Role of the near-annual mode in the ENSO cycle

existence of a stronger NAM for the post-1998 years (after the major 1997-98 El Niño event) than during the 1980s and 1990s when the thermocline feedback was most active and ENSO variability dominated by Jin (1997)’s recharge oscillator paradigm (Jin, 1997a,b), supporting the afore- mentioned modelling results.

The co-existence of these two coupled modes in the real world has several important implications. One is that they are responsible to a large extent to the richness of the spectrum of tropical climate variability, which extends from near-annual and quasi-biennial (Meehl, 1987;

Ropelewski et al., 1992) to decadal (Tourre et al., 1999; Zhang et al., 1999) time scales and beyond.

Their inverse dependencies to the mean state suggest a strong interaction between both coupled modes: in particular, minor nearly-annual El Niño events of the warm early 1990s and La Niña events of the cold early 2000s may be due to the NAM (J03; K04). This means that the NAM is likely to modify ENSO and its periodicity. However, the superimposition of both coupled modes associated to the relatively short observational records make the NAM and its interaction with ENSO difficult to observe in the real world (Mantua and Battisti, 1995; J03). Long term CGCM simulations in which the NAM can develop and be identified offer the opportunity to document its role on the ENSO variability and mean state. In this respect, Dewitte et al. (2007 –hereafter D07) demonstrated from a previous version of the CNRM-CM3 model (member of the CMIP3 dataset), that the ENSO mode and the NAM could interact to produce a quasi-biennial ENSO (Cibot et al., 2005). Such interaction takes place through non-linearities associated with the zonal advective feedback: D07 showed that the cold bias of the coupled model over the western Pacific warm pool was due to the asymmetry of both ENSO and the NAM. The latter is the result of advection of anomalous temperature by the mean zonal currents (−u.(T^')_x) and non-linear zonal advection (−u^'.(T^')_x), the main contributors to the zonal advective feedback in this model. As mentioned in the previous section, we suggest that biases in the mean temperature field are likely to feedback on the dynamical regime of ENSO, and thereby on the time scale of ENSO variability: cold biases tend to enhance the zonal advective feedback and the NAM, whereas warm biases tend to favour the thermocline feedback and damp the NAM. In the following, we extend the approach of D07 to a more comprehensive data set: the CGCMs of the CMIP3 archive. It is verified that in most cases, the over- estimation (resp. under-estimation) of the zonal advective feedback can be related to the high (resp.

low) energy level of near-annual activity. Note that only the 16 CGCMs with a statistically significant ENSO (see previous section) are considered here.

Fig. 2.1. First SVD mode between the 12-month high-pass-filtered wind stress and surface zonal current anomalies (11°S-11°N, 134°E-81°W): (a) SODA 1.4.2, (b) CNRM-CM3, (c) CSIRO-MK3.0, (d) GFDL-CM2.0, (e) INGV- ECHAM4, (f) MIUB-ECHO-G, (g) MPI-ECHAM5, (h) UKMO-HadCM3. For each model, from left to right and top to bottom: spatial patterns for zonal wind stress and current anomalies, associated adimensionalized time series (full line for currents, dashed line for wind stress anomalies), and the corresponding frequency spectra adimensionalized by the energy integrated over the whole frequency domain (full line for currents, dashed line for red noise). CI = 0.2 units.

Spatial patterns are adimensionalized by their respective variance over the domain and multiplied by 10. Percentage of explained variance for zonal currents and wind stress are indicated on the corresponding panels. Percentage of explained covariance is also provided. Correlation value between time series is indicated above the corresponding panel and the dominant NAM period is mentioned on the spectrum plot if significant.

