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--> --> -->In recent years, changes in the precipitation phase in the Arctic have attracted considerable attention and substantial research efforts (e.g., Screen and Simmonds, 2012; Berghuijs et al., 2014; Dou et al., 2019). In recent decades, increasing amounts of precipitation have fallen as rain, leading to earlier snow melt and increased spring river runoff (Barnett et al., 2005; Knowles et al., 2006; Dou et al., 2019). At the same time, a decline in snowfall was shown to be a significant contributor to the thinning of sea ice (Screen and Simmonds, 2012). According to a recent CMIP5 model evaluation, rain will likely become the dominant phase of precipitation in the Arctic by 2091?2100, under the RCP8.5 scenario (Bintanja and Andry, 2017).
Like the Arctic, West Antarctica and the Antarctic Peninsula (AP) have been characterized by large-scale climate shifts since the 1950s (Vaughan et al., 2003; Turner et al., 2005; Steig et al., 2009; Thomas et al., 2009). However, only few studies on Antarctic precipitation phase changes have been conducted (e.g., Turner et al., 1997; Kirchg??ner, 2011). Significant shifts in the precipitation phase can be found only in areas where the summer mean temperature is close to the freezing point, thereby limiting this phenomenon within the western AP region. At Vernadsky Station (previously named Faraday Station), it was found that the proportion of liquid to solid precipitation increased by 2.1% over the summers of 1956?93, based on the observation of precipitation events (Turner et al., 1997). The proportion of annual (as well as spring and autumn) non-frozen precipitation events at Vernadsky Station also increased significantly during 1960?99 (Kirchg??ner, 2011) as a consequence of the increased air temperature. However, since the late 1990s, there has been statistically significant cooling on the AP due to natural variability (Hawkins et al., 2016; Turner et al., 2016), which may influence the precipitation phase and lead to consequential impacts on glaciers, ice shelves, biodiversity etc. Thus, it is necessary to analyze the changes in the precipitation phase over the coastal western AP in recent years.
A primary factor controlling the rainfall or snowfall change is the air temperature, because the water phase is temperature-dependent (Stewart, 1992; Sankaré and Thériault, 2016). As a result of this relationship, a broadly warming climate will lead to more liquid-phase rain (Bintanja and Andry, 2017). However, the background atmospheric circulation is also essential for precipitation changes in a specific region through controlling local weather (especially air temperature and wind) and cloud physics, such as the El Ni?o?Southern Oscillation (ENSO), Southern Annular Mode (SAM) and Amundsen Sea low (ASL) (e.g., Fyke et al., 2017; Marshall et al., 2017; Paolo et al., 2018). ENSO may lead to great climate fluctuations around Antarctica (e.g., Schneider et al., 2012; Raphael et al., 2016). The SAM has a strong influence on the climate variability in the Southern Hemisphere (e.g., Marshall, 2003). Additionally, by controlling the meridional winds, the ASL can modulate the air temperature, sea ice, and precipitation in the coastal and ice-shelf regions from the AP to the Ross Sea (Hosking et al., 2013); therefore, the ASL is considered a major driver of climate variability in West Antarctica (Turner et al., 2013; Hosking et al., 2016; Deb et al., 2018).
In this paper, we analyze the total precipitation amount, total precipitation days, and changes in rainfall and snowfall days at the Great Wall Station, based on observational records during the austral summers of 1985?2014. We investigate the causal factors by characterizing the relationships between the precipitation phase, surface air temperature and large-scale circulation at the Great Wall Station. In the conclusion, we emphasize the importance of the ASL location to recent precipitation phase changes at the Great Wall Station.
