Today’s Big Picture Question Regarding Predicting and
Quantifying the ‘Breathing of the Biosphere’:
• Can We Produce Flux Information that is ‘Everywhere
All the Time’ with a Mechanistic Biophysical Model?
Dennis Baldocchi
Youngryel Ryu & Hideki Kobayashi
University of California, Berkeley
How Do We Transcend Flux
Information from the Scales
of the Stomata to the Leaf,
Plant, Lanscape and Globe?
Globe: 10,000 km (107 m)
Continent: 1000 km (106 m)
Landscape: 1-100 km
Canopy: 100-1000 m
Plant: 1-10 m
Leaf: 0.01-0.1 m
Stomata: 10-5 m
Bacteria/Chloroplast: 10-6 m
A Challenge for Leaf to Landscape Upscaling:
Transform Weather Conditions from
a Weather Station to that of the Leaves in a Canopy with Their
Assortment of Angles and Layers Relative to the Sun and Sky
And use that information to drive a variety of Non-Linear
Functions (photosynthesis, energy balance, stomatal
conductance)
Hierarchy of Canopy Abstractions
Reality
2-D Representation
3-D Representation
1-D Representation
The Perils of Upscaling Leaf-Scale Fluxes
‘.. To build a model we have to consider and join two levels
of knowledge. The level with the sort of relaxation times is
then the level which provides the explanation or the
explanatory level and the one with the long relaxation
times, the level which is to be explained or the explainable
level…’
Cornelus T deWit (1970)
Upscaling from Landscapes to the Globe
‘Space: The final frontier … To boldly go where
no man has gone before’
Captain James Kirk, Starship Enterprise
Global-Scale SVAT Modeling is Possible Today
Piers Sellers, Biometeorologist (and Astronaut),
broke the ‘deWit’ barrier by attempting to incorporate
Soil-Vegetation-Atmosphere Transfer models (SVATS)
into Global Circulation Climate Models, but at coarse
spatial resolution
Challenge for Landscape to Global Upscaling
Converting Virtual ‘Cubism’ back to Virtual ‘Reality’
Realistic Spatialization of Flux Data
Requires the Merging Numerous Data Layers with
varying Time Stamps (hourly, daily, weekly), Spatial
Resolution (1 km to 0.5 degree) and Data Sources
(Satellites, Flux Networks, Climate Stations)
FLUXNET 2007
90
75
60
45
Latitude
30
15
0
-15
-30
-45
-60
-75
-90
-180
-150
-120
-90
-60
-30
0
Longitude
To Develop a Scientifically Defensible Virtual World
‘You Must get your boots dirty’, too
Collecting Real Data Gives you Insights on What is Important &
Data to Parameterize and Validate Models
30
60
90
120
150
180
• Motivation for a High-Resolution, Space-Driven, and
Mechanistic Trace Gas Exchange Model
– Current Global-Scale Remote Sensing Products tend to rely on
• Highly-Tuned Light Use Efficiency Approach
– GPP=PAR*fPAR*LUE (since Monteith 1960’s)
• Empirical, Data-Driven Approach (machine learning technique)
• Some Forcings come from Satellite Remote Sensing Snap Shots, at fine
Spatial scale ( < 1 km)
• Other Forcings come from coarse reanalysis data (several tens to
hundreds of km resolution)
– Hypothesis, We can do Better by:
• Applying the Principles taught in Biometeorology 129 and Ecosystem
Ecology 111 which Reflect Intellectual Advances in these Fields over
the past Decade and Emerging Scaling Rules
• Merging Vast Environmental Databases at same resolution
• Utilizing Microsoft Cloud Computational Resources
Lessons Learned from the John Norman, Experience with the
CanOak Model, and Reading the Literature
•
•
•
•
•
We Must:
Couple Carbon and Water Fluxes
Assess Non-Linear Biophysical Functions with Leaf-Level
Microclimate Conditions
Consider Sun and Shade fractions separately
Consider effects of Clumped Vegetation on Light Transfer
Consider Seasonal Variations in Physiological Capacity of
Leaves and Structure of the Canopy
BESS, Breathing-Earth Science Simulator
Atmospheric
radiative
transfer
Beam PAR
NIR
Canopy
photosynthesis,
Evaporation,
Radiative transfer
Diffuse PAR
NIR
Rnet
LAI, Clumping-> canopy radiative transfer
shade
sunlit
Albdeo->Nitrogen -> Vcmax, Jmax
dePury & Farquhar two leaf
Photosynthesis model
Surface conductance
Penman-Monteith
evaporation model
Radiation at understory
Soil evaporation
Soil evaporation
Necessary Attributes of Global Biophysical ET Model:
Applying Lessons from the Berkeley Biomet Class and CANOAK
•
•
•
•
•
Treat Canopy as Dual Source (Sun/Shade), Two-Layer (Vegetation/Soil) system
– Treat Non-Linear Processes with Statistical Rigor (Norman, 1980s)
Requires Information on Direct and Diffuse Portions of Sunlight
– Monte Carlo Atmospheric Radiative Transfer model (Kobayashi + Iwabuchi,, 2008)
Couple Carbon-Water Fluxes for Constrained Stomatal Conductance Simulations
– Photosynthesis and Transpiration on Sun/Shade Leaf Fractions (dePury and Farquhar,
1996)
– Compute Leaf Energy Balance to compute Leaf Saturation Vapor Pressure and Respiration
Correctly
– Photosynthesis of C3 and C4 vegetation Must be considered Separately
Light transfer through canopies MUST consider Leaf Clumping to Compute
Photosynthesis/Stomatal Conductance correctly (Baldocchi and Harley, 1995)
– Apply New Global Clumping Maps of Chen et al./Pisek et al.
