text:
1994)007<1184:AMSOTR>2.0.CO;2.
   Liu ZY, 2007, REV GEOPHYS, V45, DOI 10.1029/2005RG000172.
   NITTA T, 1987, J METEOROL SOC JPN, V65, P373, DOI 10.2151/jmsj1965.65.3\_373.
   Peixoto J.P., 1983, VARIATIONS GLOBAL WA, P5, DOI DOI 10.1007/978-94-009-6954-4\_2.
   Peixoto J.P., 1992, PHYS CLIMATE.
   PICARD RR, 1984, J AM STAT ASSOC, V79, P575, DOI 10.2307/2288403.
   Sherwood SC, 2010, REV GEOPHYS, V48, DOI 10.1029/2009RG000301.
   Smith TM, 2008, J CLIMATE, V21, P2283, DOI 10.1175/2007JCLI2100.1.
   Son HY, 2014, CLIM DYNAM, V42, P3171, DOI 10.1007/s00382-013-2008-1.
   STONE M, 1974, J R STAT SOC B, V36, P111, DOI 10.1111/j.2517-6161.1974.tb00994.x.
   Taylor KE, 2012, B AM METEOROL SOC, V93, P485, DOI 10.1175/BAMS-D-11-00094.1.
   Trenberth KE, 1998, J GEOPHYS RES-OCEANS, V103, P14291, DOI 10.1029/97JC01444.
   Trenberth KE, 2011, CLIM RES, V47, P123, DOI 10.3354/cr00953.
   Wang B, 2000, J CLIMATE, V13, P1517, DOI 10.1175/1520-0442(2000)013<1517:PEATHD>2.0.CO;2.
   Wu AM, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2003GL018885.
   Wu RG, 2003, J CLIMATE, V16, P3742, DOI 10.1175/1520-0442(2003)016\<3742:EOERAI\>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.
   Yeh SW, 2009, NATURE, V461, P511, DOI 10.1038/nature08316.
   Yu J.-Y., 2013, US CLIVAR VARIATIONS, V11.
   Zhang T, 2014, J CLIMATE, V27, P4070, DOI 10.1175/JCLI-D-13-00454.1.}},
Number-of-Cited-References = {{39}},
Times-Cited = {{2}},
Usage-Count-Last-180-days = {{4}},
Usage-Count-Since-2013 = {{10}},
Journal-ISO = {{J. Geophys. Res.-Atmos.}},
Doc-Delivery-Number = {{GX5RG}},
Unique-ID = {{ISI:000447807300022}},
DA = {{2020-08-06}},
}

@article{ ISI:000444364400065,
Author = {Zhai, Mengyu and Huang, Guohe and Liu, Lirong and Su, Shuai},
Title = {{Dynamic input-output analysis for energy metabolism system in the
   Province of Guangdong, China}},
Journal = {{JOURNAL OF CLEANER PRODUCTION}},
Year = {{2018}},
Volume = {{196}},
Pages = {{747-762}},
Month = {{SEP 20}},
Abstract = {{The development of human society is inseparable from energy. The
   exploration of urban energy metabolism plays an essential role in
   improving sustainable development. Combing input-output analysis with
   ecological network analysis help academics to shed light into the
   complicated system interactions and interior energy flows. In this
   study, using Guangdong as a case study, the Energy Ecological Network
   model is developed to account for the intensity of the embodied energy
   consumption using monetary input-output tables from 2000, 2002, 2005,
   2007, 2010, and 2012. Sectors and energy flows are treated as nodes and
   paths to compile the corresponding physical input-output tables, which
   can facilitate a more comprehensive and balanced understanding of urban
   energy consumption by integrating various accounting perspectives. In
   detail, network control analysis is extended to reveal inter
   relationships and relative contribution rate of each sector. Network
   utility analysis gives an overall consideration of the dynamic changes
   in energy metabolism relations from multiple perspective. Furthermore,
   the modified robustness method penetrates into how each sector affects
   the stability of the system. The results show that the energy metabolic
   level in Guangdong is relatively low and indirect flows are the key to
   improving the system efficiency. The advanced manufacturing (AM) sector
   relied on other sectors in energy trade and have limited reciprocal
   relationships in the study period. Therefore, it is urgent to adjust the
   external structure and internal circulation of AM sector. The
   comprehensive dynamic analysis will give a scientific support to guide
   the development of energy reform in an attempt to promoting healthier
   development of energy metabolism system. (C) 2018 Elsevier Ltd. All
   rights reserved.}},
Publisher = {{ELSEVIER SCI LTD}},
Address = {{THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Huang, GH (Corresponding Author), North China Elect Power Univ, Inst Energy Environm \& Sustainabil Res, UR NCEPU, Beijing 102206, Peoples R China.
   Zhai, Mengyu; Su, Shuai, North China Elect Power Univ, Sino Canada Resources \& Environm Res Acad, Beijing 102206, Peoples R China.
   Huang, Guohe, North China Elect Power Univ, Inst Energy Environm \& Sustainabil Res, UR NCEPU, Beijing 102206, Peoples R China.
   Liu, Lirong, Univ Regina, Inst Energy Environm \& Sustainable Communities, Regina, SK S4S 0A2, Canada.}},
DOI = {{10.1016/j.jclepro.2018.06.084}},
ISSN = {{0959-6526}},
EISSN = {{1879-1786}},
Keywords = {{Urban energy metabolism system; Integral flow control intensity;
   Indirect effect indicator; Revised robustness analysis; Ecological
   network analysis}},
Keywords-Plus = {{ECOLOGICAL NETWORK ANALYSIS; VIRTUAL WATER NETWORK; URBAN METABOLISM;
   MODEL DEVELOPMENT; EMERGY SYNTHESIS; FLOW-ANALYSIS; SUSTAINABILITY;
   CITIES; CONSUMPTION; FOOTPRINT}},
Research-Areas = {{Science \& Technology - Other Topics; Engineering; Environmental
   Sciences \& Ecology}},
Web-of-Science-Categories  = {{Green \& Sustainable Science \& Technology; Engineering, Environmental;
   Environmental Sciences}},
Author-Email = {{huang@iseis.org}},
ResearcherID-Numbers = {{Huang, Guohe/H-5306-2011
   Huang, Guohe/AAA-7305-2019
   }},
ORCID-Numbers = {{Huang, Guohe/0000-0003-4974-3019
   Liu, Lirong/0000-0003-2872-0870}},
Funding-Acknowledgement = {{National Key Research and Development Plan {[}2016YFC0502800,
   2016YFA0601502]; Natural Sciences FoundationNational Natural Science
   Foundation of China {[}51520105013, 51679087]; Natural Science and
   Engineering Research Council of CanadaNatural Sciences and Engineering
   Research Council of Canada}},
Funding-Text = {{This research was supported by the National Key Research and Development
   Plan (2016YFC0502800, 2016YFA0601502), the Natural Sciences Foundation
   (51520105013, 51679087), and the Natural Science and Engineering
   Research Council of Canada.}},
Cited-References = {{Barles S, 2010, J ENVIRON PLANN MAN, V53, P439, DOI 10.1080/09640561003703772.
   Baynes T, 2011, ENERG POLICY, V39, P7298, DOI 10.1016/j.enpol.2011.08.053.
   Boltzmann L., 1970, SITZUNGSBERICHTE AKA.
   Bristow DN, 2013, J IND ECOL, V17, P656, DOI 10.1111/jiec.12038.
   Chen GQ, 2013, COMMUN NONLINEAR SCI, V18, P2493, DOI 10.1016/j.cnsns.2012.12.029.
   Chen S., 2014, NETWORK ENV PERSPECT, DOI {[}10.1021/es204662k, DOI 10.1021/ES204662K].
   Chen SQ, 2015, ENVIRON SCI TECHNOL, V49, P11594, DOI 10.1021/acs.est.5b02654.
   Chen SQ, 2015, APPL ENERG, V138, P99, DOI 10.1016/j.apenergy.2014.10.055.
   Chen SQ, 2011, ECOL INDIC, V11, P1664, DOI 10.1016/j.ecolind.2011.04.013.
   Collins A, 2006, J IND ECOL, V10, P9, DOI 10.1162/jiec.2006.10.3.9.
   Fang DL, 2015, ENVIRON SCI TECHNOL, V49, P6722, DOI 10.1021/es505388n.
   Fath BD, 1998, ECOL MODEL, V107, P127, DOI 10.1016/S0304-3800(97)00213-5.
   Fath B, 2007, ECOL MODEL, V208, P56, DOI 10.1016/j.ecolmodel.2007.04.021.
   Forkes J, 2007, RESOUR CONSERV RECY, V52, P74, DOI 10.1016/j.resconrec.2007.02.003.
   Goerner SJ, 2009, ECOL ECON, V69, P76, DOI 10.1016/j.ecolecon.2009.07.018.
   Goldstein B, 2013, ENVIRON RES LETT, V8, DOI 10.1088/1748-9326/8/3/035024.
   Guangdong Statistical Bureau (GSB), 2011, INP OUTP TABL GUANGD.
   Guangdong Statistical Bureau (GSB), 2000, INP OUTP TABL GUANGD.
   Guangdong Statistical Bureau (GSB), 2006, INP OUTP TABL GUANGD.
   Guangdong Statistical Bureau (GSB), 2013, INP OUTP TABL GUANGD.
   Guangdong Statistical Bureau (GSB), 2008, INP OUTP TABL GUANGD.
   Guo RP, 2016, APPL ENERG, V168, P110, DOI 10.1016/j.apenergy.2016.01.063.
   Haberl H, 2006, ENERGY, V31, P87, DOI 10.1016/j.energy.2004.04.045.
   Haberl Helmut, 2001, Journal of Industrial Ecology, V5, P71, DOI 10.1162/10881980152830141.
   HANNON B, 1973, J THEOR BIOL, V41, P535, DOI 10.1016/0022-5193(73)90060-X.
   Hillman T, 2010, ENVIRON SCI TECHNOL, V44, P1902, DOI 10.1021/es9024194.
   Hoornweg D., 2012, CITIES CLIMATE CHANG, DOI {[}10.1007/s10584, DOI 10.1007/S10584].
   Huang JN, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0109996.
   Huang SL, 2007, ECOL MODEL, V201, P495, DOI 10.1016/j.ecolmodel.2006.10.019.
   Huang SL, 2003, LANDSCAPE URBAN PLAN, V63, P61, DOI 10.1016/S0169-2046(02)00152-4.
   Kennedy C, 2012, J IND ECOL, V16, P780, DOI 10.1111/j.1530-9290.2012.00548.x.
   Kharrazi A, 2013, ECOL ECON, V90, P177, DOI 10.1016/j.ecolecon.2013.03.018.
   Kim E, 2012, REG ENVIRON CHANGE, V12, P295, DOI 10.1007/s10113-011-0275-0.
   Li SS, 2012, ENVIRON POLLUT, V170, P169, DOI 10.1016/j.envpol.2012.07.010.
   Li Y, 2011, ECOL MODEL, V222, P1771, DOI 10.1016/j.ecolmodel.2011.03.001.
   Li Y, 2009, ECOL MODEL, V220, P3163, DOI 10.1016/j.ecolmodel.2009.08.007.
