Dennis Baldocchi, University of California, Berkeley

Dennis Baldocchi, University of California, Berkeley
Integrating Information on ‘Biosphere Breathing’ from Chloroplast to the Globe Dennis Baldocchi, University of California, Berkeley ESPM 228 Adv Topics Biomet and Micromet

ESPM 228 Adv Topics Biomet and Micromet
Outline Overview on Principles of Leaf-Canopy Scaling and Integration Concepts Show Tests of Such Models over Multiple Time Scales Use the CANVEG Model to Ask Ecophysiological and Micrometeorological Questions Relating to Trace Gas Fluxes ESPM 228 Adv Topics Biomet and Micromet

Big Picture Question Regarding Predicting and Quantifying the ‘Breathing of the Biosphere’: Wedding at Cana, Veronese, the Louvre, Paris How can We Be ‘Everywhere All the Time?’ ESPM 228 Adv Topics Biomet and Micromet

Spatial Scales of Inquiry: Span orders of Magnitude Globe: 10,000 km (107 m) Continent: 1000 km (106 m) Landscape: km Canopy: m Plant: 1-10 m Leaf: m Stomata: 10-5 m ESPM 228 Adv Topics Biomet and Micromet Bacteria/Chloroplast: 10-6 m

The Breathing of an Ecosystem is Defined by the Sum of an Array of Coupled, Non-Linear, Biophysical Processes that Operate across a Hierarchy/Spectrum of Time and Space Scales ESPM 228 Adv Topics Biomet and Micromet

Biometeorology/Ecosystem Ecology, v2, the Processes
Numerous and Coupled Biophysical Processes, Fast and Slow Numerous Feedbacks, Positive and Negative Many coupled processes; They operate across a number of time scales; involve a number of ‘spheres’ ESPM 228 Adv Topics Biomet and Micromet

Processes and Linkages: Roles of Time and Space Scales
ESPM 228 Adv Topics Biomet and Micromet

Biophysical Modeling, Circa 1969 Cornelius T. deWit ‘Seven-stage simulation models by means of which ecosystems may be explained on basis of the molecular sciences are impossible large and detailed and it is naïve to pursue their construction’ ESPM 228 Adv Topics Biomet and Micromet

Sources of Model Complexity and Uncertainty
Representation of System Complexity Geometrical Processes and Feedbacks Non-Linearities Model Parameters, f(x,z,t) Driving, or Input, Variables and their Transformation Spatial and Temporal Resolution Accuracy/Representativeness of Test-Bed Flux Data ESPM 228 Adv Topics Biomet and Micromet

Model Pitfalls Garbage In = Garbage Out Watch out for Non-Linearities Apply at Proper Time-Step and Space-Scale Validate, Validate, Validate Don’t Parameterize Model Algorithms with the Same data used to Validate Equifinality, a combination of parameters yield the same answer An appeal to Multiple Constraints Avoid Auto-Correlation, y =f(y) Avoid Extrapolating Empirical Regression models beyond the range of the dataset Use Mechanistic and Prognostic Models to predict the future and to upscale information Closure: Equal number of Equations and Unknowns is needed ESPM 228 Adv Topics Biomet and Micromet

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 ESPM 228 Adv Topics Biomet and Micromet

Challenge for Landscape to Global Upscaling
Converting Virtual ‘Cubism’ into 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) ESPM 228 Adv Topics Biomet and Micromet

Mathematical Representation: Model Algorithms
Empirical, Regression Based Multiplicative Additive Mechanistic/Diagnostic Prognostic ESPM 228 Adv Topics Biomet and Micromet

Examples: Non-Linear Biophysical Processes
Photosynthesis Transpiration Respiration Leaf Temperature ESPM 228 Adv Topics Biomet and Micromet

Why Non-linearity is Important?
Jensen’s Inequality Taylor’s Series Expansion ESPM 228 Adv Topics Biomet and Micromet

Inverting LAI from Radiation Transfer Measurements L = 3.0 [P0]=0.05 [ln(P0)]=-2.996 Ln[P0]=-2.996 L*=2.996 L = 1.5 [P0]=0.525 [ln(P0)]=-1.497 Ln[P0]=-0.644 L*=0.644 ESPM 228 Adv Topics Biomet and Micromet

Hierarchy of Canopy Radiative Transfer Models ESPM 228 Adv Topics Biomet and Micromet

