The region of southern Africa (SAF) is highly vulnerable
to the impacts of climate change and is projected to experience severe
precipitation shortages in the coming decades. Ensuring that our modeling
tools are fit for the purpose of assessing these changes is critical. In
this work we compare a range of satellite products along with gauge-based
datasets. Additionally, we investigate the behavior of regional climate
simulations from the Coordinated Regional Climate Downscaling Experiment
(CORDEX) – Africa domain, along with simulations from the Coupled Model
Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6). We identify
considerable variability in the standard deviation of precipitation between
satellite products that merge with rain gauges and satellite products that
do not, during the rainy season (October–March), indicating high observational
uncertainty for specific regions over SAF. Good agreement both in spatial
pattern and the strength of the calculated trends is found between satellite
and gauge-based products, however. Both CORDEX-Africa and CMIP ensembles
underestimate the observed trends during the analysis period. The CMIP6
ensemble displayed persistent drying trends, in direct contrast to the
observations. The regional ensembles exhibited improved performance compared
to their forcing (CMIP5), when the annual cycle and the extreme
precipitation indices were examined, confirming the added value of the
higher-resolution regional climate simulations. The CMIP6 ensemble displayed
a similar behavior to CMIP5, but reducing slightly the ensemble spread.
However, we show that reproduction of some key SAF phenomena, like the
Angola Low (which exerts a strong influence on regional precipitation),
still poses a challenge for the global and regional models. This is likely a
result of the complex climatic processes that take place. Improvements in
observational networks (both in situ and satellite) as well as continued
advancements in high-resolution modeling will be critical, in order to
develop a robust assessment of climate change for southern Africa.
Introduction
The region of Sub-Saharan Africa has been characterized as one of the most
vulnerable regions to climate change (Kula et
al., 2013; Serdeczny et al., 2017), and more specifically, the region of
southern Africa (SAF) has been identified as a climate change hotspot
(Diffenbaugh and Giorgi, 2012). Taking into
consideration that the majority of the population living in SAF (70 %) is
dependent on rainfed agriculture (Mabhaudhi et
al., 2018), any climate-change-induced alteration of the spatiotemporal
patterns of precipitation will require a rapid adaptation of the
agricultural sector. Concurrently, SAF is also characterized by low adaptive
capacity to changes in climatic conditions (Davis and
Vincent, 2017); hence, it emerges as a high-risk region. In addition,
approximately 26 % of the SAF population is undernourished
(AFDB, 2019). This figure is expected to increase significantly
by 2050 (Tirado et al., 2015). Apart from the
impacts on the agricultural sector though, climatic changes are expected to
alter the spatiotemporal patterns of vector-borne disease occurrence
(Rocklöv and Dubrow, 2020), cause severe damage to
infrastructure and road networks (Chinowsky et al.,
2015), and exacerbate poverty (Azzarri and
Signorelli, 2020). Due to these impacts it is critical that the current
spatiotemporal patterns of precipitation are accurately reproduced by our
modeling systems and observations (whether in situ, reanalysis or
satellite) over SAF. Only then can we credibly assess future climate change
impacts and inform strategies aiming to mitigate their effects on local
communities.
Towards this end, satellite, gauge-based and reanalysis products are
extensively used, in order to monitor current spatial and temporal
precipitation patterns and to further characterize precipitation variability
and change during recent decades. For future projections however, climate
models able to simulate the (thermo)dynamical processes of the atmosphere
are employed. Such an endeavor has been performed in the context of the
Coupled Model Intercomparison Project Phase 5 (CMIP5)
(Taylor et al., 2012) using General Circulation Models
(GCMs) and in the context of the Coordinated Regional Climate Downscaling
Experiment (CORDEX) – Africa domain (Giorgi and
Gutowski, 2015) using Regional Climate Models (RCMs). The latest advancement
in the climate modeling community involves GCMs and Earth system models
(ESMs), participating in the CMIP6 ensemble, providing input for the
6th Assessment Report of the Intergovernmental Panel on Climate Change
(IPCC) (Eyring et al., 2016).
However, the confidence with which one can claim future climate projections
produced by GCMs, ESMs or RCMs are fit for purpose is usually assessed
based on their ability to simulate current climatic conditions. For
instance, Munday and Washington (2018) showed that the CMIP5
ensemble displayed a systematic wet bias over the SAF region that was caused
by the misrepresentation of orographic features located over the area of
Tanzania. A wet bias caused by structural model errors was also identified
in the dynamically downscaled and higher-resolution CORDEX-Africa ensemble
(Kim et al., 2014). Therefore, a
valid question arises as to what the most suitable dataset is, with which
climate impact studies can be fed when the SAF region is the focus. In
addition, before the task of characterizing future precipitation trends is
addressed, it is imperative to diagnose the degree to which observed
precipitation trends over the recent decades are reproduced by GCMs and
RCMs.
A comprehensive analysis of the performance of the CORDEX-Africa ensemble
over Africa was first presented in Nikulin
et al. (2012). They showed that during the rainy season (January–March as used in
Nikulin et al., 2012) there is a weak wet
bias over southern Africa and that the use of the ensemble mean enabled individual models to be outperformed, highlighting the importance of ensemble-based
approaches. The Nikulin et al. (2012)
analysis was conducted on a pan-African scale. Similarly,
Kalognomou et al. (2013) analyzed the
same ensemble of CORDEX-Africa simulations, focusing on southern Africa,
and reported similar findings. In
Meque and Abiodun (2015) the same ensemble of 10 evaluation simulations was again used, but it was
also compared with a set of CMIP5 GCM simulations, with the purpose of identifying a causal association between ENSO and drought events over southern
Africa. In Meque and Abiodun (2015) it was stated that RCMs were able to provide added value, compared to
their driving GCMs. A comprehensive assessment of the added value between
historical CORDEX-Africa RCM simulations and of their driving CMIP5 GCMs on
a seasonal timescale over the whole of Africa was performed in
Dosio et al. (2019). The first
time the CORDEX-Africa ensemble is compared to both CMIP5 and CMIP6
ensembles is presented in
Dosio et al. (2021). More
specifically, in Dosio et al. (2021) the analysis is performed on a seasonal time step and on pan-African scale
and its particular emphasis is placed on the projected changes of future
precipitation, although a part of the analysis is dedicated to the period
1981–2010.
Satellite and gauge-based datasets display increasing trends during the
historical period for annual precipitation over SAF (32–41 mm yr-1 per decade), an observation that is also identifiable in the Atmospheric
Model Intercomparison Project (AMIP), but not in CMIP5
(Maidment et al., 2015). During DJF,
precipitation trends over SAF display a remarkably robust signal in
gauge-based, satellite and AMIP datasets
(Maidment et al., 2015). In addition,
Onyutha (2018) also
reported on the increasing precipitation trends over SAF during DJF,
especially after the 1960s. However, according to CMIP5, precipitation is
projected to decrease over SAF in the 21st century
(IPCC, 2013). This estimate also holds for
simulations performed using RCMs forced with CMIP5
(Pinto
et al., 2016; Dosio et al., 2019). The increase in the observed
precipitation trends over SAF has been attributed to the recent
strengthening of the Pacific Walker Circulation
(Maidment et al., 2015), which is
captured in observational datasets and in AMIP simulations, but not in CMIP5
(L'Heureux et al., 2013;
Yim et al., 2016). CMIP6 displays an even more robust future decline in
precipitation and increase in drought events over SAF, relative to its
predecessor (Ukkola et al., 2020). However,
although the CMIP6 ensemble exhibits multiple improvements on various levels
(Wyser et al., 2020), certain biases and
challenges identified in CMIP5 during the historical period persist in CMIP6
(Kim et al., 2020).
