ESSDEarth System Science DataESSDEarth Syst. Sci. Data1866-3516Copernicus PublicationsGöttingen, Germany10.5194/essd-10-815-2018The WASCAL high-resolution regional climate simulation ensemble for West Africa:
concept, dissemination and assessmentThe WASCAL high-resolution regional climate simulation ensemble for West AfricaHeinzellerDominikusdom.heinzeller@noaa.govhttps://orcid.org/0000-0003-2962-1049DiengDiarraSmiatekGerhardhttps://orcid.org/0000-0002-0938-9804OlusegunChristianahttps://orcid.org/0000-0001-7208-0095KleinCorneliaHamannIlseSalackSeyniBliefernichtJanKunstmannHaraldKarlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Garmisch-Partenkirchen, GermanyUniversity of Augsburg, Institute of Geography, Augsburg, GermanyCentre for Ecology & Hydrology, Wallingford, UKGerman Climate Computing Center, Hamburg, GermanyWASCAL Competence Center, Ouagadougou, Burkina Fasonow at: University of Colorado Boulder, Cooperative Institute for Research in Environmental Sciences,
NOAA/OAR/ESRL/Global Systems Division, Boulder, CO, USADominikus Heinzeller (dom.heinzeller@noaa.gov)23April201810281583521August201722September201723March201825March2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://essd.copernicus.org/articles/10/815/2018/essd-10-815-2018.htmlThe full text article is available as a PDF file from https://essd.copernicus.org/articles/10/815/2018/essd-10-815-2018.pdf
Climate change and constant population growth pose severe
challenges to 21st century rural Africa. Within the framework of the West
African Science Service Center on Climate Change and Adapted Land Use
(WASCAL), an ensemble of high-resolution regional climate change scenarios
for the greater West African region is provided to support the development of
effective adaptation and mitigation measures. This contribution presents the
overall concept of the WASCAL regional climate simulations, as well as
detailed information on the experimental design, and provides information on
the format and dissemination of the available data. All data are made
available to the public at the CERA long-term archive of the German Climate
Computing Center (DKRZ) with a subset available at the PANGAEA Data Publisher
for Earth & Environmental Science portal
(https://doi.pangaea.de/10.1594/PANGAEA.880512). A brief assessment of
the data are presented to provide guidance for future users.
Regional climate projections are generated at high (12 km) and intermediate
(60 km) resolution using the Weather Research and Forecasting Model (WRF).
The simulations cover the validation period 1980–2010 and the two future
periods 2020–2050 and 2070–2100. A brief comparison to observations and two
climate change scenarios from the Coordinated Regional Downscaling Experiment
(CORDEX) initiative is presented to provide guidance on the data set to
future users and to assess their climate change signal. Under the RCP4.5
(Representative Concentration Pathway 4.5) scenario, the results suggest an
increase in temperature by 1.5 ∘C at the coast of Guinea and by up
to 3 ∘C in the northern Sahel by the end of the 21st century, in
line with existing climate projections for the region. They also project an
increase in precipitation by up to 300 mm per year along the coast of
Guinea, by up to 150 mm per year in the Soudano region adjacent in the north
and almost no change in precipitation in the Sahel. This stands in contrast
to existing regional climate projections, which predict increasingly drier
conditions.
The high spatial and temporal resolution of the data, the extensive list of
output variables, the large computational domain and the long time periods
covered make this data set a unique resource for follow-up analyses and
impact modelling studies over the greater West African region. The
comprehensive documentation and standardisation of the data facilitate and
encourage their use within and outside of the WASCAL community.
Introduction
With climate change being one of the most severe challenges to
rural Africa in the 21st century, West Africa is facing an urgent need to
develop effective adaptation and mitigation measures to protect its
constantly growing population
. The West
African Science Service Center on Climate Change and Adapted Land Use
(WASCAL) is a large-scale research-focused program designed to help tackle
this challenge and thereby enhance the resilience of human and environmental
systems to climate change and increasing variability. It does so by
strengthening the research infrastructure and capacity in West Africa related
to climate change and by pooling the expertise of 10 West African countries
and Germany
http://www.wascal.org, last access: 14 April
2018
. Funded by the German Federal Ministry of Education and Research
(BMBF), the research activities of WASCAL in Africa are coordinated by its
Competence Center in Ouagadougou, Burkina Faso, supported by a Core Research
Program in Germany under the leadership of the Center for Development
Research (ZEF) at the University of Bonn. An integral part of the Core
Research Program of WASCAL is the provisioning of a novel set of
high-resolution regional climate projections for West Africa. In parallel, a
meteorological observation network is set up in the region and significant
efforts are made to compile a database of historical meteorological
observations from various sources such as universities or meteorological and
hydrological agencies across the WASCAL member countries.
Available data at CERA
(https://cera-www.dkrz.de/WDCC/ui/Project.jsp?acronym=WASCAL, last
access: 14 April 2018) and PANGAEA. Note that the previous URL opens a search
mask on the CERA database for all available data sets (ensemble members),
while the links behind the individual DOIs preselect the corresponding
ensemble member. On PANGAEA, a single DOI is assigned to the data from all
ensemble members.
DOIDescriptionData available at CERA 10.1594/WDCC/WRF12_ERAINT_CTRL12 km resolution, forcing ERA-Interim, control run 1979–201410.1594/WDCC/WRF12_GFDLESM_HIST12 km resolution, forcing GFDL-ESM2M, historical run 1979–200510.1594/WDCC/WRF12_GFDLESM_RCP4512 km resolution, forcing GFDL-ESM2M, RCP4.5 run 2006–210010.1594/WDCC/WRF12_HADGEM2_HIST12 km resolution, forcing HadGEM2-ES, historical run 1979–200510.1594/WDCC/WRF12_HADGEM2_RCP4512 km resolution, forcing HadGEM2-ES, RCP4.5 run 2006–210010.1594/WDCC/WRF12_MPIESM_HIST12 km resolution, forcing MPI-ESM MR, historical run 1979–200510.1594/WDCC/WRF12_MPIESM_RCP4512 km resolution, forcing MPI-ESM MR, RCP4.5 run 2006–210010.1594/WDCC/WRF60_ERAINT_CTRL60 km resolution, forcing ERA-Interim, control run 1979–201410.1594/WDCC/WRF60_GFDLESM_HIST60 km resolution, forcing GFDL-ESM2M, historical run 1979–200510.1594/WDCC/WRF60_GFDLESM_RCP4560 km resolution, forcing GFDL-ESM2M, RCP4.5 run 2006–210010.1594/WDCC/WRF60_HADGEM2_HIST60 km resolution, forcing HadGEM2-ES, historical run 1979–200510.1594/WDCC/WRF60_HADGEM2_RCP4560 km resolution, forcing HadGEM2-ES, RCP4.5 run 2006–210010.1594/WDCC/WRF60_MPIESM_HIST60 km resolution, forcing MPI-ESM MR, historical run 1979–200510.1594/WDCC/WRF60_MPIESM_RCP4560 km resolution, forcing MPI-ESM MR, RCP4.5 run 2006–2100Data available at PANGAEA 10.1594/PANGAEA.880512Subset of all 12 km data for selected variables at daily or monthly temporal resolution
Regional climate simulations have gained a significant amount of interest
over the last years. The limited resolution of global circulation models
(GCMs; typically around 1∘ or 110 km) prohibits the resolution of
local features such as topographic variation, coastlines, land use and
mesoscale convection. Advances in computational power and in exploiting
parallelism in numerical codes nowadays allow us to run regional climate models
(RCMs) at resolutions of 10 km until 2100 . These RCMs
can add significant value to global reanalyses and GCMs and in particular
lead to an improved representation of the West African monsoon WAM;
seeand references therein. The dynamics of the WAM system are a
consequence of complex interactions between dynamics, thermodynamics and
surface conditions . In West
Africa, where rainfall is limited to only few months per year except for the
coastal regions, a correct representation of the WAM circulation and the
associated onset and cessation of the rainy season are of utmost interest for
farming management . In recent studies,
and showed that the ability of RCMs
in simulating onset and cessation of the rainy season over West Africa
strongly depends on how well the models reproduce the northward movement of
the monsoon system and its associated features. Since RCMs are nested in a
global solution, this tie to large-scale features can pose challenges for
regional climate modelling studies see, for
example,. Uncertainties also rise from the sparse
observational network and the considerable differences in the derived gridded
observation products for the region, against which models are validated and
calibrated .
First high-resolution RCM studies over West Africa were conducted by
using the mesoscale meteorological model MM5
at 9 km resolution for two time slices, 1991–2000 and
2030–2039, over a comparably small region covering the Volta Basin. They
showed an annual mean temperature increase of around 1.3 ∘C in the
Volta region, significantly exceeding the interannual variability, and a mean
annual change in precipitation from -20 to +50 %. While an individual
model run can provide a plausible representation of the future under a given
climate change scenario, it does not allow an estimate of the range of
outcomes expected for the assessment of risks and opportunities
. Further, large uncertainties and errors are associated
with the result of each model run as a consequence of imperfect initial
conditions, with the model being an imperfect abstraction of reality, and
from numerical errors and artifacts accumulating in long-term simulations
for example,.
