text:
Volume = {{50}},
Number = {{9-10}},
Pages = {{3485-3503}},
Month = {{MAY}},
Abstract = {{Considering the possible lag of the impact of the winter Arctic
   oscillation (AO) on the subsequent summer climate anomalies over East
   Asia and that the relationship between them may differ during different
   periods, the non-stationary relationship between these systems and the
   corresponding mechanism of connection are investigated in this study. A
   regime shift of the winter AO index was detected around 1988, with mean
   AO indices of -0.69 and 0.10 for 1958-1987 and 1988-2014, respectively,
   and their differences passed the 99\% confidence level. After the regime
   shift and especially since the early 1990s, negative (positive) winter
   AO indices corresponds with positive (negative) height anomalies over
   central East Asia (CEA) during the following summer. Meanwhile, the
   westerly jet stream tends to be northward (southward) and the East Asian
   summer monsoon (EASM) is strong (weak), which contributes to the warm
   (cold) anomalies over central China and the significantly enhanced
   (reduced) rainfall over northeast China. After the early 1990s, the
   enhanced connection of the winter AO and the following summer height
   anomalies over CEA is largely due to the enhanced connection of the
   winter AO and the following Indian summer monsoon (ISM) rainfall during
   the same period. The negative (positive) winter AO favors increasing
   (decreasing) rainfall over the ISM region, which triggers a positive
   (negative) circumglobal teleconnection (CGT), and results in
   significantly positive (negative) height anomalies over CEA. One way the
   winter AO could influence the following ISM rainfall is that the
   negative (positive) winter AO memories will be maintained by the
   positive (negative) sea surface temperature (SST) anomalies over the
   tropical Atlantic of the following summer, which are responsible for the
   La Nia (El Nio) developing phase and increases (decreases) the ISM
   rainfall.}},
Publisher = {{SPRINGER}},
Address = {{233 SPRING ST, NEW YORK, NY 10013 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Gong, ZQ (Corresponding Author), China Meteorol Adm, Natl Climate Ctr, Beijing 100081, Peoples R China.
   Qiao, Shaobo; Hu, Po; Feng, Taichen; Cheng, Jianbo; Han, Zixuan, Lanzhou Univ, Coll Atmospher Sci, Lanzhou, Gansu, Peoples R China.
   Gong, Zhiqiang; Zhi, Rong; Feng, Guolin, China Meteorol Adm, Natl Climate Ctr, Beijing 100081, Peoples R China.
   Feng, Guolin, Yangzhou Univ, Coll Phys Sci \& Technol, Yangzhou, Jiangsu, Peoples R China.}},
DOI = {{10.1007/s00382-017-3818-3}},
ISSN = {{0930-7575}},
EISSN = {{1432-0894}},
Keywords = {{Arctic oscillation; Regime shift; Central East Asia; Indian summer
   monsoon; Tropical Atlantic; Circumglobal teleconnection}},
Keywords-Plus = {{NORTH-ATLANTIC OSCILLATION; PLANETARY WAVE ACTIVITY; AIR-SEA FEEDBACK;
   INTERANNUAL VARIABILITY; ANNULAR MODES; ATMOSPHERIC CIRCULATION;
   TEMPERATURE ANOMALIES; CLIMATE VARIABILITY; INDIAN MONSOON; EARLY 1980S}},
Research-Areas = {{Meteorology \& Atmospheric Sciences}},
Web-of-Science-Categories  = {{Meteorology \& Atmospheric Sciences}},
Author-Email = {{gzq0929@126.com}},
Funding-Acknowledgement = {{National Basic Research Program of China (973 Program)National Basic
   Research Program of China {[}2013CB430204]; National Natural Science
   Foundation of ChinaNational Natural Science Foundation of China
   {[}41530531, 41575082, 41575074, 41475064, 41675107]}},
Funding-Text = {{This study acknowledges the support of the National Basic Research
   Program of China (973 Program) (2013CB430204), National Natural Science
   Foundation of China (41530531, 41575082, 41575074, 41475064, 41675107).}},
Cited-References = {{Adler RF, 2003, J HYDROMETEOROL, V4, P1147, DOI 10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.
   AMBRIZZI T, 1995, J ATMOS SCI, V52, P3661, DOI 10.1175/1520-0469(1995)052<3661:RWPATP>2.0.CO;2.
   BRETHERTON CS, 1992, J CLIMATE, V5, P541, DOI 10.1175/1520-0442(1992)005<0541:AIOMFF>2.0.CO;2.
   CAYAN DR, 1992, J CLIMATE, V5, P354, DOI 10.1175/1520-0442(1992)005<0354:LASHFA>2.0.CO;2.
   Chang TC, 2016, J CLIMATE, V29, P6509, DOI 10.1175/JCLI-D-15-0807.1.
   Chen MY, 2002, J HYDROMETEOROL, V3, P249, DOI 10.1175/1525-7541(2002)003<0249:GLPAYM>2.0.CO;2.
   Chen SF, 2015, J CLIMATE, V28, P485, DOI 10.1175/JCLI-D-14-00422.1.
   Chen W, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2004JD005669.
   Chen W, 2000, ADV ATMOS SCI, V17, P48.
   Chen W, 2015, CLIM DYNAM, V45, P2033, DOI 10.1007/s00382-014-2454-4.
   Chen W, 2013, J CLIMATE, V26, P622, DOI 10.1175/JCLI-D-12-00021.1.
   {[}陈文 Chen Wen], 2002, {[}大气科学, Scientia Atmospherica Sinica], V26, P595.
   Chen W, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008611.
   Choi KS, 2012, CLIM DYNAM, V38, P2613, DOI 10.1007/s00382-011-1088-z.
   Cui YF, 2015, CLIM DYNAM, V45, P1617, DOI 10.1007/s00382-014-2417-9.
   Ding QH, 2005, J CLIMATE, V18, P3483, DOI 10.1175/JCLI3473.1.
   Ding QH, 2011, J CLIMATE, V24, P1878, DOI 10.1175/2011JCLI3621.1.
   Frankignoul C, 1998, J CLIMATE, V11, P2310, DOI 10.1175/1520-0442(1998)011<2310:ASFITN>2.0.CO;2.
   FRANKIGNOUL C, 1985, REV GEOPHYS, V23, P357, DOI 10.1029/RG023i004p00357.
   Gong DY, 2017, CLIM DYNAM, V48, P2471, DOI 10.1007/s00382-016-3216-2.
   Gong DY, 2011, CLIM DYNAM, V37, P2199, DOI 10.1007/s00382-011-1041-1.
   Gong DY, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD002193.
   Gong DY, 2001, GEOPHYS RES LETT, V28, P2073, DOI 10.1029/2000GL012311.
   Gong ZQ, 2014, THEOR APPL CLIMATOL, V117, P195, DOI 10.1007/s00704-013-0997-7.
   Gu W, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL038843.
   Hartmann DL, 1998, J ATMOS SCI, V55, P1303, DOI 10.1175/1520-0469(1998)055<1303:WDZFVI>2.0.CO;2.
   He WP, 2006, ACTA PHYS SIN-CH ED, V55, P6192.
   Hong CC, 2014, J GEOPHYS RES-ATMOS, V119, P3715, DOI 10.1002/2013JD021394.
   Huang BH, 2005, J CLIMATE, V18, P1652, DOI 10.1175/JCLI3368.1.
   Huang JP, 1998, GEOPHYS RES LETT, V25, P2707, DOI 10.1029/98GL01936.
   HURRELL JW, 1995, SCIENCE, V269, P676, DOI 10.1126/science.269.5224.676.
   Hurrell JW, 1997, CLIMATIC CHANGE, V36, P301, DOI 10.1023/A:1005314315270.
   Jeong JH, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL023024.
   Kalnay E, 1996, B AM METEOROL SOC, V77, P437, DOI 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
   Kucharski F, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2007GL033037.
   Kucharski F, 2009, Q J ROY METEOR SOC, V135, P569, DOI 10.1002/qj.406.
   Kumar KK, 1999, SCIENCE, V284, P2156, DOI 10.1126/science.284.5423.2156.
   Kushnir Y, 2002, J CLIMATE, V15, P2233, DOI 10.1175/1520-0442(2002)015<2233:AGRTES>2.0.CO;2.
   Li JP, 2013, GEOPHYS RES LETT, V40, P5497, DOI 10.1002/2013GL057877.
   Li JP, 2003, ADV ATMOS SCI, V20, P661.
   Limpasuvan V, 2000, J CLIMATE, V13, P4414, DOI 10.1175/1520-0442(2000)013<4414:WMAMOC>2.0.CO;2.
   Limpasuvan V, 1999, GEOPHYS RES LETT, V26, P3133, DOI 10.1029/1999GL010478.
   Lo TT, 2008, CLIM DYNAM, V31, P449, DOI 10.1007/s00382-007-0311-4.
   Lu RY, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL027655.
   Ogi M, 2003, GEOPHYS RES LETT, V30, DOI 10.1029/2003GL017280.
   Ogi M, 2004, J METEOROL SOC JPN, V82, P905, DOI 10.2151/jmsj.2004.905.
   Pak G, 2014, J CLIMATE, V27, P8185, DOI 10.1175/JCLI-D-13-00500.1.
   Park TW, 2011, J CLIMATE, V24, P68, DOI 10.1175/2010JCLI3529.1.
   Qian BD, 2003, J CLIMATE, V16, P4108, DOI {[}10.1175/1520-0442(2003)016<4108:SUTAIL>2.0.CO;2, 10.1175/1520-0442(2003)016\<4108:SUTAIL\>2.0.CO;2].
   Qiao SB, 2016, J GEOPHYS RES-ATMOS, V121, P10074, DOI 10.1002/2016JD025007.
   Qiao SB, 2015, ATMOS SCI LETT, V16, P479, DOI 10.1002/asl.585.
   Rodionov S, 2005, ICES J MAR SCI, V62, P328, DOI 10.1016/j.icesjms.2005.01.013.
   Smith TM, 2003, J CLIMATE, V16, P1495, DOI 10.1175/1520-0442-16.10.1495.
   Sung MK, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL027253.
   Thompson DWJ, 2000, J CLIMATE, V13, P1000, DOI 10.1175/1520-0442(2000)013<1000:AMITEC>2.0.CO;2.
   Thompson DWJ, 1998, GEOPHYS RES LETT, V25, P1297, DOI 10.1029/98GL00950.
   Wang B, 2000, J CLIMATE, V13, P1517, DOI 10.1175/1520-0442(2000)013<1517:PEATHD>2.0.CO;2.
   Wang B, 2001, J CLIMATE, V14, P4073, DOI 10.1175/1520-0442(2001)014<4073:IVOTAS>2.0.CO;2.
   Wang B, 2003, J CLIMATE, V16, P1195, DOI 10.1175/1520-0442(2003)16\<1195:AOIAII\>2.0.CO;2.
   Wang H, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2012GL052371.
   Wang L, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2010JD013860.
   Wang L, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2012JD017509.