RCMs are known to add value to climate simulations over regional scales,
mainly because the spatial resolution increases, resolving atmospheric waves
in a more detailed manner, and also because surface characteristics
interacting with the atmosphere are represented more accurately
(Denis et al.,
2003; Giorgi et al., 2014). Considering the aforementioned challenges
displayed in the CMIP5 simulations to accurately capture precipitation
amounts under current climatic conditions and recent precipitation trends,
we investigate the degree to which this observation holds also for RCMs
forced with GCMs participating in the CMIP5 ensemble. Theory tells us that
RCMs develop their own physics. However, oftentimes the impact of the
driving GCMs on the RCM simulations is evident
(Denis
et al., 2003; Laprise et al., 2008; Di Luca et al., 2013; Giorgi, 2019).
Therefore, in this paper we expand on previous research to investigate how
monthly precipitation during the rainy season over southern Africa is
simulated by different modeling systems, by analyzing the monthly
precipitation climatologies, the interannual variability, specific
precipitation indices and monthly precipitation trends during the period
1986–2005, in four different modeling systems (CORDEX0.22∘/0.44∘,
CMIP5/6) and observational ensembles (satellite, reanalysis and gridded
datasets). Our main goal is to provide a comprehensive overview with regards
to precipitation climatology over SAF as simulated by the state-of-the-art
tools used by climate scientists. In addition, we investigate whether higher-resolution models are able to provide an improved representation of
precipitation over southern Africa and we investigate how a particularly
important atmospheric feature, the Angola Low (AL) pressure system, is
simulated in the RCM and GCM ensembles.
In Sect. 2 the data used are presented along with the methodology
employed. In Sect. 3 the results are presented. More specifically, the
results are analyzed based on the monthly climatology, the annual cycle of
precipitation, the AL pressure system, the precipitation indices and the
monthly precipitation trends. Lastly, in Sect. 4 we provide the discussion
of the analysis along with some concluding remarks.
Data and methodologyData
We analyze daily and monthly precipitation from five types of datasets, namely
observational datasets (OBS), GCMs and ESMs that comprise the CMIP5 and
CMIP6 ensembles, and regional climate models (RCMs) that comprise the
CORDEX-Africa ensemble at 0.44∘ of spatial resolution (CORDEX0.44) and
at 0.22∘ of spatial resolution (CORDEX0.22). The analysis is concerned
with the SAF region, which is defined as the area between 10 and
42∘ E and 10 to 35∘ S. The analyzed period
is 1986–2005, as this is the period during which the estimates of all 5
aforementioned datasets overlap. Although satellite and reanalysis products
cannot be termed as purely “observational”, in the context of the current
work they are classified as such, in order to differentiate them from
climate model datasets (CORDEX0.44, CORDEX0.22, CMIP5, CMIP6). Hereafter
“OBS” refers to satellite, gauge-based and reanalysis products.
Observational data
The OBS data used are based on the analysis of Le Coz and van
de Giesen (2020) and are comprised of five gauge-based products (datasets that
are derived by spatial interpolation of rain gauges and station data:
CRU.v4.01, UDEL.v7, PREC/L.v0.5, GPCC.v7, CPC-Global.v1), six satellite
products (given below) and one reanalysis product, ERA5. The datasets have a
temporal coverage that extends through the analyzed period (1986–2005). The
gauge-based products were chosen so that they have a spatial resolution less
than or equal to 0.5∘× 0.5∘and the satellite products have a
spatial resolution less or equal to 0.25∘× 0.25∘. For satellite
products, however, there was an exception for two products (CMAP.v19.11and
GPCP.v2.2) with a resolution equal to 2.5∘× 2.5∘ that were also
included in the analysis due to their widespread use in the literature. The OBS
ensemble is made of 12 products. More details concerning the OBS datasets
are provided in Table S1 in the Supplement. In certain parts of the following analysis the OBS
products are either used collectively or they are split into sub-ensembles,
based on the methods used for their production. More specifically, these
sub-ensembles are the mean of all gauge-based precipitation products
(Gauge-Based), the ensemble mean of satellite products that merge with rain
gauges (Satellite-Merge) (ARC.v2, CMAP.v19.11, GPCP.v2.2) and the ensemble
mean of satellite products that do not merge directly with rain gauges
(Satellite-NoMerge) (CHIRPS.v2, TAMSAT.v3, PERSIANN-CDR), but they use
alternative methods such as calibration, bias adjustment or artificial
neural network techniques (Le Coz and van de Giesen, 2020).
Climate model simulations
We retrieved daily precipitation for a set of 26 RCM simulations performed
as part of CORDEX-Africa historical simulations at 0.44∘
(∼ 50 km) spatial resolution, comprising the CORDEX0.44
ensemble. We also retrieved a set of 10 RCM simulations performed
within CORDEX-Africa, as part of the CORDEX-CORE project
(Coppola et al., 2021), available at
0.22∘ (∼ 25 km) spatial resolution (CORDEX0.22). In
addition, daily precipitation was retrieved for a set of 10 CMIP5 GCMs, with
3 additional simulations with variations in the GCM's resolution
(IPSL-LR/IPSL-MR), the ocean model (GFDL-ESM2M/GFDL-ESM2G) and
Realization/Initialization/Physics (ICHCE-EC-EARTH-r1i1p1/ICHCE-EC-EARTH-r12i1p1). The CMIP5 models selected were the ones used as
forcing in the CORDEX0.44 historical simulations. In total, precipitation
from a set of 13 CMIP5 simulations was used. Additionally, we exploited
daily precipitation from a set of 8 CMIP6 GCM and ESM simulations. The CMIP6
simulations selected were performed with the updated versions of the same
models that were part of the CMIP5 ensemble. This selection served to
construct CMIP5 and CMIP6 ensembles that were comparable. Precipitation data
for all simulations were retrieved from the Earth System Grid Federation
(ESGF). In addition, we retrieved temperature at 850 hPa for both
CORDEX0.44/0.22 from ESGF. For the CMIP5 and CMIP6 simulations temperature
and geopotential height at 850 hPa was retrieved from the Climate Data Store
(CDS). Geopotential height at 850 hPa was not available for CORDEX-Africa
simulations. Lastly, elevation data for CORDEX-Africa and CMIP5 were
obtained from ESGF, while the Shuttle Radar Topography Mission (SRTM)
(Farr et al., 2007) digital elevation model was
used as the observed elevation in the topography transects for a selected
latitude over SAF. Details about the models used are provided in Tables S2–S5.