Using an ensemble of climate simulations, these uncertainties can be
addressed, and statistical estimates on projected future changes can be made
at a considerable increase in computational costs. On a global scale, the
Coupled Model Intercomparison Project Phase 5 (CMIP5) provides a framework for
coordinated climate change experiments and contributed to the IPCC AR5 with a
larger number of GCMs and future realisations . For the
region of West Africa, several regional ensemble modelling experiments were
conducted in recent years for example,. Within CORDEX , a
large number of long-term climate projections were generated by combining
different forcing data sets (i.e. GCMs) and RCMs. With a horizontal
resolution of 50 km, these projections cover the entire West African
continent and at least the time period 1980–2100. At shorter timescales, a
10-member ensemble of regional climate projections at a resolution of 25 km
and for selected regions in West Africa is available from CORDEX
. Spanning a significantly larger region, the RegCM4 model
was used to downscale three different GCMs at the same horizontal resolution
of 25 km . A consistent finding from these experiments was
that simulations at higher resolution can improve the representation of the
annual cycle of precipitation and reduce the uncertainty in the response to
global warming. These studies showed that an increased resolution allows for
a more accurate representation of the coastline and topographic gradients and
thus leads to a more realistic simulation along the Gulf of Guinea, among
others.
The work presented here advances the regional downscaling efforts for the
region through the generation of a high-resolution, ensemble regional climate
simulation experiment for large areas of continental West Africa and
extensive periods of the 21st century at a horizontal resolution of 12 km.
Three GCMs are downscaled using the Weather Research and Forecasting Model
to narrow down uncertainties and provide estimates on
the range of climate change impact on the region. A control run using
reanalysis data as forcing is added to assess the RCM bias. The simulations
provide a large set of output variables at very high temporal resolution for
climate change analysis, impact modelling and convection-permitting
downscaling experiments. The model data generated in this experiment are
freely available at two different data portals.
In Sect. , we describe the design of our ensemble experiment
and provide further details on the currently available data.
Section briefly illustrates the scientific value of
these projections and assesses the validity of the chosen setup of this
modelling experiment, while Sect. 4 provides details
about the dissemination of the data. Section is devoted
to conclusions and an outlook on the future modelling experiments.
MethodsEnsemble experiment design
The WASCAL ensemble presented here consists of a combination of three GCMs
with one RCM for the greenhouse gas emission scenario RCP4.5
Representative Concentration Pathway 4.5;. The
choice of RCP4.5 was made because of limited computational resources and is
based on the fact that the differences between RCP4.5 and RCP8.5
become apparent only after 2040. The selected GCMs, on the other hand,
cover the extremes in temperature and precipitation of the ensemble of GCM forcing data used in CORDEX . They also
span a larger range in future conditions until about 2060 than the two
scenarios RCP4.5 and RCP8.5 and are able to reproduce the dominant,
large-scale atmospheric features over West Africa . Further, a control run using reanalysis data is included for
model verification and future bias correction.
Reanalyses and global circulation models (earth system models) used
as forcing data for the long-term regional climate simulations, and regional
climate model used to conduct the ensemble experiment. The characteristics of
the forcing models for Africa and their climate change signal (CCS) are taken
from ; OBS denotes observations and MMM denotes the CMIP5
multi-model ensemble mean.
GCM/ESMCharacteristics for West AfricaCCSReferenceERA-Interimreanalysis, “perfect atmosphere”–MPI-ESM MRtemp. close to OBS/MMMmediumHadGEM2-ESprecip. close to OBS/MMMlargeGFDL-ESM2Mboth differ from OBS/MMMsmallRCMModel configuration for West Africa ReferenceWRFV3.5.1See Table –
Table summarises the forcing data sets and the limited area
model employed in this ensemble experiment. The control run using reanalysis
forcing data is conducted for the period 1979–2014. The historical runs are
generated for the period 1979–2005 and extended by the RCP4.5 runs until
2010. This approach allows us to derive statistics for the climatological
reference period 1980–2010, as defined by the . Future
projections are calculated for the periods 2019–2050 and 2069–2100 to
provide similar 30-year windows for the mid- and end of the 21st century. It
should be noted that the three selected GCMs are based on different
calendars, which makes model verification and comparison difficult on
timescales shorter than 1 month: while the MPI-ESM-MR model (as well as
ERA-Interim) employs a Gregorian calendar, the GFDL-ESM2M and HadGEM2-ES
models are based on a 365-day (no-leap year) and a 360-day
(12 × 30 days) calendar, respectively.
The generation of an ensemble of climate projections at a resolution of
12 km and for at least 90 years in total is a process over several years and
requires the use of different high-performance computing (HPC) centres. To
ensure consistency within each model run, the entire integration for a
particular combination of GCM and RCM is conducted on the same system.
Table summarises the HPC systems used in this
ensemble experiment.
High-performance computing systems used for the WASCAL
high-resolution regional climate ensemble experiment. Control runs are
conducted for the period 1979–2014, historical runs for the period
1979–2010 (for details, see text), and RCP4.5 projection runs for the
periods 2019–2050 and 2069–2100.
DKRZ: German Climate Computing Centre, http://www.dkrz.de, last access: 14 April
2018;
JSC: Jülich Supercomputing Centre of the Research Centre Jülich,
http://www.fz-juelich.de/ias/jsc, last access: 14 April
2018; SCC: Steinbuch Centre for Computing
of the Karlsruhe Institute of Technology,
http://scc.kit.edu, last access: 14 April
2018.
Nested domain configuration with 60 and 12 km. Also shown
are the three distinct regions used in the assessment.
WRF model configuration
In limited area modelling, the size of the computational domain can have a
significant influence on the quality of the results . In a
recent study, demonstrated that the ability of a RCM to
spin up the regional- and large-scale patterns associated with the West
African monsoon flow depends on a suitably large extent of the RCM domain.
Figure displays the nested domain configuration for the
ensemble experiment, using an outer domain at 60 km resolution to downscale
the coarse global forcing data sets and to provide boundary and initial
conditions for the inner domain at 12 km horizontal resolution. The figure
also defines three analysis regions, following a north–south gradient in
increasing annual precipitation. Such a partitioning is commonly used in
climate studies e. g.and references therein and
approximates the three dominant agro-climatological regions in West Africa.
In addition to the domain configuration, a common standard for the model
output was defined for all model runs to facilitate the use of the results.
All data are provided in a commonly used binary format for climate data
(netCDF CF-1.6) on a regular latitude–longitude grid for a predefined,
extensive set of variables and pressure levels (see
Sect. 4 for further details).
An inherent problem of limited area modelling is that supplying lateral
boundary conditions to nested models can cause severe problems, up to the
point where the RCM solution becomes inconsistent with the forcing data. This
is problematic for long-term transient simulations associated with a large
computational domain, where the solution is no longer an initial value but a
boundary value problem . The
different approaches to address this issue that are discussed in the
literature range from daily to weekly re-initialisation, sometimes even
including soil conditions , to transient runs covering the
entire period of interest . In general, more
frequent re-initialisation is suitable for studying individual weather
events, whereas a longer re-initialisation is useful in climate applications.
Here, we adopt an intermediate solution by conducting 11-year time-slice
experiments, which allows for 1 year spin-up of the soil conditions each
time. For instance, the ERA-Interim-driven control run, providing data for
the period 1980–2014, consists of the four time-slice experiments
1979–1990, 1989–2000, 1999–2010 and 2009–2014. Together with a spectral
nudging approach on the outer domain
, this approach allows the WRF
model to spin up and evolve the necessary fine-scale structures, embedded in
the large-scale features of the forcing global model, without departing too
far from the global conditions.
An optimal configuration of the WRF model is paramount to address key
questions regarding the impact of climate change. For the West African
region, this equates to an accurate representation of the West African
monsoon features in the model. In several studies it was shown that the
choice of physical parameterisations available in WRF can greatly influence
the model's skills, mostly measured in near-surface temperature and
precipitation accuracy . For this experiment, we
employ WRFV3.5.1 in a configuration summarised in
Table . This setup is based on the
WRF parameter study of 27 combinations of microphysics,
planetary boundary layer and cumulus schemes for two extreme years (dry and
wet), forced by ERA-Interim reanalysis data. To account for the different
characteristics and resolutions of reanalysis data and GCM data, we extended
their study and tested their most promising configurations using MPI-ESM MR
close to the CMIP5 multi model mean; as forcing data.
The resulting optimal setup of WRF used in the WASCAL high-resolution
ensemble experiment is thus a compromise to obtain good performance for both
ERA-Interim and MPI-ESM-MR forcing and also accounts for the higher
resolution 12 km versus 24 km in.
WRFV3.5.1 supports the Gregorian and 365-day calendar types, but not the
360-day calendar type employed by the HadGEM2-ES model. It was therefore
necessary to add an implementation of the 360-day calendar to WRFV3.5.1. The
360-day calendar caused further complication to the preprocessing of the GCM
data, since the grib standard does not support this calendar type. The
standard geographic data sets for WPSV3.5.1, available from the WRF Users'
website
http://www2.mmm.ucar.edu/wrf/users, last
access: 14 April 2018.
were used in this work. Of the available land use
classifications, the MODIS+lakes data set at 30′′ resolution was chosen.
