Emergence of decadal linkage between Western Australian coast and Western–central tropical Pacific

Domain: earth_science
Published: 2024-05-25
Editorial outcome: minor_revision

Abstract
The impact of interbasin linkage on the weather/climate and ecosystems is significantly broader and profounder than that of only appearing in an individual basin. Here, we reveal that a decadal linkage of sea surface temperature (SST) has emerged between western Australian coast and western–central tropical Pacific since 1985, associated with continuous intensification of decadal variabilities (8–16 years). The rapid SST changes in both tropical Indian Ocean and Indo-Pacific warm pool in association to greenhouse gases and volcanoes are emerging factors resulting in enhanced decadal co-variabilities between these two regions since 1985. These SST changes induce enhanced convection variability over the Maritime Continent, leading to stronger easterlies in the western–central tropical Pacific during the warm phase off western Australian coast. The above changes bring about cooling in the western–central tropical Pacific and strengthened Leeuwin Current and anomalous cyclonic wind off western Australian coast, and ultimately resulting in enhanced coupling between these two regions. Our results suggest that enhanced decadal interbasin connections can offer further understanding of decadal changes under future warmer conditions.

Introduction
Compounded with substantial warming, the variability in sea surface temperature (SST) off western Australian coast1–3 and in the western–central tropical Pacific4–6 can greatly affect the local weather, climate, and marine ecosystems4,7. Adjacent to these two regions, the tropical Indian Ocean and Indo-Pacific warm pool have experienced the most rapid warming associated with climate change8–11 and became less influenced by the Interdecadal Pacific Oscillation (IPO)12,13. This rapid warming can cause changes in the Walker circulation14–16 via modulating convective activities and further influence the emergence of interbasin connections within the tropics17–19. The interbasin linkage between off western Australian coast and the western–central tropical Pacific, linked by the rapid warming regions (tropical Indian ocean and Indo-Pacific warm pool) and the overlying atmospheric circulation, can generate broader and profounder climate impacts than appearing only in an individual basin.

The interannual SST variabilities off western Australian coast and in the western–central tropical Pacific20–22 have received a great deal of attention over the last decade. The interannual variation in SST off western Australian coast, characterized by the Ningaloo Niño, is related to both local and remote processes3,23,24. The western–central tropical Pacific (Niño4w) region is a remote area with a strong linear correlation with Ningaloo Niño variability4,7. Conversely, the change off western Australian coast can actively affect variability in the Niño4w region, building a two-way interaction on an interannual timescale4,7. The interannual SST variability off western Australian coast is also affected by IPO25–28, such as an amplified Ningaloo Niño amplitude during the negative phase of the IPO29,30.

Although the interannual SST variabilities both off western Australian coast and in the western–central tropical Pacific have been studied extensively, the decadal SST variabilities in these regions are much less studied, and their causes remain elusive. The emergence of significant decadal SST variability off western Australian coast is not well reconciled yet1,2,29. Feng et al.29 indicated a timing of the late 1990s, whereas ref. 2 suggested a timing of post-1980. Trenery and Han31 showed enhanced influences of the tropical Pacific winds on sea level and thermocline depth in the tropical south Indian Ocean and western Australian coast since the early 1990s. Furthermore, it is unclear whether the decadal SST variability off western Australian coast is linked to the decadal variability in the western–central tropical Pacific like that on interannual timescale.

Here, we reveal an enhanced decadal SST variability off western Australian coast and an emergence of a decadal linkage with the western–central tropical Pacific over the past four decades based on three sets of observational data. The analysis of two pacemaker experiments related to tropical Pacific SST from two climate models show that negative IPO can partially influence this decadal linkage. Further analysis of the pacemaker experiments related to Indian Ocean SST, two large ensemble simulations from two climate models, and four single forcing large ensembles, we confirmed that the SST changes in the tropical Indian Ocean and Indo-Pacific warm pool owing to volcanic activities can enhance SST decadal variabilities and their linkage between off western Australian coast and in the western–central tropical Pacific in recent decades, leading in an intensification of the easterly wind anomalies in the western–central tropical Pacific. Moreover, the decadal linkage is related to changes in the variability of convection over the Maritime Continent.

Results
Here, the SST variability off western Australian coast is represented by the Ningaloo Niño index (NNI), which is defined as the area-weighted mean SST anomaly (SSTA) in the wedge area shown in Fig. 1a (see methods). The standard deviations (STDs) of NNI are 0.44 °C (1950–1985) and 0.51 °C (1985–2020). For these two periods, the STDs of the Niño4w index are 0.42 °C and 0.5 °C, respectively. The changes in NNI amplitude (denoted by STD) show that a substantial increase in SST variability off western Australian coast has occurred since the 1980s (Figs. 1b and S1a, e). This increased variability is consistently shown by all observational datasets. Meanwhile, the SST decadal variability before 1950 is less consistent among these observations. After 1980, the dominant periodicity of NNI changes consistently from the interannual timescale to the decadal timescale (Figs. 1b and S1a, e). Wavelet analysis reveals that the dominant NNI period over the past 40 years (1980–2020) is 8–16 years, with statistical significance at the 95% confidence level (1980–2020; Figs. 1d and S1b, f, S2b, e). Moreover, the NNI amplitude on the decadal timescale (0.33 °C) exceeds that on the interannual timescale (0.26 °C). Note that the enhanced variability since the 1980s is not due to data inconsistency associated with the availability of satellite observations (Fig. S3). In contrast, no evident enhancement in the decadal SST variability during 1980–2020 is found in other areas around the region off western Australian coast (Fig. S4). This confirms the uniqueness of the change in SST variability in regions off western Australian coast since the 1980s.

The SST variability in the western–central tropical Pacific is represented by the Niño4w index (see methods). Studies show that the Niño4w index can be the leading indicator of the NNI change on interannual timescale4,5. Approximately 50% of the year-to-year variation in the Ningaloo Niño occurs in conjunction with La Niña3,22,32, and the Niño4w index is the one most representative of the effect of La Niña on the Ningaloo Niño4. Similar to the Ningaloo Niño, the Niño4w index also shows strong decadal variability in recent decades (Figs. 1c, e and S1c, g), and such decadal variability begins to increase in the early 1960s. Its spectral peak becomes strong and statistically significant in the 1970s (Fig. 1e). Since 1985, the Niño4w amplitude on decadal timescale (0.32 °C) is comparable to that on interannual timescale (0.29 °C). The coincidence of the enhanced decadal variability in both the Niño4w and the Ningaloo Niño regions implies a potential linkage between them since the 1980s.

The NNI and the Niño4w index are negatively correlated from their correlation coefficients using 21-year sliding windows (Figs. 1f and S1d, h, S2a, d). This negative correlation coefficient is enhanced around 1960 (~−0.1), becomes statistically significant (−0.5) in the early 1970s, and is maintained after 1985 (−0.75). To further identify the changing characteristics of the NNI and the Niño4w index, we conducted a cross-spectrum analysis (Fig. 1g) for two periods: 1950–1985 and 1985–2020. There is one common significant peak in the coherence spectrum at 2–7 years in both periods. However, after 1985, a second significant coherence peak appears at 8–16 years (Figs. 1g and S5). This suggests that these two indices covary on interannual timescales before 1985 and covary on both interannual and decadal timescales after 1985, implying a potential decadal linkage between these two regions after 1985. Here, we denote “1985” as the “time of emergence” for the NNI-Niño4w linkage on a decadal timescale, defined as when the cross-spectrum of these two indexes is significant at a 95% level, and their correlation coefficient is stably exceeding 95% significance. In the following, we focus on decadal timescales with 8–16 years.

To gain insight into why the decadal variability in SST enhanced after 1985 off western Australian coast, we examined the changes in air-sea interactions before 1985 (Fig. 2a, c) and after 1985 (Fig. 2b, d) in association with the NNI. The regressions of SSTA onto the decadal NNI in boreal winter (December–January–February, DJF) display similar positive anomaly patterns over the region off western Australian coast for both periods, with some enhancement for the latter period. In contrast, over the Niño4w region, the anomalies are negative. The center of the negative SSTA shifts westward from the central-eastern Pacific near the dateline before 1985 (Fig. 2a; shading; Fig. S6) to the western–central tropical Pacific after 1985 (Fig. 2b; shading; Fig. S6). Note that positive SSTAs appear in the western tropical Pacific west of the Niño4w region after 1985 while they are negligible before 1985. As the center of negative anomaly spans the entire Niño4w region and both the Niño4w index and NNI are strengthened after 1985, it indicates a strengthened connection between the Niño4w index and the NNI on decadal timescale.

Change in surface wind plays an important role in modulating the SST variability in these regions. Associated with the positive SSTA off western Australian coast, significant wind anomalies appeared in both the tropical Pacific and the Indian Oceans during the two periods. However, the wind anomalies after 1985 are substantially stronger than those before 1985 (Fig. 2a, b; vectors; Fig. S6). On one hand, easterly wind anomalies in the equatorial Pacific after 1985 led to an enhanced Ningaloo Niño amplitude on the decadal timescale (Fig. S7). Meridional heat advection primarily originates from the western tropical Pacific29, driving more warm water toward the northwestern Australian coast. With the strengthening of the cyclonic wind anomaly, the resulting eastward geostrophic current anomalies carrying warmer water lead to an overall increase in SST off western Australian coast. Thus, the dynamical process increases the chances of local SSTs rising above the threshold for deep convection, increasing convective cloud30,35, and reducing solar radiation, which explain the negative effect of solar radiation discussed above.

The decadal change in surface wind is associated with changes in both atmospheric convective activity and the circulation pattern. The regression of precipitation onto the decadal part of boreal winter (DJF) NNI shows a positive precipitation anomaly centered around the tropical eastern Indian Ocean (Fig. S8) before 1985, but over the Maritime Continent (Fig. S8) after 1985, implying enhanced ascending (descending) motion over the Maritime Continent associated with Ningaloo Niño (Niña) event after 1985. Correspondingly, the center of the 200-hPa divergence moves to the Maritime Continent, i.e., the pattern of divergence (convergence) over the Indian Ocean (Pacific) before 1985 changes to divergence (convergence) over the Maritime Continent (the Indian Ocean and central-eastern Pacific) after 1985 (Fig. 2c, d; Fig. S6; shading and vectors). The enhanced NN-Niño4w coupling (Fig. 2a, b) is accompanied by enhanced convective activity over the Maritime Continent. This marked enhancement in convective variability in the past four decades (after 1985) is also evident from the singular value decomposition (SVD) results. This is consistent with the spatial pattern of the warming off western Australian coast during these two periods (Figs. S9–S11). Overall, the enhanced decadal variability of convective activity presented in DJF over the Maritime Continent after 1985 has led to strengthened variability of the Walker circulation and its associated easterly wind anomalies over the tropical Pacific, and to a much lesser extent westerly wind anomalies over the central-east Indian Ocean around 5°S–15°S. Stronger/weaker easterly wind in the Niño4w region will directly cause convergence/divergence over the Maritime Continent region within the warm pool and thus affect convection variability over the Maritime Continent. The decadal changes in the convection center can induce decadal linkage between the western Australian coast and the western–central tropical Pacific.

The above analysis confirms that the decadal linkage between the western Australian coast and the western–central tropical Pacific is associated with the enhanced convection variability over the Maritime Continent. The IPO should affect this decadal linkage because the western–central region (especially the Niño4w region) is the equatorial part of the IPO, according to ref. 36. Next, we will not only confirm the effects of the IPO on this interbasin linkage, but also uncover some different factors.

Two sets of pacemaker experiments with two different climate models are employed to investigate the effect of the IPO on the decadal NNI by restoring the simulated SSTAs to those observed in the tropical Pacific east of the dateline (see Methods). With the same observed SSTAs, enhanced decadal SST variabilities off western Australian coast and in the Niño4w region are found in two sets of Pacific Ocean pacemaker experiments, albeit with slightly different timing for the emergence of linkage: since 1990 for CESM1.2 and since 1985 for FGOALS-f3-L (Figs. S2, S12). This confirms that the IPO has played a role in enhancing the decadal variabilities in these two regions.

Although the equatorial Pacific SST of each ensemble member is constrained by the same observations, the spread of the NNI between the ensemble members still exists (Fig. S13), suggesting that the IPO might not be the sole factor influencing the decadal variability in SST off western Australian coast. We found that the NNI amplitude shows a continuous increase since 1970 (Fig. 3a), especially for the decadal NNI amplitude (Fig. 3b). The enhancement of the decadal NNI is not consistent with the IPO phase transition in the observations (Fig. 3a, b). Meanwhile, the stable decadal linkage had already appeared (Fig. 1f) before the IPO turned to its negative phase. Thus, the emergence of the interbasin (Indo-Pacific) decadal linkage since the 1980s cannot be attributed solely to the IPO.

The strengthened easterly wind anomalies across the western–central tropical Pacific over the past several decades are linked to the tropical Indian Ocean warming10,25. This rapid warming inspires us to explore the role of the tropical Indian Ocean in enhancing the decadal linkage between the western Australian coast and the western–central tropical Pacific (denoted as NNI-Niño4w linkage). Two sets of CESM1 pacemaker experiments are used to examine the effects of the tropical Indian Ocean SST on the NNI-Niño4w linkage: One is TIOGA (tropical Indian Ocean Global-Atmosphere) experiment in which the model SSTAs are restored to observed ones in the tropical Indian and western Pacific (west of the dateline), and the other, IOGA (Indian Ocean Global-Atmosphere) experiment in which the model SSTAs are restored to observations in the full Indian Ocean basin north of 50°S (see Methods). Results from TIOGA experiments reveal that the changes in the tropical Indian Ocean and part of western Pacific can generate significant SST changes in the central-western tropical Pacific and off western Australian coast (Fig. S14), accounting for a large fraction of the observed NNI after 1985 (Fig. 3c); Before 1985, however, the simulated NNI has a weaker amplitude and opposes to the observation (Fig. 3c). The simulated NNI and Niño4w index in TIOGA are out of phase with a correlation coefficient of −0.74 (Fig. 3d). These results suggest that SSTA in tropical Indian Ocean and western Pacific play an important role in causing the enhanced NNI-Niño4w linkage in recent decades. This is because a warm (cold) SSTA in the tropical Indian-western Pacific basin drives equatorial easterly (westerly) wind anomaly in the central-western tropical Pacific25 (Fig. S15), enhances (reduces) equatorial upwelling and causes cold (warm) SSTA in Niño4w region. The enhanced equatorial easterly (westerly) wind anomaly increases (reduces) the Indonesian Throughflow, strengthening Ningaloo Niño (Niña) events. Meanwhile, the colder (warmer) SSTA in the Niño4w region can induce cyclonic (anticyclonic) circulation in the Southeast Indian Ocean, together with local air-sea feedback, further strengthening Ningaloo Niño (Niña) magnitudes7,29,37,32. Moreover, the Ningaloo Niño (Niña) SSTA can also provide positive feedback to the equatorial zonal wind anomalies and SSTA in Ninño4w region7.

Note that NNI and Niño4w index are out of phase in TIOGA experiments both before and after 1985 (Fig. 3d), whereas the simulated NNI agrees with the observed one only after 1985 (Fig. 3c). This is because before 1985, the Niño4w SSTA generated by Indian Ocean SSTA is out of phase with that of the IPO, whereas after 1985 they are generally in phase (Fig. S16b). The warm (cold) Indian Ocean SSTA and negative (positive) IPO act in concert to intensify Niño4w SSTA and thus NNI after 1985, while the Indian Ocean SSTA weakens the IPO effect before 1985 (Fig. 3f), causing the enhanced decadal linkage between NNI and Niño4w index after 1985 as shown by observations.

To understand what causes the decadal variability of Indian Ocean SST, we analyzed the results from large ensemble experiments using CESM2 and MPI climate models (Fig. 3e). The ensemble means of these experiments show that external forcing is the major cause for the decadal SSTA of the tropical Indian Ocean after 1985 (Fig. 3e), whereas IPO is the major cause before 1985 with a positive (negative) IPO inducing warm (cold) SSTA over the Indian Ocean25 (Fig. 3f). Upon examining the impacts of various single external forcings (Fig. 4), we show that the increase in greenhouse gases (GHGs; Fig. 4, light blue line) has significantly contributed to the recent trend of rapid warming in the Tropical Indian Ocean. External forcings that exclude anthropogenic aerosols (experiment xAER; Fig. 4, blue line) produce a stronger warming trend, which means anthropogenic aerosols somewhat decrease the warming trend in the Indian Ocean. In addition to a warming trend, experiment xAER also generates decadal variability in the tropical Indian Ocean SST (Fig. 4, blue line), mainly owing to the forcings by volcanoes13 (included in everything else experiment, EE; Fig. 4, purple line). This finding suggests that external forcings by volcanoes is the main cause for decadal TIO SSTA and result in its out-of-phase relationship with IPO after 1985.

Methods
Observation datasets
The monthly SST data from 1900–2020 used in this study are obtained from the Extended Reconstructed Sea Surface Temperature dataset, version 5 (ERSSTv5)43, the Hadley Centre Sea Ice and SST dataset (HadISST1)44, and the Institute of Atmospheric Physics (IAP) analysis dataset (1940–2020)45,46. We use the monthly SST data (1900–2019) from Extended Reconstruction Sea Surface Temperature Version 3b (ERSSTv3b)47, which excludes satellite data. The surface wind and 200-hPa wind data are from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis dataset (1950–2020)48. The precipitation, latent heat flux, shortwave radiation, longwave radiation, and sensible heat flux data are from the Japanese 55-year Reanalysis (JRA55)49 from 1958–2015. The Potential temperature, Zonal velocity, and Meridional velocity are from Ocean Reanalysis System 5 (ORAS5)50 global ocean reanalysis monthly data from 1958–2015. All datasets are interpolated to 1°×1° grids.

Simulation datasets
The effects of interbasin linkage are investigated using the results from Pacific pacemaker experiments with two coupled general circulation models: the Community Earth System Model, version 1 (CESM1)51 from NCAR, and the low-resolution version of the Chinese Academy of Sciences (CAS) Flexible Global Ocean–Atmosphere–Land System model, finite-volume version 3 (FGOALS-f3-L)52. The model SSTs for the tropical central-eastern Pacific (20°N–20°S, 175°E–75°W) are restored to the model’s climatological mean plus the observed anomaly (HadISST1). Tropical Indian Ocean Global-Atmosphere (TIOGA)39,53 using CESM1, where SST in the tropical Indian Ocean and part of the western Pacific warm pool region is restored to observed SST from Extended Reconstructed SST version 3b (ERSSTv3b), with fully restored SSTA for 15°N–15°S, from the African Coast to 161°E, and buffer zones at 15°N–20°N, 15°S–20°S, and 161°E–180°. Another experiment is IOGA (full Indian Ocean Global-Atmosphere)54 using CESM1, where SST in the Indian Ocean (north of 50°S) is restored to observed SSTA from ERSSTv3b. Meanwhile, the oceanic and atmospheric components are freely coupled in other ocean areas55. Historical radiative forcing is used to drive CESM1 pacemaker (CESM1 POGA, with eight members) for 1870–2005 (is extended to 2014), and FGOALS-f3-L pacemaker (FGOALS-f3 POGA, with ten members) for 1870–2014. TIOGA (ten members) is from 1920 to 2013. IOGA (ten members) is from 1920–2019.

In addition to the pacemaker experiments, two large ensembles for the 1950–2014 period are also used: the 100-member Community Earth System Model version 2 Large Ensemble (CESM2-LE)56, and the 100-member Max Planck Institute for Meteorology Grand Ensemble (MPI-GE)57 to explore the effects of the external forcings on the enhanced decadal variability. To further isolate the effects of different external forcings on these decadal variability, four CESM2 single forcing large ensembles58 are analyzed: greenhouse gases only (GHG, 15 members); biomass burning aerosol only (BMB, 15 members); everything else (EE, excluding greenhouse gases, and anthropogenic aerosols; 15 members); and excluding anthropogenic aerosols (xAER, i.e., all forcings are time-evolving except anthropogenic aerosols, 10 members). In those “only” experiments, the time-evolving forcing is only the named one, and all other external forcings are fixed at the 1850 level. In EE and xARE experiments, all forcings are time-evolving except those excluded forcings. other than those that are time-evolving in GHG or BMB are time-evolving. The data we used is from 1900 to 2014 except xAER which the data is only available between 1920 to 2014.

Definition of indices
The SST field is regressed onto the global mean SSTA for detrending to remove the influence of anthropogenically induced global warming59, and the residual terms of the regression are used for analysis. The area-weight-averaged SSTA in the wedge-like area7,32 (22°S–16°S, 102°E–108°E; and 32°S–16°S, 108°E–115°E; see blue box in Fig. 1a) is used to define the NNI. This definition can comprehensively capture the warming pattern off western Australian coast as being wedge-shaped. Compared with the traditional definition5,21 whose area-weight-averaged SSTA is based on the region 110°-116°E, 22°-32°S, similar NNI variation and amplitude (denoted as STD) are obtained.

Ensemble empirical mode decomposition (EEMD)60 is applied to isolate components of the NNI and the Niño4w index (5°N–5°S, 160°E–175°W; see red box in Fig. 1a). The added white noise in each of the 1000 EEMD ensemble members had a standard deviation of 0.261. The NNI is decomposed into seven different timescale components. The power spectra of each component are calculated to obtain the timescale. According to the power spectrum, the third and fourth components are combined as interannual signals for 1–7 years. The fifth and sixth components are combined as decadal signals with periods of more than seven years. The IPO index is following the Tripole Index definition36, the SSTA used is from region 1: 25°N–45°N, 140°E–145°W; region 2: 10°S–10°N, 170°E–90°W; and region 3: 50°S–15°S, 150°E–160°W. By using the 8–16-year band-pass filter, we extracted decadal fluctuations in indices, such as the decadal NNI. In analyzing the mixed-layer heat budget, we calculated these decadal heat budget terms and also determined average values based on the development period of the decadal Ningaloo Niño events, before and after 1985. We select periods from when the decadal NNI increased from 0 to its peak, corresponding to the development period of the decadal Ningaloo Niño events. Before 1985, the development periods are from December 1970 to December 1972 and from January 1981 to August 1983; while after 1985, the periods are from June 1986 to June 1988 and from October 1995 to August 1997.

