Description and basic evaluation of Beijing Normal ... · Earth System Model (BNU-ESM) version 1 D....

Geosci Model Dev 7 2039ndash2064 2014wwwgeosci-model-devnet720392014doi105194gmd-7-2039-2014copy Author(s) 2014 CC Attribution 30 License

Description and basic evaluation of Beijing Normal UniversityEarth System Model (BNU-ESM) version 1

D Ji1 L Wang1 J Feng1 Q Wu1 H Cheng1 Q Zhang1 J Yang2 W Dong2 Y Dai1 D Gong2 R-H Zhang34X Wang4 J Liu5 J C Moore1 D Chen6 and M Zhou7

1College of Global Change and Earth System Science Beijing Normal University Beijing 100875 China2State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Beijing 100875 China3Key Laboratory of Ocean Circulation and Waves Institute of Oceanology Chinese Academy of SciencesQingdao 266071 China4Earth System Science Interdisciplinary Center (ESSIC) University of Maryland College Park MD 20742 USA5Department of Atmospheric and Environmental Sciences University at Albany State University of New YorkAlbany NY USA6National Parallel Computer Engineering Technology Research Center Beijing 100190 China7Jiangnan Institute of Computing Technology Wuxi 214083 China

Correspondence toL Wang (wanglnbnueducn) and D Ji (duoyingjibnueducn)

Received 31 January 2014 ndash Published in Geosci Model Dev Discuss 4 March 2014Revised 6 July 2014 ndash Accepted 10 August 2014 ndash Published 12 September 2014

Abstract An earth system model has been developed at Bei-jing Normal University (Beijing Normal University EarthSystem Model BNU-ESM) the model is based on severalwidely evaluated climate model components and is used tostudy mechanisms of ocean-atmosphere interactions natu-ral climate variability and carbon-climate feedbacks at inter-annual to interdecadal time scales In this paper the modelstructure and individual components are described brieflyFurther results for the CMIP5 (Coupled Model Intercom-parison Project phase 5) pre-industrial control and historicalsimulations are presented to demonstrate the modelrsquos perfor-mance in terms of the mean model state and the internal vari-ability It is illustrated that BNU-ESM can simulate manyobserved features of the earth climate system such as the cli-matological annual cycle of surface-air temperature and pre-cipitation annual cycle of tropical Pacific sea surface tem-perature (SST) the overall patterns and positions of cells inglobal ocean meridional overturning circulation For exam-ple the El Nintildeo-Southern Oscillation (ENSO) simulated inBNU-ESM exhibits an irregular oscillation between 2 and5 years with the seasonal phase locking feature of ENSOImportant biases with regard to observations are presentedand discussed including warm SST discrepancies in the ma-jor upwelling regions an equatorward drift of midlatitude

westerly wind bands and tropical precipitation bias over theocean that is related to the double Intertropical ConvergenceZone (ITCZ)

1 Introduction

Climate models are the essential tools to investigate the re-sponse of the climate system to various forcings to makeclimate predictions on seasonal to decadal time scales andto make projections of future climate (Flato et al 2013)At Beijing Normal University with collaboration from sev-eral model development centers in China the BNU-ESM(Beijing Normal University Earth System Model) compris-ing atmospheric land oceanic and sea ice components alongwith carbon cycles has recently been developed The de-velopment of BNU-ESM was prompted by foundation ofa new multidisciplinary research center committed to studyglobal change and earth system science in Beijing NormalUniversity The BNU-ESM takes advantage of contemporarymodel achievements from several well-known modeling cen-ters and its components were chosen based on the specificexpertise and experience available to the research center and

Published by Copernicus Publications on behalf of the European Geosciences Union

2040 D Ji et al Description and basic evaluation of BNU-ESM

furthermore with an eye to how the research strengths of thecenter can improve and develop it

The coupling framework of BNU-ESM is based on aninterim version of the Community Climate System Modelversion 4 (CCSM4) (Gent et al 2011 Vertenstein et al2010) developed at the National Center for Atmospheric Re-search (NCAR) on behalf of the Community Climate SystemModelCommunity Earth System Model (CCSMCESM)project of the University Corporation for Atmospheric Re-search (UCAR) Notably BNU-ESM differs from CCSM4in the following major aspects (i) BNU-ESM utilizes theModular Ocean Model version 4p1 (MOM4p1) (Griffies2010) developed at Geophysical Fluid Dynamics Labora-tory (GFDL) (ii) The land surface component of BNU-ESM is the Common Land Model (CoLM) (Dai et al 20032004 Ji and Dai 2010) initially developed by a commu-nity and further improved at Beijing Normal University(iii) The CoLM has a global dynamic vegetation sub-modeland terrestrial carbon and nitrogen cycles based on the LundndashPotsdamndashJena model (LPJ) (Sitch et al 2003) and the LundndashPotsdamndashJena Dynamic Nitrogen scheme (LPJ-DyN) (Xuand Prentice 2008) The LPJ-DyN based terrestrial carbonand nitrogen interaction schemes are very different from thebiogeochemistry Carbon-Nitrogen scheme used in CLM4 orCCSM4 (Thornton and Rosenbloom 2005 Oleson et al2010 Lawrence et al 2011) (iv) The atmospheric compo-nent is an interim version of the Community AtmosphericModel version 4 (CAM4) (Neale et al 2010 2013) modifiedwith a revised ZhangndashMcFarlane deep convection scheme(Zhang and McFarlane 1995 Zhang 2002 Zhang and Mu2005a) (v) The sea ice component is the Community IceCodE (CICE) version 41 (Hunke and Lipscomb 2010) de-veloped at Los Alamos National Lab (LANL) while the seaice component of CCSM4 is based on Version 4 of CICEThese variations illustrate how the BNU-ESM adds to themuch-desired climate model diversity and thus to the hierar-chy of models participating in the Climate Model Intercom-parison Projects phase 5 (CMIP5) (Taylor et al 2012)

As a member of CMIP5 BNU-ESM has completed allcore simulations within the suite of CMIP5 long-term ex-periments and some of related tier-1 integrations intended toexamine specific aspects of climate model forcing responseand processes The long-term experiments performed withBNU-ESM include a group forced by observed atmosphericcomposition changes or specified concentrations (egpi-Control historical rcp45and rcp85 labeled by CMIP5)and a group driven by time-evolving emissions of con-stituents from which concentrations can be computed in-teractively (egesmControl esmHistoricaland esmrcp85labeled by CMIP5) At the same time BNU-ESM joinedthe Geoengineearing Model Intercomparison Project (Ge-oMIP) and completed its first suite of experiments (G1ndashG4Kravitz et al 2011) concentrating on solar radiation manage-ment (SRM) schemes (eg Moore et al 2014) Data for allCMIP5 and GeoMIP simulations completed by BNU-ESM

have been published via an Earth System Grid Data Nodelocated at Beijing Normal University (BNU) and can be ac-cessed athttpesgbnueducn as a part of internationallyfederated distributed data archival and retrieval system re-ferred to as the Earth System Grid Federation (ESGF)

Many studies have utilized CMIP5 results from BNU-ESM and the model has received comprehensive eval-uations For example Wu et al (2013) evaluated theprecipitation-surface temperature (PndashT ) relationship ofBNU-ESM among 17 models in CMIP5 and found BNU-ESM has better ability in simulatingPndashT pattern correla-tion than other models especially over ocean and tropicsBellenger et al (2013) used the metrics developed withinthe Climate Variability and Predictability (CLIVAR) PacificPanel and additional metrics to evaluate the basic El Nintildeo-Southern Oscillation (ENSO) properties and associated feed-backs of BNU-ESM and other CMIP5 models BNU-ESMperforms well on simulating precipitation anomalies over theNintildeo-4 region the ratio between the ENSO spectral energyin the 1ndash3 year band and in 3ndash8 year band is well consis-tent with observational result but the model has stronger seasurface temperature (SST) anomalies than observational esti-mates over Nintildeo-3 and Nintildeo-4 regions Fettweis et al (2013)reported BNU-ESM can simulate the 1961ndash1990 variabilityof the JunendashAugust (JJA) North Atlantic Oscillation (NAO)well and the sharp decrease of the NAO index over the last10 years as observed and the model projects similar negativeNAO values into the future under RCP 85 scenario Gillettand Fyfe (2013) reported no significant Northern AnnularMode (NAM) decrease in any season between 1861 and 2099in historical and rcp45 simulations of BNU-ESM as with theother 36 models from CMIP5 Bracegirdle et al (2013) as-sessed the modelrsquos simulation of near-surface westerly windsover the Southern Ocean and found an equatorward bias inthe present-day zonal mean surface jet position in commonwith many of the CMIP5 models Among other studies Chenet al (2013) evaluated the cloud and water vapor feedbacksto El Nintildeo warming in BNU-ESM Vial et al (2013) diag-nosed the climate sensitivity radiative forcing and climatefeedback of BNU-ESM Roehrig et al (2013) assessed theperformance of BNU-ESM on simulating the West AfricanMonsoon Sillmann et al (2013) evaluated the model per-formance on simulating climate extreme indices defined bythe Expert Team on Climate Change Detection and Indices(ETCCDI) Wei et al (2012) utilized BNU-ESM in assess-ment of developed and developing world responsibilities forhistorical climate change and CO2 mitigation

Although the simulation results from BNU-ESM arewidely used in many climate studies a general descriptionof the model itself and its control climate is still not avail-able Documenting the main features of the model structureand its underlying parameterization schemes will help the cli-mate community to further understand the results from BNU-ESM

Geosci Model Dev 7 2039ndash2064 2014 wwwgeosci-model-devnet720392014

D Ji et al Description and basic evaluation of BNU-ESM 2041

This paper provides a general description and basic evalu-ation of the historical climate simulated by BNU-ESM Par-ticular focus is put on the model structure the simulated cli-matology internal climate variability and terrestrial carboncycle deduced from the piControl and historical simulationssubmitted for CMIP5 The climate response and scenarioprojections in BNU-ESM will be covered elsewhere The pa-per is organized as follows In Sect 2 a general overview ofBNU-ESM is provided elaborating on similarities and dif-ferences between the original and revised model componentsin BNU-ESM In Sect 3 the design of the piControl and his-torical model experiments is briefly presented as well as thespin-up strategy In Sect 4 the general model performanceis evaluated by using the Taylor diagram (Taylor 2001) Thefollowing two sections focus on the model performance onsimulating physical climatology and climate variability Sev-eral key modes of internal variability on different timescalesranging from interseasonal to interdecadal are evaluated Theterrestrial carbon cycle is evaluated in Sect 7 and particularfocus is put on terrestrial primary productions and soil or-ganic carbon stocks Finally the paper is summarized anddiscussed in Sect 8

2 Model description

21 Atmospheric model

The atmospheric component in BNU-ESM is based on Com-munity Atmospheric Model version 35 (CAM35) which isan interim version of the Community Atmospheric Modelversion 4 (CAM4) (Neale et al 2010 2013) Here the maindifference of the atmospheric component in BNU-ESM rela-tive to the original CAM35 model is the process of deep con-vection The BNU-ESM uses a modified ZhangndashMcFarlanescheme in which a revised closure scheme couples convec-tion to the large-scale forcing in the free troposphere insteadof to the convective available potential energy in the atmo-sphere (Zhang 2002 Zhang and Mu 2005a) On the otherhand CAM35 adopts a ZhangndashMcFarlane scheme (Zhangand McFarlane 1995) modified with the addition of convec-tive momentum transports (Richter and Rasch 2008) and amodified dilute plume calculation (Neale et al 2008) fol-lowing Raymond and Blyth (1986 1992) BNU-ESM usesthe Eulerian dynamical core in CAM35 for transport cal-culations with a T42 horizontal spectral resolution (approx-imately 281 times 281 transform grid) with 26 levels in thevertical of a hybrid sigma-pressure coordinates and modeltop at 2917 hPa Atmospheric chemical processes utilizethe tropospheric MOZART (TROP-MOZART) frameworkin CAM35 (Lamarque et al 2010) which has prognos-tic greenhouse gases and prescribed aerosols Note that theaerosols do not directly interact with the cloud scheme sothat any indirect effects are omitted in CAM35 as well as inBNU-ESM

22 Ocean model

The ocean component in BNU-ESM is based on the GFDLModular Ocean Model version 4p1 (MOM4p1) released in2009 (Griffies 2010) The oceanic physics is unchangedfrom the standard MOM4p1 model and the main modifica-tions are in the general geometry and geography of the oceancomponent MOM4p1 uses a tripolar grid to avoid the po-lar singularity over the Arctic in which the two northernpoles of the curvilinear grid are shifted to land areas overNorth America and Eurasia (Murray 1996) In BNU-ESMMOM4p1 uses a nominal latitude-longitude resolution of 1