WRF model configuration for the two domains at 60 and 12 km
resolution.
Annual cycle of near-surface temperature for the historical period
1980–2010 (a), the near future 2020–2050 (b), and the end
of the 21st century 2070–2100 (c), averaged over all land area and
for the different regions displayed in Fig. . WRF-R:
ERA-Interim control run; WRF-M: MPI-ESM-MR historical run; WRF-H: HadGEM2-ES
historical run; WRF-G: GFDL-ESM2M historical run; WRF-E: multi-model ensemble
of WRF-M, WRF-H and WRF-E. The shaded areas encompass the entire spread of
the 12 and 60 km members of the multi-model
ensembles.
The forcing model data were obtained from different sources and in different
formats. ERA-Interim reanalysis data were downloaded from the European Centre
for Medium-Range Forecasting ECMWF MARS
archive
http://apps.ecmwf.int/mars-catalogue, last
access: 14 April 2018.
, while MPI-ESM-MR data were obtained from DKRZ's CERA
archive
http://cera-www.dkrz.de, last access: 14 April 2018.
,
both in grib format. GFDL-ESM2M and HadGEM2-ES data were downloaded from the
Earth System Grid Federation
(ESGF)
http://www.earthsystemgrid.org, last access: 14 April
2018.
in netCDF format. This implied slightly different preprocessing steps
for using the data as boundary conditions in WRF. For ERA-Interim and MPI-ESM
MR, the standard preprocessing chain of WRF could be used, which consists of
converting forcing model grib data to an intermediate format (“un-grib”)
used by the WRF preprocessing system WPS, which in turn is interpolated
horizontally and vertically. For GFDL-ESM2M and HadGEM2-ES data, we
implemented a separate tool to convert the netCDF data directly into the WPS
intermediate format (“un-netcdf”), thereby avoiding the problem of an
unsupported 360-day calendar in the grib standard.
To generate model output in a standard format, the latest developments in the WRF
model were employed and extended further: WRFV3.5.1 provides the capability
to interpolate model-level data to pressure levels during the integration.
This capability was extended to include additional variables (in particular
hydrometeors). Further, climate diagnostics such as minimum and maximum daily
temperatures are calculated using the climate diagnostics features of the
model. While these interpolations require additional calculations during the
integration that slow down the model integration, it was found that writing
smaller amounts of data to disk (25 pressure levels instead of 40 model
levels) overcompensated for this increase and led to a faster model integration.
The WRF model output was further post-processed by a suite of parallelised
Python utilities to calculate additional variables, add CMIP5/CORDEX variable
attributes and provide the desired netCDF-CF compliance.
Assessment
In this section, we present a qualitative overview of the different WRF model
runs and provide guidance to future users of the data. It is also meant to
assess the assumptions on the basis of which the ensemble experiment was
designed, e.g. the characteristics of the different forcing models mentioned
in the previous section. For an in-depth analysis of the WRF simulations and
a thorough comparison with existing products from, for example, the CORDEX
initiative, the reader is referred to future publications. An evaluation of
27 WRF configurations, including the one used in this experiment, can be
found in .
Figure displays the annual cycle of mean
near-surface temperatures for the historical reference period 1980–2010, the
near future 2020–2050 and the end of the century 2070–2100. For the
historical period, observations are obtained from the University of Delaware
at 0.5∘ resolution 55 km, UDEL v3.01;. Also
displayed are data from the AgMERRA climate forcing data sets for agricultural
modelling at 0.25∘ resolution 27 km,
AgMERRA;. The WASCAL climate change projections are
displayed at 60 and 12 km resolution for the different model runs WRF-R
(forced by ERA-Interim), WRF-M (MPI-ESM MR), WRF-G (GFDL-ESM2M), WRF-H
(HadGEM2-ES) and the WRF multi-model ensemble WRF-E, composed of the three
GCM-driven runs WRF-M, WRF-G and WRF-H.
For the assessment in this section, all data were interpolated to the
high-resolution grid of the WRF 12 km simulations. The lack of
high-resolution observations for the West African region impose several
limitations on the quality of gridded observational data sets and of
reanalysis products, both of which require interpolation and/or satellite
blending techniques. As such, any conclusions drawn from a comparison of
model data at a substantially higher resolution than that of the observations
should be treated with caution. This is in particular important when trying
to assess the added value of the 12 km simulations, compared to the 60 km
simulations with a resolution similar to the observations.
The AgMERRA data set matches closely with the observations in all regions and
throughout the year. Averaged over the different areas and on a monthly
timescale, the differences between the 60 and 12 km runs for the same
forcing data set are small, compared to differences between model runs with
the same resolution and different forcing data sets. The reanalysis run
WRF-R agrees with the observations for most parts of the year, except for the
height of the monsoon season (July–September), for which the observations
show a dip in temperatures that is absent in the WRF-R run. For the Soudano
area (see Fig. ), the WRF-R run shows a larger positive bias
than for the other regions. The multi-model ensemble WRF-E shows a cold bias
of ∼ 2–4 ∘C for most parts of the year except during the
monsoon season, where it matches the observed temperatures closely. The
individual components of the ensemble are characterised by WRF-G being
consistently colder than, WRF-H being close to, and WRF-M being consistently
warmer than WRF-E. Among the three GCM-driven runs, WRF-M fits the observed
temperatures best. With respect to future conditions, all model runs show
increasing temperatures by 2.5–3 ∘C on average until the end of the
century, with WRF-H exhibiting the strongest climate change signal
(∼ 4 ∘C) and WRF-G the weakest (< 2 ∘C).
In a similar fashion, Fig. displays the annual
cycle of monthly precipitation. Again, AgMERRA fits the observations from
UDEL closely. The difference between the 12 and 60 km WRF runs is larger for
precipitation than it is for temperature, with a tendency to generate more
precipitation in the higher-resolution runs than the lower-resolution runs.
This is true for all cases except between July and October along the coast of
Guinea. In this particular case, the high-resolution runs, and foremost the
WRF-R run, show a distinct drop in precipitation that is absent in the
lower-resolution runs and in the observations and reanalyses. All WRF runs tend
to overestimate precipitation between February and June along the coast of
Guinea and for entire continental West Africa (labelled as “land”) in
general, with WRF-R showing the largest excess in precipitation and WRF-G
matching the observations best. Among the three GCM-driven ensemble members,
WRF-M tends to highest and WRF-G to lowest precipitation amounts, while WRF-H
lies in between. Consequently, WRF-E overpredicts precipitation slightly
along the coast of Guinea and matches the observations well in the Soudano
and Sahel regions. With respect to future conditions, all WRF ensemble
members show an increase in precipitation along the coast of Guinea and to
some extent in the Soudano region, whereas almost no change can be detected
for the Sahel. As for temperature, WRF-H shows the largest and WRF-G the
smallest climate change signal.
Annual cycle of precipitation for the historical period
1980–2010 (a), the near future 2020–2050 (b), and the end
of the 21st century 2070–2100 (c), averaged over all land area and
for the different regions displayed in Fig. . WRF-R:
ERA-Interim control run; WRF-M: MPI-ESM-MR historical run; WRF-H: HadGEM2-ES
historical run; WRF-G: GFDL-ESM2M historical run; WRF-E: multi-model ensemble
of WRF-M, WRF-H and WRF-E. The shaded areas encompass the entire spread of
the 12 and 60 km members of the multi-model
ensembles.
Near-surface temperature averaged over the historical reference
period 1980–2010 (a) and differences to AgMERRA reanalysis
data (b) for the WRF control runs WRF-R, the WRF multi-model
ensemble WRF-E, a CCLM control run CCLM-R and a two-member ensemble
RCA4-CORDEX.
Annual precipitation averaged over the historical reference period
1980–2010 (a) and differences to AgMERRA reanalysis
data (b) for the WRF control runs WRF-R, the WRF multi-model
ensemble WRF-E, a CCLM control run CCLM-R and a two-member ensemble
RCA4-CORDEX.
Seasonal (June–July–August) mean zonal wind cross section,
averaged between 25∘ E and 25∘ W for the historical period
1980–2010 for ERA-Interim and NCEP reanalysis data and the four 12 km WRF
runs WRF-R, WRF-M, WRF-H and WRF-G.
Near-surface temperature averaged over the historical reference
period 1980–2010 (a) and climate change signal for the near future
2020–2050 (b) and the end of the 21st century
2070–2100 (c) for the WRF multi-model ensemble WRF-E and a
two-member ensemble RCA4-CORDEX.
Annual precipitation averaged over the historical reference period
1980–2010 (a) and climate change signal for the near future
2020–2050 (b) and the end of the 21st century
2070–2100 (c) for the WRF multi-model ensemble WRF-E and a
two-member ensemble RCA4-CORDEX.
