GMDGeoscientific Model DevelopmentGMDGeosci. Model Dev.1991-9603Copernicus GmbHGöttingen, Germany10.5194/gmd-8-3987-2015Sources of interannual yield variability in JULES-crop and implications for forcing with seasonal weather forecastsWilliamsK. E.https://orcid.org/0000-0002-1185-535XFalloonP. D.Met Office Hadley Centre, FitzRoy Road, Exeter, Devon EX1 3PB, UKK. Williams (karina.williams@metoffice.gov.uk)15December20158123987399728April201522June201527October201516November2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://gmd.copernicus.org/articles/8/3987/2015/gmd-8-3987-2015.htmlThe full text article is available as a PDF file from https://gmd.copernicus.org/articles/8/3987/2015/gmd-8-3987-2015.pdf
JULES-crop is a parametrisation of crops in the Joint UK Land Environment
Simulator (JULES). We investigate the sources of the interannual variability
in the modelled maize yield, using global runs driven by reanalysis data,
with a view to understanding the impact of various approximations in the
driving data and initialisation. The standard forcing data set for JULES
consists of a combination of meteorological variables describing
precipitation, radiation, temperature, pressure, specific humidity and wind,
at subdaily time resolution. We find that the main characteristics of the
modelled yield can be reproduced with a subset of these variables and using
daily forcing, with internal disaggregation to the model time step. This has
implications in particular for the use of the model with seasonal forcing
data, which may not have been provided at subdaily resolution for all
required driving variables. We also investigate the effect on annual yield of
initialising the model with climatology on the sowing date. This
approximation has the potential to considerably simplify the use of the model
with seasonal forecasts, since obtaining observations or reanalysis output
for all the initialisation variables required by JULES for the start date of
the seasonal forecast would present significant practical challenges.
Introduction
The ability to forecast crop yield on a seasonal timescale has significant
economic and humanitarian benefits . Climate variability and extremes
can have significant impacts on crops
e.g., and improvements
in the seasonal forecast of meteorological variables such as
temperature and rainfall therefore have the potential to improve yield
forecasts. However, existing studies of crop model performance focused on
seasonal forecast applications show considerable variation in skill depending
on the region, scale, processes and crops involved . Crop model simulations driven by
statistically downscaled seasonal hindcasts for European wheat
, and specifically for
wheat in Italy , showed that reliable crop yield
predictions could be produced using an ensemble multi-model approach and the
Joint Research Centre (JRC) crop model, for instance, estimating a high probability of a positive
yield anomaly in 1996 and a negative yield anomaly in 1998 in the UK, consistent
with observations. Similarly, used an ensemble of
bias-corrected and disaggregated seasonal forecasts to simulate maize yields
over southern Brazil, with the General Large-Area Model for annual crops (GLAM) crop model. The model showed generally
good agreement with observations, with observed yields within the 95 %
forecast interval for most years. Using a statistical approach to assess the
reliability of hindcasts of global-scale yield decreases of at least 5 %,
Iizumi et al. (2013) found that within-season hindcasts with lead times of
1–3 months generally reproduced interannual variability in observed yields
in major wheat-exporting countries better than pre-season hindcasts with lead
times of 3–5 months. modelled global yields of
major crops by combining satellite-derived net primary productivity (NPP) data and global agricultural
data sets for crop calendar, harvested area and country yield statistics. This
statistical model mostly performed well compared to observations, with
modelled yields explaining 45–81 % of the spatial variation of observed
yields in 2000, and correlation coefficients between modelled yield time
series and sub-national yield statistics for 1982–2006 in major
crop-producing regions generally greater than 0.8.
found some positive skill in reproducing both severe crop failure (yields
below 10th percentile of climatology) and less severe crop failure (yields
below the 25th percentile of climatology) of groundnut in West Africa with
GLAM driven by seasonal forecast data; they found that these results were
relatively independent of assumptions on the varieties of groundnut modelled.
