Implications of climate model biases and downscaling on crop model simulated climate change impacts

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Implications of climate model biases and downscaling on crop model simulated climate change impacts

Davide Cammarano, Mike Rivington, Keith Matthews, Dave Miller, Gianni Bellocchi

To cite this version:

Davide Cammarano, Mike Rivington, Keith Matthews, Dave Miller, Gianni Bellocchi. Implica- tions of climate model biases and downscaling on crop model simulated climate change impacts.

ASA/CSSA/SSSA/ESA 2015 Joint Annual Meeting, Nov 2015, Minneapolis, MN, United States. 3 p. �hal-01519329�

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18/02/2016

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Implications of climate model biases and downscaling on crop model simulated climate change impacts

Davide Cammarano¹ Mike Rivington¹ Keith Matthews¹ Dave Miller¹ Gianni Bellocchi²

1The James Hutton Institute, Aberdeen & Dundee, UK.

2Grassland Ecosystem Research Unit, French National Institute of Agricultural Research, Clermont-Ferrand, France

Outline

•Introduction

•Objectives

•Materials and methods

•Results

•Conclusion

Introduction

Area of uncertainty of studies on modelling Climate Change (CC) impacts in agriculture:

Projections from climate model runs provide both biased and uncertain representations of observed data hence there are likely to be errors associated with the use of future projections (Flato et al., 2013).

Utility of data representing future weather projections

Introduction

Input of projected climate changes into crop models achieved through:

• Fine-scale climate data from raw Global Climate Models (GCM)

• Dynamical downscaling using Regional Climate Models (RCM) Projections converted through:

• Weather generators

• Applying climate change anomalies to observed time series

• Bias correction (BC) methods

However, the sources of the climate data contain significant systematic spatial and temporal errors (e.g. solar radiation, precipitations) that lead to bias in crop models estimations.

Introduction

then the potential exists for meaningful adjustment using either statistical downscaling or bias correction (BC) methods.

Because of the existence of such errors

the modelled weather data should be evaluated against observations prior to their use

Hindcast

Objectives

•evaluate the RCM hindcast estimates against observed data

•apply BC methods to the hindcast data and re- evaluate to investigate the range and magnitude of differences between observed and modelled data

•investigate how these differences manifest themselves as errors in simulated outputs made by three crop models

•investigate the consequences of application of BC to future projection data and use in the crop models Note: this makes estimates of future crop responses, but is not specifically an impacts study, as there are other considerations required

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Materials and methods

•Observed data from the British Atmospheric Data Centre (BADC) at 12 meteorological stations (1960-1990) [ObWT]

•HadRM3 RCM initial realisation original hindcast (50x50 km) [OrH]

•OrH data downscaled using the BC method of Rivington et al. (2008) [DsH]

•HadRM3 future projections [OrF]

•OrF data downscaled with BC method [DsF]

Cammarano et al., 2015 submitted

Materials and methods

• Three crop simulation models were used:

• Decision Support Systems for Agrotechnology Transfer (DSSAT v 4.6, Jones et al., 2003)

• Agricultural Production Systems sIMulator (APSIM v 7.7, Keating et al., 2003)

• Cropping Systems Simulation Model (CropSyst, Stöckle et al., 2003)

• Crop: Spring Barley (Sowing 15^th of March)

• Calibration: variety trials (HCGA, 2006)

• No nitrogen stress and re-initialization every year

• Sandy-loam soil used for the 12 locations (Scotland soil database: 40,000 samples, from more than 13,000 locations across the country dating back to the 1930; From 2007 to 2010, a third survey was carried out on which also provided the samples for the first national database of soil DNA)

Thickness Bulk

Density LL^a FC^b SATc Sand Clay Silt SOC^d

(cm) (g cm-3) (cm cm-3) (cm cm-3) (cm cm-3) (%) (%) (%) (%)

0-20 1.446 0.129 0.232 0.418 65 9 26 5.0

20-60 1.433 0.143 0.236 0.423 64 23 13 4.0

60-150 1.445 0.129 0.231 0.418 60 20 20 2.8

aLower Limit; bField Capacity; cSaturation; dSoil Organic Carbon.

