South Pacific Decadal Climate Variability and Potential Predictability

Lou, ‪Jiale; Holbrook, Neil J.; O’Kane, Terence J.

doi:10.1175/jcli-d-18-0249.1

Cited by 21 publications

(38 citation statements)

References 72 publications

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“…However, where the decadal signals originate remains unclear. Many studies show that the subsurface plays an important role in reddening the surface processes in the midlatitude Pacific (e.g., Power and Colman 2006;Lou et al 2019) and contributes to the observed SST decadal variability. With this in mind, we extended the LIM by introducing subsurface temperature variability in the form of VAT (Exp2).…”

Section: Dynamics Of Combined Tp Sst Sp Sst and Sp Vat Systemmentioning

confidence: 99%

“…The South Pacific decadal oscillation (SPDO; Chen and Wallace 2015; Lou et al 2019) provides the Southern Hemisphere decadal contribution to the Pacific-wide interdecadal Pacific oscillation (IPO: Power et al 1999;Folland et al 2002), and is analogous to the Pacific decadal oscillation (PDO; Mantua et al 1997) centered in the North Pacific. On interannual time scales, El Niño-Southern Oscillation (ENSO) over the tropical Pacific is the most energetic mode of climate variability and has significant impact over the globe (McPhaden et al 2006).…”

Section: Introductionmentioning

confidence: 99%

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A Linear Inverse Model of Tropical and South Pacific Seasonal Predictability

Lou

O’Kane

Holbrook

2020

Preprint

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<p>A multivariate linear inverse model (LIM) is developed to demonstrate the mechanisms and seasonal predictability of the dominant modes of variability from the tropical and South Pacific Oceans. We construct a LIM whose covariance matrix is a combination of principal components derived from tropical and extra-tropical sea surface temperature, and South Pacific Ocean vertically-averaged temperature anomalies. Eigen-decomposition of the linear deterministic system yields stationary and/or propagating eigenmodes, of which the least damped modes resemble the El-Ni&#241;o Southern Oscillation (ENSO) and the South Pacific Decadal Oscillation (SPDO). We show that although the oscillatory periods of ENSO and SPDO are distinct, they have very close damping time scales, indicating the predictive skill of the surface ENSO and SPDO is comparable. The most damped noise modes occur in the mid-latitude South Pacific Ocean, reflecting atmospheric eastward-propagating Rossby wave train variability. We argue that these ocean wave trains occur due to the atmospheric high-frequency variability of the Pacific South American pattern imprinting onto the surface ocean. The ENSO spring predictability barrier is apparent in LIM predictions initialized in Mar-May (MAM) but nevertheless displays significant correlation skill of up to ~3 months. For the SPDO, the predictability barrier tends to appear in June-September (JAS), indicating remote but delayed influences from the Tropics. We demonstrate that subsurface processes in the South Pacific Ocean are the main source of decadal variability, and further that by characterizing the upper ocean temperature contribution in the LIM the seasonal predictability of both ENSO and the SPDO variability is increased.</p>

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Section: Dynamics Of Combined Tp Sst Sp Sst and Sp Vat Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Linear Inverse Model of Tropical and South Pacific Seasonal Predictability

Lou

O’Kane

Holbrook

2020

Preprint

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“…The Pacific-wide interdecadal Pacific oscillation (IPO) (Power et al 1999) characterizes the decadal variability of SST over the entire Pacific basin, comprising variations due to ENSO, the North Pacific decadal oscillation (PDO) (Mantua et al 1997), and the South Pacific decadal oscillation (Chen and Wallace 2015; Lou et al 2019). Changes in the phase of the IPO over recent decades have been associated with variations in global mean surface temperature and attributed to the rate of external forcing [i.e., anthropogenic global warming (Kosaka and Xie 2013) and or natural and internal processes (Risbey et al 2014)].…”

