3.1. Siberian high
The SH intensity in the various reanalysis data sets is shown by the SLP time series in Figure 2(a). All data sets agree well with each other in terms of the interannual variability. As shown in Table II, the correlation coefficients between the Trenberth–Paolino data and other data sets are significantly high. To depict the interdecadal variability, the SLP time series are smoothed by a 20-year low-pass filter with the minimum slope constraint following Mann (2004). This low-pass method gives the least spurious trends on both ends of the time series. After filtering, the late 20th century decline of the SH in the Trenberth–Paolino data is considerably deeper than that in all other data sets, including HadSLP2 (Figure 2(b)). Except NCEP2, all data sets depict a similar phase of the interdecadal variation during the 1976–2001 period, although the magnitude of the variation in the Trenberth–Paolino data is notably larger than the others.
Table II. Correlation coefficient (r) between reanalysis and observation for the SLP over Siberia and the cold surge frequencies in southeast stations during 1950–2008
|Interannual time scale|
|r[SH(reanalysis × observation)]||0.96||0.92||0.97||0.96||0.95||0.95|
|r[Sg(reanalysis × observation)] Wuhan||—||0.34||0.35||0.24||0.46||0.22|
|r[Sg(reanalysis × observation)] Taipei||—||0.30||0.34||0.22||0.64||0.43|
|r[Sg(reanalysis × observation)] Hong Kong||—||0.45||0.42||0.46||0.24||0.14|
|Interdecadal time scale|
|r[SH(reanalysis × observation)]||0.73||0.30||0.25||0.35||− 0.23||0.20|
|r[Sg(reanalysis × observation)] Wuhan||—||0.47||0.20||− 0.18||0.40||− 0.11|
|r[Sg(reanalysis × observation)] Taipei||—||− 0.21||− 0.19||− 0.13||0.30||0.05|
|r[Sg(reanalysis × observation)] Hong Kong||—||0.70||0.82||0.28||0.35||0.24|
Before 1979, there is a substantial difference between NCEP1 and ERA40/I and between these two reanalyses and the observations (Figure 2(b)). After 1979, all the reanalyses become increasingly consistent with one another and with HadSLP2, but none of them depicts the SH decline as dramatic as that shown in Panagiotopoulos et al. (2005) with the Trenberth–Paolino data. Such an inconsistency is reflected by the relatively low correlation coefficients between the Trenberth–Paolino data and other data sets in the interdecadal time scale compared to the interannual time scale (Table II). In 1979, a global observing system was established during the Global Weather Experiment with the implementation of a comprehensive space-borne observing system. It is plausible that the inclusion of the global observational records beginning from 1979 resulted in considerable differences between surface station-based and assimilation data sets. From Figure 2(a) it is also visible that the SH trend levels off in 1993 in all data sets except the Trenberth–Paolino data. The timing of the difference coincides with the 1994 launch of the second-generation Geostationary Operational Environmental Satellites replacing the first-generation ones that covered 1980–1993. How the upgraded satellite data contributed to such an inconsistency in the post-1993 SH variation is unclear. However, we note that all reanalyses agree well with the HadSLP2, signalling that even different observational data sets can yield different results regarding the SH variability.
Possible causes for the discrepancy in trends between reanalyses have been discussed by a number of studies (Sturaro, 2003; Sterl, 2004; Salstein et al., 2008). Because reanalyses are naturally sensitive to historical changes in the observing system (such as the establishments of global radiosonde network in 1958 and satellite observations thriving since the 1970s), different assimilation schemes between reanalyses could lead to large discrepancies in temporal variations (Bengtsson et al., 2004; Bromwich and Fogt, 2004). For example, NCEP1 only assimilates retrieved atmospheric temperature profiles, while ERA40 includes satellite observations (Cai and Kalnay, 2005; Chen et al., 2008). Furthermore, because NCEP1 was conducted earlier than ERA40, newer observations were purposely excluded in NCEP1 in order to maintain a stable output over time (Sterl, 2004). This might have led to the sudden increase of discrepancies in various fields during the late 1960s between ERA40 and NCEP1. Moreover, as the assimilated surface pressure is linked to the total mass of the atmosphere, errors from any factors affecting the total mass of the atmosphere would likely contribute to the trend of SH and, in turn, affect the trend of cold surge frequencies. For example, NCEP1 only has water vapour data input from radiosonde observations, whereas water vapour retrieved from satellite data is assimilated in ERA40 after 1987, suggesting that more water vapour is included in ERA40 than in NCEP1 (Chen et al., 2008).