Methodology
Precipitation climatologies are investigated on a monthly basis, due to the
fact that precipitation over SAF arises as the result of atmospheric
mechanisms that display high variability during the rainy season. The
aggregation of precipitation to seasonal means might often obscure certain
spatial characteristics that are better identified on a monthly basis. The
within-ensemble agreement is investigated using the sample standard
deviation (SD), which is calculated using monthly mean values over the
period 1986–2005 for each model (or observational dataset) separately. We
also employ four precipitation indices constructed in the context of the Expert
Team on Climate Change Detection and Indices (ETCCDI) (Peterson
and Manton, 2008), utilizing daily precipitation amounts for the period
1986–2005. The four ETCCDI indices are used to describe total annual
precipitation (PRCPTOT), annual maximum daily precipitation (Rx1Day), annual
number of days with daily precipitation > 10 mm (R10mm) and
annual number of days with daily precipitation > 20 mm (R20mm).
These indices are calculated for each individual simulation of each ensemble
(CMIP5, CMIP6, CORDEX0.44 and CORDEX0.22), and OBS products, separately and
yield a value for every year (January–December) during the period 1986–2005. The
calculation of indices required data with a daily temporal resolution;
hence, observational datasets that provided monthly aggregates are excluded.
The spatial averages calculated over SAF for the annual cycle and the ETCCDI
indices consider land pixels only. For the construction of ensemble means,
either in observational or model ensembles, datasets were remapped to the
coarser grid using conservative remapping for precipitation and bilinear
interpolation for temperature and geopotential height at 850 hPa.
In order to investigate some basic thermodynamical aspects that may
differentiate precipitation in the CMIP5/6 and the CORDEX0.44/0.22
ensembles, we look into the seasonal representation of the Angola Low (AL)
pressure system over SAF. The AL pressure system is a semi-permanent
synoptic scale system that plays a strong role in modulating precipitation
over SAF
(Reason
and Jagadheesha, 2005; Lyon and Mason, 2007; Crétat et al., 2019; Munday
and Washington, 2017; Howard and Washington, 2018). More specifically, the
reason why we chose to put an emphasis on the AL pressure system is that
the AL redistributes low-tropospheric moisture entering SAF from the
southern Atlantic and the southern Indian oceans and also moisture
transport originating from the Congo basin. In addition, AL events precede
the formation of tropical temperate troughs (TTTs) and hence, they can be
considered as their precursor in the “climate process chain”
(Daron et al., 2019). As stated in
Howard and Washington (2018), it is common that AL events
precede TTT events, since the AL pressure system functions as a key process
necessary for the transport of water vapor from the tropics towards the
extratropics (Hart et al., 2010).
In Munday and Washington (2017) AL events were
identified using geopotential height at 850 hPa. However, since geopotential
height at 850 hPa is not available for CORDEX0.44/0.22 simulations, we could
not employ this method. Hence, based on the variables that are already
available within both CORDEX and CMIP5/6 ensembles, we use potential
temperature at 850 hPa (theta850) as an alternative “proxy” variable that
provides thermodynamical information. In order to ensure that theta850 can
be used instead of zg850, we examine the relationship between theta850 and
zg850 over the study region in ERA5, for each month of the rainy season
(October–March), using the climatological mean monthly values for the period
1986–2005 (Figs. S1, S2). As shown in Fig. S1, during October over the
southeastern part of Angola, there is a region of low pressure. Moving
towards the core of the rainy season, the low-pressure system deepens, while
there seems to be a weak extension of low pressure towards the south. Also,
as shown in Fig. S2, during October there is an area of high theta850 values
located over southeastern Angola, coinciding with the region of low zg850
values. As stated in Munday and Washington (2017), this
is indicative of the dry convection processes that are at play during the
beginning of the rainy season over the region. Moving towards DJF, the high
theta850 values move southwards, indicating that in the core of the rainy
season, convection over the greater Angola region is not thermally induced,
but there is a rather dynamical large-scale driver. In Fig. S3 the
scatterplots between zg850 (x axis) and theta850 (y axis) for each month of
the rainy season are shown, over all of southern Africa (land pixels
only). The same plot, but with pixels only from the greater Angola region
(14 to 25∘ E and from 11 to 19∘ S),
is displayed in Fig. S4. Although the relationship between the two variables
is not linear, they display a considerable association, especially over the
greater Angola region.
In Howard and Washington (2018) AL events are identified using
daily relative vorticity (ζ) at 800 hPa. Since u and v wind
components are not available at 800 hPa (but at 850 hPa) for the CORDEX
ensembles, we investigate whether the 850 hPa pressure level can be used
instead. We also examine whether the ζ threshold has to be adjusted.
In Howard and Washington (2018), AL events are identified
within the region ranging from 14 to 25∘ E and from
11 to 19∘ S for mean daily ζ values <-4 × 10-5 s-1. An additional issue that we take into
account is that u and v wind components are not available on a daily
time step for CMIP6, but only on a monthly time step. Hence, for consistency
reasons we work with monthly files in all ensembles (both CMIP, CORDEX) and
in ERA5.
With regards to the question of whether the 850 hPa pressure level can be
used instead of 800 hPa, we examine monthly relative vorticity in ERA5 in
both pressure levels, within the region from 14 to 25∘ E and
from 11 to 19∘ S (Fig. S5). Both distributions are very similar
in shape, maxima and spread, although the distribution of ζ values at
800 hPa appear to have a shorter tail. On both panels, both the
Howard and Washington (2018) and the
Desbiolles et al. (2020) thresholds are
indicated. We conclude that the 850 hPa pressure level can be used instead
of 800 hPa. With regards to the fact that u and v wind components are
available only on a monthly time step in CMIP6, we compare the daily and
monthly relative vorticity values at 800 hPa in ERA5 for all the months of
the rainy season (October–March) (Fig. S6). The difference in the y axis results
from the fact that when ζ is calculated using a daily time step, the
histogram is drawn using 5 421 825 values, while when the ζ is
calculated using monthly u and v values, it is drawn using 178 200 values
(for the period 1986–2005). As shown, the distribution of the monthly values
has a much shorter tail and the Howard and Washington (2018)
threshold appears to be very strict, as a criterion for the identification
of AL events.
Concerning the question of what the optimal threshold for the identification
of AL events in all datasets is, we investigate the statistical distribution
of mean monthly cyclonic vorticities in all ensembles used, for the 850 hPa
pressure level (Fig. S7). We conclude that the threshold used in
Desbiolles et al. (2020) (ζ values <-1.5 × 10-5 s-1) is reasonable, considering
the shape of the distributions examined. However, when the
Desbiolles et al. (2020) threshold is
applied to the data, it is also found to be too strict, especially for
CMIP5/6. Hence, we identify AL events having ζ<-0.00001 s-1. Lastly, we use geopotential height at 850 hPa for visual
inspection only in ERA5 and CMIP5/6 ensembles.Lastly, the Theil–Sen slope
(Theil, 1992; Sen, 1968) for monthly
precipitation during the period 1986–2005 is calculated for each dataset.
