Comentarios acerca de laspresentaciones de los paísesAndy ChallinorA.J.Challinor@leeds.ac.ukSchool of Earth andEnvironment
Temas1. “Considera que los escenarios se acercan a loque realmente sucediera?”– Incertidumbre (CIAT-PNUMA,IDEAM, SENHAMI)–...
• We don’t know by how much our models are inerror because we don’t know the error:– in model inputs (e.g. initial conditi...
Predictability varies spatially and temporallyHawkins and Sutton (2009) – Bull. Am. Met. Soc.Signal to noise ratio for dec...
Schlenker & Roberts (2009) - PNAS Vara Prasad et al (2001)DailyTmax of 29-30°CFlower bud temperature (oC)24 28 32 36 40 44...
Importancia de cuantificar incertidumbreEnsemble crop-climate modelling to inform adaptationPercentageofharvestsfailingAda...
Increase in GMT (oC)2 x σ crop failure eventsPercentageofharvestsfailing0-2 (6720) 2-4 (5832) 4-6 (2352) 6-8 (56)Error bar...
Identifying key sources of uncertainty:focus on processes not rangesThe use of models as black boxes, with the associated ...
Relationship between spatial scale and uncertaintyDo increases in model resolution improve simulation skill?Yes! For mean ...
Examine count of Tmax>30oC as this is known to be importantCan use observations to measure error, and to correct for it in...
IPSL SRES A1Bminus A2 (raw)Nudging minusDelta whenQUMP used topredict IPSL2xσ across QUMPwith Bias cor.2030-2059Tmax > 30....
Como presentar incertidumbreAnalysis of climate models to tell us ‘when’ (rather than ‘if’)• A1B and A2 are similar ifyou ...
“Improved treatments ofuncertainty: recent progress andimplications” March 13th and 14th2013, London• Review EQUIP progres...
2. Predictibilidad actual del clima• Variabilidad del clima• Detectación del cambio climático• Cambios en variabilidad – p...
Detection of climate change:importance of internal climate variabilityEd HawkinsCentralEnglandTemperature
The role of internal climate variability: example of Central EnglandTemperature – very different oC/decade climate change!...
Emergence of signals in impacts:means vs variability• In impacts studies the focus is often on mean changes, e.g. in crop ...
Changes in variability may become clearersooner than changes in the meanChallinor et al. (2013)
Australian wheatharvest failureRussian wheatharvest failureChanges in variability, and their numerousinteractions, may alr...
3. Vulnerabilidad y adaptacion• Two paradigms• Importance of social sciences
Notes: Yellow arrows: the cycle of cause and effect among the four quadrants.Blue arrow: societal response to climate chan...
Dominant perspective: 2. social scienceSustainable livelihoods frameworkThe arrows within the framework are used as shorth...
4. SíntesisChallinor et al. (2009b)“insufficientlyconstrained” (?)Impreciso einútil (?)Preciso /exacto peroincorrecto (?)
Data assimilation – the ‘fourth dimension’Importancia de las observaciones para reducirincertidumbre• Porque se pueden usa...
Conclusiones• Tratamiento de incertidumbre– Muy importante cuantificar incertidumbre y estarconsciente de construir buenas...
References• Challinor et al (2012) available athttp://www.sciencedirect.com/science/article/pii/S016819231200281X• Challin...
Upcoming SlideShare
Loading in …5
×

Presentación Andy Challinor - Foro Construcción Escenarios de Cambio Climático en los Andes

300 views

Published on

El Profesor Andy Challinor compartió sus experiencias acerca de la construcción de escenarios de cambio climático, con base en algunas consultas realizadas por autoridades nacionales en el tema de Colombia, Ecuador y Perú.

Andy Challinor es líder en el tema adaptación en el Programa de Investigación CCAFS (Cambio Climático, Agricultura y Seguridad Alimentaria) del Grupo Consultivo CGIAR; investigador principal en "NERC EQUIP: cuantificación de la incertidumbre para la predicción de impactos"; y director de investigación en el Africa College Partnership.

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
300
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
5
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Presentación Andy Challinor - Foro Construcción Escenarios de Cambio Climático en los Andes

