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Impact of tropical cyclones over the eastern North Pacific on El Niño–Southern Oscillation intensity

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Abstract

Most tropical cyclones (TCs) generated over the eastern North Pacific (ENP) do not make landfall. Consequently, TCs in this basin have received less attention, especially those that occur away from the mainland. Furthermore, there have been few studies of the climatic effects of ENP TCs. This study explores the feedback relationship between ENP TCs and the intensity of the El Niño–Southern Oscillation (ENSO), including El Niño and La Niña events, from the perspective of accumulated cyclone energy (ACE). Observational and modeling results indicate that the ENP ACE 3 months earlier can still affect the intensity of El Niño and La Niña events, although the SST persistence is main contributor. Thereinto, the impact of ENP TCs on El Niño appears to be approximately equal to that on La Niña. Moreover, this impact is independent of the persistence of the sea surface temperature (SST) in the Niño 3.4 region and the Madden–Julian Oscillation. Generally, the greater the ENP ACE, the stronger the El Niño, and the smaller the ENP ACE, the stronger the La Niña; this is especially the case for those TCs that develop over the July‒September period. In addition, results show that the ENP TCs modulate ENSO intensity by changing anomalous zonal wind at the low-level atmospheric layer. And the joint impacts of the low-level zonal wind anomalies on the Walker circulation and the east–west thermocline gradient lead to the time characteristics that ENP TCs lead ENSO intensity by about 3 months.

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Data availability

TCs dataset is available online at https://www.ncdc.noaa.gov/ibtracs/. The daily SST data is available online at https://cds.climate.copernicus.eu/cdsapp#!/home. The daily OLR data is available online at https://psl.noaa.gov/data/gridded/data.interp_OLR.html. The monthly wind dataset is available online at https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.pressure.html. The monthly SST data is available online at https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html. Ocean variables are available online at http://apdrc.soest.hawaii.edu/las/v6/dataset?catitem=4867.

Code availability

Computer code used for the analysis was written in NCL, all types of figures that occur in this study can be found in NCL application examples (available online at https://www.ncl.ucar.edu/Applications/). More specific codes in this study are available to readers upon request.

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Acknowledgements

This work is jointly supported by the National Natural Science Foundation of China (42192555, 42105014), the China Postdoctoral Science Foundation (2021T140302, 2021M701652) and the Fundamental Research Funds for the Central Universities (201962009).

Funding

This work is jointly supported by the National Key R&D Program of China under Grants 2017YFC1501601, the National Natural Science Foundation of China (61827901, 42105014), the China Postdoctoral Science Foundation (2021T140302, 2021M701652) and the Fundamental Research Funds for the Central Universities (201962009).

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Authors and Affiliations

  1. Key Laboratory of Mesoscale Severe Weather/Ministry of Education and School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China

    Qiuyun Wang & Zhe-Min Tan

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  1. Qiuyun Wang
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Contributions

Z-MT and QYW designed the study and contributed to the data analysis, interpretation, and writing of the paper.

Corresponding author

Correspondence to Zhe-Min Tan.

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Appendices

Appendix I : Abbreviations occurred in the text or figures

Abbreviations

Illustrations

Related figures

ENSO

El Niño–Southern Oscillation

–

SST

Sea surface temperature

TC

Tropical cyclone

ENP

Eastern North Pacific

ACE

Accumulated cyclone energy

MJO

Madden–Julian Oscillation

J-A-S

July, August and September

O-N-D

October, November and December

N3.4

Niño 3.4 index

Figures 2, 3, 4, 5, 6

N3.40

Preceding (three months earlier) Niño 3.4 index

N3.4_dN3.40

Niño 3.4 index after removing the preceding Niño 3.4 index

Figure 2

ACE_dN3.40

ENP ACE index after removing the preceding Niño 3.4 index

N3.4(N3.40)

Niño 3.4 indices associated with N3.40

Figures 3, 4, 5, 6

N3.40*

Preceding Niño 3.4 index after removing the preceding ENP ACE, i.e., ACE-independent Niño 3.4 index

N3.4(N3.40*)

Niño 3.4 indices associated with N3.40*

ACE0

Preceding ENP ACE anomalies

N3.4(ACE0)

Niño 3.4 indices associated with ACE0

ACE0*

Preceding ENP ACE anomalies after removing the preceding Niño 3.4 index, i.e., Niño 3.4 index-independent ACE

N3.4(ACE0*)

Niño 3.4 indices associated with ACE0*

ACE0 + N3.40

Factor consists of the preceding ENP ACE and Niño 3.4 index

Figures 4, 5, 6

N3.4(ACE0 + N3.40)

Niño 3.4 index associated with ACE0 + N3.40.,

ACE0* + N3.40

Factor consists of the preceding ACE0* and Niño 3.4 index

N3.4(ACE0* + N3.40)

Niño 3.4 index associated with ACE0* + N3.40

U

Regionally zonal wind anomalies (0°–15°N, 125°W–180°)

Figure 16

Walker

Walker circulation index

U(ACE)

Regionally zonal wind anomalies related to the ENP ACE

ACE_dWalker0

ENP ACE index which the preceding Walker circulation index is removed

Walker_dWalker0

Walker circulation index which the preceding Walker circulation index is removed

THC-E

Eastern thermocline (5°S–5°N, 90°–170°W)

Figure 18

THC-E(ACE)

Eastern thermocline related to the ENP ACE

THCG

East–west thermocline gradient index

ACE_dTHCG0

ENP ACE which the preceding east–west thermocline gradient index is removed

THCG_dTHCG0

East–west thermocline gradient which the preceding east–west thermocline gradient index is removed

Appendix II: Details of experimental set-up

Firstly, because of the limitations of the current models, it’s still a great challenge to examine the effect of TC on ENSO directly using TC/ACE as the initial forcing (Hu et al. 2019; Wang et al. 2019b; Ren et al. 2020; Fang and Zheng 2021; Vidale et al. 2021). Secondly, previous studies (Wang et al. 2019b; Wang and Li 2022a, b, c) have shown that TCs can affect ENSO by modulating wind field. Thirdly, LDEO5 can simulate the response of SST to anomalous wind field, and forecast subsequent SST during ENSO events (Chen et al. 2004; Wang et al. 2019b; Gao et al. 2020). Hence, the experiments applied the surface horizontal wind anomalies related to the different impact factors (based on the simultaneous regression) as initial forcing.

