Extreme weather: envisioning Ontario agriculture Scott Mitchell 1 , Anna Zaytseva 1 , Dan MacDonald 2 , and Ruth Waldick 1,2 27 Feb 2017 (1) (2)
Extreme weather: envisioning Ontario agriculture
ScottMitchell1,AnnaZaytseva1,DanMacDonald2,andRuthWaldick1,2
27Feb2017
(1)(2)
What’s this project about?
• createanddeliverinformationaboutcurrentandfutureclimateextremes*thatwillaffectOntario’sagriculturesectorandruralcommunities• *whatdoWEmeanbyextreme?
• developadecisionsupporttooltocharacterizeriskandvulnerabilitiesassociatedwithclimatechangeandextremesinagriculture,allowinguserstoplanforandmitigaterisksbyevaluatingdifferentadaptationchoices
• spatialscenariodevelopment–impactsoncropsandlivestock*• map-based,field-levelmapping;expectations• datarealities:weatherstations(time),GCMresolution• howtotranslatewhattheweatherdataandclimatemodelstellusintopossibleimpactstocropsandlivestock
• useofseasonal,phenology-linkedindiceswithlinkstospecificcropsandoperations
(some) Issues with existing information
• thereareproblemsusinglimitedweatherdata,orclimatemodelprojections,tocharacterizeextremeweather• howextremesusuallyconsidered?(climatemodelvariability)• spatial-temporalresolutionofmodels≠farm-scale/locallevelplanning
• many“challenges”makingsenseofexistingdata,dealingwithgaps,figuringoutwhichdatasetsarerelevanttowhatlocations
• afterthedata(andclimatemodelpredictions)are“cleanedup”andassignedtodifferentpartsofastudyregion,howdowemakesenseofthem,andmakethemrelevanttoagriculture?
• DISSEMINATE
Why focus on scenarios & phenological impacts?
• everyclimatechangemodelrunisascenario,notaprediction
• thosemodelslackspatialandtemporaldetail,butthereisdemandforinformationrelevanttolocallyevaluatinglevelsofriskandpotentialtradeoffs
• cropmodellingtypicallyfocusesonyield,
• usuallyworkbestatverylocallevels,havehighdataneeds,assumeconditionsnotchanging
• focusingonphenologicalimpactallowsustoidentifytimeswhencropsareparticularlyvulnerabletoclimatologicalevents,andassignatypicalimpacttocropyield;concentrateonrelativeimpactsratherthanspecificphysiologicalprocesses
Study area: eastern Ontario
A.Zaytseva’sM.Sc.Thesis(CarletonUniversity).
Indices derived from “just” weather data
• E.Ontarionotexpectedtobeahotspotofweatherextremes• buttypesofextremesofparticularrelevancein“regular”agriculturaloperationsarenotnecessarilywhatpeoplefirstthinkofas“extreme”
• “standard”indicesareavailabletoanalyseandcompareweather/extremes• usefultodescribegeneraltrends
• some,however,maskprocessesthatareimportanttoagriculture
Why extremes? This is NOT the whole story!
A.Zaytseva’sM.Sc.Thesis(CarletonUniversity).
Why are we here?
• Expandoninvitation,motivation• Introductions