Download litreview12forword_wm_review_9feb2012

The Current Status of Climate
Projections over West Africa
February 6, 2012 Karina Williams, Carlo Buontempo, Kate
Brown Reviewed by Wilfran Moufouma-Okia
Executive Summary
This note summarises the current status of climate projections for precipitation in Liberia and the Nimba
mountain range for the twenty-first century. Modelling the West African climate is extremely challenging since
there are many competing processes, which interact in a complex way. The amount and distribution of
precipitation is governed by the West Africa Monsoon (WAM) system, which influences the Inter-Tropical
Convergence Zone (ITCZ) and its associated rain-belt. Variations in the position of the ITCZ and the intensity of
the summer monsoon rain strongly influence the annual rainfall in Liberia and therefore a robust simulation of the
WAM system is essential if we are to have confidence in future projections of precipitation in this region.
We focus on three sets of climate projections:
•
The projections produced by phase 3 of the Coupled Model Intercomparison Projection (CMIP3) for the
Fourth Assessment Report of the Intergovernmental Panel on Climate Change. This is a multi-model ensemble of
projections, contributed by a many different climate modelling centres.
•
Projections from the Quantifying Uncertainty in Model Predictions (QUMP) ensemble. This is a perturbedphysics ensemble of projections based on the The Met Office Hadley Centre Coupled Model Version 3 (HadCM3)
global climate model.
•
Projections from the Ensembles-based Predictions of Climate Changes and Their Impacts (ENSEMBLES)
and African Monsoon Multidisciplinary Analyses (AMMA) projects, created from a variety of regional climate
models, driven by one of two global climate models.
We show that, although much progress has been made in this area, many of the major climate models are
unable to simulate the full behaviour of the WAM system over the course of the twentieth century. Moreover, a
successful description of the twentieth century West African precipitation may not necessarily imply a more
reliable projection for the twenty-first century, since there are indications that the dominating processes will
change as the global climate changes. The available projections also differ significantly from each other. We
therefore conclude that the change in precipitation in the Nimba mountain range over the course of this century
is uncertain.
1 Introduction
Climate models are essential tools for climate adaptation and impact studies. Simulations of the Earth’s climate
are extremely sophisticated and used in many regions of the globe to ensure that current climate variability and
anticipated future climate changes are taken into consideration in the strategic planning activities. However,
climate projections are also subjects to a wide number of inherent limitations which arise from the lack of
understanding of the future emissions of greenhouse gases, uncertainty in the description of key physical
processes that occur at the scales smaller than the model spatial resolution and not directly resolved by climate
models, and the mechanisms responsible for the inter-annual and longer timescales variations of the climae.
The limitations of general circulation models (GCMs) are particularly striking in West Africa where the
current models poorly capture the key features of present-day variations of the climate, notably its associated
precipitation pattern and temporal variability. These limitations will necessarily have an impact on the suitability
and reliability of the future climate projections for the Nimba mountain region within Liberia.
This report examines the current status of climate model projections over West Africa, particularly in the
areas surrounding the Fouta Djallon massifs and Nimba mountains region within Liberia. Section 2 summarises
the main characteristics of the variations of precipitation during the twentieth century, which are expected to be
captured by a climate model. Section 3 provides and overview of the available climate projections for West Africa
with a discussion on their general features and limitations. We will focus on three different data collections of
climate model projections. Bearing this in mind, we will examine their projections for the future and discuss further
the precipitation changes which Liberia may experience.
2 Rainfall in West Africa
The Fouta Djallon and the Nimba mountains region are located near the south-western flank of the West African
coast. The area is under the south-westerly wind regime between the surface and approximately the altitude of
800 hPa, and under the easterly wind above 800 hPa. The precipitation is governed by the seasonal migration of
1
the Inter-Tropical Convergence Zone (ITCZ); that is a quasi-zonal band of moist convection associated with high
2
values of precipitation . As the ITCZ migrates northwards in spring, a low latitude large-scale circulation pattern
3
4
develops from the meridional boundary layer gradient of dry and moist static energy between the warm subSaharan continent and the tropical Atlantic Ocean, which is known as the West Africa Monsoon (WAM) system.
This is a thermally direct land-ocean-atmosphere coupled circulation which combines several wind components.
The south-westerly monsoon flows in low levels, African Easterly Jet (AEJ) in mid-troposphere, and the Tropical
Easterly Jet in upper troposphere. The WAM results from the combination and scale interactions between moist
convection
in the ITCZ, dry convection in the transverse circulation associated with the Saharan heat low,
easterly waves, AEJ, TEJ, and moisture flux convergence (Lafore et al., 2011). Due to strong gradients of energy
and humidity, the convection is organised and maximised in the ITCZ. Fast moving convective systems (MCSs)
are also important source and account for about half of the rainfall in the wetter Guinea coast and Soudanian
regions (~5-12ºN), and for most of the rainfall over the Sahel region (~12-20ºN).
Good model performances in capturing both the present-day variations of the WAM climate, the multiscale interactions between its various components, and the seasonal migration of the ITCZ are critical to ensure
the suitability of climate models for impact studies and to increase the confidence in future climate models
projections over West Africa. This section describes the main characteristics of the WAM climate which we will
use later to validate the climate models performances.
1
parallel to lines of latitude
There are number of ways to define the position of the ITCZ. We will use the location of the precipitation
maximum, since this is easy to identify in climate simulations.
parallel to lines of longitude
The dry and moist static energy of an air parcel are thermodynamic properties which depend on variables such
as its temperature, height above sea level and (in the case of moist static energy) its water vapour content.
2
3
4
Figure 1: Mean annual precipitation for the period 1901-2009 using the CRU observational data [Mitchell and
Jones, 2005].
Figure 2: Number of years in which one or more stations in the grid box contributed precipitation data to the
CRU observational data [Mitchell and Jones, 2005] (the range of the CRU data is 19012009).
Figure 1(a) shows the distribution of annual rainfall across West Africa and averaged from Jan 1901 to
December 2009. It uses the CRU TS 2.1 data set (CRU), which is a database of monthly climatic variables
constructed from observational data [Mitchell and Jones, 2005] and interpolated to a 0.5◦ grid. Due to the relative
scarcity of the observational stations network in West Africa, compared to Europe (the location of stations
contributing to the CRU precipitation data set is shown in Figure 2), local precipitation characteristics are not well
represented. However, the CRU data set is very useful for looking at large scale and long-term precipitation
patterns over the course of the twentieth century. Figure 1 shows two costal regions with particularly high annual
rainfall, one centred on the coastal areas of Sierra Leone and Liberia, and the other on the coastal areas of
Cameroon. These two costal regions are located near to the main reliefs of West Africa, notably the Fouta Djallon
Massif in Guinea and the Adamaoua Massif in Cameroon. The rainfall height varies strongly in space and
displays a strong gradient southwest of the Fouta Djallon massif, which is the water tower and the primary source
of the West African hydrographic network Also visible is the transition zone between the arid Sahara desert and
the wetter climate of tropical Africa Sahel region, known as the Sahel, which stretches across Africa at
approximately 12ºN to 20ºN.
