The mechanisms to maintain the summertime low-level anomalous anticyclone (AAC) over western North Pacific (WNP) are clarified, with respect to different El Niño conditions during the decaying summer, JJA(1). Observed El Niño events from 1958-2016 are categorized into fast-decay, slow-decay, and prolonged types, based on ONI in JJA(1). Composite method is utilized to analyze the sea SSTA, low-level atmospheric circulation and rainfall.
The composite fields of the fast-decay events in El Niño developing fall, SON(0), are featured with a strong low-level quadrupled circulation, driven by tripole heating distribution (heating-cooling-heating) over tropical Eastern Pacific (EP)-Eastern Indian Ocean (IO)-Western IO that correspond to warm-cold-warm SSTA. The AAC is located over IO, in the northwestern quarter of the quadruple. In the following winter DJF(1), the cooling center and the AAC migrate eastward from Eastern IO to Western Pacific (WP) because of the abrupt demise of the Eastern IO pole and Wind-Evaporation-SST (WES) feedback. During MAM(1), the local WES feedback over WNP is still clear, but the EP SSTA decays so dramatically that the IO Capacitor Effect (IOCE) manifests significantly.
In JJA(1), the equatorial Pacific turns to a La Niña condition, and the AAC is largely maintained by equatorial atmospheric cooling associated with cold SSTA over EP.
For the slow-decay type, its SSTA and atmospheric circulation patterns are similar to the fast-decay type in the El Niño developing seasons. However, the El Niño condition remains so the WNP AAC is maintained by the WES feedback caused by the warm SSTA over tropical CP. On the other hand, SSTA contrast over WP and northern IO induces easterly anomaly that provides negative vorticity to WNP AAC.
The composite fields of prolonged events show a relatively weak AAC, equatorial Pacific SSTA, and IO SSTA during SON(0), compared with those of fast-decay events.
The AAC does not form clearly until MAM(1), which might be attributed to a band of uniform atmospheric cooling over southern tropical IO in SON(0) and D(0)JF(1). The AAC is then maintained by persistent equatorial Pacific warm SSTA from MAM(1) to SON(1); thus, it stays almost stationary over India and South Asia. Although IO does warm from MAM(1) to JJA(1), they are mainly corresponded to atmospheric cooling, which suggests that IO plays a passive role.
This study shows that WNP AAC in the following summers can be resulted from different mechanisms in different types of El Niño. In the fast-decay type, the AAC is in the subsidence area of the local Hadley cell induced by the enhanced convections over the maritime continent. In the slow-decay and the prolonged types, the AAC is anchored in WNP because the warm phase of ENSO still maintains the WES feedback.
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Appendix
Comparison of the Data Sets
Because ERA-40 and ERA-interim are merged by averaging values in the overlapping time period to cover more El Niño events, we needed to check the consistence of these two datasets. Thus, the root-mean-square error is calculated from ERA-40 and ERA-interim to insure that these two datasets do not differ much from each other. In addition, to show the consistence between the heating field and reanalysis precipitation data, we compared 〈Q1〉, 〈Q2〉 with the precipitation rate from Global Precipitation Climatology Project (GPCP) version 2.3 for the time period from January 1979 to December 2016.
We compared 3-month running averaged data of 850hPa geopotential height, U-wind component and V-U-wind component between ERA-40 and ERA-interim because this study focuses on the evolution of large-scale low-level circulation. Figure 11 shows the root-mean-square of the difference of 850hPa geopotential height between ERA-40 and ERA-interim. The value of the root-mean-square is small over open oceans (1~3 m), and becomes larger in mountain ranges and the extra tropics but it is still relatively smaller than the seasonal mean of 850hPa geopotential height. Figure 14 presents the Hovmöller diagram of the difference of 850hPa geopotential height between two datasets for certain latitudes showing that the value of 850hPa geopotential height in ERA-interim is slightly smaller than the value in ERA-40 in general. Then, we applied the same method to 850hPa U-wind component (Figure 12 and 15) and V-wind component (Figure 13 and 16). The ratios of the root-mean-square to mean value of 850hPa U-wind and V-wind are both larger than the one of 850hPa geopotential height. The Hovmöller diagrams show that the difference of the wind between two datasets is larger in the tropics than the subtropics.
