This document discusses land surface modeling in weather and climate models. It describes the role of land surface models in providing surface boundary conditions, such as heat and water fluxes. It outlines the requirements for land models, including atmospheric forcing data, appropriate physical representations, land cover and soil data, and initial land states. It then covers applications for weather and climate modeling, validation of land models, ongoing work to improve them, and their expanding role within Earth system modeling.

1. Land Surface in Weather and
Climate Models
“IV WorkEta: Esquema de superfície”
Michael Ek
(michael.ek@noaa.gov)
Environmental Modeling Center (EMC)
National Centers for Environmental Prediction (NCEP)
NOAA Center for Weather and Climate Prediction (NCWCP)
5830 University Research Court
College Park, MD 20740
National Weather Service (NWS)
National Oceanic and Atmospheric Administration (NOAA)
IV WorkEta – Workshop em Modelagem Numérica de Tempo e Clima em
Mesoescala utilizando o Modelo Eta: Aspectos Físicos e Numéricos
CPTEC/INPE, Cachoeira Paulista, São Paulo, 3-8 de Março de 20131

2. NOAA Center for Weather and Climate
Prediction (NCWCP), College Park, Maryland, USA
2
World Weather Building

3. www.noaa.gov
www.nws.noaa.gov
Development, upgrade,
transition, maintain models
www.ncep.noaa.gov
www.emc.ncep.noaa.gov

4. www.wmo.int
“www.wmo.int “WWRP”
“…improve accuracy, lead time and
utilization of weather prediction.”
www.wcrp-climate.org
“Determine the predictability of climate
and effect of human activities on climate.”
“Weather, Water,
Climate”
www.gewex.org

5. Outline
• Role of Land Surface Models (LSMs)
• Requirements:
- atmospheric forcing, valid physics, land data sets/
parameters, initial land states, cycled land states
• Applications for Weather & Climate
• Validation
• Improving LSMs
• Expanding role of Land Modeling as part of
an integrated Earth System
• Summary
5

6. Outline
• Role of Land Surface Models (LSMs)
• Requirements:
- atmospheric forcing, valid physics, land data sets/
parameters, initial land states, cycled land states
• Applications for Weather & Climate
• Validation
• Improving LSMs
• Expanding role of Land Modeling as part of
an integrated Earth System
• Summary
6

7. Role of Land Models
• Traditionally, from a coupled (atmosphere-ocean-
land-ice) Numerical Weather Prediction (NWP)
and climate modeling perspective, a land-
surface model provides quantities to a parent
atmospheric model:
- Surface sensible heat flux,
- Surface latent heat flux (evapotranspiration)
- Upward longwave radiation
(or skin temperature and surface emissivity),
- Upward (reflected) shortwave radiation
(or surface albedo, including snow effects),
- Surface momentum exchange.
7

8. Weather & Climate a “Seamless Suite”
• Products and models are integrated & consistent throughout
time & space, as well as across forecast application & domain.
Land Static vegetation, Dynamic Dynamic
e.g. climatology vegetation, ecosystems,
modeling or realtime e.g. plant e.g. changing 8
example: observations growth land cover

9. Atmospheric Energy Budget
…to close the surface energy budget, & provide surface
boundary conditions to NWP and climate models.
seasonal storage
9

10. Water Budget (Hydrological Cycle)
• Land models close the surface water budget, and
provide surface boundary conditions to models.
10

11. History of Land Modeling (e.g. at NCEP)
– 1960s (6-Layer PE model): land surface ignored
• Aside from terrain height and surface friction effects
– 1970s (LFM): land surface ignored
– Late 1980s (NGM): first simple land model introduced (Tuccillo)
• Single layer soil slab (“force-restore” model: Deardorff)
• No explicit vegetation treatment
• Temporally fixed surface wetness factor
• Diurnal cycle is treated (as is PBL) with diurnal surface radiation
• Surface albedo, skin temperature, surface energy balance
• Snow cover (but not depth)
– Early1990s (Global Model): OSU land model (Mahrt& Pan, 1984)
• Multi-layer soil column (2-layers)
• Explicit annual cycle of vegetation effects
• Snow pack physics (snowdepth, SWE)
– Mid-90s (Meso Model): OSU/Noah LSM replaces Force-Restore
– Mid 2000s (Global Model): Noah replaces OSU (Ek et al 2003)
– Mid 2000s (Meso Model: WRF): Unified Noah LSM with NCAR
– 2000s-10s: Noah “MP” with explicit canopy, ground water,CO2

12. MODEL PREDICTIONS:
PROCESSES
CLOUDS
INCOMING
SOLAR AND
LONGWAVE BOUNDARY-LAYER
TURBULENT MIXING
EVAPORATION
HEATING
THE AIR
& OUTGOING
LONGWAVE SOIL HEATING

13. MODEL PREDICTIONS: STATES
FREE ATMOSPHERE
ATMOSPHERIC
ATMOSPHERIC: BOUNDARY
TEMPERATURE LAYER
HUMIDITY
WINDS
SOIL: LAND-SURFACE
TEMPERATURE
MOISTURE

14. Land-Atmosphere Interaction
• Many land-surface/atmospheric processes.
• Interactions & feedbacks between the land and
atmosphere must be properly represented.
• Global Energy and Water Exchanges (GEWEX)
Program / Global Land/Atmosphere System Study
(GLASS) project: Accurately understand,
model, and predict the role of Local Land-
Atmosphere Coupling “LoCo” in water and
energy cycles in weather and climate models.
• All land-atmosphere coupling is local…
…initially.
14

15. LoCo: Local land-atmospheric modeling
• NEAR-SURFACE
LOCAL COUPLING
METRIC: Evaporative
fraction change with
changing soil moisture
for both bare soil &
vegetated surface =
function of near-
surface turbulence,
canopy control, soil
hydraulics & soil
thermodynamics,
• Utilize GHP/CEOP
reference site data
sets (“clean” fluxnet),
• Extend to coupling
with atmos. boundary-
layer and entrainment
processes,
• Link with approaches
by Santanello et al.
From “GEWEX
Imperatives: Plans for
2013 and Beyond” 15
15

