Comentarios acerca de las presentaciones de los países

Andy Challinor A.J.Challinor@leeds.ac.uk

School of Earth and Environment

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

• 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?

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)

Schlenker & Roberts (2009) - PNAS Vara Prasad et al (2001)

Daily Tmax of 29-30°C

Flower bud temperature (oC)

24 28 32 36 40 44 48

Frui

t set

(%)

0

20

40

60

Groundnut in controlled environments Maize using county-level yields

Daily T 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

Importancia de cuantificar incertidumbre Ensemble crop-climate modelling to inform adaptation

Per

cent

age

of h

arve

sts

faili

ng

Adaptation None Temperature Water Temp+Wat None Temperature Water Temp+Wat

Adaptation

1 x σ events 2 x σ events

Per

cent

age

of h

arve

sts

faili

ng

Challinor et al. (2010) – Environmental Research Letters

Increase in GMT (oC)

2 x σ crop failure events

Per

cent

age

of h

arve

sts

faili

ng

0-2 (6720) 2-4 (5832) 4-6 (2352) 6-8 (56)

Error bars or contingent statements?

Δ food system

Prec

isio

n

Relevance / complexity

Δyield

ΔCO2 Δclimate

Challinor (2009a)

A1B QUMP(17) GLAM(8)

Challinor et al. (2010)

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”

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

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.

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

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

“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 ….

2. Predictibilidad actual del clima

• Variabilidad del clima • Detectación del cambio climático

• Cambios en variabilidad – poco investigado

Detection of climate change: importance of internal climate variability

Ed Hawkins

Central England

Temperature

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

Ed Hawkins

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

Changes in variability may become clearer sooner than changes in the mean

Challinor et al. (2013)

Australian wheat harvest failure

Russian wheat harvest failure

Changes in variability, and their numerous interactions, may already be emerging as key drivers

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 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?

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?

4. Síntesis

Challinor et al. (2009b)

“insufficiently constrained” (?)

Impreciso e inútil (?)

Preciso / exacto pero incorrecto (?)

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

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

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