translating climate forecasts into agricultural terms: advances and challenges james hansen, andrew...
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Translating Climate Forecasts into Agricultural Terms:
Advances and Challenges
James Hansen, Andrew Challinor, Amor Ines, Tim Wheeler, Vincent Moron
presented at theInternational Workshop on Climate
Prediction and Agriculture – Advances and Challenges
WMO, Geneva, 11 May 2005
Motivation
• Information relevant to decisions
• Ex-ante assessment for credibility and targeting
• Fostering and guiding management
Overview• Six years ago
– Dominance of historic analogs
– Doubts about crop predictability
• Recent advances– The challenge, and potential approaches
– Synthetic weather conditioned on climate forecasts
– Use of daily climate model output
– Statistical prediction of crop simulations
– Downscaling and upscaling
• Opportunities and challenges– Embedding crop models within climate models
– Enhanced use of remote sensing, spatial data bases
– Robustness of alternative coupling approaches
– Forecast assessment and uncertainty
– Climate research questions
Six Years Ago:Dominance of Historic Analogs
• Advantages– Intuitive probabilistic interpretation– Accounts for any differences in “signal strength”– May incorporate useful higher-order statistics
• Concerns– Small sample size,
confidence, artificial skill– Are differences in
distribution real?– How to use with dynamic
prediction systems without discarding information?
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3 6 12 16
Number of "phases"
Var
ian
ce e
xpla
ined
cross-validated no cross-validation
Six Years Ago:Doubts About Crop Predictability
• Spatial variability of rainfall limits predic-tability at farm scale
• Accumulation of error from SSTs, to local climatic means, to crop response
• Impact of wrong fore-cast on farmers’ risk Barrett, 1998. Am. J. Agric.
Econ. 80:1109-1112
The Challenge
• Nonlinearities. Crop response to environment can be nonlinear, non-monotonic.
• Dynamics. Crops respond not to mean conditions but to dynamic interactions:– Soil water balance
– Phenology
• The scale mismatch problem.
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Gra
in y
ield
(M
g/h
a)
0 200 400 600 800OND precipitation (mm)
The Scale Mismatch Problem
• Crop models:– Homogeneous plot spatial scale– Daily time step (w.r.t. weather)
• GCMs:– Spatial scale 10,000-100,000 km2
– Sub-daily time step, BUT... Output meaningful only at (sub)seasonal scale
– Tend to over-predict rainfall frequency, under-predict mean intensity
• Temporal scale problem more difficult than spatial scale.
Effect of Spatial Averaging
Inverse-distance interpolation of daily weather data, north Florida, at a scale comparable to a GCM grid cell.
Hansen & Jones, 2000. Agric. Syst. 65:43-72.
Effect of Spatial Averaging
• Spatial averaging distorts variability, increases frequency, decreases mean intensity.
• Similar spatial averaging occurs within GCM.
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Rain
fall t
ota
l (m
m)
Jan MarMay Jul SepNov Month
interpolatedobserved
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tive f
req
uen
cy
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nsit
y (
mm
/d)
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Effect of Spatial Averaging
Simulated maize yields, CERES-Maize
Information Pathways
predicted crop yields
observed climate predictors
?
Information Pathways
crop model
analogyears
predicted crop yields
observed climate predictors
categorize
Information Pathways
downscaleddynamicmodel
stochasticgenerator
crop model(hindcast weather)
analogyears
predicted crop yields
statisticalclimatemodel
observed climate predictors
categorize
Information Pathways
downscaleddynamicmodel
stochasticgenerator
crop model(observedweather)
crop model(hindcast weather)
analogyears
predicted crop yields
statisticalclimatemodel
statisticalyield model
observed climate predictors
categorize
Approaches
• Classification and selection of historic analogs (e.g., ENSO phases)
• Synthetic daily weather conditioned on forecast: stochastic disaggregation
• Statistical function of simulated response
– Nonlinear regression
– Linear regression with transformation or GLM
– Probability-weighted historic analogs
• (Corrected) daily climate model output
Advances:Synthetic Weather Inputs
Two Approaches:
• Adjusting generator input parameters:– Flexibility to produce statistics of interest
– Assumed role of intensity vs. frequency
• Constraining generator outputs:– No assumptions re. frequency vs. intensity
Option 2. Constraining generated output
First step:First step:-- Iterative procedure – Using climatological parameters, accept the first realization with Rm near target:
|1-Rm/Rm,S|j <= 5%
Second step: Second step: - - Apply multiplicative rescaling to exactly match target monthly target.
