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Translating Climate Forecasts into Agricultural Terms: Advances and Challenges

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Title: Translating Climate Forecasts into Agricultural Terms: Advances and Challenges


1
Translating Climate Forecasts into Agricultural
Terms Advances and Challenges
  • James Hansen, Andrew Challinor, Amor Ines, Tim
    Wheeler, Vincent Moron
  • presented at the
  • International Workshop on Climate Prediction and
    Agriculture Advances and Challenges
  • WMO, Geneva, 11 May 2005

2
Motivation
  • Information relevant to decisions
  • Ex-ante assessment for credibility and targeting
  • Fostering and guiding management

3
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

4
Six Years AgoDominance 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?

5
Six Years AgoDoubts 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. 801109-1112
6
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.

7
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.

8
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. 6543-72.
9
Effect of Spatial Averaging
  • Spatial averaging distorts variability, increases
    frequency, decreases mean intensity.
  • Similar spatial averaging occurs within GCM.

10
Effect of Spatial Averaging
Simulated maize yields, CERES-Maize
11
Information Pathways
observed climate predictors
?
predicted crop yields
12
Information Pathways
13
Information Pathways
14
Information Pathways
15
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

16
AdvancesSynthetic 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

17
Option 1 Conditioning input parameters
  • Mean rainfall frequency mean intensity
  • Conditioning intensity parameters
  • Conditioning frequency parameters

18
Option 2. Constraining generated output
First step - Iterative procedure Using
climatological parameters, accept the first
realization with Rm near target
1-Rm/Rm,Sj lt 5
Second step - Apply multiplicative rescaling to
exactly match target monthly target.
Hansen Ines, Submitted. Agric. For. Meteorol.
19
Constraining generator outputs reproduces
correlations better than adjusting inputs.
20
Constraining generator output requires fewer
replicates for given accuracy.
21
Maize simulated from disaggregated monthly GCM
hindcasts, Katumani, Kenya
Rm
p
22
AdvancesUse of Daily Climate Model Output
  • Options
  • Calibrate simulated yields
  • Challinor et al., 2005. Tellus 57A198-512
  • Correct GCM mean bias
  • Additive shift for temperatures
  • Multiplicative shift for rainfall
  • Rainfall frequency-intensity correction
  • Ines Hansen, In preparation

23
Correcting Bias in Daily GCM Output Rainfall
Frequency
24
Correcting Bias in Daily GCM Output Rainfall
Intensity
25
Corrects rainfall total, frequency, intensity.
  • Katumani, Kenya
  • ECHAM4 observed OND daily rainfall (1970-95)
  • Intensity corrections
  • EG empirical (GCM) to gamma (observed)
  • GG gamma (GCM and observed)

26
Predicts yields from GCM, perhaps better than
stochastic disaggregation
  • CERES-Maize simulated with
  • Disaggregated MOS-corrected monthly hindcasts
  • Gamma-gamma transformation of daily rainfall

27
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

28
Nonlinear Regression
  • Katumani maize prediction example
  • Yields as f(PC1)
  • Mitscherlitch
  • functional form
  • Cross-validation

29
K Nearest Neighbor
  • Unequally-weighted analogs
  • Weights w
  • Based on rank distance (predictor state space)
  • Interpreted as probabilities
  • Forecast y a weighted mean
  • Optimize k
  • A non-parametric regression

30
Linear Regression TransformationRegional-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.
12777-92
31
Linear Regression TransformationRegional-Scale
Wheat, Qld, Australia
32
Linear Regression TransformationRegional-Scale
Wheat, Qld, Australia
33
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

34
Opportunities ChallengesCrop 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

35
Opportunities ChallengesRemote 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?

36
Opportunities ChallengesRobustness of
Alternative Approaches?
Hansen Indeje, 2004. Agric. For. Meterol.
125143
37
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?

38
Are differences in dispersion real?
Raw Transformed skewness 1.243 -0.032
p ENSO influence on means 0.0001
0.0004 dispersion 0.0001 0.91
n.s.
Junin, Argentina, 1934-2001
39
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?

40
Opportunities ChallengesClimate 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

41
  • THANK YOU
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