uncertainties of climate change impacts in agriculture

Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014

Uncertainties of climate change impacts in agriculture

Senthold Asseng

F. Ewert, P. Martre, R.P. Rötter, D.B. Lobell, D. Cammarano, B.A. Kimball, M.J. Ottman, G.W. Wall, J.W. White, M.P. Reynolds, P.D. Alderman, P.V.V. Prasad, P.K. Aggarwal, J. Anothai, B. Basso,

C. Biernath, A.J. Challinor, G. De Sanctis, J. Doltra, E. Fereres, M. Garcia-Vila, S. Gayler, G. Hoogenboom, L.A. Hunt, R.C. Izaurralde, M. Jabloun, C.D. Jones, K.C. Kersebaum,

A.-K. Koehler, C. Müller, S. Naresh Kumar, C. Nendel, G. O’Leary, J. E. Olesen, T. Palosuo, E. Priesack, E. Eyshi Rezaei, A.C. Ruane, M.A. Semenov, I. Shcherbak, C. Stöckle, P. Stratonovitch, T. Streck, I. Supit, F. Tao, P. Thorburn, K. Waha, E. Wang, D. Wallach, J. Wolf, Z. Zhao and Y. Zhu


Overview

1. AgMIP

2. Crop models – modeling CO2

3. Model uncertainty

a) What is it?

b) Quantification

c) Comparison with other sources

d) Can it be reduced?

4. Conclusions


AgMIP Agricultural Model Intercomparison and Improvement Project

Goals

To improve the characterization of risk of hunger and world food security

due to climate change,

To enhance adaptation capacity in both developing and developed countries.

www.agmip.org

Led by Cynthia Rosenzweig, James W. Jones, Jerry Hatfield & John Antle

http://www.agmip.org/


AgMIP : • combines climate – crop – economic models in a multi-model approach • started in 2010, open, > 600 members from around the world, >30 projects

AgMIP Wheat

AgMIP

AgMIP Wheat

30 wheat models

Rosenzweig et al. 2013 AFM


CO2Light Temperature H2O

Management

Carter 2013

Wheat yield and climate

Genotype

Soil


Scale

Crop modelsPE x M

G


APSIM - NWheat

Assimilation

Root

Shoot + Leaf Grain

Residues(surface)

Residues(roots)

BIOMC:N

HUMC:N

FOM

carbohydartes

lignin

cellulose

Mineral-NNH4 NO3urea NH4

Mineralisation

Immobilisation

Harvest

Leaching

C

N

C

N

N

C,N

C,N

CO2

TUE

Es

Ep

Denitrification

FertiliserCO2

CO2

CO2

CO2

LL SATDUL

runoff

Drainage

1

2

3

n

rainfallmax & min

temperaturesolar

radiation

C

SoilWAT

SoilN

Nwheat


Model output


Models vs observations

Modelinput output observation

output observation1. wrong input

2. wrong/poor estimate for input

3. wrong observation

4. wrong model/routine a) wrong number

b) wrong unit

c) value with large variability

d) outside model design

c) ‘not a measurement’ - just another ‘model’


Modeling CO2


Photosynthesis

CO2 + H2O + light energy ---> C6H12O6 + O2 + H2O

CO2 H2O

leaf

cell


Monteith 1977 PTRSLSinclair and Weiss (2010) In: Principles of Ecology in Plant Production

Simple approaches to compute Photosynthesis: RUE - model

RUE


Climate change - Photosynthesis

CO2 + H2O + light energy ---> C6H12O6 + O2 + H2O

CO2 H2O

leaf

Radiation use effciency (RUE) and transpiration effciency (TE) both increases with increased CO2


RUE model

RUE = Radiation use efficiency in g[crop] MJ-1[intercepted light]

dW/dt = RUE x FCO2 x I0 x [1-exp(-k . LAI)]

Atmospheric CO2 (ppm)

