Development of a combined crop and climate forecasting system Tim Wheeler and Andrew Challinor email@example.com Crops and Climate Group
A combined crop and climateforecasting systemReport 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?
Development of a combinedcrop / climate forecasting system Fully coupled crop-climate simulation Find spatial scale of weather-crop relationships Osborne (2004) Challinor et. al. (2003) Ensemble methods Climate change (Challinor et al, 2005b,c) (2005c,d) Crop modelling at the working spatial scale Hindcasts with observed weather data Challinor et. al. (2004) (Challinor et al, 2004) and reanalysis (Challinor et al, 2005a)
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
General Large Area Modelfor Annual Crops (GLAM) • 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 Challinor et. al. (2004)
1200 National Yield Statistics ) 1100 -1 GLAM simulation 1000 900 800 Groundnut yield (kg ha 700 600 500 400 1965 1970 1975 1980 1985 1990 Year Hindcasts of groundnut yield for all India using GLAM
Capturing the effects ofintra-seasonal variability 1975 Total rainfall: 394mm Model: 1059 kg/ha Obs: 1360 kg/ha 1981 Total rainfall 389mm Model: 844 kg/ha Obs: 901 kg/ha
Andhra Pradesh Gujarat Using ERA40 reanalysis data • 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.
Using probabilistic climate forecasts Model average 63 ensemble members 713 kg ha-1 Observed 775 kg ha-1 Use of DEMETER multi-model ensemble for groundnut yield in Gujarat, 1998 from Challinor et al (2005)
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
The impact of water and temperature stress at flowering under climate change Hadley Centre PRECIS model, A2 (high emission) scenario 1960-1990 2071-2100 1 = no impact 0 = max. impact Groundnut • Current risk is dominated by water stress; in the future climate run temperature stress dominates in the north.
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 TAMSAT Teo Chee-Kiat David 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