model inter-comparison on climate change in relation to grassland productivity shaoxiu ma, gianni...
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Model inter-comparison on climate change in relation to grassland
productivityShaoxiu Ma, Gianni Bellocchi
Romain Lardy, Haythem Ben-Touhami, Katja Klumpp and modelling teams
lNRA Clermont-Theix-LyonUR 874 - Grassland Ecosystem (UREP)
Ecosystem functioning & valuation web services and workflowsJune 6-7, 2013
ELTE - Eötvös Loránd University, Budapest, Hungary
Outline
Overview the concepts and objectives of MACSUR
Methodology
Preliminary results
Outlook
Networking
Modelling
Integration
Coordination of Knowledge Hub
Capacity building
CropM
TradeM
LiveM
Met
hodo
logi
cal
Case
stu
dies
Regi
onal
pilo
t st
udie
s
http://www.macsur.eu
WP2Model inter-comparison on climate change in relation to livestock and grassland
Task 2.4
Modelinter-comparison
Task 2.1
Identification ofavailable models
Task 2.2
Development of methodsfor model evaluation
Task 2.3
Definition of a protocolfor model inter-comparison
WP3Improving the assessmentof climate change impact
on livestock and grasslandat farm level
WP4Contribution to cross-cutting activities with integrated studies at
regional level
WP1Building and exploring datasets and models on climate change in relation
to livestock and grassland
MACSUR perspective (Grassland)
To quantify uncertainties due to model
structure
To discover strengths and weaknesses in
grassland models
MACSUR perspective (Grassland)
The focus of grassland model inter-comparison in MACSUR project
http://www.macsur.eu
Data supplier Modelling team
(datasets) (model runs)
Coordinator(data segregation; output evaluation, uncertainty
analysis)
Questionaire for modelling teams
Guideline and minimum dataset requirement for model
evaluation
A common protocol for model inter-comparison
Model inter-comparison at selected sites in Europe
The pathway for model inter-comparison
Methodology
Identification of available grassland models
Documentation of core algorithms
Sensitivity tests to changes of CO2, temperature and precipitation
Evaluation of model performances
Uncertainty analyses
Run of un-calibrated and calibrated models
Modelling framework
Methodology
Gra
ssla
nd
-sp
ecifi
c
Biome models
Selected models:
Crop models (adapted to grasslands)
Methodology
Methodology
Climate manipulation
experiments
FACE, Warming and
precipitation
Field experiments
Cutting, grazing, fertilization
Eddy flux measurements
Interested datasets:
Location of observational sites
Eddy flux measurements( e.g. NEE, GPP, RECO, ET, SWC)
Methodology
InputClimate scenarios
scen1 scen2 scen3 scen4 scen5 scen6 baseline
Temperature(°C)
Standard deviation -25% -10% -5% 5% 10% 25% current
Precipitation(mm)
Standard deviation -25% -10% -5% 5% 10% 25% current
CO2 (ppm) 380pm 5% 10% 15% 25% 50% 100% 380
Sensitivity tests
Methodology
use of multiple evaluation metrics
use of fuzzy-logic to aggregate metrics into synthetic
indicators
Enlarged concept of model performance:
agreement with data + model structure
Model evaluation
Methodology
Fuzzy-logic based integrated indicators / 1
Methodology
Rivington et al., 2005, Agr. Forest Meteorol.
MethodologyFuzzy-logic based integrated indicators / 2
Confalonieri et al., 2009, Ecol. Modell.
agreement with data
model structure
single VS multiple site evaluation
Methodology
Robustness: variability of model performance with the variability of conditions
(-, worst; 1, best)
(0, best; +, worst)
Confalonieri et al., 2010, Ecol. Modell.
(-1, +1)
Ratio of relevance parameters (Rp)F Partial U
≥ 0.10 ↔ ≤ 0.50
AIC relative weight (wk)F Partial U
≥ 0.70 ↔ ≤ 0.30
0.000.500.501.00
F FF UU FU U
ComplexityF Partial U0 ↔ 1
AgreementF Partial U0 ↔ 1
0.000.250.500.750.250.500.751.00
MCIm
0.000.200.600.800.200.400.801.00
F F FF F UF U FF U UU F FU F UU U FU U U
membership functionS[x; a = min (F, U); b = max (F, U)]
membershipfunction
S[x; a = 0; b = 1
Index of agreement (d)F Partial U
≥ 0.90 ↔ ≤ 0.70
Probability of equal means (P(t))F Partial U
≥ 0.10 ↔ ≤ 0.05
Correlation coefficient (R)F Partial U
≥ 0.90 ↔ ≤ 0.70expertweight
Index of robustness (IR)F Partial U
1 ↔ 10
0.001.00
FU
RobustnessF Partial U0 ↔ 1
F F FF F UF U FF U UU F FU F UU U FU U U
membership functionS[x; a = min (F, U); b = max (F, U)]
membership functionS[x; a = min (F, U); b =max (F, U)]
Methodology
agreement with data
model structure
Robustness
Preliminary resultsObserved GPP vs Estimated GPP (g C/ Monthly) for Oensingen
Uncertainty of the simulated GPP from different grassland models
for Oensingen
Preliminary results
Model range
Uncertainty of the simulated GPP from different grassland models
for Oensingen
Preliminary results
Sensitivity of GPP (Oensingen from PaSim model)
Temperature
Preliminary results
Precipitation
CO2
OutlookSensitivity of GPP of different grassland models on the
same site(virtual results)Temperature
Precipitation
CO2
Observed
Uncertainty of the simulated yield from different grassland models and sites (virtual results)
Outlook
adapted from Palosuo, 2011
Mod
el1
Mod
el2
Mod
el3
Mod
el4
Mod
el5
Mod
el6
Mod
el7
Mod
el8
obse
rved
Crop
?
Biome
?
Uncertainty of the simulated yield from different grassland models for each sites (virtual results)
Outlook
adapted from Palosuo, 2011
Site
1
Site
2
Site
3
Site
4
Site
5
Site
6
Site
7
Site
8
Semi-arid
?
Humid
?
Document all models in the inter-comparison
Run sensitivity tests and evaluate models at a variety of sites
Expand the number of models (process-based) and datasets (representative of European grassland regions) …
Future actions
Thanks a lot for your attention!