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
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Translating Climate Forecasts into Agricultural Terms: Advances and Challenges James Hansen, Andrew Challinor, Amor Ines, Tim Wheeler, Vincent Moron presented.
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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?
0%
10%
20%
30%
40%
50%
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
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