1. GPP(GEE),RE(ER) and comparisons
Oh My!
Ankur Desai, et al.
The Gap Filling Society
Jena, 20 Sept 2006

2. What we’re doing (status quo)
Using same dataset from NEE gap filling (12 site-years, 51 scenarios/site) to comparing GPP & RE across methods
13 of 15 methods produce GPP & RE
9 of these analyzed so far
With variants, currently at 19 analyzed
Unlike NEE, no benchmark
However, BETHY model is part of group
Can use BETHY as benchmark as a 1st try
Perhaps synthetic noisy data would help
Questions on how to fill + decompose NEE

3. Daily and annual sums of GPP & RE computed
What we’ve done so far
To date, all datasets have been processed and put in common binary format
Daily and annual sums of GPP & RE computed
Mean and variance across 51 replicates and across methods computed
Diagnostic plots made
Box plots look at GPP/RE across methods (letters) , replicates (gray bars), mean and st.dev (+) across methods and range (box)
Colors delineate method type

4. Who’s doing what ANN Neural network Name Abbrev. Author Type Color
BETHY_365D
B365
Kattge
process model
blue
BETHY_12D
B12
NLR_A NA
Noormets
regression (AQRTa)
magenta
NLR_EM NE
Desai
regression (Eyring)
orange
NLR_FM_AD
NFA
Richardson
regression (abs dev)
brown
NLR_FM_OLS
NFO
regression (least sq)
NLR_LM_INT_DC
NLID
Falge
regression (int)
red
NLR_LM_INT_LU
NLIL
NLR_LM_INT_RE
NLIR
NLR_LM_TA_DC
NLTD
regression (air temp)
NLR_LM_TA_LU
NLTL
NLR_LM_TA_RE
NLTR
NLR_LM_TS_DC
NLSD
regression (soil temp)
NLR_LM_TS_LU
NLSL
NLR_LM_TS_RE
NLSR
NLR_FCRN_1
NC1
Barr
regression
red brown
NLR_FCRN_2
NC2
regression (with intercept)
SPM
Stauch
semi-parametric
green
UKF
Hollinger
Kalman filter
MDS
Reichstein
Marginal sampling
ANN
Moffzt
Neural network

5. What we’ve found so far

6. What we’ve found so far

7. What’s next (action items)
Get all data for methods that want to be part of comparison
Skipping analysis of il1_2002, it3_2001
Only testing 10 Mixed gaps only + r0 (no artificial gaps)
Fill and decompose as you would when gapfilling and publishing GPP & RE for your sites – due end of October for inclusion
Run other benchmarks and tests
Run GPP and RE analysis with synthetic noisy daya
Jens K. to give Dave H. BETHY data (all sites), Dave H. to corrupt (add noise), Antje to gapify (10 mixed scenarios w/ 35% missing + 0% missing) - next 3-4 mos
Run “corrupt” data through GPP/RE decomp. methods – by Feb
Produce diurnal plots by season and scatter plots
Perform ANOVA of GPP and RE for site x method
Compare to independent data (chamber, inventory, etc…)
Share data/results with group – as acquired/analyzed - Ankur D.
Write 1st draft manuscript – Dec-Jan (Ankur D.)
Discuss – Ankur D. to create wiki this fall
Submit sometime afterward (interest in rapid publication, co-publication with gapfilling paper)

8. Manuscript outline Title Abstract Introduction Methods Results
GPP and RE rock
Abstract
See below
Introduction
Why?, Hypotheses, Prior work
Methods
Decomposition, methods, sites, comparison, statistics, synthetic data analysis,
Results
Pretty pictures that corroborate or refute hypotheses
Discussion
What does it all mean, can we trust GPP & RE from EC data and with what confidence, what does synthetic analysis imply
Conclusion
We told you so