3.2. Cold surge frequencies
Next, we evaluate the representation of cold surge frequencies from the six reanalyses. Cold surge frequencies in Wuhan, Taipei, and Hong Kong constructed from reanalyses are shown, respectively, in Figure 2(c)–(e), following Figure 2(a)–(b). It is evident that the cold surge frequency at the three stations (grey line in Figure 2(c)–(e)) decreases considerably during the 1976–2001 period and then increases afterward, except in Hong Kong where cold surges remain less active throughout the 21st century, as was noted in Wu and Leung (2008). Such low-frequency variations in the cold surge frequency are highly correlated with the SH variation (grey line in Figure 2(b)), particularly in Wuhan and Hong Kong where ocean modification of cold surges is minimal. Similar interdecadal correspondence between cold surges and the SH has also been noted in Taiwan by Hong et al. (2008) based on surface observations.
As shown in Table II, the interannual variations of cold surge frequencies are positively correlated with the observations, although the correlation is not as high as that of the SH variations. This may reflect a limitation of the coarse-resolution reanalyses in replicating station records based on which the cold surge criteria are defined. In the interdecadal time scale (i.e. filtered by the 20-year lowpass), the coherence between cold surge frequencies in reanalyses and observation remains weak (Figure 2(c)–(e) and Table II). For example, at Wuhan only NCEP1 and JRA25 give a trend in the post-1980 cold surge frequency similar to that in the observations, albeit weak. The interdecadal variation in ERA40/I and GEOS5 is nearly opposite to the observation (Figure 2(c)). In Taipei, the disagreement among data sets is more serious; only JRA25 depicts a coherent but much weaker trend (Figure 2(d)). In Hong Kong, however, JRA25, NCEP1, and NCEP2 show relatively consistent trends in the cold surge frequency with the observations (Figure 2(e)). Table II summarises the correlation coefficients between reanalyses and observations which support the analysis made in Figure 2.
The reanalyses that are more consistent with the observations in cold surges do not necessarily depict the SH trend better (e.g. NCEP1). Some reanalyses (GEOS5 and ERA40/I) even reveal opposite decadal variations between the SH and the cold surge frequency. Because surface air temperature change in southeast stations is one of the criteria for cold surge identification (i.e. Section 2), the inconsistency between the trend of SH and cold surge frequency in Figure 2 leads to a speculation that reanalyses, which do not represent the SH trend well, may handle surface air temperature in southeast stations differently. Visual inspection of the trend and variability of surface air temperature in Wuhan, Taipei, and Hong Kong (Figure 3(a)–(c)) suggests that, after 1960, all six reanalyses are in good agreement with the station records. In contrast, substantial differences between reanalyses and observations are found over Siberia (Figure 3(d)). Examinations of correlation coefficients (Table III) between the observed surface air temperature and other data sets from Figure 3 indicate that the correlation is consistently lower over Siberia than that in southern Asia, even though the correlation remains significant in all areas (except GEOS5). This contrast between the correlations over Siberia and southeast stations (Table III) suggests that reanalyses represent the surface air temperature better in Southeast Asia than Siberia.
