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
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Motivation
• Information relevant to decisions
• Ex-ante assessment for credibility and targeting
• Fostering and guiding management
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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
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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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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
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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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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.
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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.
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Effect of Spatial Averaging
• Spatial averaging distorts variability, increases frequency, decreases mean intensity.
• Similar spatial averaging occurs within GCM.
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Effect of Spatial Averaging
Simulated maize yields, CERES-Maize
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Information Pathways
predicted crop yields
observed climate predictors
?
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Information Pathways
crop model
analogyears
predicted crop yields
observed climate predictors
categorize
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Information Pathways
downscaleddynamicmodel
stochasticgenerator
crop model(hindcast weather)
analogyears
predicted crop yields
statisticalclimatemodel
observed climate predictors
categorize
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Information Pathways
downscaleddynamicmodel
stochasticgenerator
crop model(observedweather)
crop model(hindcast weather)
analogyears
predicted crop yields
statisticalclimatemodel
statisticalyield model
observed climate predictors
categorize
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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
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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
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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.
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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
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Constraining generator output requires fewer replicates for given accuracy.
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Maize simulated from disaggregated monthly GCM hindcasts, Katumani, Kenya
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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
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1
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F(xhist=0.0)
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GCMHistorical
Correcting Bias in Daily GCM Output: Rainfall Frequency
calibratedthreshold
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Correcting Bias in Daily GCM Output: Rainfall Intensity
GCMHistorical
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• 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.
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Predicts yields from GCM, perhaps better than stochastic disaggregation
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• CERES-Maize simulated with:• Disaggregated MOS-corrected monthly hindcasts• Gamma-gamma transformation of daily rainfall
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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
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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
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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
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ˆn
t i ii i t
y w y
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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
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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
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Linear Regression & Transformation:Regional-Scale Wheat, Qld, Australia
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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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Obs. vs. pred. rainfall, Ceará, NE Brazil, as function of aggregation. Gong et al., 2003. J. Climate 16:3059-71.
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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
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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?
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1960 1970 1980 1990
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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
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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?
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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
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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?
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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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