Figures
and display spatial distributions of
annual mean near-surface temperature and annual precipitation at 12 and
60 km resolution for the reanalysis runs WRF-R; the multi-model ensemble
WRF-E; an additional high-resolution (12 km) reanalysis run with the CCLM
regional climate model, obtained within the WASCAL programme
; and a two-member ensemble from CORDEX
at 50 km resolution. The two CORDEX simulations use the regional model RCA4
to downscale MPI-ESM LR and GFDL-ESM2M
forcing data. These simulations were chosen because of their
similar/identical forcing models. Also shown are differences of these data
sets with respect to AgMERRA at 27 km resolution. To calculate these
differences, all data sets were remapped to the 12 km grid of the
high-resolution WRF simulations.
With respect to temperature, beyond the findings discussed above, the spatial
plots reveal a distinct bipolar cold bias at approximately the location of
the Saharan Heat Low (SHL; 20∘ N, 5∘ W) and 15∘
east of it. This feature is present in all WRF, CCLM and CORDEX runs. The
spatial patterns of all WRF runs are similar and show relatively higher
temperatures in the Soudano region, leading to a warm bias in WRF-R and
nearly no bias in WRF-E over this region. The warm belt present in the WRF
runs around 10∘ N is confined to west of the Meridian in the CCLM-R
run and absent in the CORDEX runs.
For precipitation, the spatial plots shed further light on the zonal
distribution of precipitation and the biases relative to AgMERRA. All WRF
runs tend to a dry bias along the southwest coast of Guinea, Sierra Leone
and Liberia, presumably related to the complex interplay of onshore winds,
the coastline and the elevated topography of the Guinea Highlands. Further to
the east, the WRF runs tend towards a wet bias, in particular the reanalysis
run WRF-R. The CCLM-R run, on the contrary, displays a strong dry bias along
the entire coastline and further inland, while the CORDEX runs exhibit a wet
bias in most parts of the domain. North of 15∘ latitude, the WRF and
CCLM runs fit observed precipitation well, while the CORDEX runs still show a
wet bias.
The main dynamical large-scale features associated with the monsoon rainfall
across West Africa are illustrated in
Fig. . For the reference period
1980–2010, the figure displays the zonal wind profile during boreal summer
(June–July–August) averaged between 25∘ W and 25∘ E for
the two reanalysis data sets ERA-Interim (80 km horizontal resolution, 38
pressure levels) and NCEP/NCAR 275 km horizontal resolution, 17
pressure levels; and for the four 12 km WRF model runs WRF-R,
WRF-M, WRF-H and WRF-G. In general, the reanalysis data and the WRF data show
the expected stratified structure of the atmospheric circulation that place
the monsoon flow (0–15∘ N) and the harmattan fluxes
(20–25∘ N) below 850 hPa, the African easterly jet (AEJ,
13∘ N) at mid-levels around 600 hPa, and the tropical easterly jet
(TEJ, 10∘ N) in the upper troposphere at 150 hPa. Notably, the
WRF-R simulation using ERA-Interim forcing shows a stronger contrast (wind
speeds between -20 and +10 m s-1) than the original ERA-Interim
reanalysis data (wind speeds -14 to +10 m s-1). Among the
GCM-driven WRF runs, WRF-M fits the expected large-scale pattern best, while
WRF-H shows a weaker AEJ and WRF-G exhibits an additional jet feature at
5∘ S around 550 hPa. This is insofar interesting as WRF-G shows the
strongest cold temperature bias among all WRF runs and at the same time
reproduces the observed precipitation patterns and amounts best. The relation
of these aspects will be studied in detail in future publications.
Figures
and display spatial distributions of
the climate change signal on temperature and precipitation for the WRF
multi-model ensemble WRF-E and the RCA4 ensemble from CORDEX. With respect to
temperature, the WRF ensemble shows a gradient in warming, running from south
to north and ranging between 1.5 ∘C at the coast of Guinea to
3 ∘C in Mauritania and northern Mali. The CORDEX runs display a
gradient running from southwest to northeast between 1.5 and 2.5 ∘C.
For precipitation, the differences between the WASCAL WRF runs and the CORDEX
RCA4 runs is more pronounced. This can be partly attributed to the fact that
the CORDEX RCA4 ensemble used here lacks a HadGEM2-driven member, which,
among the three WRF runs, shows the strongest climate change signal. The
WRF ensemble shows a clearly wetter future for the coast of Guinea (up to
300 mm per year) and the Soudano region (up to 200 mm per year) and
slightly higher amounts of precipitation in the Sahel region than at present.
The CORDEX runs predict larger amounts of annual precipitation only along the
southwestern coast of Guinea, a slight drying over Nigeria and no
precipitation changes otherwise.
The data are provided in a netCDF CF-1.6-compliant
format using netCDF4 compression. All data are interpolated to a regular
latitude–longitude grid for a predefined, extensive set of variables and
pressure levels. The data are organised in streams with different output
intervals as a compromise between the requirements of follow-up studies and
storage constraints. A surface stream bundles all variables at and below the
surface at 3-hourly intervals, whereas pressure-level variables are provided
every 6 h on 25 levels in a pressure stream. Climate diagnostics such as
minimum/maximum temperatures are provided daily in a climate stream, and
time-invariant information such as land cover and terrain height are
collected in a static stream. The naming convention adopted here follows
closely the CMIP5 and CORDEX conventions. All data are made available to the
public via two different portals. The full data set, i.e. all variables at
full temporal and spatial resolution, can be obtained from the CERA database
at DKRZ see also
Table
https://cera-www.dkrz.de/WDCC/ui/cerasearch,
last access: 14 April 2018.
. A subset of the data at daily and monthly
temporal resolution is also made available through the PANGAEA Data Publisher
for Earth & Environmental Science portal see also
Table
https://www.pangaea.de,
last access: 14 April 2018.
. The WASCAL climate simulation data are freely
accessible to all users, albeit CERA requires a user registration. A full
description of the available data and the file naming conventions is provided
in Appendix A, alongside the data on
CERA
https://cera-www.dkrz.de/WDCC/ui/Entry.jsp?acronym=WASCAL_WRF_README,
last access: 14 April 2018.
and on
PANGAEA
https://doi.pangaea.de/10013/epic.51574.d001, last
access: 14 April 2018.
. On PANGAEA, we also provide the modified versions of
WRFV3.5.1 and WPSV3.5.1 used in this work for reference, as well as the
configuration (namelists) for all experiments described here.
Conclusions and outlook
A novel set of
high- and medium-resolution climate change simulations for the greater West
African region is provided to the research community within the framework of
WASCAL, which advances significantly beyond currently available data sets.
The ensemble uses the Weather Research and Forecasting Model (WRF) to downscale
three different global circulation models for three 30-year periods between
1980 and 2100, completed by a reanalysis-driven control run for the
historical period 1980–2014. These data sets are made available freely
through different data portals. A standardised and documented data structure,
closely following the CMIP5 and CORDEX conventions, is adopted to foster easy
and quick use of the data and effective collaboration. Apart from the higher
spatial resolution (12 and 60 km) than existing regional climate change
experiments 25–50 km; or global simulations
100–200 km;, our data are provided at high temporal
resolution (3-hourly, 6-hourly, daily) on 25 pressure levels and four
subsurface levels. A large number of 76 output variables in total are
available to enable a diversity of climate change analyses, impact modelling
studies and further downscaling to convection-permitting resolutions.
It is important to note that the brief analysis presented here is performed
on monthly and annual timescales, averaged over 30-year time slices, and
compared to observations and reanalysis data at ∼ 30 km resolution. A
detailed analysis of the data using observational data sets at high temporal
and spatial resolution (where available), focussing on local-scale features,
is beyond the scope of this contribution. The main characteristics of the WRF
simulations can be summarised as follows: in general, our WRF setup tends to
increase both temperature and precipitation amounts, compared to the original
forcing data set. These positive biases, in combination with the
characteristics (i.e. biases) of the forcing data sets
(Table ), lead to an overall positive bias of the ERA-Interim-driven
WRF runs in temperature and precipitation. For the MPI-ESM-MR-driven
WRF runs, they imply a good agreement with respect to temperature, alongside
a positive bias in precipitation. Conversely, for the GFDL-ESM2M-driven
WRF runs, they lead to a good agreement with respect to precipitation and a
large negative bias in temperature. The HadGEM2-ES-driven WRF runs lie in
between for both temperature and precipitation.
With respect to climate change, the WRF simulations project an increase in
temperature between 1.5 and 3 ∘C, with higher values in the Sahel,
in an overall agreement with existing global and regional climate projections
. For precipitation, our simulations
project an increase in precipitation between 150 and 300 mm per year all
south of 15∘ N, in line with the majority of the CMIP5 models
for example,. However, this stands in contrast to existing
regional climate modelling studies, for example the findings of
, who analysed the response of West African climate zones to
anthropogenic climate change in the late 21st century. Based on ensemble data
from CORDEX and their own higher-resolution RegCM4 experiments, they
concluded that West Africa evolves towards increasingly torrid, arid and
semi-arid conditions. These contradicting signals do highlight the large
uncertainty in projected future rainfall, even on a continental scale.