Analysis for the formation of SST patterns
To explore the mechanisms underlying the formation of the SST patterns, we employ wind—evaporation—sea surface temperature (WES) feedback analysis33,34. According to ref. 34, changes in ocean heat transport (D0) and net surface heat flux are balanced against each other on long timescales:

C \frac{\partial T0}{\partial t} = D0 + Q0net (1)

where the left-hand side of Eq. (1) equals zero; T0 is the SST change; and Q0net is the net surface heat flux anomaly (Q0net = Q0sw + Q0lw + Q0sh + Q0lh; positive downward), which consists of shortwave radiation (Q0sw), longwave radiation (Q0lw), sensible heat flux (Q0sh), and latent heat flux (Q0lh). Furthermore, the latent flux is decomposed into a Newtonian cooling Qo lh 0 component and a residual that represents atmospheric forcing Qa lh 0:

Qo lh 0 = \alpha Q lh T0 (2)

Qa lh 0 = Q0 lh - Qo lh 0 (3)

The equation of SST pattern can be written as follows:

T0 = \frac{D0 + Q0sw + Q0lw + Q0sh + Qa lh 0}{\alpha Q lh} (4)

\alpha is ~0.06 K−1 in the tropics. The WES feedback is represented by Qa lh 0 and D0, showing three-dimensional oceanic temperature advection.

Analysis of the mixed-layer heat budget
We calculate the mixed-layer heat budget, and the equation is as follows62,63:

\frac{\partial T0}{\partial t} = - (u' \frac{\partial T0}{\partial x} + u0 \frac{\partial T'}{\partial x} + u0 \frac{\partial T0}{\partial x}) - (v' \frac{\partial T0}{\partial y} + v0 \frac{\partial T'}{\partial y} + v0 \frac{\partial T0}{\partial y}) + \frac{Q0net}{\rho 0 Cp h0} + R0 (5)

T, u, and v represent the ocean temperature, zonal and meridional current velocities averaged in the mixed-layer depth, respectively. \frac{\partial T0}{\partial t} is temperature tendency. A variable (such as T, u, and v) can be decomposed into the sum of the climatological mean state (e.g., u') and anomaly state (e.g., u0)64. Qnet0 is the “net surface heat flux”, which accounts for the sum of shortwave radiation, longwave radiation, latent heat fluxes, and sensible heat fluxes. \rho 0 indicates sea water density (1026 kg/m3) and Cp is the ocean heat capacity (3986 J/(kg*K), h0 is mixed-layer depth. R0 is the “residual” term, which accounts for the remaining heat budget components not explicitly resolved by the used data in the equation. The results presented above are obtained through the vertical integration of the heat budget across the mixed layer.

To calculate the mixed-layer depth, we follow65:

\delta k = \sqrt{\frac{1}{k-1} \sum_{i=1}^{k} (\lambda i - \lambda k)2} (6)

\delta k is the standard deviation, \lambda(z) represents the temperature, and z is the depth of data. When k equals 1, it is on the sea surface. \lambda' k is the mean between z1 and zk.

The maximum variation over the same depth range is:

\sigma k = max_{i=1}^{k} \lambda i - min_{i=1}^{k} \lambda i (7)

The relative variance of \lambda between z1 and zk is:

\chi k = \frac{\sigma k}{\delta k} (8)

When \chi k gets smaller, the zk reaches zn1. zn1 is the layer below and slightly deeper than the mixed layer. Therefore, we locate zn2 by the following:

\frac{\delta n2 + 1 - \delta n1}{\delta n2 + 1 - \delta n1} \le \Delta (9)

Usually, the value of \Delta is between 0.1 and 0.5, and we use 0.5. zn2 is the mixed-layer depth.

Statistical analysis
The wavelet transform is calculated based on the Morlet function of the NNI and the Niño4w index to test whether they show similar periodicities. The sliding correlation and coherence spectrum between the NNI and the Niño4w index are also calculated. We use regression and correlation maps between seasonal mean variables (e.g., the SSTA and precipitation anomaly averaged for December-February) and the decadal NNI based on DJF mean to visualize the spatial pattern of the influence of the tropical Pacific on the region off western Australia, as previous studies have indicated that the Ningaloo Niño reaches its peak during boreal winter5,21,29,32. Additionally, singular value decomposition (SVD) is employed to demonstrate the relationship between SST and convective activity.

The wavelet results are tested statistically against red noise66. The statistical significance of the correlation coefficients is assessed using the two-tailed Student’s t-test. Given our application of low-pass filtering and similar smoothing methods over decadal periods, which reduces degrees of freedom, we utilize effective degrees of freedom in the test67:

N1 eff \approx N1 + N2 \sum_{j=1}^{N} \frac{1 - 2N}{N} \rho XX (j) \rho YY (j) (10)