   Liang S, 2012, WASTE MANAGE, V32, P220, DOI 10.1016/j.wasman.2011.08.018.
   Loiseau E, 2014, J CLEAN PROD, V80, P17, DOI 10.1016/j.jclepro.2014.05.059.
   Lu YSY, 2016, J CLEAN PROD, V133, P391, DOI 10.1016/j.jclepro.2016.05.174.
   Lundin M., 2002, URBAN WATER, V4, P145, DOI DOI 10.1016/S1462-0758(02)00015-8.
   Mao XF, 2012, ECOL INFORM, V10, P17, DOI 10.1016/j.ecoinf.2011.05.006.
   Mi ZF, 2017, MITIG ADAPT STRAT GL, V22, P45, DOI 10.1007/s11027-015-9660-1.
   Mi ZF, 2015, J CLEAN PROD, V103, P455, DOI 10.1016/j.jclepro.2014.06.011.
   NBSC (National Bureau of Statistics China), 2000, CHIN EN STAT YB 2000.
   NBSC (National Bureau of Statistics of P. R. China), 2008, CHIN EN STAT YB 2000.
   NBSC (National Bureau of Statistics of P. R. China), 2013, CHIN EN STAT YB 2000.
   NBSC (National Bureau of Statistics of P. R. China), 2011, CHIN EN STAT YB 2000.
   NBSC (National Bureau of Statistics of P. R. China), 2006, CHIN EN STAT YB 2000.
   Newman PWG, 1999, LANDSCAPE URBAN PLAN, V44, P219, DOI 10.1016/S0169-2046(99)00009-2.
   Ngo N. S., 2008, Urban Ecosystems, V11, P121, DOI 10.1007/s11252-008-0051-1.
   Samaniego H, 2008, J TRANSLAND USE, V1, P21, DOI DOI 10.5198/JTLU.V1I1.29.
   Shaikh F, 2016, J CLEAN PROD, V139, P1196, DOI 10.1016/j.jclepro.2016.09.002.
   Small GE, 2014, ECOL MODEL, V293, P150, DOI 10.1016/j.ecolmodel.2014.02.001.
   SONG T., 2014, CHINESE GEOGR SCI, V25, P113, DOI DOI 10.1007/S11769-014-0680-7.
   Sun XQ, 2018, J CLEAN PROD, V174, P548, DOI 10.1016/j.jclepro.2017.10.228.
   The State Council of the People's Republic of China (SCC), 2014, NAT PLAN NEW URB 201.
   Ulanowicz R. E, 2012, QUANTITATIVE METHODS, DOI {[}10.1016/13978-0-12-374711-2.00904-9, DOI 10.1016/13978-0-12-374711-2.00904-9].
   Ulanowicz RE, 2014, ECOL LETT, V17, P127, DOI 10.1111/ele.12216.
   Ulanowicz RE, 2009, ECOL MODEL, V220, P1886, DOI 10.1016/j.ecolmodel.2009.04.015.
   Ulanowicz RE, 2009, ECOL COMPLEX, V6, P27, DOI 10.1016/j.ecocom.2008.10.005.
   Wackernagel M, 2006, ENVIRON URBAN, V18, P103, DOI 10.1177/0956247806063978.
   Wang SG, 2016, APPL ENERG, V178, P773, DOI 10.1016/j.apenergy.2016.06.112.
   WOLMAN A, 1965, SCI AM, V213, P179.
   Wu B, 2016, J CLEAN PROD, V112, P3138, DOI 10.1016/j.jclepro.2015.11.009.
   Xia LL, 2016, SCI TOTAL ENVIRON, V544, P103, DOI 10.1016/j.scitotenv.2015.11.110.
   Xia XH, 2015, ECOL INFORM, V26, P60, DOI 10.1016/j.ecoinf.2014.09.008.
   Yang DW, 2014, ECOL MODEL, V272, P40, DOI 10.1016/j.ecolmodel.2013.09.014.
   Yang SY, 2016, APPL ENERG, V184, P882, DOI 10.1016/j.apenergy.2016.04.087.
   Yang WC, 2016, RENEW SUST ENERG REV, V61, P341, DOI 10.1016/j.rser.2016.04.015.
   Yang ZF, 2014, LANDSCAPE URBAN PLAN, V121, P19, DOI 10.1016/j.landurbplan.2013.09.004.
   Yang ZF, 2012, ENVIRON SCI TECHNOL, V46, P1796, DOI 10.1021/es203657t.
   Yi I, 2007, INT J LIFE CYCLE ASS, V12, P353, DOI 10.1065/lca2007.06.339.
   Zhang Y, 2016, J CLEAN PROD, V112, P4304, DOI 10.1016/j.jclepro.2015.06.052.
   Zhang Y, 2015, ENVIRON SCI TECHNOL, V49, P11247, DOI 10.1021/acs.est.5b03060.
   Zhang Y, 2014, ENERG POLICY, V73, P540, DOI 10.1016/j.enpol.2014.04.029.
   Zhang Y, 2014, ECOL MODEL, V272, P188, DOI 10.1016/j.ecolmodel.2013.10.003.
   Zhang Y, 2014, SCI TOTAL ENVIRON, V468, P642, DOI 10.1016/j.scitotenv.2013.08.047.
   Zhang Y, 2011, ECOL MODEL, V223, P14, DOI 10.1016/j.ecolmodel.2011.08.005.
   Zhang Y, 2010, ECOL MODEL, V221, P1865, DOI 10.1016/j.ecolmodel.2010.05.006.
   Zhang Y, 2009, ECOL MODEL, V220, P1431, DOI 10.1016/j.ecolmodel.2009.02.001.
   Zhang Y, 2009, ECOL MODEL, V220, P1690, DOI 10.1016/j.ecolmodel.2009.04.002.
   Zheng HM, 2017, J IND ECOL, V21, P166, DOI 10.1111/jiec.12432.}},
Number-of-Cited-References = {{82}},
Times-Cited = {{21}},
Usage-Count-Last-180-days = {{7}},
Usage-Count-Since-2013 = {{55}},
Journal-ISO = {{J. Clean Prod.}},
Doc-Delivery-Number = {{GT2XU}},
Unique-ID = {{ISI:000444364400065}},
DA = {{2020-08-06}},
}

@article{ ISI:000444364400076,
Author = {Zhang, Bo and Guan, Shihui and Wu, Xiaofang and Zhao, Xueli},
Title = {{Tracing natural resource uses via China's supply chains}},
Journal = {{JOURNAL OF CLEANER PRODUCTION}},
Year = {{2018}},
Volume = {{196}},
Pages = {{880-888}},
Month = {{SEP 20}},
Abstract = {{This paper makes an in-depth analysis on demand-driven natural resource
   requirements in China via the methods of thermodynamic input-output
   analysis and structural path analysis, in order to reveal the
   connections between the country's rapid economic development and its
   intensive use of natural resources. The main natural resources
   investigated include crops, forestry, rangeland, aquatic products, coal,
   crude oil \& natural gas, ferrous metal ores, nonferrous metal ores,
   nonmetallic minerals and other primary energy, and exergy is adopted as
   a common metric for the resource accounting. In 2012, the total domestic
   resource exergy input into Chinese economic system amounted to 130.1 EJ,
   of which 44.6\% was induced by investment demands. The embodied resource
   use (ERU) in China's exports was equivalent to over one fifth of its
   domestic resource supply. The two integrative sectors of Manufacturing
   and Construction accounted for 44.1\% and 28.7\% of the national total
   ERU, respectively. We identified critical supply chain paths starting
   from resource extraction to final demand, as well as key industrial
   sectors in driving the extraction, transmission and final use of
   embodied resources. The top 50 paths were responsible for 30.4 EJ of the
   ERU. The identification of resource supply chains from a systemic
   perspective is of great importance when resource and environmental
   policies are to be applied to concrete industrial sectors and other
   economic agents. Integrated approaches that take account of
   consumption-based resource indicators should be developed for resource
   conservation and cleaner production, particularly for the economic
   system with a complex supply network. (C) 2018 Elsevier Ltd. All rights
   reserved.}},
Publisher = {{ELSEVIER SCI LTD}},
Address = {{THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Zhang, B (Corresponding Author), China Univ Min \& Technol Beijing, Sch Management, Beijing 100083, Peoples R China.
   Wu, XF (Corresponding Author), Zhongnan Univ Econ \& Law, Econ Sch, Wuhan 430073, Hubei, Peoples R China.
   Zhang, Bo; Guan, Shihui; Zhao, Xueli, China Univ Min \& Technol Beijing, Sch Management, Beijing 100083, Peoples R China.
   Zhang, Bo, Harvard Univ, Sch Engn \& Appl Sci, Harvard China Project, Cambridge, MA 02138 USA.
   Wu, Xiaofang, Zhongnan Univ Econ \& Law, Econ Sch, Wuhan 430073, Hubei, Peoples R China.}},
DOI = {{10.1016/j.jclepro.2018.06.109}},
ISSN = {{0959-6526}},
EISSN = {{1879-1786}},
Keywords = {{Natural resources; Embodied exergy; Thermodynamic input-output analysis;
   Structural path analysis; Supply chains in China}},
Keywords-Plus = {{EXERGY CONSUMPTION; CARBON EMISSIONS; SYSTEMS ACCOUNT; INDUSTRY SECTORS;
   UNITED-STATES; ECONOMY 2007; ENERGY USE; INVENTORY; FOOTPRINT;
   SUSTAINABILITY}},
Research-Areas = {{Science \& Technology - Other Topics; Engineering; Environmental
   Sciences \& Ecology}},
Web-of-Science-Categories  = {{Green \& Sustainable Science \& Technology; Engineering, Environmental;
   Environmental Sciences}},
Author-Email = {{zhangbo@cumtb.edu.cn
   xfwu@zuel.edu.cn}},
ResearcherID-Numbers = {{Wu, Xiaofang/O-7253-2014}},
Funding-Acknowledgement = {{National Natural Science Foundation of ChinaNational Natural Science
   Foundation of China {[}71774161, 71403270, 71373262]; National Key
   Research and Development Program of China {[}2017YFB0601900]; Yue Qi
   Young Scholar Project, China University of Mining \&Technology (Beijing)}},
Funding-Text = {{This study has been supported by the National Natural Science Foundation
   of China (Grant nos. 71774161, 71403270 and 71373262), the National Key
   Research and Development Program of China (Grant no. 2017YFB0601900),
   and the Yue Qi Young Scholar Project, China University of Mining
   \&Technology (Beijing). Very helpful comments by the anonymous reviewers
   and the editor are highly appreciated.}},
Cited-References = {{An Q, 2015, ECOL MODEL, V318, P26, DOI 10.1016/j.ecolmodel.2015.01.020.
   An Q, 2014, ENERGY, V74, P67, DOI 10.1016/j.energy.2014.03.030.
   Chen B, 2006, ECOL MODEL, V196, P313, DOI 10.1016/j.ecolmodel.2006.02.019.
   Chen B, 2006, ENERGY, V31, P1115, DOI 10.1016/j.energy.2005.06.003.
   Chen GQ, 2007, ECOL MODEL, V208, P102, DOI 10.1016/j.ecolmodel.2007.04.017.