(down to 13 within 10 of the equatorial tropics) with 360longitudinal grids and 200 latitudinal grids and there are50 vertical levels with the uppermost 23 layers each being10143 m thick The mixed layer is represented by theK pro-file parameterization (KPP) of vertical mixing (Large et al1994) The idealized ocean biogeochemistry (iBGC) mod-ule is used in BNU-ESM which carries a single prognos-tic macronutrient tracer (phosphate PO4) and simulates twomain representative biogeochemical processes ie the netbiological uptake in the euphotic zone due to phytoplank-ton activity as a function of temperature light and phosphateavailability and regeneration of phosphate as an exponentialfunction below the euphotic zone

23 Sea ice model

The BNU-ESM sea ice component is the Los Alamos seaice model (CICE) version 41 (Hunke and Lipscomb 2010)The CICE was originally developed to be compatible withthe Parallel Ocean Program (POP) but has been greatly en-hanced in its technical and physical compatibility with differ-ent models in recent years In particular supporting tripolargrids makes it easier to couple with MOM4p1 code In BNU-ESM CICE uses its default shortwave scheme in which thepenetrating solar radiation is equal to zero for snow-coveredice that is most of the incoming sunlight is absorbed nearthe top surface The visible and near infrared albedos forthick ice and cold snow are set to 077 035 096 and 069respectively slightly smaller than the standard CICE config-uration as they are used as tuning parameters during modelcontrol integration The surface temperature of ice or snow iscalculated in CICE without exploiting its ldquozero-layerrdquo ther-modynamic scheme and the ldquobubbly brinerdquo model based pa-rameterization of ice thermal conductivity is used

24 Land model

The land component in BNU-ESM is the Common LandModel (CoLM) which was initially developed by incorpo-rating the best features of three earlier land models thebiospherendashatmosphere transfer scheme (BATS) (Dickinsonet al 1993) the 1994 version of the Chinese Academyof Sciences Institute of Atmospheric Physics LSM (IAP94)

wwwgeosci-model-devnet720392014 Geosci Model Dev 7 2039ndash2064 2014


(Dai and Zeng 1997) and the NCAR Land Surface Model(LSM) (Bonan 1996 1998) The CoLM was documented byDai et al (2001) and introduced to the modeling commu-nity in Dai et al (2003) The initial version of CoLM wasadopted as the Community Land Model (CLM) for use withthe Community Climate System Model (CCSM) The landmodel was then developed separately at NCAR and BNUCurrently the CoLM is radically different from its initial ver-sion and the CLM (Dai et al 2004 Bonan et al 2011)including the following (i) improved two stream approxi-mation model of radiation transfer of the canopy with at-tention to singularities in its solution and with separate in-tegrations of radiation absorption by sunlit and shaded frac-tions of canopy (ii) A photosynthesis-stomatal conductancemodel for sunlit and shaded leaves separately and for the si-multaneous transfers of CO2 and water vapor into and outof the leaf (iii) LundndashPotsdamndashJena (LPJ) model (Sitch etal 2003) based dynamical global vegetation model and ter-restrial carbon cycle and LPJ-DyN (Xu and Prentice 2008)based scheme on carbon-nitrogen cycle interactions Notethat in all BNU-ESMrsquos CMIP5 and GeoMIP simulationscarbon-nitrogen cycle interactions are turned off as the ni-trogen cycle has not yet been fully evaluated

25 Component coupling

The coupling framework of BNU-ESM is largely basedon the coupler in NCAR CCSM35 (an interim version ofNCAR CCSM4) with changes on grid mapping interpola-tion to allow for the identical tripolar grids used in both oceanand sea ice components The time evolution of the wholemodel and communication between various component mod-els are all synchronized and controlled by the coupler in theBNU-ESM Since MOM4p1 and CICE41 are both ArakawaB-grid models the coupling between them is efficient andthe exchanged fields need no transformation or additionaltreatment (eg vector rotation grid remapping grid-pointshifting etc) The different model components are run si-multaneously from their initial conditions The atmosphericcomponent uses a 1 h time step for atmospheric radiation and20 min time step for other atmospheric physics The oceansea ice and land components have a 2 h 1 h and 30 min timestep respectively while direct coupling occurs hourly amongatmospheric sea ice and land components and daily with theocean component without any flux adjustment

All biogeochemical components are driven by the phys-ical climate with the biogeochemical feedback loops com-bined The terrestrial carbon cycle module determines theexchange of CO2 between the land and the atmosphere It iscoupled to the physical climate through the vegetation distri-bution and leaf area index which affects the surface albedothe evapotranspiration flux and so on As with the terrestrialcarbon cycle module the ocean biogeochemistry module cal-culates the ocean-atmosphere exchange of CO2 and both are

Figure 1 The global mean TOA and surface net radiation fluxglobal mean SST over the piControl simulation period The blacklines are linear regressions

coupled with the TROP-MOZART framework in the atmo-spheric component to form a closed carbon cycle

3 Experiments

Following CMIP5 specifications (Taylor et al 2009) BNU-ESM has performed all CMIP5 long-term core experimentsand part of the tier-1 experiments The CMIP5 specifica-tion requires each model to reach its equilibrium states be-fore kicking off formal simulations especially for long-termcontrol experiments BNU-ESM adopted a two-step spin-upstrategy to achieve model equilibrium Firstly the land com-ponent including vegetation dynamics and terrestrial carboncycle and the ocean component including biogeochemicalmodule were separately spun-up to yield an initial estimateof equilibrium states In these off-line integrations of the firststep spin-up surface physical quantities such as winds tem-perature precipitation moisture and radiation flux are takenas the climatology of a pre-industrial run of the fully coupledBNU-ESM with carbon cycles turned off Then the resultantequilibrated physical and carbon cycle states were fed intothe coupled model as initial conditions to do on-line spin-upto achieve final equilibrium states During the second stagethe coupled model was forced with constant external condi-tions as specified for CMIP5 pre-industrial control simula-tion as stated below



Table 1Observationally based reference data sets

Variable ID Description Reference1Reference2 Domain

ta temperature [C] ERA-InterimaJRA-55b 200 850 hPaua zonal wind [m sminus1] ERA-InterimaJRA-55b 200 850 hPava meridional wind [m sminus1] ERA-InterimaJRA-55b 200 850 hPazg geopotential height [m] ERA-InterimaJRA-55b 500 hPahus specific humidity [kg kgminus1] ERA-InterimaMERRAc 400 850 hParlut TOA outgoing long-wave radiation [W mminus2] ERBEdCERES-EBAFe

rsnt TOA net shortwave radiation [W mminus2] ERBEdCERES-EBAFe

rlwcrf long-wave cloud radiative forcing [W mminus2] ERBEdCERES-EBAFe equatorward of 60

rswcrf shortwave cloud radiative forcing [W mminus2] ERBEdCERES-EBAFe equatorward of 60

pr total precipitation [mm dayminus1] GPCPfCMAPg

clt total cloud cover [] ISCCP-D2hCLOUDSATi

prw precipitable water [g kgminus1] RSS(v7)jNVAPk

psl sea level pressure [Pa] ERA-InterimaJRA-55b ocean onlyuas surface (10 m) zonal wind speed [m sminus1] ERA-InterimaJRA-55b ocean onlyvas surface (10 m) meridional wind speed [m sminus1] ERA-InterimaJRA-55b ocean onlytos sea surface temperature [C] HadISSTlOISST(v2)m ocean only equatorward of 50

tauu ocean surface zonal wind stress [Pa] ERA-InterimaNOCSn ocean onlytauv ocean surface meridional wind stress [Pa] ERA-InterimaNOCSn ocean onlyhfls(ocn) ocean surface latent heat flux [W mminus2] ERA-InterimaNOCSn ocean onlyhfss(ocn) ocean surface sensible heat flux [W mminus2] ERA-InterimaNOCSn ocean onlyhfls(lnd) land surface latent heat flux [W mminus2] ERA-InterimaFLUXNET-MTEo land onlyhfss(lnd) land surface sensible heat flux [W mminus2] ERA-InterimaFLUXNET-MTEo land onlygpp gross primary productivity [kg mminus2 sminus1] FLUXNET-MTEo land onlyfgco2 surface CO2 flux [kg mminus2 sminus1] LDEOp ocean only

a ERA-Interim (Dee et al 2011)b JRA-55 (Ebita et al 2011)c MERRA (Rienecker et al 2011)d ERBE (Barkstrom 1984)e CERES-EBAF (Loeb et al 2009)f GPCP(Adler et al 2003)g CMAP (Xie and Arkin 1997)h ISCCP-D2 (Rossow and Schiffer 1999 Rossow and Duentildeas 2004)i CLOUDSAT (LrsquoEcuyer et al 2008)j RSS(Wentz 2000 2013)k NVAP (Simpson et al 2001)l HadISST (Rayner et al 2003)m OISST (Reynolds et al 2002)n NOCS (Josey et al 1999)o FLUXNET-MTE(Jung et al 2011)p LDEO (Takahashi et al 2009)

In this paper we focus on the 559 year (from model year1450 to 2008) pre-industrial control simulation (piControl)and 156 year historical simulation representing the histori-cal period from year 1850 to 2005 The piControl simula-tion is integrated with constant external forcing prescribedat 1850 conditions (the solar constant is 1365885 W mminus2the concentrations of CO2 CH4 N2O are 284725 ppmv790979 ppbv and 275425 ppbv respectively CFC-11 CFC-12 and volcanic aerosols are assumed to be zero) In termsof energy balance and model stability the global mean top-of-atmosphere (TOA) net radiation flux over piControl pe-riod is 088 W mminus2 while the global mean surface net radi-ation flux is 086 W mminus2 The global mean sea surface tem-perature over piControl period is 1769C with a warmingdrift of 002C per century (Fig 1) The historical simula-tion is initialized with the model states of 1850 year from pi-Control simulation and forced with natural variation of so-lar radiation (Lean et al 2005 Wang et al 2005) anthro-pogenic changes in greenhouse gases concentrations strato-spheric sulphate aerosol concentrations from explosive vol-canoes (Ammann et al 2003) and aerosol concentrations ofsulfate black and organic carbon dust and sea salt according

to Lamarque et al (2010) Note that there is no land-coverchange related to (anthropogenic) land use because the vege-tation distributions evolve according to the model-simulatedclimate and the areal fraction of non-vegetated regions (lakewetland glacier and urban) are fixed according to the GlobalLand Cover Characterization (GLCC) Database Thereforechanges in physical and biogeochemical properties of thevegetation due to actual land-cover changes are excluded bydesign

4 General model performance

To systematically evaluate the general performance of BNU-ESM we use the Taylor diagram (Taylor 2001 Gleckler etal 2008) which relates the ldquocenteredrdquo root-mean square(RMS) error the pattern correlation and the standard de-viation of particular climate fields We selected 24 fields(Table 1) and compared model simulations with two differ-ent reference data sets (only one data set was available forgross primary production over land and surface CO2 fluxover ocean) The selection rationale for the fields and ref-erence data sets follows Gleckler et al (2008) where most



of reference data sets are briefly described One notabledifference is that we use ERA-Interim (Dee et al 2011)and JRA-55 (Ebita et al 2011) reanalysis data instead ofERA40 and NCEP to reflect recent advances in reanalysissystems We use estimates of specific humidity from Na-tional Aeronautics and Space Administration (NASA) Mod-ern Era Retrospective analysis for Research and Applications(MERRA Rienecker et al 2011) instead of the AtmosphericInfrared Sounder (AIRS) experiment as Tian et al (2013)indicated MERRA specific humidity probably has a smalleruncertainty than the AIRS data set The International Satel-lite Cloud Climatology Project (ISCCP Rossow and Schif-fer 1999 Rossow and Duentildeas 2004) D2 and CLOUDSAT(LrsquoEcuyer et al 2008) data sets are used to examine the to-tal cloud cover The Clouds and the Earthrsquos Radiant EnergySystem ndash Energy Balanced and Filled (CERES-EBAF) dataset (Loeb et al 2009) is used instead of the CERES observa-tions because the energy balanced characteristics of CERES-EBAF made it more suitable for the near balanced energeticsof the earth system Two carbon cycle fields (gpp and fgco2)were added to fill the gap between climate system modeland earth system model The reference data used to exam-ine gross primary production (gpp) over land is FLUXNETModel Tree Ensembles (FLUXNET-MTE) estimates (Jung etal 2011) which are restricted to vegetated land surface Thereference data used to examine surface CO2 flux over ocean(fgco2) is from LamontndashDoherty Earth Observatory (LDEOTakahashi et al 2009) this climatology data set was createdfrom about 3 million direct observations of seawaterpCO2around the world between 1970 and 2007

