Towards the prediction of multi-year to decadal climate variability in the Southern Hemisphere

Scott Power1, Ramiro Saurral2, Christine Chung1, Rob Colman1, Viatcheslav Kharin3, George Boer3, Joelle Gergis4, Benjamin Henley4, Shayne McGregor5, Julie Arblaster5, Neil Holbrook6, Giovanni Liguori7

Introduction

Multi-year (2-7 years) and decadal climate variability (MDCV) can have a profound influence on lives, livelihoods and economies. Consequently, learning more about the causes of this variability, the extent to which it can be predicted, and the greater the clarity that we can provide on the climatic conditions that will unfold over coming years and decades is a high priority for the research community. This importance is reflected in new initiatives by WCRP, CLIVAR, and in the Decadal Climate Prediction Project (Boer et al., 2016) that target this area of research. Here we briefly examine some of the things we know, and have recently learnt, about the causes and predictability of Southern Hemisphere MDCV (SH MDCV), and current skill in its prediction.

Causes of SH MDCV

As with other parts of the globe, internally generated climate variability is a major element of SH MDCV (Kirtman et al., 2013). Major components of internal variability for the Southern Hemisphere are decadal variations in ENSO, the Interdecadal Pacific Oscillation (IPO, Garreaud and Battisti, 1999; Power et al., 1999; Salinger et al., 2001; Folland et al., 2002; Wu and Hsieh, 2003; Holbrook et al., 2011; Christensen et al., 2013; Kosaka and Xie, 2013; England et al., 2014; Holbrook et al., 2014; Watanabe et al., 2014; Henley et al., 2015; Meehl et al., 2016), the Southern Annular Mode (SAM (Shiotani, 1990; Thompson et al., 2000; Watterson, 2009; Pohl et al., 2009; Yuan and Yonekura, 2011; Jones et al., 2016), and the Indian Ocean Dipole.

North Pacific decadal variability has recently been described by Di Lorenzo et al. (2015) as a seasonally-based red noise process involving the interaction between extratropical atmospheric variability and ENSO via the North Pacific Meridional Modes (Chiang and Vimont, 2004). Although a similar mechanism has not yet been proposed for the South Pacific DCV, tropical-extratropical interactions, including via both the ocean and atmosphere, are thought to be at least partly responsible for inducing decadal to multidecadal variability in the South Pacific too (e.g., McGregor et al., 2007, 2008, 2009a and b; Farneti et al., 2014; Zhang et al., 2014; Ding et al., 2015). Inter-basin interactions are also thought to play a role in driving Pacific Ocean decadal and multi-decadal variability (e.g., McGregor et al., 2014; Kucharski et al., 2016, Chikamoto et al., 2016). Palaeoclimate reconstructions of MDCV in the Pacific vary significantly in their spectral characteristics, likely due to spectral biases in the different proxy records, non-stationarity of teleconnections and the use of various statistical reconstructions methods (Bateup et al., 2015).

External forcing, both natural and anthropogenic, can also drive decadal and longer-term variability in the SH (e.g., Bindoff et al., 2013 and references therein). Natural drivers include volcanic eruptions (e.g. Church et al., 2005), while anthropogenic external drivers include changes in greenhouse gas concentrations, aerosols (Cai et al., 2010) and stratospheric ozone (Kirtman et al., 2013; Arblaster et al., 2014; Eyring et al., 2013).

How well do climate models simulate MDCV in the SH?

The ability of climate models to simulate Earth's climate was assessed in the IPCC Fifth Assessment Report (Flato et al., 2013). They concluded that models reproduce many important modes of variability. This includes modes of relevance to the SH: ENSO, the Indian-Ocean Dipole and the Quasi-Biennial Oscillation. Models have improved in some respects since the last generation although, in the case of ENSO, some of this improvement might not be entirely for the right reasons.

While models are extremely valuable tools that enable us to improve our understanding of SH MDCV, they are not without their limitations. For example, while CMIP5 models tend to capture the spatial pattern of the IPO, models tend to underestimate the magnitude of SST variability associated with the IPO (Power et al., 2016; Henley et al., 2017), and both the magnitude of DCV in trade winds (England et al., 2014; McGregor et al., 2014) and multidecadal changes in the strength of the Walker Circulation (Kociuba and Power, 2015). These deficiencies appear to be due, at least in part, to modeled ENSOs that tend to be too oscillatory on too short a time-scale. This makes it hard for the models to maintain multi-year and longer-term anomalies (Kociuba and Power, 2015; Power et al., 2016).

Figure 1: Time series of the (detrended) 10-year running annual mean surface air temperature, averaged over the SH from the CMIP5 models (K), ordered from smallest to largest. Values calculated from the first 200 years of pre-industrial experiments offsets for display purposes.

