Selection of climate change scenario data for impact modelling

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Standard

Selection of climate change scenario data for impact modelling. / Sloth Madsen, M.; Maule, C. Fox; MacKellar, N.; Olesen, J. E.; Christensen, J. Hesselbjerg.

I: Food Additives and Contaminants - Part A Chemistry, Analysis, Control, Exposure and Risk Assessment, Bind 29, Nr. 10, 01.10.2012, s. 1502-1513.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Sloth Madsen, M, Maule, CF, MacKellar, N, Olesen, JE & Christensen, JH 2012, 'Selection of climate change scenario data for impact modelling', Food Additives and Contaminants - Part A Chemistry, Analysis, Control, Exposure and Risk Assessment, bind 29, nr. 10, s. 1502-1513. https://doi.org/10.1080/19440049.2012.712059

APA

Sloth Madsen, M., Maule, C. F., MacKellar, N., Olesen, J. E., & Christensen, J. H. (2012). Selection of climate change scenario data for impact modelling. Food Additives and Contaminants - Part A Chemistry, Analysis, Control, Exposure and Risk Assessment, 29(10), 1502-1513. https://doi.org/10.1080/19440049.2012.712059

Vancouver

Sloth Madsen M, Maule CF, MacKellar N, Olesen JE, Christensen JH. Selection of climate change scenario data for impact modelling. Food Additives and Contaminants - Part A Chemistry, Analysis, Control, Exposure and Risk Assessment. 2012 okt 1;29(10):1502-1513. https://doi.org/10.1080/19440049.2012.712059

Author

Sloth Madsen, M. ; Maule, C. Fox ; MacKellar, N. ; Olesen, J. E. ; Christensen, J. Hesselbjerg. / Selection of climate change scenario data for impact modelling. I: Food Additives and Contaminants - Part A Chemistry, Analysis, Control, Exposure and Risk Assessment. 2012 ; Bind 29, Nr. 10. s. 1502-1513.

Bibtex

@article{d80539485d60435cafea399eef68e2e1,
title = "Selection of climate change scenario data for impact modelling",
abstract = "Impact models investigating climate change effects on food safety often need detailed climate data. The aim of this study was to select climate change projection data for selected crop phenology and mycotoxin impact models. Using the ENSEMBLES database of climate model output, this study illustrates how the projected climate change signal of important variables as temperature, precipitation and relative humidity depends on the choice of the climate model. Using climate change projections from at least two different climate models is recommended to account for model uncertainty. To make the climate projections suitable for impact analysis at the local scale a weather generator approach was adopted. As the weather generator did not treat all the necessary variables, an ad-hoc statistical method was developed to synthesise realistic values of missing variables. The method is presented in this paper, applied to relative humidity, but it could be adopted to other variables if needed.",
keywords = "crop phenology, method validation, mycotoxins, precipitation, relative humidity, temperature",
author = "{Sloth Madsen}, M. and Maule, {C. Fox} and N. MacKellar and Olesen, {J. E.} and Christensen, {J. Hesselbjerg}",
year = "2012",
month = "10",
day = "1",
doi = "10.1080/19440049.2012.712059",
language = "English",
volume = "29",
pages = "1502--1513",
journal = "Food Additives & Contaminants: Part A",
issn = "1944-0049",
publisher = "Taylor & Francis Online",
number = "10",

}

RIS

TY - JOUR

T1 - Selection of climate change scenario data for impact modelling

AU - Sloth Madsen, M.

AU - Maule, C. Fox

AU - MacKellar, N.

AU - Olesen, J. E.

AU - Christensen, J. Hesselbjerg

PY - 2012/10/1

Y1 - 2012/10/1

N2 - Impact models investigating climate change effects on food safety often need detailed climate data. The aim of this study was to select climate change projection data for selected crop phenology and mycotoxin impact models. Using the ENSEMBLES database of climate model output, this study illustrates how the projected climate change signal of important variables as temperature, precipitation and relative humidity depends on the choice of the climate model. Using climate change projections from at least two different climate models is recommended to account for model uncertainty. To make the climate projections suitable for impact analysis at the local scale a weather generator approach was adopted. As the weather generator did not treat all the necessary variables, an ad-hoc statistical method was developed to synthesise realistic values of missing variables. The method is presented in this paper, applied to relative humidity, but it could be adopted to other variables if needed.

AB - Impact models investigating climate change effects on food safety often need detailed climate data. The aim of this study was to select climate change projection data for selected crop phenology and mycotoxin impact models. Using the ENSEMBLES database of climate model output, this study illustrates how the projected climate change signal of important variables as temperature, precipitation and relative humidity depends on the choice of the climate model. Using climate change projections from at least two different climate models is recommended to account for model uncertainty. To make the climate projections suitable for impact analysis at the local scale a weather generator approach was adopted. As the weather generator did not treat all the necessary variables, an ad-hoc statistical method was developed to synthesise realistic values of missing variables. The method is presented in this paper, applied to relative humidity, but it could be adopted to other variables if needed.

KW - crop phenology

KW - method validation

KW - mycotoxins

KW - precipitation

KW - relative humidity

KW - temperature

UR - http://www.scopus.com/inward/record.url?scp=84866718834&partnerID=8YFLogxK

U2 - 10.1080/19440049.2012.712059

DO - 10.1080/19440049.2012.712059

M3 - Journal article

C2 - 22889171

AN - SCOPUS:84866718834

VL - 29

SP - 1502

EP - 1513

JO - Food Additives & Contaminants: Part A

JF - Food Additives & Contaminants: Part A

SN - 1944-0049

IS - 10

ER -

ID: 186940310