Introduction
Global and regional climate models (GCMs and RCMs) are used to produce
projections of future climates driven by various types of greenhouse gas
emission scenarios. The last Coupled Model Intercomparison Project
CMIP;, CMIP5, provides simulations for the
preindustrial period (CO2 concentration at a level of 280 ppm),
historical period (1860–2005; including real evolutions of CO2 and
other greenhouse gas concentrations, anthropogenic and volcanic eruption
aerosol contents, solar activity), and future climate projections based on
different CO2 emission trajectory scenarios, Representative
Concentration Pathways RCPx.x (; x.x corresponding to the radiative forcing in
W m-2 in 2100), RCP2.6, RCP4.5, RCP6.0, and RCP8.5 .
Scientific communities working on evaluation and modelling of climate change
impacts (in terms of crop yields, water resources, health, etc.) are
increasingly using these simulation outputs either to compute related impact
metrics or to run impact models. However robust biases are still present in
climate models due to ill-defined processes and associated parametrizations,
leading to biased statistical distributions of simulated physical and
dynamical variables e.g.. Then statistical
bias corrections must be applied to variables used in impact model
simulations . For instance, warmer-than-normal sea surface
temperatures in the equatorial Atlantic lead to a too southern location of
the Inter-Tropical Convergence Zone (ITCZ) in boreal summer over West Africa. This bias has not been reduced
between CMIP3 and CMIP5 GCM simulations see.
This too southern ITCZ location over West Africa leads to too weak
precipitation over the Sahel and too weak crop yields whose values cannot be
used as relevant information for stakeholders and farmers.
GCM and RCM output data have to be adjusted to statistical distributions of
observation-based reference data. However, the use of different
bias-correction methods in combination with different reference datasets
contributes to the total uncertainty in climate projections and can
contribute in some contexts more than the use of different GCMs or RCMs
. Thus using multiple bias-correction
techniques
and reference datasets can be recommended. For instance, a bias correction
of a subset of five GCMs of the CMIP5 database was realized at a global scale
through the ISIMIP project , the first
Inter-Sectorial Impact Model Intercomparison
Project. These corrections were applied at
a daily scale from 1 January 1950 to 31 December 2099 to historical and all
RCP scenarios for five GCMs at a 0.5∘ × 0.5∘ grid using WATCH Forcing Data
(WFD)
data as observation-based reference. More recently a ISIMIP2b bias correction
using an improved reference dataset, EWEMBI, has been realized for three out of the
five CMIP5 GCMs' data, and the results have been compared to the bias-corrected
ISIMIP/WFD data . Significant differences have been
highlighted that are closely related to differences between WDF and EWEMBI data.
The objectives of this paper are to present and evaluate bias-corrected GCM
data obtained by performing the cumulative distribution function transform (CDF-t)
method over Africa to quantify the sensitivity of the bias-corrected
data to different reference datasets and to illustrate this in terms of
simulated crop yields. It is a contribution to the
AMMA-2050 project, centred on West
Africa, the goals of which are to significantly improve scientific understanding of climate
variability and change across Africa and the impact of climate change on
specific development decisions, to introduce flexible methods for integrating
improved climate information and tools in specific decision-making contexts,
and to improve medium to long-term (5–40 years) decision-making, policies,
planning, and investment by African stakeholders and donors.
Bias correction has been applied to daily data of six variables critical for
these types of impact: precipitation (pr), mean near-surface air temperature
(tas), near-surface maximum air temperature (tasmax), near-surface minimum
air temperature (tasmin), surface downwelling shortwave radiation (rsds),
and wind speed (wind). The bias correction has been performed using the
CDF-t method ,
a method that has been widely used and
validated for various variables and in various contexts
e.g.,
including tropical areas ,
but not Africa. These corrections have been applied to 29 GCMs over the
1950–2005 period and RCP2.6, RCP4.5, and RCP8.5 2006–2099 projections. The
observation-based reference dataset used for bias corrections is WFDEI, the
WATCH Forcing Data WFD; methodology applied to
ERA-Interim data, for the period from 1 January 1979 to 31 December 2013 on a
0.5∘ × 0.5∘ grid .
Section 2 presents the reference data. A first intercomparison of WFD, WFDEI,
and EWEMBI is presented in terms of mean seasonal fields over West Africa. In
Sect. 3 the CDF-t bias-correction method is shortly presented. Then tests
are carried out over 1979–2013 to evaluate the sensitivity of the corrections
to the calibration period. In Sect. 4, the evaluation of the CDF-t
bias correction is detailed over West Africa, first on mean seasonal fields,
then on daily metrics. CDF-t bias-corrected GCM data are also compared
with ISIMIP/WFD bias-corrected data for the five GCMs used in ISIMIP. The
significant impact induced by some improvements introduced in WFDEI data will
be shown. CDF-t outputs are also compared to products from EWEMBI. To go
further into this evaluation, a crop model has been used to test the impact
on simulated crop yields (specifically a local maize cultivar) of
bias-correction data with one GCM and of the three reference data. A
sensitivity analysis to individual forcing variables (temperature,
pr, and rsds) is also
presented. Finally the bias-correction impact on crop simulations in the
context of RCP8.5 climate change projections is shown. Conclusions are given in Sect. 5.
Climate input data
The AMMA-2050 dataset comprises bias-corrected daily data for the variables
pr, tas, maximum air temperature and
minimum air temperature, rsds, and wind
speed. It covers the domain 20∘ W–55∘ E/40∘ S–40∘ N,
including all of Africa. In this paper, results are
presented for West Africa (20∘ W–20∘ E/0–25∘ N)
in boreal summer as it is the focus of AMMA-2050.
List of available CMIP5 models used for historical and RCP
simulations. The five GCMs also used in ISIMIP are in italics. The number in each
column is the number of ensemble member used in this work. Zero indicates
that no run was used. The last line shows the total number of runs used for
each simulation.