850hPa U-component in ERA-interim is more westerly than ERA-40 in low latitudes (Figure 15). The V-component in ERA-interim is more northerly than ERA-40 in the tropics and southern subtropics, and more southerly in the northern subtropics (Figure 16).
In the following, we compared 〈Q1〉, 〈Q2〉 with precipitation data from a reanalysis dataset, GPCP. According to Yanai et al. 1973, the budgets of Q1 and Q2 can be interpreted as following equations:
𝑄1 = 𝑄𝑅 + 𝐿(𝑐 − 𝑒) − 𝜕
𝜕𝑝𝑠′𝜔′̅̅̅̅̅ (9) 𝑄2 = 𝐿(𝑐 − 𝑒) + 𝐿 𝜕
𝜕𝑝𝑞′𝜔′̅̅̅̅̅̅ (10) Where QR is the heating rate of radiation, L is the specific latent heat for vaporization of water, s’ is the perturbation of the dry static energy, ω’ is the perturbation of the p-velocity, c is the condensation rate per unit mass of air, e is the re-evaporation rate of cloud droplet and q’ is perturbation of the specific humidity. Integrating equation (9) and (10) from ptop to psfc, we obtain
〈𝑄1〉 = 〈𝑄𝑅〉 + 𝐿𝑃 + 𝑆 (11)
〈𝑄2〉 = 𝐿(𝑃 − 𝐸) (12) Where P is the precipitation rate, S is the surface sensible heat flux and E is the evaporation rate. We separately derive the heating rate due to net condensation (LP) by subtracting the radiation heating (〈QR〉) and the surface sensible heat flux (S) from 〈Q1〉 , and subtracting the surface latent heat flux (-LE) from 〈Q2〉. We plotted the composite maps of the major terms in equation (11) and equation (12) for each season (Figure 17-22). Figure 20 and 24 show that the heating rates of net condensation from both 〈Q1〉 and
〈Q2〉 are highly correlated with precipitation rate from GPCP over the tropical region. The relationship between the precipitation data from GPCP and the heating rate of net
condensation derived from 〈Q2〉 is better than the relationship between the precipitation data from GPCP and the heating rate of net condensation derived from 〈Q1〉 in higher latitudes. However, we still chose 〈Q1〉 to represent the large-scale atmospheric heating because the radiation heating is important to the evolution of large-scale circulation besides the heating due to condensation, especially over the areas with little precipitation in the climatology.
Tables
Table 1: The list of each El Niño type. The years with the principle component of the first EOF mode larger than one standard deviation are denoted in red, blue for the second mode, and purple for both.
Years
PC1> 1 STD 1965, 1969, 1979, 1980, 1983, 1987, 1992, 1993, 1997, 1998 PC2> 1 STD 1958, 1970, 1973, 1975, 1988, 1998, 2003, 2008, 2010
Table 2: The list of years that the principle components are larger than one standard deviation (positive only) for the first two modes.
Figures
Figure 1: The evolution of Oceanic Niño Index (ONI) of each El Niño type from JJA (-1) to JJA (2). Figure (a) for the fast-decay type, figure (b) for the slow-decay type, and figure(c) for the prolonged type El Niño. Black triangles indicate D(0)JF(1), and dashed lines indicate JJA(1) of each El Niño event. The line of the mean value of the fast-decay type ends at D(1)JF(2) because the datasets used in this study are from 1958JAN to 2016DEC. Thus, there is no data for year(2) of 2015-2016 event.
Figure 2: Composite maps for the fast-decay type El Niño events from SON(0) to JJA(1). Shadings show SST anomalies (c.i. =0.2°C) and dotted area is for p-value<0.05.
Figure 3: Composite maps for the fast-decay type El Niño events from SON(0) to JJA(1). Shadings for 〈Q1〉 anomalies (c.i. =20 W/m2), vectors for 850hPa wind anomalies (p-value<0.15) and contours for 850hPa stream function anomalies (c.i = 100x106 m2/s; solid lines for positive values, dashed lines for negative values and thick solid lines for zero value).
Figure 4: Same as figure 2 but for the slow-decay type.
Figure 5: Same as figure 3 but for the slow-decay type.
Figure 6: Same as figure 2 but for the prolonged type.
Figure 7: Same as figure 3 but for the prolonged type.