Hansen & Ines, Submitted. Agric. For. Meteorol.
Constraining generator outputs reproduces correlations better than adjusting inputs.
Tifton, Georgia
Gainesville, Florida
Scenario RM vs. π RM vs. μI μI vs. π RM vs. πRM vs. μI μI vs. π
Observed daily rainfall 0.649 0.577 -0.165
0.668 0.706 0.046
Disaggregated monthly rainfall
constrain RM 0.681 0.676 -0.004
0.649 0.697 0.014
condition π 0.822 0.473 0.013
0.831 0.121 0.052condition μI 0.491 0.856 0.071
0.458 0.837 0.052
Constraining generator output requires fewer replicates for given accuracy.
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Gainesville Katumani
constrain RM
condition μI
condition π
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e
Tifton
Number of realizations
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N o . o f r e a l i z a t i o n s
RM
SE
, kg
ha-1
R m
p i
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N o . o f r e a l i z a t i o n s
R
R m
p i
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1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Year
Yie
ld,
kg h
a-1
Simulated Hindcasts
R=0.44
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1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Year
Yie
ld,
kg h
a-1
Simulated Hindcasts
R=0.41
Rm
π
Maize simulated from disaggregated monthly GCM hindcasts, Katumani, Kenya
Advances:Use of Daily Climate Model Output
Options
• Calibrate simulated yieldsChallinor et al., 2005. Tellus 57A:198-512
• Correct GCM mean bias– Additive shift for temperatures– Multiplicative shift for rainfall
• Rainfall frequency-intensity correctionInes & Hansen, In preparation
1
F(xGCM=0.0)
F(xhist=0.0)
00
GCMHistorical
Correcting Bias in Daily GCM Output: Rainfall Frequency
calibratedthreshold
Correcting Bias in Daily GCM Output: Rainfall Intensity
GCMHistorical
0
Daily rainfall (x), mm
1
00
F(x
)
x'i
F(xi)
xi
1, ,( ( ))i obs m GCM m ix F F x
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Mean
rai
nfa
ll fre
quency
, w
d d
-10
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ean
rain
fall inte
nsity, m
m w
d-1
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1970 1975 1980 1985 1990 1995
Year
Mea
n r
ainfa
ll a
mount, m
m d
-1
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Obs
EG
GG
Uncorr
Rm
μ
π
• Katumani, Kenya
• ECHAM4 & observed OND daily rainfall (1970-95)
• Intensity corrections:
• EG: empirical (GCM) to gamma (observed)
• GG: gamma (GCM and observed)
Corrects rainfall total, frequency, intensity.
Predicts yields from GCM, perhaps better than stochastic disaggregation
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1970 1975 1980 1985 1990 1995
Year
Mean
mo
nth
ly r
ain
fall
(R m
), m
m d
-1
rMOS=0.59
rGG =0.74
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1970 1975 1980 1985 1990 1995
Obs
MOS
GG
• CERES-Maize simulated with:• Disaggregated MOS-corrected monthly hindcasts• Gamma-gamma transformation of daily rainfall
Advances: Statistical Prediction of Crop Simulations
• Seasonal predictors of local climate potential predictors of crop response
• Predictand: Yields simulated with observed weather
• Eliminates need for daily weather conditioned on climate forecast
• Poor statistical behavior
Nonlinear Regression
Katumani maize prediction example:
• Yields as f(PC1)
• Mitscherlitch functional form:
• Cross-validation
ex py a b c x 1y=3.33+1.34(1-exp(-0.133x))R2 = 0.400
K Nearest Neighbor
• Unequally-weighted analogs
• Weights w:
– Based on rank distance (predictor state space)
– Interpreted as probabilities
• Forecast ŷ a weighted mean:
• Optimize k
• A non-parametric regression
1
1/
1/j k
i
jw
i
1,
ˆn
t i ii i t
y w y
Linear Regression & Transformation:Regional-Scale Wheat, Qld, Australia
• Wheat simulations: water satisfaction index
• ECHAM4.5, persisted SSTs, optimized (MOS)
• Yield prediction by c-v linear regression
• Box-Cox normalizing transformation
• Forecast distribution:
