The efects of remote SST forcings on ENSO dynamics, variability and diversity

Transcription

1 Clim Dyn DOI.7/s The efects of remote SST forcings on ENSO dynamics, variability and diversity Dietmar Dommenget Yanshan Yu Received: 7 February 26 / Accepted: 28 November 26 Springer-Verlag Berlin Heidelberg 26 Abstract Air-sea interactions with remote regions in the tropical Indian and Atlantic, and extra-tropical oceans can inluence ENSO features in the tropical Paciic. In this study these efects are explored by using an AGCM coupled with a Slab Ocean and a simple recharge oscillator ENSO model through switched on/of air-sea interaction in respective ocean area. It is shown that the decoupling in diferent remote regions has diferent impacts on ENSO dynamics, variability and diversity. The most interesting result is that the air-sea interactions with remote tropical oceans provide a delayed negative feedback to ENSO similar to that of the tropical Paciic Ocean internal wave dynamics. This is caused by the ENSO teleconnections: they lead to a delayed remote warming and cooling, which in turn feedbacks to ENSO efectively giving a delayed negative feedback. The model simulations suggest that this remote delayed feedback may contribute about 4% to the total delayed negative feedback of ENSO. Thus a central element of ENSO dynamics is partly due to interactions with other tropical ocean basins by atmospheric teleconnections. Furthermore, all remote regions efectively provide stochastic forcings for the ENSO variability and therefore increase the ENSO variability. The inluence from the remote regions also causes diferent patterns of sea surface temperature (SST) variability in the tropical Paciic, contributing to the diversity of the ENSO mode. In particular the extra-tropical Paciic regions force SST variability that is diferent from the equatorial ENSO mode of variability. The inluence that the remote regions have on the * Dietmar Dommenget dietmar.dommenget@monash.edu School of Earth, Atmosphere and Environment, Monash University, Clayton, VIC 38, Australia ENSO dynamics and variability is signiicantly altered by the interaction between the equatorial recharge oscillator dynamics and the simple thermodynamic slab ocean processes. Keywords El Nino Climate variability ENSO Teleconnections Introduction El Nino-Southern Oscillation (ENSO), the dominant mode of interannual climate variability in the tropical Paciic, is known to afect tropical Indian Ocean, tropical Atlantic Ocean and extra-tropical Paciic Ocean through teleconnections. Conversely, sea surface temperature (SST) variability in these ocean areas also inluences ENSO variability in the tropical Paciic Ocean in various ways. Indian Ocean warming afects atmospheric circulation over the western Paciic and then modulates ENSO evolution (Kug and Kang 26; Dommenget et al. 26; Ohba and Ueda 27; Jansen et al. 29; Izumo et al. 2; Frauen and Dommenget 22; Santoso et al. 22; Kajtar et al. 25; Dayan et al. 25). Tropical Atlantic SST variability can also modulate ENSO variability through the Walker circulation (Dommenget et al. 26; Jansen et al. 29; Rodriguez-Fonseca et al. 29; Frauen and Dommenget 22; Ding et al. 22; Ham et al. 23; McGregor et al. 24). A number of studies also suggest that the SST variability in the North Paciic inluences El Nino events occurring in the central Paciic through wind-evaporation-sst feedback (Vimont et al. 2; Chiang and Vimont 24; Yu and Kim 2; Larson and Kirtman 23). The Southern Hemisphere also inluences ENSO at diferent time scales (e.g. Matei et al. 28; Vol.:( )

2 D. Dommenget, Y. Yu Clement et al. 2; Okumura 23; Tatebe et al. 23; Boschat et al. 23; Zhang et al. 24). However, there s no consensus on the efect of each individual ocean area on the ENSO s amplitude and frequency. Some of the previous studies found that Indian Ocean coupling (e.g. interactive SST variability) could enhance ENSO variability (Yu et al. 22; Wu and Kirtman 24) while others found the reverse (Dommenget et al. 26; Kug et al. 26; Frauen and Dommenget 22; Santoso et al. 22) or no impact of the Indian Ocean coupling on ENSO amplitude (Yeh et al. 27). Frauen and Dommenget (22) and Jansen et al. (29) argued that the decoupling (e.g. ixed prescribed seasonal cycle of SST) of the tropical Atlantic has almost no efect on the ENSO dynamics, but Dommenget et al. (26) found a shift in ENSO period when decoupling the tropical Atlantic Ocean. Terray et al. (26) found that SSTs in both the Indian Ocean and the Atlantic Ocean have a signiicant damping efect on ENSO variability and promote a shorter ENSO cycle. Thus it is important to understand how the diferent remote forcings afect the ENSO internal dynamics. In general ENSO dynamics can be described by the interactions between the zonal wind stress, SST and the thermocline depth, often summarized as the Bjerknes feedbacks (Bjerknes 969). The simplest and widely accepted way of combining these simpliied interactions is in the recharge oscillator toy model of Jin (997) and in the further simpliied model of Burgers et al. (25), which describes ENSO as a recharge and discharge of heat content along the whole equatorial Paciic forced by zonal wind stress in the central equatorial Paciic and atmospheric heat luxes over the NINO3 region (5 S 5 N/5 W 9 W). The recharge oscillator model coupled to a fully complex atmosphere general circulation model (AGCM) has been used to study ENSO dynamics and the efect of ocean basin interactions on ENSO by Frauen and Dommenget (2, 22). However, in their study the SSTA variability in the tropical Paciic is simpliied to only one variable NINO3 index, hence it cannot capture the spatial variability of SSTA and ENSO diversity. Furthermore, it needs to be noted that ENSO variability can result from a number of diferent processes (e.g. Neelin et al. 998; Dommenget 2; Dommenget et al. 24). Yu et al. (26; referred to as Y5) employed a slab ocean model interacting with the recharge oscillator and showed that this model is capable of simulating ENSO amplitude, period, diversity and other statistics more realistic then the recharge oscillator model by itself. They further pointed out that both Slab Ocean processes and recharge oscillator contribute to the SSTA variability in the tropical Paciic and that the modes of the Slab Ocean interact with the ReOsc mode in non-linear way. A natural question will arise: how the other remote ocean areas afect ENSO features in this model framework and what is the role of Slab Ocean processes in this inter-basin interaction. The aim of this study here is to analyze the efect of airsea interaction in various ocean areas on the ENSO features in a framework where both slab ocean process and recharge oscillator contribute to the ENSO variability. We will focus here on model simulations with decoupling remote SST interactions. This modeling approach will allow us to clearly illustrate causality and it will help us to evaluate the dynamical efects on ENSO that would otherwise (with analysis of observations only or coupled model simulations) be diicult to achieve. We will illustrate that the tropical Indian Ocean and the tropical Atlantic Ocean tend to have a stronger efect on ENSO variability and dynamics, whereas the extra-tropical Paciic Ocean have a stronger efect on ENSO diversity. We will also show the results in a recharge oscillator only model where Slab Ocean processes are not included, which is similar to the model used in Frauen and Dommenget (22; here referred to as FD2) but with a diferent AGCM component. Using the reduced complexity slab ocean and recharge oscillator model of ENSO allows us to more easily understand and quantify the dynamical impact on ENSO. However, this model approach will also have some limitations, which we will point out in more detail in the following sections. This paper is organized as follows. The models and experimental setups are described in Sect. 2. The characteristics of the ENSO variability changes due to the remote inluences from various ocean basins are studied in Sect. 3. Section 4 will focus on understanding how the remote regions change the dynamics of ENSO and therefore explain the changes in the characteristics of the ENSO variability. The inal analysis section focuses on the ENSO diversity and changes in SST patterns. The study is concluded with a Sect Model, experimental setups and methods In this study three diferent hybrid model simulations are studied that all use the same atmosphere model, but differ only in the formulation of the simpliied ocean models. These are the same model simulations studied in Y5. The main elements of the model simulations are presented below, but for details on the model development see Y5. The AGCM in all three simulations is a low-resolution version ( ) of the Australian Community Climate and Earth System Simulator (ACCESS) model of the UK Meteorological Oice Uniied Model AGCM with HadGEM2 physics (Davies et al. 25; Martin et al. 2, 2 ; Bi et al. 23). In all three simulations the ocean is simulated with simpliied models and SST climatologies are forced to be similar to the observed by either prescribing the