var=17.5% var=10.4% var=14.7% var=18.4%

var=12.1% var=14.8% var=11.2% var=14.8%

var=8.6% var=15.0% var=14.1% var=21.2%

var=15.0% var=31.0% var=7.2% var=17.6%

Following D07, the NAM in the models was diagnosed from the results of the SVD between the 12-month high-pass-filtered surface zonal current and wind stress anomalies in the tropical Pacific Ocean area (defined as 11°S-11°N, 120°E-70°W) for the whole available time periods of simulation (table 1 from previous section). Surface zonal currents are estimated here as the average of the top four model levels, since these correspond to the first thirty or fourty meters depth for most CGCMs. Anomalies, frequency spectra and NAM periods are computed in a similar way to that of the ENSO mode (see previous section). Results are presented in figure 2.1 for the same models as in figure 1 from the previous section. Most models, including SODA, show similar spatial patterns: a relatively strong maximum variability in the western equatorial Pacific for zonal currents, with a symmetric horseshoe pattern for several models (CNRM-CM3, CSIRO-MK3.0, GFDL-CM2.0, MIUB-ECHO-G, UKMO-HadCM3), typical of Rossby waves and consistently with D07. The associated wind stress patterns also show a maximum variability in the western equatorial Pacific.

The coupled nature of this fast mode of variability is confirmed by the rather strong correlation values between time series associated to currents and wind stress (table 2.1).

Table 2.1. Near-Annual Characteristics of the models.

Model Name Model Number

NAM Period (months)

Correlation between time series

Near-Annual Activity Index (x10^-3 (m/s)²) SODA 1.4.2 ---- 10.3 0.62 11.58

BCCR-BCM2.0 1 ---- 0.59 2.35

CNRM-CM3 4 9.0 0.74 9.25

CSIRO-MK3.0 (run1) 5 8.0 0.58 1.11

CSIRO-MK3.5 6 10.3 0.61 1.24

GFDL-CM2.0 7 ---- 0.66 2.41

GFDL-CM2.1 8 8.0 0.68 5.41

IAP-FGOALS1.0-g (run1) 12 9.0 0.76 3.05

INGV-ECHAM4 13 8.0 0.48 2.59

INM-CM3.0 14 9.0 0.68 2.98

IPSL-CM4 15 9.0 0.58 3.78

MIUB-ECHO-G 18 ---- 0.62 6.88

MPI-ECHAM5 19 ---- 0.71 2.66

MRI-CGCM2.3.2A 20 7.2 0.87 5.51

NCAR-CCSM3.0 (run2) 21 8.0 0.68 2.50 UKMO-HadCM3 (run1) 22 10.3 0.62 3.33

UKMO-HadGEM1 23 10.3 0.61 3.02

Concerning temporal characteristics, results are more contrasted. A NAM seems to be detectable for most models in the [7.5 - 10.5] month^-1 frequency band (table 2.1), although it is not statistically significant for some of them, like GFDL-CM2.0, MIUB-ECHO-G or MPI-ECHAM5 for example. This is believed to be due to the fact that NAM activity characteristics (in particular its period) are sensitive to changes in the mean state, which the SVD analysis cannot grasp. However, the similar structure of the mode captured by the SVD analysis among the models suggests the

existence of such mode for most CGCMs. For this reason, and because we do not focus here on the time scale of the NAM, this lack of statistical significance for some models will not be considered in the rest of the study. Conversely, some models have a very marked peak, CNRM-CM3 or even UKMO-HadCM3 being good examples. SODA also features a clear peak at 10.3 months associated to the NAM. The period of the NAM is quite heterogenous (table 2.1), going from 7.2 months for MRI-CGCM2.3.2A to 10.3 months for CSIRO-MK3.5 and the UKMO models.

In order to infer an index of NAM activity, zonal current anomalies are first averaged over the Niño-4 box (5°S-5°N, 160°E-150°W), the region over which the NAM exhibits a peak in variance (figure 2.1). The scale-average time series of the corresponding wavelet energy spectrum in the [6 – 18] month^-1 frequency band is then derived and the mean value over the whole time period provides an index of NAM activity. The near-annual activity index presents a wide range of values (table 2.1). Note that SODA exhibits a rather strong value compared to the models which has to be related to the shorter time period of the record and the fact that SODA has a richer spectrum of variability than the CGCMs. Other indices of NAM activity were tested, based on the results of the SVDs. They lead to comparable results. We chose to retain the NAM activity index based on the full zonal current anomalies for simplicity. An index based on SST anomalies was also tested. Such index exhibits similar tendency than the one based on zonal current anomalies although with a much reduced standard deviation between models. This is due to the fact that the NAM has a weaker signature in SST than in surface zonal current (Wu and Kirtman, 2005; D07). The index of NAM activity can then be used to assess the dependence of the NAM on the dominant ENSO feedback mechanism.

