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The influence of shelf-sea fronts on winter monsoon over East China Sea

Abstract

Strong sea surface temperature fronts in open seas are known to affect the atmosphere. Shelf-sea fronts in winter have comparable strengths, yet their impacts on winds have not been studied. In January of 2012, a persistent, narrow band of cloud stretching 600–1,000 km was observed along the front of East China Sea (ECS). Numerical and analytical models show that the cloud was formed atop a recirculating cell induced by the front and, more generally, that β-plumes of low and high pressures emanate and spread far from fronts. Consistent with the theory, observations show that in ECS at inter-annual time scales, strong fronts co-vary with on-shelf convergent wind, strong northeasterly monsoon, and alongshelf alignment of clouds with low clouds near the coast and higher clouds offshore. Our results suggest that shelf-sea fronts are potentially an important dynamic determinant of climate variability of East Asia.

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Notes

  1. 1.

    Schneider and Lindzen (1976) showed that, in a zonally symmetric model with stable stratification, the usual approximation of an un-changing pressure gradient penetrating into the boundary layer does not hold near the equator. As the ECS coastal front is situated sufficiently off the equator, the approximation is assumed here.

  2. 2.

    The linearization assumes that flow advection (first term in Jacobian) is small compared to the beta term (second term) which is not strictly valid at the frontal scales. Nonetheless, the simple model yields qualitatively useful solutions.

  3. 3.

    The factor (1−k+..) in the above formula for "u" was mistakenly written as (1+k+..) in the Supplementary Material of Chang and Oey 2013; but fortunately the error did not alter their conclusions.

  4. 4.

    The ∇dwT is also strong, but its connection with ∇·τ remains tightly linear (high R2) as described above (Fig. 12e).

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Acknowledgments

Authors thank the reviewers and editor, Dr. Schneider, for their comments which improve the manuscript. LYO thanks Drs Shu-Hua Chen and Su-Chih Yang for help with running WRF. LYO is grateful for the award from the Taiwan Foundation for the Advancement of Outstanding Scholarship. We acknowledge partial support from the National Science Council of Taiwan. All data are public and available from links given in the Method section.

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Correspondence to L.-Y. Oey.

Appendix

Appendix

Estimation of “k”

Figure 14 shows how the “k” in the assumed exponential decay of effects of SST in the lower troposphere is calculated.

Fig. 14
figure14

Perturbation potential temperature (K) profile due to SST front obtained by subtracting the Exp.NCEP θ* from the Exp.OPHI θ*, averaged over the ECS section as in Fig. 9 and also across the coastal front from 121.2 to 122.2°E (black line); it is the temperature in the (model) atmosphere that is due to the SST front. The red line is the least square best-fit curve: θFIT = 0.32 – e −kζ (K), where k = 0.8, ζ = z*E, δE is PBLH ≈ 500 m and z* is vertical coordinate measured upward from the surface taken as the 1,000 hPa. The PBLH (indicated by the dashed line) is the average of Exp.NCEP and Exp.OPHI—both shown in Fig. 9d, averaged over the same cross-section as the temperature

Fig. 15
figure15

The functions ϕ1 = sinζ + (k2/2)·(cosζ − e(1−k)ζ), ϕ2 = (k2/2)sinζ − (cosζ − e(1−k)ζ), and ϕ3 = 1 − e−ζ·[cosζ + ϕ2(ζ;k)/(1 − k+k2/2)) plotted for 0 ≤ ζ, k ≤ 3 showing positive values for ζ < 1.8 and k < 3

Vertical velocity “w”

Figure 16 shows the vertical velocity of the idealized solutions described in Sect. 4.

Fig. 16
figure16

Non-dimensional vertical velocity w (ζ = ∞) outside the PBL in the idealized model of an SST front that is aligned zonally (γ = 0 corresponding to Fig. 11c; left) and at 45° anticlockwise from east (γ = π/4 corresponding to Fig. 11d; right). Shown are total w (top), w due to friction (middle) and w due to the SST front (bottom). For clarity, a smaller domain centered at the front is plotted

Chelton et al. (2007) analysis

In order to ensure the correctness of our algorithm for analyzing the relations between wind stress curl and crosswind gradient of SST, and between wind stress divergence and downwind gradient of SST (referred to below as the “gradient-analysis”), we repeated the analyses of Chelton et al. (2007) and compare our results with theirs in the followings. For our calculations, we used the gridded product of GHRSST at ¼° × ¼° resolution instead of the AMSR-E measurements of SST. For the wind stress, we used the gridded CCMP wind product at ¼° × ¼° resolution together with the bulk wind stress formula given in Oey et al. (2006), instead of the QuikSCAT measurements. The mean wind stress for the same four summer periods analyzed by Chelton et al. are shown in Fig. 17, and these are to be compared with their Fig. 5 (top 4 panels). In this and the below Fig. 17 (left column), the CCMP wind stress has been multiplied by a factor of 1.25 for display purposes to make the magnitudes comparable to the QuikSCAT values; actual wind stresses are used in the gradient-analysis however. Other than this adjustment, it can be seen by comparing Fig. 17 with Fig. 5 of Chelton et al. that the agreements between the two are good. Figure 18 then shows the same gradient-analysis as in Fig. 6 of Chelton et al., and Fig. 19 the same binned scatterplots as their Fig. 7 (lower 2 panels). While the values do not exactly match, the general patterns can be seen to agree well.

Fig. 17
figure17

Mean wind stress vectors and magnitudes (shading and contours) averaged in the same way and for the same summer periods as in top 4 panels of Fig. 5 of Chelton et al. (2007), for the coastal region off California and Oregon, USA. For display purpose, the wind stress has been multiplied by a factor of 1.25 to make colors comparable to those of Chelton et al.

Fig. 18
figure18

Left mean wind stress (×1.25 for display purpose to make values similar to those of Chelton et al. 2007; vectors in N m−2) and SST (shading °C); middle: k·(∇ × τ) (shading in N m−2 per 104 km) and ∇cwT (contours with interval 0.5 °C/100 km); right ∇·τ (shading in N m−2 per 104 km) and ∇dwT (contours with interval 0.5 °C/100 km). To be compared with Fig. 6 of Chelton et al. (2007)

Fig. 19
figure19

Binned scatterplots of (top) wind stress curl k·(∇ × τ) versus crosswind gradient of SST ∇cwT and (bottom) wind stress divergence ∇·τ versus downwind gradient of SST ∇dwT. The anomaly fields are used and calculated as in bottom 2 panels of Fig. 7 of Chelton et al. (2007) using their domain indicated in rectangle in Fig. 18

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Oey, LY., Chang, MC., Huang, SM. et al. The influence of shelf-sea fronts on winter monsoon over East China Sea. Clim Dyn 45, 2047–2068 (2015). https://doi.org/10.1007/s00382-014-2455-3

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Keywords

  • Winter winds and climate
  • SST fronts
  • East Asian winter monsoon
  • Shelf-sea fronts
  • Air–sea coupling
  • Hadley cell