Due to the migration of the ITCZ, the precipitation pattern across West Africa shows strong seasonal
variability. Consequently the climate of the costal areas near the Fouta Djallon Massif is wet equatorial with
alternation of a dry season from November to March and a rainy season from April to October, providing an
average rainfall height higher than 2500 mm. The coastal rainy season moves regularly northward until May and
is characterised by a progressive increase of the moist air from the ocean into the continent, associated with the
seasonal migration of the ITCZ [Lebel et al., 2003]. In May-June, the ITCZ typically shifts abruptly from a quasistationary position at 5º N to another quasi-stationary location at 10º N in July-August, before retreating south in
September-October [Sultan and Janicot, 2000]. This seasonal migration of rainfall with the latitude is
demonstrated in Figure 3, which shows the average precipitation during the months of June, July and October in
the CRU data set. This abrupt jump of rainfall maxima in mid-June is illustrated by the time-latitude Hovmoller
plot in Figure 4. This characteristic pattern is due to a combination of factors, including a westward travelling
monsoon depression [Grodsky and Carton], the local orography [Drobinski et al., 2005], surface albedo [Ramel et
al., 2006] , the dynamics of the Saharan heat low [Sultan and Janicot, 2003, Sijikumar et al., 2006], the
development of the oceanic cold tongue in the Gulf of Guinea [Nguyen et al., 2011].
In addition to successfully capturing the structure and spatial characteristics of precipitation in West Africa in a
typical year, a successful climate model should be able to reproduce the strong variation which is seen on
interannual and decadal timescales, such as the severe drought which was experienced in the Sahel in the late
1960s to late 1980s. As shown in Figure 5 (left), parts of Liberia experienced the largest absolute decrease in
annual precipitation seen in the region. The drought is expressed in terms of the decrease in precipitation as a
fraction of the climatological value, defined here using the CRU data for 1901-2009, which emphasizes the effect
of the drought in the Sahel region. Although this region experienced a lower drop in absolute terms as compared
to Liberia, this drop represented a larger proportion of the total rainfall received.
There is a strong consensus that anomalies in sea surface temperatures (SSTs) play an important role in the
variations of West African precipitation on interannual and decadal timescales. The north-south SST contrast in
the tropical Atlantic is an important factor, with anomalously large SSTs in the Gulf of guinea associated both with
wetter conditions in the Guinea coasts and drier conditions further north in the Sahel [Vizy and cook, 2001;
Hastenrath, 1990]. The SSTs in the Indian ocean and their relation to the SST anomalies in the Pacific are also a
contributing factor [Palmer,1986; Shinoda and Kawamura,1994; Rowell, 2001] as are other SST patterns in the
Pacific, such as those associated with the El Nino-Southern Oscillation5 [Janicot et al.,1996; Rowell, 2001] and
SST anomalies in the Mediterranean [Rowell, 2003; Jung et al.,2006]. Recently, a study by Ackerley et al. [2011],
has provided further evidence for the argument that the change in sea surface temperatures responsible for the
Sahelian drought [Folland et al., 1986, Giannini et al., 2003] is linked to the emission of sulphate aerosols by
industrial countries. Other mechanisms, such as land-surface feedback [ Charney et al.,1975; Zeng et al.,1999]
and desert dust [Prospero and Lamb, 2003;Yoshioka et al.,2007] are also thought to be relevant.
In addition to interpolated observational data sets such as CRU, other valuable tools for understanding the
climate of West Africa are meteorological reanalyses, such as the ERA-interim reanalysis by ECMWF [Dee et al.,
2011] and the NCEP-NCAR reanalysis [Kalnay et al., 1996]. In a reanalysis, past observations are assimilated
consistently into a forecast model, to produce a data set representing the status of the atmosphere system at
regular time intervals in the past. This reconstruction of the atmosphere can be used to study the structure of
climatological features such as the WAM system (although, since these reconstructions have been mediated by a
model, they will inherit any model biases). For example, Cook and Vizy [2006] used the three dimensional data on
wind speed and direction in the NCEP-NCAR reanalysis to identify three southward wind maxima over West
Africa during the monsoon season, which they then looked for when evaluating the representation of the WAM in
model simulations. In addition, as we will discuss in Section 3.4,
5
NEED DESCRIPTION
Figure 3: Mean precipitation in the months of June, July and October for the period 1901-2009 using the CRU
observational data [Mitchell and Jones, 2005].
© Crown Copyright 2012 5 Figure 4: Hovm¨
oller diagram showing the zonal pattern of precipitation using the GPCP 1-Degree Daily
◦
observational data [Huffman et al., 2001]. It has been produced by averaging the daily precipitation at each 1
latitude interval over 20W-20E for each day in the years 1996-2007. It illustrates the migration of the rain band
(which marks the position of the ITCZ) over the course of the year, including the shift in the maximum from ∼ 5N
in May-June to ∼ 10N in July-August.
Figure 5: Left: Mean annual precipitation anomaly in 1970-1989, calculated by subtracting the climatological value
(defined using the 1901-2009 data shown in Figure 1) from the mean annual precipitation in 1970-1989. Right:
The relative anomaly, calculated by dividing the mean annual precipitation anomaly in 1970-1989 (as given in the
left plot) by the climatological value. Both plots use the CRU observational data [Mitchell and Jones, 2005].
Comparison with Figure 2 shows that the local maximum on Liberian coast occurs in the grid box containing the
observational stations, which indicates that this could be an artefact produced by the scarcity of CRU observations
in the rest of Liberia.
meteorological reanalyses can be used to drive regional climate models (RCMs) when investigating the ability of
the RCM to simulate the past climate.
3 Overview of climate projections
This section describes the general features and limitations of current climate models, before providing more detail
on the three different ensembles of climate projections used in our analysis. There is also a discussion on the
models performance in capturing the past and present climate variability in West Africa and whether there is any
consensus in their projections for the twenty-first century climate.
.
3.1 Introduction to climate modelling
A climate model is a mathematical representation of the climate system. It consists of a set of equations modelling
the various physical and dynamical processes, such as radiative transfer, atmospheric and ocean circulation,
cloud formation and precipitation, and the interaction and feedbacks between these processes and other factors,
such as changes in atmospheric composition (including the concentration of greenhouse gases), vegetation and
ice sheets. This set of equations are solved at finite intervals in space and time. The size of these intervals
(resolution) is limited by the available computing power. Many important processes, such as cloud formation and
the impact of local topography, occur at scales smaller than the grid resolution and so these processes have to be
parametrised within the model.
Climate models are subject to certain inherent uncertainties:
Forcing uncertainty
Changes in the radiative balance of the atmosphere (‘forcing’) arise from, for example, changes in the incident
solar radiation, changes in the concentration of greenhouse gases, ozone and aerosols in the atmosphere and
land-use changes and can be natural or human induced. It is not possible to accurately predict the forcing which
will be experienced in the future.
An important element of forcing is the increase in the concentration of greenhouse gases as a result of human
6
activity . The IPCC produced a Special Report on Emission Scenarios [Nakicenovic et al., 2000], which identified a
number of possible ‘storylines’ which took into account driving forces such as demographic, social, economic,
technological, and environmental developments. Possible emission ‘scenarios’ were defined, based on these
scenarios. These are described in more detail in Appendix A. These scenarios have been widely use by the
climate modelling community when producing climate projections. There are additional uncertainties when relating
the future emissions to the future concentrations in the Earth’s atmosphere.
“Global atmosphere concentrations of carbon dioxide, methane and nitrous oxide have increased markedly as
6
a result of human activities since 1750, and now far exceed pre-industrial values [...] Most of the observed
increase in global average temperatures since the mid-20th century is very likely due to the observed increase in
anthropogenic greenhouse gas concentrations” [Alley et al., 2007] In the IPCC 4th Assessment Report, ‘very
likely’ indicates > 90 % probability.
Uncertainty arising from natural variability
The climate has many internal modes of variability, arising from natural cycles such as the El Nino Southern
Oscillation, which has a period of 2-7 years. The global weather experienced in any particular year therefore
depends not only on the long-term trend in the climate, such as greenhouse gas-induced warming, but also on the
timing of these natural cycles. In order to study the long-term trend in the climate, it is therefore useful to average
the results over a time period (typically 30 years) which is greater than most of the natural cycles. However, note
that there are known cycles that act over larger time-scales and that additional natural oscillations are still being
identified in observations.