   Wang L, 2009, J CLIMATE, V22, P4860, DOI 10.1175/2009JCLI2973.1.
   Wang L, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD008054.
   Wang YM, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010929.
   Watanabe M, 2004, J CLIMATE, V17, P4674, DOI 10.1175/JCLI-3228.1.
   Webster PJ, 1999, NATURE, V401, P356, DOI 10.1038/43848.
   Wu RG, 2006, J CLIMATE, V19, P4914, DOI 10.1175/JCLI3904.1.
   Wu RG, 2002, INT J CLIMATOL, V22, P1879, DOI 10.1002/joc.845.
   Wu ZW, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2009JD011733.
   Xu TT, 2016, THEOR APPL CLIMATOL, V124, P1, DOI 10.1007/s00704-015-1396-z.
   Zhang RH, 2015, SCI BULL, V60, P1222, DOI 10.1007/s11434-015-0824-x.
   Zheng F, 2016, J APPL METEOROL CLIM, V55, P1459, DOI 10.1175/JAMC-D-15-0199.1.
   Zuo JQ, 2016, J CLIMATE, V29, P7477, DOI 10.1175/JCLI-D-15-0873.1.
   Zuo JQ, 2015, J CLIMATE, V28, P4015, DOI 10.1175/JCLI-D-14-00687.1.
   Zuo JQ, 2013, ADV ATMOS SCI, V30, P1173, DOI 10.1007/s00376-012-2125-5.
   孙柏民, 1994, Advances in Atmospheric Sciences, V11, P79.}},
Number-of-Cited-References = {{77}},
Times-Cited = {{7}},
Usage-Count-Last-180-days = {{3}},
Usage-Count-Since-2013 = {{25}},
Journal-ISO = {{Clim. Dyn.}},
Doc-Delivery-Number = {{GC2YM}},
Unique-ID = {{ISI:000429650700019}},
DA = {{2020-08-06}},
}

@article{ ISI:000429650700021,
Author = {Zhu, Zhiwei and Li, Tim},
Title = {{Amplified contiguous United States summer rainfall variability induced
   by East Asian monsoon interdecadal change}},
Journal = {{CLIMATE DYNAMICS}},
Year = {{2018}},
Volume = {{50}},
Number = {{9-10}},
Pages = {{3523-3536}},
Month = {{MAY}},
Abstract = {{The strength of the dominant variability of contiguous United States
   (CONUS) summer rainfall during 1960-2013 experiences an interdecadal
   change in the early 1990s. Before the early 1990s, the variation in
   CONUS summer rainfall is relatively small in amplitude (standard
   deviation: 0.64 mm day(-1)), whereas after it amplifies remarkably, with
   its standard deviation (1.31 mm day(-1)) roughly doubling. Observational
   diagnoses and simulation results show that enhanced East Asian
   subtropical monsoon variability plays a direct role in strengthening the
   CONUS summer rainfall dipole variability. Besides, a northward shift of
   the East Asian summer monsoon is also responsible for the amplification
   of the CONUS summer rainfall variability. This northward shift of the
   East Asian rain belt pushes the rainfall perturbation farther to the
   north, much closer to the subtropical East Asian upper-level westerly
   jet stream. As a result, the East Asian subtropical monsoon heating
   induces the upper-level Asia-North America teleconnection pattern more
   effectively, leading to the larger amplitude of CONUS summer rainfall
   variability.}},
Publisher = {{SPRINGER}},
Address = {{233 SPRING ST, NEW YORK, NY 10013 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Zhu, ZW; Li, T (Corresponding Author), Nanjing Univ Informat Sci \& Technol, Joint Int Res Lab Climate \& Environm Change ILCEC, Collaborat Innovat Ctr Forecast \& Evaluat Meteoro, Key Lab Meteorol Disaster,Minist Educ KLME, Nanjing, Jiangsu, Peoples R China.
   Zhu, ZW; Li, T (Corresponding Author), Univ Hawaii Manoa, Dept Atmospher Sci, SOEST, IPRC, 1680 East West Rd,POST Bldg 401, Honolulu, HI 96822 USA.
   Zhu, Zhiwei; Li, Tim, Nanjing Univ Informat Sci \& Technol, Joint Int Res Lab Climate \& Environm Change ILCEC, Collaborat Innovat Ctr Forecast \& Evaluat Meteoro, Key Lab Meteorol Disaster,Minist Educ KLME, Nanjing, Jiangsu, Peoples R China.
   Zhu, Zhiwei; Li, Tim, Univ Hawaii Manoa, Dept Atmospher Sci, SOEST, IPRC, 1680 East West Rd,POST Bldg 401, Honolulu, HI 96822 USA.}},
DOI = {{10.1007/s00382-017-3821-8}},
ISSN = {{0930-7575}},
EISSN = {{1432-0894}},
Keywords = {{Contiguous United States summer rainfall; Rainfall variability;
   Interdecadal change; Asia-North America teleconnection; Upper-level
   westerly jet stream}},
Keywords-Plus = {{WESTERN NORTH PACIFIC; ATLANTIC MULTIDECADAL OSCILLATION; SEA-SURFACE
   TEMPERATURE; SOUTHERN CHINA; CALIFORNIA DROUGHT; INTERANNUAL
   VARIABILITY; DECADAL CHANGE; INDIAN-OCEAN; EL-NINO; PRECIPITATION}},
Research-Areas = {{Meteorology \& Atmospheric Sciences}},
Web-of-Science-Categories  = {{Meteorology \& Atmospheric Sciences}},
Author-Email = {{zhiweizh@hawaii.edu
   timli@hawaii.edu}},
ResearcherID-Numbers = {{Zhu, Zhiwei/N-2679-2016}},
ORCID-Numbers = {{Zhu, Zhiwei/0000-0002-4885-1299}},
Funding-Acknowledgement = {{NSFCNational Natural Science Foundation of China {[}41630423, 41605035];
   National Key Research {[}2017YFA0603802, 2015CB453200]; NSFNational
   Science Foundation (NSF) {[}AGS-1643297, AGS-1565653]; Jiangsu projects
   {[}BK20150062, R2014SCT001]; Priority Academic Program Development of
   Jiangsu Higher Education Institutions (PAPD)}},
Funding-Text = {{Comments from two anonymous reviewers are greatly appreciated. This work
   was supported by NSFC Grant 41630423, National Key Research Projects
   2017YFA0603802 and 2015CB453200, NSFC Grant 41605035, NSF Grants
   AGS-1643297 and AGS-1565653, Jiangsu projects BK20150062 and
   R2014SCT001, and the Priority Academic Program Development of Jiangsu
   Higher Education Institutions (PAPD). This paper is SOEST contribution
   number 10108, IPRC contribution number 1279, and ESMC contribution
   number 171.}},
Cited-References = {{BARNSTON AG, 1987, MON WEATHER REV, V115, P1083, DOI 10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2.
   Chang CP, 2000, J CLIMATE, V13, P4326, DOI 10.1175/1520-0442(2000)013<4326:IAIVOT>2.0.CO;2.
   Chen JP, 2017, CLIM DYNAM, V48, P191, DOI 10.1007/s00382-016-3069-8.
   Chen MY, 2002, J HYDROMETEOROL, V3, P249, DOI 10.1175/1525-7541(2002)003<0249:GLPAYM>2.0.CO;2.
   Cressman GP, 1959, MON WEATHER REV, V87, P367, DOI DOI 10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2.
   Curtis S, 2008, CLIM DYNAM, V30, P343, DOI 10.1007/s00382-007-0295-0.
   Dai AG, 2013, CLIM DYNAM, V41, P633, DOI 10.1007/s00382-012-1446-5.
   Enfield DB, 1996, GEOPHYS RES LETT, V23, P3305, DOI 10.1029/96GL03231.
   Fan K, 2014, METEOROL ATMOS PHYS, V124, P23, DOI 10.1007/s00703-013-0301-5.
   GILL AE, 1980, Q J ROY METEOR SOC, V106, P447, DOI 10.1256/smsqj.44904.
   Gotvald A. J., 2010, EPIC FLOODING GEORGI.
   Griffin D, 2014, GEOPHYS RES LETT, V41, P9017, DOI 10.1002/2014GL062433.
   He JH, 2015, INT J CLIMATOL, V35, P4547, DOI 10.1002/joc.4305.
   HELD IM, 1994, B AM METEOROL SOC, V75, P1825, DOI 10.1175/1520-0477(1994)075<1825:APFTIO>2.0.CO;2.
   Higgins R W, 2000, IMPROVED US PRECIPIT.
   HOREL JD, 1981, MON WEATHER REV, V109, P813, DOI 10.1175/1520-0493(1981)109<0813:PSAPAW>2.0.CO;2.
   Hu Q, 2011, J CLIMATE, V24, P5555, DOI 10.1175/2011JCLI4060.1.
   Kalnay E, 1996, B AM METEOROL SOC, V77, P437, DOI 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
   Kendall M.G., 1975, RANK CORRELATION MET.
   Kwon M, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL023026.
   LEATHERS DJ, 1991, J CLIMATE, V4, P517, DOI 10.1175/1520-0442(1991)004<0517:TPATPA>2.0.CO;2.
   LEPAGE Y, 1971, BIOMETRIKA, V58, P213, DOI 10.2307/2334333.
   Li CH, 2010, J CLIMATE, V23, P825, DOI 10.1175/2009JCLI2762.1.
   Li LF, 2012, CLIM DYNAM, V39, P1401, DOI 10.1007/s00382-011-1214-y.
   Mann HB, 1945, ECONOMETRICA, V13, P245, DOI 10.2307/1907187.
   Manuel J, 2008, ENVIRON HEALTH PERSP, V116, pA168, DOI 10.1289/ehp.116-a168.
   Mao YX, 2015, GEOPHYS RES LETT, V42, P2805, DOI 10.1002/2015GL063456.
   McCabe GJ, 2004, P NATL ACAD SCI USA, V101, P4136, DOI 10.1073/pnas.0306738101.
   Mo KC, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL034656.
   NORTH GR, 1982, MON WEATHER REV, V110, P699, DOI 10.1175/1520-0493(1982)110<0699:SEITEO>2.0.CO;2.
   PALMER TN, 1989, NATURE, V338, P54, DOI 10.1038/338054a0.
   Ren Q, 2017, INT J CLIMATOL, V37, P409, DOI 10.1002/joc.4714.
   ROPELEWSKI CF, 1986, MON WEATHER REV, V114, P2352, DOI 10.1175/1520-0493(1986)114<2352:NAPATP>2.0.CO;2.
   Smith TM, 2008, J CLIMATE, V21, P2283, DOI 10.1175/2007JCLI2100.1.
   Ting MF, 1997, J CLIMATE, V10, P1853, DOI 10.1175/1520-0442(1997)010<1853:SUSPVA>2.0.CO;2.
   Trenberth KE, 1998, J GEOPHYS RES-OCEANS, V103, P14291, DOI 10.1029/97JC01444.
