RESEARCH ARTICLE
10.1029/2019MS001916
Special Section:
Community Earth System
Model version 2 (CESM2)
Special Collection
Key Points:• Community Earth System Model
Version 2 includes many substantial
science and infrastructure
improvements since its previous
version• Preindustrial control and historicalsimulations were performed with
low‐top and high‐top withcomprehensive chemistry
atmospheric models• Comparisons to observations are
improved relative to previous
versions, including major reductions
in radiation and precipitation biases

Correspondence to:
G. Danabasoglu,
gokhan@ucar.edu

Citation:
Danabasoglu, G., Lamarque, J.‐F.,
Bacmeister, J., Bailey, D. A., DuVivier,A. K., Edwards, J., et al. (2020). The
Community Earth System Model
Version 2 (CESM2). Journal ofAdvances in Modeling Earth Systems,
12, e2019MS001916. https://doi.org/
10.1029/2019MS001916
Received 30 SEP 2019
Accepted 10 JAN 2020
Accepted article online 16 JAN 2020

The Community Earth System Model Version 2 (CESM2)
G. Danabasoglu1 , J.‐F. Lamarque1 , J. Bacmeister1, D. A. Bailey1 , A. K. DuVivier1 ,J. Edwards1, L. K. Emmons2 , J. Fasullo1 , R. Garcia2 , A. Gettelman1,2 , C. Hannay1 ,M. M. Holland1 , W. G. Large1, P. H. Lauritzen1 , D. M. Lawrence1 , J. T. M. Lenaerts3 ,K. Lindsay1, W. H. Lipscomb1 , M. J. Mills2 , R. Neale1 , K. W. Oleson1 ,B. Otto‐Bliesner1 , A. S. Phillips1 , W. Sacks1, S. Tilmes2 , L. van Kampenhout4,
M. Vertenstein1 , A. Bertini1, J. Dennis5 , C. Deser1 , C. Fischer1, B. Fox‐Kemper6 ,J. E. Kay7 , D. Kinnison2 , P. J. Kushner8 , V. E. Larson9 , M. C. Long1 , S. Mickelson5 ,J. K. Moore10, E. Nienhouse5, L. Polvani11 , P. J. Rasch12 , and W. G. Strand1
1

Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA, 2Atmospheric
Chemistry Observations and Modeling Laboratory, National Center for Atmospheric Research, Boulder, CO, USA,3
Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO, USA, 4Institute forMarine and Atmospheric Research Utrecht, Utrecht University, Utrecht, Netherlands, 5Computational and Information
Systems Laboratory, National Center for Atmospheric Research, Boulder, CO, USA, 6Department of Earth,Environmental, and Planetary Sciences, Brown University, Providence, RI, USA, 7Cooperative Institute for Research in
Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA, 8Department of Physics, University ofToronto, Toronto, ON, Canada, 9Department of Mathematical Sciences, University of Wisconsin‐Milwaukee, Milwaukee,
WI, USA, 10Department of Earth System Science, University of California Irvine, Irvine, CA, USA, 11Applied
Mathematics/Earth and Environmental Sciences, Columbia University, New York, NY, USA, 12Atmospheric Science andGlobal Change Division, Paciﬁc Northwest National Laboratory, Richland, WA, USA

Abstract An overview of the Community Earth System Model Version 2 (CESM2) is provided, including
a discussion of the challenges encountered during its development and how they were addressed. In
addition, an evaluation of a pair of CESM2 long preindustrial control and historical ensemble simulations ispresented. These simulations were performed using the nominal 1° horizontal resolution conﬁguration of
the coupled model with both the “low‐top” (40 km, with limited chemistry) and “high‐top” (130 km, withcomprehensive chemistry) versions of the atmospheric component. CESM2 contains many substantial
science and infrastructure improvements and new capabilities since its previous major release, CESM1,
resulting in improved historical simulations in comparison to CESM1 and available observations. These
include major reductions in low‐latitude precipitation and shortwave cloud forcing biases; better
representation of the Madden‐Julian Oscillation; better El Niño‐Southern Oscillation‐related
teleconnections; and a global land carbon accumulation trend that agrees well with observationally based
estimates. Most tropospheric and surface features of the low‐ and high‐top simulations are very similar toeach other, so these improvements are present in both conﬁgurations. CESM2 has an equilibrium
climate sensitivity of 5.1–5.3 °C, larger than in CESM1, primarily due to a combination of relatively smallchanges to cloud microphysics and boundary layer parameters. In contrast, CESM2's transient climate
response of 1.9–2.0 °C is comparable to that of CESM1. The model outputs from these and many othersimulations are available to the research community, and they represent CESM2's contributions to the
Coupled Model Intercomparison Project Phase 6.

Plain Language Summary The Community Earth System Model (CESM) is an open‐source,©2020. The Authors.
This is an open access article under theterms of the Creative Commons
Attribution License, which permits use,
distribution and reproduction in any
medium, provided the original work is
properly cited.

DANABASOGLU ET AL.

comprehensive model used in simulations of the Earth's past, present, and future climates. The newest
version, CESM2, has many new technical and scientiﬁc capabilities ranging from a more realistic
representation of Greenland's evolving ice sheet, to the ability to model in detail how crops interact with thelarger Earth system, to improved representation of clouds and rain, and to the addition of wind‐driven
waves on the model's ocean surface. The data sets from a large set of simulations that include integrations
for the preindustrial conditions (1850s) and for the 1850‐2014 historical period are available to thecommunity, representing CESM2's contributions to the Coupled Model Intercomparison Project Phase
6 (CMIP6).

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1. Introduction
The Community Earth System Model Version 2 (CESM2) is the latest generation of the coupledclimate/Earth system models developed as a collaborative effort between scientists, software engineers,and students from the National Center for Atmospheric Research (NCAR), universities, and other research
institutions. The CESM2 builds on many successes of its predecessors. The ﬁrst global coupled climate modelthat did not use any ﬂux corrections, which were previously required to stabilize coupled simulations even in
the absence of changed radiative forcing, was developed at NCAR. Washington and Meehl (1989) performed
several short (multidecadal) simulations with this model to study climate sensitivity. The ﬁrst coupled climate model to achieve a stable present‐day control simulation without any ﬂux corrections after long multicentury integrations was the Climate System Model (CSM; Boville & Gent, 1998). This latter effort was
followed by the Community Climate System Model Version 2 (CCSM2; Kiehl & Gent, 2004), the CCSM3(Collins et al., 2006), and the CCSM4 (Gent et al., 2011). Additional capabilities such as interactive
carbon‐nitrogen cycling, global dynamic vegetation and land use change due to anthropogenic activities, amarine biogeochemistry module, a dynamic ice sheet model, and new chemical and physical processes for
direct and indirect aerosol effects were added to develop CCSM into an Earth system model. With these
new capabilities, the model was renamed the Community Earth System Model (CESM1; Hurrell et al., 2013).
These CCSM and CESM versions have been at the forefront of national and international efforts to understand and predict the behavior of Earth's climate. Output from numerous simulations using CCSM andCESM has been routinely used in studies to better understand the processes and mechanisms responsible
for climate variability and change. Most of these studies make use of CCSM's and CESM's contributions to
the various phases of the Coupled Model Intercomparison Project (CMIP). Evaluation of those contributions
has identiﬁed CCSM and CESM as among the most realistic climate models in the world based on a few
metrics that compare the model outputs against present‐day observationally based data sets (Knutti et al.,2013). In addition, CESM1 has also been used in key activities to advance our understanding of the climate
system and its variability and predictability, by supplementing CESM1's contributions to the CMIPs withother community driven science efforts. These include the CESM1 Large Ensemble (CESM1(LENS); Kayet al., 2015), the CESM Decadal Prediction Large Ensemble (CESM‐DPLE; Yeager et al., 2018), the CESM
Last Millennium Ensemble (CESM‐LME; Otto‐Bliesner et al., 2016), and the CESM Stratospheric Aerosol
Geoengineering Large Ensemble (GLENS; Tilmes et al., 2018).
The CESM2 was released to the community in June 2018 (available at www.cesm.ucar.edu:/models/cesm2/).
Simulations with the model have been done as contributions to the CMIP Phase 6 (CMIP6; Eyring et al., 2016)using the nominal 1° horizontal resolution conﬁguration with both the so‐called “low‐top” (limited chemistry) and “high‐top” with comprehensive chemistry versions of the atmospheric model (see section 2).
Following the notation introduced in Hurrell et al. (2013), these conﬁgurations will be referred to as
CESM2(CAM6) and CESM2(WACCM6), respectively. CMIP6‐required core simulations are the so‐called
Diagnostic, Evaluation, and Characterization of Klima (DECK) experiments that consist of a long preindustrial (PI) control simulation; an abrupt quadrupling of CO2 concentration simulation; a 1% per year CO2 concentration increase simulation; and an AMIP (Atmospheric Model Intercomparison Project) simulation
forced with prescribed observed sea surface temperatures (SSTs) and sea‐ice concentrations. These are complemented by multiple simulations of the historical (1850–2014) period. In addition, CESM2 is participating
in about 20 Model Intercomparison Projects (MIPs) within CMIP6, including the ScenarioMIP, which
requests future‐projection simulations driven by alternative plausible futures of emissions and land use(O'Neill et al., 2016). The output ﬁelds from these CESM2 simulations have been posted on the Earth
System Grid Federation (ESGF; https://esgf‐node.llnl.gov/search/cmip6). To expedite the use of CESM2 by
the community primarily for CMIP6‐related science and simulations, two incremental releases of CESM2
with the same base code as in the June 2018 release were made available in December 2018 (CESM2.1.0)
and in June 2019 (CESM2.1.1). These two releases contain additional conﬁgurations from which many of
the CMIP6 experiments can be run as out‐of‐the‐box simulations.
This manuscript provides an overview of the CESM2 as well as an evaluation of the solutions from CESM2(CAM6) and CESM2(WACCM6) PI control simulations and from the subsequent ensemble sets of historicalsimulations with 11 and 3 members, respectively. Section 2 summarizes the major new features and capabilities introduced in each component model since CESM1. Section 3 brieﬂy documents challenges

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Figure 1. Schematic of the CESM2 component models.

encountered during the development process and the strategies adopted to address them. Initial conditions
and forcing data sets used in the simulations are detailed in sections 4 and 5, respectively. Information onmodel tuning, spin‐up, and drifts in the PI control simulations is presented in section 6. Highlights fromprimarily the historical simulations that include variables from many component models are presented in
sections 7–11. CESM2 results are shown with respect to CESM1 PI control and CESM1(LENS) historicalsimulations as well as available observations. Section 12 brieﬂy discusses the new model's equilibrium
climate sensitivity and transient climate response. Finally, section 13 provides a summary and discussion.
While much of our analysis makes use of the full set of ensemble members available, in a few instances
fewer (or even one) ensemble members are used for clarity and simplicity, as many of the simulation
characteristics or improvements are robust across the ensemble members. Solutions from the PI control
and historical simulations as well as many other CESM2 DECK and MIP simulations are analyzed anddocumented in more detail in the manuscripts collected as part of the AGU CESM2 Virtual Special Issue.

