The issue of properly ordering climate indices calculation and bias correction before identifying spatial analogs for agricultural applications

Abstract A spatial analog is a type of climate scenario that associates a projected future climate at a target location with similar recent-past climates at remote locations. Identifying spatial analogs is usually a step preceding the analysis of local climate impacts and adaptation actions in regions already experiencing a plausible future climate for the target. Such methodology involves several important technical choices. For instance, climates must be synthesized, for example as distributions of a few study-specific annual indices calculated from daily temperature and precipitation time series, before a metric is used to rank candidate analogs in terms of similarity with the target. Moreover, simulations used as target plausible future climates must often be bias-corrected, raising the issue of the order of operations between indices calculation and bias correction (BC). This study investigates the impact of the order of operations on the subsequent identification of analogs, by considering three approaches: BC after (the calculation of the indices); BC before; and BC both before and after. The study, part of a broader project related to agricultural pest management, considers five farms as the targets, which are located in southern Quebec, Canada. Target climate scenarios are built from an ensemble of twenty CMIP5 simulations. The reference product, used for both synthesizing recent-past analog climates and bias-correcting simulations, is the MERRA reanalysis. Results led to the formulation of recommendations for climate services centres providing users with spatial analogs, including the recourse to the self-analog test, proposed as a verification procedure to potentially reveal methodological deficiencies.