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Daily characteristics of Central African rainfall
in REMO model
Tamoffo T. Alain1*, D. A. Vondou, M. W. Pokam, A. H¨
ansler, D. Z. Yepdo, D. T. Lucie
1Laboratory of Environmental Modeling and Atmospheric Physics, University of Yaounde 1, P.O. Box 812, Yaounde - Cameroon
*e-mail: alaintamoffotchio@gmail.com
Abstract
To study the daily characteristics of Central African (0◦–40◦E ; 20◦S–
20◦N) rainfall, we have used historicals and scenarios (RCP2.6, RCP4.5
and RCP8.5) of Regional Model (REMO) in the middle and at the end
of twenty-first (21st) century. The model was driven by GCMs (Global
Climate Models) MPI-ESM (Max Planck Institute-Earth System Model:
REMO-MPI) and EC-Earth (Europe-wide Consortium Earth System Model:
REMO-EC). REMO’s skills were assessed using many metrics. In general,
REMO is able to simulate rainfall in Central Africa. However, we note dif-
ferences at some places. Projected frequency of wet days show localised
increase/decrease during the 21st century. Analysis of the variations of the
thresholds and maximums of 90th percentile reveals increase of extreme
precipitation along century. Results show that although there is a good
agreement between the RCM and reanalysis in ITCZ migration, the RCM
fails to capture the monsoon jump over ocean and simulates it with a delay
on the continent.
Study area, data used and Methods
Study area
Figure 1: Topography of study area and division in subregions [1].
Data used
• REMO simulations data [1] (REMO-EC and REMO-MPI) with a reso-
lution of 0.44◦×0.44◦over the period 1981–2005 for historicals and
2041–2065 then 2071–2095 for scenarios RCP2.6, RCP4.5 and RCP8.5 .
• Three observationals data: GPCP (1◦×1◦, 1997–2005) ; TRMM
(0.25◦×0.25◦, 1998–2005) ; CHIRPS (0.05◦×0.05◦, 1981–2005)
Methods
• Mean Bias (MB), Patterns Correlations Coefficients (PCC) and Root-
Mean-Square-Difference (RMSD) are the statistical values that we have
computed for more assess REMO model.
• The frequency of wet days is obtained by the ratio of total days of season
where rain is greater or equal to 1 mm.
• In our study we define an extreme event of rain at a grid point as the 90th
percentile of total precipitation fallen at this point.
• The DPID represents the ratio of daily cumulated precipitation within a
range, by the number of grid points where rain has been detected. It
allows to see the share of contribution of each range to the total precipi-
tation [2].
Results
Frequency of wets days over the current period
• Spatial distribution of that frequency show that, the model is in agreement
with the observations because it manages to follow the movements of the
ITCZ by season (areas of maximum rainfall are well detected during sea-
sons).
• Concerning intensity of the frequency distribution of wets days, it results
in very few differences (less than 15% of MB in general).
• The differences between the two simulations may be due to errors trans-
mitted originally by the different internal dynamics and physical schemes
of GCMs [3,4,5].
These results are in line with values of Mean Bias (MB), Patterns Corre-
lations Coefficients (PCC) and Root-Mean-Square-Difference (RMSD) of
Table.
Figure 2: Mean frequency of wets days (expressed in percent of total annual days) in DJF,
MAM, JJA and SON seasons from CHIRPS (1st column) and GPCP (2nd column) obser-
vations, and historical REMO-EC and REMO-MPI (3th and 4th columns respectively) for
the period 1981-2005.
MB
CHIRPS GPCP
Season DJF MAM JJA SON DJF MAM JJA SON
REMO-EC -2.30 2.32 3.01 3.50 5.74 12.02 8.89 10.12
REMO-MPI -4.15 -2.51 1.06 3.92 3.37 7.15 7.01 10.15
PCC
CHIRPS GPCP
Season DJF MAM JJA SON DJF MAM JJA SON
REMO-EC 0.94 0.89 0.89 0.94 0.80 0.76 0.84 0.83
REMO-MPI 0.93 0.93 0.88 0.92 0.80 0.79 0.83 0.83
RMSD
CHIRPS GPCP
Season DJF MAM JJA SON DJF MAM JJA SON
REMO-EC 14.76 14.83 18.37 11.44 25.47 25.05 22.14 20.26
REMO-MPI 15.65 12.25 19.84 12.77 25.35 21.42 22.68 20.62
Table 1: Summary of statistical evaluation of annual rainfall (REMO-EC and REMO-MPI)
with the CHIRPS and GPCP data for the current 25-yr period (1981–2005) over the entire
Central African domain.
Projected changes in frequency of wets days
Figure 3: Projected changes in seasonal-mean frequency of wets days (expressed in percent
of total annual days. Scenarios[2041–2065] minus CHIRPS[1981–2005]) in the middle of
the 21st century. From scenarios RCP2.6, RCP4.5 and RCP8.5 .