   WALLACE JM, 1981, MON WEATHER REV, V109, P784, DOI 10.1175/1520-0493(1981)109<0784:TITGHF>2.0.CO;2.
   Wang B, 2000, J CLIMATE, V13, P1517, DOI 10.1175/1520-0442(2000)013<1517:PEATHD>2.0.CO;2.
   Wang H, 2010, J HYDROMETEOROL, V11, P1007, DOI 10.1175/2010JHM1229.1.
   Williams AP, 2015, GEOPHYS RES LETT, V42, P6819, DOI 10.1002/2015GL064924.
   Wu B, 2010, J CLIMATE, V23, P2974, DOI 10.1175/2010JCLI3300.1.
   Wu RG, 2012, THEOR APPL CLIMATOL, V108, P119, DOI 10.1007/s00704-011-0519-4.
   Wu RG, 2010, J CLIMATE, V23, P2389, DOI 10.1175/2009JCLI3336.1.
   Xie SP, 2009, J CLIMATE, V22, P730, DOI 10.1175/2008JCLI2544.1.
   YAMAMOTO R, 1986, J METEOROL SOC JPN, V64, P273, DOI 10.2151/jmsj1965.64.2\_273.
   Yim SY, 2008, J CLIMATE, V21, P3165, DOI 10.1175/2007JCLI1948.1.
   Yu JH, 2016, CLIM DYNAM, V46, P865, DOI 10.1007/s00382-015-2618-x.
   Zhang WJ, 2014, GEOPHYS RES LETT, V41, P4020, DOI 10.1002/2014GL060130.
   Zhang YS, 2004, J CLIMATE, V17, P2780, DOI 10.1175/1520-0442(2004)017\<2780:DCOTSS\>2.0.CO;2.
   Zhu ZW, 2012, J TROP METEOROL, V18, P305.
   Zhu ZW, 2016, J CLIMATE, V29, P7313, DOI 10.1175/JCLI-D-16-0137.1.
   Zhu ZW, 2014, J CLIMATE, V27, P1083, DOI 10.1175/JCLI-D-13-00180.1.}},
Number-of-Cited-References = {{52}},
Times-Cited = {{23}},
Usage-Count-Last-180-days = {{2}},
Usage-Count-Since-2013 = {{18}},
Journal-ISO = {{Clim. Dyn.}},
Doc-Delivery-Number = {{GC2YM}},
Unique-ID = {{ISI:000429650700021}},
DA = {{2020-08-06}},
}

@article{ ISI:000428008000016,
Author = {Rehkamp, Sarah and Canning, Patrick},
Title = {{Measuring Embodied Blue Water in American Diets: An EIO Supply Chain
   Approach}},
Journal = {{ECOLOGICAL ECONOMICS}},
Year = {{2018}},
Volume = {{147}},
Pages = {{179-188}},
Month = {{MAY}},
Abstract = {{Food systems worldwide rely on water resources that are facing demand
   from a growing population while regional water supplies are increasingly
   uncertain due to climate change. In this environment, dietary changes
   may have the potential to reduce water used in food production. At the
   same time, it is well established that American diets need to change in
   order to align with Federal healthy eating guidelines. In this article,
   we examine if there are synergies between healthier diets and blue water
   conservation in the U.S. food system. We estimate blue water use by
   supply chain stage using a multi-regional environmental input-output
   model. Then, we link this blue water to individual food items and use
   mathematical optimization to model healthy diet scenarios that meet the
   Dietary Guidelines for Americans. We find that while healthier U.S. diet
   outcomes and blue water conservation can be synergistic, these goals may
   also be competing. Making minimal changes from current American
   consumption to a healthy omnivore or vegetarian diet, blue water use
   increases by 16\%, but the omnivore and vegetarian diets reduce embodied
   blue water by 63 and 66\%, respectively, when the objective is to
   minimize water use.}},
Publisher = {{ELSEVIER SCIENCE BV}},
Address = {{PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Rehkamp, S (Corresponding Author), ERS, USDA, 1400 Independence Ave SW,Mail Stop 1800, Washington, DC 20250 USA.
   Rehkamp, Sarah; Canning, Patrick, ERS, USDA, 1400 Independence Ave SW,Mail Stop 1800, Washington, DC 20250 USA.}},
DOI = {{10.1016/j.ecolecon.2017.12.028}},
ISSN = {{0921-8009}},
EISSN = {{1873-6106}},
Keywords = {{Blue water; Environmental input-output model; Food policy; Healthy diet;
   Sustainability; US food system}},
Keywords-Plus = {{INPUT-OUTPUT-ANALYSIS; GREENHOUSE-GAS EMISSIONS; ENVIRONMENTAL IMPACTS;
   FOOD-CONSUMPTION; SUSTAINABLE DIET; FOOTPRINT; AGGREGATION; ENERGY;
   TRADE; MODEL}},
Research-Areas = {{Environmental Sciences \& Ecology; Business \& Economics}},
Web-of-Science-Categories  = {{Ecology; Economics; Environmental Sciences; Environmental Studies}},
Author-Email = {{sarah.rehkamp@ers.usda.gov
   pcanning@ers.usda.gov}},
Cited-References = {{Baroni L, 2007, EUR J CLIN NUTR, V61, P279, DOI 10.1038/sj.ejcn.1602522.
   Birney CI, 2017, ENVIRON RES LETT, V12, DOI 10.1088/1748-9326/aa8494.
   Blackhurst M, 2010, ENVIRON SCI TECHNOL, V44, P2126, DOI 10.1021/es903147k.
   Canning P., 2017, ERR224 DEP AGR EC RE.
   Canning P, 2016, AGR ECON-BLACKWELL, V47, P505, DOI 10.1111/agec.12250.
   Canning P, 2013, ECON SYST RES, V25, P245, DOI 10.1080/09535314.2012.730991.
   Cazcarro I, 2013, ENVIRON SCI TECHNOL, V47, P12275, DOI 10.1021/es4019964.
   CDC, 2013, NHANES 2007 2008 DIE.
   Dalin C, 2017, NATURE, V543, P700, DOI 10.1038/nature21403.
   Dang Q., 2014, WATER RESOUR RES, V51.
   Daniels PL, 2011, ECON SYST RES, V23, P353, DOI 10.1080/09535314.2011.633500.
   Debaere P, 2017, J ASSOC ENVIRON RESO, V4, P155, DOI 10.1086/689833.
   Dietzenbacher E, 2007, REG STUD, V41, P185, DOI 10.1080/00343400600929077.
   Distefano T, 2017, ECOL ECON, V142, P130, DOI 10.1016/j.ecolecon.2017.06.019.
   Donati M, 2016, APPETITE, V106, P48, DOI 10.1016/j.appet.2016.02.151.
   Duarte R, 2002, ECOL ECON, V43, P71, DOI 10.1016/S0921-8009(02)00183-0.
   Eshel G, 2006, EARTH INTERACT, V10, DOI 10.1175/EI167.1.
   Eshel G, 2014, P NATL ACAD SCI USA, V111, P11996, DOI 10.1073/pnas.1402183111.
   Feng KS, 2011, ECON SYST RES, V23, P371, DOI 10.1080/09535314.2011.638276.
   Gephart JA, 2016, SCI TOTAL ENVIRON, V553, P120, DOI 10.1016/j.scitotenv.2016.02.050.
   GUSSOW JD, 1986, J NUTR EDUC, V18, P1.
   Gustafson D, 2016, SUSTAINABILITY-BASEL, V8, DOI 10.3390/su8030196.
   Hadjikakou M, 2017, ECOL ECON, V131, P119, DOI 10.1016/j.ecolecon.2016.08.006.
   Heller MC, 2015, J IND ECOL, V19, P391, DOI 10.1111/jiec.12174.
   Hoekstra AY, 2012, P NATL ACAD SCI USA, V109, P3232, DOI 10.1073/pnas.1109936109.
   Hoekstra AY, 2011, WATER FOOTPRINT ASSE.
   Kenny J.F., 2009, 1344 US GEOL SURV.
   Kenny J. F., 2004, 4A4 US GEOL SURV.
   Lenzen M, 2001, WATER POLICY, V3, P321, DOI DOI 10.1016/S1366-7017(01)00072-1.
   Lenzen M, 2013, ECON SYST RES, V25, P20, DOI 10.1080/09535314.2013.769938.
   Lenzen M, 2011, ECON SYST RES, V23, P73, DOI 10.1080/09535314.2010.548793.
   LEONTIEF W, 1970, REV ECON STAT, V52, P262, DOI 10.2307/1926294.
   LEONTIEF W, 1967, REV ECON STAT, V49, P412, DOI 10.2307/1926651.
   Leontief W, 1941, STRUCTURE AM EC.
   Leontief W, 1953, STUDIES STRUCTURE AM.
   Lovelace JK, 2009, 20095041 US GEOL SUR.
   Macdiarmid JI, 2012, AM J CLIN NUTR, V96, P632, DOI 10.3945/ajcn.112.038729.
   Marlow HJ, 2009, AM J CLIN NUTR, V89, pS1699, DOI 10.3945/ajcn.2009.26736Z.
   Masset G, 2014, AM J CLIN NUTR, V99, P1460, DOI 10.3945/ajcn.113.077958.
   Mekonnen KM., 2010, VALUE WATER RES REPO, V47.
   Mekonnen M, 2010, VALUE WATER RES REPO, V48.
   Mekonnen MM, 2011, HYDROL EARTH SYST SC, V15, P1577, DOI 10.5194/hess-15-1577-2011.
   Mekonnen MM, 2012, ECOSYSTEMS, V15, P401, DOI 10.1007/s10021-011-9517-8.
   Mubako S, 2013, ECOL ECON, V93, P230, DOI 10.1016/j.ecolecon.2013.06.005.
   National Research Council, 2002, EST WAT US US NEW PA.
   Peters C, 2016, ELEMENTA, P4.
   Peters CJ, 2007, RENEW AGR FOOD SYST, V22, P145, DOI 10.1017/S1742170507001767.
   Pimentel D, 2003, AM J CLIN NUTR, V78, p660S, DOI 10.1093/ajcn/78.3.660S.
   Pinero P, 2015, ECOL ECON, V119, P217, DOI 10.1016/j.ecolecon.2015.09.002.
   Rehkamp S., 2017, FOOD STUD, V7, P11.
   Reynolds CJ, 2015, ECOL ECON, V111, P58, DOI 10.1016/j.ecolecon.2015.01.007.
   Saxe H, 2013, CLIMATIC CHANGE, V116, P249, DOI 10.1007/s10584-012-0495-4.
   Scarborough P, 2014, CLIMATIC CHANGE, V125, P179, DOI 10.1007/s10584-014-1169-1.
   Tom Michelle S., 2016, Environment Systems \& Decisions, V36, P92, DOI 10.1007/s10669-015-9577-y.
   Tukker A, 2011, ECOL ECON, V70, P1776, DOI 10.1016/j.ecolecon.2011.05.001.
