Zong-Liang Yang

1
Noah at UT

• Outline
• Four talks
• Liang Yang Overview (20 slides)
• Xiaoyan Jiang Feedback between the atmosphere,
  vegetation and groundwater represented in
  WRF/Noah (27 slides)
• Guo-Yue Niu Modeling snow and groundwater in
  atmospheric models (83 slides)
• Enrique Rosero Evaluating Noah with IHOP data (9
  slides)

Zong-Liang Yang
Prepared for NCEP-NCAR-NASA-OHD-UT Noah
Telecon March 20, 2007
2
Model Development at UT-Austin (http//www.geo.ute
xas.edu/climate/Research/publications.htm)

• Improved TOPMODEL runoff (Yang and Niu, 2003,
  GPC Niu and Yang, 2003, GPC Niu et al., 2005,
  JGR)
• Improved frozen soil scheme (Niu and Yang, 2006,
  JHM)
• Multi-layer snow (Yang and Niu, 2003, GPC)
• Snow and vegetation canopy interaction (Niu and
  Yang, 2004, JGR)
• Snow cover fraction (Niu and Yang, 2007, JGR)
• Global unconfined aquifer/groundwater component
  (Niu et al., 2007, JGR)
• Comparison of stochastic and physically-based
  subgrid snow cover fraction for snow assimilation
  (Su et al., 2007 Yang et al., 2007)

These physical parameterizations are expected to
work for both climate and weather models.
3
Noah _at_ UT

• To put a dynamic vegetation and groundwater
  component in the Noah LSM
• understand the feedback mechanisms between
  precipitation, soil moisture, and
  vegetation/groundwater dynamics
• To improve weather and climate forecasts on time
  scales from hourly to seasonal (Jiang et al.,
  2007a, in preparation Yang et al., 2007, in
  preparation)
• To interpret and transfer weather and climate
  forecasts for water resources management
  applications
• To put a biogenic VOC emission component in the
  Noah LSM
• understand the impacts of climate change and land
  use/land cover change on BVOC emissions and the
  formation of near ground ozone (Gulden and Yang,
  2006, Atmospheric Environment Jiang et al.,
  2007b, in preparation)

4
The Modeling Domains Multiple interactive
nesting grids
Weather Research and Forecast (WRF) Model,
version 2.02 (http//www.wrf-model.org/) The
model is developed by NCAR, NCEP and Universities.
30 km grid 31 Levels Temperature, wind,
humidity, pressure predicted

3-10 km grid 31 Levels Temperature, wind,
humidity, pressure predicted
90 km grid 31 Levels Temperature, wind,
humidity, pressure prescribed from large-scale
analyses
5
Vegetation Distribution in the Model
Other Land Data Vegetation LAI Albedo Roughness
length Stomatal parameters Soil Topography Valida
tion Weather stations Hydrologic stations Remote
sensing Field data
6
Stomatal Conductance in Noah

• Transpiration parameterized through surface
  conductance based on the model of Jarvis (1976).
  For a single leaf

7
Noah with Modifications (1)
The ability of roots to uptake soil water depends
on the availability of soil water. In Noah, this
is a linear function, which may work if soil is
near saturated. We make it a step-function,
guided by observations. We call the experiment
Noah SWF (soil water factor).
8
Role of Land Surface Processes in Modulating NAMS
Rainfall
OBS
WRF/SLAB
WRF/NOAH
WRF/NOAH SWF
JUN
JUL
AUG
WRF/SLAB fails to produce rainfall for June thru
August.
WRF/Noah fails to capture rainfall in August.
WRF/Noah-SWF captures rainfall in August.
9
Noah with Modifications (2)

• Noah prescribes fractional green vegetation.
• In the real world, vegetation growth depends on
  precipitation, temperature, nutrients, and
  others.
• This can be modeled by
• Relating stomatal conductance to photosynthesis
  environmental conditions, and
• Allocating assimilated carbon to leaves, stem,
  wood, and roots.

10
Leaf Anatomy
Stomate (pl. stomata)
11
Carbon and Water

• Plants eat CO2 for a living
• They open their stomata to let CO2 in
• Water gets out as an (unfortunate?) consequence
• For every CO2 molecule fixed about 400 H2O
  molecules are lost

12
Photosynthesis and Conductance
Stomatal conductance is linearly related to
photosynthesis
(The Ball-Berry-Collatz parameterization)
RH at leaf sfc
photosynthesis
stomatalconductance
CO2 at leaf sfc
Photosynthesis is controlled by three
limitations(The Farquahar-Berry model)
Enzyme kinetics(rubisco)
Light
Starch
13
Photosynthesis and Carbon Allocation
14
Comparison of Observed Simulated Rainfall
WRF/Noah DV
WRF/Noah SWF
OBS
JUN
JUL
AUG
15
Conclusions

1. Strong sensitivity to land surface processes
2. SLAB (without vegetation) fails to simulate the
   monsoon rainfall in all months
3. Noah (with prescribed vegetation) is better, but
   under-estimates the August rainfall
4. Noah (with improved root water uptake and
   response) is even better
5. Noah (with improved carbon and water coupling and
   leafs dynamic response to rainfall) gives the
   best overall simulation of the warm season
   rainfall
6. In the southern monsoon regions
7. In the southern Great Plains.
8. Dynamic vegetation has the largest impacts on the
   simulation of rainfall in the SGP, which is the
   area that shows the largest memory to soil
   moisture conditions.

16
Groundwater
In LSMs without groundwater, water is NOT
conserved over irrigated areas.
With our simple aquifer scheme added to LSM, we
could more realistically model

• Agriculture (irrigation)
• Water resources (pumpage, river flow)

17
Representing Irrigation in CLM-GW
Case Study High Plains Aquifer
Data courtesy of B. Scanlon
18
Representing Irrigation in CLM-GW
Pumpage?ET (wet season)
Pumpage?ET (dry season)
19
PILPS-GW Project for Intercomparison of Land
Surface Parameterizations Groundwater

• Objective Improve LSM subsurface hydrology
• Interested to become a contributor?
• Please contact
• Lindsey Gulden
• gulden_at_mail.utexas.edu 812-603-3873
• Zong-Liang Yang
• liang_at_mail.utexas.edu 512-471-3824

Gulden et al.,2007, GRL (submitted)
20
Conclusions

1. We have developed schemes for topography-based
   runoff, frozen soil, interactive vegetation,
   groundwater, and sub-grid-scale variability of
   snow cover.
2. An LSM augmented with above treatments simulates
   better terrestrial water storage dynamics across
   a wide range of spatial scales (see Nius talk)
3. An LSM with a groundwater component makes it
   logical to deal with irrigation through pumpage
   rates.
4. The augmented LSMs (Noah), when operated in the
   coupled mode, can improve the simulations of
   near-surface climate variables (see Jiangs talk)
5. Parameter estimation and calibration is important
   to guide the model development (see Roseros
   talk).