Exploring the role of global terrestrial ecosystems in the climate-carbon cycle interactions: An Integrated modeling and remote sensing approach Robert E. Dickinson ([e-mail]), Qing Liu, Yuhong Tian, Liming Zhou

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Exploring the role of global terrestrial
ecosystems in the climate-carbon cycle
interactions An Integrated modeling and remote
sensing approachRobert E. Dickinson
(robted_at_eas.gatech.edu), Qing Liu, Yuhong Tian,
Liming Zhou
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School of Earth and Atmospheric Sciences, Georgia
Institute of Technology, 311 Ferst Drive NW,
Atlanta, GA 30332-0340

• KEY PAST PROGRESSES
• Some key advances were made in the following
  areas in our past NASA projects in conjunction
  with our proposed NASA work.

OVERVIEW OF SCIENTIFIC PLAN

• ABSTRACT
• Terrestrial ecosystems play important role in
  the global climate system and carbon cycle.
  Coupled climate-carbon models constrained by
  remote sensing products provide important means
  to address how the terrestrial ecosystem affects
  and reacts to changing climate and global carbon
  cycle. However, our current capability is still
  limited because of inadequately developed
  modeling and remote sensing data approaches. Our
  current and past NASA projects have improved
  satellite data representation of global
  terrestrial properties and are developing new
  model schemes for the merging of satellite data
  and climate models. We propose to implement these
  schemes to explore various terrestrial
  biophysical coupling mechanisms that contribute
  to the trajectory of atmospheric carbon, and to
  develop a data assimilation system that allows
  direct inference of vegetation dynamical
  properties from remote sensed radiation fluxes in
  weather and climate prediction. The proposed work
  will be conducted using NASA MODIS products and
  the modeling framework of the Community Climate
  System Model (CCSM), and will significantly
  improve the carbon and climate predictability of
  the CCSM.

• Derived new land datasets from MODIS products for
  use in climate models (1) a new land surface
  dataset created from the latest MODIS high
  quality products of LAI, vegetation continuous
  field, IGBP land cover, and PFTs from the period
  20002001 (2) a more realistic broadband
  emissivity dataset created from MODIS thermal
  infrared bands (3) a global monthly green
  vegetation fraction dataset derived from MODIS
  data and (4) an annual maximum FVC dataset
  created from MODIS and AVHRR data. These datasets
  significantly improved land surface climate and
  energy balance simulations in the NCAR CLM2 and
  Noah land model.

• Spatial pattern of percent cover difference
  (new-old) in grass/ crop, tree, shrub, and bare
  soil at the model spatial resolution as inferred
  from MODIS land cover classification versus use
  of AVHRR.

Objective 1 To improve and evaluate several
terrestrial processes in a coupled carbon climate
model that are important for determining future
concentrations of CO2 and CH4. Objective 2 To
develop and test an approach for the assimilation
of vegetation properties as a dynamic system in a
climate model from MODIS data.

• Spatial pattern of LAI difference between the
  new and old land surface datasets (new-old as
  derived respectively from MODIS and AVHRR land
  products) in winter (DJF) and summer (JJA) .

• RESEARCH QUESTIONS TECHNICAL APPROACHES
• What determines the seasonal dynamics of leaf
  level radiation fluxes, how can these be better
  included in a climate model, and what are the
  consequences of these fluxes for leaf level
  carbon assimilation?
• Approaches We continue to revise and test the
  current CLM-DGVM (Dynamical Global Vegetation
  Model) phenology scheme and the radiation scheme
  for complex canopy. The phenology scheme will be
  strongly constrained by MODIS LAI and EVI data
  and validated using the MODIS derived Phenology
  product. The canopy radiation scheme will be
  evaluated against canopy light measurements from
  BOREAS, LBA and the Safari 2000 MODIS validation
  in southern Africa. We will also evaluate the
  modeled sunlit leaf fraction against
  climatological data of MODIS fPAR.
• What determines soil moisture and water table
  levels and consequently the distribution of
  terrestrial seasonal and permanent wetlands?
  Under what conditions will a wetland dry out?
  What will make boreal and tropical peatland
  vulnerable to climate change?
• Approaches We are using a simple
  groundwater model developed in our current IDS
  work as an efficient approach to represent
  groundwater dynamics and will extend it with an
  inter-grid horizontal water transport for use in
  GCMs. Methane emissions are to be estimated using
  models by Walter et al. (2001). The simulated
  changes in wetland areas and in the net fluxes of
  carbon and methane over these regions will then
  be assessed.
• What climate processes condition rapid losses
  from and transitions between different forms of
  terrestrial biomass carbon stores?
• Approaches We are reformulating modules in the
  CLM-DGVM by including will adding the current
  CLM-DGVM fire module fire probability based on
  the concept of fuel beds related to various
  carbon stores. We will also design some rules to
  characterize fractions of various carbon stores
  removed on average by occurrence of fire at model
  time step based on analyses of climatological and
  remote sensing indices. The MODIS daily fire
  product at 1-km will be used to constrain the
  model simulated timing and spatial distribution
  of fires.
• Can we develop a data simulation framework that
  could derive MODIS LAI in a forecast mode,
  prototyped by use of MODIS albedos and a dynamic
  vegetation model with atmospheric forcing for the
  period of MODIS data?
• Approaches We will develop and test a canopy
  radiation model to relate the vegetation
  dynamical properties with the albedo more
  realistically for complex canopies. A filter-type
  assimilation technique will be used to perform
  data-model fusion. A statistical dynamic
  vegetation model will be used to validate the
  physical dynamical vegetation model and to test
  the developed data assimilation system.
• Can we establish proper quantifications of data
  and model statistics for applications in the
  global terrestrial ecosystem in which remote
  sensing data and dynamic vegetation models can be
  optimally integrated, i.e., What statistics of
  the albedo do we use as input? How can we compute
  matching statistics from the dynamic model? What
  are the statistical properties of the dynamic
  model we need to have for the model-data fusion
  to be successful? How can we quantify the albedo
  statistics of both the dynamic model and the
  MODIS data?
• Approaches The land surface model uncertainty
  is largely attributed to uncertainties of model
  parameters, and hence will be quantified through
  Monte Carlo-type approach. We will use MODIS
  pixel albedos to obtain an optimal estimate of
  model grid level albedos and their data errors.
  The albedo scaling-up can be as simple as
  averaging the pixel values for homogenous grids
  and more complex for highly heterogeneous grids.
  Statistically-based criteria will be used to
  determine the heterogeneity of each grid cell
  before computing the optimal grid estimate.