This is a non-parametric approach to estimate trends that is insensitive to
outliers. Statistical significance is assessed using the Mann–Kendall test
(Mann, 1945; Kendall, 1948).
ResultsClimatology
Figure 1 displays monthly precipitation climatologies during October–March (rainy
season over the study region) for ERA5 and for the ensemble means of seven additional types of datasets. At the beginning of the rainy season (October) all
products display precipitation maxima at the northwestern part of the study
region. Another precipitation maximum is observed in eastern South Africa.
For both regions, there is a slight tendency for gauge-based products to
yield approximately 1 mm d-1 less precipitation than reanalysis and
satellite products. The CMIP5, CMIP6, CORDEX0.44 and CORDEX0.22 ensembles
are also in agreement with regards to the location and amounts; however,
CORDEX0.44 displays approximately 2 mm d-1 more precipitation over
Angola. During November, the rainband extends southwards, and the region over
South Africa experiencing high precipitation enlarges.
Monthly precipitation climatologies during the period 1986–2005 in
mm d-1. More specifically, from top to bottom – ERA5 reanalysis dataset;
Gauge-based: ensemble mean of datasets that were produced by employing
spatial interpolation methods using rain gauges/station data;
Satellite-Merge: ensemble mean of all satellite products that merge with
rain gauges/station data; Satellite-NoMerge: ensemble mean of satellite
products that do not merge with rain gauges/station data; CORDEX-0.44∘:
ensemble mean of regional climate model simulations performed in the context
of the Coordinated Regional Climate Downscaling Experiment (CORDEX) –
Africa domain with a spatial resolution equal to 0.44∘× 0.44∘;
CORDEX-0.22∘: CORDEX-Africa simulations with a spatial
resolution equal to 0.22∘× 0.22∘; CMIP5: ensemble mean of general
circulation models participating in the Coupled Model Intercomparison
Project Phase 5 (CMIP5) that were used as forcing in the CORDEX-Africa
simulations; CMIP6: ensemble mean of general circulation models
participating in the Coupled Model Intercomparison Project Phase 6.
Moving towards the core of the rainy season (DJF) the precipitation maxima
extends southwards following the collapse of the Congo air boundary (CAB)
(Howard and Washington, 2019), and high precipitation amounts
are also observed over the eastern part of the study region. More
specifically during January, high precipitation amounts (> 10 mm d-1) are observed over an extended region in northern Mozambique for
non-merging satellite products (Satellite-NoMerge). This area is also
identified as a region of high precipitation in gauge-based products and in
merging satellite products, however, with a smaller magnitude. In ERA5, the
spatial pattern of precipitation is more patchy and exhibits precipitation amounts higher than
observed in the wider region of Lake Malawi, reaching
extremely high values (34 mm d-1), as also indicated in the known
precipitation issues of ERA5 over Africa
(Hersbach et al., 2020). During DJF
both CORDEX0.44 and CORDEX0.22 ensembles display precipitation values
> 3 mm d-1 over almost all of the SAF region. This
observation is also consistent in CMIP5 and CMIP6; however, maximum
precipitation amounts in CMIP5 and CMIP6 are approximately > 3 mm d-1 larger than in the CORDEX ensembles. It is noteworthy that in
CORDEX0.22 during DJF, there are parts over northern SAF experiencing
precipitation amounts > 10 mm d-1, a
feature that is not seen in any of the observational products. After
investigating the individual ensemble members used in the CORDEX0.22
ensemble (Fig. S8), we see that the excess amount of precipitation is
removed from the CORDEX0.22 ensemble mean when RegCM4-7 simulations are not
included (Fig. S9). In March, the rainband starts its northward shift;
nevertheless, high precipitation amounts are still observed over the eastern
parts of the study region and over the coastal region of Angola. The retreat
of the rainband is evident in both CORDEX0.44 and CORDEX0.22; however, CMIP5
and CMIP6 still exhibit extended regions of high precipitation.
In Fig. 2, SD values for the 7 ensembles are presented during the months October–March
for the period 1986–2005 expressed as millimeters per day (mm d-1). SD is used as a measure
of the within-ensemble agreement. As shown for gauge-based products,
during October and November high SD values are observed primarily over
Angola. For the months December–March Angola remains a high-SD region; however,
increased SD values are also observed over the eastern parts of SAF and
especially over northern Mozambique. An important aspect influencing
gauge-based products is the spatiotemporal coverage of the rain gauges used
(Le Coz and van de Giesen, 2020), which is highly variable
between regions and reporting periods. More specifically, after the 1970s
the rain gauge coverage over Africa has decreased significantly
(Janowiak, 1988), and the gauge network has been
particularly sparse over the SAF region
(Lorenz and Kunstmann, 2012; Giesen
et al., 2014), which further implies that gauge-based products depend on
extrapolating values from surrounding gauges. Therefore, station density and
the interpolation method employed are key factors in determining the
accuracy of the final product (Le Coz and van de Giesen,
2020). The high SD values over Angola are mainly due to the scarcity of
available rain gauges used in the interpolation method (Fig. S10). After
1995, there is a noticeable reduction of the station and rain gauge data used
over the SAF region (Fig. S11) for three of the gauge-based products.
Standard deviation of monthly precipitation [mm d-1] during
the period 1986–2005. Rows indicate the ensemble means analyzed. From top to
bottom – Gauge-based: ensemble mean of datasets that were produced by
employing spatial interpolation methods using rain gauges/station data;
Sat-Merge: ensemble mean of all satellite products that merge with rain
gauges/station data; Sat-NoMerge: ensemble mean of satellite products that
do not merge with rain gauges/station data; CORDEX-0.44∘: ensemble mean
of regional climate model simulations performed in the context of the
Coordinated Regional Climate Downscaling Experiment – Africa domain with a
spatial resolution equal to 0.44∘× 0.44∘; CORDEX-0.22∘: CORDEX-Africa simulations with a spatial resolution equal to 0.22∘× 0.22∘; CMIP5: ensemble mean of general circulation models participating
in the Coupled Model Intercomparison Project Phase 5 (CMIP5) that were used
as forcing in the CORDEX-Africa simulations; CMIP6: ensemble mean of general
circulation models participating in the Coupled Model Intercomparison
Project Phase 6.
A similar spatiotemporal pattern of SD is also observed in satellite-based
products (Sat-Merge) which employ algorithms that merge rain gauges with
thermal-infrared (TIR) images. This is indicative of the strong impact that
the location and number of rain gauges exert on satellite algorithms that
employ merging techniques
(Maidment
et al., 2014, 2015). The spatiotemporal pattern of SD for satellite-based
products that do not merge with gauges (Sat-NoMerge) displays low SD values
for October and November; however, during DJF localized areas of high SD
appear over Angola, Zambia, Malawi and Mozambique. The satellite products
used in this ensemble are based on TIR images, and precipitation is
indirectly assessed through cloud top temperature
(Tarnavsky et
al., 2014; Ashouri et al., 2015; Funk et al., 2015). Hence, the occurrence
and severity of precipitation is calculated based on a temperature
threshold. In cases that the threshold is set to very low cloud top
temperature values, the algorithm has high skill at identifying deep
convection; however, warm rain events are not adequately captured
(Toté et al., 2015). As shown in Fig. 2, high SD values in non-merging satellite products are
primarily observed over coastal regions and over regions where the elevation
increases rapidly. These type of regions can be associated with orographic
or frontal lifting of air masses (Houze,
2012), resulting in precipitation, without the threshold temperature of the
cloud top being reached.