300 400 500 600 700

FCO2

0.95

1.00

1.05

1.10

1.15

1.20

1.25

1.30

10 oC

15 oC

20 oC

25 oC

FCO2

Reyanga et al. 1999 EMS

incoming light interception


0

2

4

6

8

10

12

Grain Yield (t/ha)

drydry+CO 2

wetwet+CO 2

Observed grain yield – CO2 effect

Observed data after Kimball et al. 1995 GCB

+CO2 = 550ppm (by 2050)


0

2

4

6

8

10

12

low N

low N+CO 2

Grain Yield (t/ha)

Observed data after Kimball et al. 1995 GCB

+CO2 = 550ppm (by 2050)

Observed grain yield – CO2 effect


0

2

4

6

8

10

12

observed & simulated

Grain Yield (t/ha)

drydry+CO 2

wetwet+CO 2

low N

low N+CO 2

Asseng et al. 2004 FCR

Observed & simulated grain yield – CO2 effect


Uncertainty


Climate models

Impact model Impact model

Climate models (+scenarios)

e.g. Crop model (or model for:- hydrology,- biodiversity, - health…)

e.g. Economic model (or model for:- land-use…)

Impact model

Climate models

Modeling climate impact


Challinor et al. 2014 Nature CC

A meta-analysis of crop yields (wheat)


Lehmann & Rillig 2014 Nature CC

Distinguishing variability from uncertainty

Variability = Natural variability in space & time

Due to model, process, measurements errors

e.g. impact simulation


After Lehmann & Rillig 2014 Nature CC

Time

Distinguishing variability from uncertainty

Natural variability

Uncertainty


AgMIP Wheat - Background

1. Crop model = main tool to assess climate change impact2. But, simulated effect due to chosen crop model ?


27 wheat models

AgMIP Wheat Pilot

4 contrasting field experiments (natural variability)

Standardized protocols• “Blind test”• Full calibration• Sensitivity analysis


ME 11, High rainfall; cold temperature, winter wheatME 2, High rainfall; temperate temperature, spring wheatME 1, Irrigated; temperate temperature, spring wheatME 4, Low rainfall; temperate temperature, spring wheat

27 wheat models

4 contrasting field experiments

AgMIP Wheat Pilot

Wheat area after Monfreda et al. (2008)

CIMMYT’s mega-environments (ME) for wheat

Days after sowing

50 100 150 200 250 300

Above-ground biomass (t/ha)

0

5

10

15

20


Calibrated

NL

Grain yield (t/ha)0 2 4 6 8 10

"Blind"

Observed

AR

IN

AU

Calibrated

"Blind"

Observed

Calibrated

"Blind"

Observed

Calibrated

"Blind"

Observed

Observations versus simulations

Line = medianBox = 50%Bars = 80%

Asseng et al. 2014 Nature CC


Number of models within 13.5%of observation

0510152025

Location

N.a.N.NLARIN AUNL+ARNL +INNL+AUAR + INAR +AUIN + AUNL+AR+INAR+IN+AUNL+IN+AUNL+AR+AUNL+AR+IN+AU

13.5% = coefficient of variation for field experimental observation (Taylor et al. 1999)


“Blind”

Fully calibrated



Days after sowing

50 100 150 200 250 300

Above-ground biomass (t/ha)

0

5

10

15

20


RMSE %

0 10 20 30 40

Grain Yield

Grain Number

Grain Protein

Harvest Index (HI)

Biomass @ Anthesis

Biomass @ Maturity

Maximum LAI

Cumulative ET

Crop N @ Anthesis

Crop N @ Maturity

Grain N

“Blind”

Fully calibrated



Model detail


Model detail

0 5 10 15 20 250

102030405060708090

f(x) = 0.1980233579047 x² − 4.1646109474828 x + 23.760063915835R² = 0.208789768659186

Number of cultivar parameter (#)

Relative RMSE (%)