2. CESM2 and Its Components
The CESM2 is an open‐source community coupled model consisting of ocean, atmosphere (both low‐top andhigh‐top with comprehensive chemistry), land, sea‐ice, land‐ice, river, and wave models that exchange states
and ﬂuxes via a coupler (Figure 1). It contains many substantial science and infrastructure improvements
and capabilities since its previous version, CESM1. In this section, each component model is introduced,
and new developments are summarized. As noted below, the individual version numbers across component
models differ, reﬂecting their independent development paths.
2.1. Atmosphere
The Community Atmosphere Model Version 6 (CAM6) uses the same Finite Volume (FV) dynamical core
(Lin & Rood, 1997) as in CCSM4 and CESM1, but it includes many changes to the existing representationof physical processes. With the exception of radiation (Rapid Radiative Transfer Model for General circulation models, RRTMG; Iacono et al., 2008), all parameterizations have undergone some level of change. The
primary change is the inclusion of the uniﬁed turbulence scheme, Cloud Layers Uniﬁed By Binormals(CLUBB; Golaz et al., 2002; Larson, 2017). CLUBB uniﬁes the description of cloudy turbulent layers bydirectly replacing the separate shallow‐convection, boundary layer, and grid‐scale condensation schemespresent in CAM5 (Bogenschutz et al., 2013). It is a high‐order closure representation of moist turbulence thatcloses many terms by novel use of a simple, multivariate binormal probability density function (PDF)
describing subgrid scale variations in temperature, humidity, and vertical velocity. The Morrison‐
Gettelman cloud microphysics scheme (MG1; Morrison & Gettelman, 2008) has been replaced by an
updated version (MG2; Gettelman & Morrison, 2015), which is now able to forecast, instead of diagnose,
the mass and number concentration of falling condensed species (rain and snow). Notably, mixed phase
ice nucleation depends on aerosols, rather than just temperature, following Hoose et al. (2010), as implemented in CESM2 by Wang et al. (2014) with modiﬁcations of Shi et al. (2015). Aerosols are treated using
the Modal Aerosol Model version 4 (MAM4; Liu et al., 2016). A new subgrid orographic form drag parameterization (Beljaars et al., 2004) replaces the Turbulent Mountain Stress (TMS) scheme and now operates
with a diagnosed proﬁle that may extend above the surface level. An anisotropic orographic gravity wave
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drag scheme following Scinocca and Mcfarlane (2000) represents vertically propagating gravity waves andnear‐surface drag as a function of mountain ridge orientation and height to more accurately represent thedependence on near‐surface ﬂow direction and mountain orientation. The workhorse version of CAM6 uses
a nominal 1° (1.25° in longitude and 0.9° in latitude) horizontal resolution with 32 vertical levels and amodel top at 2.26 hPa (about 40 km). This version of the model has relatively coarse stratospheric representation (and no prognostic chemistry module for ozone and other stratospheric constituents) and is thus
referred to as a “low‐top” model.
The Whole Atmosphere Community Climate Model Version 6 (WACCM6) in CESM2 is conﬁgured identically to CAM6, except that it uses 70 vertical levels and its model top is at 4.5 × 10‐6 hPa (about 130 km).
CAM6 and WACCM6 share the same vertical level structure between the surface and 87 hPa. Compared
to CAM6, this version of the model has superior stratospheric representation and is thus referred to as a“high‐top” model. WACCM6 features a comprehensive chemistry mechanism with a description of tropospheric, stratospheric, and upper‐atmospheric chemistry. The default WACCM6 chemical mechanism contains 228 prognostic chemical species with reactions relevant for the whole atmosphere: Troposphere,
Stratosphere, Mesosphere, and Lower Thermosphere (TSMLT). In particular, it includes an extensive representation of secondary organic aerosols (Tilmes et al., 2019). MAM4 has been modiﬁed to incorporate a new
prognostic stratospheric aerosol capability (Mills et al., 2016). The modiﬁcations include mode widthchanges, growth of sulfate aerosol into the coarse mode, and the evolution of stratospheric sulfate aerosol
from natural and anthropogenic emissions of source gases, including carbonyl sulﬁde (OCS) and volcanic
sulfur dioxide (SO2). As described by Gettelman, Mills, et al. (2019) and as also shown below, WACCM6
simulates nearly the same climate as CAM6 in CESM2, with slight differences due to aerosol responses tochemistry, and upper atmospheric processes above the CAM6 model top. WACCM6 also simulates internalvariability in the stratosphere, including Stratospheric Sudden Warming (SSW) events on the intraseasonal
timescales and the explicitly‐resolved Quasi‐Biennial Oscillation.
2.2. Ocean
The CESM2 ocean component is the same as in CCSM4 and CESM1 but features several physics and numerical improvements; it is a level‐coordinate model based on the Parallel Ocean Program Version 2 (POP2;
Smith et al., 2010), as described in Danabasoglu et al. (2012). The physics advances include a new parameterization for mixing effects in estuaries, which improves the representation of the exchange of freshwater
between the terrestrial and marine branches of the hydrologic cycle (Sun et al., 2019); increased mesoscale
eddy (isopycnal) diffusivities at depth to improve the representation of passive tracers; use of prognostic
chlorophyll for shortwave absorption; use of salinity‐dependent freezing‐point together with the sea‐ice
model (Assur, 1958); and a new Langmuir mixing parameterization in conjunction with the new wave
model component (Li et al., 2016; also see below). The numerical improvements include a new iterative solver for the barotropic mode to reduce communication costs, particularly advantageous for high‐resolution
simulations on large processor counts (Huang et al., 2016); a tracer‐conserving time ﬁltering scheme basedon an adaption of the Robert‐Asselin ﬁlter to enable subdiurnal coupling of the ocean model (Asselin, 1972;
Robert, 1966; Williams, 2011); and subsequently, use of a 1‐hr coupling frequency to explicitly resolve (sub)diurnal and inertial periods. In addition, the K‐Proﬁle vertical mixing Parameterization (KPP; Large et al.,1994) is incorporated via the Community ocean Vertical Mixing (CVMix) framework, and the Caspian Sea
is treated as a lake in the land model, and thus, it is no longer included in the ocean model as a marginal
sea. The model uses spherical coordinates in the Southern Hemisphere. In the Northern Hemisphere, thegrid North Pole is displaced into Greenland. The horizontal resolution is nominal 1° with a uniform resolution of 1.125° in the zonal direction. The resolution varies signiﬁcantly in the meridional direction with theﬁnest resolution of 0.27° at the equator. In the Southern Hemisphere, the resolution monotonically increases
to 0.53° at 32°S, remaining constant further south. In the Northern Hemisphere high latitudes, the ﬁnest
resolution is about 0.38°, occurring in the northwestern Atlantic Ocean, and the coarsest resolution is about
0.64°, located in the northwestern Paciﬁc Ocean. There are 60 levels in the vertical, with a maximum depthof 5,500 m. The vertical resolution is uniform at 10 m in the upper 160 m and increases monotonically to a
maximum of 250 m by a depth of 3,500 m.
Ocean biogeochemistry in CESM2 is represented by the Marine Biogeochemistry Library (MARBL), conﬁgured to implement an updated version of what has previously been known as the Biogeochemistry
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Elemental Cycle (BEC; Moore et al., 2002, 2004, 2013). MARBL represents multiple nutrient colimitation (N,P, Si, and Fe) and includes three explicit phytoplankton functional groups (diatoms, diazotrophs, andpico/nano phytoplankton) and one implicit group (calciﬁers), and there is one zooplankton group (Moore
et al., 2004). MARBL includes fully prognostic carbonate chemistry and simulates sinking particulateorganic matter implicitly, subject to ballasting by mineral dust, biogenic CaCO3, and Si following
Armstrong et al. (2002). Major updates relative to CESM1 include a representation of subgrid‐scale variations in light (Long et al., 2015), variable C:P stoichiometry (Galbraith & Martiny, 2015), burial of material
at the seaﬂoor that matches riverine inputs in the preindustrial climate, both semilabile and refractory dissolved organic material pools (Letscher et al., 2015), prognostic oceanic emission of ammonia (Paulot et al.,2015), and an explicit ligand tracer that complexes iron. Atmospheric deposition of iron is computed prognostically as a function of dust and black carbon deposition simulated by CAM6. Riverine nutrient, carbon,
and alkalinity ﬂuxes are supplied to the ocean from a data set. These ﬂuxes are held constant using data from
GlobalNEWS (Mayorga et al., 2010) with the exception of inorganic nitrogen and phosphorus ﬂuxes, which
are taken from the Integrated Model to Assess the Global Environment‐Global Nutrient Model (IMAGE‐
GNM) (Beusen et al., 2015, 2016), and vary from 1900 to 2005. Outside this period, the ﬂuxes are held constant using the closest temporal value.
Version 3.14 of the NOAA WaveWatch‐III ocean surface wave prediction model (Tolman, 2009) is incorporated in CESM2 as a new component. The wave model is solved on a coarse resolution, near‐global grid(78.4°S to 78.4°N) with a resolution of 4° in longitude and 3.25° in latitude with 25 frequency and 24 direction bins, optimized for climate applications of the Stokes drift, called the ww3a grid. The waves are driven
by winds, air‐sea temperature differences, mixed layer depth, and ocean currents provided through the coupler, and they deliver three new beneﬁts. First, the waves provide the forcing necessary for including
Langmuir, or wave‐driven, turbulence in the KPP vertical mixing scheme in POP2 (Li et al., 2016).
Second, wave statistics from CMIP6 experiments will be contributed to the Coordinated Ocean Wave
Climate Project (COWCLIP) experiment (Hemer et al., 2012), which studies changes in wave climate underclimate change. The CESM2 contribution is expected to be unique in COWCLIP comparison as the waves
will be run in a fully coupled system. Third, wave statistics are now available for development of otherwave‐related process studies and parameterizations, such as drag and gas transfer coefﬁcients that dependon sea state (Reichl et al., 2014), waves in the marginal ice zones (Roach et al., 2018), wave effects on themean ﬂow that become important at mesoscale‐permitting ocean model resolutions (e.g., McWilliams &Fox‐Kemper, 2013; Suzuki & Fox‐Kemper, 2016), and other climate effects of waves (Cavaleri et al., 2012).
The CESM2 default version of Langmuir mixing includes only enhanced mixing within the mixed layer(Li et al., 2016; McWilliams & Sullivan, 2000), but a scheme with more accurate entrainment at the mixed
layer base is also available (Li et al., 2017). When the prognostic wave model is not used, a statistical theory
prediction of the needed variables of the wave state based on empirical relationships observed in surface
waves can be used instead. This “theory wave” feature in CVMix provides similar Langmuir mixing effects
at a much lower cost (Li & Fox‐Kemper, 2017; Reichl & Li, 2019), with a minimal loss of accuracy for theLangmuir mixing application.
2.3. Sea Ice
CESM2 uses CICE Version 5.1.2 (CICE5; Hunke et al., 2015) as its sea‐ice component. Compared to CESM1,
the sea‐ice model now incorporates mushy‐layer thermodynamics (Turner & Hunke, 2015), with a prognostic vertical proﬁle of ice salinity. To accomplish this, the model parameterizes the gravity drainage of brine,
melt water ﬂushing within the ice, and salinity effects of snow‐ice formation. The sea‐ice vertical resolution
has been enhanced from four layers in CESM1 to eight layers in CESM2 to better resolve the salinity andtemperature proﬁles. Also, the snow model now resolves three layers to represent the vertical temperature
proﬁle. The melt pond parameterization has been updated (Hunke et al., 2013) and now accounts for the factthat ponds preferentially form on undeformed sea ice. In conjunction with the ocean model, a salinity‐
dependent freezing temperature is incorporated (Assur, 1958). CICE5 shares the same horizontal grid withthe ocean component.
2.4. Land Ice
The land‐ice modeling capabilities introduced in CESM1 (Hurrell et al., 2013; Lipscomb et al., 2013) havebeen extended in CESM2. CESM1 used the Glimmer Community Ice Sheet Model (a serial code withDANABASOGLU ET AL.

