Abstract
The East Asian subtropical westerly jet (EASWJ) is one of the most crucial subtropical circulation systems affecting the precipitation over East Asia. Based on the ERA5 dataset, the dominant modes of the intraseasonal variability of the EASWJ in early (May and June) and late (July and August) summer are investigated, respectively, through the empirical orthogonal function (EOF) analysis. The EOF1 in early summer is characterized by the anomalous westerlies centered over the North China and anomalous easterlies centered over the south of Japan. This mode is led by the south-eastward propagating wave train initiating from the Barents Sea, where the ridge of the wave train coincides with an anomalous warm advection in the low level, due to the negative phase of Arctic Dipole (AD). Since lag − 4 days, the East Asia/Western Ruassia (EAWR) teleconnection contributes to the wave trains. The EOF1 in late summer is characterized by the anomalous westerlies centered over the south of Baikal and anomalous easterlies centered over the Central China, which is affected by the two wave trains along different directions. One wave train propagate zonally across Eurasia initiated from North Atlantic, where significant signal of East Atlantic (EA) teleconnection is found as a precursor. When the wave train disperses downstream to Eurasia, the EAWR play a dominant role on the growth and persistence of the EASWJ variability. The other one is similar to the East Asia–Pacific (EAP) teleconnection propagating poleward from the Southern Asia and Western North Pacific, where the active convection anomalies may be a key driver. The intraseasonal wave trains that influence the EASWJ are different between early and late summer probably due to the discrepancies of background status, such as the background temperature gradient and the curviness of the jet climatology.
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Data availability
The ECMWF ERA5 reanalysis data is available for public use from their website https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5. Data for CPC indices are available at https://www.cpc.ncep.noaa.gov/products/site_index.shtml. Output data of this study are available from the authors upon request.
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Acknowledgements
We would like to thank the European Centre for Medium–Range Weather Forecasts for providing us the reanalysis data, and Climate Prediction Center for the teleconnection indices. The figures S1 and S2 are produced following the example of “unique_11.ncl” on NCL website (https://www.ncl.ucar.edu/Applications/unique.shtml). This research was jointly supported by National Natural Science Foundation of China (Grant No. 42030601, 41875087, 42175066, 41621005), Shanghai International Science and Technology Partnership Project (Grant No. 21230780200), Shanghai Municipal Natural Science Fund (Grant No. 20ZR1407400) and Shanghai Pujiang Program(Grant No. 20PJ1401600). Xiaodan Chen is supported by the National Natural Science Foundation of China (Grant No. 42105017).
Funding
This work was jointly supported by National Natural Science Foundation of China (Grant No. 42030601, 41875087, 42175066, 41621005), Shanghai International Science and Technology Partnership Project (Grant No. 21230780200), Shanghai Municipal Natural Science Fund (Grant No. 20ZR1407400) and Shanghai Pujiang Program(Grant No. 20PJ1401600). Xiaodan Chen is supported by the National Natural Science Foundation of China (Grant No. 42105017). The authors have no relevant financial or non-financial interests to disclose.
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Appendix
Appendix
Derivation of temperature advection formula:
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1.
The formula of temperature advection:
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2.
We separate all the variables into the climatological mean (overbars) and anomalies (primes)
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4.
Taking the climatological mean of (A4) yields
where
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Zhou, Y., Yuan, J., Wen, Z. et al. The influence of the wave trains on the intraseasonal variability of the East Asian subtropical westerly jet in early and late summer. Clim Dyn 60, 2081–2095 (2023). https://doi.org/10.1007/s00382-022-06412-z
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DOI: https://doi.org/10.1007/s00382-022-06412-z