Uncertainties of climate change impacts in agriculture
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Transcript of 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
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
CO2Light Temperature H2O
Management
Carter 2013
Wheat yield and climate
Genotype
Soil
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
Scale
Crop modelsPE x M
G
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
Model output
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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’
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
Modeling CO2
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
Photosynthesis
CO2 + H2O + light energy ---> C6H12O6 + O2 + H2O
CO2 H2O
leaf
cell
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
Monteith 1977 PTRSLSinclair and Weiss (2010) In: Principles of Ecology in Plant Production
Simple approaches to compute Photosynthesis: RUE - model
RUE
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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)
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
Uncertainty
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
Challinor et al. 2014 Nature CC
A meta-analysis of crop yields (wheat)
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
After Lehmann & Rillig 2014 Nature CC
Time
Distinguishing variability from uncertainty
Natural variability
Uncertainty
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
AgMIP Wheat - Background
1. Crop model = main tool to assess climate change impact2. But, simulated effect due to chosen crop model ?
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
27 wheat models
AgMIP Wheat Pilot
4 contrasting field experiments (natural variability)
Standardized protocols• “Blind test”• Full calibration• Sensitivity analysis
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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)
Observations versus simulations
“Blind”
Fully calibrated
Asseng et al. 2014 Nature CC
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
Days after sowing
50 100 150 200 250 300
Above-ground biomass (t/ha)
0
5
10
15
20
Observations versus simulations
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
Asseng et al. 2014 Nature CC
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
Model detail
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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
Asseng et al. 2014 Nature CC
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
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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 (%)
Line = medianBox = 50%Bars = 80%
Asseng et al. 2014 Nature CC
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
-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
Line = medianBox = 50%Bars = 80%
Asseng et al. 2013 Nature CC
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
What about other crops?
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
Bassu et al. 2014 GCB
Maize model response
23 models
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
Crop models vs GCMs
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
Impact model
Climate models (+scenarios)
e.g. Crop model (or model for:- hydrology,- biodiversity, - health…)
Climate models
Modelling climate impact
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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
Asseng et al. 2014 Nature CC
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
Reducing uncertainty
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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)
Asseng et al. 2014 Nature CC
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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
Asseng et al. 2014 Nature CC
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
Reducing uncertaintyvia model improvements
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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
Senthold Asseng, Seminar for NCAR ASP Summer Colloquium on Uncertainty, Bolder, CO, July 22, 2014
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]