A further illustration of the limitations of CMIP5 models in simulating SH MDCV is seen in Fig. 1. It shows time-series of the decadal variability in SH surface air temperature from pre-industrial runs of CMIP5 models. The model-to-model range in the magnitude and character of the variability is remarkable. Some models exhibit variability that has a range of a few tenths of a degree, while the range of some other models is three or more times larger. The character of the variability also differs markedly among models. In some models there is pronounced multi-decadal variability, whereas in other models variability occurs on much shorter time-scales. While further research is needed to ascertain which of these simulations more realistically captures SH MDCV. Clearly, given the large model to model differences evident in Fig.1, care is needed in assigning confidence to conclusions drawn on the basis of SH MDCV simulated in the current generation of climate models.

There have been other studies investigating SH MDCV of the past 1000 years in climate model simulations and reconstructions For example, a recent study by Hope et al. (2016) examined the decadal characteristics of ENSO spectra based on seven published ENSO reconstructions, and indices of Nino 3.4 SSTs and the Southern Oscillation Index calculated from six CMIP5–PMIP3 last millennium simulations. The post-1850 spectrum of each modelled or reconstructed ENSO series captures the observed spectrum to varying degrees. However, no single model or ENSO reconstruction completely reproduces the instrumental spectral characteristics across the multiyear or decadal bands.

Appreciable changes in the level of decadal ENSO variability is observed in the reconstructions and simulations of the pre-1850 period. While much of this represents internally generated variability (see e.g. Power et al., 2006), some of the variability may be linked to intermittent major volcanic eruptions (Hope et al., 2016). Nevertheless, Hope et al. (2016) report inconsistencies between reconstructions, models and instrumental indices used to evaluate changes over past centuries, reflecting the complexity of reconstructing and simulating past changes of a highly variable coupled system. In the reconstructions, variability arises from internal climate processes, different forced responses, or non-climatic proxy processes that are still not well understood (Ault et al., 2013).

Improving the simulation of SH MDCV would be greatly assisted by the availability of additional data from high latitude regions. The palaeoclimate reconstruction of the SAM by Abram et al. (2014) displays good agreement with CMIP5–PMIP3 last millennium simulations. Although the reconstruction shows a progressive shift towards SAM's positive phase as early as the fifteenth century, the positive trend in the SAM since 1940 is reproduced by multi-model climate simulations forced with rising greenhouse gas levels and ozone depletion (Abram et al.. 2014). These results are likely to reflect the brevity of the instrumental data from high latitudes, and associated deficiencies in model representation of SH climate (Jones et al., 2016).

Predictability of MDCV in the SH

Figure 2: The relative importance of initial conditions and external forcing for the climate prediction and predictability of time-averaged temperature in the SH. The correlation skill scores for the ensemble mean of initialized temperature forecasts, and the model-based "potential" correlation skill scores, area averaged over the SH, are presented as orange lines. The same quantities, but for uninitialized climate simulations, are plotted as green lines. All lines represent area-averaged SH values. Solid lines: hindcast skill; Dashed lines: potential skill. Results are for temperature averaged over periods from a month to a decade. (Figure prepared by V.V. Kharin based on the results reported in Boer et al. (2013)).

In climate science the term “predictability” has a different meaning to “predictive skill”. Predictability provides an estimate of the upper limit to predictive skill, in the absence of technical problems - apart from uncertainty in initial conditions. Predicability is usually estimated from a model’s ability to predict its own evolution given imperfect initial conditions. The presumption is that the results from a well-behaved climate model can provide information on the predictability of the actual climate system. Predictability arises from internal variability, external forcing (e.g. increasing greenhouse gas concentrations), and interactions between them. One estimate of the relative importance of initial conditions and external forcing for the predictability of time-averaged temperature in the Southern Hemisphere is depicted in Fig. 2 (dashed lines). Initialisation provides additional potential skill on average over the southern hemisphere on all time-scales, from one month to 10 years. The relative contribution of initialisation to the potential skill tends to diminish the longer the time-scale, as the relative contribution from external forcing increases.

The estimated potential predictability of five-year means across the SH is dominated by the contribution of external forcing, with internal variability making comparatively little contribution (Kirtman et al., 2013, Fig. 11.1). This is consistent with the findings of Meehl and Hu (2010), with the possible exception of the east Antarctic region south of Africa. Whether or not this represents a robust exception remains unclear.

Additional studies have examined the regional predictability of MDCV in the SH using idealised experiments in which model conditions are perturbed at a particular point in time in an integration and the degree to which the ensuing variability is affected is assessed. Power and Colman (2006) used this strategy to assess the predictability on internally generated variability in their climate model. They found that off-equatorial regions in the South Pacific exhibited variability that was a delayed, low pass-filtered version of preceding ENSO variability. The ocean had acted as a low pass filter on the wind-stress and heat flux forcing it received, in an ENSO-modified Frankignoul and Hasselmann process (Hasselmann, 1976; Frankignoul and Hasselmann, 1977). At ocean depths of 300 m, this gave rise to highly predictable multi-year variability. This is consistent with the findings of Shakun and Shaman (2009), who showed that the leading mode of SST variability in the South Pacific could be reasonably well-simulated as a response to preceding ENSO-driven heat flux forcing, in analogy with the Pacific Decadal Oscillation in the North Pacific (Newman et al., 2003; 2016).