Geometrical Abstraction of the Canopy
One-Dimensional Big-Leaf Dual Source, Sun-Shade 2-Layer Vegetation and soil Multi-Layered Two-Dimensional Dual source sunlit and shaded Vegetated vs Bare Soil Three-Dimensional Individual Plants and Trees After Hanson et al Ecol Appl 2004 ESPM 228 Adv Topics Biomet and Micromet

Role of Proper Model Abstraction ESPM 228 Adv Topics Biomet and Micromet

Simple, but More Applicable and Defensible Dual Source: Sun/Shade Two Layer: Vegetation/Soil ESPM 228 Adv Topics Biomet and Micromet

Dual Source Model: Discrete Form
Whole Canopy Surface Conductance, G, Wt function of sun and shade leaf areas Sunlit leaf area Shaded leaf area ESPM 228 Adv Topics Biomet and Micromet

Probability of Sunlit (Beam, Pb) and Shaded Leaves (Psh)
P0 is probability of Beam Penetration, zero hits ESPM 228 Adv Topics Biomet and Micromet

Random Spatial Distribution: Poisson Prob Distr.
Prob of Beam Penetration, P0 is a function of: Leaf area index, L, the cosine between the leaf normal and sun, G, and the solar elevation angle, b Prob of Sunlit Leaf ESPM 228 Adv Topics Biomet and Micromet

Sunlit Leaf Area ESPM 228 Adv Topics Biomet and Micromet

Clumped Leaves Omega varies between 0 and 1

Sunlit Leaf Area, clumped Leaves ESPM 228 Adv Topics Biomet and Micromet

Sources of Spatial Heterogeneity
Vertical Variations in: Leaf area index Leaf inclination angles Leaf Clumping Leaf N + photosynthetic capacity Stomatal conductance Light, Temperature, Wind, Humidity, CO2 ESPM 228 Adv Topics Biomet and Micromet

Vertical Profiles in Leaf Area

Multi-Layer Models ESPM 228 Adv Topics Biomet and Micromet

Quantifying Sources and Sinks
Biology: Leaf area density: a(z); Internal Concentration: Ci; Stomatal Resistance, rs Physics: Boundary Layer Resistance, rb; Atmospheric Concentration, C(z) ESPM 228 Adv Topics Biomet and Micromet

Weight Source/Sink by Fraction of Sunlit and Shaded Leaves and Their Environment ESPM 228 Adv Topics Biomet and Micromet

Sun Angles and the probability of beam penetration, P0
Beer’s Law L: Leaf area Index G: direction cosine, leaf normal vs solar zenith angle ESPM 228 Adv Topics Biomet and Micromet

G Function, the relation between leaf normals and the sun

G functions for different leaf inclination angle distributions

Extinction coefficients, k, with different solar zenith angles

The extinction coefficient, k, equals
the fraction of hemi-surface leaf area (A) that is projected onto the horizontal (Ah), from a particular zenith angle. ESPM 228 Adv Topics Biomet and Micromet

Light interception by Inclined leaves k goes to zero as Ah goes to zero k= Ah/A; it equals 1 As Ah and A equal One another k goes to infinity As Ah goes to infinity ESPM 228 Adv Topics Biomet and Micromet

Leaf Angle Distribution G, direction cosine K, extinction coefficient Horizontal cos(q) 1 Vertical 2/π sin(q) 2 tan(q/π) Conical cos(q) cos(ql) Spherical or random 0.5 1/(2 cos(q)) Heliotropic 1/ cos(q) Ellipsoidal * ** ESPM 228 Adv Topics Biomet and Micromet

Many Forests have Clumped Foliage

Probability of beam penetration with clumped and randomly distributed foliage Beer’s Law W, clumping coef ESPM 228 Adv Topics Biomet and Micromet

Sunlit leaves are illuminated by direct and diffuse light Direct Light is Directional Diffuse Light is Isotropic and Hemispherical ESPM 228 Adv Topics Biomet and Micromet

Radiative Transfer Scheme Downward Diffuse Upward Diffuse D U After Norman, 1979 ESPM 228 Adv Topics Biomet and Micromet

Turbulence, Philosophical Side
‘There are two great unexplained mysteries in our understanding of the universe. One is the nature of a unified generalized theory to explain both gravity and electromagnetism. The other is an understanding of the nature of turbulence. After I die, I expect God to clarify the general field theory to me. I have no such hope for turbulence’. Theodore Von Karman ESPM 129 Biometeorology