16. Land-Atmosphere Interaction
Betts et al
(1996)
Diurnal
time-scales
Seasonal
Century
16

17. Outline
• Role of Land Surface Models (LSMs)
• Requirements:
- atmospheric forcing, valid physics, land data sets/
parameters, initial land states, cycled land states
• Applications for Weather & Climate
• Validation
• Improving LSMs
• Expanding role of Land Modeling as part of
an integrated Earth System
• Summary
17

18. Land Model Requirements
• To provide proper boundary conditions, land model
must have:
- Necessary atmospheric forcing to drive the land
model,
- Appropriate physics to represent land-surface
processes (for relevant time/spatial scales),
- Corresponding land data sets and associated
parameters, e.g. land use/land cover (vegetation
type), soil type, surface albedo, snow cover,
surface roughness, etc., and
- Proper initial land states, analogous to initial
atmospheric conditions, though land states may
carry more “memory” (e.g. especially in deep soil
moisture), similar to ocean SSTs. 18

19. Atmospheric Forcing to Land Model
Precipitation Incoming solar Wind speed
Air temperature Incoming Longwave Specific humidity
19
+ Atmospheric Pressure Example from 18 UTC, 12 Feb 2011

20. Unified NCEP-NCAR Noah land model
• Four soil layers (10, 30,
60, 100 cm thick).
• Linearized (non-
iterative) surface
energy budget;
numerically efficient.
• Soil hydraulics and
parameters follow
Cosby et al.
• Jarvis-Stewart “big-
leaf” canopy cond.
• Direct soil evaporation.
• Canopy interception.
• Vegetation-reduced soil
thermal conductivity.
• Patchy/fractional snow
cover effect on surface • Freeze/thaw soil physics.
fluxes; coverage treated • Snowpack density and snow water
as function of equivalent.
20
snowdepth & veg type • Veg & soil classes parameters.

21. Prognostic Equations
Soil Moisture (Θ):
• “Richard’s Equation”; DΘ (soil water diffusivity) and
KΘ (hydraulic conductivity), are nonlinear functions of
soil moisture and soil type (Cosby et al 1984); FΘ is a
source/sink term for precipitation/evapotranspiration.
Soil Temperature (T):
• CT (thermal heat capacity) and KT (soil thermal
conductivity; Johansen 1975), non-linear functions of
soil/type; soil ice = fct(soil type/temp./moisture).
Canopy water (Cw):
• P (precipitation) increases Cw, while Ec (canopy
water evaporation) decreases Cw. 21

22. Surface Energy Budget
Rn = Net radiation = Sê - Sé + Lê - Lé
Sê = incoming shortwave (provided by atmos. model)
Sé = reflected shortwave (snow-free albedo (α) provided
by atmos. model; α modified by Noah model over snow)
Lê = downward longwave (provided by atmos. model)
Lé = emitted longwave = εσTs4 (ε=surface emissivity,
σ=Stefan-Boltzmann const., Ts=surface skin temperature)
H = sensible heat flux
LE = latent heat flux (surface evapotranspiration)
G = ground heat flux (subsurface soil heat flux)
SPC = snow phase-change heat flux (melting snow)
• Noah model provides: α, Lé, H, LE, G and SPC.
22

23. Surface Water Budget
ΔS = change in land-surface water
P = precipitation
R = runoff
E = evapotranspiration
P-R = infiltration of moisture into the soil
• ΔS includes changes in soil moisture, snowpack (cold
season), and canopy water (dewfall/frostfall and
intercepted precipitation, which are small).
• Evapotranspiration is a function of surface, soil and
vegetation characteristics: canopy water, snow cover/
depth, vegetation type/cover/density & rooting depth/
density, soil type, soil water & ice, surface roughness.
• Noah model provides: ΔS, R and E. 23

24. Potential Evaporation
(Penman)
open water surface
Δ = slope of saturation vapor pressure curve
Rn-G = available energy"
ρ = air density
cp = specific heat
Ch = surface-layer turbulent exchange coefficient
U = wind speed
δe = atmos. vapor pressure deficit (humidity)
γ = psychrometric constant, fct(pressure)" 24

25. Surface Latent Heat Flux
(Evapotranspiration)
Canopy Water
Evap. (LEc) Transpiration
(LEt) Bare Soil
canopy water
Evaporation (LEd)
canopy
soil
• LEc is a function of canopy water % saturation.
• LEt uses Jarvis (1976)-Stewart (1988) “big-leaf”
canopy conductance.
• LEd is a function of near-surface soil % saturation.
• LEc, LEt, and LEd are all a function of LEp. 25

26. Surface Latent Heat Flux (cont.)
Canopy Water Evaporation (LEc):
• Cw, Cs are canopy water & canopy water saturation,
respectively, a function of veg. type; nc is a coeff.
Transpiration (LEt):
max sê
• gc is canopy conductance, gcmax is maximum canopy
conductance and gSê, gT, gδe, gΘ are solar, air
temperature, humidity, and soil moisture availability
factors, respectively, all functions of vegetation type.
Bare Soil Evaporation (LEd):
• Θd, Θs are dry (minimum) & saturated soil moisture
contents, =fct(soil type); nd is a coefficient (nom.=2).
26

27. Latent Heat Flux over Snow
LE (shallow snow) < LE (deep snow)
Sublimation
(LEsnow)
LEsnow = LEp
LEsnow = LEp snowpack
LEns < LEp LEns = 0
soil
Shallow/Patchy Snow Deep snow
Snowcover<1 Snowcover=1
• LEns = “non-snow” evaporation (evapotranspiration terms).
• 100% snowcover a function of vegetation type, i.e.
shallower for grass & crops, deeper for forests.
27

28. Surface Sensible Heat Flux
(from canopy/soil
snowpack surface)
canopy bare soil snowpack
soil
ρ, cp = air density, specific heat
Ch = surface-layer turbulent exchange coeff.
U = wind speed
Tsfc-Tair = surface-air temperature difference
• “effective” Tsfc for canopy, bare soil, snowpack. 28

29. Ground Heat Flux
(to canopy/soil/snowpack surface)
canopy bare soil snowpack
soil
KT = soil thermal conductivity (function of soil type:
larger for moister soil, larger for clay soil; reduced
through canopy, reduced through snowpack)
Δz = upper soil layer thickness
Tsfc-Tsoil = surface-upper soil layer temp. difference
• “effective” Tsfc for canopy, bare soil, snowpack. 29

