Water vapor isotopes indicating rapid shift among multiple moisture sources for the 2018/2019 winter extreme precipitation events in Southeast China

In the East Asian monsoon region, winter extreme precipitation events occasionally occur and bring great social and economic losses. From December 2018 to February 2019, Southeast China experienced a record-breaking number of extreme precipitation events. In this study, we analyzed the variation of water vapor isotopes and their controlling factors during the extreme precipitation events in Nanjing, Southeast China. The results show that the variations of water vapor 15 isotopes are closely linked to the change of moisture sources. Using a water vapor d-excess weighted trajectory model, we identified five most important moisture source regions: South China, East China Sea, South China Sea, Bay of Bengal, and Continental regions (Northwest China and Mongolia). Moreover, the variations of water vapor d-excess during a precipitation event reflect rapid shifts of moisture source regions. These results indicate that rapid shifts among multiple moisture sources are important conditions for sustaining wintertime extreme precipitation events over extended periods. 20


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In order to trace moisture source, we used the NOAA Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and calculated backward trajectories of air masses associated with individual extreme precipitation events, using the Global Data Assimilation System (GDAS) with a spatial resolution of 1° × 1° as the background meteorological data (ftp://ftp.arl.noaa.gov/pub/archives/gdas1). Eight-day backward trajectories were calculated every one hour with the starting height of 1500 m above ground.

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Based on the HYSPLIT results, we calculated the Concentration Weighted Trajectory (CWT) field at 0.5° × 0.5° resolution to establish potential source regions that influence the isotopic variability of water vapor at the study site (Salamalikis et al., 2015;Bedaso and Wu, 2020;Li et al., 2020b). The CWT (Cij) was calculated with the following equation: where (i, j) are grid indices, k is the trajectory index, K is the total number of trajectories that pass each 0.5° × 0.5° grid, Ck 105 the concentration (d-excess) measured upon arrival of trajectory k, and τijk the residence time of trajectory k in grid cell (i, j).

Meteorological and reanalysis data
We obtained long-term monthly mean     Figure 2 shows the hourly average δ 18 Ov and dv, daily average of δ 18 Op and dp, and hourly air temperature, relative humidity, and precipitation amount for those five events. δ 18 Ov varies from −23.6‰ to −12.4‰ (with an 120 average of −18.1‰), and dv ranges from 16.3‰ to 35.9‰ (with an average of 24.6‰). δ 18 Op has an range from −15.5‰ to −1.3‰ and an average of −7.0‰. dp ranges from 13.5‰ to 32.5‰, with an average of 23.6‰. Stable isotopes in precipitation and water vapor have similar variation pattern. Therefore, only high temporal resolution water vapor isotope data are used for further analysis. Based on the large-scale atmospheric circulation patterns (Fig. 3), we group these precipitation events into three classes.

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The first class (including event a and b) is defined as cold air mass dominated events. The beginning of the precipitation event was characterized by the southerly wind ( Fig. 3a, b) and higher temperature ( Fig. 2a, b). With the invasion of the cold air mass through the majority of the event period, the study site experienced northerly wind and temperature decrease.
Towards the end of the event, the site returned to southerly wind with temperature increase. Under this circulation background, the δ 18 Ov value was generally high at the beginning, decreased significantly during the events, and gradually 130 increased again toward the end of the events, whereas the dv value showed the opposite trends ( Fig. 2a, b). The lowest δ 18 Ov and highest dv values were usually observed at the lowest temperature and relative humidity. However, changes in temperature and relative humidity cannot completely explain the variation in the δ 18 Ov and dv values. Air temperature and relative humidity data obtained from the meteorological station only reflect the local conditions at the sampling site (He et al., 2018), and the large-scale atmospheric circulation plays a more important role.

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The second class (event c and d) is defined as warm air mass dominated events. Northerly wind and low temperature occurred only at the beginning of the precipitation event, whereas the majority of the even period was characterized by southerly wind and warm temperature (Fig. 3c, d). Similar to the first class, the δ 18 Ov value was generally high at the beginning, decreased significantly during the events, and gradually increased again toward the end of the events (Fig. 2c).
However, different from the first class, both δ 18 Ov and dv values in this class showed many more fluctuations throughout the 140 event (Fig. 2c, d). The lowest δ 18 Ov and dv values occurred at the lowest temperature and highest relative humidity, it is significantly different from the first class.
In addition to the above two classes, the third class (event e) is characterized by alternating cold and warm air masses.
The event started with northerly wind and low temperature, followed by southerly wind and temperature increase, and ended with northerly wind and temperature decrease (Fig. 3e). The δ 18 Ov value remained constant in the early stage until it 145 decreased suddenly at the end, whereas the dv value showed great fluctuations (Fig. 2e).

Intra-event variation of water vapor isotopic compositions
Significant variations in δ 18 Ov and dv values of water vapor are observed within each event. We used the concentrated rainfall period of each event and divided it into different stages (Fig. 2), based on the above three classifications and the temporal patterns of variation in water vapor δ 18 Ov and dv.