   Chen GQ, 2007, ENERG POLICY, V35, P2038, DOI 10.1016/j.enpol.2006.06.009.
   Chen GQ, 2010, ENERG POLICY, V38, P6180, DOI 10.1016/j.enpol.2010.06.004.
   Chen GQ, 2010, COMMUN NONLINEAR SCI, V15, P3647, DOI 10.1016/j.cnsns.2009.12.024.
   Chen G. Q., 2006, COMMUN NONLINEAR SCI, V11, P531, DOI DOI 10.1016/J.CNSNS.2004.11.009.
   Chen GQ, 2005, ECOL MODEL, V184, P363, DOI 10.1016/j.ecolmodel.2004.10.015.
   Chen WD, 2017, RESOUR CONSERV RECY, V117, P264, DOI 10.1016/j.resconrec.2016.10.008.
   Chen ZM, 2013, COMMUN NONLINEAR SCI, V18, P1757, DOI 10.1016/j.cnsns.2012.11.004.
   CSY, 2016, CHIN STAT YB 2016.
   Dewulf J, 2008, ENVIRON SCI TECHNOL, V42, P2221, DOI 10.1021/es071719a.
   Dincer I., 2007, EXERGY ENERGY ENV SU.
   Fu F, 2014, ENERG CONVERS MANAGE, V85, P573, DOI 10.1016/j.enconman.2014.05.046.
   Hawkins J, 2015, ENERG ECON, V48, P81, DOI 10.1016/j.eneco.2014.12.002.
   Hermann WA, 2006, ENERGY, V31, P1685, DOI 10.1016/j.energy.2005.09.006.
   Hoekstra AY, 2014, SCIENCE, V344, P1114, DOI 10.1126/science.1248365.
   Kuishuang Feng, 2016, Energy, Ecology and Environment, V1, P39, DOI 10.1007/s40974-016-0015-x.
   Lenzen M, 2016, ECON SYST RES, V28, P119, DOI 10.1080/09535314.2016.1170991.
   Lenzen M, 2012, ENVIRON SCI TECHNOL, V46, P8374, DOI 10.1021/es300171x.
   Llop M, 2015, ECOL ECON, V109, P203, DOI 10.1016/j.ecolecon.2014.11.011.
   Meng J, 2015, ENVIRON RES LETT, V10, DOI 10.1088/1748-9326/10/5/054005.
   Miller R. E., 2009, INPUT OUTPUT ANAL FD.
   NBSC (National Bureau of Statistics of China), 2015, 2012 INP OUTP TABL C.
   Nguyen HX, 2007, RESOUR CONSERV RECY, V51, P870, DOI 10.1016/j.resconrec.2007.01.004.
   O'Rourke D, 2014, SCIENCE, V344, P1124, DOI 10.1126/science.1248526.
   Oshita Y, 2012, ENERG ECON, V34, P1041, DOI 10.1016/j.eneco.2011.08.013.
   Rosen MA, 2008, ENERG POLICY, V36, P128, DOI 10.1016/j.enpol.2007.09.006.
   Sciubba E, 2005, INT J ENERG RES, V29, P613, DOI 10.1002/er.1096.
   Sciubba E, 2007, INT J THERMODYN, V10, P1.
   Shao L, 2013, ECOL MODEL, V252, P83, DOI 10.1016/j.ecolmodel.2012.09.001.
   Skelton A, 2011, ENVIRON SCI TECHNOL, V45, P10516, DOI 10.1021/es202313e.
   Sun XD, 2017, APPL ENERG, V196, P118, DOI 10.1016/j.apenergy.2016.12.088.
   Szargut J, 2005, EXERGY METHOD ITS AP.
   Treloar G., 1997, ECON SYST RES, V9, P375, DOI DOI 10.1080/09535319700000032.
   Ukidwe NU, 2008, COMPUT CHEM ENG, V32, P2050, DOI 10.1016/j.compchemeng.2008.02.006.
   Ukidwe NU, 2007, ENERGY, V32, P1560, DOI 10.1016/j.energy.2006.11.005.
   Ukidwe NU, 2004, ENVIRON SCI TECHNOL, V38, P4810, DOI 10.1021/es035367t.
   WALL G, 1977, 7742 I THEOR PHYS.
   Warr B, 2008, ECOL ECON, V68, P126, DOI 10.1016/j.ecolecon.2008.02.019.
   Wiedmann TO, 2015, P NATL ACAD SCI USA, V112, P6271, DOI 10.1073/pnas.1220362110.
   Wu XF, 2017, ENERG POLICY, V108, P81, DOI 10.1016/j.enpol.2017.05.048.
   Yang ZY, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0135727.
   Zhang B, 2018, RESOUR CONSERV RECY, V132, P324, DOI 10.1016/j.resconrec.2017.05.011.
   Zhang B, 2018, EARTHS FUTURE, V6, P103, DOI 10.1002/2017EF000707.
   Zhang B, 2017, APPL ENERG, V191, P425, DOI 10.1016/j.apenergy.2017.01.066.
   Zhang B, 2013, ENERG POLICY, V63, P1169, DOI 10.1016/j.enpol.2013.08.062.
   Zhang B, 2012, SCI WORLD J, DOI 10.1100/2012/692746.
   Zhang B, 2010, RENEW SUST ENERG REV, V14, P1527, DOI 10.1016/j.rser.2010.01.021.
   Zhang LX, 2017, J CLEAN PROD, V142, P829, DOI 10.1016/j.jclepro.2016.03.063.
   Zhang Q, 2015, SUSTAINABILITY-BASEL, V7, P8223, DOI 10.3390/su7078223.}},
Number-of-Cited-References = {{53}},
Times-Cited = {{6}},
Usage-Count-Last-180-days = {{11}},
Usage-Count-Since-2013 = {{55}},
Journal-ISO = {{J. Clean Prod.}},
Doc-Delivery-Number = {{GT2XU}},
Unique-ID = {{ISI:000444364400076}},
DA = {{2020-08-06}},
}

@article{ ISI:000445727500047,
Author = {Hu, Chundi and Chen, Dake and Huang, Gang and Yang, Song},
Title = {{Dipole Types of Autumn Precipitation Variability Over the Subtropical
   East Asia-Western Pacific Modulated by Shifting ENSO}},
Journal = {{GEOPHYSICAL RESEARCH LETTERS}},
Year = {{2018}},
Volume = {{45}},
Number = {{17}},
Pages = {{9123-9130}},
Month = {{SEP 16}},
Abstract = {{Recent autumns have exhibited two dipole types of precipitation
   variability (DIPO1 and DIPO2) over the subtropical East Asia-western
   Pacific (SEAWP). Meanwhile, El Nino-Southern Oscillation (ENSO) has
   undergone significant mode shift, exhibiting as the eastern Pacific or
   central Pacific ENSO (EPSO or CPSO). Whether there was any physical
   linkage between the two remains unclear. Here we demonstrate for the
   first time that interannual variations of DIPO1 and DIPO2 are closely
   linked to EPSO and CPSO, respectively. Shifting ENSO modulates the
   patterns of SEAWP precipitation dipole mainly via changing the path of
   tropospheric moisture transport and the location of anomalous water
   vapor convergence/divergence due to different low-level
   cyclone/anticyclone activities induced by EPSO or CPSO. Further analysis
   shows that the interdecadal variability of SEAWP precipitation is also
   linked to the La Nina-like climate shift in 1998. Hence, our study
   provides some new insight into the ongoing hot debate on the shifting
   ENSO and increasing autumn drought in southeastern China.
   Plain Language Summary We explore the mode changes in the subtropical
   East Asia-western Pacific (SEAWP) precipitation variability during
   boreal autumn over 1979-2016, which is important for crop ripening and
   harvest from the autumn drought perspective. Results show that the
   eastern Pacific ENSO (El Nino-Southern Oscillation) and central Pacific
   ENSO primarily drive the DIPO1 and DIPO2 via modifying the low-level
   atmospheric circulation and moisture response over the SEAWP,
   respectively. So it should be emphasized that shifting ENSO is
   responsible for modulating the SEAWP precipitation dipoles to certain
   extent in the context of global warming.}},
Publisher = {{AMER GEOPHYSICAL UNION}},
Address = {{2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Hu, CD (Corresponding Author), Second Inst Oceanog, State Key Lab Satellite Ocean Environm Dynam, Hangzhou, Zhejiang, Peoples R China.
   Hu, CD (Corresponding Author), Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci \& Geop, Beijing, Peoples R China.
   Hu, CD (Corresponding Author), Sun Yat Sen Univ, Sch Atmospher Sci, Guangzhou, Guangdong, Peoples R China.
   Hu, Chundi; Chen, Dake, Second Inst Oceanog, State Key Lab Satellite Ocean Environm Dynam, Hangzhou, Zhejiang, Peoples R China.
   Hu, Chundi; Huang, Gang, Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci \& Geop, Beijing, Peoples R China.
   Hu, Chundi; Yang, Song, Sun Yat Sen Univ, Sch Atmospher Sci, Guangzhou, Guangdong, Peoples R China.
   Yang, Song, Sun Yat Sen Univ, Guangdong Prov Key Lab Climate Change \& Nat Disas, Guangzhou, Guangdong, Peoples R China.
   Yang, Song, Sun Yat Sen Univ, Inst Earth Climate \& Environm Syst, Guangzhou, Guangdong, Peoples R China.}},
DOI = {{10.1029/2018GL078982}},
ISSN = {{0094-8276}},
EISSN = {{1944-8007}},
Keywords = {{Autumn Precipitation Variability; Two Dipole Types; Subtropical East
   Asia-Western Pacific; Cimate Teleconnection; EP ENSO; CP ENSO}},
Keywords-Plus = {{SEA-SURFACE TEMPERATURE; EL-NINO; IMPACTS; OSCILLATION; DROUGHT; CANADA;
   OCEAN}},
Research-Areas = {{Geology}},
Web-of-Science-Categories  = {{Geosciences, Multidisciplinary}},
Author-Email = {{chundi\_nju@yeah.net}},
ResearcherID-Numbers = {{huang, gang/D-6427-2012
   }},
ORCID-Numbers = {{huang, gang/0000-0002-8692-7856
   Hu, Chundi/0000-0002-3519-3784
   Chen, Dake/0000-0002-8193-7158}},
Funding-Acknowledgement = {{National Natural Science Foundation of ChinaNational Natural Science
   Foundation of China {[}41705050, 41690121, 41690123, 41690120, 41621064,
   41730535]; National Postdoctoral Program for Innovative Talents
   {[}BX201600039]; China Postdoctoral Science FoundationChina Postdoctoral
   Science Foundation {[}2018M630646]; Scientific Research Fund of the
   Second Institute of Oceanography {[}JB1702]; National Program on Global
   Change and Air-Sea Interaction {[}GASI-IPOVAI-04]}},
Funding-Text = {{This research is supported by the National Natural Science Foundation of
   China (grants 41705050, 41690121, 41690123, 41690120, 41621064, and
   41730535); the National Postdoctoral Program for Innovative Talents
   (grant BX201600039); the China Postdoctoral Science Foundation (grant
   2018M630646); the Scientific Research Fund of the Second Institute of
   Oceanography (grant JB1702); and the National Program on Global Change
   and Air-Sea Interaction (grant GASI-IPOVAI-04). We appreciate the
   valuable comments from the anonymous reviewers. We are grateful to all
   data providers: The ERSST-v5, GPCP-v2.3, and NOAA's PREC/L data products
   are provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, available
   at https://www.esrl.noaa.gov/psd/; the ERA-Interim atmospheric data sets
   are obtained from http://apps.ecmwf.int/datasets/; and the Hadley SST
   data is available at https://www.metoffice.gov.uk/hadobs/hadisst/.}},
Cited-References = {{Adler RF, 2003, J HYDROMETEOROL, V4, P1147, DOI 10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.