Figure 4: Same as Fig.3 but at the end of the 21st century (scenarios[2071–2095] minus
CHIRPS[1981–2005]).
• In general, 21st century will be caracterized by a decrease of the fre-
quency of wet days in Central Africa except Gulf of Guinea where will
be noted an increase according to three scenarios;
• The maximum of decrease will reach 57% of MB according to REMO-
EC scenarios in MAM season over Lake Victoria in the middle of century
and 44% at the end. Concerning increasing, the maximum reach 43% of
MB in the middle of 21st century and 52% at the end over Gabon during
JJA season according to three scenarios.
The threshold of extreme rainfall over Central Africa
Figure 5: Threshold of the extreme rainfall (90th percentile) as observed TRMM (1998–
2005), GPCP (1997–2005), CHIRPS (1981–2005) and simulated REMO-EC and REMO-
MPI (1981–2005).
• Observations have high threshold values (>22 mm/day) followed by
model (Fig.10 d and e) on the oceanic part of Gulf of Guinea and low
thresholds (<14 mm/day) at north in the Sahel region and south in An-
gola, in Zambia, in Malawi, Mozambique (TRMM, GPCP and CHIRPS)
followed by models on the mainland.
Projected changes in the extreme precipitation threshold
Figure 6: Threshold of the extreme rainfall (90th percentile of daily rainfall, mm/day) as
observed TRMM (1998–2005), GPCP (1997–2005), CHIRPS (1981–2005) and simulated
REMO-EC and REMO-MPI (1981–2005).
• By mid-century and for all scenarios of REMO-EC, the maximum thresh-
old will increase ∼2 mm/day (RCP8.5, Fig.6c) and 1 mm/day (RCP2.6
and RCP4.5, Fig.6 a and b respectively). This increase is extended to
the end of the century with added values up to 8 mm/day (RCP8.5), 6
mm/day (RCP4.5).
• REMO-MPI scenarios will reduce the maximum of threshold extreme
precipitation ∼2 mm/day (RCP2.6 and RCP4.5, Fig.6d and 6e respec-
tively) and of 1 mm/day (RCP8.5, Fig.6f) in mid-century. At the end of
the century, only RCP4.5 will maintain the reduction at 2 mm/day.
Dynamic Central african rainfall
Figure 7: Latitude-time diagram of the mean annual cycle of precipitation (mm/day) av-
eraged between 0◦–10◦E (over ocean : Fig.4 a,b,c,d) and 14◦E–30◦E (over continent :
Fig.4 e,f,g,h) for the period 1998–2005 using GPCP and TRMM observationals, REMO-
MPI and REMO-EC simulations.
• REMO fails to simulate the jump of mooson over the ocean and rather
shows its ability to follow the migration of the ITCZ.
• Although this phenomenon is detected on the continent, April,9th–
July,18th for GPCP and TRMM (Fig.4e and Fig.5f respectively), the
”jump” of monsoon starts late in REMO model (May,19th–July,18th;
Fig.4 g and h respectively).
Daily precipitation intensity distributions
Figure 8: Mean daily precipitation intensity distribution (DPID) simulated by REMO-
MPI (1981–2005, blue); REMO-EC (1981–2005, cyan); as well as from TRMM (1998–
2005, black); GPCP (1997–2005, red) and CHIRPS (1981–2005, forestgreen) observation-
als datasets.
For example: in the subregion 1, at the JJA season (Fig.5c) REMO-EC
simulation (cyan) simulates a value of 1 mm/day (y-axis) in the range of
precipitation intensities 8–16 mm/day (x-axis), which corresponds to 92 mm
that is to say 1 mm/day times 92 days of JJA season. So to know the part of
contribution of any precipitation of the range whose intensities are between
8–16 mm/day, simply divide its DPID by the mean seasonal [2].
Conclusions
• The aims of our study was to assess the capacity of the REMO model
driven by EC-Earth and MPI-ESM to simulate current climate in Central
Africa and carry out climate projections using scenarios RCP2.6, RCP4.5
and RCP8.5 from REMO-EC and REMO-MPI.
• The results obtained show that despite some slight discrepencies, REMO
simulate in line precipitation over Central Africa compared to TRMM,
GPCP and CHIRPS.
References
1. H¨
ansler et al.,2013 Climate Service Centre Report ,219 , 2192–4058.
2. Laprise et al.,2013 Climate Dynamics ,41 , 3219–3246.
3. Van Noije et al.,2014 Geosci. Model Dev. Discuss ,7, 1933–2006.
4. Bechtold et al.,2008 Q. J. R. M. S. ,134 , 1337–1352.
5. Ilyina et al.,2013 J. A. M. Earth Systems ,5, 287–315.
Acknowledgements
We thank the organisation commitee of the International Conference on Re-
gional Climate-CORDEX 2016 (ICRC-CORDEX 2016) for financial sup-
port. We thank GERICS for REMO data. We also thank all producers of
observationals data used in this study and that allowed us to validate REMO
model.
