CarbonFusion meeting, 4 or 5 June 2008

Building from the bottom-up and learning as we go: data requirements for upscaling ecosystem function

Paul Stoy1*, Mathew Williams1

1 School of GeoSciences, University of Edinburgh, UK

Jon Evans2, Colin Lloyd2

2 Center for Ecology and Hydrology, Wallingford, UK

Ana Prieto-Blanco3, Mathias Disney3

3 Department of Geography, University College London, London, UK

Gaby Katul4, Mario Siqueira4, Kim Novick4, Jehn-Yih Juang4, Ram Oren4

4 Nicholas School of the Environment and Earth Sciences, Duke University, USA

1) Introa) Motivation

2) Examplesa) Duke sitesb) Tundra sitec) IC

3) Summarya) What

modelsneed

Motivation

‘The Leuning 7’ [after Liu and Gupta (2007)]

LSMs consist of 7 components:

1) the system boundary, B2) inputs, u3) initial states, x0 4) parameters, θ5) model structure, M 6) model states, x and 7) outputs, y

9) How should the (FLUXNET) flux data be processed?

10) What ancillary data (including EO) can and should be used?

Motivate these q’s using the upscaling challenge 1) Introa) Motivation

2) Examplesa) Duke sitesb) Tundra sitec) IC

3) Summarya) What

modelsneed

The challenge:

3.5

0.0

0 200m

LAI

Oren et al., (2006) GCB

…interpreting ecosystemfunction from dynamicEC measurements.

Example: The Duke FACESite (PP) measures a footprint with relatively low LAI.

NEEA would be ca. 50 g C m-2 y-1 if the towerwas located centrally

How do we move from leaf to tree to tower to region?

N gradient

1) Introa) Motivation

2) Examplesa) Duke sitesb) Tundra sitec) IC

3) Summarya) What

modelsneed

The challenge (continued):

Oishi et al., (in press) AFM

The adjacent DBF ecosystem (HW) has: wet & dry subplots, multiple species, LAI variability

95% peak s.w.f.

50% peak s.w.f.

sapflux

Litterbaskets

1) Introa) Motivation

2) Examplesa) Duke sitesb) Tundra sitec) IC

3) Summarya) What

modelsneed

A small part of a complicated landscape

Juang et al., (2007) WRR

Stoy et al., (2007) GCB

1) Introa) Motivation

2) Examplesa) Duke sitesb) Tundra sitec) IC

3) Summarya) What

modelsneed

MODIS GPP algorithm for PP

Heinsch et al., (2006) IEEE-TGRS

ENF or MF?

Savanna?

Observational bias(remote sensing) plays a central rolefor modelling & measurement

1) Introa) Motivation

2) Examplesa) Duke sitesb) Tundra sitec) IC

3) Summarya) What

modelsneed

Sources of bias (tundra)

Burba et al., (2008) GCB

Asner et al., (2003) GEB

Flux observation bias is an additional challenge

1) Introa) Motivation

2) Examplesa) Duke sitesb) Tundra sitec) IC

3) Summarya) What

modelsneed

‘De-biasing (?)’ using a footprint model

Left: LAI map of Abisko Tundra (AT)With ½ hr. footprint

Right: pdf of tower-measured (daily, black) vs. footprint NDVI 1) Intro

a) Motivation

2) Examplesa) Duke sitesb) Tundra sitec) IC

3) Summarya) What

modelsneed

‘De-biasing (?)’ using a footprint model

1) Introa) Motivation

2) Examplesa) Duke sitesb) Tundra sitec) IC

3) Summarya) What

modelsneed

Upscaling = preserving information?

Stoy et al. (in review) Ecosystems

Finding spatial averaging operator(s) that preserve fine-scale information content (IC)[via Shannon Entropy, Kullback-Liebler divergence]

IC for parameter space analysis?

1) Introa) Motivation

2) Examplesa) Duke sitesb) Tundra sitec) IC

3) Summarya) What

modelsneed

NE

E R

esidu

al Sp

ectrum

(mg C

m-2 s

-1)2

2000 2000.5 2001 2001.5 2002 2002.5 2003 2003.5 Year

Tim

e S

cale

(y)

Tim

e S

cale

(y)

10-4

10-3

10-2

10-1

100

10-4

10-3

10-2

10-1

100

2000 2000.5 2001 2001.5 2002 2002.5 2003 2003.5

H

D

W

M

Y

H

D

W

M

Y

Wavelet half plane model residual analysis: Duke PP and HW

Color = residual energy

Suggestions for LSMs

Problems for upscaling and models• Observational bias Measurement bias (and random error)

Potential for de-biasing using additional ecological information

Future directions / needs for FLUXNET

The ‘super site’ concept (e.g. IMECC)Ray’s 20 ecosystems? - We need temporal and spatial data for: Ecosystem structure and (with parameters), function

- How much?

- Probably just enough to describe ecosystem change over time.

How does flux ‘resonate’ with climate?

1) Introa) Motivation

2) Examplesa) Duke sitesb) Tundra sitec) IC

3) Summarya) What

modelsneed

The important time scales of variability are long

1) Introa) Motivation

2) Examplesa) Duke sitesb) Tundra sitec) IC

3) Summarya) What

modelsneed

Few high frequency(bi-monthly or less)Differences amongVeg/climate types

We need PFTs afterThe bi-monthly t.s.

Questions?

Funding: NERC (IPY)

1) Introa) Motivation

2) Examplesa) Duke sitesb) Tundra sitec) IC

3) Summarya) What

modelsneed

Reducing uncertainty with data assimilation

Early SeasonImprovement

PLIRTgapfillingmodel(Burba GCB ’08?)

Adding data increases confidence

State (t)

(Shaver et al. Parameters)

Initial Forecast

State (t+1)

g C m-2

Cum

ulat

ive

Obs (t+1)

Forecast (t+1) Assimilation

77±3

127±2

140±3

168±13

model (PLIRT)

(Ensemble Kalman Filter) 1) Introa) Motivationb) Model

2) Methodsa) Siteb) Measc) Movie

3) Resultsa) Modelb) Data

assimilationc) FLUXNET