   U. S. Deparment of Commerce Bureau of Economic Analysis (DOC-BEA), 2007, INP OUTP ACC.
   U. S. Department of Agriculture Agricultural Research Service Food Surveys Research Group (USDA-ARS-FSRG), 2010, USDA FOOD NUTR DAT D, V4. 1.
   U. S. Department of Agriculture Agricultural Research Service Food Surveys Research Group (USDA-ARS-FSRG), 2010, USDA FOOD NUTR DAT D, V4. 1.
   U. S. Department of Agriculture Agricultural Research Service (USDA-ARS), 2014, FPED 2007 2008 FOOD.
   U.S. Department of Agriculture and U. S. Department of Health and Human Services, 2010, DIET GUID AM 2010.
   U. S. Department of Agriculture Economic Research Service (USDA-ERS), 2013, IRR WAT US.
   U. S. Department of Agriculture National Agricultural Statistics Service (USDA-NASS), 2008, FARM RANCH IRR SURV.
   U. S. Department of the Interior U. S. Geological Survey (DOI-USGS), 2005, WAT US US EST US WAT.
   United Nations European Commission International Monetary Fund Organisation for Economic Co-operation and Development and World Bank (UN EC IMF OECD and World Bank), 2003, STESASTATSERF61REV1, V61.
   United States Department of Agriculture, 2015, 2015 2020 DIET GUID.
   Vanham D, 2016, SCI TOTAL ENVIRON, V573, P96, DOI 10.1016/j.scitotenv.2016.08.111.
   Vanham D, 2013, ENVIRON INT, V61, P45, DOI 10.1016/j.envint.2013.09.011.
   Vanham D, 2013, ECOL INDIC, V32, P1, DOI 10.1016/j.ecolind.2013.02.020.
   Velazquez E, 2006, ECOL ECON, V56, P226, DOI 10.1016/j.ecolecon.2004.09.026.
   Vieux F., 2013, AM J CLIN NUTR.
   Vorosmarty CJ, 2000, SCIENCE, V289, P284, DOI 10.1126/science.289.5477.284.
   Wallen A, 2004, ENVIRON SCI POLICY, V7, P525, DOI 10.1016/j.envsci.2004.08.004.
   Weber CL, 2008, ENVIRON SCI TECHNOL, V42, P3508, DOI 10.1021/es702969f.
   Wiedmann T, 2007, ECOL ECON, V61, P15, DOI 10.1016/j.ecolecon.2006.12.003.
   Zhang C, 2014, ECOL ECON, V100, P159, DOI 10.1016/j.ecolecon.2014.02.006.
   Zhao X, 2009, ECOL MODEL, V220, P245, DOI 10.1016/j.ecolmodel.2008.09.016.}},
Number-of-Cited-References = {{76}},
Times-Cited = {{7}},
Usage-Count-Last-180-days = {{2}},
Usage-Count-Since-2013 = {{25}},
Journal-ISO = {{Ecol. Econ.}},
Doc-Delivery-Number = {{GA0MO}},
Unique-ID = {{ISI:000428008000016}},
DA = {{2020-08-06}},
}

@article{ ISI:000442019700023,
Author = {Nagashima, Fumiya},
Title = {{The sign reversal problem in structural decomposition analysis}},
Journal = {{ENERGY ECONOMICS}},
Year = {{2018}},
Volume = {{72}},
Pages = {{307-312}},
Month = {{MAY}},
Abstract = {{A structural decomposition analysis (SDA) based on the input-output
   model disaggregates fluctuations in the total factor budget into shifts
   in its determinants. The essence of SDA is its ability to quantify the
   critical factors that contribute to changes in phenomena. However, it is
   well-known that various uncertainties are manifest in input-output
   datasets and SDA results may be vulnerable to substantive biases
   including erroneous sign reversals. This study employs Monte Carlo
   simulations and investigates this sign reversal problem. The simulations
   reveal instability in the decomposition results, particularly the
   effects of the intensity term and the economic structure term. In
   contrast, the decomposition effect of the final demand term is
   relatively insusceptible in this regard. (C) 2018 Elsevier B.V. All
   rights reserved.}},
Publisher = {{ELSEVIER SCIENCE BV}},
Address = {{PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Nagashima, F (Corresponding Author), Kyushu Univ, Grad Sch Econ, Higashi Ku, 6-19-1 Hakozaki, Fukuoka 8128581, Japan.
   Nagashima, Fumiya, Kyushu Univ, Grad Sch Econ, Higashi Ku, 6-19-1 Hakozaki, Fukuoka 8128581, Japan.}},
DOI = {{10.1016/j.eneco.2018.04.027}},
ISSN = {{0140-9883}},
EISSN = {{1873-6181}},
Keywords = {{Structural decomposition analysis; Uncertainty; Input-output analysis;
   Monte Carlo analysis; Carbon footprint}},
Keywords-Plus = {{INPUT-OUTPUT MODELS; CARBON EMISSIONS; ENERGY INTENSITY; AGGREGATE
   ENERGY; WATER FOOTPRINT; UNITED-STATES; GLOBAL ENERGY; INDEX;
   CONSUMPTION; CHINA}},
Research-Areas = {{Business \& Economics}},
Web-of-Science-Categories  = {{Economics}},
Author-Email = {{3EC16105M@s.kyushu-u.ac.jp}},
ResearcherID-Numbers = {{Nagashima, Fumiya/F-8987-2018}},
ORCID-Numbers = {{Nagashima, Fumiya/0000-0001-6112-9105}},
Funding-Acknowledgement = {{JSPS Research Fellowships for Young ScientistsMinistry of Education,
   Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for
   the Promotion of Science {[}17J03544]}},
Funding-Text = {{This research was supported by JSPS Research Fellowships for Young
   Scientists (No. 17J03544).}},
Cited-References = {{Akpan US, 2015, ENVIRON RESOUR ECON, V61, P165, DOI 10.1007/s10640-014-9787-7.
   Albrecht J, 2002, ENERG POLICY, V30, P727, DOI 10.1016/S0301-4215(01)00131-8.
   Ang BW, 2007, ENERG POLICY, V35, P238, DOI 10.1016/j.enpol.2005.11.001.
   Ang BW, 1997, ENERGY J, V18, P59.
   Ang BW, 2003, ENERG POLICY, V31, P1561, DOI 10.1016/S0301-4215(02)00206-9.
   Ang BW, 1998, ENERGY, V23, P489, DOI 10.1016/S0360-5442(98)00016-4.
   Ang BW, 2000, ENERGY, V25, P1149, DOI 10.1016/S0360-5442(00)00039-6.
   Arto I, 2014, ENVIRON SCI TECHNOL, V48, P5388, DOI 10.1021/es5005347.
   Caster SD, 1998, ENVIRON RESOUR ECON, V11, P349.
   Choi KH, 2003, ENERG ECON, V25, P615, DOI 10.1016/S0140-9883(03)00038-0.
   de Haan M, 2001, ECON SYST RES, V13, P181, DOI DOI 10.1080/09537320120052452.
   Dietzenbacher E., 2002, TRADE NETWORKS HIERA.
   Dietzenbacher E., 1998, ECON SYST RES, V10, P307, DOI DOI 10.1080/09535319800000023.
   Dietzenbacher E, 2006, J REGIONAL SCI, V46, P773, DOI 10.1111/j.1467-9787.2006.00477.x.
   Dietzenbacher E, 2013, ECON SYST RES, V25, P71, DOI 10.1080/09535314.2012.761180.
   FELDMAN SJ, 1987, REV ECON STAT, V69, P503, DOI 10.2307/1925539.
   Feng KS, 2015, NAT COMMUN, V6, DOI 10.1038/ncomms8714.
   Feng L, 2017, J CLEAN PROD, V163, pS322, DOI 10.1016/j.jclepro.2015.09.047.
   Fujii H, 2017, ENVIRON RES LETT, V12, DOI 10.1088/1748-9326/aa9c66.
   Guan D, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2008GL036540.
   Heijungs R, 2014, INT J LIFE CYCLE ASS, V19, P1445, DOI 10.1007/s11367-014-0751-0.
   Hoekstra R, 2003, ENERG ECON, V25, P39, DOI 10.1016/S0140-9883(02)00059-2.
   Hoekstra R, 2016, ECON SYST RES, V28, P151, DOI 10.1080/09535314.2016.1166099.
   Hogg R, 2012, INTRO MATH STAT.
   Jacobsen H. K., 2000, ECON SYST RES, V12, P319, DOI DOI 10.1080/09535310050120916.
   KAGAWA S, 2004, ECON SYST RES, V16, P279, DOI DOI 10.1080/0953531042000239374.
   Kagawa Shigemi, 2001, ECON SYST RES, V13, P339, DOI DOI 10.1080/09535310120089752.
   Lan J, 2016, APPL ENERG, V163, P436, DOI 10.1016/j.apenergy.2015.10.178.
   Lenzen M, 2006, APPL ENERG, V83, P185, DOI 10.1016/j.apenergy.2005.02.005.
   Lenzen M, 2000, J IND ECOL, V4, P127, DOI DOI 10.1162/10881980052541981.
   Lenzen M, 2016, ECON SYST RES, V28, P119, DOI 10.1080/09535314.2016.1170991.
   Lenzen M, 2013, CLIMATIC CHANGE, V121, P815, DOI 10.1007/s10584-013-0930-1.
   Lenzen M, 2013, ECON SYST RES, V25, P20, DOI 10.1080/09535314.2013.769938.
   Lenzen M, 2012, ENVIRON SCI TECHNOL, V46, P8374, DOI 10.1021/es300171x.
   Lenzen M, 2010, ECON SYST RES, V22, P43, DOI 10.1080/09535311003661226.
   Liao H, 2007, ENERG POLICY, V35, P4640, DOI 10.1016/j.enpol.2007.03.028.
   Malik A, 2016, ECON SYST RES, V28, P168, DOI 10.1080/09535314.2016.1172475.
   Malik A, 2016, ENVIRON SCI TECHNOL, V50, P4722, DOI 10.1021/acs.est.5b06162.
   MARTIN RP, 1992, GROWTH CHANGE, V23, P446, DOI 10.1111/j.1468-2257.1992.tb00944.x.
   Mi ZF, 2017, NAT COMMUN, V8, DOI 10.1038/s41467-017-01820-w.
   Miller R. E., 2009, INPUT OUTPUT ANAL FD.
   Nansai K., 2001, ENERGY RESOUR, V22.
   Nansai K, 2012, ENVIRON SCI TECHNOL, V46, P9146, DOI 10.1021/es2043257.
   Oshita Y, 2012, ENERG ECON, V34, P1041, DOI 10.1016/j.eneco.2011.08.013.
   Peters GP, 2007, ENVIRON SCI TECHNOL, V41, P5939, DOI 10.1021/es070108f.
   ROLANDHOLST DW, 1989, REV ECON STAT, V71, P718, DOI 10.2307/1928119.
   Rose A., 1996, ECON SYST RES, V8, P33, DOI DOI 10.1080/09535319600000003.