The WRF simulations in this WASCAL high-resolution ensemble are conducted as
a time-slice experiment over 10 years, preceded by an additional year for
model spin-up. As discussed in and
, the internal variability of regional models is
generally small compared to the interannual variability in decadal
simulations. On the other hand, it is less clear whether differences between
individual 10-year periods are dominated by climate variability rather than
climate change. We therefore advocate the utilisation of the entire 30-year periods,
each consisting of three consecutive decadal simulations, for the present day
(1980–2010), the near future (2030–2050) and the distant future
(2070–2100). To do so, the spin-up period of 1 year for each of the
decadal runs has to be neglected.
Despite its widespread use, classical limited area modelling as it is used in
the regional downscaling experiments presented here suffers from several
limitations such as numerical artefacts at domain and nest boundaries,
diverging solutions between the regional model and the global forcing model
and the inability to provide feedback from the regional to the forcing model,
to name a few. Alternative modelling systems such as the global Model for
Prediction Across Scales MPAS; make use of
innovative variable-resolution mesh geometries with smooth transitions
between different areas of refinement and provide excellent scaling on modern
high-performance computing systems. MPAS was tested recently, using uniform
and variable-resolution meshes over the region of West Africa, and showed
promising results . For instance, the 60–12 km
variable-resolution mesh used in their study (i.e. with a 60 km resolution
globally and a 12 km resolution over the entire North African continent)
shows a clear bipolar pattern of high temperatures in the locations where all
of the regional climate simulations presented here exhibit a cold bias
Fig. here versus Figs. 9
and 11 in. This could be related to an improved
representation of large-scale patterns governing the West African climate in
global models and requires further investigation.
Within the framework of WASCAL, additional high-resolution climate
simulations are carried out, employing other regional climate models such as
CCLM or focusing on specific areas such as the
agriculturally important Volta Basin . Also, to improve the
representation of the diurnal cycle of precipitation and of extreme
precipitation events in the models, convection-permitting and coupled
atmospheric–hydrological modelling experiments are pursued
. The climate modelling efforts
presented here are undertaken in parallel to the setup of a dense network of
automatic weather stations in the region with the goal of assessing and
reducing model uncertainties and biases. Together, the modelling and observational
activities of WASCAL will enable researchers and stakeholders to develop
effective climate change mitigation measures for West Africa with a higher
level of confidence from local to continental scales.
Additional information on the WASCAL WRF climate simulation data
The full description of the available data and the file naming conventions
provided in this appendix are identical to the information contained in the
WASCAL_WRF_README on CERA
(https://cera-www.dkrz.de/WDCC/ui/Entry.jsp?acronym=WASCAL_WRF_README,
last access: 14 April 2018.) and on PANGAEA
(https://doi.pangaea.de/10013/epic.51574.d001, last access: 14 April
2018.).
Description of variables
Table summarises the list of output variables
of the WASCAL climate simulations. The table includes all variables that are
produced by the WRF model runs. Static variables are provided only once in
the static output stream. For further information on the meaning and
calculation of these fields, the user is referred to the WRF Users' Guide, in
particular to
Chapter 5
http://www2.mmm.ucar.edu/wrf/users/docs/user_guide_V3/users_guide_chap5.htm,
last access: 14 April 2018.
. The variable types are “acc” (accumulated
values), “coord” (coordinate variables), “const” (constant values),
“min” (minimum over last output interval), “max” (maximum over last
output interval) and “inst” (instantaneous values). The variables are
classified into different streams with different output frequency (see
Sect. below).
Note 1. The climate output diagnostic variables
contained in stream wrfclm are only available for the high-resolution
(12 km) experiments, not for the low-resolution (60 km) experiments.
Note 2. The following time-slice experiments are
missing the accumulated radiation budgets (rlds, rldt, rlus, rlut, rsds,
rsdt, rsus, rsut; see Sect. and
for details on the time slices and naming conventions):
WRF12_MPIESM_HIST/{1979–1990, 1999–2006},
WRF12_MPIESM_RCP45/{2006–2010, 2039–2050, 2089–2100}.
Description of streams
The output variables are classified into different output streams, which are
described in Table . The streams have different output
frequencies. Note that the stream classification does not appear in the
directory structure or file names (see Sect. below).
Pressure levels for stream wrfprs
The native model output is interpolated to 25 pressure levels, see
Table . Variables on pressure levels are set to
missing values below ground.
Description of files
The data are provided in compressed netCDF4 CF-1.6-compliant format. All data
are combined into monthly output files, independent of the output frequency
and size of the variables. The coarser 60 km runs provide the same data as
the 12 km runs except for the climate output diagnostics (stream wrfclm is
not present). A consistent filename convention is adopted and described in
Table .
Description of nesting strategy and time slices
The domain configuration is displayed and described in detail in the main
text. The high-resolution runs (12 km) are carried out as a nested
simulation, using the output of the coarser resolution (60 km) model runs as
the forcing data set. The coarser model runs are forced by the different
reanalysis and GCM data sets described above. An offline-nesting approach is
adopted, which implies no feedback from the 12 km experiments to the 60 km
experiments. Thus, the 60 km experiments can be considered as stand-alone
experiments at a relatively coarse resolution.
The experiments are conducted as time-sliced runs of 11-year duration each,
where the first year is considered as a spin-up period and should not be used in
the analysis. The historical run 1999–2006 is carried over into the
projection run 2006–2010 to be able to provide model data for the WMO
reference period 1980–2010 by combining the three decadal time-slice
experiments 1979–1990, 1989–2000 and 1999–2010 and neglecting the spin-up year
for each of them. The available time slices are summarised in
Table .
List of output variables of the WASCAL WRF climate simulations. The
variable types are “acc” (accumulated values), “coord” (coordinate
variables), “const” (constant values), “min” (minimum over last output
interval), “max” (maximum over last output interval) and “inst”
(instantaneous values).
WRF nameOutput nameUnitsStreamTypeDescription (long name)ACLWDNBrldsJ m-2wrfsfcaccAccumulated surface downwelling longwave radiationACLWDNTrldtJ m-2wrfsfcaccAccumulated TOA incident longwave radiationACLWUPBrlusJ m-2wrfsfcaccAccumulated surface upwelling longwave radiationACLWUPTrlutJ m-2wrfsfcaccAccumulated TOA outgoing longwave radiationACSWDNBrsdsJ m-2wrfsfcaccAccumulated surface downwelling shortwave radiationACSWDNTrsdtJ m-2wrfsfcaccAccumulated TOA incident shortwave radiationACSWUPBrsusJ m-2wrfsfcaccAccumulated surface upwelling shortwave radiationACSWUPTrsutJ m-2wrfsfcaccAccumulated TOA outgoing shortwave radiationALBEDOalb1wrfsfcinstAlbedoCANWATcanwatkg m-2wrfsfcinstCanopy waterCLDFRAcl1wrfprsinstCloud area fractionDEPTHdepthmwrfsfccoordDepthEMISSems1wrfsfcinstSurface emissivityGHTzgmwrfprsinstGeopotential heightGRDFLXhfgW m-2wrfsfcinstGround heat fluxHFXhfssW m-2wrfsfcinstSurface upward sensible heat fluxHGTorogmwrfstainstTerrain heightISLTYPsltype1wrfstaconstDominant soil categoryIVGTYPvegtype1wrfstaconstDominant vegetation categoryLANDMASKsftlf1wrfstaconstLand binary mask (1 for land, 0 for water)LATlatdegrees_northwrfclm, wrfprs,coordLatitude; south is negativewrfsfc, wrfstLHhflsW m-2wrfsfcinstSurface upward latent heat fluxLONlondegrees_eastwrfclm, wrfprs,coordLongitude; west is negativewrfsfc, wrfstaMUamdryPawrfsfcinstDry air mass in columnPBLHzmlamwrfsfcinstAtmosphere boundary layer thicknessPLEVplevhPawrfprscoordPressurePMSLpslPawrfsfcinstSea level pressurePSFCpsPawrfsfcinstSurface air pressureQ2vapskg kg-1wrfsfcinstNear-surface water vapour mixing ratioQCLOUDclwkg kg-1wrfprsinstCloud water mixing ratioQFXmfskg m-2 s-1wrfsfcinstSurface upward moisture fluxQICEclikg kg-1wrfprsinstIce mixing ratioQRAINclrkg kg-1wrfprsinstRain water mixing ratioQSNOWclskg kg-1wrfprsinstSnow mixing ratioQVAPORvapkg kg-1wrfprsinstWater vapour mixing ratioRAINprmmwrfsfcaccAccumulated precipitationRHhur%wrfprsinstRelative humidityRH2hurs%wrfsfcinstNear-surface relative humiditySEAICEsic1wrfsfcinstSea ice binary mask (1 for sea ice, 0 for water)SHDMAXvegmax1wrfstaconstAnnual max vegetation fractionSHDMINvegmin1wrfstaconstAnnual min vegetation fractionSKINTEMPMAXtsmaxKwrfclmmaxDaily maximum surface skin temperatureSKINTEMPMINtsminKwrfclmminDaily minimum surface skin temperatureSMCRELmrrlsl1wrfsfcinstRelative soil moistureSMOISmrlslm3 m-3wrfsfcinstWater content of soil layerSMOISTmrsom3 m-3wrfsfcinstTotal soil moisture contentSNOALBalbmax1wrfstaconstAnnual max snow albedo in fractionSNOWsnwkg m-2wrfsfcinstSnow water equivalentSNOWHsndmwrfsfcinstPhysical snow depthSPDUVwindm s-1wrfprsinstWind speedSPDUV10sfcWindm s-1wrfsfcinstNear-surface wind speedSPDUV10MAXsfcWindmaxm s-1wrfclmmaxDaily maximum near-surface wind speed
Continued.