Modelling centre (or group)
CMIP5
Resolution
Simulations
Models
(lat × long × lev)
Historical
RCP2.6
RCP4.5
RCP8.5
Commonwealth Scientific and Industrial Research Organization (CSIRO) and Bureau of
ACCESS1-0
1.25∘ × 1.875∘ × 38
1
0
1
1
Meteorology (BOM), Australia
ACCESS1-3
1
0
1
1
Beijing Climate Center, China Meteorological Administration
bcc-csm1-1
1.875∘ × 1.875∘ × 16
1
1
1
1
bcc-csm1-1-m
1
1
1
1
College of Global Change and Earth System Science, Beijing Normal University
BNU-ESM
2.81∘ × 2.81∘ × 26
1
1
1
1
Canadian Centre for Climate Modelling and Analysis
CanESM2
2.790∘ × 2.81∘ × 35
1
1
1
1
Centro Euro-Mediterraneo per I Cambiamenti Climatici
CMCC-CESM
3.443∘ × 3.75∘ × 39
1
0
0
1
CMCC-CM
0.748∘ × 0.75∘ × 31
1
0
1
1
CMCC-CMS
3.711∘ × 3.75∘ × 95
1
0
1
1
Centre National de Recherches Météorologiques/Centre Européen de Recherche et
CNRM-CM5
1.4∘ × 1.4∘ × 31
1
1
1
1
Formation Avancée en Calcul Scientifique
Commonwealth Scientific and Industrial Research Organization in collaboration with
CSIRO-Mk3-6-0
1.875∘ × 1.875∘ × 18
1
1
1
1
Queens land Climate Change Centre of Excellence
NOAA Geophysical Fluid Dynamics Laboratory
GFDL-CM3
2∘ × 2.5∘ × 48
1
1
1
1
GFDL-ESM2G
2∘ × 2.5∘ × 24
1
1
1
1
GFDL-ESM2M
2∘ × 2.5∘ × 24
1
1
1
1
Met Office Hadley Centre (additional HadGEM2-ES realizations contributed by Instituto
HadGEM2-AO
1.25∘ × 1.875∘ × 38
1
1
1
1
Nacional de Pesquisas Espaciais)
HadGEM2-CC
1.25∘ × 1.875∘ × 38
1
0
1
1
HadGEM2-ES
1.25∘ × 1.875∘ × 38
1
1
1
1
Institute for Numerical Mathematics
Inmcm4
1.5∘ × 2∘ × 21
1
0
1
1
Institut Pierre-Simon Laplace
IPSL-CM5A-LR
1.9∘ × 3.75∘ × 39
1
1
1
1
IPSL-CM5A-MR
1.25∘ × 2.5∘ × 39
1
1
1
1
IPSL-CM5B-LR
1.9∘ × 3.75∘ × 39
1
0
1
1
Atmosphere and Ocean Research Institute (University of Tokyo), National Institute for
MIROC5
1.4∘ × 1.4∘ × 40
1
1
1
1
Environmental Studies, and Japan Agency for Marine-Earth Science and Technology
Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research
MIROC-ESM
2.8125∘ × 2.8125∘ × 80
1
1
1
1
Institute (University of Tokyo), and National Institute for Environmental Studies
MIROC-ESM-CHEM
1
1
1
1
Max-Planck-Institut für Meteorologie (Max Planck Institute for Meteorology)
MPI-ESM-LR
1.8653∘ × 1.875∘ × 47
1
1
1
1
MPI-ESM-MR
1.8653∘ × 1.875∘ × 95
1
1
1
1
Meteorological Research Institute
MRI-CGCM3
1.12148∘ × 1.125∘ × 48
1
1
1
1
MRI-ESM1
1
0
0
1
Norwegian Climate Centre
NorESM1-M
1.9∘ × 2.5∘ × 26
1
1
1
1
Total
29
29
20
27
29
Simulations
We use daily data extracted from the CMIP5 archive, covering the period from
1 January 1950 to 31 December 2099. Based on availability of daily data, it
comprises 29 GCMs for the 1950–2005 historical period and RCP8.5
2006–2099 projection, 27 GCMs for the RCP4.5 projection, and 20 GCMs for the RCP2.6 projection
(see Table for more details). Only one run has been used for
each GCM. For an easier comparison with observation, these “raw” data have
been interpolated on the 0.5∘ × 0.5∘ grid of WFDEI using a
bilinear approach for temperatures and wind and using a “nearest neighbour”
approach for precipitation. Then, bias-corrected data are available on the
0.5∘ × 0.5∘ grid.
Reference observation datasets
The observation-based reference dataset is critical for the correction of GCM
biases, especially when corrections are applied to daily data. The reference
dataset must also have a global coverage on a regular grid, which may induce
large uncertainties in void in situ data areas as in Africa. So we used the
available WFD, WFDEI, and EWEMBI reference datasets
to compare to each other and to compare bias-corrected (with WFD) ISIMIP data
with bias-corrected (with WFDEI) AMMA-2050 data.
The WFD dataset is a combination of ERA-40 daily
reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF)
at a grid resolution of 2.5∘ and the Climate Research Unit (CRU) TS2.1
dataset that provides observed time series of monthly variations in the
climate on a resolution grid of 0.5∘. A correction for monthly
mean rainfall is included using the Global Precipitation Climatology Centre (GPCC)
version 4 dataset . The WFD data are
available over the period 1958–2001 on a 0.5∘ grid over land area
points. The WFD dataset has been used over 1979–2001.
WFDEI, an improved version of WFD, has been produced based on ERA-Interim
reanalysis, over the period from 1 January 1979 to 31 December 2013 on a
0.5∘ × 0.5∘ grid . Improvements
come from the 4D-var data assimilation system with 6 h windows in ERA-Interim
instead of 3D-var in ERA-40. Compared to ERA-40, ERA-Interim uses a more
extensive suite of satellites, atmospheric soundings, and surface observations
and provides substantial improvement in surface meteorological variables
, in particular with a new aerosol loading
distributions and corrections for downward shortwave fluxes (leading in
particular to larger average WFDEI values over the Sahara and northern
Africa),
leading to less bias compared to globally distributed observations.
ERA-Interim also has a reduced Gaussian grid spectral model resolution of
T255 instead of T159 for ERA-40, leading to data much closer to the
regular 0.5∘ × 0.5∘ spatial resolution and to the
elevation distribution used for WFDEI. A correction for monthly mean rainfall
is included using the GPCCv5/v6 dataset. The WFDEI dataset has been used over 1979–2013.
More recently, the EWEMBI dataset has been produced within ISIMIP
. Over land, EWEMBI is identical to
the WFDEI dataset for pr, daily mean, minimum, and maximum
near-surface air temperature, and 10 m wind speed but different for surface
downwelling shortwave radiation. Data sources of EWEMBI are ERA-Interim data,
WFDEI, eartH2Observe forcing data E2OBS;, and
NASA/GEWEX Surface Radiation Budget data SRB;
primary-algorithm estimates of daily mean rsds from SRB release 3.0
. Significant differences have been
highlighted between WFD-based and EWEMBI-based bias-corrected data that are
closely related to similar improvements from WDF to EWEMBI data. The EWEMBI
dataset has been used over 1979–2013.
