RGMA Project sponsored by 


Pacific Ocean Decadal Climate Variability and Change (PODX)

GOAL. This project (2019-2022) aims at developing a fundamental understanding and synthesis of the processes that drive Pacific decadal variability (PDV) in Earth System Models (ESMs) under different external climate forcing scenarios using simulations from the Coupled Model Intercomparison Project (CMIP) and  E3SM v1.  In this project we diagnose, and compare across ESMs, the role of coupled ocean‐atmosphere processes and feedbacks, determine how they contribute to predictable PDV dynamics and how they are affected by unpredictable stochastic forcing, and evaluate mean state dependencies including those associated with anthropogenic external forcing. 

DATA & MODELS. The data and methods for this project include (a) data archives from the latest available CMIP, (b) data archives from existing large ensembles from CESM, GFDL ESM2M, IPSL CM5A, and CanESM (c) a hierarchy of reduced complexity stochastic models, (d) a set of simulations from a new hybrid coupled climate model based on the CESM and E3SM, and finally (e) a 15‐member large ensemble of the DOE E3SM v1 model, newly generated in this project, where each member undergoes the same historical/projected 1850‐2100 external forcing.

APPROACH. Our approach is to first construct an integrated stochastic model that incorporates the mechanisms and teleconnections of the observationally‐based hypothesis, using it to quantify and synthesize the dominant pathways energizing PDV in each ESM. Next, individual processes of PDV in the tropics (e.g. stochastic forcing, ENSO precursors and feedback dynamics) and extra‐tropics (e.g. tropical/extra‐tropical teleconnections, stochastic forcing and memory dynamics) will be further examined using the hybrid simulations and advanced budget analyses. The influence of anthropogenic external forcing (e.g.changes in mean state) will also be investigated using similar approaches applied to the existing large‐ensemble simulations from CMIP5‐era models and the new CMIP6‐era E3SM large ensemble. Substantial internal climate variability has been shown to impact regional estimation of forced signals within ESMs, requiring large ensembles for robust identification of the links between climate change and PDV. Our approach moves beyond simple statistical comparisons and analyses of the patterns of the PDV modes to deliver mechanistic understanding and synthesis of the dynamics and processes that energize their variance. 



Emanuele Di Lorenzo

Georgia Tech

Large-scale ocean & climate dynamics, solutions for coastal resilience, climate and marine ecosystems dynamics


Youngji Joh

NOAA Geophysical Fluid Dynamics Laboratory

Climate variability and change, air-sea interactions, and tropical extra-tropical teleconnections


Yingying Zhao

Georgia Tech

Climate change and modeling, empirical dynamical models


Matt Newman

University of Boulder Colorado

Linear inverse modeling, atmospheric dynamics, Pacific climate variability


Xing Chen

University of California Santa Barbara

Pacific decadal variability, ENSO, climate modeling


Tongtong Xu


Coastal ocean modeling and prediction, linear inverse modeling, machine learning


Samantha Stevenson

University of California Santa Barbara

Climate and paleo-climate modeling, ENSO dynamics, Tropical Pacific climate variability


Antonietta Capotondi

NOAA Physical Science Laboratory

ENSO dynamics, Pacific Ocean climate variability, Linear Inverse Modeling


Sheng Wu

Georgia Tech

Climate dynamics and change, linear inverse modeling



*Publications contributed by this project

It has been long recognized that Modes of Variability (MOV) such as the El Niño Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), the North Pacific Gyre Oscillation (NPGO) are foundational and interlinked modes of Pacific climate variability. Together, these modes have substantial impacts on the statistics of heat and precipitation extremes over North America, yet their dynamic interactions in a changing climate remain unclear.  Below we provide a short synthesis of the current understanding of the mechanisms that energize and dynamically link the MOV, and highlight contributions from this DOE research project.


(1) One-way teleconnections from tropics to extra-tropics (C1). Ocean and atmospheric teleconnections from the tropics to the extra-tropics forced by Eastern Pacific ENSO (EP-ENSO) are known to energize the Aleutian Low (AL) and PDO system on timescales of 6m-1yr. In turn, the oceanic adjustment to the PDO excites long ocean Rossby waves that propagate the signal into the Kuroshio-Oyashio Extension (KOE) region on timescales of 2-4yr. Collectively, this quasi-deterministic cycle EP-ENSO -> PDO -> KOE (C1 in the schematic) carries a multi-year memory that energizes the North Pacific Decadal Variability (PDV).

