The El Niño–Southern Oscillation (ENSO) is tightly linked to the
intraseasonal tropical variability (ITV) that contributes to energise the
deterministic ocean dynamics during the development of El Niño. Here, the
relationship between ITV and ENSO is assessed based on models from the
Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) taking into
account the so-called diversity of ENSO, that is, the existence of two types
of events (central Pacific versus eastern Pacific El Niño). As a first
step, the models' skill in simulating ENSO diversity is assessed. The
characteristics of the ITV are then documented revealing a large dispersion
within an ensemble of 16 models. A total of 11 models exhibit some skill in
simulating the key aspects of the ITV for ENSO: the total variance along the
Equator, the seasonal cycle and the characteristics of the propagation along
the Equator of the Madden–Julian oscillation (MJO) and the convectively
coupled equatorial Rossby (ER) waves. Five models that account realistically
for both the two types of El Niño events and ITV characteristics are used
for the further analysis of seasonal ITV
The El Niño–Southern Oscillation (ENSO) is the dominant mode of climate
variability at interannual
timescale in the Pacific (Bjerknes, 1969; Rasmusson and Carpenter, 1982). It
originates in the equatorial Pacific and induces important climate and
weather anomalies in many parts of the globe through so-called
teleconnections (Horel and Wallace, 1981; Keshavamurti , 1982; Trenberth et
al., 1998; Diaz et al., 2001). Therefore, predicting El Niño occurrence
and amplitude, both in the current conditions and for the next century, is a key societal need (Cai et al.,
2015). The coupled ocean–atmosphere models in a wide range of complexity
from “Earth system models” to intermediate coupled models have demonstrated
encouraging skill in ENSO forecast
(
The dominant intraseasonal mode in the tropics – the Madden–Julian
oscillation (MJO) – was shown to be tightly related to ENSO through its
relationship to episodes of westerly wind events (WWEs), which are short-lived, but
strong westerlies developing over the western Pacific warm pool (e.g. Luther
et al., 1983; Keen, 1982) that can trigger downwelling intraseasonal Kelvin
waves (Kessler et al., 1995), a precursor to El Niño onset (Zhang and
Gottschalck, 2002; McPhaden et al., 2006; Hendon et al., 2007; Fedorov, 2002;
Lengaigne et al., 2003; Boulanger et al., 2004). However, the MJO is not the
only important component of the ITV involved in the development of WWEs.
Puy et al. (2016) highlighted the role of equatorial
Rossby (ER) waves in the generation of WWEs and show that 41 % of WWEs are
associated with the combined occurrence of the ER and MJO convective phase.
Consistently, Gushchina and Dewitte (2012) suggested that the activity of ER
waves is associated with the enhanced intraseasonal Kelvin waves during the
development of El Niño. While the anomalous westerlies related to the
convective phase of MJO are associated with the forcing of oceanic Kelvin waves
in the western Pacific in March–April, preceding the El Niño peak, the
intensification of the ER activity is observed in June–July over the
equatorial central Pacific and tends to compensate for the Kelvin wave
dissipation along its way through the eastern Pacific. Gushchina and
Dewitte (2012) also highlight the different characteristics of the
ENSO
Description of the 23 CMIP5 coupled models analysed in this study. Names in bold indicate the model retained for the evaluation of ITV (Sect. 3.2).
Other studies have focused on the assessment of the ITV in the CMIP databases. Hung et al. (2013) evaluated the skill of 20 models from CMIP5 in simulating the MJO and convectively coupled equatorial waves (CCEWs) and compared their result with the one obtained from CMIP3 models (Lin et al., 2006). They showed that CMIP5 models exhibit an overall improvement in the simulation of ITV, especially the MJO and several CCEWs, as compared to CMIP3 models. The CMIP5 models produce larger total intraseasonal variance of precipitation than the CMIP3 models, including larger variances of MJO, Kelvin, ER and eastward inertio-gravity (EIG) waves. About one-third of the CMIP5 models generate the spectral peak of MJO precipitation between 30 and 70 days; however, the model MJO period tends to be longer than in the observations and only one of the 20 models is able to simulate a realistic eastward propagation of the precipitation patterns associated with MJO.
While the ITV and ENSO characteristics in CMIP5 have been documented separately, to the authors' knowledge, the evaluation of how the ITV relates to the El Niño cycle in CMIP5 models is lacking. This paper addresses this issue, incorporating recent progress in our understanding of ENSO, in particular its diversity (Capotondi et al., 2015). While a long-term motivation is to address the climate change issue, we are also guided by the will to identify the most skilful model in order to carry out process studies and document model biases within a physically based framework.