SRprfz1wrfsfcinstFraction of frozen precipitationSSTtsoKwrfsfcinstSea surface temperatureSWDDIFswddifW m-2wrfsfcinstShortwave surface downward diffuse irradianceSWDDIRswddirW m-2wrfsfcinstShortwave surface downward direct irradianceSWDDNIswddniW m-2wrfsfcinstShortwave surface downward direct normal irradianceTtaKwrfprsinstAir temperatureT2tasKwrfsfcinstNear-surface air temperatureT2MAXtasmaxKwrfclmmaxDaily maximum near-surface air temperatureT2MINtasminKwrfclmminDaily minimum near-surface air temperatureTCLDFRAclt1wrfsfcinstTotal cloud fractionTDtdKwrfprsinstDew point temperatureTD2tdsKwrfsfcinstNear-surface dew point temperatureTH2thetasKwrfsfcinstNear-surface potential temperatureTIMEtimehours since 1 Jan 1970wrfclm, wrfprs, wrfsfc, wrfstainstTimeTMNtsllKwrfsfcinstTemperature of soil at lower boundaryTSKtsKwrfsfcinstSurface skin temperatureTSLBtslKwrfsfcinstTemperature of soilUuam s-1wrfprsinstEastward windU10uasm s-1wrfsfcinstEastward near-surface windU10MAXuasmaxm s-1wrfclmmaxDaily maximum eastward near-surface windVvam s-1wrfprsinstNorthward windV10vasm s-1wrfsfcinstNorthward near-surface windV10MAXvasmaxm s-1wrfclmmaxDaily maximum northward near-surface windVEGFRAveg1wrfsfcinstVegetation fractionWwam s-1wrfprsinstUpward wind
Description of streams into which the WASCAL WRF output variables
are classified.
Stream nameDescriptionOutput intervalwrfclmclimate variables (extremes), 2Ddaywrfprspressure-level variables, 3D6 hwrfsfcsurface, subsurface and other 2D variables3 hwrfstastatic variables, 2Dfx
Pressure levels to which three-dimensional atmospheric variables are
interpolated.
File naming convention for the WASCAL WRF ensemble. Here, {sr}
denotes the spatial resolution in km, {forcing} the forcing model,
{scenario} the scenario, {var} the variable,
{yyyy}–{mm} the year and month, and {tr} the output
interval (temporal resolution).
Subset of data available at PANGAEA. The variables and
de-accumulation steps are described in Sects.
and , with the parameters enclosed in curly brackets in
Sect. .
Variables de-accumulated, daily sums/averagespr, rlds, rldt, rlus, rlut, rsds, rsdt, rsus, rsutdaily averageshfls, hfss, hurs, mrso, psl, tas, tasmax, tasmin, tdsmonthly averagesswddif, swddir, swddni, ua, va, wa, zgPressure levels (hPa) for variables ua, va, wa, zg1000, 850, 750, 700, 650, 600, 550, 450, 350, 250, 150Naming convention de-accumulated variables, daily sumsDAC_wa12clmN_{forcing}_{scenario}_{var}_{yyyy}_{yyyy}_DAYSUM.ncde-accumulated variables, daily averagesDAC_wa12clmN_{forcing}_{scenario}_{var}_{yyyy}_{yyyy}_DAYMEAN.ncother variables, daily averageswa12clmN_{forcing}_{scenario}_{var}_{yyyy}_{yyyy}_DAYMEAN.ncother variables, monthly averageswa12clmN_{forcing}_{scenario}_{var}_{yyyy}_{yyyy}_MONMEAN.ncSubset of data available at PANGAEA
To facilitate the use of the WASCAL data for applications that do not require
the full set of variables or the full temporal resolution of the data, a
subset of the data set available at CERA is provided through the PANGAEA
portal. This subset is derived from the data provided at CERA as follows:
Only data from the high-resolution 12 km runs are considered, not from the
intermediate-resolution 60 km runs.
A subset of variables of potentially high interest are selected (see Table ).
Accumulated data (rainfall; radiation budgets are de-accumulated into precipitation sums and radiation averages between two output time steps).
Data at high temporal resolution (3-hourly, 6-hourly) are aggregated to daily or monthly timescales.
Atmospheric variables on pressure levels are extracted for 11 out of the 25 available pressure levels (see Table ).
Data are concatenated into 30-year periods 1980–2010 (control, historical), 2020–2050 (RCP4.5), 2070–2100 (RCP4.5), thereby neglecting the
1-year spin-up period for each of the time-slice experiments.
A slightly different file naming convention is adopted to reflect the above modifications of the data (see Table ).
Note 1. The accumulated radiation budgets are
missing for the runs using MPI-ESM as forcing data set (see also
Sect. ).
Note 2. For the periods 1980–2010, the historical
runs 1999–2005 are completed by the (continuation) runs 2006–2010 from the
RCP4.5 scenario, but the 30-year data sets are labelled as “historical”.
Rights of use
The data are provided under the Creative Commons license 4.0. For details
about the licensing model, see the following web page:
https://creativecommons.org/licenses/by/4.0/, last access: 14 April
2018.
Liability and warranty
The data are made available to the user without any warranty. The user is
aware that the data have been obtained according to current state-of-the-art
science and computational engineering.
The data producer must not be taken into any obligation to third parties
on the basis of this agreement. Any liability of the data producer for damage
of all kinds resulting from the provision and further processing of the data
is ruled out.
The liability disclaimer stated under (1) and (2) does not apply insofar
as the data producer has acted in gross negligence or with wilful intent.
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was funded by the German Federal Ministry of Education and Research
(BMBF) through the West African Science Service Center on Climate Change and
Adapted Land Use (WASCAL), and by the Bavarian State Ministry of Education,
Sciences and the Arts through a KONWIHR (Competence Network for Scientific
High-Performance Computing) project. We acknowledge that the results of this
research were achieved using computational resources at the German Climate
Computing Center (DKRZ) and the Research Centre Jülich. The authors
acknowledge the European Centre for Medium-Range Weather Forecasts (ECMWF)
for the dissemination of ERA-Interim, as well as the Global Modeling and
Assimilation Office (GMAO) for the dissemination of AgMERRA. Further, we
acknowledge the NOAA/OAR/ESRL Physical Sciences Division for providing UDEL air temperature and precipitation as
well as the NCEP reanalysis derived data, the Earth System Grid Federation
(ESGF) for providing CMIP5 model data from the GFDL-ESM2M and HadGEM2-ES
earth system models, and DKRZ for providing MPI-ESM-MR model data. We also
thank the DKRZ long-term archiving service CERA and the PANGAEA Data
Publisher for Earth & Environmental Science platform for providing the
necessary storage to disseminate the data generated in our ensemble
experiment. The authors are particularly grateful for the extensive and
valuable support from Heinke Hoeck and Peter Lenzen of DKRZ and Stefanie
Schumacher and Rainer Sieger of PANGEA with ingesting the data and generating
the necessary metadata for the data dissemination. We also thank the two anonymous referees for useful comments
and corrections that helped to improve this manuscript.
Edited by: David Carlson
Reviewed by: two anonymous referees
ReferencesAnnor, T., Lamptey, B., Wagner, S., Oguntunde, P., Arnault, J., Heinzeller,
D., and Kunstmann, H.: High-resolution long-term WRF climate simulations over
Volta Basin. Part 1: validation analysis for temperature and precipitation,
Theor. Appl. Climatol., 1–21, 10.1007/s00704-017-2223-5, 2017.Anon, A.: GFDL's ESM2 Global Coupled Climate-Carbon Earth System Models.
Part I: Physical Formulation and Baseline Simulation Characteristics,
J. Climate, 25, 6646–6665, 10.1175/JCLI-D-11-00560.1, 2012.Arnault, J., Wagner, S., Rummler, T., Fersch, B., Bliefernicht, J., Andresen,
S., and Kunstmann, H.: Role of runoff-infiltration partitioning and resolved
overland flow on land-atmosphere feedbacks: A case-study with the WRF-Hydro
coupled modeling system for West Africa, J. Hydrometeorol., 17, 1489–1516,
10.1175/JHM-D-15-0089.1, 2016.Browne, N. A. K. and Sylla, M. B.: Regional climate model sensitivity to
domain size for the simulation of the West African summer monsoon rainfall,
International Journal of Geophysics, 2012, 625831, 10.1155/2012/625831,
2012.