ran the Système d'Analyse Régional des Risques Agroclimatiques-Habillé (System for Regional Analysis of Agro-Climatic Risks) (SARRA-H) crop model at five locations in
Burkina Faso, showing that, in most cases, incorporating seasonal rainfall
forecasts improved sorghum yield predictions made early in the season.
and also found
that downscaling seasonal hindcasts improved crop model performance – the
r2 value of simulated biomass for the whole of Europe increased from 0.62
to 0.69 with greater regional improvements when downscaled seasonal forecasts
were used instead of the original, pre-downscaled versions. On the other hand
found that bias correction of general circulation model (GCM)-derived
seasonal hindcasts data had generally small effects for simulation of
groundnut yields in India. found that errors in
rainfall data had the largest impact on crop model skill for groundnut in
India, mainly because the study region was rainfall limited, while generally
the largest yield errors were caused by errors in interannual variability in
temperature and precipitation. In contrast, for French maize, temperature
errors had a stronger influence on yield estimates from both a statistical
model and a process-based model than precipitation
.
The ability of crop models to represent interannual effects of climate
variables also varies depending on the processes represented in the
models . For example, high-temperature stress
around anthesis (the onset of flowering) can have strong impacts on crop
yields, but not all models include this effect, and responses vary across
models that do . In general, there is little
information on the role of initial conditions such as soil moisture in crop
model performance on seasonal timescales ,
although hydrological studies have shown that different spin-up approaches
may be needed for different impacts and different
regions.
The JULES-crop model was developed with the dual
aim of being able to simulate the impact of weather and climate on crop
productivity and the impact that croplands have on weather and climate. It
is a component of the Joint UK Land Environment Simulator
(JULES) , which is a community land surface
model that can be used both online as part of the Met Office Unified
Modelling system and offline for impact studies. As part of the EU FP7
project EUPORIAS European Provision Of Regional Impacts
Assessments on Seasonal and Decadal Timescales;, JULES-crop will be driven by
seasonal forecasts and its ability to produce probabilistic forecasts of crop
yield will be investigated. EUPORIAS aims to maximise the societal
benefit of seasonal and decadal forecasts by making the predictions directly
relevant to decision makers. As part of this project, a multi-model ensemble
of seasonal meteorological forecasts will be used to drive an ensemble of
impacts models, including JULES-crop.
However, using JULES-crop on a seasonal timescale introduces a number of
technical and scientific issues. The aim of this paper is to address those
issues that are centred around the availability of data, by investigating to
what extent the interannual variability of the modelled yield can be captured
if some of these data requirements are relaxed.
The first data availability issue concerns the driving data. JULES is driven
by a combination of meteorological variables describing air temperature,
precipitation, radiation, wind speed, humidity and pressure (for a full
description, see the JULES User Guide, available at
https://jules.jchmr.org/) for each grid box in the model domain,
ideally at subdaily resolution. Output in this format for each ensemble
member requires a large amount of storage space and is typically not made
externally available by seasonal forecast centres. It is therefore useful to
investigate whether the yield variability can be modelled sufficiently well
if only a subset of the forcing variables are taken from the seasonal
forecast and the others set to climatology, or if the model is forced with
daily meteorological data and disaggregated internally to the model time step.
To gain a better understanding of the dependence of the yield on the
different forcing variables, we look at the effect of removing water stress
and the correlation of the yield with the total grid box precipitation during
the crop-growing season.
The second data availability issue concerns the variables required to
initialise the JULES-crop runs, such as the moisture content of each soil
layer (as a fraction of the water content at saturation). Obtaining accurate
values for these variables on the start date of the seasonal forecast runs
would present a significant practical challenge, as recent observations would
be required to estimate these values directly or as input to a reanalysis
run. Therefore, we investigate the loss in predictability of yield if the
JULES-crop model run is started on the sowing date of the crop in that
grid box and initialised by the climatological values for that date. This
set-up would be simple to reproduce with seasonal forecast forcing that has
been bias-corrected to a reanalysis data set, such as those available as part
of EUPORIAS, since JULES-crop can be run with this reanalysis data set to
produce a climatology of the initialisation variables. Starting the run
before or on the sowing date means that the initialisation of crop variables
(e.g. height) is trivial since the crop either does not yet exist or only
exists as a seed. It has also been suggested that the initialisation of
impact model runs driven by seasonal forecasts is more critical for some
impacts and regions than others; for example, it may be more critical for
water resources in cold regions where snow stores are important than for dry
land cropping .