Section: A Interdecadal Pacific Oscillation and The Atlantic Multidecadal Oscillationmentioning

confidence: 99%

CAFE60v1: A 60-year large ensemble climate reanalysis. Part II: Evaluation

et al. 2021

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The CSIRO Climate retrospective Analysis and Forecast Ensemble system: version 1 (CAFE60v1) provides a large (96 member) ensemble retrospective analysis of the global climate system from 1960 to present with sufficiently many realizations and at spatio-temporal resolutions suitable to enable probabilistic climate studies. Using a variant of the ensemble Kalman filter, 96 climate state estimates are generated over the most recent six decades. These state estimates are constrained by monthly mean ocean, atmosphere and sea ice observations such that their trajectories track the observed state while enabling estimation of the uncertainties in the approximations to the retrospective mean climate over recent decades. For the atmosphere, we evaluate CAFE60v1 in comparison to empirical indices of the major climate teleconnections and blocking with various reanalysis products. Estimates of the large scale ocean structure, transports and biogeochemistry are compared to those derived from gridded observational products and climate model projections (CMIP). Sea ice (extent, concentration and variability) and land surface (precipitation and surface air temperatures) are also compared to a variety of model and observational products. Our results show that CAFE60v1 is a useful, comprehensive and unique data resource for studying internal climate variability and predictability, including the recent climate response to anthropogenic forcing on multi-year to decadal time scales.

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“…Potential predictability is similar to signalto-noise ratio and describes the potential predictable ability, which is widely used to evaluate the precipitation predictability in previous studies (G. Boer, 2009;G. Boer & Lambert, 2008;Kang et al, 2004;Lou et al, 2019;W. Wei et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Potential predictability (G. J. Boer, 2004) is another way to quantify predictability from a variance fraction perspective. It describes the fraction of long‐term variability that may be distinguished from the internally generated natural variability which is not predictable on long time scales and therefore may be considered as “noise.” Potential predictability is similar to signal‐to‐noise ratio and describes the potential predictable ability, which is widely used to evaluate the precipitation predictability in previous studies (G. Boer, 2009; G. Boer & Lambert, 2008; Kang et al, 2004; Lou et al, 2019; W. Wei et al, 2017). Therefore, potential precipitation predictability (PPP) is introduced to estimate precipitation predictability over global lands.…”

Section: Introductionmentioning

confidence: 99%

Potential Precipitation Predictability Decreases Under Future Warming

Zhang

Chen

et al. 2020

Geophysical Research Letters

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Precipitation predictability is likely to decrease with water cycle intensification under global warming, yet how much it will change spatiotemporally is unclear. We quantify the precipitation predictability changes under future warming using model simulations from Coupled Model Intercomparison Project Phase 5. The global-averaged potential precipitation predictability (PPP) is likely to decrease 0.3% per 1°C increase of global sea surface temperature (SST), with a decrease of 0.8% and 0.1% in tropical and extratropical regions, respectively, under future warming scenarios. The PPP changes are divergent among different models with considerable uncertainty. The estimated declining PPP is closely related to the increase of SST and the decrease of potential SST predictability based on a statistical regression analysis. These results unravel the changing pattern of precipitation predictability under future warming. Plain Language Summary Precipitation prediction for several weeks or months in advance provides opportunities for the preparation of extreme weather events such as droughts and floods. The predictable ability of precipitation determines how long it can be forecasted. We found that precipitation is becoming more difficult to predict when the global sea surface temperature (SST) continues to increase in the future, although different models show divergent results. The decreasing precipitation predictable ability is well explained by the increasing SST and the decreasing predictable ability of SST. Although extreme precipitation events may increase in a warmer world in the context of the enhanced SST variability and other changes such as sulfate aerosols and land use, precipitation is probably less predictable as a result of lower predictable ability of SST.

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South Pacific Decadal Climate Variability and Potential Predictability

Cited by 21 publications

References 72 publications

A Linear Inverse Model of Tropical and South Pacific Seasonal Predictability

A Linear Inverse Model of Tropical and South Pacific Seasonal Predictability

CAFE60v1: A 60-year large ensemble climate reanalysis. Part II: Evaluation

Potential Precipitation Predictability Decreases Under Future Warming

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