manusc ip submi ed o Jou nal o Geophysical Resea ch: A mosphe es
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Po en ial Dis inc Impac s o Global Wa ming on Mesoscale Con ec i e Sys ems and
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Isola ed Deep Con ec ion in he Cen al-eas e n Uni ed S a es
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Jian eng Li1, Yun Qian1, L. Ruby Leung1, Wei an Liu2, Kai Zhang1, Paul Ull ich2,3,
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Lingcheng Li1, Ye Liu1, Huilin Huang1, Zeyu Xue1
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1A mosphe ic, Clima e, and Ea h Sciences Di ision, Paci ic No hwes Na ional Labo a o y;
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Richland, Washing on, USA
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2Depa men o Land, Ai , and Wa e Resou ces, Uni e si y o Cali o nia, Da is, Da is, CA,
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USA
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3Di ision o Physical and Li e Sciences, Law ence Li e mo e Na ional Labo a o y, Li e mo e,
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CA, USA
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Co esponding au ho s: Jian eng Li ([email p o ec ed]) and Yun Qian ([email p o ec ed])
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Key Poin s:
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A egionally- e ined global cloud- esol ing model is used o in es iga e how global
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wa ming a ec s con ec i e sys ems o di e en sizes
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In a wa me summe a mosphe e, mesoscale con ec i e sys ems (MCSs) become mo e
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equen , while isola ed deep con ec ion (IDC) diminishes
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Summe ime MCSs and IDC end o occu close o he sou he n and eas e n coas al a eas
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o he Uni ed S a es unde wa ming
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manusc ip submi ed o Jou nal o Geophysical Resea ch: A mosphe es
Abs ac
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Using he egionally e ined Simple Cloud-Resol ing E3SM (Ene gy Exascale Ea h Sys em
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Model) A mosphe e Model and he pseudo-global wa ming expe imen amewo k, we
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in es iga e he po en ial impac o global wa ming on con ec i e s o ms o di e en sizes and
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li e imes in he cen al-eas e n Uni ed S a es om July 1 o Augus 19, 2020. We ind an inc ease
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in he numbe o la ge and long-las ing mesoscale con ec i e sys ems (MCSs) bu a dec ease in
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small and sho -du a ion isola ed deep con ec ion (IDC) e en s in a wa ming scena io ollowing
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he Sha ed Socioeconomic Pa hways (SSP5-8.5). Al hough he mean MCS li e ime becomes
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sho e unde wa ming, IDC e en s pe sis longe . Also, egions wi h he mos equen MCS and
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IDC occu ences shi signi ican ly: MCSs and IDC end o occu close o he sou he n and
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eas e n coas s, whe e he ela i e humidi y o he en i onmen inc eases unde global wa ming.
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This wo k un eils he complexi y o he esponse o con ec i e sys ems o clima e change.
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Plain Language Summa y
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Con ec i e sys ems a e i al o he Ea h’s hyd ological cycle, a mosphe ic ci cula ion, and
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adia ion balance. This s udy uses a egionally e ined global cloud- esol ing model and he
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pseudo-global wa ming app oach o in es iga e he po en ial impac o global wa ming on
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summe ime con ec i e s o ms o di e en sizes in he cen al-eas e n Uni ed S a es. Resul s
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show ha wa ming has con as ing impac s on he la ge and long-las ing mesoscale con ec i e
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sys ems and small and sho -du a ion isola ed deep con ec ion e en s when i comes o e en
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numbe and mean li e ime. In addi ion, bo h ypes o con ec i e sys ems end o occu close o
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he sou he n and eas e n coas s unde u u e wa ming. This s udy highligh s he complexi y o
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how con ec i e sys ems espond o global wa ming.
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1 In oduc ion
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Deep con ec ion p oduces hea y p ecipi a ion, which has c i ical impac s on he wa e
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and biogeochemical cycles (Chapman e al., 2021; Hu e al., 2020; Kanchebe De bile & Abudu
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Kasei, 2012; Mo ew e al., 2018). I can also a ec la ge-scale en i onmen s and adia ion
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balance h ough he e ical edis ibu ion o hea , mass, and momen um wi hin he a mosphe e
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(Feng e al., 2011; Houze, 2004). Fu he mo e, deep con ec ion is associa ed wi h many na u al
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haza ds, such as o nadoes, hail, ligh ning, looding, and damaging gus s, se e ely h ea ening
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human secu i y and p ope y (Folge , 2013; Koehle , 2020; Tasza ek e al., 2020).