It is important to note that while this study provides a practical
methodology for driving JULES-crop with seasonal forecasts, given commonly
available forcing and initialisation data, there are many aspects of the
uncertainty chain that remain to be addressed. For example, once an
application has been identified (e.g. a decision threshold based on the yield
of a particular crop in a particular region), a thorough validation would
need to be performed of the relevant model diagnostic against observational
data and against hindcast-driven runs.
This paper is organised as follows. Section
describes the JULES-crop model and how it interacts with the other JULES
components, Sect. describes the model set-up used for the
runs presented in this paper, Sect. presents the results
and Sect. draws conclusions from these runs about the
model behaviour and sensitivities and how these can inform the design of
JULES-crop runs forced with seasonal forecasts.
Model description
JULES is a process-based model that simulates fluxes of carbon, water, energy
and momentum between the land surface and the atmosphere. Sub-grid
heterogeneity is represented through tiles of various surface types, such as
broadleaf trees, bare soil and C3 grass. As of JULES version 4.0, it includes
a crop parametrisation (JULES-crop) which introduces an additional tile for
each crop simulated. We refer the reader to and
for a fuller description of JULES and to for a
description of JULES-crop in particular; here we focus on features that are
particularly relevant to this article, such as the influence of temperature
on crop growth stage, the influence of soil moisture on photosynthesis and the partitioning of
carbon into different parts of the plant.
The status of development of the crop on each tile is parametrised by the
crop development index (DVI), which is -2 before sowing, -1 at sowing, 0 at
emergence and 1 at flowering. Under normal conditions, harvest occurs at a
DVI of 2. The progression between the development stages is determined by
crop-specific thermal time parameters, set by the user. For the purposes of
this paper, thermal time is an accumulation of effective temperature between
one development stage and the next (since we do not include a photoperiod
dependence). Effective temperature is defined by
Teff=0forT<TbT-TbforTb≤T≤To(To-Tb)(1-T-ToTm-To)forTo<T<Tm0forT≥Tm,
where T is the air temperature of the tile at that time step and Tb,
To and Tm are crop-specific cardinal temperatures.
Potential leaf-level photosynthesis (unstressed by water availability and
ozone effects) is calculated as the smoothed minimum of three potentially
limiting rates, based on :
(a) the Rubisco-limited rate, which depends on the maximum rate of
carboxylation of Rubisco, (b) the light-limited rate and (c) the rate
associated with the transport of photosynthetic products for C3 plants or
PEP carboxylase limitation for C4 plants. The vertical profile of radiation
through the canopy can use either the big-leaf approach (following Beer's
law) or a multi-layered canopy radiation scheme, which treats the direct and
diffuse components of the radiation separately. The latter can optionally
include the direct component of the direct beam radiation (“sunflecks”). The
potential leaf-level photosynthesis is scaled by a soil water factor β,
to account for soil moisture stress. This factor is 0 when the mean soil
moisture content in the root zone θ is less than or equal to a wilting
point concentration θw, 1 when θ is greater than the critical
concentration θc and linearly increasing in between (i.e. a slant
step function). As of JULES version 4.1, it is possible to irrigate part of
each grid box, which involves adding water to the soil until β=1 during
certain times of the year.
NPP is calculated by scaling the leaf-level
photosynthesis to the canopy level and subtracting plant maintenance and
growth respiration. Crop growth is modelled by integrating NPP over the
course of a day and splitting this carbon between the crop root, stem, leaf,
harvest and stem reserve carbon pools for that tile (Croot, Cleaf,
Cstem, Charv, and Cresv respectively). The proportion of carbon
given to each pool depends on the DVI of the crop and the crop type.