In the CORDEX0.44 ensemble SD values are > 0.8 mm d-1 over
almost all of the SAF region; however, very high SD values (3–9.8 mm d-1) are observed in the coastal part of Angola and over the Lake Malawi region during November–March. SD values in CORDEX0.22 are considerably
larger throughout the greater part of SAF, especially during DJF. In the
CMIP5 ensemble the spatiotemporal pattern of SD values exceeds 2 mm d-1
during November–March throughout the whole SAF region. CMIP6 displays a similar SD
pattern. During March, however, CMIP6 displays a substantial improvement in
the agreement between its ensemble members. Overall, for the whole extent of
SAF, the CORDEX-Africa ensembles display greater agreement among ensemble
members, however SD values become large over specific localized regions,
mainly in western Angola and in the Malawi region. The CMIP5 and CMIP6
ensembles, although not displaying the localized extreme SD values as
CORDEX-Africa, display generally high SD values throughout the whole extent
of SAF.
Annual cycle
Figure 3 displays the annual cycle of precipitation in the CORDEX0.44,
CORDEX0.22, CMIP5, CMIP6 and observational ensembles for land grid points.
All datasets capture the unimodal distribution of precipitation over SAF;
however, considerable differences in precipitation amount and spread are
observed.
Annual cycle of monthly precipitation during 1986–2005 for the
ensemble of observational data (gauge-based, satellite and reanalysis),
CMIP5 (Coupled Model Intercomparison Project Phase 5), CMIP6 (Coupled Model
Intercomparison Project Phase 6), CORDEX0.44 (Coordinated Regional Climate
Downscaling Experiment – Africa domain with a spatial resolution equal to
0.44∘× 0.44∘) and CORDEX-0.22∘ (CORDEX-Africa simulations
with a spatial resolution equal to 0.22∘× 0.22∘). The thick
horizontal black lines indicate the ensemble median for each month, the box
encloses the interquartile range and the tails denote the full ensemble
range. Circles represent the outliers for each ensemble. Only grid points
are considered.
Specifically, the CMIP5 ensemble exhibits significantly higher precipitation
amounts than both CORDEX and observational ensembles. This difference
becomes particularly pronounced during the rainy season, with CMIP5 yielding
approximately 2 mm d-1 more precipitation than the observational
ensemble. It is also notable that for November–February, even the driest ensemble
members of CMIP5 yield approximately 1 mm d-1 more precipitation than
the wettest ensemble members of the observational data. This is in agreement
with Munday and Washington (2018), who identified a systematic
wet bias over SAF in CMIP5 that was associated with an intensified
northeasterly transport of moisture that erroneously reaches SAF, due to
the poorly represented orography in the region of Tanzania and Malawi (which
would hinder moisture originating from the Indian ocean from reaching SAF
and instead force it to recurve towards the region of Madagascar). The
behavior of CMIP6 is similar to CMIP5, with a slightly smaller ensemble
spread during January–March and a considerable reduction in spread during
November.
The CORDEX0.44 ensemble reduces precipitation amounts during the core of the
rainy season (DJF) compared to CMIP5; however, its behavior during the rest
of the months is complicated. More specifically, during August–October CORDEX0.44
displays slightly higher precipitation amounts compared to CMIP5. During
November, the difference between the CORDEX0.44 and the CMIP5 ensembles
becomes noticeable, with the CMIP5 ensemble mean becoming 0.4 mm d-1
larger than the CORDEX0.44 ensemble mean. During DJF the differences between
the two ensembles maximize, with the CORDEX0.44 ensemble displaying good
agreement with the OBS ensemble (< 1 mm d-1 difference in the
ensemble means of CORDEX0.44 and OBS). From March until July, the difference
between the CORDEX0.44 and CMIP5 ensembles starts to reduce gradually. The
ensemble mean of the CORDEX0.22 ensemble is similar to that of the
CORDEX0.44 ensemble; however, its spread during the rainy season is
considerably larger. Taking into consideration that excess precipitation in
the CORDEX0.22 ensemble is introduced by RegCM4-7, we observe that the
ensemble spread of the CORDEX0.22 ensemble is reduced, when RegCM4-7 is not
included in the CORDEX0.22 ensemble (Fig. S12).
Since the maximum impact of the northeasterly moisture transport into SAF
responsible for the wet bias in CMIP5 occurs during DJF
(Munday and Washington, 2018), the impact of the CORDEX0.44
and CORDEX0.22 increase in resolution and the effect of the improved
representation of topography is also more intensely identified during DJF.
As displayed in Fig. 4, surface orography is substantially improved in
the CORDEX ensembles, relative to CMIP5/6. The improvement of orography has
a further effect in blocking moisture transport entering SAF from the
northeast, especially during December–January, as seen in Fig. 5.
Cross section of surface elevation at 11∘ S across southern
Africa for the Shuttle Radar Topography Mission (SRTM) digital elevation
model (in green), the surface altitude as represented in the CMIP5 (Coupled
Model Intercomparison Project Phase 5) global climate models (in red), the
surface altitude as represented in the CORDEX0.44 (Coordinated Regional
Climate Downscaling Experiment – Africa domain with a spatial resolution
equal to 0.44∘× 0.44∘) (in blue) and the surface altitude as
represented in the CORDEX-0.22∘ (CORDEX-Africa simulations with a
spatial resolution equal to 0.22∘× 0.22∘) (in yellow).
Angola low
In Fig. 6 the mean monthly climatology of the AL pressure system during the
rainy season is displayed for the period 1986–2005. The AL is explored by
means of relative vorticity, only within the region extending from 14 to 25∘ E and from 11 to 19∘ S. This
region is characterized by Howard and Washington (2018) as the
main region of interest for the AL. The relative vorticity for ζ<-0.00001 s-1 over the whole SAF is shown in Fig. S13. In
addition, potential temperature at 850 hPa (theta850) is overlaid on
relative vorticity, with the first contour set at 308 K, the last contour
set at 318 K and the increment between the isotherms being set to 2 K. For
ERA5 and the ensemble means of CMIP5/6 the geopotential height at 850 hPa
was also available.
Mean monthly moisture flux and divergence at 850 hPa during the
period 1986–2005. Rows indicate the ensemble means analyzed. From top to
bottom: ERA5, ensemble mean of CORDEX0.44∘, CORDEX0.22∘, CMIP5 and
CMIP6 simulations.