Power et al. (2006) used the same strategy and concluded that there is limited predictability in interdecadal changes in ENSO activity and its associated teleconnections to Australian climate. More recently, Wittenberg et al. (2014) concluded that potential predictability was evident in ENSO variability in their model several years ahead, but not on decadal time-scales. Power and Colman (2006) also concluded that the relative influence of decadal variability on ocean variability as a whole tended to be weak in the tropical Pacific and more pronounced as depth and latitude increased. This general pattern is consistent with the more recent analysis of predictability in SST (Frederiksen et al. 2016).

The excitation of Rossby waves from ENSO-driven wind-stresses also drives subsequent and therefore predictable sea-level variability (e.g., Luo, 2003; Qiu and Chen, 2006; Holbrook et al., 2011; 2014). Other studies have examined lagged associations with extra-tropical regions and the tropics, with results suggesting that South Pacific driven changes in both the ocean and atmosphere may provide a source of predictability for multi-year variability in the tropics (McGregor et al., 2007, 2008, 2009a and b; Luo et al., 2003; Tatebe et al., 2013; Zhang et al., 2014). For example, Zhang et al. (2014) identified a South Pacific meridional mode in idealised climate simulations, in which wind variability in the South Pacific underpins a sub-component of subsequent climatic variability in the tropics, while the studies of McGregor et al. highlight ocean links between the extra-tropics and the tropics and their role in driving climate variability in the tropics.

More recently, the re-emergence of remnant mixed layers to the surface has been identified as a possible source of predictability for SH MDCV (D. Dommenget, pers. comm.). Finally, predictability may arise in the Pacific, including the South Pacific, via atmospheric teleconnections driven by partially predictable Atlantic surface temperature variability (e.g. Rashid et al., 2010).

Prediction of SH MDCV

Unlike “predictability” assessment, a “prediction” is an estimate or collection of estimates of the future state of the real world. Research on decadal climate prediction (Smith et al., 2007; Meehl et al., 2009; Meehl et al., 2016) aims to provide forecasts of MDCV. Part of the scientific basis for producing such predictions is given by the predictability that exists in some areas and for some variables, as discussed in the previous section. MDCV prediction research was an important element of the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) (Taylor et al., 2012) and was a subject assessed in the Fifth Assessment Report of the IPCC (Kirtman et al., 2013). CMIP5 provided a new set of forecasts that were initialized using the observed state of the climate system. This allowed an assessment of the benefit of initialization by comparison with parallel experiments where no information on the initial state of the climate system was provided. The quantification of such changes is important since near-term climate predictions are affected by the initial conditions as well as by changes in the external forcing (Meehl et al., 2009; Kirtman et al., 2013; Meehl and Teng 2016).

Initialization, which is accomplished using a range of different techniques (e.g. Hawkins and Sutton, 2009, 2011; Mochizuki et al. 2010; Doblas-Reyes et al., 2011), produce ensembles of simulations which are intended to account for uncertainties in the initial conditions. As with any other forecast, a set of skill scores is usually computed to quantify the quality of such predictions for previous years (i.e. prediction of past years or hindcasts), providing a quantitative assessment of the performance of the different forecast systems.

In spite of the relative youth of this research area, there are a growing number of papers that address predictability and prediction skill over areas of the Northern Hemisphere (e.g. Griffies and Bryan, 1997; Boer, 2000; Collins, 2002; Hawkins and Sutton, 2009; Smith et al., 2010; Zanna, 2012; García-Serrano and Doblas-Reyes, 2012; Boer et al., 2013; Guémas et al., 2013; among many others). However, considerably less attention has been paid to the SH. Part of the explanation is likely related to the smaller scientific community that exists in the SH compared to the northern hemisphere. Most importantly, there is a relative lack of in situ climate records in most of the SH, with the exception of some ship routes in the Indian and Atlantic Oceans. Limited in situ data poses a difficulty for the assessment of SST variability and prediction skill. The advent of the satellite era increased the amount of available information, but since that source of data only exists since the late 1970s or early 1980s, most of the SST hindcasts prior to the 1970s cannot be validated against ground truth, thus decreasing the sample size and significance of most of the statistical tests applied therein. In an assessment of the decadal climate prediction skill over southern Africa, Reason et al. (2006) had already noticed how the lack of data and the decrease in density and quality of the information in several regions in Africa was a major concern.

The relative importance of initial conditions and external forcing for the skill of existing hindcasts of time-averaged temperature in the Southern Hemisphere is depicted in Fig. 2 (solid lines). Initialisation provides additional hindcast skill over the Southern Hemisphere for averages of up to three years, beyond which initialisation in current systems appears to have little impact. The difference between the actual skill scores (solid lines) and the potential skill scores (dashed lines) suggests that greater skill may be achieved as the technical issues associated with MDCV prediction are overcome.