Figure 2 shows six climatological annual-cycle space-timeTaylor diagrams for the 24 selected fields in Table 1 for thetropical (20 Sndash20 N) and the northern extra-tropical (20ndash90 N) zones It is clear from Fig 2 that the accuracy ofthe model varies between fields and domains Some simu-lated fields over the northern extra-tropics have correlationswith the reference data of greater than 095 (eg zg-500hPata-850hPa rlut rsnt tos) and most of fields have correla-tions with the reference data of greater than 08 whereasone field has much lower correlation of 038 (fgco2 over thenorthern extra-tropics) The amplitude of spatial and tempo-ral variability simulated by the model is reasonably close tothat of observationally based reference data The normalizedstandard deviations between the simulation and the referencedata of most fields have a bias of less than 025 and sev-eral fields have a bias of less than 01 (eg ta-850hPa hus-850hPa rlut rsnt psl tos) One outlier in Fig 2 (NHEX G3and TROP G3) is the sensible heat flux over ocean (hfss) ex-amined with National Oceanography Centre Southampton(NOCS) reference data (Josey et al 1999) The model showsbetter skills when compared to ERA-Interim reanalysis al-though the pattern correlations against two reference datasets are both of about 06 Previous studies suggest that thereare large uncertainties in NOCS data set and their pattern hasbetter agreement with reanalysis products than the magnitude

Figure 2 Multivariate Taylor diagrams of the 20th century annualcycle climatological (1986ndash2005) for the tropical (20 Sndash20 NTROP) and the northern extra-tropical (20ndash90 N NHEX) zonesEach field is normalized by the corresponding standard deviation ofthe reference data which allows multiple fields to be shown in eachsub-figure RedBlue markers represent the simulation field evalu-ated against the Reference1Reference2 data defined in Table 1

of their fluxes (eg Taylor 2000) In general most of fieldsover the tropics are closer to reference data than those overthe northern extra-tropics in Taylor diagrams but some fieldswith relatively high correlations in the northern extra-tropicshave a lower skill in the tropics These features are consistentwith Gleckler et al (2008)

5 Climatology in the late 20th century

51 Atmospheric mean state

Figure 3 shows the zonally averaged mean atmospheric tem-perature zonal wind and specific humidity for the histori-cal simulation of the BNU-ESM and its deviations from theERA-Interim reanalysis (Dee et al 2011) The air temper-ature in the troposphere is in general cold for both borealsummer and winter especially during the boreal summer(Fig 3a) Near the polar tropopause (about 250 hPa) thereis a relatively large cold bias up to 8 K over the Arctic duringJJA and up to 10 K over the Antarctica during DecemberndashFebruary (DJF) This tropospheric cold bias is one com-mon problem in many CMIP5 models (Charlton-Perez etal 2013 Tian et al 2013) In the lower polar troposphere



Figure 3 Zonally averaged air temperature(a) zonal wind(b) andspecific humidity(c) climatology from BNU-ESM historical sim-ulation (black contours) and bias relative to the ERA-Interim cli-matology (color filled color bar is of same units except as forspecific humidity) for 1986ndash2005

during JJA there is a notable cold bias over the Antarctic Inthe stratosphere the very low winter temperature at 50 hPa inthe Southern Hemisphere associated with the polar night jetis overestimated in the model

With respect to zonally averaged winds (Fig 3b) theseasonal mitigation of the northern tropospheric jet is wellcaptured in the simulation but the westerlies at 200 hPa inthis jet are too strong by up to 4 m sminus1 during DJF and8 m sminus1 during JJA compared with ERA-Interim reanalysisThe southern tropospheric jet during DJF is also too strongby up to 12 m sminus1 while the westerlies from the surfaceto about 100 hPa at 60 S during DJF are weak relative tothe reanalysis The westerly wind maximum in the South-ern Hemisphere during JJA extends upward into the strato-sphere at higher latitudes as is observed In the stratospherethe polar-night jets in both hemispheres are shifted slightlypolewards relative to the reanalysis Over the equator in the

upper tropopause the model overestimates the easterly veloc-ities the largest biases occur at roughly 50 hPa

Figure 3c shows the modeled zonally averaged specific hu-midity and their differences relative to the ERA-Interim re-analysis shown as percentages because the relative error pro-vides a better measure of the water vaporrsquos impact on the ra-diative transfer than does the absolute errors (Soden et al2005) The model can simulate the strong meridional andvertical gradients in tropospheric specific humidity that de-crease with both latitude and altitude For example the spe-cific humidity decreases from around 14 g kgminus1 at 1000 hPanear the equator to around 1 g kgminus1 at 1000 hPa near the polesand around 05 g kgminus1 at 300 hPa over the equator In com-parison with ERA-Interim reanalysis the model has a moisttendency in the southern tropical upper troposphere (above700 hPa) and a slightly dry tendency in the tropical lower tro-posphere In terms of relative difference the modelrsquos dry biasin the tropical lower troposphere approaches 15 and thewet bias in the tropical upper troposphere approaches 50 This humidity bias pattern is also presented in many CMIP5models (Tian et al 2013)

Clouds are always a major source of uncertainty in cli-mate models In BNU-ESM the total cloud fraction is gen-erally underestimated (Fig 4a) the global mean value forthe years 1976ndash2005 of the historical simulation gives a biasof minus14 with a root-mean-square error (RMSE) of 18 compared with the ISCCP observational data set A notableexception is Antarctica where there are too many cloudsThe tropical central eastern Pacific and southern Africa alsohave more clouds than observations The latitudinal averagedcloud fraction bias within the tropics and subtropics is muchlower than at higher latitudes (Fig 4b) and is similar to re-sults from the original CAM35 and CAM4 at 2

times 2 hori-zontal resolution (Neale et al 2013) At the same time theliquid water in clouds over ocean is generally exaggerated inthe simulation (Fig 4c) and is particularly pronounced in theextratropical storm track regions

Clouds have a significant impact on the global radia-tive balance that is often assessed using TOA shortwavecloud forcing (SWCF) and long-wave cloud forcing (LWCF)(Ramanathan et al 1989) In BNU-ESM the simulatedshortwave cooling effect of clouds is too strong in the trop-ics and too weak in the mid-latitudes (Fig 5b) especiallyover oceans these biases are common in climate models(Trenberth and Fasullo 2010) BNU-ESM also overestimatesLWCF in the tropics due to the presence of a double In-tertropical Convergence Zone (ITCZ) (Fig 5d) and it largelyoffsets the bias of SWCF in the tropics In AMIP simulationwith sea surface temperature and sea ice boundary conditionsspecified the SWCF biases in BNU-ESM (not shown) re-semble that in CAM4 except for Eurasian continent (Kayet al 2012) Over Eurasia BNU-ESM simulates moderateshortwave cooling effects while CAM4 simulates oppositewarming effects In South Africa and Amazon regions bothmodels exhibit strong shortwave cloud cooling effects



Figure 4 (a) Total cloud fraction bias relative to ISCCP D2 re-trievals (Rossow and Schiffer 1999 Rossow and Duentildeas 2004)(b)Zonally averaged total cloud fraction compared with ISCCP D2 re-trievals and CLOUDSAT retrievals (LrsquoEcuyer et al 2008)(c) Zon-ally averaged total liquid water path (LWP) compared with SpecialSensor MicrowaveImager (SSMI) retrievals (Wentz 2000 2013)over oceans

52 Surface temperature and precipitation

The mean observed and modeled climatological annual cy-cles of surface-air temperature and precipitation for nine rep-resentative land regions are shown in Figs 6 and 7 Themost prominent differences from observations in modeledsurface-air temperature are a positive bias in Europe of upto 4C and negative bias in Eastern Siberia up to nearly

7C In Central Canada China and India the biases are rel-atively small In addition to Europe eight of nine regionsexhibit cold biases in annual mean surface-air temperatureand the model generally underestimates the annual temper-ature over the global land area (excluding Antarctica) byminus047C (minus028C) with an RMSE of 225C (240C)compared with CRU TS31 (Matsuura and Willmott MW)data Compared with two observational precipitation datasets BNU-ESM has a wet bias at high latitudes Excessiverainfall during winter seasons in Europe results from toostrong mid-latitude westerlies in particular over the NorthAtlantic which carry moist maritime air to the continentThe wet season precipitation in the Amazon exhibits a drybias and this tendency extends to August In SoutheasternAsia the monsoon rainfall in India is more realistic than inChina this is consistent with Sabeerali et al (2013) whofound that the BNU-ESM can simulate a climatologicallyrealistic spatial pattern of June to September precipitationover the Asian summer monsoon region Globally BNU-ESM overestimates the annual precipitation over the land(excluding Antarctica) by 047 mm dayminus1 (044 mm dayminus1)with a RMSE of 142 mm dayminus1 (133 mm dayminus1) comparedwith CMAP (MW) data These regional biases may causedynamic vegetation models in BNU-ESM to produce unreal-istic vegetation in affected regions

In Fig 8 global surface temperature for the period 1976ndash2005 of historical simulation is compared with observationsThe globally averaged bias isminus017C with a RMSE of183C Over ocean positive sea surface temperature (SST)biases are seen in the major eastern coastal upwelling re-gions probably due to coastal winds that are not favorable forupwelling or underestimation of stratocumulus cloud coverwhich is also an issue with other models (eg Washingtonet al 2000 Roberts et al 2004 Lin 2007 Gent et al2011) Negative SST biases are mainly found in South At-lantic South Indian and subpolar North Pacific Oceans An-other notable negative SST bias is seen in a narrow regionassociated with East Greenland and Labrador cold currentsIn South Atlantic and South Indian Oceans a tendency fornegative SST biases along the northern flank of the Antarc-tic Circumpolar Current (ACC) are mostly due to insufficientsouthward transport of heat out of the tropics and a position-ing error of the ACC caused by equatorward shift of the west-erlies although there is a small positive bias of the shortwavecloud radiation effect at the cold band between 40 and 50 S(Fig 5b) Gupta et al (2009) noted that relatively small errorsin the position of the ACC lead to more obvious biases in theSST Over continents the temperature biases are likely con-sistent with cloud fraction and TOA shortwave cloud forcing(SWCF) biases (Figs 8b and 5b) Such as the negative tem-perature bias over South Africa is likely linked to the nega-tive SWCF bias and excessive cloud fraction and the positivetemperature bias over central USA is probably linked to lesscloud fraction (Ma et al 2014)



Figure 5 Global map of shortwave cloud forcing (SWCF) and long-wave cloud forcing (LWCF) the following(a) SWCF of observedCERES-EBAF(b) BNU-ESM SWCF bias relative to CERES-EBAF(c) LWCF of observed CERES-EBAF(d) BNU-ESM LWCF biasrelative to CERES-EBAF

The global average precipitation in BNU-ESM is018 mm dayminus1 larger over the period of 1979ndash2005 year(Fig 9) than the Global Precipitation Climatology Project(GPCP) data set which combines surface observations andsatellite precipitation data (Adler et al 2003) While theGPCP data has been claimed to be an underestimate overocean by Trenberth et al (2007) the magnitude of tropi-cal precipitation is clearly overestimated by BNU-ESM Incommon with many climate models (eg Li and Xie 2014Lin 2007) we note a bias in precipitation characterized bya double Intertropical Convergence Zone (ITCZ) structureover much of the Tropics This produces excess precipita-tion over the Northern Hemispherersquos ITCZ Southern Hemi-spherersquos South Pacific convergence zone (SPCZ) the Mar-itime Continent and the tropical Indian Ocean together withinsufficient precipitation over the equatorial Pacific BNU-ESM displays the characteristic pattern of the double ITCZproblem with too much precipitation in the central Pacificnear 5 S and too little precipitation in the west and centralPacific between 15 and 30 S which is similar to CCSM4(Gent et al 2011) BNU-ESM underestimates precipitationat 5 N latitude but overestimates it along the 5 S paral-lel in the tropical Atlantic Compared with observations theBNU-ESM develops too weak a latitudinal asymmetry intropical precipitation and SST over the eastern Pacific and

Atlantic Oceans The negative precipitation bias in the Southand Northwest Atlantic is closely associated with local neg-ative SST biases (Fig 8) The band of excessive precipita-tion over the Southern Ocean between the southernmost ofSouthern Africa (about at 35 S 30 E) to southwest of Aus-tralian is consistent with the spatial pattern of warm SST bi-ases and is along the northern flank of a cold SST bias whichprobably produces more convective precipitation Over con-tinents there is excessive precipitation in India northernChina western USA South Africa and west coast of SouthAmerica and less precipitation in southern China and Ama-zon

The frequency and intensity of precipitation in the modelis highly dependent on the formulation of the convection pa-rameterization (Wilcox and Donner 2007) Figure 10 showsfrequency versus daily precipitation rate over land in thetropics between 20 N and 20 S and compared with the ob-servational estimates from the GPCP 1-degree daily data set(Huffman et al 2001) and the Tropical Rainfall MeasuringMission (TRMM) satellite observations (Kummerow et al2000) It is clear that BNU-ESM produces a realistic num-ber of precipitation events at a wide range of precipitationrates although the model has a tendency to underestimate ex-treme precipitation events (over 50 mm dayminus1) We note that