Model uncertainty
An additional source of uncertainty arises from the representation of key climate processes within the model. No
model is an exact representation of the real world and different modelling centres make use of different
formalisms and approximations. For example, the parametrisation of cloud formation varies from institution to
institution. The parametrisations also involve parameters which have been fixed based on the current scientific
understanding of the process and these parameters will themselves have an associated uncertainty.
One way of addressing model uncertainty is to use projections created by models from a variety of institutions
(a ‘multi-model ensemble’). Model validation, where climate models are tested to see whether they can reproduce
characteristics of the current and past climate, is very important. A common method of exploring model
uncertainty is to run one model with many different values for the parameters describing key processes (a
‘perturbed physics ensemble’). The examples we will discuss in the subsequent sections will explore all three of
these strategies.
3.2 CMIP3 Projections
The CMIP3 multi-model data set was produced by phase 3 of The Coupled Model Intercomparison Project, in
order to inform the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). It is a
co-ordinated set of atmosphere-ocean general circulation model (AOGCM) experiments, organized by the World
Climate Research Programme (WCRP) Climate Variability and Predictability (CLIVAR) Working Group on
Coupled Models (WGCM) Climate Simulation Panel [Meehl et al., 2007], with 16 modelling groups from 11
countries contributing to the original data set, using 23 models. The experiments included simulations of the
twentieth century climate and projections for the twenty-first century climate, based on the emission scenarios
described in Appendix A. This data set has been made freely available for analysis by the international community
and remains one of the largest and most comprehensive multi-model experiments ever attempted.
Figure 6: “Temperature anomalies with respect to 1901 to 1950 for four African land
regions for 1906 to 2005 (black line) and as simulated (red envelope) by MMD models
incorporating known forcings; and as projected for 2001 to 2100 by MMD [Multi-Model
Data set] models for the A1B scenario (orange envelope). The bars at the end of the
orange envelope represent the range of projected changes for 2091 to 2100 for the B1
scenario (blue), the A1B scenario (orange) and the A2 scenario (red). The black line is dashed where
observations are present for less than 50% of the area in the decade concerned.” Figure and caption reproduced
from the IPCC AR4 Christensen et al. [2007].
Figure 7: “Temperature and precipitation changes over Africa from the MMD-A1B
simulations. Top row: Annual mean, DJF and JJA temperature change between 1980 to 1999 and 2080 to
2099, averaged over 21 models. [...] Bottom row: number of models out of 21 that project increases in
precipitation.” Figure and caption reproduced from IPCC AR4 Christensen et al. [2007].
Figure 6 has been reproduced from Christensen et al. [2007] and shows the projected temperature changes
in 2001 to 2100 (with respect to 1901 to 1950) for the models in the CMIP3 data set, for the B1 (blue), the A1B
(orange) and the A2 (red) emission scenarios (see Appendix A for scenario definitions). The projected
temperature depends on the emission scenario used, but in all regions is expected to increase by between
◦
◦
approximately 1 C and 7 C. The width of the coloured bar represents the 5 to 95% confidence range of the
model results; as we can see the models give a very consistent picture for temperature. The ‘Sahara’ subregion (defined as 35S,10E to 12S,52E) is projected to have slightly higher temperature increases than the
‘West Africa’ sub-region (defined here as 12S,20W to 22N,18E) for all three emission scenarios. The top plots
in Figure 7 (also reproduced from Christensen et al. [2007]) gives further regional detail for the A1B scenario
and shows that the interior of West Africa has a higher projected temperature increase than the coast.
The model consensus for precipitation over Africa in the twenty-first century is much poorer, with a large
spread between projections from different model integrations for the same emission scenario. In such a
situation, it is useful look at the ability of individual model integrations in representing the climate of the
twentieth century, with the aim of reducing the spread of results by discarding projections from models with a
particularly poor representation of key physical processes.
Cook and Vizy [2006] note that about a third of the model integrations (including both ECHAM5/MPIOM and
7
the Met Office Hadley Centre Coupled Model (UKMO-HadCM3) ) fail to generate a West African Monsoon,
because the precipitation maximum remains off the coast. They note that many simulations fail to reproduce the
pattern of total precipitation seen in Figure 1 of a maximum on the
We use the naming scheme defined at http://www-pcmdi.llnl.gov/ipcc/model documentation/ipcc model
documentation.php
7
west coast and on the east side of the Guinean Coast. Some models miss the former
maximum, some miss the latter and some add in extra maxima. In a subset of the models, the onshore flow is too
deep, extending into the middle troposphere. The circulation pattern was also examined, and it was found that a
selection of the models are able to capture the three southerly wind maxima successfully, although this includes
simulations which place the centre of monsoon circulation in the southern hemisphere instead of the North, so that
there is rising instead of sinking motion over the Gulf. In addition, Cook and Vizy [2006] were able to show that a
number of the models were able to reproduce some of the characteristics of the dipole structure between
precipitation in the Sahel and the Guinean Coast and the SST anomalies in the Gulf of Guinea.
Cook and Vizy [2006] use this criteria to identify the three most successful simulations (GFDLCM2.0,
8
MIROC3.2(medres) and MRI-CGCM2.3.2) and examine their projections for the A2 and B1 scenarios . These
three model simulations project a dramatically different West African climate by the end of the twenty-first century,
as illustrated in Figure 8. GFDL-CM2.0 projects strong drying both the Guinean Coast and the Sahel, due to
◦
pronounced surface warming north of of 12 N, leading to a strong temperature gradient, which strengthens the
9
African easterly jet . The MIROC3.2(medres) projection show a dramatically wetter Sahel and drier Guinean
Coast. This is due to large increases in the sea surface temperature in the Gulf of Guinea, which breaks the
monsoon and results in westerly flow enhancements across much of West Africa, with an additional northerly flow
enhancement on the Guinean Coast. Compared to the GFDL-CM2.0 and MIROC3.2(medres) projections, the
precipitation changes projected by the MRI-CGCM2.3.2 model are more modest. For both the A2 and B1
scenarios, the model projects a small increase in precipitation in the Guinean Coast. The projection under the A2
scenario shows noticeable drying in the Sahel, which does not occur for the B1 scenario. Unlike the GFDL-CM2.0
and MIROC3.2(medres) projections, the twenty-first century West Africa monsoon system in the MRI-CGCM2.3.2
integrations retains many of its current characteristics. It is interesting to note from Figure 8 that the projected
relative change in precipitation in the grid-box over the Nimba Mountains do not show the rather extreme spread
which these three models give for the Sahel, although, as we will see, this greatly underestimates the spread of
the CMIP3 precipitation projections as a whole in this region.
Other authors have used different criteria to determine which of the CMIP3 simulations are most successful at
reproducing the climate of West Africa in the twentieth century. For example, Joly et al. [2007] focusses on the
ability of model integrations to capture the relation between SST anomalies and the WAM system using a
maximum covariance analysis in 12 CMIP3 models. They look particularly at the ENSO teleconnection (i.e. the
correlation with SSTs in the Pacific Ocean) and the Gulf of Guinea SST anomalies. The Pacific teleconnection is
judged realistic in five of the models considered (including MIROC3.2(medres), MRI-CGCM2.3.2 and UKMOHadCM3) and found to be absent in two of the models studied (including GISS ER) and to exist with the wrong
8
The GISS EH simulation also performs successfully by this criteria, but had no data available for the A2 and B1
scenarios.
The African easterly jet is a jet stream (concentrated air current) which occurs over the Sahara in the summer.