where N is the sample size, \rho XX (j) and \rho YY (j) are the autocorrelation functions of two time series X and Y at time lag.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Recommendafion: Minor revisions\nThe paper discusses the inter-basin linkage between western Australian coast and western-central tropical Pacific on the decadal fime scale (8-16 years) based on the analysis of mulfiple datasets, including three sets of observafional data, two pacemaker experiments of climate models, a large ensemble of CESM2 simulafions, and CMIP6 simulafions. The results suggest that a decadal linkage of SST between the two regions emerged around 1985, coinciding with a confinuous intensificafion of decadal variability. The mechanism of the decadal linkage is further discussed by examining the role of external forcing and the IPO. Warming in the western tropical Pacific induced by external forcing causes enhancement and eastward-shift of the center of deep convecfion from the Indian Ocean to Marifime Confinent, leading to stronger westerlies in the tropical Indian Ocean. These changes cause anomalous cyclonic circulafions and SST changes in the west coast of Australia, resulfing in enhanced coupling in the two regions. These findings provide valuable insights into decadal variability over the Ind-Pacific region and its predicfion.\nWhile the availability and quality of observafional data are generally challenges for this type of studies on decadal variability, the authors conclusions are obtained from the analysis that incorporates mulfiple datasets including both observafions and model experiments. Therefore, the decadal linkage scenario described above are plausible, making it worthwhile to publish the results. I have some minor comments, which are mostly concerns on wrifing.\n1. Title\n“Western-Central Pacific” is not very clear. Perhaps “Western-Central Tropical Pacific” is befter.\n2. Abstract\nL25 “The interbasin linkage generates a broad and profound impact.”\nAdding a liftle more background informafion at the beginning of the abstract (perhaps two sentences) would help readers befter understand the implicafion of the results.\nL29 “external forcing”\nIn the discussion (L241), the term “the external forcing” is used to signify “anthropogenic forcing”. Using a more explicit expression or term related to global warming (e.g., increase of greenhouse gas) would clarify the meaning of “external forcing”.\n3. L42 “the western Pacific (including eastern Indian Ocean….”\nThis sentence is confusing. The western Pacific does not include the eastern Indian Ocean.\n4. L51“varia”\nShould be “variafion”.\n5. L54 “most important”\nImportant for what? Does this mean “strongest”?\n6. L84 “NNI, which is defined as the area-weighted mean SST anomaly (SSTA) in the wedged area”\nThis definifion is different from the most commonly used NNI (area average SST for 110°-116°E, 22°-32ºS or 108ºE-116ºE, 22º-28ºS). Are the results sensifive to the definifion used? For example, is STD on the decadal fime scale similar when using the commonly used NNI? The jusfificafion for the choice of this specific definifion could be included in the method secfion.\n6. L86. “The standard deviafions (STDs) of NNI are 0.92 (1950-1985) and 1.07\n87 (1985-2020).”\n℃ ℃\nThe quality of the SST data could be different in these two periods because of the satellite measurements in recent years. Is the uncertainty due to the SST data quality smaller than the difference in STD between the two periods?\n7. L93 “8-16 years”\nIt is beneficial to state more explicitly in this secfion that the decadal fime scale in this study is “8-16 years”, although it is stated in the method secfion later.\n8. L134 “The center of negafive anomaly shifts westward”\nBefore 1985, strong correlafions exist in the eastern tropical Pacific. However, this may not necessarily indicate an interbasin linkage between western Australian coast and eastern tropical Pacific during this period. Or is it an interbasin linkage? Please explain.\n9. L145 “eastern”\nShould be “easterly”.\n10. L148 “norther”\nShould be “northerly”.\n11. L151 “Weakening of coastal upwelling”\nAlso, northerly winds reduce the wind speed and thus evaporafive cooling (latent heat flux) decreases.\n12. L159 “The latent heat flux is largely unchanged between these two periods”\nFig. 2 shows that local winds (and thus wind speed) are largely changed between these two periods. So the latent heat flux should be largely changed as well, which is inconsistent with the results. Please clarify.\n12. L166 “northern wind anomaly”\nShould be “northerly wind anomaly”.\n13. L179 “centra-eastern”\nShould be “central-eastern”\n14. L205 “centra-eastern”\nShould be “central-eastern”\n15. L223 “This indicates external that”\nCorrect to “This indicates that external”\n16. L245 “convecfion in situ”\nNot clear what this means.\n17. L255 “northern surface wind anomaly”\nShould be “northerly surface wind anomaly”\n18. L279 “eastern wind anomaly”\nShould be “easterly wind anomaly”\n19. L332 “Japanese 55-year Reanalysis (JRA55)”\nThe fime period covered in OAFlux is similar. Is the decadal variability in surface flux data from JRA55 consistent with OAFlux? Also, the uncertainty of surface flux data in this region should be menfioned (e.g., Feng and Shinoda 2019).\n20. Reference secfion\nMost of the references lack journal names.\n21. L592 Data and materials availability\nAvailability of two pacemaker simulafions is not provided.", "reviewer_2": "Review of the paper enfitled ‘Emergence of Decadal Linkage between Western Australian Coast and Western-Central Pacific\" by Dr Lin and colleagues.\nMain\nThis work invesfigates the recent enhancement of decadal variability observed in the northwest Australian coast. Through observafional data analyses and model experiments, the paper suggests that the anomalous warming of the western Pacific tropical Ocean contributes to enhanced variability via atmospheric teleconnecfions. The Paper also suggests an emergence of decadal linkage between the two regions under the western Pacific warming which is likely to persist in the future given the projected warming of the tropical Pacific.\nThe role of western Pacific variability on the development of Ningaloo Nino (NN) has been reported earlier. Feng et al. (2015) also suggested that anthropogenic warming in the western Pacific may have an addifional role in modulafing the NN at decadal fime scales. This paper takes this idea further with detailed research to show that recent decadal variafions of NN might have an independent existence from IPO under strong external warming, leading to a ‘new decadal link’ between the two regions. This is an excifing finding!\nI appreciate the enthusiasm of the authors to tackle a complex research problem that has narrow lines separafing the role of anthropogenic warming and natural variafions for controlling variability over different fime scales. Given the complexifies of the problem, I sfill have quite a few concerns and quesfions which I list below. I hope clearing these quesfions will bring some more clarity to the presentafion of the paper.\nMajor comments:\n1) What is the rafionale behind using EEMD to obtain components of variability over different fime scales? For instance, how do the authors make sure that the decadal component (fifth and sixth components combined) of indices has variability only above 7 years? Also, it seems that only indices are filtered for different fime scales but not the variables (SST, wind, etc.). You may get spurious regression esfimates if the dependent (SST for example) and independent (indices) operate over different fime scales. I would like the authors to make a liftle more serious effort to clarify these aspects in the paper.\n2) (Page 17) Analysis of SST paftern: This secfion doesn’t speak of ocean heat transport (D) well. The balance between D and Q is set as an assumpfion but hasn’t been tested explicitly. In fact, previous works on low-frequency NN and its mechanisms (Feng et al. 2015) showed that heat advecfion from the western Pacific plays an important role. The present paper summarises the WES and ocean transport contribufions in the form of regression coefficients (table S2) which is very hard to understand. I do not even understand what are the two sets of values presented for each period in this table. I find that the way of treatment of ocean heat transport is an important drawback of this study. In fact, there is no descripfion of how and where heat transport anomalies are calculated. Atmospheric teleconnecfions might be playing an important role as discussed in this paper, but I would like to see if the role of oceanic heat transport can be expelled more undoubtedly. For instance, there might be a reason why ITF doesn’t contribute much to the decadal linkage, however, this study doesn’t explore that.\n3) Related to the previous comment: The role of (slow) upper ocean heat content variafions in the western Pacific and its transmission to the West Australian coast (WAC), affecfing SST and sea level there, is indeed discussed earlier (Feng et al. 2015). This paper almost fully neglects the role of ocean waveguides in shaping the low-frequency SST variafions in the WAC. I was wondering how fide-gauge sea-level measurements at Fremantle which is idenfified to be a good indicator of heat content variafions in the WAC (Feng et al. 2003, 2015) would complement the overall result of this paper. Even though the authors try to establish the absence of oceanic teleconnecfion (S3?), the descripfion of figure 2 (Page 7) uses the ocean transport (Leeuwin current) to explain the spafial structures. Do authors believe that the influence of Leeuwin current is confined only to the NN region?\n4) There is a systemafic difference between the NNI-regressed spafial SST maps in Figure 1 and Figure 3. There is no variability seen in the western Pacific in figure 1 (observafion) but there are strong posifive SST anomalies in the western Pacific in figure 3 (coupled model). Can the authors elaborate on this difference further? An intercomparison of figures 1, 3, and S8 prompts me to think that the NN in coupled models is fightly linked to canonical ENSO and affected by its interannual variafions but this is not the case for observafion. This may have strong implicafions for the overall result presented in this paper. For instance, in the pacemaker experiments, the ENSO variafions dominate and the effect of Marifime confinent warming after 1990 might be nil or weak. But, in observafions, Marifime warming might play an important role in shaping the decadal linkage. I would ask the authors to redo figure S5 but for models (pacemaker experiments) for the two periods to see this further.\n5) Related to the previous comment - (Page 10) authors say ‘surprisingly, the nni-nino4w decadal linkage is found in the ensemble mean of members whose IPO phases are totally opposite to observed phase’ – this may simply indicate that, in the ensemble mean, it is not IPO but interannual variability in the equatorial Pacific affects the linkage. In fact, Fig. S11 suggests that there is virtually no decadal power in the ensemble mean Nino4w for R-, indicafing that the spafial paftern seen in figure 3f comes from interannual variability. This is contradictory to the argument made, and I would ask the authors to make a revision accordingly either for the analysis or the text at the beginning of Page 11. Also, I would like to ask authors to show spafial maps similar to Figures 3e, f but for the enfire 100-member ensemble mean SST (not for R+/- groups).\n6) Figure 3: Do the wavelets represent the ensemble mean spectrum or the spectrum of the ensemble mean index? I think these two are different. Also, there is a 4-year periodicity in the Nino4w in figure 3d which is absent in figure 1e. Shouldn’t the ensemble mean spectrum eliminate such interannual signals? Can authors explain why this is so?\nOther comments:\n1) Page 4; line 71: Do we have enough observafions before 1980 in the western Australian coast to confidently state a post-1980 change? I would like to hear from the authors if they think the enhancement of decadal variafions since 1980 is because we have robust measurements since 1980 (satellite) in this region.\n2) Stafisfical significance of the coherence over a 21-year period and other regression esfimates. The methodology says the test employs a student’s t-test but doesn’t explicitly state how the effecfive sample size (ESS) is esfimated. Since the signals are smoothed over decadal periods, the autocorrelafion of the indices/fime series would reduce the independent samples, and significance tests should consider the ESS.\n3) The IPO definifion doesn’t follow the reference (Henley et al. 2015) given. Henley et al. suggested a tripolar index instead of EOF.\n4) Introducfion:\nPage 3; line 45: ‘Variafions in the interbasin linkage between off western Australian coast and the western–central Pacific, linked by the Indo-Pacific warm pool and the overlying atmospheric circulafion, can generate a profound impact on the local and remote climate’.\n‘Befter rephrase to ‘can impact local and remote climate’.\n5) Page 3; line 50: Not discussing ‘local’ processes well: How much variance is explained by local and remote processes respecfively? Similarly, what is the main mode of low-frequency (interannual and above) SST variability on the West Australian coast? Is it Ningaloo Nino or warming associated with the Leeuwin current?\n6) Page 8; line 172: The convecfive centers change over P1 (IO) and P2 (WEP). Does that really mean an ‘eastward movement’ as suggested in the paper?\n7) Page 11, and elsewhere in the paper: The physical explanafions presented in the paper suggest that the western Pacific warming (anthropogenic) can enhance an ‘exisfing’ decadal variability in the WAC – but doesn’t induce a new mode of decadal variability. In that perspecfive, is it feasible to say an ‘emergence of decadal linkage’? More appropriate would be to say, ‘influence of western Pacific warming on the amplitude of decadal variability off the WAC’?\n8) Fig. 4: Why does the trend esfimafion is limited to 1970 – 1995 for CMIP6 models? Is the phase of the IPO in CMIP models consistent with the observed IPO phase? If not necessarily (I think that is the case), how would the authors reject the fact that the linear relafionship between the western Pacific SST and NNI amplitude might arise through IPO? I would suggest the authors add one more panel by redoing the same figure but once the IPO is regressed out from the western Pacific SST, to check this.\n9) Pacemaker experiment – The SST anomalies are nudged to the model climatology and HadISST1 anomalies. I would like to see the climatological SST map in the Indo-Pacific region from HadISST1 and the two models considered for the pacemaker experiment (left) and the spafial map of the standard deviafion of the SST anomalies (for HadiSST and two models) for the two periods (post and before 1980) in the right columns. And, please show the Nino4w & NN regions (box) over each of these maps.\nMinor comments:\n1) Previous studies indicate that half of the NN is NOT related to the western Pacific (La Nina), indicafing an important local driving mechanism that controls wind and SST off the WAC (see Kataoka et al. 2013; Marshall et al. 2014). The respecfive role of local processes driving the enhanced decadal amplitudes is not well addressed in the present study. Can the authors include a few more notes about the anthropogenic effects on the local drivers, in the discussion?\n2) Anthropogenic heat enters the ocean through natural variability (e.g., IPO). The global warming hiatus during the last decade is an example. It is hard to disfinguish, hence, the decadal SST response to natural variability and external forcing over short periods. A coupled model experiment by Feng et al. (2015) indeed suggested that, even though external warming influences the precondifioning of the NN development, it (external forcing) itself may not cause the decadal modulafion of the occurrence of NN over recent decades – I would ask authors to discuss these aspects and previous literature in the discussion of the paper.\n3) Please provide the fime series of decadal (>7 years) Nino4w and NNI together over 1950 – 2020 and provide the correlafion coefficient for the periods 1950 – 2020, 1950 – 1985, and 1985 – 2020.", "reviewer_3": "Based on observafions together with several sets of model simulafions, i.e., pacemaker simulafions from CESM1.2 and FGOALS-f3-L models, single-model CESM2-LE large ensemble simulafions, and CMIP6 mulfi-model simulafions, the authors invesfigated the coherence in decadal variafions of sea surface temperature (SST) between the western Australian coast and the western-central Pacific. The coherence of SST variafions on the decadal fime scale between these two regions emerged in 1985. The authors claimed that the enhanced covarying decadal variafions were aftributed to the rapid warming in the western Pacific, however, the influence of Interdecadal Pacific Oscillafion phase changes is relafively less prominent. The topic is important and interesfing. The manuscript is in general well-organized and wriften, but some parts especially the methods could benefit from greater clarity. The conclusions that aftribute the coherent decadal variafions in the western Australian coast and the western-central Pacific to global warming merit further analyses/discussions to bring this forward more robustly.\nMajor comments:\n- The main conclusion and novelty of this study is aftribufing the decadal linkage between the western Australian coast and the western-central Pacific to the global warming induced by external forcing. This part of the results and discussion needs to be further enhanced. In the present manuscript, the authors use CESM2-LE large ensemble to invesfigate this and the ensemble mean of 100 members represents the forced signal. By comparing Fig. 3(c) and (d) based on CESM2-LE large ensemble mean to Fig. 1(d) and (e) based on observafions, the decadal variafions from the fime scale of 8-16 years are quite different in the wavelet analysis results, especially the Niño4w index is mainly dominated by 3-7 year variafions. The wavelet results in Fig. 3(c) and (d) don’t have informafion of significance test as in Fig. 1 (d) and (e). This needs to be added and it will be helpful to add the fime series of the ensemble mean NNI and Ninñ4w from CESM2-LE.\n- Fig. 4: What would the figure be like if the Y-axis was subsfituted with the correlafion between NNI and Niño4w in the period before and after 1985? This would be more straighfforward in supporfing the enhanced decadal coherence between NNI and Niño4w due to external forcing-induced warming.\n- The authors selected subsamples from CESM2-LE to invesfigate the influence of IPO on the enhanced covarying SST decadal variabilifies in the western Australian coast and the western-central Pacific. It is unclear how the IPO phases of the selected members match observafions. The Methods secfion explained shortly that the members are selected based on the IPO paftern correlafion coefficient between the model and observafion. How are the fime-evolving IPO phases combined with the spafial pafterns? I would suggest simply selecfing the ensemble members that could resemble the IPO index phase changes from observafions.\n- L295-298 “Most climate models present relafively even warming in the tropical Pacific ….This model bias may affect the emergence fiming of Indo-Pacific decadal linkage,” What is the bias for CESM2? As CESM2-LE is crucial for this study, the warming paftern from this model should be presented.\n- It seems that the main analyses are on boreal winter fime, i.e., December-January-February (DJF). In some places, it is stated, and in others not. It would be much clearer if the authors could state in general if seasonal or annual data was used in this study at the beginning of the manuscript and in the Method part. In case all the analyses are using DJF data, this informafion could be considered to be added to the abstract and even the fitle of the study.\n- The Methods are not explained very clearly.\ni. The secfion “Analysis for the formafion of SST pafterns” is missing some informafion. The way leading from equafion (1) to (2) is not well explained, and the Newtonian cooling component Q_lh^(o^' ) is not found in equafion (2).\nii. The amplitude of NNI on the decadal fimescale is larger than that on the interannual fimescale (refer to L95-96). Normally the interannual variability is more prominent with a larger amplitude than the decadal variability because the fime series on the decadal fime scale is more smoothed. I wonder how the amplitude is defined.\niii. The IPO paftern correlafion coefficient (PCC) is not introduced clearly how it is calculated. This is crifical for understanding the results.\n- Please review and ensure the grammar is correct and keep the consistency in the use of either past or present tense throughout the manuscript.\nMinor comments:\n- The first sentence in the abstract is incomplete or lacks informafion. Some words could be added to the end of the sentence to specify what impact the interbasin linkage would bring.\n- L42-43: “…has experienced the most rapid warming…” I feel some condifions should be added, for instance “in the tropical regions”, because the high lafitude warming signal is stronger than the warming in these regions.\n- L55: “…affect that in the Niño4w region…” What does the “that” refer to?\n- L97: “in other areas around the region off western Australian coast,” this is duplicated shown another fime by the end of the sentence.\n- L135-136: “Note that the posifive anomaly appears stronger in the southern South China Sea and the western Pacific”, what does that mean?\n-L148: “the norther wind…”-> “the northern wind…”\n-L166: “the dynamic process has led to…” -> “the dynamic process via ocean heat transport has led to…”\n-L173: “winter NNI and precipitafion” -> It sounds a bit weird because the locafion of the NN region is in the southern hemisphere, the winter there is not in DJF. To keep consistent and be more precise, either stafing the months or referring to the hemisphere would work.\n-L182-183: “…more in accord with their posifion in the climatological mean state…” Adding a figure of the climatological mean state will make it clearer.\n-L204: “Two sets of pacemaker experiments” Is it intended to say “with two models”?\n-L209: Why is the fime period for CESM1.2 shorter than that for FGOALS-f3-L?\n-L222: “is similar to that observed exists for NNI…” -> “is similar to that observed for NNI…”\nActually, the decadal variability seems not similar between model simulafions and observafions, see my major comment.\n-L225-226: “The external forcing explains about 42.6% of the decadal variance of the NNI.” Where does this number come from? It is befter to show the fime series.\n-L233-234: “The decadal linkage is consistent in the individual members due to large Signal-to-Noise.” I don’t understand this sentence.\n-L245: “convecfion in situ”-> “in situ convecfion”\n-L250: “correlafion coefficient (0.6) between the decadal NNI and the warming trend”, what would the value of correlafion coefficient be when the decadal NNI is subsfituted with the correlafion between decadal NNI and Nino4w?\n-L303: “by Feng et al.24 and Trenery and Han26.” Why not just add the numbers as citafions?\n-L308: “It is unnecessary to cite the paper here again. It reads like reference #37 is a previous publicafion of the authors. BTW, the informafion on this paper in the reference list is incomplete.”\n-L345: “with eight simulafions” do you mean “ensemble members”?\n-L346: “ 31 simulafions” models? Simulafions from 31 Earth system models?\nL359: “ 1000 EEMD ensemble members …0.2. …“ Is 1000 a typical number of members? What does the magnitude of standard deviafion of 0.2 mean to the results? How was the number determined, would a smaller value of 0.1 or a bigger value of 0.3 work?\nL366: “by dividing each monthly anomaly by the standard deviafion”-> “by dividing each monthly anomaly with the standard deviafion”\nL369: to have the full name of WES\nL370: the citafion should be #29, the #32 is not Xie et al.\nL377: Q_lh^(o^' ) This term does not appear in the equafion (2)\nL381: “where Q_lw^' and Q_sh^' are neglected to owe to their..” This is confusing. These two terms are presented in Table S2. Is this statement based on the results there? Then it should be in the main text. This sentence is not grammarly correct.\nL390: decadal NNI Is NNI also based on DJF mean or annual mean data?\nL392-393: From observafion, we choose the CESM2-LE members with IPO phases similar (R+) /opposite (R-). I don’t understand this. Maybe the authors meant “According to” or “Based on” observafion? “with IPO phases similar (R+) /opposite (R-).” -> “with similar (R+) /opposite (R-) IPO phases.”\nL393-394: “Before choosing” could be removed.\nL394-395: “PCC” is only used here and in the next sentence, I would suggest not introducing a new abbreviafion.\nFig. 1: There is a typo in the fitle of Fig. 1(g): “Corss” -> “Cross”.\nFig. 2: Regarding the decadal NNI, there are only 3-4 cycles within 35 years, what is the degree of freedom for the stafisfically significant test?\nThe precipitafion contours in Fig. 2(c)-(d) are hard to find and the values are missing.\n“Ningaloo Niño generally peaks in boreal winter; thus, only the changes in boreal winter are presented.”\n-Is this a general statement that could be applied to the whole study and all the figures shown in the manuscript? In that case, this needs to be menfioned at the beginning of the manuscript and introduced in the methods.\nFig. 3: The way of choosing the R+/R- group in this study might be affected by the model’s response of SSTA to IPO. It's possible that some SSTA pafterns may not be related to certain IPO phases. As the purpose is to check the IPO, it is more straighfforward to choose the ensemble members by the simulated IPO indices. Would this way end up with two similar sets of ensemble members?\nFig. 4: I would suggest adding figures with subsfitufing the y-axis with correlafion coefficients between NNI and Nino4w before and after 1985.\nTable S2: It will be easier to add the long names of the terms into the table after each term."