Figure 6 Climatological annual cycle of 2 m air temperature forselected regions for BNU-ESM and two observational estimatesfor the period 1976ndash2005 Color shading indicates interannual vari-ability (standard deviation) MW denotes version 201 05

times 05

monthly time series from Matsuura and Willmott (2009a) CRUis the Climatic Research Unit 05

times 05 TS 31 data set (Harriset al 2014) Regions are defined as follows Alaska (56ndash75 N167ndash141 W) Central Canada (46ndash61 N 123ndash97 W) EasternSiberia (51ndash66 N 112ndash138 E) eastern United States (27ndash47 N92ndash72 W) Europe (37ndash57 N 0ndash32 E) China (18ndash42 N 100ndash125 E) Amazon (14 Sndash5 N 74ndash53 W) Sahel (4ndash19 N 0ndash32 E) and India (4ndash28 N 68ndash94 E)

CCSM4 also produces similar precipitation characteristics at1 and 2 resolutions (Gent et al 2011)

53 Tropical Pacific SST

The tropical Pacific SST is closely associated with the ElNintildeondashSouthern Oscillation (ENSO) and exerts a strong in-fluence on the East Asian monsoon (Chang et al 2000 Liet al 2010) Figure 11 shows the 20th century mean and an-nual cycle of SSTs along the equator averaged between 2 Sand 2 N in the Pacific Oceans from HadISST observationsand the BNU-ESM historical run The modeled mean SST iscolder by about 04C than the observations over most of thewestern Pacific and by nearly 13C over the eastern basinwhile warmer than reality at both the western and easternboundaries of the Pacific (Fig 11a) These biases are causedby the strong easterly winds in the central and western Pacificand weaker zonal wind at the equatorial boundaries of the Pa-cific which result in cold and warm SST biases through en-hanced or weakened Ekman pumping in these regions The

Figure 7 As for Fig 6 but for precipitation for the period 1979ndash2005 Color shading indicates interannual variability (standard de-viation) CMAP comes from the Climate Prediction Center (CPC)Merged Analysis of Precipitation 1979ndash2009 ldquostandardrdquo (no reanal-ysis data) monthly time series at 25

times 25 (Xie and Arkin 1997)MW is version 201 05 times 05 monthly time series from Matsuuraand Willmott (2009b) for the years 1979ndash2005

different cold SST biases in the central eastern Pacific alongthe equator result in a stronger equatorial westward SST gra-dient than observed In terms of seasonal variation the obser-vations show a dominant annual cycle in SST in the easternPacific Ocean with anomaly patterns propagating westwardacross the central Pacific (Fig 11b) BNU-ESM reasonablyreproduces features of the annual cycle structure in the east-ern Pacific (Fig 11c) such as its transition phases and theamplitude and the position of the cold tongue but the warmseason peak is 1 month later in the model than in observa-tions The westward propagation of positive SST anomalypatterns in BNU-ESM is at about the correct speed betweenApril and November with 05C seasonal warming extend-ing to a little west of 160 W while the observed anomaly re-mains east of 160 W On the other hand the observed 05Cseasonal cooling near the dateline in March is not seen in themodel The semiannual cycle in SST that dominates in thewestern Pacific in the HadISST observations is also reason-ably simulated in BNU-ESM

54 Sea ice extent

Sea ice has long been recognized as a critical aspect of theglobal heat balance Unrealistic simulation of sea ice usu-ally exposes deficiencies in both atmospheric and oceanic



Figure 8 Climatological mean surface temperature from the05 times 05 CRU TS 31 (Harris et al 2014) and 1

times 1 HadISST(Rayner et al 2003) observations for the period 1976ndash2005(a)Annual mean surface temperature bias ( C) of BNU-ESM relativeto the CRU TS 31 and HadISST data sets for the period 1976ndash2005(b) All data sets are regridded to 1

times 1 resolution Dottedarea indicates non-significant regions at the 95 confidence level

forcing (eg Losch et al 2010) The observational dataused to evaluate the BNU-ESM is monthly climatologicalsea ice concentrations from the Special Sensor MicrowaveImager (SSMI) data set (Comiso 1999) obtained from theNational Snow and Ice Data Center (NSIDC) We also usethe NSIDCrsquos Sea Ice Index (Fetterer et al 2002) whichcontains monthly values of sea ice extent and sea ice areaFigure 12 shows the climatological sea ice concentrationin the Arctic and Antarctica for the period 1979ndash2005 ofBNU-ESM historical simulation and the solid black linesare the 15 mean concentration values from SSMI satel-lite observations The sea ice extent is overestimated inMarch (Fig 12a) and slightly underestimated in September(Fig 12b) following the summer in the Northern Hemisphere(the average mean sea ice extents of March and Septemberare 1846 and 587 million km2 while the NSIDC sea ice ex-tents for the same periods are 1548 and 667 million km2)In the Southern Hemisphere both March (Fig 12c) andSeptember (Fig 12d) extents are overestimated (the aver-age mean sea ice extents of March and September are 496and 2594 million km2 while the NSIDC sea ice extents are

Figure 9 Climatological mean precipitation from the GPCP (Adleret al 2003) observations(a) and annual mean precipitation bias(mm dayminus1) of BNU-ESM relative to the GPCP climatology for theperiod 1979ndash2005(b) Dotted area indicates non-significant regionsat the 95 confidence level

Figure 10 Frequency () of daily precipitation rate over landbetween 20 N and 20 S from BNU-ESM historical simulationover the period 1990ndash1999 the GPCP 1-degree daily data set andTRMM 3B42 daily observations over the period 1999ndash2008 Alldata are regridded to the T42 spectral resolution (approximately281 times 281 transform grid)



Figure 11 Mean SST (C) along the equator in the PacificOcean(a) color shading indicates interannual variability (standarddeviation) Annual cycle of SST anomalies for the period 1976ndash2005 from HadISST(b) and the BNU-ESM historical run(c)

402 and 1845 million km2) The excessive sea ice extentfollowing the winter in the Northern Hemisphere is mostlydue to too much sea ice in the Labrador Sea Bering SeaSea of Okhotsk and adjacent North Pacific The modeledgeographic distribution of ice in the Northern Hemisphereis close to observations in summer In the Southern Hemi-sphere the main overestimation in summer is in WeddellSea The too extensive sea ice simulated in both hemispheresis consistent with the cold SST bias found in correspondingareas (Fig 8) The simulated atmospheric fields are at leastpartly responsible for the Southern Hemisphere sea ice biasOne notable bias is that the annual average zonal wind stressfrom about 35 to 55 S latitudes over ocean is 232 strongercompared with ERA-Interim reanalysis and 428 strongercompared with NCEP reanalysis which likely inhibits suf-ficient southward transport of heat and contributes to coldsurface temperatures that are directly linked to a biased iceextent

In terms of seasonal cycle of sea ice extent the simulatedArctic sea ice extent for the period 1980ndash1999 is within therange of 42 CMIP5 models reported by Flato et al (2013)In Antarctica BNU-ESM estimates reasonable sea ice ex-tents for February but overestimates them in September(26 million km2) which is somewhat above the range of 42CMIP5 models BNU-ESM and CCSMCESM adopt simi-lar sea ice schemes and both models can simulate both the

Figure 12 Mean sea ice concentration () over years 1976ndash2005of the BNU-ESM historical run for both hemispheres and for March(a c) and September(b d) The solid black lines show the 15 mean sea ice concentration from SSMI observations (Comiso1999)

September Arctic sea ice extent and the rate of Arctic sea icedecline over recent decades better than many other CMIP5models (Liu et al 2013) While for Antarctica BNU-ESMand CCSM both have a tendency to overestimate sea ice ex-tent

55 Ocean meridional overturning circulation

The meridional overturning circulation (MOC) of the globalocean is a system of surface and deep currents encompassingall ocean basins It transports large amounts of water heatsalt carbon nutrients and other substances around the globeand is quite important for the chemical and biological proper-ties of the ocean The Atlantic MOC (AMOC) is an importantpart of the system and is responsible for a considerable partof northward oceanic heat transport Figure 13 shows 30 yearmeans of the global MOC and the AMOC over the 1976ndash2005 period of the BNU-ESM historical run the overall pat-terns and positions of cells water masses and overturningare similar to observed patterns (Lumpkin and Speer 2007)North Atlantic deep-water circulation can reach most of theocean bottom between 30 and 60 N The maximum over-turning of Atlantic water occurs near 35 N and is 284 Sv(1 Sv= 106 m3 sminus1) at a depth of about 15 km Many othermodels have maximum overturning at a depth of 1 km thereason for the deeper position in BNU-ESM is not well un-derstood The maximum annual mean AMOC strength at265 N in BNU-ESM is about 254 Sv which is somewhat



Figure 13Atlantic meridional overturning circulation (MOC) (Sv)and global MOC (Sv) for the period 1976ndash2005 from the BNU-ESM historical run

above the estimate of 187plusmn 48 Sv for the AMOC strengthat the same latitude found by the RAPIDMOCHA monitor-ing array for the years 2004ndash2011 (Rayner et al 2011) Overthe historical simulation period (1850ndash2005) the maximumannual mean AMOC strength at 265 N decreases 126 from 269 to 235 Sv

The BNU-ESM global MOC possesses a strong Deaconcell of about 40 Sv between 60 and 45 S which penetratesto 4 km depth and is a result of increased zonal wind stressdriving the ocean The mean transport of the Antarctic Cir-cumpolar Current (ACC) through Drake Passage is about1017 Sv This is less than the measured value of 134plusmn 11 Sv(Cunningham et al 2003) and at the low end of the rangeof 90ndash264 Sv from 23 CMIP5 models (Meijers et al 2012)One reason for weaker ACC transport through the Drake Pas-sage is that the model-simulated westerly wind stress max-imum is shifted equatorward The mean zonal wind stressover ocean is 26 lower than ERA-Interim reanalysis prod-ucts at the latitude of the Drake Passage Antarctic BottomWater (AABW) is located north of 50 S at depths greaterthan 35 km and the deep MOC in the Southern Hemisphereis about 4 Sv and weak compared with estimates of 8ndash95 Svfrom observations (Orsi et al 1999)

6 Climate variability

61 Tropical intraseasonal oscillation

The dominant component of the tropical intraseasonal oscil-lation (ISO) is the MaddenndashJulian Oscillation (MJO) (Mad-den and Julian 1971 1972) which affects tropical deep con-vection and rainfall patterns During the boreal winter aneastward propagating component affects rainfall over thetropics while during the boreal summer a northward prop-agating ISO affects much of southern Asia (eg Krishna-murti and Subrahmanyam 1982 Lau and Chan 1986 Anna-malai and Sperber 2005 Yang et al 2008) The MJO playsthe prominent role in tropical climate variability but is stillpoorly represented in climate models (Lin et al 2006 Kimet al 2009 Xavier et al 2010 Lau and Waliser 2012 Sper-ber and Kim 2012) Here we adopt the set of communitydiagnostics developed by the CLIVAR MJO Working Groupto examine simulated MJO characteristics In BNU-ESM thewinter eastward propagation is well detectable in zonal windsat 850 hPa (U850) over a region from the maritime continentto the western Pacific but is absent over the Indian Ocean andnot evident in precipitation (Fig 14a and b) Meanwhile thenorthward propagation in summer can be realistically simu-lated particularly in the off-equatorial region from 5 to 20 N(Fig 14c and d) The quadrature relationship between precip-itation and U850 is also well reproduced in northward prop-agation signals consistent with observations

The observed MJO (Fig 15a) exhibits peak power atzonal wavenumber 1 at a period of 30ndash80 days in both bo-real winter and summer (eg Weickmann et al 1985 Ki-ladis and Weickmann 1992 Zhang et al 2006) The powerspectrum of BNU-ESM shows that the zonal wave num-ber power distribution is well captured during boreal win-ter (Fig 15b) but the eastward propagating power tends tobe concentrated at lower than observed frequencies (peri-odsgt 80 days) The power density for westward propaga-tion is overestimated and consequently the eastndashwest ratio ofMJO spectral power is smaller than observed As with BNU-ESM the power spectra maximum produced by CCSM35using its default convection parameterization is also greaterthan 80 days (Kim et al 2009) while spectra computed byZhang and Mu (2005b) for CCM3 adopting the same convec-tion parameterization scheme as BNU-ESM peaks at approx-imately 40 days These studies suggest that the ability of aclimate model to simulate realistic MJO depends not only onits convective parameterization but also on interactions be-tween convection and other physical processes in the modelBNU-ESM simulation shows a northward propagating modeof precipitation during boreal summer at wavenumber 1 witha maximum variance between 30 and 50 days (Fig 15d)but the northward propagating band is weaker than observed(Fig 15c) Sabeerali et al (2013) analyzed the boreal sum-mer ISO of BNU-ESM along with 32 CMIP5 models Theyfound that BNU-ESM is one of six models which captures



Figure 14NovemberndashApril lag-longitude diagram of 10 Sndash10 Naveraged intraseasonal precipitation anomalies (colors) and in-traseasonal 850 hPa zonal wind anomalies (contours) correlatedagainst intraseasonal precipitation in the Indian Ocean referenceregion (10 Sndash5 N 75ndash100 E) for NCEP observation(a) andBNU-ESM (b) MayndashSeptember lag-latitude diagram of 65ndash95 Eaveraged intraseasonal precipitation anomalies (colors) and in-traseasonal 850 hPa zonal wind anomalies (contours) correlatedagainst intraseasonal precipitation at the Indian Ocean reference re-gion for NCEP observation(c) and BNU-ESM(d) The averagingperiod is 1980ndash2005 for BNU-ESM historical run and 1997ndash2006for observations

the three peak centers of boreal summer ISO variance overthe Indian summer monsoon region adequately