Turbulent Transfer Lagrangian Eulerian ESPM 228 Adv Topics Biomet and Micromet

Eulerian Closure Problems
1st Order closure: 1 Eq: 2 unknowns (F,C) 2st Order closure: 2 Eq: 3 unknowns (F,C, <w’w’c’>) ESPM 228 Adv Topics Biomet and Micromet

Higher Order Closure Equations and Unknowns ESPM 228 Adv Topics Biomet and Micromet

Scalar Conservation Budget Equations: What the Terms Mean
Flux Divergence Time Rate of Change Source/Sink ESPM 228 Adv Topics Biomet and Micromet

Flux Conservation Budget: What the Terms Mean
Non-local Transport Buoyancy prod Shear Prod Pressure Interactions ESPM 228 Adv Topics Biomet and Micromet

K Theory in the Surface Layer Short vegetation Tall vegetation ESPM 228 Adv Topics Biomet and Micromet

K Theory in the Plant Canopy ESPM 228 Adv Topics Biomet and Micromet

Stanley Corrsin (1974) wrote that several conditions must hold to apply K-theory: the length scales of the turbulent transfer must be less than the length scales associated with the curvature of the concentration gradient of the scalar. the turbulent length scale must be constant over the distance where the concentration gradient changes significantly. ESPM 228 Adv Topics Biomet and Micromet

Lagrangian Near- and Far-Field Theory ESPM 228 Adv Topics Biomet and Micromet

Concentration is function of conditional probability, P,
and Source Strength, S ESPM 228 Adv Topics Biomet and Micromet

Form of Langevin’s Equation for Heterogeneous turbulence

Dispersion Matrix ESPM 228 Adv Topics Biomet and Micromet

Turbulent Mixing ESPM 228 Adv Topics Biomet and Micromet

Vertical Gradients in CO2

Vertical Gradients in q and T

Model Hierarchy ESPM 228 Adv Topics Biomet and Micromet Juang et al 2008 BLM

Do We Need to Consider Canopy Microclimate [C] Feedbacks on Fluxes?
2% Error in LE 40% Error in H 1.5% Error in Fc ESPM 228 Adv Topics Biomet and Micromet

Lessons Learned from the CanVeg Model years of Developing and Testing a Hierarchy of Scaling Models with Flux Measurements at Contrasting Oak Woodland Sites in Tennessee and California 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 ESPM 228 Adv Topics Biomet and Micromet

Summary of Points, P2 K Theory Fails in Plant Canopies due to Non-Local Turbulent Transport 2nd Order Closure Eulerian or Lagrangian Models can Accommodate Non-Local Transport Errors are Greatest for Sensible Heat Transfer Errors will propagate into errors in PBL growth, Clouds and Convective Rain and Soil Moisture Feedbacks Errors induced by K theory may be Academic for Latent Heat and CO2 Exchange ESPM 228 Adv Topics Biomet and Micromet

Part 2, Upscaling from Landscapes to the Globe ‘Space: The final frontier … To boldly go where no man has gone before’ Captain James Kirk, Starship Enterprise Watch split infinitive ESPM 228 Adv Topics Biomet and Micromet

Motivation 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 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 Merging Vast Environmental Databases Utilizing Microsoft Cloud Computational Resources ESPM 228 Adv Topics Biomet and Micromet

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) Light transfer through canopies MUST consider Leaf Clumping Apply New Global Clumping Maps of Chen et al./Pisek et al. 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 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;Schulze et al.; Wright et al.) Seasonality in Vcmax is considered (Wang et al.) ESPM 228 Adv Topics Biomet and Micromet

BESS, Breathing-Earth Science Simulator
Atmospheric radiative transfer Beam PAR NIR Diffuse PAR NIR Rnet shade sunlit LAI, Clumping-> canopy radiative transfer Canopy photosynthesis, Evaporation, Radiative transfer Albdeo->Nitrogen -> Vcmax, Jmax Surface conductance dePury & Farquhar two leaf Photosynthesis model Penman-Monteith evaporation model Berkeley Evaporation Science Simulator, Biometeorology-Lab Evaporation Science Simulator Radiation at understory Soil evaporation Soil evaporation ESPM 228 Adv Topics Biomet and Micromet