30. Land Data Sets (e.g. uncoupled Noah)
Vegetation Type Soil Type Max.-Snow Albedo
(1-km, USGS) (1-km, STATSGO-FAO) (1-deg, Robinson)
Jan July winter summer
Green Vegetation Fraction Snow-Free Albedo
(monthly, 1/8-deg, NESDIS AVHRR) (seasonal, 1-deg, Matthews)
• Fixed climatologies or (near real-time) observations.
• Some quantities predicted/assimilated (e.g. soil moist., snow,
vegetation). 30

31. Land Data Sets (e.g. meso NAM)
Vegetation Type Soil Type Max.-Snow Albedo
(1-km, IGBP-MODIS) (1-km, STATSGO-FAO) (1-km, UAz-MODIS)
mid-Jan mid-July Jan July
Green Vegetation Fraction Snow-Free Albedo
(weekly, 1/8-deg, (monthly, 1-km,
new NESDIS/AVHRR) BU-MODIS) 31

32. Land Data Sets (e.g. GFS/CFS, GLDAS)
Vegetation Type Soil Type Max.-Snow Albedo
(1-deg, UMD) (1-deg, Zobler) (1-deg, Robinson)
Jan July Jan July
Green Vegetation Fraction Snow-Free Albedo
(monthly, 1/8-deg, NESDIS/ (seasonal, 1-deg,
AVHRR) Matthews) 32

33. Land surface model physics
parameters (examples)
• Surface momentum roughness dependent on
vegetation/land-use type.
• Stomatal control dependent on vegetation
type, direct effect on transpiration.
• Depth of snow (snow water equivalent, or s.w.e.) for
deep snow and assumption of maximum snow
albedo is a function of vegetation type.
• Heat transfer through vegetation and into the
soil a function of green vegetation fraction
(coverage) and leaf area index (density).
• Soil thermal and hydraulic processes highly
dependent on soil type (vary by orders of
magnitude). 33

34. Initial Land States
• Valid land state initial conditions are necessary
for NWP and climate models, & must be consistent
with the land model used in a given weather or
climate model, i.e. from same cycling land model.
• Land states spun up in a given NWP or climate
model cannot be used directly to initialize another
model without a rescaling procedure because of
differing land model soil moisture climatologies.
May Soil Moisture
Climatology from 30-year
NCEP Climate Forecast
System Reanalysis (CFSR),
spun up from Noah land
model coupled with CFS.
34

35. Initial Land States (cont.)
• In addition to soil moisture: the land model
provides surface skin temperature, soil
temperature, soil ice, canopy water, and
snow depth & snow water equivalent.
National Ice Air Force Weather Agency
Center snow cover snow cover & depth
35

36. Initial Land States (cont.)
• In seasonal (and longer) climate
simulations, land states are cycled so that
there is an evolution in land states in
response to atmospheric forcing and land
model physics.
• Land data set quantities may be observed
and/or simulated, e.g. green vegetation
fraction & leaf area index, and even land-use
type (evolving ecosystems).
36

37. Land Data Assimilation:
Motivation
• Better initial conditions of land states
for numerical weather prediction (NWP) and
seasonal climate model forecasts.
• Assess model (physics) performance
and make improvements by assimilating
real land data (sets).
• Application to regional and global
drought monitoring and seasonal
hydrological prediction.
37

38. Land Data Assimilation (LDA)
• Observations incorporated into weather, seasonal
climate and hydrological models in analysis cycles.
• In each analysis cycle, observations of current
(and possibly past) state of a system are combined
with results from a weather/climate/hydro model.
• Analysis step is considered “best” estimate of the
current state of the weather/climate/hydro system.
• Analysis step balances uncertainty in data and in
forecast. The model is then advanced in time & its
results becomes the forecast in next analysis cycle.
38

39. Land Data Assimilation (LDA)
• Minimization of a "cost function” has the effect of
making sure that the analysis does not drift too far
away from observations and forecasts that are
known to usually be reliable.
39

40. Developing Land Data Assimilation Capabilities
Figure 1: Snow water equivalent (SWE)
based on Terra/MODIS and Aqua/AMSR-E. Figure 2: Annual average precipitation from 1998 to
Future observations will be provided by JPSS/ 2009 based on TRMM satellite observations. Future
observations will be provided by GPM.
VIIRS and DWSS/MIS.
Figure 5: Current lakes and reservoirs monitored by
Figure 3: Daily soil moisture based on Aqua/ OSTM/Jason-2. Shown are current height variations
AMSR-E. Future observations will be Figure 4: Changes in annual-average terrestrial relative to 10-year average levels. Future
provided by SMAP. water storage (the sum of groundwater, soil water, observations will be provided by SWOT.
surface water, snow, and ice, as an equivalent height
C. Peters-Lidard et al of water in cm) between 2009 and 2010, based on 40
(NASA) GRACE satellite observations. Future observations
will be provided by GRACE-II.

41. NASA Land Information System
Inputs Physics Outputs Applications
Topography, Surface
Land Surface Models Energy
Soils
SAC-HT/SNOW17 Fluxes
(Static) Water
Noah, CLM, VIC, Catchment (Qh,Qle) Supply &
Demand
Land Cover,
Leaf Area Index Soil Moisture Agriculture
(MODIS, AMSR, Evaporation Hydro-
TRMM, SRTM) Electric
Power,
Meteorology: Water
Modeled & Observed Surface Quality
(NLDAS, DMIP II, Water
GFS,GLDAS,TRMM Fluxes Improved
GOES, Station) (e.g., Runoff) Short Term
&
Observed States Data Assimilation Modules Long Term
(Snow Surface Predictions
(DI, EKF, EnKF) States
Soil Moisture
Temperature) (Snowpack)
41
41
Christa Peters-Lidard et al., NASA/GSFC/HSB

