CPClimate of the PastCPClim. Past1814-9332Copernicus PublicationsGöttingen, Germany10.5194/cp-14-1377-2018Inter-annual variability in the tropical Atlantic from the Last Glacial Maximum into future climate projections simulated by CMIP5/PMIP3Simulated Atlantic variabilityBrierleyChrisc.brierley@ucl.ac.ukhttps://orcid.org/0000-0002-9195-6731WainerIlanaEnvironmental Change Research Centre, Department of Geography, University College London, Gower St, London, WC1E 6BT, UKDepartamento de Oceanografia Física, Química e Geológica, Instituto Oceanográfico da Universidade
de São Paulo, Praça do Oceanográfico, 05508-120, São Paulo, BrasilChris Brierley (c.brierley@ucl.ac.uk)1October20181410137713906November201728November201729August201817September2018This 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://cp.copernicus.org/articles/14/1377/2018/cp-14-1377-2018.htmlThe full text article is available as a PDF file from https://cp.copernicus.org/articles/14/1377/2018/cp-14-1377-2018.pdf
Tropical Atlantic variability (TAV) plays an important role in driving
year-to-year changes in rainfall over Africa and South America. In this
study, its response to global climate change is investigated through a series
of multi-model experiments. We explore the leading modes of TAV during the
historical, Last Glacial Maximum, mid-Holocene, and future simulations in the
multi-model ensemble known as PMIP3/CMIP5. Despite their known sea surface
temperature biases, most of the models are able to capture the tropical
Atlantic's two leading modes of SST variability patterns – the Atlantic
Meridional Mode (AMM) and the Atlantic zonal mode (also called the Atlantic
Niño or ATL3). The ensemble suggests that AMM amplitude was less during
the mid-Holocene and increased during the Last Glacial Maximum, but is
equivocal about future changes. ATL3 appears stronger under both the Last
Glacial Maximum and future climate changes, with no consistent message about
the mid-Holocene. The patterns and the regions under the influence of the two
modes alter a little under climate change in concert with changes in the mean
climate state. In the future climate experiment, the equatorial mode weakens,
and the whole Northern Hemisphere warms up, while the South Atlantic displays
a hemisphere-wide weak oscillating pattern. For the LGM, the AMM projects
onto a pattern that resembles the pan-Atlantic decadal oscillation. No robust
relationships between the amplitude of the zonal and meridional temperature
gradients and their respective variability was found.
IntroductionTropical Atlantic variability and its importance
Variability in the tropical Atlantic Ocean occurs at different timescales
ranging from seasonal, inter-annual to decadal, and longer
. The dominant
frequency for this region is the seasonal cycle, which combined with
continental monsoon forcing and air–sea interaction regulates the
latitudinal displacement of the rain-producing Inter-Tropical Convergence
Zone (ITCZ). The marine portion of the ITCZ, in turn, is locked to the
Atlantic's sea surface temperature (SST). In the tropical Atlantic, changes
in SST are in phase with the meridional displacement of the ITCZ and
associated meridional wind stress . SST departures from the
seasonal cycle are primarily driven by changes in surface winds that result
from local air–sea interaction associated with the latitudinal migration of
the ITCZ or remotely forced by external factors (e.g. variability associated
with ENSO). There is also significant inter-annual variability in the tropical
Atlantic that is represented by its two leading SST modes.
The most well-known mode of tropical Atlantic variability (TAV) is a zonal
mode ATL3; that is governed by equatorial ocean
dynamics in response to surface winds, much like ENSO in the Pacific. It
involves changes in the Atlantic equatorial cold tongue and the associated
displacement of the equatorial thermocline. Although this mode has a weaker
impact on the meridional displacement of the ITCZ, it can nonetheless impact
South America precipitation . The positive phase of this
equatorial mode presents positive SST anomalies in the eastern part of the
basin and is associated with increased precipitation over north-east Brazil
and the western Amazon. The negative phase is associated with weakening of
the African monsoon.
The leading mode of TAV is a meridional mode characterized by a north–south
inter-hemispheric SST gradient (Atlantic Meridional Mode, hereafter referred
to as AMM), which significantly impacts changes in the position and
intensity of the ITCZ-related rainfall. The inter-hemispheric SST gradient is
accompanied by a cross-equatorial atmospheric flow
. Furthermore, the AMM has been
linked to changes in surface winds and the associated evaporation feedbacks
. These feedbacks are important in
defining the spatial and temporal features of the AMM and its impact on
rainfall. The AMM is associated with a shift in the distribution of
ITCZ-related precipitation towards the hemisphere with anomalously warm SST
(relative to the other). Put simply, the ITCZ tends to follow the warmest
hemisphere , although see
for a description of the true nuances of this relationship.
In both modes, the coupling between SST and the ITCZ is an important driver
of rainfall variability for north to north-east Brazil and for the Sahel
region in Africa. It is regulated by the combined changes in intensity and
meridional displacement of the ITCZ driven by the underlying SST gradient
associated with a surface wind response
. The reader is referred to
for a detailed review of the patterns, mechanisms, and impacts
of TAV.
What do we know about TAV in past climates?
Several studies of past climates have attempted to understand predominant
characteristics of the Last Glacial Maximum (LGM) and mid-Holocene relative
to pre-industrial controls (PI) e.g.among
others.
A common thread among these studies is the idea that in response to the
changes in meridional SST gradient, the mean position of the ITCZ shifts to
the hemisphere with warmer temperatures (cf. the AMM). It is known that
during the LGM, the tropics cooled less than extratropical latitudes
e.g..
examine and quantify changes in the north–south location of the ITCZ from
simulation results from PMIP2 for the Last Glacial Maximum (LGM) and the
mid-Holocene (MH). They establish that changes in the associated meridional
SST gradient in the tropical Atlantic during summer at the MH are in phase
with changes in precipitation over West Africa. linked
changes in the meridional SST gradient with changes in the Hadley circulation
and north–south thermal contrast. discuss the fact that for the LGM,
the marine portion of the ITCZ does not reach the South American continent
during DJF, contributing to weakened precipitation. find that
for the LGM and mid-Holocene the latitudinal shift in the mean ITCZ is less
than 1∘ latitude. They discuss how the position of the ITCZ is
associated with the heat transport between the hemispheres. An important
conclusion of their work also noted by is that
tropical SST gradients for past climates can be reconstructed with greater
certainty than the ITCZ position, which means that understanding the
fluctuations of the anomalous SST variability patterns allows for the assessment
of past changes in ITCZ position and the related rainfall patterns.