   An SI, 2005, J CLIMATE, V18, P876, DOI 10.1175/JCLI-3298.1.
   Ashok K, 2007, J GEOPHYS RES-OCEANS, V112, DOI 10.1029/2006JC003798.
   Barriopedro D, 2012, J HYDROMETEOROL, V13, P1251, DOI 10.1175/JHM-D-11-074.1.
   Cai WJ, 2015, NAT CLIM CHANGE, V5, P849, DOI {[}10.1038/nclimate2743, 10.1038/NCLIMATE2743].
   Capotondi A, 2015, B AM METEOROL SOC, V96, P921, DOI 10.1175/BAMS-D-13-00117.1.
   Chen MY, 2002, J HYDROMETEOROL, V3, P249, DOI 10.1175/1525-7541(2002)003<0249:GLPAYM>2.0.CO;2.
   Collins M, 2010, NAT GEOSCI, V3, P391, DOI 10.1038/NGEO868.
   Dee DP, 2011, Q J ROY METEOR SOC, V137, P553, DOI 10.1002/qj.828.
   Ding QH, 2014, NATURE, V509, P209, DOI 10.1038/nature13260.
   Gu W, 2015, INT J CLIMATOL, V35, P1543, DOI 10.1002/joc.4075.
   Henley BJ, 2015, CLIM DYNAM, V45, P3077, DOI 10.1007/s00382-015-2525-1.
   Hong CC, 2016, CLIM DYNAM, V47, P2587, DOI 10.1007/s00382-016-2986-x.
   Hsu PC, 2014, J CLIMATE, V27, P4296, DOI 10.1175/JCLI-D-13-00417.1.
   Hu CD, 2018, CLIM DYNAM, V51, P2455, DOI 10.1007/s00382-017-4022-1.
   Hu CD, 2016, CLIM DYNAM, V47, P4031, DOI 10.1007/s00382-016-3059-x.
   Hu CD, 2016, NAT COMMUN, V7, DOI 10.1038/ncomms11721.
   Hu CD, 2016, J CLIMATE, V29, P2109, DOI 10.1175/JCLI-D-15-0403.1.
   Huang BY, 2017, J CLIMATE, V30, P8179, DOI 10.1175/JCLI-D-16-0836.1.
   Jia XJ, 2016, INT J CLIMATOL, V36, P3384, DOI 10.1002/joc.4562.
   Jo HS, 2015, GEOPHYS RES LETT, V42, P7171, DOI 10.1002/2015GL065049.
   Lee T, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2010GL044007.
   Mantua NJ, 1997, B AM METEOROL SOC, V78, P1069, DOI 10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2.
   McPhaden MJ, 2006, SCIENCE, V314, P1740, DOI 10.1126/science.1132588.
   Power S, 2006, J CLIMATE, V19, P4755, DOI 10.1175/JCLI3868.1.
   Rayner NA, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD002670.
   Wang B, 2000, J CLIMATE, V13, P1517, DOI 10.1175/1520-0442(2000)013<1517:PEATHD>2.0.CO;2.
   Wang L, 2015, CLIM DYNAM, V45, P2527, DOI 10.1007/s00382-015-2490-8.
   Wei T, 2018, J GEOPHYS RES-ATMOS, V123, P835, DOI 10.1002/2017JD027092.
   Wu LG, 2015, GEOPHYS RES LETT, V42, P1537, DOI 10.1002/2015GL063450.
   Yeh SW, 2018, REV GEOPHYS, V56, P185, DOI 10.1002/2017RG000568.
   Yeh SW, 2009, NATURE, V461, P511, DOI 10.1038/nature08316.
   Yu JY, 2013, INT J CLIMATOL, V33, P2105, DOI 10.1002/joc.3575.
   Zhang RH, 2017, SCI CHINA EARTH SCI, V60, P1124, DOI 10.1007/s11430-016-9026-x.
   Zhang RH, 1996, J METEOROL SOC JPN, V74, P49, DOI 10.2151/jmsj1965.74.1\_49.
   Zhang RH, 2002, J METEOROL SOC JPN, V80, P213, DOI 10.2151/jmsj.80.213.
   Zhang RH, 1999, ADV ATMOS SCI, V16, P229, DOI 10.1007/BF02973084.
   Zhang WJ, 2014, GEOPHYS RES LETT, V41, P4020, DOI 10.1002/2014GL060130.
   Zhang WJ, 2013, J CLIMATE, V26, P8392, DOI 10.1175/JCLI-D-12-00851.1.
   Zhang WJ, 2011, J METEOROL SOC JPN, V89, P563, DOI 10.2151/jmsj.2011-510.
   Zhang Y, 1997, J CLIMATE, V10, P1004, DOI 10.1175/1520-0442(1997)010<1004:ELIV>2.0.CO;2.
   Zhao HK, 2016, CLIM DYNAM, V47, P315, DOI 10.1007/s00382-015-2837-1.}},
Number-of-Cited-References = {{42}},
Times-Cited = {{0}},
Usage-Count-Last-180-days = {{0}},
Usage-Count-Since-2013 = {{6}},
Journal-ISO = {{Geophys. Res. Lett.}},
Doc-Delivery-Number = {{GV0DN}},
Unique-ID = {{ISI:000445727500047}},
OA = {{Other Gold}},
DA = {{2020-08-06}},
}

@article{ ISI:000445727500059,
Author = {Williams, Ian N. and Patricola, Christina M.},
Title = {{Diversity of ENSO Events Unified by Convective Threshold Sea Surface
   Temperature: A Nonlinear ENSO Index}},
Journal = {{GEOPHYSICAL RESEARCH LETTERS}},
Year = {{2018}},
Volume = {{45}},
Number = {{17}},
Pages = {{9236-9244}},
Month = {{SEP 16}},
Abstract = {{We show that the well-known failure of any single index to capture the
   diversity and extremes of El Nino-Southern Oscillation (ENSO) results
   from the inability of existing indices to uniquely characterize the
   average longitude of deep convection in the Walker Circulation. We
   present a simple sea surface temperature (SST)-based index of this
   longitude that compactly characterizes the different spatial patterns,
   or flavors of observed and projected ENSO events. It recovers the
   familiar global responses of temperature, precipitation, and tropical
   cyclones to ENSO and identifies historical extreme El Nino events.
   Despite its simplicity, the new longitude index describes the nonlinear
   relationship between the first two principal components of SST, and
   unlike previous indices, accounts for background SST changes associated
   with the seasonal cycle and climate change. The index reveals that
   extreme El Nino, El Nino Modoki, and La Nina events are projected to
   become more frequent in the future at the expense of neutral ENSO
   conditions.
   Plain Language Summary It is widely known that every El Nino is
   different. The 2015-2016 El Nino event is a case in point. Despite
   having similar sea surface temperature warming as historical events
   (1982-1983 and 1997-1998), the 2015-2016 El Nino had unexpectedly weak
   impacts on precipitation in the Western United States. No single index
   has been able to capture the diversity of El Nino-Southern Oscillation
   (ENSO) events (including La Nina), which has led to a proliferation of
   indices for each kind of ENSO event. Yet none of the existing indices
   measures the key atmospheric feature of ENSOthe west-to-east oscillation
   in the longitude of deep convection (thunderstorms) across the
   equatorial Pacific. We quantified this longitude and found that it
   compactly characterizes the diversity of ENSO events, clearly indicates
   extreme El Nino events (like 1982-1983 and 1997-1998) that produce the
   greatest impacts on global climate, and performs as well as existing
   indices in revealing the remote climate impacts of ENSO. Widespread
   adoption of this practical new index will simplify model evaluation of
   ENSO and more effectively monitor and communicate the state of the
   coupled climate system.}},
Publisher = {{AMER GEOPHYSICAL UNION}},
Address = {{2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Williams, IN (Corresponding Author), Lawrence Berkeley Natl Lab, Climate \& Ecosyst Sci Div, Berkeley, CA 94720 USA.
   Williams, Ian N.; Patricola, Christina M., Lawrence Berkeley Natl Lab, Climate \& Ecosyst Sci Div, Berkeley, CA 94720 USA.}},
DOI = {{10.1029/2018GL079203}},
ISSN = {{0094-8276}},
EISSN = {{1944-8007}},
Keywords = {{ENSO diversity; extreme El Nino; Modoki; precipitation teleconnection;
   convective threshold; ENSO index}},
Keywords-Plus = {{NINO SOUTHERN-OSCILLATION; EL-NINO; PRECIPITATION ANOMALIES; SEASONAL
   CYCLE; NORTH-AMERICA; PACIFIC; ATMOSPHERE; IMPACTS; MODEL; VARIABILITY}},
Research-Areas = {{Geology}},
Web-of-Science-Categories  = {{Geosciences, Multidisciplinary}},
Author-Email = {{inwilliams@lbl.gov}},
ResearcherID-Numbers = {{Patricola, Christina M/L-9902-2016
   Williams, Ian/G-3256-2015}},
ORCID-Numbers = {{Patricola, Christina M/0000-0002-3387-0307
   Williams, Ian/0000-0003-0355-1310}},
Funding-Acknowledgement = {{U.S. Department of Energy, Office of Science, Office of Biological and
   Environmental Research, Climate and Environmental Sciences Division,
   Regional \& Global Climate Modeling ProgramUnited States Department of
   Energy (DOE) {[}DE-AC02-05CH11231]; U.S. Department of Energy, Office of
   Science, Office of Biological and Environmental ResearchUnited States
   Department of Energy (DOE); Office of Science of the U.S. Department of
   EnergyUnited States Department of Energy (DOE) {[}DE-AC02-05CH11231];
   U.S. Department of Energy, Office of Science, Office of Biological and
   Environmental Research, Climate and Environmental Sciences Division,
   Atmospheric System Research ProgramUnited States Department of Energy
   (DOE) {[}DE-AC02-05CH11231]}},
Funding-Text = {{We thank two anonymous reviewers for their helpful comments and
   suggestions. This material is based upon work supported by the U.S.
   Department of Energy, Office of Science, Office of Biological and
   Environmental Research, Climate and Environmental Sciences Division,
   Regional \& Global Climate Modeling and Atmospheric System Research
   Programs, under Award Number DE-AC02-05CH11231. Simulation data from
   E3SMv1 were obtained from the Energy Exascale Earth System Model
   project, sponsored by the U.S. Department of Energy, Office of Science,
   Office of Biological and Environmental Research. The data were produced
   using resources of the National Energy Research Scientific Computing
   Center, a DOE Office of Science User Facility supported by the Office of
   Science of the U.S. Department of Energy under Contract No.