Figure1. (a) Location of Great Wall Station on the AP. (b) Average precipitation amount and number of precipitation days in summer for the years 1985?2014. (c) Summer longitudinal locations of the ASL (top); average summer surface air temperature (middle); number of rain days (dark line) and snow days (light line) (bottom) at Great Wall Station (1985?2014).In this paper, precipitation days are defined as the total number of precipitation (rain or snow) days accumulated over the austral summer, while rain (snow) days are defined as the number of days with rain (snow). As rain and snow events could overlap on the same day, the sum of rain days and snow days was larger than the number of precipitation days. Because of strong local winds, snow can be periodically blown into and out of the precipitation gauge, resulting in large uncertainty in the precipitation record; thus, the precipitation phase was also recorded by an observer as a supplement. Additionally, in this paper, instead of directly using the precipitation quantity, we herein apply precipitation days to study the changes in precipitation phase. The precipitation and synoptic records at Great Wall Station are available through the WMO GTS system (No. 89058) and the Chinese National Arctic and Antarctic Data Center (
ERA-Interim daily data from December to February in 1985 to 2014 were analyzed, including precipitation (mm d?1) and snowfall as water equivalents (mm d?1) for 1200 UTC and 0000 UTC, at time steps of 12 h (Dee et al., 2011). We summed the 12-h accumulated totals for 1200 UTC and 0000 UTC to give the daily accumulated precipitation and snowfall as water equivalents (Screen and Simmonds, 2012). The rainfall data were obtained by subtracting the snowfall from the total precipitation in the ERA-Interim data. Screen and Simmonds (2012) found no obvious tendencies or discontinuities in the rainfall/snowfall to precipitation ratio (RPR) difference between observations and ERA-Interim as a function of time. The monthly means of the daily mean sea level pressure (MSLP) and 10-m wind data were also used in this study. The ERA-Interim data used for this analysis are available from the ECMWF (
Monthly sea surface temperature (SST) anomalies (1981?2010 base period) in the Ni?o3.4 region (5°N?5°S, 120°?170°W) were our primary metric for ENSO. The monthly SAM index was created by Marshall (2003). The monthly ASL index was created by Hosking et al. (2016), in which the background signal of the SAM is removed. The central pressure index was simply defined as the central pressure of the ASL. The latitude and longitude of the ASL were identified using a minimum finding algorithm within the ASL sector, defined as the range of 170°?298°E and 80°?60°S (Hosking et al., 2016). The SST anomaly data can be downloaded from the NCEP (
In this study, we applied a least-squares linear regression method for linear trend analysis, which was tested using an ordinary Student’s t-test. If the trends were significant at the 95% confidence level or higher, then the changes were considered statistically significant. When analyzing the data for the years 1985?2001 and for the years 2001?14, 2001 is included in both periods.
3.1. Precipitation variability
The annual average total summer precipitation during 1985?2014 was 136.3 mm, a value that may have large uncertainty, as pointed out above. The average numbers of summer precipitation days, rain days and snow days observed at Great Wall Station during 1985?2014 were approximately 70.0, 44.7 and 35.7 d yr?1, respectively. During 1985?2014, the trends of the total summer precipitation and total number of summer precipitation days were statistically non-significant (Fig. 1b). However, the trends of the total number of summer rain days and snow days exhibited a clear shift around 2001 (Fig. 1c), with the onset of significantly decreasing total summer rain days and significantly increasing total summer snow days (Figs. 2a and b). During 1985?2001, the total summer rain days displayed a non-significant trend of 5.17 d (10 yr)?1. After 2001, the number of summer rain days decreased significantly at a rate of ?14.13 d (10 yr)?1 (p < 0.05) (Table 1). In contrast to rain days, the total summer snow days decreased with a non-significant trend of ?0.29 d (10 yr)?1 during 1985?2001. However, after 2001, snow days began to increase significantly at a rate of 14.31 d (10 yr)?1 (p < 0.05) (Table 1). In contrast to the significant rain and snow day trends, the total precipitation and precipitation days displayed no significant trends after 2001 (Table 1). In summary, the observational record indicates that precipitation has occurred increasingly in the form of snow at the Great Wall Station location since 2001 at the expense of rain.| Precipitation [mm (10 yr)?1] | Precipitation days [d (10 yr)?1] | Rain days [d (10 yr)?1] | Snow days [d (10 yr)?1] | Temperature [°C (10 yr)?1] | Longitudinal location of the ASL [° (10 yr)?1] | |
| 1985?2001 | 7.31 | 3.92 | 5.17 | ?0.29 | 0.34 | 15.82 |
| 2001?14 | ?36.37 | ?0.86 | ?14.13* | 14.31* | ?0.46 | ?41.11* |
| 1985?14 | ?0.46 | 0.73 | ?1.09 | 3.84 | ?0.10 | ?9.24 |
Table1. Trends in the longitudinal locations of the ASL, temperature, and rain and snow days at Great Wall Station during 1985?2001, 2001?14 and 1985?2014. An asterisk indicates statistical significance at the greater than 95% confidence level.