   Roson R, 2015, SUSTAINABILITY-BASEL, V7, P5304, DOI 10.3390/su7055304.
   Shapley L. S., 1953, CONTRIBUTIONS THEORY, V2.
   SKOLKA J, 1989, J POLICY MODEL, V11, P45, DOI 10.1016/0161-8938(89)90024-0.
   Su B, 2012, ENERG ECON, V34, P177, DOI 10.1016/j.eneco.2011.10.009.
   Sun JW, 2000, ENERGY, V25, P1177, DOI 10.1016/S0360-5442(00)00038-4.
   Sun JW, 1998, ENERG ECON, V20, P85, DOI 10.1016/S0140-9883(97)00012-1.
   Weber CL, 2009, ENERG POLICY, V37, P1561, DOI 10.1016/j.enpol.2008.12.027.
   WEBER CL, 2008, INT INP OUTP M MAN E.
   Weinzettel J, 2011, J IND ECOL, V15, P893, DOI 10.1111/j.1530-9290.2011.00378.x.
   Wiedmann T, 2007, ECOL ECON, V61, P15, DOI 10.1016/j.ecolecon.2006.12.003.
   Wood R, 2006, ENERG POLICY, V34, P1326, DOI 10.1016/j.enpol.2004.11.010.
   Wood R, 2009, J IND ECOL, V13, P847, DOI 10.1111/j.1530-9290.2009.00177.x.
   Wood R, 2009, ENERG POLICY, V37, P4943, DOI 10.1016/j.enpol.2009.06.060.
   Xu Y, 2014, ECOL ECON, V101, P10, DOI 10.1016/j.ecolecon.2014.02.015.
   Yang L, 2010, CHINA ECON REV, V21, P557, DOI 10.1016/j.chieco.2010.05.012.
   Yoshida Y., 2002, TECHNOLOGY, V8, P229.
   Yu SW, 2016, ECON SYST RES, V28, P273, DOI 10.1080/09535314.2015.1102714.
   Zhang HY, 2014, ECON SYST RES, V26, P119, DOI 10.1080/09535314.2014.880663.
   Zhang ZY, 2012, ENVIRON SCI TECHNOL, V46, P12373, DOI 10.1021/es302576u.}},
Number-of-Cited-References = {{66}},
Times-Cited = {{5}},
Usage-Count-Last-180-days = {{1}},
Usage-Count-Since-2013 = {{15}},
Journal-ISO = {{Energy Econ.}},
Doc-Delivery-Number = {{GQ8PU}},
Unique-ID = {{ISI:000442019700023}},
DA = {{2020-08-06}},
}

@article{ ISI:000431054400002,
Author = {Zhao, X. and Li, Y. P. and Yang, H. and Liu, W. F. and Tillotson, M. R.
   and Guan, D. and Yi, Y. and Wang, H.},
Title = {{Measuring scarce water saving from interregional virtual water flows in
   China}},
Journal = {{ENVIRONMENTAL RESEARCH LETTERS}},
Year = {{2018}},
Volume = {{13}},
Number = {{5}},
Month = {{MAY}},
Abstract = {{Trade of commodities can lead to virtual water flows between trading
   partners. When commodities flow from regions of high water productivity
   to regions of low water productivity, the trade has the potential to
   generate water saving. However, this accounting of water saving does not
   account for the water scarcity status in different regions. It could be
   that the water saving generated from this trade occurs at the expense of
   the intensified water scarcity in the exporting region, and exerts
   limited effect on water stress alleviation in importing regions. In this
   paper, we propose an approach to measure the scarce water saving
   associated with virtual water trade (measuring in water withdrawal/use).
   The scarce water is quantified by multiplying the water use in
   production with the water stress index (WSI). We assessed the scarce
   water saving/loss through interprovincial trade within China using a
   multi-region input-output table from 2010. The results show that
   interprovincial trade resulted in 14.2 km(3) of water loss without
   considering water stress, but only 0.4 km(3) scarce water loss using the
   scarce water concept. Among the 435 total connections of virtual water
   flows, 254 connections contributed to 20.2 km(3) of scarce water saving.
   Most of these connections are virtual water flows from provinces with
   lower WSI to that with higherWSI. Conversely, 175 connections
   contributed to 20.6 km(3) of scarce water loss. The virtual water flow
   connections between Xinjiang and other provinces stood out as the
   biggest contributors, accounting for 66\% of total scarce water loss.
   The results show the importance of assessing water savings generated
   from trade with consideration of both water scarcity status and water
   productivity across regions. Identifying key connections of scarce water
   saving is useful in guiding interregional economic restructuring towards
   water stress alleviation, a major goal of China's sustainable
   development strategy.}},
Publisher = {{IOP PUBLISHING LTD}},
Address = {{TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Zhao, X; Li, YP (Corresponding Author), Hohai Univ, Coll Environm, Key Lab Integrated Regulat \& Resource Dev Shallow, Minist Educ, Nanjing 210098, Jiangsu, Peoples R China.
   Zhao, X.; Li, Y. P.; Wang, H., Hohai Univ, Coll Environm, Key Lab Integrated Regulat \& Resource Dev Shallow, Minist Educ, Nanjing 210098, Jiangsu, Peoples R China.
   Yang, H.; Liu, W. F., Eawag, Swiss Fed Inst Aquat Sci \& Technol, Ueberlandstr 133, CH-8600 Dubendorf, Switzerland.
   Yang, H., MGU Univ Basel, Dept Environm Sci, Peterspl 1, CH-4003 Basel, Switzerland.
   Tillotson, M. R., Univ Leeds, Sch Civil Engn, Leeds LS2 9JT, W Yorkshire, England.
   Guan, D., Univ East Anglia, Sch Int Dev, Water Secur Res Ctr, Norwich Res Pk, Norwich NR4 7TJ, Norfolk, England.
   Yi, Y., Beijing Normal Univ, Sch Environm, Minist Educ, Key Lab Water \& Sediment Sci, Beijing 100875, Peoples R China.}},
DOI = {{10.1088/1748-9326/aaba49}},
Article-Number = {{054012}},
ISSN = {{1748-9326}},
Keywords = {{virtual water trade; virtual scarce water; water saving; input-output
   analysis}},
Keywords-Plus = {{FOOD TRADE; FOOTPRINT; TRANSFERS; LCA; SECURITY; CRITIQUE; NATIONS;
   STRESS}},
Research-Areas = {{Environmental Sciences \& Ecology; Meteorology \& Atmospheric Sciences}},
Web-of-Science-Categories  = {{Environmental Sciences; Meteorology \& Atmospheric Sciences}},
Author-Email = {{xuzhao@hhu.edu.cn
   liyiping@hhu.edu.cn}},
ResearcherID-Numbers = {{Liu, Wenfeng/D-3715-2017
   Guan, Dabo/Y-2406-2019
   Zhao, Xu/A-6972-2015
   }},
ORCID-Numbers = {{Liu, Wenfeng/0000-0002-8699-3677
   Guan, Dabo/0000-0003-3773-3403
   Zhao, Xu/0000-0002-7461-852X
   Tillotson, Martin/0000-0002-3362-021X}},
Funding-Acknowledgement = {{National Key RAMP;D Program of China {[}2017YFC0405203, 2016YFC0401703];
   Fundamental Research Funds for the Central UniversitiesFundamental
   Research Funds for the Central Universities {[}2016B13814, 2016B06814];
   National Natural Science Foundation of ChinaNational Natural Science
   Foundation of China {[}5177090079, 51579071, 51779075]}},
Funding-Text = {{This work was supported by the National Key R\&D Program of China
   (2017YFC0405203, 2016YFC0401703), the Fundamental Research Funds for the
   Central Universities (No. 2016B13814, 2016B06814), and the National
   Natural Science Foundation of China (No. 5177090079, 51579071,
   51779075).}},
Cited-References = {{Allan T., 1997, 1997 BRIT ASS FEST S.
   Chapagain AK, 2006, HYDROL EARTH SYST SC, V10, P455, DOI 10.5194/hess-10-455-2006.
   Dalin C, 2015, P NATL ACAD SCI USA, V112, P4588, DOI 10.1073/pnas.1504345112.
   Dalin C, 2014, P NATL ACAD SCI USA, V111, P9774, DOI 10.1073/pnas.1404749111.
   Dalin C, 2012, P NATL ACAD SCI USA, V109, P5989, DOI 10.1073/pnas.1203176109.
   Dang Q, 2015, WATER RESOUR RES, V51, P973, DOI 10.1002/2014WR015919.
   Fader M, 2011, HYDROL EARTH SYST SC, V15, P1641, DOI 10.5194/hess-15-1641-2011.
   Feng KS, 2014, ENVIRON SCI TECHNOL, V48, P7704, DOI 10.1021/es500502q.
   Feng KS, 2011, ECON SYST RES, V23, P371, DOI 10.1080/09535314.2011.638276.
   Guan D, 2007, ECOL ECON, V61, P159, DOI 10.1016/j.ecolecon.2006.02.022.
   Hoekstra AY, 2016, ECOL INDIC, V66, P564, DOI 10.1016/j.ecolind.2016.02.026.
   Hoekstra AY, 2012, P NATL ACAD SCI USA, V109, P3232, DOI 10.1073/pnas.1109936109.
   Hoekstra AY, 2009, P NATL ACAD SCI USA, V106, pE114, DOI 10.1073/pnas.0909948106.
   Konar M, 2013, HYDROL EARTH SYST SC, V17, P3969, DOI 10.5194/hess-17-3969-2013.
   Konar M, 2013, HYDROL EARTH SYST SC, V17, P3219, DOI 10.5194/hess-17-3219-2013.
   Konar M, 2012, WATER RESOUR RES, V48, DOI 10.1029/2012WR011959.
   Liu JG, 2015, SCIENCE, V347, DOI 10.1126/science.1258832.
   Liu JG, 2013, GLOBAL ENVIRON CHANG, V23, P633, DOI 10.1016/j.gloenvcha.2013.02.002.
   Liu W., 2014, CHINA 30 PROVINCE IN.
   Ma J, 2006, PHILOS T R SOC B, V361, P835, DOI 10.1098/rstb.2005.1644.
   Orlowsky B, 2014, ENVIRON RES LETT, V9, DOI 10.1088/1748-9326/9/7/074007.
   Pfister S, 2017, ECOL INDIC, V72, P352, DOI 10.1016/j.ecolind.2016.07.051.
   Pfister S, 2009, P NATL ACAD SCI USA, V106, pE93, DOI 10.1073/pnas.0908069106.
   Pfister S, 2009, ENVIRON SCI TECHNOL, V43, P4098, DOI 10.1021/es802423e.
   Provincial Water Resources Bureau, 2010, WAT RES B.
   Ridoutt BG, 2010, GLOBAL ENVIRON CHANG, V20, P113, DOI 10.1016/j.gloenvcha.2009.08.003.
   The State Council Leading Group Office of Second China Economic Census, 2008, CHIN EC CENS YB.
   van Vliet MTH, 2017, NAT GEOSCI, V10, P800, DOI 10.1038/ngeo3047.