Once the proportion of carbon given to the stem pool drops below 0.01, carbon
from the stem reserve pool is mobilised to the harvest pool, by reducing
Cresv by 10 % each day and adding this carbon to the harvest pool.
Similarly, once the DVI is above 1.5, carbon from the leaf pool is mobilised
to the harvest pool, by reducing Cleaf by 5 % each day and adding this
carbon to Charv, to simulate leaf senescence. At harvest, the carbon in
the harvest pool becomes yield and each crop carbon pool is reset.
The model does not include a way of calibrating against yield observations
(e.g. a yield gap parameter which accounts for the impact of pests, diseases
and non-optimal management on the crop yield). Therefore the outputted yield
is the water-limited potential yield when irrigation is switched off, and the
potential yield when the crop is fully irrigated.
Experimental set-up
All runs were performed with JULES 4.2.
Combinations of driving variables that are
allowed to vary in the sens-* runs. Each column is a separate run.
All driving variables not marked with an “×” are set to their daily
climatology.
Namesens-Tsens-Psens-TPsens-TPRsens-TPWMean temperature (T)××××Precipitation (P)××××Downward shortwave radiation (R)×Wind speed (W)×Control run (control)
The experimental set-up for the control run follows the global
set-up in . The control run was forced by
6-hourly CRU-NCEPv4 climate data as used by the Global Carbon
Project , regridded to a n96 grid (i.e. grid boxes
are 1.875 degrees by 1.25 degrees). The main run was from 1960 to 2009. The
initialisation variables were taken from a CRU-NCEP-forced run with the crop
model switched off and the model was spun up by repeating the first 10 years
five times, before starting the main run, in order to remove the sensitivity
to this initialisation. Wheat, soybean, maize and rice were modelled, with
the crop parameters listed in . A multi-layer
canopy radiation scheme was used, which accounts for direct/diffuse radiation
components including sunflecks (can_ran_mod=5). The crop-sowing
dates were taken from and extended using
nearest-neighbour interpolation. The crop tile fractions were taken from
, and other ancillaries were taken from
HadGEM2-ES . Irrigation was not
switched on.
Fully irrigated run (irrig)
We repeated the control run with irrigation demand switched on, such
that, when one of the crops on the grid box had DVI >-1, water was added to
the top two soil levels until the critical soil moisture content θc
was reached, so that the soil water factor β was 1, with no constraint
on water availability. The run was initialised and spun up in the same way as
the control run.
Full disaggregated run (disagg)
We created daily means and daily temperature ranges from the CRU-NCEPv4
driving data, and we used this to drive a JULES run. The internal JULES
disaggregator (described in ) was used to
disaggregate these forcing data to the internal model time step of 1 h.
For temperature, this involves adding a sinusoidal diurnal cycle.
Precipitation in a day is modelled as occurring in one rainfall event of
constant intensity, with a duration that depends on the precipitation type.
The run was initialised and spun up in the same way as the control
run. All other settings were the same as the control run.
Results from the global runs described
in Sect. . First column is the run name; second column is the mean
maize yield (in Mgha-1); third column is the standard deviation of the
annual global yield time series (in Mgha-1). The fourth column
gives the Pearson correlation coefficient with the global yield in the
control run, and the fifth column gives the Pearson correlation
coefficient with the global yield in the disagg run. All results
have been weighted as described in Sect. . These results
are presented as scatter plots in Appendix A.
NameMeanStandard deviationGlobal corr with controlGlobal corr with disaggcontrol10.60.55irrig16.20.180.48init10.30.480.91disagg10.20.530.98sens-T10.70.230.23sens-P10.90.420.87sens-TP11.10.510.92sens-TPR11.10.500.92sens-TPW10.30.520.96Disaggregated runs with some forcing from climatology (sens-*)
In order to investigate the sensitivity to variability
in different parts of the driving data, we created daily climatologies of
each driving data variable in the full disaggregated run. For example, for
each grid box, the value used for 1 January in the precipitation
climatology was the mean over the CRU-NCEP precipitation on every 1 January
from 1960 to 2009 in that grid box. We then repeated the runs (for 1960 to
2009, as before) with climatological driving data for all variables apart
from certain combinations. The combinations we refer to in this paper are
shown in Table . The run was initialised and spun up in the
same way as the control run.