We also compared space-time spectra of daily tropical pre-cipitation from BNU-ESM with observed precipitation esti-mates from GPCP 1-degree daily data set from 1997 to 2005using the methodology of Wheeler and Kiladis (1999) Fig-ure 16 shows the results of dividing the symmetric raw spec-tra by estimates of their background spectra Kelvin equa-torial Rossby (ER) westward inertia-gravity (WIG) wavesand the MJO are readily identified in the observational GPCPsymmetric spectra Signals of convectively coupled Kelvinand ER waves appear in the model and the spectral signa-ture of the MJO is also represented In observations thereis a clear distinction between eastward power in the MJOrange (20 dayndash80 day) and westward power associated withER waves The BNU-ESM model exhibits this distinctionto some extent with the eastward power lying at a con-stant frequency across all wavenumbers and the westward

Figure 15 NovemberndashApril wavenumber-frequency spectra of10 Sndash10 N averaged daily zonal 850 hPa winds for NCEP ob-servation(a) and BNU-ESM(b) MayndashSeptember wavenumber-frequency spectra of 15 Sndash30 N 65ndash95 E averaged daily pre-cipitation for GPCP observation(c) and BNU-ESM(d) Individ-ual spectra were calculated for each year and then averaged overall years of data Only the climatological seasonal cycle and timemean for each NovemberndashApril or MayndashSeptember segment wereremoved before calculation of the spectra The averaging period is1980ndash2005 for BNU-ESM historical run and 1997ndash2006 for obser-vations

power lying more along the ER dispersion curves BNU-ESM represents signals of convectively coupled equatorialwaves (CCEWs) similarly as CCSM4 (Hung et al 2013)such as the equivalent depth of the waves and the low powerof WIG waves (Fig 4 in Hung et al 2013) The powers ofeastward propagating components near the MJO spatial andtemporal scale in BNU-ESM are more distinctive than thatof their westward propagating counterparts compared withCCSM4 (Hung et al 2013)

62 El Nintildeo-Southern Oscillation

The El Nintildeo-Southern Oscillation (ENSO) phenomenon isthe dominant mode of climate variability on seasonal to in-terannual time scales (Zhang and Levitus 1997 Wang andPicaut 2004 Zhang et al 2013) Bellenger et al (2013) an-alyzed several aspects of ENSO from the BNU-ESM and



Figure 16 Spacendashtime spectrum of the 15 Nndash15 S symmetriccomponent of precipitation divided by the background spectrumSuperimposed are the dispersion curves of the odd meridional modenumbered equatorial waves for 12 25 and 50 m equivalent depthsFrequency spectral width is 1128 cpd

here we present several different aspects of Nintildeo-34 Fig-ure 17 shows time series of detrended monthly SST anoma-lies of the Nintildeo-34 region (5 Sndash5 N 170ndash120 W) for theHadISST observations and BNU-ESM historical simulationfor the years 1900ndash2005 as well as SST anomalies fromthe corresponding years of BNU-ESM piControl simulationOverall the BNU-ESM exhibits strong interdecadal varia-tions in the amplitude and period in the ENSO frequencyband The model overestimates the amplitude of Nintildeo-34SST variability considerably with respect to HadISST obser-vations with a standard variability 147 K for both the piCon-trol and historical simulations compared with the standarddeviation of HadISST of 075 K A well-known characteristicof observed ENSO events is the tendency for phase-lockingto the seasonal cycle The standard deviation of the observedNintildeo-34 SST index maximizes (097 K) in December andreaches a minimum (056 K) in May and the Nintildeo-34 SSTindex of BNU-ESM historical run also maximizes (171 K)in December and reaches a minimum (121 K) in May BNU-ESM exhibits realistic timing of the seasonal cycle with onepeak and one minimum but the amplitude is much strongerthan in observations

Figure 18 shows the power spectra of the normalized timeseries of Fig 17 (the detrended SST anomalies normalized bytheir long-term standard deviation) The observation basedNintildeo-34 index has most power between 3 and 7 years whileboth BNU-ESM indices have the most prominent variabil-ity between 2 and 5 years with a narrow peak at 35 yearsOn timescales longer than 10 year the piControl and histor-ical simulations have similar power spectra but less powercompared with HadISST observations The presence of vari-ability in the external forcing during the historical simulationdoes not induce significant changes in decadal and longer pe-riod variability

Figure 17 Time series of detrended monthly SST anomalies ofthe Nintildeo-34 region (5 Sndash5 N 170ndash120 W) from HadISST theBNU-ESM historical and piControl runs The anomalies are foundby subtracting the monthly means for the whole time series Thebottom sub-figure is standard deviation of monthly Nintildeo-34 SSTanomalies from HadISST and the BNU-ESM historical run

Another aspect of the BNU-ESM ENSO historical sim-ulation shown in Fig 19 is the correlation of monthlymean Nintildeo-34 SST anomalies with global SST anomaliescompared with that from HadISST observations The figureshows a realistic but narrower meridional width of the pos-itive correlations in the central and eastern tropical PacificA horseshoe pattern of negative correlations in the westerntropical Pacific is seen in HadISST but is less pronounced inthe model The positive correlation in the western part of theIndian Ocean is well simulated in BNU-ESM but the exten-sion of this positive pattern into the Bay of Bengal Gulf ofThailand and South China Sea is missing from the modelThe correlation patterns in the Atlantic Ocean are similar be-tween HadISST and BNU-ESM but more pronounced in themodel

The Southern Oscillation is the atmospheric componentof El Nintildeo Figure 20 shows the Southern Oscillation In-dex (SOI) from BNU-ESM compared to observation Theobserved SOI is calculated using station data from Darwinand Tahiti For the model areal averages of mean sea-level



Figure 18Power spectra of the Nintildeo-34 index (the SST anomaliesof Fig 17 normalized with the standard deviation) using the multi-taper method (Ghil et al 2002) with resolutionp = 4 and numberof taperst = 7

Figure 19 Correlation of monthly mean Nintildeo-34 SST anoma-lies with global SST anomalies for the HadISST and BNU-ESMThe anomalies are found by subtracting the monthly means for thewhole time series that span the years 1900ndash2005 Hatched area in-dicates regions where the correlation is not significantly differentfrom zero at the 95 confidence level

pressure over 125ndash135 E 17ndash7 S and 155ndash145 W 22ndash12 S (10 times 10 areas centered close to the Darwin andTahiti stations) are used The interannual variability in themodeled SOI due to ENSO events is well reproduced andshows the expected negative correlation with Nintildeo-34 SSTanomalies (Fig 17) The modeled regression coefficient be-tween monthly deseasonalized SOI and Nintildeo34 SST anoma-lies isminus052 hPa Kminus1 while the observed isminus152 hPa Kminus1Hence the model underestimates the strength of the atmo-spheric response to ENSO

Figure 20Time series of Southern Oscillation index (5 month run-ning mean) from 1951 to 2005 The observed SOI is calculated us-ing station data from Darwin and Tahiti Absolute rather than nor-malized time series are used here

63 Pacific Decadal Oscillation

Another prominent structure of low-frequency climate vari-ability in the North Pacific with extensions to the tropicalIndo-Pacific is the Pacific Decadal Oscillation (PDO) (Man-tua et al 1997) PDO and ENSO exhibit similar spatialpatterns of SST variability but with different regional em-phasis (Zhang et al 1997 Deser et al 2007) During thepositive (negative) phase of PDO waters in the east tropicalPacific and along the North American west coast are anoma-lously warm (cool) while waters in the northern western andsouthern Pacific are colder (warmer) than normal Coupledclimate models can simulate some aspects of PDO althoughlinkages between the tropical and North Pacific are usuallyweaker than observed (Stoner et al 2009 Furtado et al2011) Figure 21 shows the regression maps of monthly SSTanomalies upon the normalized leading principal componenttime series of monthly SST anomalies over the North Pacificdomain (20ndash40 N) The first empirical orthogonal function(EOF) mode of BNU-ESM and HadISST observations ex-plains 224 and 258 variance respectively BNU-ESM ex-hibits generally realistic PDO spatial patterns and its con-nections to the tropical Pacific are of comparative strengthwith respect to HadISST observations but with a narrowermeridional extent in the tropical Pacific region The maxi-mum amplitude of the negative SST anomalies in the NorthPacific shifts a little too far west to the east of Japan ratherthan in the central basin Figure 22 shows time series of thenormalized first EOF mode of SST anomalies of BNU-ESMand HadISST observations over the North Pacific domain Itis evident that both patterns show prominent decadal vari-ability



Figure 21 Leading EOF of monthly SST anomalies for the NorthPacific domain (outlined by the box) for HadISST and the BNU-ESM historical run over the period 1900ndash2005 The results areshown as SST anomaly regressions upon the normalized principalcomponent time series (C per standard deviation) The numbers atthe bottom left corner of each panel denote the percentage of vari-ance explained by the leading EOF

7 Terrestrial carbon cycle

71 Terrestrial primary production

Carbon flux components are hard to measure directly pre-senting a challenge in evaluating the model performanceGlobal products for land gross primary production (GPP)and net primary production (NPP) exist but are model-basedand have large uncertainties (Anav et al 2013 Ito 2011)Figure 23 shows regional averages of monthly land grossprimary production (GPP) for BNU-ESM compared withFLUXNET-MTE estimates (Jung et al 2011) BNU-ESMreplicates the annual cycle of GPP in arctic mid-latitudesand tropical regions but the model has a tendency for un-derestimation during boreal summer especially over Alaskathe eastern USA and Europe Differences between the es-timates from our model and those from FLUXNET-MTEmay be caused both by differences in the near surface cli-matology and land cover characteristics as BNU-ESM dy-namically simulates vegetation characteristics as a functionof climate and atmospheric CO2 concentration In Alaskathe model simulates more C3 arctic grass and less borealshrub compared with the observed International Geosphere-Biosphere Programme (IGBP) vegetation distribution (not

Figure 22 Time series of the normalized leading EOF mode ofSST anomalies in the North Pacific domain (as Fig 21) over theperiod 1900ndash2005 for HadISST and BNU-ESM The solid blacklines show decadal variations after 10 year running average

shown) While in Europe although the model simulates morebroadleaf deciduous temperate tree cover and less grasslandthe biased high temperature and low precipitation duringboreal summer suppress GPP significantly In the Amazonthe model simulates a reasonable vegetation distribution ofbroadleaf and evergreen tropical trees but the wet seasonprecipitation suffers a dry bias until August (Fig 7) and themodel systematically underestimates GPP The interannualvariability of the GPP estimated by the model is larger thanthe observational estimates from FLUXNET-MTE and thismay be connected with the stronger interannual variability ofthe physical fields

The global terrestrial GPP simulated in the BNU-ESMis 1063 Pg C yrminus1 over the period 1986ndash2005 Variousstudies estimated the global terrestrial GPP to be about120plusmn 6 Pg C yrminus1 over similar periods (Sabine et al 2004Beer et al 2010 Jung et al 2011) However these are wellbelow the range of 150ndash175 Pg C yrminus1 from recent observa-tional estimates (Welp et al 2011) The global simulatedNPP over the period 1986ndash2005 is 49 Pg C yrminus1 which isconsistent with the range of 42ndash70 Pg C yrminus1 from earlierstudies (Schimel et al 2001 Gruber et al 2004 Zhao etal 2005 Ito 2011) Net biosphere production (NBP) sim-ulated in the model for the 1990s and 2000ndash2005 are 16and 14 Pg C yrminus1 which is also consistent with estimatesof 15plusmn 08 and 11plusmn 08 Pg C yrminus1 respectively reported byCiais et al (2013)

72 Soil organic carbon

Soil organic carbon is a large component of the carboncycle that can participate in climate change feedbacksparticularly on decadal and centennial timescales (Todd-Brown et al 2013) The amount of soil organic carbonsimulated by models is strongly dependent on their de-sign especially the number of soil-carbon pools turnoverrate of decomposition and their response to soil mois-ture and temperature change Figure 24a b show the



distribution of global soil organic carbon content includ-ing litter from BNU-ESM compared with the most recenthigh-resolution observation-based Harmonized World SoilDatabase (HWSD FAOIIASAISRICISSCASJRC 2012)The HWSD data provides soil-carbon estimates for topsoil(0ndash30 cm) and subsoil (30ndash100 cm) at 30 arc-second resolu-tion Overall the ecosystem carbon content follows the pre-cipitation and temperature distribution (Figs 8 and 9) TheBNU-ESM model can capture the large store of soil organiccarbon in the boreal and tundra regions of Eurasia and NorthAmerica and the small storage in tropical and extra-tropicalregions (Fig 24b) The model underestimates soil-carbondensity in the upper 1 m globally compared with the HWSD(Fig 24a) especially in boreal regions Soil carbon is over-estimated in the model on the Tibetan plateau because thecoarse horizontal resolution does not correctly represent therugged terrain and overestimates vegetation cover