Figure2. Trends of (a) rain days and (b) snow days at Great Wall Station in each period. Black squares: statistically significant at the greater than 95% confidence level.Owing to the other stations nearby not recording precipitation observations, the trend of summer RPR in the AP region was calculated with ERA-Interim data to further assess whether or not the phenomenon was accidental. It was found that the summer RPR on the AP increased during 1985?2001 and then decreased significantly during 2001 (Figs. 3a and b), which was consistent with observations at Great Wall Station. Water vapor flux patterns before and after 2001 (Figs. 3c and d) showed that the anomalous water vapor flux around the AP region during the latter period (2001?14) was evidently distinct from that during the earlier period (1985?2001). The anomalous northward water vapor flux emanating from the colder Bellingshausen Sea might also provide a favorable background for more snow days. This further indicates that the switch of precipitation phase not only happened at Great Wall Station, but also appeared over the AP region under the control of colder water vapor flows from the Bellingshausen Sea during 2001?14.
Figure3. Trends of the summer RPR in the AP during (a) 1985?2001 and (b) 2001?14. Dashed shading indicates statistical significance at the greater than 95% confidence level. Anomalous vertically (surface?200 hPa) integrated water vapor fluxes during (c) 1985?2001 and (d) 2001?14. The shading denotes the absolute value of anomalous water vapor fluxes. Anomalous fluxes below 1 g cm?1 s?1 have been omitted.2
3.2. Possible causal factors
Generally, the surface air temperature and tropospheric vertical temperature profiles are the most direct determinants of the precipitation phase (e.g., Stewart, 1992; Ye and Cohen, 2013; Ding et al., 2014; Sankaré and Thériault, 2016). At Great Wall Station, the summer average surface air temperature was approximately 1.3°C during 1985?2014. Within this period, the temperature displayed initially increasing and subsequently decreasing variations—a trend that is consistent with the changes in rain days (Table 1, Fig. 1c). The correlation coefficient between the surface air temperature and rain days/snow days at Great Wall Station is 0.75/?0.53 (p < 0.05), indicating that the surface air temperature was strongly correlated with the precipitation phase during 1985?2014. Considering the air temperature at Great Wall Station may have spatial limitation to explain the precipitation pattern across the AP region, we therefore collected the data of eight WMO meteorological stations on the AP and calculated the general tendency of climate change by composite analysis (Fig. 4). the results showed that the increased summer surface air temperature over the AP clearly reversed after 2001, with a warming rate of 0.36°C (10 yr)?1 during 1984?2001 and a cooling rate of ?0.87°C (10 yr)?1 during 2001?14. It happens that there was a similar case in which Turner et al. (2016) identified an absence of warming over the AP from 1998 to 2014 with observations at six sites along with ice-core records. They suggested that this pattern was a consequence of an increased frequency of cold, east-to-southeasterly winds due to increased cyclonic activities over the northern Weddell Sea.
