Transcript Simulation of the components of GPP and NPP in Amazonia

Detailed Simulation of Carbon Cycle
Components in 4 Amazonian Sites
David Galbraith, Brad Christofferson, Hewlley
Imbuzeiro, Naomi Levine, Yadvinder Malhi
Talk Format
• 1) Intensive C-cycle measurements: background.
• 2) Simulation of Total GPP, NPP and CUE: model
overview, comparison approach and results.
• 3) Simulation of autotrophic respiration
components: model overview, comparison
approach and results.
• 4) Simulation of NPP components: model
overview, comparison approach and results.
PART 1: Background to C-cycle
Measurements and Study Overview
Flux towers are great, but they don’t tell
us the whole story ...
• Flux towers are great as they provide:
• High resolution meteorological data
• High resolution data of above-canopy
fluxes (momentum, energy, NEE)
• Flux towers don’t provide:
• Information on NPP, a critical process
for ecosystem models;
• Information on the individual
components of GPP and NPP (e.g.
Allocation of NPP, respiration rates of
individual carbon pools and organs)
Flux tower in Caxiuanã, Brazil
We would like our models to get NPP right for the
right reasons!
KM 34: NPP
KM 34: GPP
14
50
10
ED
8
IBIS
6
CLM
4
JULES
Observed
2
0
GPP (t C ha-1 yr-1)
NPP (t C ha-1 yr-1)
12
40
ED
30
IBIS
CLM
20
JULES
10
Observed
0
ED
IBIS
CLM
JULES
Observed
ED
IBIS
CLM
JULES
Observed
Results Presented at Cusco Meeting, May 2010: JULES
simulated NPP for Manaus well but it did so by simulating
GPP and Autotrophic Respiration that were much too high
Intensive C-cycle plots provide a unique
dataset to test our models against
Malhi et al. 2009
Individual C-cycle components measured separately to construct
bottom-up estimates of GPP. Provides much-needed information on
carbon use efficiency and allocation to different carbon pools.
Intensive C-cycle plots: measurements
being made
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•
•
•
•
•
•
•
•
•
LAI
Litterfall
Specific Leaf Area
Stem growth (> 10 cm dbh)
Stem growth (> 2 cm dbh)
Stem respiration
Leaf Respiration
Coarse woody debris
Branchfall (coarse litter)
Soil Respiration
Root Production
Partitioning of soil
respiration
(soil, root, rhizotron)
LAI
Litterfall
Root Growth
Soil Respiration
Target Questions
• 1) How well do our models simulate total
GPP, NPP and CUE (=GPP/NPP)?
• 2) How well do our models simulate the
components of autotrophic respiration?
• 3) How well do our models simulate the
components of NPP (e.g. Belowground vs.
Aboveground Productivity)?
Network of Intensive C-Cycle Plots
Africa: 5 plots in 3 sites (installed in 2010;
proposal for 16 plots)
Partners: Forest Research Institute (Ghana);
Tropical Research Center (Gabon), University of
Tuscia (Italy)
South America: 17 plots in 7 sites (Funded up to 2012)
Partners: Museu Goeldi (Brazil), Universidade Federal do
Pará (Brazil), IPAM (Brazil),Museo de Historia Natural Noel
Kempff (Bolivia), Universidad Nacional de San Antonio
Abad de Cusco (Peru), IIAP (Peru)
Borneo: 8 plots in 3 sites
Partners: University of Sabah (Malaysia),
CTFS, Royal Society
Site Simulated for this Study
• Manaus KM34, Tapajós KM67, Caxiuanã
Tower Site (CAX-06), (Observations from Malhi
et al. 2009). {‘Average’ data over several years}
• Tambopata (Partially published in Aragão et al.
2009). {‘Average’ data over several years}
PART 2: SIMULATION OF TOTAL GPP, NPP
and CUE
Data Comparison Approach: Total GPP,
NPP, CUE
• Model Data: Mean annual values over SOI period
with long spin-up (K34,KM67,CAX06), mean
annual values over Sheffield period with long spinup (means calculated based on 2000 -2005)
(KM34,KM67,CAX06,TAM)
• Field Data – Validation data from Malhi et al. 2009
and Aragao et al. 2010
Results (1): K34 – CUE, SOI Drivers
Manaus K34 - SOI Drivers (2001 - 2004)
0.5
Carbon Use Efficiency
0.45
0.4
0.35
CLM
0.3
ED2
0.25
IBIS
0.2
JULES
OBS
0.15
0.1
0.05
0
CLM
ED2
IBIS
JULES
OBS
RESULTS (2): K67 – CUE, SOI Drivers
CUE: K67 (Santarem)
0.7
Carbon Use Efficiency
0.6
0.5
CLM
0.4
ED2
IBIS
0.3
JULES
OBS
0.2
0.1
0
CLM
ED2
IBIS
JULES
OBS
RESULTS: K34 – GPP, SOI Drivers
Manaus K34 - SOI Drivers (2001 - 2004)
45
GPP (t C ha-1 yr-1)
40
35
30
CLM
25
ED2
20
IBIS
JULES
15
OBS
10
5
0
CLM
ED2
IBIS
JULES
OBS
RESULTS: K67 – GPP, SOI Drivers
GPP: K67 (Santarem)
45
GPP (t C ha-1 yr-1)
40
35
30
CLM
25
ED2
IBIS
20
JULES
15
OBS
10
5
0
CLM
ED2
IBIS
JULES
OBS
RESULTS: K34 – NPP, SOI Drivers
Manaus K34 - SOI Drivers (2001 - 2004)
14
NPP (t C ha-1 yr-1)
12
10
CLM
8
ED2
IBIS
6
JULES
OBS
4
2
0
CLM
ED2
IBIS
JULES
OBS
RESULTS: K67 NPP, SOI Drivers
GPP: K67 (Santarem)
15
NPP (t C ha-1 yr-1)
14.5
14
13.5
CLM
ED2
13
IBIS
JULES
12.5
OBS
12
11.5
11
CLM
ED2
IBIS
JULES
OBS
What drives model differences in
simulated GPP and NPP?