The total simulated soil organic carbon including lit-ter is 700 Pg C for the period 1986ndash2005 is well be-low the 1260 Pg C (with a 95 confidence interval of890ndash1660 Pg C) estimated from HWSD data (Todd-Brownet al 2013) and 1502 Pg C estimated by Jobbaacutegy andJackson (2000) for the upper 1 m of soil However thereis still considerable uncertainty for those observation-basedestimates because of limited numbers of soil profiles withorganic carbon analyses (Tarnocai et al 2009) In additionthe soil-carbon sub-model of BNU-ESM is not yet designedto simulate the large carbon accumulations in organic peatsoils or the stocks and dynamics of organic matter in per-mafrost a common failure of many CMIP5 models It is thusto be expected that simulations without these processes un-derestimate the global soil organic carbon stock Especiallythe temperature sensitivity of soil-carbon decomposition isdescribed by theQ10 equation (Lloyd and Taylor 1994)in BNU-ESM and the environmental controls of moistureand temperature are diagnosed at 025 m depth In Fig 24cthe zonally averaged soil-carbon density from BNU-ESM iscompared with those from HWSD and IGBP-DIS for upper03 m and upper 10 m depth ranges The model simulatessubstantially less soil carbon than those from the HWSD andIGBP-DIS for the upper 10 m but agrees much better withupper 03 m soil-carbon density estimates on magnitude andlatitudinal gradients

8 Summary and discussion

In this study the BNU-ESM is described and results forthe CMIP5 pre-industrial and historical simulations are eval-uated in terms of climatology and climate variability Theclimatological annual cycles of surface-air temperature andprecipitation generally agree with observations but with theannual temperature underestimated and the annual precipita-tion overestimated over global land areas (excluding Antarc-tica) The sea ice extent of both polar regions agrees better

Figure 23 As for Fig 6 but for GPP for the period 1986ndash2005The observations (MTE) are from FLUXNET-MTE estimates (Junget al 2011)

with the observations in summer seasons than in winter sea-sons and the model has a tendency to have excessive ice ex-tent during winter seasons The global and Atlantic oceanmeridional overturning circulation patterns are similar tothose observed With respect to climate variability BNU-ESM captures some features of tropical intraseasonal oscilla-tion such as the quadrature relationship between precipitationand zonal wind in the northward propagation direction TheMJO signal in large-scale circulation (U850) is not as wellsimulated as it is in convection (precipitation) but the north-ward and eastward propagating motions are both weaker thanobserved The annual cycle patterns of tropical equatorial Pa-cific SST the periods of ENSO and the leading EOF modeof PDO in the historical simulation are reasonably well sim-ulated As BNU-ESM has similarities and some heritage incommon with CCSM4 in particular for the atmosphere landand sea ice components many characteristics in BNU-ESMare probably shared by CCSM4 such as some notable sur-face climate biases over land (Lawrence et al 2012) and thedipole precipitation bias in the Indian Ocean

BNU-ESM has significant biases that need to be improvedsuch as the tropical precipitation bias over ocean related tothe double ITCZ that has long been a problem among manyclimate models (Lin 2007) Note that BNU-ESM uses the re-vised ZhangndashMcFarlane scheme on deep convection (Zhang2002 Zhang and Mu 2005a) and CCSM4 also uses a re-vised ZhangndashMcFarlane scheme but with different emphasis



Figure 24Soil-carbon density in the top 1 m depth from the HWSD(a) and BNU-ESM(b) and zonal average soil-carbon density ofBNU-ESM compared with that of upper 03 m and upper 1 m soilfrom HWSD IGBP-DIS data sets

(Richter and Rasch 2008 Neale et al 2008) It turns out thatneither of them eliminates the double ITCZ problem (Gent etal 2011) so further parameterization improvements are cer-tainly required Land surface-air temperature simulated forthe last few decades of the 20th century exhibit a mean biasgreater than 2C over significant regions compared with ob-servations which also shows room for further improvementsAnother related discrepancy is that modeled temperatures in-crease significantly during the last few years of the historicalsimulation relative to observations (not shown) This is verylikely related to the lack of indirect aerosol effects in the at-mospheric component (eg Gent et al 2011) and we notethat NorESM which is also based on CCSM4 but whichincludes indirect of aerosol effects does not exhibit similarproblems (Bentsen et al 2013)

The positive SST biases prevailing at major coastal up-welling regions are clearly related with the relatively coarsehorizontal resolution used by the atmospheric componentAccording to Gent et al (2010) the most important factor forSST improvements in CCSM35 is the finer resolution andbetter representation of topography which produces stronger

upwelling and favorable winds right along the model coastsrather than being located somewhat offshore The cold biasesin mean SST along the equator in the Pacific Ocean have sev-eral causes One is the stronger easterly winds on the equa-tor which result in stronger equatorial upwelling anothermay be weaker activity of tropical instability waves in theocean The ocean component MOM4p1 uses the horizontalanisotropic friction scheme from Large et al (2001) whichinduces more frictional dissipation and prohibits vigoroustropical instability wave activity (Wittenberg et al 2006)Stronger activity of tropical instability waves could preventthe cold tongue water from cooling down by mixing with thewarm off-equatorial water (Jochum and Murtugudde 2006Menkes et al 2006 Seo et al 2006 Zhang and Busalacchi2008) The negative SST bias in the southern ocean and ex-cessive sea ice extent in the Antarctic suggest a need to cor-rect the wind stress field to ensure sufficient southern oceanheat transport and proper ocean gyre boundaries

The strength and frequency of ESNO variability in BNU-ESM highlights potential improvements The model has arobust ENSO with an irregular oscillation between 2 and5 years and a peak at about 35 years whereas the HadISSTobservations show an oscillation between 3 and 7 yearsThe seasonal phase locking feature of ENSO is well cap-tured in the model although the standard deviation of Nintildeo-34 SST anomalies from the historical simulation is signifi-cantly large than in the observations The causes of biases inENSO occurrence and amplitude in BNU-ESM may involvemany different physical processes and feedbacks Becauseof the dominant role of the atmospheric component in set-ting ENSO characteristics (Schneider 2002 Guilyardi et al2004 Kim et al 2008 Neale et al 2008 Wu and Kirtman2007 Sun et al 2009) previous studies have diagnosedthe dynamical Bjerknes feedback (Bjerknes 1969 Neelinand Djikstra 1995) and the heat flux feedback (Waliseret al 1994 Jin et al 2006) during ENSO Bellenger etal (2013) found that BNU-ESM underestimates both thepositive Bjerknes and the negative heat flux feedbacks byabout 45 and 50 respectively which could be the majorcauses of the ENSO biases in the model This also raises theimportance of further improvements on the deep convectionparameterization scheme as the representation of deep con-vection is central in defining both the dynamical and the heatflux atmospheric feedbacks (Guilyardi et al 2009) Anotherpossible cause for the excessive ENSO amplitude is the lackof a sufficient surface heat flux damping of SST anomaliesin the model as weaker heat flux damping tends to destabi-lize and amplify ENSO (Wittenberg 2002 Wittenberg et al2006) Further studies on these topics are warranted

Despite the drawbacks of the model in simulating somedetails of the climate system BNU-ESM has proven to bea useful modelling tool and is being actively used by manyresearchers in prognostic simulations for both anthropogenicand geoengineering forcing scenarios The BNU-ESM repre-sents an addition to the diversity of earth system simulators



and currently is evolving in many respects As global biogeo-chemical cycles are recognized as being evermore significantin mediating global climate change improvements of BNU-ESM are underway in the terrestrial and marine biogeochem-istry schemes On terrestrial biogeochemistry the LPJ-DyNbased carbon-nitrogen interaction scheme (Xu and Prentice2008) will be evaluated and activated in the future The soil-carbon scheme will be further improved to simulate the largecarbon accumulations in organic peat soils the stocks anddynamics of organic matter in permafrost A dynamic marineecosystem scheme will replace the current iBGC module thenew marine ecosystem scheme has improved parameteriza-tions of dissolved organic materials and detritus (Wang et al2008) a phytoplankton dynamic module that produces a vari-able of carbon to chlorophyll ratio (Wang et al 2009a) andrefined nitrogen regeneration pathways (Wang et al 2009b)Additionally a three-dimensional canopy radiative transfermodel (Yuan et al 2014) will be adopted to replace the tradi-tional one-dimensional two-stream approximation scheme inthe land component to calculate terrestrial canopy radiationmore realistically The spatial resolution of the BNU-ESMwill be increased to better the simulation of surface phys-ical climate especially for the atmospheric and land com-ponents Currently a 09

times 125 resolution land and atmo-sphere components adapted from the finite-volume dynamiccore in CAM is being tested We also note that CAM5 hasmade significant progress such as correcting well-knowncloud biases from CAM35 (Kay et al 2012) Further discus-sions of how to incorporate these developments from CAM5into BNU-ESM are underway

Code availability

Please contact Duoying Ji (E-mail duoyingjibnueducn)to obtain the source code of BNU-ESM

AcknowledgementsWe thank four anonymous reviewers for theirconstructive suggestions This research was sponsored by theNational Key Program for Global Change Research of China Grant2010CB950500 the National Natural Science Foundation of ChinaGrant 40905047 41305083 We acknowledge the World ClimateResearch Programmersquos Working Group on Coupled Modellingwhich is responsible for CMIP the Center of Information andNetwork Technology at Beijing Normal University for assistancein publishing the CMIP5 data set ERA-Interim data used in thisstudy is provided from the European Centre for Medium-RangeWeather Forecasts (ECMWF) JRA-55 data used in this study isprovided from the Japanese 55-year Reanalysis (JRA-55) projectcarried out by the Japan Meteorological Agency (JMA)

Edited by M-H Lo

References

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Li H Dai A Zhou T and Lu J Responses of East Asian sum-mer monsoon to historical SST and atmospheric forcing during1950ndash2000 Clim Dynam 34 501ndash514 2010

Lin J-L The Double-ITCZ Problem in IPCC AR4 CoupledGCMs Ocean-Atmosphere Feedback Analysis J Climate 204497ndash4525 doi101175JCLI42721 2007

Lin J-L Kiladis G N Mapes B E Weickmann K M Sper-ber K R Lin W Wheeler M C Schubert S D Genio A DDonner L J Emori S Gueremy J-F Hourdin F Rasch P JRoeckner E and Scinocca J F Tropical intraseasonal variabil-ity in 14 IPCC AR4 climate models Part I Convective signalsJ Climate 19 2665ndash2690 doi101175JCLI37351 2006

Liu J Song M Horton R M and Hu Y Reducingspread in climate model projections of a September ice-

free Arctic Proc Natl Acad Sci USA 110 12571ndash12576doi101073pnas1219716110 2013

Lloyd J and Taylor J A On the temperature dependence of soilrespiration Funct Ecol 8 315ndash323 1994

Loeb N G Wielicki B A Doelling D R Smith G L KeyesD F Kato S Manalo-Smith N and Wong T Toward opti-mal closure of the earthrsquos top-of-atmosphere radiation budget JClimate 22 748ndash766 2009

Losch M Menemenlis D Campin J-M Heimbach P and HillC On the formulation of sea-ice models Part 1 Effects ofdifferent solver implementations and parameterizations OceanModel 33 129ndash144 2010

Lumpkin R and Speer K Global ocean meridional overturningJ Phys Oceanogr 37 2550ndash2562 2007

Ma H-Y Xie S Klein S A Williams K D Boyle J S BonyS Douville H Fermepin S Medeiros B Tyteca S Watan-abe M and Williamson D On the correspondence betweenmean forecast errors and climate errors in CMIP5 models J Cli-mate 27 1781ndash1798 doi101175JCLI-D-13-004741 2014

Madden R and Julian P Detection of a 40-50 day oscillation inthe zonal wind in the tropical Pacific J Atmos Sci 28 702ndash708 1971

Madden R and Julian P Description of global-scale circulationcells in the tropics with a 40-50 day period J Atmos Sci 291109ndash1123 1972

Mantua N J Hare S R Zhang Y Wallace J M and FrancisR C A Pacific interdecadal oscillation with impacts on salmonproduction Bull Am Meteor Soc 78 1069ndash1079 1997

Matsuura K and Willmott C J Terrestrial air temperature1900ndash2008 gridded monthly time series version 201 avail-able athttpclimategeogudeledu~climate(last access Octo-ber 2013) 2009a

Matsuura K and Willmott C J Terrestrial precipitation 1900ndash2008 gridded monthly time series version 201 available athttpclimategeogudeledu~climate(last access October 2013)2009b

Meijers A J S Shuckburgh E Bruneau N Sallee J-B Brace-girdle T J and Wang Z Representation of the AntarcticCircumpolar Current in the CMIP5 climate models and fu-ture changes under warming scenarios J Geophys Res 117C12008 doi1010292012JC008412 2012

