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Richard G. Williams, Anna Katavouta, and Vassil Roussenov

these passive and dynamic effects are taken into account in how the anthropogenic heat is viewed in terms of added and redistributed heat components ( Xie and Vallis 2012 ; Gregory et al. 2016 ). The ocean carbon is often separated into anthropogenic carbon and a much larger background of natural carbon. The anthropogenic carbon is usually diagnosed from observations using a quasi-conserved carbon tracer ( Gruber et al. 1996 ; Sabine et al. 2004 ; Clement and Gruber 2018 ) or inferred from

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Seungmok Paik and Seung-Ki Min

1. Introduction The observed increase in anthropogenic greenhouse gases has been identified as a major cause of the global and continental-scale surface warming during past decades ( Hegerl et al. 2007 ; Bindoff et al. 2013 ). The Northern Hemisphere (NH) extratropical land has experienced strong warming and the NH spring snow-cover extent (SCE) has decreased accordingly ( Brown and Robinson 2011 ; Brutel-Vuilmet et al. 2013 ; Hori et al. 2017 ; Bormann et al. 2018 ; Meredith et al. 2019

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Ariaan Purich and Seok-Woo Son

changes induced by the increase in anthropogenic greenhouse gases ( Fyfe et al. 1999 ; Kushner et al. 2001 ; Cai et al. 2003 ), such changes in austral summer have also been influenced by Antarctic ozone depletion ( Thompson and Solomon 2002 ; Shindell and Schmidt 2004 ; Arblaster and Meehl 2006 ; Perlwitz et al. 2008 ; Son et al. 2009 , 2010 ; McLandress et al. 2011 ; Polvani et al. 2011 ; Kang et al. 2012 ). It is known that both increasing greenhouse gases, occurring year-round, and ozone

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Nikolaos Christidis, Andrew Ciavarella, and Peter A. Stott

extremes begs consideration of both thermodynamic and dynamic changes. The latter arise from anthropogenic influence on the atmospheric circulation and, although they are generally less easily detected, their contribution has been considered in the literature (e.g., Schaller et al. 2016 ; Vautard et al. 2016 ). Here we only employ a single threshold to define extreme events and do not attempt a separation of the thermodynamic and dynamic effects, but focus on the overall change in the likelihood of

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Evan Weller, Bo-Joung Park, and Seung-Ki Min

et al. 2014 ; Pfleiderer et al. 2019 ). Despite various limitations in simulating regional changes ( Hao et al. 2013 ), climate models project these trends to continue with increasing anthropogenic forcing ( Meehl and Tebaldi 2004 ; Coumou and Robinson 2013 ). Therefore, any change in the seasonal cycle that alters the summer season, such as a lengthening of the summer season (e.g., Peña-Oritz et al. 2015 ; Park et al. 2018 ), will simply provide a larger window for these extreme summer heat

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José L. Hernández, Syewoon Hwang, Francisco Escobedo, April H. Davis, and James W. Jones

better understand their interconnection. These investigations acknowledged the importance of considering anthropogenic land cover change when studying climate trends. This study consisted of two complementary parts addressing key questions about the factors driving landscape conversion in central Florida during recent decades and its hypothetical effect on climatic conditions. The first part ( section 2 ) investigates observed LUCs in and around the Southwest Florida Water Management District (SWFWMD

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F. Salamanca, M. Georgescu, A. Mahalov, and M. Moustaoui

1. Introduction Built-up areas alter the surface energy balance components relative to their rural counterparts ( Oke 1988 ). Radiative trapping by building walls and roads, high thermal storage capacity of unnatural surfaces, and anthropogenic heat release (originating from human activities that consume energy) reduces built environment nocturnal cooling rates, promoting the formation of the urban heat island (UHI) phenomenon ( Howard 1833 ; Arnfield 2003 ). The UHI phenomenon is the

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Adeline Bichet, Paul J. Kushner, and Lawrence Mudryk

time period 1900–2040 by multiplying g ( t ) with h ( x ) [i.e., forming the product g ( t ) h ( x )] before adding the climatological mean Hurrell SST–SICE. In this study, we only use S GW over the period 1980–2010, and in future work we will consider the time period up to 2040. As illustrated in Fig. 1 , the temporal pattern g ( t ) consists of a slowly varying rate of anthropogenic greenhouse warming, modulated by effects such as aerosol forcing and internal variability. The method

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Chris Jones, Eddy Robertson, Vivek Arora, Pierre Friedlingstein, Elena Shevliakova, Laurent Bopp, Victor Brovkin, Tomohiro Hajima, Etsushi Kato, Michio Kawamiya, Spencer Liddicoat, Keith Lindsay, Christian H. Reick, Caroline Roelandt, Joachim Segschneider, and Jerry Tjiputra

1. Introduction The global carbon cycle has long been known to be a crucial component of future climate change, closely linking anthropogenic CO 2 emissions with future changes in atmospheric CO 2 concentration and hence climate (e.g., Prentice et al. 2001 ). Including the carbon cycle as an interactive component in comprehensive climate models has become common, and the Coupled Carbon Cycle Climate Model Intercomparison Project (C 4 MIP; Friedlingstein et al. 2006 ) presented results of 11

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A. P. Sokolov, P. H. Stone, C. E. Forest, R. Prinn, M. C. Sarofim, M. Webster, S. Paltsev, C. A. Schlosser, D. Kicklighter, S. Dutkiewicz, J. Reilly, C. Wang, B. Felzer, J. M. Melillo, and H. D. Jacoby

1. Introduction Projections of anthropogenic global warming have from the start been confounded by the many economic and scientific uncertainties that affect forecasts of anthropogenic emissions and the response of the climate system to these emissions (e.g., Houghton et al. 2001 ; Solomon et al. 2007 ). Up until 2001, the uncertainties in the projected climate changes were generally dealt with by giving ranges of projected changes but without any likelihoods being associated with these

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