Among other recent contributions to this topic, Rea et al., (2016) quantified prediction quality over high SH latitudes, concluding that a proper representation of the stratospheric processes and the stratosphere-troposphere coupling is crucial in order to obtain skillful predictions. Saurral et al., (2016) analyzed the influence of initialization and climate drift on hindcasts of SST in the South Pacific in a set of coupled GCMs. They noticed differences in the hindcast skill depending upon initialization under strong or weak ENSO conditions. Overall, their results showed lower skill in decadal predictions than that found for the North Pacific basin (Lienert and Doblas-Reyes, 2013).

The increased number of observations that are being made in the SH, particularly of the ocean’s surface and sub-surface through e.g. the Global Temperature and Salinity Profile Programme, ARGO, and other critical elements of the global observing network, will provide invaluable data for the assessment of predictability and prediction skill in coming years. At the same time, modelling efforts focused on improving the representation of processes underpinning decadal variability and initial conditions are also of great importance for the field. The prediction of SH MDCV could benefit from the improvements in sea ice concentration and thickness reanalysis and the ingestion of those data into the GCMs. For instance, the upcoming Year of Polar Prediction could prove a very interesting opportunity to gather new information on sea ice in order to improve understanding of high-to-mid latitude linkages and to see how these new observations impact the skill of MDCV predictions.

Making further progress

Our understanding of the causes and predictability of MDCV in the Southern Hemisphere and our ability to predict its behavior could be increased by improving, e.g.:

- the documentation, quality controlling and analysis of MDCV evident in instrumental records, other historical records (see, e.g. Callaghan and Power, 2011; 2014), and paleo records

- the quality and increasing the range of relevant instrumental, historical and paleoclimate records.

- our understanding of the impact/sensitivity of methods used for multi-proxy paleoclimate records in order to develop methods that are not sensitive to the non-stationarity of teleconnections.

- climate models and their simulation of observed SH MDCV, including the underlying dynamical processes and statistics of SH MDCV and particular important events (e.g., strengthening the of Walker circulation over the past few decades and the rate of warming in the Pacific over the past half-century)

- understanding of the causes of SH MDCV through e.g. process studies, by continuing to develop theoretical understanding of the relevant processes involving both observations and model simulations, using a hierarchy of modelling approaches

- understanding of MDC predictability and its underlying dynamics in models, through further experimentation and further analysis of experiments already conducted

- initialisation methods, and predictive systems more broadly

- our ability to reliably determine hindcast skill.

These objectives can be assisted by, e.g.:

- participation in CMIP6, the Decadal Climate Prediction Project and other relevant international initiatives

- maintaining and expanding observing networks to better monitor SH MDCV

- continuing to foster links between paleoclimate, climate modelling and model analysis communities

- digitising and quality controlling early instrumental records from SH locations (Allan et al., 2016; Freeman et al., 2016), and by

- collecting critical palaeoclimate data in key regions (e.g. Abram et al., 2015).

affiliations

1Bureau of Meteorology, Australia
2CIMA, Ciudad Universitaria, Argentina
3Environment and Climate Change, Canada
4University of Melbourne, Australia
5 Monash University, Australia
6University of Tasmania, Australia
7Georgia Institute of Technology, USA

references

Abram, N. J., Dixon, B. C., Rosevear, M. G., Plunkett, B., Gagan, M. K., Hantoro, W. S. and Phipps, S. J., 2015: Optimized coral reconstructions of the Indian Ocean Dipole: An assessment of location and length considerations. Paleoceanography, 30 (10), 2015PA002810.

Abram, N. J., Mulvaney, R., Vimeux, F., Phipps, S. J., Turner, J. and England, M. H., 2014: Evolution of the Southern Annular Mode during the past millennium. Nature Climate Change, 4, 564–569.

Allan, R., Endfield, G., Damodaran, V., Adamson, G., Hannaford, M., Carroll, F., Macdonald, N., Groom, N., Jones, J., Williamson, F., Hendy, E., Holper, P., Arroyo-Mora, J. P., Hughes, L., Bickers, R. and Bliuc, A.-M., 2016: Toward integrated historical climate research: the example of Atmospheric Circulation Reconstructions over the Earth. WIREs Climate Change, 7, 164-174, doi: 10.1002/wcc.379.

Arblaster, J.M., and N.P Gillett (Lead Authors), N. Calvo, P.M. Forster, L.M. Polvani, S.-W. Son, D.W. Waugh, and P.J. Young, 2014: Stratospheric ozone changes and climate, Chapter 4. Scientific Assessment of Ozone Depletion: 2014, Global Ozone Research and Monitoring Project – Report No. 55, World Meteorological Organization, Geneva, Switzerland.

