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Human emissions drive recent trends in North Pacific climate variations

Nature volume 644, pages 684–692 (2025)Cite this article

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Abstract

The Pacific decadal oscillation (PDO)—the leading pattern of climate variability driving changes over the North Pacific and surrounding continents—is now thought to be generated by processes internal to the climate system1,2. According to this paradigm, the characteristic, irregular oscillations of the PDO arise from a collection of mechanisms involving ocean and atmosphere interactions in the North and tropical Pacific3,4,5. Recent variations in the coupled ocean–atmosphere system, such as the 2015 El Niño, ought to have shifted the PDO into its positive phase6. Yet, the PDO has been locked in a consistent downward trend for more than three decades, remanding nearby regions to a steady set of climate impacts. Here we show that the main multidecadal variations in the PDO index during the twentieth century, including the ongoing, decades-long negative trend, were largely driven by human emissions of aerosols and greenhouse gases rather than internal processes. This anthropogenic influence was previously undetected because the current generation of climate models systematically underestimate the amplitude of forced climate variability. A new attribution technique that statistically corrects for this error suggests that observed PDO impacts—including the ongoing multidecadal drought in the western United States—can be largely attributed to human activity through externally forced changes in the PDO. These results indicate that we need to rethink the attribution and projection of multidecadal changes in regional climate.

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Fig. 1: External forcing explains the timing and pattern of the PDO.
Fig. 2: The role of forcing in the PDO is statistically robust.
Fig. 3: Forcing from anthropogenic aerosols and greenhouse gases explain the timing of recent trends in the PDO index.
Fig. 4: The signal-to-noise paradox in the PDO is associated with weak Aleutian Low variability.
Fig. 5: The long-term meteorological drought in the western United States is attributable to human emissions of aerosols and greenhouse gases by their influence on the PDO.
Fig. 6: A stronger internal PDO in models does not solve the signal-to-noise error.

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Data availability

All climate model output is publicly available at the Earth System Grid Federation (https://esgf.llnl.gov/). NOAA Extended Reconstructed SST v.5 (ERSSTv5), NOAA/CIRES/DOE 20th Century Reanalysis (v.3) and Global Precipitation Climatology Project (GPCP) Monthly Analysis Product data provided by the NOAA PSL at https://psl.noaa.gov. Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) data were provided by the Met Office (United Kingdom) and are available at https://www.metoffice.gov.uk/hadobs/hadisst/. This study used the MATLAB Mapping Toolbox to construct maps, including the Coastlines dataset71.

Code availability

This study used MATLAB and the MATLAB Signal Processing Toolbox and the Statistics and Machine Learning Toolbox for statistical analysis. This included the use of code from the WEACLIM toolbox, available on the MATLAB File Exchange72. The code used to perform the statistical tests and produce the figures is available at Zenodo73 (https://doi.org/10.5281/zenodo.15658555).

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Acknowledgements

We thank J. Fasullo, J. Kay and I. Simpson for their comments. We acknowledge the grants received from the National Science Foundation (grant AGS-2002528) (J.M.K., P.N.D. and T.M.S.), the NOAA Climate Program Office (grant NA20OAR4310400) and the National Science Foundation (grant AGS-2241752) (A.C.C.). The National Center for Atmospheric Research is sponsored by the National Science Foundation.

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Authors and Affiliations

  1. Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO, USA

    Jeremy M. Klavans & Pedro N. DiNezio

  2. Rosenstiel School of Marine, Atmospheric, and Earth Science, University of Miami, Miami, FL, USA

    Amy C. Clement

  3. National Center for Atmospheric Research, Boulder, CO, USA

    Clara Deser

  4. Jackson School of Geosciences, University of Texas at Austin, Austin, TX, USA

    Timothy M. Shanahan

  5. Lamont Doherty Earth Observatory, Columbia University, New York, NY, USA

    Mark A. Cane

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Contributions

J.M.K. and P.N.D. conceptualized the study. J.M.K., P.N.D. and M.A.C. devised the methodology. J.M.K. conducted the investigation. J.M.K. performed the visualization. P.N.D. and T.M.S. helped with the funding acquisition. J.M.K. wrote the original draft. J.M.K., P.N.D., A.C.C., C.D., T.M.S. and M.A.C. wrote, reviewed and edited the paper.

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Correspondence to Jeremy M. Klavans.

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Extended data figures and tables

Extended Data Fig. 1 The limited effect of ENSO removal on the observed PDO index.

Observed PDO index timeseries (from NOAA) with and without linearly-removing ENSO for unfiltered (annual average) and low-pass (LP) filtered data.

Extended Data Fig. 2 The forced component of the PDO is relatively insensitive to the PDO index definition.