Runs initialised from climatology (init)
We created a climatology for each initialisation variable, for each day of
the year, using daily means outputted from the control run and
averaging over 1960–2009. The model requires 16 initialisation variables, on
multiple model layers or tiles, such as tile surface temperature and moisture
in soil layers as a fraction of water content at saturation (see JULES user
guide for full list). The model domain was split by sowing date, and we
performed a separate run for each sowing date for each crop for each year,
initialised by the climatology for that sowing date, without spin up. For
example, for maize, we modelled 77 different sowing dates across the globe
for 48 years, which involved 77×48 individual JULES runs. The full
6-hourly driving data were used. Each run lasted 1 year, and the annual yields
were concatenated to get a 48-year time series for each crop in each grid box.
Results
Global time series for each crop were constructed from the model output by
first masking any grid boxes which had one or more years in which the crop
did not reach a DVI of 1.5 or greater or had a yield less than the seed
carbon 0.01 kgCm-2 (which we assumed was due to a failure
on the part of the model or model settings to represent the crops in this
grid box) and then weighting according to grid box size and crop tile
fraction. We define a year as 1 January to 31 December (i.e. the model
year). In a small fraction of the grid boxes with harvest dates around the end
of December/beginning of January, this definition caused issues, as two
harvests could fall in one year and none in the next. These points were
masked out, as the zero yield appears as a model failure.
found that maize yield in the control
run had the highest correlation with detrended global FAO yield observations
out of the four crop types modelled (maize, soybean, rice and wheat);
therefore we will explicitly discuss the results for maize only, although we
have confirmed that our overall conclusions apply to each of the four crops
individually. Results from the other crops are given in the Supplement.
All plots show the correlations with the
annual maize yield in the control run for each grid box. Top left:
the correlation between yield in control run and crop season
precipitation. Top right, bottom left and bottom right: the correlation
between yield in control run and yield in the disagg,
init and irrig runs respectively.
Using daily forcing data and disaggregating rather than using the full 6-hourly
data results in a slightly lower mean global yield (10.2 Mgha-1
for the disaggregated run, compared to 10.6 Mgha-1; see Table ). The global
yield time series from the disaggregated run correlates very well with the
global yield time series from the control run: the Pearson
correlation coefficient is 0.98. The annual control yield is plotted
against the annual disagg yield in Fig. ,
and it shows no obvious deviations from linearity, even at the extremes. Figure (top right) shows the correlation for each grid
box, 94 % of which are greater than 0.85 (note that there will be spatial
correlation between grid boxes and autocorrelation in the time series for each
grid box. Also the Pearson correlation coefficient is not resistant to
outliers). It is interesting to note that many of the grid boxes with low
correlations are in Brazil, a region where the disaggregator has been seen
previously to reproduce the climatology of key variables such as evaporation
better than runs driven with 3-hourly
data . As discussed in
, since the 3-hourly data are more
representative of the underlying driving data than the disaggregated data,
this apparent “improvement” with the disaggregator is likely to be result of
the extra parameters involved in the disaggregation being tuned to compensate
for a bias elsewhere in the model. As a result, the maize yield from the
disaggregated run can actually have a higher correlation with FAO country
yield data than the control run for Brazil (not shown here). We can
therefore conclude that using daily forcing data and disaggregating is a very
good approximation to the control run, for the purposes of looking
at variability in the maize yield.
The correlations between the annual maize
yield in the disagg run and the annual maize yield from the
sens-P (top left), sens-TP (top right), sens-T
(bottom left) and sens-TPR (bottom right) runs for each grid box.