3 The efects of remote SST forcings on ENSO dynamics, variability and diversity mean SST or by lux corrections. Sea ice is kept ixed to seasonally changing climatologies. Thus no anomalous sea ice variability is present in these model simulations. The irst model is a Slab Ocean model with constant mixed layer depth of 5 m: dsst( x, t ) ( ) ( ) = F () dt atmos x, t + FQ x, tj where γ is the heat capacity of the 5 m mixed layer and F atmos is the net heat lux into the ocean. The lux correction, F Q, is a state-independent lux correction that forces the model to have the same mean SST climatology as observed. Both SST and F atmos are functions of location, x, and time, t, and F Q is a function of location and calendar day of the year, t j. Thus this SST tendency Eq. () models the SST at every grid point for each time step. We refer to this model as the Slab simulation. The second model (referred to as ReOsc) is the same as the Slab simulation except the slab ocean component in the tropical Paciic (2 S 2 N, 3 E 7 W) is replaced by a low order two-dimensional recharge oscillator model. This two-dimensional recharge oscillator model from Burgers et al. (25) is given by two coupled diferential equations of SST anomalies in the NINO3 region, T, and the thermocline depth anomalies over the whole equatorial Paciic, h: dsst( x, t ) dt dh(t) dt = a 2O T(t)+a 22 h(t)+ λ 2 a 22 τ(t) where f is the averaged F atmos anomaly in NINO3 region and τ is averaged zonal wind stress anomaly in the central Paciic (6 S 6 N, 6 E 4 W). λ is a free coupling parameter; a O and a 2O are variations of the original parameters a and a 2 by excluding the linear relation to τ and f from atmospheric feedbacks with regression coeicients C τt and C ft, respectively. To get basin-wide temperatures the anomaly T value is multiplied with the irst EOF mode of observed SSTA in the tropical Paciic and SST climatology of the Slab model, SST clim ( x, tj ), are added: SST ( x, t ) = SST c lim ( x, tj ) + T(t) EOFst ( x ). = F atmos ( x, t ) + FQ ( x, tj ) + PRe Osc ( x ) [ao T(t)+a 2 h(t)+a 2 (t)] This model is identical to the model developed in Frauen and Dommenget (2) except that a diferent AGCM is used and the parameter values in Eqs. (3 5) are diferent, relecting the diferent AGCMs (see Table 2 in Y5 for details). Frauen and Dommenget (2) and FD2 used the ECHAM5 AGCM in a similar low-resolution. The third model (referred to as ReOsc-Slab) considers both Slab Ocean Eq. () and the ReOsc model Eqs. (3, 4) in the tropical Paciic leading to the SST equations: (3) (4) (5) dt(t) dt dh(t) dt The two equations are forced by stochastic noise terms ζ and ζ 2. The stochastic noise terms ζ and ζ 2 result from the complex atmospheric dynamics simulated by the atmospheric model. The overall strength of these noise terms are comparable to those observed (Y5) giving a good irst order approximation of atmospheric noise forcing to the large scale ENSO dynamics. However, small-scale noise resulting from ocean dynamics is not considered in this model. The model parameters a and a 22 represent the damping (or growth rate) of T and h, and the parameters a 2 and a 2 the coupling between T and h. In the ReOsc simulation the Eq. (2) are coupled to the atmosphere model leading to the equations: dt(t) dt = a T(t)+a 2 h(t)+ζ = a 2 T(t)+a 22 h(t)+ζ 2 = a O T(t)+a 2 h(t)+a 2 λτ(t)+ f(t) γ (2) where the pattern of the recharge oscillator component is revised from EOF- to P ReOsc ( x ) (see Y5). The PReOsc ( x ) pattern is more conined to the equator compared to the EOF st ( x ) pattern used in the ReOsc simulation, as it was assumed that in the ReOSc-Slab simulation the EOF- of SST variability in the tropical Paciic is the combined efect of the recharger oscillator pattern and the slab ocean processes. Thus by construction the efective SST EOF- pattern in the ReOSc-Slab and ReOsc simulation are very similar (see Y5 for further discussions). Further, to make the ENSO amplitude in both the ReOsc-Slab model and the ReOsc model similar to observation, the parameters are set diferently in these two models (see Table 2 in Y5 for details; ReOsc model is denoted as ReOsc-Obs model there). Each of the three models has thermal coupling (heat luxes) to the atmospheric variability where the slab ocean is active and additional dynamical coupling to atmospheric zonal wind stress in the central tropical Paciic box where the ReOsc model is active. Each of the three simulations is 5 years long and are refereed to as control simulations (CTRL). The initial conditions of SST climatology (no initial anomalies) and h = are not afecting any of the analysis results, given the length of the simulations.

