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Impacts of bias correction over climate
change projections applied to crop yield simulations
Santiago V. Cuadra, Rosmeri P. da Rocha, Marta Pereira
Llopart, Kenneth J.Boote, Daniel De Castro Victoria
e-mail: santiago.cuadra@embrapa.br
São Paulo, Brasil, June 25-27/2018.
CORDEX Central America and South America Training Workshop on Downscaling Techniques
Climate Change Impacts
for BrazilAverage Air Temperature
Climate Change Impacts
for BrazilPrecipitation Anomaly
Climate Change Impacts
for BrazilWater Defict Anomaly
Crop Development, Growth and Yield
Crop Cycle
Sow.| Emerg. | Vegetative Phase | Flowering Phase | Pod Formation and Maturation
Phase 4
-80
-60
-40
-20
0
20
40
60
80
100
120
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
sWater Balance
Phase 3
Phase 2
Phase 1
Summary of projected changes in crop yield
(combines 1090 evaluations)
Source: IPCC/WG1AR5
Expect Impacts for food
production
Goald
Present the climate simulations systematic errors and their impacts over climate change
scenarios for food production – soybean over south Brazil.
RegCM4-HadGEM Bias
Minimum Temperature Bias Maximum Temperature Bias
RegCM4-HadGEM Bias
Precipitation Bias Solar Radiation Bias
Consequences of climate
model Bias
Soybean Simulated Yield
Observed Climate RegCM4-Original
NOT Handling Bias - REGO
Handling Bias -
REGDDELTA
DELTA
Handling Bias - REGB
Handling Bias -
REGQ
Consequences of climate
model Bias
Soybean Simulated Yield
Observed Climate RegCM4-Original RegCM4-Bias Correct
Bias correction of climate
model fieldsOriginal RegCM4 outputs
YIELD WITH OBS DATA
YIE
LD W
ITH
REG
O
Bias correction of climate
model fieldsRemove Average Bias from RegCM4 outputs
YIELD WITH OBS DATA
YIE
LD W
ITH
REG
B
Bias correction of climate
model fieldsQuantil-Quatil correction RegCM4 outputs
YIELD WITH OBS DATA
YIE
LD W
ITH
REG
Q
Bias correction of climate
model fields
Original RegCM4 outputsObserved Climate Data
YIELD (kg/ha)
Fre
qu
en
cy
YIELD (kg/ha)
Bias correction of climate
model fields
Remove Average Bias from RegCM4 Observed Climate Data
YIELD (kg/ha)
Fre
qu
en
cy
YIELD (kg/ha)
Bias correction of climate
model fields
Quantil-Quatil correction RegCMObserved Climate Data
Impacts of Bias correction for
climate change scenarios
TMAX anomaly - DELTA (RegCM4)
TMAX anomaly - Bias correction TMAX anomaly - Quantil correction
TMAX anomaly - original RegCM4
TMAX - DELTA (RegCM4) TMAX - original RegCM4
TMAX - Bias correction TMAX - Quantil correction
TMAX
Fre
qu
en
cy
PRCP Anomaly - DELTA (RegCM4)
PRCP Anomaly - Bias correction PRCP Anomaly - Quantil correction
PRCP Anomaly - original RegCM4
PREC - DELTA (RegCM4) PREC - original RegCM4
PREC - Bias correction PREC - Quantil correction
PREC
Fre
qu
en
cy
SRAD Anomaly - DELTA (RegCM4)
SRAD Anomaly - Bias correction SRAD Anomaly - Quantil correction
SRAD Anomaly - original RegCM4
SRAD - DELTA (RegCM4) SRAD - original RegCM4
SRAD - Bias correction SRAD - Quantil correction
Solar Radiation
Fre
qu
en
cy
Impacts through DELTA (RegCM4)
Impacts with Bias correction Impacts with Quantil correction
Impacts with original RegCM4
Impacts through DELTA (RegCM4)
Impacts with Bias correction Impacts with Quantil correction
Impacts with original RegCM4
Impacts through DELTA (RegCM4) Impacts with original RegCM4
Impacts with Bias correction Impacts with Quantil correction
YIELD (kg/ha)
Fre
qu
en
cy
• Yield anomalies with the original RegCM4 data presented differences in
relation to the simulations with bias correction, reaching up to 40%
differences;
• Simulations with all bias correction methods presented similar results for
average yield anomalies;
• Delta method underestimated the climate change impacts on the average
soybean yield and its interannual variability;
• The results shows the need to correct the systematic errors of the climate
models for impacts assessments applications.
Conclusions and
Recommendations
Recommended