Comparing the control run with the fully irrigated run allows us to
determine how much of the modelled yield variability is driven by soil
moisture variability. Removing the effect of soil moisture stress increases
global NPP as expected, which results in considerably higher global mean
yields: maize yield rises from 10.6 to 16.2 Mgha-1 (Table ). This increase in NPP also has the effect of
increasing the number of grid boxes which contribute to the global yield time
series, since fewer grid boxes have crops that are harvested prematurely in
the model due to lack of growth. Removing soil moisture stress also
significantly decreases the (year-to-year) standard deviation for maize
yield, which has a global standard deviation of 0.55 Mgha-1
in the control run and 0.18 Mgha-1 in the irrigated
run.
We also calculated the Pearson correlation coefficient between the global
control run yield and irrigated run yield for each grid box, as shown
in Fig. , bottom right. There was a high
correlation coefficient between the two runs in areas with high rainfall
during the model maize-growing season such as Southeast Asia, central
Brazil, the northern part of the Amazon Basin and Bangladesh/east India,
where we would not expect soil moisture to be a limiting factor in crop
growth, even with no irrigation. However, in drier regions, these
correlations were much lower, as expected. The percentage of unmasked
grid boxes with correlations above 0.85 was just 20 % for maize, showing that
in most regions soil moisture variability is an import contribution to the
yield variability in the control run.
Moving on from soil moisture to precipitation, we constructed a time series
for the crop season precipitation by integrating the rainfall between the
sowing and harvesting dates for each crop in each grid box. In many regions,
this crop season precipitation index correlates reasonably well with the crop
yield for the unmasked grid boxes, particularly outside of Southeast Asia,
central Brazil, the northern part of the Amazon Basin and Bangladesh/east
India, where, as we have already identified, the modelled yield variability
does not follow soil moisture variability.
It is therefore interesting to look at how much of the modelled yield
variability can be reproduced if the daily precipitation is used to drive the
model, while keeping all other variables at their climatological value for
each day of the year (sens-P). A priori we can not assume this
will be a good approximation to using the full daily driving data
(disagg) from the result for the crop season precipitation index
above, since, in the control run, the precipitation is not
independent of the other driving data. However, Fig. (top left) shows that the sens-P run does
indeed correlate well with the disagg run in areas outside of
Southeast Asia, central Brazil, the northern part of the Amazon Basin and
Bangladesh/east India. Seventy-four percent of the grid boxes shown have a correlation of 0.85
or more. The correlation between the global yield time series from the
sens-P run and the disagg run is 0.87. The sens-P
run does have a slightly higher mean global maize yield than the
disagg run: 10.9 Mgha-1 as compared to 10.2 Mgha-1.
If temperature is the only variable allowed to vary between years (i.e. the
sens-T run), then the global mean maize yield is 10.7 Mgha-1, with standard deviation 0.23 Mgha-1.
This reduction in standard deviation compared to the disagg run is
consistent with the reduction in standard deviation seen when the effect of
soil moisture was removed (the irrig run). Unsurprisingly, Fig. (bottom left) shows that the sens-T run does not
correlate well with the disagg run in areas where the sens-P run had a higher
correlation.
If both daily precipitation and daily mean temperature are allowed to vary
(sens-PT), the grid box correlations with the disagg run are
much more spatially uniform than when either of these variables are varied on
their own: in the sens-PT run, 81 % of the grid boxes have a
correlation higher than 0.85 (Fig. , top right).
Many of the areas with low correlations in the sens-P run are much
higher in the sens-PT run, such as parts of Brazil, Columbia,
Bangladesh and Southeast Asia, although these still remain lower than
surrounding regions. The correlation between the global maize yield time
series in the sens-TP run and the disagg run is 0.92. The
scatter plot of these yield time series (Fig. )
shows that the relation between the outputted yield is well approximated by a
linear fit. In general, therefore, driving the model with daily precipitation
and mean temperature and using climatology for all other driving variables is
a good approximation to make when looking at the interannual yield
variability across the majority of global maize-growing regions.
