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.

1. Comentarios acerca de las
presentaciones de los países
Andy Challinor
A.J.Challinor@leeds.ac.uk
School of Earth and
Environment

2. Temas
1. “Considera que los escenarios se acercan a lo
que realmente sucediera?”
– Incertidumbre (CIAT-PNUMA,IDEAM, SENHAMI)
– Downscaling (INHAMI, SENHAMI)
2. Predictibilidad actual del clima (SENHAMI,
IDEAM)
– Variabilidad del clima
– Detectación del cambio climático
3. Vulnerabilidad y adaptación (INHAMI, SENHAMI,
CIAT-PNUMA)
4. Síntesis

3. • We don’t know by how much our models are in
error because we don’t know the error:
– in model inputs (e.g. initial conditions, boundary
conditions, parameters, driving variables)
– in model structure (inc. spatial and temporal
discretization)
– resulting from intrinsic stochastic variability
What is uncertainty?

4. Predictability varies spatially and temporally
Hawkins and Sutton (2009) – Bull. Am. Met. Soc.
Signal to noise ratio for decadal mean surface air temperature predictions
4
Este análisis se puede hacer para cultivos (Vermuelen et al., 2013)

5. Schlenker & Roberts (2009) - PNAS Vara Prasad et al (2001)
DailyTmax of 29-30°C
Flower bud temperature (oC)
24 28 32 36 40 44 48
Fruitset(%)
0
20
40
60
Groundnut in controlled environmentsMaize using county-level yields
DailyT of 32-39 °C ,
depending on timing
Scale dependency of biophysical relationships
• If this scale dependency can be further understood then models could
improve, thus reducing uncertainty
• To do this, need to put together diverse types of models

6. Importancia de cuantificar incertidumbre
Ensemble crop-climate modelling to inform adaptation
Percentageofharvestsfailing
Adaptation
None Temperature Water Temp+Wat None Temperature Water Temp+Wat
Adaptation
1 x σ events 2 x σ events
Percentageofharvestsfailing
Challinor et al. (2010) – Environmental Research Letters

7. Increase in GMT (oC)
2 x σ crop failure events
Percentageofharvestsfailing
0-2 (6720) 2-4 (5832) 4-6 (2352) 6-8 (56)
Error bars or contingent statements?
Δ food
system
Precision
Relevance / complexity
Δyield
ΔCO2
Δclimate
Challinor (2009a)
A1B QUMP(17) GLAM(8)
Challinor et al. (2010)

8. Identifying key sources of uncertainty:
focus on processes not ranges
The use of models as black boxes, with the associated focus on model outputs, places
a significant burden on the model to correctly reproduce the interactions between
processes.
• 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 which
processes 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 not
See Challinor et al. (2012), part of a special issue of Ag. For. Met. “Agricultural prediction using climate model ensembles”

9. Relationship between spatial scale and uncertainty
Do increases in model resolution improve simulation skill?
Yes! For mean temperature
Not really… For precipitation
Dashed lines are the means of CMIP3
Julian Ramirez

10. Examine count of Tmax>30oC as this is known to be important
Can use observations to measure error, and to correct for it in
projections
• A number of methods exist for doing this with GCMs
• Unclear which is best
Downscaling as a ‘source’ of
uncertainty
“Nudging” “Delta approaches”
Observations
GCM baseline GCM raw
Prediction Obs
GCM b GCM raw
Pred
Hawkins et al. (2012) – Ag. For. Met.

11. IPSL SRES A1B
minus A2 (raw)
Nudging minus
Delta when
QUMP used to
predict IPSL
2xσ across QUMP
with Bias cor.
2030-2059
Tmax > 30.C
Uncertainty in the bias of the climate model is significant – i.e. the choice of climate
model error correction is a significant source of uncertainty in crop impacts assessments
Hawkins et al. (2012)
 “Perfect sibling” approach: reference simulation of current climate treated as
future observations
HADCM3 QUMP sibling models and IPSL, which is structurally different

12. Como presentar incertidumbre
Analysis of climate models to tell us ‘when’ (rather than ‘if’)
• A1B and A2 are similar if
you are posing the question
“when will 2oC be
exceeded?”
• But for 3oC they are
significantly different
Joshi et al. (2012) – Nature Climate Change