Summer climatology from different observation datasets (WFD, WFDEI,
and EWEMBI): (a–c) for near-surface temperature (∘C)
over 1979–2001, (d–f) for precipitation rate (mm day-1)
over the 1979–2001 period, (g–i) for solar radiation (W m-2)
over the 1984–2001 period, and the difference among WFD (j) over 1984–2007,
WFDEI (k) over 1984–2007, EWEMBI (l) over 1984–2001, and
SRB solar radiation. The red box (18∘ W–10∘ E; 10–20∘ N)
and blue box (18∘ W–10∘ E; 3–10∘ N) respectively represent
the Sahel and Guinea regions used in this study.
Intercomparison of WFD, WFDEI, and EWEMBI on mean seasonal fields over West Africa
In the following, to reduce the number of figures, the results are presented
only for the summer season, July–September (JAS), which is the main rainy
season over the Sahel. Similar computations have been performed over the
other seasons, especially over spring, which is the main rainy season over
the Guinean coast, and some of the results will be commented on.
Figure presents the July–September mean seasonal fields of
WFD, WFDEI, and EWEMBI for tas, pr, and rsds. Regarding tas, the mean fields of
the three reference datasets are very close, showing the set-up in northern
spring and summer of the high-temperature area associated with the Saharan and
Saudi Arabia heat lows. Regarding pr, the seasonal fields are also very
close, showing the seasonal migration of the ITCZ
between spring and summer. Local maxima associated with highlands like the
Fouta Djalloon or Cameroon mountains are also clearly highlighted. Regarding
rsds, more differences are evident between the three reference datasets. The
mean seasonal fields show similar patterns with low values within the ITCZ
area due to the high cloud coverage and high values over the Sahara due to
low moisture and cloud coverage, but the range of values are quite different.
Over the ITCZ, WFD rsds values are the weakest and EWEMBI values the highest. Over
the Sahara WFD values are also the weakest but WFDEI values are a bit higher
than for EWEMBI. In the remaining panels, differences are produced in respect
to SRB data. Compared to SRB, EWEMBI data are very similar, which is logical
since SRB data were used to correct ERA-Interim. WFDEI has moderate negative
biases in the ITCZ area and weak positive biases over the Sahara, while WFD
has high negative biases over the whole area.
The CDF-t bias correction
The CDF-t method
In this work, we use the CDF-t method developed by
to adjust climate models. It consists
in matching the CDF of a climate variable simulated by a model (here the GCM)
to the CDF of this variable in observations (here WFDEI) through a
mathematical function. CDF-t is a variant of the non-parametric
quantile–quantile (QQ) method . But contrary to the
QQ method that projects the GCM CDF of simulated future data onto the CDF of
historical data, CDF-t considers the CDF change between historical and
future GCM simulations. Let FGh and FSh define
the CDFs of a variable from the GCM (subscript G) and from a given
reference location (subscript S) over a historical calibration period
(subscript h). The transformation T allows going from FGh to FSh:
TFGh(x)=FSh(x).
Replacing x with FGh-1(u), where u is any probability in [0, 1]:
T(u)=FShFGh-1(u),
which provides a definition of T. Assuming T is stationary in time, the
transformation can be applied to FGf, the CDF of the variable over
a future or validation period f, to generate FSf, the CDF at the
reference location for the same period f:
TFGf(x)=FSf(x).
That is
FSf(x)=FShFGh-1FGf(x).
Once FSf has been determined from Eq. (), a QQ
approach is carried out between FGf and FSf to generate
local time series. While in , QQ is applied directly
between FGh and FSh, the CDF-t method generates quantile
values through a QQ performed between FGf (and not FGh)
and FSf (and not FSh). Values are then
generated according to FSf in chronological agreement
with future climate simulations. More details on the CDF-t method can be
found in .
Application
This CDF-t approach has been applied to five out of the six variables (tas,
tasmax, tasmin, rsds, and wind) over the period 1950–2099 (historical and
RCP2.6, RCP4.5, and RCP8.5 runs). For pr, an updated CDF-t
approach has been used, referred to as “singularity stochastic removal” (SSR),
addressing rainfall occurrence and intensity issues seefor more details.
CDF-t has been applied month by month to take into account the strong
seasonality over Africa. It has been applied using a moving window to smooth
discontinuities : a moving 17-year window is used as the
“target” CDF, and the GCM data of the central 9 years are corrected. This
process is repeated by moving the window forward by 9 years, covering
the whole period of 1950-2099. Moreover, CDF-t preserves any long-term trend in
the GCM data but neither trends in moments nor in quantiles
. GCM data have been interpolated to the WFDEI grid
before being bias corrected, using a bilinear method for tas, tasmax, tasmin,
rsds, and wind and a nearest neighbour method for pr.
Examples of CDF-t bias correction applied to mean West Africa daily
pr data for the five GCMs used in ISIMIP are shown (Fig. S1 in the Supplement). It is
represented in terms of cumulative distribution function. The distributions
of raw GCM data are clearly different from the WFDEI data. Some of them show
more low pr values in GCMs than in WFDEI while others have more
low pr values. The CDF-t bias correction appears very effective as the
WFDEI and bias-corrected GCM data distributions are closely superimposed.
Sensitivity of the correction to the calibration period over West Africa
Before applying the CDF-t correction through the moving window process
over 1950–2099, the bias-correction method has to be calibrated individually for
every GCM over a reference period. In order to have a calibration dataset as
representative as possible of the variability in the various variables,
especially pr, the time period 1979–2013 has finally been used for
calibration of the bias-correction method. However the sensitivity to the
calibration period has been explored over West Africa by testing it on two
sub-periods, 1979–1996 and 1996–2013, to prevent any overestimation of the
bias-correction performance. This has been performed on the five GCMs used in
ISIMIP, and it is more specifically shown in the IPSL-CM5A-LR model in summer for
tas, pr, and rsds (Supplement).
Three calibration periods have been tested: 1979–1996, 1996–2013, and
1979–2013 (see Fig. S2). First, it is clear that the bias correction is
powerful to remove the cold bias of the raw data. Second, the positive trend
present in the raw data over the period 1979–2013, as in WFDEI but with a
weaker range, is preserved after the bias correction. This is probably due to
the dry bias of pr over the Sahel in raw data that induces a
higher sensitivity to the impact of anthropogenic global warming over the
period than in observations. Third, the effect of the calibration period is
clear. By using the calibration period 1979–1996, the remaining bias of
corrected data is near zero and is weakly positive over 1997–2013, while by
using the calibration period 1996–2013, the remaining bias of corrected data
is near zero and is weakly negative over 1979–1995. Using the calibration
period 1979–2013, the remaining bias is overall very weak and on average near
zero. Similar tests have been carried out for the variables pr and rsds, and
for the other seasons, with similar conclusions. Thus, while it can be thought
that using the whole observational period to calibrate the bias-correction
process may lead to overestimation of the fit between observations and
bias-corrected data, it in fact provides a more robust correction. Therefore we
choose the longest period 1979–2013 to perform the calibration process.
