Preparing suitable climate scenario data to assess impacts on local food safety

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Abstract

Quantification of climate change impacts on food safety requires food safety assessment with different past and future climate scenario data to compare current and future conditions. This study presents a tool to prepare climate and climate change data for local food safety scenario analysis and illustrates how this tool can be used with impact models, such as bacterial and mycotoxin growth and pesticide models. As an example, coarse gridded data from two global climate models (GCMs), HadGEM2-ES and CCSM4, are selected and downscaled using the “Delta method” with quantile-quantile correction for Ukkel, Belgium. Observational daily temperature and precipitation data from 1981 to 2000 are used as a reference for this downscaling. Data are provided for four future representative concentration pathways (RCPs) for the periods 2031–2050 and 2081–2100. These RCPs are radiative forcing scenarios for which future climate conditions are projected. The climate projections for these RCPs show that both temperature and precipitation will increase towards the end of the century in Ukkel. The climate change data are then used with Ratkowsky's bacterial growth model to illustrate how projected climate data can be used for projecting bacterial growth in the future. In this example, the growth rate of Lactobacillus plantarum in Ukkel is projected to increase in the future and the number of days that the bacteria are able to grow is also projected to increase. This example shows that this downscaling method can be applied to assess future food safety. However, we only used two GCMs. To obtain a more realistic uncertainty range, using many different GCM output datasets and working directly with climate modellers is recommended. Our approach helps food safety researchers to perform their own climate change scenario analysis. The actual algorithm of the downscaling method and its detailed manual is available in the supplementary material.
Original languageEnglish
Pages (from-to)31-40
JournalFood Research International
Volume68
DOIs
Publication statusPublished - 2015

Keywords

  • stochastic weather generator
  • multimodel ensemble
  • change projections
  • model
  • precipitation
  • cmip5
  • uncertainty
  • temperature
  • calibration
  • growth

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