Ault, T. R., C. Deser, M. Newman, J. Emile‐Geay, 2013: Characterizing decadal to centennial variability in the equatorial Pacific during the last millennium. Geophysical Research Letters, 40, 3450-3456.

Bateup, R., S. McGregor and A. J. E. Gallant, 2015: The influence of non-stationary teleconnections on palaeoclimate reconstructions of ENSO variance using a pseudoproxy framework, Clim. Past, 11, 1733-1749, doi:10.5194/cp-11-1733-2015

Bindoff, N.L., P.A. Stott, K.M. AchutaRao, M.R. Allen, N. Gillett, D. Gutzler, K. Hansingo, G. Hegerl, Y. Hu, S. Jain, I.I. Mokhov, J. Overland, J. Perlwitz, R. Sebbari and X. Zhang, 2013: Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Boer, G. J., 2000: A study of atmosphere-ocean predictability on long time scales. Climate Dyn., 16, 469–477.

Boer, G. J., V. V. Kharin, and W. J. Merryfield, 2013: Decadal predictability and forecast skill. Clim. Dyn., 41, 1814-1833, doi:10.1007/s00382-013-1705-0.

Boer, G., and Coauthors, 2016: The Decadal Climate Prediction Project (DCPP) contribution to CMIP6. Geosci. Model Dev., 9, 3751-3777.

Cai, W., T. Cowan, J. M. Arblaster, and S. Wijffels, 2010: On potential causes for an under-estimated global ocean heat content trend in CMIP3 models, Geophys. Res. Lett., 37, L17709, doi:10.1029/2010GL044399.

Callaghan, J., and S. B. Power, 2014: Major coastal flooding in southeastern Australia 1860–2012, associated deaths and weather systems. Aust. Meteor. Oceanogr. J., 64, 183–213.

Christensen, J.H., K. Krishna Kumar, E. Aldrian, S.-I. An, I.F.A. Cavalcanti, M. de Castro, W. Dong, P. Goswami, A. Hall, J.K. Kanyanga, A. Kitoh, J. Kossin, N.-C. Lau, J. Renwick, D.B. Stephenson, S.-P. Xie and T. Zhou, 2013: Climate Phenomena and their Relevance for Future Regional Climate Change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Church, J., N.J. White and J.M. Arblaster, 2005: Significant decadal-scale impact of volcanic eruptions on sea level and ocean heat content. Nature, 438, 74-77, doi:10.1038/nature04237

Collins, M., 2002: Climate predictability on interannual to decadal time scales: The initial value problem. Clim. Dyn., 19, 671–692.

CSIRO, Australian Bureau of Meteorology and SPREP, 2015: Climate in the Pacific: a regional summary of new science and management tools, Pacific-Australia Climate Change Science and Adaptation Planning Program Summary Report. Commonwealth Scientific and Industrial Research Organisation, Melbourne, Australia.

Di Lorenzo, E., G. Liguori, N. Schneider, J. C. Furtado, B. T. Anderson, and M. A. Alexander, 2015: ENSO and meridional modes A null hypothesis for Pacific Climate Variability. Geophys. Res. Lett., 42, 9440-9448, doi: 10.1002/2015GL066281.

Ding, R., J. Li, and Y. Tseng, 2015: The impact of South Pacific extratropical forcing on ENO and comparisons with the North Pacific. Climate Dynamics, 44, DOI: 10.1007/s00382-014-2303-5.

Doblas-Reyes, F. J., M. A. Balmaseda, A. Weisheimer, and T. N. Palmer, 2011: Decadal climate prediction with the ECMWF coupled forecast system: Impact of ocean observations. J. Geophys. Res., 116, D19111.

Dong L., T. Zhou, A. Dai, F. Song, B. Wu, X. Chen, 2016: The Footprint of the Inter-decadal Pacific Oscillation in Indian Ocean Sea Surface Temperatures. Scientific Reports, 6, 21251, doi:10.1038/srep21251.

England, M. H., S. McGregor, P. Spence, G. A. Meehl, A. Timmermann, W. Cai, A. Sen Gupta, M. J. McPhaden, A. Purich and A. Santoso, 2014: Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nature Climate Change, 4, 222-227, doi: 10.1038/nclimate2106

Eyring, V., J. M. Arblaster, I. Cionni, J. Sedláček, J. Perlwitz, P. J. Young, S. Bekki, D. Bergmann, P. Cameron-Smith, W. J. Collins, G. Faluvegi, K.-D. Gottschaldt, L. W. Horowitz, D. E. Kinnison, J.-F. Lamarque, D. R. Marsh, D. Saint-Martin, D. T. Shindell, K. Sudo, S. Szopa and S. Watanabe, 2013: Long-term ozone changes and associated climate impacts in CMIP5 simulations, J. Geophys. Res. Atmos., 118, 5029–5060, doi:10.1002/jgrd.50316.

Farneti, R., F. Molteni, and F. Kucharski, 2014: Pacific interdecadal variability driven by tropical-extratropical interactions. Climate Dynamics, 42(11–12), 3337–3355.