MOD04 aerosol MOD05 Precipitable water MOD06 cloud MOD07
Challenge for a Computationally-Challenged Biometeorology Lab: Extracting Data Drivers from Global Remote Sensing to Run the Model Atmospheric radiative transfer MOD04 aerosol MOD05 Precipitable water Net radiation Youngryel was lonely with 1 PC MOD06 cloud MOD07 Temperature, ozone MCD43 albedo MOD11 Skin temperature MOD15 LAI Canopy radiative transfer POLDER Foliage clumping ESPM 228 Adv Topics Biomet and Micromet

Size and Number of Candidate Data Sets is Enormous US: 15 tiles FluxTower: 32 tiles Global: 193 tiles Global 1-year source data: 2.4 TB (10 yr: 24 TB) How to know which source files are missed among >0.1 million files ESPM 228 Adv Topics Biomet and Micromet

Barriers to Global Remote Sensing by the Berkeley Biometeorology Lab
Data processing Global 1-year calculation: 9000 CPU hours That is, 375 days. 1-year calculation takes 1 year! ESPM 228 Adv Topics Biomet and Micromet

Photosynthetic Capacity Leaf Area Index Solar Radiation Humidity Deficits ESPM 228 Adv Topics Biomet and Micromet

Test of BESS Model with Flux Towers

Test of BESS model with Data-Driven Model (Jung et al.) and Basin Water Balance Ryu et al 2012 GBC ESPM 228 Adv Topics Biomet and Micromet

What is Globally Integrated GPP? ESPM 228 Adv Topics Biomet and Micromet

UpScaling GPP Regionally with Sun-Shade Coupled Energy Balance Photosynthesis Model Ryu et al. unpublished ESPM 228 Adv Topics Biomet and Micromet

Global Evaporation at 1 to 5 km scale <ET> = 503 mm/y == m3/y An Independent, Bottom-Up Alternative to Residuals based on the Global Water Balance, ET = Precipitation - Runoff ESPM 228 Adv Topics Biomet and Micromet

CANOAK MODEL Physiology Photosynthesis Stomatal Conductance Transpiration Micrometeorology Leaf/Soil Energy Balance Radiative Transfer Lagrangian Turbulent Transfer ESPM 228 Adv Topics Biomet and Micromet

CANOAK Schematic ESPM 228 Adv Topics Biomet and Micromet

Leaf Energy Balance R: is shortwave solar energy, W m-2 L: is Longwave, terrestrial energy, W m-2 lE: Latent Heat Flux Density, W m-2 H: Sensible Heat Flux Density, W m-2 ESPM 228 Adv Topics Biomet and Micromet

Leaf Energy Balance, Wet, Transpiring Leaf
Net Radiation is balanced by the sum of Sensible and Latent Heat exchange ESPM 228 Adv Topics Biomet and Micromet

Derivation 1: Leaf Energy Balance 2: Resistance Equations for H and lE 3: Linearize T4 and es(T) ESPM 228 Adv Topics Biomet and Micromet

Linearize with 1st order Taylor’s Expansion Series ESPM 228 Adv Topics Biomet and Micromet

Linearize the Saturation Vapor Pressure function ESPM 228 Adv Topics Biomet and Micromet

Wc, the rate of carboxylation when ribulose bisphosphate (RuBP) is saturated Wj, the carboxylation rate when RuBP regeneration is limited by electron transport. Wp carboxylation rate with triose phosphate utilization ESPM 228 Adv Topics Biomet and Micromet

Analytical Equation for Leaf Photosynthesis Baldocchi 1994 Tree Physiology ESPM 228 Adv Topics Biomet and Micromet

Model Parameters Leaf Area Index Photosynthetic Capacity, Jmax, Vcmax Kinetics Basal Respiration, leaf/soil ESPM 228 Adv Topics Biomet and Micromet

Seasonality in LAI, Deciduous Forests Baldocchi and Wilson ESPM 228 Adv Topics Biomet and Micromet

Jmax and Vcmax scale with one another Wullschleger 199 J exp bot Wullschleger, 1993 J Expt Bot ESPM 228 Adv Topics Biomet and Micromet

Practical Assessment for Vcmax in sites with many species and spatial variability ESPM 228 Adv Topics Biomet and Micromet

Seasonality in Vcmax Wilson et al Tree Physiol ESPM 228 Adv Topics Biomet and Micromet