In order to improve the approximation further, it may be desirable to
additionally allow downward shortwave radiation to vary (sens-PTR)
or additionally allow wind speed to vary (sens-PTW). Allowing
downward shortwave radiation to vary improves performance (i.e. grid box
correlations with the disagg run) in the areas which still have
relatively low performance in the sens-PT run, i.e. Brazil, Columbia,
Bangladesh and Southeast Asia (Fig. , bottom
right). Alternatively, allowing wind speed to vary results in a mean global
yield that is closer to the mean global of the disagg run (Table ).
The final remaining question concerns the model initialisation. The set of
runs that are initialised on each sowing date with climatology
(init) in general reproduce the spatial distribution of yield from
the control run. The global yield is generally lower than in the
control run in each year, which results in slightly lower mean
global yield (10.3 Mgha-1) compared to the control
run (10.6 Mgha-1). The correlation between the global maize
yield in the init run and the control run is 0.91 (see
Fig. for scatter plots), and 70 % of individual
grid boxes have a correlation above 0.85 (Fig. ,
bottom left). The correlations are relatively poor in some parts of India,
the Congo basin and south/southeastern Brazil. However, outside these areas,
initialising on the sowing date has the potential to be a very useful
approximation.
Conclusions
In this article, we have investigated a number of possible approximations that could be made when running JULES-crop:
use driving data at daily rather than subdaily resolution, and disaggregate internally to the model time
step;
use a subset of daily driving data, and set the rest to a daily
climatology.
initialise with climatology on the crop-sowing date.
Each of these approximations significantly simplifies the use of JULES-crop for
seasonal crop yield forecasts, due to the reduction in required driving and
initialisation data. With this usage in mind, we have concentrated on the
effect of these approximations on the interannual variability of the modelled
yield.
Using daily forcing data and disaggregating performs the best out of these
approximations, although care should be taken if modelling the Amazon basin,
where the precipitation disaggregation parameters may have been tuned to
compensate for biases in JULES.
We have shown that, in most regions outside Southeast Asia, central Brazil,
the northern part of the Amazon Basin and Bangladesh/east India, the
interannual variability of the yield from a JULES-crop run in the control
configuration is mainly driven by precipitation, which affects the crop via
water availability from the soil, which we have confirmed with a fully
irrigated run. As a result, in these regions, it is a good approximation to
drive the model with forecast precipitation and leave the other driving data
at their climatological values for each day of year. It should be noted that
the processes and parameters which govern the response of the crop model to
the soil moisture distribution, such as the soil water factor β and the
root distributions in JULES, are therefore keys areas for future model
development. Driving the model with both precipitation and temperature
improves the performance in areas with high soil moisture, and some further
improvement in these areas can be obtained from the addition of downward
shortwave radiation.
Perhaps the most important approximation considered here is initialising with
climatology on the sowing date, since obtaining accurate initialisation data
on the timescales needed for seasonal forecast runs is a particularly
significant practical challenge. We have confirmed that this approximation
performs well across the majority of maize-growing regions and identified
areas where the approximation breaks down.
Taken together, these approximations allow JULES-crop to be driven by
seasonal meteorological forecasts where ensembles of bias-corrected daily
precipitation and daily temperature (and possibly downward shortwave
radiation) are available. The reference data set used for the bias correction
can be used to generate the climatology of the initialisation variables and
the other driving variables. Since these data are widely available, this
provides a practical methodology by which to obtain seasonal crop forecasts
with JULES-crop.
Scatter plots of global yield from model runs
Scatter plots comparing the global mean maize yield from different model runs.
Scatter plots comparing the global mean maize yield from different model runs.
The Supplement related to this article is available online at doi:10.5194/gmd-8-3987-2015-supplement.
Acknowledgements
The authors are grateful to Jemma Gornall, Tom Osborne and Camilla Mathison for useful discussions. This article was supported by the
Joint DECC–Defra Met Office Hadley Centre Climate Programme
(GA01101) and the EUPORIAS project, funded by the European
Commission 7th Framework Programme for Research, grant agreement
308291. This work contributed to EUPORIAS D23.5.
Edited by: D. Lawrence
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