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Supplementary Material Herzschuh Et Al., 2019 Position and Orientation Of Supplementary Material Herzschuh et al., 2019 Position and orientation of the westerly jet determined Holocene rainfall patterns in China Nature Communications Supplementary Tables Supplementary Tab. 1 Summary statistics for canonical correspondence analyses for the whole dataset from China and Mongolia (Cao et al., 2014). Pann – annual precipitation, Mtwa – mean temperature of the warmest month; Mtco – mean temperature of the coldest month; Tann mean annual temperature, Pamjjas – precipitation between March and September, Pamjjas – precipitation between June and August. Results indicate that Pann explains most variance in the modern pollen dataset. Neither temperature nor seasonal precipitation explains more variance. Climatic variables as sole Marginal contribution based on VIF VIF predictor climatic variables Climatic 1/2 variables (without (add Explained Explained Tann) Tann) variance P-value variance P-value (%) (%) Pann 3.8 3.8 1.58 4.9 0.005 1.50 0.005 Mtco 4.3 221.7 1.36 4.2 0.005 0.67 0.005 Mtwa 1.5 116.6 0.61 2.7 0.005 1.30 0.005 Tann - 520.4 - - - - Pamjjas - - 1.50 4.9 0.005 Pjja - - 1.30 4.4 0.005 Cao, X., Herzschuh, U., Telford, R.J., Ni, J. A modern pollen-climate dataset from China and Mongolia: assessing its potential for climate reconstruction. Review of Palaeobotany and Palynology 211, 87-96 (2014). Supplementary Tab. 2 Summary statistics for canonical correspondence analyses for the southern China dataset <30°N (Cao et al., 2014). Pann – annual precipitation, Mtwa – mean temperature of the warmest month; Mtco – mean temperature of the coldest month; Tann mean annual temperature. Results indicate that Pann explains most variance in the modern pollen dataset. Neither temperature nor seasonal precipitation explains more variance. Results indicate that even in the southern part of China precipitation is the variable that explains most variance in the modern pollen dataset. Climatic variables as Marginal contribution based on VIF VIF sole predictor climatic variables Climatic 1/2 variables (without (add Explained Explained Tann) Tann) variance P-value variance (%) P-value (%) Pann 2.5 2.6 0.89 4.5 0.001 1.82 0.001 Mtco 5.0 400.1 0.83 4.1 0.001 1.45 0.001 Mtwa 4.5 342.5 0.80 4.4 0.001 1.50 0.001 Tann - 1355.9 - - - - Supplementary Tab. 3. Supplementary information for each pollen record. We assessed the age-model reliability (age score) and pollen data quality (data score) of each record to be high, intermediate, or low. An age-model was considered highly reliable if it had more than 3 reliable dates within the 10–2 cal ka BP interval, and was considered to be of low reliability if it had only one reliable date, or no dating at all, within that interval. The data quality was considered to be high if it possessed a complete pollen assemblage and original pollen data. The component (Comp) means the selected component of the WA-PLS model; r2 is the coefficient of determination between observed and predicted environmental values; RMSEP is the root mean square error of prediction; RMSEP percentage means the RMSEP as a percentage of the modern annual precipitation gradient range (Pann gradient); Sig. is the p-value of the statistical significance test of reconstruction (Telford & Birks, 2011). Site information Quality of pollen data Reliability of reconstruction ID Site Long. Lat. Alt. Modern Age Data Available No. of Pann Comp. r2 RMSEP RMSEP Sig. Reference Pann score score number Sample gradient percentag e Gunin et al., 1 Achit Nur Lake 90.60 49.50 1435 273 2 2 11 292 60-541 2 0.67 80 16.67 0.01 1999 Blyakharchuk et 4 Akkol Lake 89.63 50.25 2204 411 3 3 44 283 60-541 2 0.67 81 16.84 0.04 al., 2007 Cheng et al., 7 Ayongwama Co 98.20 