In the following, we investigate to which extent characteristics of the NAM can be related to the biases in the mean state identified in the previous section: according to previous studies (J03;

K04; D07), it is expected that models with a dominant zonal advective feedback favor the enhancement of the NAM, leading to a ‘fast’ ENSO period (see previous section). On the other hand, models exhibiting a dominant thermocline feedback and thereby a ‘slow’ ENSO period (see previous section) should have a damped NAM.

Figure 2.2 presents the near-annual activity index for the 16 models, classified into the 3 groups: models dominated by the zonal advective feedback, models dominated by the thermocline feedback, and those with the combination of both mechanisms. Interestingly, models from the thermocline feedback group (group 3) have lower levels of near-annual activity. Models from the zonal advective feedback group (group 1) have a high near-annual activity index on average, though large discrepancies are found within the members of this group, while models from the hybrid feedback group (group 2) exhibit intermediate values that are more homogeneous. Most models from

group 1 (see corresponding error bars)have higher NAM index values or at least of the same order of magnitude than all models from group 2, and similarly, most models from group 2 have higher values or of the same order of magnitude than all models from group 3 (except GFDL-CM2.1). This confirms the fact that an enhanced (resp. diminished) zonal advective feedback is associated to a relatively larger (resp. smaller) NAM variability, itself leading to faster (resp. slower) interannual variabilities through time scale interactions (D07). Models with a hybrid dynamical regime are generally associated to intermediate energy levels of the NAM index. A similar tendency is observed when adimensionalizing the NAM index by the Niño3-SST index variability (not shown), even if the models with a dominant zonal advective feedback have on average a larger ENSO variability than the models of the other groups.

Fig. 2.2. Histogram of the Near-Annual Activity Index of the CMIP3 models. Model names are referenced in table 1 from previous section. The colour code is that of figure 6 from previous section. The black dot represents the mean value for each group. The mean was calculated excluding the models deviating from the mean by more than the standard deviation of the considered group. Error bars are provided, that correspond to the highest and lowest values of the models retained for the calculation of the mean value.

To summarize, it is shown here that consistent with theory, the NAM in IPCC climate models is enhanced when the mean state is colder over the western Pacific warm pool than observed, leading to favour the zonal advective feedback and a fast advective-reflective-type ENSO (Picaut et al., 1997), and damped when the mean state is warmer over the warm pool, setting the conditions for a dominant thermocline feedback together with a slow recharge-oscillator-type ENSO (Jin, 1997a,b).

a)

b)

Fig. 2.3. Schematic of the mechanisms involved in the interaction between ENSO, NAM and the mean state for a) a dominant zonal advective feedback and b) a dominant thermocline feedback. The colour code is for the temperature

bias associated to the different processes: blue is for a colder state than observed, red is for a warmer state, and light blue is for a slightly colder state. In a), strong easterlies over the warm pool (τxwest) tend to have a cooling effect on the surface ocean (Twest) through mixing and entrainment, and through the forcing of strong westward surface flow (uwest) that advects colder waters from the east towards the western Pacific. uwest is an index of the zonal advective feedback, involving overestimated advection of the temperature anomalies by the mean zonal currents (^−u

( )

overestimated non-linear zonal advection ie the advection of temperature anomalies by the zonal current anomalies (^−u'

( )

Based on previous studies and results from the previous section, it is suggested that the NAM has a specific role in the modulation of ENSO by changes in the mean state and in the related feedback mechanisms (figure 2.3): the asymmetry of SST over the warm pool due to ENSO- and NAM-related non-linearities (see previous section) produces a non-zero residual which sign and amplitude depend on the dominant ENSO feedback mechanism. Thus models having a dominant zonal advective feedback will favour a cooling tendendy of the mean state over the warm pool region, producing a positive feedback on the NAM (cooler mean state will enhance NAM activity), whereas models having a dominant thermocline feedback through ENSO nonlinearity will tend to reduce the impact of the NAM on the mean state leading to a negative feedback. Such hypothesis will need to be investigated further in the light of the results of the analyses of the non-linear advection terms at both interannual and near-annual frequencies.