(2) Two-way coupling between tropics and extra-tropics (C2). While C1 emphasizes the one-way coupling from the tropics to the extra-tropics, results from this DOE project show that two-way coupling between tropics and extra-tropics is likely a more important mechanism for explaining South, North, and Tropical PDV (C2 in the schematic). By combining Linear Inverse Models (LIMs) with reanalysis and large-ensembles of CMIP5 & 6 models, including an E3SM1 ensemble generated by this team (Stevenson et al. submitted
*), the PIs show that extra-tropical ENSO precursor dynamics excited by the weakening of the off-equatorial trade winds energize the tropical Pacific decadal variability, specifically the Central Pacific ENSO pattern (CP-ENSO). In the Northern Hemisphere, these wind fluctuations are forced by the North Pacific Oscillation (NPO) and its oceanic expression -- the North Pacific Gyre Oscillation (NPGO). The resulting tropical PDV is then exported again to the extra-tropics through the ENSO teleconnections NPO/NPGO (extra-tropics) -> CP-ENSO (tropics) -> NPO/NPGO (extra-tropics) (C2 in the schematic) leading to the canonical PDV pattern over the entire Pacific basin (Zhao and Di Lorenzo, 2020*; Zhao et al. 2021a*). Beyond its implication for PDV, this two-way coupling is also an important mechanism for multi-year ENSO in CMIP6 NPO -> CP-ENSO -> NPO -> CP-ENSO (Ding, Di Lorenzo et al. 2022*). Furthermore, given that the NPO leads to different flavors of ENSO, the PIs found that the two-way coupling NPO/NPGO -> CP-ENSO -> EP-ENSO -> AL/PDO is a primary mechanism supporting the evolutions and persistence of multi-year climate extremes of the North Pacific such as the recent 2013-2015 marine heatwaves and droughts over North America (Capotondi et al., 2019*; Xu et al, 2020*). In model simulations, when this coupling is interrupted, the spatial and temporal structure of the PDO is significantly changed featuring a reduction in variance in the eastern North Pacific and the emergence of a stronger variance pattern in the KOE (Zhao et al. 2021b*). 

(3) Coupling between KOE and basin-scale PDV in a changing climate (C3). Further analyses reveal that the KOE may play a bigger role in controlling the basin-scale PDV, especially as the climate is changing towards warmer conditions. While several studies have explored how ocean variability in the KOE can trigger downstream atmospheric teleconnections in the central and eastern North Pacific, the PIs are able to show that these downstream teleconnections dynamics can also impact the tropical Pacific and give rise to PDV with a preferred quasi-decadal timescale. Specifically, the atmospheric response to the low-frequency oceanic forcing of the KOE acts as a nudging of the NPO conditions on timescale of 1-2yr and energize the KOE -> NPO -> CP-ENSO -> NPO/NPGO -> KOE (Joh and Di Lorenzo, 2019
*; C3 in the schematic). This mechanism has been preliminarily confirmed in a high-resolution ocean-atmosphere model (Joh, Di Lorenzo, Kirtman et al. 2021*) with suggestions that C3 may be explaining a larger fraction of PDV under global warming. The exact mechanism linking the KOE-driven PDV and climate change is still unknown but may be linked to changes in the average location of the storm tracks with significant effects on the statistics of extremes over North America. Analyses that use a LIM to decompose Pacific basin dynamics in both nature (Newman et al. 2016; Capotondi et al., submitted*) and CMIP models (Wu, Di Lorenzo, et al., in review*) clearly indicate that this tropical-extra-tropical coupling emerges as one of the dominant dynamical modes of Pacific climate, with areas of high amplitude in the KOE and Central Tropical Pacific. We refer to this new pattern as the North Pacific-Central Pacific Mode (NP-CP). Although previous studies of statistical variability patterns have identified various elements of the NP-CP mode, the PIs have shown it is a dynamical mode of the Pacific system, suggesting it is generated by a sequence of dynamical events (e.g. C3 + C2), that impacts predictability of phenomena including the PDO, NE Pacific marine heatwaves, and CP ENSO events. The emergence of the NP-CP mode along with the intensification of Pacific low frequency variability seen in observations and climate models (Joh and Di Lorenzo, 2017; 2021*; Liguori and Di Lorenzo, 2018) raises key questions about its underlying mechanisms and their relation to climate change.  



Image by Roman Mager

A Linear Inverse Modeling Toolbox for Pacific Climate


New E3SMv1 20-Member Large-Ensemble




  1. Stevenson, S., Z. Huang, Y. Zhao, E. Di Lorenzo, M. Newman, L. van Roekel, A. Capotondi and T. Xu: Ocean Initial State Contributions to Ensemble Spread: Insights from the Energy Exascale Earth System Model version 1 Large Ensemble. Journal of Climate, submitted.

  2. Capotondi, A., M. Newman, T. Xu and E. Di Lorenzo, 2022: An Optimal Precursor of Northeast Pacific Marine Heatwaves and Central Pacific El Nino Events. Geophysical Research Letters, 49(5) 10, doi:10.1029/2021gl097350.

