Excluding the Kunming station improves the correlation coefficient to 0.84 due to the weak correlation between. Kunming δ18Or and JJAS NINO34. Exclusion of ...
Text S1 Sensitivity test on excluding records from the ASMOI As the low elevation records respond less strongly to ENSO variability, we conducted sensitivity analyses to test whether or not excluding some records, especially low elevation records, changes ASMOI-ENSO correlation statistics significantly. The results suggest that inclusion of the four longest records significantly improves the correlation with JJAS NINO34 (Table S1 and S5). More specifically, when excluding any of the four longest records (Bangkok, Hong Kong, Qiangtang, Lhasa; Table S1) the correlation coefficient between the ASMOI and JJAS NINO34 significantly decreases. Excluding the Kunming station improves the correlation coefficient to 0.84 due to the weak correlation between Kunming δ18Or and JJAS NINO34. Exclusion of each of the other three records does not significantly change the correlation with JJAS NINO34. Large decreases in correlation coefficient and its significant level were observed when the two long records at low elevation (Bangkok and Hong Kong) or the two long records on the Tibetan Plateau (Qiangtang and Lhasa) are excluded from the ASMOI. Although excluding the Dasuopu record alone does not significantly change ASMOI correlation with JJAS NINO34, when all three records on the Tibetan Plateau are removed from the ASMOI the correlation coefficient declines to 0.53. In summary, the integration of records from different locations does improve the correlation of the ASMOI with ENSO variability, with both lowland and high elevation records contributing significantly.
Table S1. Site characteristics, including latitude (Lat, °N), longitude (Lon, °E), altitude (Alt, m), record period, duration of the rainy season (Rainy), and percentage of rainy season precipitation to annual precipitation (Ratio, %), from the six stations and the two ice core sites used to construct the ASMOI. Name Lat Lon Alt Period Rainy Ratio Bangkok 13.73 100.50 2 1979-2014 May-Oct 84.6 Hong Kong 22.37 114.17 66 1979-2013 May-Sep 77.6 Lhasa 29.70 91.13 3649 1986,1989-1993, 1995-2008, 2013-2014 Jun-Sep 87.2 Kunming 25.02 102.68 1892 1986-1992,1996-2003 May-Sep 78.6 Diliman 14.64 121.04 42 2000-2014 Jun-Oct 77.4 Chengdu 30.67 104.02 506 1986-1992,1996,1997 Jun-Sep 77 Dasuopu 28.38 85.72 7200 1979-1996 --Qiangtang 33.30 88.69 5890 1979-2011 ---
Table S2. Gap periods for each GNIP record that were filled when calculating rainy season precipitation δ18O values. Station Gaps Bangkok 1981/6-7, 1981/9-10, 1990/6, 2014/10 Hong Kong 1979/5, 1982/7-8, 2005/7 Lhasa 1988/8 Kunming 1992/5, 1998/6 Diliman 2008/10 Chengdu 1986/6
Table S3. Correlation coefficient between JJAS NINO34 and weighted (r_w) or arithmetic (r_a) mean rainy season precipitation δ18O values from the six stations and the two ice core sites. Boldface italic, boldface, and italic values indicate correlation coefficients exceeding the 99%, 95%, and 90% confidence levels, respectively. Bangkok Hong Kong Lhasa Kunming Diliman Chengdu Dasuopu Qiangtang r_w 0.63 0.11 0.63 0.56 0.66 0.64 0.59 0.67 r_a 0.76 0.48 0.43 0.57 --0.55 0.62
Table S4. Linear temporal trend (‰ per decade) of δ18Or at each site and the p value (F-test). Bongkok Hong Kong Lhasa Kunming Diliman Chengdu Dasuopu Trend 0.01 -0.24 0.18 0.36 -0.27 1.20 0.15 p 0.97 0.19 0.72 0.49 0.65 0.36 0.90
Qiangtang -0.71 0.11
Table S5 Correlation coefficient and its significant level (-log(p), in which p is the F-test derived significant value) between the ASMOI and JJAS NINO34 when excluding one or more stations from the ASMOI. Station(s) listed in the table are those excluded stations. Excluded Station(s) r -log(p) 0.75 6.97 Bangkok 0.76 7.00 Hong Kong 0.77 7.35 Qiangtang 0.74 6.60 Lhasa 0.80 8.28 Dasuopu 0.79 8.10 Diliman 0.84 9.77 Kunming 0.80 8.29 Chengdu 0.68 5.38 Bangkok and Hong Kong 0.64 4.40 Bangkok, Hong Kong, and Diliman 0.70 5.64 Qiangtang and Lhasa 0.75 6.89 Qiangtang and Dasuopu 0.74 6.62 Lhasa and Dasuopu 0.53 3.05 Qiangtang, Lhasa, and Dasuopu
Fig. S1. Left panel: Locations of air parcels (dots) 72 (a), 120 (b), and 168 (c) hours prior arrival at Lhasa for high-δ18Or years. Right panel: Same as left panel, but for low-δ18Or years. Stars denote the location of Lhasa. Black lines denote the 3000 m above sea level contour, delineating the TP.
Fig. S2. Spatial distribution of trajectory frequency indicting how frequently trajectories travel across a location on the map during high- (a) and low- (b) δ18Or years for trajectories starting at Lhasa. Stars denote the location of Lhasa. Black lines denote the 3000 m above sea level contour, delineating the TP.
Fig. S3. Same as Fig. S1, but for trajectories starting at Bangkok.
Fig. S4. Same as Fig. S2, but for trajectories starting at Bangkok.
Fig. S5. Same as Fig. S1, but for trajectories starting at Hong Kong.
Fig. S6. Same as Fig. S2, but for trajectories starting at Hong Kong during high- (a) and low- (b) δ18Or years as well as May-Sep 1998 (c).
Fig. S7. Spatial distribution of cloud top pressure (CTP, hPa) anomaly (a) and total column vapor divergence (10-5 kg/m2/s) anomaly (b) during May-Sep 1998. Stars denote the location of Hong Kong.
Fig. S8. Time series for the ASM precipitation δ18O index (ASMOI). The dashed line indicates the linear trend of the ASMOI.