Folland, C. K., J. A. Renwick, M. J. Salinger, and A. B. Mullan, 2002: Relative influences of the Interdecadal Pacific Oscillation and ENSO on the South Pacific Convergence Zone. Geophys. Res. Lett., 29, 1643-1647.

Frankignoul, C., and K. Hasselmann, 1977: Stochastic climate models, Part II Application to sea-surface temperature anomalies and thermocline variability. Tellus, 29, 10.1111/j.2153-3490.1977.tb00740.x.

Frederiksen, C.S., X. Zheng, and S. Grainger, 2016: Simulated modes of inter-decadal predictability in sea surface temperature. Climate Dynamics, 46, 2231-2245. DOI 10.1007/s00382-015-2699-6.

Freeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E., Berry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J., Rayner, N. A., Rosenhagen, G. and Smith, S. R., 2016: ICOADS Release 3.0: a major update to the historical marine climate record. International Journal of Climatology: doi:10.1002/joc.4775.

Garreaud, R.D. and D.S. Battisti, 1999: Interannual ENSO and Interdecadal ENSO-like Variability in the Southern Hemisphere Tropospheric Circulation. J. Climate, 12, 2113-2123

Garcia-Serrano, J., F. J. Doblas-Reyes, and C. A. S. Coelho, 2012: Understanding Atlantic multi-decadal variability prediction skill. Geophys. Res. Lett., 39, L18708, doi:10.1029/2012GL053283.

Griffies, S. M., and K. Bryan, 1997: A predictability study of simulated North Atlantic multidecadal variability. Clim. Dyn., 13, 459–487.

Guémas, V., F. J. Doblas-Reyes, I. Andreu-Burillo, and M. Asif, 2013: Retrospective prediction of the global warming slowdown in the last decade. Nature Clim. Change, 3, 649-653, doi:10.1038/nclimate1863.

Hasselmann, K., 1976: Stochastic climate models. Part I. Theory. Tellus, 6, 473-485, 10.1111/j.2153-3490.1976.tb00696.x.

Han, W., J. Vialard, M. J. McPhaden, W. P. M. de Ruitjer, 2014: Indian Ocean Decadal Variability: A Review. Bull. American Meteorol. Soc., 95, 1679-1703.

Hawkins, E., and R. Sutton, 2009: The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc., 90, 1095-1107, doi: 10.1175/2009BAMS2607.1.

Henley, B.J. and Coauthors, 2015: A Tripole Index for the Interdecadal Pacific Oscillation. Climate Dynamics, 45(11–12), 3077–3090.

Henley, B.J. and Coauthors, 2017. Spatial and Temporal Agreement in Climate Model Simulations of the Interdecadal Pacific Oscillation. Environmental Research Letters, in press, 1–33.

Holbrook N. J., I. D. Goodwin, S. McGregor, E. Molina, S. B. Power, 2011: ENSO to multi-decadal time scale changes in East Australian Current transports and Fort Denison sea level: Oceanic Rossby waves as the connecting mechanism. Deep Sea Research, Part II,, 58, 547-558, doi:10.1016/j.dsr2.2010.06.007.

Holbrook NJ, J Li, M Collins, E Di Lorenzo, F-F Jin, T Knutson, M Latif, C Li, SB Power, R Huang and G Wu, 2014: Decadal climate variability and cross-scale interactions: ICCL 2013 Expert Assessment Workshop. Bulletin of the American Meteorological Society, 95, ES155-ES158. doi: 10.1175/BAMS-D-13-00201.1.

Hope, P., Henley, B. J., Gergis, J., Brown, J. and Ye, H., 2016: Time-varying spectral characteristics of ENSO over the Last Millennium. Climate Dynamics: DOI: 10.1007/s00382-016-3393-z.

Jones, J. M., S. T. Gille, H. Goosse, N. J. Abram, P. O. Canziani, D. J. Charman, K. R. Clem, X. Crosta, C. de Lavergne, I. Eisenman, M. H. England, R. L. Fogt, L. M. Frankcombe, G. J. Marshall, V. Masson-Delmotte, A. K. Morrison, A. J. Orsi, M. N. Raphael, J. A. Renwick, D. P. Schneider, G. R. Simpkins, E. J. Steig, B. Stenni, D. Swingedouw, T. R. Vance, 2016: Assessing recent trends in high-latitude Southern Hemisphere surface climate. Nature Climate Change, 6, 917–926.

Kirtman, B., S.B. Power, J.A. Adedoyin, G.J. Boer, R. Bojariu, I. Camilloni, F.J. Doblas-Reyes, A.M. Fiore, M. Kimoto, G.A. Meehl, M. Prather, A. Sarr, C. Schär, R. Sutton, G.J. van Oldenborgh, G. Vecchi and H.J. Wang, 2013: Near-term Climate Change: Projections and Predictability. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Kociuba G. and S. B. Power, 2015: Inability of CMIP5 Models to Simulate Recent Strengthening of the Walker Circulation: Implications for Projections. J. Clim., 28, 20-35.