Figure4. (a) Eight stations on the AP and (b) the summer surface air temperature at these eight stations during 1985?2014.Besides that, it has also been reported that other spatial patterns of large-scale atmospheric circulation also have considerable impacts on Antarctic climate anomalies (e.g., Genthon et al., 2003; Fyke et al., 2017; Marshall et al., 2017). In particular, the Antarctic Oscillation (i.e., SAM) and ENSO are closely associated with the first two leading modes of atmospheric variability in the Antarctic region, respectively (Genthon et al., 2003). Clearly, the effects of ENSO and the SAM can play important roles in modulating Antarctic climate, which also signifies that the influence of these two factors on the climate variability of the AP also deserves investigation. Thus, we performed composite analyses to investigate the circulation and climate influences of ENSO and the SAM, respectively (Figs. 5 and 6). During the El Ni?o years, a north?south anomalous MSLP pattern controls the AP, and meanwhile an anomalous easterly flow prevails around the AP (Fig. 5a). During the La Ni?a years, an east?west anomalous MSLP pattern governs the AP, which induces an anomalous northward flow, facilitating low temperatures and associated snow in this region (Fig. 5b). However, from the time series of Ni?o3.4 index (Fig. 5a), or from the selection of El Ni?o/ La Ni?a years, we can infer that El Ni?o/La Ni?a seems to be able to modulate the variability of air temperature and rain or snow days on interannual time scales, rather than on interdecadal time scales. In particular, there was no significant linear trend in ENSO during the period 2001?14, and thus it cannot explain the trends in rain and snow days or air temperature during this period. As a result, the correlation coefficients of Ni?o3.4 index with rain days (?0.35), snow days (0.02) and air temperature (?0.27) are all non-significant during 2001?14 (Table 2). The results imply that ENSO is not the main reason for the variation of the precipitation phase and the air temperature over the AP.
| Precipitation days | Rain days | Snow days | Air temperature | |
| 1985?2014 Ni?o3.4 | ?0.43* | ?0.22 | ?0.08 | ?0.15 |
| 2001?14 Ni?o3.4 | ?0.44 | ?0.35 | 0.02 | ?0.27 |
| 1985?2014 SAM | 0.39* | 0.47** | ?0.03 | 0.48** |
| 2001?14 SAM | 0.67** | 0.39 | 0.35 | 0.41 |
| 1985?2014 AMO | 0.09 | 0.20 | ?0.01 | 0.003 |
| 2001?14 AMO | ?0.18 | 0.11 | ?0.47 | ?0.10 |
| 1985?2014 IPO | ?0.50** | ?0.22 | ?0.15 | ?0.07 |
| 2001?14 IPO | ?0.46 | ?0.33 | 0.04 | ?0.22 |
Table2. Correlation coefficients of the Ni?o3.4 and SAM indices with the precipitation, rain and snow days, and air temperature during the periods 1985?2014 and 2001?2014. Single and double asterisks denote statistical significance at the greater than 95% and 99% confidence levels, respectively.
Figure5. (a) SST anomalies in the Ni?o3.4 region during the austral summers of 1985?2014 (light lines indicate one standard deviation); and composite anomalous MSLP and 10-m winds for (b) five El Ni?o years (1986, 1991, 1994, 1997, 2009) and (c) five La Ni?a years (1988, 1998, 1999, 2007, 2010).
Figure6. (a) SAM index during the austral summers of 1985?2014 (light lines indicate one standard deviation); and composite summer anomalous MSLP and 10-m winds for (b) the top five highest SAM index years (1998, 1999, 2001, 2007, 2014) and (c) the top five lowest SAM index years (1985, 1986, 1991, 2000, 2005).Similarly, through modulating MSLP anomalies and correspondingly inducing anomalous 10-m winds around the AP (Figs. 6b and c), a higher/lower SAM may, to some extent, manipulates air temperatures and rain/snow days over the AP on interannual time scales. Nevertheless, the SAM seems to be unavailable in adjusting the trend of rain or snow days during the period 2001?14. This SAM?precipitation phase inconsistency in linear trend can also be revealed by the non-significant correlations between the SAM index and rain or snow days during the period 2001?14 (Table 2). Through the above analysis, it can be seen that neither ENSO nor the SAM can account for the changes in the precipitation phase since 2001.