Considering the significant impact that TAV has on the position of the ITCZ
and the distribution of rainfall of the adjacent continental regions, and
given that it has been changing with global climate change, we seek to
characterize the SST modes of TAV for different climates. The idea is to
identify, if any, changes in TAV for past climates and understand its
behaviour in future projections using simulations from complex climate models.
The present study has the goal to examine the performance of Earth system
models relative to the simulation of TAV in terms of SST for different
climates in the context of the Paleoclimate Model Intercomparison Project
(PMIP). Hopefully by understanding the link between the modes of variability
of the tropical Atlantic for different climates, we can improve our
understanding of the related monsoon dynamics and mechanisms in the region.
MethodsModel simulations
Coupled atmosphere–ocean general circulations models (GCMs) are routinely
used for climate research. Simulations of future climate are coordinated by
the Coupled Model Intercomparison Project (CMIP) through the use of
collectively defined experiments . The fifth phase
of CMIP was heavily relied upon by the IPCC for their fifth assessment report
. Additionally, a series of past climate experiments has been
coordinated by PMIP . Three such experiments
formed part of the third phase of PMIP: the mid-Holocene, the Last Glacial
Maximum, and the last millennium (although this latter experiment is not
analysed here). A pre-industrial control and an idealized warming scenario
were also requested to establish the baseline and forced climate response,
respectively. Further details of these experiments will be introduced later
when relevant.
Anomalous SSTs are calculated separately for each individual simulation. The
climatology is calculated across all years for the following: the pre-industrial,
mid-Holocene, and LGM experiment; the average of 1971–2000 for the historical
simulation; and the final 40 years of the idealized warming experiment. For all
simulations, the resulting SST anomalies are then linearly detrended to
remove any residual drift or aliasing from changes in mean state. Anomalous
precipitation is similarly computed with respect to the same climatologies
and linearly detrended.
Patterns associated with the ATL3 and AMM are calculated as linear regression
slopes of the anomalous SST and precipitation to the derived time series. No
statistical significance testing of this regression is performed. This
pragmatic choice is motivated by the challenges posed by averaging pattern
changes over an ensemble with each individual field having its own missing
data mask.
Not every modelling group within PMIP was able to perform all the requested
simulations, although only in the case of the GFDL and CSIRO-Mk3L Last
Glacial Maximum runs was this for scientific rather than resourcing reasons.
Here we investigate every simulation that has posted the required data on the
Earth System Grid Federation's data nodes (Table 1). Three modelling groups
provided multiple realizations of the simulations differing only by their
initial conditions (Table 1). Every simulation is considered equally likely
during the creation of any ensemble averages. The ensemble mean change
patterns shown are the average of the difference for each
model that has run both simulations (rather than the difference in the two
ensemble means). The spread within the ensemble is illustrated throughout
this analysis by stippling to indicate consistency. We consider the
ensemble's signal to be consistent if two-thirds or more of the participant
models show a change of the same sign as the ensemble mean. Whilst this
particular measure is not overly stringent, it does allow for ready
identification of regions where the signal is more likely to be robust.
The number of simulated years of monthly output used to calculate
the tropical Atlantic variability in each model simulation. The models can be
identified by their established acronyms from the Earth System Grid
Federation database.
a The name of this simulation is “midHolocene” on
the Earth System Grid Federation. b Indicates models that only
form part of PMIP3, but not CMIP5. c CCSM4, GISS-E2-R, and
MPI-ESM-P deposited multiple ensemble members.
Observations
This research involves the joint investigation of sea surface temperature and
precipitation. We adopt a combination of the twentieth century reanalysis
for the atmospheric variables with HadISST1.1
for the SST. HadISST1.1 forms the underlying boundary
conditions for the twentieth century reanalysis , providing
internal consistency between the datasets. These datasets exist over the
period 1871–2012 CE, although there is an increased amount of uncertainty
in the early portion of the record e.g.. For
the mean precipitation field shown in Fig. , we use GPCP
. Despite having only a shorter available record than
the reanalysis, it looks to give better rainfall over the elevated topography
of South America. We follow in using a
climatological period of 1971–2000, as the historical simulations only
extend until 2005.
Definition of modes
Climate modes of variability are preferred spatial patterns associated with
time variations that have global to regional impacts. Both modes of tropical
Atlantic variability analysed here have been identified using area-averaged
SST anomaly indices. We avoid using definitions based upon empirical
orthogonal functions (EOFs) as preliminary analysis indicated they could have
alternate ordering in the various models and simulations.
PMIP4 is endeavouring to perform a routine evaluation of the simulated
climate variability using the ESMValTool software
. This includes a collection of standardized
analyses to look at coupled climate modes . In
particular, this research was performed using the Climate Variability
Diagnostics Package (CVDP v5.0.0). We have expanded the software to
additionally incorporate analysis of the predominant modes of tropical
Atlantic climate variability (TAV) listed below. The main source code is
freely available at http://www.cesm.ucar.edu/working_groups/CVC/cvdp/;
last access: 29 August 2018. The full results of the software on the
simulations described here are visible via the PMIP variability database
currently hosted at http://www2.geog.ucl.ac.uk/~ucfaccb/PMIPVarData/;
last access: 29 August 2018. A summary table for the climate modes mentioned
here is provided as the Supplement to this article.
Atlantic zonal mode – ATL3
The Atlantic zonal mode is the second mode of tropical
Atlantic variability and represents changes in the cold tongue at the eastern
part of the basin, just south of the Equator. We adopt the ATL3 region of
as a metric. It is defined to be the area average of
the detrended SST anomaly over the region 3∘ N–3∘ S,
20–0∘ W. This index definition is somewhat analogous to that of a
Niño region in the Pacific, leading to this mode sometimes being termed
the “Atlantic Niño” .
Atlantic meridional mode – AMM
The AMM is the leading mode of variability in the Atlantic. It represents
variations in the north–south SST gradient that exhibits opposite SST
anomalies on either side of the mean position of the ITCZ
. The underlying SST distribution has an influence on the
position of the ITCZ, which in turn affects the regional rainfall
distribution. Here we adapt the SST-based index of . The AMM
state is defined as the basin-wide, area average, detrended SST anomaly
difference between the two hemispheres. More precisely, it is the average of
15–5∘ N, 50–20∘ W minus the average of
15∘ S–5∘ N, 20∘ W–10∘ E.
The sea surface temperature representation and changes in the
ensemble. The climatological (1971–2000) sea surface temperature seen in
HadISST for DJF (a) and JJA (b). Even on
the ensemble mean, there are model biases in the seasonal temperatures of the
historical simulation for DJF (c) and JJA (d). The ensemble
mean biases in the seasonal temperatures in the PI control simulations for
DJF (e) and JJA (f) are very similar to those of the
historical simulations. Stippling indicates regions where two-thirds or more
of the models agree with the sign of the ensemble mean bias
(Sect. ).