• Different sensitivities of plant physiological processes
to environmental variables (SWRad, CO2, Leaf T, θ,
Humidity, etc.)
• Differences in the way leaf photosynthesis is scaled to
the canopy level
• Parameterisation of photosynthetic capacity
Why are JULES results different to those shown at
Cuzco meeting?
OLD RUNS: CUSCO
KM 34: NPP
Manaus K34 - SOI Drivers (2001 2004)
12
10
ED
8
IBIS
6
CLM
4
JULES
2
Observed
0
ED
IBIS
CLM
JULES
Observed
NPP (t C ha-1 yr-1)
NPP (t C ha-1 yr-1)
14
15
10
ED2
5
IBIS
JULES
0
CLM
KM 34: GPP
ED
30
IBIS
20
CLM
JULES
10
Observed
0
CLM
JULES
Observed
GPP (t C ha-1 yr-1)
GPP (t C ha-1 yr-1)
40
IBIS
ED2
IBIS
JULES OBS
OBS
Manaus K34 - SOI Drivers (2001 2004)
50
ED
CLM
60
CLM
40
ED2
20
IBIS
JULES
0
CLM
ED2
IBIS
JULES
OBS
OBS
Conclusions: Total GPP, NPP & CUE
• CLM, IBIS and JULES typically simulate a CUE
of 0.30 – 0.35 for Amazonian sites (in close
agreement with field studies) while ED
simulates a higher CUE (~ 0.45);
• ED generally simulates lower GPP than the
other three models;
• Estimates of GPP and CUE have improved in
JULES following the implementation of a new
canopy radiation scheme
Part 3: Components of Plant Respiration
Data-model comparison approach
• Respiration Components, Malhi et al. 2009:
Rtotal = Rleaf + Rstem + Rroot
(Rroot is mainly fine roots but includes some
smaller coarse roots; perhaps underestimating
coarse root respiration)
Typical Modelling Approach:
Rtotal = Rleaf + Rwood + Rfroot
For now assume Rroot(data) = Rfroot(model)
Results: K34 – Autotrophic Respiration Components, SOI
Drivers
Components of Plant Respiration: K34, SOI Drivers (2001-2004)
30
Respiration (t C ha-1 yr-1)
25
20
WoodResp
15
RootResp
LeafResp
10
5
0
CLM
ED2
JULES
OBS
Results K67 – Autotrophic Respiration
Components, SOI Drivers
K67 - Respiration Components, SOI Drivers
30
Respiration (t C ha-1 yr-1)
25
20
WoodResp
15
RootResp
LeafResp
10
5
0
CLM
ED2
JULES
OBS
Part 3: Conclusions
• Not much differences in leaf respiration across
models, but very large differences in root
respiration; root respiration is especially low
in ED;
• Treatment of coarse root complicates
comparison with field root respiration studies
Part 4: Components of NPP
Data-model comparison approach
Calculation of NPP – field studies
Malhi et al. 2009:
NPPtotal = NPPcanopy + NPPbranch + NPPtrunk + NPPcroot + NPPfroot
+ NPPVOC
Typical Modelling Approach:
NPPtotal = NPPleaf + NPPwood + NPPfroot
NPPleaf = NPPcanopy (Actually includes flowers & fruits, but these results have
now been refined)
NPPwood = NPPbranch + NPPtrunk + NPPcroot
NPProot = NPPfroot
Results: K34, NPP Components, SOI Drivers
NPP Components: K34
20
NPP (t C ha-1 yr-1)
18
16
14
12
10
NPPwood
8
NPPfroot
NPPleaf
6
4
2
0
ED2
(Unadjusted)
ED2
(Adjusted)
IBIS
JULES
OBS
Results: K67, NPP components, SOI Drivers
20
18
NPP (t C ha-1 yr-1)
16
14
12
NPPwood
10
NPPfroot
8
NPPleaf
6
4
2
0
ED2
(Unadjusted)
ED2 (Adjusted)
IBIS
JULES
OBS
A Key Problem: Mismatch between Observed
Components and Modelled Components
• For each PFT, total NPP split between “growth” and “spreading” based
on LAI
• “Growth” NPP split between leaves, woody biomass and leaves by each
PFT
NPP
PFT fraction
PFT
Cleaf + Cstem + Croot
Balanced LAI
PFT height
JULES
Results: aboveground vs. belowground allocation
(comparison with Aragao et al. 2009)
K34 - Fraction AG/BG NPP
1
0.8
Fraction of NPP
0.6
0.4
0.2
0
ED2 (Adjusted)
-0.2
-0.4
-0.6
IBIS
JULES
OBS
Part 4: Conclusions
• Considerable differences among models in
allocation of NPP
• Model allocation schemes can be difficult to
compare against field data
• Modelling groups still need to think a bit more
about this analysis