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Deep con ec ion is gene ally p ojec ed o inc ease in equency and in ensi y in a wa me
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a mosphe e due o enhanced a mosphe ic mois u e and la ge con ec i e a ailable po en ial
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ene gy (CAPE) (Di enbaugh e al., 2013; P ein e al., 2017; T app e al., 2007; Wes a e al.,
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2014). Howe e , wa ming also o en leads o inc eased con ec i e inhibi ion (CIN) and
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dec eased inland ela i e humidi y (RH) (By ne & O’Go man, 2016; Chen e al., 2020), which
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a e belie ed o supp ess summe ime deep con ec ion in some a eas o he Uni ed S a es
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(G abowski & P ein, 2019; Hoogewind e al., 2017; Tasza ek e al., 2021; T app e al., 2019).
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The compe i ion be ween hese wo g oups o ac o s (inc eased mois u e and CAPE e sus
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g ea e CIN and lowe RH) complica es how con ec i e sys ems espond o global wa ming.
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CIN and CAPE a ec deep con ec ion in di e en ways. CIN quan i ies he nega i e buoyancy
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ha mus be o e come o ini ia e con ec ion, while CAPE measu es a mosphe ic ins abili y and
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po en ial con ec i e in ensi y bu akes e ec only a e CIN is o e come o con ec i e
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ini ia ion (Di enbaugh e al., 2013; T app e al., 2007). Rasmussen e al. (2020) demons a ed
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manusc ip submi ed o Jou nal o Geophysical Resea ch: A mosphe es
ha , unde global wa ming, inc eased CIN would supp ess weak and mode a e con ec ion, while
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la ge CAPE would esul in mo e s ong con ec ion. Howe e , hey used g id-scale ada
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e lec i i y o es ima e he changes in he con ec ion popula ions o di e en scales, which migh
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no ep esen he ac ual sizes and in ensi ies o deep con ec i e sys ems. Small and sho -
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du a ion isola ed deep con ec ion (IDC) can p oduce ada e lec i i y as la ge as ha o la ge
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and long-las ing mesoscale con ec i e sys ems (MCSs) (Li e al., 2021b).
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This s udy exploi s he upda ed FLExible objec TRacKeR (FLEXTRKR) algo i hm o
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dis inguish small and sho -du a ion IDC om la ge and long-las ing MCS e en s, enabling us o
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in es iga e he po en ial impac s o u u e wa ming on hese wo ypes o con ec i e sys ems
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wi h dis inc p ope ies (Li e al., 2021a). Using a egionally e ined global con ec ion-
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pe mi ing model – he Simple Cloud-Resol ing E3SM (Ene gy Exascale Ea h Sys em Model)
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A mosphe e Model (SCREAM) (Caldwell e al., 2021), we simula e con ec i e sys ems o
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di e en sizes o e he Uni ed S a es eas o he Rocky Moun ains, as hey we e obse ed in he
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summe o 2020 and unde u u e wa ming condi ions. We apply he pseudo-global wa ming
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(PGW) expe imen amewo k o cons uc he u u e wa ming scena io o SCREAM (Schä e
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al., 1996). The SCREAM model and con igu a ion employed, he upda ed FLEXTRKR
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algo i hm, and an obse a ional MCS-IDC da ase a e desc ibed in de ail in Sec ion 2. Sec ion 3
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e alua es SCREAM’s pe o mance in ep oducing obse ed MCS and IDC cha ac e is ics and
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analyzes he po en ial impac s o global wa ming on MCS and IDC by compa ing he simula ed
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MCS and IDC e en s unde obse ed and pseudo-wa ming condi ions. The unce ain ies and
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limi a ions o he s udy a e also discussed in Sec ion 3. Finally, we summa ize his s udy in
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Sec ion 4.
