Title: Adjusting climate model bias for agricultural impact assessment: how to cut the mustard
Authors: GALMARINI STEFANOCANNON ALEXCEGLAR ANDREJCHRISTENSEN OLEDE NOBLET N.DENTENER FRANCISCUSDOBLAS REYES FRANCISCODOSIO ALESSANDROGUTIERREZ JOSE MANUELITURBIDE M.JURY M.LANGE S.LOUKOS HARILAOSMAIORANO ANDREAMARAUN DOUGLASMCGINNIS SETNIKULIN GRIGORYRICCIO A.SANCHEZ ENRIQUESOLAZZO EFISIOTORETI ANDREAVRAC MATHIEUZAMPIERI MATTEO
Citation: Climate Services vol. 13 p. 65-69
Publisher: Elsevier BV
Publication Year: 2019
JRC N°: JRC114597
ISSN: 2405-8807 (online)
URI: https://www.sciencedirect.com/science/article/pii/S2405880718300608?via%3Dihub
https://publications.jrc.ec.europa.eu/repository/handle/JRC114597
DOI: 10.1016/j.cliser.2019.01.004
Type: Articles in periodicals and books
Abstract: The growing conditions and key plant physiological processes of the most important staple crops for the human and animal food chains depend on climate conditions and are affected by climate extremes such as heat stress and drought (e.g. Porter and Semenov 2005, Lobell et al. 2014, Zampieri et al. 2017a). They will therefore be highly influenced by climate change in the coming decades (i. e. Rosenzweig et al. 2014, Iizumi et al. 2017). Climate change effects on crops can be estimated by using process-based crop models. Based on formulations of the essential physiological plant-growth processes (Zampieri et al., 2017b), they are used for both operational seasonal prediction of crop yields (i.e. MARS) and assessing climate-change impacts and adaptation measures for food production (Asseng et al. 2014, Rosenzweig et al. 2014; Caubel et al. 2015, 2017). For the latter, crop models rely on multi-decadal climate projections computed by global climate models, e.g. in the framework of the Climate Model Intercomparison Projects (CMIP), or within regional initiatives such as the Coordinated Regional Climate Downscaling Experiment (CORDEX). It is well known that climate model simulations are affected by both systematic and random errors, which prevent them from correctly simulating the occurrence and the intensity of extreme events such as droughts, heat waves, and extreme precipitation. Model-bias normally refers to only one component of the error, i.e. the average difference between the modelled and observed values (e.g. glossary in CLIMATE4IMPACTS, Potempski and Galmarini, 2008). In climate science, however, the term has a broader meaning and it is used to represent the systematic errors (mean and higher moments) of a given variable (Teutschbein and Seibert, 2013; Maraun, 2016; Maraun and Widmann, 2018). Prior to using climate model simulation for (among many other applications) crop modeling, an adjustment of the variables is required so that their statistical properties are more similar to the ones observed. Bias-correction or more properly adjustment (B-A) methods, developed as generic statistical techniques, have been devised in large varieties also for climate applications. It is important to notice that, in agreement with the bias definition in climate science, the expression bias-adjustment does not imply a correction of the bias only, but relates to the overall systematic error and its distribution.
JRC Directorate:Sustainable Resources

Files in This Item:
There are no files associated with this item.


Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.