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Project:

ClimAfrica

Project full title:

Climate change predictions in Sub-Saharan Africa:

impacts and adaptations

European Commission - FP7

Small-Medium Scale Collaborative Project

Grant agreement no.: 244240

Del. no: 3.5

Deliverable name: Decadal forecast runs to test effects of these on predictability of yield

Version: Final version

WP no: 3

Lead beneficiary: LU(n.2)/CEA(n.3)

Delivery date from Annex I: 40

Actual / Forecast delivery date (project month):41

Authors:

Per Bodin, Abdoul Traoré, Nicolas Vuichard, Jens Heinke, Jonas Jaegermeyr, Valentina Mereu,

Stefano Materia, Donatella Spano, Almut Arneth

1. Introduction 1.1 Short summary

Despite showing initially promising results, current climate decadal forecast are not reliable enough for the

purpose of the deliverable. Instead we use statistically and dynamically downscaled and/or bias corrected

GCM output to force the three AgroDVMs (LPJmL, LPJ-GUESS and ORCHIDEE-STICS) and the site

based DSSAT model. Using the same GCM output there is large difference in model result depending on the

downscaling/bias correction method used with sometimes contrasting signs in the resulting difference

between future and current yield. The difference between models is however larger. This is not overly

surprising as the model differ slightly in how they represent different processes and in how they are tuned.

For example, the DSSAT model has been parameterized specifically for the different sites for which the

models were run and so should be able to more accurately represent the reality. Judging by this LPJmL

performs better than LPJ-GUESS when it comes to simulating the change in the length of the growing

period. However, LPJ-GUESS does take into account an adaptation of the length of the growing period to

future climate by a form of selection of “varieties” and thus a smaller effect on the length of the growing

period is expected. Despite the differences between the models some consistencies can be found between

LPJ-GUESS and LPJ-mL in relation to the location of regions with a projected increase in yield. For the

Sahel this is relatively consistent across downscaling/bias correction methods (with a much larger are for

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LPJ-GUESS compared to LPJmL) and for most other regions consistent between the models for the same

downscaling/bias correction method.

1.2 Rationale for this deliverable

Sub Saharan Africa (SSA) is a region expected to be particularly sensitive to climate change effects on crop

yield (Barrios et al. 2008). Annual precipitation, calculated as averages for each African country, is expected

to change by −39 to +64 mm by 2030 (Jarvis et al. 2012). The effect of climate also becomes larger as ~97 %

of all agricultural land in SSA is rainfed (Rockström et al. 2004). When it comes to precipitation, it is not

only the amount of precipitation that is of importance in SSA. Equally or of more importance is the timing of

rain events (Funk et al., 2003).

Many climate impact studies focus on longer time scales up until the end of the 21st Century. But adaptation

to climate change in agriculture needs to be made at a much shorter time scale (~10 years for annual crops

and pastures) (Vera et al. 2010) and it is therefore of key importance to develop credible projections for the

near turn future (Verdin et al., 2005; Mishra et al., 2008). At the seasonal time scale land surface areas such

as SSA have recently been evidenced as a very promising contributor to forecasts skill (Koster, 2004;

Alessandri and Navarra, 2008; Kirtman and Pirani, 2008).

Within ClimAfrica the aim was to develop improved climate predictions on seasonal to decadal climatic

scales. Climate output from these models were then to be used to improve the current understanding of

climate impacts in key sectors in SSA, such as water resources and agriculture. Deliverable 3.5 focuses on

the decadal forecast of crop yield. These types of forecasts would be of great importance to the Impact

Assessment community and would therefore function as a strong link between climate modelling

community, the crop modelling community and Impact Assessment related research.

1.3 Problems encountered and envisaged solutions

Despite some promising preliminary results in relation to the predictive skill of decadal forecast models,

several researchers have pointed out that the skill of decadal predictions currently are not at the level as to be

useful to Impact Assessment research (Goodard et al., 2012; Jones, 2013; Meehl et al., Accepted). Meehl et

al. (Accepted) summarize these findings and especially for precipitation the predictive skill of decadal

forecasts is poor. They also question whether future decadal climate predictions will prove useful to most

stakeholders, with precipitation presenting a larger challenge than temperature.

A large portion of climate variability for sea surface temperatures within five years from model initialization

is due to El Nino Southern Oscillation (ENSO) modulation and episodic volcanic eruptions, while longer

time-scale variability is most likely related to changes in the external forcing (i.e. increase in the

concentration of greenhouse gases, Bellucci et al., 2013). The poor sampling of the observed state

implemented in the protocol of CMIP5 (on which the ClimAfrica experimental setup was built), with only

one start-date every five years, creates a dramatic under-sampling for ENSO, since no major episodes of El

Nino / La Nina occur during the years selected for initialization. In this way, the main driver of predictability

for the first prediction years is cut out, and radiative forcing change remains the only factor impacting multi-

year variability.