Bruyère, C. L.: Regional Climate Research using WRF and MPAS: Overview
and Future Development, 14th Annual WRF Workshop, Boulder, Colorado, USA,
June 2013.Bruyère, C., Raktham, C., Done, J., Kreasuwun, J., Thongbai, J., and
Promnopas, W.: Major weather regime changes over Southeast Asia in a
near-term future scenario, Clim. Res., 72, 1–18, 10.3354/cr01442,
2016.Buontempo, C., Mathison, C., Jones, R., Williams, K., Wang, C., and
McSweeney, C.: An ensemble climate projection for Africa, Clim. Dynam., 44,
2097–2118, 10.1007/s00382-014-2286-2, 2015.Cook, K. H.: Generation of the African easterly jet and its role in
determining West African precipitation, J. Climate, 12, 1165–1184,
10.1175/1520-0442(1999)012<1165:GOTAEJ>2.0.CO;2, 1999.Davies, H. C.: Limitations of Some Common Lateral Boundary Schemes used in
Regional NWP Models, Mon. Weather Rev., 111, 1002–1012,
10.1175/1520-0493(1983)111<1002:LOSCLB>2.0.CO;2, 1983.Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy,
S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P.,
Köhler, M., Matricardi, M., Mcnally, A. P., Monge-Sanz, B. M.,
Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C.,
Thépaut, J. N., and Vitart, F.: The ERA-Interim reanalysis:
Configuration and performance of the data assimilation system, Q. J. Roy.
Meteor. Soc., 137, 553–597, 10.1002/qj.828, 2011.Dieng, D., Smiatek, G., Bliefernicht, J., Heinzeller, D., Sarr, A., Gaye,
A. T., and Kunstmann, H.: Evaluation of the COSMO-CLM high-resolution climate
simulations over West Africa, J. Geophys. Res.-Atmos., 122, 1437–1455,
10.1002/2016JD025457, 2017.
Eguavoen, I.: Climate change and trajectories of blame in northern ghana,
Anthropol. Noteb., 19, 5–24, 2013.Elguindi, N., Giorgi, F., and Turuncoglu, U.: Assessment of CMIP5 global
model simulations over the subset of CORDEX domains used in the Phase I
CREMA, Climatic Change, 125, 7–21, 10.1007/s10584-013-0935-9, 2014.Flaounas, E., Janicot, S., Bastin, S., Roca, R., and Mohino, E.: The role of
the Indian monsoon onset in the West African monsoon onset: Observations and
AGCM nudged simulations, Clim. Dynam., 38, 965–983,
10.1007/s00382-011-1045-x, 2012.
Giorgi, F., Jones, C., and Asrar, G. R.: Addressing climate information needs
at the regional level: the CORDEX framework, WMO Bulletin, 58, 175–183,
2009.Grell, G. A., Dudhia, J., and Stauffer, D. R.: A description of the
Fifth-generation Penn State/NCAR Mesoscale Model (MM5), NCAR Technical Note
NCAR/TN-398+STR, 121 pp., 10.5065/D60Z716B, 1994.Harris, L. M. and Durran, D. R.: An Idealized Comparison of One-Way and
Two-Way Grid Nesting, Mon. Weather Rev., 138, 2174–2187,
10.1175/2010MWR3080.1, 2010.Heinzeller, D., Duda, M. G., and Kunstmann, H.: Towards convection-resolving,
global atmospheric simulations with the Model for Prediction Across Scales
(MPAS) v3.1: an extreme scaling experiment, Geosci. Model Dev., 9, 77–110,
10.5194/gmd-9-77-2016, 2016.Heinzeller, D., Dieng, D., Smiatek, G., Olusegun, C., Klein, C., Hamann, I.,
Bliefernicht, B., and Kunstmann, H.: West African Science Service Centre on
Climate Change and Adapted Land Use (WASCAL) High-Resolution Climate
Simulation Data, 10.1594/WDCC/WRF60_GFDLESM_HIST,
10.1594/WDCC/WRF60_GFDLESM_RCP45,
10.1594/WDCC/WRF60_HADGEM2_RCP45, 10.1594/WDCC/WRF60_MPIESM_HIST,
10.1594/WDCC/WRF60_MPIESM_RCP45, 10.1594/WDCC/WRF60_ERAINT_CTRL,
10.1594/WDCC/WRF60_HADGEM2_HIST, 10.1594/WDCC/WRF12_ERAINT_CTRL,
10.1594/WDCC/WRF12_MPIESM_RCP45,
10.1594/WDCC/WRF12_GFDLESM_RCP45,
10.1594/WDCC/WRF12_HADGEM2_RCP45,
10.1594/WDCC/WRF12_GFDLESM_HIST, 10.1594/WDCC/WRF12_HADGEM2_HIST,
10.1594/WDCC/WRF12_MPIESM_HIST,
https://cera-www.dkrz.de/WDCC/ui/Project.jsp?acronym=WASCAL (last
access: 14 April 2018), 2017a.Heinzeller, D., Dieng, D., Smiatek, G., Olusegun, C., Klein, C., Hamann, I.,
Bliefernicht, B., and Kunstmann, H.: West African Science Service Centre on
Climate Change and Adapted Land Use (WASCAL) high-resolution climate
simulation data, links to subset of variables at daily and monthly temporal
resolution in NetCDF format, PANGAEA, 10.1594/PANGAEA.880512, 2017b.Jones, C. D., Hughes, J. K., Bellouin, N., Hardiman, S. C., Jones, G. S.,
Knight, J., Liddicoat, S., O'Connor, F. M., Andres, R. J., Bell, C., Boo,
K.-O., Bozzo, A., Butchart, N., Cadule, P., Corbin, K. D., Doutriaux-Boucher,
M., Friedlingstein, P., Gornall, J., Gray, L., Halloran, P. R., Hurtt, G.,
Ingram, W. J., Lamarque, J.-F., Law, R. M., Meinshausen, M., Osprey, S.,
Palin, E. J., Parsons Chini, L., Raddatz, T., Sanderson, M. G., Sellar, A.
A., Schurer, A., Valdes, P., Wood, N., Woodward, S., Yoshioka, M., and
Zerroukat, M.: The HadGEM2-ES implementation of CMIP5 centennial simulations,
Geosci. Model Dev., 4, 543–570, 10.5194/gmd-4-543-2011, 2011.Jung, G. and Kunstmann, H.: High-resolution regional climate modeling for the
Volta region of West Africa, J. Geophys. Res., 112, 1–17,
10.1029/2006JD007951, 2007.Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M.,
Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang,
J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR
40-Year Reanalysis Project, B. Am. Meteorol. Soc., 77, 437–472,
10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996.Kirtman, B., Power, S. B., Adedoyin, J. A., Boer, G. J., Bojariu, R.,
Camilloni, I., Doblas-Reyes, F. J., Fiore, A. M., Kimoto, M., Meehl, G. A.,
Prather, M., Sarr, A., Schär, C., Sutton, R., van Oldenborgh, G. J.,
Vecchi, G., and Wang, H.-J.: Near-term Climate Change: Projections and
Predictability, in: Climate Change 2013: The Physical Science Basis.
Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Stocker, T., Chap. 11,
953–1028, 10.1017/CBO9781107415324.023, 2013.Klein, C., Heinzeller, D., Bliefernicht, J., and Kunstmann, H.: Variability
of West African monsoon patterns generated by a WRF multi-physics ensemble,
Clim. Dynam., 45, 2733–2755, 10.1007/s00382-015-2505-5, 2015.Klein, C., Bliefernicht, J., Heinzeller, D., Gessner, U., Klein, I., and
Kunstmann, H.: Feedback of observed interannual vegetation change: a regional
climate model analysis for the West African monsoon, Clim. Dynam., 48,
2837–2858, 10.1007/s00382-016-3237-x, 2017.Kupiainen, M., Jansson, C., Samuelsson, P., Jones, C., Willén, U.,
Hansson, U., Ullerstig, A., Wang, S., and Döscher, R.: Rossby Centre
regional atmospheric model, RCA4, Rossby Center News Letter, available at:
https://www.smhi.se/en/research/research-departments/climate-research-rossby-centre2-552/rossby-centre-regional-atmospheric-model-rca4-1.16562
(last access: 14 April 2018), 2014.Laprise, R.: Resolved Scales and Nonlinear Interactions in Limited-Area
Models, J. Atmos. Sci., 60, 768–779,
10.1175/1520-0469(2003)060<0768:RSANII>2.0.CO;2, 2003.Leduc, M. and Laprise, R.: Regional climate model sensitivity to domain size,
Clim. Dynam., 32, 833–854, 10.1007/s00382-008-0400-z, 2008.Lee, J. Y. and Wang, B.: Future change of global monsoon in the CMIP5, Clim.