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2 Ma e ials and Me hods
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2.1 SCREAM and Regional Re ined Mesh
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SCREAM is a global con ec ion-pe mi ing a mosphe ic-land model de eloped by he
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U.S. Depa men o Ene gy (DOE) designed o u ilize DOE’s high-pe o mance supe compu e
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esou ces o esol e some o he long-s anding p oblems a ibu ed o coa se- esolu ion in E3SM
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simula ions (Caldwell e al., 2021). SCREAM uses he non-hyd os a ic e sion o he High O de
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Me hod Modeling En i onmen (HOMME-NH) as he luid-dynamic sol e (Taylo e al., 2020).
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The model's physical pa ame e iza ions include he Simpli ied Highe O de Closu e (SHOC)
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bounda y laye u bulence scheme (Bogenschu z & K uege , 2013), he P edic ed Pa icle
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P ope ies (P3) mic ophysics scheme (Mo ison & Milb and , 2015), and he combina ion o he
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Radia i e T ans e o Ene ge ics (RTE) and he Rapid Radia i e T ans e Model o Gene al
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ci cula ion models – Pa allel (RRTMGP) o longwa e and sho wa e adia ion (Pincus e al.,
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2019). The model does no include a deep con ec i e pa ame e iza ion, and ae osol
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concen a ions a e p esc ibed (Caldwell e al., 2021).
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Al hough SCREAM is demons ably supe io o coa se- esolu ion E3SM in simula ing
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p ecipi a ion and con ec i e sys ems, i is compu a ionally expensi e (Caldwell e al., 2021).
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This s udy, hus, cons uc s a egional e ined mesh o no only educe he compu a ional bu den
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bu also exploi he ad anced ea u es o SCREAM (Liu e al., 2023; Tang e al., 2019).
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Regionally e ined model (RRM) suppo s highe esolu ion in he egion o in e es and lowe
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esolu ion in o he a eas o he model domain (Figu e 1). Since he coa se esolu ion egion does
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no explici ly esol e deep con ec ion, he lack o a deep con ec i e pa ame e iza ion in
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manusc ip submi ed o Jou nal o Geophysical Resea ch: A mosphe es
SCREAM is a po en ial issue, pa icula ly o long- unning simula ions. Consequen ly, we apply
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nudging o e he coa se- esolu ion egion o cons ain he a mosphe ic s a es by hose o a global
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eanalysis bu keep he high- esolu ion egion o in e es ee- unning and able o espond o
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ex e nal o cing (Figu e 1). We expec nudging o gene a e ealis ic bounda y condi ions o he
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egion o in e es , mimicking con ec ion-pe mi ing egional clima e model simula ions
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cons ained by bounda y condi ions (Li e al., 2023). Howe e , nudging allows some la ge-scale
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ci cula ion eedback be ween he egion o in e es and he su ounding a eas in SCREAM RRM,
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which is en i ely absen in egional clima e models because o he imposed bounda y condi ions.
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Using he egional e ined mesh con igu a ion in Figu e 1, which has a dynamical
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ho izon al esolu ion o ~3.2 km o he cen al-eas e n Uni ed S a es and ~25 km o o he a eas,
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we conduc an AMIP- ype (AMIP: A mosphe ic Model In e compa ison P ojec ) SCREAM
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RRM con ol simula ion (he ea e named he CTRL simula ion) om June 28 o Augus 19,
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2020, wi h he i s h ee days as spin-up. I s componen se comp ises an ac i e a mosphe ic
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componen , i.e., SCREAM, an ac i e land componen – E3SM Land Model (ELM) (Golaz e al.,
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2019; Golaz e al., 2022), a simpli ied ac i e sea ice componen , a da a ocean model wi h
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p esc ibed hou ly sea su ace empe a u e (SST) and sea ice ac ions om he Eu opean Cen e
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o Medium-Range Wea he Fo ecas s (ECMWF) Reanalysis 5 (ERA5) eanalysis da ase , and
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he Model o Scale Adap i e Ri e T anspo (MOSART) o i e ou ing (Li e al., 2013).
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SCREAM has 128 e ical le els wi h a e ical esolu ion o ~50 m in he bounda y laye and a
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model op a 2.25 hPa (Caldwell e al., 2021). The model has a dynamics ime s ep o 8.33
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seconds and a physics ime s ep o 100 seconds. The a mosphe ic ini ial condi ion is based on a
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combina ion o he hou ly High-Resolu ion Rapid Re esh (HRRR) and ERA5 eanalysis
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da ase s (Dowell e al., 2022; He sbach e al., 2020). HRRR has a ho izon al esolu ion o 3 km,
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co e ing he con iguous Uni ed S a es, while ERA5 has a global ho izon al esolu ion o 0.25.
