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Signature of the western boundary currents in local climate variability

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Abstract

The western boundary currents are characterized by narrow, intense ocean jets and are among the most energetic phenomena in the world ocean. The importance of the western boundary currents to the mean climate is well established: they transport vast quantities of heat from the subtropics to the midlatitudes1, and they govern the structure of the climatological mean surface winds2,3,4,5,6, precipitation4,5,6 and extratropical storm tracks7,8,9,10,11,12,13. Their importance to climate variability is much less clear, as the tropospheric response to extratropical sea surface temperature (SST) variability is generally modest relative to the internal variability in the midlatitude atmosphere12,13,14. Here we exploit novel local analyses based on high-spatial-resolution data to demonstrate that SST variability in the western boundary currents has a more robust signature in climate variability than has been indicated in previous work. Our results indicate that warm SST anomalies in the major boundary currents of both hemispheres are associated with a distinct signature of locally enhanced precipitation and rising motion anomalies that extend throughout the depth of the troposphere. The tropospheric signature closely mirrors that of ocean dynamical processes in the boundary currents. Thus, the findings indicate a distinct and robust pathway through which extratropical ocean dynamical processes influence local climate variability. The observational relationships are also reproducible in Earth system model simulations but only when the simulations are run at high spatial resolution.

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Fig. 1: Observed signature of the western boundary currents in climatological mean vertical motion and precipitation.
Fig. 2: Observed signature of the western boundary currents in month-to-month variability in vertical motion and precipitation.
Fig. 3: Vertical motion variability associated with SST variability in the four western boundary current regions.
Fig. 4: Simulated signature of the western boundary currents in a high-resolution coupled atmosphere–ocean global climate model.
Fig. 5: Simulated signature of the western boundary currents in a low-resolution coupled atmosphere–ocean global climate model.

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Data availability

Reanalysis and observed data were obtained from ERA5 (https://cds.climate.copernicus.eu/), OISST (https://www.ncei.noaa.gov/products/optimum-interpolation-sst) and IMERG (https://gpm.nasa.gov/data/imerg). iHESP model data were obtained from https://ihesp.github.io/archive/. Base maps use freely available data from https://www.naturalearthdata.com/downloads/, plotted with the Cartopy software77.

Code availability

The code used to process the data and produce these figures can be found at the Open Science Framework78. This code is licensed under the Open Software License 3.0.

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Acknowledgements

We thank R. J. Wills, M. Alexander and the anonymous reviewers for their comments on the paper. We thank L. Sun for his assistance with the numerical output and comments on the figures and methods. We thank R. Justin Small and Y.-O. Kwon for the conversations and comments on the results. J.G.L., D.W.J.T. and J.W.H. are supported by the National Science Foundation (grant no. AGS-2055121). D.W.J.T. is supported by the National Aeronautics and Space Administration (NASA) under 80NSSC23K0113 and the NSF CLD Program under AGS-2116186. The analysis and simulations benefited from the high-performance computing support from Casper (https://arc.ucar.edu/knowledge_base/70549550) carried out in the Computational and Information Systems Laboratory of NSF NCAR.

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  1. Department of Atmospheric Science, Colorado State University, Fort Collins, CO, USA

    James G. Larson, David W. J. Thompson & James W. Hurrell

  2. School of Environmental Sciences, University of East Anglia, Norwich, UK

    David W. J. Thompson

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J.G.L., D.W.J.T. and J.W.H. conceived the study and wrote the paper. J.G.L. performed the analysis and generated the figures.

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Correspondence to David W. J. Thompson.

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Extended data figures and tables

Extended Data Fig. 1 Signature of western boundary currents in remotely sensed SST and precipitation.

(a, d, g, j) The standard deviations of monthly grid point SST anomalies; (b, e, h, k) grid point vertical motion anomalies at 850 hPa regressed onto grid point SST anomalies; and (c, f, i, l) grid point precipitation anomalies regressed onto grid point SST anomalies. The rows correspond to the four western boundary currents of interest. The SST anomalies are based on the National Oceanic and Atmospheric Administration (NOAA) 1/4° Daily Optimum Interpolation Sea Surface Temperature (OISST) dataset, the vertical motion field is based on ERA5, and the precipitation field is based on the National Aeronautics and Space Administration’s (NASA) Global Precipitation Measurement Mission (GPM). Each dataset is based on monthly mean output.