13. “Improved treatments of
uncertainty: recent progress and
implications” March 13th and 14th
2013, London
• Review EQUIP progress and take
a forward-looking view of
uncertainty quantification at both
weather and climate timescales.
• Use of uncertain climate and
impacts information
• Africa-focussed session
EQUIP: un proyecto sobre el incertidumbre
en clima y sus impactos
www.equip.leeds.ac.uk
Special issue of Climatic Change: improving the
quantification of uncertainty across models of
climate and its impacts.
Quantifying and communicating uncertainty in climate and its impacts Anna
Weisslink, Andy Challinor
Using observations to constrain climate forecasts Friederike Otto, Myles
Allen, …
Statistical benchmark models for impacts prediction Emma Suckling, Lenny
Smith
Required weather characteristics for climate impact projections Hawkins,
Ferro & Stephenson
Evaluating climate predictions: when is hindcast performance a guide to
forecast performance? Friederike Otto, Emma Suckling, Chris Ferro, Tom
Fricker
Attributing impacts of external climate drivers on extreme precipitation events
in Europe Sue Rosier
Predicting impact relevant changes in heatwaves and water availability /
Benefit of intialisation for decadal prediction of summer heatwave indices
Helen Hanlon, G. Hegerl, Chris Kilsby, S Tett,
Assessment of risk of marine eutrophication, past present and future. Stefan
Saux Picart & Momme Butenschon
The communication of science and uncertainty in European National
Adaptation Strategies Susanne Lorenz, Suraje Dessai, Jouni Paavola, Piers
Forster
….

14. 2. Predictibilidad actual del clima
• Variabilidad del clima
• Detectación del cambio climático
• Cambios en variabilidad – poco investigado

15. Detection of climate change:
importance of internal climate variability
Ed Hawkins
Central
England
Temperature

16. The role of internal climate variability: example of Central England
Temperature – very different oC/decade climate change!
Ed Hawkins

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 century
Challinor et al. (2013)
Trop and temp
Mostly tropical

18. Changes in variability may become clearer
sooner than changes in the mean
Challinor et al. (2013)

19. Australian wheat
harvest failure
Russian wheat
harvest failure
Changes in variability, and their numerous
interactions, may already be emerging as key drivers

20. 3. Vulnerabilidad y adaptacion
• Two paradigms
• Importance of social sciences

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 sciences
Integrated assessment framework for considering anthropogenic climate change.
Questions of interest:
Predictive: How will
people respond?
Prescriptive: How should
people respond?

22. Dominant perspective: 2. social science
Sustainable livelihoods framework
The arrows within the framework are used as shorthand to denote a variety of different
types of relationships, all of which are highly dynamic. None of the arrows imply direct
causality, though all imply a certain level of influence.
Question:
How can we
reduce social
vulnerability to
climate impacts?

23. 4. Síntesis
Challinor et al. (2009b)
“insufficiently
constrained” (?)
Impreciso e
inútil (?)
Preciso /
exacto pero
incorrecto (?)

24. Data assimilation – the ‘fourth dimension’
Importancia de las observaciones para reducir
incertidumbre
• Porque se pueden usar para cuantificar los errores
de los modelos
• Los institutos nacionales de meteorología tienen
una extensa red meteorológica – se podían usar
para esto

25. Conclusiones
• Tratamiento de incertidumbre
– Muy importante cuantificar incertidumbre y estar
consciente de construir buenas “contingent statements” o
“descriptions of trade-offs”
– Puede que haya menos incertidumbre en zonas
montañosas (Vermuelen et al. 2013, Laderach et al.) – cf
CIAT-PNUMA
– Método de downscaling tiene implicaciones para
incertidumbre
• Presentar incertidumbre using the time axis
• Importancia de cuantificar cambios de variabilidad
• Importancia de ciencias sociales para analizar a la
vulnerabilidad

26. References
• Challinor et al (2012) available at
http://www.sciencedirect.com/science/article/pii/S016819231200281X
• Challinor AJ, Simelton ES, Fraser EDG, Hemming D, & Collins M (2010) Increased crop
failure due to climate change: assessing adaptation options using models and socio-economic
data for wheat in China. Environmental Research Letters 5(3):034012.
• Challinor, A. J., T. Osborne, A. Morse, L. Shaffrey, T. Wheeler, H. Weller (2009b). Methods
and resources for climate impacts research: achieving synergy. Bulletin of the American
Meteorological 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 of
Experimental Botany 60(10):2775-2789.
• Challinor AJ & Wheeler TR (2008) Use of a crop model ensemble to quantify CO2
stimulation of water-stressed and well-watered crops. Agricultural and Forest Meteorology
148(6-7):1062-1077.
• Joshi M, Hawkins E, Sutton R, Lowe J, & Frame D (2011) Projections of when temperature
change will exceed 2 [deg]C above pre-industrial levels. Nature Clim. Change 1(8):407-412.
• Hawkins et al (2012) available at
http://www.sciencedirect.com/science/article/pii/S0168192312001372
• Watson and Challinor (2012) available at
http://www.sciencedirect.com/science/article/pii/S0168192312002535