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