Extended Data Fig. 2 Statistical significance testing of grid point air-sea correlations.

Grid point correlation coefficients of (a, c, e, g) vertical motion anomalies at 850 hPa correlated with SST anomalies and (b, d, f, h) high-pass spatially filtered precipitation anomalies correlated with SST anomalies. Hatching indicates statistically significant values using a two-tail Student’s t-test at 99% confidence with 96 degrees of freedom. See Methods for more details. The rows correspond to the four western boundary currents of interest. Results are based on monthly mean ERA5.

Extended Data Fig. 3 Variance explained by grid point air-sea correlations.

The square of grid point correlation coefficients of (a, c, e, g) vertical motion anomalies at 850 hPa correlated with SST anomalies and (b, d, f, h) high-pass spatially filtered precipitation anomalies correlated with SST anomalies. See Methods for more details. Results are based on monthly mean ERA5.

Extended Data Fig. 4 Spatial masks applied to determine the vertical profile of vertical motion associated with SST variability.

The masks, used in the spatial averaging to calculate the results in Fig. 3, exclude all grid points whose regression coefficients of vertical motion at 850 hPa regressed onto SST anomalies fall below the threshold of 0.8 mm s−1 σ−1 for the (a) Gulf Stream, (b) Kuroshio-Oyashio Extension, (c) Agulhas, and (d) Brazil-Malvinas currents. Results are based on monthly mean ERA5.

Extended Data Fig. 5 The effect of high-pass spatial filtering the precipitation field.

(a, d, g, j) The standard deviations of grid point SST anomalies; (b, e, h, k) grid point unfiltered convective precipitation anomalies regressed onto grid point SST anomalies (c, f, i, l) grid point unfiltered total precipitation anomalies regressed onto grid point SST anomalies. The rows correspond to the four western boundary currents of interest. Results are based on monthly mean ERA5.

Extended Data Fig. 6 Composites of daily-mean precipitation during anomalously warm and cold days at a representative location in the Gulf Stream region.

The results in Extended Data Fig. 6 explore the signature of the SST field in daily-mean precipitation as a function of precipitation amplitude, and thus indicate whether the covariability observed on month-to-month timescales arises primarily from large amplitude daily precipitation events, or from daily precipitation events across a range of amplitudes. To construct the figure, we: 1) obtained daily values of SST and precipitation (hereafter P) from the grid point identified in the inset in the figure (in the inset, the shading reproduces the vertical motion covariability from Fig. 2 panel b, and the grid point lies in a region of large SST-vertical motion covariability in the Gulf Stream region); 2) removed the seasonal-cycle and long-term trend from the SST data at the selected grid point; 3) formed composites of wintertime precipitation based on days when the SST anomaly time series at the grid point was higher than normal (SST > 1 standard deviation) and lower than normal (SST < −1 standard deviation); and 4) binned the composite precipitation values for warm and cold conditions by the amplitude of the daily-mean precipitation. The analyses are based on ~1300 days in both the SST > 1 standard deviation and SST < −1 standard deviation bins. The bars show the results as histograms, where the x-axis indicates the daily-mean precipitation amplitude and the y-axis indicates the number of days within each precipitation amplitude bin. The key result is that warm days (red bars) are marked by an increased incidence of precipitation events relative to cold days (blue bars) across a range of precipitation amplitudes. That is, they are marked by an increased incidence of not only large-amplitude precipitation events (right part of the plot) but also small amplitude events (left part of the plot). Similar conclusions emerge from analyses at other sample grid points within the different western boundary currents.

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Larson, J.G., Thompson, D.W.J. & Hurrell, J.W. Signature of the western boundary currents in local climate variability. Nature (2024). https://doi.org/10.1038/s41586-024-08019-2

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