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Alexis Berg ( LOCEAN-LSCE ), Benjamin Sultan ( LOCEAN ), Nathalie de Noblet ( LSCE )

What are the dominant features of rainfall leading to realistic large-scale yield prediction over West Africa ?. Alexis Berg ( LOCEAN-LSCE ), Benjamin Sultan ( LOCEAN ), Nathalie de Noblet ( LSCE ). Group Meeting VARCLIM 11/12/09. Context & objectives.

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Alexis Berg ( LOCEAN-LSCE ), Benjamin Sultan ( LOCEAN ), Nathalie de Noblet ( LSCE )

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  1. What are the dominant features of rainfall leading to realistic large-scale yield prediction over West Africa ? Alexis Berg (LOCEAN-LSCE), Benjamin Sultan (LOCEAN), Nathalie de Noblet (LSCE) Group Meeting VARCLIM 11/12/09

  2. Context & objectives • Climate impacts on crop production (variability, mean). Particularly true in the Tropics (subsistence farming, low levels of managements, high climate variability) • Linking climate models and crop models impact assessment (seasonal time-scale, climate change) • Many sources of error: climate model, crop model, combination of both. • GCM biases: in particular, rainfall (key variable for crop simulation…) What consequence on the performance of yield prediction ? Case study on West Africa, focus on rainfall. Model rainfall progressively corrected towards observations: how does the model “skill” respond (=ability to simulate observed yield variability) ?

  3. What is a crop model ? • Plot-scale: homogenous conditions, ’one’ plant Leaves (LAI) Roots Stems Grains Weather, CO2, radiation (+ nutrient stress) Carbon assimilation Biomass (Monteith, Farqhuar…) (allometric rules…) = f(stage) LAI Sowing Vegetative stage Reproductive stage Harvest Grain filling Maturation/dessication Stages=f(T° sum)

  4. DGVM What is a DGVM ? Global Climate Model Atmosphere model (1 grid cell)‏ Atmosphere Climate, CO2 Surface fluxes (LE, H, CO2), albedo, roughness Vegetation Sea-ice Ocean Ex:ORCHIDEE : croplands = grasslands. But they ARE different… My work : to include a more realistic representation of tropical croplands in ORCHIDEE.

  5. Context & objectives • Climate impacts on crop production (variability, mean). Particularly true in the Tropics (subsistence farming, low levels of managements, high climate variability) • Linking climate models and crop models impact assessment (seasonal time-scale, climate change) • Many sources of error: climate model, crop model, combination of both. • GCM biases: in particular, rainfall (key variable for crop simulation…) What consequence on the performance of yield prediction ? Case study on West Africa, with a large-scale crop model ORCHIDE-mil, focus on rainfall: Model rainfall progressively corrected towards observations: how does the model “skill” respond (=ability to simulate observed yield variability) ?

  6. Experimental setup Yield simulations over West Africa, with a range of different forcing datasets where rainfall is increasingly realistically represented, from “model rain” to observations - using NCEP, CRU and IRD data. • NCEP: interpolated at 1°x1°, 6h (Ngo Duc et al) • CRU: 1°x1°, monthly • IRD: 1°x1°, daily “Model” Obs. – but CRU and IRD amounts are different.

  7. Yield simulations over West Africa, with a range of different forcing datasets where rainfall is increasingly realistically represented, from “model rain” to observations - using NCEP, CRU and IRD data. Experimental setup • NCEP: interpolated at 1°x1°, 6h (Ngo Duc et al) • CRU: 1°x1°, monthly • IRD: 1°x1°, daily “Model” Obs. 5 levels of realism: “Model” Model + cumulative rainfall interannual variability Model + cum. interannual variability + monthly cycle • “Raw” NCEP • NCEP with corrected annual cumulative rainfall (CRU or IRD data) • NCEP with corrected monthly cumulative rainfall (CRU or IRD data) • “monthly-permuted” IRD daily events (CRU or IRD annual amounts) • IRD daily events (CRU or IRD annual amounts) Realism Model + cum. interannual variability + monthly cycle + frequency Model + cum.interannual variability + monthly cycle + frequency + real chronology of rainfall events

  8. Average (1961-1990) time-lat. rainfall in NCEP and IRD

  9. Pdf of rainfall events in IRD and NCEP Difference in simulated sowing dates between IRD and NCEP (blank = missing data) Exemple of seasonal rainfall over one pixel, one year • NCEP overestimates rainfall frequency (« drizzle rains ») • First rains occur too late in NCEP

  10. a) Mali Senegal Niger NCEP CRU IRD Annual rainfall (1968-1990) over different countries in NCEP, CRU and IRD Burkina-Faso • CRU and IRD annual amounts are well correlated, but CRU rainfall is more abundant • NCEP rainfall tends to be too small, and not correlated with observations

  11. Model skill Simulated yields are aggregated at national scale (pixels are averaged). We are only interested in interannual variability: all time series are detrended. Model skill: correlation between observed (FAO) and simulated national yields over 1968-1990.

