Model inter-comparison on climate change in relation to grassland productivity

1. Model inter-comparison on climate change in relation to grassland productivity Shaoxiu Ma, Gianni Bellocchi Romain Lardy, Haythem Ben-Touhami, KatjaKlumpp and modelling teams lNRA Clermont-Theix-Lyon UR 874 - Grassland Ecosystem (UREP) Ecosystem functioning & valuation web services and workflows June 6-7, 2013 ELTE - EötvösLoránd University, Budapest, Hungary

2. Outline • Overview the concepts and objectives of MACSUR • Methodology • Preliminary results • Outlook

3. Coordination of Knowledge Hub CropM Networking Regional pilot studies Methodological Case studies Modelling TradeM Integration LiveM Capacity building http://www.macsur.eu

4. Task 2.4 Model inter-comparison Task 2.1 Identification of availablemodels Task 2.2 Development of methods for model evaluation Task 2.3 Definition of a protocol for model inter-comparison WP4 Contribution to cross-cutting activities with integrated studies at regional level WP3 Improving the assessment of climate change impact on livestock and grassland at farm level MACSUR perspective (Grassland) WP1 Building and exploring datasets and models on climate change in relation to livestock and grassland WP2 Model inter-comparison on climate change in relation to livestock and grassland

5. MACSUR perspective (Grassland) The focus of grassland model inter-comparison in MACSUR project • To quantify uncertainties due to model structure • To discover strengths and weaknesses in grassland models http://www.macsur.eu

6. Methodology The pathway for model inter-comparison • 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 Data supplierModelling team (datasets) (model runs) Coordinator (data segregation; output evaluation, uncertainty analysis)

7. Methodology Modelling framework Identification of available grassland models Documentation of core algorithms Sensitivity tests to changes of CO2, temperature and precipitation Run of un-calibrated and calibrated models Evaluation of model performances Uncertainty analyses

8. Methodology Selected models: Crop models (adapted to grasslands) Biome models Grassland-specific

9. Methodology Interested datasets: • Climate manipulation experiments • FACE, Warming and precipitation • Field experiments • Cutting, grazing, fertilization • Eddy flux measurements

10. Methodology Location of observational sites Eddy flux measurements( e.g. NEE, GPP, RECO, ET, SWC)

11. Methodology Sensitivity tests

12. Methodology Model evaluation • 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

13. Methodology Fuzzy-logic based integrated indicators / 1 Rivington et al., 2005, Agr. Forest Meteorol.

14. Methodology Fuzzy-logic based integrated indicators / 2 agreement with data model structure Confalonieri et al., 2009, Ecol. Modell.

15. Methodology single VS multiple site evaluation Robustness: variability of model performance with the variability of conditions (0, best; +, worst) (-, worst; 1, best) (-1, +1) Confalonieri et al., 2010, Ecol. Modell.

16. MCIm Methodology membership function S[x; a = min (F, U); b =max (F, U)] Correlation coefficient (R) F Partial U ≥ 0.90 ↔ ≤ 0.70 expert weight Index of agreement (d) F Partial U ≥ 0.90 ↔ ≤ 0.70 Probability of equal means (P(t)) F Partial U ≥ 0.10 ↔ ≤ 0.05 membershipfunction S[x; a = 0; b = 1 0.00 0.20 0.60 0.80 0.20 0.40 0.80 1.00 F FF F F U F U F F U U U F F U F U U U F U UU agreement with data membership function S[x; a = min (F, U); b = max (F, U)] Complexity F Partial U 0 ↔ 1 Agreement F Partial U 0 ↔ 1 Robustness F Partial U 0 ↔ 1 Ratio of relevance parameters (Rp) F Partial U ≥ 0.10 ↔ ≤ 0.50 AIC relative weight (wk) F Partial U ≥ 0.70 ↔ ≤ 0.30 0.00 0.50 0.50 1.00 F F F U U F U U 0.00 0.25 0.50 0.75 0.25 0.50 0.75 1.00 F FF F F U F U F F U U U F F U F U U U F U UU model structure Index of robustness (IR) F Partial U 1 ↔ 10 Robustness F U 0.00 1.00 membership function S[x; a = min (F, U); b = max (F, U)]

17. Preliminary results Observed GPP vs Estimated GPP (g C/ Monthly) for Oensingen

18. Preliminary results Uncertainty of the simulated GPP from different grassland models for Oensingen

19. Preliminary results Uncertainty of the simulated GPP from different grassland models for Oensingen Model range

20. Preliminary results Sensitivity of GPP (Oensingen from PaSim model) Temperature Precipitation CO2

21. Outlook Sensitivity of GPP of different grassland models on the same site (virtual results) Observed Temperature Precipitation CO2

22. Outlook Uncertainty of the simulated yield from different grassland models and sites (virtual results) ? ? Biome Crop Model5 Model6 Model4 Model8 Model2 Model3 Model7 Model1 observed adapted from Palosuo, 2011

23. Outlook Uncertainty of the simulated yield from different grassland models for each sites (virtual results) ? ? Semi-arid Humid Site8 Site2 Site3 Site7 Site1 Site5 Site6 Site4 adapted from Palosuo, 2011

24. Future actions • 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) • …

25. Thanks a lot for your attention!