   DE-AC02-05CH11231. E3SMv1 data obtained through the Earth System Grid
   Federation (ESGF). Simulation data from CESM-LENS were produced by the
   CESM Large Ensemble Community Project and obtained through the NCAR
   Climate Data Gateway. GPCC Precipitation data and NOAA\_ERSST\_V5 data
   provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their
   Website at https://www.esrl.noaa.gov/psd/. HadISST data provided by the
   Met Office Hadley Centre, from their Website at
   http://www.metoffice.gov.uk/hadobs.}},
Cited-References = {{Ashok K, 2007, J GEOPHYS RES-OCEANS, V112, DOI 10.1029/2006JC003798.
   BATTISTI DS, 1989, J ATMOS SCI, V46, P1687, DOI 10.1175/1520-0469(1989)046<1687:IVIATA>2.0.CO;2.
   Bell GD, 2000, B AM METEOROL SOC, V81, pS1, DOI 10.1175/1520-0477(2000)81{[}s1:CAF]2.0.CO;2.
   Bellenger H, 2014, CLIM DYNAM, V42, P1999, DOI 10.1007/s00382-013-1783-z.
   Biasutti M, 2018, NAT GEOSCI, V11, P392, DOI 10.1038/s41561-018-0137-1.
   Cai WJ, 2015, NAT CLIM CHANGE, V5, P132, DOI {[}10.1038/NCLIMATE2492, 10.1038/nclimate2492].
   Cai WJ, 2014, NAT CLIM CHANGE, V4, P111, DOI {[}10.1038/nclimate2100, 10.1038/NCLIMATE2100].
   Capotondi A, 2015, B AM METEOROL SOC, V96, P921, DOI 10.1175/BAMS-D-13-00117.1.
   CHANG P, 1995, J ATMOS SCI, V52, P2353, DOI 10.1175/1520-0469(1995)052<2353:IBTSCA>2.0.CO;2.
   Chen C, 2017, J CLIMATE, V30, P775, DOI 10.1175/JCLI-D-15-0901.1.
   Chen MY, 2018, MON WEATHER REV, V146, P909, DOI 10.1175/MWR-D-17-0116.1.
   Chiang JCH, 2004, J CLIMATE, V17, P4143, DOI 10.1175/JCLI4953.1.
   Chiodi AM, 2015, J CLIMATE, V28, P6133, DOI 10.1175/JCLI-D-14-00387.1.
   Chiodi AM, 2013, J CLIMATE, V26, P822, DOI 10.1175/JCLI-D-12-00097.1.
   Deser C, 2018, J CLIMATE, V31, P4991, DOI 10.1175/JCLI-D-17-0783.1.
   Deser C, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2010GL043321.
   Dommenget D, 2013, CLIM DYNAM, V40, P2825, DOI 10.1007/s00382-012-1475-0.
   Fedorov AV, 2006, SCIENCE, V312, P1485, DOI 10.1126/science.1122666.
   Galeotti S, 2010, GEOLOGY, V38, P419, DOI 10.1130/G30629.1.
   GILL AE, 1980, Q J ROY METEOR SOC, V106, P447, DOI 10.1256/smsqj.44904.
   Hoerling MP, 2002, J CLIMATE, V15, P2184, DOI 10.1175/1520-0442(2002)015<2184:ARPAWT>2.0.CO;2.
   HOREL JD, 1982, MON WEATHER REV, V110, P1863, DOI 10.1175/1520-0493(1982)110<1863:OTACOT>2.0.CO;2.
   Hoyos CD, 2012, CLIM DYNAM, V38, P1501, DOI 10.1007/s00382-011-1181-3.
   Huang BY, 2017, J CLIMATE, V30, P8179, DOI 10.1175/JCLI-D-16-0836.1.
   Huber M, 2003, SCIENCE, V299, P877, DOI 10.1126/science.1078766.
   Ivany LC, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL048635.
   Johnson NC, 2016, CLIM DYNAM, V47, P3737, DOI 10.1007/s00382-016-3039-1.
   Johnson NC, 2010, NAT GEOSCI, V3, P842, DOI {[}10.1038/ngeo1008, 10.1038/NGEO1008].
   Kanamitsu M, 2002, B AM METEOROL SOC, V83, P1631, DOI {[}10.1175/BAMS-83-11-1631(2002)083<1631:NAR>2.3.CO;2, 10.1175/Bams-83-11-1631].
   Kay JE, 2015, B AM METEOROL SOC, V96, P1333, DOI 10.1175/BAMS-D-13-00255.1.
   Knapp KR, 2010, B AM METEOROL SOC, V91, P363, DOI 10.1175/2009BAMS2755.1.
   L'Heureux ML, 2017, B AM METEOROL SOC, V98, P1363, DOI 10.1175/BAMS-D-16-0009.1.
   Landsea C. N., 2013, ATLANTIC HURRICANE D.
   Lee SK, 2018, GEOPHYS RES LETT, V45, P907, DOI 10.1002/2017GL076197.
   Li Y, 2017, J GEOPHYS RES-OCEANS, V122, P8524, DOI 10.1002/2017JC013052.
   Manucharyan GE, 2014, J CLIMATE, V27, P5836, DOI 10.1175/JCLI-D-13-00759.1.
   McBride JL, 2003, J CLIMATE, V16, P2905, DOI 10.1175/1520-0442(2003)016<2905:RBTMCH>2.0.CO;2.
   Neale RB, 2008, J CLIMATE, V21, P5904, DOI 10.1175/2008JCLI2244.1.
   NEELIN JD, 1987, MON WEATHER REV, V115, P3, DOI 10.1175/1520-0493(1987)115<0003:MTCBOT>2.0.CO;2.
   Paek H, 2017, GEOPHYS RES LETT, V44, P1848, DOI 10.1002/2016GL071515.
   Patricola CM, 2018, J CLIMATE, V31, P5395, DOI 10.1175/JCLI-D-17-0678.1.
   Patricola CM, 2016, NAT GEOSCI, V9, P155, DOI 10.1038/NGEO2624.
   Rayner NA, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD002670.
   Schneider T, 2014, NATURE, V513, P45, DOI 10.1038/nature13636.
   Sobel AH, 2000, J CLIMATE, V13, P4378, DOI 10.1175/1520-0442(2000)013<4378:MTPIAS>2.0.CO;2.
   SUAREZ MJ, 1988, J ATMOS SCI, V45, P3283, DOI 10.1175/1520-0469(1988)045<3283:ADAOFE>2.0.CO;2.
   Swain DL, 2018, NAT CLIM CHANGE, V8, P427, DOI 10.1038/s41558-018-0140-y.
   Takahashi K, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL047364.
   Trenberth KE, 1997, B AM METEOROL SOC, V78, P2771, DOI 10.1175/1520-0477(1997)078<2771:TDOENO>2.0.CO;2.
   Trenberth KE, 2001, J CLIMATE, V14, P1697, DOI 10.1175/1520-0442(2001)014<1697:LIOENO>2.0.CO;2.
   TZIPERMAN E, 1995, J ATMOS SCI, V52, P293, DOI 10.1175/1520-0469(1995)052<0293:IALTTS>2.0.CO;2.
   Williams IN, 2017, GEOPHYS RES LETT, V44, P1503, DOI 10.1002/2016GL072202.
   Williams IN, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL039849.
   Yeh SW, 2009, NATURE, V461, P511, DOI 10.1038/nature08316.
   ZEBIAK SE, 1987, MON WEATHER REV, V115, P2262, DOI 10.1175/1520-0493(1987)115<2262:AMENO>2.0.CO;2.
   Zheng XT, 2016, J CLIMATE, V29, P7265, DOI 10.1175/JCLI-D-16-0039.1.}},
Number-of-Cited-References = {{56}},
Times-Cited = {{11}},
Usage-Count-Last-180-days = {{1}},
Usage-Count-Since-2013 = {{12}},
Journal-ISO = {{Geophys. Res. Lett.}},
Doc-Delivery-Number = {{GV0DN}},
Unique-ID = {{ISI:000445727500059}},
OA = {{Bronze}},
DA = {{2020-08-06}},
}

@article{ ISI:000445617500027,
Author = {Chen, Shangfeng and Wu, Renguang and Song, Linye and Chen, Wen},
Title = {{Combined Influence of the Arctic Oscillation and the Scandinavia Pattern
   on Spring Surface Air Temperature Variations Over Eurasia}},
Journal = {{JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES}},
Year = {{2018}},
Volume = {{123}},
Number = {{17}},
Pages = {{9410-9429}},
Month = {{SEP 16}},
Abstract = {{The present study investigated the joint impacts of the spring Arctic
   Oscillation (AO) and the Scandinavia (SCAND) teleconnection pattern on
   spring surface air temperature (SAT) variations over the midhigh
   latitude Eurasia. It is found that the connection of the spring AO
   (SCAND) to the first two empirical orthogonal function (EOF) modes of
   the spring Eurasian SAT variations depends strongly on the phase of the
   spring SCAND (AO) index. The EOF1 and EOF2 modes display same sign
   variation and tripole pattern, respectively. In the in-phase AO-SCAND
   years, the mid-high latitude Eurasia is covered by same sign SAT
   anomalies, and the spring AO and SCAND indices both have a strong (weak)
   connection with the EOF1 (EOF2) mode. In the out-of-phase AO-SCAND
   years, the SAT anomalies over the mid-high latitude Eurasia are featured
   by a tripole pattern, and the connection of the spring AO and SCAND
   indices with the EOF1 (EOF2) is insignificant (significant). Differences
   in the distribution of SAT anomalies between the in-phase and
   out-of-phase AO-SCAND years are attributed to the constructive and
   destructive superposition of atmospheric circulation anomalies related
   to the spring AO and SCAND indices. The observed joint impacts of the
   spring AO and SCAND on spring Eurasian SAT variations are captured by a
   historical simulation of the flexible global ocean-atmosphere-land
   system model, spectral version 2. Analysis indicates that the prediction
   skill of the spring Eurasian SAT variations related to the first two EOF
   modes is enhanced when taking both the spring AO and SCAND signals into
   account.}},
Publisher = {{AMER GEOPHYSICAL UNION}},
Address = {{2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Wu, RG (Corresponding Author), Chinese Acad Sci, Inst Atmospher Phys, Ctr Monsoon Syst Res, Beijing, Peoples R China.
   Wu, RG (Corresponding Author), Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci \& Geop, Beijing, Peoples R China.
   Chen, Shangfeng; Wu, Renguang; Chen, Wen, Chinese Acad Sci, Inst Atmospher Phys, Ctr Monsoon Syst Res, Beijing, Peoples R China.
   Song, Linye, China Meteorol Adm, Inst Urban Meteorol, Beijing, Peoples R China.