Present dayMean state
Prior to investigating the inter-annual variability, it will be instructive to
look at the representation of the mean climate state. The highest
temperatures in the Atlantic occur on the Equator throughout the year
(Fig. ). The west Atlantic warm pool shifts to stay in the
summer hemisphere. The eastern equatorial Atlantic has a tongue of cold
upwelling that peaks in JJA. The warmest SSTs are associated with the strong
precipitation of the ITCZ (Fig. ). Both South America and
West Africa experience heavy monsoonal rainfall in their respective summers.
General circulation models provide our best tool for modelling the climate and
generally provide a fair representation. However, all models have some biases
in their mean climate state. On the ensemble mean, models are unable to
simulate the correct magnitude of equatorial upwelling
(Fig. c, d). Furthermore, the west Atlantic warm pool extent
is underestimated. Both of these biases are also seen for the PI control
ensemble mean simulations (Fig. e, f) and persist throughout
the year. There are precipitation biases as well (Fig. ). In
general, the models are unable to realistically represent the distribution of
the ITCZ-related rainfall; there is too much rainfall on the southern flank
of the ITCZ. The West African monsoon is biased dry, whilst the models have
too much rainfall over NE Brazil, likely due to issues with the simulated
convection. The ensemble mean biases discussed are relatively consistent
within the ensemble (the majority of the biases demonstrated in
Figs. and are stippled, meaning that
two-thirds or more of the models have the same sign bias as plotted). These
biases have been reported by other studies that looked at TAV in CMIP models
. The
bias in many of the models is related to either a weak eastern equatorial
cold tongue or failure to reproduce it. examine twentieth
century simulations in nine GCMs and identify strong interactions between the
Atlantic zonal and the meridional modes that are not realistic. They discuss
the fact that the models that seem to best represent the meridional mode show its
weakening in future climate conditions. examined
precipitation from 22 atmosphere-only models and identify an annual mean
east–west bias associated with the ITCZ. They find that models with the east
Atlantic bias tend to be high-resolution models which rain excessively over
the Gulf of Guinea. analyse the simulation results of 33
models, of which 29 display biases relative to the mean state that can
include an annual mean zonal equatorial SST gradient whose sign is opposite
to observations. compare the pre-industrial simulation
results of 36 different models and show that although there are errors in the
annual cycles of SST, wind stress, and heat content, the relationship between
them is well simulated. More recently, consider the validity
of eastern equatorial Atlantic upwelling in the CMIP5 models when discussing
their ability to predict the cold tongue SST development. Despite the mean
state biases reported, the models are able to reproduce the dominant modes of
climate variability in the tropical Atlantic.
The seasonal cycle of precipitation and its changes across the
ensemble. The climatological (1979–1999) precipitation seen in the Global
Precipitation Climatology Project in DJF
(a) and JJA (b). The ensemble mean biases of the seasonal
precipitation in the historical simulation for DJF (c) and JJA
(d). The ensemble mean biases of the seasonal precipitation in the
PI control simulation for DJF (e) and JJA (f). Stippling
indicates regions where two-thirds or more of the models agree with the sign
of the ensemble mean bias.
Representation of tropical Atlantic variability and its relationship to precipitation
To address the question of how AMM and ATL3 are simulated, we present a
comparison between the ensemble mean pattern seen in the historical and
PI control simulations (Table ) together with the HadISST
temperature observations (Fig. ). The time series for
both modes are determined through area-averaged, detrended SST anomalies for
both the AMM (Sect. ) and ATL3 (Sect. ). The
standard deviations of the resulting monthly time series are calculated and
shown in each panel (Fig. ). The amplitude variations
of the ATL3 region are 0.18 ∘C, which is approximately identical to
the ensemble mean amplitude of 0.17 and 0.16 ∘C (given the ensemble
spread) for the historical and PI control simulations, respectively. The
standard deviation SST gradient used as a metric for the AMM has a stronger
amplitude in the observations (0.28 ∘C) than the models suggest
(0.18 and 0.19 ∘C in the historical and pre-industrial, respectively).
It should be noted that one should expect the GCMs to sample different phases
of the low-frequency natural variability, so a direct comparison of the
time series is not appropriate. Additionally, there are uncertainties in the
observational record, which may be considerable in the early portion of the
record .
Sea surface temperature patterns related to ATL3 (left) and AMM
(right) indices in observations (a, b) and CMIP5/PMIP3 ensemble
average for the historical simulations (c, d) and the pre-industrial
control (e, f). The standard deviations of the SST indices are also
shown.
The spatial patterns associated with the tropical Atlantic variability are
demonstrated through simple linear regression of the area-averaged indices
onto the monthly anomalies (Sect. ). This regression is
extended across the globe, which highlights correlations with other modes of
internal variability. This does not imply that a causal relationship
extending out of the Atlantic to other ocean basins exists. The relationship
with ENSO differs between models, which is interesting. However, given this
model dependence, we leave analysis of this feature for future work.
The spatial extent of the ATL3 does not extend far beyond the tropical
Atlantic (Fig. ). In fact, in both observations and
models it has little effect on the North Atlantic. The projection of the ATL3
in models is predominantly onto the Equator itself and there is a muted
effect on the upwelling region. This is likely due to an under-representation
of the upwelling in the model as demonstrated by the substantial warm biases
in the mean state (Fig. ).
The SST pattern associated with the AMM in the GCM ensembles appears to be
generally well represented in the Atlantic when compared to the HadISST dataset (Fig. ). There is too much extension of the
negative SSTs across the South Atlantic, however.
Precipitation patterns related to ATL3 (left) and AMM (right)
indices in reanalysis (top) and CMIP5/PMIP3. The patterns across the
historical simulations (c, d) and pre-industrial simulations
(e, f) are an ensemble mean across the available models (Table 1).
To evaluate the relationship of the TAV modes with tropical rainfall across
the region, the ATL3 and AMM indices are regressed onto precipitation for the
ensemble mean historical and PI control simulations and compared to the
equivalent regressions from the reanalysis. The resulting patterns are shown
for two ensembles of simulations and the reanalysis
(Fig. ). It is clear from
Fig. that the AMM and ATL3 rainfall patterns for
the simulations closely resemble that of the reanalysis. Differences are seen
mostly over the continents where the relationship with the TAV modes are
stronger (towards the west for South America and east–south-east for Africa).
The weaker regression relationships in West Africa in the model happen to
correspond to the low bias in the mean precipitation.