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We use HRRR whe e e i is a ailable and ERA5 o a eas no co e ed by HRRR when
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cons uc ing he SCREAM a mosphe ic ini ial condi ion. The land ini ial condi ion is spun up
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h ough a 3-yea (June 28, 2017, o June 28, 2020) SCREAM RRM land-only simula ion
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cons ained by a mosphe ic o cing in 2020 om ERA5 (Liu e al., 2023). The simula ion uses
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p esc ibed hou ly SST om ERA5. Th ee-dimensional zonal (U) and me idional (V) winds,
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empe a u e (T), and speci ic humidi y (Q) abo e 850 hPa a e nudged owa ds hou ly ERA5
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eanalysis wi h a elaxa ion imescale o 6 hou s o g ids ou side he ed box in Figu e 1
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(Ba hel So ensen e al., 2024).
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manusc ip submi ed o Jou nal o Geophysical Resea ch: A mosphe es
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Figu e 1. The SCREAM RRM domain in a cylind ical equidis an p ojec ion. Blue lines
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ep esen he bounda ies o spec al elemen g ids, and each spec al elemen comp ises 4
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physical g id boxes. The ed ec angle ou lines he ee- unning egion wi hin, while he g een
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ec angle ou lines he egion wi h obse a ional MCS-IDC da a.
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2.2 PGW se up
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To in es iga e he po en ial impac s o global wa ming on MCSs and IDC, we conduc
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ano he simula ion using he PGW app oach (Schä e al., 1996; Xue e al., 2023) (he ea e
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named he PGW simula ion), which is nea ly iden ical o he CTRL simula ion bu wi h ou
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excep ions. Fo he PGW simula ion, 1) mon hly PGW del as (Equa ion 1) a e added o he s a e
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a iables in he a mosphe ic ini ial condi ion (U, V, T, Q, su ace p essu e, and skin empe a u e)
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and he ERA5 nudging ields (U, V, T, and Q); 2) mon hly PGW del as a e added o he
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a mosphe ic o cing s a e a iables (10-me e U and V, 2-me e T, 2-me e Q, and su ace
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p essu e) and mon hly PGW a ios (Equa ion 2) a e mul iplied wi h he lux a iables om he
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a mosphe ic o cing (p ecipi a ion and su ace downwelling longwa e and sho wa e luxes)
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(Law ence e al., 2018) as o cing o he land-only simula ion o p o ide ini ial condi ions o
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he land componen ; 3) empo ally-in e pola ed hou ly PGW del as a e added o p esc ibed SST;
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4) he Six h Assessmen Repo o he Uni ed Na ions In e go e nmen al Panel on Clima e
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Change (IPCC AR6) p ojec ed g eenhouse gas concen a ions in 2100 a e used ins ead o he
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alues in 2020 (Meinshausen e al., 2019). Simila o Li e al. (2023), we selec 11 models om
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he Coupled Model In e compa ison P ojec Phase 6 (CMIP6) a chi e (excep o E3SM 1.1) o
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compu e he mul i-model mean PGW del as and a ios be ween he his o ical pe iod (1981–2010)
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and he end o his cen u y (2071–2100) unde he Sha ed Socioeconomic Pa hways (SSP5-8.5)
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scena io, ea u ing a adia i e o cing o 8.5 W m−2 by 2100. Figu e S1 summa izes he
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wo k low o he PGW simula ion.
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∆𝑃𝐺𝑊= 𝑋2071−2100 − 𝑋1981−2010 (1)
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manusc ip submi ed o Jou nal o Geophysical Resea ch: A mosphe es
𝑅𝑃𝐺𝑊 =𝑋2071−2100
𝑋1981−2010 (2)
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whe e X is any me eo ological a iable (e.g., U, V, T, and Q); X2071-2100 deno es he mul i-yea
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a e aged mon hly alue o X be ween 2071 and 2100, simila o X1981-2010;
PGW e e s o he
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PGW del a o X, and RPGW ep esen s he PGW a io o X o he gi en mon h.