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simpliﬁed shallow‐ice dynamics) and was limited to one‐way coupling; the ice sheet could respond dynamically to surface mass balance (SMB) forcing from the land model, but land surface topography and surface
types could not evolve. The ice sheet component of CESM2 is the Community Ice Sheet Model Version
2.1 (CISM2.1; Lipscomb et al., 2019), a state‐of‐the‐art parallel model with higher‐order velocity solvers(valid for fast‐ﬂowing ice streams and ice shelves) and improved treatments of basal sliding, iceberg calving,and other physical processes. The simulations in this manuscript assume ﬁxed ice sheets, but CESM2 also
supports two‐way coupling between the Greenland ice sheet and the land and atmosphere models. For
two‐way coupling, land surface types and surface elevation evolve as the ice sheet advances and retreats,with total water mass conserved. The ice sheet model typically is run on the Greenland domain at 4‐km grid
resolution with a depth‐integrated higher‐order solver (Goldberg, 2011), a pseudo‐plastic basal sliding law(Aschwanden et al., 2016), and with ﬂoating ice immediately calved. CESM2 simulations with dynamic
Antarctic and paleo ice sheets are not yet supported but are under development. With either ﬁxed or
dynamic ice sheets, the SMB and surface temperature over ice sheets are computed in CLM5 in multiple elevation classes for each glaciated grid cell (Lipscomb et al., 2013). By default, these calculations are done for
any CESM2 simulation with an active land model, unlike CESM1 in which ice sheet SMB was computed
only in a few custom simulations (e.g., Vizcaíno et al., 2013). For Greenland, the SMB and surface temperature are passed to the coupler and downscaled to the ﬁner CISM grid, with linear interpolation between elevation classes in the vertical direction and bilinear interpolation in the horizontal. For Antarctica, the SMB is
computed in multiple elevation classes in CLM5 but is not downscaled by the coupler.
2.5. Land
The Community Land Model Version 5 (CLM5; Lawrence et al., 2019) includes an extensive suite of new andupdated processes and parameterizations. Collectively, these developments improve the model's hydrological and ecological realism and enhance the representation of anthropogenic land use activities on climate
and the carbon cycle. Speciﬁcally, the main updates are as follows: photosynthesis updates (canopy scaling,
colimitation (Bonan et al., 2011), and temperature acclimation (Lombardozzi et al., 2015)); soil hydrology
improvements (frozen soil hydrology updates (Swenson et al., 2012), spatially explicit soil depth (Brunke
et al., 2016), dry surface layer control on soil evaporation (Swenson & Lawrence, 2014), and updated groundwater scheme (Swenson & Lawrence, 2015)); snow model updates (new snow cover fraction parameterization (Swenson & Lawrence, 2012), new fresh snow density parameterization based on temperature and wind
resulting in more realistic denser snowpacks, new fresh grain size parameterization based on temperature,revised canopy snow interception, canopy snow radiation, and snow unloading parameterizations, and 10‐msnow water equivalent maximum snowpack allowing for ﬁrn formation (van Kampenhout et al., 2017));
introduction of a mechanistic plant hydraulics and hydraulic redistribution scheme, which explicitly models
water transport through roots, stems, and leaves according to a simple hydraulic framework and which
replaces the empirical soil moisture stress function (Kennedy et al., 2019); a completely updated lake model(Subin et al., 2012); vertically resolved soil biogeochemistry scheme that allows for simulation of permafrost
carbon (Koven et al., 2013); addition of a global crop model that represents planting, harvest, grain ﬁll, andgrain yields for six crop types and includes time‐evolving, spatially explicit fertilization rates and irrigatedareas (Levis et al., 2018); inclusion of a new ﬁre model that has representation of natural and anthropogenic
ﬁre triggers and suppression as well as agricultural, deforestation, and peat ﬁres (F. Li et al., 2013; Li &Lawrence, 2017); multiple urban classes and updated urban energy model (Oleson & Feddema, 2019); andimproved representation of plant N dynamics to address known deﬁciencies in CLM4. This latter improvement is accomplished by (1) introducing ﬂexible plant carbon : nitrogen (C:N) stoichiometry (Ghimire et al.,2016), which circumvents the problematic CLM4 separation of potential and actual productivity ﬂuxes that
effectively decoupled leaf‐level C exchange from associated water and energy ﬂuxes; (2) explicitly simulating
how photosynthetic capacity responds to environmental conditions through the Leaf Utilization of Nitrogenfor Assimilation (LUNA) module (Ali et al., 2016); and (3) accounting for how changes in N availability haveconsequences for plant productivity through the Fixation and Uptake of Nitrogen (FUN) module, which calculates the carbon costs of N acquisition and allocates C expenditure on N uptake among biological ﬁxation,
active uptake, and retranslocation (Shi et al., 2016). CLM5 receives instantaneous N ﬂuxes directly throughthe coupler from WACCM6 when it is used. Otherwise, CLM5 uses monthly N‐deposition derived from prior
CESM2(WACCM6) simulations. Also, irrigation is applied either with water taken from rivers, if available,or diffusely from the ocean if there is not enough river water. Detailed description, assessment, and
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characterization of CLM5 performance compared to prior CLM versions in land‐only conﬁgurations are provided in Lawrence et al. (2019), Wieder et al. (2019), Fisher et al. (2019), and Bonan et al. (2019).
2.6. River Transport
The River Transport Model (RTM) used in CESM1 has been replaced with the Model for Scale Adaptive
River Transport (MOSART; H. Y. Li et al., 2013). In standard CESM2 conﬁgurations, the MOSART grid resolution is 0.5° × 0.5°. In each MOSART grid cell, surface runoff received from CLM5 is routed across hillslopes
and then discharged along with subsurface runoff, also received from CLM5, into a tributary subnetwork.
This tributary subnetwork then feeds into the main river channel of that grid cell that links river water fromupstream grid cells to downstream grid cells. A primary difference between RTM and MOSART is how river
ﬂow is calculated. RTM uses a simple linear reservoir method wherein the ﬂux of water transferred from an
RTM grid cell to its downstream neighbor is dependent only on the river water storage in that grid cell, themean distance between grid cells, and a globally constant effective water velocity. In MOSART, river ﬂow
rates are calculated explicitly via the physically based kinematic wave method, a commonly used method
in hydrology that is based on mass and momentum equations. As a consequence, whereas RTM only
simulates streamﬂow (m3/s), MOSART additionally simulates time‐varying main channel ﬂow velocity(m/s) and water depth, as well as subgrid surface water ﬂow in hillslopes and tributaries. MOSART requires
detailed information on river hydrography (i.e., parameters describing river and tributary width, depth, average slope, roughness coefﬁcient, and dominant river length), which is derived from the global 1‐km
HydroSheds data set (Lehner et al., 2008).
2.7. Coupling
CESM2 is accompanied by a new collaborative software infrastructure for building and running the model
system as well as for controlling state and ﬂux exchanges among the components. This framework is known
as the Common Infrastructure for Modeling the Earth (CIME; http://github.com/ESMCI/cime). CIME contains a signiﬁcantly rewritten coupling infrastructure that includes modularity, multi‐instance capabilities
for data assimilation, and new support for land‐ice coupling. It also includes rewritten data components thathave new Python capabilities and are more easily extensible. As in CESM1, the data components can replace
prognostic components, thereby enabling component feedbacks to be easily controlled. Examples of suchconﬁgurations include ocean ‐ sea‐ice coupled integrations in which the atmospheric data sets are provided
by a data atmosphere model, and a land‐only setup where the land model is the only prognostic component.
CIME also provides a new Case Control System (CCS) for conﬁguring, compiling, and executing complex
Earth system model experiments. CCS is an object‐oriented set of Python utilities that provide many new
capabilities for easier portability, case generation, and user customization. It records details of the model version used as well as user customization of the experimental setup. Additionally, CCS has system and unit
testing functionality that leads to a more robust and ﬂexible code base. CIME can be ported and tested ina stand‐alone mode, without any of the CESM prognostic components, as a ﬁrst step in porting CESM toother machines. Finally, CIME contains additional tools, such as a new statistical consistency test, that allow
users to quickly verify if their port of CESM2 is successful.
The atmosphere, land, wave, and sea‐ice components communicate state information and ﬂuxes throughthe coupler every atmospheric time step of 30 min. The only ﬂuxes calculated in the coupler are thosebetween the atmosphere and ocean, and the coupler communicates them to the ocean component every
hour and to the atmosphere every 30 min. The land‐ice component communicates with the coupler oncea day. The nominal 1° horizontal version of the CESM2(CAM6) has a throughput of about 30 simulated
years per day on 4,320 processors on the Cheyenne Supercomputer, corresponding to about 3,450 cpu hours
per simulation year. The CESM2(WACCM6) version has a throughput of about 4 simulated years per day on
3,780 processors on the same machine, corresponding to about 22,700 cpu hours per simulation year,
roughly seven times more expensive than CESM2(CAM6). In both CESM2(CAM6) and CESM2(WACCM6) conﬁgurations, the atmosphere is scaled to near the maximum allowed by the FV dycore:
1,152 tasks by 3 threads. Although CAM6 and WACCM6 can run on more processors, it is not cost‐effective
to do so. Thus, the lower processor count in CESM2(WACCM6) simulations is due to the ocean component
that uses 256 tasks by 3 threads in CESM2(CAM6) but only 24 tasks by 3 threads in CESM2(WACCM6). Thisis because the atmospheric model is much slower with WACCM6, so fewer processors are needed for theocean to keep pace.
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Figure 2. Thirty‐year mean Arctic sea‐ice concentration distributions (in %) from CESM2(CAM6) PI simulations (left)
without and (right) with excessive sea‐ice formation in the Labrador Sea region. The black lines are the 15% concentration values from SSM/I observations for the 1981–2005 period (Cavalieri et al., 1996).

3. Development Strategy and Challenges
Evaluations of the component model developments summarized above were done ﬁrst by the respective
developers using both component‐only and fully‐coupled conﬁgurations with various CCSM4 and CESM1
versions. This is in contrast to the process used during the CCSM4 development where initial assessments
were done primarily in component‐only simulations. Preliminary versions of the component models werethen brought together to start the ﬁrst CESM2 coupled simulations in October 2015. During this development and evaluation process, particular attention was paid to conservation of heat and freshwater acrossthe component models, climate drifts, reasonableness of mean climate and of variability in various ﬁelds
such as El Niño‐Southern Oscillation (ENSO) and Atlantic meridional overturning circulation (AMOC) as
well as making sure that the model simulations were able to reproduce observed key features of the 20th century climate such as evolution of global‐mean surface temperatures, present‐day SSTs, and sea‐ice distributions and trends. Unfortunately, the path to a fully‐coupled CESM2 version that produced acceptable
simulations for CMIP6 turned out to be much longer than anticipated and very challenging. Along theway, errors and bugs were discovered and addressed. Progress was particularly delayed due to two major
unacceptable features that were not obviously related to bugs or implementation errors. The ﬁrst concerned
the formation of unrealistically extensive sea‐ice cover over the Labrador Sea region in PI control simulations. The second was that the global‐mean surface temperature time series in historical simulations showeda signiﬁcant and quite unrealistic cooling particularly during the second half of the 20th century, with end‐of‐the‐century temperatures being actually colder than in the 1850s. Substantial human and computational
resources were dedicated to address these major challenges, as well as less serious issues, as described below.
After executing over 300 simulations, the vast majority being fully coupled, ranging in duration from several
decades to centennial, a near‐ﬁnal CESM2 version was created in April 2018.
The formation of unrealistically extensive sea‐ice cover over the Labrador Sea region, extending into thenorthern North Atlantic, in PI control simulations proved to be very challenging to address robustly. In fact,
this feature—not supported by available observations—emerged twice during the development process.
Figure 2 shows 30‐year average sea‐ice concentration distributions from two CESM2(CAM6) PI simulations
with and without such an extensive Labrador Sea ice cover. The more extensive Labrador Sea ice extent isalso accompanied by enhanced (and more unrealistic) sea ice in the Sea of Okhotsk region. In simulations
with extensive ice cover, this feature emerges as early as around year 40 and as late as around year 105 relative to initialization of the coupled simulation. In no simulation did the Labrador Sea ice cover become
unrealistically extensive after year 105 if it had not already emerged earlier in the simulation, indicating thatthe model likely has multiple equilibria and the trajectory through phase space following initialization
passes near a bifurcation point. It was found that extensive ice cover persisted when the simulations wereextended by 50 or more years further. It was not feasible, given time constraints, to undertake longer integrations to investigate long‐term behavior of this feature. Simulation characteristics when extensive ice cover is
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present include annual‐mean sea‐ice concentrations that are usually around 60% in the Labrador Sea;
Labrador Sea SSTs that remain around freezing point with salinities <34 psu; curtailed Labrador Sea deep
water formation/convection; compensating enhanced deep‐water formation to the east of Greenland andin the Irminger Sea; and a slightly weaker AMOC maintained by the switch in the location of deep‐water
formation. Detailed analyses of many simulations with extensive ice cover in comparison to simulations
without this feature, unfortunately, did not identify any robust mechanisms involving a particular processor phenomenon such as surface ﬂuxes, liquid and solid runoff, lateral advection, vertical mixing, and ice
drag as instigators or drivers. Indeed, when ensemble simulations were performed using the same model
conﬁguration but with only differences in their initial atmospheric potential temperatures introduced byround‐off level perturbations, some members showed extensive ice cover, and others did not. Given thisexperience, a practical—though not fully satisfactory—approach was adopted to use an ensemble member
without an extensive Labrador Sea ice cover after a sufﬁciently long simulation (see section 4) to provide
ocean and sea‐ice initial conditions going forward.
The second unacceptable feature was an unrealistic cooling of global‐mean surface temperatures in the historical simulations (see Figure 7, green line). The cooling started around the 1920s, mildly at ﬁrst and thengetting worse during roughly the 1950–1980 period, with surface temperature anomalies exceeding −0.4 °C
with respect to the 1850s. Although the model warmed after the 1980s, the end‐of‐the‐century temperatures
remained unrealistically colder than in the preindustrial 1850s. This behavior ﬁrst appeared when the new
CMIP6‐based emissions data sets were applied in our simulations. The same model versions with CMIP5‐
based emissions did not cool in this way. The observational time series (see Figure 7, black and gray lines)
show decadal ﬂuctuations that have signiﬁcant contributions from internal variability. The coolingdescribed above clearly exceeds this baseline internal variability range, and this is what is addressed here.
The goal is not to reproduce exactly the 20th century global‐mean surface temperature time series in eachrealization but to eliminate the unrealistic cooling behavior. Initial investigations uncovered some inconsistencies and errors in the CMIP6 emissions data sets (which were resolved in their ﬁnal release) and theirapplication to CAM6, particularly for anthropogenic sulfur. Simulations with the corrected data sets did
show some reductions of the unrealistic cooling, but the cooling behavior remained, particularly during
the mid‐1940s to 1980 period, with end‐of‐the‐century temperatures only slightly warmer than in the
1850s (not shown). Analysis of CAM6 against recent volcanic sulfur emission events indicated a potentially
excessive response (Malavelle et al., 2017), which could be traced to the impact of aerosols on the liquidwater path. Several modiﬁcations were, therefore, made to aerosol—cloud interaction processes, focusing
on the warm rain formation process (autoconversion and accretion) to reduce the magnitude of these effects.
As a result of these modiﬁcations, the global historical (2000 minus 1850) aerosol effective radiative forcingchanged from −1.82 W/m2 in CAM5 with CMIP5‐based emissions to −1.67 W/m2 in CAM6 with CMIP6‐
based emissions (see Gettelman, Hannay, et al., 2019, for further details). These changes were instrumental
in enabling the model to simulate a realistic 20th century surface temperature time series as discussed below(see section 7), but they did not target a particular equilibrium climate sensitivity value (see section 12).