The climatological sea surface temperature changes shown by the
ensemble. The ensemble mean difference between the mid-Holocene and
pre-industrial simulations demonstrates the temperature impacts in DJF
(a) and JJA (b). The Last Glacial Maximum is simulated as
being substantially colder than the pre-industrial in both DJF (c)
and JJA (d). In contrast, the ensemble mean average of the final
40 years of the 1 % per year increasing carbon dioxide concentration run
is warmer than pre-industrial in both DJF (e) and JJA (f).
Stippling indicates regions where two-thirds or more of the models agree with
the sign of the ensemble mean change. Overlaid contour lines represent the
mean state in the ensemble mean of the pre-industrial control simulations.
Past climatesMid-Holocene
Around 6000 years ago was the warmest portion of the Holocene
, although there are suggestions this may
only represent the summer rather than annual average temperatures
. The magnitude of the simulated temperature changes
relative to pre-industrial conditions were comparatively small
(Fig. a, b), with several areas of cooling on the Equator
. The climate change was caused by differences in the
orbital precession that drove the movement of the ITCZ seasonal cycle to
favour the Northern Hemisphere . Most notably, this
increased the mean precipitation over Northern Africa, influencing its
variability and supporting green vegetation in the
Sahara . The ensemble simulates a noticeable
northward shift in precipitation over Africa (Fig. b). This
is, however, significantly less than observed in the region for the
mid-Holocene . It has been shown that, when
imposing mid-Holocene vegetation reconstruction as a boundary condition to
the model, inter-annual climate variability can be impacted
. Over NE Brazil, the monsoon rainfall reductions
are relatively moderate (Fig. b), although there is a
general decrease in summer rainfall across South America
(Fig. a).
The changes in the seasonal cycle in precipitation across the ensemble.
The ensemble mean difference between the mid-Holocene and pre-industrial
simulations shows the movement of the ITCZ in DJF (a) and JJA
(b). The Last Glacial Maximum is simulated as having less intense
rain bands than the pre-industrial in both DJF (g) and JJA
(h). In contrast, the ensemble mean average of the final 40 years
of the 1 % per year increasing carbon dioxide concentration run
demonstrates more enhanced activity over the ITCZ than in the pre-industrial for both
DJF (i) and JJA (j). Stippling indicates regions where
two-thirds or more of the models agree with the sign of the ensemble mean
change. Overlaid contour lines represent the ensemble mean of the
pre-industrial control simulations.
The precession-related changes in the mid-Holocene led to changes in the
amplitude of TAV in many of the ensemble members (Fig. ).
These changes rarely exceed a 20 % change in amplitude. The ensemble is
equivocal about the response of the ATL3 during the mid-Holocene. The
ensemble mean change of 1.3 % is heavily influenced by the dramatic changes
seen in KCM1-2-2 (Fig. ). More than two-thirds of the
simulations show a reduction in amplitude of the AMM
(Fig. c), with a mean reduction of 7.5 %.
The standard deviations of the (a) ATL3 and (b)
AMM indices in the mid-Holocene (green) and pre-industrial results (grey);
(c) changes expressed as a percentage across the ensemble for both
ATL3 (yellow) and AMM (light green).
There are some small local spatial patterns associated with TAV shift at the
mid-Holocene (Fig. ). The ATL3 shows hints of a northward shift
in its spatial pattern (Fig. a). A mid-Holocene weakening of
the El Niño–Southern Oscillation has been seen in observations and models
. Despite this, there is a stronger
relationship hinted at between the AMM and this mode (as seen by the
increasing regressions in the Pacific in Fig. b). The
relationships between AMM, ATL3, and ENSO are model dependent
(Sect. ), so were not investigated. The amplitude and
correlations for each simulation are included as a Supplement table should
subsequent researchers be interested in the links for specific models. The
mid-Holocene AMM sees a poleward shift in its pattern over the North
Atlantic, which is likely related to the shift in the ITCZ location in the
mean state (Fig. a, b). There appears to be little change in
the precipitation patterns associated with TAV over the continents
(Fig. c, d). Again we must stress that the patterns shown are
the ensemble mean and may average out some substantial variation in response
between the individual models.
Mid-Holocene changes relative to pre-industrial in the temperature
patterns related to (a) the ATL3 and (b) AMM indices. The
changes in precipitation patterns are also shown for the ATL3
(c) and AMM (b). The changes are stippled with greater than
two-thirds of the ensemble showing the same direction as the ensemble mean
change. The contours show the change in regression strength onto the index in
question, i.e. the change in local expression of the +1 ∘C index. The
overlaid contours show the ensemble mean strength of the relationship in the
pre-industrial control. Note that the temperature regression contours are
linearly spaced.
Last Glacial Maximum
21 000 years ago saw the maximum extent of the ice sheets during the last
glacial. The orbital configuration then differed only slightly from the
pre-industrial. The large ice sheets were accompanied by substantial cooling
across the globe . Tropical sea
surface temperatures cooled by roughly 2 ∘C
(Fig. ), predominantly controlled by a drop in CO2
of around 100 ppm .
The patterns of SST change are approximately uniform, although there is a
slight weakening of the north–south gradient in the tropical Atlantic
(Fig. ). The ensemble is equivocal about changes in the
equatorial zonal SST gradient. The intensity of tropical rainfall was
generally reduced and the position of the ITCZ moved marginally southward
(Fig. ). The ensemble shows a strong propensity for
increased climate variability in the tropical Atlantic
(Fig. ). The average increase in the ATL3 amplitude is
25.5 %, with only one dissenter suggesting a decrease. All but two models
show an increasing amplitude of the AMM, with the ensemble mean increase
being 31.3 % (Fig. ).
The standard deviations of (a) the ATL3 and (b)
AMM indices in the Last Glacial Maximum simulations (blue) shown alongside
the values from their respective pre-industrial control simulation (grey);
(c) changes expressed as a percentage across the ensemble for both
ATL3 (yellow) and AMM (light green).
These increases in amplitude are associated with small but robust changes in
the spatial pattern of the modes (Fig. ). The Last Glacial
Maximum sees a slight reduction in the influence of the ATL3 in the equatorial
Atlantic (Fig. ). We interpret that to represent the ATL3
further constricting onto the Equator as the ITCZ moves slightly southward
(Fig. ). The AMM sees an increasing influence over the South
Atlantic (Fig. b). The North Atlantic has something similar, but
likely overlaid with changes caused by the imposition of large ice sheets
over North America impacting the atmospheric dynamics .
The impact of TAV on rainfall in South America is reduced, in places by 50 %
(Fig. c, d). These TAV-related reductions are proportionally
much larger that the LGM change in mean precipitation
(Fig. ). Therefore, PMIP3 potentially suggests rainfall
over NE Brazil that was simultaneously weaker, but less variable. There are
few changes in the TAV patterns over Africa (Fig. c, d).