Menkes C Vialard J Kennan S C Boulanger J-P and MadecG V A modeling study of the impact of tropical instabilitywaves on the heat budget of the eastern equatorial Pacific JPhys Oceanogr 36 847ndash865 2006

Moore J C Rinke A Yu X Ji D Li Y Alterskjaeligr K Cui XKristjaacutensson J E Muri H Boucher O Huneeus N KravitzB Robock A Niemeier U Schulz M Tilmes S WatanabeS and Yang S Arctic sea ice and atmospheric circulation un-der the GeoMIP G1 scenario J Geophys Res 119 567ndash583doi1010022013JD021060 2014

Murray R J Explicit generation of orthogonal grids for oceanmodels J Comput Phys 126 251ndash273 1996

Neale R B Richter J H and Jochum M The impact of convec-tion on ENSO From a delayed oscillator to a series of events JClimate 21 5904ndash5924 2008

Neale R B Richter J H Conley A J Park S Lau-ritzen P H Gettelman A Williamson D L Rasch PJ Vavrus S J Taylor M A Collins W D Zhang M



and Lin S-J Description of the NCAR Community At-mosphere Model (CAM 40) NCAR TECHNICAL NOTENCARTN-485+STR available athttpwwwcesmucaredumodelsccsm40camdocsdescriptioncam4_descpdf(last ac-cess October 2013) 2010

Neale R B Richter J Park S Lauritzen P H Vavrus S JRasch P J and Zhang M The Mean Climate of the Commu-nity Atmosphere Model (CAM4) in Forced SST and Fully Cou-pled Experiments J Climate 26 5150ndash5168 doi101175JCLI-D-12-002361 2013

Neelin J D and Dijkstra H A Oceanndashatmosphere interaction andthe tropical climatology Part I The dangers of flux correction JClimate 8 1325ndash1342 1995

Oleson K W Lawrence D M Bonan G B Flanner M GKluzek E Lawrence P J Levis S Swenson S C Thorn-ton P E Dai A Decker M Dickinson R E FeddemaJ Heald C L Hoffman F Lamarque J-F Mahowald NNiu G-Y Qian T Randerson J Running S Sakaguchi KSlater A Stoumlckli R Wang A Yang Z-L Zeng X andZeng X Technical description of version 40 of the CommunityLand Model NCAR Tech Note NCARTN-478+STR availableat httpwwwcesmucaredumodelscesm10clmCLM4Tech_Notepdf(last access October 2013) 2010

Orsi A H Johnson G C and Bullister J L Circulation mixingand production of Antarctic bottom water Prog Oceanogr 4355ndash109 1999

Ramanathan V Cess R D Harrison E F Minnis P BarkstromB R Ahmad E and Hartmann D Radiative forcing and cli-mate Results from the Earth Radiation Budget Experiment Sci-ence 243 57ndash63 doi101126science243488757 1989

Raymond D J and Blyth A M A stochastic mixing modelfor non-precipitating cumulus clouds J Atmos Sci 43 2708ndash2718 1986

Raymond D J and Blyth A M Extension of the stochastic mix-ing model to cumulonimbus clouds J Atmos Sci 49 1968ndash1983 1992

Rayner D Hirschi J J-M Kanzow T Johns W E Wright PG Frajka-Williams E Bryden H L Meinen C S BaringerM O Marotzke J Beal L M and Cunningham S A Moni-toring the Atlantic meridional overturning circulation Deep SeaRes Pt II 58 1744ndash1753 2011

Rayner N A Parker D E Horton E B Folland C K Alexan-der L V Rowell D P Kent E C and Kaplan A Globalanalyses of sea surface temperature sea ice and night marine airtemperature since the late nineteenth century J Geophys Res108 4407 doi1010292002JD002670 2003

Reynolds R W Rayner N A Smith T M Stokes D C andWang W An improved in situ and satellite SST analysis forclimate J Climate 15 1609ndash1625 2002

Richter J H and Rasch P J Effects of convective momentumtransport on the atmospheric circulation in the Community At-mosphere Model version 3 J Climate 21 1487ndash1499 2008

Rienecker M M Suarez M J Gelaro R Todling R Bacmeis-ter J Liu E Bosilovich M G Schubert S D Takacs LKim G-K Bloom S Chen J Collins D Conaty A daSilva A Gu W Joiner J Koster R D Lucchesi R MolodA Owens T Pawson S Pegion P Redder C R ReichleR Robertson F R Ruddick A G Sienkiewicz M andWoollen J MERRA NASArsquos Modern-Era Retrospective Anal-

ysis for Research and Applications J Climate 24 3624ndash3648doi101175jcli-d-11-000151 2011

Roberts M J Banks H Gedney N Gregory J Hill RMullerworth S Pardaens A Rickard G Thorpe R andWood R Impact of an Eddy-Permitting Ocean Resolu-tion on Control and Climate Change Simulations with aGlobal Coupled GCM J Climate 17 3ndash20 doi1011751520-0442(2004)017lt0003IOAEORgt20CO2 2004

Roehrig R Bouniol D Guichard F Hourdin F and Re-delsperger J-L The Present and Future of the West AfricanMonsoon A Process-Oriented Assessment of CMIP5 Simula-tions along the AMMA Transect J Climate 26 6471ndash6505doi101175JCLI-D-12-005051 2013

Rossow W B and Schiffer R A Advances in understandingclouds from ISCCP Bull Am Meteor Soc 80 2261ndash22871999

Rossow W B and Duentildeas E N The International SatelliteCloud Climatology Project (ISCCP) Web Site An Online Re-source for Research Bull Am Meteor Soc 85 167ndash172doi101175BAMS-85-2-167 2004

Sabeerali C T Dandi A R Dhakate A Salunke K MahapatraS and Rao S A Simulation of boreal summer intraseasonal os-cillations in the latest CMIP5 coupled GCMs J Geophys Res-Atmos 118 4401ndash4420 doi101002jgrd50403 2013

Sabine C L Feely R A Gruber N Key R M Lee K Bullis-ter J L Wanninkhof R Wong C S Wallace D W RTilbrook B Millero F J Peng T-H Kozyr A Ono T andRios A F The oceanic sink for anthropogenic CO2 Science305 367ndash371 2004

Schimel D S House J I Hibbard K A Bousquet P Ciais PPeylin P Braswell B H Apps M J Baker D Bondeau ACanadell J Churkina G Cramer W Denning A S FieldC B Friedlingstein P Goodale C Heimann M HoughtonP A Melillo J M Moore B III Murdiyarso D Noble IPacala S W Prentice I C Raupach M R Rayner P J Sc-holes R J Steffen W L and Wirth C Recent patterns andmechanisms of carbon exchange by terrestrial ecosystems Na-ture 414 169ndash172 2001

Schneider E K Understanding differences between the equatorialPacific as simulated by two coupled GCMs J Climate 15 449-469 2002

Seo H Jochum M Murtugudde R and Miller A J Ef-fect of ocean mesoscale variability on the mean state oftropical Atlantic climate Geophys Res Lett 33 L09606doi1010292005GL025651 2006

Sillmann J Kharin V V Zhang X Zwiers F W and BronaughD Climate extremes indices in the CMIP5 multimodel ensem-ble Part 1 Model evaluation in the present climate J GeophysRes-Atmos 118 1716ndash1733 doi101002jgrd50203 2013

Simpson J J Berg J S Koblinsky C J Hufford G L andBeckley B The NVAP global water vapor dataset Independentcross-comparison and multiyear variability Remote Sens Envi-ron 76 112ndash129 2001

Sitch S Smith B Prentice I C Arneth A Bondeau ACramer W Kaplan J O Levis S Lucht W Sykes M TThonicke K and Venevsky S Evaluation of ecosystem dynam-ics plant geography and terrestrial carbon cycling in the LPJ dy-namic global vegetation model Glob Change Biol 9 161ndash185doi101046j1365-2486200300569x 2003



Soden B J Jackson D L Ramaswamy V SchwarzkopfM D and Huang X L The radiative signature ofupper tropospheric moistening Science 310 841ndash844doi101126science1115602 2005

Sperber K and Kim D Simplified metrics for the identificationof the Madden-Julian oscillation in models Atmos Sci Lett 13187ndash193 doi101002asl378 2012

Stoner A M K Hayhoe K and Wuebbles D J As-sessing General Circulation Model Simulations of Atmo-spheric Teleconnection Patterns J Climate 22 4348ndash4372doi1011752009JCLI25771 2009

Sun D-Z Yu Y and Zhang T Tropical Water Vapor andCloud Feedbacks in Climate Models A Further Assess-ment Using Coupled Simulations J Climate 22 1287ndash1304doi1011752008JCLI22671 2009

Takahashi T Sutherland S C Wanninkhof R Sweeney CFeely R A Chipman D W Hales B Friederich G ChavezF Sabine C Watson A Bakker D C E Schuster U MetzlN Yoshikawa-Inoue H Ishii M Midorikawa T Nojiri YKoumlrtzinger A Steinhoff T Hoppema M Olafsson J Arnar-son T S Tilbrook B Johannessen T Olsen A Bellerby RWong C S Delille B Bates N R and de Baar H J W Cli-matological mean and decadal change in surface oceanpCO2and net seandashair CO2 flux over the global oceans Deep Sea ResPt II 56 554ndash577 doi101016jdsr2200812009 2009

Tarnocai C Canadell J G Schuur E A G Kuhry P Mazhi-tova G and Zimov S Soil organic carbon pools in the north-ern circumpolar permafrost region Global Biogeochem Cy 23GB2023 doi1010292008GB003327 2009

Taylor K E Summarizing multiple aspects of model performancein a single diagram J Geophys Res 106 7183ndash7192 2001

Taylor K E Stouffer R J and Meehl G A A Summary of theCMIP5 Experiment Design available athttpcmip-pcmdillnlgovcmip5docsTaylor_CMIP5_designpdf(last access October2013) 2009 (with updatescorrections made 22 January 2011)

Taylor K E Stouffer R J and Meehl G A An Overview ofCMIP5 and the Experiment Design Bull Am Meteor Soc 93485ndash498 doi101175BAMS-D-11-000941 2012

Taylor P K (Ed) Final report of the Joint WCRPSCOR Work-ing Group on Air-Sea Fluxes Intercomparison and validation ofocean-atmosphere energy flux fields WCRP-112 available athttpeprintssotonacuk695221wgasf_final_reppdf(last ac-cess May 2014) 2000

Thornton P E and Rosenbloom N A Ecosystem model spin-upestimating steady state conditions in a coupled terrestrial carbonand nitrogen cycle model Ecol Model 189 25ndash48 2005

Tian B Fetzer E J Kahn B H Teixeira J Manning E andHearty T Evaluating CMIP5 Models using AIRS TroposphericAir Temperature and Specific Humidity Climatology J Geo-phys Res-Atmos 118 114ndash134 doi1010292012JD0186072013

Todd-Brown K E O Randerson J T Post W M Hoffman FM Tarnocai C Schuur E A G and Allison S D Causesof variation in soil carbon simulations from CMIP5 Earth systemmodels and comparison with observations Biogeosciences 101717ndash1736 doi105194bg-10-1717-2013 2013

Trenberth K E and Fasullo J T Simulation of present-day andtwenty-first-century energy budgets of the Southern Oceans JClimate 23 440ndash454 doi1011752009JCLI31521 2010

Trenberth K E Smith L Qian T Dai A and Fasullo J Es-timates of the global water budget and its annual cycle usingobservational and model data J Hydrometeorol 8 758ndash769doi101175JHM6001 2007

Vertenstein M Craig T Middleton A Feddema D and Fis-cher C CCSM40 Userrsquos Guide available athttpwwwcesmucaredumodelsccsm40ccsm_docugpdf(last access October2013) 2010

Vial J Dufresne J-L and Bony S On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates Clim Dy-nam 41 3339ndash3362 doi101007s00382-013-1725-9 2013

Waliser D E Blanke B Neelin J D and Gautier C Short-wave feedbacks and El Nintildeo-Southern Oscillation Forced oceanand coupled ocean-atmosphere experiments J Geophys Res99 25109ndash25125 1994

Wang C and Picaut J Understanding ENSO Physics ndash A Reviewin Earthrsquos Climate The Ocean-Atmosphere Interaction editedby Wang C Xie S P and Carton J A American GeophysicalUnion 21ndash48 doi101029147GM02 2004

Wang X J Le Borgne R Murtugudde R Busalacchi A Jand Behrenfeld M Spatial and temporal variations in dissolvedand particulate organic nitrogen in the equatorial Pacific bio-logical and physical influences Biogeosciences 5 1705ndash1721doi105194bg-5-1705-2008 2008

Wang X J Behrenfeld M Le Borgne R Murtugudde R andBoss E Regulation of phytoplankton carbon to chlorophyllratio by light nutrients and temperature in the Equatorial Pa-cific Ocean a basin-scale model Biogeosciences 6 391ndash404doi105194bg-6-391-2009 2009a