Figure 3. The times series of DJF-averaged temperature in (a) Wuhan, (b) Taipei, and (c) Hong Kong obtained from surface stations (solid thin line) and the reanalyses (dotted thin lines with open circles). The 20-year lowpass filtered temperatures are added in (a)–(c) by thick solid lines with the corresponding colour. (d) Similar to (a)–(c), but for DJF-averaged temperature area-averaged over 92.5°–95°E, 50°–52.5°N (located by a blue open triangle in Figure 4; a centre over Siberia with largest decline of SLP trend). The legend for those thick lines and thin lines is given in the lower right in (a)–(d) and atop (a). The time period between 1976 and 2001 where the great SH decline was noted is shaded in light yellow. This figure is available in colour online at wileyonlinelibrary.com/journal/joc
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Table III. Correlation coefficient (r) between reanalysis and observation for the surface air temperature in southeast stations and Siberia during 1950–2008
|Interannual time scale|
|r[Ts(reanalysis × observation)] Wuhan||0.76||0.84||0.93||0.96||0.94|
|r[Ts(reanalysis × observation)] Taipei||0.90||0.85||0.88||0.91||0.82|
|r[Ts(reanalysis × observation)] Hong Kong||0.86||0.89||0.73||0.90||0.88|
|r[ΔTs(reanalysis × observation)] (92.5°–95°E, 50°–52.5°N)||0.63||0.54||0.55||0.49||0.35|
|Interdecadal time scale|
|r[Ts(reanalysis × observation)] Wuhan||0.79||0.89||0.93||0.95||0.97|
|r[Ts(reanalysis × observation)] Taipei||0.95||0.98||0.94||0.99||0.96|
|r[Ts(reanalysis × observation)] Hong Kong||0.84||0.99||0.83||0.90||0.80|
|r[ΔTs(reanalysis × observation)] (92.5°–95°E, 50°–52.5°N)||0.69||0.63||0.56||0.53||0.48|
To further explain the geographic difference of trends in both SLP and surface air temperature between reanalyses and observations, we examine the horizontal distributions of the difference of SLP (i.e. ΔSLP; contours in Figure (4)) and surface air temperature (i.e. ΔTs; shadings in Figure (4)) between the 1979–1993 and 1994–2009 periods. The selection of these two time periods is based on the fact that the SH trend levels off in 1993 in most data sets except the Trenberth–Paolino data (Figure 2(a)). It appears that the magnitudes of ΔSLP between observation and other data sets are larger over Siberia than Southeast Asia. A clear anomalous low of ΔSLP located over Siberia is revealed in the Trenberth–Paolino data (Figure 4(a)), but none of the reanalyses (Figure 4(b)–(f)) depicts this anomalous low with the same intensity. Previous studies (Yang et al., 2002; Inoue and Matsumoto, 2004) have also noted a similar discrepancy of SLP of NCEP1 in the region.
Figure 4. Decadal change of DJF averaged [SLP (contour), Ts (shading)] between the time periods of 1979–1993 and 1994–2009 for observation and reanalyses. The colour scale of ΔTs is given in the right bottom. The contour interval of ΔSLP is 0.5 hPa. The location of Wuhan, Taipei, and Hong Kong is denoted by a red open circle in (a)–(f). The location of blue open triangle within Siberia (blue boxed area) is selected for the analysis of Figure 3(d). This figure is available in colour online at wileyonlinelibrary.com/journal/joc
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It is also revealed in Figure 4 that the magnitude of surface air temperature warming is larger in northern China than Southeast Asia (Wang and Gaffen, 2001). Over Siberia, ΔTs also exhibits pronounced spatial differences between observation and reanalyses, but the differences are not as significant as those revealed in ΔSLP (Figure 4). This explains why the correlation coefficients between observation and reanalyses are lower for the SH trend (seventh row in Table II) than the Ts trend over Siberia (tenth row in Table III). As for the spatial variation of SLP and Ts trends in Southeast Asia shown in Figure 4, the differences between observation and reanalyses are not as significant as those revealed over Siberia.
It is noted that in the inner parts of China such as Wuhan (Figure 3(a)), surface air temperature in the earlier period of NCEP1 (before 1960) does not correspond well with the observation, possibly due to insufficient early observations. Also noteworthy is the widespread warming in all data sets accompanied by intensified warming corresponding to the SH declining period (1978–2001). The warming levelled off at the end of the 20th century and began to cool from 2000 onward. Such temperature characteristics are consistent with those observed worldwide (Knight et al., 2009). More importantly, the consistency in surface temperature speaks for the capability of reanalyses in replicating surface features over Southeast Asia, despite their general disagreements in the variations of the SH and Southeast Asian cold surge frequency. The documented discrepancies ought to be taken into account in future research for the EAWM interdecadal variability.