Last Glacial Maximum changes relative to pre-industrial in the
temperature patterns related to (a) the ATL3 and (b) AMM
indices. The changes in precipitation patterns are also shown for the ATL3
(c) and AMM (b). The changes are stippled with greater than
two-thirds of the ensemble showing the same direction as the ensemble mean
change. The contours show the change in regression strength onto the index in
question, i.e. the change in local expression of the +1 ∘C index. The
overlaid contours show the ensemble mean strength of the relationship in the
pre-industrial control. Note that the temperature regression contours are
linearly spaced.
Future changes
The climate simulated for both the mid-Holocene (Sect. ) and Last
Glacial Maximum (Sect. ) represent equilibrated conditions
between the climate and its forcing. The climate is expected to still be in a
transient state throughout the coming century. Rather than selecting a
particular plausible future scenario, we analyse the idealized simulations
in which the atmospheric CO2 concentrations are increased by 1 % per
year until it is quadrupled . The mean climate
during the final 40 years of these simulations is substantially warmer
(Fig. ) with an intensified hydrological cycle
(Fig. ). To have sufficient years to assemble robust SST
patterns of climate variability, we consider the full length of these
transient simulations as having first removed a linear trend from each model
grid point after.
The standard deviations of (a) the ATL3 and (b)
AMM indices in the 1 % per year until quadrupled CO2 experiment
(red) shown alongside the values from their respective pre-industrial control
simulation (grey); (c) changes expressed as a percentage across the
ensemble for both ATL3 (yellow) and AMM (light green).
The mean SST and rainfall patterns are very similar to a reverse of those for
the cold LGM (Figs. , ). However, the
changes in tropical Atlantic variability are not. There is an indication that
there will be an increase in amplitude of ATL3 (Fig. a),
with an average increase of 13.8 %. However, the ensemble is split evenly
as to whether the AMM (Fig. b) will increase in
amplitude as well (mean change of +5.1 %). Despite that, there is a
robust poleward expansion of the AMM influence in the Atlantic
(Fig. b). The influence of ATL3 also expands polewards, but
only in the Northern Hemisphere (Fig. a).
demonstrate that the Pacific Ocean response to Atlantic
warming is a La Niña-like cooling response, much like the AMM-related
future changes in Fig. b. Interestingly, the precipitation
response to the ATL3 is weaker in the future scenarios
(Fig. c), with a slight contraction onto the Equator. This
does not bear a strong relationship to the changes in the mean state
(Fig. ). The AMM shows a similar contraction in related
rainfall, but the amplitude of the changes is generally small
(Fig. d).
The 1 % per year until quadrupled CO2 forced changes relative
to pre-industrial in the temperature patterns related to (a) the
ATL3 and (b) AMM indices. The changes in precipitation patterns are
also shown for the ATL3 (c) and AMM (b). The changes are
stippled with greater than two-thirds of the ensemble showing the same
direction as the ensemble mean change. The contours show the change in
regression strength onto the index in question, i.e. the change in local
expression of the +1∘C index. The overlaid contours show the ensemble
mean strength of the relationship in the pre-industrial control. Note that the
temperature regression contours are linearly spaced.
TAV amplitude changes as a function of the SST gradient
Changes in the amplitude of the ATL3 mode have previously been linked to
changes in the zonal SST gradient since 1950 . In
Fig. , we investigate whether this link holds
across the ensemble and multiple climates. We use the difference in the
area-averaged SST between 3∘ N–3∘ S, 45–25∘ W
and 3∘ N–3∘ S, 20–0∘ W to characterize the
west–east SST gradient after and only consider the
climate change signal to prevent the model biases from hiding any relationship.
The gradient changes in the future simulations are only analysed over the
final 40 years (once the main climate change signal is dominant), unlike
the TAV calculations, which detrend and use the full time series
(Sect. ).
The change in the standard deviation of the zonal mode (ALT3) as a
function of the change in the west–east SST gradient. The gradient is calculated
using the regions (see text). The colours indicate the
different experiments: 1pctCO2 (red), mid-Holocene (green), and Last Glacial
Maximum (blue).
Despite the ensemble showing robust changes in mean state and often robust
changes in variability, there is no apparent relationship emerging between
the change in the standard deviation of the ATL3 and changes in the zonal SST
gradient (Fig. ). This perhaps questions the
previous conclusions of . In fact, extending their time
series of ATL3 earlier within the instrumental period indicates little
persistence of the trends they find between 1950 and 2000 (not shown).
highlight aerosols as the cause of their trends –
something which is not really explored across these simulations – leading us
to conclude that further work is required to understand the future of the
ATL3.
The AMM is defined as variations in the inter-hemispheric tropical SST
gradient (Sect. ). It would seem logical to think that as the
inter-hemispheric gradient changes the inter-annual variability of that
gradient would also change. Recently have suggested
there was an increased climate variability during the last glacial, which is
well supported by Fig. . They propose that the increased
meridional temperature gradients are the underlying cause of the greater
variability. We explore this suggestion to search for an emergent constraint
of the future AMM response. There appears to be no robust relationship
between the AMM amplitude and the mean meridional gradient
(Fig. ). A decrease in the AMM amplitude change can
be associated with an increase in the meridional SST gradient for the
mid-Holocene, while the opposite occurs for the 1 % per year until
quadrupled CO2 (1pctCO2). Overall, the relationship between the
amplitude change of the AMM and the changes in the meridional SST gradient
depends on the climatic period considered.
Conclusions
This study has used the multi-model CMIP5/PMIP3 ensemble to investigate
changes in tropical Atlantic variability across several climate states. All
models are able to represent the main characteristics of the dominant modes
of variability, with similar mean state bias, for all climate periods
analysed. These biases are consistent among all models
(Fig. ), especially in the equatorial cold tongue. They are
also consistent in showing precipitation biases over South America, Africa,
and over the tropical Atlantic. Despite their mean state biases, the
simulation results show reasonable representation of the observed patterns of
tropical Atlantic variability. Analysis of the idealized warming scenarios
alone suggests a spectrum of future climate change responses. The additional
analysis of the palaeoclimate simulations provides some valuable context for
those responses. For example, the simulated future ATL3 (Atlantic Niño)
amplitude increase is not simply a response to the warmer temperatures, as
a similar increase is seen during the Last Glacial Maximum.
The change in the standard deviation of the meridional mode (AMM) as a
function of the change in the meridional SST gradient. The gradient is calculated
using the same regions as for the AMM index itself (Sect. ).
The colours indicate the different experiments: 1pctCO2(red), mid-Holocene
(green), and Last Glacial Maximum (blue).