   Vorosmarty CJ, 2015, SCIENCE, V349, P478, DOI 10.1126/science.aac6009.
   Vorosmarty CJ, 2010, NATURE, V467, P555, DOI 10.1038/nature09440.
   Yang H, 2006, HYDROL EARTH SYST SC, V10, P443, DOI 10.5194/hess-10-443-2006.
   Yang H, 2013, CURR OPIN ENV SUST, V5, P599, DOI 10.1016/j.cosust.2013.10.003.
   Zhang C, 2017, APPL ENERG, V187, P438, DOI 10.1016/j.apenergy.2016.11.052.
   Zhang C, 2014, ECOL ECON, V100, P159, DOI 10.1016/j.ecolecon.2014.02.006.
   Zhao X, 2016, WATER RESOUR RES, V52, P6916, DOI 10.1002/2016WR018595.
   Zhao X, 2015, P NATL ACAD SCI USA, V112, P1031, DOI 10.1073/pnas.1404130112.}},
Number-of-Cited-References = {{36}},
Times-Cited = {{19}},
Usage-Count-Last-180-days = {{4}},
Usage-Count-Since-2013 = {{40}},
Journal-ISO = {{Environ. Res. Lett.}},
Doc-Delivery-Number = {{GE2OL}},
Unique-ID = {{ISI:000431054400002}},
OA = {{DOAJ Gold, Green Accepted, Green Published}},
DA = {{2020-08-06}},
}

@article{ ISI:000429529900014,
Author = {Gonzalez, Sergi and Vasallo, Francisco and Recio-Blitz, Cayetana and
   Guijarro, Jose A. and Riesco, Jesus},
Title = {{Atmospheric Patterns over the Antarctic Peninsula}},
Journal = {{JOURNAL OF CLIMATE}},
Year = {{2018}},
Volume = {{31}},
Number = {{9}},
Pages = {{3597-3608}},
Month = {{MAY}},
Abstract = {{Using clustering analysis for the sea level pressure field of the
   ERA-Interim reanalysis between 1979 and 2016, five synoptic pressure
   patterns have been obtained for the Drake area and Antarctic Peninsula
   (AP) region (458-758S, 208-1208W), and the resulting daily series has
   been made available to the scientific community. The five patterns have
   been named according to their most important features as follows: low
   over the Weddell Sea (LWS), low over the Amundsen and Bellingshausen
   Seas (LAB), low over the Drake Passage (LDP), zonal flow over the Drake
   Passage (ZDP), and ridge over the Antarctic Peninsula (RAP). Each
   atmospheric pattern is described after analyzing its development and
   evolution. A frequency analysis shows that the five atmospheric patterns
   present a similar annual frequency but a large seasonal variability. The
   transitions from one pattern to another tend to follow a cycle in which
   synoptic atmospheric waves are displaced eastward by a quarter
   wavelength. Four of the five atmospheric patterns (all except RAP) are
   very influenced by the southern annular mode (SAM); however, only LAB
   and LWS are influenced to some degree by ENSO. The occurrence of the LAB
   pattern presents a positive trend showing agreement with other studies
   that indicate an enhancement of the Amundsen-Bellingshausen Seas low.
   Finally, atmospheric circulation patterns have been related to the
   airmass advection and precipitation in Livingston Island, showing the
   potential application for studying the changes in the surface mass
   balance on the AP cryosphere.}},
Publisher = {{AMER METEOROLOGICAL SOC}},
Address = {{45 BEACON ST, BOSTON, MA 02108-3693 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Gonzalez, S (Corresponding Author), Univ Barcelona, Antarctic Grp, Spanish Meteorol Serv AEMET, Barcelona, Spain.
   Gonzalez, S (Corresponding Author), Univ Barcelona, Dept Appl Phys, Barcelona, Spain.
   Gonzalez, Sergi, Univ Barcelona, Antarctic Grp, Spanish Meteorol Serv AEMET, Barcelona, Spain.
   Gonzalez, Sergi, Univ Barcelona, Dept Appl Phys, Barcelona, Spain.
   Vasallo, Francisco, Spanish Meteorol Serv AEMET, Antarctic Grp, Rota, Spain.
   Recio-Blitz, Cayetana, Univ Politecn Madrid, Dept Matemat Aplicada Tecnol Informac \& Comunicac, Madrid, Spain.
   Guijarro, Jose A., Spanish Meteorol Serv AEMET, Balearic Isl Off, Palma De Mallorca, Spain.
   Riesco, Jesus, Spanish Meteorol Serv AEMET, Antarctic Grp, Malaga, Spain.}},
DOI = {{10.1175/JCLI-D-17-0598.1}},
ISSN = {{0894-8755}},
EISSN = {{1520-0442}},
Keywords-Plus = {{SOUTHERN ANNULAR MODE; REGIONAL CLIMATE VARIABILITY; SEA-LEVEL RISE;
   SEMIANNUAL OSCILLATION; SURFACE TEMPERATURES; ENSO TELECONNECTION;
   MOUNTAIN GLACIERS; NEW-ZEALAND; MEAN STATE; ICE CAPS}},
Research-Areas = {{Meteorology \& Atmospheric Sciences}},
Web-of-Science-Categories  = {{Meteorology \& Atmospheric Sciences}},
Author-Email = {{sgonzalezh@aemet.es}},
ResearcherID-Numbers = {{Guijarro, Jose A/L-3744-2014
   Gonzalez, Sergi/Y-7279-2018
   }},
ORCID-Numbers = {{Guijarro, Jose A/0000-0002-9527-9758
   Gonzalez, Sergi/0000-0002-2505-2435
   Vasallo, Francisco/0000-0002-3907-4897}},
Funding-Acknowledgement = {{Ministry of Economy and Competitiveness (MINECO) through the AEMET
   Antarctic program; Spanish Investigation Agency (AEI); European Regional
   Development Fund (FEDER)European Union (EU) {[}CTM2016-79741-R]}},
Funding-Text = {{This work is supported by the Ministry of Economy and Competitiveness
   (MINECO) through the AEMET Antarctic program and by the Spanish
   Investigation Agency (AEI) and the European Regional Development Fund
   (FEDER), Grant CTM2016-79741-R for MICROAIRPOLAR project (to S. Gonzalez
   and F. Vasallo). Integrated Data Viewer (IDV) software from UCAR/Unidata
   was used in the analysis and visualization of the data. We also thank
   the two anonymous reviewers whose feedback helped to greatly improve
   this text. Finally, we want also thank all those meteorologists from the
   AEMET Antarctic Group who, during 30 years, have maintained the
   observations of the Juan Carlos I meteorological station; some of these
   data are used in this paper.}},
Cited-References = {{Arthur D, 2007, PROCEEDINGS OF THE EIGHTEENTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P1027.
   Bamston A. G., 1997, ATMOS OCEAN, V35, P367, DOI {[}10.1080/07055900.1997.9649597, DOI 10.1080/07055900.1997.9649597].
   Bliss A, 2013, ANN GLACIOL, V54, P191, DOI 10.3189/2013AoG63A377.
   Bracegirdle TJ, 2012, J CLIMATE, V25, P7138, DOI 10.1175/JCLI-D-11-00685.1.
   Carleton AM, 2003, J GEOPHYS RES-OCEANS, V108, DOI 10.1029/2000JC000379.
   Carrasco J. F., 2013, ATMOSPHERE CLIMATE S, V3, P275, DOI DOI 10.4236/ACS.2013.33029.
   Clem KR, 2016, J GEOPHYS RES-ATMOS, V121, P9324, DOI 10.1002/2016JD025305.
   Clem KR, 2013, J GEOPHYS RES-ATMOS, V118, P11481, DOI 10.1002/jgrd.50860.
   Codron F, 2005, J CLIMATE, V18, P320, DOI 10.1175/JCLI-3255.1.
   Codron F, 2007, J ATMOS SCI, V64, P3328, DOI 10.1175/JAS4012.1.
   Cohen L, 2013, J CLIMATE, V26, P636, DOI 10.1175/JCLI-D-11-00690.1.
   Dee DP, 2011, Q J ROY METEOR SOC, V137, P553, DOI 10.1002/qj.828.
   Fogt RL, 2006, J CLIMATE, V19, P979, DOI 10.1175/JCLI3671.1.
   Fogt RL, 2012, J CLIMATE, V25, P6253, DOI 10.1175/JCLI-D-11-00474.1.
   Fogt RL, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2011JD017337.
   Fogt RL, 2011, CLIM DYNAM, V36, P1555, DOI 10.1007/s00382-010-0905-0.
   Gallego D, 2005, CLIM DYNAM, V24, P607, DOI 10.1007/s00382-005-0006-7.
   Gardner AS, 2013, SCIENCE, V340, P852, DOI 10.1126/science.1234532.
   Genthon C, 2003, GEOPHYS RES LETT, V30, DOI 10.1029/2003GL018280.
   Govorukha L. S., 2002, PROBL KLIMATOL POLAR, V10, P159.
   Guijarro J. A., 2017, P 9 SEM HOM QUAL CON, P175.
   Hobbs WR, 2010, CLIM DYNAM, V35, P859, DOI 10.1007/s00382-009-0663-z.
   Hock R, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2008GL037020.
   Hosking JS, 2013, J CLIMATE, V26, P6633, DOI 10.1175/JCLI-D-12-00813.1.
   HOUSSOS EE, 2008, ADV GEOSCI, V17, P5, DOI DOI 10.5194/ADGEO-17-5-2008.
   IPCC, 2013, CLIM CHANG 2013 PHYS.
   Irving D, 2016, J CLIMATE, V29, P6109, DOI 10.1175/JCLI-D-15-0843.1.
   Irving D, 2015, J CLIMATE, V28, P9041, DOI 10.1175/JCLI-D-15-0287.1.
   Jonsell UY, 2012, CRYOSPHERE, V6, P539, DOI 10.5194/tc-6-539-2012.
   Kejna M., 1993, P 20 POL S LUBL POL, P369.
   Kidson JW, 2000, INT J CLIMATOL, V20, P299, DOI 10.1002/(SICI)1097-0088(20000315)20:3<299::AID-JOC474>3.0.CO;2-B.
   Kidston J, 2009, J CLIMATE, V22, P4759, DOI 10.1175/2009JCLI2640.1.
   Lana A., 2007, ADV GEOSCI, V12, P27, DOI {[}10.5194/adgeo-12-27-2007, DOI 10.5194/ADGEO-12-27-2007].
   Lefebvre W, 2004, J GEOPHYS RES-OCEANS, V109, DOI 10.1029/2004JC002403.
   Lubin D, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2007GL032170.
   Ludescher J, 2016, CLIM DYNAM, V46, P263, DOI 10.1007/s00382-015-2582-5.
   Marshall GJ, 2007, INT J CLIMATOL, V27, P373, DOI 10.1002/joc.1407.
   Marshall GJ, 2006, J CLIMATE, V19, P5388, DOI 10.1175/JCLI3844.1.