Therefore we instead use the climate scenario runs used in Deliverable 3.2 for current climate, but extended

to a future climate (2011-2040). Here the future climate is based on GCM output from three GCMs, and

using three different statistically downscaling and/or bias correction techniques. These simulations present

both a larger spatial and temporal resolution than the decadal forecasts, but nevertheless represent the state of

the art in relation to climate modelling, downscaling and bias correction for climate scenarios at regional

scale.

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2. Method

The model description and modeling setup are taken from the model runs used in D3.2 with a focus on

current (2001-2010) to near future changes (2021-2030) in the length of the growing period and yield.

2.1 The models:

a°) The LPJ models:

The original Lund-Potsdam-Jena (LPJ) model is a process-based dynamic global vegetation model designed

to simulate patterns and dynamics of natural vegetation patterns and corresponding fluxes of carbon and

water (Smith et al., 2001; Sitch et al., 2003). LPJ distinguishes 9 plant functional types (PFTs) that are

characterized by the general traits of plant types (e.g. tropical evergreen tree, temperate deciduous trees,

grass) rather than plant species. Herbaceous vegetation is distinguished into C3 and C4 types with the

respective differences in the photosynthetic pathway. Gross primary production (GPP) is computed based on

a generalized version of the widely applied Farquhar et al. leaf-photosynthesis model (Farquhar et al., 1980,

Collatz et al., 1992). CO2 assimilation for photosynthesis and water loss through transpiration are

intrinsically coupled and influence each other mutually. High photosynthesis rates are accompanied by high

transpiration rates; reduced transpiration from water stress causes a reduction of photosynthesis. Allocation

of assimilated carbon to plant organs and respiration form different carbon pools is modeled explicitly based

on PFT specific traits and ambient conditions. Distribution of PFTs is determined by bioclimatic limits and

competition for light and water.

LPJmL

LPJmL is a version of the original LPJ model maintained and further developed at the Potsdam Institute for

Climate Impact Research (PIK). Main developments include the introduction of managed lands (mL) by

extending the concept of functional types to crops, so called crop functional types (CFTs), each representing

a major crop or crop type (Bondeau et al., 2007). Currently, 12 CFTs are implemented: temperate cereals,

rice, maize, tropical cereals, temperate roots, tropical roots, pulses, rapeseed, soybean, sunflower, groundnut,

and sugar cane. In addition, pasturelands are included as managed grassland with a distribution of C3 and C4

types based on the parameterization for natural grasses. River routing and limitation of irrigation by surface

water availability was introduced by Rost et al. (2008). Plantation of grass and tree biomass for bioenergy

use was implemented by Beringer et al. (2011). Permafrost and a new hydrology scheme were implemented

by Schaphoff et al. (2013). An improved scheme for the estimation of sowing dates was implemented by

Waha et al. (2012).

LPJ-GUESS

LPJ-GUESS is a version of the original LPJ model maintained and further developed at the Lund University

(LU). Cropland was introduced to LPJ-GUESS (Lindeskog et al., 2013) by modifying the approach of

Bondeau et al. (2007). A new sowing algorithm, based on the seasonality of precipitation and temperature

and minimum monthly temperature (Waha et al., 2012) was also implemented. Crop phenology in LPJ-

GUESS, represented by LAI, is calculated based on heat unit sums and photosynthesis. Heat unit sums also

controls carbon allocation to roots and leaves. This scheme is a development from the crop phenology in

Bondeau et al. (2007) where LAI is prescribed and heat unit sums only controls carbon allocation.

b°) ORCHIDEE-STICS

ORCHIDEE is a land surface model simulating carbon, water, energy (and in some versions nutrient)

exchange within ecosystems and between ecosystems and the overlying atmosphere (Krinner et al., 2005).

The land surface is described using a number (12) of plant functional types (PFTs) and bare soil. Phenology

and carbon fluxes between the different ecosystem carbon pools are also accounted for. ORCHIDEE

represents key carbon-related processes by modeling the gross primary production (GPP), net primary

productivity (NPP), growth and maintenance respiration, mortality, litter and soil organic decomposition.

CO2 assimilation by GPP for C3 and C4 plants are calculated at a half-hour time step using a big leaf

approximation, based on the Farquhar et al. (1980) and Collatz et al. (1992) leaf-scale equations respectively.