9
Figure 8: Projected precipitation change (2070-2100 with respect to a baseline of 1961-1990) for the A2 scenario
as produced by three of the CMIP3 models: GFDL-CM2.0 (top row), MIROC3.2(medres)(middle row) and MRICGCM2.3.2(bottom row). The plots in the left column show the absolute difference between 2070-2100 and the
baseline values whereas the right column show the relative difference i.e. the absolute difference divided by the
baseline values.
Figure 9: The region defined as the ‘Liberia region’ in this report, which stretches from 3.75N to 11.25N and
5.625W to 13.125W (chosen to correspond to 6 gridboxes in UKMO-HadCM3). The colour bar indicates height of
the orography with respect to sea level, with a spatial resolution of approximately 50km in the longitude and
latitude directions. This orography data was used in the SMIRCA ENSEMBLES-AMMA runs and can be
downloaded at http://ensemblesrt3.dmi.dk/.
Figure 10: Blue crosses: the projected precipitation change versus projected projected temperature change for
2070-2100 with respect to a baseline of 1961-1990 for the A1B scenario for 17 of the CMIP3 models in the Liberia
region (as defined in Figure 9) for each month. The blue box plots indicate the mean and the spread of the runs.
The points for GFDL-CM2.0, MIROC3.2(medres) and MRI-CGCM2.3.2 are hilighted with black, red and green
circles respectively.
sign in five of the models (including ECHAM5/MPI-OM and GFDL-CM2.0). Only one
model (GFDLCM2.0) is found to capture a teleconnection with the Gulf of Guinea SST independent of the
Pacific. The response of West African precipitation to the inter-hemispheric SST pattern was found to be
captured in only five of the twelve models examined, including MIROC3.2(medres), ECHAM5/MPIOM, MRICGCM2.3.2, UKMO-HadCM3.
Another study, Lau et al. [2006], evaluated the CMIP3 model runs based on their ability to capture the
Sahelian drought. They found that only eight of the models were able reproduce the Sahelian drought, with
GFDL-CM2.0 judged the best, which requires the correct coupling between West African precipitation and
Indian Ocean SST and Atlantic Ocean SST anomalies (they also found land surface feedback to be
important). Seven models were found to produce excessive rainfall over Sahel during the observed drought
period (including MIROC3.2(medres), ECHAM5/MPI-OM, UKMO-HadCM3 and MRI-CGCM2.3.2), and four
models showed no significant deviation from normal.
It is clear that many CMIP3 models still have deficiencies in their modelling of the twentieth-century West
African climate and that the future projections of models which perform relatively well in the present day
climate can differ dramatically. This has been investigated in detail by Biasutti et al. [2008], who found that,
while the sensitivity of the twentieth century Sahelian rainfall to sea surface temperatures was broadly
captured by the CMIP3 simulations, the difference in their rainfall projections for the twenty-first century
could not be explained simply by differences in the projected sea surface temperatures. If other mechanisms
become important in the twenty-first century, the successful reproduction of the twentieth century climate of a
model does not determine how well it is able to simulate twenty-first century climate.
Given these limitations, it is still possible to draw some conclusions from the CMIP3 projections. For
example, Biasutti and Sobel [2009] found that, despite the huge differences in the seasonal rainfall,
analysing the projections on a month-by-month basis showed that there was an robust agreement that the
rainy season in the Sahel would be shifted to later in the year.
Figure 10 shows the change in average annual precipitation against the change in temperature for 17 of the
10
11
CMIP3 models for the Liberia region (defined in Figure 9) projected for 2070-2100 compared to 1961-1990 for
the A1B scenario, including GFDL-CM2.0 (black circle), MIROC3.2(medres) (red circle) and MRI-CGCM2.3.2
(green circle). As we would expect from Figure 6 and Figure 7, all models show a projected increase in
temperature. However, the projected precipitation diverges markedly, with a maximum spread in July. It is
interesting to note that, out of the three projections which we have discussed in detail (GFDL-CM2.0,
MIROC3.2(medres) and MRI-CGCM2.3.2), GFDL-CM2.0 and MRI-CGCM2.3.2 are not outliers. This is perhaps
surprising in the case of GFDLCM2.0, since, as we have discussed, it projects severe drying in the Sahel. It is
therefore important that these three models are not taken as an approximation of the spread in CMIP3 projections
in
10
Where multiple runs are available for a model, we take the first run
This standard WMO climatological baseline [WMO, 1988] was recommended in Carter et al. [1994] for
use in climate impacts and adaptation studies.
11
the Liberia region, as would possible for the Sahelian region. MIROC(medres), as
expected, shows a tendency towards a lower precipitation projection in the Liberia region than the ensemble
mean, particularly in July and September, when it projects more drying than any other CMIP3 model run shown.
3.3 QUMP
The QUMP projections were developed under the Quantifying Uncertainty in Model Predictions program run by
the Met Office Hadley Centre. Whereas the CMIP3 data set utilises a multi-model ensemble, the QUMP
projections are from a perturbed-physics ensemble [Murphy et al., 2004, Stain-forth et al., 2005], where one
model is run with many different values for the parameters controlling key physical and biogeochemical
processes. The central values and the ranges of these parameters are chosen, based on the current scientific
knowledge. The QUMP [Murphy et al., 2007, Collins et al., 2011] ensemble has 17 members and uses the UKMOHadCM3 coupled ocean-atmosphere global climate model run with adjustments to the heat and water fluxes to
correct for biases in the sea surface temperature and salinity (we will denote the unperturbed QUMP run by
‘HadCM3Q0’). Perturbed-physics ensembles can lend themselves better to probabilistic interpretations as they
allow more control over exactly what is being varied across the ensemble.
For many regions, the range of climate futures projected by the QUMP ensemble is greater than or equivalent
to the range of the CMIP3 projections [Collins et al., 2011]. For these cases, QUMP is extremely useful for
investigating the uncertainties of the projections. However, for the Sahel and the Liberia region, the range of the
QUMP ensemble projections does not encompass the CMIP3 projections, as can be seen in Figure 11, which is
similar to Figure 10, but also includes the QUMP ensembles members (black). Interestingly, the QUMP ensemble
even fails to encompass the full range of CMIP3 temperature projections, particularly in the first half of the year,
where the QUMP ensemble show a preference for larger temperature increases as compared to the CMIP3
ensemble. For some months, such as August, the QUMP ensemble extends the range of projected precipitation
seen in the CMIP3 projections, which could be a valuable indication that the CMIP3 range in this case does not
sufficiently represent the current model uncertainty in climate projections over the Liberia region. The QUMP
ensemble exhibits a preference for a reduction in precipitation in February-September and an increase in October
to January, implying a delayed rainy season, which is especially interesting in the context of the trend towards a
delayed rainy season in the Sahel which was identified by Biasutti and Sobel [2009] in the CMIP3 projections.
In Figure 11, the unperturbed QUMP run HadCM3Q0 and the HadCM3 run used in CMIP3 are highlighted by
red circles. Recall that these runs differ because HadCM3Q0 has had SST biases corrected using flux
adjustments. It can be seen that this difference results in a considerably drier projection over the Liberia region.
Figure 12 illustrates the difference in annual precipitation between the HadCM3 run used in CMIP3 (left) and
unperturbed QUMP run HadCM3Q0 (right) for the A1B scenario (2070-2100 w.r.t. 1961-1990). The strong
influence of the SSTs leads to two very different projected patterns of precipitation across West Africa. This was
also found by McSweeney et al. [2012] for South East Asia, who concluded that, while flux adjustments may give
a more realistic baseline, it is unclear whether they produce more reliable projections.