  1. 1. Comentarios acerca de laspresentaciones de los paísesAndy ChallinorA.J.Challinor@leeds.ac.ukSchool of Earth andEnvironment
  2. 2. Temas1. “Considera que los escenarios se acercan a loque realmente sucediera?”– Incertidumbre (CIAT-PNUMA,IDEAM, SENHAMI)– Downscaling (INHAMI, SENHAMI)2. Predictibilidad actual del clima (SENHAMI,IDEAM)– Variabilidad del clima– Detectación del cambio climático3. Vulnerabilidad y adaptación (INHAMI, SENHAMI,CIAT-PNUMA)4. Síntesis
  3. 3. • We don’t know by how much our models are inerror because we don’t know the error:– in model inputs (e.g. initial conditions, boundaryconditions, parameters, driving variables)– in model structure (inc. spatial and temporaldiscretization)– resulting from intrinsic stochastic variabilityWhat is uncertainty?
  4. 4. Predictability varies spatially and temporallyHawkins and Sutton (2009) – Bull. Am. Met. Soc.Signal to noise ratio for decadal mean surface air temperature predictions4Este análisis se puede hacer para cultivos (Vermuelen et al., 2013)
  5. 5. Schlenker & Roberts (2009) - PNAS Vara Prasad et al (2001)DailyTmax of 29-30°CFlower bud temperature (oC)24 28 32 36 40 44 48Fruitset(%)0204060Groundnut in controlled environmentsMaize using county-level yieldsDailyT of 32-39 °C ,depending on timingScale dependency of biophysical relationships• If this scale dependency can be further understood then models couldimprove, thus reducing uncertainty• To do this, need to put together diverse types of models
  6. 6. Importancia de cuantificar incertidumbreEnsemble crop-climate modelling to inform adaptationPercentageofharvestsfailingAdaptationNone Temperature Water Temp+Wat None Temperature Water Temp+WatAdaptation1 x σ events 2 x σ eventsPercentageofharvestsfailingChallinor et al. (2010) – Environmental Research Letters
  7. 7. Increase in GMT (oC)2 x σ crop failure eventsPercentageofharvestsfailing0-2 (6720) 2-4 (5832) 4-6 (2352) 6-8 (56)Error bars or contingent statements?Δ foodsystemPrecisionRelevance / complexityΔyieldΔCO2ΔclimateChallinor (2009a)A1B QUMP(17) GLAM(8)Challinor et al. (2010)
  8. 8. Identifying key sources of uncertainty:focus on processes not rangesThe use of models as black boxes, with the associated focus on model outputs, placesa significant burden on the model to correctly reproduce the interactions betweenprocesses.• Often unclear which processes have been simulated within a given ag. impacts study(White et al., 2011).• Points to need for impacts model intercomparison projects to clearly document whichprocesses are simulated and synthesise the results of numerous models.Use contingent statements to express trade-offs:‘What are the limiting processes?’ vs ‘what will happen to impact variable x?’“Warmer temperatures will reduce the time to maturity of crops, thus reducing yield.Increases in rainfall compensate for this in 40-60% of cases”vs.“yields decrease by 10-70%.”Identify key uncertainties, determine which are reducible and which are notSee Challinor et al. (2012), part of a special issue of Ag. For. Met. “Agricultural prediction using climate model ensembles”
  9. 9. Relationship between spatial scale and uncertaintyDo increases in model resolution improve simulation skill?Yes! For mean temperatureNot really… For precipitationDashed lines are the means of CMIP3Julian Ramirez
  10. 10. Examine count of Tmax>30oC as this is known to be importantCan use observations to measure error, and to correct for it inprojections• A number of methods exist for doing this with GCMs• Unclear which is bestDownscaling as a ‘source’ ofuncertainty“Nudging” “Delta approaches”ObservationsGCM baseline GCM rawPrediction ObsGCM b GCM rawPredHawkins et al. (2012) – Ag. For. Met.
  11. 11. IPSL SRES A1Bminus A2 (raw)Nudging minusDelta whenQUMP used topredict IPSL2xσ across QUMPwith Bias cor.2030-2059Tmax > 30.CUncertainty in the bias of the climate model is significant – i.e. the choice of climatemodel error correction is a significant source of uncertainty in crop impacts assessmentsHawkins et al. (2012) “Perfect sibling” approach: reference simulation of current climate treated asfuture observationsHADCM3 QUMP sibling models and IPSL, which is structurally different
  12. 12. Como presentar incertidumbreAnalysis of climate models to tell us ‘when’ (rather than ‘if’)• A1B and A2 are similar ifyou are posing the question“when will 2oC beexceeded?”• But for 3oC they aresignificantly differentJoshi et al. (2012) – Nature Climate Change