The scaling of GPP within the canopy assumes exponential attenuation of light. Other carbon related

processes are calculated at a daily time step. Variables such as the foliage density (LAI) or plant water stress

that characterize the state of the vegetation are prognostic variables that will impact for instance assimilation,

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allocation, senescence and other related processes (Krinner et al., 2005). Water exchange through

transpiration is related to photosynthesis via stomatal conductance following the formulation of Ball et al.

(1987).

The soil water diffusion based on a simple two-layer bucket-type mode in the ORCHIDEE standard version

has been modified to introduce a more realistic soil hydrology parameterization scheme. The soil physics in

the ORCHIDEE new hydrology is based on the CWRR model (Bruen, 1997; Dooge et al., 1993) with an 11

layers soil-discretization. The vertical discretization is finer near the surface (the 4 topmost layers being 1cm

part) where the soil moisture mostly varies. This ensures that rapid exchanges in soil moisture near the

surface can be well represented.

Furthermore, the development of ORCHIDEE-STICS has been included in the ORCHIDEE new hydrology

and used in this deliverable. ORCHIDEE-STICS is the ORCHIDEE land surface model in which we include

the STICS (Simulateur mulTIdisciplinaire pour les Cultures Standard) crop model as an interface in order to

simulate specific crops such as cereals (maize, wheat, etc.) over Africa.

c°) DSSAT-CSM crop model

DSSAT-CSM (Decision Support System for Agrotechnology Transfer-Cropping System Model) (Jones et

al., 2003; Hoogenboom et al., 2010) is a dynamic, process-oriented, software that allows the simulation of

crop growth and development overt time on a uniform surface of land, under certain conditions of crop

management (ordinary or assumed). Moreover, DSSAT-CSM allows the simulation of water, carbon and

nutrients dynamics that occur in soil and the effects of different agronomic practices on a cropping system

over time. The DSSAT-CSM version 4.5 includes crop models that simulate over 25 crops. The crop growth

models are sub modules of the Plant primary module. Inside the Plant module, three different levels of

complexity are considered: crop specie, ecotype and variety.

A platform for DSSAT-CSM spatial application was developed by CMCC, allowing multiple runs exploring

different soil, climate and crop management options. The platform is written in the programming language R

and makes it possible to link crop simulation models with a GIS software and to directly use climate data in

Netcdf format.

2.2. The datasets

Climate forcing

Eight meteorological input variables were used to drive the three agro-DGVMs and DSSAT-CSM crop

model: rainfall (mm.day-1), solar radiation (Wm-2), minimum and maximum temperature (K), wind speed

(m.s-1), the surface air pressure (hPa) and the specific humidity (kg.kg-1). All the forcing data was available

at a daily time step and at 0.5° resolution. Note that ORCHIDEE-STICS needs all these eight variables,

DSSAT needs four variables (rain, min and max temperature and solar radiation) whereas LPJ models only

needs three variables (rain, surface air temperature and solar radiation).

Bias-correction/downscaling methods applied to GCM

Three GCMs were selected to force the Agro-DVMs and DSSAT-CSM crop models: CanESM2, GFDL-

ESM2M and MIROC5. The selection was made based partly on availability and partly on the spread in

climate means. The selected GCMs displayed both a large range in climate means and were available from

both the statistical and dynamical downscaling products. Of the different emission scenarios we selected the

RCP8.5, giving the largest difference in simulated climate compared to today.

In order to explore the impact of different ways of processing GCM output for the use in impact models

(downscaling and bias-correction), three alternative methods named respectively SOMD, QMBC and SMHI-

RCM data are employed. The SOMD method was applied on rainfall, surface air temperature (Tmin and

Tmax) and solar downward radiation. The QMBC method was applied on rainfall, surface air temperature,

solar and infrared radiation, specific humidity, wind speed and surface air pressure. All the meteorological

variables needed to run the Agro-DVMs and DSSAT crop model are available with SMHI-RCM data.

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The rationale for using a range of different downscaling/bias-correction methods is to assess the impact of

this process in the modelling chain, which has been shown to be of similar importance than the selection of

the climate or impact model.

SOMD:

The SOMD (Self-Organizing Maps Downscaling) method is a statistical downscaling described in Hewitson

and Crane (2006) and further developed and applied by UCT (University of Capte Town, South Africa).

QMBC

The QMBC (Quantile-Mapping Bias-Correction) is a new bias-correction method based on a methodology

described in Watanabe et al. (2013) and further developed to allow for a separate correction of biases in

inter-annual and day-to-day variability.