The paper is organised as follows.
The model database and the observed datasets used for the validation as
well as the diagnostic methods used in this study are described in Sect. 2.
The simulations of two types of El Niño, ITV components and the ITV
The outputs of 23 models from the CMIP5 used for the Intergovernmental Panel
on Climate Change (IPCC) Fifth Assessment Report (AR5) has been analysed (see
model list in Table 1). The 250-year long simulations of the pre-industrial
(hereafter PI) experiment (Taylor et al., 2012) are used for the evaluation
of ENSO types, while a selected 20 years among these simulations are used to
diagnose the ITV characteristics. The motivation for focusing on the PI
experiment and not on the historical simulations as it is commonly done for
model evaluation stands in the fact that it eases the interpretation of the
results since there is no external forcing in the PI experiments, which
provides a benchmark for further assessment of the sensitivity of the
ENSO
To document the ITV properties, we use the technique proposed by Wheeler and
Kiladis (1999). This method is identical to those used in previous studies
evaluating the realism of MJO and CCEW in CMIP3 (Lin et al., 2006) and CMIP5
(Hung et al., 2013) models. It is based on the decomposition of the symmetric
and antisymmetric components relative to the Equator components of the field
in the frequency–wavenumber space. Inversed Fourier transform is then used
to recompose the signal in the desired frequency and wavenumber bands. The
frequency and wavenumber intervals were derived from the normalised
space–time spectrum for U850 and are centred on the spectral maximum of U850
(see Gushchina and Dewitte, 2011). In the models, the localisation of
spectral maximum may differ from the reanalysis. However, sensitivity tests
show that slight changes in the frequency–wavenumber interval do not
significantly change the characteristics of the recomposed signal; therefore,
fixed boundaries in the frequency and zonal wavenumber domain were used.
These are, for MJO, zonal wavenumbers 1–3 and a period of 30–96 days; for
equatorial Rossby waves, zonal wavenumbers
Following Hayashi (1979), only the part of the eastward power that is
incoherent with its equivalent westward power represents the true
eastward-propagating signal. Moreover, the results of Jiang et al. (2015)
emphasise the dominant stationary signals in many model simulations. To
verify if the westward counterpart is present in the models, we recomposed
the signal in the same frequency intervals as for MJO and Rossby waves but
for the opposite sign of zonal wavenumbers:
The amplitude of ER and MJO was calculated by taking the root mean square (rms) of the recomposed signal in a running window whose span depends on the wave's type (90 and 48 days for MJO and equatorial Rossby waves, respectively). Then, the running rms was considered as monthly averaged. To calculate the anomalies, the mean climatology over the investigated period was removed.
We use here U850 field for ITV filtering instead of outgoing longwave radiation (OLR) or brightness temperature signals from satellite data that are commonly used to derive the frequency–wavenumber of ITV, noting that the filter bands are similar for OLR and U850 as predicted by a simple dynamical model of ITV (Thual et al., 2014). Moreover, the use of zonal wind field eases the interpretation of the results since it is the westerly wind anomalies that serve as a physical conduit from the ITV to the ENSO dynamics. This approach follows previous relevant studies (McPhaden et al., 2006; Hendon et al., 2007).
In order to depict ENSO variability in terms of its two flavours (or regimes),
we used the indices defined by Takahashi et al. (2011), the so-called
The CP and EP events were selected using the time series of
As a first step, the models' skill in simulating ENSO diversity is assessed
based on the comparison of the
Spatial correlation between observed (HadISST) and simulated (CMIP5
models)
The characteristics of ITV are documented here with the focus on its intensity, seasonality and propagating features. Earlier studies have evidenced biases in the simulation of MJO and CCEW in CMIP models (Guo et al., 2015; Jiang et al., 2015; Klingaman et al., 2015; Xavier et al., 2015), however, with the CMIP5 models being more realistic (Hung et al., 2013) than the CMIP3 models (Lin et al., 2006). Our analysis here is based on the most realistic models in terms of their skill in simulating the two types of El Niño. Some modes are not considered in the analyses because the daily data of U850 were not available in open access. We thus retain 16 models: ACCESS1-3, BNU-ESM, CanESM2, CCSM4, CMCC-CM, CNRM-CM5, EC-EARTH, HadGEM2-CC, HadGEM2-ES, INMCM4, IPSL-CM5A-MR, MIROC5, MPI-ESM-LR, MPI-ESM-P, MRI-CGCM3 and NorESM1-M. The 20 years of the PI experiment for each model are analysed, the results of which are compared to the NCEP/NCAR Reanalysis data over the period 1980–1999.