   Wu, Renguang; Chen, Wen, Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci \& Geop, Beijing, Peoples R China.}},
DOI = {{10.1029/2018JD028685}},
ISSN = {{2169-897X}},
EISSN = {{2169-8996}},
Keywords = {{Arctic Oscillation; Scandinavia teleconnection pattern; joint impact;
   spring surface air temperature; Eurasia}},
Keywords-Plus = {{ASIAN WINTER MONSOON; NORTH-ATLANTIC OSCILLATION; ATMOSPHERIC
   CIRCULATION; EASTERN CHINA; SEA-ICE; GEOPOTENTIAL HEIGHT; SOUTHERN
   CHINA; SYSTEM MODEL; HEAT-WAVE; VERSION 2}},
Research-Areas = {{Meteorology \& Atmospheric Sciences}},
Web-of-Science-Categories  = {{Meteorology \& Atmospheric Sciences}},
Author-Email = {{renguang@mail.iap.ac.cn}},
ResearcherID-Numbers = {{Chen, Wen/G-6058-2011
   chen, shangfeng/AAO-9288-2020
   chen, Shangfeng/AAP-2223-2020}},
ORCID-Numbers = {{Chen, Wen/0000-0001-9327-9079
   chen, shangfeng/0000-0003-4347-8592
   }},
Funding-Acknowledgement = {{National Key Research and Development Program of China
   {[}2016YFA0600603]; National Natural Science Foundation of ChinaNational
   Natural Science Foundation of China {[}41605050, 41530425, 41775080,
   41661144016]; Young Elite Scientists Sponsorship Program by the China
   Association for Science and Technology {[}2016QNRC001]; China
   Postdoctoral Science FoundationChina Postdoctoral Science Foundation
   {[}2017 T100102]}},
Funding-Text = {{We thank the three anonymous reviewers for their constructive
   suggestions and comments, which helped to improve the paper. This study
   is supported by the National Key Research and Development Program of
   China grant (2016YFA0600603), the National Natural Science Foundation of
   China grants (41605050, 41530425, 41775080, and 41661144016), the Young
   Elite Scientists Sponsorship Program by the China Association for
   Science and Technology (2016QNRC001), and the China Postdoctoral Science
   Foundation (2017 T100102). The NCEP-NCAR reanalysis data are obtained
   from ftp://ftp.cdc.noaa.gov/Datasets/ncep.reanalysis.derived/. The SAT
   data of the University of Delaware are derived from
   https\%3A\%2F\%2Fwww.esrl.noaa.gov\%2Fpsd\%2Fdata\%2Fgridded\%2Fdata.
   The NOAA CPC teleconnection indices are obtained from
   http://www.cpc.ncep.noaa.gov/. The FGOALS-s2 historical experiments are
   obtained from https://esgf-node.llnl.gov/search/cmip5/.}},
Cited-References = {{{[}Anonymous], 2013, SUMM POL 5 ASS REP I.
   Bao Q, 2013, ADV ATMOS SCI, V30, P561, DOI 10.1007/s00376-012-2113-9.
   Bao Q, 2010, MON WEATHER REV, V138, P2375, DOI 10.1175/2009MWR2950.1.
   BARNSTON AG, 1987, MON WEATHER REV, V115, P1083, DOI 10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2.
   Beniston M, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2003GL018857.
   Chen SX, 2018, ADV MECH ENG, V10, DOI 10.1177/1687814017740710.
   Chen SF, 2018, CLIM DYNAM, V51, P2523, DOI 10.1007/s00382-017-4026-x.
   Chen SF, 2016, J CLIMATE, V29, P1109, DOI 10.1175/JCLI-D-15-0524.1.
   Chen SF, 2014, CLIM DYNAM, V42, P973, DOI 10.1007/s00382-012-1654-z.
   Chen SF, 2013, ADV ATMOS SCI, V30, P1712, DOI 10.1007/s00376-013-2296-8.
   Cheung HN, 2012, J CLIMATE, V25, P4242, DOI 10.1175/JCLI-D-11-00225.1.
   Collins WD, 2006, J CLIMATE, V19, P2122, DOI 10.1175/JCLI3761.1.
   D'Arrigo R, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL027507.
   Ding QH, 2005, J CLIMATE, V18, P3483, DOI 10.1175/JCLI3473.1.
   Ding RQ, 2015, J GEOPHYS RES-ATMOS, V120, P964, DOI 10.1002/2014JD022364.
   DUCHON CE, 1979, J APPL METEOROL, V18, P1016, DOI 10.1175/1520-0450(1979)018<1016:LFIOAT>2.0.CO;2.
   Feudale L, 2011, CLIM DYNAM, V36, P1691, DOI 10.1007/s00382-010-0788-0.
   Gong DY, 2001, GEOPHYS RES LETT, V28, P2073, DOI 10.1029/2000GL012311.
   Graf HF, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2011JD016493.
   Guan YH, 2015, GLOBAL PLANET CHANGE, V124, P79, DOI 10.1016/j.gloplacha.2014.11.008.
   Ham YG, 2013, NAT GEOSCI, V6, P112, DOI {[}10.1038/NGEO1686, 10.1038/ngeo1686].
   Henderson-Sellers A, 1996, GLOBAL PLANET CHANGE, V13, P3, DOI 10.1016/0921-8181(95)00034-8.
   Hurrell J. W., 1997, CLIMATIC CHANGE HIGH, P69, DOI DOI 10.1007/978-94-015-8905-5\_4.
   HURRELL JW, 1995, SCIENCE, V269, P676, DOI 10.1126/science.269.5224.676.
   Kalnay E, 1996, B AM METEOROL SOC, V77, P437, DOI 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
   Keellings D, 2012, THEOR APPL CLIMATOL, V108, P579, DOI 10.1007/s00704-011-0553-2.
   Kunkel KE, 1999, B AM METEOROL SOC, V80, P1077, DOI 10.1175/1520-0477(1999)080<1077:TFIWAC>2.0.CO;2.
   Labat D, 2004, ADV WATER RESOUR, V27, P631, DOI 10.1016/j.advwatres.2004.02.020.
   Lin PF, 2013, ADV ATMOS SCI, V30, P175, DOI 10.1007/s00376-012-2042-7.
   Liu HL, 2012, ACTA METEOROL SIN, V26, P318, DOI 10.1007/s13351-012-0305-y.
   Liu JP, 2012, P NATL ACAD SCI USA, V109, P4074, DOI 10.1073/pnas.1114910109.
   Liu XD, 2001, Q J ROY METEOR SOC, V127, P909, DOI 10.1002/qj.49712757311.
   Liu YY, 2014, CLIM DYNAM, V42, P2817, DOI 10.1007/s00382-014-2163-z.
   Matsuura K, 2009, TERRESTRIAL AIR TEMP.
   NORTH GR, 1982, MON WEATHER REV, V110, P699, DOI 10.1175/1520-0493(1982)110<0699:SEITEO>2.0.CO;2.
   NORTH GR, 1982, MON WEATHER REV, V110, P319, DOI 10.1175/1520-0493(1982)110<0319:TLDOTV>2.0.CO;2.
   Ogi M, 2003, GEOPHYS RES LETT, V30, DOI 10.1029/2003GL017280.
   Oleson K. W, 2004, NCARTN461STR CLM.
   Stott PA, 2004, NATURE, V432, P610, DOI 10.1038/nature03089.
   Sun CH, 2016, CLIM DYNAM, V47, P481, DOI 10.1007/s00382-015-2851-3.
   Sun Y, 2014, NAT CLIM CHANGE, V4, P1082, DOI 10.1038/NCLIMATE2410.
   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.
   WALLACE JM, 1981, MON WEATHER REV, V109, P784, DOI 10.1175/1520-0493(1981)109<0784:TITGHF>2.0.CO;2.
   Wu BY, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2002GL015373.
   Wu RG, 2016, CLIM DYNAM, V47, P2425, DOI 10.1007/s00382-015-2972-8.
   Wu RG, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2011JD015779.
   Wu RG, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2010JD014422.
   Xia JJ, 2016, INT J CLIMATOL, V36, P1291, DOI 10.1002/joc.4424.
   Yao PZ, 1995, J CATASTROPHOL, V10, P51.
   Ye KH, 2015, J GEOPHYS RES-ATMOS, V120, P2738, DOI 10.1002/2015JD023148.
   Ye LM, 2013, ADV ATMOS SCI, V30, P382, DOI 10.1007/s00376-012-1252-3.
   Zhai PM, 2005, J CLIMATE, V18, P1096, DOI 10.1175/JCLI-3318.1.
   Zuo JQ, 2016, CLIM DYNAM, V47, P2331, DOI 10.1007/s00382-015-2966-6.
   Zuo QJ, 2016, J METEOROL RES-PRC, V30, P694, DOI 10.1007/s13351-016-5253-5.}},
Number-of-Cited-References = {{55}},
Times-Cited = {{9}},
Usage-Count-Last-180-days = {{5}},
Usage-Count-Since-2013 = {{19}},
Journal-ISO = {{J. Geophys. Res.-Atmos.}},
Doc-Delivery-Number = {{GU8UH}},
Unique-ID = {{ISI:000445617500027}},
DA = {{2020-08-06}},
}

@article{ ISI:000445727500057,
Author = {Fasullo, J. T. and Otto-Bliesner, B. L. and Stevenson, S.},
Title = {{ENSO's Changing Influence on Temperature, Precipitation, and Wildfire in
   a Warming Climate}},
Journal = {{GEOPHYSICAL RESEARCH LETTERS}},
Year = {{2018}},
Volume = {{45}},
Number = {{17}},
Pages = {{9216-9225}},
Month = {{SEP 16}},
Abstract = {{On interannual to decadal time scales, the climate mode with many of the
   strongest societal impacts is the El Nino-Southern Oscillation (ENSO).
   However, quantifying ENSO's changes in a warming climate remains a
   formidable challenge, due to both the noise arising from internal
   variability and the complexity of air-sea feedbacks in the tropical
   Pacific Ocean. In this work, we use large (30-member) ensembles of
   climate simulations to show that anthropogenic climate change can
   produce systematic increases in ENSO teleconnection strength over many
   land regions, driving increased interannual variability in regional
   temperature extremes and wildfire frequency. As the spatial character of
   this intensification exhibits strong land-ocean contrasts, a causal role
   for land-atmosphere feedbacks is suggested. The identified increase in
   variance occurs in multiple model ensembles, independent of changes in
   sea surface temperature variance. This suggests that in addition to
   changes in the overall likelihoods of heat and wildfire extremes, the
   variability in these events may also be a robust feature of future
   climate.
   Plain Language Summary Changes in climate variability strongly affect
   the overall impacts of climate change. In this work, increases in the
   intensity of heat waves and wildfire driven by El Nino/La Nina in a
   business-as-usual climate scenario are identified in recently produced
   climate simulations spanning the 20th and 21st centuries. The
   intensification in temperature extremes occurs mainly over land regions
   and independently of changes in eastern Pacific sea surface temperature
   variability. It is argued that land atmosphere feedbacks are likely to
   play a key role in the simulated amplification, with relevance to
   impacts such as heat waves and wildfire frequency.}},
Publisher = {{AMER GEOPHYSICAL UNION}},
Address = {{2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Fasullo, JT (Corresponding Author), Natl Ctr Atmospher Res, Boulder, CO 80305 USA.
   Fasullo, J. T.; Otto-Bliesner, B. L., Natl Ctr Atmospher Res, Boulder, CO 80305 USA.