The spatial patterns associated with each of the tropical modes are very
robust and closely related to the SST anomalies. Mode shifts actually reflect
changes in intensity and amplitude rather than changes in spatial distribution.
Results have shown that for the AMM behaviour in particular, the distinction
between climatic periods is clear. The LGM and mid-Holocene AMM show
opposite behaviour: at the LGM there is an increase in the AMM amplitude and
the north–south SST gradient decreases, while at the mid-Holocene there is an
increase in the north–south SST gradient accompanied by a decrease in the
amplitude of the AMM. The behaviour of the AMM for the 1pctCO2 shows an
overall weakening of the AMM with a decrease in both the AMM amplitude
and the associated north–south SST gradient.
The study of past climate change to place future changes in context is itself
worthwhile . The advantage of using models is that
they can be equally applied to the past and future (as shown here). Ideally
one could use observations of past climates to constrain future projections
. Unfortunately there are currently no
reconstructions of past tropical Atlantic variability to form that
constraint. An alternate approach would be to detect an emergent relationship
between the mean state and climate variability. Reconstructions of
changes in the mean state could then be used as emergent constraints on
future behaviour . We investigate whether there are such
quantifiable links between TAV and the mean state in CMIP5/PMIP3: no
significant relationships emerge. Nonetheless, we feel the approach of
analysing several different multi-model climate experiments, some with direct
or proxy observations available, promises to constrain the uncertainty in
future projections.
The software is freely available at
http://www.cesm.ucar.edu/working_groups/CVC/cvdp/ (last access: 29
August 2018), with the modification plotting scripts for this paper to be
found at https://bitbucket.org/cbrierley/cvdp_pmip (Brierley, 2018).
The results for individual models are freely available for inspection and
download from the PMIP variability database at
http://www.geog.ucl.ac.uk/ucfaccb/PMIPVarData/ (last access: 29 August
2018), along with results for many other modes of climate variability.
The supplement related to this article is available online at: https://doi.org/10.5194/cp-14-1377-2018-supplement.
Both authors contributed equally to devising the study and writing the paper. CB performed the data analysis.
The authors declare that they have no conflict of
interest.
Acknowledgements
This analysis would not have been possible without the sterling effort by
John Fasullo and Adam Phillips. Their foresight and generosity in building
and freely distributing the Climate Variability Diagnostics Package is
wonderfully refreshing. We acknowledge the World Climate Research Programme's
Working Group on Coupled Modelling, which is responsible for CMIP, and we
thank the climate modelling groups (listed in Table of this
paper) for producing and making available their model output. For CMIP, the
US Department of Energy's Program for Climate Model Diagnosis and
Intercomparison provides coordinating support and led the development of software
infrastructure in partnership with the Global Organization for Earth System
Science Portals. This study was supported by the Belmont Forum's PACMEDY
project through awards by NERC (NE/P006752/1) and FAPESP (15/50686-1); Ilana Wainer
was additionally supported by grants CNPq-301726/2013-2 and
CNPq-405869/2013-4. Edited by: Pascale
Braconnot Reviewed by: Pascale Braconnot and two anonymous
referees
References
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak, J.,
Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.: The version-2
global precipitation climatology project (GPCP) monthly precipitation
analysis (1979–present), J. Hydrometeorol., 4, 1147–1167, 2003.
Amaya, D. J., DeFlorio, M. J., Miller, A. J., and Xie, S.-P.: WES feedback and
the Atlantic Meridional Mode: observations and CMIP5 comparisons, Clim.
Dynam., 49, 1–15, 2016.Annan, J. D. and Hargreaves, J. C.: A new global reconstruction of
temperature changes at the Last Glacial Maximum, Clim. Past, 9, 367–376,
10.5194/cp-9-367-2013, 2013.
Annan, J. and Hargreaves, J.: A perspective on model-data surface temperature
comparison at the Last Glacial Maximum, Quaternary Sci. Rev., 107,
1–10, 2015.
Bischoff, T. and Schneider, T.: The equatorial energy balance, ITCZ position,
and double-ITCZ bifurcations, J. Climate, 29, 2997–3013, 2016.Braconnot, P., Otto-Bliesner, B., Harrison, S., Joussaume, S., Peterchmitt,
J.-Y., Abe-Ouchi, A., Crucifix, M., Driesschaert, E., Fichefet, Th., Hewitt,
C. D., Kageyama, M., Kitoh, A., Loutre, M.-F., Marti, O., Merkel, U.,
Ramstein, G., Valdes, P., Weber, L., Yu, Y., and Zhao, Y.: Results of PMIP2
coupled simulations of the Mid-Holocene and Last Glacial Maximum – Part 2:
feedbacks with emphasis on the location of the ITCZ and mid- and high
latitudes heat budget, Clim. Past, 3, 279–296,
10.5194/cp-3-279-2007, 2007.
Braconnot, P., Harrison, S. P., Kageyama, M., Bartlein, P. J.,
Masson-Delmotte, V., Abe-Ouchi, A., Otto-Bliesner, B., and Zhao, Y.:
Evaluation of climate models using palaeoclimatic data, Nat. Clim. Change, 2,
417–424, 2012.Breugem, W.-P., Hazeleger, W., and Haarsma, R.: Multimodel study of tropical
Atlantic variability and change, Geophys. Res. Lett., 33, 10.1029/2006GL027831, 2006.Brierley, C.: PMIP fork of the Climate Variabiltiy Diagnostics Package Last,
available at: https://bitbucket.org/cbrierley/cvdp-pmipT, last access:
29 August 2018.Broccoli, A. and Manabe, S.: The influence of continental ice, atmospheric
CO2, and land albedo on the climate of the last glacial maximum,
Clim.
Dynam., 1, 87–99, 1987.
Broccoli, A. J.: Tropical cooling at the Last Glacial Maximum: An
atmosphere–mixed layer ocean model simulation, J. Climate, 13,
951–976, 2000.
Chang, P., Yamagata, T., Schopf, P., Behera, S., Carton, J., Kessler, W.,
Meyers, G., Qu, T., Schott, F., Shetye, S., and Xie, S.-P.: Climate fluctuations of
tropical coupled systems the role of ocean dynamics, J. Climate,
19, 5122–5174, 2006.
Chiang, J. C.: The tropics in paleoclimate, Annu. Rev. Earth
Pl. Sc., 37, 263–297, 2009.
Chiang, J. C., Kushnir, Y., and Giannini, A.: Deconstructing Atlantic
Intertropical Convergence Zone variability: Influence of the local
cross-equatorial sea surface temperature gradient and remote forcing from the
eastern equatorial Pacific, J. Geophys. Res.-Atmos.,
107, 4530–4544, 2002.
