Title: Translating Climate Forecasts into Agricultural Terms: Advances and Challenges
1Translating 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
2Motivation
- Information relevant to decisions
- Ex-ante assessment for credibility and targeting
- Fostering and guiding management
3Overview
- 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
4Six 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?
5Six 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
6The 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.
7The 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.
8Effect 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.
9Effect of Spatial Averaging
- Spatial averaging distorts variability, increases
frequency, decreases mean intensity. - Similar spatial averaging occurs within GCM.
10Effect of Spatial Averaging
Simulated maize yields, CERES-Maize
11Information Pathways
observed climate predictors
?
predicted crop yields
12Information Pathways
13Information Pathways
14Information Pathways
15Approaches
- 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
16AdvancesSynthetic 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
17Option 1 Conditioning input parameters
- Mean rainfall frequency mean intensity
- Conditioning intensity parameters
- Conditioning frequency parameters
18Option 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.
19Constraining generator outputs reproduces
correlations better than adjusting inputs.
20Constraining generator output requires fewer
replicates for given accuracy.
21Maize simulated from disaggregated monthly GCM
hindcasts, Katumani, Kenya
Rm
p
22AdvancesUse 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
23Correcting Bias in Daily GCM Output Rainfall
Frequency
24Correcting Bias in Daily GCM Output Rainfall
Intensity
25Corrects 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)
26Predicts yields from GCM, perhaps better than
stochastic disaggregation
- CERES-Maize simulated with
- Disaggregated MOS-corrected monthly hindcasts
- Gamma-gamma transformation of daily rainfall
27Advances 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
28Nonlinear Regression
- Katumani maize prediction example
- Yields as f(PC1)
- Mitscherlitch
- functional form
- Cross-validation
29K 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
30Linear 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
31Linear Regression TransformationRegional-Scale
Wheat, Qld, Australia
32Linear Regression TransformationRegional-Scale
Wheat, Qld, Australia
33Advances 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
34Opportunities 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
35Opportunities 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?
36Opportunities ChallengesRobustness of
Alternative Approaches?
Hansen Indeje, 2004. Agric. For. Meterol.
125143
37Opportunities 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?
38Are 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
39Opportunities 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?
40Opportunities 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