Monthly climatologies of the Angola Low pressure system during the
rainy season for the period 1986–2005. Filled contours indicate cyclonic
relative vorticity (ζ) for ζ<-0.00001 s-1 over the region extending from 14 to 25∘ E and
from 11 to 19∘ S. Red lines indicate the isotherms of
potential temperature at 850 hPa, having an increment of 2 K. Blue lines
indicate isoheights of the geopotential height at 850 hPa, having an
increment of 5 m. CORDEX0.44 and CORDEX0.22 are not plotted with geopotential
isoheights, because this variable was not available for CORDEX simulations.
From top to bottom: ERA5, ensemble mean of CORDEX0.44∘,
CORDEX0.22∘, CMIP5 and CMIP6 simulations. Black box indicates the
region from 14 to 25∘ E and from 11 to
19∘ S.
As shown in Fig. 6, ζ values for October are greater than
>-0.000025 s-1 for ERA5 and CORDEX0.44/0.22 and are
relatively weaker in CMIP5 and even weaker in CMIP6. The high cyclonic
vorticity values overlap with the 312 K isotherm for all datasets. We also
observe that the isoheights in the ERA5 and CMIP5/6 ensembles are closely
collocated with the 312 K isotherms, indicating that the low pressure system
observed over the region is caused by the excess heating of the air, and
hence it is indicative of a typical low-pressure heat system
(Munday and Washington, 2017; Howard and
Washington, 2018). Moving to November, the picture is similar; however, the
isotherms display a southward extension, while the 850 hPa isoheights deepen
by ∼ 5 m in ERA5 and CMIP5/6. In December, all datasets display
an increase in cyclonic vorticity; however, the maximum heating area has
migrated southwards over the Kalahari region. This fact indicates that
cyclonic activity over the AL region is no longer due to thermal causes.
During December and January the cyclonic activity is enhanced in all
datasets and the isotherms have migrated even more southwards, forming the
Kalahari heat low, which is distinct from the AL. We also observe that
during January, the isoheights in ERA5 and CMIP5/6 become even deeper. We
also note that the elongated trough during December–January can be indicative of the
formation of TTTs, which account for a large proportion of rainfall over SAF
(Hart et al., 2010). February
displays similar spatial patterns to January for all datasets, albeit
slightly weakened for all variables. In March, cyclonic activity over the
region has seized. Taking into consideration the distribution of the
cyclonic vorticity field, we observe that in higher-resolution datasets
(ERA5, CORDEX0.22) high vorticity values are more severe, in very localized
regions. With respect to potential temperature, we observe for October and
November all datasets having a similar distribution of theta850 values. We
also note that CMIP6, in general, displays higher theta850 values and lower
geopotential heights, relative to CMIP5.
Precipitation indices
Total annual precipitation (PRCPTOT) is displayed in Fig. 7a. The mean of
the CMIP6 ensemble displays the largest amounts of PRCPTOT (approximately
1000 mm yr-1), with CMIP5 following closely. The CORDEX0.44 and
CORDEX0.22 ensembles display a very similar behavior, systematically
reducing PRCPTOT amounts seen in CMIP5/6 by approximately 200 mm yr-1, yielding PRCPTOT values closer to that of the observational
datasets. Both CMIP5/6 and CORDEX0.22/0.44 ensembles display similar
within-ensemble variability. The ensemble mean of the observational datasets
is considerably lower than CORDEX ensembles and displays an interannual
variability between 500–800 mm yr-1. The ensemble means of both
CMIP5/6 and CORDEX0.44/0.22 fail to reproduce the interannual variability of
the observational ensemble. In Fig. 7b the annual maximum 1 d
precipitation (Rx1Day) is displayed. For Rx1Day, the mean of the CMIP5
ensemble is in close agreement with the mean of the observational ensemble
(approximately 40 mm d-1). The ensemble mean of CORDEX0.44 yields
larger precipitation amounts (approximately 55 mm d-1) than CMIP5 and
the observational ensemble. The CORDEX0.22 ensemble mean displays even
higher values (approximately 75 mm d-1). As shown in Fig. 7b,
the CORDEX0.44 ensemble mean is influenced by higher Rx1Day values,
originating from ensemble members that cluster within the range 65–85 mm d-1. The spread of the CMIP5 ensemble is comparable to that of the
observational data; however, the CORDEX0.44/0.22 ensemble spreads are still
larger, ranging from 25–85 and from 55–100 mm d-1, respectively. The
CMIP6 ensemble falls between the CORDEX0.44 and CMIP5 ensembles, with a
spread comparable to that of CMIP5. In Fig. 7c the annual number of days
with daily precipitation greater than 10 mm (R10mm) is presented. It is
noted that the ensemble mean of the CORDEX0.44 ensemble is close to that of
the observational datasets (∼ 25 d yr-1 with daily
precipitation greater than 10 mm), while the ensemble mean of CORDEX0.22
almost coincides with the mean of the observational datasets. The mean of
the CMIP5 ensemble yields approximately 34 d of extreme precipitation
annually. It is also highlighted that the CMIP5 ensemble displays a large
range of R10mm values (10–55 d yr-1). Again, the CMIP6 ensemble
mean coincides with that of CMIP5. In Fig. 7d the annual number of days
with daily precipitation greater than 20 mm (R20mm) is shown. There is close
agreement between the CMIP5 and CORDEX0.44 ensembles; however, both datasets
overestimate R20mm relative to the observational data. Again, the CMIP5
ensemble displays the largest spread, and a very weak interannual variability
is seen on both CMIP5 and CORDEX0.44 ensemble means. The CMIP6 ensemble mean
is slightly larger than its predecessor. R20mm in CORDEX0.22 mean is almost
identical to the mean of the CMIP6 ensemble.
Time series of the ETCCDI indices over southern Africa (10 to 42∘ E and 10 to 35∘ S) for the
observational ensemble in red (gauge-based, satellite and reanalysis), CMIP5
(Coupled Model Intercomparison Project Phase 5) ensemble in green, CMIP6
(Coupled Model Intercomparison Project Phase 6) ensemble in purple,
CORDEX-0.44∘ ensemble mean of regional climate model simulations
performed in the context of the Coordinated Regional Climate Downscaling
Experiment – Africa domain with a spatial resolution equal to 0.44∘× 0.44∘ in blue and CORDEX-0.22∘ in orange. Thin lines display
single ensemble members, thick lines display ensemble means. The y axis on each
panel depicts (a) PRCPTOT (total annual precipitation), (b) Rx1Day (annual
maximum daily precipitation), (c) R10mm (annual number of days with daily
precipitation > 10 mm) and (d) R20mm (annual number of days with
daily precipitation > 20 mm).
Trends
In Fig. 8 the monthly precipitation trends for the rainy season of the
period 1986–2005 are displayed for all three observational data (gauge-based,
SatelliteMerge, Satellite-NoMerge) and for the CORDEX0.44, CORDEX0.22, CMIP5
and CMIP6 ensembles. Precipitation trends display considerable agreement
among all three observational datasets, concerning both the signal and the
magnitude of the trend. However, the CORDEX0.44/0.22 and CMIP5/6 ensembles
display trends that are considerably smaller in magnitude. In addition,
CORDEX0.44, CMIP5 and CMIP6 ensembles display fairly distinct spatial
patterns that are not in agreement either among themselves or with the spatial
pattern of precipitation trends displayed by the observational datasets. In
general, we observe that the signal between CORDEX0.44 and CORDEX0.22 is
consistent, with trends in CORDEX0.22 displaying a larger magnitude.
