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![Page 1: Today’s Big Picture Question Regarding Predicting and Quantifying the ‘Breathing of the Biosphere’: Can We Produce Flux Information that is ‘Everywhere.](https://reader035.fdocuments.net/reader035/viewer/2022070406/56649ddf5503460f94ad8839/html5/thumbnails/1.jpg)
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 BaldocchiYoungryel Ryu & Hideki KobayashiUniversity of California, Berkeley
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Stomata: 10-5 m
Leaf: 0.01-0.1 m
Plant: 1-10 m
Canopy: 100-1000 m
Landscape: 1-100 km
Continent: 1000 km (106 m)
Globe: 10,000 km (107 m)
Bacteria/Chloroplast: 10-6 m
How Do We Transcend Flux Information from the Scales of the Stomata to the Leaf, Plant, Lanscape and Globe?
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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)
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Hierarchy of Canopy Abstractions
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Cornelus T deWit (1970)
‘.. 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…’
The Perils of Upscaling Leaf-Scale Fluxes
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Upscaling from Landscapes to the Globe
‘Space: The final frontier … To boldly go where no man has gone before’
Captain James Kirk, Starship Enterprise
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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
Global-Scale SVAT Modeling is Possible Today
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Challenge for Landscape to Global Upscaling
Converting Virtual ‘Cubism’ back to Virtual ‘Reality’
Realistic Spatialization of Flux DataRequires 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)
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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
-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180
Longitude
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FLUXNET 2007
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• 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
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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
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Atmosphericradiativetransfer
Canopy photosynthesis,Evaporation, Radiative transfer
Soil evaporation
Beam PAR NIR
Diffuse PAR NIR
Albdeo->Nitrogen -> Vcmax, Jmax
LAI, Clumping-> canopy radiative transfer
dePury & Farquhar two leaf Photosynthesis model
Rnet
Surface conductance
Penman-Monteithevaporation model
Radiation at understory
Soil evaporation
shade sunlit
BESS, Breathing-Earth Science Simulator
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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 )
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Role of Proper Model Abstraction
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But, We Need Big Iron to Play with the Big Guys and Gals
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MOD04
MOD05
MOD06
MOD07
aerosol
Precipitable water
cloud
Temperature, ozone
MCD43 albedo
MOD11 Skin temperatureAtm
ospheric radiative transfer
Net radiation
MOD15 LAI
POLDER Foliage clumping
Canopy radiativetransfer
Challenge for a Computationally-Challenged Biometeorology Lab:Extracting Data Drivers from Global Remote Sensing to Run the Model
Youngryel was lonely with 1 PC
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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!
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Help from ModisAzure -Azure Service for Remote Sensing Geoscience
Scientific Results Download
Reduction #1 Queue
Source Metadata
AzureMODIS Service Web Role Portal
Request Queue
Analysis Reduction Stage
Data Collection Stage
Source Imagery Download Sites
. .
.
Reprojection Queue
Derivation Reduction Stage Reprojection Stage
Reduction #2 Queue
DownloadQueue
Scientists
Science results
AZURE Cloud with 200 CPUs cuts 1 Year of Processing to <2 days
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Photosynthetic Capacity Leaf Area Index
Solar RadiationHumidity Deficits
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Ryu et al (Accepted) Global Biogeochemical Cycles
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118±26 PgC yr-1
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BESS vs Machine Learning Upscaling Method
Ryu et al (Accepted) Global Biogeochemical Cycles
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Global Evaporation at 1 to 5 km scale
An Independent, Bottom-Up Alternative to Residuals based on the Global Water Balance, ET = Precipitation - Runoff
<ET> = 503 mm/y == 6.5 1013 m3/y
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BESS vs Machine Learning Upscaling Method
Ryu et al (Accepted) Global Biogeochemical Cycles
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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
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Gross Photosynthesis, GPP, Across the US
Lessons for Biofuel Production
Indicates Less GPP in the Corn Belt, than the Adjacent Temperate Forests
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Key point: 4. Temporal upscaling of fluxes from snap-shots to 8-day mean daily sum estimates
d gPOT
gPOT
d
ddttR
tRs
dttE
tEstSF
)(
)(1800
)(
)(1800)(
8
18 )(
)(1800
8
1
d dd
dday tSF
tEsE
Ryu et al (2011) Agricultural and Forest Meteorology Accepted
RgPOT =f(latitude, longitude, time)
Instantaneous LE
Rg at TOA
Day (1-8)
30 minSatellite overpass time
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Ryu et al (Accepted) Agricultural and Forest Meteorology
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Tested the scheme using 33 flux tower data from the Arctic to the Tropics
Ryu et al (Accepted) Agricultural and Forest Meteorology
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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 Next-Generation 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
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• Data standardization
MODIS Land products: standardized tiles (sinusoidal projection)
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Barriers for global RS study
• 2. Data standardization
MODIS Atmospheric products: swath=> Should be gridded to overlay with the land products
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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
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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.
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Seasonal pattern of Vmax@25 follows the seasonal pattern of LAI(modified version of Houborg et al 2009 AFM)
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Size and Number of Candidate Data Sets is Enormous
US: 15 tilesFluxTower: 32 tilesGlobal: 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
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• 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 – 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.
Reprojected Data (Sinusoidal format - equal land area pixel)
Tasked Performed with MODIS-AZURE
h12v04h13v04h11v04h10v04h09v04h08v04
h12v05h11v05h10v05h09v05h08v05
h11v06h10v06h09v06h08v06
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remote sensingof CO2
Tem
pora
l sca
le
Spatial scale [km]
hour
day
week
month
year
decade
century
local 0.1 1 10 100 1000 10 000 global
forestinventory
plot
Countries EUplot/site
talltowerobser-
vatories
Forest/soil inventories
Eddycovariance
towers
Landsurface remote sensing
Components of an Integrated Earth System EXIST, but are Multi-Faceted
From: Markus Reichstein, MPI
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Computing Carbon Dioxide and Water Vapor Fluxes Everywhere, All of the Time
Dennis BaldocchiYoungryel Ryu & Hideki KobayashiUniversity of California, Berkeley
AGU, Fall 2011