– Regression residuals in transformed space
– n antecedent X n within-season weather years
Hansen et al., 2004. Agric. For. Meteorol. 127:77-92
Linear Regression & Transformation:Regional-Scale Wheat, Qld, Australia
N
200 0 200 400 km
1 July
1 June
1 August
1 MayCorrelation
<0.34 (n.s.)0.34-0.450.45-0.550.55-0.650.65-0.750.75-0.85 > 0.85
Linear Regression & Transformation:Regional-Scale Wheat, Qld, Australia
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in y
ield
(M
g ha
-1)
Climatology
Date of forecast
ENSO phase
198
9 (n
eutr
al)
GCM-based
198
2 (E
l N
iño
)19
88
(La
Niñ
a)
1Jun 1Jul 1Aug Harvest1May 1Jun 1Jul 1Aug Harvest1May1Jun 1Jul 1Aug Harvest1May
Observed90th percentile75th percentile50th percentile25th percentile10th percentile
Advances: Downscaling &
Upscaling• Spatial climate downscaling:
– Methods advancing
– Uncertain impact on skill
• Crop model upscaling:
– Understanding and methods for aggregating point models
– Increasing set of reduced form large-area models
Predictability (r) of groundnut yields with large area model, W India. Challinor et al., 2005. Tellus 57A:198-512
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Co
rre
latio
n
point ~1°×1°~2°×1° stateScale
Jan-MarApr-Jun
Obs. vs. pred. rainfall, Ceará, NE Brazil, as function of aggregation. Gong et al., 2003. J. Climate 16:3059-71.
Opportunities & Challenges:Crop Models Within Climate Models
• Run crop models within GCM or RCMs
• Allow crop to influence atmosphere– Alternative land surface scheme– Intended benefit is atmosphere response to
crop
• Likely to require calibration of crop results for foreseeable future
• Match scale of climate model grid
Opportunities & Challenges:Remote Sensing, Spatial Data Bases
• Enhanced georeferenced soil, land use, cultivar data bases
• Assimilation of real-time, contiguous antecedent weather into forecasts
• Estimation of cropped areas, dates
• Correction of simulated state variables
• Eventual farm-specific crop forecasts?
1960 1970 1980 1990
observedpredicted
0
1
2
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4
5
0
1
2
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4
5
0
1
2
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1960 1970 1980 1990
Stochastic disaggregation + k nearest neighbors, 1 PC
Regression
k nearest neighbors, 2 PCs
Stochastic disaggregationr = 0.57
r = 0.53
r = 0.53
r = 0.58
r = 0.55
Opportunities & ChallengesRobustness of Alternative Approaches?
Hansen & Indeje, 2004. Agric. For. Meterol. 125:143
Opportunities & Challenges: Forecast Assessment and Uncertainty
• Does predictability (climate and impacts) change from year to year?– Artifact of skewness?– Real impacts of climate state?– Captured by GCM ensembles?
• Interpretation of forecasts based on categorical vs. continuous predictors?
• Consistency of hindcast error vs. GCM ensemble distributions?
Are differences in dispersion real?
ENSO phase
Dec
embe
r rai
nfal
l
La Nina neutral El Nino
ENSO phase
La Nina neutral El Nino
Raw Transformedskewness 1.243 -0.032p ENSO influence on: means 0.0001 *** 0.0004 *** dispersion 0.0001 *** 0.91 n.s.
Junin, Argentina, 1934-2001
Opportunities & Challenges: Forecast Assessment and Uncertainty
• Does predictability (climate and impacts) change from year to year?– Artifact of skewness?– Real impacts of climate state?– Captured by GCM ensembles?
• Interpretation of forecasts based on categorical vs. continuous predictors?
• Consistency of hindcast error vs. GCM ensemble distributions?
Opportunities & Challenges:Climate Research Questions
• Past prediction efforts driven by skill– Relative shifts– Large areas– 3-month climatic means
• Stimulating interest in “weather within climate”– Skill at sub-seasonal time scales– Higher-order rainfall statistics– Shifts in timing, onset, cessation– Methods to translate into weather realizations
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