3.4 ENSEMBLES-AMMA
Global Climate Models are limited by their relatively coarse resolution. One method of deriving more detailed
regional information is by using ‘nested’ Regional Climate Models (RCMs), which are driven at the boundaries by
information from a Global Climate Model. The limited spatial extent of an RCM enables it to be run at a higher
resolution that the global models and are thus able to simulate small-scale structure missed by the GCM (recall
UKMO-HadCM3 covered the Liberia region with six grid boxes). RCMs also allow the flexibility of choosing the
values of the internal model parameters to be those most suited to the particular region under study, rather than
requiring that the choice of value is suitable for all regions of the globe (see e.g. [Rockel and Geyer, 2008]). On
the other hand, if there are large-scale processes which are not captured well by the driving GCM, this will be
inherited by the RCM.
There has been considerable recent progress in understanding and modelling the current West African climate
using individual Regional Climate Models (see Paeth et al. [2011] for a review). The first multi-model ensemble of
RCM runs over West Africa was coordinated by the Projections from the Ensembles-based Predictions of Climate
Changes and Their Impacts (ENSEMBLES) and African Monsoon Multidisciplinary Analyses (AMMA) projects
[Van der Linden and Mitchell, 2009]. One of two Global Climate Models to set the boundary conditions:
HadCM3Q0 or ECHAM5-r3. The ENSEMBLES-AMMA resolution is 50km and A1B was chosen as the emission
scenario. Figure 9 shows the orography used by SMIRCA, as an example of the level of orographic detail which
can be resolved at ENSEMBLES-AMMA resolution.
Additional ENSEMBLES-AMMA runs were also carried out which were driven by the ERA-interim reanalysis
rather than a GCM, over the period 1990-2007, in order to evaluate the ability of each RCM to capture various
aspects of the current West African climate. As discussed in Paeth et al. [2011], despite the common boundary
conditions, the individual ENSEMBLES-AMMA members runs produced significantly different climatologies for
1990-2007, with different spatial distributions of errors when compared to observations from the Global
Precipitation Climatology Centre (GPCC). These error distributions were also markedly different from those of the
ERA-interim analysis with respect to the GPCC observations, which implies that the models are not just inheriting
the biases from the driving conditions at the region boundaries. However, most of the ENSEMBLESAMMA runs
do show a dry bias over Liberia and the Nimba Mountains, with respect to the GPCC observations. Paeth et al.
[2011] suggest that this is due to the convection scheme in off-shore gridboxes being incited too strongly by
orography in the coastal grid boxes, which could possibly be
Figure 11: Projected precipitation change versus projected projected temperature change for 20702100 with
respect to a baseline of 1961-1990 for the A1B scenario for the CMIP3 runs (blue, as in Figure 10) and
QUMP runs (black) in the Liberia region(as defined in Figure 9) for each month. The box plots indicate the
mean and the spread. The points corresponding to the unperturbed QUMP run HadCM3Q0 and the HadCM3
run used in CMIP3 are hilighted by red circles.
c19 Figure 12: Projected precipitation change (2070-2100 with respect to a baseline of 1961-1990) produced for
the A1B scenario by the HadCM3 runs in CMIP3 ensemble (left) and the HadCM3 runs used in the QUMP
ensemble (right). The projections differ because the HadCM3 runs used in the QUMP ensemble include flux
adjustments.
solved by increasing the resolution of the simulations. It is interesting to note that the ERA-interim reanalysis has
a predominantly wet bias in this region.
In D35, the ability of the RCMs to reproduce the seasonal cycle of the WAM is examined. The three phases of
the monsoon (as discussed in Section 2) are reproduced by the models but significant discrepancies in the
magnitude and timing of the WAM are identified. In particular, many models (including HadRM3) have a wet bias
º
at 5º N in the first phase of the monsoon system, as discussed above. D35 also identify common biases in the
timing of the phases of WAM system, such as an early end to the first phase, which they suggest are inherited
from the ERA-interim boundary conditions.
The projections of the ENSEMBLES-AMMA ensembles for precipitation over West Africa in the 2001-2050
period for the A1B scenario show substantial variation, both between different ensemble members and spatially
for individual ensemble members [Paeth et al., 2011], with projections ranging from sizable increases to sizable
decreases. Since there are large differences between ensemble members driven by the same GCM, this suggests
that these characteristics are not simply inherited from the boundary conditions. In Figure 13 we show the
ENSEMBLE-AMMA projections for the Liberia region in 2030-2050 with respect to 1990-2010 for the A1B
scenario (the choice of time period is determined by the availability of the data), with ensemble members driven
by HadCM3Q0 in orange crosses and ECHAM5-r3 in orange diamonds. The runs which will be used in our
extreme value analysis, HadRM3P and SMHIRCA3, are highlighted in red and blue respectively. The bar plots
show the mean and spread for the entire ENSEMBLES-AMMA ensemble (i.e. they are calculated from RCM runs
driven by both HadCM3Q0 and ECHAM5-r3). It can be seen that these projections do not show a consensus over
whether the precipitation will increase or decrease over this area in this time period. In addition, the independence
of the projection from the driving GCM is also illustrated .
Figure 14 shows a comparison between the CMIP3 and QUMP projections for the Liberia region for the A1B
scenario for 2070-2100 (w.r.t. a baseline of 1961-1990) and the three ENSEMBLESAMMA runs which have data
available for these time periods (HadRM3P, INMRCA3 and SMHIRCA, which are all driven by HadCM3Q0). The
temperature projections for these three runs are within the range of the CMIP3 and QUMP temperature
projections. However, these three runs do not all have precipitation projections which lie within the ranges given
by CMIP3 and QUMP. This implies that using simply the CMIP3 and QUMP ranges of precipitation projections
may be an underestimate of the current model uncertainty.
The ENSEMBLES-AMMA runs shown in Figure 13 did not include the effect of land12
cover changes , which could arise from human activity such as agriculture, shifting cultivation, pasture,
urbanization, and transport infrastructure or as a natural response to climate change in the region. When REMO
was re-run with assumed land-cover assumptions based on the A1B and B1 story-lines for future population
growth and urbanization in Africa, a large and extensive drying trend was generated in the twenty-first century
projection in both scenarios [Paeth et al., 2011, 2009].
4 Conclusion
Considerable progress has been made in modelling the West African climate in recent decades. However, the
current generation of models still have difficulties capturing all aspects of the climate of this region, leading to
dramatically different projections for the precipitation changes in different greenhouse-gas and sulphur emission
pathways, with a lack of consensus even on the sign of the change. It is therefore difficult to arrive at
conclusions on how precipitation will change in the Nimba Mountains over the course of the century.
Currently, model centres across the world are producing CMIP5, which will inform the fifth assessment report
of the IPCC (AR5). There are already indications that this collection of GCM models will show substantial
improvements in the modelling of the West African Monsoon system (see
e.g. CSRP [2011]). In addition, the progress made by the African Monsoon Multidisciplinary Analysis [Lafore et al.,
2011], including observations of the monsoon taken by the during their intensive field campaign in 2006 [Janicot et
al., 2008], will be very valuable for tuning model parametrisations and providing a reference for model validation.
There is ongoing research into integrating other important influences into the model projections, such as landcover changes.
The immediate successor to the ENSEMBLES-AMMA project will be A COordinated Regional climate
Downscaling EXperiment (CORDEX) Giorgi et al. [2009], sponsored by the World Climate Research Programme
(WCRP), which will create an ensemble of dynamical and statistical down-scaling models, driven by the CMIP5
GCMs. The initial focus will be on a 50km resolution grid over Africa. Therefore, while the projected twenty-first
century climate is, as yet, uncertain, it is the subject of much current work, which will hopefully lead to a greater
consensus between model projections in the next few years.
12
i need to be absolutely sure!