Mean near-surface air temperature (∘C) in JAS 1979–2001 from
WFDEI and from five CMIP5 GCMs' raw data (the GCMs also used in ISIMIP).
Results
User-based metrics and diagnostics
A list of priority metrics has been established between scientists and
stakeholders involved in AMMA-2050. We are presenting results based on some
of these metrics related to the three variables, pr,
near-surface air temperature (tas), and surface downwelling shortwave
radiation (rsds). These metrics are
the seasonal mean for pr, tas, and rsds;
the mean time–latitude annual cycle over (15∘ W–15∘ E) for pr, tas, and
rsds;
the 95th percentile of daily values for tas;
the number of days with tas > 30 ∘C;
the 95th percentile of daily values for pr;
the number of wet days (pr > 1 mm day-1);
the number of days with pr > 10 mm day-1;
the number of dry days (pr < 1 mm day-1);
the 95th percentile of the duration of consecutive dry days sequences.
Taylor diagrams relative to the mean of near-surface air temperature
over 1979–2001 from 29 individual models (a) and five out of them (b)
were also used in ISIMIP. Two areas are considered: Sahel and Guinea (see these
boxes in Figs. , , or ).
Data are compared to WFDEI data. Taylor diagrams provide three statistics: the
correlation coefficient between any tested field and the reference field
(related to the azimuthal angle), the normalized standard deviation of the
tested field in respect to the standard deviation of the reference field
(proportional to the radial distance from the origin), and the centred root-mean-square difference between the tested field and the reference field
(proportional to the distance from the REF point on the x axis; grey circles
from 1 (with the lowest radius) to 4 (the highest radius)). “Observations” represents WFD and EWEMBI data (in black). Raw GCM data are in blue,
CDF-t bias-corrected GCM data in red, and ISIMIP bias-corrected GCM data in
green.
Spatial correlation, standard deviation (SD), and root-mean-square
error (RMSE) computed for different observation datasets over the Sahel
(18∘ W–10∘ E; 10–20∘ N) and Guinea
(18∘ W–10∘ E; 3–10∘ N) areas in JAS. All scores are
computed relative to WFDEI for seasonal mean precipitation (Mean pr), seasonal
near-surface air temperature (Mean tas), seasonal surface downwelling shortwave
radiation (Mean rsds), the 95th percentile of daily values for precipitation (R95p)
and near-surface air temperature (T95p), the number of wet days (R1mm), the
number of heavy days (R10mm), the number of dry days, the 95th percentile of
consecutive dry days, and the number of days with tas greater than 30 ∘C. CDD: consecutive dry days.
Correlation
SD
RMSE
Metrics
WFDEI
WFD
EWEMBI
WFDEI
WFD
EWEMBI
WFDEI
WFD
EWEMBI
Sahel
Mean tas
–
0.997
1.000
2.797
2.581
2.797
–
0.414
0.000
Mean pr
–
0.999
1.000
3.176
3.237
3.176
–
0.203
0.000
Mean rsds
–
0.980
0.938
43.125
47.945
30.944
–
39.115
18.687
T95p
–
0.994
1.000
3.290
2.830
3.290
–
0.577
0.000
R95p
–
0.970
1.000
6.740
12.979
6.740
–
8.424
0.000
R10mm
–
0.965
1.000
3.751
3.202
3.751
–
3.214
0.000
Number of day with tas > 30 ∘C
–
0.996
1.000
35.114
35.988
35.114
–
4.487
0.000
R1mm
–
0.961
1.000
27.421
15.859
27.421
–
21.527
0.000
Number of dry days
–
0.961
1.000
9.140
5.286
9.140
–
7.176
0.000
95th percentile of CDD
–
0.977
1.000
9.800
5.618
9.800
–
6.609
0.000
Guinea
Mean tas
–
0.887
1.000
0.733
0.624
0.736
–
0.741
0.000
Mean pr
–
0.995
1.000
3.647
3.644
3.680
–
0.352
0.000
Mean rsds
–
0.824
0.390
15.387
14.419
13.946
–
54.940
28.532
T95p
–
0.844
1.000
0.789
0.655
0.795
–
1.005
0.000
R95p
–
0.948
1.000
7.866
11.735
7.957
–
13.676
0.000
R10mm
–
0.969
1.000
17.860
10.681
17.860
–
7.972
0.000
Number of day with tas > 30 ∘C
–
0.571
1.000
0.005
0.075
0.005
–
0.075
0.000
R1mm
–
0.717
1.000
7.440
10.897
7.440
–
29.305
0.000
Number of dry days
–
0.717
1.000
2.480
3.632
2.480
–
9.768
0.000
95th percentile of CDD
–
0.886
1.000
7.910
4.750
7.910
–
9.374
0.000
Same as Fig. but for precipitation rate in millimetres per day.
Same as Fig. but for precipitation rate.
Same as Fig. but for solar radiation in watts per square metre.
Same as Fig. but for solar radiation. In
the right column, EWEMBI is used as “REF”.
Mean seasonal fields over West Africa
In the following, the Taylor diagram will be
used to quantify the distance between the raw, bias-corrected GCM data and
WFDEI data. This diagram provides three statistics, the spatial correlation
coefficient between the tested field and the reference field, the normalized
standard deviation of the tested field in respect to the standard deviation
of the reference field, and the centred root-mean-square error (RMSE)
between the tested field and the reference field. The Taylor diagram has also been
used to evaluate the distance between the reference datasets WFD and
EWEMBI relative to WFDEI. Table sums up the three Taylor
statistics of these reference datasets for all the metrics.
Regarding the seasonal mean metrics, WFDEI and EWEMBI statistics are similar
except for rsds, for which they are quite different over the Guinean coast. WFD is
also very close to WFDEI but all statistics are a bit different, with again
more differences for the Guinea coast.
Figure presents the mean JAS temperature fields over
West Africa for WFDEI data and for raw data from the five GCMs used in
ISIMIP. Figure S3 shows similar fields but for CDF-t bias-corrected data.
Figure shows the Taylor diagrams computed on JAS over
the Sahel and Guinea areas for the 29 raw and bias-corrected GCM data
compared to WFDEI data (first column) and the five GCMs used in ISIMIP in
terms of raw data of CDF-t bias-corrected data and of ISIMIP bias-corrected
data (second column). WFD and EWEMBI data are also plotted in these diagrams.
In the Taylor diagrams the means of the fields are subtracted out before
computing their second-order statistics, so these diagrams do not provide
information about overall biases but characterize biases associated with
centred pattern errors. Hence maps in Fig. and Taylor
diagrams in Fig. provide complementary bias information.