The PDO index in all panels is defined as the 1st principal component of North Pacific sea-surface temperatures after removing North Pacific average SSTs (Methods). a, The observed PDO index (black) compared with the ensemble mean PDO index from the all-forcings simulations (dark blue) and the normalized ensemble mean PDO index from the all-forcings simulations (light blue). We normalize the forced PDO index strictly to illustrate the timing of the shifts in both indices; the amplitudes of each timeseries are listed in Extended Data Table 2 and discussed in-text. b, Regression of observed SST (colours) and sea-level pressure (contours; hPa per unit of the PDO index) on the observed PDO index. We draw contours every −0.5 hPa in purple; the zero contour is in black. The KOE region is outlined in solid black. c, Regression of forced SST (colours) and sea-level pressure (contours) on the normalized, forced PDO index. d, Correlation coefficients and their significance levels for the three PDO definitions in the main text. As in the main text, the KOE SST index is detrended. Significance levels are calculated empirically via phase re-shuffling (Methods).

Extended Data Fig. 3 The role of external forcing is similar in a N. Pacific sea-surface temperature index.

An index of linearly detrended, spatial-average sea-surface temperatures in the Kuroshio-Oyashio Extension region of the North Pacific is calculated (31° – 36° N, 140° – 165° E; black outline) and regressed on SSTs in observations and models, which shows that the forced signal described in-text is not an artifact of the method we use to calculate the PDO index. a, The detrended, observed KOE sea-surface temperature index (black) compared and the detrended ensemble mean KOE SST index (dark blue) and the normalized, detrended, ensemble mean KOE SST index from the all-forcings simulations (light blue). We normalize the forced KOE index strictly to illustrate the timing of the shifts in both indices; the amplitudes of each timeseries are listed in Extended Data Table 2 and discussed in-text. b, Regression of observed SST (colours) on the observed, detrended, KOE index. c, Regression of forced SST (colours) on the normalized, detrended, forced KOE index.

Extended Data Fig. 4 There is a meaningful forced component in the PDO in most single-model large ensembles.

All panels: for each ensemble size, in each single model large ensemble, we randomly select members from the full ensemble (with replacement), average, and correlate with observations to calculate the mean correlation coefficient (dot) and the 95% confidence interval (cloud). Please note that this figure reports the correlation coefficient not its square, explained variance (as in Fig. 2d), because some values are negative. Additionally, re-sampling with replacement may give a false sense of stability and significance for the correlations calculated from smaller ensembles.

Extended Data Fig. 5 As forcing intensifies, the role of forcing in the PDO grows larger.

a,b, External forcing explains more PDO variance after 1950 on both (a) interannual and (b) multidecadal timescales. Please note we only plot bars where model output allows; not all models were initialized in or before 1870 (see Extended Data Table 1). Also, the number of simulations in each single-model ensemble varies (listed below model name and in Extended Data Table 1) implying that these bars may not be directly comparable to each other, especially for those models with fewer simulations. Please note that the “all models” value varies slightly from the text because we calculate the first principal component of North Pacific SST earlier than 1950 in those models that allow. The black dots correspond to the empirical 90% confidence level, as calculated via phase re-shuffling (Methods).

Extended Data Fig. 6 The model-generated internal and forced PDO patterns appear similar to observations and each other.

a, Regression of observed SST on the observed PDO index. b, The average regression pattern of the internal PDO SST pattern across all ensemble members. In each ensemble member, we calculate the regression of SST on the PDO index at each grid point. We then average these regression patterns across the full multi-model ensemble with each member weighted equally. c, Regression of ensemble mean SST on the normalized, ensemble mean PDO index from the all-forcings simulations.

Extended Data Fig. 7 The explanatory power of the forced PDO is not sensitive to model configuration.

Left panels: the observed PDO index (black) compared with the ensemble mean PDO index from the all-forcings simulations (dark blue), the normalized ensemble mean PDO index from the all-forcings simulations (light blue), and the two standard deviation ensemble spread from the all-forcings simulations (blue cloud). Right panels: regression of ensemble mean SST (colours) on the normalized, ensemble mean PDO index. The number of members in each ensemble is listed in parentheses next to the description.

Extended Data Fig. 8 The forced PDO pattern in each large single-model large ensemble.

All panels: regression of ensemble mean SST on the normalized, ensemble mean PDO index from each single model large ensemble we consider.

Extended Data Table 1 Components of the multi-model large ensemble
Full size table
Extended Data Table 2 Decomposition of North Pacific SST variance
Full size table
Extended Data Table 3 The signal-to-noise error in response to external forcing
Full size table

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Klavans, J.M., DiNezio, P.N., Clement, A.C. et al. Human emissions drive recent trends in North Pacific climate variations. Nature 644, 684–692 (2025). https://doi.org/10.1038/s41586-025-09368-2

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