Figure 8: Empirical orthogonal functions (EOF) analysis of JJA 850hPa geopotential height over Asian-Australian monsoon domain (20°S-40°N, 30°-180°E). Figure (a) is the eigenvalue for the largest 10 modes. The contour of figure (b) is the pattern of the 1st EOF mode, and the figure(c) for the 2nd EOF mode. Figure (d) is the normalized principle component of the 1st and the 2nd EOF mode.
Figure 9: Regression maps for the 1st EOF mode. Figure (a) contours for the EOF pattern of 850hPa geopotential height (c.i. = 10 m; solid lines for positive values, dashed lines for negative values and thick solid lines for zero value), and shadings for the regression of SST on normalized principle component of the 1st EOF mode. Figure (b) contours for the regression of 850hPa stream function (c.i. = 50x106 m2/s; solid lines for positive values, dashed lines for negative values and thick solid lines for zero value), and shadings for the regression of 〈Q1〉 on normalized principle component of the 1st EOF mode.
Figure 10: Same as figure 9 but for the 2nd EOF mode.
Figure 11: The comparison of 850hPa geopotential height from 40 and ERA-interim for each season during the overlapping period (January 1979 to August 2002).
Shadings present root-mean-square of the difference between 850hPa geopotential height from ERA-40 and ERA-interim (c.i. = 1 m). Contours shows the seasonal mean of 850hPa geopotential height from ERA-interim (c.i. = 50 m). The gray shadings indicate the areas with a topography higher than 1500 m.
Figure 12: Same as figure 1 but for 850hPa U-wind component (c.i. = 0.2 for shadings;
c.i. = 4 m/s for contours).
Figure 13: Same as figure 1 but for 850hPa V-wind component (c.i. = 0.2 for shadings;
c.i. = 2 m/s for contours).
Figure 14: The Hovmöller diagram for the difference of 850hPa geopotential height between ERA-40 and ERA-interim. Shadings show the evolution of the meridional averaged difference (ERA-interim minus ERA-40, c.i. = 1m) between these two datasets. Contours indicate the 3-month running averaged value of meridional averaged 850hPa geopotential height from ERA-interim (c.i. = 50 m). Figure (a) for 15°S-25°S, figure (b) for 5°S-5°N, and figure (c) for 15°N-25°N.
Figure 15: Same as figure 14 but for 850hPa U-wind component (c.i. = 0.2 for shadings;
c.i. = 4 m/s for contours).
Figure 16: Same as figure 14 but for 850hPa V-wind component (c.i. = 0.2 for shadings;
c.i. = 2 m/s for contours).
Figure 17: Seasonal composite maps for the vertically integrated apparent heat source (〈Q1〉) and GPCP precipitation rate. Shadings for 〈Q1〉 (c.i. = 50 W/m2) and contours for the precipitation rate (c.i. = 50 W/m2, 1 mm/day ~ 29 W/m2).
Figure 18: Seasonal composite maps for the radiation heating (〈QR〉) (c.i. = 50 W/m2).
Figure 19: Seasonal composite maps for the net condensation heating (LP) derived from 〈Q1〉 and GPCP precipitation rate. Shadings for LP (c.i. = 50 W/m2) and contours for the precipitation rate (c.i. = 50 W/m2).
Figure 20: Shading shows the correlation coefficient between precipitation data from GPCP and the heating rate of net condensation derived from 〈Q1〉 for each season.
Contours indicate the annual mean precipitation rate from GPCP (c.i. = 50 W/m2).
Figure 21: Seasonal composite maps for the vertically integrated apparent moisture sink (〈Q2〉) and GPCP precipitation rate. Shadings for 〈Q2〉 (c.i. = 50 W/m2) and contours for the precipitation rate (c.i. = 50 W/m2).
Figure 22: Seasonal composite maps for the surface latent heat flux (-LE) (c.i. = 50 W/m2).
Figure 23: Seasonal composite maps for the net condensation heating (LP) derived from 〈Q2〉 and GPCP precipitation rate. Shadings for LP (c.i. = 50 W/m2) and contours for the precipitation rate (c.i. = 50 W/m2).
Figure 24: Shading shows the correlation coefficient between precipitation data from GPCP and the heating rate of net condensation derived from 〈Q2〉 for each season.
Contours indicate the annual mean precipitation rate from GPCP (c.i. = 50 W/m2).