}, "author_response": "Reviewer #1 (Remarks to the Author):\nRecommendation: Minor revisions\nThe paper discusses the inter-basin linkage between western Australian coast\nand western–central tropical Pacific on the decadal time scale (8–16 years)\nbased on the analysis of multiple datasets, including three sets of observational\ndata, two pacemaker experiments of climate models, a large ensemble of\nCESM2 simulations, and CMIP6 simulations. The results suggest that a decadal\nlinkage of SST between the two regions emerged around 1985, coinciding with\na continuous intensification of decadal variability. The mechanism of the\ndecadal linkage is further discussed by examining the role of external forcing\nand the IPO. Warming in the western tropical Pacific induced by external forcing\ncauses enhancement and eastward-shift of the center of deep convection from\nthe Indian Ocean to Maritime Continent, leading to stronger westerlies in the\ntropical Indian Ocean. These changes cause anomalous cyclonic circulations\nand SST changes in the west coast of Australia, resulting in enhanced coupling\nin the two regions. These findings provide valuable insights into decadal\nvariability over the Ind-Pacific region and its prediction.\nWhile the availability and quality of observational data are generally challenges\nfor this type of studies on decadal variability, the authors conclusions are\nobtained from the analysis that incorporates multiple datasets including both\nobservations and model experiments. Therefore, the decadal linkage scenario\ndescribed above are plausible, making it worthwhile to publish the results. I\nhave some minor comments, which are mostly concerns on writing.\nResponse: We would like to thank the reviewer for the constructive review\ncomments/suggestions, which give us a big help to improve the quality of our\nmanuscript. We have done our best to address the reviewer’s concerns and\nmodified the manuscript. Point-by-point responses to the reviewer’s comments\nare listed below. We made our effort to correct errors in the writing, discussed\nthe impact of uncertainties in heat flux data on the SST pattern off the western\nAustralian coast. We also show that the uncertainty of SST data due to satellite\nmeasurements does not affect the decadal variability and linkage between\nthese two regions. We added two sets of pacemaker experiments related to the\nIndian Ocean and four single forcing large ensembles, complementing the\ndefinition and effects of external forcing.\n1. Title\n“Western–central Pacific” is not very clear. Perhaps “Western–central Tropical\nPacific” is better.\nResponse: We thank the reviewer for this suggestion. “Western–central\nTropical Pacific” is more concise. We have revised the title.\n2. Abstract\nL25 “The interbasin linkage generates a broad and profound impact.”\nAdding a little more background information at the beginning of the abstract\n(perhaps two sentences) would help readers better understand the implication\nof the results.\nResponse: We appreciate the reviewer's suggestion. To address the incomplete\ninformation in the first sentence of the abstract, we have included the following\nsentence: “The impact of interbasin linkage on the weather/climate and\necosystems is significantly broader and profounder than that of only appearing\nin an individual basin.” This addition aims to specify the wide-ranging impacts\nof the teleconnection between the Indo-Pacific basins.\nL29 “external forcing”\nIn the discussion (L241), the term “the external forcing” is used to signify\n“anthropogenic forcing”. Using a more explicit expression or term related to\nglobal warming (e.g., increase of greenhouse gas) would clarify the meaning of\n“external forcing”.\nResponse: Thank you for you good suggestion. In our revised manuscript, we\nspecified the external forcing as the greenhouse gases and volcanoes, which is\nimportant to affect the Tropical Indian Ocean warming (by the warming trend)\nand decadal variabilities (by the volcanoes) last decades. We further elucidate\nhow \"external forcing\" impacts the relationship between western Australian\ncoast and the Western–central tropical Pacific. We have added two pacemaker\nexperiments related to Indian Ocean and four single forcing large ensembles:\nOne pacemaker experiment is TIOGA using the National Center for Atmospheric\nResearch (NCAR) Community Earth System Model version 1 (CESM1), in which\nthe simulated SST in the tropical Indian Ocean and part of the western Pacific\nwarm pool region is restored to observed SST from Extended Reconstructed\nSST version 3b (ERSSTv3b), with fully restored SST anomalies (SSTA) for\n15°N-15°S, from the African Coast to 161°E, and buffer zones at 15°N-20°N,\n15°S-20°S, and 161°E-180°(Hurrell et al., 2013; Zhang et al., 2019; Yang et\nal., 2020). This TIOGA demonstrates the influence of the tropical Indian Ocean\nand Western warm pool.\nAnother pacemaker experiment is IOGA using NCAR CESM1, in which the\nsimulated SST in the Indian Ocean (north of 50°S) is restored to observed SST\nfrom ERSSTv3b (Hurrell et al., 2013; Zhang et al., 2021). This IOGA highlights\nthe influence of the Indian Ocean, specifically, the effect of the western\nAustralian coast area on Niño4w and their relationship.\nFour single forcing large ensembles (Simpson et al., 2023) are employed to\nexamine the role of external forcings further. They are GHG only (only\ngreenhouse gases evolving, having 15 members); BMB only (Only biomass\nburning aerosols evolving, having 15 members); EE (everything else evolving,\nhaving 15 members, that is, all forcings are time evolving except GHGs, AAERs\nincluding BMB which means greenhouse gases and anthropogenic (including\nbiomass burning) aerosols are held fixed at 1850 level; xAER (Everything time\nevolving except anthropogenic aerosols, having 10 members).\nResults from TIOGA experiments reveal that the changes in tropical Indian\nOcean and part of western Pacific can generate significant SST changes in the\ncentral-western tropical Pacific and off western Australian coast, accounting for\na large fraction of the observed NNI after 1985; Before 1985, however, the\nsimulated NNI has a weaker amplitude and opposes to the observation (Fig.\nRA1c; Fig. 3c of revised manuscript). The simulated NNI and Niño4w index in\nTIOGA are out of phase with a correlation coefficient of -0.74 (Fig. RA1d; Fig.\n3d of revised manuscript). These results suggest that SSTA in tropical Indian\nOcean and western Pacific play an important role in causing the enhanced NN-\nNiño4w linkage in recent decades. This is because a warm (cold) SSTA in\ntropical Indian-western Pacific basin drives equatorial easterly (westerly) wind\nanomaly in the central-western tropical Pacific (Fig. RA2; Fig. S14 of revised\nmanuscript), enhances (reduces) equatorial upwelling and causes cold (warm)\nSSTA in Niño4w region. The enhanced equatorial easterly (westerly) wind\nanomaly increases (reduces) the Indonesian Throughflow, strengthening\nNingaloo Niño (Niña) events. Meanwhile, the colder (warmer) SSTA in Niño4w\nregion can induce cyclonic (anticyclonic) circulation in the Southeast Indian\nOcean, together with local air-sea feedback, further strengthening Ningaloo\nNiño (Niña) magnitudes.\nBefore 1985, the warming of the tropical Indian Ocean was primarily influenced\nby the Interdecadal Pacific Oscillation (IPO). Then, the decadal variability in the\nIndian Ocean was in phase with the IPO (Dong et al., 2017; Zhang et al., 2018).\nAfter 1985, the external forcings dominate the changes in the Indian Ocean\n(Fig. RA1e; Fig. 3e of revised manuscript). Under this influence, the out of\nphase decadal relationship between Indian Ocean and Pacific basin appears (Fig.\nR1Af; Fig. 3f of revised manuscript). The warming in the Indian Ocean and the\nnegative phase of IPO can both lead to easterly wind anomalies and cooling in\nthe Niño4w region, further enhancing the heat advection from the western\nPacific, ultimately causing the warming off the western Australian coast area\n(Fig. RA2b; Fig. S14b of revised manuscript).\nTherefore, the \"external forcing\" referred to here primarily denotes the external\nforcing causing decadal variability in the tropical Indian Ocean and resulting in\nan enhanced decadal co-variability between these two regions after 1985. we\nshow that the increase in greenhouse gases (GHGs; Fig. RA3; Fig. 4 of revised\nmanuscript, green line) has significantly contributed to the recent trend of rapid\nwarming in the Tropical Indian Ocean. External forcings that exclude\nanthropogenic aerosols (experiment xAER; Fig. RA3; Fig. 4 of revised\nmanuscript, blue line) produce a stronger warming trend, which means\nanthropogenic aerosols somewhat decrease the warming trend in the Indian\nOcean. In addition to a warming trend, Experiment xAER also generates\ndecadal variability in the tropical Indian Ocean SST Fig. RA3; (Fig. 4 of revised\nmanuscript, purple line). This finding suggests that external forcings by volcanoes is the main\ncause for TIO SSTA and result in its out of phase relationship with IPO after\n1985.\nFigure RA1 Possible influencing factors (IPO and external forcing) for\ndecadal variabilities of NNI. a Time series of the 21-year sliding variance of\nthe NNI (black line) and the IPO index (blue line) based on ERSSTv5, in which\nthe red shading identifies the positive phase of the IPO during 1978–1997. b\nTime series of the 21-year sliding variance of interannual NNI (blue line) and\ndecadal NNI (red line). Red shading identifies the positive phase of the IPO\nduring 1978–1997. c The 8–16 years band-pass filtered decadal NNI in\nERSSTv5 and TIOGA. d The 8–16 years band-pass filtered decadal NNI and\nNiño4w index in TIOGA. e The 8 years low-pass filtered tropical Indian ocean\nSSTA (15°N–15°S, 50°–160°E) in observations and the ensemble mean of\nCESM2-LE, and MPI-LE. Blue shading identifies the period 1960-1985. Red\nshading identifies the period after 1985. f The normalized 8-year low-pass\nfiltered Tropical Indian ocean SSTA and IPO index (detrend) in observations.\nFigure RA2 Teleconnections for the decadal SST variability off western\nAustralian coast in DJF in different Indian Ocean pacemaker\nexperiments. Correlation between DJF decadal SSTA and the DJF decadal NNI\n(shading; vectors for wind anomaly; contours for precipitation anomaly) during\na 1950–1990 and b 1990–2013 in CESM1 tropical Indian ocean (TIOGA)\npacemaker experiments. c and d as in a and b but for the 200-hPa velocity\npotential anomaly (shading; vectors for divergent wind; contours for\nprecipitation). Red/blue contours represent positive/negative precipitation\nsignificant at the 95% confidence level. Stippled areas are where the regression\nresults are statistically significant at the 95% confidence level. e, f, g, and h\nare for 1950–1990 and 1990–2019 in CESM1 Indian ocean (IOGA) pacemaker\nexperiments.\nFigure RA3 The contribution of single external forcing to the tropical\nIndian Ocean SST.\nThe 8 years low-pass filtered tropical Indian ocean SSTA (15°N–15°S, 50°–\n160°E) in ERSSTv5 (black) and the ensemble mean of CESM2-LE (red) and four\nsets of CESM2 single forcing large ensembles: xAER (blue), GHG (green), BMB\nand EE (orange), and EE (purple). Light blue shading identifies the period 1960-\n1985. Light red shading identifies the period after 1985.\nReference:\n1. Hurrell JW, et al. The community earth system model: a framework for\ncollaborative research. Bulletin of the American Meteorological Society 94,\n1339-1360 (2013).\n2. Zhang L, et al. Indian Ocean warming trend reduces Pacific warming\nresponse to anthropogenic greenhouse gases: An interbasin thermostat\nmechanism. Geophysical Research Letters 46, 10882-10890 (2019).\n3. Yang D, et al. Role of tropical variability in driving decadal shifts in the\nSouthern Hemisphere summertime eddy-driven jet. Journal of Climate 33,\n5445-5463 (2020).\n4. Zhang L, Han W. Indian ocean dipole leads to Atlantic Niño. Nature\nCommunications 12, 5952 (2021).\n5. Zhang L, Han W, Sienz F. Unraveling causes for the changing behavior of\nthe tropical Indian Ocean in the past few decades. Journal of Climate 31, 2377-\n2388 (2018).\n6. Dong L, McPhaden MJ. Why has the relationship between Indian and Pacific\nOcean decadal variability changed in recent decades? Journal of climate 30,\n1971-1983 (2017).\n7. Zhang L, Han W, Sienz F. Unraveling causes for the changing behavior of\nthe tropical Indian Ocean in the past few decades. Journal of Climate 31, 2377-\n2388 (2018).\n8. Simpson IR, et al. The CESM2 single-forcing large ensemble and comparison\nto CESM1: Implications for experimental design. Journal of Climate 36, 5687-\n5711 (2023).\n3. L42 “the western Pacific (including eastern Indian Ocean….”\nThis sentence is confusing. The western Pacific does not include the eastern\nIndian Ocean.\nResponse: We thank the reviewer for this suggestion. We have deleted “the\neastern Indian Ocean”.\n4. L51“varia”\nShould be “variation”.\nResponse: We thank the reviewer for this suggestion. We have corrected this\nword.\n5. L54 “most important”\nImportant for what? Does this mean “strongest”?\nResponse: Yes, according to the precious studies, the SST index of western–\ncentral tropical Pacific (Niño4w) region has the strong correlation with NNI\n(Feng et al., 2013; Zhang et al., 2018). We have corrected this sentence to\n“The western–central Pacific (Niño4w) region is the remote area with strong\nlinear correlation with Ningaloo Niño variability.”\nReference:\n1. Feng M, et al. Decadal increase in Ningaloo Niño since the late 1990s.\nGeophysical Research Letters 42, 104-112 (2015).\n2. Zhang L, Han W. Impact of Ningaloo Niño on tropical Pacific and an interbasin\ncoupling mechanism. Geophysical Research Letters 45, 11,300-311,309 (2018).\n6. L84 “NNI, which is defined as the area-weighted mean SST anomaly (SSTA)\nin the wedged area”\nThis definition is different from the most commonly used NNI (area average\nSST for 110°-116°E, 22°-32ºS or 108ºE-116ºE, 22°-28ºS). Are the results\nsensitive to the definition used? For example, is STD on the decadal time scale\nsimilar when using the commonly used NNI? The justification for the choice of\nthis specific definition could be included in the method section.\nResponse: We adopted the NNI definition from Zhang (2018). In the regression\nresults, a wedge-shaped sea surface temperature mode was displayed. The\nwedge-shaped is similar to 'wedge of SST warming' pattern employed the\ncommonly used NNI. Therefore, we opted for the definition of the NNI region\nbased on this wedge-shaped sea surface temperature pattern. We calculated\nthe STD of different chosen region on the decadal time scale as suggested, for\nthe region in our manuscript, the STD is 0.51℃, for the region 110°-116°E,\n22°-32ºS is 0.51℃, and for the region 108ºE-116ºE, 22º-28ºS is 0.52℃. The\nabove results show the rationality of our region selection. We have added this\ndiscussion to method section: “This definition can comprehensively capture the\nwarming pattern off western Australian coast as being wedge-shaped.\nCompared with the traditional definition (Marshall et al., 2015; Kataoka et al.,\n2014) whose area-weight-averaged SSTA is based on the region 110°-116°E,\n22°-32ºS , similar NNI variation and amplitude (denoted as STD) are obtained.”\nReference:\n1. Zhang L, Han W, Li Y, Shinoda T. Mechanisms for generation and\ndevelopment of the Ningaloo Niño. Journal of Climate 31, 9239-9259 (2018).\n2. Marshall AG, Hendon HH, Feng M, Schiller A. Initiation and amplification of\nthe Ningaloo Niño. Climate Dynamics 45, 2367-2385 (2015).\n3. Kataoka T, Tozuka T, Behera S, Yamagata T. On the Ningaloo Niño/Niña.\nClimate dynamics 43, 1463-1482 (2014).\n6. L86. “The standard deviations (STDs) of NNI are 0.92 ℃ (1950-1985) and\n1.07 ℃\n87 (1985-2020).”\nThe quality of the SST data could be different in these two periods because of\nthe satellite measurements in recent years. Is the uncertainty due to the SST\ndata quality smaller than the difference in STD between the two periods?\nResponse: Thank for your comments. Extended Reconstruction Sea Surface\nTemperature Version 3b (ERSSTv3b; Smith et al., 2008) does not include\nsatellite data. Therefore, we used ERSSTv3b to test the uncertainty due to\nsatellite measurements (Fig. RA4). We calculated the wavelet of NNI and\nNiño4w and the 21-year sliding correlation between these two regions, and the\nresults are similar with that in ERSSTv5, HadISST1, and IAP. This confirms that\nthe enhanced decadal variability and linkage between these two regions in our\nstudy aren’t influenced by the satellite measurement data since the 1980s.\nFigure RA4 SST variability off western Australian coast and the western–\ncentral tropical Pacific and their relationship in ERSSTv3b. a The 21-year sliding\ncorrelation coefficients between the ensemble mean NNI and the ensemble\nmean Niño4w index, in which the red line signifies results statistically significant\nat the 95% confidence level. b Wavelet spectrum of the ensemble mean NNI.\nThe red lines mark the periodicity of 8–16 years. c Wavelet spectrum of the\nensemble mean Niño4w index.\nReference:\n1. Smith TM, Reynolds RW, Peterson TC, Lawrimore J. Improvements to NOAA's\nhistorical merged land–ocean surface temperature analysis (1880–2006).\nJournal of climate 21, 2283-2296 (2008).\n7. L93 “8–16 years”\nIt is beneficial to state more explicitly in this section that the decadal time scale\nin this study is “8–16 years”, although it is stated in the method section later.\nResponse: We thank the reviewer for this suggestion. At the end of the first\npart of the results, we have emphasized that the decadal timescale in our study\nis mainly 8–16 years as suggested. We added “In the following, we focus on\nthe decadal timescale with 8–16 years.”\n8. L134 “The center of negative anomaly shifts westward”\nBefore 1985, strong correlations exist in the eastern tropical Pacific. However,\nthis may not necessarily indicate an interbasin linkage between western\nAustralian coast and eastern tropical Pacific during this period. Or is it an\ninterbasin linkage? Please explain.\nResponse: We thank the reviewer for this suggestion. It represents an\ninterbasin linkage between the western Australian coast and the eastern\ntropical Pacific. This linkage has experienced a westward shift after 1985,\nshowing the Western–central tropical Pacific the primary area of interaction\nbetween the NNI and the tropical Pacific.\n9. L145 “eastern”\nShould be “easterly”.\nResponse: We thank the reviewer for correction. We have modified this word.\n10. L148 “norther”\nShould be “northerly”.\nResponse: We thank the reviewer for correction. We have modified this word.\n11. L151 “Weakening of coastal upwelling”\nAlso, northerly winds reduce the wind speed and thus evaporative cooling\n(latent heat flux) decreases.\nResponse: We thank the reviewer for this crucial comment. Moreover, the WES\nfeedback we previously calculated is not solely focused on the contribution of\nwind to original latent heat. The\n𝑄𝑎′\nincludes wind speed, relative humidity,\n𝑙ℎ\nand stability effects, which are not directly tied to Sea Surface Temperature\n(SST) but are due to atmospheric adjustments (Xie et al., 2010). Therefore,\nthe changes in 'latent heat flux' and the wind might have some inconsistencies.\nReference:\n1. Xie S-P, Deser C, Vecchi GA, Ma J, Teng H, Wittenberg AT. Global warming\npattern formation: Sea surface temperature and rainfall. Journal of Climate 23,\n966-986 (2010).\n12. L159 “The latent heat flux is largely unchanged between these two periods”\nFig. 2 shows that local winds (and thus wind speed) are largely changed\nbetween these two periods. So the latent heat flux should be largely changed\nas well, which is inconsistent with the results. Please clarify.\nResponse: We thank the reviewer for this comments. Please see the response\nto comment 11. We used the\n𝑄𝑎′\n, which includes wind speed, relative humidity,\n𝑙ℎ\nand stability effects. The changes in 'latent heat flux' and the wind might have\nsome inconsistencies.\n12. L166 “northern wind anomaly”\nShould be “northerly wind anomaly”.\nResponse: We thank the reviewer for correction. We have modified this word.\n13. L179 “centra-eastern”\nShould be “central-eastern”\nResponse: We thank the reviewer for correction. We have modified this word.\n14. L205 “centra-eastern”\nShould be “central-eastern”\nResponse: We thank the reviewer for correction. We have modified this word.\n15. L223 “This indicates external that”\nCorrect to “This indicates that external”\nResponse: We thank the reviewer for correction. We have modified this\nsentence.\n16. L245 “convection in situ”\nNot clear what this means.\nResponse: We thank the reviewer for correction. We modified this to “in situ\nconvection” to show that the convection is also over the Maritime Continent\nand western Pacific.\n17. L255 “northern surface wind anomaly”\nShould be “northerly surface wind anomaly”\nResponse: We thank the reviewer for correction. We have modified this word.\n18. L279 “eastern wind anomaly”\nShould be “easterly wind anomaly”\nResponse: We thank the reviewer for correction. We have modified this word.\nWe also corrected all the similar grammar mistakes.\n19. L332 “Japanese 55-year Reanalysis (JRA55)”\nThe time period covered in OAFlux is similar. Is the decadal variability in surface\nflux data from JRA55 consistent with OAFlux? Also, the uncertainty of surface\nflux data in this region should be mentioned (e.g., Feng and Shinoda 2019).\nResponse: Thank you for your valuable comments. The uncertainty between\ndifferent datasets is important for this study. According to previous studies\n(Feng and Shinoda 2019), especially during the boreal winter when the\nNingaloo Niño develops, the results of surface heat flux from different datasets\nvary greatly, which poses certain difficulties to our qualitative research. We\nhave added it in discussion: “In heat budget analysis, the surface heat fluxes\nvary substantially with the datasets, especially during the boreal winter when\nthe Ningaloo Niño develops (Feng and Shinoda 2019). However, the increased\nheat advection is supported by different datasets and methods.”\nTo further validate the importance of oceanic dynamic, we did a composite\nanalysis of the mixed-layer heat budget off western Australian coast (in\nNingaloo Niño region, (Table RA1) during the development phase of decadal\nNingaloo Niño events. In this phase, Ningaloo Niño region experiences\nsignificant warming for both periods, but the warming rate is larger for the\npost-1985 period (Table RA1), consistent with the intensified SSTA. Increased\nocean heat advection plays a dominant role in causing the intensified SST\nvariability. As shown in Table RA1, the largest terms of the heat budget are the\nsurface heat flux and the residue. Here the residue term represents the vertical\nheat transport due to upwelling of subsurface water into the mixed layer and\nall other non-resolved processes. The net effect of the horizontal advection\nterms is to diverge the heat away from the Ningaloo Niño region, and this effect\nis larger before 1985 than after 1985. Combined with residue term, our budget\nanalysis suggests that the reduced heat divergence due to horiz to horizonal\nadvection and the reduced upwelling after 1985 lead to an enhanced Ningaloo\nNiño amplitude on decadal timescale. Meridional heat advection primarily\noriginates from the western tropical Pacific, driving more warm water towards the\nnorthwestern Australian coast. With the strengthening of the cyclonic wind\nanomaly, the resulting eastward geostrophic current anomalies carrying\nwarmer water have led to an overall increase in SST off western Australian\ncoast.\nThe equation for mixed-layer heat budget follows:\n𝜕𝑇′ 𝜕𝑇′ 𝜕𝑇̅ 𝜕𝑇′ 𝜕𝑇′ 𝜕𝑇̅ 𝜕𝑇′ 𝑄𝑛𝑒𝑡′\n=−(𝑢̅ +𝑢′ +𝑢′ )−(𝑣̅ +𝑣′ +𝑣′ )+ +𝑅′\n𝜕𝑡 𝜕𝑥 𝜕𝑥 𝜕𝑥 𝜕𝑦 𝜕𝑦 𝜕𝑦 𝜌 𝐶 ℎ\n0 𝑃\nT, u, and v represent the oceanic temperature, zonal and meridional current\n𝜕𝑇′\nvelocities averaged in the mixed-layer depth, respectively. is temperature\n𝜕𝑡\nTendency. A variable (such as T, u, and v) can be decomposed into the sum of\nthe climatological mean state (e.g. 𝑢̅) and anomaly state (e.g.𝑢′). 𝑄𝑛𝑒𝑡′ is the\n\"net heat flux\", which accounts for the sum of shortwave radiation, longwave\nradiation, latent heat fluxes, and sensible heat fluxes. 𝜌 indicates sea water\n0\ndensity (1026 kg/𝑚3) and 𝐶 is the ocean heat capacity (3986 J/(𝑘𝑔∙𝐾) , h is\n𝑃\nmixed-layer depth. 𝑅′ is the \"residual\" term, which accounts for the remaining\nheat budget components not explicitly resolved by the used data in the\nequation. The results presented above were obtained through the vertical\nintegration of the heat budget across the mixed layer.\nTable RA1 The average of mixed-layer heat budget terms of Ningaloo\nNiño region by choosing decadal Ningaloo Niño events (before 1985\nand after 1985). The chosen events are December 1970 to December 1972,\nand January 1981 to August 1983 (before 1985); June 1986 to June 1988, and\nOctober 1995 to August 1997 (after 1985), monthly data after 8–16 years\nband-pass filter is used.\nAverage of decadal\nNingaloo Niño events Before 1985 After 1985\n(10−9 K/s)\n𝜕𝑇′\n1.44 1.86\n𝜕𝑡\n𝑄𝑛𝑒𝑡′\n-12.88 -9.16\n𝜌 𝐶 h\n0 𝑃\n𝑅′ 27.37 15.26\n𝜕𝑇′\n𝑢′ 0.77 0.51\n𝜕𝑥\n𝜕𝑇̅\n𝑢′ 0.16 2.23\n𝜕𝑥\n𝜕𝑇′\n𝑢̅ -1.51 0.64\n𝜕𝑥\n𝜕𝑇′\n𝑣′ 0.79 -0.74\n𝜕𝑦\n𝜕𝑇̅\n𝑣′ -9.94 -7.05\n𝜕𝑦\n𝜕𝑇′\n𝑣̅ -6.08 1.21\n𝜕𝑦\nReference:\n1. Feng X, Shinoda T. Air-sea heat flux variability in the southeast Indian Ocean\nand its relation with Ningaloo Niño. Frontiers in Marine Science 6, 266 (2019).\n20. Reference section\nMost of the references lack journal names.\nResponse: Thank you for your comments. We have revised all the references.\n21. L592 Data and materials availability\nAvailability of two pacemaker simulations is not provided.\nResponse: Thank you for your comments. We have added the data availability\nfor Tropical Indian Ocean pacemaker experiment. We have Data from some\npacemaker experiments (CESM1 Pacific pacemaker experiment, FGOALS-f3\npacemaker experiment, and Indian Ocean pacemaker experiment): “The\nrelated data of some pacemaker experiments (CESM1 Pacific pacemaker\nexperiment, FGOALS-f3 Pacific pacemaker experiment, and Indian Ocean\npacemaker experiment) are available via\nhttp://data.lasg.ac.cn/lpf/data_availability-POGA-IOGA.zip.”