4 D. Dommenget, Y. Yu In addition to the three CTRL simulations, we perform partial coupling experiments based on the above three models: Slab, ReOsc and ReOsc-Slab. For each model, there are ive 5-year long sensitivity simulations as: uncoupled tropical Indian Ocean simulation (NO-IND), uncoupled tropical Atlantic Ocean simulation (NO-ATL), uncoupled extra-tropical Oceans simulation (NO-EXTROP), uncoupled tropical Indian and Atlantic Ocean simulation (NO-INDATL), and everywhere uncoupled except tropical Paciic Ocean simulation (NO-ALL). In each of the sensitivity experiments the SST in the uncoupled region is prescribed by monthly varying climatology obtained from the control simulation. Thus the SST in these regions is not responding to any changes in the atmospheric forcings, but will always be the same for the same calendar day of the year. See Fig. for the domain boundaries for each of the sensitivity experiments. We need to point out here that the model setup described above does have some important advantages, but also does have some important limitations. The main advantage of using a low-resolution model is that we will be able to discuss 5 years long integrations, which will allow us to detect even small efects. This, however, comes with the disadvantage of limited skill in simulating some of the smaller scale features and some of the more complex dynamics. The simpliication of the tropical ocean dynamics to the simple linear framework of the ReOsc model allows us to directly diagnose the impact of the remote regions on the model parameters (e.g. a, a 2, a 2 and a 22 ). On the other hand the ReOsc model is a strong simpliication of the ENSO ocean dynamics. Processes that do not project onto these simpliied dynamics will not be considered. Simulating the remote ocean by a lux corrected slab ocean model does have the advantage that the control mean SST is close to those observed and therefore the atmospheric response to SST anomalies will be more realistic than in mean state biased fully coupled general circulation model (CGCM) simulations. However, slab ocean models do respond diferently to atmospheric wind stress forcing then CGCMs (e.g. subtropical gyres respond to wind stress curl changes). In summary the simpliied model setup discussed here will have some limitations that need to be considered for the following analysis. All analyses presented here are based on monthly mean data, with the anomalies deined for each model simulation individually relative to the model s mean monthly climatology. The 2-dimensional model Eq. (2) can also be used to estimate the efective model parameters from simulated T and h statistics (e.g. Burgers et al. 25; Jansen et al. 29; Frauen and Dommenget 22). The efective recharge oscillator parameters a, a 2, a 2 and a 22 are estimated from each model simulation by multivariate linear regressing the monthly mean tendencies of T and h against monthly mean T and h respectively. The four parameters set in the model s equations (Eqs. 3, 4) and the efective estimated values from the SST statistics do not need to be the same and in general will not be the same due the interaction of diferent processes and due to the atmospheric forcings not just being a linear relation to the NINO3 SST. The residual of the linear regression it can be interpreted, as the random noise forcing with the standard deviation of the residuals Fig. Standard deviation of monthly mean SST variability in the ReOsc-Slab simulation. The domain boundaries of all decoupling experiments are shown in the red, green and blue box frames

