation phenology and
water table in a climate model improve seasonal
precipitation forecasts?
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66
WRF Simulated & Observed Monthly and Seasonal Mean
Precipitation in Central Great Plains
The WRF model with a dynamic vegetation growth (DV) improves, over the DEFAULT, the rainfall simulation in Central Great Plains in the USA, i.e., the transition zones between arid/semi-arid and humid regions, especially in July, August, and the entire summertime (JJA).
Further consideration of the dynamic water table (DVGW) improves the simulation even more.
These improvements are due to the improved coupling between soil moisture and precipitation through lowered lifting condensation level (see next slide).
This study suggests incorporating vegetation and groundwater dynamics into a regional climate model would be beneficial for seasonal precipitation forecast in the transition zones.
Jiang, X., G.-Y. Niu, and Z.-L. Yang, 2009: Impacts of vegetation and groundwater dynamics on warm season precipitation
over the Central United States, J. Geophys. Res., 114, D06109, doi:10.1029/2008JD010756.
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Lifting condensation level (LCL) height versus soil
moisture index (SMI) in the soil layers
Jiang, X., G.-Y. Niu, and Z.-L. Yang, 2009: Impacts of vegetation and groundwater dynamics on warm
season precipitation over the Central United States, J. Geophys. Res., 114, D06109,
doi:10.1029/2008JD010756.
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Noah LSM with multi-physics options
1. Leaf area index (prescribed; predicted) 2. Turbulent transfer (Noah; NCAR LSM) 3. Soil moisture stress factor for transpiration (Noah; BATS; CLM) 4. Canopy stomatal resistance (Jarvis; Ball-Berry) 5. Snow surface albedo (BATS; CLASS) 6. Frozen soil permeability (Noah; Niu and Yang, 2006) 7. Supercooled liquid water (Noah; Niu and Yang, 2006) 8. Radiation transfer: Modified two-stream: Gap = F (3D structure; solar zenith angle;
...) ≤ 1-GVF Two-stream applied to the entire grid cell: Gap = 0 Two-stream applied to fractional vegetated area: Gap = 1-GVF 9. Partitioning of precipitation to snowfall and rainfall (CLM; Noah) 10. Runoff and groundwater: TOPMODEL with groundwater TOPMODEL with an equilibrium water table (Chen&Kumar,2001) Original Noah scheme BATS surface runoff and free drainage More to be added
Niu, Yang, et al. (2011)
Collaborators: UT, NCAR, NCEP/NOAA, and others
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Maximum # of Combinations 1. Leaf area index (prescribed; predicted) 2 2. Turbulent transfer (Noah; NCAR LSM) 2 3. Soil moisture stress factor for transp. (Noah; BATS; CLM) 3 4. Canopy stomatal resistance (Jarvis; Ball-Berry) 2 5. Snow surface albedo (BATS; CLASS) 2 6. Frozen soil permeability (Noah; Niu and Yang, 2006) 2 7. Supercooled liquid water (Noah; Niu and Yang, 2006) 2 8. Radiation transfer: 3 Modified two-stream: Gap = F (3D structure; solar zenith angle;
...) ≤ 1-GVF Two-stream applied to the entire grid cell: Gap = 0 Two-stream applied to fractional vegetated area: Gap = 1-GVF 9. Partitioning of precipitation to snow- and rainfall (CLM; Noah) 2 10. Runoff and groundwater: 4 TOPMODEL with groundwater TOPMODEL with an equilibrium water table (Chen&Kumar,2001) Original Noah scheme BATS surface runoff and free drainage
2x2x3x2x2x2x2x3x2x4 = 4608 combinations
Process understanding, probabilistic forecasting, quantifying uncertainties
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71
36 Ensemble Experiments
1. Runoff scheme is shown as the dominant player in the SM-ET relationship: SIMTOP (bottom sealed; green) produces the wettest soil and greatest ET; BATS (greatest surface runoff: grey) produces the driest soil and smallest ET.
2. Runoff scheme plays as a provider of soil water (besides precipitation) while surface schemes plays as a “consumer” of soil water.
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Traditionally, land surface modeling
• treats land as a lower boundary condition in weather and climate models;
• determines the coupling strength and land–atmosphere interactions and feedbacks;
• calculates, in both coupled and offline modes, evapotranspiration (ET), other fluxes (sensible heat, reflected solar radiation , upward longwave radiation, runoff), and state variables (soil moisture, snow water equivalent, soil temperature).
Driven by IPCC and hydrologic/environmental applications, land surface models
• have evolved greatly in the past three decades;
• are becoming more complex as we are facing the emerging need to – understand climate variability and change on all time/space scales,
– quantify the climatic impacts on energy/water resources and environmental conditions for decision making.
• demand cross-cutting efforts from multi-disciplinary groups.
Summary
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Thank you!
Prof. Zong-Liang Yang
+1-512-471-3824
liang@jsg.utexas.edu
http://www.geo.utexas.edu/climate
Major References
Yang, Z.-L., 2004: Modeling land surface processes in short-term weather and climate studies, in Observations, Theory, and
Modeling of Atmospheric Variability, (ed. X. Zhu), World Scientific Series on Meteorology of East Asia, Vol. 3, World Scientific
Publishing Corporation, Singapore, 288-313.
Yang, Z.-L., 2008: Description of recent snow models, in Snow and Climate, Edited by R. L. Armstrong and E. Martin, Cambridge
University Press, 129-136.
Yang, Z.-L., 2010: Global Land Atmosphere Interaction Dynamics, Graduate Course, The University of Texas at Austin,
http://www.geo.utexas.edu/courses/387H/SyllabusLAID.htm
Other citations can be found at http://www.geo.utexas.edu/climate/recent_publications.html
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