\nReviewer #2 (Remarks to the Author):\nReview of the paper entitled ‘Emergence of Decadal Linkage between Western\nAustralian Coast and Western–central Pacific\" by Dr Lin and colleagues.\nMain\nThis work investigates the recent enhancement of decadal variability observed\nin the northwest Australian coast. Through observational data analyses and\nmodel experiments, the paper suggests that the anomalous warming of the\nwestern Pacific tropical Ocean contributes to enhanced variability via\natmospheric teleconnections. The Paper also suggests an emergence of decadal\nlinkage between the two regions under the western Pacific warming which is\nlikely to persist in the future given the projected warming of the tropical Pacific.\nThe role of western Pacific variability on the development of Ningaloo Niño (NN)\nhas been reported earlier. Feng et al. (2015) also suggested that anthropogenic\nwarming in the western Pacific may have an additional role in modulating the\nNN at decadal time scales. This paper takes this idea further with detailed\nresearch to show that recent decadal variations of NN might have an\nindependent existence from IPO under strong external warming, leading to a\n‘new decadal link’ between the two regions. This is an exciting finding!\nI appreciate the enthusiasm of the authors to tackle a complex research\nproblem that has narrow lines separating the role of anthropogenic warming\nand natural variations for controlling variability over different time scales. Given\nthe complexities of the problem, I still have quite a few concerns and questions\nwhich I list below. I hope clearing these questions will bring some more clarity\nto the presentation of the paper.\nResponse: We would like to thank the reviewer for the constructive\ncomments/suggestions. We have significant modifications based on the\nreviewers' comments, adding two sets of pacemaker experiments related to\nthe Indian Ocean and four single forcing large ensembles to explore the specific\neffects of external forcing. We also applied the 8–16 years band-pass\nfiltering to both the NNI and variable fields to ensure our study focusing on the\ndecadal timescale, and we discussed the linkage on an interannual scale. To\nverify the role of oceanic dynamic, we added calculations of the mixed-layer\nheat budget. We also added some important discussion.\nMajor comments:\n1) What is the rationale behind using EEMD to obtain components of\nvariability over different time scales? For instance, how do the authors make\nsure that the decadal component (fifth and sixth components combined) of\nindices has variability only above 7 years? Also, it seems that only indices are\nfiltered for different time scales but not the variables (SST, wind, etc.). You\nmay get spurious regression estimates if the dependent (SST for example)\nand independent (indices) operate over different time scales. I would like the\nauthors to make a little more serious effort to clarify these aspects in the\npaper.\nResponse: EEMD, through adaptive decomposing steps, decomposes the time\nseries into oscillatory components at different time scales. EEMD is an improved\nversion of Empirical Mode Decomposition (EMD). In comparison to EMD, EEMD\naddresses the 'mode mixing' issue, resulting in a more stable decomposition\noutcome. EMD represents data through a series of filtering steps, expressing it\nas a combination of a finite number of periodic components (IMFs) and a trend\nterm. This process involves connecting local maxima and minima of the original\ndata or the series obtained by subtracting several oscillatory components using\ncubic spline functions to form upper and lower envelopes. The original data is\nthen subtracted from these two envelopes' local means, and this process is\nrepeated until the local mean is zero everywhere, yielding the oscillatory\ncomponent for a specific period. EEMD, on the other hand, leverages the\nprinciple of multiple measurements averaging by introducing appropriately\nsized white noise into the original data to simulate scenarios of multiple\nobservations. Through multiple calculations and ensemble averaging, the final\nresult is obtained.\nTo ensure that the decadal component includes variability only above 7 years,\nthe EEMD process involves decomposing the time series into different IMFs,\neach representing a specific frequency or timescale. We used power spectra to\ncalculate the peak for each IMFs, thus determining their timescales variations\n(Fig. RB1). By combining the fifth and sixth IMFs, which correspond to\ntimescales longer than 7 years, we isolated and obtained the decadal variability.\nWe also used 8–16 year band-pass filter in revised manuscript.\nWe have provided correlation maps of the decadal NNI and the decadal variable\nfields (SST, surface wind, etc.), utilizing an 8–16 years bandpass filter for the\ndecadal analysis. The results show that the positive SST anomaly mode still\noccurs in the Ningaloo Niño region after 1985. Meanwhile, in the tropical Pacific,\nthe strong negative correlation areas have slightly shifted westward, and the\nstronger easternly wind over the western–central tropical Pacific (Niño4w\nregion; Fig. RB2, red box). The connection between the decadal NNI and\nNiño4w has strengthened after 1985 on decadal timescale.\nFigure RB1 Power spectra of the EEMD components using different datasets,\nwith the first column for ERSSTv5, the second column for HadISST, and the\nthird column for IAP.\nFigure RB2 Correlation between the decadal DJF SSTA (shading;\nvectors: surface wind) and the decadal DJF NNI based on ERSSTv5 and\nHadISST. The blue/red box represents the Ningaloo Niño and the Niño4w\nregions, respectively. Stippled areas denote where the regression results are\nstatistically significant at the 95% confidence level.\n2) (Page 17) Analysis of SST pattern: This section doesn’t speak of ocean heat\ntransport (D) well. The balance between D and Q is set as an assumption but\nhasn’t been tested explicitly. In fact, previous works on low-frequency NN and\nits mechanisms (Feng et al. 2015) showed that heat advection from the western\nPacific plays an important role. The present paper summarises the WES and\nocean transport contributions in the form of regression coefficients (table S2)\nwhich is very hard to understand. I do not even understand what are the two\nsets of values presented for each period in this table. I find that the way of\ntreatment of ocean heat transport is an important drawback of this study. In\nfact, there is no description of how and where heat transport anomalies are\ncalculated. Atmospheric teleconnections might be playing an important role as\ndiscussed in this paper, but I would like to see if the role of oceanic heat\ntransport can be expelled more undoubtedly. For instance, there might be a\nreason why ITF doesn’t contribute much to the decadal linkage, however, this\nstudy doesn’t explore that.\nResponse: We thank the reviewer for this remarkable comment. In our previous\nstudy, we worked under the assumption that ocean heat transport and net\nsurface heat flux are balanced against each other over long timescales,\nmeaning that D′ is essentially represented by the negative of Qnet. In addition,\n0\nwe calculated the contributions of different items in the Ningaloo Niño region:\nlatent heat representing atmospheric forcing, shortwave, longwave, and\nsensible heat. In the previous manuscript, the periods represented inside and\noutside the parentheses were not clearly expressed; we have now corrected\nthis: outside the brackets represents the decadal contributions, while inside\nbrackets the contributions from the raw data. Our findings agree what you\nmentioned: changes in ocean heat transport are important.\nTo further validate the importance of oceanic dynamic, we did a composite\nanalysis of the mixed-layer heat budget off western Australian coast (in\nNingaloo Niño region, (Table RB1) during the development phase of decadal\nNingaloo Niño events. In this phase, Ningaloo Niño region experiences\nsignificant warming for both periods, but the warming rate is larger for the\npost-1985 period (Table RB1), consistent with the intensified SSTA. Increased\nocean heat advection plays a dominant role in causing the intensified SST\nvariability. As shown in Table RB1, the largest terms of the heat budget are the\nsurface heat flux and the residue. Here the residue term represents the vertical\nheat transport due to upwelling of subsurface water into the mixed layer and\nall other non-resolved processes. The net effect of the horizontal advection\nterms is to diverge the heat away from the Ningaloo Niño region, and this effect\nis larger before 1985 than after 1985. Combined with residue term, our budget\nanalysis suggests that the reduced heat divergence due to horizonal\nadvection and the reduced upwelling after 1985 lead to an enhanced Ningaloo\nNiño amplitude on decadal timescale. Meridional heat advection primarily\noriginates from the western tropical Pacific, driving more warm water towards the\nnorthwestern Australian coast. With the strengthening of the cyclonic wind\nanomaly, the resulting eastward geostrophic current anomalies carrying\nwarmer water have led to an overall increase in SST off western Australian\ncoast. The results are consistent with the diagnosis that the ocean dynamical\nrole is important (Table S1 of original manuscript) and the findings of other\nresearchers (Feng et al., 2015).\nThe equation for mixed-layer heat budget follows:\n𝜕𝑇′ 𝜕𝑇′ 𝜕𝑇̅ 𝜕𝑇′ 𝜕𝑇′ 𝜕𝑇̅ 𝜕𝑇′ 𝑄𝑛𝑒𝑡′\n=−(𝑢̅ +𝑢′ +𝑢′ )−(𝑣̅ +𝑣′ +𝑣′ )+ +𝑅′\n𝜕𝑡 𝜕𝑥 𝜕𝑥 𝜕𝑥 𝜕𝑦 𝜕𝑦 𝜕𝑦 𝜌 𝐶 ℎ\n0 𝑃\nT, u, and v represent the oceanic temperature, zonal and meridional current\n𝜕𝑇′\nvelocities averaged in the mixed-layer depth, respectively. is temperature\n𝜕𝑡\nTendency. A variable (such as T, u, and v) can be decomposed into the sum of\nthe climatological mean state (e.g. 𝑢̅) and anomaly state (e.g.𝑢′). 𝑄𝑛𝑒𝑡′ is the\n\"net heat flux\", which accounts for the sum of shortwave radiation, longwave\nradiation, latent heat fluxes, and sensible heat fluxes. 𝜌 indicates sea water\n0\ndensity (1026 kg/𝑚3) and 𝐶 is the ocean heat capacity (3986 J/(𝑘𝑔∙𝐾) , h is\n𝑃\nmixed-layer depth. 𝑅′ is the \"residual\" term, which accounts for the remaining\nheat budget components not explicitly resolved by the used data in the\nequation. The results presented above were obtained through the vertical\nintegration of the heat budget across the mixed layer.\nTable RB1 The average of mixed-layer heat budget terms of Ningaloo\nNiño region by choosing decadal Ningaloo Niño events (before 1985\nand after 1985). The chosen events are December 1970 to December 1972,\nand January 1981 to August 1983 (before 1985); June 1986 to June 1988, and\nOctober 1995 to August 1997 (after 1985), monthly data after 8–16 years\nband-pass filter is used.\nAverage of decadal\nNingaloo Niño events Before 1985 After 1985\n(10−9 K/s)\n𝜕𝑇′\n1.44 1.86\n𝜕𝑡\n𝑄𝑛𝑒𝑡′\n-12.88 -9.16\n𝜌 𝐶 h\n0 𝑃\n𝑅′ 27.37 15.26\n𝜕𝑇′\n𝑢′ 0.77 0.51\n𝜕𝑥\n𝜕𝑇̅\n𝑢′ 0.16 2.23\n𝜕𝑥\n𝜕𝑇′\n𝑢̅ -1.51 0.64\n𝜕𝑥\n𝜕𝑇′\n𝑣′ 0.79 -0.74\n𝜕𝑦\n𝜕𝑇̅\n𝑣′ -9.94 -7.05\n𝜕𝑦\n𝜕𝑇′\n𝑣̅ -6.08 1.21\n𝜕𝑦\nReference:\n1. Feng M, et al. Decadal increase in Ningaloo Niño since the late 1990s.\nGeophysical Research Letters 42, 104-112 (2015).\n3) Related to the previous comment: The role of (slow) upper ocean heat\ncontent variations in the western Pacific and its transmission to the West\nAustralian coast (WAC), affecting SST and sea level there, is indeed discussed\nearlier (Feng et al. 2015). This paper almost fully neglects the role of ocean\nwaveguides in shaping the low-frequency SST variations in the WAC. I was\nwondering how tide-gauge sea-level measurements at Fremantle which is\nidentified to be a good indicator of heat content variations in the WAC (Feng et\nal. 2003, 2015) would complement the overall result of this paper. Even though\nthe authors try to establish the absence of oceanic teleconnection (S3?), the\ndescription of figure 2 (Page 7) uses the ocean transport (Leeuwin current) to\nexplain the spatial structures. Do authors believe that the influence of Leeuwin\ncurrent is confined only to the NN region?\nResponse: We thank the reviewer for such a insightful comment. As we stated\nand obtained in Table RB1, the reduced heat divergence due to horizonal\nadvection and the reduced upwelling after 1985 lead to an enhanced Ningaloo\nNiño amplitude on decadal timescale. The results show that, both in terms of\nthe previous SST pattern analysis and the mixed layer heat budget calculation,\nthe importance of heat advection is confirmed, just as Feng previously\nmentioned. The heat advection north side of the NNI region (including western\nPacific) is significant. This leads to an intensification of the Leeuwin Current,\nultimately causing an increase in sea temperatures off western Australian coast.\nAlso, Due to the cyclonic anomaly off western Australian coast, the induced\neastward geostrophic current anomalies have brought more warm water to the\ncoast. Therefore, we believe that the role of the Leeuwin Current is not limited\nonly to the Ningaloo Niño region.\n4) There is a systematic difference between the NNI-regressed spatial SST\nmaps in Figure 1 and Figure 3. There is no variability seen in the western Pacific\nin figure 1 (observation) but there are strong positive SST anomalies in the\nwestern Pacific in figure 3 (coupled model). Can the authors elaborate on this\ndifference further? An intercomparison of figures 1, 3, and S8 prompts me to\nthink that the NN in coupled models is tightly linked to canonical ENSO and\naffected by its interannual variations but this is not the case for observation.\nThis may have strong implications for the overall result presented in this paper.\nFor instance, in the pacemaker experiments, the ENSO variations dominate and\nthe effect of Maritime continent warming after 1990 might be nil or weak. But,\nin observations, Maritime warming might play an important role in shaping the\ndecadal linkage. I would ask the authors to redo figure S5 but for models\n(pacemaker experiments) for the two periods to see this further.\nResponse: Thank you very much for your valuable comments, which are of\ngreat importance for enhancing the integrity of the conclusions in our\nmanuscript. Firstly, we recognize the existence of enhanced coherence of\ninterannual SSTA between NNI and Niño4w after 1985, such as in Fig. S4 of\noriginal manuscript where IAP SST seems an outlier for capturing the 4-6yr\npeak, compared to the period before 1985. Additionally, in previous Fig 3b,\nwe also demonstrated the growth of NNI on interannual scale. La Niña can also\npromote a stronger Leeuwin Current (Feng et al., 2015). Therefore, as a crucial\ninterannual signal in the tropical Pacific, the role of ENSO in the linkage between\nthe two regions on interannual scale cannot be underestimated. However, the\nfocus of this manuscript is on decadal timescale. In light of your comments and\nsuggestions, we have made significant modifications and enhancements to\nsection 3, including the addition of two sets of pacemaker experiments related\nto Indian Ocean to explore the sources of decadal scale fluctuations and\nlinkages between the NNI and Niño4w regions, and four single forcing large\nensembles to study the role of external forcing.\nFirstly, we refined our filtering and ensemble mean methods. We now apply an\n8–16 years bandpass filter to the NNI and variable fields in both observation\nand pacemaker experiments. This will ensure the analysis is on the decadal\ntimescale. In the CESM1.2 Pacific pacemaker, correlation maps before and after\n1985 show enhanced warming along the western Australian coast and an\nintensified negative pattern of the IPO. We also added correlation maps similar\nto Fig S5 (Fig. RB3; Fig. S11 of revised manuscript), indicating that the IPO\nindeed contributes to the linkage between the NNI and Niño4w regions on\ndecadal timescale. From observations, it is evident there are strong positive\nSST anomalies in the western Pacific now (Fig. RB2 b and d).\nTherefore, we introduce two new pacemaker experiments: TIOGA using the\nNational Center for Atmospheric Research (NCAR) Community Earth System\nModel version 1 (CESM1), in which the simulated SST in the tropical Indian\nOcean and part of the western Pacific warm pool region is restored to observed\nSST from Extended Reconstructed SST version 3b (ERSSTv3b), with fully\nrestored SST anomalies (SSTA) for 15°N-15°S, from the African Coast to 161°E,\nand buffer zones at 15°N-20°N, 15°S-20°S, and 161°E-180°(Hurrell et al.,\n2013; Zhang et al., 2019; Yang et al., 2020). Another experiment is IOGA using\nNCAR CESM1, in which the simulated SST in the Indian Ocean (north of 50°S)\nis restored to observed SST from ERSSTv3b (Hurrell et al., 2013; Zhang and\nHan, 2021).\nResults from TIOGA experiments reveal that the changes in tropical Indian\nOcean and part of western Pacific can generate significant SST changes in the\ncentral-western tropical Pacific and off western Australian coast, accounting for\na large fraction of the observed NNI after 1985; Before 1985, however, the\nsimulated NNI has a weaker amplitude and opposes to the observation (Fig.\nRB4c; Fig. 3c of revised manuscript). These results suggest that SSTA in\ntropical Indian Ocean and western Pacific play an important role in causing the\nenhanced NN-Niño4w linkage in recent decades. This is because a warm (cold)\nSSTA in tropical Indian-western Pacific basin drives equatorial easterly\n(westerly) wind anomaly in the central-western tropical Pacific (Fig. RB5; Fig.\nS14 of revised manuscript), enhances (reduces) equatorial upwelling and\ncauses cold (warm) SSTA in Niño4w region. The enhanced equatorial easterly\n(westerly) wind anomaly increases (reduces) the Indonesian Throughflow,\nstrengthening Ningaloo Niño (Niña) events. Meanwhile, the colder (warmer)\nSSTA in Niño4w region can induce cyclonic (anticyclonic) circulation in the\nSoutheast Indian Ocean, together with local air-sea feedback, further\nstrengthening Ningaloo Niño (Niña) magnitudes.\nFrom ensemble mean results from CESM2-LE and MPI-LE, it can be seen that\nbefore 1985, the TIO SSTA did not exhibit strong decadal fluctuations under\nthe influence of external forcing. However, after 1985, the TIO SST decadal\nfluctuations in large ensembles and observations agree well (Fig. RB4e; Fig. 3e\nof revised manuscript), indicating that the decadal fluctuations in TIO after\n1985 are primarily controlled by external forcing, consistent with previous\nstudies (Dong et al., 2017; Zhang et al., 2018). Initially, TIO SST was under\nthe control of the IPO, but subsequently, external forcing became the dominant\ninfluence, resulting in decadal fluctuations in TIO SST (Dong et al., 2017;\nZhang et al., 2018). In contrast, IPO, after 1985, does not resemble\nobservations (Fig. RB6c; Fig. S15c of revised manuscript).\nThe IOGA highlights the influence of the Indian Ocean, specifically, the effect\nof the western Australian coast area on Niño4w region and their relationship.\nOur results capture an enhanced decadal linkage between NNI and Niño4w after\n1990 (Fig. RB5f; Fig. S14f of revised manuscript), indicating that the NNI region\ncan also influence the Niño4w region.\nIn the TIOGA experiments, the NNI and Niño4w index consistently shows\nnegative correlation before and after 1985 (Fig. RB4d; Fig. 3d of revised\nmanuscript), whereas the simulated NNI agrees with observed one only after\n1985 (Fig. RB4c; Fig. 3c of revised manuscript). This is because before 1985,\nthe Niño4w SSTA generated by Indian Ocean SST is out of phase with that of\nthe IPO, whereas after 1985 they are generally in phase (Fig. RB5b; Fig. S15b\nof revised manuscript). The warm (cold) Indian Ocean SSTA and negative\n(positive) IPO act in concert to intensify Niño4w SSTA and thus NNI after 1985,\nwhile the Indian Ocean SSTA weakens the IPO effect before 1985 ((Fig. RB4f;\nFig. 3 f of revised manuscript), causing the enhanced decadal linkage between\nNNI and Niño4w index after 1985 as shown by observations. Thus, the decadal\nlinkage between NNI and Niño4w has been explained.\nFour single forcing large ensembles (Simpson et al., 2023) are employed to\nexamine the role of external forcings further. They are GHG (only greenhouse\ngases evolving, having 15 members); BMB (Only biomass burning aerosols\nevolving, having 15 members); EE (everything else evolving, having 15\nmembers, that is, all forcings other than those that are time evolving in GHG,\nAAER or BMB are time evolving. Greenhouse gases and anthropogenic and\nbiomass burning aerosols are held fixed); xAER (Everything time evolving\nexcept anthropogenic aerosols, having 10 members).\nThe above results all stem from the fact that, after 1985, external forcing\ncaused TIO SST to be out of phase with the IPO, leading to the warming trend\nand decadal fluctuation. Upon examining the impacts of various single external\nforcings, we show that the increase in greenhouse gases (GHGs; Fig. RB7; Fig.\n4 of revised manuscript, green line) has significantly contributed to the recent\ntrend of rapid warming in the Tropical Indian Ocean. External forcings that\nexclude anthropogenic aerosols (experiment xAER; Fig. RB7; Fig. 4 of revised\nmanuscript, blue line) produce a stronger warming trend, which means\nanthropogenic aerosols somewhat decrease the warming trend in the Indian\nOcean. In addition to a warming trend, Experiment xAER also generates\ndecadal variability in the tropical Indian Ocean SST Fig. RB7; (Fig. 4 of revised\nmanuscript, purple line). This finding suggests that external forcings by volcanoes is the main\ncause for TIO SSTA and result in its out of phase relationship with IPO after\n1985.\nFigure RB3 Teleconnections for the decadal SST variability off western\nAustralian coast in DJF in Pacific pacemaker experiments. Correlation\nbetween the DJF decadal SSTA and the DJF decadal NNI (shading; vectors for\nwind anomaly; contours for precipitation anomaly) during a 1950–1990 and b\n1990–2014 in CESM1.2 (Pacific pacemaker experiments). c and d as in a and\nb but for the 200-hPa velocity potential anomaly (shading; vectors for divergent\nwind; contours for precipitation). Red/blue contours represent\npositive/negative precipitation significant at the 95% confidence level. Stippled\nareas are where the regression results are statistically significant at the 95%\nconfidence level. e, f, g, and h are for 1950–1985 and 1985–2014 in FGOALS-\nf3-L (Pacific pacemaker experiments).\nFigure RB4 Possible influencing factors (IPO and external forcing) for\ndecadal variabilities of NNI. a Time series of the 21-year sliding variance of\nthe NNI (black line) and the IPO index (blue line) based on ERSSTv5, in which\nthe red shading identifies the positive phase of the IPO during 1978–1997. b\nTime series of the 21-year sliding variance of interannual NNI (blue line) and\ndecadal NNI (red line). Red shading identifies the positive phase of the IPO\nduring 1978–1997. c The 8–16 years band-pass filtered decadal NNI in\nERSSTv5 and TIOGA. d The 8–16 years band-pass filtered decadal NNI and\nNiño4w index in TIOGA. e The 8 years low-pass filtered tropical Indian ocean\nSSTA (15°N–15°S, 50°–160°E) in observations and the ensemble mean of\nCESM2-LE, and MPI-LE. Blue shading identifies the period 1960-1985. Red\nshading identifies the period after 1985. f The normalized 8-year low-pass\nfiltered Tropical Indian ocean SSTA and IPO index (detrend) in observations.\nFigure RB5 Teleconnections for the decadal SST variability off western\nAustralian coast in DJF in different Indian Ocean pacemaker\nexperiments. Correlation between the DJF decadal SSTA and the DJF decadal\nNNI (shading; vectors for wind anomaly; contours for precipitation anomaly)\nduring a 1950–1990 and b 1990–2013 in CESM1 tropical Indian ocean (TIOGA)\npacemaker experiments. c and d as in a and b but for the 200-hPa velocity\npotential anomaly (shading; vectors for divergent wind; contours for\nprecipitation). Red/blue contours represent positive/negative precipitation\nsignificant at the 95% confidence level. Stippled areas are where the regression\nresults are statistically significant at the 95% confidence level. e, f, g, and h\nare for 1950–1990 and 1990–2019 in CESM1 Indian ocean (IOGA) pacemaker\nexperiments.\nFigure RB6 a The 8–16 years band-pass filtered decadal NNI in ERSSTv5 and\nIOGA. b The 8–16 years band-pass filtered decadal Niño4w index in ERSSTv5\nand IOGA. c The 8 years low-pass filtered IPO index (15°N–15°S, 50°–160°E)\nin observations and the ensemble mean of CESM2-LE, and MPI-LE. Blue\nshading identifies the period 1960-1985. Red shading identifies the period after\n1985.\nFigure RB7 The contribution of single external forcing to the tropical\nIndian Ocean SST.\nThe 8 years low-pass filtered tropical Indian ocean SSTA (15°N–15°S, 50°–\n160°E) in ERSSTv5 (black) and the ensemble mean of CESM2-LE (red) and four\nsets of CESM2 single forcing large ensembles: xAER (blue), GHG (green), BMB\nand EE (orange), and EE (purple). Light blue shading identifies the period 1960-\n1985. Light red shading identifies the period after 1985.\nReference:\n1. Hurrell JW, et al. The community earth system model: a framework for\ncollaborative research. Bulletin of the American Meteorological Society 94,\n1339-1360 (2013).\n2. Zhang L, et al. Indian Ocean warming trend reduces Pacific warming\nresponse to anthropogenic greenhouse gases: An interbasin thermostat\nmechanism. Geophysical Research Letters 46, 10882-10890 (2019).\n3. Yang D, et al. Role of tropical variability in driving decadal shifts in the\nSouthern Hemisphere summertime eddy-driven jet. Journal of Climate 33,\n5445-5463 (2020).\n4. Zhang L, Han W. Indian ocean dipole leads to Atlantic Niño. Nature\nCommunications 12, 5952 (2021).\n5. Dong L, McPhaden MJ. Why has the relationship between Indian and Pacific\nOcean decadal variability changed in recent decades? Journal of climate 30,\n1971-1983 (2017).\n6. Zhang L, Han W, Sienz F. Unraveling causes for the changing behavior of\nthe tropical Indian Ocean in the past few decades. Journal of Climate 31, 2377-\n2388 (2018).