4. Initial Conditions
The ocean and sea‐ice initial conditions used in both CESM2(CAM6) and CESM2(WACCM6) PI control
simulations were obtained through the following series of integrations (Figure 3). First, a three‐member
ensemble set of simulations was started from the CESM1(LENS) PI control integration (Kay et al., 2015)
at Year 402 (all simulations start from 01 January of a given year), which itself was initialized from theJanuary‐mean potential temperature and salinity from the Polar Science Center Hydrographic
Climatology (PHC2; Steele et al., 2001; Levitus et al., 1998). Two ensemble members developed excessive
Labrador Sea ice cover, starting at about Year 80. The third member (Simulation 262c) did not show sucha behavior. Therefore, it was decided to use this simulation to provide ocean and sea‐ice states for initializing
subsequent simulations. Indeed, the simulation was later extended to >350 years to conﬁrm that the
Labrador Sea ice cover remained realistic. A series of simulations with minor adjustments and bug ﬁxes
in the coupled model were then performed, starting with a simulation (293) from Year 161 of Simulation
262c. This simulation was followed by another (Simulation 297) that started from Year 130 of Simulation
293. Finally, a third simulation (299) was initialized from Year 249 of Simulation 297. The ocean and sea‐
ice states from year 134 of the ﬁnal simulation (299) were used as the initial conditions for the main
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Figure 3. Schematic of the sequence of simulations used to obtain ocean and sea‐ice initial conditions for the CESM2 PI
control simulations. The simulation names are given in blue. The orange numbers represent the year of a simulationfrom which a subsequent simulation started. The black horizontal lines represent the length of a given simulation in years.
The red line shows the cumulative integration time in years. The black and red tick marks indicate 100‐year intervals.
CESM1(LENS) PI control simulation started from the PHC2 observations.

CESM2(CAM6) and CESM2(WACCM6) PI control simulations. Thus, in total, these ocean potential
temperature and salinity initial conditions represent states integrated for about 1,070 years after
initialization starting from the PHC2 data sets.
The runoff initial conditions also come from Year 134 of Simulation 299 for both CESM2(CAM6) andCESM2(WACCM6) PI controls. Although their origins can be traced back to Year 161 of Simulation 262c,
their in‐between paths differ from that of ocean and sea‐ice initial conditions. The atmospheric initial conditions come from Year 134 of Simulation 299 for CESM2(CAM6) PI control and Year 11 of a preliminary
CESM2(WACCM6) PI simulation (Simulation 298) for the main CESM2(WACCM6) PI control.
Initial conditions for the land states and ocean biogeochemical tracers, including abiotic radiocarbon, wereobtained with long spin‐up runs of the land and ocean models, respectively. In these spin‐up runs, the active
atmospheric component was replaced with a data‐atmosphere component that simply read in and repeatedly cycled through multiple years of surface forcing data sets that were needed to construct surface ﬂuxes.
Twenty‐one years of forcing data sets extracted from Simulation 297 were used, in order to capture some
aspects of interannual variability. The land model was spun up from bare ground, utilizing the accelerated
decomposition mode to equilibrate the slow carbon pools, followed by several hundred years of normal
mode spin‐up (Lawrence et al., 2019). The ocean tracer spin‐up was applied only to the biogeochemical tracers of the ocean model. To avoid drift of the ocean physical state, it was reset to its initial condition at the
beginning of each 21‐year cycle, keeping it synchronized with the surface forcing. A subset of the ocean biogeochemical tracers was spun up using a Newton‐Krylov based solver (Lindsay, 2017).
The initial conditions for the 11 members of the CESM2(CAM6) historical simulations come from Years 501,
601, 631, 661, 691, 721, 751, 781, 811, 841, and 871 of the corresponding PI control simulation (Figure 4). The
three members of the CESM2(WACCM6) historical simulations are initialized from Years 56, 61, and 71 of
the corresponding PI control integration. The late and early start dates for the CESM2(CAM6) and CESM2(WACCM6) historical simulations simply reﬂect their respective PI control integration lengths and are notintended to avoid or sample any particular variability characteristics.

5. Forcing Data Sets for Preindustrial and Historical Simulations
CESM2(CAM6) PI control and historical simulations used several forcing data sets that were obtained fromthe corresponding CESM2(WACCM6) simulations. This is a new approach in CESM2 adopted to ensure thata physically consistent pair of low‐ and high‐top atmospheric models that share the same code base and contain the same physics in their common range of altitude is used. It represents a degree of compatibility andconsistency not available to most modeling groups. CESM2(WACCM6) uses CMIP6‐provided forcings wherever appropriate but calculates itself chemical and aerosol constituents from emissions that are based on theanthropogenic and biomass burning inventories speciﬁed by CMIP6. Anthropogenic emissions for 1850 to2014 are provided from the Community Emissions Data System (CEDS; Hoesly et al., 2018). Biomass
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Figure 4. Schematic of the CESM2 PI control and historical simulations. CESM2(WACCM6) PI simulation was started
ﬁrst (black) to obtain the forcing data sets used for the CESM2(CAM6) PI integration (red). These forcings were basedon the average of Years 21–50 (green shading). Three historical CESM2(WACCM6) simulations were started from Years56, 61, and 71 of the corresponding PI simulation (gray lines). As described in section 5, three‐member ensemble
averages of these WACCM6 simulations (blue shading) were then used to obtain the forcings needed for the CESM2(CAM6) historical simulations (orange lines). The CESM2(CAM6) historical simulations started from Years 501, 601, 631,
661, 691, 721, 751, 781, 811, 841, and 871 of the corresponding PI simulation. The black and red tick marks indicate 100‐
year intervals.

burning emissions are described by van Marle et al. (2017) and are all speciﬁed at the surface (without any
plume‐rise or speciﬁed vertical distribution of the emissions). Further details of the emissions and chemistry
used in CESM2(WACCM6), including the speciﬁcation of ozone‐depleting substances, are described in
Gettelman, Mills, et al. (2019). CESM2(CAM6) simulations use the CMIP6‐provided forcings except for (i)tropospheric oxidants for chemistry; (ii) stratospheric ozone for radiative effects; (iii) stratosphericaerosols for radiative effects; (iv) H2O production rates due to CH4 oxidation in the stratosphere; and (v)N deposition to the land and ocean components. These WACCM6‐based forcings are described below.
First, CESM2(CAM6) PI simulation 299 was run with prescribed forcings from a CESM2(WACCM6) simulation (295). Then the CESM2(WACCM6) PI control was conducted, using initial states for the ocean, sea‐
ice, and land components from Simulation 299 (see section 4). Distributions of tropospheric oxidants, stratospheric ozone, stratospheric aerosol, and methane oxidation rates were derived from the average of Years
21–50 of this CESM2(WACCM6) PI simulation and used in the subsequent CESM2(CAM6) PI control.
For historical simulations, an ensemble of three CESM2(WACCM6) simulations was run ﬁrst for the
1850–2015 period. Corresponding forcings were derived from the CESM2(WACCM6) ensemble average,
for use in the CESM2(CAM6) historical simulations. The averaging of 30 years in the PI control and of threeensemble members in the historical simulations is intended to reduce the inﬂuence of natural variability onthese forcings. A schematic summary of our integration strategy to obtain the necessary forcing data sets toperform the CESM2(CAM6) simulations is provided in Figure 4.
The tropospheric oxidant species (O3, OH, NO3, and HO2) are prescribed in CESM2(CAM6), which uses
them in gas‐phase and aerosol chemistry calculations to allow tropospheric aerosol formation. Monthly
averaged values are used in both the PI control and historical simulations. For the CESM2(CAM6) PI control, a repeating 12‐month annual cycle is derived from averaged values over Years 21–50 of the CESM2(WACCM6) PI control simulation. CESM2(CAM6) interpolates cyclically between midmonth date values.
For CESM2(CAM6) historical simulations, 12‐month annual cycles are provided at quasi‐decadal frequency(see Appendix A). CESM2(CAM6) interpolates annual cycles for years between those provided.
Stratospheric ozone and stratospheric aerosol are radiatively active species important to the radiative budget,
temperature, general circulation, chemistry, and cloud physics of CESM2. In CESM2(CAM6), stratosphericdistributions of these species are prescribed from 5‐day zonal means of CESM2(WACCM6) output. Ozone
volume mixing ratio is prescribed for the entire vertical extent of the model for use in radiativetransfer calculations.
A major advance in CESM2(WACCM6) is the development of prognostic stratospheric aerosols derived fromgas‐phase sulfur emissions. As described in Mills et al. (2016), this new feature provides CESM2 with stratospheric aerosol forcing that is much more consistent with observations than previous climatologies
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Figure 5. Time evolution of the globally averaged stratospheric aerosol optical depth (SAOD) at 550 nm as simulated by
CESM2(WACCM6) (three‐member average) and prescribed in CESM2(CAM6) (black) in comparison to that provided by
CMIP6 (red). There is uncertainty in the time, location, and intensity of volcanic emissions, especially further back in time.

developed for climate models. This self‐consistent treatment of volcanic aerosols and chemistry has beenshown to produce realistic radiative responses to eruptions over the 1979–2015 period (Mills et al., 2017;
Schmidt et al., 2018). For CMIP6, volcanic SO2 emissions for 1850–2015 from VolcanEESM database(Neely & Schmidt, 2016) are used. In addition, observed OCS concentrations are speciﬁed as time‐varying
boundary conditions. As shown in Figure 5, this leads to a realistic simulation of the stratospheric aerosol
optical depth (SAOD) compared to the CMIP6 data that are constrained by satellite observations startingin 1979. Before this date, when the information on volcanic emissions is sparser, CESM2(WACCM6) andCMIP6 SAOD can differ; for example, the eruptions of Hekla (1947) and Bezymianny (1956) are missing
from the CMIP6 database. Prior to the satellite era, there is also a very small (~0.001) difference between
CESM2(WACCM6) and CMIP6 data in the background component of SAOD at 550 nm that persists
during volcanically quiescent periods. Historical variability in this background is largely due to variability
in the source gas OCS, which WACCM6 accounts for as a time‐varying lower boundary condition for OCS(Montzka et al., 2004). CMIP6 prescribes a time‐varying background based on the 1999–2005 average,
scaled following the calculations of Sheng et al. (2015).
Although CESM2(CAM6) does not include the interactive chemistry needed to realistically simulate thedevelopment of stratospheric aerosol following a major volcanic eruption, model consistency is maintainedby prescribing aerosol properties above the tropopause in CESM2(CAM6) using 5‐day zonally averaged output from CESM2(WACCM6). Prescribed stratospheric aerosol properties are the sulfate mass mixing ratio
and diameter of each of the three sulfate‐bearing modes of MAM4, aerosol surface area density, and ice condensation nuclei number concentration. Each of these properties is prescribed consistently with its use in
CESM2(WACCM6), which includes prognostic stratospheric aerosol in MAM4. As a result, the radiative
impacts of stratospheric aerosol from large volcanic eruptions are consistent between CESM2(CAM6) andCESM2(WACCM6) simulations. For the CESM2(CAM6) PI control, a 5‐day zonal‐mean annual cycle wascreated from the average of Years 21–50 of the CESM2(WACCM6) PI control. For the CESM2(CAM6) historical simulations, 5‐day zonal‐mean values were calculated for each year over the 1850–2014 historical period from the average of the three CESM2(WACCM6) historical ensemble members. The 5‐day frequency
allows resolution of the impacts of major volcanic eruptions, as well as the annual development of polarozone loss (Neely et al., 2014). The averaging of three CESM2(WACCM6) historical ensemble members is
especially important to the evolution of stratospheric aerosol resulting from volcanic eruptions, which canbe particularly sensitive to the circulation present at the time of the eruption.
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The oxidation of methane (CH4) is an important source of water vapor to the middle atmosphere. Because
water vapor is a greenhouse gas, production rates of water vapor in CESM2(CAM6) are prescribed using
methane oxidation rates from CESM2(WACCM6). It is assumed that the oxidation of each CH4 molecule
results in the production of two molecules of H2O (Nicolet, 1970). For the CESM2(CAM6) PI control, arepeating 12‐month annual cycle is derived from averaged values over Years 21–50 of the CESM2(WACCM6) PI control simulation. For CESM2(CAM6) historical simulations, 12‐month annual cycles areprovided at the same quasi‐decadal frequency as the tropospheric oxidants (see above and Appendix A).
Regarding Chloroﬂuorocarbons (CFCs), in CESM2(CAM6), they are speciﬁed as a global average mixing
ratio derived from the lower boundary conditions provided by CMIP6. The radiative transfer module in both
CESM2(CAM6) and CESM2(WACCM6) considers only CFC‐12 and CFC‐11eq (equivalent). The CFC‐11eq
time series is supplied by CMIP6 (Meinshausen et al., 2017). Because CESM2(WACCM6) includes most of
the chemical species contained in CFC‐11eq, the global distribution of CFC‐11eq is ﬁrst derived interactively
in WACCM6. The derived global average distribution at the surface is then scaled to match a given CMIP6
scenario that contains additional species not included in the WACCM6 chemistry, that is, CFC‐11eq = CFC‐
11eq [CMIP6]/CFC‐11eq [WACCM6]).
Updated land cover and land use data have been created for CLM5. The new data set combines updated versions of present‐day satellite land cover descriptions with the past and future transient land use time series
from the Land Use Harmonization Version 2 product (LUH2, http://luh.umd.edu/). CLM5 (and ocean biogeochemistry) uses monthly N‐deposition derived from CESM2(WACCM6) simulations. Additional information on this data set is provided in Lawrence et al. (2019) and references therein.