Figure shows that the raw GCMs capture the
spatial structure of temperature over Africa rather well, characterized by high values
over the Sahara in summer as well as in spring (not shown), and low values in
northern fall and winter (not shown). However moderate cold biases exist over
most of the area. Inter-model dispersion is also present. For instance,
temperatures in MIROC-ESM-CHEM are about 2 ∘C higher than
temperatures in HadGEM2-ES or IPSL-CM5A-LR. The bias-correction process
improves quite well the simulations (see Fig. S3) and provides corrected mean
seasonal fields very similar to WFDEI, even at small spatial scales as for
lower temperatures over the Fouta Djalloon and Cameroon mountains. The Taylor
diagrams (Fig. ) quantify this improvement very
clearly for the 29 GCMs. The raw GCMs (Fig. left
column) are quite scattered with spatial correlations, with WFDEI distributed
between +0.1 and more than +0.9. For the Sahel area, correlations are quite
high in JAS (centred around +0.9 between 0.5 and 0.98), while for the Guinean
area correlations are globally centred around +0.4 (from 0.1 to 0.8). GCMs
are also scattered in terms of normalized variances, from 0.6 to more than 2.
The performance of the CDF-t bias correction is clearly high since all the
GCMs are very close to the WFDEI reference point. Taylor diagrams enable
comparison of the five GCMs used in ISIMIP in reference to WFDEI
(Fig. right column), for raw data and bias-corrected
data using the CDF-t and ISIMIP methods. WFD and EWEMBI data are also plotted.
CDF-t bias-corrected GCMs are very close to WFDEI. ISIMIP bias-corrected GCMs
are centred around WFD and also near WFDEI (correlation higher than +0.9
and normalized standard deviation close to 1); however WFD is a bit more
distant from WFDEI for the Guinean area (see also Table ).
EWEMBI data are even closer to WFDEI.
Figures , , and S4 show similar
results but for pr. The seasonal fields of WFDEI show the mean location of
the ITCZ in JAS (Fig. ). Local maxima associated with
highlands like the Fouta Djalloon or Cameroon mountains are also clearly
highlighted. Raw GCMs reproduce this pattern but a lot of discrepancies can
be noticed for all GCMs, in terms of pr amplitude, spatial
pattern, and latitude extension. HadGEM2-ES has the weakest values while the
four others produce pr amounts generally higher than WFDEI. The
CDF-t bias correction very efficiently improves the GCM mean seasonal
pr fields since examination must be very detailed to discern
differences with WFDEI fields and among the GCMs (see Fig. S4). This
improvement is clearly quantified with the Taylor diagrams over the Sahel and
Guinea areas in Fig. . For raw GCMs the standardized
standard deviation is very scattered from 0.25 to more than 2. Spatial
correlations are higher in the Sahel (from +0.7 to +0.95) than in Guinea area
(from +0.2 to +0.8). The CDF-t bias correction is quite effective in removing
these biases and bringing the raw data closer to WFDEI, with some small
remaining discrepancies, higher than for tas. The ISIMIP bias correction is
also effective due to the proximity between WFD and WFDEI (see also Table ).
Hovmöller diagrams of daily temperature (∘C) averaged
between 15∘ W and 15∘ E and for the period 1979–2001 for
EWEMBI, WFDEI, and WFD observations, each of the five GCMs for raw data (first
column panels), CDF-t data (second column panels), and ISIMIP data (third column panels).
Same as Fig. but for precipitation rate in millimetres per day.
Same as Fig. but for solar radiation in watts per square metre.
The 95th percentile of daily values for temperature from various
observation datasets in JAS: WFD (a), WFDEI (b),
EWEMBI (c),
and the difference relative to WFDEI data from five individual CDF-t bias-corrected
models (d–h) over the period 1979–2001.
Same as Fig. but for the 95th percentile of
near-surface temperature.
The 95th percentile of daily precipitation rate (mm day-1) from
various observation datasets in JAS: WFD (a), WFDEI (b),
EWEMBI (c), and the difference relative to WFDEI data from five individual
CDF-t bias-corrected models (d–h) over the period 1979–2001.
Same as Fig. but for the 95th percentile of
daily precipitation rate (mm day-1).
Seasonal mean of number of days with precipitation greater than or equal to
10 mm day-1 from various observation datasets in JAS: WFD (a),
WFDEI (b), EWEMBI (c), and the difference relative to WFDEI data
from five individual CDF-t bias-corrected models (d–h) over the period 1979–2001.
Same as Fig. but for the number of days
when
precipitation is greater than or equal to 10 mm day-1.
Figures , , and S5 show
similar results, but for rsds. The mean seasonal field of WFDEI is a pattern
with low values within the ITCZ area due to the high cloud coverage and high
values for the Sahara due to low moisture and cloud coverage. We have noticed
previously (Fig. ) that high differences exist among
WFDEI, WFD, and EWEMBI. WFD rsds values are the weakest and EWEMBI values the
highest in the ITCZ area. WFD values are also the weakest and WFDEI values
are a bit higher than for EWEMBI over the Sahara (see also Table ).
The five raw GCMs have, in agreement with their
pr mean seasonal fields, a reasonable latitudinal evolution of low
rsds values associated with the ITCZ, but the range of rsds differences with
WFDEI data as well as the inter-GCM dispersion are very high. There is an
overall positive bias over West Africa, except for GFDL-ESM2M. The CDF-t
bias correction is once more very effective at removing biases in respect to WFDEI
data (see Fig. S5). The Taylor diagrams (Fig. )
provide some more quantification over the Sahel and Guinea areas. In terms of
spatial correlation and normalized standard deviation in respect to WFDEI,
raw GCMs have rather good performances over the Sahel (correlations higher
than +0.8). Again, results are less good over the Guinea area (correlations
less than +0.8) with a high dispersion of the GCMs. The ISIMIP
bias correction highly reduces the inter-GCM dispersion around WFD, but WFD
rsds data are a bit far from WFDEI rsds data. EWEMBI rsds data are also far
from WFDEI. This is illustrated by the Taylor diagnostics using EWEMBI as
“REF”. Bias-corrected data from both the CDF-t and ISIMIP methods stay far from
EWEMBI and do not improve the performance of raw GCMs.
Figures to display other features of the mean
fields in terms of Hovmöller diagrams computed over West Africa
(15∘ W–15∘ E) for the whole year. They show the mean
fields of EWEMBI, WFDEI, and WFD (first row) and of the five GCMs used in
ISIMIP (rows 2 to 6) for raw data (first column), CDF-t bias-corrected data
(second column), and the ISIMIP bias-correction method (third row). For tas
(Fig. ), the WFDEI, WFD, and EWEMBI fields are very similar and
highlight the set-up of the high-temperature area associated with the
Saharan heat low in spring and summer . Raw
GCMs show a similar timing but their temperature values are lower by
2 to 4 ∘C depending on the model and some increase in
the northward progression around June that is not present in observations.