Wang X J Murtugudde R and Le Borgne R Nitrogen uptakeand regeneration pathways in the equatorial Pacific a basin scalemodeling study Biogeosciences 6 2647ndash2660 doi105194bg-6-2647-2009 2009b

Wang Y-M Lean J L and Sheeley Jr N R Modeling thesunrsquos magnetic field and irradiance since 1713 Astrophys J625 522ndash538 doi101086429689 2005

Washington W M Weatherly J W Meehl G A Semtner JrA J Bettge T W Craig A P Strand Jr W G ArblasterJ Wayland V B James R and Zhang Y Parallel climatemodel (PCM) control and transient simulations Clim Dynam16 755ndash774 doi101007s003820000079 2000

Wei T Yang S Moore J C Shi P Cui X Duan Q Xu BDai Y Yuan W Wei X Yang Z Wen T Teng F Gao YChou J Yan X Wei Z Guo Y Jiang Y Gao X Wang KZheng X Ren F Lv S Yu Y Liu B Luo Y Li W Ji DFeng J Wu Q Cheng H He J Fu C Ye D Xu G andDong W Developed and developing world responsibilities forhistorical climate change and CO2 mitigation Proc Natl AcadSci USA 109 12911ndash12915 doi101073pnas12032821092012

Weickmann K M Lussky G R and Kutzbach J E Intrasea-sonal (30ndash60 Day) fluctuations of Outgoing Longwave Radia-tion and 250 mb streamfunction during northern winter MonWeather Rev 113 941ndash961 1985

Welp L R Keeling R F Meijer H A J Bollenbacher A FPiper S C Yoshimura K Francey R J Allison C E andWahlen M Interannual variability in the oxygen isotopes of at-mospheric CO2 driven by El Nintildeo Nature 477 579ndash582 2011



Wentz F J A well-calibrated ocean algorithm for SSMI J Geo-phys Res 102 8703ndash8718 2000

Wentz F J SSMI Version-7 Calibration Report Re-mote Sensing Systems Santa Rosa CA available athttpwwwremsscompaperstech_reports2012_Wentz_011012_Version-7_SSMI_Calibrationpdf(last access May2014) 2013

Wheeler M C and Kiladis G N Convectively coupled equatorialwaves Analysis of clouds and temperature in the wavenumberndashfrequency domain J Atmos Sci 56 374ndash399 1999

Wilcox E M and Donner L J The Frequency of ExtremeRain Events in Satellite Rain-Rate Estimates and an Atmo-spheric General Circulation Model J Climate 20 53ndash69doi101175JCLI39871 2007

Wittenberg A T ENSO response to altered climates PhD thesisPrinceton University 475 pp 2002

Wittenberg A T Rosati A Lau N-C and Ploshay J JGFDLrsquos CM2 Global Coupled Climate Models Part III Trop-ical Pacific Climate and ENSO J Climate 19 698ndash722doi101175JCLI36311 2006

Wu R and Kirtman B P Regimes of seasonal air-sea interactionand implications for performance of forced simulations ClimDynam 29 393ndash410 2007

Wu R G Chen J P and Wen Z P Precipitation-surface temper-ature relationship in the IPCC CMIP5 Models Adv Atmos Sci30 766ndash778 doi101007s00376-012-2130-8 2013

Xavier P K Duvel J-P Braconnot P and Doblas-Reyes F JAn Evaluation Metric for Intraseasonal Variability and its Appli-cation to CMIP3 Twentieth-Century Simulations J Climate 233497ndash3508 doi1011752010JCLI32601 2010

Xie P P and Arkin P A Global precipitation A 17-year monthlyanalysis based on gauge observations satellite estimates and nu-merical model outputs Bull Am Meteor Soc 78 2539ndash25581997

Xu R and Prentice I C Terrestrial nitrogen cycle simulationwith a dynamic global vegetation model Glob Change Biol14 1745ndash1764 doi101111j1365-2486200801625x 2008

Yang J Wang B and Wang B Anticorrelated intensitychange of the quasi-biweekly and 30ndash50 day oscillationsover the South China Sea Geophys Res Lett 35 L16702doi1010292008GL034449 2008

Yuan H Dickinson R E Dai Y Shaikh M J Zhou L andShangguan W Ji D A 3D Canopy Radiative Transfer Modelfor Global Climate Modeling Description Validation and Ap-plication J Climate 27 1168ndash1192 doi101175JCLI-D-13-001551 2014

Zhang C Dong M Hendon H H Maloney E D MarshallA Sperber K R and Wang W Simulations of the Madden-Julian oscillation in four pairs of coupled and uncoupled globalmodels Clim Dynam 27 573ndash592 doi101007s00382-006-0148-2 2006

Zhang G J Convective quasi-equilibrium in midlatitude con-tinental environment and its effect on convective parame-terization J Geophys Res 107 ACL 12-1ndashACL 12-16doi1010292001JD001005 2002

Zhang G J and McFarlane N A Role of convective scale mo-mentum transport in climate simulation J Geophys Res 1001417ndash1426 1995

Zhang G J and Mu M Effects of modifications to the Zhang-McFarlane convection parameterization on the simulation of thetropical precipitation in the National Center for Atmospheric Re-search Community Climate Model version 3 J Geophys Res110 D09109 doi1010292004JD005617 2005a

Zhang G J and Mu M Simulation of the MaddenndashJulian Oscil-lation in the NCAR CCM3 Using a Revised ZhangndashMcFarlaneConvection Parameterization Scheme J Climate 18 4046ndash4064 doi101175JCLI35081 2005b

Zhang R-H and Levitus S Interannual variability of the coupledTropical Pacific ocean-atmosphere system associated with the ElNintildeoSouthern Oscillation J Climate 10 1312ndash1330 1997

Zhang R-H and Busalacchi A J Rectified effects of trop-ical instability wave (TIW)-induced atmospheric wind feed-back in the tropical Pacific Geophys Res Lett 35 L05608doi1010292007GL033028 2008

Zhang R-H Zheng F Zhu J and Wang Z A successful real-time forecast of the 2010-11 La Nintildea event Sci Rep 3 1108doi101038srep01108 2013

Zhang Y Wallace J M and Battisti D S ENSO-like inter-decadal variability 1900ndash93 J Climate 10 1004ndash1020 1997

Zhao M S Heinsch F A Nemani R R and Running S WImprovements of the MODIS terrestrial gross and net primaryproduction global data set Remote Sens Environ 95 164ndash176doi101016jrse200412011 2005



furthermore with an eye to how the research strengths of thecenter can improve and develop it

The coupling framework of BNU-ESM is based on aninterim version of the Community Climate System Modelversion 4 (CCSM4) (Gent et al 2011 Vertenstein et al2010) developed at the National Center for Atmospheric Re-search (NCAR) on behalf of the Community Climate SystemModelCommunity Earth System Model (CCSMCESM)project of the University Corporation for Atmospheric Re-search (UCAR) Notably BNU-ESM differs from CCSM4in the following major aspects (i) BNU-ESM utilizes theModular Ocean Model version 4p1 (MOM4p1) (Griffies2010) developed at Geophysical Fluid Dynamics Labora-tory (GFDL) (ii) The land surface component of BNU-ESM is the Common Land Model (CoLM) (Dai et al 20032004 Ji and Dai 2010) initially developed by a commu-nity and further improved at Beijing Normal University(iii) The CoLM has a global dynamic vegetation sub-modeland terrestrial carbon and nitrogen cycles based on the LundndashPotsdamndashJena model (LPJ) (Sitch et al 2003) and the LundndashPotsdamndashJena Dynamic Nitrogen scheme (LPJ-DyN) (Xuand Prentice 2008) The LPJ-DyN based terrestrial carbonand nitrogen interaction schemes are very different from thebiogeochemistry Carbon-Nitrogen scheme used in CLM4 orCCSM4 (Thornton and Rosenbloom 2005 Oleson et al2010 Lawrence et al 2011) (iv) The atmospheric compo-nent is an interim version of the Community AtmosphericModel version 4 (CAM4) (Neale et al 2010 2013) modifiedwith a revised ZhangndashMcFarlane deep convection scheme(Zhang and McFarlane 1995 Zhang 2002 Zhang and Mu2005a) (v) The sea ice component is the Community IceCodE (CICE) version 41 (Hunke and Lipscomb 2010) de-veloped at Los Alamos National Lab (LANL) while the seaice component of CCSM4 is based on Version 4 of CICEThese variations illustrate how the BNU-ESM adds to themuch-desired climate model diversity and thus to the hierar-chy of models participating in the Climate Model Intercom-parison Projects phase 5 (CMIP5) (Taylor et al 2012)

As a member of CMIP5 BNU-ESM has completed allcore simulations within the suite of CMIP5 long-term ex-periments and some of related tier-1 integrations intended toexamine specific aspects of climate model forcing responseand processes The long-term experiments performed withBNU-ESM include a group forced by observed atmosphericcomposition changes or specified concentrations (egpi-Control historical rcp45and rcp85 labeled by CMIP5)and a group driven by time-evolving emissions of con-stituents from which concentrations can be computed in-teractively (egesmControl esmHistoricaland esmrcp85labeled by CMIP5) At the same time BNU-ESM joinedthe Geoengineearing Model Intercomparison Project (Ge-oMIP) and completed its first suite of experiments (G1ndashG4Kravitz et al 2011) concentrating on solar radiation manage-ment (SRM) schemes (eg Moore et al 2014) Data for allCMIP5 and GeoMIP simulations completed by BNU-ESM

have been published via an Earth System Grid Data Nodelocated at Beijing Normal University (BNU) and can be ac-cessed athttpesgbnueducn as a part of internationallyfederated distributed data archival and retrieval system re-ferred to as the Earth System Grid Federation (ESGF)

Many studies have utilized CMIP5 results from BNU-ESM and the model has received comprehensive eval-uations For example Wu et al (2013) evaluated theprecipitation-surface temperature (PndashT ) relationship ofBNU-ESM among 17 models in CMIP5 and found BNU-ESM has better ability in simulatingPndashT pattern correla-tion than other models especially over ocean and tropicsBellenger et al (2013) used the metrics developed withinthe Climate Variability and Predictability (CLIVAR) PacificPanel and additional metrics to evaluate the basic El Nintildeo-Southern Oscillation (ENSO) properties and associated feed-backs of BNU-ESM and other CMIP5 models BNU-ESMperforms well on simulating precipitation anomalies over theNintildeo-4 region the ratio between the ENSO spectral energyin the 1ndash3 year band and in 3ndash8 year band is well consis-tent with observational result but the model has stronger seasurface temperature (SST) anomalies than observational esti-mates over Nintildeo-3 and Nintildeo-4 regions Fettweis et al (2013)reported BNU-ESM can simulate the 1961ndash1990 variabilityof the JunendashAugust (JJA) North Atlantic Oscillation (NAO)well and the sharp decrease of the NAO index over the last10 years as observed and the model projects similar negativeNAO values into the future under RCP 85 scenario Gillettand Fyfe (2013) reported no significant Northern AnnularMode (NAM) decrease in any season between 1861 and 2099in historical and rcp45 simulations of BNU-ESM as with theother 36 models from CMIP5 Bracegirdle et al (2013) as-sessed the modelrsquos simulation of near-surface westerly windsover the Southern Ocean and found an equatorward bias inthe present-day zonal mean surface jet position in commonwith many of the CMIP5 models Among other studies Chenet al (2013) evaluated the cloud and water vapor feedbacksto El Nintildeo warming in BNU-ESM Vial et al (2013) diag-nosed the climate sensitivity radiative forcing and climatefeedback of BNU-ESM Roehrig et al (2013) assessed theperformance of BNU-ESM on simulating the West AfricanMonsoon Sillmann et al (2013) evaluated the model per-formance on simulating climate extreme indices defined bythe Expert Team on Climate Change Detection and Indices(ETCCDI) Wei et al (2012) utilized BNU-ESM in assess-ment of developed and developing world responsibilities forhistorical climate change and CO2 mitigation

Although the simulation results from BNU-ESM arewidely used in many climate studies a general descriptionof the model itself and its control climate is still not avail-able Documenting the main features of the model structureand its underlying parameterization schemes will help the cli-mate community to further understand the results from BNU-ESM




2 Model description



22 Ocean model



23 Sea ice model


24 Land model










3 Experiments























































times 05









54 Sea ice extent





















































































Code availability



Edited by M-H Lo

References



























































































































































































2 Model description



22 Ocean model



23 Sea ice model


24 Land model










3 Experiments























































times 05









54 Sea ice extent





















































































Code availability



Edited by M-H Lo

References
































































































































































































3 Experiments























































times 05









54 Sea ice extent





















































































Code availability



Edited by M-H Lo

References













































































































































































































































times 05









54 Sea ice extent





















































































Code availability



Edited by M-H Lo

References



























































































































































































times 05









54 Sea ice extent





















































































Code availability


AcknowledgementsWe thank four anonymous reviewers for theirconstructive suggestions This

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