\n7. Feng M, et al. Decadal increase in Ningaloo Niño since the late 1990s.\nGeophysical Research Letters 42, 104-112 (2015).\n8. Simpson IR, et al. The CESM2 single-forcing large ensemble and comparison\nto CESM1: Implications for experimental design. Journal of Climate 36, 5687-\n5711 (2023).\n5) Related to the previous comment - (Page 10) authors say ‘surprisingly, the\nnni-Niño4w decadal linkage is found in the ensemble mean of members whose\nIPO phases are totally opposite to observed phase’ – this may simply indicate\nthat, in the ensemble mean, it is not IPO but interannual variability in the\nequatorial Pacific affects the linkage. In fact, Fig. S11 suggests that there is\nvirtually no decadal power in the ensemble mean Niño4w for R-, indicating that\nthe spatial pattern seen in figure 3f comes from interannual variability. This is\ncontradictory to the argument made, and I would ask the authors to make a\nrevision accordingly either for the analysis or the text at the beginning of Page\n11. Also, I would like to ask authors to show spatial maps similar to Figures 3e,\nf but for the entire 100-member ensemble mean SST (not for R+/- groups).\nResponse: Thank you for the reviewer's valuable comments. The chosen R- and\nR+ cannot split the role of IPO and rapid warming. So, in the revision, we do\nnot employ the figures based on the R- and R+. To explain this, two sets of\npacemaker experiments related to Indian Ocean are adding to explore the\nsources of decadal scale fluctuations and linkages between the NNI and Niño4w\nregions, and we use 8–16 years band-pass filter for both index and variable\nfields to focus on decadal timescale. Four single forcing large ensembles to\nstudy the role of external forcing. We believe these experiments can explain\nthe role of rapid warming, IPO and external forcings in modulating decadal\nlinkage between the NNI and Niño4w index.\nWe also show the mean SST map and the SST bias between ERSSTv5 and\nCESM2-LE (CESM2-LE minus ERSSTv5). The warmer tropical Pacific Ocean in\nCESM2-LE indicate that it fails to capture observed La Niña-like cooling in the\neastern Pacific (Fig. RB8).\nFigure RB8 a The mean state of SST in CESM2-LE ensemble mean. b The\nmean state of SST in ERSSTv5. c The bias between the CESM2-LE ensemble\nmean and ERSSTv5 (CESM2-LE ensemble mean minus ERSSTv5).\n6) Figure 3: Do the wavelets represent the ensemble mean spectrum or the\nspectrum of the ensemble mean index? I think these two are different. Also,\nthere is a 4-year periodicity in the Niño4w in figure 3d which is absent in figure\n1e. Shouldn’t the ensemble mean spectrum eliminate such interannual signals?\nCan authors explain why this is so?\nResponse: We appreciate your comment. We initially calculated the wavelet for\neach ensemble member and then average them in original manuscript. This\napproach is chosen because the spectrum of the ensemble mean index tends\nto filter out decadal-scale variations. To ensure that the presented results are\nentirely generated by external forcing, we have corrected the results. Now, all\nrelated results are first subjected to ensemble mean before further processing\n(such as wavelet analysis).\nOther comments:\n1) Page 4; line 71: Do we have enough observations before 1980 in the western\nAustralian coast to confidently state a post-1980 change? I would like to hear\nfrom the authors if they think the enhancement of decadal variations since\n1980 is because we have robust measurements since 1980 (satellite) in this\nregion.\nResponse: Thank for your comments. Extended Reconstruction Sea Surface\nTemperature Version 3b (ERSSTv3b; Smith et al., 2008) does not include\nsatellite data due to a cold bias in the satellite-derived SSTs that proved difficult\nto correct. Therefore, we used ERSSTv3b to test the uncertainty due to\nsatellite measurements (Fig. RB9). We calculated the wavelet of NNI and\nNiño4w and the 21-year sliding correlation between these two regions, and the\nresults is similar with that in ERSSTv5, HadISST1, and IAP. This confirms that\nthe enhanced decadal variability and linkage between these two regions don’t be\ninfluenced by uncertainty due to the SST data quality such as satellite\nmeasurements since the 1980s.\nFigure RB9 SST variability off western Australian coast and the tropical\nwestern–central tropical Pacific and their relationship in ERSSTv3b. a The 21-\nyear sliding correlation coefficients between the ensemble mean NNI and the\nensemble mean Niño4w index, in which the red line signifies results statistically\nsignificant at the 95% confidence level. b Wavelet spectrum of the ensemble\nmean NNI. The red lines mark the periodicity of 8–16 years. c Wavelet\nspectrum of the ensemble mean Niño4w index.\nReference:\n1. Smith TM, Reynolds RW, Peterson TC, Lawrimore J. Improvements to NOAA's\nhistorical merged land–ocean surface temperature analysis (1880–2006).\nJournal of climate 21, 2283-2296 (2008).\n2) Statistical significance of the coherence over a 21-year period and other\nregression estimates. The methodology says the test employs a student’s t-\ntest but doesn’t explicitly state how the effective sample size (ESS) is estimated.\nSince the signals are smoothed over decadal periods, the autocorrelation of the\nindices/time series would reduce the independent samples, and significance\ntests should consider the ESS.\nResponse: Thank you for providing insightful feedback. To evaluate statistical\nsignificance, we applied a two-tailed Student's t-test. Yes, it's worth noting that\nour methodology involves low-pass filtering and similar techniques that smooth\ndata over decadal periods, leading to a reduction in degrees of freedom.\nConsequently, we address this by employing effective degrees of freedom in\nour analysis, calculated using the following formula (Li et al., 2022):\n𝑁−2\n1 1 2 𝑁−𝑗\n≈ + ∑ 𝜌 (𝑗)𝜌 (𝑗)\n𝑁 𝑁 𝑁 𝑁 𝑋𝑋 𝑌𝑌\n𝑒𝑓𝑓\n𝐽=1\nwhere N is the sample size, 𝜌 (𝑗) and 𝜌 (𝑗) are the autocorrelations of two\n𝑋𝑋 𝑌𝑌\ntime series X and Y at lag.\nWe have added relevant details in the revised version of the manuscript.\nReference:\n1. Li J, et al. Influence of the NAO on wintertime surface air temperature over\nEast Asia: multidecadal variability and decadal prediction. Advances in\nAtmospheric Sciences 39, 625-642 (2022).\n3) The IPO definition doesn’t follow the reference (Henley et al. 2015) given.\nHenley et al. suggested a tripolar index instead of EOF.\nResponse: Thank you for your comments. For consistency throughout the study,\nthe revised manuscript adopts the tripolar index for the IPO.\n4) Introduction:\nPage 3; line 45: ‘Variations in the interbasin linkage between off western\nAustralian coast and the western–central Pacific, linked by the Indo-Pacific\nwarm pool and the overlying atmospheric circulation, can generate a profound\nimpact on the local and remote climate’.\n‘Better rephrase to ‘can impact local and remote climate’.\nResponse: Thank you for this insightful comment. We have revised this\nsentence.\n5) Page 3; line 50: Not discussing ‘local’ processes well: How much variance is\nexplained by local and remote processes respectively? Similarly, what is the\nmain mode of low-frequency (interannual and above) SST variability on the\nWest Australian coast? Is it Ningaloo Niño or warming associated with the\nLeeuwin current?\nResponse: Thank you for this valuable comment., We use Empirical Orthogonal\nFunction (EOF) analysis to identify and understand the patterns and drivers of\nNingaloo Niño events, and the EOF1 pattern generally explains approximately\n50 % of the total variance (Kataoka et al. 2014; Doi et al. 2013). Based on our\nresults and previous study (Feng et al. 2015), the main mode for both\ninterannual and decadal variability is the wedge-shaped warming pattern along\nthe West Australian coast. The intensification of the Leeuwin Current is crucial\nfor the warming anomalies along the West Australian coast. The main mode of\nlow-frequency (interannual and above) SST variability on the West Australian\ncoast includes both Ningaloo Niño events and warming associated with the\nLeeuwin Current. We have added “Meanwhile, the colder (warmer) SSTA in\nNiño4w region can induce cyclonic (anticyclonic) circulation in the Southeast\nIndian Ocean, together with local air-sea feedback, further strengthening\nNingaloo Niño (Niña) magnitudes.”\nReference:\n1. Feng M, et al. Decadal increase in Ningaloo Niño since the late 1990s.\nGeophysical Research Letters 42, 104-112 (2015).\n2. Kataoka T, Tozuka T, Behera S, Yamagata T. On the Ningaloo Niño/Niña.\nClimate dynamics 43, 1463-1482 (2014).\nKataoka, Takahito, et al. \"On the ningaloo niño/niña.\" Climate dynamics 43\n(2014): 1463-1482.\n3. Doi T, Behera SK, Yamagata T. Predictability of the Ningaloo Niño/Niña.\nScientific Reports 3, 2892 (2013).\n6) Page 8; line 172: The convective centers change over P1 (IO) and P2 (WEP).\nDoes that really mean an ‘eastward movement’ as suggested in the paper?\nResponse: Thank you for your comments. In the revised version of the\nmanuscript, we adopted a more cautious approach, removing the direct\nconclusion regarding the \"eastward movement\" and instead described the\nconvection center as being in different locations before and after 1985.\n7) Page 11, and elsewhere in the paper: The physical explanations presented\nin the paper suggest that the western Pacific warming (anthropogenic) can\nenhance an ‘existing’ decadal variability in the WAC – but doesn’t induce a new\nmode of decadal variability. In that perspective, is it feasible to say an\n‘emergence of decadal linkage’? More appropriate would be to say, ‘influence\nof western Pacific warming on the amplitude of decadal variability off the\nWAC’?\nResponse: Thank you for your suggestion. We denote “1985” as the “time of\nemergence” for the NNI-Niño4w linkage, defined as when the cross-spectrum\nof these two indexes is significant at 95% level, and their correlation coefficient\nis stable exceeding 95% significance. Here, the \"emergence\" we describe refers\nto the connection (correlation) between two regions reaching a certain\nthreshold, rather than the creation of something new.\n8) Fig. 4: Why does the trend estimation is limited to 1970 – 1995 for CMIP6\nmodels? Is the phase of the IPO in CMIP models consistent with the observed\nIPO phase? If not necessarily (I think that is the case), how would the authors\nreject the fact that the linear relationship between the western Pacific SST and\nNNI amplitude might arise through IPO? I would suggest the authors add one\nmore panel by redoing the same figure but once the IPO is regressed out from\nthe western Pacific SST, to check this.\nResponse: We chose the period from 1970 to 1995 because we aim to\ninvestigate what leads to the sustained increase in the amplitude of the NNI\nduring the positive IPO phase. We removed the influence of the IPO and\ncalculated the correlation coefficient between the Western Pacific warming\ntrend and the decadal NNI to be -0.03, the results were not statistically\nsignificant. We attribute the insignificant results to biases present within CMIP6\nmodels. In the revised results, we have used pacemaker experiments related to\nthe Indian Ocean. We found the important role of the rapid warming of the\ntropical Indian Ocean under external forcing in influencing the decadal\nvariability of the two regions. Generally, Climate models (e.g., CMIP models,\nlarge ensemble simulations) do not adequately capture the phenomenon where\nthe warming SST rate in the tropical Indian Ocean surpasses that in the tropical\nPacific. Significant warming in the tropical Indian Ocean can induce negative\nsea-level pressure (SLP) anomalies that extend eastward to the western\ntropical Pacific, thereby intensifying the zonal SLP gradient across the Pacific\nand driving anomalous easterlies in the tropical western Pacific. However, in\ncoupled models, weaker negative SLP anomalies are showed solely off western\nAustralian coast (Zhang et al., 2019). This may explain why the results for the\ncorrelation with influence of IPO being removed were not as anticipated. The\npacemaker experiments we added do not exhibit this bias, as they are capable\nof capturing the basin-wide negative SLP anomalies (Zhang et al., 2019). The\nresults from pacemaker experiments related to Indian Ocean and large\nensembles show that the Indian Ocean produces decadal fluctuations under the\ninfluence of external forcing since 1985, and being out of phase with the Pacific\nand leading to an intensification of the easterly anomalies in the western–\ncentral tropical Pacific. Therefore, we no longer consider that a simple\nwarming trend could cause decadal fluctuations in these two regions.\nReference:\n1. Zhang L, et al. Indian Ocean warming trend reduces Pacific warming\nresponse to anthropogenic greenhouse gases: An interbasin thermostat\nmechanism. Geophysical Research Letters 46, 10882-10890 (2019).\n9) Pacemaker experiment – The SST anomalies are nudged to the model\nclimatology and HadISST1 anomalies. I would like to see the climatological SST\nmap in the Indo-Pacific region from HadISST1 and the two models considered\nfor the pacemaker experiment (left) and the spatial map of the standard\ndeviation of the SST anomalies (for HadiSST and two models) for the two\nperiods (post and before 1980) in the right columns. And, please show the\nNiño4w & NN regions (box) over each of these maps.\nResponse: Thank you for your suggestion. For the climatological SST, the\ntropical Pacific SST in pacemaker experiments are warmer than the observation.\nFor the standard deviation of the SST anomalies, the largest STD in pacemaker\nexperiments occurs within the tropical Pacific region, indicating that the results\nof pacemaker experiment are predominantly driven by changes in the tropical\nPacific.\nFigure RB10 The climatological SST in a HadISST, c CESM1 Pacific\npacemaker experiment, and e FGOALS-f3 Pacific pacemaker experiment\nbefore 1985. b, d, and f as a, c, and e, but after 1985.\nFigure RB11 The standard deviation of the SST anomalies in a HadISST, c\nCESM1 Pacific pacemaker experiment, and e FGOALS-f3 Pacific pacemaker\nexperiment before 1985. b, d, and f as a, c, and e, but after 1985.\nMinor comments:\n1) Previous studies indicate that half of the NN is NOT related to the western\nPacific (La Nina), indicating an important local driving mechanism that\ncontrols wind and SST off the WAC (see Kataoka et al. 2013; Marshall et al.\n2014). The respective role of local processes driving the enhanced decadal\namplitudes is not well addressed in the present study. Can the authors include\na few more notes about the anthropogenic effects on the local drivers, in the\ndiscussion?\nResponse: Thank you for your valuable comments. Indeed, we have primarily\ninvestigated the remote mechanisms influencing decadal timescale fluctuations\nof the Ningaloo Niño. However, local physical mechanisms are also very\nimportant for the occurrence and development of NN, especially the cyclonic\natmospheric circulation anomalies off the northwest coast of Australia (Kataoka\net al. 2014; Marshall et al. 2015). These anomalies reduce the prevailing\nsoutherly winds along the shore, leading to surface warming due to decreased\nlatent heat loss and the increased poleward transport of warm tropical waters\nby the Leeuwin Current. This process is further facilitated by a local wind-\nevaporation-sea surface temperature (WES) feedback mechanism. On decadal\ntimescale, the negative phase of IPO can induce positive sea surface height\n(SSH) anomalies in the equatorial western Pacific and the southeast Indian\nOcean. At this time, there is a significant negative correlation between sea-\nlevel pressure (SLP) anomalies off the west coast of Australia and SSH\nanomalies in the equatorial western Pacific and southeastern Indian Ocean on\ndecadal timescale (Feng et al. 2015), this explains the role of the negative IPO.\nHowever, the warming in the Indian Ocean, which also induces basin-wide\nnegative SLP anomalies (Zhang et al. 2019), is mainly controlled by external\nforcing, suggesting that external forcing impacts the local processes along the\nwest coast of Australia.\nWe have added this part to the discussion:” This study shows the enhanced\ndecadal linkage and its impact on both regions. We have demonstrated the role\nof the Tropical Indian Ocean SSTA in this linkage, but in previous studies show\nthat local processes off western Australian coast, such as cyclonic anomalies,\nare also important. On decadal scale, the negative IPO can induce positive sea\nsurface height (SSH) anomalies in the equatorial western Pacific and the\nsoutheast Indian Ocean. Associated with this, there is a significant negative\ncorrelation between sea-level pressure (SLP) anomalies off western Australian\ncoast and SSH anomalies in the equatorial western Pacific and southeastern\nIndian Ocean on decadal timescale (Feng et al. 2015). Another view, the rapid\nwarming in the Indian Ocean, which can also induce basin-wide negative SLP\nanomalies (Zhang et al. 2019), is mainly controlled by increased anthropogenic\nforcings. This basin-wide SLP anomaly would induce cyclonic wind anomalies\nand impact the local processes off western Australian coast.”\nReference:\n1. Kataoka T, Tozuka T, Behera S, Yamagata T. On the Ningaloo Niño/Niña.\nClimate dynamics 43, 1463-1482 (2014).\n2. Marshall AG, Hendon HH, Feng M, Schiller A. Initiation and amplification of\nthe Ningaloo Niño. Climate Dynamics 45, 2367-2385 (2015).\n3. Feng M, et al. Decadal increase in Ningaloo Niño since the late 1990s.\nGeophysical Research Letters 42, 104-112 (2015).\n4. Zhang L, et al. Indian Ocean warming trend reduces Pacific warming\nresponse to anthropogenic greenhouse gases: An interbasin thermostat\nmechanism. Geophysical Research Letters 46, 10882-10890 (2019).\n2) Anthropogenic heat enters the ocean through natural variability (e.g., IPO).\nThe global warming hiatus during the last decade is an example. It is hard to\ndistinguish, hence, the decadal SST response to natural variability and external\nforcing over short periods. A coupled model experiment by Feng et al. (2015)\nindeed suggested that, even though external warming influences the\npreconditioning of the NN development, it (external forcing) itself may not\ncause the decadal modulation of the occurrence of NN over recent decades – I\nwould ask authors to discuss these aspects and previous literature in the\ndiscussion of the paper.\nResponse: Thank you for your insightful comments. Restoring the sea\ntemperatures in the eastern Pacific to observed value in experiments can\nsimulate significant negative SLP anomalies in the southeastern Indian Ocean.\nHowever, in a coupled model simulation with the same radiative forcing but\nwithout restoring the equatorial eastern Pacific SST, there are no significant\nnegative sea level pressure anomalies in the southeast Indian Ocean (Feng et\nal. 2015). This precisely illustrates the importance we have emphasized on the\nrapid warming of the tropical Indian Ocean under external forcing for the\ndecadal variability of the two regions. Climate models generally fail to capture\nthe phenomenon that the warming rate of SST in the tropical Indian Ocean is\nhigher than that of the tropical Pacific. When the tropical Indian Ocean warms\nsignificantly, it will cause negative SLP anomalies extending eastward to the\nwestern tropical Pacific, enhancing the zonal SLP gradient across the Pacific and\ndriving the anomalous easterlies in the tropical western Pacific. In our\npacemaker experiments related to Indian Ocean, the basin-wide negative SLP\nanomalies are reproduced (Zhang et al. 2019).\nThis extension of discussion is very necessary, and we have added some\ndiscussions in the revised manuscript: “When the tropical Indian Ocean\nexperiences significant warming, it induces negative SLP anomalies extending\neastward to the western tropical Pacific. This process amplifies the zonal SLP\ngradient across the Pacific, which, in turn, generates anomalous easterlies in\nthe tropical western Pacific (Zhang et al. 2019). Nevertheless, a common\nshortfall among climate models is their inability to accurately represent this\ndynamic. Specifically, these models often underrepresent the discrepancy in\nwarming rates between the tropical Indian Ocean and the tropical Pacific. The\ntropical Indian Ocean warms at a faster rate than the tropical Pacific, a critical\nfactor that models fail to capture. This model bias results in an underestimation\nof the strong negative SLP anomalies. Consequently, this modeling bias could\npotentially influence the simulated SST off western Australian coast and in the\nwestern–central tropical Pacific.”\nReference:\n1. Feng M, et al. Decadal increase in Ningaloo Niño since the late 1990s.\nGeophysical Research Letters 42, 104-112 (2015).\n2. Zhang L, et al. Indian Ocean warming trend reduces Pacific warming\nresponse to anthropogenic greenhouse gases: An interbasin thermostat\nmechanism. Geophysical Research Letters 46, 10882-10890 (2019).\n3) Please provide the time series of decadal (>7 years) Niño4w and NNI\ntogether over 1950 – 2020 and provide the correlation coefficient for the\nperiods 1950 – 2020, 1950 – 1985, and 1985 – 2020.\nResponse: Thank you for your good suggestion. We show the following figure,\nit is evident that the NNI and Niño4w index are out of phase, and their decadal\nvariability enhanced after 1985.\nFigure RB12 The timeseries for NNI and Niño4w with 7 years low-pass filtered.\nThe blue line represents NNI, and the red line represents Niño4w index.\nAnd we calculated their correlation coefficient on decadal timescale for the\nperiods 1950 – 2020, 1950 – 1985, and 1985 – 2020 are -0.54, -0.63, and -\n0.68. For the raw timeseries, their correlation coefficient for the periods 1950\n– 2020, 1950 – 1985, and 1985 – 2020 are -0.15, -0.34, and -0.49. The results\nshow that their decadal linkage is stronger after 1985.\nReviewer #3 (Remarks to the Author):\nBased on observations together with several sets of model simulations, i.e.,\npacemaker simulations from CESM1.2 and FGOALS-f3-L models, single-model\nCESM2-LE large ensemble simulations, and CMIP6 multi-model simulations, the\nauthors investigated the coherence in decadal variations of sea surface\ntemperature (SST) between the western Australian coast and the western–\ncentral Pacific. The coherence of SST variations on the decadal time scale\nbetween these two regions emerged in 1985. The authors claimed that the\nenhanced covarying decadal variations were attributed to the rapid warming in\nthe western Pacific, however, the influence of Interdecadal Pacific Oscillation\nphase changes is relatively less prominent. The topic is important and\ninteresting. The manuscript is in general well-organized and written, but some\nparts especially the methods could benefit from greater clarity. The conclusions\nthat attribute the coherent decadal variations in the western Australian coast\nand the western–central Pacific to global warming merit further\nanalyses/discussions to bring this forward more robustly.\nResponse: We appreciate the insightful comments from the review, which help\nto increase the reliability of our manuscript. According to the reviewer’s\nsuggestions, we have thoroughly revised the manuscript. We have provided a\nmore detailed exposition of the method section, including the calculation of SST\npattern formation, the criteria for selecting two groups of CESM2-LE members,\nand the calculation of effective degrees of freedom. We corrected the method\nof ensemble mean and added the display of significance test. We revised some\nresults from the large ensemble and CMIP6 models, and in response to the bias\nbetween the models and observations, we added two sets of pacemaker\nexperiments related to the Indian Ocean and four single forcing large ensembles\nto explore the specific effects of warming and external forcing.\nMajor comments:\n- The main conclusion and novelty of this study is attributing the decadal\nlinkage between the western Australian coast and the western–central Pacific\nto the global warming induced by external forcing. This part of the results and\ndiscussion needs to be further enhanced. In the present manuscript, the\nauthors use CESM2-LE large ensemble to investigate this and the ensemble\nmean of 100 members represents the forced signal. By comparing Fig. 3(c) and\n(d) based on CESM2-LE large ensemble mean to Fig. 1(d) and (e) based on\nobservations, the decadal variations from the time scale of 8–16 years are quite\ndifferent in the wavelet analysis results, especially the Niño4w index is mainly\ndominated by 3-7 year variations. The wavelet results in Fig. 3(c) and (d) don’t\nhave information of significance test as in Fig. 1 (d) and (e). This needs to be\nadded and it will be helpful to add the time series of the ensemble mean NNI\nand Ninñ4w from CESM2-LE.\nResponse: Thank you for your valuable comments. In the revised version of\nour manuscript, we have taken into consideration numerous suggestions from\nyou and recognize the importance of further discussing factors beyond the\nInterdecadal Pacific Oscillation (IPO). We provide the wavelet analysis of the\nNNI and Niño4w index from CESM2-LE. They exhibit enhanced decadal\nvariabilities, but they do not pass the 95% significance test (Fig. RC1), the\nwavelet results are tested statistically against red noise (Torrence et al., 1998).\nInstead, we supplemented our study with two sets of pacemaker experiments\nrelated to the Indian Ocean and four single forcing large ensembles to broaden\nour findings. In our revised manuscript, the wavelet analysis of the ensemble\nmean Niño4w index reveals decadal variability, and all wavelet analysis here\nincludes statistical significance tests.\nThe wavelet analysis is applied to the ensemble mean NNI and Niño4w index\nfrom the CESM1 and FGOALS-f3-L Pacific pacemaker experiments (Fig. RC2;\nFig. S2 of revised manuscript). The decadal signals in the POGA of CESM1 and\nthat of FGOALS-f3-L are enhanced since the 1980s than those before the 1980s\nalthough the tests are not so significant at 95% level. The no-passing test is\nmainly because of the short time series. Beyond the IPO, there are other factors\ninfluencing the decadal variabilities and linkage between the two regions.