Use Emerging Ecosystem Scaling Rules to parameterize models, based on remote sensing
spatio-temporal inputs
– Vcmax=f(N)=f(albedo) (Ollinger et al; Hollinger et al; Wright et al.)
– Seasonality in Vcmax is considered (Wang et al., 2008)
– Vcmax scales with Jmax (Wullschleger, 1993 )
Role of Proper Model Abstraction
But, We Need Big Iron to Play with the Big Guys
and Gals
Challenge for a Computationally-Challenged Biometeorology Lab:
Extracting Data Drivers from Global Remote Sensing to Run the Model
MOD05
Precipitable water
MOD06
cloud
MOD07
Temperature, ozone
MCD43
albedo
MOD11
Skin temperature
MOD15
LAI
POLDER
Foliage clumping
Net radiation
aerosol
Atmospheric radiative transfer
MOD04
Canopy
radiative
transfer
Youngryel was lonely with 1 PC
Barriers to Global Remote Sensing by the Berkeley
Biometeorology Lab
• Data processing
– Global 1-year source data: 2.4 TB (10 yr: 24 TB)
– 150,000+ source files
– Global 1-year calculation: 9000 CPU hours
– That is, 375 days.
– 1-year calculation takes 1 year!
Help from ModisAzure -Azure Service for
Remote Sensing Geoscience
Source Imagery Download Sites
Download
Queue
Scientists
Request
Queue
..
.
Data Collection Stage
Source
Metadata
Scientific
Results
Download
AzureMODIS
Service Web Role Portal
Science results
Reprojection Queue
Reprojection Stage
Derivation Reduction Stage
Reduction #1 Queue
Analysis Reduction Stage
Reduction #2 Queue
AZURE Cloud with 200 CPUs cuts 1 Year of Processing to <2 days
Photosynthetic Capacity
Solar Radiation
Leaf Area Index
Humidity Deficits
Ryu et al (Accepted) Global Biogeochemical Cycles
118±26 PgC yr-1
BESS vs Machine Learning Upscaling Method
Ryu et al (Accepted) Global Biogeochemical Cycles
Global Evaporation at 1 to 5 km scale
<ET> = 503 mm/y == 6.5 1013 m3/y
An Independent, Bottom-Up Alternative to Residuals
based on the Global Water Balance, ET = Precipitation - Runoff
BESS vs Machine Learning Upscaling Method
Ryu et al (Accepted) Global Biogeochemical Cycles
Big Picture Question Regarding Predicting and
Quantifying the ‘Breathing of the Biosphere’:
• Can We Produce Flux Information with a Mechanistic
Model that is ‘Everywhere, All the Time?’...Yes
Gross Photosynthesis, GPP, Across the US
Lessons for Biofuel Production
Indicates Less GPP in the Corn Belt, than the Adjacent Temperate Forests
Key point: 4. Temporal upscaling of fluxes from
snap-shots to 8-day mean daily sum estimates
Satellite overpass time
30 min
1800s  E (t ) 1800s  RgPOT (t )
SFd (t ) 

 E(t )dt
 RgPOT (t )dt
d
Rg at TOA
d
Day (1-8)
E8day
1 1800s  E (t d )
 
8 d 1
SFd (t d )
8
Instantaneous LE
RgPOT =f(latitude, longitude, time)
Ryu et al (2011) Agricultural and Forest Meteorology Accepted
Ryu et al (Accepted) Agricultural and Forest Meteorology
Tested the scheme using 33 flux tower
data from the Arctic to the Tropics
Ryu et al (Accepted) Agricultural and Forest Meteorology
Conclusion
• Three-Dimensional Radiative Transfer models should be used
to compute Mass and energy exchanges of Heterogeneous
canopies
– Models can be implemented with new generation of LIDAR data and
powerful clusters of computers
• Advances in Theory, Data Availability, Data Sharing and
Computational Systems Enable us to Produce the NextGeneration of Globally-Integrated Products on the ‘Breathing