In addition to ENSO and the SAM, the ASL plays an important role in the climate variability of West Antarctica (e.g., Turner et al., 2013). The ASL is near the AP region in summer and promotes warmer northwesterly winds around the AP region (Fig. 5a; Fogt et al., 2012; Turner et al., 2013; Hosking et al., 2016). Hosking et al. (2013) suggested that the location of the ASL may also have important impacts on the surface air temperature and precipitation variability in the AP region. Thus, we analyzed the central pressure and location of the ASL during the past 30 years. It was found that the central pressure showed no obvious change, whereas the movement of the longitudinal location of the ASL showed a clear shift in summer. During 1985?2014, the average summer longitudinal location of the ASL was approximately 249.5°E, with an eastward movement of 15.8° (10 yr)?1 [1754.0 km (10 yr)?1] before 2001 (not significant), after which it started to move significantly westward [?41.1° (10 yr)?1/?4562.5 km (10 yr)?1; p < 0.05] (Table 1, Fig. 1c).
To assess the physical mechanism by which the ASL location affects the precipitation phase at Great Wall Station and in the AP region, the correlation between the ASL location and the summer surface air temperature at Great Wall Station was calculated. The summer air temperature and ASL location were strongly correlated during 2001?14 (with a coefficient of up to 0.62; p < 0.05). During the same period, the rain days/snow days decreased/increased with the westward movement of the ASL, with a coefficient of 0.63/?0.74 (p < 0.05). Scatterplots of rain and snow days versus the ASL longitudinal location (Fig. 7) further reveal that the variations of the rain and snow days are closely connected with the ASL longitudinal location during the period 2001?14. Corresponding to the farther-east ASL (i.e., positive ASL longitudinal location index), rain/snow day index generally appears in the positive/negative quadrant (Fig. 7b). On the contrary, corresponding to the farther-west ASL (i.e., negative ASL longitudinal location index), rain/snow day index generally appears in the negative/positive quadrant (Fig. 7b). As such, rain and snow days can be linearly fitted by the ASL longitudinal location during the period 2001?14 (Fig. 7b). For the period 1985?2014, the relationship between the ASL longitudinal location and rain or snow days was relatively weak (Fig. 7a), which was consistent with the non-significant trend of rain days and snow days during 1985?2014. Following the movement of the ASL, the eastern part of the ASL showed higher MSLP values, and the cyclones over the Weddell Sea became more active, favoring the advection of cold polar air masses over Great Wall Station and even the AP region (Fig. 8).
Figure7. Scatterplots of rain day (red circles) and snow day (blue crosses) indices versus ASL longitudinal location index for the periods (a) 1985?2001 and (b) 2001?14. Red and blue lines in (b) denote the least-squares fitting results for rain and snow day indices, respectively. For the period 1985?2001, the least-squares fittings are non-significant.