Trends for monthly precipitation for the period 1986–2005 [mm d-1 per 20 years] calculated using Sen's slope. Rows indicate the
ensemble mean of trends produced by each ensemble member. From top to
bottom – Gauge-Based: ensemble mean of datasets that were produced by
employing spatial interpolation methods using rain gauges/station data.
Satellite-Merge: ensemble mean of all satellite products that merge with
rain gauges/station data. Satellite-NoMerge: ensemble mean of satellite
products that do not merge with rain gauges/station data. CORDEX-0.44∘:
ensemble mean of regional climate model simulations performed in the context
of the Coordinated Regional Climate Downscaling Experiment – Africa domain
with a spatial resolution equal to 0.44∘× 0.44∘.
CORDEX-0.22∘: CORDEX-Africa simulations with a spatial
resolution equal to 0.22∘× 0.22∘. CMIP5: ensemble mean of general
circulation models participating in the Coupled Model Intercomparison
Project Phase 5 (CMIP5) that were used as forcing in the CORDEX-0.44∘
simulations. CMIP6: ensemble mean of general circulation models
participating in the Coupled Model Intercomparison Project Phase 6.
More specifically, during October, all observational products display
decreasing trends for most of SAF that reach up to -0.1 mm d-1 per 20 years. During November the signal changes and SAF experiences
increasing trends, with an exception for NW SAF, northern Mozambique and
regions of eastern South Africa. During December increasing trends become
even more spatially extended and pronounced, especially for satellite
products. During January, certain areas of decreasing trends over northern
SAF appear, while during February decreasing trends are observed over almost
the whole extent of SAF. In March, increasing trends are observed in the
region extending from southern Mozambique and stretching towards Zimbabwe
and southern Zambia.
Monthly precipitation trends in the CORDEX0.44 ensemble are significantly
weaker than in the observational datasets and display a precipitation increase
during October–December. After January certain regions of intensified decreasing
trends appear over southern Angola–northern Namibia and Botswana (January) and
over Botswana and South Africa (February). The pattern of trends is relatively
similar in CORDEX0.22; however, the trend magnitude is more enhanced. In
CMIP5 decreasing trends are observed during October, but for November
increasing trends are observed over the northern part of SAF. During
December, strong increasing trends (0.1 mm d-1 per 20 years) appear
for central SAF, while during January almost all of the SAF region (with an
exception for Mozambique) experiences decreasing precipitation trends. In
CMIP6 persistent drying trends are observed almost throughout the whole of
SAF and are particularly strong during January–February (-0.1 mm d-1 per 20 years). During March, however, the signal is reversed. Statistical
significance assessed with the Mann–Kendall test is shown in Fig. S14. The
number of ensemble members displaying increasing or decreasing trends in
each ensemble is shown in Fig. S15.
Discussion and conclusions
The analysis of the SD among the different observational products highlights
the fact that precipitation assessment requires consultation of multiple
(gauge-based, satellite and reanalysis) products. If this is not possible,
then it is highly recommended that the spatial distribution and frequency of
reporting of the underlying station data are examined, for each respective
precipitation product in use. This should also be regarded in cases when
gauge-based or satellite products are utilized for model evaluation
purposes. Moreover, satellite products that merge with rain gauges should
not be considered independent from gauge-based products that exploit similar
gauge networks. In addition, we note that SD in the CORDEX0.44 ensemble is
considerably lower than in the CMIP5/6 ensembles, supplying evidence that
the CORDEX0.44 set of simulations provide more constrained results and can
thus be considered to be a suitable dataset for climate impact assessment
studies over SAF. However, that is not entirely the case for the CORDEX0.22
ensemble, which, although it displays SD values smaller to that of CMIP5/6, still yields SD values higher than that of CORDEX0.44.
Concerning the annual cycle of precipitation, we note that although the
seasonality is captured reasonably by both the CMIP and CORDEX-Africa
ensembles, still, there are considerable differences between them. More
specifically, we conclude that the CORDEX0.44 ensemble exhibits smaller
ensemble spread for all months of the rainy season compared to the driving
GCMs (CMIP5). We also conclude that the strong wet bias over SAF in the
CMIP5 ensemble (Munday and Washington, 2018) is considerably
reduced in the CORDEX0.44 ensemble. This bias is still evident in CMIP6. A
plethora of references in the literature
(Reason
and Jagadheesha, 2005; Lyon and Mason, 2007; Crétat et al., 2019; Munday
and Washington, 2017; Howard and Washington, 2018) have highlighted the
importance of the AL pressure system in modulating precipitation over SAF.
We note that the strength of the AL as assessed in the current study was
simulated to be weaker in the CORDEX0.44 than in the CORDEX0.22 ensemble.
This may partly explain why precipitation in the CORDEX0.44 ensemble is
reduced, relative to the CORDEX0.22 ensemble. However, there is need for a
more in-depth dynamical analysis of the simulation of the AL in the
CORDEX-Africa ensemble (both CORDEX0.44 and CORDEX0.22) and its impact on
modulating precipitation seasonality and patterns over SAF.
The use of the four ETCCDI indices demonstrated that the CORDEX-Africa ensemble
yields results that are in closer agreement to the observational data,
compared to CMIP5/6 ensembles. It is, nevertheless, noticeable that the
improvement in the CORDEX-Africa ensemble is most evident when the ensemble
mean is used. This highlights the fact that the ensemble mean performance is
improved, relative to the performance of individual models
(Nikulin et al., 2012). For this reason, it is advisable
that climate impact studies employ multi-model ensemble means, as a method
of obtaining the consensus climatic information emanating from various
models (Duan et al., 2019). In addition, we
underline the fact that in all indices the ensemble means of CMIP5/6 and
CORDEX-Africa were not able to reproduce the interannual variability that
was seen in the observational ensemble. This remark is in agreement with the
fact that the task of reproducing precipitation variability across various
timescales using the CMIP5 ensemble is known to present challenges
(Dieppois et al., 2019), which
inevitably cascade into the CORDEX-Africa simulations that are forced with
CMIP5 GCMs (Dosio et al., 2015). Lastly,
even though the CORDEX-Africa ensembles reduce precipitation amounts over
SAF, their use in drought-related impact studies should take into
consideration that they still yield larger precipitation amounts than the
observational data, which might eventually lead to underestimation of
drought risk.
Precipitation trends during the rainy season displayed high spatial
variability depending on the month. All observed (gauge-based and satellite)
trends display substantial spatial agreement. The precipitation trends
obtained by the CMIP5/6 and CORDEX0.44/0.22 ensembles did not display
consistency with the trends obtained from the observational datasets. This
is not entirely unexpected, due to the role of internal variability compared
to external forcing in recent decades
(Pierce et al., 2009), unlike
temperature trends which have been shown to have a good agreement between
the CORDEX-Africa (at 0.44∘ of spatial resolution) and CMIP5
ensembles with observed temperature trends
(Dosio
and Panitz, 2016; Warnatzsch and Reay, 2019). Nonetheless, we note that the
trend signal between CORDEX0.44 and CORDEX0.22 is consistent, with
CORDEX0.22 in general enhancing the CORDEX0.44 precipitation trends.