High Vcmax must be Achieved in Seasonally- Droughted Ecosystems to attain Positive Carbon Balance Area under the Curves are Similar ESPM 228 Adv Topics Biomet and Micromet

Temperature Response Curve Ea or Ha activation energy s: change in entropy Hd: change in deactivation enthalpy R: Universal gas constant T: temperature, K Harley and Tenhunen; Bernacchi et al. ESPM 228 Adv Topics Biomet and Micromet

Model Test: Hourly to Annual Time Scale
Stress need to use ensembles of flux data, not instantaneous data points, they have errors too, both sampling and bias ESPM 228 Adv Topics Biomet and Micromet

Model Test: Hourly Data

Time Scales of Interannual Variability are recreated forcing model withi weather and seasonal changes in LAI and Vcmax ESPM 228 Adv Topics Biomet and Micromet

Who is Right and Wrong, and Why? How Good is Good Enough?
Model Validation: Who is Right and Wrong, and Why? How Good is Good Enough? Hansen et al, 2004 Ecol Monograph ESPM 228 Adv Topics Biomet and Micromet

P2. Sensitivity and Science Questions f(Time, space, Parameters & Processes) ESPM 228 Adv Topics Biomet and Micromet

What is Interannual Variability, beyond the measurement record?

Decadal Scales of Variability, Information exists at Long time scales

NEE and Growing Season Length

Importance of Vcmax Vcmax(73) Vcmax(50) % difference NEE (gC m-2 a-1) -577 -454 -21.3 lE (MJ m-2 a-1) 1690 1584 -6.3 H (MJ m-2 a-1) 1096 1199 9.3 ESPM 228 Adv Topics Biomet and Micromet

Vertical Variations in Vcmax, are they needed? ESPM 228 Adv Topics Biomet and Micromet

Light Use Efficiency and Net Primary Productivity
NPP=e f Qp ESPM 228 Adv Topics Biomet and Micromet

Emergent Processes: Impact of Leaf Clumping on Canopy Light Response Curves ESPM 228 Adv Topics Biomet and Micromet

LUE and Leaf Area ESPM 228 Adv Topics Biomet and Micromet

LUE and Ps Capacity ESPM 228 Adv Topics Biomet and Micromet

Developing Simple Model from Complex One

Role of Leaf Angle Inclination and Clumping on Fluxes clumped random spherical erectophile planophile NEE (gC m-2 a-1) -577 -354 -720 -1126 -224 lE (MJ m-2 a-1) 1690 1551 1774 2023 1473 H (MJ m-2 a-1) 1096 1032 1095 1171 1008 ESPM 228 Adv Topics Biomet and Micromet

Interaction between Clumping and Leaf Area

How Sky Conditions Affect NEE?

Conversion of direct to diffuse light increases light capture ESPM 228 Adv Topics Biomet and Micromet

Knohl and Baldocchi, 2008 JGR Biogeosci ESPM 228 Adv Topics Biomet and Micromet

Interpreting Stable Isotopes
Conventional theory ESPM 228 Adv Topics Biomet and Micromet

Isotopes Infer Leaf Temperatures of Tree Leaves are Constrained, ~ 21 C Leaf Temperature Growing Season Temperature ESPM 228 Adv Topics Biomet and Micromet Helliker and Richter 2008 Nature

Leaf Temperature, Modeled with CANOAK, as a Central Tendency near 20 C
Canoak Model ESPM 228 Adv Topics Biomet and Micromet

Isotopes Evaluate a Flux-Weighted Temperature
Transpiration (E) Weighted Leaf Temperature ESPM 228 Adv Topics Biomet and Micromet

Transpiration Weighted Leaf Temperature for Oak Savanna

Leaf size, CO2 and Temperature: why oak leaves are small in CA and large in TN ESPM 228 Adv Topics Biomet and Micromet

NEE (gC m-2 a-1) -577 -588 -586 lE (MJ m-2 a-1) 1690 1652 1615 H (MJ m-2 a-1) 1096 1164 1202 ESPM 228 Adv Topics Biomet and Micromet

Physiological Capacity and Leaf Temperature: Why Low Capacity Leaves Can’t Be Sunlit::or don’t leave the potted Laurel Tree in the Sun ESPM 228 Adv Topics Biomet and Micromet

Study area for test of CANOAK-3d Traverse radiometer system a b g f e d c 1 2 3 Fig3 ESPM 228 Adv Topics Biomet and Micromet