5 The efects of remote SST forcings on ENSO dynamics, variability and diversity being the standard deviation of the noise forcings for the T and h equations. The total variance of a linear superposition of independent source of variability, Var total, is the sum of the independent sources: Var total = i Var i Thus, if we assume independent sources, then the variance in the uncoupled experiments is Var total minus the variance from the remote regions. This in turn allows us to estimate variance from the remote region as the diference between the control simulation and the uncoupled simulation. Similarly, we can estimate all linear superposition from the combination of the control and all uncoupled simulations. 3 Inluences on the variability We start the analysis sections with looking at the changes in the SST variability and related forcings due to the decoupling of the remote regions. We will focus here on the ReOsc-Slab simulation as the most realistic simulation. The Slab simulation will be used to understand the contributions to the ENSO dynamics by the simple thermodynamics of the local SST variability. The ReOsc simulation will be used as a reference to the study of FD2 and to illustrate how the ENSO dynamics change by including the interactions between the equatorial recharge oscillator and the mostly of-equatorial slab ocean dynamics. First we take a look at the global distribution of SST variability by means of the SST standard deviation in the ReOsc-Slab simulation, see Fig.. It is important to note that the slab ocean outside the tropical Paciic gives a decent representation of SST variability in remote regions. The SST variability is of similar amount in most regions to that observed (not shown) and the patterns of the SST variability are not that diferent from observed (not shown; see also discussion in Wang et al. 25). In particular the tropical Indian Ocean variability is of similar strength and structure as observed, which is consistent with the inding of Dommenget (2). The tropical and even equatorial Atlantic SST variability is also of similar strength as observed supporting the inding of Nnamchi et al. (25) that equatorial Atlantic SST variability is to a large part simulated by slab ocean thermodynamics. Thus, to irst or order, the slab ocean is a good approximation of remote SST variability. However, we need to keep in mind that the real world SST variability is more complex and may therefore provide diferent SST forcings to ENSO. In particular, equatorial SST variability in the tropical Atlantic is more complex than simulated in the slab ocean model. Figure 2 shows the changes in the SST standard deviation (stdv) in the ReOsc-Slab and Slab simulations due to the decoupling of the remote regions. The ReOsc simulation is not shown in this igure, as all SST variability in this simulation is proportional to the NINO3 region, which is discussed later. We can irst of all note that the relative changes in both the ReOsc-Slab and Slab set of simulations are similar to each other for all remote domains. In all cases SST variability is mostly reduced with some weak indication of some enhanced variability in the ReOsc-Slab NO- IND simulation. Decoupling the Indian and Atlantic oceans has in both models a weaker impact on the SST stdv than decoupling the extra-tropics. The combined efects of the diferent regions (NO-INDATL and ALL) is to irst order a linear superposition of the individual parts assuming they are independent sources of variability (the Methods section for details). In the ReOsc-Slab simulation the NO-IND, NO-ATL and NO-INDATL simulations have enhanced relative reductions in the eastern equatorial Paciic region, indicating stronger impact on the ENSO equatorial dynamics. In the Slab simulation the relative reductions by decoupling the remote regions are in general stronger than for the ReOsc-Slab simulation, but it needs to be noted here that the SST variability in the Slab simulation is in general weaker than in the ReOsc-Slab simulation and is missing the enhanced equatorial SST variability (see Y5). The NINO3 SST variability has some signiicant changes in the power spectrum in all three models, see Fig. 3. In both the ReOsc-Slab and ReOsc simulation we can see a shift in the variance from the control peak frequency towards lower frequencies. This is the case for all uncoupled experiments with the exception being the EXTROP simulation. The shift in the peak to lower frequencies for decoupling the tropical Indian and Atlantic Oceans is qualitatively consistent with previous indings (e.g. Dommenget et al. 26; Jansen et al. 29, FD2; Santoso et al. 22; Kajtar et al. 25). In the ReOsc simulations we can also note an increase in the variance at the peak frequency. This is qualitatively consistent with the inding FD2 and is therefore suggesting that the diferent AGCM simulations coupled to the same ReOsc model result into similar interaction with remote regions. In the Slab simulations it is the extra-tropical regions that have the strongest impact on the NINO3 SST power spectrum. The decoupling the extra-tropical regions lead to reduced SST variance only on the longer interannual to decadal time scales, but not on the seasonal to monthly time scales. Following the stochastic climate model hypothesis (Hasselmann 976), a Slab Ocean SST power spectrum is a red noise power spectrum. This suggests that the extra-tropical inluence onto the tropical Paciic is a red noise forcing, which should increase the persistence of

6 D. Dommenget, Y. Yu Fig. 2 Changes in the monthly mean SST standard deviation relative to the control simulation for the diferent decoupled simulations for the ReOsc-Slab (left) and Slab (right) simulations. The last row is the diference resulting from the linear superposition of NO-IND, NO-ATL and NO-EXTROP assuming that the total variance is a sum of independent components. Note the diferent color bars for the left and right columns SST variability, which is illustrated in the power spectra by the increase on the long time scales while the shorter time scales are not changing. The tropical Indian and Atlantic Ocean have less of an inluence on the power spectrum, but do appear to have a signiicant efect on the variance on the intermediate time scales around the annual period. The changes in the oscillating behavior of the ReOscSlab ENSO variability is better illustrated on the basis of 3

7 The efects of remote SST forcings on ENSO dynamics, variability and diversity (a) ReOsc Slab Spectrum of NINO3 SST (b) ReOsc Spectrum of NINO3 SST (c) Slab Spectrum of NINO3 SST specral variance [ o C 2 yrs] CTRL NO ATL NO IND NO INDATL NO EXTROP NO ALL /2 /2 /2 (d) ratio relative to control (e) ratio relative to control (f) ratio relative to control ratio /2 frequency [/yr] /2 frequency [/yr] /2 frequency [/yr] Fig. 3 Power spectrum of NINO3 SST for the ReOsc-Slab (left), ReOsc (middle) and Slab (right). Lower panels show the ratio of the decoupled simulations SST spectrum divided by the respective control spectrum. The grey shaded area is the 95% conidence interval for the control simulation the NINO3 SST auto-correlation function, see Fig. 4. In the control simulation we see a clear damped oscillation of 2 years period. The decoupling of the other tropical ocean basins clearly afects the oscillation period and the strength of the oscillation. Here the Indian Ocean has the strongest efect on the oscillation period by shifting it to longer periods. This is qualitatively consistent with the inding FD2. This is slightly enhanced when the tropical Atlantic is also uncoupled (NO-INDATL in Fig. 4), but the Atlantic alone appears to have no inluence on the period. In addition both tropical ocean basins appear to have an impact on the damping of the NINO3 SST auto-correlation function. The control simulation is less damped than the decoupled simulations. The extra-tropical regions appear to have no signiicant efect on the NINO3 SST auto-correlation function. Next we examine how the forcings of the NINO3 SST change due to the decoupling of the remote regions, see Fig. 5. In the ReOsc-Slab (Eq. 5) and ReOsc (Eq. 3) the NINO3 SST is forced by the thermocline depth, h, the atmospheric net heat lux into the NINO3 region, f, the zonal wind stress forcing, τ, and in the Slab simulation it is only forced by f. Since the SST directly inluences h, f and τ we may to irst order expect that changes in SST stdv to project linearly onto changes in the stdv of the forcings and vice versa. Thus following a one-to-one relation as illustrated by the dotted line in Fig. 5.