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2.3 The upda ed FLEXTRKR algo i hm and he obse a ional MCS-IDC da ase
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The upda ed FLEXTRKR algo i hm, combined wi h he S o m Labeling in Th ee
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Dimensions (SL3D) algo i hm (S a zec e al., 2017), can ack MCS and IDC simul aneously
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using in a ed b igh ness empe a u e (Tb), ada e lec i i y, p ecipi a ion, and mel ing le el
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heigh (Li e al., 2021a). The algo i hm i s iden i ies cold cloud shields (CCSs) wi h Tb < 241K
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a each ime slice and hen es ablishes he spa ial connec ions o CCSs be ween wo consecu i e
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hou s using a spa ial o e lap h eshold o 50%. A ack is gene a ed by linking all he CCSs om
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he same cloud sys em. FLEXTRKR classi ies a ack as an MCS i he a eas o he acked
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CCSs a e >60,000 km2 o >6 consecu i e hou s and he ack con ains p ecipi a ion ea u es
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(PFs) wi h a majo axis leng h >100 km and an embedded in ense con ec i e cell a ea ≥16 km2
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o ≥6 con inuous hou s. He e, a PF is a con inuous upd a , con ec i e, o p ecipi a ing
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s a i o m a ea wi h p ecipi a ion >1 mm h−1, and an in ense con ec i e cell is a con inuous
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upd a o con ec i e a ea wi h composi e e lec i i y ≥45 dBZ. The SL3D algo i hm iden i ies
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upd a , con ec i e, and p ecipi a ing s a i o m pixels. A non-MCS ack is conside ed IDC i i
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con ains any PFs and con ec i e co e ea u es (CCFs) du ing i s li e ime. A CCF is a con inuous
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upd a o con ec i e a ea wi h p ecipi a ion >0 mm h−1.
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This s udy uses he upda ed FLEXTRKR algo i hm and a ious sou ce da ase s o
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de elop a high- esolu ion (4 km, hou ly) obse a ional MCS-IDC da ase , which p o ides
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acking and cha ac e is ics o MCS and IDC e en s o e he Uni ed S a es eas o he Rocky
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Moun ains om July 1 o Augus 19, 2020. The sou ce da ase s a e he same as hose used in Li
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e al. (2021a), excep ha he 3-D G idded NEXRAD (Nex Gene a ion Wea he Rada ) WSR-
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88D Rada (G idRad) e lec i i y da a has been upda ed om Ve sion 3.1 o Ve sion 4.2
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(Bowman & Homeye , 2021). The upda ed FLEXTRKR is also used o ack MCS and IDC
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e en s om he abo e SCREAM RRM simula ions wi hin he obse a ional MCS-IDC da a
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domain (Figu e 1). No ably, we emo e con ec i e sys ems associa ed wi h wo hu icanes
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(Hu icanes Hanna and Isaias) occu ing du ing he s udy pe iod om he SCREAM simula ions
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and he obse a ional MCS-IDC da ase . Mo eo e , his s udy ocuses exclusi ely on MCS and
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IDC e en s in he U.S. land a eas wi hin he MCS-IDC da a domain, which a e de ined as
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con ec i e sys ems s aying wi hin he egion o a leas hal o hei li e imes.
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3 Resul s
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3.1 E alua ion o he CTRL simula ion
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Table 1 e alua es he CTRL simula ion agains he obse a ional MCS-IDC da ase a he
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e en scale. The CTRL simula ion oughly p oduces as many MCS (66 s. 80) and IDC (19,638
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s. 15,887) e en s as obse a ions, a be e han he unde es ima ed coun s (by >70%) o MCS
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e en s in global clima e models gene a ed wi h e ined ho izon al esolu ion a 50 and 25 km
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o e No h Ame ica (Feng e al., 2021). The CTRL simula ion also well ep oduces he obse ed
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MCS and IDC espec i e mean li e ime, CCS a ea, CCS co e (Tb < 225 K) a ea, PF s a i o m
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a ea, PF con ec i e and s a i o m p ecipi a ion a es, and CCS, PF, and CCF majo axis leng hs.