Figure 13: Projected precipitation change versus projected projected temperature change for 20702100 with
respect to a baseline of 1961-1990 for the A1B scenario for the ENSEMBLES-AMMA runs driven by
HadCM3Q0 (orange crosses) and ECHAM5-r3 (orange diamonds) in the Liberia region (as defined in Figure
9) for each month. The box plots indicate the mean and the spread. The HadRM3P and SMHIRCA3
projections are hilighted in red and blue respectively.
c22
Figure 14: Projected precipitation change versus projected projected temperature change for 20702100 with
respect to a baseline of 1961-1990 for the A1B scenario for three of the ENSEMBLESAMMA (orange crosses) in
the Liberia region (as defined in Figure 9) for each month. The box plots indicate the mean and the spread of the
CMIP3 projections (blue) and QUMP projections (black).
A Emission Scenarios
The SRES scenarios [Nakicenovic et al., 2000] predict greenhouse gases emissions based on different economic,
technological, and social ‘storylines’. There are six illustrative scenarios, belonging to four families: A1, A2, B1,
B2. We will outline the three scenarios used in this report.
A.1 A1B
The major themes of the A1 storyline are convergence among regions, capacity building and increased cultural
and social interactions. It describes very rapid economic growth, with the rapid introduction of new and more
efficient technologies, a global population which peaks in the middle of the twenty-first century and a substantial
reduction in regional differences in per capita income.
The A1B scenario assumes that the technological change will lead to a balance between fossil-intensive and
non-fossil energy sources.
A.2 A2
The major themes of the A2 storyline is self-reliance and preservation of local identities. It has slower and more
fragmented economic development, per capita economic growth and technological change and a continuously
increasing population.
A.3 B1
The B1 storyline describes a world which rapidly moves towards a service and information economy, with an
emphasis on global solutions to economic, social and environmental sustainability, a reduction in material intensity
and the introduction of clean and resource-efficient technologies. It has the same population growth as the A1
storyline.
References
D3.5.3 report on RCM evaluation for the AMMA region. Technical report. URL http://www.ensembleseu.org/.
Duncan Ackerley, Ben B. B. Booth, Sylvia H. E. Knight, Eleanor J. Highwood, David J. Frame, Myles R. Allen, and
David P. Rowell. Sensitivity of Twentieth-Century sahel rainfall to sulfate aerosol and CO2 forcing. J. Climate,
24(19):4999–5014, May 2011. doi: 10.1175/JCLI-D-1100019.1. URL http://dx.doi.org/10.1175/JCLID-11-00019.1.
R. Alley, T. Berntsen, N. Bindoff, Z. Chen, A. Chidthaisong, P. Friedlingstein, J. Gregory,
G. Hegerl, M. Heimann, B. Hewitson, B. Hoskins, F. Joos, J. Jouzel, V. Kattsov, U. Lohmann, M. Manning, T.
Matsuno, M. Molina, N. Nicholls, J. Overpeck, D. Qin, G. Raga, V. Ramaswamy, J. Ren, M. Rusticucci, S.
Solomon, R. Somerville, T. Stocker, P. Stott, R. Stouffer, P. Whetton, R. Wood, and D. Wratt. Summary for
policy makers. climate change 2007: The physical science basis. contribution of working group i to the fourth
assessment report of the intergovernmental panel on climate change. 2007. ISSN 978 0 521 70596 7. URL
http://www.ipcc.ch/publications and data/ar4/wg1/en/spm.html.
M. Biasutti, I. M. Held, A. H. Sobel, and A. Giannini. SST forcings and sahel rainfall
variability in simulations of the twentieth and Twenty-First centuries. J. Climate, 21(14):3471–3486, July 2008.
doi: 10.1175/2007JCLI1896.1. URL http://dx.doi.org/10.1175/2007JCLI1896.1.
Michela Biasutti and Adam H. Sobel. Delayed sahel rainfall and global seasonal cycle in a warmer climate.
Geophysical
Research
Letters,
36(23):L23707+,
December
2009.
ISSN
0094-8276.
doi:
10.1029/2009GL041303. URL http://dx.doi.org/10.1029/2009GL041303.
T. R. Carter, M. L. Parry, H. Harasawa, and S. Nishioka. IPCC technical guidelines for assessing climate change
impacts and adaptations. Technical report, Department of Geography, University College London, UK and the
Center for Global Environmental Research, National Institute for Environmental Studies, Japan, 1994. URL
http://www.ipcc.ch/publications and data/publications and data reports.shtml.
Jule Charney, Peter H. Stone, and William J. Quirk. Drought in the sahara: A biogeophysical feedback
mechanism.
Science,
187(4175):434–435,
February
1975.
ISSN
1095-9203.
doi:
10.1126/science.187.4175.434. URL http://dx.doi.org/10.1126/science.187.4175.434.
J. H. Christensen, B. Hewitson, A. Busuioc, A. Chen, X. Gao, I. Held, R. Jones, R. K. Kolli, W. T Kwon, R. Laprise,
V. Maga˜endez, J. R¨anen, A. Rinke, A. Sarr,
na Rueda, L. Mearns, C. G. Men´ais¨and P. Whetton. Regional climate projections.
Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change, chapter 11. Cambridge University Press, Cambridge
UK, 2007. ISBN 978 0 521 70596 7. URL http://www.ipcc.ch/publications and
data/ar4/wg1/en/contents.html.
Matthew Collins, Ben B. B. Booth, B. Bhaskaran, Glen R. Harris, James M. Murphy, David
M. H. Sexton, and Mark J. Webb. Climate model errors, feedbacks and forcings: a comparison of perturbed
physics and multi-model ensembles. Climate Dynamics, 36(9): 1737–1766, May 2011. ISSN 0930-7575. doi:
10.1007/s00382-010-0808-0. URL http://dx.doi.org/10.1007/s00382-010-0808-0.
Kerry H. Cook and Edward K. Vizy. Coupled model simulations of the west african monsoon system: Twentiethand Twenty-First-century simulations. J. Climate, 19(15):3681–3703, August 2006. doi: 10.1175/JCLI3814.1.
URL http://dx.doi.org/10.1175/JCLI3814.1.
The DFID Met Office Hadley Centre Climate Science Research Partnership CSRP.
Briefing note on CSRP progress in modelling the west african monsoon (internal note). 2011.
D. P. Dee, S. M. Uppala, A. J. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae, M. A. Balmaseda, G.
Balsamo, P. Bauer, P. Bechtold, A. C. M. Beljaars, L. van de Berg, J. Bid-lot, N. Bormann, C. Delsol, R.
Dragani, M. Fuentes, A. J. Geer, L. Haimberger, S. B. Healy,
H. Hersbach, E. V. H´allberg, M. K¨
olm, L. Isaksen, P. K˚ohler, M. Matricardi, A. P. McNally, B. M. Monge-Sanz, J. J.
Morcrette, B. K. Park, C. Peubey, P. de Rosnay, C. Tavolato, J. N. Th´
epaut, and F. Vitart.
The ERA-interim reanalysis: configuration and performance of the data assimilation system. Q.J.R. Meteorol.
Soc., 137(656):553–597, 2011. doi: 10.1002/qj.828. URL http://dx.doi.org/10.1002/qj.828.
Philippe Drobinski, Benjamin Sultan, and Serge Janicot. Role of the hoggar massif in the west african monsoon
onset.
Geophysical
Research
Letters,
32(1):L01705+,
January
2005.
ISSN
0094-8276.
doi:
10.1029/2004GL020710. URL http://dx.doi.org/10.1029/2004GL020710.
C. K. Folland, T. N. Palmer, and D. E. Parker. Sahel rainfall and worldwide sea temperatures, 190185. Nature,
320(6063):602–607, April 1986. doi: 10.1038/320602a0. URL http://dx.doi.org/10.1038/320602a0.