Challinor et al. 2014 Nature CC


10 20 30

10

20

30

102030

10

20

30

NL

AR

IN

AU

CV% of model response

Model response to changes in T, rainfall and CO2

“Blind”fully calibrated 50% of models with the closest simulations to the observed yields across all location 50% of models with closest simulation per location


to climate change scenario (A2 2100)

e.g. the best models (i.e. smallest RMSE with observations) have smallest CV at 3 locations, but not at AU; i.e. performance of models with historical data is no guidance for future impact studies


Model response to rainfall

Argentina Australia

-25

25

Simulated % yield change

-120 -80 -40 0 40 80 120 -120 -80 -40 0 40 80 120

10

-10

0

Rainfall change (%)




-120 -80 -40 0 40 80 120

0

0

0

+3

+3

+3

+6

+6

+6

Tem

per

atu

re c

han

ge

(oC

)

A

tmo

sph

eric

CO

2 co

nce

ntr

atio

n (

pp

m)

7

20

5

40

360

Plot 1

-120 -80 -40 0 40 80 120

Observed (% yield change)

-80 -40 0 40 80

550 ppm

+3oC

+6oC

observed

0

+3

+6

Model response to CO2 and T

Simulated % yield change

CO2 response: Amthor 2001, Ewert et al. 2002, Hogy et al. 2010, Kimball 2011, Ko et al., 2010, Li et al. 2007T response (extrapolated): Amthor 2001, Singh et al. 2008, Xiao et al. 2005

Argentina Australia




Model response to heat stress

Line = medianBox = 50%Bars = 80%Models with heat stress routine

7 x 35 oC after anthesis

Asseng et al. 2013 Nature CCSimulated relative heat impact (%)

-40 -20 0 20

NL

AR

IN

AU


What about other crops?


Bassu et al. 2014 GCB

Maize model response

23 models


Crop models vs GCMs


Impact model

Climate models (+scenarios)

e.g. Crop model (or model for:- hydrology,- biodiversity, - health…)

Climate models

Modelling climate impact


Netherlands Argentina India Australia

Coefficient of variation (%)

0

5

10

15

20

25

Impact uncertainties

Uncertainty due to 16 GCM’s scenarios

Mean exp CV% (Taylor et al. 1999)

Model uncertainty in simulating climate change yield impact

A2 scenario for Mid-Century



Reducing uncertainty


India: +3oC & 450ppm

Number of Models (#)

0 2 4 6 8 10 12 14 16 18 20 22 24 26

Coefficient of Variation (%)

0

5

10

15

20

25

30

35

Multi-model ensembles to reduce uncertainty

13.5% = Mean exp CV% (Taylor et al. 1999)



Multi-model ensembles to reduce uncertainty

Changes in temperature (oC)

-6 -3 0 3 6 9 12

Required number of crop models to achieve

<13.5% simulated impact variability (-)

0

3

6

9

12

15

Colors represent different CO2 levels

(13.5% = Mean exp CV% (Taylor et al. 1999))

Mean (+/- STD) of all locations

Number of Models (#)

0 2 4 6 8 10 12 14 16 18 20 22 24 26

Coefficient of Variation (%)

0

5

10

15

20

25

30

35



Reducing uncertaintyvia model improvements


Model improvements to reduce uncertainty

CIMMYT, El Batan, Texcoco, MexicoJune 1921, 2013

PD Alderman, E Quilligan, S Asseng, F Ewert and MP Reynolds (Editors)

Improve high temperature impacts in models

Bruce Kimball

Wall et al. 2011 GCB; Ottman et al. 2012 AJ


Conclusions

1. Many of the crop models can reproduce observed experiments

2. However, there is an uncertainty in climate change impact assessments due to crop models

3. This uncertainty is similar to experimental error, but larger than from GCM’s

4. Uncertainty in modeling T and T x CO2 interactions >>> model improvements

5. Multi-model ensembles can reduce simulated impact uncertainties.

Contact: Senthold Asseng, [email protected]

uncertainties of climate change impacts in agriculture

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