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manusc ip submi ed o Jou nal o Geophysical Resea ch: A mosphe es
Howe e , he CTRL unde es ima es he MCS max 40-dBZ echo op heigh bu o e es ima es he
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MCS and IDC PF con ec i e a eas. The la e may be a ibu ed o o e es ima ed model ada
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e lec i i ies associa ed wi h con ec i e sys ems (no shown), a limi a ion also iden i ied in he
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simula ion o he June 2012 No h Ame ican de echo using SCREAM RRM and he Wea he
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Resea ch and Fo ecas ing Model (Li e al., 2023; Liu e al., 2023). The CTRL also e ec i ely
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cap u es he obse ed MCS e olu ional cha ac e is ics (Figu e S2), u he alida ing he
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excellen capabili y o SCREAM RRM in simula ing he popula ions o con ec i e sys ems.
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Figu es S3a-S3d and 2a-2d compa e he CTRL-simula ed spa ial dis ibu ions o MCS
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and IDC occu ences and hei accumula ed p ecipi a ion wi h he MCS-IDC da ase . The CTRL
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cap u es he obse ed MCS and IDC spa ial dis ibu ion pa e ns well. The uncen e ed pa e n
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co ela ions be ween CTRL and obse a ions a e 0.68 and 0.72 o MCS and IDC p ecipi a ion,
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espec i ely (Figu es 2a-2d). These alues each up o 0.93 and 0.90 o he MCS and IDC
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occu ence equencies (Figu es S3a-S3d). The CTRL co ec ly iden i ies ho spo s wi h equen
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MCS and IDC occu ences, such as he G ea Plains and Midwes o MCS and he sou heas e n
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and eas e n coas al a eas o IDC. Howe e , on a e age, he CTRL unde es ima es he MCS
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numbe s by 23% and p ecipi a ion by 16%. The unde es ima ion is mos appa en in he G ea
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Plains, wi h equen MCS occu ences, which is a common bias widely ound in egional and
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global clima e models (Feng e al., 2021; Li e al., 2022; Lin e al., 2022; P ein e al., 2020). In
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addi ion, on a e age, he CTRL o e es ima es IDC occu ences by 23%, while he IDC
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p ecipi a ion is o e es ima ed by only 5% due o an unde es ima ed IDC p ecipi a ion a e (Table
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1).
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O e all, despi e he p esence o ce ain common model biases, he CTRL simula ion
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e ec i ely cap u es he e en -scale and spa ial-scale cha ac e is ics o obse ed MCS and IDC
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e en s. This allows o a meaning ul compa ison be ween he PGW and CTRL simula ions.
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manusc ip submi ed o Jou nal o Geophysical Resea ch: A mosphe es
Table 1. Compa ison o he SCREAM RRM simula ed MCS and IDC s a is ics and mean p ope ies wi h he obse a ional MCS-IDC da ase
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S a is ics and mean p ope ies
MCS
IDC
Obs
CTRL
PGW
Obs
CTRL
PGW
E en numbe
80
66
85
15,887
19,638
16,014
Li e ime / h
18.6
18.41
17.1
1.8
1.5
1.6
CCS a ea / km2
127,560
116,552
113,209
2,970
2,927
3,546
CCS majo axis leng h / km
524
515
508
67
75
77
CCS co e a ea / km2
66,444
55,516
61,202
667
647
1,103
PF majo axis leng h / km
297
323
317
47
59
57
PF con ec i e a ea / km2
8,363
12,911
15,027
388
970
1,020
PF s a i o m a ea / km2
22,405
27,325
25,159
558
777
651
PF con ec i e p ecipi a ion a e / mm h-1
4.6
4.1
3.9
4.3
3.7
3.7
PF s a i o m p ecipi a ion a e / mm h-1
2.8
2.6
2.6
2.9
2.2
2.3
Max 40-dBZ echo op heigh / km
9.2
6.9
8.1
5.4
5.0
6.2
CCF majo axis leng h / km
122
114
129
26
42
44
1Red colo ed numbe s indica e he di e ence be ween he PGW and CTRL simula ions is s a is ically signi ican a he 5% le el.