Figure8. Summer MSLP and 10-m winds for (a) the 1985?2014 average (red line is ASL; the symbol “L” means the location of the central pressure) and (b) the difference between 1985?2001 and 2001?14.Previous studies have also pointed out that the tropical Atlantic SST and central tropical Pacific SST could generate Rossby wave responses and cause increased advection of warm air to West Antarctica through influencing atmospheric circulation over the Amundsen Sea (Ding et al., 2011; Li et al., 2014; 2015). With respect to surface temperatures over the northern AP, Yu et al. (2012) emphasized the influence of the Pacific?South American (PSA) teleconnection, but they also indicated that such a teleconnection did not show a close relationship with surface temperature over the southern AP and, furthermore, the influence of the large-range PSA teleconnection is relatively weaker than that of the ASL. The abovementioned effects mainly occur during austral wintertime, in which the deepening ASL acts to play a bridging role in linking Atlantic and Pacific SST anomalies with Antarctic climate. The aforementioned previous research motivated us to investigate whether the large-scale SST patterns, such as the Atlantic Multidecadal Oscillation (AMO) and Interdecadal Pacific Oscillation (IPO), can also exert an influence on the change in precipitation phase over the AP region through adjusting the ASL during austral summertime. The results showed that the correlation coefficient between the ASL longitudinal location and the AMO/IPO is only ?0.12/?0.13 for the period 1985?2014, and 0.14/?0.18 for the period 2001?14. These non-significant correlations disclose that the variabilities of the AMO and IPO seem to be unable to dominate the change in the location of the ASL. As such, the AMO or IPO did not show significant correlations with the air temperature at Great Wall Station and relevant rain or snow days for both the whole period (1985?2014) and the period (2001?14) with linearly increasing/decreasing snow/rain days (Table 2). Besides, the AMO index switched to its high-value phase around 1997, whereas the IPO index switched to its low-value phase around 1998. Both of these two abrupt shifts do not match the interdecadal change in rain and snow days around 2001. Moreover, the AMO and IPO indices did not show a clear linear trend after their respective abrupt changes, which does not agree with the clear linear increase/decrease in snow/rain days since 2001.
In summary, ENSO and the SAM, AMO and IPO seem not to be the main reason for the decadal variability of the precipitation phase at Great Wall Station. In contrast, the ASL location is closely linked with this decadal variability in the precipitation phase.
It should be noted that the ASL is essentially a regional pressure anomaly. The ASL seems not to be linked with larger-scale atmospheric circulation, even though it shares part of its area with the SAM. This can be clearly detected in the correlation between the ASL longitudinal location index and SLP field (Fig. 9). This figure shows that significant correlations appear over relatively smaller domains rather than over larger-scale domains (such as the SAM domain). The ASL index is obtained through subtracting the ASL actual central pressure from the area-averaged pressure over the ASL domain (Hosking et al., 2013, 2016). In this manner, the variability of the SAM is removed from that of the ASL. Interestingly, the ASL after removing the larger-scale SAM is responsible for the decadal variability in the precipitation phase at Great Wall Station, which further implies the invalidity of larger-scale climate processes in this context.
Figure9. Distribution of the correlation coefficients between the ASL longitudinal location index and SLP during 1985?2014. Contours are drawn every 0.1. Yellow/red shading denotes positive correlations significant at the 95%/99% confidence level, and blue/purple shading indicates negative correlations significant at the 95%/99% confidence level.Among the large-scale forcing factors (e.g., ENSO, the SAM and the ASL), we found that the longitudinal location of the ASL was the most important in controlling the variation of the precipitation phase in the AP region during 1985?2014. The eastward/westward trend of the ASL before/after 2001 led to a relatively warm/cold wind anomaly near the AP, producing an increased number of rain/snow days during the summertime.
Based on our analysis, we believe that the westward movement of the summer ASL is a supplementary reason for the cooling and the main reason for the changes in the summer precipitation phase over the AP region during the past decade. It should be noted that our analysis was carried out during the summer season only. Thus, further studies are still needed to determine the overall impact of the movement of the ASL location on the annual scale.
This study suggests a potential mechanism for reducing the instability of the AP ice sheet system in recent years, as rainfall can prolong the surface melting of ice sheets (Doyle et al., 2015). Further model studies are needed to clarify the mechanisms relating atmospheric circulation patterns to precipitation phase changes over the AP region, particularly in light of the potentially accelerating anthropogenically forced trends in the broader climate system of the Southern Hemisphere.
Acknowledgements. This research was supported by Strategic Priority Research Program of Chinese Academy of Sciences (XDA20100300), the National Natural Science Foundation of China (Grant No. 41771064) and the Basic Fund of the Chinese Academy of Meteorological Sciences (Grant Nos. 2018Z001 and 2019Z008).