42. Soil Moisture Data Assimilation
Data Assimilated:
• AMSR-E LPRM soil moisture
• AMSR-E NASA soil moisture
Variables Analyzed:
• Soil Moisture
• Evapotranspiration
• Steamflow
Experimental Setup:
• Domain: CONUS, NLDAS
• Resolution: 0.125 deg.
• Period: 2002-01 to 2010-01
• Forcing: NLDASII
• LSM: Noah 2.7.1,3.2
Figure 3: Daily soil moisture based on Aqua/
AMSR-E. Future observations will be
provided by SMAP.
Peters-Lidard, C.D, S.V. Kumar, D.M. Mocko, Y. Tian, 2011: Estimating evapotranspiration with land data
42
assimilation systems, Hydrological Processes, 25(26), 3979--3992, DOI: 10.1002/hyp.8387

43. LDA: Ensemble Kalman Filter (EnKF)
From Rolf Reichle (2008)
Nonlinearly propagates
ensemble of model trajectories.
y Can account for wide range of
k model errors (incl. non-additive).
Approx.: Ensemble size.
Linearized update.
xki state vector (eg soil moisture)
Pk state error covariance
Rk observation error covariance
Propagation tk-1 to tk: Update at tk:
xki+ = xki- + Kk(yki - xki- )
xki+ = f(xk-1i-) + wki
for each ensemble member i=1…N
w = model error Kk = Pk (Pk + Rk)-1
with Pk computed from ensemble spread

44. Soil moisture Assimilation -> soil moisture
ALL available (Evaluation vs SCAN)
stations (179) Anomaly OL NASA-DA LPRM-DA
correlation
Surface soil 0.55 +/- 0.49 +/- 0.56 +/-
moisture 0.01 0.01 0.01
(10cm)
Root zone soil 0.17 +/- 0.13 +/- 0.19 +/-
moisture (1m) 0.01 0.01 0.01
(21) Stations
Anomaly OL NASA-DA LPRM-DA
listed in correlation
Reichle et al. Surface soil 0.62 +/- 0.53 +/- 0.62 +/-
(2007) moisture 0.05 0.05 0.05
(10cm)
Root zone soil 0.16 +/- 0.13 +/- 0.19 +/-
moisture (1m) 0.05 0.05 0.05 44

45. Latent Heat Flux (Qle) Estimates over US
(“Observed” vs. “Modeled Open Loop” (OL))
DJF MAM JJA SON
FLUXNET
MOD16
GLDAS
NLDAS
(Noah 2.7.1)
NLDAS
(Noah 3.2)
1 Peters-Lidard, C.D, S.V. Kumar, D.M. Mocko, Y. Tian, 2011: Estimating evapotranspiration with land data Pg.
assimilation systems, Hydrological Processes, 25(26), 3979--3992, DOI: 10.1002/hyp.8387
45

46. Soil Moisture Assimilation -> Evapotranspiration (Qle)
FLUXNET MOD16
RMSE
Bias
1
Peters-Lidard, C.D, S.V. Kumar, D.M. Mocko, Y. Tian, 2011: Estimating evapotranspiration with land data Pg.
assimilation systems, Hydrological Processes, 25(26), 3979--3992, DOI: 10.1002/hyp.8387
46

47. Where Does Soil Moisture Assimilation Help Improve
Qle (i.e. Reduce RMSE) ?
FLUXNET MOD16 FLUXNET MOD16
DJF DJF
MAM MAM
JJA JJA
SON SON
1 1
LPRM-DA NASA-DA
Peters-Lidard, Christa D., Sujay V. Kumar, David M. Mocko and Yudong Tian, (2011), Estimating Pg.
Evapotranspiration with Land Data Assimilation Systems, In press, Hyd. Proc.
47

48. Where Does Soil Moisture Assimilation Help Improve
Qle (i.e. Reduce RMSE) ?
Qle RMSE % Difference FLUXNET MOD16
(DA-OL)
Landcover type NASA-DA LPRM-DA NASA-DA LPRM-DA
(Wm-2) (Wm-2) (Wm-2) (Wm-2)
Evergreen needleleaf forest 17.6 7.9 10.5 -3.6
Deciduous broadleaf forest 3.2 12.7 0.3 0.7
Mixed forest 1.8 8.0 -0.7 -0.9
Woodlands 16.4 18.9 11.5 -5.9
Wooded grassland 8.8 -0.5 9.6 0.3
Closed shrubland 7.3 3.4 2.5 8.9
Open shrubland 9.0 7.4 3.6 12.1
Grassland 23.9 7.1 32.9 46.4
Cropland 12.3 34.7 30.9 40.8
Bare soil -0.1 0.6 -0.8 1.4
Urban -0.1 -0.1 -0.2 -0.3
Pg.
48

49. Soil Moisture Assimilation -> Streamflow
Evaluation vs. USGS gauges – by major basins
49

50. Soil Moisture Assimilation -> Streamflow
(average seasonal cycles of RMSE– using Xia et al. (2011) stations)
NEW ENGLAND SOUTH ATLANTIC MID ATLANTIC
80 50 32
OL OL OL
70 LPRM-DA 30 LPRM-DA
45 LPRM-DA
28
60 40 26
RMSE (m3/s)
RMSE (m3/s)
RMSE (m3/s)
50 35 24
40 30 22
30 20
25
18
20 20 16
10 15 14
0 10 12
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
GREAT LAKES OHIO TENNESSE
26 200 18
OL OL OL
24 LPRM-DA 180 LPRM-DA LPRM-DA
16
22 160
20
RMSE (m3/s)
RMSE (m3/s)
RMSE (m3/s)
140 14
18
16 120 12
14 100
10
12 80
10
60 8
8
6 40 6
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
UPPER MISSISSIPPI LOWER MISSISSIPPI SOURIS RED RAINY
120 40 40
OL OL OL
110 LPRM-DA 35 LPRM-DA 35 LPRM-DA
100 30
30
RMSE (m3/s)
RMSE (m3/s)
RMSE (m3/s)
90
25
80 25
20
70 20
15
60
15
50 10
40 10 5 50
30 5 0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