6. Preindustrial Control Spin‐Up and Tuning
The CESM2(CAM6) and CESM2(WACCM6) PI control simulations were integrated for 1,200 and 500 years,
respectively. In such long control simulations, it is necessary to aim for a top‐of‐the‐atmosphere (TOA)
global‐ and time‐mean energy balance of <|0.1|W/m2 to avoid unrealistically warm or cold climate states
toward the end of these simulations that will be subsequently used to initialize historical integrations.
Such a small TOA energy balance is indeed desired in CESM2, because there are no additional externalsources or sinks of energy, such as geothermal heat ﬂuxes in the ocean bottom. To achieve this level of
TOA energy balance, the coupled model was tuned using two sets of adjustments. The ﬁrst involved very
minor changes in the so‐called “gamma_coef” parameter in CLUBB, which controls how bimodal the
PDF of subgrid vertical velocity is. A larger value of gamma_coef implies a less bimodal, longer‐tailed
PDF for vertical velocity. The longer tails of velocity, in turn, lead to stronger entrainment at the top of
the planetary boundary layer, which in turn reduces the amount and thickness of low‐clouds, particularly
in marine stratocumulus layers. The second set of adjustments were done in CICE5. Because the sea ice
was deemed to be too thick in the PI simulations—in comparison to the present‐day observations—to produce a realistic sea‐ice distribution at present day, the bulk dry snow grain radius standard deviation was
reduced to 1.25 from its CESM1 value of 1.75. In addition, the temperature for the onset of snowmelt was
lowered from −1.0 to −1.5 °C, and the maximum melting snow grain radius size was increased from
1,000 to 1,500 microns, both adjustments again with respect to those used in CESM1 simulations. All theseadjustments were only applied to snow on sea ice, and they are certainly within the observationally based
acceptable ranges of these tunable sea‐ice parameters. The changes collectively result in a lower snow andsea‐ice albedo and hence thinner sea‐ice overall. These parameters were adjusted identically in the two PI
control simulations, and no additional changes were made throughout the control integrations.
Furthermore, the tuning parameters remained ﬁxed at these values in all subsequent experiments.
The impacts of these tunings were assessed in decadal to multidecadal length simulations prior to the start ofthe main PI control simulations. Figure 6 (left panel) shows the time series of the global‐mean TOA energy
imbalances (ﬂuxes) for the two control simulations where the imbalances are +0.05 and +0.06 W/m2 for
CESM2(CAM6) and CESM2(WACCM6), respectively, averaged over their integration lengths. These imbalances are +0.03 W/m2 during the last 500 years of CESM2(CAM6) and +0.06 W/m2 during the last 200 yearsof CESM2(WACCM6). While these TOA imbalances are much smaller than the imbalance of about −0.15
W/m2 in CCSM4 (Gent et al., 2011), they are larger than the exceptionally small TOA imbalance of near‐zeroin the CESM1(LENS) control simulation. In both CESM2(CAM6) and CESM2(WACCM6) control
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Figure 6. Time series of (left) 5‐year average, global‐mean TOA energy imbalance (ﬂux) and (right) annual‐ and volume‐
mean ocean potential temperature from the CESM2(CAM6) and CESM2(WACCM6) PI control simulations.

simulations, this energy gain is solely reﬂected in the ocean model, as the land and sea‐ice components show
minor energy losses. The global‐mean energy gain by the ocean model is about 0.07 W/m2 during the last 500
years of CESM2(CAM6) and about 0.10 W/m2 during the last 200 years of CESM2(WACCM6). As a result,the global‐mean ocean potential temperature (Figure 6, right panel) increases from its initial value of 3.92
to about 4.23 °C by Year 1,200 in CESM2(CAM6). Similarly, it increases from 3.92 to about 4.08 °C in
CESM2(WACCM6). Although the details of the depth ranges and magnitudes of this warming differ
across the ocean basins, depths below about 2 km show warming in all major basins (not shown). The
global‐mean ocean salinity decreases slightly by about 5 × 10‐5 psu per century in both CESM2(CAM6)
and CESM (WACCM6). This salinity trend is very similar to those of CCSM4 and CESM1(LENS) PI
control simulations.

7. Surface Temperatures

Figure 7. Time series of the global‐mean surface (2‐m air) temperature
anomaly relative to the respective 1850–1870 averages during the historicalperiod from observations, CESM2(CAM6), CESM2(WACCM6), andCESM1(LENS) ensemble simulations. The solid red line and the pinkshading show the 11‐member ensemble‐mean and its spread for CESM2(CAM6) simulations. The solid blue and dashed red lines show the 3‐ and 40‐
member ensemble means for CESM2(WACCM6) and CESM1(LENS)
simulations, respectively. The green line, EXP(A), shows a sample timeseries from an earlier simulation with unrealistic cooling particularly during
the second half of the 20th century. The CESM1 simulations use theRepresentative Concentration Pathway 8.5 (RCP8.5) forcing for the 2006–
2014 period. The observations are from the HADCRU45, GISTEMP, andNOAAGlobalTemp data sets. The latter two time series are referenced to themean of HADCRU45 for the 1870–1890 period.

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The model global‐mean surface (2‐m air) temperature anomaly timeseries for the historical period are presented in Figure 7 in comparison
with observations. A reference period of 1850–1870 is chosen to easily
identify warming since the preindustrial conditions. For CESM2(CAM6), both the ensemble‐mean and its spread are shown. Only theensemble‐mean time series are included from CESM2(WACCM6) andCESM1(LENS) simulations for clarity. The observations are from theHadley Centre‐Climate Research Unit Temperature Anomalies(HADCRU4.5; Jones et al., 2012); the Goddard Institute for SpaceStudies Surface Temperature Analysis (GISTEMP; Lenssen et al.,2019); and the National Oceanic and Atmospheric Administration
mergedland‐ocean
globalsurfacetemperature
analysis(NOAAGlobalTemp; Vose et al., 2012). In general, the CESM2(WACCM6) time series tend to be slightly warmer than those of
CESM2(CAM6). Both sets of simulations capture the observed low‐frequency variability, and the observations are usually within theensemble spread of CESM2(CAM6) simulations with a few notable
exceptions. One such example is the dip around 1910 evident in observations, but absent in model simulations. This discrepancy may berelated to incorrect and/or missing representations of impacts of volcanic eruptions in both models and observations during the 1900–1910
period. Another major discrepancy is seen during the 2000–2014 periodwith model ensemble‐mean temperature anomalies being warmer thanobserved by about 0.2 to 0.4 °C at the end of the integration period in
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CESM2 simulations. Although the warming trends between 1975 and 2000 are comparable between
these two model simulations and observations, no CESM2 ensemble member is able to capture theobserved slowdown in the warming trend during roughly 2000–2012. This is not surprising as internalvariability as well as anthropogenic and natural forcings are known to play a role in the creation of thisdecadal‐scale slowdown (e.g., Deser et al., 2017; Kaufmann et al., 2011; Kosaka & Xie, 2013; Meehl
et al., 2014; Solomon et al., 2010). Indeed, as discussed in Meehl et al. (2014), a much larger ensemble
than presently used is needed as such a slowdown is present only in a very small fraction of the available CMIP5 simulations (10 out of 262). In comparison to CESM2 simulations and observations, CESM1(LENS) ensemble‐mean time series show generally colder temperature anomalies, usually remaining
below or near the lower extent of the CESM2(CAM6) spread. CESM1(LENS) does not capture theobserved decadal‐scale slowdown, either.
The 1985–2014 average SST (0‐ to 10‐m average in the ocean) distributions from CESM2(CAM6), CESM2(WACCM6), and CESM1(LENS) ensemble means are presented in Figure 8 in comparison to the
Extended Reconstructed Sea Surface Temperature Version 5 data set (ERSSTv5; Huang et al., 2017). There
are two salient features in the ﬁgure, particularly evident in the model‐observations difference distributions.
First, CESM2(CAM6) and CESM2(WACCM6) mean SSTs are almost identical to each other. Second, bothmodel SSTs are similarly warmer than observations and those of CESM1(LENS). Indeed, CESM2(CAM6)
and CESM2(WACCM6) global‐mean SSTs are warmer than observations by 0.39 and 0.33 °C, respectively,
while CESM1(LENS) is colder than observations by 0.27 °C. These global‐mean differences largely reﬂect
the SST differences in the corresponding PI control simulations from the ERSSTv5 data set for the preindustrial period, which are computed as 0.33 °C for CESM2(CAM6), 0.18 °C for CESM2(WACCM6), and −0.18 °C for CESM1(LENS) over 30‐year periods close to the mean initial start dates of the respective historicalensemble members. When root‐mean‐square (rms) differences from observations are considered, both
CESM2(CAM6) and CESM2(WACCM6) have slight degradations of the mean SST simulations with rms differences of 0.77 and 0.75 °C, respectively, compared to 0.67 °C for CESM1(LENS). The SST difference distributions depict several persistent biases. The warm‐cold SST difference dipole in the North Atlantic
associated with errors in the separation and path of the Gulf Stream‐North Atlantic Current system is present in all model simulations with comparable magnitudes. The warm biases in the Eastern boundary
upwelling regions, that is, off the coasts of California, South America, and South Africa, are more pronounced in CESM2(CAM6) and CESM2(WACCM6) than in CESM1(LENS). These CESM2 biases are muchlarger in their magnitude (and spatial extent) than the global‐ and time‐mean SST differences between
CESM2 and CESM1(LENS), so the larger CESM2 biases cannot be solely attributed to the warmer meanstate of these simulations. Reducing these upwelling‐related biases has been rather challenging in 1° horizontal resolution models. In CESM1, the upwelling biases were reduced due to more realistic wind stresscurl in a model conﬁguration with much higher horizontal resolutions in the ocean and atmospheric models
than used in the present simulations (Small et al., 2015).
Seasonal‐average surface (2‐m) air temperature model minus observations differences over land for
December‐January‐February (DJF) and June‐July‐August (JJA) for the 1985–2014 period from CESM2(CAM6), CESM2(WACCM6), and CESM1(LENS) are shown in Figure 9. The observations are from theBerkeley Earth Surface Temperatures data set (BEST; Rohde et al., 2013). As in the SST distributions,
CESM2(CAM6) and CESM2(WACCM6) distributions are very similar to each other, and, on average,
CESM2 is warmer than CESM1. Both the global mean bias and rms error are appreciably lower in CESM2
than in CESM1 in all seasons. For example, the DJF mean bias (rms error) has been reduced from −2.24 °C (1.90 °C) in CESM1(LENS) to 0.89 °C (1.51 °C) and 0.67 °C (1.38 °C) in CESM2(CAM6) and CESM2(WACCM6), respectively. In CESM2, there is a notably damped annual cycle—warm biases in the winterseason and cold biases in the summer season—in the northern high latitudes. There is also a large summertime warm bias over the central United States, which partially coincides with a dry bias (see Figure 10), andmay be related to the absence of propagating mesoscale convective systems at the 1° model resolution.
Another interesting bias is the warm bias over Northeast India, which exists in all seasons except for
March‐April‐May (MAM) (not shown). This region is among the most heavily irrigated regions in the world
and previous studies have shown that the irrigation‐induced cooling is likely mitigating against climate
warming in this area (Thiery et al., 2020). Though CESM2 includes active irrigation by default, irrigationin India is applied mainly in the MAM season due to a limitation in the crop planting date scheme thatDANABASOGLU ET AL.

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Figure 8. The 1985–2014 average sea surface temperature (in °C) from (top) ERSST observations and (left) model simulations. The right panels show the respective model minus observations difference distributions. The model distributions
represent full ensemble means. The global means for each panel and the root‐mean‐square error for the difference distributions are also provided.

incorrectly results in crops being planted in the spring over India. In reality, over much of Northern India,
the two main cropping and irrigation seasons are in summer and in winter. Improved simulation of crop
planting dates, therefore, may result in reduced surface air temperature biases in this region.