Simulated understory (1m above the ground) radiations near the tram site (W m-2) Downward PAR Upward PAR Net radiation Kobayashi et al AgForMet ESPM 228 Adv Topics Biomet and Micromet

Simulated images (RGB composite) 8/5/2007 Simulation AVIRIS 5/12/2006 Kobayashi et al AgForMet ESPM 228 Adv Topics Biomet and Micromet

Comparison of simulated and tram-measured PAR and net radiation DOY 124 DOY 194 DOY 215 PAR (obs.) PAR (Sim.) Hour Hour Hour Hour Hour Hour Kobayashi et al AgForMet ESPM 228 Adv Topics Biomet and Micromet

Comparison of top of the tower net radiation, sensible heat and latent heat Net radiation Sensible heat Latent heat Kobayashi et al AgForMet ESPM 228 Adv Topics Biomet and Micromet

Below Canopy Fluxes ESPM 228 Adv Topics Biomet and Micromet

Below Canopy Fluxes and Canopy Structure and Function

Impact of Thermal Stratification

Impact of Litter ESPM 228 Adv Topics Biomet and Micromet

Issues How Good is Good Enough? How Much Detail Is Enough? Where and When can we Simplify? Assessing Errors and Variability in Model Parameters Constraining Model Parameters Assessing Errors in Driving Meteorological Conditions Biases in Test Data used to validate Models ESPM 228 Adv Topics Biomet and Micromet

Conclusions Biophysical Models that Couple Aspects of Micrometeorology, Ecophysiology and Biogeochemistry Produce Accurate and Constrained Fluxes of C and Energy, across Multiple Time Scales Models can be used to Interpret Field Data LUE is affected by LAI, Clumping, direct/diffuse radiation, Ps capacity NEE is affected by length of growing season Interactions between leaf size, Ps capacity and position help leaves avoid lethal temperatures Below canopy fluxes are affected by T stratification and litter ESPM 228 Adv Topics Biomet and Micromet

CO2 Microclimate ESPM 228 Adv Topics Biomet and Micromet

Temperature Microclimate

Lesson/Exercise Vary CO2 and compute fluxes, ambient, +100, +300, +500 ppm Vary Temperature by -2, +2 and +4 C CoVary Temperature and CO2 (-100 ppm, -2 C; +2 C, +200 ppm; + 4C, +400 ppm ESPM 228 Adv Topics Biomet and Micromet

Family of G-Functions Computed Y. Ryu ESPM 228 Adv Topics Biomet and Micromet

Model Test: Daily Integration

Canopy Representation ESPM 228 Adv Topics Biomet and Micromet

3-d Representation of Canopy Qi Chen and D. Baldocchi ESPM 228 Adv Topics Biomet and Micromet

Carboxylation Velocity Profiles

Heterogeneous Canopies, Clumping and Radiative Transfer
Ryu et al 2011 AgForestMet ESPM 228 Adv Topics Biomet and Micromet

Interconnection of Key Ecosystem Processes
System Complexity: Interconnection of Key Ecosystem Processes ESPM 228 Adv Topics Biomet and Micromet

EcoHydrology Has Other Demands ESPM 228 Adv Topics Biomet and Micromet Thompson et al 2011, WRR

Model Detail =f(ecosystem, climate) EP: Evaporation potential; GW: Groundwater Thompson et al 2011, WRR ESPM 228 Adv Topics Biomet and Micromet

Define K in terms of Sun and Leaf Inclination Angles a,angle between leaf normal And solar zenith b, solar elevation angle k, extinction coefficient G, G-function or direction cosine ESPM 228 Adv Topics Biomet and Micromet

Project the area of a leaf normal to the sun’s beam (Ab) onto the horizontal (Ah). Project the area of a leaf onto the area normal to the solar beam ESPM 228 Adv Topics Biomet and Micromet

Voila’ ESPM 228 Adv Topics Biomet and Micromet

2nd Order Equation, General Form ESPM 228 Adv Topics Biomet and Micromet

Examples of 2nd Order Budget Equations ESPM 228 Adv Topics Biomet and Micromet

Pressure Fluctuation is a function of the drag coefficient ESPM 228 Adv Topics Biomet and Micromet

Dennis Baldocchi, University of California, Berkeley

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Dennis Baldocchi, University of California, Berkeley

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