34.83 4220 357 2 1 17 944 35-1069 1 0.79 115 11.14 0.04 2004 Demske et al., 10 Baikal Lake 105.87 52.08 130 2401 3 3 29 164 93-378 1 0.47 49 17.05 0.01 2005 16 Barkol Lake 92.80 43.62 1575 163 3 2 85 471 35-541 1 0.63 90 17.84 0.06 Tao et al., 2009 Jiang et al., 17 Bayanchagan Lake 115.21 41.65 1355 389 3 2 30 584 111-864 1 0.56 115 15.24 0.25 2006 22 Bosten Lake 86.55 41.97 1050 74 3 3 18 296 47-541 1 0.68 91 18.48 0.95 Xu, 1998 Shan et al., 24 Bunan Lake 90.83 35.95 4876 217 1 2 4 697 40-764 1 0.66 94 12.98 0.16 1996 Li and Yan, 28 Chaiwopu Lake 87.78 43.55 1100 223 1 2 6 254 35-541 1 0.70 89 17.64 0.71 1990 Changjiang 29 121.38 31.62 2 1109 3 3 9 249 488-1913 2 0.81 185 12.97 0.53 Yi et al., 2003 River_1997 Changjiang 30 120.23 32.25 6 1059 3 3 44 356 476-1913 2 0.81 178 12.42 0.74 Yi et al., 2003 River_1998 Gunin et al., 41 Daba Nur Lake 98.79 48.20 2465 365 3 2 12 309 35-541 1 0.67 78 15.39 0.55 1999 Du and Kong, 42 Dabsan Lake 95.50 37.10 2675 58 2 2 4 931 35-1062 1 0.68 115 11.15 0.64 1986 45 Dahaizi Lake 102.67 27.83 3660 1069 3 2 17 654 312-1838 1 0.78 180 11.81 0.59 Li and Liu, 1988 Zhou et al., 46 Dahu Lake 115.03 24.25 250 1780 3 2 15 576 1020-2091 2 0.60 195 18.20 0.37 2004 47 Daihai Lake_2004 112.67 40.58 1220 376 3 3 192 643 35-1129 1 0.62 117 10.67 0.05 Xiao et al., 2004 Liu H.P. et al., 49 Dajiuhu Lake 110.67 31.75 1700 1130 3 2 25 668 144-2011 1 0.81 213 11.39 0.01 2001 50 Dalai Nur Lake 116.58 43.28 1200 349 1 2 3 591 111-1006 2 0.66 102 11.43 0.2 Li et al., 1990 Yan and Xu, 52 Daluoba 88.20 47.83 2020 209 1 2 5 252 60-541 2 0.70 81 16.90 0.08 1992 Zhang et al., 58 Dengjiacun 113.65 34.47 133 661 3 1 9 615 144-1857 2 0.83 159 9.29 0.93 2007 Dodson et al., 61 Dingnan 115.03 24.75 274 1683 3 2 13 579 1020-2091 2 0.61 195 18.17 0.05 2006 Zhang et al., 64 Dongganchi 115.78 39.53 49 509 3 3 42 573 111-1325 1 0.80 115 9.46 0.4 1997 Gunin et al., 66 Dood Nur Lake 99.38 51.33 1538 334 2 2 8 187 37-378 1 0.44 57 16.79 0.01 1999 68 Dunde 96.40 38.10 5325 339 3 3 24 944 35-872 1 0.67 111 13.25 0.66 Liu et al., 1998 Wen and Qiao, 69 Ebinur Lake 82.45 44.55 212 174 3 2 7 235 53-541 2 0.76 81 16.70 0.01 1990 Zhou et al., 71 Erhai Lake 100.20 25.77 1974 785 3 1 40 462 403-1766 1 0.81 152 11.14 0.74 2003 72 Erhailongwan Lake 126.37 42.30 724 813 3 2 8 373 220-1006 1 0.60 94 11.94 0.47 Liu et al., 2008 Meng et al., 78 Ganhai Lake 112.19 38.89 1854 505 3 3 21 714 40-1348 2 0.81 110 8.44 0.16 2007 Firsov et al., 81 Gladkoye Bog 83.33 55.00 80 458 3 3 45 45 96-271 1 0.00 35 19.80 0.68 1982 Shan et al., 82 Gounong Co 92.15 34.63 4670 274 3 3 31 722 47-872 1 0.66 108 13.15 0.02 1996 Blyakharchuk et 83 Grusha Lake 89.42 50.38 2413 336 3 3 13 270 60-541 2 0.68 82 16.99 0.04 al., 2007 Atahan et al., 84 Guangfulin 121.19 31.06 4 1179 2 1 35 296 490-1913 2 0.80 177 12.42 0.59 2008 Yang et al., 87 Gucheng Lake 118.90 31.28 6 1273 2 3 153 371 476-2089 2 0.81 197 12.21 0.87 1996 Gunin et al., 88 Gun Nur Lake 106.60 50.25 600 377 3 3 21 252 35-402 1 0.49 57 15.64 0.1 1999 Chen F.H. et al., 91 Halali 99.78 36.72 3220 360 3 2 2 853 35-1062 1 0.74 116 11.30 1 1991 Wang et al., 95 Haoluku Lake 116.76 42.96 1295 382 2 3 24 595 111-1006 2 0.66 102 11.42 0.87 2001 99 Hemudu 121.35 29.97 6 1412 3 3 23 244 537-2068 2 0.76 180 11.74 0.36 Li et al., 2009 104 Hongyuanbai River 102.53 32.80 3500 772 3 3 33 935 40-1456 2 0.85 122 8.59 0.06 Wang, 1987 107 Huangjiapu 115.15 40.57 500 376 1 1 16 541 111-1051 1 0.62 117 12.42 0.73 Sun et al., 2001 108 Huangsha 113.23 23.13 40 1822 2 2 16 541 1016-2091 2 0.59 193 17.96 0.83 Li, 1991 Huguangyan Maar 111 110.28 21.15 88 1690 3 3 4 429 892-2091 1 0.50 201 16.76 0.59 Lv et al., 2003 Lake 113 Hulun Nur Lake_2006 117.51 49.13 545 284 3 2 103 427 111-710 1 0.49 93 15.49 0.77 Wen et al., 2010 Zhao Y.
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