8 D. Dommenget, Y. Yu Fig. 4 Auto-correlation of NINO3 SST for the ReOsc-Slab simulations. The grey shaded area is the 95% conidence interval for the control simulation.5 ReOsc Slab SST auto correlation CTRL NO ATL NO IND NO INDATL NO EXTROP NO ALL auto correlation time [mon] We irst focus on the ReOsc-Slab simulation. The decoupling of remote regions reduces the NINO3 SST stdv in all cases with the tropical Atlantic Ocean having the biggest impact. The decoupling of several regions together (NO- INDATL and NO-ALL) leads to further decrease in SST stdv roughly consistent with a linear superposition of independent sources of variability (square in Fig. 5a). In contrast the stdv of h is increasing when remote regions are decoupled and again the tropical Atlantic has the biggest impact here too. The increase in stdv of h when the stdv of SST is decreasing and the remote forcings are decoupled is interesting and properly unexpected. The underlying dynamics causing this will be discussed in the next section, but irst we focus on the other forcings and models. The forcings f and τ mostly follow the one-to-one relationship with the SST stdv. Thus a reduction in SST stdv roughly leads to a proportional reduction in the forcings stdv or stated the other way around: a reduction in the forcings stdv goes along with a roughly proportional reduction in SST stdv. However, the changes in the forcings are slightly above the one-to-one line in Fig. 5 suggesting that the forcings will have a non-zero stdv when the SST stdv has reached zero, which is expected as the atmospheric forcing have variability internal to the atmosphere and independent of the SST. The ReOsc simulations changes in the SST and h stdv are quite diferent from those of the ReOsc-Slab simulations. The SST stdv is strongly increasing, in particular when both tropical ocean basins are decoupled (NO- INDATL and NO-ALL). This is consistent with the inding of FD2. At the same time the h stdv is essentially following the one-to-one relation with the SST stdv. It can also be noted that, unlike in the ReOsc-Slab simulation, the combined efect of the Indian and Atlantic Oceans (NO- INDATL) is much stronger than the linear superposition of the two individual efects (NO-IND and NO-ATL). The forcings f and τ mostly follow the one-to-one relationship with the SST stdv similar as in the ReOsc-Slab simulations. The Slab model simulation is most strongly inluenced by the extra-tropical regions. The forcings f and τ somewhat follow the one-to-one relationship with the SST stdv similar to the other to models, but more weakly suggesting that a larger (relative) amount of the forcings is left for zero SST stdv. This, however, is mostly due to the fact that the SST variability in the Slab simulations is already closer to zero (weaker) and therefore the deviations from the one-toone relationship appear (relatively) stronger. 4 Inluences on the dynamics The changes we have seen in the SST statistics can be understood by changes in the ENSO dynamics. We therefore irst of all look at the changes in the efective parameters of the ENSO recharge oscillator model. We will then use the recharge oscillator model Eq. (2) to understand

9 The efects of remote SST forcings on ENSO dynamics, variability and diversity stdv tau x [N/m 2 ] (a) stdv T cline [m] (c) ReOsc Slab (b) (d) ReOsc (e).5..5 control NO ATL NO IND NO INDATL NO EXTROP NO ALL superposition Slab (f) (g) (h) stdv heat [W/m 2 ] stdv SST [C] stdv SST [C] stdv SST [C] Fig. 5 Standard deviations of SST against standard deviation of thermocline depth, h, (upper), zonal wind stress, τ, (middle) and atmos. heat lux (lower), f, for the ReOsc-Slab (left column), ReOsc (middle column) and Slab (right column). The values of h, τ and f are the regional means as deined for Eq. (3). The solid black lines represent the control simulation values and the dash lines mark the 9% conidence internals. The dotted diagonal lines are the one-to-one relation. The superposition is based on NO-IND, NO-ATL and NO-EXTROP experiment; see Methods section for details. In panels e and h the red point is behind the yellow point which of these efective parameter changes are responsible for the changes in SST and also in the h statistics. Finally, a look at the lag-lead relation between NINO3 SST and the remote regions SST will help us understand the efective parameter and related ENSO dynamic changes due to the remote regions inluences. This will lead us to the important inding that the remote region inluence is an efective delayed negative feedback. Based on the statistics of the NINO3 SST and h of the simulations we can estimate the efective parameters of the ENSO recharge oscillator model Eq. (2) (see Methods section for details). Figure 6 shows the four model parameters and the stdv of the noise forcings (see Methods section for deinition) for all simulations of the ReOsc-Slab and the ReOsc models. We can see some signiicant changes in some of the parameters and stdv of the noise forcings that have some similarity in both models, but also some diferences. We will irst focus on the ReOsc-Slab simulation. In this model we see no signiicant changes in the model parameters related to the NINO3 SST terms (a and a 2 ; Fig. 6a), but very clear and signiicant changes in the model parameters related to the thermocline depth, h, terms (a 2 and a 22 ; Fig. 6c) and in the strength of the noise forcings (Fig. 6e). The later changes in noise forcings appear to have a fairly simple relation: the more remote regions are decoupled the less noise forcings for both T and h is present. The model parameters related to the h inluence are also reduced in magnitude. It is worth noting that these changes in the efective parameters of h mean that the remote regions in the ReOsc-Slab model simulations lead to a stronger efective coupling of the recharge oscillator model

