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Daily characteristics of precipitation is investigated over Central Africa using REMO model.
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
To study the daily characteristics of Central African (040E ; 20S–
20N) 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 0.44×0.44over 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)
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].
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-
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
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.
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
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
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
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 010E (over ocean : Fig.4 a,b,c,d) and 14E–30E (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].
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,
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.
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
Full-text available
This study examines the seasonal forecast of the North American Multi-Model Ensemble (NMME) over Central Africa (CA), which encompasses a region of the world where the economies of the countries are highly dependent on agriculture and livestock breeding. Following many regional climate perspectives, we evaluated the seasonal forecast over the 4 seasons: December to February (DJF), March to May (MAM), June to August (JJA), September to November (SON) between 0 and 5 months lead time before the beginning of each season. Deterministic and categorical approaches which focus on the rainfall variable were used to assess NMME ensemble mean (MME). The observed and predicted rainfalls have been divided into three categories: below normal, normal, and above normal. The results show that for 0 to 2 months lead time, the MME reproduces well the peak rainfall of the Atlantic coast and in the East of Democratic Republic of Congo in MAM and SON between 9 and 10 mm/day. Again in the same lead time interval, values of correlation coefficients (R) between the MME and the Global Precipitation Climatology Center (GPCC) reference observation of all seasons are greater than 0.72. For 3 to 5 months lead time, lower values of R are observed. It follows that probabilities of detection (POD) are greater than 50% for all different normal seasons and less than 45% for below and above normal seasons. On the other hand, high false alarm (FAR) values and low Critical Success Index (CSI) values are observed for both below and above normal seasons. From our results, one can argue that the NMME seems to be an interesting tool during the first three forecasting lead times in CA capable of providing important seasonal characteristics before the start of each season, which will allow proper consideration of meteorological phenomena
Full-text available
Abstract It is well established that Africa is particularly exposed to climate extremes including heat waves, droughts, and intense rainfall events. How exposed Africa is to the co‐occurrence of these events is however virtually unknown. This study provides the first analysis of projected changes in the co‐occurrence of five such compound climate extremes in Africa, under a low (RCP2.6) and high (RCP8.5) emissions scenario. These changes are combined with population projections for a low (SSP1) and high (SSP3) population growth scenario, in order to provide estimates of the number of people that may be exposed to such events at the end of the 21st century. We make use of an ensemble of regional climate projections from the Coordinated Output for Regional Evaluations (CORE) project embedded in the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework. This ensemble comprises five different Earth System Model/Regional Climate Model (ESM/RCM) combinations with three different ESMs and two RCMs. We show that all five compound climate extremes will increase in frequency, with changes being greater under RCP8.5 than RCP2.6. Moreover, populations exposed to these changes are greater under RCP8.5/SSP3, than RCP2.6/SSP1, increasing by 47‐ and 12‐fold, respectively, compared to the present‐day. Regions of Africa that are particularly exposed are West Africa, Central‐East Africa, and Northeast and Southeast Africa. Increased exposure is mainly driven by the interaction between climate and population growth, and the effect of population alone. This has important policy implications in relation to climate mitigation and adaptation.
Full-text available
This paper investigates the performance of ten (10) Regional Climate Models (RCMs) hindcasts from the Coordinated Regional Climate Downscaling Experiments (CORDEX) over Central Africa, covering the period 1998-2008 and performed over a common model grid-spacing 0.44°(∼50 km). Multiple observational datasets are used to evaluate model performances over four targeted subregions. Throughout the work, a measure of observational uncertainty is made and we discuss whether or not the models are found within or outside the range of observational uncertainty. Results indicate that RCMs generally capture rainfall and temperature basic features, though important biases exist and vary for models and seasons. Dry (wet) biases are common features over the Congo basin (northern and southern part of the domain). In terms of precipitation and temperature in both seasonal and annual scale, most RCMs along with their ensemble mean generally fall in the range of observational uncertainty. Furthermore, most RCMs show a good spread of grid points where the added value of RCMs is found although the added value in temperature is not as great as with precipitation. UC-WRF is among models adding less value on ERAINT and this could explain why whatever the time scale of variability, UC-WRF outputs are generally out from the observational uncertainty. The multimodel ensemble mean is generally found within observational range when most models are there as well. This highlights the fact that the ensemble mean, built from the equal treatment of RCMs, does not always outperform individual RCMs realization as it is reported in several previous studies.
  • Hänsler
Hänsler et al.,2013 Climate Service Centre Report, 219, 2192-4058.
  • Bechtold
Bechtold et al.,2008 Q. J. R. M. S., 134, 1337–1352.
  • Laprise
Laprise et al.,2013 Climate Dynamics, 41, 3219-3246.
  • Van Noije
Van Noije et al.,2014 Geosci. Model Dev. Discuss, 7, 1933-2006.
  • Ilyina
Ilyina et al.,2013 J. A. M. Earth Systems, 5, 287-315.