   Marshall GJ, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2004GL019952.
   Marshall GJ, 2003, J CLIMATE, V16, P4134, DOI 10.1175/1520-0442(2003)016<4134:TITSAM>2.0.CO;2.
   MAYES PR, 1985, J GEOPHYS RES-ATMOS, V90, P5829, DOI 10.1029/JD090iD03p05829.
   Oliva M, 2017, SCI TOTAL ENVIRON, V580, P210, DOI 10.1016/j.scitotenv.2016.12.030.
   Orr A, 2008, J ATMOS SCI, V65, P1396, DOI 10.1175/2007JAS2498.1.
   Pezza AB, 2012, CLIM DYNAM, V38, P57, DOI 10.1007/s00382-011-1044-y.
   Radic V, 2011, NAT GEOSCI, V4, P91, DOI {[}10.1038/ngeo1052, 10.1038/NGEO1052].
   Radic V, 2010, J GEOPHYS RES-EARTH, V115, DOI 10.1029/2009JF001373.
   Raphael MN, 2016, B AM METEOROL SOC, V97, P111, DOI 10.1175/BAMS-D-14-00018.1.
   Raphael MN, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2004GL020365.
   Rayner NA, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD002670.
   Reboita MS, 2015, CLIM DYNAM, V45, P1929, DOI 10.1007/s00382-014-2447-3.
   Simmonds I, 2004, ANTARCT SCI, V16, P401, DOI 10.1017/S0954102004002226.
   Simmonds I, 2000, J CLIMATE, V13, P873, DOI 10.1175/1520-0442(2000)013<0873:MSHECB>2.0.CO;2.
   Simmonds I, 2003, MON WEATHER REV, V131, P272, DOI 10.1175/1520-0493(2003)131<0272:SAITSA>2.0.CO;2.
   Thompson DWJ, 2002, SCIENCE, V296, P895, DOI 10.1126/science.1069270.
   Trenberth KE, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2000JD000298.
   Turner J, 1998, INT J CLIMATOL, V18, P253, DOI 10.1002/(SICI)1097-0088(19980315)18:3<253::AID-JOC248>3.0.CO;2-3.
   Turner J, 2016, NATURE, V535, P411, DOI 10.1038/nature18645.
   Turner J, 2013, INT J CLIMATOL, V33, P1818, DOI 10.1002/joc.3558.
   van den Broeke M, 2000, INT J CLIMATOL, V20, P117, DOI 10.1002/(SICI)1097-0088(200002)20:2<117::AID-JOC481>3.0.CO;2-B.
   van den Broeke MR, 2003, ANTARCT RES SER, V79, P43.
   Van den Broeke MR, 1998, ANTARCT SCI, V10, P175, DOI 10.1017/S0954102098000248.
   VANLOON H, 1972, J GEOPHYS RES, V77, P992, DOI 10.1029/JC077i006p00992.
   VANLOON H, 1967, J ATMOS SCI, V24, P472, DOI 10.1175/1520-0469(1967)024<0472:THYOIM>2.0.CO;2.
   Vasallo F., 2015, AEMET ANTARTIDA CLIM.
   Vaughan DG, 2003, CLIMATIC CHANGE, V60, P243, DOI 10.1023/A:1026021217991.}},
Number-of-Cited-References = {{65}},
Times-Cited = {{6}},
Usage-Count-Last-180-days = {{0}},
Usage-Count-Since-2013 = {{15}},
Journal-ISO = {{J. Clim.}},
Doc-Delivery-Number = {{GC1GW}},
Unique-ID = {{ISI:000429529900014}},
OA = {{Bronze}},
DA = {{2020-08-06}},
}

@article{ ISI:000430803800001,
Author = {Srinivas, G. and Chowdary, Jasti S. and Kosaka, Yu and Gnanaseelan, C.
   and Parekh, Anant and Prasad, K. V. S. R.},
Title = {{Influence of the Pacific-Japan Pattern on Indian Summer Monsoon Rainfall}},
Journal = {{JOURNAL OF CLIMATE}},
Year = {{2018}},
Volume = {{31}},
Number = {{10}},
Pages = {{3943-3958}},
Month = {{MAY}},
Abstract = {{This study discusses the impact of the Pacific-Japan (PJ) pattern on
   Indian summer monsoon (ISM) rainfall and its possible physical linkages
   through coupled and uncoupled pathways. Empirical orthogonal function
   analysis of 850-hPa relative vorticity over the western North Pacific
   (WNP) is used to extract the PJ pattern as the leading mode of
   circulation variability. The partial correlation analysis of the leading
   principal component reveals that the positive PJ pattern, which features
   anticyclonic and cyclonic low-level circulation anomalies over the
   tropical WNP and around Japan respectively, enhances the rainfall over
   the southern and northern parts of India. The northwestward propagating
   Rossby waves, in response to intensified convection over the Maritime
   Continent reinforced by low-level convergence in the southern flank of
   westward extended tropical WNP anticyclone, increase rainfall over
   southern peninsular India. Meanwhile, the anomalous moisture transport
   from the warm Bay of Bengal due to anomalous southerlies at the western
   edge of the low-level anticyclone extending from the tropical WNP helps
   to enhance the rainfall over northern India. The atmospheric general
   circulation model forced with climatological sea surface temperature
   confirms this atmospheric pathway through the westward propagating
   Rossby waves. Furthermore, the north Indian Ocean (NIO) warming induced
   by easterly wind anomalies along the southern periphery of the tropical
   WNP-NIO anticyclone enhances local convection, which in turn feeds back
   to the WNP convection anomalies. This coupled nature via interbasin
   feedback between the PJ pattern and NIO is confirmed using coupled model
   sensitivity experiments. These results are important in identifying new
   sources of ISM variability/predictability on the interannual time scale.}},
Publisher = {{AMER METEOROLOGICAL SOC}},
Address = {{45 BEACON ST, BOSTON, MA 02108-3693 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Chowdary, JS (Corresponding Author), Indian Inst Trop Meteorol, Pune, Maharashtra, India.
   Srinivas, G.; Chowdary, Jasti S.; Gnanaseelan, C.; Parekh, Anant, Indian Inst Trop Meteorol, Pune, Maharashtra, India.
   Srinivas, G.; Prasad, K. V. S. R., Andhra Univ, Dept Meteorol \& Oceanog, Visakhapatnam, Andhra Prades, India.
   Kosaka, Yu, Univ Tokyo, Res Ctr Adv Sci \& Technol, Tokyo, Japan.}},
DOI = {{10.1175/JCLI-D-17-0408.1}},
ISSN = {{0894-8755}},
EISSN = {{1520-0442}},
Keywords = {{Teleconnections; Monsoons; Precipitation; Interannual variability}},
Keywords-Plus = {{SEA-SURFACE TEMPERATURE; INDO-WESTERN PACIFIC; CLIMATE FORECAST SYSTEM;
   EL-NINO; INTRASEASONAL OSCILLATION; ENSO RELATIONSHIP; TELECONNECTION
   PATTERN; SOUTHERN OSCILLATION; SCALE VARIABILITY; OCEAN CAPACITOR}},
Research-Areas = {{Meteorology \& Atmospheric Sciences}},
Web-of-Science-Categories  = {{Meteorology \& Atmospheric Sciences}},
Author-Email = {{jasti@tropmet.res.in}},
ResearcherID-Numbers = {{Kosaka, Yu/C-2792-2009}},
ORCID-Numbers = {{Kosaka, Yu/0000-0001-5575-4668}},
Funding-Acknowledgement = {{Earth System Sciences Organization (ESSO), Indian Institute of Tropical
   Meteorology (IITM); Japan Society for the Promotion of ScienceMinistry
   of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan
   Society for the Promotion of Science {[}15H05466]; Japan Science and
   Technology Agency through Belmont Forum CRA ``InterDec{''}; Japanese
   Ministry of Environment through the Environment Research and Technology
   Development Fund {[}2-1503]}},
Funding-Text = {{We thank the Director, Earth System Sciences Organization (ESSO), Indian
   Institute of Tropical Meteorology (IITM) for financial support and
   infrastructure. We thank the editor and anonymous reviewers for their
   valuable comments that helped us to improve the manuscript. We sincerely
   thank Dr. H. Annamalai, IPRC Hawaii, for his suggestions to improve the
   analysis. YK is supported by Japan Society for the Promotion of Science
   through Grant-in-Aid for Young Scientists (A) 15H05466, Japan Science
   and Technology Agency through Belmont Forum CRA ``InterDec,{''} and the
   Japanese Ministry of Environment through the Environment Research and
   Technology Development Fund 2-1503. Figures are prepared in PyFerret.}},
Cited-References = {{Achuthavarier D, 2012, J CLIMATE, V25, P2490, DOI 10.1175/JCLI-D-11-00111.1.
   Anderson JL, 2004, J CLIMATE, V17, P4641.
   Annamalai H, 2005, J CLIMATE, V18, P4150, DOI 10.1175/JCLI3533.1.
   Annamalai H, 2010, J ATMOS SCI, V67, P589, DOI 10.1175/2009JAS2991.1.
   Ashok K, 2007, J GEOPHYS RES-OCEANS, V112, DOI 10.1029/2006JC003798.
   Choi KS, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2010JD013866.
   Chowdary JS, 2013, THEOR APPL CLIMATOL, V113, P329, DOI 10.1007/s00704-012-0785-9.
   Chowdary JS, 2012, J CLIMATE, V25, P1722, DOI 10.1175/JCLI-D-11-00070.1.
   Chowdary JS, 2017, CLIM DYNAM, V48, P2707, DOI 10.1007/s00382-016-3233-1.
   Chowdary JS, 2011, CLIM DYNAM, V36, P607, DOI 10.1007/s00382-009-0686-5.
   Compo GP, 2011, Q J ROY METEOR SOC, V137, P1, DOI 10.1002/qj.776.
   Dee DP, 2011, Q J ROY METEOR SOC, V137, P553, DOI 10.1002/qj.828.
   Delsole T., 2002, 11458 COLA CTR.
   Du Y, 2009, J CLIMATE, V22, P2023, DOI 10.1175/2008JCLI2590.1.
   Gadgil S, 2005, CURR SCI INDIA, V88, P1389.
   Garreaud RD, 1999, J CLIMATE, V12, P2113, DOI 10.1175/1520-0442(1999)012<2113:IEAIEL>2.0.CO;2.
   GILL AE, 1980, Q J ROY METEOR SOC, V106, P447, DOI 10.1256/smsqj.44904.
   Goswami BN, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL023216.
   Goswami BN, 1998, J CLIMATE, V11, P501, DOI 10.1175/1520-0442(1998)011<0501:IVOISM>2.0.CO;2.
   Griffies S. M., 2004, 5 GFDL OC GROUP.
   Harris I, 2014, INT J CLIMATOL, V34, P623, DOI 10.1002/joc.3711.
   Huang BY, 2015, J CLIMATE, V28, P911, DOI 10.1175/JCLI-D-14-00006.1.