\nTo further examine the role of Indian Ocean warming in the past decades, two\npacemaker experiments related to Indian Ocean are also used: TIOGA using\nthe National Center for Atmospheric Research (NCAR) Community Earth\nSystem Model version 1 (CESM1), in which the simulated SST in the tropical\nIndian Ocean and part of the western Pacific warm pool region is restored to\nobserved SST from Extended Reconstructed SST version 3b (ERSSTv3b), with\nfully restored SST anomalies (SSTA) for 15°N-15°S, from the African Coast to\n161°E, and buffer zones at 15°N-20°N, 15°S-20°S, and 161°E-180°(Hurrell et\nal., 2013; Zhang et al., 2019; Yang et al., 2020). Another experiment is IOGA\nusing NCAR CESM1, in which the simulated SST in the Indian Ocean (north of\n50°S) is restored to observed SST from ERSSTv3b (Hurrell et al., 2013; Zhang\nand Han, 2021).\nBoth pacemaker experiments were able to demonstrate the enhancement of\ndecadal variability in the two regions (Fig. RC3 b and f; Fig. S14 of revised\nmanuscript). Results from TIOGA experiments reveal that the changes in\ntropical Indian Ocean and part of western Pacific can generate significant SST\nchanges in the central-western tropical Pacific and off western Australian coast,\naccounting for a large fraction of the observed NNI after 1985; Before 1985,\nhowever, the simulated NNI has a weaker amplitude and opposes to the\nobservation (Fig. RC4; Fig. 3c of revised manuscript). These results suggest\nthat SSTA in tropical Indian Ocean and western Pacific play an important role\nin causing the enhanced NN-Niño4w linkage in recent decades. This is because\na warm (cold) SSTA in tropical Indian-western Pacific basin drives equatorial\neasterly (westerly) wind anomaly in the central-western tropical Pacific (Fig.\nRC5b; Fig. S14 of revised manuscript), enhances (reduces) equatorial\nupwelling and causes cold (warm) SSTA in Niño4w region. The enhanced\nequatorial easterly (westerly) wind anomaly increases (reduces) the\nIndonesian Throughflow, strengthening Ningaloo Niño (Niña) events.\nMeanwhile, the colder (warmer) SSTA in Niño4w region can induce cyclonic\n(anticyclonic) circulation in the Southeast Indian Ocean, together with local air-\nsea feedback, further strengthening Ningaloo Niño (Niña) magnitudes.\nIn the TIOGA experiments, the NNI and Niño4w index consistently display\nnegative correlation before and after 1985 (Fig. RC4d; Fig. 3d of revised\nmanuscript), whereas the simulated NNI agrees with observed one only after\n1985 (Fig. RC4c; Fig. 3c of revised manuscript). This is because before 1985,\nthe Niño4w SSTA generated by Indian Ocean SST is out of phase with that of\nthe IPO, whereas after 1985 they are generally in phase (Fig. RC6b; Fig. S15b\nof revised manuscript). The warm (cold) Indian Ocean SSTA and negative\n(positive) IPO act in concert to intensify Niño4w SSTA and thus NNI after 1985,\nwhile the Indian Ocean SSTA weakens the IPO effect before 1985 ((Fig. RB4f;\nFig. 3 f of revised manuscript), causing the enhanced decadal linkage between\nNNI and Niño4w index after 1985 as shown by observations. Thus, the decadal\nlinkage between NNI and Niño4w has been explained.\nBefore 1985, the warming of the tropical Indian Ocean was primarily influenced\nby the Interdecadal Pacific Oscillation (IPO). Then the decadal variability in the\nIndian Ocean was in phase with the IPO (Dong et al., 2017; Zhang et al., 2018).\nAfter 1985, external forcing dominated the changes in the Indian Ocean (Fig.\nRC4e; Fig. 3e of revised manuscript). Under the influence of external forcing,\nthe decadal variability in Indian Ocean and the IPO are out of phase (Fig. RC4f;\nFig. 3f of revised manuscript). The warming of the Indian Ocean and the\nnegative phase of IPO can both lead to enhanced easterlies in the Niño4w\nregion (Fig. RC5b; Fig. S14b of revised manuscript).\nFour single forcing large ensembles (Simpson et al., 2023) are employed to\nexamine the role of external forcings further. They are GHG only (only\ngreenhouse gases evolving, having 15 members); BMB only (Only biomass\nburning aerosols evolving, having 15 members); EE (everything else evolving,\nhaving 15 members, that is, all forcings are time evolving except GHGs, AAERs\nincluding BMB which means greenhouse gases and anthropogenic (including\nbiomass burning) aerosols are held fixed at 1850 level; xAER (Everything time\nevolving except anthropogenic aerosols, having 10 members).\nThe above results show that, after 1985, external forcings caused TIO SST to\nbe out of phase with the IPO, leading to decadal fluctuations. To further explore\nthe role of external forcings, we compared the CESM2-LE and four single forcing\nlarge ensembles from CESM2. we showed that the increase in greenhouse\ngases (GHGs; Fig. RC7; Fig. 4 of revised manuscript, green line) has\nsignificantly contributed to the recent trend of rapid warming in the Tropical\nIndian Ocean. External forcings that exclude anthropogenic aerosols\n(experiment xAER; Fig. RC7; Fig. 4 of revised manuscript, blue line) produce a\nstronger warming trend, which means anthropogenic aerosols somewhat\ndecrease the warming trend in the Indian Ocean. In addition to a warming\ntrend, Experiment xAER also generates decadal variability in the tropical Indian\nOcean SST Fig. RC7; (Fig. 4 of revised manuscript, purple line). This finding suggests that external forcings by volcanoes is the main\ncause for TIO SSTA and result in its out of phase relationship with IPO after\n1985.\nFigure RC1 SST variability off western Australian coast and the tropical\nwestern–central tropical Pacific in CESM2-LE. a Wavelet spectrum of the\nensemble mean NNI for 1950–2014. The red lines mark the periodicity of 8–16\nyears. b Wavelet spectrum of the ensemble mean Niño4w index for 1950–2014.\nStippled results are statistically significant at the 95% confidence level.\nFigure RC2 SST variability off western Australian coast and the tropical\nwestern–central Pacific and their relationship in Pacific pacemaker\nexperiments. a The 21-year sliding correlation coefficients between the\nensemble mean NNI and the ensemble mean Niño4w index in CESM1.2\n(pacemaker experiments), in which the red line signifies results statistically\nsignificant at the 95% confidence level. b Wavelet spectrum of the ensemble\nmean NNI for 1950–2014 in CESM1.2 (Pacific pacemaker experiments). The\nred lines mark the periodicity of 8–16 years. c Wavelet spectrum of the\nensemble mean Niño4w index for 1950–2014 in CESM1.2 (Pacific pacemaker\nexperiments). d, e, and f as in a, b, and c but in FGOALS-f3-L (pacemaker\nexperiments) for 1950–2014.\nFigure RC3 SST variability off western Australian coast and the tropical\nwestern–central tropical Pacific and their relationship in different\nIndian ocean pacemaker experiments. a The 21-year sliding correlation\ncoefficients between the ensemble mean NNI and the ensemble mean Niño4w\nindex in CESM1 tropical Indian ocean (TIOGA) pacemaker experiments, in\nwhich the red line signifies results statistically significant at the 95% confidence\nlevel. b Wavelet spectrum of the ensemble mean NNI for 1950–2013 in TIOGA.\nThe red lines mark the periodicity of 8–16 years. c Wavelet spectrum of the\nensemble mean Niño4w index for 1950–2013 in TIOGA. d, e, and f as in a, b,\nand c but in CESM1 Indian ocean (IOGA) pacemaker experiments for 1950–\n2019.\nFigure RC4 Possible influencing factors (IPO and external forcing) for\ndecadal variabilities of NNI. a Time series of the 21-year sliding variance of\nthe NNI (black line) and the IPO index (blue line) based on ERSSTv5, in which\nthe red shading identifies the positive phase of the IPO during 1978–1997. b\nTime series of the 21-year sliding variance of interannual NNI (blue line) and\ndecadal NNI (red line). Red shading identifies the positive phase of the IPO\nduring 1978–1997. c The 8–16 years band-pass filtered decadal NNI in\nERSSTv5 and TIOGA. d The 8–16 years band-pass filtered decadal NNI and\nNiño4w index in TIOGA. e The 8 years low-pass filtered tropical Indian ocean\nSSTA (15°N–15°S, 50°–160°E) in observations and the ensemble mean of\nCESM2-LE, and MPI-LE. Blue shading identifies the period 1960-1985. Red\nshading identifies the period after 1985. f The normalized 8-year low-pass\nfiltered Tropical Indian ocean SSTA and IPO index (detrend) in observations.\nFigure RC5 Climate teleconnections for the decadal SST variability off\nwestern Australian coast in DJF in different Indian Ocean pacemaker\nexperiments. Correlation between the DJF decadal SSTA and the DJF decadal\nNNI (shading; vectors for wind anomaly; contours for precipitation anomaly)\nduring a 1950–1990 and b 1990–2013 in CESM1 tropical Indian ocean (TIOGA)\npacemaker experiments. c and d as in a and b but for the 200-hPa velocity\npotential anomaly (shading; vectors for divergent wind; contours for\nprecipitation). Red/blue contours represent positive/negative precipitation\nsignificant at the 95% confidence level. Stippled areas are where the regression\nresults are statistically significant at the 95% confidence level. e, f, g, and h\nare for 1950–1990 and 1990–2019 in CESM1 Indian ocean (IOGA) pacemaker\nexperiments.\nFigure RC6 Decadal NNI and Niño4w index in IOGA and observation; IPO\nindexes in two large ensembles and different observation data. a The 8–16\nyears band-pass filtered decadal NNI in ERSSTv5 and IOGA. b The 8–16 years\nband-pass filtered decadal Niño4w index in ERSSTv5 and IOGA. c The 8 years\nlow-pass filtered IPO index (15°N–15°S, 50°–160°E) in observations and the\nensemble mean of CESM2-LE, and MPI-LE. Blue shading identifies the period\n1960-1985. Red shading identifies the period after 1985.\nFigure RC7 The contribution of single external forcing to the tropical\nIndian Ocean SST.\nThe 8 years low-pass filtered tropical Indian ocean SSTA (15°N–15°S, 50°–\n160°E) in ERSSTv5 (black) and the ensemble mean of CESM2-LE (red) and four\nsets of CESM2 single forcing large ensembles: xAER (blue), GHG (green), BMB\nand EE (orange), and EE (purple). Light blue shading identifies the period 1960-\n1985. Light red shading identifies the period after 1985.\nReference:\n1. Torrence C, Compo GP. A practical guide to wavelet analysis. Bulletin of the\nAmerican Meteorological society 79, 61-78 (1998).\n2. Hurrell JW, et al. The community earth system model: a framework for\ncollaborative research. Bulletin of the American Meteorological Society 94,\n1339-1360 (2013).\n3. Zhang L, et al. Indian Ocean warming trend reduces Pacific warming\nresponse to anthropogenic greenhouse gases: An interbasin thermostat\nmechanism. Geophysical Research Letters 46, 10882-10890 (2019).\n4. Yang D, et al. Role of tropical variability in driving decadal shifts in the\nSouthern Hemisphere summertime eddy-driven jet. Journal of Climate 33,\n5445-5463 (2020).\n5. Zhang L, Han W. Indian ocean dipole leads to Atlantic Niño. Nature\nCommunications 12, 5952 (2021).\n6. Dong L, McPhaden MJ. Why has the relationship between Indian and Pacific\nOcean decadal variability changed in recent decades? Journal of climate 30,\n1971-1983 (2017).\n7. Zhang L, Han W, Sienz F. Unraveling causes for the changing behavior of\nthe tropical Indian Ocean in the past few decades. Journal of Climate 31, 2377-\n2388 (2018).\n8. Simpson IR, et al. The CESM2 single-forcing large ensemble and comparison\nto CESM1: Implications for experimental design. Journal of Climate 36, 5687-\n5711 (2023).\n- Fig. 4: What would the figure be like if the Y-axis was substituted with the\ncorrelation between NNI and Niño4w in the period before and after 1985? This\nwould be more straightforward in supporting the enhanced decadal coherence\nbetween NNI and Niño4w due to external forcing-induced warming.\nResponse: Thank you for your good suggestion. Yes, the correlations between\nNNI and Niño4w across different periods would more directly demonstrate how\nthe warming of the Western Pacific was strengthening the linkage between the\ntwo regions. In our previous manuscript, when we replaced the y-axis with the\ncorrelation between NNI and Niño4w for the periods before and after 1985, the\nresults were not statistically significant. We attribute the insignificant results\nafter modifying the y-axis to the simulated biases in the CMIP6 models.\nIn the revised results, we used pacemaker experiments related to the Indian\nOcean. We found the important role of the rapid warming of the tropical Indian\nOcean under external forcing in influencing the decadal variability of the two\nregions. Generally, Climate models (e.g., CMIP models, large ensemble\nsimulations) do not adequately capture the phenomenon where the warming\nSST rate in the tropical Indian Ocean surpasses that in the tropical Pacific.\nSignificant warming in the tropical Indian Ocean can induce negative sea-level\npressure (SLP) anomalies that extend eastward to the western tropical Pacific,\nthereby intensifying the zonal SLP gradient across the Pacific and driving\nanomalous easterlies in the tropical western Pacific. However, in coupled\nmodels, weaker negative SLP anomalies are showed solely off western\nAustralian coast (Zhang et al., 2019). This may explain why the results for the\ncorrelation between NNI and Niño4w were not as anticipated. The pacemaker\nexperiments we added do not exhibit this bias, as they are capable of capturing\nthe basin-wide negative SLP anomalies (Zhang et al., 2019). The results from\npacemaker experiments related to Indian Ocean and large ensembles show\nthat the Indian Ocean produces decadal fluctuations under the influence of\nexternal forcing since 1985, and being out of phase with the Pacific and leading\nto easterly anomalies in the western–central Pacific. Therefore, we no longer\nconsider that a simple warming trend could cause decadal fluctuations in these\ntwo regions.\nReference:\n1. Zhang L, et al. Indian Ocean warming trend reduces Pacific warming\nresponse to anthropogenic greenhouse gases: An interbasin thermostat\nmechanism. Geophysical Research Letters 46, 10882-10890 (2019).\n- The authors selected subsamples from CESM2-LE to investigate the influence\nof IPO on the enhanced covarying SST decadal variabilities in the western\nAustralian coast and the western–central Pacific. It is unclear how the IPO\nphases of the selected members match observations. The Methods section\nexplained shortly that the members are selected based on the IPO pattern\ncorrelation coefficient between the model and observation. How are the time-\nevolving IPO phases combined with the spatial patterns? I would suggest simply\nselecting the ensemble members that could resemble the IPO index phase\nchanges from observations.\nResponse: Your thoughtful comment is appreciated. In the process of selecting\nCESM2-LE members with similar (R+) / opposite (R-) IPO phases, we prioritize\nthe accurate simulation of IPO. Before engaging in time series selection, we\ncalculate the pattern correlation coefficient between individual members and\nobserved IPO to establish a baseline. We set a threshold of 0.5, ensuring that\nthe selected members possess the capability to precisely simulate the IPO\nmode. Hence, the calculation of the correlation coefficient for spatial patterns\nis merely a preliminary step to find members that simulate the IPO mode.\nFollowing this step, we selected model members with IPO indices similar to and\nopposite from the observed IPO and obtained the ensemble mean results for\neach group.\nTherefore, In the original manuscript, Fig. 3e presents the ensemble members\nthat could resemble the observed IPO index phase changes. However, taking\ninto account your feedback and the model's inability to simulate the rate of SST\nwarming in the Indian Ocean exceeding that of the Pacific (Zhang et al., 2019),\nwe felt that relying solely on results from CESM2-LE is insufficient. Therefore,\nwe have added two pacemaker experiments related to the Indian Ocean to\nreplace the selecting CESM2-LE members with similar (R+) / opposite (R-) IPO\nphases.\nReference:\n1. Zhang L, et al. Indian Ocean warming trend reduces Pacific warming\nresponse to anthropogenic greenhouse gases: An interbasin thermostat\nmechanism. Geophysical Research Letters 46, 10882-10890 (2019).\n- L295-298 “Most climate models present relatively even warming in the\ntropical Pacific ….This model bias may affect the emergence timing of Indo-\nPacific decadal linkage,” What is the bias for CESM2? As CESM2-LE is crucial\nfor this study, the warming pattern from this model should be presented.\nResponse: Thank you for your suggestion. We show the mean SST map and\nthe SST bias between ERSSTv5 and CESM2-LE (CESM2-LE minus ERSSTv5).\nThe warmer tropical Pacific Ocean in CESM2-LE indicate that it fails to capture\nobserved La Niña-like cooling in the eastern Pacific (Fig. RC8).\nFigure RC8 a The mean state of SST in CESM2-LE ensemble mean. b The\nmean state of SST in ERSSTv5. c The bias between the CESM2-LE ensemble\nmean and ERSSTv5 (CESM2-LE ensemble mean minus ERSSTv5).\n- It seems that the main analyses are on boreal winter time, i.e., December-\nJanuary-February (DJF). In some places, it is stated, and in others not. It would\nbe much clearer if the authors could state in general if seasonal or annual data\nwas used in this study at the beginning of the manuscript and in the Method\npart. In case all the analyses are using DJF data, this information could be\nconsidered to be added to the abstract and even the title of the study.\nResponse: We appreciate the reviewer's valuable suggestions. In the first\nsection of the results, the time series for NNI and Niño4w index include both\nraw data (Fig 1b and c, bars, original manuscript) and their decadal\ncomponents (Fig 1b and c, black lines, original manuscript). Wavelet spectra\nanalysis is based on the raw time series, while 21-years sliding correlations for\nboth regions used annual time series, highlighting the broader context of\ndecadal variations and connections between the two regions. In subsequent\nsections, considering warming events along Western Australia's coast occurring\nmainly during the boreal winter (Dec-Jan-Feb, DJF), we specifically focus on\nthe boreal winter (DJF) for our study. We have emphasized this aspect for\nclarity in revised manuscript: “We used regression and correlation maps\nbetween seasonal mean variables (e.g., the SSTA and precipitation anomaly\naveraged for December-February) and the decadal NNI based on DJF mean to\nvisualize the spatial pattern of the influence of the tropical Pacific on the region\noff western Australia, as previous studies have indicated that the Ningaloo Niño\nreaches its peak during boreal winter (Marshall et al, 2015; Kataoka et al,2014;\nFeng et al, 2015; Zhang et al, 2018).”\nReference:\n1. Marshall AG, Hendon HH, Feng M, Schiller A. Initiation and amplification of\nthe Ningaloo Niño. Climate Dynamics 45, 2367-2385 (2015).\n2. Kataoka T, Tozuka T, Behera S, Yamagata T. On the Ningaloo Niño/Niña.\nClimate dynamics 43, 1463-1482 (2014).\n3. Feng M, et al. Decadal increase in Ningaloo Niño since the late 1990s.\nGeophysical Research Letters 42, 104-112 (2015).\n4. Zhang L, Han W, Li Y, Shinoda T. Mechanisms for generation and\ndevelopment of the Ningaloo Niño. Journal of Climate 31, 9239-9259 (2018).\n- The Methods are not explained very clearly.\ni. The section “Analysis for the formation of SST patterns” is missing some\ninformation. The way leading from equation (1) to (2) is not well explained,\nand the Newtonian cooling component Q_lh^(o^' ) is not found in equation\n(2).\nResponse: Thank you for this comment. We have added some details and\nformulas in the revised version.\nIn the manuscript, we give the main conclusions and formulas, so Eq. (2) of\noriginal manuscript includes\n𝑄𝑜′\n. Here is the detailed derivation process:\n𝑙ℎ\n𝜕𝑇′\n𝐶 =𝐷′ +𝑄′ (1)\n𝜕𝑡 0 𝑛𝑒𝑡\nwhere the left-hand side of Eq. (1) equals zero; 𝑇′ is the SST change; and 𝑄′\n𝑛𝑒𝑡\nis the net surface heat flux anomaly (𝑄′ =𝑄′ +𝑄′ +𝑄′ +𝑄′ ; positive\n𝑛𝑒𝑡 𝑠𝑤 𝑙𝑤 𝑠ℎ 𝑙ℎ\ndownward), which consists of shortwave radiation (𝑄′ ), longwave radiation\n𝑠𝑤\n(𝑄′ ), sensible heat flux (𝑄′ ), and latent heat flux (𝑄′ ).\n𝑙𝑤 𝑠ℎ 𝑙ℎ\nOn the decadal time scale, the MLT (temperature in the mixed layer) anomalies\ntendency term on the left-hand side of Eq. (1) is smaller than the two right-\nhand-side terms by at least one order of magnitude and can thus be neglected:\n0=𝐷′ +𝑄′ (2)\n0 𝑛𝑒𝑡\nFurthermore, the latent flux is decomposed into a Newtonian cooling\n𝑄𝑜′\n𝑙ℎ\ncomponent and a residual that represents atmospheric forcing\n𝑄𝑎′\n:\n𝑙ℎ\n𝑄𝑜′ = 𝛼𝑄̅̅̅̅𝑇′ (3)\n𝑙ℎ 𝑙ℎ\n𝑄𝑎′ = 𝑄′ −𝑄𝑜′ (4)\n𝑙ℎ 𝑙ℎ 𝑙ℎ\nNow, the net surface heat flux anomaly is:\n𝑄′ =𝑄′ +𝑄′ +𝑄′ +𝑄𝑜′ +𝑄𝑎′ (5)\n𝑛𝑒𝑡 𝑠𝑤 𝑙𝑤 𝑠ℎ 𝑙ℎ 𝑙ℎ\n𝑄′ =𝑄′ +𝑄′ +𝑄′ +𝑄𝑜′ +𝛼𝑄̅̅̅̅𝑇′ (6)\n𝑛𝑒𝑡 𝑠𝑤 𝑙𝑤 𝑠ℎ 𝑙ℎ 𝑙ℎ\nThen, Eq. (2) is changed to:\n−𝐷′ =𝑄′ +𝑄′ +𝑄′ +𝑄𝑜′ +𝛼𝑄̅̅̅̅𝑇′ (7)\n0 𝑠𝑤 𝑙𝑤 𝑠ℎ 𝑙ℎ 𝑙ℎ\nand the equation of SST pattern can be written as follows:\n𝐷′ +𝑄′ +𝑄′ +𝑄′ +𝑄𝑎′\n𝑇′ =\n0 𝑠𝑤 𝑙𝑤 𝑠ℎ 𝑙ℎ\n(8)\n𝛼𝑄̅̅̅̅\n𝑙ℎ\nTherefore, equation (8) includes\n𝑄𝑜′\n. \n𝑙ℎ\nii. The amplitude of NNI on the decadal timescale is larger than that on the\ninterannual timescale (refer to L95-96). Normally the interannual variability is\nmore prominent with a larger amplitude than the decadal variability because\nthe time series on the decadal time scale is more smoothed. I wonder how the\namplitude is defined.\nResponse: Thank you for your feedback. The amplitude is defined as the\nstandard deviations of time series at interannual and decadal timescales. In\nour analysis, the NNI was decomposed into seven different timescale\ncomponents. The third and fourth components were combined as interannual\nsignals covering 1–7 years, while the fifth and sixth components were combined\nas decadal signals with periods exceeding seven years. This decomposition\nallows us to differentiate and compare the amplitudes at interannual and\ndecadal timescales.\niii. The IPO pattern correlation coefficient (PCC) is not introduced clearly how it\nis calculated. This is critical for understanding the results.\nResponse: Thank you for this comment. The pattern correlation coefficient (PCC)\nis the Pearson product-moment coefficient of linear correlation between two\nvariables. In the process of selecting CESM2-LE members with similar (R+) /\nopposite (R-) IPO phases, we prioritize the accurate simulation of IPO. Before\nengaging in time series selection, we calculate the pattern correlation\ncoefficient between individual members and observed IPO to establish a\nbaseline. We set a threshold of 0.5, ensuring that the selected members\npossess the capability to precisely simulate the IPO mode. Therefore, we select\nthe two groups of members based on the similarity of simulated and observed\ntime series (IPO index).\n- Please review and ensure the grammar is correct and keep the consistency in\nthe use of either past or present tense throughout the manuscript.\nResponse: Thank you for constructive comments. As you suggested, we have\nrevised the manuscript.\nMinor comments:\n- The first sentence in the abstract is incomplete or lacks information. Some\nwords could be added to the end of the sentence to specify what impact the\ninterbasin linkage would bring.\nResponse: We appreciate the reviewer's suggestion. To address the incomplete\ninformation in the first sentence of the abstract, we have included the following\nsentence: “The impact of interbasin linkage on the weather/climate and\necosystems is significantly broader and profounder than that of only appearing\nin an individual basin.” This addition aims to specify the wide-ranging impacts\nof the teleconnection between the Indo-Pacific basins.\n- L42-43: “…has experienced the most rapid warming…” I feel some conditions\nshould be added, for instance “in the tropical regions”, because the high latitude\nwarming signal is stronger than the warming in these regions.\nResponse: We thank the reviewer for this suggestion. We have added “tropical”\nin the sentence.\n- L55: “…affect that in the Niño4w region…” What does the “that” refer to?\nResponse: We appreciate the reviewer's insightful comment. The use of “that”\nrefers to the variability. To enhance clarity and conciseness, we have modified\nthe sentence: “Conversely, the change off western Australian coast can actively\naffect variability in the Niño4w region, building a two-way interaction on\ninterannual timescale” This revision aims to more precisely show the two-way\ninteraction between the western Australian coast and the Niño4w region.\n- L97: “in other areas around the region off western Australian coast,” this is\nduplicated shown another time by the end of the sentence.\nResponse: Thank you for your correction. We have removed the repeated\nsentence.\n- L135-136: “Note that the positive anomaly appears stronger in the southern\nSouth China Sea and the western Pacific”, what does that mean?\nResponse: Thanks for this valuable comment. This sentence meant to show\nthat the influence of Indo-Pac warm pool. To make this sentence more concise,\nwe have changed it into “Note that positive SST anomalies appear in the\nwestern tropical Pacific west of the Niño4w region after 1985 while they are\nnegligible before 1985.”\n-L148: “the norther wind…”-> “the northern wind…”\nResponse: We thank the reviewer for correction. We have modified this word.\nWe also corrected all the similar grammar mistakes.\n-L166: “the dynamic process has led to…” -> “the dynamic process via ocean\nheat transport has led to…”\nResponse: We thank the reviewer for correction. We have added it into this\nsentence.\n-L173: “winter NNI and precipitation” -> It sounds a bit weird because the\nlocation of the NN region is in the southern hemisphere, the winter there is not\nin DJF. To keep consistent and be more precise, either stating the months or\nreferring to the hemisphere would work.\nResponse: Thank you for this comments. We modified it into “boreal winter”.\n-L182-183: “…more in accord with their position in the climatological mean\nstate…” Adding a figure of the climatological mean state will make it clearer.