Dynam., 42, 101–119, 10.1007/s00382-012-1564-0, 2014.Lucas-Picher, P., Caya, D., Elía, R., and Laprise, R.: Investigation of
regional climate models' internal variability with a ten-member ensemble of
10-year simulations over a large domain, Clim. Dynam., 31, 927–940,
10.1007/s00382-008-0384-8, 2008.Miguez-Macho, G., Stenchikov, G. L., and Robock, A.: Spectral nudging to
eliminate the effects of domain position and geometry in regional climate
model simulations, J. Geophys. Res., 109, D13104, 10.1029/2003JD004495,
2004.Mounkaila, M. S., Abiodun, B. J., and 'Bayo Omotosho, J.: Assessing the
capability of CORDEX models in simulating onset of rainfall in West Africa,
Theoretical and Applied Climatology, 119, 255–272,
10.1007/s00704-014-1104-4, 2015.Naab, J., Bationo, A., Wafula, B. M., Traore, P. S., Zougmore, R., Ouattara,
M., Tabo, R., and Vlek, P. L. G.: African Perspectives on Climate Change and
Agriculture: Impacts, Adaptation and Mitigation Potential, Handbook of
Climate Change and Agroecosystems, 85–106, 10.1142/9781848169845_0006,
2012.Naabil, E., Lamptey, B. L., Arnault, J., Kunstmann, H., and Olufayo, A.:
Water resources management using the WRF-Hydro modelling system: Case-study
of the Tono dam in West Africa, Journal of Hydrology: Regional Studies, 12,
196–209, 10.1016/j.ejrh.2017.05.010, 2017.Neumann, R., Jung, G., Laux, P., and Kunstmann, H.: Climate trends of
temperature, precipitation and river discharge in the Volta Basin of West
Africa, International Journal of River Basin Management, 5, 17–30,
10.1080/15715124.2007.9635302, 2007.Niang, I., Ruppel, O., Abdrabo, M., Essel, A., Lennard, C., Padgham, J., and
Urquhart, P.: Africa, in: Climate Change 2014: Impacts, Adaptation, and
Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to
the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,
edited by: Barros, V., Field, C., Dokken, D., Mastrandrea, M., Mach, K.,
Bilir, T., Chatterjee, M., Ebi, K., Estrada, Y., Genova, R., Girma, B.,
Kissel, E., Levy, A., MacCracken, S., Mastrandrea, P., and White, L. L.,
Cambridge University Press, Chap. 22, 1199–1265, available at:
https://www.ipcc.ch/pdf/assessment-report/ar5/wg2/WGIIAR5-Chap22_FINAL.pdf
(last access: 14 April 2018), 2014.Nicholson, S. E.: The West African Sahel: A Review of Recent Studies on the
Rainfall Regime and Its Interannual Variability, ISRN Meteorology, 2013,
453521, 10.1155/2013/453521, 2013.Nikulin, G., Jones, C., Giorgi, F., Asrar, G., Büchner, M.,
Cerezo-Mota, R., Christensen, O. B., Déqué, M., Fernandez, J.,
Hänsler, A., van Meijgaard, E., Samuelsson, P., Sylla, M. B., and
Sushama, L.: Precipitation Climatology in an Ensemble of CORDEX-Africa
Regional Climate Simulations, J. Climate, 25, 6057–6078,
10.1175/JCLI-D-11-00375.1, 2012.Nikulin, G., Jones, C., Kjellström, E., and Gbobaniyi, E.: The West
African Monsoon simulated by global and regional climate models, EGU General
Assembly, Vienna, Austria, 7–12 April 2013, EGU2013-4581, available at:
http://meetingorganizer.copernicus.org/EGU2013/EGU2013-4581.pdf (last
access: 14 April 2018), 2013.Noble, E., Druyan, L., and Fulakeza, M.: The sensitivity of WRF daily
summertime simulations over West Africa to alternative parameterizations.
Part I: african wave circulation, Mon. Weather Rev., 142, 1588–1608,
10.1175/MWR-D-13-00194.1, 2014.Otte, T. L.: The impact of nudging in the meteorological model for
retrospective air quality simulations. Part I: Evaluation against national
observation networks, J. Appl. Meteorol. Clim., 47, 1853–1867,
10.1175/2007JAMC1790.1, 2008.Otte, T. L., Nolte, C. G., Otte, M. J., and Bowden, J. H.: Does nudging
squelch the extremes in regional climate modeling?, J. Climate, 25,
7046–7066, 10.1175/JCLI-D-12-00048.1, 2012.Paeth, H., Hall, N. M. J., Gaertner, M. A., Alonso, M. D., Moumouni, S.,
Polcher, J., Ruti, P. M., Fink, A. H., Gosset, M., Lebel, T., Gaye, A. T.,
Rowell, D. P., Moufouma-Okia, W., Jacob, D., Rockel, B., Giorgi, F., and
Rummukainen, M.: Progress in regional downscaling of west African
precipitation, Atmos. Sci. Lett., 12, 75–82, 10.1002/asl.306, 2011.
Park, S.-H., Klemp, J. B., and Skamarock, W. C.: A Comparison of Mesh
Refinement in the Global MPAS-A and WRF Models Using an Idealized Normal-Mode
Baroclinic Wave Simulation, Mon. Weather Rev., 142, 3614–3634, 2014.Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J.,
Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G. K., Bloom,
S., Chen, J., Collins, D., Conaty, A., Da Silva, A., Gu, W., Joiner, J.,
Koster, R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P.,
Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz,
M., and Woollen, J.: MERRA: NASA's modern-era retrospective analysis for
research and applications, J. Climate, 24, 3624–3648,
10.1175/JCLI-D-11-00015.1, 2011.Salack, S., Klein, C., Giannini, A., Sarr, B., Worou, O. N., Belko, N.,
Bliefernicht, J., and Kunstman, H.: Global warming induced hybrid rainy
seasons in the Sahel, Environ. Res. Lett., 11, 104008,
10.1088/1748-9326/11/10/104008, 2016.Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Barker, D., Duda, M., Huang,
X.-Y., Wang, W., and Powers, J.: A Description of the Advanced Research WRF
Version 3, NCAR Technical Note NCAR/TN 475+STR, 125 pp.,
10.5065/D6DZ069T, 2008.Skamarock, W. C., Klemp, J. B., Duda, M. G., Fowler, L. D., Park, S.-H., and
Ringler, T. D.: A Multiscale Nonhydrostatic Atmospheric Model Using
Centroidal Voronoi Tesselations and C-Grid Staggering, Mon. Weather Rev.,
140, 3090–3105 10.1175/MWR-D-11-00215.1, 2012.Stevens, B., Giorgetta, M., Esch, M., Mauritsen, T., Crueger, T., Rast, S.,
Salzmann, M., Schmidt, H., Bader, J., Block, K., Brokopf, R., Fast, I.,
Kinne, S., Kornblueh, L., Lohmann, U., Pincus, R., Reichler, T., and
Roeckner, E.: Atmospheric component of the MPI-M earth system model: ECHAM6,
J. Adv. Model. Earth Syst., 5, 146–172, 10.1002/jame.20015, 2013.
Strandberg, G., Bärring, L., Hansson, U., Jansson, C., Jones, C.,
Kjellström, E., Kolax, M., Kupiainen, M., Nikulin, G., Samuelsson, P.,
Ullerstig, A., and Wang, S.: CORDEX scenarios for Europe from the Rossby
Centre regional climate model RCA4, SMHI Report Meteorology and Climatology
No. 116, 84 pp., 2014.Sylla, M. B., Giorgi, F., Coppola, E., and Mariotti, L.: Uncertainties in
daily rainfall over Africa: Assessment of gridded observation products and
evaluation of a regional climate model simulation, Int. J. Climatol., 33,
1805–1817, 10.1002/joc.3551, 2013.Sylla, M. B., Giorgi, F., Pal, J. S., Gibba, P., Kebe, I., and Nikiema, M.:
Projected changes in the annual cycle of high-intensity precipitation events
over West Africa for the late twenty-first century, J. Climate, 28,
6475–6488, 10.1175/JCLI-D-14-00854.1, 2015.Sylla, M. B., Elguindi, N., Giorgi, F., and Wisser, D.: Projected robust
shift of climate zones over West Africa in response to anthropogenic climate
change for the late 21st century, Climatic Change, 134, 241–253,
10.1007/s10584-015-1522-z, 2016.Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and
the experiment design, B. Am. Meteorol. Soc., 93, 485–498,
10.1175/BAMS-D-11-00094.1, 2012.van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard,
K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J. F., Masui, T.,
Meinshausen, M., Nakicenovic, N., Smith, S. J., and Rose, S. K.: The
representative concentration pathways: An overview, Climatic Change, 109,
5–31, 10.1007/s10584-011-0148-z, 2011.von Storch, H., Langenberg, H., and Feser, F.: A Spectral Nudging Technique
for Dynamical Downscaling Purposes, Mon. Weather Rev., 128, 3664–3673,
10.1175/1520-0493(2000)128<3664:ASNTFD>2.0.CO;2, 2000.Warner, T. T., Peterson, R. A., and Treadon, R. E.: A Tutorial on Lateral
Boundary Conditions as a Basic and Potentially Serious Limitation to Regional
Numerical Weather Prediction, B. Am. Meteorol. Soc., 78, 2599–2617,
10.1175/1520-0477(1997)078<2599:ATOLBC>2.0.CO;2, 1997.Willmott, C. J. and Matsuura, K.: Terrestrial Air Temperature and
Precipitation: Monthly and Annual Time Series (1900–2010),
http://climate.geog.udel.edu/~climate/html_pages/README.ghcn_ts2.html
(last access: 14 April 2018), 2012.
World Meteorological Organization: Guide to Climatological Practices, 3rd
Edn., available at:
http://www.wmo.int/pages/prog/wcp/ccl/documents/WMO_100_en.pdf (last
access: 14 April 2018), 2011.