Bias-correction methods are very effective at reducing these discrepancies but
few differences still remain with WFDEI as for instance a bit weaker
temperature maximum around July in CDF-t-corrected data. ISIMIP
bias-corrected data are also very closer to WFD.
Figure shows similar diagrams but for pr. The Hovmöller
approach is a good way to depict the main characteristics of the ITCZ
evolution over West Africa with a first rainy season during spring over the
Guinea area followed by an abrupt jump to the north in June–July
and by a more progressive southward retreat at the
end of the summer monsoon season, leading to a second weaker rainy
season over the Guinean area in autumn. WFDEI and EWEMBI are quite similar. WFD fields
are a bit noisier. Raw GCMs have high discrepancies and produce mean fields
quite different from one model to another one. In particular, pr
data can be either very low (HadGEM2-ES) or very high (GFDL-ESM2M), and no
GCM captures the abrupt northward shift of the ITCZ well. The bias-correction
methods (CDF-t using WFDEI, ISIMIP using WFD) are very effective in capturing
back the main features of the ITCZ evolution. However, differences still
remain among the GCMs, and ISIMIP-corrected GCMs have global rainfall
maxima higher than CDF-t-corrected GCMs.
Figure shows similar diagrams but for rsds. The seasonal
evolution is in agreement with tas and pr fields and depicts high solar
radiation values over the Sahara and weak values following the ITCZ
latitudinal excursion but with a small southward lag (consistent with a
higher cover of mid-level clouds; see ). WFD
shows an overall negative bias with respect to WFDEI and EWEMBI, and WFDEI
has a higher meridional gradient than EWEMBI with lower minimum values over
the Guinea area and higher maximum values of the Sahara. The corrected GCM
data are very close to their respective observation reference (WFD for
ISIMIP, WFDEI for CDF-t), and hence different between their two respective
corrected versions due to the differences between WFD and WFDEI.
Time series of crop maize yield over the Sahel (18∘ W–10∘ E;
10–20∘ N) and Guinea (18∘ W–10∘ E;
3–10∘ N) areas
using IPSL-raw, IPSL-CDF-t, IPSL-ISIMIP, WFD, WFDEI, and EWEMBI as forcing data
over 1979–2001.
Temporal mean of maize yield (t ha-1) for IPSL-raw, IPSL-CDF-t,
WFDEI, EWEMBI, and GDHY over 1979–2001 and the difference between EWEMBI and WFDEI
simulations. The boxes indicate the Sahel (18∘ W–10∘ E;
10–20∘ N) and Guinea (18∘ W–10∘ E; 3—10∘ N) regions.
Time series of RCP8.5 projections of maize yields over the Sahel
(18∘ W–10∘ E; 10–20∘ N) and Guinea
(18∘ W–10∘ E; 3–10∘ N) areas (a) and
standardized yield anomalies with respect to 1979–2001 (b),
using IPSL-CM5A-LR raw data (green line), BC data with CDF-t (blue line), and
ISIMIP BC data (red line) as forcing data. Maps show mean maize yields from
CDF-t bias-corrected data over 1979–2001, 2077–2099, and their difference.
Daily metrics over West Africa
In the following, similar diagnostics are presented to evaluate the selected
daily metrics. To reduce the number of figures in the core of the
paper, some of them are presented in the Supplement (three metrics in
the core of the paper, three others in the Supplement). A more
complete metrics report is available at http://www.amma2050.org/content/climate-metrics.
Figures and shows
the results for the tas 95th percentile of daily values
of near-surface air temperature. WFD, WFDEI, and EWEMBI provide similar values
in summer (Fig. ; see also Table )
with the highest values over the Sahara in spring (up to 40 ∘C, not
shown), moving northward in summer, and with weaker values in autumn (up to
32 ∘C; not shown). WFD values appear to be a bit higher than the two
other reference datasets. More to the south, in the Guinea area, the 95 %
percentile is between 30 and 34 ∘C. CDF-t
bias-corrected data are also presented for the five GCMs used in ISIMIP in
terms of differences relative to WFDEI. Some biases still remain but mostly
lower than 1 ∘C in absolute value. They are generally negative over
the Sahara except for GFDL-ESM2M. The Taylor diagrams again depict the good
performance of the CDF-t bias-correction method here for extreme values. The
highly scattered raw GCM data, especially over the Guinea area, move into a
concentrated zone very near the WFDEI reference
(Fig. ). ISIMIP bias-corrected data are also well
concentrated near the WDF reference data but at some distance from the WFDEI
reference point. Here again, EWEMBI is superimposed to REF (see also Table ),
and bias-corrected GCMs are closer to REF for Sahel than for Guinea area.
Figures and provide
similar analysis for the 95th percentile of daily pr.
WFD, WFDEI, and EWEMBI provide fields consistent with the ITCZ location
including high values over the mountain areas
(Fig. ). WFDEI and EWEMBI have very similar fields
while the range of values for WDF is very different, with values higher than
30 mm day-1 in the ITCZ in summer in contrast with values lower
than 20 mm day-1 for the two other reference datasets (see also
Table ). A similar range of differences is present over the
Guinea area in spring and to a lesser extent in autumn (not shown). Such
differences are also large over the mountain areas (Fouta Djalloon, Cameroon).
CDF-t bias-corrected GCM data have remaining weak biases relative to WFDEI,
lower than ±2 mm day-1, except for IPSL-CM5A-LR, where differences
up to +5 mm day-1 are located north of 10∘ N. Compared
to the 95th percentile of daily tas, Taylor diagrams
(Fig. ) again show the good performance of the
CDF-t bias-correction method for the 29 GCMs, but with a bit higher distance
to REF for both the Sahel and Guinea areas. ISIMIP bias-corrected GCM data are
more scattered than CDF-t-corrected GCMs in relation to their respective
reference dataset, WFD and WFDEI, and WFD is located far from the WFDEI REF
in terms of normalized standard deviation and centred RMSE (see also Table 2).
Finally, Figs. and
provide similar analysis for the number of days with pr > 10 mm day-1.
WFD, WFDEI, and EWEMBI provide values consistent with the
ITCZ location including high values over the mountain areas
(Fig. ). In contrast to the previous metric, WFD has a
more similar range of values relative to WFDEI and EWEMBI, with some
overestimation, especially over Nigeria, Cameroon, and central Africa. The
spatial variance is higher than for the two previous metrics with a higher
contrast between mountain and plain areas. Remaining biases in the CDF-t-corrected data are localized over mountain areas with mostly negative biases,
but also over plains with mostly positive biases in the ITCZ area and
especially extended for IPSL-CM5A-LR. Taylor diagrams
(Fig. ) once more show a good performance of the
CDF-t correction method to remove biases and reduce inter-GCM dispersion.