Clark, P. U., Dyke, A. S., Shakun, J. D., Carlson, A. E., Clark, J., Wohlfarth,
B., Mitrovica, J. X., Hostetler, S. W., and McCabe, A. M.: The last glacial
maximum, Science, 325, 710–714, 2009.
Clement, A. C., Seager, R., and Cane, M. A.: Suppression of El Niño during
the Mid-Holocene by changes in the Earth's orbit, Paleoceanography, 15,
731–737, 2000.
Cobb, K. M., Westphal, N., Sayani, H. R., Watson, J. T., Di Lorenzo, E., Cheng,
H., Edwards, R., and Charles, C. D.: Highly variable El Niño–Southern
Oscillation throughout the Holocene, Science, 339, 67–70, 2013.
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J.,
Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P.,
Brönnimann,
S.,
Brunet, M., Crouthamel, R. I., Grant, A. N., Groisman, P. Y., Jones, P. D., Kruk, M. C.,
Kruger, A. C., Marshall, G. J., Maugeri, M., Mok, H. Y., Nordli, Ø., Ross, T. F.,
Trigo R. M., Wang, X. L., Woodruff, S. D., and Worley, S. J.,:
The twentieth century reanalysis project, Q. J. Roy.
Meteor. Soc., 137, 1–28, 2011.
D'Agostino, R., Lionello, P., Adam, O., and Schneider, T.: Factors controlling
Hadley circulation changes from the Last Glacial Maximum to the end of the
21st century, Geophys. Res. Lett., 44, 8585–8591, 2017.
Deppenmeier, A.-L., Haarsma, R. J., and Hazeleger, W.: The Bjerknes feedback in
the tropical Atlantic in CMIP5 models, Clim. Dynam., 47, 2691–2707,
2016.
Deser, C., Alexander, M. A., Xie, S.-P., and Phillips, A. S.: Sea surface
temperature variability: Patterns and mechanisms, Annu. Rev. Mar.
Sci., 2, 115–143, 2010.
Doi, T., Tozuka, T., and Yamagata, T.: Interannual variability of the Guinea
Dome and its possible link with the Atlantic Meridional Mode, Clim.
Dynam., 33, 985–998, 2009.
Donohoe, A., Marshall, J., Ferreira, D., and Mcgee, D.: The relationship
between ITCZ location and cross-equatorial atmospheric heat transport: From
the seasonal cycle to the Last Glacial Maximum, J. Climate, 26,
3597–3618, 2013.Eyring, V., Righi, M., Lauer, A., Evaldsson, M., Wenzel, S., Jones, C., Anav,
A., Andrews, O., Cionni, I., Davin, E. L., Deser, C., Ehbrecht, C.,
Friedlingstein, P., Gleckler, P., Gottschaldt, K.-D., Hagemann, S., Juckes,
M., Kindermann, S., Krasting, J., Kunert, D., Levine, R., Loew, A.,
Mäkelä, J., Martin, G., Mason, E., Phillips, A. S., Read, S., Rio,
C., Roehrig, R., Senftleben, D., Sterl, A., van Ulft, L. H., Walton, J.,
Wang, S., and Williams, K. D.: ESMValTool (v1.0) – a community diagnostic
and performance metrics tool for routine evaluation of Earth system models in
CMIP, Geosci. Model Dev., 9, 1747–1802,
10.5194/gmd-9-1747-2016, 2016.
Green, B. and Marshall, J.: Coupling of trade winds with ocean circulation
damps ITCZ shifts, J. Climate, 30, 4395–4411, 2017.Hall, A. and Qu, X.: Using the current seasonal cycle to constrain snow albedo
feedback in future climate change, Geophys. Res. Lett., 33, L03502, 10.1029/2005GL025127, 2006.Hargreaves, J. C., Annan, J. D., Yoshimori, M., and Abe-Ouchi, A.: Can the Last
Glacial Maximum constrain climate sensitivity?, Geophys. Res. Lett.,
39, L24702, 10.1029/2012GL053872, 2012.Hély, C., Lézine, A. M., and Contributors, A. P. D.: Holocene changes
in African vegetation: tradeoff between climate and water availability,
Clim. Past, 10, 681–686, 10.5194/cp-10-681-2014, 2014.
Ilyas, M., Brierley, C. M., and Guillas, S.: Uncertainty in regional
temperatures inferred from sparse global observations: Application to a
probabilistic classification of El Niño, Geophys. Res. Lett.,
44, 9068–9074, 2017.
IPCC, 2013: 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. F., Qin, D., Plattner, G.-K.,
Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and
Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA, 1535 pp., 2013.Kageyama, M., Braconnot, P., Harrison, S. P., Haywood, A. M., Jungclaus, J.
H., Otto-Bliesner, B. L., Peterschmitt, J.-Y., Abe-Ouchi, A., Albani, S.,
Bartlein, P. J., Brierley, C., Crucifix, M., Dolan, A., Fernandez-Donado, L.,
Fischer, H., Hopcroft, P. O., Ivanovic, R. F., Lambert, F., Lunt, D. J.,
Mahowald, N. M., Peltier, W. R., Phipps, S. J., Roche, D. M., Schmidt, G. A.,
Tarasov, L., Valdes, P. J., Zhang, Q., and Zhou, T.: The PMIP4 contribution
to CMIP6 – Part 1: Overview and over-arching analysis plan, Geosci. Model
Dev., 11, 1033–1057, 10.5194/gmd-11-1033-2018, 2018.Kucharski, F., Kang, I.-S., Farneti, R., and Feudale, L.: Tropical Pacific
response to 20th century Atlantic warming, Geophys. Res. Lett.,
38, L03702, 10.1029/2010GL046248,
2011.
Liu, Z., Zhu, J., Rosenthal, Y., Zhang, X., Otto-Bliesner, B. L., Timmermann,
A., Smith, R. S., Lohmann, G., Zheng, W., and Timm, O. E.: The Holocene
temperature conundrum, P. Natl. Acad. Sci. USA, 111,
E3501–E3505, 2014.
Mahajan, S., Saravanan, R., and Chang, P.: Free and forced variability of the
tropical Atlantic Ocean: Role of the wind–evaporation–sea surface
temperature feedback, J. Climate, 23, 5958–5977, 2010.
Marcott, S. A., Shakun, J. D., Clark, P. U., and Mix, A. C.: A reconstruction
of regional and global temperature for the past 11,300 years, Science, 339,
1198–1201, 2013.