8. Atmospheric Fields
8.1. Precipitation
The numerous improvements incorporated into the atmospheric model components have a positive impact
on many aspects of the climate of the fully‐coupled CESM2 system. Figure 10 shows the 1985–2014 average
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Figure 9. Seasonal‐average surface (2‐m) air temperature model minus observations differences (in °C) over land for DJF
and JJA for the 1985–2014 period from CESM2(CAM6), CESM2(WACCM6), and CESM1(LENS). The observations arefrom the BEST data set. The model distributions represent full ensemble means. The global‐mean biases and the root‐
mean‐square errors are also provided.

precipitation from CESM2(CAM6), CESM2(WACCM6), and CESM1(LENS) ensemble means in comparison
to observational estimates from the Global Precipitation Climatology Project (GPCP, Huffman et al., 2009).
As in the SST and surface temperature over land distributions, CESM2(CAM6) and CESM2(WACCM6)
simulations produce nearly identical mean precipitation ﬁelds. Both sets of simulations clearly depict asystematic reduction of most biases from CESM1(LENS) to CESM2, from a mean bias of 0.33 mm/day in
CESM1(LENS) down to 0.25 and 0.23 mm/day in CESM2(CAM6) and CESM2(WACCM6), respectively.
The rms error also decreases substantially by about 20%, from 0.87 mm/day in CESM1(LENS) to 0.71mm/day in CESM2. These improvements arise primarily from the reduction of the major tropical wet
biases of the Indian Ocean, the South Paciﬁc Convergence Zone (SPCZ), and the tropical Atlantic Inter‐
Tropical Convergence Zone (ITCZ). Over land, the dipole of Andean wet and Amazon dry biases isdecreased, and excessive precipitation over Australia and Western United States is reduced. Nevertheless,there are a few degraded regions, including a slightly larger negative bias off Brazil in CESM2 that extends
into the South Atlantic Ocean.
8.2. Shortwave and Longwave Cloud Forcing
A dramatic improvement is seen in the radiative properties and, in particular, the time‐averaged shortwave
cloud forcing (Figure 11). In comparison to the Clouds and the Earth's Radiant Energy System‐Energy
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Figure 10. The 1985–2014 average total precipitation (in mm/day) from (top) GPCP observations and (left) model simulations. The right panels show the respective model minus observations difference distributions. The model distributions
represent full ensemble means. The global means for each panel and the root‐mean‐square error for the difference distributions are also provided.

Balanced and Filled (CERES‐EBAF; Loeb et al., 2009) data set, the reﬂective biases of the clouds in CESM1(LENS) throughout the tropics over land and ocean, often in excess of −30 W/m2, are substantially alleviatedin both CESM2(CAM6) and CESM2(WACCM6). This improvement is due to several changes in different
cloud regimes. The interaction of CLUBB with the CAM6 deep convective parameterization enabled areadjustment of tropical deep convection over land to reduce biases. Net rms for shallow clouds is reducedwith CLUBB. At higher latitudes, low clouds were insufﬁciently bright in CESM1(LENS). Changes inmixed‐phase cloud properties in the MG2 microphysics scheme produce more supercooled liquid water in
CESM2 and result in more low clouds and more reﬂective clouds. Quantitatively, the global‐mean rms
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2

Figure 11. The 2000–2013 average shortwave cloud forcing (in W/m ) (a) from the CERES‐EBAF data set and (b–d) for
model minus observation differences. Only one ensemble member is used for model simulations.

error is substantially reduced from about 14 W/m2 in CESM1(LENS) to about 9.1 W/m2 in CESM2(CAM6)
and 9.7 W/m2 in CESM2(WACCM6). The subtropical stratocumulus regions show a few large‐scale
improvements. For the most part however, deﬁciencies in cloud forcing seen in CESM1(LENS) are stillseen in the CESM2(CAM6) and CESM2(WACCM6) stratocumulus regions.
In contrast, the improvements in the time‐averaged long‐wave cloud forcing (Figure 12) are much moremodest compared to those of the shortwave cloud forcing. In particular, weak cloud forcing biases in
CESM2(CAM6) and CESM2(WACCM6) remain similar to those of CESM1(LENS) outside of the tropics,
illustrating the continued challenge of representing ice microphysics and their optical characteristics.
Within the tropics, the Indo‐Paciﬁc improvements in cloud forcing largely reﬂect the patterns of tropical precipitation changes and associated upper tropospheric cloud and cloud water. These are most apparent in the
reduction of the double ITCZ bias in the Paciﬁc Ocean and the east‐west precipitation distribution in the
Indian Ocean. Quantitatively, the global‐mean rms errors are about 8.3 W/m2 in CESM2(CAM6) and 7.6
W/m2 in CESM2(WACCM6), both representing modest reductions from the rms error of about 9.6 W/m2
in CESM1(LENS).
8.3. Madden‐Julian Oscillation
An aspect of variability that was poorly represented in CESM1 is the Madden‐Julian Oscillation (MJO). In
CESM2, minor changes to the Zhang‐McFarlane deep convection scheme and the addition of CLUBB havecombined to generate a much improved MJO (Figure 13). The observation‐based Tropical Rainfall
Measuring Mission‐European Centre for Medium‐Range Weather Forecast (ECMWF) Reanalysis‐Interim(TRMM/ERAI; Kummerow et al., 2000; Dee et al., 2011) product shows convectively coupled coherent wind
and precipitation features that propagate in quadrature from the Indian Ocean into the West Paciﬁc. In
CESM1(LENS), these features had incorrect westward propagation in the Indian Ocean and a lack ofWest Paciﬁc variability. In both CESM2(CAM6) and CESM2(WACCM6), these adverse characteristics arelargely absent, such that coherent eastward propagation now occurs from the Western Indian Ocean
through the Maritime Continent and into the Central Paciﬁc. Correlations are slightly weaker in CESM2(WACCM6) than in CESM2(CAM6). Remaining bias characteristics are slightly weak coupling andphase speed.
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Figure 12. Same as in Figure 11 except for longwave cloud forcing.

Figure 13. Lag correlation relationship between total precipitation averaged in the Indian Ocean (between 60º–90ºE asshown by the green dash lines) and total precipitation (color) and 850‐mb zonal wind (contour) at all Indo‐Paciﬁc longitudes from (a) TRMM/ERAI data sets and (b–d) model simulations. The latitude range for averaging is 10ºS to 10ºN.
Based on daily data ﬁltered with a 20‐ to 100‐day Lanczos ﬁlter. Only one ensemble member is used for model simulations.

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Figure 14. Spatial composites of DJF ENSO anomalies of precipitation (top row; in mm/day), surface air temperature over land and SST over ocean (bottom row;color shading; in °C), and sea level pressure (bottom row; contours; −16 to 16 hPa by 2 hPa with negative contours dashed) from observations, CESM2(CAM6)
PI control for Years 100–1,199, CESM2(WACCM6) PI control for Years 50–499, and CESM1(LENS) PI control for Years 500–1,599. The 6 mm/day DJF climatological mean precipitation contour is shown in black for each data set in the top row. The normalized December Niño 3.4 time series are used to composite all years
greater than 1 standard deviation (El Niño) and all years less than −1 standard deviation (La Niña), and the ENSO composite is formed by subtracting the La
Niña composite from the El Niño composite. Observations are from GPCP for precipitation (1979–2017), ERSSTv5 for SST (1920–2017), BEST for 2‐m air temperature (1920–2017), and ECMWF Reanalysis for the 20th century (ERA20C; Poli et al., 2016) combined with ERA‐Interim (Berrisford et al., 2009) for sea levelpressure (1920–2017).

8.4. El Niño‐Southern Oscillation
ENSO is arguably the most important mode of tropospheric climate variability on interannual time scales.
Because it was one of the worst aspects in CCSM3 simulations with a dominant period of 2 years, improving
ENSO was the highest priority during CCSM4 development. As a result of changes in the atmospheric deep
convection parameterization, ENSO characteristics were improved in CCSM4 and CESM1 (Deser et al.,2012; Neale et al., 2008; Richter & Rasch, 2008). These improvements are retained in CESM2. Figure 14 compares ENSO (El Niño minus La Niña) spatial composites of DJF anomalies of precipitation, surface air temperature over land and SST over ocean, and sea level pressure from observations and CESM2(CAM6),
CESM2(WACCM6), and CESM1(LENS) PI control simulations. Again, CESM2(CAM6) and CESM2(WACCM6) are very similar to each other with the exception of a slightly stronger ENSO amplitude in
the former, and the spatial patterns are generally well simulated by all model versions. (The ENSO composite value of DJF SST in the Niño 3.4 region is signiﬁcantly different between CESM2(CAM6) and CESM2(WACCM6) at the 95% conﬁdence level based on a 2‐sided Student's t test [1.38 °C vs. 1.26 °C], for reasonsthat remain to be understood.) There are two notable improvements in CESM2(CAM6) and CESM2(WACCM6) with respect to CESM1(LENS). First, the orientation of the sea level pressure teleconnection
over the North Paciﬁc, in particular its northwest‐southeast tilt, is more realistic with implications for thecirculation‐induced air temperature anomalies over western North America. Second, the representation of
the tropical precipitation anomaly pattern is improved, with more prominent drying over the maritime continent and SPCZ, and a broader equatorial maximum over the western half of the Paciﬁc. These improvements are associated with a better representation of the climatological precipitation distribution as
evidenced by a more prominent northwest‐southeast oriented SPCZ that is conﬁned to the western Paciﬁcand less of a double ITCZ structure as indicated by the contours in the top row. The precipitation anomaly
pattern in CESM2(CAM6) and CESM2(WACCM6) also shows a stronger east‐west Walker Circulation component, more in line with observations, compared to the dominant narrow Hadley Circulation response
in CESM1(LENS).
8.5. Stratospheric Ozone
WACCM6 is able to accurately simulate the evolution of stratospheric ozone over the late 20th century. In
particular, WACCM6 can produce the halogen‐induced ozone loss in the Southern Hemisphere polar
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stratosphere in spring. This occurs due to the activation of chlorine andbromine via heterogeneous chemistry on the surface of polar stratosphericcloud particles (WMO, 2010). Figure 15 illustrates the annual evolution of
the Southern Hemisphere polar cap Total Column Ozone (TCO) in
October. Coupled historical and AMIP‐type simulations with WACCM6
are able to broadly represent the ozone loss from the 1970s to 1990s.
“Speciﬁed Dynamics” (SD) simulations, wherein the model is constrainedwith MERRA‐2 (Rienecker et al., 2011) meteorology (SD‐MERRA2), indicate that much of the variability in observations can be explained by interannual variability of meteorology in the Southern Hemispherepolar vortex.

9. Atlantic Meridional Overturning Circulation
One of the climatically most important ocean circulations is the AMOC,
representing a zonally integrated view of the Atlantic Ocean circulation.
Through its associated heat and salt transports, AMOC signiﬁcantly
impacts spatial and temporal variability in the North Atlantic and surrounding areas, even inﬂuencing the global climate. Many studies link
the low‐frequency variability of the North Atlantic SSTs to ﬂuctuations
in AMOC (Zhang et al., 2019 and references therein). Figure 16 presents
the 1995–2014 average AMOC distributions from CESM2(CAM6) and CESM2(WACCM6) in comparison
to CESM1(LENS). These are for the Eulerian‐mean, that is, resolved ﬂow, component only as the parameterized contributions are relatively small. The ﬁgure shows that AMOC spatial structure and transports areindistinguishable between CESM2(CAM6) and CESM2(WACCM6). The primary pattern is the clockwise
circulation cell (positive contours) associated with the southward ﬂowing North Atlantic Deep Water(NADW). The maximum NADW transport is about 24 Sv (1 Sv = 106 m3/s) in CESM2(CAM6) andCESM2(WACCM6), while it is about 26 Sv in CESM1(LENS). There are no basin‐scale observational estimates for AMOC, and only recently a few transbasin transport estimates have become available. Amongthese, the RAPID array at 26.5°N has the longest record. For the April 2004 to February 2017 period, themean and standard deviation of the estimated NADW maximum transport at 26.5°N are 17.0 ± 3.3 Sv(Frajka‐Williams et al., 2019; Smeed et al., 2018). Acknowledging the different averaging periods, the model
maximum transports at this latitude are larger than the RAPID estimate by about 2–3 Sv with CESM1(LENS)
showing the largest magnitude.

Figure 15. October 90° to 60°S Total Column Ozone (TCO, in Dobson
Units: DU) from CESM2(WACCM6) simulations (blue), ﬁxed SST (AMIP‐type) simulations (green), Speciﬁed Dynamics simulation (SD‐MERRA2;
purple), and observations (SBUV‐MOD; black). For CESM2(WACCM6) and
AMIP, dots represent individual ensemble members while the ensemblemeans are shown by the solid lines.

The penetration depth of the NADW cell as measured by the depth of the zero‐contour line is similarly shallower in CESM2(CAM6) and CESM2(WACCM6) than in CESM1(LENS). Given that the ocean component
in all model versions uses the same overﬂow parameterization (Danabasoglu et al., 2010) to represent dense‐water overﬂows through the Denmark Strait and Faroe Bank Channel, the differences in penetration depth

Figure 16. The 1995–2014 average Eulerian‐mean Atlantic meridional overturning circulation (in Sv) from (left) CESM2(CAM6), (middle) CESM2(WACCM6),and (right) CESM1(LENS). The model distributions represent full ensemble means. The positive and negative contours indicate clockwise and counterclockwise
circulations, respectively.

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between the CESM1 and CESM2 simulations reﬂect differences in water mass properties, partially arisingfrom surface ﬂux differences.
The counterclockwise (negative contours) circulation region in the abyssal ocean below the NADW cell
represents the northward ﬂowing Antarctic Bottom Water (AABW). In comparison to very limited observational estimates, all the simulations suffer from a chronic bias of very anemic AABW transport due to too
weak AABW formation, with CESM2(CAM6) and CESM2(WACCM6) showing only a marginally broader
and stronger AABW cell than in CESM1(LENS). The observational range for the AABW transport into
the Atlantic basin is about 1–5 Sv at 24.5°N based on sparse data sets dating back to 1957 (Frajka‐
Williams et al., 2011). Both CESM2 simulations have a maximum transport of about 0.5 Sv, compared to nearzero in CESM1(LENS). Such weak deep overturning circulations lead to weak ventilation at depth evident intracer distributions particularly in the North Paciﬁc, compared to available observations (not shown).