\nResponse: Thank you for your correction. Our intention was to indicate that\nduring La Niña events, the Walker Circulation usually intensifies. This is due to\nthe SST cooling in the central and eastern equatorial Pacific under La Niña\nconditions, leading to weakened atmospheric convection in that region, while\nconvection in the western–central Pacific is relatively strong. We should have\ncorrected the relevant statements as “These changes make the ascending and\ndescending motions associated with the NNI in the tropical region in accord\nwith their position in the climatological La Niña state, thereby leading to the\nstrengthening of the Walker circulation after 1985 under the La Niña”. However,\nin the revised manuscript, we have removed this sentence. Because we prefer\nto provide precise conclusions related to our results.\n-L204: “Two sets of pacemaker experiments” Is it intended to say “with two\nmodels”?\nResponse: Thank you for your comment. Yes, two sets of pacemaker\nexperiments include two models, i.e., CESM1.2 (with eight ensemble members)\nand FGOALS-f3-L (with ten ensemble members), respectively.\n-L209: Why is the time period for CESM1.2 shorter than that for FGOALS-f3-L?\nResponse: Thank you for this comment. CESM1.2 is based on the CMIP5\nhistorical forcings (~2005), and FGOALS-f3-L is based on CMIP6 forcings\n(~2014). In the revised manuscript, we have extended the CESM1.2 to 2014.\nThe two sets of IPO pacemaker experiments have covering from 1950 to 2014\nnow.\n-L222: “is similar to that observed exists for NNI…” -> “is similar to that\nobserved for NNI…”\nActually, the decadal variability seems not similar between model simulations\nand observations, see my major comment.\nResponse: Thank you for this comment. In the original manuscript, the\nsentence “Out of 100 large ensemble members, the ensemble mean shows that\ndecadal variability is similar to that observed exists for NNI (Fig. 3c) and\nNiño4w (Fig. 3d) in models.” Our purposes are to show that the SST variabilities\nof ensemble mean NNI and Niño4w. In original paper, we show the ensemble\nmean of wavelet. Now we show the wavelet analysis for ensemble mean NNI\nand Niño4w index with significance test. Both the NNI and Niño4w index exhibit\nenhanced decadal variabilities, yet the variabilities do not pass the 95%\nsignificance test (Fig. RC1). The above results also prompted us to explore the\nrole of warming and external forcing further using models beyond CESM2-LE.\n-L225-226: “The external forcing explains about 42.6% of the decadal variance\nof the NNI.” Where does this number come from? It is better to show the time\nseries.\nResponse: Thank you for your valuable comment. We calculated the variance\nof NNI on decadal timescale in large ensemble and divided it by the variance\nof observed NNI on decadal timescale to obtain above numerical value.\nWe modified the method of ensemble mean and provided the time series of\nensemble means for NNI in CESM2-LE and MPI-LE, along with three sets of\nobservational data, showing decadal fluctuations under the influence of\nexternal forcing after 1985 (Fig. RC9).\nFigure RC9 The 8–16 years band-pass filtered NNI in observations and the\nensemble mean of CESM2-LE, and MPI-LE. Blue shading identifies the period\n1960-1985. Red shading identifies the period after 1985.\n-L233-234: “The decadal linkage is consistent in the individual members due\nto large Signal-to-Noise.” I don’t understand this sentence.\nResponse: We appreciate your comments. The Signal-to-Noise Ratio (SNR)\nassesses the ratio of ensemble mean results (signal) to the standard deviation\namong all ensemble members (background noise). A higher SNR indicates a\nmore robust capture of meaningful information across all ensemble members.\nHowever, due to updates in our ensemble mean method and the pacemaker\nexperiments used, the current results now employ the Student's t-test with\neffective degrees of freedom:\n𝑁−2\n1 1 2 𝑁−𝑗\n≈ + ∑ 𝜌 (𝑗)𝜌 (𝑗)\n𝑁 𝑁 𝑁 𝑁 𝑋𝑋 𝑌𝑌\n𝑒𝑓𝑓\n𝐽=1\nwhere N is the sample size, 𝜌 (𝑗) and 𝜌 (𝑗) are the autocorrelations of two\n𝑋𝑋 𝑌𝑌\ntime series X and Y at lag.\nReference:\n1. Li J, et al. Influence of the NAO on wintertime surface air temperature over\nEast Asia: multidecadal variability and decadal prediction. Advances in\nAtmospheric Sciences 39, 625-642 (2022).\n-L245: “convection in situ”-> “in situ convection”\nResponse: We thank the reviewer for correction. We have modified this\nsentence.\n-L250: “correlation coefficient (0.6) between the decadal NNI and the warming\ntrend”, what would the value of correlation coefficient be when the decadal NNI\nis substituted with the correlation between decadal NNI and Niño4w?\nResponse: Thank you for your good suggestion. Yes, the correlations between\nNNI and Niño4w across different periods would more directly demonstrate how\nthe warming of the Western Pacific was strengthening the linkage between the\ntwo regions. We replaced the y-axis with the correlation between NNI and\nNiño4w for the periods before and after 1985. Before 1985, the value is -0.11,\nand after 1985, the value is -0.26. Although the results indicated that the\nincreased western Pacific warming led to an increase in the negative correlation\nbetween the two regions, the results did not pass the 95% significance test.\nWe attribute the insignificant results after modifying the y-axis to the simulated\nbiases in the CMIP6 models. In the revised results, we have used pacemaker\nexperiments related to the Indian Ocean. We found the important role of the\nrapid warming of the tropical Indian Ocean under external forcing in influencing\nthe decadal variability of the two regions. Generally, Climate models (e.g.,\nCMIP models, large ensemble simulations) do not adequately capture the\nphenomenon where the rate of SST warming in the tropical Indian Ocean\nsurpasses that in the tropical Pacific. Significant warming in the tropical Indian\nOcean can induce negative sea-level pressure (SLP) anomalies that extend\neastward to the western tropical Pacific, thereby intensifying the zonal SLP\ngradient across the Pacific and driving the anomalous easterlies in the tropical\nwestern Pacific. However, in coupled models, weaker negative SLP anomalies\nare showed solely off western Australian coast (Zhang et al., 2019). This may\nexplain why the results for the correlation between NNI and Niño4w were not\nas anticipated. The pacemaker experiments we added do not exhibit this bias,\nas they are capable of capturing the basin-wide negative SLP anomalies (Zhang\net al., 2019). The results from pacemaker experiments related to Indian Ocean\nand large ensembles show that the Indian Ocean produces decadal fluctuations\nunder the influence of external forcing since 1985, and being out of phase with\nthe Pacific and leading to an intensification of the easterly anomalies in the\nwestern–central tropical Pacific. Therefore, we no longer consider that a simple\nwarming trend could cause decadal fluctuations in these two regions.\nReference:\n1. Zhang L, et al. Indian Ocean warming trend reduces Pacific warming\nresponse to anthropogenic greenhouse gases: An interbasin thermostat\nmechanism. Geophysical Research Letters 46, 10882-10890 (2019).\n-L303: “by Feng et al.24 and Trenery and Han26.” Why not just add the\nnumbers as citations?\nResponse: Thank you, we have removed them to make the sentence more\nconcise.\n-L308: “It is unnecessary to cite the paper here again. It reads like reference\n#37 is a previous publication of the authors. BTW, the information on this paper\nin the reference list is incomplete.”\nResponse: Thank you, we have removed the reference #37 of original\nmanuscript and we reedited the information of the reference.\n-L345: “with eight simulations” do you mean “ensemble members”?\nResponse: Thank you. Yes, it means that CESM1.2-pacemker includes eight\nensemble members. In revised manuscript, we have corrected it to “with eight\nmembers”.\n-L346: “ 31 simulations” models? Simulations from 31 Earth system models?\nResponse: Thank you for the correction, it should be “31 climate models” from\nCMIP6.\nL359: “ 1000 EEMD ensemble members …0.2. …“ Is 1000 a typical number of\nmembers? What does the magnitude of standard deviation of 0.2 mean to the\nresults? How was the number determined, would a smaller value of 0.1 or a\nbigger value of 0.3 work?\nResponse: Yes, 1000 is a typical number for the ensemble size based on\nprevious studies. A standard deviation magnitude of 0.2 for added noise is\ncommon. In large ensemble means, the added noise theoretically cancels each\nother out, making the differences in results with various noise amplitudes minor.\nAccording to Qian (2016), increasing the ensemble size compensates for larger\nnoise amplitudes. We have added the reference in revised manuscript.\nReference:\n1. Qian C. Disentangling the urbanization effect, multi‐decadal variability, and\nsecular trend in temperature in eastern China during 1909–2010. Atmospheric\nScience Letters 17, 177-182 (2016).\nL366: “by dividing each monthly anomaly by the standard deviation”-> “by\ndividing each monthly anomaly with the standard deviation”\nResponse: Thank you for the correction, we have replaced “by” with “with”.\nL369: to have the full name of WES\nResponse: Thank you, we have added the full name of WES feedback here.\nWES feedback is wind–evaporation–SST feedback.\nL370: the citation should be #29, the #32 is not Xie et al.\nResponse: Thank you, we have corrected the citation.\nL377:\n𝑄𝑜′\nThis term does not appear in the equation (2)\n𝑙ℎ\nResponse: Thank you for this comment. The latent flux is decomposed into a\nNewtonian cooling\n𝑄𝑜′\ncomponent and a residual that represents atmospheric\n𝑙ℎ\nforcing\n𝑄𝑎′\n:\n𝑙ℎ\n𝑄𝑜′ = 𝛼𝑄̅̅̅̅𝑇′ (1)\n𝑙ℎ 𝑙ℎ\n𝑄𝑎′ = 𝑄′ −𝑄𝑜′ (2)\n𝑙ℎ 𝑙ℎ 𝑙ℎ\nand the equation of SST pattern can be written as follows:\n𝐷′ +𝑄′ +𝑄′ +𝑄′ +𝑄𝑎′\n𝑇′ =\n0 𝑠𝑤 𝑙𝑤 𝑠ℎ 𝑙ℎ\n(3)\n𝛼𝑄̅̅̅̅\n𝑙ℎ\nTherefore, equation (3) includes\n𝑄𝑜′\n. \n𝑙ℎ\nL381: “where 𝑄′ and 𝑄′ are neglected to owe to their..” This is confusing.\n𝑙𝑤 𝑠ℎ\nThese two terms are presented in Table S2. Is this statement based on the\nresults there? Then it should be in the main text. This sentence is not\ngrammarly correct.\nResponse: In previous study, 𝑄′ plays an important role of SST pattern\n𝑙ℎ\nformation (Huang, et al., 2019; Xie, et al., 2010). For the 𝑄′ , it is usually\n𝑛𝑒𝑡\ndominant by the 𝑄′ and 𝑄𝑎′ . In our manuscript, we show the similar results\n𝑠𝑤 𝑙ℎ\ntoo. In order to avoid ambiguity, we have removed this sentence from the\nmethods section.\nReference:\n1. Huang Y, Wu B, Li T, Zhou T, Liu B. Interdecadal Indian Ocean basin mode\ndriven by interdecadal Pacific oscillation: A season-dependent growth\nmechanism. Journal of Climate 32, 2057-2073 (2019).\n2. Xie S-P, Deser C, Vecchi GA, Ma J, Teng H, Wittenberg AT. Global warming\npattern formation: Sea surface temperature and rainfall. Journal of Climate 23,\n966-986 (2010).\nL390: decadal NNI Is NNI also based on DJF mean or annual mean data?\nResponse: Thank you for the comments. In the time series, we present the\nresults of filtering the original monthly indices over 8–16 years; whereas, in\nthe regression or correlation map results, we use the decadal NNI based on DJF\nmean. In the revised version, we have added the description of this part in the\nmethods: “We used regression and correlation maps between seasonal mean\nvariables (e.g., the SSTA and precipitation anomaly averaged for December-\nFebruary) and the decadal NNI based on DJF mean to visualize the spatial\npattern of the influence of the tropical Pacific on the region off western Australia,\nas previous studies have indicated that the Ningaloo Niño reaches its peak\nduring boreal winter (Marshall et al, 2015; Kataoka et al,2014; Feng et al, 2015;\nZhang et al, 2018).”\nReference:\n1. Marshall AG, Hendon HH, Feng M, Schiller A. Initiation and amplification of\nthe Ningaloo Niño. Climate Dynamics 45, 2367-2385 (2015).\n2. Kataoka T, Tozuka T, Behera S, Yamagata T. On the Ningaloo Niño/Niña.\nClimate dynamics 43, 1463-1482 (2014).\n3. Feng M, et al. Decadal increase in Ningaloo Niño since the late 1990s.\nGeophysical Research Letters 42, 104-112 (2015).\n4. Zhang L, Han W, Li Y, Shinoda T. Mechanisms for generation and\ndevelopment of the Ningaloo Niño. Journal of Climate 31, 9239-9259 (2018).\nL392-393: From observation, we choose the CESM2-LE members with IPO\nphases similar (R+) /opposite (R-). I don’t understand this. Maybe the authors\nmeant “According to” or “Based on” observation? “with IPO phases similar (R+)\n/opposite (R-).” -> “with similar (R+) /opposite (R-) IPO phases.”\nResponse: Thank you for the suggestion. Yes, we meant “based on” observation.\nThe sentence “we choose the CESM2-LE members with similar (R+) /opposite\n(R-) IPO phases” is more precise. We have revised the methods; thus, this\ncontent is no longer used.\nL393-394: “Before choosing” could be removed.\nResponse: Thank you for this comment. We have removed this sentence.\nL394-395: “PCC” is only used here and in the next sentence, I would suggest\nnot introducing a new abbreviation.\nResponse: Thank you for this suggestion. We have replaced the abbreviation\nPCC with the pattern correlation coefficient. In revised manuscript, the related\ncontent is no longer used.\nFig. 1: There is a typo in the title of Fig. 1(g): “Corss” -> “Cross”.\nResponse: Thank you for this correction. We have revised the Fig. 1(g).\nFig. 2: Regarding the decadal NNI, there are only 3-4 cycles within 35 years,\nwhat is the degree of freedom for the statistically significant test?\nResponse: Thank you for your valuable comments. We employed a two-tailed\nStudent's t-test to assess statistical significance. Given our application of low-\npass filtering and similar methods, which reduces degrees of freedom, we\nutilize effective degrees of freedom in our calculations. The formula used is:\n𝑁−2\n1 1 2 𝑁−𝑗\n≈ + ∑ 𝜌 (𝑗)𝜌 (𝑗)\n𝑁 𝑁 𝑁 𝑁 𝑋𝑋 𝑌𝑌\n𝑒𝑓𝑓\n𝐽=1\nwhere N is the sample size, 𝜌 (𝑗) and 𝜌 (𝑗) are the autocorrelations of two\n𝑋𝑋 𝑌𝑌\ntime series X and Y at lag. The details have been added in revised manuscript.\nReference:\n1. Li J, et al. Influence of the NAO on wintertime surface air temperature over\nEast Asia: multidecadal variability and decadal prediction. Advances in\nAtmospheric Sciences 39, 625-642 (2022).\nThe precipitation contours in Fig. 2(c)-(d) are hard to find and the values are\nmissing.\nResponse: Thank you for this valuable comments. We have redrawn the\nregression maps for precipitation (Fig. RC10; Fig. S7 of revised manuscript),\nand the current Fig. 2(c)-(d) now show the 200hPa velocity potential and\ndivergent winds more clearly (Fig. RC11; Fig. 2 of revised manuscript) in\nrevised manuscript. Precipitation is displayed separately in a single figure (Fig.\nRC10; Fig. S7 of revised manuscript).\nFigure RC10 The increased precipitation over the Maritime Continent.\nRegression of the DJF precipitation onto the DJF decadal NNI in a 1950–1985\nand b 1985–2020 for ERSSTv5. Stippled areas are where the regression results\nare statistically significant at the 95% confidence level. Panels c and d are\nbased on HadISST1, and panels e and f are based on IAP data.\nFigure RC11 Teleconnections for the decadal SST variability off western\nAustralian coast in December–January–February (DJF). Regression of the DJF\nSSTA onto the DJF decadal NNI (shading; unit: K; vectors for wind anomaly;\nunit: m/s) in a 1950–1985 and b 1985–2020. The blue/red box represents the\nregion off western Australian coast and the Niño4w region, respectively.\nStippled areas are where the regression results are statistically significant at\nthe 95% confidence level. c and d as in a and b but for the 200-hPa velocity\npotential anomaly (shading; unit: 106 m/s; vectors for divergent wind; unit:\nm/s;). Ningaloo Niño generally peaks in boreal winter; thus, only the changes\nin boreal winter are presented.\n“Ningaloo Niño generally peaks in boreal winter; thus, only the changes in\nboreal winter are presented.” -Is this a general statement that could be applied\nto the whole study and all the figures shown in the manuscript? In that case,\nthis needs to be mentioned at the beginning of the manuscript and introduced\nin the methods.\nResponse: Thank you for this good suggestion. For the wavelet analysis, we\npresent the results based on the original data. For the time series, we provide\nresults from either the original data or the original data subjected to 8–16 years\nband-pass filtering. For the spatial maps of regression or correlation, we use\nthe decadal NNI based on the DJF mean, as previous studies have indicated\nthat the Ningaloo Niño reaches its peak during boreal winter (Kataoka et al.\n2014; Feng et al. 2015; Marshall et al. 2015). We have explained this in a\nstraightforward manner in the methods section of our revised manuscript: “We\nused regression and correlation maps between seasonal mean variables (e.g.,\nthe SSTA and precipitation anomaly averaged for December-February) and the\ndecadal NNI based on DJF mean to visualize the spatial pattern of the influence\nof the tropical Pacific on the region off western Australia, as previous studies\nhave indicated that the Ningaloo Niño reaches its peak during boreal winter\n(Marshall et al, 2015; Kataoka et al,2014; Feng et al, 2015; Zhang et al, 2018).”\nReference:\n1. Marshall AG, Hendon HH, Feng M, Schiller A. Initiation and amplification of\nthe Ningaloo Niño. Climate Dynamics 45, 2367-2385 (2015).\n2. Kataoka T, Tozuka T, Behera S, Yamagata T. On the Ningaloo Niño/Niña.\nClimate dynamics 43, 1463-1482 (2014).\n3. Feng M, et al. Decadal increase in Ningaloo Niño since the late 1990s.\nGeophysical Research Letters 42, 104-112 (2015).\n4. Zhang L, Han W, Li Y, Shinoda T. Mechanisms for generation and\ndevelopment of the Ningaloo Niño. Journal of Climate 31, 9239-9259 (2018).\nFig. 3: The way of choosing the R+/R- group in this study might be affected by\nthe model’s response of SSTA to IPO. It's possible that some SSTA patterns\nmay not be related to certain IPO phases. As the purpose is to check the IPO,\nit is more straightforward to choose the ensemble members by the simulated\nIPO indices. Would this way end up with two similar sets of ensemble members?\nResponse: Thank you for this suggestion. Calculating the spatial correlation\ncoefficient was merely to ensure that the model's members could simulate the\nIPO mode, rather than directly selecting members based on their spatial\ncorrelation with observations. As you pointed out, we selected members based\non their similarity to or opposition against the observed IPO index. However,\nwe also recognized that this selection method might not effectively distinguish\nthe influence of the IPO. Therefore, we utilized two pacemaker experiments\nrelated to the Indian Ocean and four single forcing large ensembles to further\ninvestigate.\nFig. 4: I would suggest adding figures with substituting the y-axis with\ncorrelation coefficients between NNI and Niño4w before and after 1985.\nResponse: Thank you for your good suggestion. Yes, the correlations between\nNNI and Niño4w across different periods would more directly demonstrate how\nthe warming of the Western Pacific was strengthening the linkage between the\ntwo regions. We replaced the y-axis with the correlation between NNI and\nNiño4w for the periods before and after 1985. Before 1985, the value is -0.11,\nand after 1985, the value is -0.26. Although the results indicated that the\nincreased western Pacific warming led to an increase in the negative correlation\nbetween the two regions, the results did not pass the 95% significance test.\nWe attribute the insignificant results after modifying the y-axis to the simulated\nbiases in the CMIP6 models. In the revised results, we have used pacemaker\nexperiments related to the Indian Ocean. We found the important role of the\nrapid warming of the tropical Indian Ocean under external forcing in influencing\nthe decadal variability of the two regions. Generally, Climate models (e.g.,\nCMIP models, large ensemble simulations) do not adequately capture the\nphenomenon where the warming SST rate in the tropical Indian Ocean\nsurpasses that in the tropical Pacific. Significant warming in the tropical Indian\nOcean can induce negative sea-level pressure (SLP) anomalies that extend\neastward to the western tropical Pacific, thereby intensifying the zonal SLP\ngradient across the Pacific and driving anomalous easterlies in the tropical\nwestern Pacific. However, in coupled models, weaker negative SLP anomalies\nare showed solely off western Australian coast (Zhang et al., 2019). This may\nexplain why the results for the correlation between NNI and Niño4w were not\nas anticipated. The pacemaker experiments we added do not exhibit this bias,\nas they are capable of capturing the basin-wide negative SLP anomalies (Zhang\net al., 2019). The results from pacemaker experiments related to Indian Ocean\nand large ensembles show that the Indian Ocean produces decadal fluctuations\nunder the influence of external forcing since 1985, and being out of phase with\nthe Pacific and leading to an intensification of the easterly anomalies in the\nwestern–central Pacific. Therefore, we no longer consider that a simple\nwarming trend could cause decadal fluctuations in these two regions.\nReference:\n1. Zhang L, et al. Indian Ocean warming trend reduces Pacific warming\nresponse to anthropogenic greenhouse gases: An interbasin thermostat\nmechanism. Geophysical Research Letters 46, 10882-10890 (2019).\nTable S2: It will be easier to add the long names of the terms into the table\nafter each term.\nResponse: Thank you for suggestion. We have revised the Table S2 to make it\nconcise.\nTable RC1 Contribution of WES feedback and oceanic heat transport to the\nformation of SST pattern. Regression of the DJF SSTA (T′, unit: K; Eq. (2)) onto\nthe DJF decadal NNI (out of brackets) and DJF NNI (in brackets) for 1958–1985\nand 1985–201). Also shown are the contributions of latent heat flux to the\natmosphere (𝑄𝑎′ , unit: K), oceanic heat transport (𝐷′, unit: K), shortwave\n𝑙ℎ 0\nradiation (Q′ , unit: K), longwave radiation (𝑄 ′ , unit: K), and sensible heat\nsw 𝑙𝑤\n′\nflux (𝑄 , unit: K).\n𝑠ℎ\nBefore 1985 After1985\n𝑇′ 0.12 (0.88) 0.4 (0.92)\n𝑄𝑎′\n0.32 (1.21) 0.33 (1.29)\n𝑙ℎ\n𝐷′ −0.25 (−0.43) 0.15 (0.004)\n0\n𝑄′ −0.05 (0.04) −0.14 (−0.48)\n𝑠𝑤\n𝑄 ′ 0.03 (−0.02) 0.01 (0.03)\n𝑙𝑤\n𝑄 ′ 0.07 (0.08) 0.05 (0.08)\n𝑠ℎ\nREVIEWERS' COMMENTS\nReviewer #3 (Remarks to the Author):\nThe authors have done lots of addifional analyses and clarified further the methods. In general, my\nconcerns are mostly addressed in the revised manuscript and responses by the authors. I have only\nminor comments for clarificafion.\n- Why are the new results of wavelet spectrum of SSTA in NNI and Nino4w from CESM2-LE in Figure RC1\nso different from the previous results of wavelet spectrum of NNI and Nino4w indices shown in Fig. 3 (c)-\n(d) in the original submifted manuscript?\n- Table S1. It would be more straighfforward to have the full name of each term in the table rather than\nin the capfion. There are enough spaces in the table. The words in the capfion are unclear and should be\nrevised, this is an example: “onto the DJF decadal NNI (out of brackets) and DJF NNI (in brackets)” ->\n“onto the DJF NNI on decadal fimescale and the DJF NNI of original fime series without filtering (in\nbrackets)”\nReviewer #3 (Remarks to the Author):\nThe authors have done lots of additional analyses and clarified further the\nmethods. In general, my concerns are mostly addressed in the revised\nmanuscript and responses by the authors. I have only minor comments for\nclarification.\nResponse: We appreciate the insightful comments from the review, which help\nto increase the reliability of our manuscript. According to the reviewer’s\ncomments, we have further clarified the reasons for the slight differences\nbetween Fig. 3 (c)-(d) in the original and revised manuscripts, and revised\nTable S1 based on the reviewer's suggestions.\n- Why are the new results of wavelet spectrum of SSTA in NNI and Nino4w from\nCESM2-LE in Figure RC1 so different from the previous results of wavelet\nspectrum of NNI and Nino4w indices shown in Fig. 3 (c)-(d) in the original\nsubmitted manuscript?\nResponse: We thank the reviewer for such an insightful comment. We initially\ncalculated the wavelet for each ensemble member and then average them in\noriginal submitted manuscript. This method will cause the results to be affected\nby internal variability.\nIn revised manuscript, we focus on studying the role of external forcing in\nrevised manuscript. To ensure that the presented results are entirely generated\nby external forcing, we have corrected the analysis processes and update the\nresults. Here, all related results are first subjected to the ensemble mean from\nthe related members, then further process (including wavelet analysis) based\non the ensemble mean.\n- Table S1. It would be more straightforward to have the full name of each term\nin the table rather than in the caption. There are enough spaces in the table.\nThe words in the caption are unclear and should be revised, this is an example:\n“onto the DJF decadal NNI (out of brackets) and DJF NNI (in brackets)” -> “onto\nthe DJF NNI on decadal timescale and the DJF NNI of original time series\nwithout filtering (in brackets)”\nResponse: Thank you for the reviewer's valuable suggestions. We have revised\nthe text according to the suggestions and added the full names of each item to\nthe table to make it more comprehensible.\nTable S1. Contribution of WES feedback and oceanic heat transport to\nthe formation of SST pattern. Regression of the DJF SSTA (𝑻′, unit: K;\nEq. (2)) onto the DJF NNI on decadal timescale and the DJF NNI of\noriginal time series without filtering (in brackets) for 1958–1985 and\n1985–2015). Also shown are the contributions of latent heat flux to the\natmosphere (𝑸𝒂′ , unit: K), oceanic heat transport (𝑫′ , unit: K),\n𝒍𝒉 𝟎\nshortwave radiation (𝑸′ , unit: K), longwave radiation (𝑸 ′ , unit: K),\n𝒔𝒘 𝒍𝒘\n′\nand sensible heat flux (𝑸 , unit: K).\n𝒔𝒉\nBefore 1985 After1985\nSST (𝑇′) 0.12 (0.88) 0.4 (0.92)\nLatent heat flux to the\natmosphere\n(𝑄𝑎′\n)\n0.32 (1.21) 0.33 (1.29)\n𝑙ℎ\nOceanic heat transport (𝐷′) −0.25 (−0.43) 0.15 (0.004)\n𝑠𝑤\nLongwave radiation (𝑄 ′ ) 0.03 (−0.02) 0.01 (0.03)\n𝑙𝑤\nSensible heat flux (𝑄 ′ ) 0.07 (0.08) 0.05 (0.08)\n𝑠ℎ"}]

Decision letter