1. Assessing the impact on crop modelling of multi- and uni-variate climate model bias adjustments
- Author
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Galmarini, S., Solazzo, E., Ferrise, R., Srivastava, A.K., Ahmed, M., Asseng, S., Cannon, A.J., Dentener, F., De Sanctis, G., Gaiser, T., Gao, Y., Gayler, S., Gutierrez, J.M., Hoogenboom, G., Iturbide, M., Jury, M., Lange, S., Loukos, H., Maraun, D., Moriondo, M., McGinnis, S., Nendel, C., Padovan, G., Riccio, A., Ripoche, D., Stockle, C.O., Supit, I., Thao, S., Trombi, G., Vrac, M., Weber, T.K.D., Zhao, C., Galmarini, S., Solazzo, E., Ferrise, R., Srivastava, A.K., Ahmed, M., Asseng, S., Cannon, A.J., Dentener, F., De Sanctis, G., Gaiser, T., Gao, Y., Gayler, S., Gutierrez, J.M., Hoogenboom, G., Iturbide, M., Jury, M., Lange, S., Loukos, H., Maraun, D., Moriondo, M., McGinnis, S., Nendel, C., Padovan, G., Riccio, A., Ripoche, D., Stockle, C.O., Supit, I., Thao, S., Trombi, G., Vrac, M., Weber, T.K.D., and Zhao, C.
- Abstract
CONTEXT: Crop models are essential tools for assessing the impact of climate change on national or regional agricultural production. Starting from meteorology, soil and crop management, fertilization and irrigation practices, they predict the yield of specific crop varieties. For long term assessments, climate models are the source of primary information. To make climate model results usable in a specific time frame context, bias adjustment (BA) is required. In fact, climate models tend to deviate from day-to-day values of the physical parameters while conserving the climate variability signal. BA brings the climatic signal to the actual values observed in a specific location and period, and to be representative of a specific period in absolute terms. BA techniques come in different flavours. The broadest categorization is univariate and multivariate methods. Multivariate methods adjust the variables considering possible cross-correlations while univariate methods treat the variables one by one without accounting for possible dependence on one another. OBJECTIVE: The hypothesis tested in this paper is that since crop models require as input climate variables that are in most of the cases cross-correlated, the multi-variate bias adjustment of the latter is likely to improve performance compared to univariate bias adjusted climate model results. METHODS: To verify this hypothesis, 14 BA methods were applied to 9 variables from 8 climate models at 21 locations across Europe and Northern Africa for a period of 5 years. Twelve crop models, from the AgMIP Wheat community, were run using the climate model results. All crop models, except one, were restarted at every growing season. The crop models were also run using the AgMERRA re-analysis. The latter were used as reference to compare the results when using the other climate models treated with the various sets of bias-adjustment methods. RESULTS AND CONCLUSIONS: The results show that multivariate BA treatment should be p
- Published
- 2024