51. Soil Moisture Assimilation -> Streamflow
(average seasonal cycles of RMSE– using Xia et al. (2011) stations)
MISSOURI ARKANSAS-WHITE-RED
110 26
OL OL
100 LPRM-DA 24 LPRM-DA
90
22
80
RMSE (m3/s)
RMSE (m3/s)
70 20
60 18
50 16
40
14
30
20 12
10 10
0 8
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
UPPER COLORADO LOWER COLORADO
10 10
OL OL
9 LPRM-DA
9 LPRM-DA
8
8
7
RMSE (m3/s)
RMSE (m3/s)
7
6
6 5
5 4
4 3
3 2
2 1
1 0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
GREAT BASIN
PACIFIC NORTHWEST CALIFORNIA
10
OL 100 80
9 LPRM-DA OL OL
90 LPRM-DA 70 LPRM-DA
8
80 60
7
RMSE (m3/s)
RMSE (m3/s)
RMSE (m3/s)
6 70
50
5 60
40
4 50
30
3 40
2 30 20
1 20 10 51
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 10 0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

52. SMOS (ESA satellite) soil moisture
assimilation tests in the NCEP Global
Forecast Model
The simplified ensemble Kalman Filter (EnKF) was
"
embedded in the GFS latest version to assimilate soil
moisture observation
" Case: 00Z July 6, 2011. (GFS free forecast)
" Experiments:
CTL: Control run
EnKF: Sensitivity run
52
Weizhong Zheng and Xiwu Zhan (NESDIS/STAR)

53. Comparison of Soil Moisture between GFS & SMOS
SMOS CTL
EnKF
53

54. Comparison of Soil Moisture between GFS & SMOS
SMOS CTL
EnKF
54

55. Improved Precipitation in EnKF vs CTL
CTL EnKF
OBS EnKF-
CTL
55

56. Snow Data Assimilation
Snow Cover Fraction
Comparison of snow cover
fraction between the MODIS
(blue circles), the open loop
simulation (black line) and the
assimilation simulation (green
line).
Snow Water Equivalent
Comparison of snow water
equivalent between the open
loop simulation (green), the
assimilation simulation (red)
and the in-situ measurement
(black) averaged over all
SNOTEL sites in the study
region.
56

57. Outline
• Role of Land Surface Models (LSMs)
• Requirements:
- atmospheric forcing, valid physics, land data sets/
parameters, initial land states, cycled land states
• Applications for Weather & Climate
• Validation
• Improving LSMs
• Expanding role of Land Modeling as part of
an integrated Earth System
• Summary
57

58. APPLICATIONS: e.g. Noah land model in
NCEP Weather and Climate models
Climate led
Oceans
Coup
CFS Hurricane 2 -Way HYCOM
MOM GFDL
HWRF
WaveWatch III
1.7B Obs/Day
Satellites
99.9%
Dispersion
Global Regional NAM
WRF NMM
Global Data Forecast (including NARR)
ARL/HYSPLIT
Assimilation
System
Severe Weather
Regional Data WRF NMM/ARW
Assimilation Short-Range Workstation WRF
Ensemble Forecast
North American Ensemble
Forecast System WRF: ARW, NMM Air Quality
ETA, RSM
GFS, Canadian Global Model NAM/CMAQ For
eca
st
NLDAS Rapid Update
(drought) for Aviation (ARW-based)
58
NOAH Land Surface Model

59. NCEP-NCAR unified Noah land model
• Surface energy
(linearized) & water
budgets; 4 soil layers.
• Forcing: downward
radiation, precip., temp.,
humidity, pressure, wind.
• Land states: Tsfc, Tsoil*,
soil water* and soil ice,
canopy water*, snow depth
and snow density.
*prognostic
• Land data sets: veg. type,
green vegetation fraction,
soil type, snow-free albedo
& maximum snow albedo.
• Noah coupled with NCEP models: North American Mesoscale
model (NAM; short-range), Global Forecast System (GFS;
medium-range), Climate Forecast System (CFS; seasonal), and
uncoupled NLDAS (drought) & GLDAS (climate & drought). 59

60. Global Land Data Assimilation System (GLDAS)
• GLDAS (run Noah LSM under NASA/Land Information System)
forced with CFSv2/GDAS atmos. data assimil. output & blended
precip.
• “Blended precipitation”: satellite (CMAP; heaviest weight in
tropics where gauges sparse), surface gauge (heaviest in middle
latitudes) and GDAS (modeled; high latitudes).
• CFSv2/GLDAS ingested into CFSv2/GDAS every 24-hours (semi
–coupled) with adjustment to soil states (e.g. soil moisture).
• Snow cycled in CFSv2/GLDAS if model within 0.5x to 2.0x
observed value (IMS snow cover, and AFWA snow depth
products), else adjusted to 0.5 or 2.0 of observed value.
60
GDAS-CMAP precip Gauge locations IMS snow cover AFWA snow depth

61. Climate Forecast Syst. & 30-yr Reanalysis
• Global Land Data Assimilation System (GLDAS):
Daily update of land states via semi-coupled
Noah model run in NASA Land Information System.
• Driven by CFS assimilation cycle atmos. forcing &
“blended” precipitation obs (satellite, gauge, model).
• Reasonable land climatology: energy/water budgets.
Precipitation Evaporation Runoff Soil Moisture Snow
January (top), July (bottom) Climatology from 30-year NCEP CFS
Reanalysis (Precip, Evap, Runoff [mm/day]; Soil Moisture, Snow [mm]) 61

62. Land Modeling and Drought (NLDAS)
• North Amererican Land Data Assimilation System
• Noah (& 3 other land models) run in an uncoupled
mode driven by obs. atmos. forcing to generate
surface fluxes, land/soil states, runoff & streamflow.
• Land model runs provide 30-year climatology.
• Anomalies used for drought monitoring, and
seasonal hydrological prediction (using climate
model downscaled forcing); supports the National
Integrated Drought Information System (NIDIS).
July 30-year climatology July 1988 (drought year) July 1993 (flood year)
62
NLDAS four-model ensemble soil moisture monthly anomaly

63. • NLDAS is a multi-model land modeling & data assimil. system…
• …run in uncoupled mode driven by atmospheric forcing
(using surface meteorology data sets)…
• …with “long-term” retrospective and near real-time output of
land-surface water and energy budgets.
NLDAS Configuration: Land models

64. NLDAS Data Sets and Setup

65. NLDAS website
www.emc.ncep.noaa.gov/mmb/nldas
ldas.gsfc.nasa.gov/nldas
NLDAS NLDAS
Drought Monitor Drought Prediction
Anomaly and percentile for six variables
and three time scales:
• Soil moisture, snow water, runoff, streamflow,
evaporation, precipitation
• Current, Weekly, Monthly

66. June 1988: Drought Year
Monthly total soil moisture anonamlies for
NLDAS land models and ensemble mean

67. June 1993: Flood Year
Monthly total soil moisture anonamlies for
NLDAS land models and ensemble mean

68. NLDAS Seasonal Hydrological
Forecast System
• System uses VIC land model and includes three
forecasting approaches for necessary downscaled/
ensemble temperature and precipitation forcing data
sets: (1) NCEP Climate Forecast System (CFS), (2)
traditional ESP, and (3) CPC.
• System jointly developed by Princeton University and
University of Washington.
• Transitioned to NCEP/EMC local system in November
2009 as an experimental seasonal forecast system.
• Run at the beginning of each month with forecast
products staged on NLDAS website by mid-month.
• For CFS forcing, transitioning from use of CFSv1 to
now-operational CFSv2.