10. Sea‐Ice and Land‐Ice Fields
10.1. Sea‐Ice Thickness and Extent
The annual‐mean sea‐ice thickness and extent are shown in Figure 17, averaged for 1979–2014. For theArctic, sea‐ice in both CESM2 simulations is thinner than in CESM1(LENS), which has a total annual‐mean
Arctic ice volume of 28.9 × 103 km3. The sea ice is considerably thicker in the simulations with CESM2(WACCM6) than with CESM2(CAM6), with the respective total annual‐mean Arctic ice volumes of 21.4
× 103 and 15.4 × 103 km3. The CESM2(WACCM6) simulations agree well with observationally based estimates from the Pan‐Arctic Ice Ocean Modeling and Assimilation System (PIOMAS; Schweiger et al.,2011), which simulates a 1979–2014 average Arctic ice volume of 20.2 × 103 km3. The thicker sea ice in
CESM2(WACCM6) is related to liquid Arctic clouds, which are thicker as a result of increased aerosol concentrations predicted from the interactive chemistry module (Gettelman, Mills, et al., 2019). Speciﬁcally, theinteractive oxidants with full chemistry in CESM2(WACCM6) mean that aerosols will evolve differently.
Because the aerosols are not produced locally in remote regions such as the Arctic, the increase in aerosol
concentrations over the Arctic is due to a lengthening of the aerosol lifetime. The anticorrelation between
aerosols and oxidants is meteorologically dependent and so may not be seen in monthly average oxidants
ﬁelds used in CESM2(CAM6), leading to a different aerosol evolution, particularly in such remote areas.
The differences in sea‐ice thickness between the two model conﬁgurations have consequences for the annual
cycle of ice extent. In particular, with the thin ice bias in the CESM2(CAM6) simulations, more melting
occurs during the summer months, with a resulting low summer ice extent as compared to satellitepassive‐microwave observations (Figure 17g). In contrast, CESM2(WACCM6) and CESM1(LENS) have an
excellent simulation of summer ice extent compared to observations. In both CESM2(CAM6) and CESM2(WACCM6) simulations, the ice extent is low in winter compared to observations. This is in contrast to
CESM1(LENS), which simulates a winter ice extent that is in very good agreement with observations.
In the Southern Hemisphere, the sea ice is nearly identical in CESM2(CAM6) and CESM2(WACCM6), both
somewhat thinner than the sea ice simulated in CESM1(LENS). As in observations, a primarily seasonal ice
pack is present, and the ice remains thinner than 1 m in the annual mean over most of the sea‐ice domain for
all model versions. Sea‐ice thicker than 1 m forms in the Amundsen/Bellingshausen and Weddell Seas. This
is in reasonable agreement with the limited in situ ice thickness observations (Worby et al., 2008). The ice
extent annual cycle is also reasonably well simulated in both CESM2 simulations. The maximum simulated
ice cover is biased low by about 1.6 × 106 km2, and the ice melt‐back is delayed by about 1 month relative toobservations but reaches a similar minimum ice cover in February. This is in contrast to CESM1(LENS),
which has a good simulation of the winter maximum ice extent but retains too much ice at thesummer minimum.
10.2. Land‐Ice Surface Mass Balance
Figure 18 shows the simulated Greenland and Antarctic SMB for the 1961–1990 mean of CESM2(CAM6)
and CESM2(WACCM6) historical simulations in comparison to the regional atmospheric climate model
RACMO2 (Noël et al., 2018; van Wessem et al., 2018) and to CESM1(CAM4) (Vizcaíno et al., 2013). (Weshow CESM1(CAM4) results because ice sheet SMB was not computed for CESM1(LENS) simulations.)
CESM2(CAM6) and CESM2(WACCM6) solutions are very similar to each other, both reproducing the
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Figure 17. The annual‐mean sea‐ice thickness (in m) in CESM2(CAM6) (a and b), CESM2(WACCM6) (c and d), and2
CESM1(LENS) (e and f) simulations. The annual cycle of sea‐ice extent (in km ) for the (g) Northern Hemisphere and(h) Southern Hemisphere. All values are averages for 1979–2014. The observations are estimates from the PIOMAS data
set. The analysis uses 6, 3, and 40 ensemble members for CESM2(CAM6), CESM2(WACCM6), and CESM1(LENS),
respectively. In (h), CESM2(CAM6) and CESM2(WACCM6) lines overlap.

RACMO2 SMB reasonably well for both ice sheets. Improvements relative to CESM1(CAM4) can be
attributed in part to more realistic snow/ﬁrn physics in CLM5 (van Kampenhout et al., 2017), and to theBeljaars et al. (2004) orographic form drag scheme and new cloud microphysics in CAM6. Remaining
biases for Greenland include excessive snowfall in the south, likely associated with unresolved
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Figure 18. Surface mass balance (SMB in mm water equivalent per year) of the Greenland ice sheet (top row) and Antarctic ice sheet (bottom row) from statisticallydownscaled output of the regional atmospheric climate model RACMO2.3p2, and from CESM2(CAM6), CESM2(WACCM6), and CESM1(CAM4). The CESM
results are from historical simulations averaged over 1961–1990, with eleven ensemble members for CESM2(CAM6), three members for CESM2(WACCM6),and one member for CESM1(CAM4). For Greenland, CESM2 SMB has been downscaled using multiple elevation classes to a 5‐km grid for CESM1(CAM4) and to a
4‐km CISM grid for both versions of CESM2. For Antarctica, both CESM1 and CESM2 SMB are shown on the land model grid without downscaling.
RACMO2.3p2 data are averaged over 1961–1990 for Greenland (Noël et al., 2018) and over 1979–2010 for Antarctica (van Wessem et al., 2018) at a resolution of 11
and 27 km, respectively.

topography, excessive rainfall (van Kampenhout et al., 2019), and a generally too positive SMB bias in
the north.

11. Land Fields
The land model variables are assessed with the International Land Model Benchmarking (ILAMBv2.1;
Collier et al., 2018) package. The ILAMBv2 used here integrates analysis for 30 variables evaluated against
more than 60 data products. ILAMB package results include statistics, maps, time series, and metrics for
relative bias, rms error, seasonal cycle phase, spatial distribution, and interannual variability, as well as
variable‐to‐variable assessments that evaluate functional relationships between variables. ILAMB summary
diagrams, which integrate scores across several data sets and metrics for each variable and variable‐to‐variable comparisons, are shown in Figure 19 for three ensemble members each of CESM1(LENS) andCESM2(CAM6)—CESM2(WACCM6) results are very similar to those of CESM2(CAM6). For most variables
assessed, CESM2(CAM6) better matches the observations than CESM1(LENS). Except for precipitation, all
the land climate variables (shown in gray in the left panel of Figure 19) show modest improvement, indicating an improved surface climate simulation in CESM2(CAM6) due to a combination of improvements in
CAM6 and CLM5. The land carbon, water, and energy variable improvements are also seen in land‐onlysimulations forced with historical climate reconstructions (not shown; see Lawrence et al., 2019, for detailed
assessment), which suggests that improvements in these variables are predominantly a result of CLM5 physics and biogeochemistry model development rather than an outcome of a better‐simulated surface climate
in CESM2(CAM6). The consistently improved variable‐to‐variable scores suggests improved process representation in CLM5. These functional relationship scores are improved even for comparisons involving amean quantity that is slightly degraded in CESM2(CAM6)—for example, land precipitation is slightly
degraded, but precipitation versus gross primary productivity and precipitation versus burned area are
improved. This result supports the interpretation that the improvements derive mainly from CLM5 developments. Insight into the sources of improvement is provided in Lawrence et al. (2019) and Wieder et al. (2019).
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A caveat here is that ILAMB results should be interpreted carefully and that an improvement or degradation
in a particular ILAMB variable should not be overly interpreted as indicative of whether or not results thatare dependent on that variable are reliable or not (see Lawrence et al., 2019, and Bonan et al., 2019). For
example, soil carbon is degraded according to the ILAMB metric, but other aspects of soil carbon such as
the soil carbon turnover time appear to be improved (Lawrence et al., 2019), potentially reﬂecting a lackof consistency across observational data sets or a limitation in the applied ILAMB metric. A note of caution
on the soil carbon ﬁeld in CESM2 is that due to an error in the model spin‐up process, unrealistically large
soil carbon stocks are seen for some high‐latitude grid cells where vegetation does not survive the cold climate (e.g., Canadian Archipelago). For these grid cells, the spin‐up process was supposed to set soil carbon
stocks to near zero, but due to very small but nonzero vegetation stocks, the soil carbon stocks were not set tozero as intended. Model users can resolve this problem (which was ﬁxed in CESM2.1.1) by screening out
large soil carbon stocks when vegetation carbon is zero.
One of the clearest improvements in CESM2 and CLM5 is the simulated global land carbon accumulation
trends. CESM1/CLM4 produced an unrealistically strong nutrient limitation on photosynthesis, which limited the capacity in CESM1 for carbon uptake in response to increases in atmospheric CO2 concentrations.
Consequently, in CESM1 land use and land‐cover change carbon loss ﬂuxes dominate over the CO2 fertilization response, and CESM1 simulates an accumulated carbon loss over the period 1960 to 2004, whereas
the observationally‐based estimates indicate a carbon gain of ~40 Pg C (Figure 20). In CESM2(CAM6) andCESM2(WACCM6), the CO2 fertilization response appears to be more reasonable and, when this is combined with updates to the land use and land‐cover change carbon ﬂuxes, results in a late 20th century accumulated carbon trend that agrees well with observationally based estimates. Further discussion on thesource of improvement for this feature of CESM2, which is also seen in land‐only simulations, is provided
in Lawrence et al. (2019), Wieder et al. (2019), and Bonan et al. (2019). In addition to the improved accumulated carbon, CESM2 simulations also show evidence of considerable improvements in the amplitude of theannual cycle of net ecosystem exchange, especially at northern high latitudes (see Carbon Dioxide variable
metrics in Figure 19), which translate to improved annual cycle amplitudes of atmospheric CO2 concentrations in emissions‐driven simulations.

12. Equilibrium Climate Sensitivity and Transient Climate Response
An important emergent property of the coupled model is its Equilibrium Climate Sensitivity (ECS; e.g.,
Stocker et al., 2013), deﬁned as the equilibrium change in global‐mean surface temperature after a doublingof CO2 concentration. ECS can be estimated using a slab ocean model (SOM) conﬁguration, because (1) sucha conﬁguration equilibrates much faster compared to one with a full‐depth ocean model (about 50 vs. >1,000
years) and (2) ECS values obtained with a SOM versus a full‐depth ocean component are comparable to eachother (Danabasoglu & Gent, 2009). Therefore, SOM conﬁgurations are used here to estimate the ECS of bothmodel conﬁgurations. The control SOM experiments use a preindustrial value of CO2 set at 280 ppmv. These
respective control simulations are compared with a second set of SOM experiments in which the CO2 concentration is instantaneously doubled to 560 ppmv (2xCO2). All SOM experiments are integrated longenough to ensure that global‐mean surface temperature has equilibrated and TOA radiative imbalances
are near zero. Differencing the equilibrated surface temperatures (SST over the ice‐free oceans and a radiative temperature over land and sea‐ice) between the respective control and 2xCO2 experiments, the ECS
values are computed as 5.3 °C for CESM2(CAM6) (Gettelman, Hannay, et al., 2019) and 5.1 °C for CESM2(WACCM6). These values represent signiﬁcant increases from an ECS of around 4.0 °C in the SOM versionof CESM1(CAM5) (Gettelman et al., 2012).
Values of climate sensitivity derived from the ﬁrst 150 years of fully‐coupled 4xCO2 runs using the Gregory
method (so‐called Effective Climate Sensitivity; Gregory et al., 2004) are 5.3 °C for CESM2(CAM6) and 4.8°C for CESM2(WACCM6) compared to 4.3 °C in CESM1(CAM5) obtained with the same method (Meehl
et al., 2013). However, this level of relatively good agreement with the above SOM‐based estimates of ECS
is deceptive, because the Gregory method estimate of climate sensitivity for CESM2 is highly sensitive tothe number of simulation years used in the calculation, due to nonlinearities in the relationship between
TOA energy imbalance and temperature, with larger estimates of climate sensitivity as more years
are used.
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Figure 19. ILAMB summary diagrams for three ensemble members each of CESM1(LENS) and CESM2(CAM6) historical simulations. The left panel shows the
single‐variable assessment summary for land variables. The upper part of the right panel shows assessment of the land forcing variables as simulated by theatmosphere model. The lower part of the right panel shows a variable‐to‐variable comparison summary. See Collier et al. (2018) for details on all metrics. Full
ILAMB package results for both coupled and land‐only simulations are available online (at http://www.cesm.ucar.edu/experiments/cesm2.0/land/diagnostics/
clm_diag_ILAMB.html).