• Developed spatially and temporally more realistic
  and accurate MODIS land albedo and LAI products
  (1) improved MODIS albedo and LAI retrievals
  under snowy and cloudy conditions (2) more
  realistic LAI seasonality (3) established global
  bare soil albedo date set for climate models.
• Improved parameterizations of the terrestrial
  processes (1) a new snow cover fraction scheme
  (2) investigating canopy effects on snow surface
  energy budget (3) integration of the MODIS
  BRDF/albedo data and land modeling (4) land
  surface turbulence, bifurcation, and soil
  moisture memory (5) parameterization of canopy
  hydrological processes.

• Developed new canopy radiation model with
  consideration of the plant canopy 3-D effects We
  found that the modeling of the canopy light
  environment as implemented in current CLM is
  seriously deficient in its partitioning of light
  between vegetation canopies and underlying
  surfaces due to its unrealistic assumption of
  plane parallel canopy geometry, in particular for
  the semiarid systems with sparse shrubs, and
  northern forest with winter snow-pack. This
  consequently leads to the underestimation of the
  light loading on the canopy, and hence its carbon
  assimilation. We have been developing a more
  realistic three dimensional canopy radiation
  model for climate modeling to describe the canopy
  geometric (shadow) effect to improve the model
  climate simulations and energy partitioning
  between canopy and its underlying surface.

• Revealed the cause of the notable differences
  between the CLM modeled and MODIS observed
  radiation flux We found that the FPAR value is
  mainly determined by LAI in MODIS and both LAI
  and stem area index (SAI) in CLM. The positive
  FPAR bias is mainly attributed to CLM SAI of
  deciduous canopy and higher LAI than MODIS for
  evergreen canopy as well. The negative FPAR bias
  results from several factors, including
  differences in LAI and soil albedo between CLM
  and MODIS or limitations of the geometric optics
  scheme used in the model.

Collaborators Rong Fu, Wenhong Li, Qing Liu
(Georgia Institute of Technology) Gordon B.
Bonan (NCAR) Ruth DeFries, John Townsend,
Eugenia Kalnay, Shunlin Liang, Chris Justice
(University of Maryland) Mark Friedl, Yuri
Knyazikhin, Ranga Myneni, Nikolay Shabanov,
Crystal Schaaf, Alan Strahler (Boston
University) Nancy Kiang, Randal Koster, Jeffrey
Privette (GSFC, NASA) Wenge Ni-Meister (Hunter
College) Xubin Zeng (Univeristy of Arizona)
Zong-Liang Yang, Guo-Yue Niu (University of Texas
at Austin) Hanqin Tian (Auburn University)
Yong-Jiu Dai (Beijing Normal University, China)
David Erickson, John Drake (Oak Ridge National
Laboratory) Xiaowen Li, Qinhou Liu (CAS
Institute of Remote Sensing, China)

• FPAR differences (FPARag0.0-FPARag0.23)
  simulated by the CLM and the MODIS FPAR
  algorithm with the canopy underlying soil albedo
  changing from 0 to 0.23 at SZA 30.

Please visit http//climate.eas.gatech.edu/dickins
on for more project information and published
papers.