69. Weekly Drought Forecast
System Using CFS forcing
Drought Monitoring (top) & Corresponding Monthly Fcst (bottom)
03 May 2012 28 June 2012 02 August 2012
Courtesy Dr. L. Luo at Michigan State U.

70. Global Land Data Assimilation
System (GLDAS) for drought
Ÿ GLDAS running Noah LSM part of NCEP Climate
Forecast System (CFSv2) Reanalysis (CFSR)
1979-2010; similar to NLDAS.
Ÿ GLDAS/Noah uses atmospheric forcing from Climate
Data Assimilation System (CDAS) from CFSR and
observed precipitation.
Ÿ GLDAS part of the now operational CFSv2 (January
2011).
Ÿ GLDAS being developed for Global Drought
Information System (GDIS); connections with World
Climate Research Programme.

71. CFSR/GLDAS Soil Moisture Climatology
71
71

72. GLDAS CFSR Soil Moisture Anomaly
May 1988
(US drought)
May 1993
(US floods)
72
72

73. GLDAS CFSR Soil Moisture Anomaly
July 2012
(US drought)

74. Outline
• Role of Land Surface Models (LSMs)
• Requirements:
- atmospheric forcing, valid physics, land data sets/
parameters, initial land states, cycled land states
• Applications for Weather & Climate
• Validation
• Improving LSMs
• Expanding role of Land Modeling as part of
an integrated Earth System
• Summary
74

75. Land Model Validation
• Land model validation uses (near-) surface
observations, e.g. routine weather observations of
air temperature, dew point and relative humidity,
10-meter wind, along with upper-air validation,
precipitation scores, etc.
• To more fully validate land models, surface fluxes
and soil states (soil moisture, etc) are also used.
• Comparing monthly diurnal composites useful to
assess systematic model biases (averaging out
transient atmospheric conditions).
• Such validations suggest land model physics
upgrades.
75

76. Near-surface verification regions
Western US! Eastern US!
76

77. July 2008 diurnal monthly mean
NAM (dashed line) vs obs (solid)
00-84hr, 00z cycle, 2-m temp & RH
temp
1C daytime warm bias slight
1C daytime warm bias! nighttime warm bias!
Western US Eastern US
daytime dry bias
(increasing dry trend) slight daytime dry bias
RH
nighttime dry bias
slight nighttime
dry bias
77

78. Validation with flux site data
(e.g. via Fluxnet , GEWEX/
CEOP reference sites).
78

79. Compare monthly diurnal composites of model!
output versus observations from flux sites to!
assess systematic model biases. !
SURFX (Surface Flux) Network!
NOAA/ATDD, Tilden Meyers et al! 79

80. Ft. Peck,!
Montana!
(grassland)! January!
80

81. Ft. Peck,!
Montana!
(grassland)! July!
81

82. Audubon!
Grassland,!
Arizona! January!
NOAA/ATDD Surface Flux Network!
82

83. Audubon!
Grassland,!
Arizona! July !
NOAA/ATDD Surface Flux Network!
83

84. Ozarks,! January!
Missouri!
(mixed forest)!
NOAA/ATDD Surface Flux Network!
84

85. Ozarks,! July !
Missouri!
(mixed forest)!
NOAA/ATDD Surface Flux Network!
85

86. January!
Walker Branch,!
Tennessee!
(deciduous forest)!
NOAA/ATDD Surface Flux Network!
86

87. July !
Walker Branch,!
Tennessee!
(deciduous forest)!
NOAA/ATDD Surface Flux Network!
87

88. Ft. Peck, Montana
(grassland)
SURFACE ENERGY
BUDGET TERMS
Downward Solar January 2008Upward Longwave
monthly averages:
model vs obs
Reflected Solar Downward Longwave
Sensible Heat Flux Latent Heat Flux Ground Heat Flux
88

89. Ft. Peck, Montana
(grassland)
Sensible heat flux
monthly averages:
model vs obs
January 2008 October 2008
better surface-
layer physics in
NAM vs GFS
July 2007
April 2008 July 2008 89

90. Ft. Peck, MT Brookings, SD
model (grassland) (grassland)
high bias
obs
Walker Branch, TN
(deciduous)
low bias
Black Hills, SD Ozarks, MO
(conifer) (deciduous)
July 2005, Sensible heat flux
monthly averages: NAM
(North American Mesoscale) Bondville, IL
model vs obs (cropland)
90

91. Land Model Benchmarking
• Comprehensive evaluation of land models that goes beyond
traditional validation, with an established level of a priori
performance for a number of metrics, e.g. modeled surface
fluxes within ±ε [W/m2] of the observations, PDFs of observed
& modeled surface fluxes overlap by at least X%, an annual
carbon budget within Y%, etc.
• Metrics depend on community, e.g. climate, weather, carbon,
hydrology.
• Empirical approaches: e.g. statistical, neural network, other
machine learning; relationship between training and testing
data sets manipulated to see how well a model utilizes
available information.
• Land model (physics) should be able to e.g. out-perform
simple empirical models, persistence (for NWP models),
climatology (for climate models). 91

92. Benchmarking vs Evaluation
(Martin Best et al, GEWEX/GLASS)
• Evaluation
Model A
Ø Run model
Model B
Ø Compare output with observations and
ask: Benchmark
Error
q How good is the model?
• Benchmarking
Ø Decide how good model needs to be
Ø Run model and ask: Site / Variable
ü Does model reach the level
required?
PALS Land sUrface Model Benchmarking Evaluation pRoject (PLUMBER)
© Crown copyright Met Office