   HUANG RH, 1992, J METEOROL SOC JPN, V70, P243, DOI 10.2151/jmsj1965.70.1B\_243.
   Jiang XW, 2013, J CLIMATE, V26, P3708, DOI 10.1175/JCLI-D-12-00437.1.
   Jiang XN, 2004, J CLIMATE, V17, P1022, DOI 10.1175/1520-0442(2004)017<1022:SAMOTN>2.0.CO;2.
   Joseph PV, 2013, ATMOS OCEAN SCI LETT, V6, P398, DOI 10.3878/j.issn.1674-2834.13.0044.
   Joshi MK, 2017, CLIM DYNAM, V48, P2375, DOI 10.1007/s00382-016-3210-8.
   Joshi MK, 2015, CLIM DYNAM, V44, P3339, DOI 10.1007/s00382-014-2333-z.
   Kawamura R, 2001, J GEOPHYS RES-ATMOS, V106, P4681, DOI 10.1029/2000JD900610.
   Kemball-Cook S, 2001, J CLIMATE, V14, P2923, DOI 10.1175/1520-0442(2001)014<2923:EWAASI>2.0.CO;2.
   Kosaka Y, 2006, Q J ROY METEOR SOC, V132, P2009, DOI 10.1256/qj.05.204.
   Kosaka Y, 2013, P NATL ACAD SCI USA, V110, P7574, DOI 10.1073/pnas.1215582110.
   Kosaka Y, 2010, J CLIMATE, V23, P5085, DOI 10.1175/2010JCLI3413.1.
   Krishnamurthy L, 2014, CLIM DYNAM, V42, P2397, DOI 10.1007/s00382-013-1856-z.
   Krishnamurthy V, 2000, J CLIMATE, V13, P579, DOI 10.1175/1520-0442(2000)013<0579:IMEROI>2.0.CO;2.
   Krishnamurti TN, 2000, J CLIMATE, V13, P4196, DOI 10.1175/1520-0442(2000)013<4196:MEFFWA>2.0.CO;2.
   Krishnan R, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL025811.
   Krishnan R, 2003, CLIM DYNAM, V21, P233, DOI 10.1007/s00382-003-0330-8.
   Kubota H, 2016, INT J CLIMATOL, V36, P1575, DOI 10.1002/joc.4441.
   Kumar KK, 2006, SCIENCE, V314, P115, DOI 10.1126/science.1131152.
   Kumar KK, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2004GL021979.
   Kumar KK, 1999, SCIENCE, V284, P2156, DOI 10.1126/science.284.5423.2156.
   LAU KM, 1990, J ATMOS SCI, V47, P1443, DOI 10.1175/1520-0469(1990)047<1443:OOLFOI>2.0.CO;2.
   Lau NC, 2000, J CLIMATE, V13, P4287, DOI 10.1175/1520-0442(2000)013<4287:IOEOTV>2.0.CO;2.
   Lawrence DM, 2002, J ATMOS SCI, V59, P1593, DOI 10.1175/1520-0469(2002)059<1593:TBSIOR>2.0.CO;2.
   Li RCY, 2014, J CLIMATE, V27, P140, DOI 10.1175/JCLI-D-13-00183.1.
   Ma J, 2017, J CLIMATE, V30, P3927, DOI {[}10.1175/jcli-d-16-0638.1, 10.1175/JCLI-D-16-0638.1].
   Malik A, 2017, CLIM DYNAM, V49, P3551, DOI 10.1007/s00382-017-3529-9.
   Mantua NJ, 1997, B AM METEOROL SOC, V78, P1069, DOI 10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2.
   Matsuno T., 1966, J METEOR SOC JAPAN, V44, P25, DOI {[}DOI 10.2151/JMSJ1965.44.1\_25, 10.2151/jmsj1965.44.1\_25].
   Meehl GA, 2009, J CLIMATE, V22, P780, DOI 10.1175/2008JCLI2552.1.
   NITTA T, 1987, J METEOROL SOC JPN, V65, P373, DOI 10.2151/jmsj1965.65.3\_373.
   Rajeevan M, 2007, CLIM DYNAM, V28, P813, DOI 10.1007/s00382-006-0197-6.
   Rayner NA, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD002670.
   Reynolds RW, 2007, J CLIMATE, V20, P5473, DOI 10.1175/2007JCLI1824.1.
   Saha S, 2014, J CLIMATE, V27, P2185, DOI 10.1175/JCLI-D-12-00823.1.
   Saha S, 2010, B AM METEOROL SOC, V91, P1015, DOI 10.1175/2010BAMS3001.1.
   Schneider U, 2017, ATMOSPHERE-BASEL, V8, DOI 10.3390/atmos8030052.
   SHAPIRO SS, 1965, BIOMETRIKA, V52, P591, DOI 10.1093/biomet/52.3-4.591.
   SHUKLA J, 1983, MON WEATHER REV, V111, P1830, DOI 10.1175/1520-0493(1983)111<1830:TSOALR>2.0.CO;2.
   Shukla J, 1984, PROBLEMS PROSPECTS L, P155, DOI DOI 10.1007/978-3-642-82132-5\_6.
   Srinivas G, 2018, CLIM DYNAM, V50, P1659, DOI 10.1007/s00382-017-3713-y.
   Stuecker MF, 2015, J CLIMATE, V28, P1093, DOI 10.1175/JCLI-D-14-00225.1.
   Sun XG, 2010, Q J ROY METEOR SOC, V136, P829, DOI 10.1002/qj.635.
   Terray P, 2003, CLIM DYNAM, V21, P593, DOI 10.1007/s00382-003-0354-0.
   TRENBERTH KE, 1994, CLIM DYNAM, V9, P303, DOI 10.1007/BF00204745.
   Turner AG, 2012, NAT CLIM CHANGE, V2, P587, DOI 10.1038/NCLIMATE1495.
   Wang B, 2013, P NATL ACAD SCI USA, V110, P2718, DOI 10.1073/pnas.1214626110.
   Wang SYS, 2015, GEOPHYS RES LETT, V42, P8140, DOI 10.1002/2015GL065211.
   WEBSTER PJ, 1992, Q J ROY METEOR SOC, V118, P877, DOI 10.1256/smsqj.50704.
   Webster PJ, 1998, J GEOPHYS RES-OCEANS, V103, P14451, DOI 10.1029/97JC02719.
   Wu RG, 2012, J CLIMATE, V25, P903, DOI 10.1175/JCLI-D-11-00039.1.
   Wu RG, 2004, J CLIMATE, V17, P3037, DOI {[}10.1175/1520-0442(2004)017<3037:IOTIOO>2.0.CO;2, 10.1175/1520-0442(2004)017\<3037:IOTIOO\>2.0.CO;2].
   Wu RG, 2003, Q J ROY METEOR SOC, V129, P3439, DOI 10.1256/qj.02.214.
   Wu RG, 2002, J CLIMATE, V15, P3266, DOI 10.1175/1520-0442(2002)015<3266:ACOTEA>2.0.CO;2.
   Xie PP, 1997, B AM METEOROL SOC, V78, P2539, DOI 10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2.
   Xie SP, 2016, ADV ATMOS SCI, V33, P411, DOI 10.1007/s00376-015-5192-6.
   Xie SP, 2010, J CLIMATE, V23, P3352, DOI 10.1175/2010JCLI3429.1.
   Xie SP, 2009, J CLIMATE, V22, P730, DOI 10.1175/2008JCLI2544.1.
   Yang JL, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2006GL028571.
   Yang S, 1998, J CLIMATE, V11, P3230, DOI 10.1175/1520-0442(1998)011<3230:IOSSTA>2.0.CO;2.
   Zhang Y, 1997, J CLIMATE, V10, P1004, DOI 10.1175/1520-0442(1997)010<1004:ELIV>2.0.CO;2.}},
Number-of-Cited-References = {{82}},
Times-Cited = {{15}},
Usage-Count-Last-180-days = {{4}},
Usage-Count-Since-2013 = {{30}},
Journal-ISO = {{J. Clim.}},
Doc-Delivery-Number = {{GD8ZW}},
Unique-ID = {{ISI:000430803800001}},
OA = {{Bronze}},
DA = {{2020-08-06}},
}

@article{ ISI:000434410000011,
Author = {Yeh, Sang-Wook and Won, You-Jin and Hong, Jin-Sil and Lee, Kang-Jin and
   Kwon, Minho and Seo, Kyong-Hwan and Ham, Yoo-Geun},
Title = {{The Record-Breaking Heat Wave in 2016 over South Korea and Its Physical
   Mechanism}},
Journal = {{MONTHLY WEATHER REVIEW}},
Year = {{2018}},
Volume = {{146}},
Number = {{5}},
Pages = {{1463-1474}},
Month = {{MAY}},
Abstract = {{It is important to understand the dynamical processes that cause heat
   waves at regional scales. This study examined the physical mechanism
   that was responsible for a heat wave in South Korea in August 2016.
   Unlike previous August heat waves over the Korean Peninsula, the
   intensity of the geopotential height over the Kamchatka Peninsula in
   August 2016 was the strongest since 1979, which acted as an atmospheric
   blocking in the downstream region of the Korean Peninsula. Therefore,
   the anomalous high geopotential height in Mongolia, where the surface
   temperature was quite high, was observed persistently in August 2016.
   This anomalous high in Mongolia induced northerly winds with warm
   temperatures onto the Korean Peninsula, which contributed to a heat wave
   in August 2016. We further showed that the anomalous high geopotential
   height over the Kamchatka Peninsula in August 2016 was triggered by
   strong convection in the western-to-central subtropical Pacific through
   atmospheric teleconnections, which was quite different from a typical
   heat wave over the Korean Peninsula, in which convective forcing around
   the South China Sea is strong. This implies that convective forcing in
   the subtropical Pacific should also be monitored to predict heat wave
   events in East Asia, including South Korea. On the other hand, the zonal
   wave train associated with the circumglobal teleconnection pattern is
   also associated with the anomalous high geopotential height around
   Mongolia and the Kamchatka Peninsula, which may have contributed to the
   heat wave in August 2016.}},
Publisher = {{AMER METEOROLOGICAL SOC}},
Address = {{45 BEACON ST, BOSTON, MA 02108-3693 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Yeh, SW (Corresponding Author), Hanyang Univ, Dept Marine Sci \& Convergence Technol, Ansan, South Korea.
   Yeh, Sang-Wook; Won, You-Jin; Hong, Jin-Sil, Hanyang Univ, Dept Marine Sci \& Convergence Technol, Ansan, South Korea.
   Won, You-Jin, Korea Meteorol Adm, Seoul, South Korea.
   Lee, Kang-Jin; Kwon, Minho, KIOST, Busan, South Korea.
   Seo, Kyong-Hwan, Pusan Natl Univ, Busan, South Korea.
   Ham, Yoo-Geun, Chonnam Natl Univ, Dept Oceanog, Gwangju, South Korea.}},
DOI = {{10.1175/MWR-D-17-0205.1}},
ISSN = {{0027-0644}},
EISS