Quantification of modelling uncertainties in an ensemble of carbon simulations in grasslands and croplands.
Sandor, R., Bellocchi, G., Ehrhardt, F., Bhatia, A., Brilli, L., de Antoni Migliorati, M., Carozzi, M., Doltra, J., Dorich, C., Doro, L., Fitton, N., Fuchs, K., Gongadze, K., Grace, P., Grant, B., Giacomini, S., Klumpp, K., Léonard, L., Liebig, M., Martin, R., Massad, R. S., Merbold, L., Newton, P., Pattey, E., Rees, B., Rolinski, S., Sharp, J., Smith, P., Smith, W., Snow, V., Soussana, J.-F., Zhang, Q., Recous, S. (2019). Poster: Quantification of modelling uncertainties in an ensemble of carbon simulations in grasslands and croplands. Presented at 3rd Agriculture and Climate Change Conference, Budapest, HUN (2019-03-24 - 2019-03-26). https://prodinra.inra.fr/record/472277
Croplands and grasslands are agricultural systems that contribute to land–atmosphere exchanges of carbon (C). We evaluated and compared gross primary production (GPP), ecosystem respiration (RECO), net ecosystem exchange (NEE=RECO-GPP) of CO2, and two derived outputs - C use efficiency (CUE=-NEE/GPP) and C emission intensity (IntC= -NEE/Offtake [grazed or harvested biomass]). The outputs came from 23 models (11 crop-specific, eight grassland-specific, and four models covering both systems) at three cropping sites over several rotations with spring and winter cereals, soybean and rapeseed in Canada, France and India, and two temperate permanent grasslands in France and the United Kingdom. The models were run independently over multi-year simulation periods in five stages (S), either blind with no calibration and initialization data (S1), using historical management and climate for initialization (S2), calibrated against plant data (S3), plant and soil data together (S4), or with the addition of C and N fluxes (S5). Here, we provide a framework to address methodological uncertainties and contextualize results. Most of the models overestimated or underestimated the C fluxes observed during the growing seasons (or the whole years for grasslands), with substantial differences between models. For each simulated variable, changes in the multi-model median (MMM) from S1 to S5 was used as a descriptor of the ensemble performance. Overall, the greatest improvements (MMM approaching the mean of observations) were achieved at S3 or higher calibration stages. For instance, grassland GPP MMM was equal to 1632 g C m-2 yr-1 (S5) while the observed mean was equal to 1763 m-2 yr-1 (average for two sites). Nash-Sutcliffe modelling efficiency coefficients indicate that MMM outperformed individual models in 91.4% of cases (S3 and S5). Our study suggests a cautious use of large-scale, multi-model ensembles to estimate C fluxes in agricultural sites if some site-specific plant and soil observations are available for model calibration. The further development of crop/grassland ensemble modelling will hinge upon the interpretation of results in light of the way models represent the processes underlying C fluxes in complex agricultural systems (grassland and crop rotations including fallow periods).