   Stevenson, S., Univ Calif Santa Barbara, Bren Sch Environm Sci \& Management, Santa Barbara, CA 93106 USA.}},
DOI = {{10.1029/2018GL079022}},
ISSN = {{0094-8276}},
EISSN = {{1944-8007}},
Keywords = {{ENSO; climate variability; climate change; large ensembles;
   teleconnections}},
Keywords-Plus = {{EL-NINO; CALIFORNIA DROUGHT; PACIFIC-OCEAN; WATER; TELECONNECTIONS;
   FREQUENCY; DEMAND; IMPACT; SEASON; CYCLE}},
Research-Areas = {{Geology}},
Web-of-Science-Categories  = {{Geosciences, Multidisciplinary}},
Author-Email = {{fasullo@ucar.edu}},
ResearcherID-Numbers = {{Fasullo, John/H-4552-2019
   }},
ORCID-Numbers = {{Fasullo, John/0000-0003-1216-892X
   Otto-Bliesner, Bette/0000-0003-1911-1598}},
Funding-Acknowledgement = {{NSFNational Science Foundation (NSF) {[}AGS-1243125]; DOEUnited States
   Department of Energy (DOE) {[}DE-SC0012711]}},
Funding-Text = {{This work was supported by NSF Award ID AGS-1243125 and DOE Award ID
   DE-SC0012711. The CESM and CMIP5 simulations are available on the Earth
   System Grid (https://esgf.llnl.gov). The ESM 2M simulations have been
   made available by Keith Rodgers of GFDL (krodgers@princeton.edu). The
   ECMWF 20th century reanalysis was obtained from
   https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-
   datasets/era-20c.}},
Cited-References = {{Abatzoglou JT, 2016, P NATL ACAD SCI USA, V113, P11770, DOI 10.1073/pnas.1607171113.
   Barbero R, 2015, GEOPHYS RES LETT, V42, P3538, DOI 10.1002/2015GL063428.
   Bell GD, 1999, B AM METEOROL SOC, V80, pS1.
   Bellenger H, 2014, CLIM DYNAM, V42, P1999, DOI 10.1007/s00382-013-1783-z.
   Berg A, 2016, NAT CLIM CHANGE, V6, P869, DOI {[}10.1038/nclimate3029, 10.1038/NCLIMATE3029].
   Burn DH, 2016, CAN WATER RESOUR J, V41, P139, DOI 10.1080/07011784.2015.1026844.
   Cai WJ, 2015, NAT CLIM CHANGE, V5, P849, DOI {[}10.1038/nclimate2743, 10.1038/NCLIMATE2743].
   Cai WJ, 2014, NAT CLIM CHANGE, V4, P111, DOI {[}10.1038/nclimate2100, 10.1038/NCLIMATE2100].
   Capotondi A, 2015, B AM METEOROL SOC, V96, P921, DOI 10.1175/BAMS-D-13-00117.1.
   Chikamoto Y, 2015, CLIM DYNAM, V45, P2213, DOI 10.1007/s00382-015-2469-5.
   Collins M, 2010, NAT GEOSCI, V3, P391, DOI 10.1038/NGEO868.
   Deser C, 2017, J CLIMATE, V30, P5059, DOI 10.1175/JCLI-D-16-0844.1.
   Dunne JP, 2013, J CLIMATE, V26, P2247, DOI 10.1175/JCLI-D-12-00150.1.
   Fasullo JT, 2010, J CLIMATE, V23, P4677, DOI 10.1175/2010JCLI3451.1.
   Funk CC, 2015, J CLIMATE, V28, P4309, DOI 10.1175/JCLI-D-14-00334.1.
   GLYNN PW, 1991, SCIENCE, V253, P69, DOI 10.1126/science.253.5015.69.
   Graham FS, 2017, CLIM DYNAM, V48, P2045, DOI 10.1007/s00382-016-3189-1.
   Guilyardi E, 2016, B AM METEOROL SOC, V97, P817, DOI 10.1175/BAMS-D-15-00287.1.
   Herrera D, 2017, J CLIMATE, V30, P7801, DOI 10.1175/JCLI-D-16-0838.1.
   Jolly WM, 2015, NAT COMMUN, V6, DOI 10.1038/ncomms8537.
   Kay JE, 2015, B AM METEOROL SOC, V96, P1333, DOI 10.1175/BAMS-D-13-00255.1.
   Lawrence DM, 2012, J CLIMATE, V25, P2240, DOI 10.1175/JCLI-D-11-00103.1.
   Lewis SL, 2011, SCIENCE, V331, P554, DOI 10.1126/science.1200807.
   Marengo JA, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL047436.
   McPhaden MJ, 2006, SCIENCE, V314, P1740, DOI 10.1126/science.1132588.
   Newman M, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL047658.
   Perry SJ, 2017, GEOPHYS RES LETT, V44, P10608, DOI 10.1002/2017GL074509.
   Phillips Adam S., 2014, Eos, Transactions American Geophysical Union, V95, P453, DOI 10.1002/2014EO490002.
   Poli P, 2016, J CLIMATE, V29, P4083, DOI 10.1175/JCLI-D-15-0556.1.
   Power S, 2013, NATURE, V502, P541, DOI 10.1038/nature12580.
   RASMUSSON EM, 1983, MON WEATHER REV, V111, P517, DOI 10.1175/1520-0493(1983)111<0517:TRBEEP>2.0.CO;2.
   Rodgers K. B., 2015, BIOGEOSCIENCES, V11, P18189, DOI DOI 10.5194/BGD-11-18189-2014.
   ROPELEWSKI CF, 1987, MON WEATHER REV, V115, P1606, DOI 10.1175/1520-0493(1987)115<1606:GARSPP>2.0.CO;2.
   Seager R, 2014, J HYDROMETEOROL, V15, P39, DOI 10.1175/JHM-D-13-024.1.
   Stevenson SL, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2012GL052759.
   Stevenson S, 2012, J CLIMATE, V25, P2129, DOI 10.1175/JCLI-D-11-00252.1.
   Swain DL, 2018, NAT CLIM CHANGE, V8, P427, DOI 10.1038/s41558-018-0140-y.
   Taylor KE, 2012, B AM METEOROL SOC, V93, P485, DOI 10.1175/BAMS-D-11-00094.1.
   Trenberth KE, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL022760.
   Udall B, 2017, WATER RESOUR RES, V53, P2404, DOI 10.1002/2016WR019638.
   Walker G. T., 1923, MEMOIRS INDIA METEOR, V24, P75.
   Wang SY, 2014, GEOPHYS RES LETT, V41, P3220, DOI 10.1002/2014GL059748.
   Wang SYS, 2015, GEOPHYS RES LETT, V42, P8140, DOI 10.1002/2015GL065211.
   Wang SYS, 2015, ATMOS SCI LETT, V16, P338, DOI 10.1002/asl2.565.
   Westerling A.L., 2003, EOS, V84, P545, DOI {[}DOI 10.1029/2003E0490001, 10.1029/2003EO490001].
   Williams AP, 2018, GEOPHYS RES LETT, V45, P5653, DOI 10.1029/2018GL077319.
   Williams AP, 2015, GEOPHYS RES LETT, V42, P6819, DOI 10.1002/2015GL064924.
   Williams AP, 2014, J APPL METEOROL CLIM, V53, P2671, DOI 10.1175/JAMC-D-14-0053.1.
   Wittenberg AT, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL038710.
   Yeh SW, 2018, REV GEOPHYS, V56, P185, DOI 10.1002/2017RG000568.
   Yeh SW, 2009, NATURE, V461, P511, DOI 10.1038/nature08316.
   Yoon JH, 2015, B AM METEOROL SOC, V96, pS5, DOI 10.1175/BAMS-D-15-00114.1.
   Yoon JH, 2015, NAT COMMUN, V6, DOI 10.1038/ncomms9657.
   Zheng XT, 2016, J CLIMATE, V29, P7265, DOI 10.1175/JCLI-D-16-0039.1.
   Zhou ZQ, 2014, J CLIMATE, V27, P9050, DOI 10.1175/JCLI-D-14-00254.1.}},
Number-of-Cited-References = {{55}},
Times-Cited = {{18}},
Usage-Count-Last-180-days = {{5}},
Usage-Count-Since-2013 = {{26}},
Journal-ISO = {{Geophys. Res. Lett.}},
Doc-Delivery-Number = {{GV0DN}},
Unique-ID = {{ISI:000445727500057}},
OA = {{Bronze}},
DA = {{2020-08-06}},
}

@article{ ISI:000445727500058,
Author = {Chen, Y. and Langenbrunner, B. and Randerson, J. T.},
Title = {{Future Drying in Central America and Northern South America Linked With
   Atlantic Meridional Overturning Circulation}},
Journal = {{GEOPHYSICAL RESEARCH LETTERS}},
Year = {{2018}},
Volume = {{45}},
Number = {{17}},
Pages = {{9226-9235}},
Month = {{SEP 16}},
Abstract = {{Many climate models from the fifth phase of the Coupled Model
   Intercomparison Project predict decreases in precipitation in Central
   America and northern South America by the year 2100 for the
   Representative Concentration Pathway 8.5 scenario. Here we show that the
   fifth phase of the Coupled Model Intercomparison Project models able to
   more accurately simulate warm North Atlantic sea surface temperatures
   (SSTs) for the present climate (strong Atlantic meridional overturning
   circulation models) are more likely to project a larger precipitation
   decrease. Drought amplification from the slowdown of Atlantic meridional
   overturning circulation is more significant during the wet season in the
   Northern Hemisphere, with an SST-constrained model estimate yielding a
   73\% larger decrease in precipitation (-1.11mm/day) than the multimodel
   mean (-0.64mm/day). Since most Earth system models underestimate
   contemporary SSTs in the North Atlantic, the use of the multimodel mean
   for impact analysis likely underestimates drought stress and the
   vulnerability of neotropical forests to increasing drought from climate
   change.
   Plain Language Summary Many climate models predict that in response to
   global warming, rainfall will decrease in Central America and northern
   South America. However, there is large disagreement regarding the
   magnitude of this drying in different models. We separate these models
   into different groups based on their ability to accurately simulate sea
   surface temperatures in the North Atlantic Ocean. One group of models
   simulates higher present-day sea surface temperatures, which agree
   better with present-day air temperature and ocean current observations.
   We find that these models predict more future drying in Central America
   and northern South America, particularly during the wet season, relative
   to the full model archive. By using sea surface temperature observations
   as a constraint, we create an optimal prediction and estimate that the
   drying will be intensified by an additional 73\% by 2100 compared to the
   multimodel mean. Our work suggests that future drought stress and the
   vulnerability of tropical forests to climate change may be potentially
   larger than previous estimates.}},
Publisher = {{AMER GEOPHYSICAL UNION}},
Address = {{2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Chen, Y (Corresponding Author), Univ Calif Irvine, Dept Earth Syst Sci, Irvine, CA 92697 USA.