10 D. Dommenget, Y. Yu Fig. 6 Changes in the efective parameters of the ReOsc-Slab (left) and ReOsc (right) simulations due to the decoupling of the diferent regions. The solid black lines represent the control simulation values, and the dash lines and grey shaded areas mark the 9% conidence internals. The superposition is based on NO-IND, NO-ATL and NO-EXTROP experiment; see Methods section for details. Note the diferent axis ranges and scaling for the diferent model simulations (a) a2 [m/k/mon] (c) ReOsc Slab Temperature parameters a [/mon] T cline depth parameters (b) a2 [m/k/mon] (d) ReOsc Temperature parameters a [/mon] T cline depth parameters control NO ATL NO IND NO INDATL NO EXTROP NO ALL superposition a22 [/mon].2.25 a22 [/mon] (e) a2 [K/m/mon] Noise forcing parameters (f) a2 [K/m/mon] Noise forcing parameters noise h [m/mon] noise h [m/mon] noise T [K/mon] noise T [K/mon] Eq. (2) to h. This, however, may appear unexpected and do require some more discussion. The recharge oscillator model Eq. (2) are an efective, statistical, way of describing the relation between T and h. The parameter values are the efective values of these relations, resulting from all processes in the complex climate system. However, these parameters are not implying causality as they just represent a statistical relation. For instance, a change in the efective coupling to h (a 2 and a 22 ; Fig. 6c) does not necessarily imply that the system s coupling to h itself has changed. A change in the efective coupling to h can also result from a change to coupling in atmospheric forcings that look like the time evolution of h. In Eq. (3) we see that in the ReOsc-Slab model the tendencies of T and h are forced by τ and f as well. Depending on the time evolution of τ and f, this can be an efective coupling to T or h in a statistical sense. Since the SST of the remote regions can inluence τ and f, they can efectively inluence the coupling to T and h depending on the time evolution of the remote SST. This is illustrated in Fig. 7 by the lag-lead correlation of the NINO3 SST with the remote regions SST and with h in the ReOsc-Slab control simulation. The Indian and Atlantic Oceans SST have an out of phase relation with the NINO3 SST similar to that of h, but with reversed signs. The extra-tropical North and South Paciic regions have a more in-phase relation with the NINO3 SST, but are also slightly shifted to an out of phase relation with the NINO3 SST similar to that of h. The combined efect of all the remote regions is again similar to the out of phase relation of h. Thus efectively the remote regions have a similar out of phase relation to the NINO3 SST as that of h. The observed cross-correlations are qualitatively very similar (not shown), in particular for the combined efect of all the remote regions. The largest diferences is in crosscorrelation to the North Paciic, which in the observations has stronger negative correlation at lag zero (.4) and the strongest negative correlation is at lag 5 mon. We can quantify these simulated remote cross-correlations in Eq. (2) by including the combined efect of all the remote regions, T remote, into the two terms that are related to h: a 2 h(t) a 2h h(t)+a 2r c T remote (t) (a 2h + a 2r ) h(t) (6)

11 The efects of remote SST forcings on ENSO dynamics, variability and diversity Fig. 7 Cross-correlation between NINO3 SST and SST in diferent remote regions in the ReOsc-Slab control simulation ReOSc Slab cross correl SST boxes with NINO3 SST NINO3 SST ( )IND ( )ATL S PAC N PAC ( )IND ATL all SST box combined T CLINE correlation time [mon] a 22 h(t) a 22h h(t)+a 22r c T remote (t) (a 22h + a 22r ) h(t) The combined remote SST, T remote, includes the Indian and Atlantic oceans with reversed signs. Since this combined T remote time evolution relative to the SST time evolution is similar to that of h (see Fig. 7), we can approximately replace c T remote with h in Eq. (6). Where c is a constant scaling factor. Thus the efective h coupling parameters, a 2 and a 22, are approximately replaced by direct coupling to h, a 2h and a 22h, and a coupling to T remote, a 2r and a 22r. The direct coupling h, estimated by the ratio of a 2 and a 22 in the NO-ALL experiment relative to a 2 and a 22 of the control of ReOsc-Slab (see Fig. 6c) is only about 53 and 63%, respectively. Thus the indirect efect from the remote regions is about 47% and 37% in the ReOsc-Slab model. Thus the remote regions in the ReOsc-Slab model simulations lead to a stronger efective coupling of the recharge oscillator model Eq. (2) to h. The relative importance of each of the parameter and noise forcing changes in the ReOsc-Slab model can be analyzed by Monte Carlo (MC) integrations of Eq. (2) with diferent parameter combinations. This is similar to the approach in FD2 and Y5. We therefore integrate Eq. (2) for 5 years with the efective parameters and noise forcing strength from the diferent ReOsc-Slab simulations as shown in Fig. 6 and noise forcings as white noise. In all integrations we use the same noise realizations, which essentially eliminates all statistical uncertainties in the comparison of two runs and therefore allows us to discuss all diferences in the statistical parameters of the resulting T and h variability. Figure 8 shows the stdv of the resulting T and h variability, and the auto-correlation of T for all MC integrations with the parameters from the diferent ReOsc-Slab simulations. The changes in the stdv of T and h for the diferent decoupling experiments in the MC integrations (Fig. 8a) are qualitatively similar to those seen in ReOsc-Slab simulations (Fig. 5a). Also the changes in the auto-correlation of T are fairly similar (compare Fig. 8b against Fig. 4). Thus the MC integrations of Eq. (2) roughly reproduce the main changes in the T and h variability by decoupling the remote regions. We can therefore use the MC toy model approach to explore which of the individual efective parameter changes are most important to explain the changes in the T and h variability. In Fig. 9 we present the results from the MC integrations together with simulations in which just one parameter was changed from the control values to the decoupled value. We can irst take a look at the NO-ALL experiments in which the changes in T and h stdv are essentially a result of the changes in the parameter a 2, T noise forcing strength and some smaller contribution from the a 22 parameter changes. The linear combination of the three changes in T and h stdv essentially gives the total changes (similar to black circle in Fig. 9g). All the remaining parameter changes have no signiicant impact on T and