Kosaka, Y. and S.-P. Xie, 2013: Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature, 501(7467), 403–7.

Kucharski, F., Ikram, F., Molteni, F. et al., 2016: Atlantic forcing of Pacific decadal variability, Clim Dyn, 46: 2337. doi:10.1007/s00382-015-2705-z

Lienert F., F. J. Doblas-Reyes, 2013: Decadal prediction of interannual tropical and North Pacific sea surface temperature. J. Geophys. Res. Atmospheres, 118, 5913-5922.

Luo, J.-J., S. Masson, S. Behera, P. Delecluse, S. Gualdi, A. Navarra, T. Yamagata, 2003: South Pacific origin of the decadal ENSO-like variation as simulated by a coupled GCM. Geophys. Res. Lett., 30 (24), 2250, doi:10.1029/2003GL018649.

McGregor, S., A. Sen Gupta, N. J. Holbrook and S. B. Power, 2009: The Modulation of ENSO Variability in CCSM3 by Extratropical Rossby Waves. J. Climate, 22, 5839-5383.

McGregor, S., N. J. Holbrook and S. B. Power, 2009: The response of a stochastically forced ENSO model to observed off-equatorial wind-stress forcing. J. Climate. 2512-2525.

McGregor, S., N. J. Holbrook, and S. B. Power, 2007: Interdecadal sea surface temperature variability in the equatorial Pacific Ocean. Part I: The role of off-equatorial wind stresses and oceanic Rossby waves. J. Clim., 20 (11), 2643-2658

McGregor, S., N. J. Holbrook, and S. B. Power, 2008: Interdecadal sea surface temperature variability in the equatorial Pacific Ocean. Part II: the role of equatorial/off-equatorial wind stresses in a hybrid coupled model. J. Clim., 21 (17), 4242-4256.

McGregor, S., A. Timmermann, M. F. Stuecker, M. H. England, M. Merrifield, F.-F. Jin, Y. Chikamoto, 2014: Recent Walker Circulation strengthening and Pacific cooling amplified by Atlantic warming, Nature Climate Change, 4, 888-892, doi:10.1038/NCLIMATE2330

Meehl, G. A., J. M. Arblaster, C. M. Bitz, C. T. Y. Chung, and H. Teng, 2016: Antarctic sea-ice expansion between 2000 and 2014 driven by tropical Pacific decadal climate variability. Nature Geoscience, doi:10.1038/ngeo2751.

Meehl, G.A., A Hu, H Teng, 2016: Initialized decadal prediction for transition to positive phase of the Interdecadal Pacific Oscillation, Nature Communications, 7, doi:10.1038/ncomms11718.

Mochizuki, T., et al., 2010: Pacific decadal oscillation hindcasts relevant to near-term climate prediction. Proc, Natl. Acad. Sci., 107, 1833–1837.

Newman, M., G. P. Compo, and M. A. Alexander, 2003: ENSO-forced variability of the Pacific Decadal Oscillation, J. Climate, 16, 3853–3857, doi:10.1175/1520-0442.

Newman, M., et al. 2016: The Pacific Decadal Oscillation, revisited. J. Climate, 29, 4399-4427, DOI: http://dx.doi.org/10.1175/JCLI-D-15-0508.1

Palmer, J., Cook, E., Turney, C., Allen, K., Fenwick, P., Cook, B., O’Donnell, A., Lough, J., Grierson, P. and Baker, P., 2015: Drought variability in the eastern Australia and New Zealand summer drought atlas (ANZDA, CE 1500–2012) modulated by the Interdecadal Pacific Oscillation. Environmental Research Letters, 10 (12), 124002.

Power, S. B., T. Casey, C. Folland, A. Colman and V. Mehta, 1999: Inter-decadal modulation of the impact of ENSO on Australia. Climate Dynamics, 15, 319-324.

Power, S. B. and R. Colman, 2006: Multi-year predictability in a coupled general circulation model. Climate Dynamics, 2, 247-272.

Power, S., M. Haylock, R. Colman, and X. Wang, 2006: The predictability of interdecadal changes in ENSO and ENSO teleconnections. J. Climate, 8, 2161-2180.

Power, S., F. Delage, G. Wang, and G. Kocuiba, 2016: Inability of climate models to simulate recent multi- decadal change in Pacific surface temperature: implications for global temperature projections. Climate Dynamics, doi:10.1007/s00382-016-3326-x.

Power, S. B. and J. Callaghan, 2016: Variability in Severe Coastal Flooding, Associated Storms, and Death Tolls in Southeastern Australia since the Mid–Nineteenth Century. Journal of Applied Meteorology and Climatology, 55, 1139-1149.