Results: Climatic variables at Mylnefield

0 20 40 60 80 100

Proportional Difference

-0.5 0.0 0.5 1.0 1.5 2.0

Hindcast Downscaled

0 5 10 15 20

Observed Hindcast

0 100 200 300

-6 -4 -2 0 2 4 6 8

Day of Year

0 100 200 300

Difference in mean daily solarradiation, So (Estimated - Observed) (MJ m-2 day-1)

Mean daily Maximum (Tmax) and Minimum (Tmin) air temperature (o C)

Observed Downscaled Hindcast Precipitation amount (mm)

0 20 40 60 80 100

Difference (mm)

-10 0 10 20 30 40 50 60

Hindcast Downscaled

20 40 60 80 100

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Hindcast Observed Downscaled

10 12 14 16 18 20

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

0 2 4 6 8 10

Probability of Excedence (%)

20 40 60 80

Rain events

OrH DsH

Results: Solar radiation difference at the 12 locations

Inverness (So from CD) -8 -6 -4 -202468

Sutton Bonington -8 -6 -4 -202468

Rothamstead -8 -6 -4-202468

Day of Year 050 100 150 200 250 300 350 Before downwscaling

(Original Hindcast - Observed) After Downscaling (Downscaled Hindcast - Observed)

Auchincruive

-8 -6 -4 -202468

Difference in mean daily solar radiation, So (Estimated - Observed) (MJ m-2 day-1)

East Malling 050 100 150 200 250 300 350 -8-6

-4 -202468

Cawood -8 -6 -4 -202468

Aberdeen -8 -6 -4 -202468

Everton

Day of Year 050 100 150 200 250 300 350 -8-6

-4 -202468

050 100 150 200 250 300 350 Before downscaling

(Original Hindcast - Observed)

After downscaling (Downscaled Hindcast - Observed)

Mylnefield -8 -6 -4 -202468

Bush (So from CD) -8-6 -4 -202468

Galashiels (So from JW) -8-6 -4 -202468

Wallingford -8 -6 -4-202468

Results: Frequency distribution of simulated yield

16 12 8 4 0 16 12 8 4 0 16 12 8 4 0 16 12 8 4 0

10000 6000 2000 16 12 8 4 0

10000 6000 2000

Simulated yield (kg/ha)

Frequency

Everton Malling East Wallingford Rothamsted Bonington Sutton Cawood Auchinc've Galashiels Bush Mylnefield Aberdeen Inverness

ObWT

OrH

DsH

OrF

DsF

North UK South UK

Results

DsF

-60 -40 -20 0 20 40 60

OrF

-60 -40 -20 0 20 40 60

DsH

Relative change (%)

-60 -40 -20 0 20 40 60

OrH

-60 -40 -20 0 20 40 60

CS AP DS

Anthesis Maturity Yield Cumulative

Evapotranspiration

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Results

h)

Days after sowing

0 20 40 60 80 100 120 140

0.0 0.2 0.4 0.6 0.8 1.0 g)

0 2 4 6 f)

0 2 4 6 e)

0 2 4 6 d)

0 10 20 30 40 c)

0 10 20 30 40 b)

0 10 20 30 40

Precipitation (mm)

0 10 20 30 40 a)

CS

DS AP

AP AP

DS

DS CS CS Solar radiation (MJ m2 day-1)Air temperature (C)Potential, actual evapotranspiration, plant transpiration (mm d-1)Water Stress Index

OrH

DsH

growing season rainfall 178 mm lower than DsH

Conclusions

•Input of climate data has consequences on crop models outputs

•Bias Correction increased the confidence in the quality of the climate outputs

•Disentangle the input error effect and response to climate change

•Daily simulation of evapotranspiration and water stress diverged significantly under lower rainfall.

Acknowledgments

The results of this research were obtained within an international research project named “FACCE MACSUR – Modelling European Agriculture with Climate Change for Food Security, a FACCE JPI knowledge hub”.

The authors would like to thank the Scottish Government’s Rural and Environment Science and Analytical Services Division (RESAS) and the meta-programme Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA) for their funding support of this research.

Thanks to Mr. K. Marsh at the BADC for processing the HadRM3 model data and to the Meteorological Office and Hadley Centre for permission to use their data.