12 D. Dommenget, Y. Yu (a) stdv(h) [m] 8 6 Standard deviations MC Toymodel ReOsc Slab control NO ATL NO IND NO INDATL NO EXTROP NO ALL (b) correlation.5 SST auto correlation control NO ATL NO IND NO INDATL NO EXTROP NO ALL stdv(t) [K] time [mon] Fig. 8 Monte Carlo simulation results of the ReOsc-Slab toy model for the standard deviation of T and h (left), and for the auto-correlation of T (right). See text for details h stdv. The T auto-correlation is only afected by the a 2 changes. All other parameter changes have no signiicant impact on it. The picture is similar for the other decoupled experiments. In all cases only changes in the a 2 parameter afect the T auto-correlation and therefore the period of ENSO. In the NO-EXTROP there are no signiicant changes in the T auto-correlation as there are also no signiicant changes in a 2 (see Fig. 6c). All remote regions inluence the T noise forcing strength and the NO-IND and NO-ATL experiments both show similar impacts from the a 2 changes. We can now summarize how the remote regions inluence the T and h variability and dynamics in the ReOsc- Slab simulations. All remote regions provide stochastic forcing in both the T and h tendency equations, where the stochastic forcing in T is most important. Since T is mostly forced by wind stress, it suggests that the wind stress noise forcing from remote regions increases the ENSO variability. The NO-IND and NO-ATL regions do afect the delayed negative feedback, which in the recharge oscillator model Eq. (2) is given by a 2. Both NO-IND and NO-ATL regions respond to ENSO with a delayed SST response, which then feedbacks back onto the ENSO dynamics by afecting the wind stress. Since this delayed feedback has a similar timing as h it acts like a stronger coupling to h. This also explains why the h variability increases, as the stronger a 2 then feedbacks onto h via the coupling to T. The ReOsc simulations are similar to the ReOsc-Slab simulations in a number of parameter changes, but some changes are also substantially diferent. The parameters related to the h coupling (middle row in Fig. 6) behave qualitatively the same, but the relative change in the parameters are weaker. In turn the changes in the parameters related to the T coupling (upper row in Fig. 6) are stronger in the ReOsc simulations. Both the NO-IND and NO-ATL decoupling leads to a signiicantly weaker T damping (a ). The noise forcing is reduced in the NO-ATL and NO-EXTROP simulations showing a reduction similar to the ReOsc-Slab simulation, but the NO-IND and the combined NO-ALL and NO-INDATL show no change or even an increase in the noise forcings, which is quite diferent from the ReOsc- Slab simulation. However, these indings are largely consistent with the indings of FD2. Exploring the details of why the ReOsc simulations behave so diferently from the ReOsc-Slab simulation is beyond the scope of this study, but a few indications can be given here. First the only diference between the two simulations is the presence of the Slab ocean dynamics in the tropical Paciic and the diferent parameters for the recharge oscillator Eqs. (3 5) (see Table 2 in Y5 for details). It is also worth noting that in the ReOsc simulations the tropical Paciic SST anomaly is only a one-dimensional scalar value (T), whereas in the ReOsc-Slab simulation the Slab Ocean equations provide high dimensional tropical Paciic SST anomalies. In the case when both Indian and Atlantic tropical oceans are decoupled (NO-INDATL and NO-ALL) the whole tropical SST variability is reduced to one scalar value (T) in the ReOsc simulation. This may provide a key aspect of why the ReOsc simulations shows such pronounced increase in SST variability in these two simulations.

13 The efects of remote SST forcings on ENSO dynamics, variability and diversity (a) stdv(h) [m] 8 6 Standard deviations NO ATL MC toymodel ReOsc Slab ctrl a a2 a2 a22 t noise h noise Exp: NO ATL (b) correlation.5 SST auto correlation ctrl a a2 a2 a22 t noise h noise Exp: NO ATL (c) NO IND (d) stdv(h) [m] 8 6 correlation (e) NO EXTROP (f) stdv(h) [m] 8 6 correlation (g) NO ALL (h) stdv(h) [m] 8 6 correlation stdv(sst) [K] time [mon] Fig. 9 Monte Carlo toy model results for diferent ReOsc Slab simulations with single parameters changed to the decoupled experiments

14 D. Dommenget, Y. Yu 5 Spatial pattern and diversity In this inal analysis section we focus on changes in the spatial structure and modes of SST variability due to coupling to remote regions. In the ReOsc-Slab model the P ReOsc ( x ) pattern of the recharge oscillator Eqs. (3, 5) is ixed and does not allow for variations in spatial structure or pattern of SST variability. However, in combination with the Slab Ocean Eq. () the ReOsc-Slab model SST variability can have variations in spatial structure or pattern of SST variability. This can also afect the absolute and relative amplitude of the P ReOsc ( x ) pattern as discussed in Y5. In the following we look at a number of diferent statistics to illustrate how the remote regions inluence the spatial structure and mode of SST variability. In this we will in particular focus on the diversity of SST variability along the equatorial Paciic, which is often related to the central vs. east Paciic ENSO events or warm pool vs. cold tongue ENSO events. Figure shows the two leading EOF-modes of the ReOsc-Slab control simulation. The two patterns are similar to the observed ones, but the EOF-2 does miss some equatorial signatures (see Y5 for a more detailed discussion). In all decoupled simulations the two leading EOFmodes are essentially the same two patterns, with pattern correlations >.98 for all decoupled runs except for the NO-EXTROP and NO-ALL in which the EOF-2 has pattern correlation with the control of.9, which is still very similar. Thus, to irst order the modes of variability are not fundamentally altered by the remote SST interactions. However, the remote regions do have an efect onto the relative importance of the leading modes of variability. In Fig. c the eigenvalues (in percentage) of the two leading EOFs are shown for all ReOsc-Slab simulations. The relative importance of the two leading EOF-modes is changing when diferent remote regions are decoupled. The strongest inluence here comes from the extra-tropical Paciic (NO- EXTROP). Decoupling it strongly reduces the variance of EOF-2. The reduction in EOF-2 is not unexpected considering that the nodes of EOF-2 are in subtropical regions and the mode strongly projects onto the extra-tropical regions. It also increases the relative importance of EOF-, but it should be noted here that the variance of EOF- in absolute terms is however decreased, as the total variance in the NO-EXTROP experiments is weaker overall. The tropical Atlantic has a weaker, but somewhat opposite efect on the leading EOF-modes. Decoupling the tropical Atlantic increases the relative and total (not shown) variance in EOF-2. This in turn means that the interaction with the tropical Atlantic SST anomalies does reduces the importance of the EOF-2 and increases the variance in EOF- and thus in the equatorial ENSO mode. The tropical Indian Ocean has little impact on the leading EOF-modes and the combined efect of the remote regions essentially is a superposition of the individual regions. An alternative way of looking at changes in the leading modes in the decoupled simulations relative to the control simulations is the distinct EOF (DEOF) analysis (Dommenget 27; Bayr and Dommenget 24). In the DEOF analysis the diferences in the leading modes between two data sets is estimated by a rotation of the leading EOFmodes towards the most distinct EOF (DEOF) modes (a) (c) 9 8 ReOsc Slab Eigenvalues CTRL NO ATL NO IND NO INDATL NO EXTROP NO ALL superposition (b) EOF 2 [%] EOF [%] Fig. ReOsc-Slab EOF-modes and 2 (left) and the eigenvalues for the diferent decoupling experiments (right). The superposition in (c) is based on NO-IND, NO-ATL and NO-EXTROP experiments relative to the control experiment