Qiu, B., and S. Chen, 2006: Decadal Variability in the Large-Scale Sea Surface Height Field of the South Pacific Ocean: Observations and Causes. J. Phys. Oceanogr., 36, 1751-1762.

Rashid, H., S.B. Power, and J. Knight, 2010: Impact of multi-decadal fluctuations in the Atlantic thermohaline circulation on Indo-Pacific climate variability in a coupled GCM. J. Climate, 23, 4038-4044.

Rea G, C. Cagnazzo, A. Riccio, F. Fierli, and F. Cairo, 2016: Multi-decadal changes and predictions over the Southern Hemisphere region: role of the stratospheric representation in CMIP5 models. EGU General Assembly Conference Abstracts, Vienna, Austria, European Geosciences Union.

Reason CJ, W Landman, and W Tennant, 2006: Southern African climate and its links with variability of the Atlantic Ocean. Bull. Amer. Meteor. Soc., 87, 941-955.

Salinger, M.J, J.A. Renwick, and A. B. Mullan, 2001: Interdecadal Pacific Oscillation and South Pacific Climate. Int. J. Climatol., 21, 1705-1721.

Saurral R. I., F. J. Doblas-Reyes, and J. García-Serrano, 2016: Initial shock, drift and trends in decadal climate predictions in the South Pacific region upon different phases of ENSO. SPECS/PREFACE/WCRP Workshop on initial shock, drift and bias adjustment. 9-13 May 2016, Barcelona, Spain.

Shakun, J.D., and J. Shaman, 2009: Tropical origins of North and South Pacific decadal variability. Geophys. Res. Lett., 36, L19711, doi:10.1029/2009GL040313.

Shiotani, M., 1990: Low-frequency variations of the zonal mean state of the Southern Hemisphere troposphere, J. Meteorol. Soc. Jpn., 68, 461–471.

Smith, D. M., S. Cusack, A. W. Colman, C. K. Folland, G. R. Harris, and J. M. Murphy, 2007: Improved surface temperature prediction for the coming decade from a global climate model. Science, 317, 796–799.

Smith, D. M., R. Eade, N. J. Dunstone, D. Fereday, J. M. Murphy, H. Pohlmann, and A. A. Scaife, 2010: Skilful multi-year predictions of Atlantic hurricane frequency. Nature Geosci., 3, 846–849.

Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc., 93, 485–498.

Tierney, J. E., Abram, N. J., Anchukaitis, K. J., Evans, M. N., Giry, C., Halimeda Kilbourne, K., Saenger, C. P., Wu, H. C. and Zinke, J., 2015: Tropical sea-surface temperatures for the past four centuries reconstructed from coral archives. Paleoceanography, 30, 226-252, doi:10.1002/2014PA002717.

Thompson, D. W. J., J. M. Wallace, and G. C. Hegerl, 2000: Annular modes in the extratropical circulation, part 2, Trends, J. Clim., 13, 1018–1036.

Tozer, C. R., T. R. Vance, J. L. Roberts, A. S. Kiem, M. A. J. Curran, and A. D. Moy, 2016: An ice core derived 1013-year catchment-scale annual rainfall reconstruction in subtropical eastern Australia. Hydrology and Earth System Science, 20 (5), 1703-1717.

Vera, C., 2003: Interannual and interdecadal variability of atmospheric synoptic-scale activity in the Southern Hemisphere. J. Geophys. Res: Oceans, 108 (C4), 8077, doi:10.1029/2000JC000406.

Watanabe, M., H. Shiogama, H. Tatabe, M. Hayashi, M. Ishii, and M. Kimoto, 2014: Nature Climate Change, 4, 893–897.

Watterson, I. G., 2009: Components of precipitation and temperature anomalies and change associated with modes of the Southern Hemisphere. Int. J. Climatol., 29: 809–826. doi:10.1002/joc.1772.

Waugh, D.W., F. Primeau, T. DeVries, and M. Holzer, Recent changes in the ventilation of the southern oceans, Science, 339 (6119), 568-570, doi: 10.1126/science.1225411, 2013

Wittenberg, A.T., A. Rosati, T.L. Delworth, G.A. Vecchi, F. Zeng, 2014: ENSO Modulation: Is it decadally predictable? J. Climate, 27, 2667-2681, doi: http://dx.doi.org/10.1175/JCLI-D-13-00577.1.

Wu, A., and W. W. Hsieh, 2003: Nonlinear interdecadal changes of the El Nino-Southern Oscillation. Climate Dynamics, 21, 719-730.

Yuan X. and E. Yonekura, 2011: Decadal variability in the Southern Hemisphere, J. Geophys. Res., 116, D19115, doi:10.1029/2011JD015673.

Zanna, L., 2012: Forecast skill and predictability of observed Atlantic sea surface temperatures. J. Climate., 25, 5047–5056.

Zhang, H., A. Clement and P. Di Nezio, 2014: The South Pacific Meridional Mode: A Mechanism for ENSO-like Variability. J. Climate, 27, 769-783.