ISIMIP bias-corrected GCMs have a higher dispersion than CDF-t-corrected GCMs
relative to their respective reference dataset.
Crop yield simulations and sensitivity to bias-corrected variables
The sensitivity of simulated crop yields over West Africa to raw and
bias-corrected forcing data is now evaluated. A crop model forced by
atmospheric variables integrates biases and variability in these forcing data
in a non-linear way. This integration may reduce or amplify the variability
induced from these forcing data.
This has been tested by using the crop model SARRA-O (System of
Agroclimatological Regional Risk Analysis; version O). The model simulates
yield attainable under water-limited conditions by simulating the soil water
balance, potential and actual evapotranspiration, phenology, potential and
water-limited carbon assimilation, and biomass partitioning seefor
a detailed review of model concepts. The simulation
of these processes makes SARRA-O particularly suited for the analysis of
climate impacts on cereal growth and yield in dry tropical environments
see for instance. Several sensitivity
simulations have been carried out. First SARRA-O has been forced for each
year from 1979 to 2001 by WFD, WFDEI, and EWEMBI data. Second, the IPSL-CM5A-LR
model has been used to force SARRA-O over the same years, with raw, CDF-t
bias-corrected, and ISIMIP bias-corrected data. The simulations have been
compared to the “GDHY” dataset (1981–2001) of 1.125∘ gridded
yield estimation. GDHY is a hybrid of FAO country yield data,
satellite-derived crop-specific vegetation index and global crop datasets on
crop calendar, harvested area, and production shares achieved by different
growing season. Subnational yield statistics have been used to validate the
grid-cell yield estimates . Note that SARRA-O
provides potential yields that can be different from observed yields, so this
comparison with the GDHY dataset must be considered as indicative only.
Finally, sensitivity to individual variables has been conducted by comparing
the SARRA-O simulation forced with WFDEI data with simulations where one
WFDEI variable is replaced by the corresponding raw IPSL-CM5A-LR data.
Figure compares the simulated crop yields over the
Sahel and Guinea areas when SARRA-O is forced either by WFD, WFDEI, or EWEMBI
and by the raw, CDF-t, or ISIMIP bias-corrected IPSL-CM5A-LR model. GDHY data are
also shown as an evaluation. Over the Guinea area, the differentiation of
ensembles of simulations is quite clear. The raw IPSL-CM5A-LR simulation has the
highest yields (∼ 2200 kg ha-1) while WFD and associated ISIMIP
bias-corrected simulations have the lowest yields (∼ 240 and 180 kg ha-1
respectively). The four remaining simulations, based on
WFDEI and associated CDF-t bias-corrected data and EWEMBI and GDHY data, have
intermediate yields, between 700 and 1000 kg ha-1. Thus it is shown
first that SARRA-O maize yields are quite sensitive to the different forcing
datasets, second that WFD lead to simulated yields far from the GDHY data
while WFDEI and EWEMBI leads to quite better yields, and finally that the raw GCM
and GCM corrected with WFD are also quite far from the validation data while
the
GCM corrected with WFDEI has a rather good performance. The simulation forced
by EWEMBI has a higher mean value than WFDEI (∼ 760 and 1030 kg ha-1
respectively), and GDHY has yields ranging between WFDEI
and EWEMBI (∼ 980 kg ha-1), close to EWEMBI. Over the Sahel area,
the curves are closer but some similar conclusions can be drawn. WFD and
associated ISIMIP bias-corrected simulations provide the lowest yields
(∼ 400 and 370 kg ha-1 respectively). WFDEI, EWEMBI, and CDF-t
bias-corrected simulations are very close (∼ 660, 650, and 710 kg ha-1
respectively). Finally, in contrast to the Guinea area,
GDHY data have the highest yields (∼ 980 kg ha-1), far from other
simulations. The raw simulation (∼ 590 kg ha-1) is close to the WFDEI,
EWEMBI, and CDF-t bias-corrected simulations. This last point is quite
surprising since raw IPSL-CM5A-LR data have large biases.
Sensitivity experiment means and biases (kg ha-1) in
respect to WFDEI simulations for the Sahel and Guinea areas. Simulations of sensitivity
to individual variables have been conducted by forcing the
SARRA-O model with WFDEI data and by replacing one of the WFDEI variables
with
the corresponding raw IPSL-CM5A-LR data. These variables are pr, rsds,
tasmin,
and tasmax, and also rsds from ISIMIP bias-corrected IPSL-CM5A-LR (using WFD
as a reference).
Sahel
Guinea
Mean
Bias
Mean
Bias
WFDEI
658
0
757
0
WFD
398
-260
241
-516
EWEMBI
646
-12
1029
272
GDHY
979
321
978
221
IPSL-CM5A-LR Raw
586
-72
2201
1444
IPSL-CM5A-LR CDF-t
706
48
693
-64
IPSL-CM5A-LR ISIMIP
367
-291
184
-573
WFDEIpr
668
10
716
-41
WFDEIrsds
717
59
786
29
WFDEItminmax
658
0
767
10
WFDEIWFDrsds
317
-341
195
-562
Figure shows the maps of mean simulated yields for
raw IPSL-CM5A-LR, WFDEI, and CDF-t bias-corrected EWEMBI simulations, GDHY
data, and the difference between EWEMBI and WFDEI simulations. For raw
simulations, yields are highly underestimated over the central Sahel but
highly overestimated over the western Sahel and especially near the
Fouta Djalloon. The boundary between the Sahel and Guinea areas being at
10∘ N, the spatial average over the Sahel combine positive and
negative biases in respect to WFDEI. This explains the point raised at the
end of the previous paragraph. The other maps show that yields obtained from
EWEMBI are closer to GDHY data than yields from WFDEI, mostly due to better
realistic values over the Guinea area (see also Table ).
Yields from EWEMBI are higher than yields from WFDEI mostly south of
10∘ N. Underestimation of yields simulated from WFDEI over
Fouta Djalloon, southern Cameroon, and south-eastern Nigeria can be
clearly associated with underestimation of WFDEI rsds compared to EWEMBI rsds
(see Fig. ). Comparisons of WFDEI and EWEMBI interannual
time series of yields and associated tas, pr, and rsds on individual grid
points in these areas confirm that these yield differences are linked
exclusively to rsds differences. Finally, maps of simulated yields from WFD
and ISIMIP bias correction confirm the weak values over all of West Africa
due to an underestimation of rsds south of 10∘ N (not shown).