The SOMD is related to climatic processes such long-term oscillations in the climate system (e.g., ENSO)

whereas the QMBC is determined by processes on a much smaller temporal and spatial scale. The new

method should therefore be superior to approaches that do not distinguish between these two types (e.g.,

Hempel et al., 2013).

SMHI-RCM

GCM output data that has been dynamically downscaled using the Swedish Meteorological and Hydrological

Institute Regional Climate model (SMHI-RCM) were made available by the SMHI. No bias correction was

applied to this data. These model runs were made within the Coordinated Regional Downscaling Experiment

(CORDEX), a program for generating improved regional climate change projections worldwide. Results

from the CORDEX analysis is used as input to the IPCC Fifth Assessment Report as well as for high-

resolution downscaled projections to inform climate change impact and adaptation studies.

2.3 Simulations set up

A set of continental model runs was conducted using the three different methods for processing GCMs

outputs (SOMD from UCT, QMBC from PIK and SMHI-RCM raw data) applied to the three GCMs

(MIROC5, CanESM2 and NOAA-GFDL), making a total of 9 climate forcing datasets. Transient CO2

concentrations were assumed in these simulations using the CO2 scenarios associated with RCP 8.5.

Due to the differences in the number of climate drivers needed to force the models not the all agro-DVMs

and the DSSAT-CSM crop model were forced with all climate datasets (Table 1). The SMHI-RCM dataset

and the QMBC GCMs bias corrected data was used to force all three AgroDVMs over the entire African

continent whereas the SOMDS applied to GCM output data was used to force the two LPJ models.

Local modelling activities concern the parameterization of crop simulation models at the field scale, inside

the case study areas, using data from field experiments and information on ordinary agronomic crop

management, provided by WP6. Specifically, weather, soil, crop management and yield data for maize in

Lake Chirwa (Malawi), and Makueni-Kitale (Kenya) case study areas, data for sorghum, millet and rice in

Sanmantenga (Burkina Faso), for maize and cassava in Togogo (Togo), and Ankasa (Ghana), and for

sorghum in Algadambalia (Sudan) case study areas were collected. The missing data were derived from

literature or online database. The parameterized models were then used to simulate crop phenology and yield

under rainfed conditions, and different management options. DSSAT was forced with SMHI-RCA and

QMBC for simulation of current and future yields at these sites.

Land-cover and soil data

In the simulations the three AgroDVMs, land-cover and cropland fractions were assumed to be constant and

at the year 2000 level for the all simulations. Land-cover and cropland cover data were taken from

Ramankutty et al. (2008) and Portmann et al. (2010) as proposed by Fader et al. (2010).

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For runs using LPJ-GUESS soil texture data were adapted from the FAO soil texture database (Sitch et al.,

2003) and for model runs with LPJmL soil information from the Harmonized World Soil Database (version

1.2) (FAO/IIASA/ISRIC/ISSCAS/JRC, 2012).

Table 1. Climatologies and GCMs used in the simulations using the models from the respective WP

partners.

Downscaling method GCM Participants

SMHI-RCM MIROC5, CanESM2,

GFDL

LU (LPJ-GUESS),

PIK (LPJmL),

CEA (ORCHIDEE),

CMCC (DSSAT)

SOMD MIROC5, CanESM2,

GFDL

LU (LPJ-GUESS),

PIK (LPJmL) ,

CMCC (DSSAT)

QMBC MIROC5, CanESM2,

GFDL

LU (LPJ-GUESS),

PIK (LPJmL),

CEA (ORCHIDEE)

3. Main Results

For the four sites with field trial data for maize and the two sites with field data for sorghum all four models

were compared in relation to differences in yield between near future (2011-2040) and current (1981-2010)

yield. As simulated yield might be affected by a change in the length of the growing period (LGP) the

difference in LGP between near future and current climate was also compared for all models except for

ORCHIDDE-STICS.

For sorghum, DSSAT predicts a decrease in the length of the growing season between 2 and 4 days (Figure

1) for both sites. For the same downscaling/bias correction techniques LPJmL predicts a decrease between 4

and 6 days. LPJ-GUESS however predicts a smaller change and for SOMD in Burkina Faso even a positive

change is observed. The change in yield is less consistent between models when it comes to the direction of

change in yield but the change absolute numbers is small across all models (Figure 2). ORCHIDEE-STICS

displays a decrease for all climate data sets at both sites whereas LPJ-GUESS displays an increase across

sites and climate datasets (except SOMDS in Burkina Faso).