15 The efects of remote SST forcings on ENSO dynamics, variability and diversity (Bayr and Dommenget 24). The DEOF method inds the patterns (modes) that show the largest diferences in the explained variance between the control simulation and the decoupling simulations. This can be done in two ways: looking for the modes dominant in the control run relative to the decoupled runs or looking for the modes dominant in the decoupled runs relative to the control. Figure shows the DEOF-modes for the projection of the decoupled run modes onto the control simulation modes (left column), which highlights the modes that are more dominant in the control than in the decoupled simulations. These modes can be interpreted as modes that are ampliied (in terms of relative contribution to the total variance) by the coupling with the remote regions. The right column shows the DEOF-modes for the projection of the control modes onto the decoupled run modes, which highlights the modes that are more dominant in the decoupled run than in the control simulation. These modes can be interpreted as modes that are more independent from the remote regions. The DEOF-modes are largely consistent with the previous discussion about the changes in the leading EOFmodes eigenvalues, but also provide some more interesting details. The NO-EXTROP simulation again indicates that the extra-tropical Paciic enhances the of-equatorial modes (Fig. e) and the decoupling enhances the relative importance of the equatorial ENSO mode (Fig. f). The NO-ATL run shows the almost opposite behavior. The coupling to the tropical Atlantic enhances the equatorial mode (Fig. c) and decreases the relative importance of of-equatorial modes (Fig. d), which is mostly contrary to what most other regions do. The coupling to the tropical Indian Ocean (NO-IND) enhances an equatorial mode, which is a dipole between the far western edge of the Paciic basin and the eastern equatorial (Fig. a), and it decreases the equatorial ENSO mode (Fig. b). Interestingly, the combined efect of both the tropical Indian and Atlantic Oceans (NO-INDATL) is to slightly enhance an equatorial dipole mode (Fig. g), which in combination with the leading EOF- equatorial ENSO mode suggests enhanced variations of ENSO variability from central to east Paciic events. Thus slightly increasing the diversity of ENSO events. Finally, we take a look at the diversity of ENSO variability along the equatorial Paciic, focusing on the east-to-west shifts of ENSO events. We therefore follow the approach of Kug et al. (29, 2) by analyzing the scatter between the normalized seasonal mean NINO3 and NINO4 (averaged SST anomalies over 5 S 5 N, 6 E 5 W) indices during December February when central Paciic (CP) or east Paciic (EP) El Nino events occur. CP (EP) El Nino events are deined by the normalized seasonal mean NINO4 (NINO3) indices being greater than one and being greater than the seasonal mean normalized NINO3 (NINO4) indices, see Fig. 2. A large scatter (low correlation) in this diagram suggests a large diversity and thus strong variations between CP and EP El Nino events. In observations the scatter is much larger (correlation of.2) than in the ReOsc-Slab and in most other coupled climate models (Y5). The coupling to the remote regions does afect the ENSO diversity. The largest impact is again from the extratropical Paciic (NO-EXTROP) and the tropical Atlantic (NO-ATL), but with opposing efects. The extra-tropical Paciic does increase the diversity, thus leading to a more pronounced diference between the NINO3 and NINO4 indices. The tropical Atlantic does reduce the diversity, leading to stronger alignment between NINO3 and NINO4 indices. The Indian Ocean does not have much of an impact in the scatter of the NINO3 and NINO4 indices. This is somewhat in disagreement with DEOF-analysis, which did suggested that the east-to-west shifts of ENSO events are enhanced by the tropical Indian Ocean (Fig. a, g). However, the DEOF-patterns western node was further to the west than the NINO4 index and therefore do not project onto the diversity between NINO3 and NINO4 indices. 6 Summary and discussion In this study we investigated the efect of remote air-sea interaction in various ocean areas on ENSO variability and dynamics. We based our analysis on a series of hybrid coupled model simulations with a strongly simpliied ocean component in which we decoupled the air-sea interactions in a number of remote regions to investigate the impact they have on ENSO variability and dynamics. This approach has a number of advantages, but also has some drawbacks. The main advantages of using the simpliied hybrid coupled model simulations are that it allows to more easily diagnose the changes in dynamics and therefore understand what is happening in the complex system. It also allows for much longer integrations and therefore more data, as it is numerically, relatively cheap. The drawbacks, on the other hand are that it does not include all processes active in ENSO and also not all processes active in remote regions. The results therefore may not give the complete picture, but can be considered a zero order approximation of the more complex real world. The decoupling experiments suggest that the tropical Indian, Atlantic and extra-tropical regions do have an impact on ENSO variability, diversity and dynamics. However, each of the three regions studied have diferent impacts on ENSO. In the concept on the ReOsc model (Eqs. 2 3) the inluence of the remote regions can be quantiied as an inluence on the coupling to the NINO3 SST