  13. 13. “Improved treatments ofuncertainty: recent progress andimplications” March 13th and 14th2013, London• Review EQUIP progress and takea forward-looking view ofuncertainty quantification at bothweather and climate timescales.• Use of uncertain climate andimpacts information• Africa-focussed sessionEQUIP: un proyecto sobre el incertidumbreen clima y sus impactoswww.equip.leeds.ac.ukSpecial issue of Climatic Change: improving thequantification of uncertainty across models ofclimate and its impacts.Quantifying and communicating uncertainty in climate and its impacts AnnaWeisslink, Andy ChallinorUsing observations to constrain climate forecasts Friederike Otto, MylesAllen, …Statistical benchmark models for impacts prediction Emma Suckling, LennySmithRequired weather characteristics for climate impact projections Hawkins,Ferro & StephensonEvaluating climate predictions: when is hindcast performance a guide toforecast performance? Friederike Otto, Emma Suckling, Chris Ferro, TomFrickerAttributing impacts of external climate drivers on extreme precipitation eventsin Europe Sue RosierPredicting impact relevant changes in heatwaves and water availability /Benefit of intialisation for decadal prediction of summer heatwave indicesHelen Hanlon, G. Hegerl, Chris Kilsby, S Tett,Assessment of risk of marine eutrophication, past present and future. StefanSaux Picart & Momme ButenschonThe communication of science and uncertainty in European NationalAdaptation Strategies Susanne Lorenz, Suraje Dessai, Jouni Paavola, PiersForster….
  14. 14. 2. Predictibilidad actual del clima• Variabilidad del clima• Detectación del cambio climático• Cambios en variabilidad – poco investigado
  15. 15. Detection of climate change:importance of internal climate variabilityEd HawkinsCentralEnglandTemperature
  16. 16. The role of internal climate variability: example of Central EnglandTemperature – very different oC/decade climate change!Ed Hawkins
  17. 17. Emergence of signals in impacts:means vs variability• In impacts studies the focus is often on mean changes, e.g. in crop yields.Variability is often not reported, or it is used as an error bar• Clear signals in mean yields may not be possible until late in the centuryChallinor et al. (2013)Trop and tempMostly tropical
  18. 18. Changes in variability may become clearersooner than changes in the meanChallinor et al. (2013)
  19. 19. Australian wheatharvest failureRussian wheatharvest failureChanges in variability, and their numerousinteractions, may already be emerging as key drivers
  20. 20. 3. Vulnerabilidad y adaptacion• Two paradigms• Importance of social sciences
  21. 21. Notes: Yellow arrows: the cycle of cause and effect among the four quadrants.Blue arrow: societal response to climate change impacts.Dominant perspective: 1. physical sciencesIntegrated assessment framework for considering anthropogenic climate change.Questions of interest:Predictive: How willpeople respond?Prescriptive: How shouldpeople respond?
  22. 22. Dominant perspective: 2. social scienceSustainable livelihoods frameworkThe arrows within the framework are used as shorthand to denote a variety of differenttypes of relationships, all of which are highly dynamic. None of the arrows imply directcausality, though all imply a certain level of influence.Question:How can wereduce socialvulnerability toclimate impacts?
  23. 23. 4. SíntesisChallinor et al. (2009b)“insufficientlyconstrained” (?)Impreciso einútil (?)Preciso /exacto peroincorrecto (?)
  24. 24. Data assimilation – the ‘fourth dimension’Importancia de las observaciones para reducirincertidumbre• Porque se pueden usar para cuantificar los erroresde los modelos• Los institutos nacionales de meteorología tienenuna extensa red meteorológica – se podían usarpara esto
  25. 25. Conclusiones• Tratamiento de incertidumbre– Muy importante cuantificar incertidumbre y estarconsciente de construir buenas “contingent statements” o“descriptions of trade-offs”– Puede que haya menos incertidumbre en zonasmontañosas (Vermuelen et al. 2013, Laderach et al.) – cfCIAT-PNUMA– Método de downscaling tiene implicaciones paraincertidumbre• Presentar incertidumbre using the time axis• Importancia de cuantificar cambios de variabilidad• Importancia de ciencias sociales para analizar a lavulnerabilidad
  26. 26. References• Challinor et al (2012) available athttp://www.sciencedirect.com/science/article/pii/S016819231200281X• Challinor AJ, Simelton ES, Fraser EDG, Hemming D, & Collins M (2010) Increased cropfailure due to climate change: assessing adaptation options using models and socio-economicdata for wheat in China. Environmental Research Letters 5(3):034012.• Challinor, A. J., T. Osborne, A. Morse, L. Shaffrey, T. Wheeler, H. Weller (2009b). Methodsand resources for climate impacts research: achieving synergy. Bulletin of the AmericanMeteorological Society, 90 (6), 825-835• Challinor AJ, Ewert F, Arnold S, Simelton E, & Fraser E (2009a) Crops and climate change:progress, trends, and challenges in simulating impacts and informing adaptation. Journal ofExperimental Botany 60(10):2775-2789.• Challinor AJ & Wheeler TR (2008) Use of a crop model ensemble to quantify CO2stimulation of water-stressed and well-watered crops. Agricultural and Forest Meteorology148(6-7):1062-1077.• Joshi M, Hawkins E, Sutton R, Lowe J, & Frame D (2011) Projections of when temperaturechange will exceed 2 [deg]C above pre-industrial levels. Nature Clim. Change 1(8):407-412.• Hawkins et al (2012) available athttp://www.sciencedirect.com/science/article/pii/S0168192312001372• Watson and Challinor (2012) available athttp://www.sciencedirect.com/science/article/pii/S0168192312002535

×