Observations and reanalysis data
We use the monthly mean SLP, winds at 850-hPa and 500-hPa, and precipitation rate provided by the National Centers for Environmental Prediction-National Center for Atmospheric Research reanalysis dataset from January 1948 to the present47, and monthly mean SST provided by the National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed SST version 5 from January 1854 to the present48. Monthly mean precipitation data from January 1979 to the present are obtained from the Climate Prediction Center Merged Analysis of Precipitation (CMAP) dataset49. In addition, monthly mean surface wind stress, sea surface height, and subsurface sea water potential temperature are extracted from the Global Ocean Assimilation System reanalysis dataset from January 1980 to the present50. Subsurface sea water potential temperature extends from 5 m to 4478 m below sea level.
Historical simulations of CMIP6
To examine the effect of global warming on the background circulation, we compare the historical simulations and the climate change projection simulations under the SSP585 scenario from 34 coupled climate models that participated in CMIP651 (Supplementary Table 2). As a number of models only provide one member in the historical simulation or the climate change simulation, we only employ the first run from these 34 CMIP6 models. Mean state differences of interested variables between SSP585 scenario (2020-2099) and historical (1920-1999) simulations from the 34 CMIP6 models are used to represent the influence of the global warming. Note that we use the 80-year mean to reduce the impact of internal climate variability.
Large ensemble simulations of CanESM2
We use the 50-member large ensemble climate simulations conducted with the second-generation Canadian Earth System Model to further confirm the effect of the global warming on the mean climate, which largely exclude the contribution of the internal climate variability (CanESM2)52-53. The 50-member climate simulations of CanESM2 are briefly summarized as follows. First, five simulations are constructed over 1850-1950 to develop five different oceanic conditions in 1950. Second, ten simulations are further performed from each of the above five simulations with small different initial conditions in 1950. Due to the chaotic nature of climate systems, the small different initial condition in 1950 results in quite different atmospheric states a few days later54. This produces 50 members of simulations over the period 1950-2100. Over 1950-2005, each of the simulations is forced with the same historical radiative forcings, sulfate aerosols, and greenhouse gas concentration. Over 2006-2100, the simulations are forced by the RCP8.5 scenario forcing55. We compare the mean state differences between 2045-2100 and 1950-2005 in the CanESM2 simulations. Because each of the simulations is forced by the same external forcing with only slight differences in the initial condition, the difference of the projected changes among the 50 members is due solely to internal climate variability. In addition, projected changes due to the external forcing can be obtained by averaging the results of the CanEMS2 50 members.
PMM and air-sea coupling strength
Studies have indicated that the PMM is the first leading air-sea coupling mode over the subtropical Northeastern Pacific29-30,41. The PMM is defined as the first singular value decomposition (SVD, also called maximum covariance analysis, MCA) mode of the SST and surface winds anomalies over the subtropical Northeastern Pacific29-30,41. As in previous studies, strength of the air-sea coupling (i.e., WES feedback) over the subtropical northeastern Pacific is defined as the correlation coefficient between the PMM-SST and PMM-wind indices41-42,56. The PMM-SST (PMM-wind) index is represented by expansion coefficient time series of SST (surface winds) corresponding to the first SVD of SST and surface winds over the subtropical Northeastern Pacific.
Efficiency of the WES feedback
Following previous studies21,40,43, the efficiency of the WES feedback ( , representing change in the surface latent heat flux per unit anomalies of the surface wind) is expressed as follows:
Here w represents the background wind speed perturbation, is the background mean zonal wind. It should be mentioned that the zonal wind component dominants the change in the WES feedback, and thus the contribution of the meridional wind is not considered21,40,43.
AL and NPO
We use EOF analysis to obtain the AL and NPO variation patterns. The first two leading EOF modes of March SLP anomalies over North Pacific (20o-70oN, 120oE-100oW) during 1979-2019 are shown in Supplementary Figure 1. EOF1 is characterized by same-sign SLP anomalies over mid-high latitudes of the North Pacific, with the largest loading around the region where the climatological Aleutian Low generally is located. Thus, EOF1 represents variation of the Aleutian Low intensity (ALI). According to the spatial pattern shown in Supplementary Fig. 1a, we define an ALI as region-mean SLP anomalies over the area of 30o-65oN, 160oE-140oW. Note that positive value of ALI corresponds to a weakened AL intensity. The correlation coefficient between the ALI and the principal component (PC) time series of EOF1 is as high as 0.95 over 1979-2019. EOF2 of the SLP anomalies over the North Pacific is featured by a meridional dipole pattern, which represents the NPO (Supplementary Fig. 1b). The NPO index is defined as the PC time series corresponding to the EOF2 of SLP anomalies.
The Atlantic Multidecadal Oscillation (AMO) is the dominant SST variability on the multidecadal time scale in the North Atlantic44. Monthly mean smoothed AMO index since the January 1948 is obtained from the NOAA Physical Science Division (Fig. 5a). A positive (negative) AMO phase is defined as those years when the normalized AMO index is larger (smaller) than zero.
The Pacific Decadal Oscillation (PDO) is the first leading mode of SST variability on the decadal time scale over the North Pacific57. Monthly mean PDO index is extracted from the NOAA Physical Sciences Laboratory available from January 1948 to the present (Supplementary Fig. 6a). A positive (negative) PDO phase is defined as those years when the normalized PDO index is larger (smaller) than zero.
The North Pacific Gyre Oscillation (NPGO) is the second EOF mode of sea surface height variation over the North Pacific, and it is characterized by interdecadal changes in the oceanic gyres over the subtropical and subpolar North Pacific58. Monthly mean North Pacific Gyre Oscillation (NPGO) index is derived from http://www.o3d.org/npgo/, covering the period from January 1950 to the present (Fig. 5b). A positive (negative) NPGO phase is defined as those years when the normalized NPGO index is larger (smaller) than zero.
ENSO variability is characterized by the Niño-3.4 SST index, which is defined as region-mean SST anomalies over 5oS-5oN, 120o-170oW. As ENSO has a strong quasi-biennial oscillation feature and has a strong impact on the AL, we remove preceding winter (December-March-mean, DJFM) Niño-3.4 SST index influence from the March AL index and all other variables based on a linear regression. This ensures that the connection of the March AL with the following winter ENSO is independent of the influence of preceding ENSO.
Long-term linear trend and climatological seasonal cycle of all variables have been removed. Significance levels of regression and correlation coefficients are estimated by a two-tailed Student’s t test.
Fisher’s r-z transformation
Fisher’s r-z transformation is used to estimate significance level of the difference between two correlation coefficients (denoted as R1 and R2). The Fisher transform of R1 and R2 can be written as follows59:
Then, the standard parametric test is employed to estimate the null hypothesis of the equality of the Z1 and Z2. The test statistic u is written as:
Here, N1 and N2 are the sizes of the data used to calculate R1 and R2, respectively. The test statistic u is normal distribution. Hence, the significance levels are evaluated based on the two-tailed Student’s t test.
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