The evolution of CESM2's climate sensitivity through several development versions of the model is examined in detail in Gettelman, Hannay, et al. (2019). This study suggests that the increased climate sensitivity
in CESM2 has arisen from a combination of relatively small changes to cloud microphysics and boundary
layer parameters as well as from land parameter changes that were introduced late in the development process, along with the introduction of more realistic clouds with CLUBB and MG2.

Figure 20. Land carbon accumulation for the period 1960–2014 for threehistorical ensemble members of CESM2(CAM6), CESM2(WACCM6), andCESM1(LENS) along with observationally based estimates. The latter are
from the Global Carbon Project (Le Quéré et al., 2016). Gray shading shows
uncertainty range for the observational estimates, using an uncertainty of
0.8 Pg C/year and calculated assuming that the errors are additive in time asin Koven et al. (2013).

DANABASOGLU ET AL.

Another important emergent property of the coupled model is its
Transient Climate Response (TCR). It is calculated from a pair of fully
coupled simulations: a control simulation, again usually for PI conditions, and a 1% per year CO2 concentration increase simulation.
Following the ESMValTool (Eyring et al., 2020) procedure, TCR in
CESM2 is calculated as the difference of the 20‐year average (Years
60–79) global mean surface temperature, centered around the time ofCO2 doubling at about Year 70, from the 140‐year linear trend (ﬁt) of
the corresponding PI control, overlapping the 1% per year CO2 increaserun. The TCR values are obtained as 2.0 °C for CESM2(CAM6) and 1.9°C for CESM2(WACCM6). These values are very comparable to a TCR
value of 2.3 °C for CESM1(CAM5) reported by Meehl et al. (2013).
Processes leading to CESM2's TCR value are being investigated.
Preliminary analysis suggests that, in CESM2, while tropics and theSouthern Ocean warm faster, the Northern Hemisphere at middle
and high latitudes warms at a slower rate, in comparison to CESM1.
It is important to stress that all estimates of ECS and TCR are subject tosubstantial uncertainties arising from the details of the calculation procedures. These uncertainties include the selection of reference periods for

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Figure 21. Mean pattern correlations across climatological mean, seasonal contrasts (JJA‐DJF), and ENSO teleconnections for three ensemble members of CESM2(CAM6), CESM2(WACCM6), and CESM1(LENS) for various ﬁelds: precipitable water (prw), sea level pressure (psl), 500‐hPa relative humidity (hur500), outgoing longwave radiation (rlut), 500‐hPa geopotential height (zg500), 2‐m air temperature (tas), top of atmosphere net shortwave ﬂux (rsnt), longwave andshortwave cloud forcing (lwcf, swcf), rainfall (pr), and 500‐hPa vertical velocity (wap500).

the PI simulations, the length of the averaging periods, and whether detrending is applied or not. For theabove CESM values, such details may result in 0.1–0.2 °C differences.

13. Summary and Discussion
The new version of the Community Earth System Model, CESM2, contains many substantial science andinfrastructure improvements and capabilities. The CESM project is contributing simulations to the CMIP6
effort with two CESM2 model conﬁgurations differing only in their atmospheric components. While one
conﬁguration uses the low‐top CAM6, CESM2(CAM6), the other employs WACCM6 with its high‐top andcomprehensive chemistry, CESM2(WACCM6). Both conﬁgurations use the same nominal 1° horizontal
resolution. A noteworthy new approach in CESM2 is that CESM2(WACCM6) PI and historical simulations
were performed ﬁrst to obtain some of the necessary forcing data sets to run the corresponding CESM2(CAM6) simulations. This was critical to ensure that, for the ﬁrst time for CESM, a physically consistent pairof low‐ and high‐top atmospheric models are used. This manuscript documents the CESM2 development
process and evaluates solutions from the 1,200‐year CESM2(CAM6) and 500‐year CESM2(WACCM6) PI
control integrations and from the subsequent historical simulations. The focus has been primarily on the historical simulations, as they can be more easily and directly compared with modern observations.
The new developments and capabilities in CESM2 result in many improvements in solutions in comparison
to available observations and those of CESM1. These improvements include major reductions in the tropical
wet biases of the Indian Ocean, the SPCZ, and the Atlantic ITCZ, generally with less of a double ITCZ structure in both the Paciﬁc and Atlantic Basins; reductions in the dipole of Andean wet and Amazon dry biases
as well as in Australian and Western U.S. excessive precipitation biases; substantial reductions in the reﬂective biases of the clouds throughout the tropics; a much better representation of MJO characteristics withcoherent eastward propagation of wind and precipitation anomalies; more realistic representation of thenorthwest‐southeast tilt of the sea level pressure anomaly teleconnection patterns associated with ENSO
over the North Paciﬁc; a global land carbon accumulation trend that matches the observationally based
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trend remarkably well; and better representation of the SMB for both the Greenland and Antarctic ice sheets.
An important ﬁnding is that CESM2(CAM6) and CESM2(WACCM6) historical simulations produce solutions that are very similar to each other in most variables. So these improvements are present in both setsof simulations. An exception is the Arctic sea‐ice thickness with CESM2(WACCM6) showing thicker sea
ice than in CESM2(CAM6) in better agreement with observations. The thicker sea ice in CESM2(WACCM6) is related to liquid Arctic clouds, which are thicker as a result of increased aerosol concentrations predicted from the interactive chemistry module.
Figure 21 presents a summary analysis comparing CESM2 solutions to those of CESM1(LENS). The ﬁgureshows mean pattern correlations across climatological mean, seasonal contrasts (JJA‐DJF), and ENSO teleconnections for the ﬁrst three ensemble members of CESM2(CAM6), CESM2(WACCM6), and CESM1(LENS) historical simulations for 11 atmospheric ﬁelds with respect to many observational‐based data sets.
Indeed, CESM2 simulations have notable improvements in many of the metrics considered here comparedto those of CESM1(LENS).
Undoubtedly, many biases still remain in the model solutions. These biases range from relatively minor
ones, such as local precipitation errors without discernable global impacts to biases that can have signiﬁcantclimate impacts such as the thin Arctic sea ice in CESM2(CAM6) simulations. The sea‐ice bias can perhaps
be tuned away at the expense of possible appearance of other biases. There are also much more persistentbiases such as the incorrect separation and path of the Gulf Stream‐North Atlantic Current system, withaccompanying large SST and surface salinity biases. This latter class of biases can have signiﬁcant climate
impacts as well, but no robust remedy exists to alleviate them in noneddy‐permitting/noneddy‐resolving
ocean models usually used in climate simulations.
The path to ﬁnalizing a CESM2 version that produced acceptable simulations took much longer than anticipated. The process was hindered particularly by two major unacceptable features in simulations. The ﬁrst
concerned formation of unrealistically extensive sea‐ice cover over the Labrador Sea region in PI control
simulations. Unfortunately, extensive analysis of simulations did not reveal a particular process (or processes) as the culprit. To avoid further delays, a practical approach was adopted that involved performing
several member ensemble simulations, which differed at round‐off levels in their atmospheric potential temperature initial conditions, and then using one of the members without an extensive Labrador Sea ice cover
to provide the ocean and sea‐ice initial conditions. The second issue was that the global‐mean surface temperature time series in historical simulations showed a substantial and unrealistic cooling, particularly during the second half of the 20th century, with end‐of‐the‐century temperatures being colder than in the 1850s.
Adoption of updated CMIP6 emissions data sets, along with several modiﬁcations in the aerosol‐cloud interaction processes based on observational data from recent volcanic sulfur emission events, resulted in surface
temperature time series that evolve realistically during the historical period. Nevertheless, there are several
discrepancies in the details of the modeled and observed time series. For example, the model surface temperatures are warmer in the early part of the 21st century than in observations by 0.2 to 0.4 °C due at least
partly to the missing decadal‐scale slowdown in warming during roughly 2000–2012 in the simulations.
However, such a difference is not unexpected because the observed slowdown is associated with internal climate variability in addition to anthropogenic and natural forcings. This warm bias is also reﬂected in the SST
and over‐land surface temperature distributions.
An emergent property of climate simulations is their ECS, which in CESM2 is calculated as 5.3 °C for CESM2(CAM6) and 5.1 °C for CESM2(WACCM6) based on CO2 doubling experiments with CESM2 SOM conﬁgurations. These values represent a signiﬁcant increase from an ECS of 4.0 °C calculated for CESM1, alsousing a SOM conﬁguration. Climate sensitivities based on the Gregory method from fully‐coupled 4xCO2
runs are calculated as 5.3 °C for CESM2(CAM6) and 4.8 °C for CESM2(WACCM6), again larger than thevalue of 4.3 °C in CESM1(CAM5) obtained with the same method (Meehl et al., 2013). However, these latter
estimates are highly sensitive to the number of simulation years used in the calculation due to nonlinearities
in the relationship between TOA energy imbalance and temperature. Our analysis suggests that theincreased climate sensitivity in CESM2 has arisen from a combination of seemingly small changes to cloud
microphysics and boundary layer parameters as well as from land parameter changes that were introduced
late in the development process, along with the introduction of more realistic clouds with CLUBB and MG2(see Gettelman, Hannay, et al., 2019). In contrast, the CESM2 TCR values, calculated as 2.0 °C for CESM2DANABASOGLU ET AL.

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Table A1
Forcing Data Sets Date Information
Seasonal cycle date

(CAM6) and 1.9 °C for CESM2(WACCM6), are comparable to a TCR
value of 2.3 °C for CESM1(CAM5) reported in Meehl et al. (2013).

WACCM6 experiment

Years averaged

PI control
Historical
Historical
Historical
Historical
Historical
Historical
Historical
Historical
Historical
Historical
Historical
Historical
Historical
Historical
Historical
Historical
Historical
Historical

21–50
1856–1864
1866–1874
1876–1884
1886–1894
1896–1904
1906–1914
1916–1924
1926–1934
1936–1944
1946–1954
1956–1965
1966–1974
1976–1984
1986–1994
1996–2004
2006–2014
2012–2014
2012–2014

1849
1860
1870
1880
1890
1900
1910
1920
1930
1940
1950
1960
1970
1980
1990
2000
2010
2013
2015

Acknowledgments
Previous and current CESM versions
are freely available online (at www.
cesm.ucar.edu:/models/cesm2/). The
CESM data sets used in this study arealso freely available from the Earth
System Grid Federation (ESGF; at esgf‐
node.llnl.gov/search/cmip6) or fromthe NCAR Digital Asset Services Hub(DASH; at data.ucar.edu) or from thelinks provided from the CESM website(at www.cesm.ucar.edu). The CESM
project is supported primarily by the
National Science Foundation (NSF).
This material is based upon work
supported by the National Center for
Atmospheric Research (NCAR), which
is a major facility sponsored by the NSF
under Cooperative Agreement 1852977.
Computing and data storage resources,
including the Cheyenne supercomputer(doi:10.5065/D6RX99HX), were
provided by the Computational andInformation Systems Laboratory (CISL)at NCAR. We thank all the scientists,
software engineers, and administrators
who contributed to the development of
CESM2. GPCP Precipitation data areprovided by the NOAA/OAR/ESRL
PSD, Boulder, Colorado, USA, fromtheir website (at https://www.esrl.noaa.
gov/psd/). The implementation of the
WaveWatch‐III has beneﬁtted from the
input and collaborations with the wave
model developers at the NOAA
National Centers for Environmental
Prediction (NCEP).

DANABASOGLU ET AL.

10.1029/2019MS001916

The CESM community has been participating in about 20 CMIP6‐
endorsed MIPs. The data sets from these MIPs and all the DECK simulations are being made available on the ESGF site for use of the broader
research community. These simulations, including the PI control and historical integrations presented here, are analyzed in more detail in over 70
manuscripts collected in the AGU CESM2 Virtual Special Issue. Our preliminary analyses suggest that the CESM2 simulations exhibit agreementwith satellite era observations of the climate mean state, seasonal cycle,
and interannual variability that is among the closest of coupled climate
models in the present CMIP6 archive.

Appendix A.: Construction of Forcing Data Sets FromCESM2(WACCM6) Historical Simulations for Use in
Historical Simulations With CESM2(CAM6)For CESM2(CAM6) historical simulations, 12‐month annual cycles areprovided at quasi‐decadal frequency (Table A1). The ﬁrst year in the forcing ﬁle is 1849, which is derived from Years 21–50 of the CESM2(WACCM6) PI control simulation. Subsequent annual cycles are provided
using the ensemble average of the three CESM2(WACCM6) historical simulations. One annual cycle per
decade is provided from 1860 to 2010, each averaged over 9 years centered on the year. For example, oxidants
provided for the midmonth date of 16 January 1860 are derived from the average of January averages forYears 1856–1864 of three CESM2(WACCM6) historical ensemble members. An annual cycle is also provided
for 2015, averaged from Years 2012–2014, as the end year needed for time interpolation. CESM2(CAM6)
interpolates annual cycles for years between those provided.

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