Space–time spectrum averaged between 15
Variance (rms) of MJO
Summary of the model skill according to the diagnostics performed in
our study. The
Definition of the scale for classifying the models' skill for Table 3.
Figure 2 presents the space–time spectra normalised above the background
spectra for the symmetric component of U850 wind for the observations
(Fig. 2, upper panel) and for the CMIP5 models. Superimposed upon these plots
are the dispersion curves for the odd meridional mode number of equatorial
waves for various equivalent depths (
Seasonal variances (rms) of MJO averaged zonally over the tropical
Pacific (120
Seasonal variances (rms) of Rossby waves averaged zonally over
the tropical Pacific (120
Root mean square error (RMSE) between modelled and observed seasonal
variance of MJO
The distribution of the variance of the MJO and ER along the equatorial band
is also key to accounting for the relationship between ENSO and ITV
considering that the balance between oceanic feedbacks which depends on the
sloping mean thermocline determines the nature of the coupled instability
during ENSO (An and Jin, 2001). The rms values of the ITV components over
20 years averaged between 15
In the following, the seasonality of the ITV is assessed considering the
focus of this study on the seasonal dependence of the ENSO
The MJO has a maximum intensity in the summer hemisphere (i.e. in the
Northern Hemisphere in July and in the Southern Hemisphere in January), which
implies that the MJO variance peaks along the Equator in boreal spring (Zhang
and Dong, 2004) when it may act efficiently as an ENSO trigger. Therefore, the
MJO cross-equatorial seasonal migration is a key feature that needs to be
realistically simulated in the models. The MJO seasonal variability is thus
estimated over the three latitudinal belts: 10–15
Lag correlation of the MJO filtered U850 averaged along the Equator
between 5
Lag correlation of the ER filtered U850 averaged along the Equator
between 5
Periods used for the statistics of Figs. 10 and 11.
Further, the propagating characteristics of the MJO and ER along the Equator
are documented for the most skilful models in terms of the amplitude and
seasonal cycle of the ITV. Figures 7 and 8 show the lag correlation of the
MJO and ER filtered U850 time series averaged between 5
Evolution of the predictive score (see Eq. 1 in the text) for
MJO
Monthly lagged correlation of
As Fig. 10 but for Rossby waves' activity index.
In this section, our objective is to illustrate the large dispersion among
models' skill in simulating the ITV
Consistently with the results conveyed in Fig. 9, the ITV
Regarding the Rossby wave, the observations indicate that the ER activity
intensifies in February–April and July–September of the year prior to the
EP El Niño peak (Fig. 11a). During CP El Niño, the Rossby wave
activity also appears to be a good precursor, and the relationship with SST
anomalies persists after the peak phase (Fig. 11g). All five models have some
skill in simulating the ER
In this paper, we question the extent to which the models that are used for
assessing the change in ENSO properties under global warming (i.e. CMIP5) are
able to account for a fundamental ENSO property found in the observations,
that is, the tendency of ITV activity to increase one to two seasons prior to
the ENSO peak (McPhaden et al., 2006; Hendon et al., 2007; Gushchina and
Dewitte, 2012). Five CMIP5 models (BNU-ESM, CCSM4, CMCC-CM, INM-CM4 and
MIROC5) are retained that have been evaluated among a total of 16 that
exhibit relatively good skills in simulating many aspects of the ITV, that
is, its variance along the Equator, its seasonality and the propagation
characteristics of the MJO and ER. These five models have also some skills in
accounting for the so-called ENSO diversity, that is, the existence of two
types of El Niño events, the EP and CP events. Despite the ability of
these models to simulate relatively realistically both the ITV
characteristics and the ENSO diversity, they exhibit limited skill in
simulating the seasonal ENSO
The codes in Fortran and MATLAB are available from the corresponding author upon request (Daria Gushchina, dasha155@mail.ru).
Model data can be downloaded from the CMIP (Coupled Model Intercomparison
Project) data portal (
The supplement related to this article is available online at:
This study is supported by the Russian Foundation of Basic Research grant nos. 18-05-00767 and 16-35-00394/16. The study is carried out in the framework of the scientific program of Faculty of Geography of Moscow State University (no. AAAA-A16-116032810086-4). Boris Dewitte acknowledges supports from FONDECYT (grant nos. 1151185 and 1171861) and from LEFE-GMMC. Edited by: Richard Neale Reviewed by: three anonymous referees