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

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Case

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Regi

onal

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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

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-sp

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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!