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The first class (Fig. 2a, b) of the precipitation events can be divided into four stages. (In the last stage of event b was not delineated because of missing data.) In stages 1 and 2, the δ 18 Ov values continued to decrease, which was consistent with the temperature effect (i.e. stable isotopes had a significant positive correlation with air temperature). The dv value first decreased (or remained stable), then increased, suggesting a gradual shift of water vapor source from ocean to land (Fig. 2a,   b). Generally, atmospheric water vapor from the dry and cold regions shows a more negative δ 18 O value and relatively high 155 d-excess value (Uemura et al., 2008;Kostrova et al., 2020). In stage 3, the δ 18 Ov and dv value fluctuated without any obvious trends as a whole, mainly due to the mixing of oceanic and inland water vapor. In stage 4, the δ 18 Ov value continued to decline, consistent with the rainout effect (i.e. stable isotopes continued to decrease with the increase of precipitation amount); the dv value was significantly lower than the stage 3 with no obvious trend, reflecting the stable influence of oceanic water vapor (Fig. 2a).

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The second class (Fig. 2c, d)  source from land to ocean. In stage 2, both the δ 18 Ov value and dv values increased, but still lower than the initial value in stage 1, suggesting increasing contribution of local inland water vapor in the mixture of oceanic and inland water vapor. In stage 3, the δ 18 Ov value and dv values showed a downward trend, reflecting the continuous influence of oceanic water vapor.

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The third class (Fig. 2e) of the precipitation event can be divided into two stages. The δ 18 Ov value was stable and relatively high in stage 1, possibly due to the influence of local inland water vapor in South China, where the air temperature remained high, leading to enriched isotopic values. The δ 18 Ov value started to decline in stage 2, likely caused by the rainout effect. The dv value is relatively high in stage 1, likely due the contribution of local inland water vapor. The rapid decrease of dv in the middle may indicate the influence of oceanic water vapor. In stage 2, the dv value decreases rapidly, reflecting the 170 rapid change of moisture sources, and a shift from mixed water vapor to oceanic water vapor.

Moisture sources for five precipitation events
Previous studies demonstrated that seasonal variations in the stable isotopic compositions of precipitation are caused not only by local meteorological conditions (Dansgaard, 1964), but also by the different moisture sources (Bonne et al., 2020).

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The above analysis results show that the large-scale circulation patterns (850 hPa meridional wind and temperature) have an important influence on the event-scale stable isotopes in water vapor, and the variation of meridional wind often corresponds to the change of moisture source regions. Some studies indicated that the air masses could obtain specific isotopic signatures based on the meteorological conditions in the moisture source region before reaching a given sampling site (Salamalikis et al., 2015;Kostrova et al., 2020). Therefore, based on the CWT model, we calculated the dv value concentration fields to 180 investigate differences among moisture source regions and water vapor transport pathways.
As seen in Fig. 4, we identified five major moisture source regions that affect the sampling site base on cluster analysis of backward trajectories during these precipitation events: South China, East China Sea, South China Sea, Bay of Bengal, and Continental regions (Northwest China and Mongolia). The air parcels passing areas indicated with warm colors exhibit high d-excess values in the sampling site (Salamalikis et al., 2015). Trajectories passing North China, Northwest China, and Mongolia correspond to higher dv values in the sampling site (Fig. 4), as they are associated with relatively dry air masses from the inland region. Moisture from other sources show lower dv values, due to higher relative humidity at the oceanic source regions (Fig. 4). These results clearly indicate that the changes of moisture source regions could play an important role in the variation of water vapor isotopic compositions in winter extreme precipitation events. Our observations are in good agreement with the observation of summer extreme precipitation event by Li et al., (2015b). We believe that abundant 190 moisture supply through multiple moisture sources is one of the necessary conditions for the 2018/2019 winter extreme precipitation events to last for a long time. was mainly controlled by the change of moisture sources. Therefore, we believe that the turning points at the blue dashed lines reflected rapid shifts of moisture source regions. In order to verify this hypothesis, we plotted the relationship between the dv value and 850 hPa wind direction in the study region. Figure 5 shows that variation of the dv value is closely related to rapid change in the wind direction, especially near the turning point. For example, during event a (Fig. 5a), the dv value was relatively low in the early stage, and the main wind directions are easterly and southeasterly, reflecting the influence of water 200 vapor from the East China Sea. From the first vertical blue dashed line, the wind direction turned northerly. As a result, the dv value gradually increased and remained high, mainly due to the influence of water vapor transported by cold air mass from Northwest China and Mongolia. In the later stage, the wind direction near the second vertical blue dashed line turned to southerly and southeasterly, and the dv value decreased due to the water vapor from the East China Sea. Therefore, the high temporal resolution dv value in water vapor can be used to identify the rapid shift of moisture source regions during the 205 continuous extreme precipitation process.

Conclusions
In this study, we presented stable isotopes in atmospheric water vapor and precipitation for five extreme winter precipitation