For maize, DSSAT displays a decrease in LGP between 2 and 8 days (Figure 3), LPJ-mL between 7 and 27

days and LPJ-GUESS displays no difference for all sites and climate datasets except for QMBC in Kenya.

The decrease is largest in Kenya and Malawi for LPJmL and Malawi and Togo for DSSAT. For yield the

LPJmL model and DSSAT predicts the largest changes in yield with up to a 800 kg ha-1 increase for LPJmL

in Malawi for SMHI-RCA data and a 1000 kg decrease for DSSAT in Ghana for the same climate dataset

(Figure 4). Again, LPJ-GUESS and ORCHIDEE-STICS predict small changes in yield. All four models

agree on an increase or no change in yield for Kenya (except for ORCHIDEE-STICS forced by SMHI-RCM)

and for Togo LPJmL and DSSAT display a small decrease whereas the other two models display a small

increase. For Ghana and Malawi there is a larger inconsistency between models and climate datasets.

Looking at the entire continent the general pattern with a larger difference for LPJmL compared to LPJ-

GUESS identified at the site level for maize can also be seen (Figure 5). For large parts of southern Africa

the length of the growing period decreases up to 30 days for the LPJmL model. The region with the largest

difference differs slightly between the downscaling/bias correction methods. For LPJ-GUESS the absolute

change in the length of the growing period is much smaller. It is interesting to note that for some regions the

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simulated change has different signs between the models using the same climate dataset (e.g. South Africa,

Kenya and Cameroon).

The difference in the simulated difference in yield is also much larger for LPJmL compared to LPJ-GUESS

(Figure 6). The regions for which there is an increase in projected yield are however relatively similar for the

LPJ models across downscaling/bias correction methods in particular for the SMHI-RCM. For LPJmL there

is a large region with a positive trend in yields stretching from Angola in to Ethiopia in the north east. These

regions in general have a positive trend in yield (albeit smaller in absolute terms) for LPJ-GUESS as well.

For LPJ-GUESS there is a general positive trend in yield for the Sahel. This increase can also be found

ORCHIDEE-STICS and partly for LPJmL. The large region with a decrease in yield found in central Africa

for ORCHIDEE-STICS using QMBC can also be found for LPJmL but not for LPJ-GUESS. ORCHIDEE-

STICS also agree on an increase in yield for parts of Ethiopia found for LPJmL.

4. Conclusion

The main objective of this deliverable was to study the change in yield using decadal forecasts.

Here we present simulations using climate from three GCMs and three different downscaling/bias

correction methods forcing three AgroDVMs and one site based crop models. The results show a

large difference in results depending on the selection of downscaling method and/or bias correction

method. For any analysis made with future climate projections, the selection of bias downscaling

and bias correction method is therefore of great importance. The difference between models is

partly expected and also shows the importance of using a multi-model approach. Some general

trends can however be seen across models such as which regions for which an increase in yield is

expected.

Figure 1. Simulated difference in the length of the growing period for sorghum between future (2011-

2040) and current (1981-2010) climate for three of the crop models and three climate datasets at two

ClimAfrica field trial sites.

Figure 2. Simulated difference in yield for sorghum between future (2011-2040) and current (1981-

2010) climate for the four crop models and three climate datasets at two ClimAfrica field trial sites.

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Figure 3. Simulated difference in the length of the growing period for sorghum between future (2011-

2040) and current (1981-2010) climate for three of the crop models and three climate datasets at four

ClimAfrica field trial sites.

Figure 4. Simulated difference in yield for maize between future (2011-2040) and current (1981-2010)

climate for the four crop models and three climate datasets at four ClimAfrica field trial sites.

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a) LPJmL

b) LPJ-GUESS

c)

d)

e)

f)

Figure 5. Simulated difference the in length of the growing period for maize between future (2011-

2040) and current (1981-2010) climate. Left panel represent LPJmL and right panel LPJ-GUESS. Top

panels (a-b) represent simulation runs using SMHI-RCM data, center panels (c-d) SOMDS

downscaling, and bottom panels (e-f) QMBC bias correction.

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a) LPJmL

b) LPJ-GUESS

c) ORCHIDEE

d)

e)

f)

Not sufficient number of climate

variables available to run the model

g)

h)

i)

Figure 6. Simulated difference in yield for maize between future (2011-2040) and current (1981-2010)

climate. Left panel represent LPJmL, centre panel LPJ-GUESS and Right panel ORCHIDEE. Top

panels (a-c) represent simulation runs using SMHI-RCM data, center panels (e-f) SOMDS

downscaling, and bottom panels (h-j) QMBC bias correction.

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