93. Joint GEWEX/GLASS-GHP project:
Land Model Benchmarking
• GLASS tools, i.e. Protocol for the Analysis of Land
Surface models (PALS): www.pals.unsw.edu.au.
• GEWEX Hydroclimatology Panel (GHP) provides
reference site/model output data sets for different
regions, seasons, &variables: evaluate energy, water
& carbon budgets; coupled, uncoupled model output.
GHP “CEOP” reference sites Fluxnet 93 93

94. Joint GEWEX/GLASS-GHP project:
Land Model Benchmarking
• PALS example: CABLE (BOM/Australia) land model, Bondville, IL, USA
(crolandp), 1997-2006, average diurnal cycles
. Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv
400
400
Total score: 1.1 Score: 0.91 Score: 2.9 Total score: 0.46 Score: 1.7 Score: 0.37
winter summer
(NME) (NME) (NME) (NME) (NME) (NME)
Sensible heat flux W/ m2
Sensible heat flux W/ m2
300
300
250
250
Latent heat flux W/ m2
Latent heat flux W/ m2
Observed Observed
Modelled Modelled
obs
200
200
150
150
100
100
model
0 50
0 50
0
0
0 6 12 18 23 0 6 12 18 23 0 6 12 18 23 0 6 12 18 23
DJF hour of day JJA hour of day DJF hour of day JJA hour of day
Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv
400
400
Score: 0.51 Score: 0.55 Score: 0.57 Score: 0.28
spring autumn
(NME) (NME) (NME) (NME)
LE
Sensible heat flux W/ m2
Sensible heat flux W/ m2
H
300
300
250
250
Latent heat flux W/ m2
Latent heat flux W/ m2
200
200
150
150
100
100
0 50
0 50
0
0
0 6 12 18 23 0 6 12 18 23 0 6 12 18 23 0 6 12 18 23
MAM hour of day SON hour of day MAM hour of day SON hour of day
Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv
100 150
100 150
Total score: 0.3 Score: 0.59 Score: 0.37 Total score: 1.6 Score: 3.6 Score: 1.5
(NME) (NME) (NME) (NME) (NME) (NME)
600
600
Ground heat flux W/ m2
Ground heat flux W/ m2
Net radiation W/ m2
Net radiation W/ m2
Observed Observed
Modelled Modelled
400
400
50
50
200
200
0
0
−50
−50
0
0
0 6 12 18 23 0 6 12 18 23 0 6 12 18 23 0 6 12 18 23
DJF hour of day JJA hour of day DJF hour of day JJA hour of day
Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv
100 150
100 150
Score: 0.19 Score: 0.22 Score: 1.2 Score: 1.8
(NME) (NME) (NME) (NME)
600
600
Rn G
Ground heat flux W/ m2
Ground heat flux W/ m2
Net radiation W/ m2
Net radiation W/ m2
400
400
50
50
200
200
0
0
−50
−50
0
0
94
0 6 12 18 23 0 6 12 18 23 0 6 12 18 23 0 6 12 18 23
MAM hour of day SON hour of day MAM hour of day SON hour of day

95. Outline
• Role of Land Surface Models (LSMs)
• Requirements:
- atmospheric forcing, valid physics, land data sets/
parameters, initial land states, cycled land states
• Applications for Weather & Climate
• Validation
• Improving LSMs
• Expanding role of Land Modeling as part of
an integrated Earth System
• Summary
95

96. Land Model Improvement
Surface flow
Snowpack & Saturated Urban-canopy
frozen soil subsurface flow model
• Urban-canopy & land-use/land cover changes
• Refined surface layer turbulence (esp. stable cond.)
• Refined evapotranspiration (empirical Jarvis-Stewart)
• Surface/subsurface lateral flow (hydrology)
• Dynamic (growing) veg. with multi-layer canopy*
• CO2-based photosynthesis* *Noah-MP upgrades
• Groundwater/water table*
• Multi-layer snowpack*, refined frozen soil processes
96

97. Land Model Improvement:
Transpiration
Ÿ Use surface fluxes (e.g. latent and sensible) to
evaluate land-surface physics formulations and
parameters, e.g. invert transpiration formulation to
infer canopy conductance. (Similarly for aero. cond.)
97

98. Noah-MP: a land component in
Mesoscale Weather Research &
Forecasting (WRF) model
• Noah-MP developed by Niu et al (2011, JGR), Univ.
Texas-Austin, NCAR, NCEP, Univ. Arizona
• Explicit canopy/subcanopy layers, dynamic (growing)
vegetation, and canopy conductance based on CO2-
photosynthesis
• Ground water (connection with hydrology models)
• Multi-layer snowpack
• Implicit solution of surface energy budget (SEB, for
better SEB closure)
• Recent NCAR Improvements to Noah-MP version 3.5
98

99. Canopy Absorbed Solar Radiation in Noah-MP
SWdn
What are the units of solar absorbed
radiation in the canopy?
- W/m2grid : original two stream makes this assumption
- W/m2veg : Noah-MP calculates absorption for the grid shaded fraction
but then applies it to the vegetated fraction
Original Noah-MP uses both fractional vegetation cover (turbulent fluxes) and
vegetation stem density (radiation fluxes). In the original code, these were
inconsistent.
For Evergreen Needleleaf Forest, the prescribed stem density gives a constant
horizontal coverage of 72% while turbulent flux fractional vegetation can be any 99
value
from 0% to 100%

100. Noah-MP vs. Noah: Improved canopy
radiation effect and surface-layer changes
in coupled mesoscale WRF v3.5
Before (V3.4.1) After (Pre V3.5)
• Reduced low-level (2-meter) air temperature bias 100

101. Tiled or Separate Land Model Grid
Ÿ A land model grid may comprise sub-atmospheric-
grid-scale “tiles” of e.g. grass/crop/shrubland,
forest, water, etc, of O(1-4km, or even less), with
coarser-resolution atmospheric forcing, and an
aggregate flux fed to the “parent” atmospheric.
Aggregated ABL “blending” height
surface
• Boundary-layer issues,
flux e.g. when blending
height > BL depth?
• Land-surface tiling run
under NASA Land
Information System
(LIS); other LIS tools.
• NASA/LIS to be part of
5-50km NOAA Environmental
Modeling System
surface tiles with different fluxes (NEMS). 101