In conclusion, while CORDEX0.44 displays marked improvement over coarser-resolution products, there are still further improvements to be made. More
specifically, since the wet bias in RCM simulations persists (although
considerably reduced relative to GCMs), it is necessary that precipitation
over southern Africa is no longer assessed based on bulk descriptive
statistics, but that there will be a shift towards process-based evaluation,
where the dynamical and thermodynamical characteristics of specific
atmospheric features are investigated more thoroughly in the CORDEX-Africa
simulations. For this reason, it is imperative that all institutes
submitting RCM simulations in data repositories such as the Earth System
Grid Federation or the Copernicus Climate Data Store provide model output
data on multiple pressure levels, so that a fair comparison with the CMIP
community would be possible. In addition, since the climate of southern
Africa is highly coupled with the moisture transport coming from the
adjacent oceans, it is necessary that the next generation of RCM simulations
within CORDEX-Africa are performed coupled with ocean models. Lastly, since
convection over southern Africa has a strong thermal component during
specific months of the year (October–November), it is necessary that the
land–atmosphere coupling processes within each RCM are examined in more
detail, with coordinated efforts such as the LUCAS Flagship Pilot Study
(https://ms.hereon.de/cordex_fps_lucas/index.php.en, last access: 1 November 2021), as performed in the Euro-CORDEX domain. In the world
of regional climate modeling community, the 0.44∘ resolution of
CORDEX-Africa is no longer state of the art, and ensemble efforts are now
approaching convection-permitting grid spacing (i.e., < 4 km) in
some parts of the world (Ban et al.,
2021; Pichelli et al., 2021). We also note that increasing effort should be
made with regards to understanding the improvements made from CORDEX0.44
simulations to CORDEX0.22. Although higher resolution is a desired target in
the climate modeling community due to the more realistic representation of
processes that it offers, still it should not be used as a panacea. In the
current work we identified certain weaknesses in the CORDEX0.22 ensemble,
that should be addressed before the community populates further its
simulation matrix. The next generation ensembles for Africa will hopefully
provide insight and improvements to the challenges described here.
Code and data availability
Analysis was performed using the R Project for Statistical Computing
(https://www.r-project.org/,
R Core Team, 2021), the Climate Data Operators (CDO)
(https://code.mpimet.mpg.de/projects/cdo/, Schulzweida, 2021) and Bash programming
routines. Processing scripts are available via ZENODO under the following DOI: 10.5281/zenodo.4725441 (Karypidou, 2021). CMIP5, CMIP6 and CORDEX-Africa
daily precipitation data were retrieved from the Earth System Grid
Federation (ESGF) portal (https://esgf-data.dkrz.de/projects/esgf-dkrz/, ESGF-DKRZ, 2021). CMIP5 temperature data at
850 hPa were retrieved from the Climate Data Store (CDS) (2021) (https://cds.climate.copernicus.eu/#!/home). CORDEX-Africa (both at
0.44 and 0.22∘ spatial resolution) temperature data at 850 hPa
were retrieved from ESGF. Surface elevation data for CMIP5 and CORDEX-Africa
were retrieved from ESGF. The Shuttle Radar Topography Mission (SRTM)
digital elevation model was retrieved from https://srtm.csi.cgiar.org/ (last access: 21 June 2018, Farr et al., 2007). ERA5 data were retrieved from CDS. Climate
Research Unit (CRU) data are available at https://crudata.uea.ac.uk/cru/data/hrg/ (last access: 22 April 2020, Harris et al., 2014 ). The University of Delaware (UDEL)
data are available at https://psl.noaa.gov/data/gridded/data.UDel_AirT_Precip.html (last access: 14 May 2020, Willmott and Matsuura, 1995). The CPC Global Unified Gauge-Based
Analysis of Daily Precipitation (CPC-Unified) was retrieved from https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html (last access: 18 April 2020, Chen et al., 2008). NOAA's
PRECipitation REConstruction over Land dataset (PREC/L) was retrieved from
https://psl.noaa.gov/data/gridded/data.precl.html (last access: 22 November 2019, Chen et al., 2002). The dataset
of the Global Precipitation Climatology Centre (GPCC) was retrieved from
https://psl.noaa.gov/data/gridded/data.gpcc.html (last access: 22 November 2020, Schneider et al., 2015). The Tropical
Applications of Meteorology using SATellite (TAMSAT) data were retrieved
from http://www.tamsat.org.uk/ (last access: 26 November 2020, Tarnavsky et al., 2014; Maidment et al., 2017). The Precipitation Estimation
from Remotely Sensed Information using Artificial Neural Networks – Climate
Data Record (PERSIANN-CDR) is available at https://chrsdata.eng.uci.edu/ (last access: 26 November 2020, Ashouri et al., 2015). The Climate Hazards Group InfraRed
Precipitation with Station data (CHIRPS) products are available at
https://www.chc.ucsb.edu/data/chirps (last access: 20 May 2020, Funk et al., 2015). The CPC Merged Analysis
of Precipitation (CMAP) dataset was retrieved from https://psl.noaa.gov/data/gridded/data.cmap.html (last access: 22 April 2020, Xie and Arkin, 1997). The Global Climatology
Precipitation Project (GPCP) dataset was retrieved from https://psl.noaa.gov/data/gridded/data.gpcp.html (last access: 22 April 2020, Adler et al., 2012). The African Rainfall
Climatology (ARC) dataset is available at https://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.FEWS/.Africa/.DAILY/.ARC2/.daily/index.html?Set-Language=en (last access: 25 April 2020, Novella and Thiaw, 2013).
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-15-3387-2022-supplement.
Author contributions
MCK, EK and SPS designed the research. MCK implemented the analysis and
prepared the paper. EK and SPS edited the paper and provided
corrections.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
Maria Chara Karypidou was funded by the Hellenic Foundation for Research & Innovation,
under the second call for PhD candidates (application no. 1323).
This article is funded by the AfriCultuReS project “Enhancing Food Security
in African Agricultural Systems with the Support of Remote Sensing”
(European Union's Horizon 2020 Research and Innovation Framework Programme
under grant agreement no. 774652). The authors would like to thank the
Scientific Support Centre of the Aristotle University of Thessaloniki
(Greece) for providing computational and storage infrastructure and technical
support.
This work is dedicated to the beautiful memory of Ms Anatoli Karypidou, “who lived faithfully a hidden life”.
Financial support
This research has been supported by the Hellenic Foundation for Research & Innovation (H.F.R.I.) under the 2nd call for PhD Candidates (grant No. 1323).
Publication fees are covered by the Horizon 2020 AfriCultuReS project (grant no. 774652).
Review statement
This paper was edited by Augustin Colette and reviewed by two anonymous referees.
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