233
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manusc ip submi ed o Jou nal o Geophysical Resea ch: A mosphe es
235
Figu e 2. Spa ial dis ibu ions o accumula ed p ecipi a ion p oduced by (le panel) MCS and
236
( igh panel) IDC e en s om (a, b) he obse a ional MCS-IDC da ase , (c, d) he CTRL
237
simula ion, and (e, ) he PGW simula ion. We only coun hou s wi h hou ly p ecipi a ion la ge
238
han 0.01 mm o each g id cell.
239
3.2 Impac o global wa ming on e en -scale s a is ics
240
By compa ing he PGW and CTRL simula ions, we ind consis en inc eases in he mean
241
PF con ec i e a eas o MCS and IDC e en s, which aligns wi h la ge max 40-dBZ echo op
242
heigh s unde wa ming (Figu e 3 and Table 1). In con as , MCS and IDC PF s a i o m a eas a e
243
educed in he PGW compa ed o he CTRL simula ions (Figu e 3 and Table 1). The wa ming-
244
induced inc ease in MCS con ec i e a eas and dec ease in s a i o m a eas is consis en wi h
245
Feng e al. (2024), who in es iga ed he po en ial impac o global wa ming on a clus e o
246
sp ing ime MCSs in he sou he n G ea Plains du ing May 2015. They a ibu ed he wide
247
con ec i e a eas o la ge CAPE, esul ing in mo e in ense con ec i e upd a s, and he educed
248
s a i o m a eas o ele a ed s a i o m cloud bases, leading o s onge p ecipi a ion e apo a ion
249
in a wa me a mosphe e (Feng e al., 2024). Addi ionally, Figu e 3 and Table 1 show a s onge
250
manusc ip submi ed o Jou nal o Geophysical Resea ch: A mosphe es
Open Resea ch
401
The SCREAM sou ce code is a ailable a h ps://gi hub.com/E3SM-P ojec /sc eam (las access: Sep embe 26,
402
2022). The MCS-IDC da ase used in he s udy is a ailable a
403
h ps://po al.ne sc.go /p ojec /m3780/jli628/SCREAM_MCS_IDC/ (las access: Janua y 26, 2023). The
404
G idRad 4.2 da a is om h ps:// da.uca .edu/da ase s/ds841-1/ (las access: No embe 3, 2022) (Bowman &
405
Homeye , 2021). We download he ERA5 nudging ields and SST om h ps:// da.uca .edu/da ase s/ds633-0/
406
(las access: Oc obe 2, 2022) (ECMWF, 2019). The HRRR eanalysis da a is downloaded om he Google
407
Cloud Pla o m (h ps://console.cloud.google.com/ma ke place/p oduc /noaa-public/h ?p ojec =py hon-
408
232920&pli=1; las access, Decembe 7, 2022). Su ace a iables in he ERA5 ini ial condi ion and he ERA5
409
o cing da a used in he SCREAM land-only simula ions a e downloaded om
410
h ps://cds.clima e.cope nicus.eu/cdsapp#!/da ase / eanalysis-e a5-single-le els? ab=o e iew (las access:
411
Decembe 26, 2022) (He sbach e al., 2023b). P essu e-le el a iables in he ERA5 ini ial condi ion a e
412
downloaded om h ps://cds.clima e.cope nicus.eu/cdsapp#!/da ase / eanalysis-e a5-p essu e-
413
le els? ab=o e iew (las access: Decembe 26, 2022) (He sbach e al., 2023a). We download he CMIP6 da a
414
om h ps://aims2.llnl.go /sea ch/cmip6/ (las access: Decembe 26, 2022) (Bouche e al., 2019; Bouche e
415
al., 2018; Danabasoglu, 2019a, 2019b; Dix e al., 2019a, 2019b; EC-Ea h, 2019a, 2019b; Guo e al., 2018a;
416
Guo e al., 2018b; Jungclaus e al., 2019; Lo a o & Peano, 2020a, 2020b; Seland e al., 2019a, 2019b;
417
Shiogama e al., 2019; Swa e al., 2019a, 2019b; Ta ebe & Wa anabe, 2018; Wiene s e al., 2019; Yu, 2019a,
418
2019b).
419
420
421
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422
423
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In usi e Machine Lea ning F amewo k o Debiasing Long-Time Coa se Resolu ion Clima e Simula ions
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