Development of a combined crop and climate forecasting system Tim Wheeler and Andrew Challinor [email protected] Crops and Climate Group.

Development of a combined crop and climate forecasting system

Tim Wheeler and Andrew Challinor

[email protected]

Crops and Climate Group

A combined crop and climateforecasting system

Report from:‘Food Crops in a Changing Climate’

Linking climate informationto crop models

general circulation model

crop model

At what scale should information pass between crop and climate models?

Find spatial scale of weather-crop

relationships

Crop modelling at the working

spatial scale Hindcasts with observed weather data

Ensemble methodsClimate

change

Challinor et. al. (2003)

Development of a combinedcrop / climate forecasting system

Challinor et. al. (2004)(Challinor et al, 2004)

and reanalysis(Challinor et al, 2005a)

(Challinor et al, 2005b,c)

(2005c,d)

Fully coupled crop-climate simulation

Osborne (2004)

Simple correlations betweenrainfall and crop yield

Seasonal rainfall and groundnut yields for all India.

Time trend removed. rainfall yield

Patterns of seasonal rainfall and yield of groundnut in India

District level groundnut yields (kg ha-1)

Mean of 1966 - 1990

Data source: ICRISAT

Patterns of seasonal rainfall and yield of groundnut in India

Sub-divisional level seasonal rainfall (JJAS, cm)

Mean of 1966 - 1990

Data source: IITM

• Aims to combine:– the benefits of more empirical approaches (low

input data requirements, validity over large spatial scales) with

– the benefits of a process-based approach (e.g. the potential to capture intra-seasonal variability, and so cope with changing climates)

• Uses a Yield Gap Parameter to account for the impact of differing nutrient levels, pests, diseases, non-optimal management to simulate farm yields

General Large Area Modelfor Annual Crops (GLAM)

Challinor et. al. (2004)

Hindcasts of groundnut yield forall India using GLAM

400

500

600

700

800

900

1000

1100

1200

1965 1970 1975 1980 1985 1990

Year

Gro

un

dn

ut

yie

ld (

kg

ha

-1)

National Yield Statistics

GLAM simulation

Capturing the effects ofintra-seasonal variability

1975Total rainfall: 394mmModel: 1059 kg/haObs: 1360 kg/ha

1981Total rainfall 389mmModel: 844 kg/haObs: 901 kg/ha

Using ERA40 reanalysis data

Andhra Pradesh

Gujarat

• Gujarat: bias correction of climatological mean rainfall works well- Correlation with observed yields 0.49 0.60

• Andhra Pradesh: simulated mean yield < observed, variability >> observed- Incorrect seasonal cycle (both mean and variability) though Jun and Sept good. This is harder to correct.

0

5

10

15

20

25

200 300 400 500 600 700 800 900 1000 1100 1200

Yield (kg ha-1)

Fre

qu

ency

Using probabilistic climate forecasts

Use of DEMETER multi-model ensemble for groundnut yield in Gujarat, 1998 from Challinor et al (2005)

Model average 63 ensemble members

Observed

775 kg ha-1

713 kg ha-1

Probabilistic forecasting of crop failure

• The number of ensemble members predicting yield below a given threshold is an indication of probability of occurrence

• Found predictability in crop failure

• Current risk is dominated by water stress; in the future climate run temperature stress dominates in the north.

The impact of water and temperature stress at flowering under climate change

1960-1990 1 = no impact

0 = max. impact

2071-2100

Hadley Centre PRECIS model, A2 (high emission) scenario

Groundnut

Variety response to temperature stress alone under climate change

Hadley Centre PRECIS model, A2 (high emission) scenario 2071-2100

Number of years when the total number of pods setting is below 50%.

Sensitive variety Tolerant variety

An integrated approach to climate impact assessments

• Crops can modify their own environment

– The water cycle and surface temperatures vary according to land use

• Integrate biological and physical modelling– By working on common spatial scale– By fully coupling the models

Fully coupled crop-climate simulation

Crops ‘growing’ in HadAM3

All-India groundnut yield (red) with simulated mean yield (black) and spatial standard deviation (grey shading).

Fully coupled crop-climate simulation

Using satellite estimates of rainfall

TAMSATTeo Chee-KiatDavid Grimes

Conclusions

• A combined crop and climate modelling system has been developed and tested for the current climate.– It shows skill in seasonal hindcasts and with

climate ensembles– It has been used to study crop responses to

climate change– Can be fully coupled to a GCM, and driven by

satellite data