Experiments

Details

Illustrations

Experiment I

Step 1, the surface horizontal wind anomalies related to Niño 3.4 index from 1970 to 2020, as an initial forcing, are added to simulate Niño 3.4 index;

 

Step 2, the discrepancy between the observed and simulated Niño 3.4 indices is corrected

Ensure a better simulation of LDEO5 on Niño 3.4 index

Step 3, the simulated Niño 3.4 index after correction are employed to predict Niño 3.4 index three months later in LDEO5

The role of SST-persistence in the SST three months later

Step 4, Select the forecasting Niño 3.4 index during ENSO developing year

Obtain the Fig. 5a, g

Experiment II

Step 1, the surface horizontal wind anomalies related to Niño 3.4 index after removing the signal of simultaneous ENP ACE (i.e. ACE-independent Niño 3.4 index) from 1970 to 2020, as an initial forcing, are added to simulate Niño 3.4 index;

 

Step 2, the discrepancy between the observed and simulated Niño 3.4 indices is corrected using the correction coefficient obtained by the experiment I

Same correction coefficient as Experiment I can ensure obtained the relative contribution of SST persistence and SST persistence after removing the influence of ENP ACE

Step 3, the simulated Niño 3.4 index after correction are employed to predict Niño 3.4 index three months later in LDEO5

Step 4, Select the forecasting Niño 3.4 index during ENSO developing year

Obtain the Fig. 5b, h

Experiment III

Step 1, the surface horizontal wind anomalies related to the ENP ACE from 1970 to 2020, as an initial forcing, are added to simulate Niño 3.4 index;

 

Step 2, the discrepancy between the observed and simulated Niño 3.4 indices is corrected using the correction coefficient obtained by the experiment I

Same correction coefficient as Experiment I can ensure obtained the relative contribution of ACE and the aforementioned factors

Step 3, the simulated Niño 3.4 index after correction are employed to predict Niño 3.4 index three months later in LDEO5

Step 4, Select the forecasting Niño 3.4 index during ENSO developing year

Obtain the Fig. 5c, i

Experiment IV

Step 1, the surface horizontal wind anomalies related to the ENP ACE after removing the signal of simultaneous Niño 3.4 index (Niño 3.4 index-independent ACE) from 1970 to 2020, as an initial forcing, are added to simulate Niño 3.4 index;

 

Step 2, the discrepancy between the observed and simulated Niño 3.4 indices is corrected using the correction coefficient obtained by the experiment I

Same correction coefficient as Experiment I can ensure obtained the relative contribution of ACE after removing the signal of simultaneous Niño 3.4 SST and the aforementioned factors

Step 3, the simulated Niño 3.4 index after correction are employed to predict Niño 3.4 index three months later in LDEO5

Step 4, Select the forecasting Niño 3.4 index during ENSO developing year

Obtain the Fig. 5d, j

Experiment V

Step 1, the surface horizontal wind anomalies related to the Niño 3.4 index and ENP ACE from 1970 to 2020, as an initial forcing, are added to simulate Niño 3.4 index;

 

Step 2, the discrepancy between the observed and simulated Niño 3.4 indices is corrected using the correction coefficient obtained by the experiment I

Same correction coefficient as Experiment I can ensure obtained the relative contribution of joint factor (Niño 3.4 index and ENP ACE) and the aforementioned factors

Step 3, the simulated Niño 3.4 index after correction are employed to predict Niño 3.4 index three months later in LDEO5

 

Step 4, Select the forecasting Niño 3.4 index during ENSO developing year

Obtain the Fig. 5e, k

Experiment VI

Step 1, the surface horizontal wind anomalies related to ACE-independent Niño 3.4 index and ENP ACE from 1970 to 2020, as an initial forcing, are added to simulate Niño 3.4 index;

 

Step 2, the discrepancy between the observed and simulated Niño 3.4 indices is corrected using the correction coefficient obtained by the experiment I

Same correction coefficient as Experiment I can ensure obtained the relative contribution of joint factor (ACE-independent Niño 3.4 index and ENP ACE) and the aforementioned factors

Step 3, the simulated Niño 3.4 index after correction are employed to predict Niño 3.4 index three months later in LDEO5

 
 

Step 4, Select the forecasting Niño 3.4 index during ENSO developing year

Obtain the Fig. 5f, l

Experiments VII and VIII are similar to VI, but for the joint factors consist of ACE and Niño 3.4 index-independent ACE, and ACE-independent Niño 3.4 index and Niño 3.4 index-independent ACE, respectively

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Wang, Q., Tan, ZM. Impact of tropical cyclones over the eastern North Pacific on El Niño–Southern Oscillation intensity. Clim Dyn 61, 3103–3126 (2023). https://doi.org/10.1007/s00382-023-06723-9

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