102. Upgraded land/surface-layer physics:
Improvement of Satellite Data Utilization over
Desert & Arid Regions in NCEP Operational NWP
Modeling and Data Assimilation Systems
• Change to surface-layer turbulence physics (thermal
roughness change) and surface microwave emissivity
MW model…
•…yields a reduction of large bias in calculated
brightness temperatures was found for infrared and
microwave satellite sensors for surface channels…
•…so many more satellite measurements can be
utilized in GSI data assimilation system.
102

103. LST [K] Verification with GOES 3-Day Mean: July 1-3, 2007
(a) and SURFRAD
GFS-GOES: (b GFS-GOES: New
)
CTR Zot
Large cold
(c) (d
bias LST ) Ch
Improved significantly Aerodynamic 103103
during daytime! conductance: CTR vs Zot

104. Outline
• Role of Land Surface Models (LSMs)
• Requirements:
- valid physics, land data sets/parameters, initial
land states, atmospheric forcing, cycled land states
• Applications for Weather & Climate
• Validation
• Improving LSMs
• Expanding role of Land Modeling as part of
an integrated Earth System
• Summary
104

105. Expanding Role of Land Models
Ÿ In more fully-coupled Earth System models, role is
changing, with Weather & Climate connections to:
Hydrology (soil moisture & ground water/water
tables, irrigation and groundwater extraction,
water quality, streamflow and river discharge to
oceans, drought/flood, lakes, and reservoirs with
management, etc),
Biogeochemical cycles (application to ecosystems
--terrestrial & marine, dynamic vegetation and
biomass, carbon budgets, etc.)
Air Quality (interaction with boundary-layer,
biogenic emissions, VOC, dust/aerosols, etc.)
Ÿ More constraints, i.e. we must close the energy
and water budgets, and those related to air quality
and biogeochemical cycles. 105

106. NCAR Common Land Model (CLM)
106

107. Hurricanes and Inland Flooding
• Physical-based Noah model included in (mesoscale)
Hurricane Weather Research & Forecasting model,
with little degradation in track & intensity & precip.
• Inland flood Hurricane Katrina
forecasting
(right) using GFS
Soil Moisture
Noah runoff Initial
Condictions
& streamflow USGS gauge
station
(IC; default)
model.
NAM
• Extend to IC
global/climate
models: river NLDAS
discharge to observations IC
ops
oceans. NAM
107

108. Hydro-Coastal Linkage
Ÿ Impact of adding freshwater input to Gulf of Mexico
(implemented RTOFS June 2007).
Freshwater
nearshore
108

109. Sea Nettle Forecasting
SST
Likelihood of Chrysaora
NCEP, with
NESDIS, NOS
Habitat Model
Salinity
109

110. Lake modeling
• Thousands of lakes in N. America on the scale of
4km (NAM target), not resolved by SST analysis.
• Influence of previously unresolved lakes may be felt
on this scale and can no longer just be filled in .
110

111. Lake modeling
• Freshwater lake FLake model (Dmitrii Mironov, DWD).
• In use in regional COSMO, HIRLAM (European
models), UKMO, ECMWF (global).
- two-layer
- temperature &
energy budget
- mixed-layer &
thermocline
- snow-ice module
- atmospheric
forcing inputs
- specified depth
& turbidity
111

112. Outline
• Role of Land Surface Models (LSMs)
• Requirements:
- atmospheric forcing, valid physics, land data sets/
parameters, initial land states, cycled land states
• Applications for Weather & Climate
• Validation
• Improving LSMs
• Expanding role of Land Modeling as part of
an integrated Earth System
• Summary
112

113. Land Surface: Summary
• Provide surface boundary conditions for weather &
climate models (e.g. NCEP models), proper
representation of interactions with atmosphere.
• Valid physics, land data sets & parameters, initial
land states, atmospheric forcing, cycled land states.
• Land model validation using (near-) surface
observations, e.g. air temperature, relative
humidity, wind, soil moisture, surface fluxes, etc …
suggests model physics improvements.
• Expanding role of land models for weather & climate
in more fully-coupled Earth System models with
connections between Weather & Climate and
Hydrology, Biogeochemical cycles (e.g. carbon),
and Air Quality communities and models. 113

114. Land-Hydrology Partners/Projects
• Noah land model development: NCAR, U.
Ariz., U. Texas/Austin, U. Wash., WRF land &
PBL working groups, other national/internat’l
partners; freshwater “FLake” community),
• NLDAS/GLDAS drought: Princeton, Univ.
Wash., NASA, NWS Hydro., Climate
Prediction Center),
• Remote sensing/land data sets/data assimil.
of soil moisture, vegetation, surface temp,
snow: JCSDA, NESDIS, NIC, NASA, AFWA,
• NOAA research: NLDAS, GLDAS support,
• WCRP/GEWEX: GLASS/land-hydrology,
GABLS/boundary-layer, GHP/hydroclimate 114
projects, UKMO, ECMWF, Meteo-France, etc.

115. Thank you!
Obrigado!
¡Gracias!
115

116. Michael Ek
(michael.ek@noaa.gov)
Environmental Modeling Center (EMC)
National Centers for Environmental Prediction (NCEP)
NOAA Center for Weather and Climate Prediction (NCWCP)
5830 University Research Court
College Park, MD 20740
National Weather Service (NWS)
National Oceanic and Atmospheric Administration (NOAA)
116

117. Local Land-Atmosphere Interactions
above-ABL above-ABL
dryness cloud cover stability
incoming precipitation downward
solar 8 longwave
1 entrainment
wind boundary-
layer growth
7 1
turbulence 2
2 4
3
relative
humidity temperature
5 7
3 4 5
emitted
* + moisture longwave sensible reflected
canopy flux surface heat flux solar
conductance temperature albedo
8 6 6
soil moisture soil heat flux soil temperature
+positive feedback for C3 & C4 plants, negative feedback for CAM plants positive feedback
*negative feedback above optimal temperature 117
negative feedback
land-surface processes surface layer & ABL radiation