of the Earth’
• Data-Mining these Products has Much Potential for Regional
and Locale Decision making on Environmental and Agricultural
Management
• Data standardization
MODIS Land products: standardized tiles (sinusoidal projection)
Barriers for global RS study
• 2. Data standardization
MODIS Atmospheric products: swath
=> Should be gridded to overlay with the land products
Current status
• The Cloud includes
– 10-year MODIS Terra and Aqua data over the US
(1 km resolution)
– 3-year MODIS Terra for the global land (5 km
resolution)
• Quota:
– 200 CPUs
– 100TB storage
Help from MODIS-AZURE
Necessary Attributes of the Next-Generation Global
Biophysical Model, BESS
• Direct and Diffuse Sunlight
– Monte Carlo Atmospheric Radiative Transfer model (Kobayashi, xxxx)
– Light transfer through canopies consider leaf clumping
• Coupled Carbon-Water for Better Stomatal Conductance
Simlulations
– Photosynthesis and Transpiration on Sun/Shade Leaf Fractions (dePury
and Farquhar, 1996)
– Photosynthesis of C3 and C4 vegetation considered
• Ecosystem Scaling Relations to parameterize models, based on
remote sensing spatio-temporal inputs
– Vcmax=f(N)=f(albedo) (Ollinger et al; Hollinger et al;Schulze et al.;
Wright et al.
– Seasonality in Vcmax is considered
• Model Predictions should Match Fluxes Measured at Ecosystem
Scale hourly and seasonally.
Seasonal pattern of Vmax@25 follows the seasonal pattern of LAI
(modified version of Houborg et al 2009 AFM)
Size and Number of Candidate Data Sets is Enormous
US: 15 tiles
FluxTower: 32 tiles
Global: 193 tiles
1. Global 1-year source data: 2.4 TB (10 yr: 24 TB)
2. How to know which source files are missed
among >0.1 million files
Tasked Performed with MODIS-AZURE
•
Automation
–
•
Downloads thousands of files of MODIS data from NASA ftp
Reprojection
– Converts one geo-spatial representation to another.
– Example: latitude-longitude swaths converted to sinusoidal
cells to merge MODIS Land and Atmosphere Products
•
Spatial resampling
– Converts one spatial resolution to another.
– Example is converting from 1 km to 5 km pixels.
•
Temporal resampling
– Converts one temporal resolution to another.
– Converts daily observation to 8 day averages.
•
Gap filling
Reprojected
Data
(Sinusoidal
format equal land
area
pixel)
– Assigns values to pixels without data either due to inherent
data issues such as clouds or missing pixels.
•
Masking
– Eliminates uninteresting or unneeded pixels.
– Examples are eliminating pixels over the ocean when
computing a land product or outside a spatial feature such as
a watershed.
h08v04
h09v04
h10v04
h11v04
h12v04
h13v04
h08v05
h09v05
h10v05
h11v05
h12v05
h08v06
h09v06
h10v06
h11v06
Components of an Integrated Earth System EXIST, but are Multi-Faceted
century
forest
inventory
plot
Forest/soil inventories
decade
Temporal scale
year
Landsurface remote sensing
month
Eddy
covariance
towers
week
tall
tower
observatories
remote sensing
of CO2
day
hour
local 0.1
1
plot/site
From: Markus Reichstein, MPI
10
100
1000
10 000 global
Countries EU
Spatial scale [km]
Computing Carbon Dioxide and Water Vapor
Fluxes Everywhere, All of the Time
Dennis Baldocchi
Youngryel Ryu & Hideki Kobayashi
University of California, Berkeley
AGU, Fall 2011