Marcott, S. A., Bauska, T. K., Buizert, C., Steig, E. J., Rosen, J. L., Cuffey,
K. M., Fudge, T., Severinghaus, J. P., Ahn, J., Kalk, M. L., McConnell, J. R., Kendrick, T. S., James, C. T., White, W. C., and Edward, J. B.:
Centennial-scale changes in the global carbon cycle during the last
deglaciation, Nature, 514, 616–619, 2014.
McGee, D., Donohoe, A., Marshall, J., and Ferreira, D.: Changes in ITCZ
location and cross-equatorial heat transport at the Last Glacial Maximum,
Heinrich Stadial 1, and the mid-Holocene, Earth Planet. Sci.
Lett., 390, 69–79, 2014.
Muñoz, E., Weijer, W., Grodsky, S. A., Bates, S. C., and Wainer, I.: Mean
and variability of the tropical Atlantic Ocean in the CCSM4, J.
Climate, 25, 4860–4882, 2012.
Okumura, Y. and Xie, S.-P.: Interaction of the Atlantic equatorial cold tongue
and the African monsoon, J. Climate, 17, 3589–3602, 2004.
Pancost, R. D.: Climate change narratives, Nat. Geosci., 10, 466–468,
2017.Pausata, F. S. R., Li, C., Wettstein, J. J., Kageyama, M., and Nisancioglu,
K. H.: The key role of topography in altering North Atlantic atmospheric
circulation during the last glacial period, Clim. Past, 7, 1089–1101,
10.5194/cp-7-1089-2011, 2011.Pausata, F. S., Zhang, Q., Muschitiello, F., Lu, Z., Chafik, L., Niedermeyer,
E. M., Stager, J. C., Cobb, K. M., and Liu, Z.: Greening of the Sahara
suppressed ENSO activity during the mid-Holocene, Nat. Commun., 8, 16020,
10.1038/ncomms16020,
2017.Perez-Sanz, A., Li, G., González-Sampériz, P., and Harrison, S. P.:
Evaluation of modern and mid-Holocene seasonal precipitation of the
Mediterranean and northern Africa in the CMIP5 simulations, Clim. Past, 10,
551–568, 10.5194/cp-10-551-2014, 2014.Phillips, A. S., Deser, C., and Fasullo, J.: Evaluating Modes of Variability in
Climate Models, EOS, 95, 453–455,
10.1002/2014eo490002, 2014.
Pinot, S., Ramstein, G., Harrison, S., Prentice, I., Guiot, J., Stute, M., and
Joussaume, S.: Tropical paleoclimates at the Last Glacial Maximum: comparison
of Paleoclimate Modeling Intercomparison Project (PMIP) simulations and
paleodata, Clim. Dynam., 15, 857–874, 1999.Rayner, N., Parker, D. E., Horton, E., Folland, C., Alexander, L., Rowell, D.,
Kent, E., and Kaplan, A.: Global analyses of sea surface temperature, sea
ice, and night marine air temperature since the late nineteenth century,
J. Geophys. Res.-Atmos., 108, 4407, 10.1029/2002JD002670, 2003.
Rehfeld, K., Münch, T., Ho, S. L., and Laepple, T.: Global patterns of
declining temperature variability from the Last Glacial Maximum to the
Holocene, Nature, 554, 356–359, 2018.
Richter, I., Xie, S.-P., Behera, S. K., Doi, T., and Masumoto, Y.: Equatorial
Atlantic variability and its relation to mean state biases in CMIP5, Clim.
Dynam., 42, 171–188, 2014.
Ruiz-Barradas, A., Carton, J. A., and Nigam, S.: Structure of
interannual-to-decadal climate variability in the tropical Atlantic sector,
J. Climate, 13, 3285–3297, 2000.
Saravanan, R. and Chang, P.: Thermodynamic coupling and predictability of
tropical sea surface temperature, Earth's Climate, in: Earth's Climate: The Ocean-Atmosphere Interaction, AGU's Geophysical Monograph Series 147, 171–180, 2004.Schneider, T., Bischoff, T., and Haug, G. H.: Migrations and dynamics of the
intertropical convergence zone, Nature, 513, 45–53, 2014.
Servain, J., Wainer, I., McCreary, J. P., and Dessier, A.: Relationship between
the equatorial and meridional modes of climatic variability in the tropical
Atlantic, Geophys. Res. Lett., 26, 485–488, 1999.
Servain, J., Wainer, I., Ludos Ayina, H., and Roquet, H.: The relationship
between the simulated climatic variability modes of the tropical Atlantic,
Int. J. Climatol., 20, 939–953, 2000.
Siongco, A. C., Hohenegger, C., and Stevens, B.: The Atlantic ITCZ bias in
CMIP5 models, Clim. Dynam., 45, 1169–1180, 2015.
Solomon, S., Qin, D., Manning, M., Marquis, M., Averyt, K., Tignor, M., Miller,
H. L., and Zhenlin, C.: 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
Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA,
2007.Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and
the Experiment Design, Bull. Am. Meteorol. Soc., 93, 485–498,
10.1175/bams-d-11-00094.1, 2011.
Tokinaga, H. and Xie, S.-P.: Weakening of the equatorial Atlantic cold tongue
over the past six decades, Nat. Geosci., 4, 222–226, 2011.
Wainer, I. and Soares, J.: North northeast Brazil rainfall and its
decadal-scale relationship to wind stress and sea surface temperature,
Geophys. Res. Lett., 24, 277–280, 1997.Wainer, I., Clauzet, G., Ledru, M.-P., Brady, E., and Otto-Bliesner, B.: Last
Glacial Maximum in South America: paleoclimate proxies and model results,
Geophys. Res. Lett., 32, L08702, 10.1029/2004GL021244, 2005.
Wainer, I., Servain, J., and Clauzet, G.: Is the decadal variability in the
tropical Atlantic a precursor to the NAO?, 26, 4075–4080, 2008.
Wang, L.-C., Jin, F.-F., and Wu, C.-R.: Dynamics of simulated Atlantic
upwelling annual cycle in CMIP5 models, J. Geophys. Res.-Ocean, 122, 5774–5785, 2017.
Xie, S.-P. and Carton, J. A.: Tropical Atlantic variability: Patterns,
mechanisms, and impacts, Earth's Climate, in: Earth's Climate: The Ocean-Atmosphere Interaction, AGU's Geophysical Monograph Series 147, 121–142, 2004.
Zebiak, S. E.: Air–sea interaction in the equatorial Atlantic region, J.
Climate, 6, 1567–1586, 1993.
Zhao, Y., Braconnot, P., Harrison, S. P., Yiou, P., and Marti, O.: Simulated changes in the relationship between tropical ocean temperatures and the western African monsoon during the
mid-Holocene, Clim. Dynam., 28, 533–551, 2007.