  3. Di Lorenzo, E., T. Xu, Y. Zhao, M. Newman, A. Capotondi, S. Stevenson, D. Amaya, B. T. Anderson, R. Ding, J. C. Furtado, Y. Joh, G. Liguori, J. Lou, A. J. Miller, G. Navarra, N. Schneider, D. Vimont, S. Wu and H. Zhang, 2022: Modes and Mechanisms of Pacific Decadal-Scale Variability. Annual Reviews of Marine Science, in press.

  4. Ding, R. Q., Y. H. Tseng, E. Di Lorenzo, L. Shi, J. P. Li, J. Y. Yu, C. Z. Wang, C. Sun, J. J. Luo, K. J. Ha, Z. Z. Hu and F. F. Li, 2022: Multi-year El Nino events tied to the North Pacific Oscillation. Nature Communications, 13(1) 11, doi:10.1038/s41467-022-31516-9.

  5. Liguori, G., S. McGregor, M. Singh, J. Arblaster and E. Di Lorenzo, 2022: Revisiting ENSO and IOD Contributions to Australian Precipitation. Geophysical Research Letters, 49(1) 12, doi:10.1029/2021gl094295.

  6. Xu, T., M. Newman, A. Capotondi, S. Stevenson and E. Di Lorenzo, 2022: An increase in marine heatwaves despite decreasing surface ocean temperature variability. Nature Communications, accepted


  7. Joh, Y., E. Di Lorenzo, L. Siqueira, and B. P. Kirtman (2021), Enhanced interactions of Kuroshio Extension with tropical Pacific in a changing climate, Sci Rep, 11(1), 12, doi:10.1038/s41598-021-85582-y.

  8. Shin, S. I., and M. Newman (2021), Seasonal Predictability of Global and North American Coastal Sea Surface Temperature and Height Anomalies, Geophysical Research Letters, 48(10), 10, doi:10.1029/2020gl091886.

  9. Shin, S. I., P. D. Sardeshmukh, M. Newman, C. Penland, and M. A. Alexander (2021), Impact of Annual Cycle on ENSO Variability and Predictability, Journal of Climate, 34(1), 171-193, doi:10.1175/jcli-d-20-0291.1.

  10. Stevenson, S., Wittenberg, A.T., Fasullo, J., Coats, S. and Otto-Bliesner, B., 2021. Understanding Diverse Model Projections of Future Extreme El Niño. Journal of Climate, 34(2), pp.449-464.

  11. Xu, T., M. Newman, A. Capotondi, and E. Di Lorenzo (2021), The Continuum of Northeast Pacific Marine Heatwaves and Their Relationship to the Tropical Pacific, Geophysical Research Letters, 48, doi:10.1029/2020gl090661.

  12. Zhao, Y., M. Newman, A. Capotondi, and E. Di Lorenzo (2021), Removing the Effects of Tropical Dynamics from North Pacific Climate Variability, Journal of Climate, https://doi.org/10.1175/JCLI-D-21-0344.1.

  13. Zhao, Y. Y., E. Di Lorenzo, D. X. Sun, and S. Stevenson (2021), Tropical Pacific Decadal Variability and ENSO Precursor in CMIP5 Models, Journal of Climate, 34(3), 1023-1045, doi:10.1175/jcli-d-20-0158.1.


  14. Zhao, Y., and E. Di Lorenzo (2020), The impacts of Extra-tropical ENSO Precursors on Tropical Pacific Decadal-scale Variability, Nature Scientific Reports, DOI: 10.1038/s41598-020-59253-3.

  15. Ding, H., M. Newman, M. A. Alexander, and A. T. Wittenberg (2020), Relating CMIP5 Model Biases to Seasonal Forecast Skill in the Tropical Pacific, Geophysical Research Letters, 47(5), e2019GL086765, doi:10.1029/2019GL086765.


  16. Capotondi, A., P. D. Sardeshmukh, E. Di Lorenzo, A. C. Subramanian, and A. J. Miller (2019), Predictability of US West Coast Ocean Temperatures is not solely due to ENSO, Nature Scientific Reports, 9, 10, doi:10.1038/s41598-019-47400-4.

  17. Joh, Y., and E. Di Lorenzo (2019), Interactions between Kuroshio Extension and Central Tropical Pacific lead to preferred decadal-timescale oscillations in Pacific climate, Nature Scientific Reports, 9, 12, doi:10.1038/s41598-019-49927-y.

  18. Liguori, G., and E. Di Lorenzo (2019), Separating the North and South Pacific Meridional Modes Contributions to ENSO and Tropical Decadal Variability, Geophysical Research Letters, 46(2), 906-915, doi:10.1029/2018gl080320.