   Chen, Y.; Langenbrunner, B.; Randerson, J. T., Univ Calif Irvine, Dept Earth Syst Sci, Irvine, CA 92697 USA.}},
DOI = {{10.1029/2018GL077953}},
ISSN = {{0094-8276}},
EISSN = {{1944-8007}},
Keywords = {{drought; AMOC; teleconnection; emergent constraint; CMIP5; precipitation}},
Keywords-Plus = {{CLIMATE-CHANGE; THERMOHALINE CIRCULATION; EMERGENT CONSTRAINTS;
   RAINFALL; CMIP5; CYCLE; PRECIPITATION; TEMPERATURE; PROJECTIONS;
   MECHANISMS}},
Research-Areas = {{Geology}},
Web-of-Science-Categories  = {{Geosciences, Multidisciplinary}},
Author-Email = {{yang.chen@uci.edu}},
ResearcherID-Numbers = {{Randerson, James/Y-2550-2019
   Chen, Yang/C-6529-2008
   }},
ORCID-Numbers = {{Randerson, James/0000-0001-6559-7387
   Chen, Yang/0000-0002-0993-7081
   Langenbrunner, Baird/0000-0002-3844-9706}},
Funding-Acknowledgement = {{Gordon and Betty Moore FoundationGordon and Betty Moore Foundation
   {[}GBMF3269]; NASA SUSMAP program; NASA IDS program; NASA CMS program;
   U.S. Department of Energy Office of Science RUBISCO Science Focus
   AreaUnited States Department of Energy (DOE)}},
Funding-Text = {{This work was conducted with the support of the Gordon and Betty Moore
   Foundation (GBMF3269), NASA SUSMAP, IDS, and CMS programs, and the U.S.
   Department of Energy Office of Science RUBISCO Science Focus Area. We
   thank Jin-Yi Yu for valuable suggestions and comments. CMIP5 output
   variables were downloaded from the Program for Climate Model Diagnosis
   and Intercomparison (PCMDI) Earth System Grid Federation database at
   Lawrence Livermore National Laboratory (http://esgf-node.llnl.gov/). We
   thank the PCMDI for making the CMIP5 model data sets available.}},
Cited-References = {{Anderson BT, 2015, GEOPHYS RES LETT, V42, P1190, DOI 10.1002/2014GL062593.
   Boisier JP, 2015, NAT CLIM CHANGE, V5, P656, DOI {[}10.1038/nclimate2658, 10.1038/NCLIMATE2658].
   Borodina A, 2017, J CLIMATE, V30, P3655, DOI 10.1175/JCLI-D-16-0662.1.
   Bozbiyik A, 2011, CLIM PAST, V7, P319, DOI 10.5194/cp-7-319-2011.
   Broecker W, 1991, OCEANOGRAPHY, V4, P79, DOI DOI 10.5670/0CEAN0G.1991.07.
   Brown JN, 2015, GEOPHYS RES LETT, V42, P10856, DOI 10.1002/2015GL066184.
   Buckley MW, 2016, REV GEOPHYS, V54, P5, DOI 10.1002/2015RG000493.
   Byrne MP, 2016, GEOPHYS RES LETT, V43, P11350, DOI 10.1002/2016GL070396.
   Byrne MP, 2015, J CLIMATE, V28, P8078, DOI 10.1175/JCLI-D-15-0369.1.
   Caesar L, 2018, NATURE, V556, P191, DOI 10.1038/s41586-018-0006-5.
   Chadwick R, 2016, NAT CLIM CHANGE, V6, P177, DOI {[}10.1038/NCLIMATE2805, 10.1038/nclimate2805].
   Cheng W, 2013, J CLIMATE, V26, P7187, DOI 10.1175/JCLI-D-12-00496.1.
   Chiang JCH, 2012, ANNU REV EARTH PL SC, V40, P383, DOI {[}10.1146/annurev-earth-042711-105545, 10.1146/annurev-earth-042711-185545].
   Chou C, 2009, J CLIMATE, V22, P1982, DOI 10.1175/2008JCLI2471.1.
   Collins M, 2014, CLIMATE CHANGE 2013: THE PHYSICAL SCIENCE BASIS, P1029.
   Davidson EA, 2012, NATURE, V481, P321, DOI 10.1038/nature10717.
   DeClerck FAJ, 2010, BIOL CONSERV, V143, P2301, DOI 10.1016/j.biocon.2010.03.026.
   Diffenbaugh NS, 2012, CLIMATIC CHANGE, V114, P813, DOI 10.1007/s10584-012-0570-x.
   Drijfhout S, 2012, J CLIMATE, V25, P8373, DOI 10.1175/JCLI-D-12-00490.1.
   Friedl MA, 2010, REMOTE SENS ENVIRON, V114, P168, DOI 10.1016/j.rse.2009.08.016.
   Frierson DMW, 2013, NAT GEOSCI, V6, P940, DOI {[}10.1038/ngeo1987, 10.1038/NGEO1987].
   Frierson DMW, 2012, J CLIMATE, V25, P720, DOI 10.1175/JCLI-D-11-00116.1.
   Gregory JM, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL023209.
   Hoorn C, 2010, SCIENCE, V330, P927, DOI 10.1126/science.1194585.
   Jackson LC, 2015, CLIM DYNAM, V45, P3299, DOI 10.1007/s00382-015-2540-2.
   Kang SM, 2008, J CLIMATE, V21, P3521, DOI 10.1175/2007JCLI2146.1.
   Kang SM, 2018, NPJ CLIM ATMOS SCI, V1, DOI 10.1038/s41612-017-0004-6.
   Keenlyside NS, 2008, NATURE, V453, P84, DOI 10.1038/nature06921.
   Kent C, 2015, J CLIMATE, V28, P4390, DOI 10.1175/JCLI-D-14-00613.1.
   Klein SA, 2015, CURR CLIM CHANGE REP, V1, P276, DOI 10.1007/s40641-015-0027-1.
   Knutti R, 2004, NATURE, V430, P851, DOI 10.1038/nature02786.
   Kooperman GJ, 2018, NAT CLIM CHANGE, V8, P434, DOI 10.1038/s41558-018-0144-7.
   L'Heveder B, 2015, J CLIMATE, V28, P2650, DOI 10.1175/JCLI-D-14-00377.1.
   Lau WKM, 2015, P NATL ACAD SCI USA, V112, P3630, DOI 10.1073/pnas.1418682112.
   Lovejoy TE, 2018, SCI ADV, V4, DOI 10.1126/sciadv.aat2340.
   McSweeney CF, 2013, CLIMATIC CHANGE, V119, P617, DOI 10.1007/s10584-013-0781-9.
   Mittermeier RA, 2003, P NATL ACAD SCI USA, V100, P10309, DOI 10.1073/pnas.1732458100.
   Neelin JD, 2006, P NATL ACAD SCI USA, V103, P6110, DOI 10.1073/pnas.0601798103.
   Parsons LA, 2014, GEOPHYS RES LETT, V41, P146, DOI 10.1002/2013GL058454.
   Rahmstorf S, 2015, NAT CLIM CHANGE, V5, P475, DOI {[}10.1038/NCLIMATE2554, 10.1038/nclimate2554].
   Rauscher SA, 2011, J CLIMATE, V24, P2003, DOI 10.1175/2010JCLI3536.1.
   Reynolds JF, 2007, SCIENCE, V316, P847, DOI 10.1126/science.1131634.
   Schneider U, 2014, THEOR APPL CLIMATOL, V115, P15, DOI 10.1007/s00704-013-0860-x.
   Seager R, 2010, J CLIMATE, V23, P4651, DOI 10.1175/2010JCLI3655.1.
   Shepherd TG, 2014, NAT GEOSCI, V7, P703, DOI 10.1038/NGEO2253.
   Skinner CB, 2017, J CLIMATE, V30, P2319, DOI {[}10.1175/jcli-d-16-0603.1, 10.1175/JCLI-D-16-0603.1].
   Srokosz M, 2012, B AM METEOROL SOC, V93, P1663, DOI 10.1175/BAMS-D-11-00151.1.
   Stanfield R., 2015, CLIM DYNAM, P1.
   Stocker T. F., 2013, CLIM CHANG 2013, P33.
   Stouffer RJ, 2006, J CLIMATE, V19, P1365, DOI 10.1175/JCLI3689.1.
   Swann ALS, 2016, P NATL ACAD SCI USA, V113, P10019, DOI 10.1073/pnas.1604581113.
   Taylor KE, 2012, B AM METEOROL SOC, V93, P485, DOI 10.1175/BAMS-D-11-00094.1.
   Timmermann A, 2007, J CLIMATE, V20, P4899, DOI 10.1175/JCLI4283.1.
   Trenberth KE, 2014, SURV GEOPHYS, V35, P515, DOI 10.1007/s10712-012-9214-y.
   Vecchi GA, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2006GL028905.
   Vellinga M, 2008, CLIMATIC CHANGE, V91, P43, DOI 10.1007/s10584-006-9146-y.
   Wang CZ, 2007, CLIM DYNAM, V29, P411, DOI 10.1007/s00382-007-0243-z.
   Wang CZ, 2014, NAT CLIM CHANGE, V4, P201, DOI {[}10.1038/NCLIMATE2118, 10.1038/nclimate2118].
   Wang CZ, 2012, J CLIMATE, V25, P5404, DOI 10.1175/JCLI-D-11-00413.1.
   Weaver AJ, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2012GL053763.
   Woollings T, 2012, NAT GEOSCI, V5, P313, DOI {[}10.1038/NGEO1438, 10.1038/ngeo1438].
   Yin L, 2013, CLIM DYNAM, V41, P3127, DOI 10.1007/s00382-012-1582-y.
   Zhang R, 2005, J CLIMATE, V18, P1853, DOI 10.1175/JCLI3460.1.}},
Number-of-Cited-References = {{63}},
Times-Cited = {{2}},
Usage-Count-Last-180-days = {{1}},
Usage-Count-Since-2013 = {{5}},
Journal-ISO = {{Geophys. Res. Lett.}},
Doc-Delivery-Number = {{GV0DN}},
Unique-ID = {{ISI:000445727500058}},
OA = {{Bronze}},
DA = {{2020-08-06}},
}

@article{ ISI:000441688100020,
Author = {Liao, Xiawei and Zhao, Xu and Hall, Jim W. and Guan, Dabo},
Title = {{Categorising virtual water transfers through China's electric power
   sector}},
Journal = {{APPLIED ENERGY}},
Year = {{2018}},
Volume = {{226}},
Pages = {{252-260}},
Month = {{SEP 15}},
Abstract = {{Water consumption in thermoelectric and hydropower plants in China
   increased from 1.6 and 6.1 billion m(3), respectively, to 3.8 and 14.6
   billion m(3) from 2002 to 2010. Using the concept of virtual water, we
   attribute to different electricity users the total water consumption by
   the electric power sector. From 2002 to 2010, virtual water embodied in
   the final consumption of electricity (hereinafter referred to as VWEF)
   increased from 1.90 to 7.35 billion m(3), whilst virtual water in
   electricity used by industries (hereinafter referred to as VWEI)
   increased from 5.82 to 11.13 billion m(3). The inter-provincial virtual
   water trades as a result of spatial mismatch of electricity production
   and consumption are quantified. Nearly half (47.5\% in 2010) of the
   physical water inputs into the power sector were virtually transferred
   across provincial boundaries in the form of virtual water embodied in
   the electricity produced, mainly from provinces in northeast, central
   and south China to those in east and north China. Until 2030, VWEF and
   VWEI are likely to increase from 5.27 and 14.89 billion m(3) to 7.19 and
   20.33 billion m(3), respectively. Climate change mitigation and water
   conservation measures in the power sector may help to relieve the
   regional pressures on water resources imposed by the power

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