  12. Model skill Simulated yields are aggregated at national scale (pixels are averaged). We are only interested in interannual variability: all time series are detrended. Model skill: correlation between observed (FAO) and simulated national yields over 1968-1990.

  13. Effect of cumulative rainfall variability

  14. Effect of cumulative rainfall variability Sudano-Sahelian West Africa: water-limited environment, rainfed crops Observed yields are strongly correlated with observedannual rainfall – not with NCEP rainfall (since NCEP and observed rainfall are not well correlated) Annual rainfall is the first “climate signal” in yield data.

  15. Effect of cumulative rainfall variability Accordingly, simulated yields are (~always) significantly correlated with annual rainfall in input. Correlations over 1968-1990 between simulated yields and annual rainfall. Dotted line shows the 5% significance level. Black bars are simulations with IRD annual rainfall, grey bars the ones with CRU annual rainfall

  16. … As a consequence from these two relationships (in observations and in the model), yields simulated with NCEP can not be expected to correlate well with observations In other words, one can not simulate yield variability without the right cumulative rainfall variability in input.

  17. Effect of daily rainfall distribution

  18. Effect of daily rainfall distribution More realistic representation of daily rainfall temporal characteristics (frequency, intensity) higher rain/yield correlations in the model…

  19. Effect of daily rainfall distribution Rainfall/yield correlations are a first order measure of how water-limited crop productivity is in the model. Yield/rainfall correlation

  20. Rainfall with a proper frequency acts as a stronger constrain on crop productivity than “drizzle” rainfall. < Positive bias in simulated plant productivity caused by drizzle rainfall: small and frequent rain events reduce water stress, increasing the plant’s ability to assimilate carbon. Well-known bias in crop modelling: using large-scale climate model outputs as forcing tends to artificially increase crop production (e.g., Baron et al., 2005).

  21. Observed Yields Simulated yields Annual Rainfall Stronger rainfall/sim correlations result in an increase of obs./sim correlations. ‘Drizzle bias’ also undermines the model skill, as it weakens the correlation between input rainfall and simulated yield.

  22. Effect of intraseasonal distribution At the scale considered here, information on the chronology of rainfall – whether monthly or daily - does not add to the model skill.

  23. Counterintuitive: intraseasonal distribution of rainfall (in particular, dry spells) has a significant impact on crop yield (Winkel et al., 1997) + the model (daily time step) is able to capture sub-seasonal effects = model score should improve ?

  24. Effect of intraseasonal distribution Frequency distribution of the relative differences in yields between FREQ and OBS simulations. Calculations are done at the pixel scale (empty bars) and at the country scale (full grey bars) - all pixels (or countries) and all years considered.

  25. This suggests that intraseasonal distribution variability does not show a spatial consistency large enough to impact simulated yields aggregated on a wider scale. Around each pixel, the area within which intraseasonal rainfall events are significantly correlated is no larger than a few pixels (1.27 on average). Area (pixel) of >0.5 inter-pixel correlation of 4-day dry spell occurrence, over JJAS 68-90 Similarly, interannual variations in sowing dates are not spatially correlated beyond a few pixels

  26. Conclusions • The two essential rainfall features for the model to skilfully simulate large-scale yield variability are cumulative annual rainfall variability and rainfall temporal characteristics (frequency/intensity). • At this scale, having the right chronology of rain events does not increase the model score. Resolution-dependant ? Region-dependant ? These results give indications on the characteristics of rainfall that climate models should ideally be able to simulate (or that should be bais-corrected/downscaled…) if seasonal climate forecasts are to be used to drive crop simulations • The increase in model score here, as reanalysis rainfall is progressively corrected, suggests that improvements in GCM simulations are likely to translate into more accurate yield predictions.

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