A. Giannini, R. Saravanan, and P. Chang. Oceanic forcing of sahel rainfall on interannual to interdecadal time
scales. Science, 302(5647):1027–1030, November 2003. ISSN 1095-9203. doi: 10.1126/science.1089357. URL
http://dx.doi.org/10.1126/science.1089357.
F. Giorgi, C. Jones, and G. R. Asrar. Addressing climate change needs at the regional level: the CORDEX
framework.
WMO
Bulletin,
58
(3),
July
2009.
URL
http://euro-
cordex.net/uploads/media/Download.pdf.
Semyon A. Grodsky and James A. Carton. Coupled land/atmosphere interactions in the west african monsoon.
Geophysical
Research
Letters,
28(8):null+.
ISSN
0094-8276.
doi:
10.1029/2000GL012601.
URL
http://dx.doi.org/10.1029/2000GL012601.
Stefan Hastenrath. Decadal-scale changes of the circulation in the tropical atlantic sector associated with sahel
drought.
Int.
J.
Climatol.,
10(5):459–472,
1990.
doi:
10.1002/joc.3370100504.
URL
http://dx.doi.org/10.1002/joc.3370100504.
George J. Huffman, Robert F. Adler, Mark M. Morrissey, David T. Bolvin, Scott Curtis, Robert Joyce, Brad
McGavock, and Joel Susskind. Global precipitation at One-Degree daily resolution from multisatellite
observations.
J.
Hydrometeor,
2(1):36–50,
7541(2001)002%3C0036:GPAODD%3E2.0.CO;2.
7541(2001)002%3C0036:GPAODD%3E2.0.CO;2.
February
URL
2001.
doi:
10.1175/1525-
http://dx.doi.org/10.1175/1525-
S. Janicot, C. D. Thorncroft, A. Ali, N. Asencio, G. Berry, O. Bock, B. Bourles, G.
Caniaux, F. Chauvin, A. Deme, L. Kergoat, J. P. Lafore, C. Lavaysse, T. Lebel, B. Marticorena, F. Mounier, P.
Nedelec, J. L. Redelsperger, F. Ravegnani, C. E. Reeves, R. Roca, P. de Rosnay, H. Schlager, B. Sultan, M.
Tomasini, A. Ulanovsky, and ACMAD forecasters team. Large-scale overview of the summer monsoon over
West Africa during the amma field experiment in 2006. Annales Geophysicae, 26(9):2569–2595, September
2008. URL http://www.ann-geophys.net/26/2569/2008/.
Serge Janicot, Vincent Moron, and Bernard Fontaine. Sahel droughts and enso dynamics. Geophysical Research
Letters,
23(5):null+,
1996.
ISSN
0094-8276.
doi:
10.1029/96GL00246.
URL
http://dx.doi.org/10.1029/96GL00246.
oire, Herv´e Douville, Pascal Terray, Jean-Franc¸ois Royer, Mathieu Joly, Aurore Voldoire, Herv´
e Douville, Pascal Terray, and Jean-Franc¸ois Royer. African monsoon teleconnections
with tropical SSTs: validation and evolution in a set of IPCC4 simulations. Climate Dynamics, 29(1):1–20, July
2007. ISSN 0930-7575. doi: 10.1007/s00382-006-0215-8. URL http://dx.doi.org/10.1007/s00382006-0215-8.
Thomas Jung, Laura Ferranti, and Adrian M. Tompkins. Response to the summer of 2003 mediterranean SST
anomalies over europe and africa. J. Climate, 19(20):5439–5454, October 2006. doi: 10.1175/JCLI3916.1. URL
http://dx.doi.org/10.1175/JCLI3916.1.
E. Kalnay, M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha,
G. White, J. Woollen, Y. Zhu, A. Leetmaa, R. Reynolds, M. Chelliah, W. Ebisuzaki,
W. Higgins, J. Janowiak, K. C. Mo, C. Ropelewski, J. Wang, Roy Jenne, and Dennis Joseph. The NCEP/NCAR
40-Year reanalysis project. Bull. Amer. Meteor. Soc., 77(3):437– 471, March 1996. doi: 10.1175/15200477(1996)077%3C0437:TNYRP%3E2.0.CO;2.
URL
http://dx.doi.org/10.1175/1520-
0477(1996)077%3C0437:TNYRP%3E2.0.CO;2.
J. P. Lafore, C. Flamant, F. Guichard, D. J. Parker, D. Bouniol, A. H. Fink, V. Giraud, M. Gosset,
N. Hall, H. H¨
oller, S. C. Jones, A. Protat, R. Roca, F. Roux, F. Sa¨ıd, and C. Thorncroft. Progress in
understanding of weather systems in west africa. Atmosph. Sci. Lett., 12(1):7–12, 2011. doi: 10.1002/asl.335.
URL http://dx.doi.org/10.1002/asl.335.
K. M. Lau, S. S. P. Shen, K. M. Kim, and H. Wang. A multimodel study of the twentieth-century simulations of
sahel drought from the 1970s to 1990s. Journal of Geophysical Research, 111(D7):D07111+, April 2006. ISSN
0148-0227. doi: 10.1029/2005JD006281. URL http://dx.doi.org/10.1029/2005JD006281.
Carol F. McSweeney, Richard G. Jones, and Ben B. B. Booth. Selecting ensemble members to provide regional
climate change information. 2012.
Gerald A. Meehl, Curt Covey, Karl E. Taylor, Thomas Delworth, Ronald J. Stouffer, Mojib Latif, Bryant McAvaney,
and John F. B. Mitchell. THE WCRP CMIP3 multimodel dataset: A new era in climate change research. Bull.
Amer.
Meteor.
Soc.,
88(9):1383–1394,
September
http://dx.doi.org/10.1175/BAMS-88-9-1383.
2007.
doi:
10.1175/BAMS-88-9-1383.
URL
Timothy D. Mitchell and Philip D. Jones. An improved method of constructing a
database of monthly climate observations and associated high-resolution grids. International Journal of
Climatology,
25(6):693–712,
May
2005.
ISSN
0899-8418.
doi:
10.1002/joc.1181.
URL
http://dx.doi.org/10.1002/joc.1181.
J. M. Murphy, B. B. B. Booth, M. Collins, G. R. Harris, D. M. H. Sexton, and M. J. Webb. A methodology for
probabilistic predictions of regional climate change from perturbed physics ensembles. Philosophical
Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1857):1993–2028,
August
2007.
ISSN
1471-2962.
doi:
10.1098/rsta.2007.2077.
URL
http://dx.doi.org/10.1098/rsta.2007.2077.
James M. Murphy, David M. H. Sexton, David N. Barnett, Gareth S. Jones, Mark J. Webb, Matthew Collins, and
David A. Stainforth. Quantification of modelling uncertainties in a large ensemble of climate change simulations.
Nature,
430(7001):768–772,
August
2004.
ISSN
0028-0836.
doi:
10.1038/nature02771.
URL
http://dx.doi.org/10.1038/nature02771.
Nebojsa Nakicenovic, Joseph Alcamo, Gerald Davis, Bert de Vries, Joergen Fenhann, Stuart Gaffin, Kenneth
Gregory, Arnulf Gr¨ubler, Tae Y. Jung, Tom Kram, Emilio L. La Rovere, Laurie Michaelis, Shunsuke Mori,
Tsuneyuki Morita, William Pepper, Hugh Pitcher, Lynn Price, Keywan Riahi, Alexander Roehrl, Hans-Holger
Rogner, Alexei Sankovski, Michael Schlesinger, Priyadarshi Shukla, Steven Smith, Robert Swart, Sascha van
Rooijen, Nadejda Victor, and Zhou Dadi. Special report on emissions scenarios. Technical report, IPCC, 2000.