To go further, a sensitivity analysis to individual variables has been
conducted by comparing the SARRA-O simulation forced with WFDEI data with
simulations where one of these WFDEI variables is replaced by the
corresponding raw IPSL-CM5A-LR data. These variables are pr, rsds, tasmin, and
tasmax, and also rsds from ISIMIP bias-corrected IPSL-CM5A-LR (using WFD as
reference). Table shows the mean yields for the Sahel and
Guinea areas and the resulting biases relative to WFDEI simulations. Biases
are very weak with tasmin–tasmax simulations (WFDEItminmax), a bit higher for
pr simulations (WFDEIpr) and for rsds simulations (WFDEIrsds) and drastically large
for rsds from ISIMIP bias-corrected simulations (WFDEIWFDrsds). Thus
rsds appears as a very critical variable for maize yields simulated with
SARRA-O, confirming a previous study based on an older version, SARRA-H, of
the crop model .
SARRA-O has also been run over the period 1950–2099 using the RCP8.5
projection, forced by ISPL-CM5A-LR in terms of raw, CDF-t bias-corrected, and
ISIMIP bias-corrected data. Figure shows, on the one hand, the
resulting time series of maize yields over the Sahel and Guinea boxes and, on
the other hand, the maps of yields from CDF-t bias-corrected data
over 1979–2001 and
2077–2099 and the resulting difference between these two periods.
Time series of standardized yield anomalies to their respective mean
over 1979–2001 are also displayed. In agreement with the previous analysis, ISIMIP
bias-corrected forcing data (with WFD as reference data) lead to the lowest
yields over both the Sahel and Guinea areas at the present time but also over
the whole 21st century. Over the Guinea area, the very high
simulated yields coming from raw data are drastically reduced with CDF-t
bias-corrected forcing data (with WFDEI as reference data) while over the
Sahel area these yields are rather similar. After CDF-t bias correction,
yields are quite similar over the two areas. Interannual variability in
simulated yields is proportional to the mean with a very weak variability for
ISIMIP yield and higher variability for CDF-t and raw simulations. More
precisely, standardized yield anomalies (right panels) have a similar range
over the Sahel, around 1 standard deviation after 2060, and a range around
2 standard deviations after 2070 over the Guinea area, except for ISIMIP
yields,
which reach 4 standard deviations. All projections show a clear decrease in
maize yields by a factor of ∼ 2 over all of West Africa for the
21st century. The map of the difference between 2077–2099 and 1979–2001
shows that the yield decrease is located mostly south of 13∘ N, except
between Mali and Niger, and that a slight increase is present north of 13∘ N.
Conclusions
The objectives of this paper are (i) to introduce a new bias-corrected
dataset for which the CDF-t correction method has been applied to CMIP5 GCM
daily data for the first time over Africa, (ii) to quantify the effect of
using different reference datasets on the corrected data, (iii) and to
illustrate this effect on crop simulations over West Africa. This
bias correction has been applied over the period 1950–2099, combining
historical runs and RCP scenarios with 29/27/20 GCMs for RCP8.5/4.5/2.6
respectively. It has been applied to six variables critical for agricultural
impacts: daily accumulated pr, daily mean, minimum and maximum
near-surface air temperature, daily mean surface downwelling shortwave radiation, and
daily mean wind speed.
The use of different bias-correction methods also based on different
reference datasets contributes to the total uncertainty in climate
projections and can contribute in some contexts more than the use of
different GCMs or RCMs . So using multiple
bias-correction techniques and reference datasets is highly recommended. In
this context, CDF-t bias-corrected GCM data have been compared to the five GCMs
ISIMIP bias-corrected data, and the impact of the different reference datasets, WFD (used in ISIMIP bias corrections), WFDEI (used in CDF-t
bias corrections), and the more recent EWEMBI (used in a second version of
ISIMIP bias corrections), has been examined in detail. Crop simulations have
also been carried out to test how the impact of bias corrections in forcing
data (temperature, pr, rsds)
is integrated in terms of crop (maize) yields. Finally, bias corrections have
also been presented in the context of RCP8.5 scenarios.
The whole observational period, 1979–2013, has been chosen to calibrate the
bias-correction process. It has been shown that using various calibration
sub-periods has a weak impact, in particular on the time evolution over the 21st century.
The evaluation of CDF-t bias correction applied to the 29 GCMs, both to mean
seasonal data and to daily metrics, has shown that CDF-t is very
effective in removing the biases in respect to the reference WFDEI data and
in reducing the high inter-GCM scattering. It has also shown some distance,
depending on variables and metrics, from bias-corrected ISIMIP GCM data,
mainly due to the differences between WFDEI and WFD reference data. WFDEI
(and associated CDF-t bias-corrected GCMs) appears closer to EWEMBI than WFD
(and associated ISIMIP bias-corrected GCMs). Metrics based on temperature are
very close for the three reference datasets, and some differences exist in
pr-based metrics. In contrast, significant differences have been
highlighted in terms of rsds. This has
consequences in terms of crop (maize) yields over West Africa. Sensitivity
simulations performed with one GCM have shown that bias corrections improve
the yields simulated by the raw GCM. However, the ISIMIP bias-corrected GCM still
underestimate them as CDF-t bias-corrected GCMs do but with yield estimates
closer to observed ones. EWEMBI provides the closest yields to observed
estimates. This is mainly due to rsds
whose values are underestimated in WFDEI south of 10∘ N. Finally,
in agreement with maize yield sensitivity simulations, projections of future
yields over West Africa have quite different levels depending on the
bias-correction method. However, they all show a similar relative decreasing
trend over the 21st century.
The main perspective of this work is to go on exploring the uncertainty
linked to bias-correction methods and their associated reference data in RCP
climate scenarios by producing a second version of this bias-corrected
29-GCM ensemble over Africa using more recent reference data like EWEMBI or
others like those used in AgMIP based on other reanalyses (AgMERRA or AgCFSR;
). The main divergence among all those reference
datasets is probably expected from rsds. Bias correction for other variables useful for user-based metrics
like specific humidity is also scheduled. Comparison between CDF-t and ISIMIP
bias-correction methods based on the same reference dataset is also ongoing.
The CFD-t bias correction has been applied independently for each of the six
variables. However, this may be a problem since existing spatial coherency and
dependence among variables may be destroyed by the application of univariate
calibrations. Recently, to address this issue, improved calibrations have
been developed in terms of multivariate correction and spatial and/or temporal
dependences see for instancefor a synthesis.
Implementation of more sophisticated methods using multivariate correction is
also ongoing.
This work constitutes a first step in producing bias-corrected datasets over
Africa within AMMA-2050. An atlas is in preparation that will provide
extensive results over Africa to the FCFA stakeholders and end-user
communities. These communities will be accompanied by FCFA climate scientists
in order to be aware of the way to use these data and their limitations.