URL http://www.grida.no/publications/other/ipcc sr/?src=/climate/ipcc/emission/.
Heiko Paeth, Kai Born, Robin Girmes, Ralf Podzun, and Daniela Jacob. Regional climate change in tropical and
northern africa due to greenhouse forcing and land use changes. J. Climate, 22(1):114–132, January 2009. doi:
10.1175/2008JCLI2390.1. URL http://dx.doi.org/10.1175/2008JCLI2390.1.
Heiko Paeth, Nicholas M. J. Hall, Miguel A. Gaertner, Marta D. Alonso, Sounma¨ıla Moumouni, Jan Polcher,
Paolo M. Ruti, Andreas H. Fink, Marielle Gosset, Thierry Lebel, Amadou T. Gaye, David P. Rowell, Wilfran
Moufouma-Okia, Daniela Jacob, Burkhardt Rockel, Filippo Giorgi, and Markku Rummukainen. Progress in
regional downscaling of west african precipitation. Atmospheric Science Letters, 12(1):75–82, 2011. ISSN
1530261X. doi: 10.1002/asl.306. URL http://dx.doi.org/10.1002/asl.306.
T. N. Palmer. Influence of the atlantic, pacific and indian oceans on sahel rainfall. Nature, 322(6076): 251–253,
July 1986. doi: 10.1038/322251a0. URL http://dx.doi.org/10.1038/322251a0.
Joseph M. Prospero and Peter J. Lamb. African droughts and dust transport to the
caribbean: Climate change implications. Science, 302(5647):1024–1027, November 2003. ISSN 1095-9203.
doi: 10.1126/science.1089915. URL http://dx.doi.org/10.1126/science.1089915.
ubert Gall´On the northward shift of the west ee, and Christophe Messager. african monsoon. Climate Dynamics, 26(4):429–
440, March 2006. ISSN 0930-7575. doi: 10.1007/s00382-005-0093-5. URL
http://dx.doi.org/10.1007/s00382-005-0093-5.
Burkhardt Rockel and Beate Geyer. The performance of the regional climate model CLM in different climate
regions, based on the example of precipitation. Meteorologische Zeitschrift, 17(4):487–498, August 2008. ISSN
0941-2948.
doi:
10.1127/0941-2948/2008/0297.
URL
http://dx.doi.org/10.1127/0941-
2948/2008/0297.
David
Dav
id
P Rowell.
.
Teleconnections
betwee
n
P.
t
h
e
tropical pacific
an
d
th
e
sahel.
Q.J.
R.
7
Soc. 127(575):1683–
10.1002/qj.4
1
Meteorol.
2001.
doi:
57512. URL
,
1706,
9
2
Rowell. The impact of mediterranean SSTs on the sahelian rainfall season. J. Climate,
7 16 (5):849–862, March
http://dx.doi.org/10.1002/qj.49712757
2003.
doi:
10.1175/1520-0442(2003)016%3C0849:TIOMSO%3E2.0.CO;2.
URL
512.
http://dx.doi.org/10.1175/1520-0442(2003)016%3C0849:TIOMSO%3E2.0.CO;2.
Met Office
Tel: 0870 900 0100
FitzRoy Road,
Fax: 0870 900 5050
Exeter
M. Shinoda
and R. Kawamura. Tropical rainbelt, circulation, and sea surface temperatures associated with the
enquiries@metoffice.gov.
3PB
sahelianDevon,
rainfallEX1
trend.
Journaluk
of the Meteorological Society of Japan, 72:341–357, 1994.
UK
www.metoffice.gov.uk
S. Sijikumar, Pascal Roucou, and Bernard Fontaine. Monsoon onset over Sudan-Sahel: Simulation by the
regional scale model MM5. Geophysical Research Letters, 33(3): L03814+, February 2006. ISSN 0094-8276.
doi: 10.1029/2005GL024819. URL http://dx.doi.org/10.1029/2005GL024819.
D. A. Stainforth, T. Aina, C. Christensen, M. Collins, N. Faull, D. J. Frame, J. A. Kettleborough,
S. Knight, A. Martin, J. M. Murphy, C. Piani, D. Sexton, L. A. Smith, R. A. Spicer, A. J. Thorpe, and M. R. Allen.
Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature, 433(7024):403–
406,
January
2005.
ISSN
0028-0836.
doi:
10.1038/nature03301.
URL
http://dx.doi.org/10.1038/nature03301.
Benjamin Sultan and Serge Janicot. Abrupt shift of the ITCZ over west africa and intraseasonal variability.
Geophysical Research Letters, 27(20):null+, 2000. ISSN 0094-8276. doi: 10.1029/1999GL011285. URL
http://dx.doi.org/10.1029/1999GL011285.
Benjamin Sultan and Serge Janicot. The west african monsoon dynamics. part II: The preonset and onset of the
summer
monsoon.
J.
Climate,
16(21):3407–3427,
0442(2003)016%3C3407:TWAMDP%3E2.0.CO;2.
0442(2003)016%3C3407:TWAMDP%3E2.0.CO;2.
URL
November
2003.
doi:
10.1175/1520-
http://dx.doi.org/10.1175/1520-
P. Van der Linden and J. F. B. Mitchell. ENSEMBLES: Climate change and its impacts:
Summary of researchand results from the ENSEMBLES project. Technical report, Met Office Hadley Centre,
FitzRoy Road, Exeter EX1 3PB, UK, 2009. URL http://www.ensembles-eu.org/.
Edward K. Vizy and Kerry H. Cook. Mechanisms by which gulf of guinea and eastern north atlantic sea surface
temperature anomalies can influence african rainfall. J. Climate, 14(5):795– 821, March 2001. doi:
10.1175/1520-0442(2001)014%3C0795:MBWGOG%3E2.0.CO;2.
URL
http://dx.doi.org/10.1175/1520-0442(2001)014%3C0795:MBWGOG%3E2.0.CO;2.
Wmo. Technical regulations vol 1: General meteorological standards and recommended practices. Technical
Report 49, World Meteorological Organization, 1988. URL http://www.wmo.int/.
Masaru Yoshioka, Natalie M. Mahowald, Andrew J. Conley, William D. Collins, David W. Fillmore, Charles S.
Zender, and Dani B. Coleman. Impact of desert dust radiative forcing on sahel precipitation: Relative
importance of dust compared to sea surface temperature variations, vegetation changes, and greenhouse gas
warming.
J.
Climate,
20(8):1445–1467,
April
2007.
doi:
10.1175/JCLI4056.1.
URL
http://dx.doi.org/10.1175/JCLI4056.1.
Ning Zeng, J. David Neelin, K. M. Lau, and Compton J. Tucker. Enhancement of interdecadal climate variability in
the sahel by vegetation interaction. Science, 286(5444):1537– 1540, November 1999. ISSN 1095-9203. doi:
10.1126/science.286.5444.1537. URL http://dx.doi.org/10.1126/science.286.5444.1537.
䘀椀最甀爀攀.
.........................................................................................
.
...
................................
.................................................................................................................................................................
......................................................................................
..................................................................................
.
...
................................
.....................................................................................................................................................................................
...................
Dav
id
P Rowell.
.
Teleconnections
Soc.
,
127(575):1683–
1706,
Meteorol.
betwee
n
t
h
e
2001.
http://dx.doi.org/10.1002/qj.49712757
512.
Met Office
Tel: 0870 900 0100
FitzRoy Road,
Fax: 0870 900 5050
Exeter
enquiries@metoffice.gov.
Devon, EX1 3PB
uk
UK
www.metoffice.gov.uk
tropical pacific
doi:
an
d
th
e
10.1002/qj.4
9
7
1
2
7
sahel.
57512.
U