The Whole Atmosphere Community Climate Model · X - 2 GETTELMAN ET AL.: WACCM6 3 Abstract. The Whole Atmosphere Community Climate Model version 4 6 (WACCM6) is a major update of the

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. ???, XXXX, DOI:10.1029/,

The Whole Atmosphere Community Climate Model1

Version 6 (WACCM6)2

A. Gettelman1, M. J. Mills

1, D. E. Kinnison

1, R. R. Garcia

1, A.K. Smith

1,

D.R. Marsh1,3

, S. Tilmes1, F. Vitt

1, C. G. Bardeen

1, J. McInerny

1, H.-L.

Liu1, S. C. Solomon

1, L. M. Polvani

2, L. K. Emmons

1, J.-F. Lamarque

1, J. H.

Richter1, A. S. Glanville

1, J. T. Bacmeister

1, A. S. Phillips

1, R. B. Neale

1, I.

R. Simpson1, A. K. DuVivier

1, A. Hodzic

1, W. J. Randel

1

1National Center for Atmospheric

Research, Boulder, CO, USA.

2Columbia University, New York, NY,

USA.

3University of Leeds, Leeds, UK.

D R A F T May 2, 2019, 10:00pm D R A F T

X - 2 GETTELMAN ET AL.: WACCM6

Abstract. The Whole Atmosphere Community Climate Model version3

6 (WACCM6) is a major update of the whole atmosphere modeling capa-4

bility in the Community Earth System Model (CESM), featuring enhanced5

physical, chemical and aerosol parameterizations. This work describes WACCM66

and some of the important features of the model. WACCM6 can reproduce7

many modes of variability and trends in the middle atmosphere, including8

the Quasi-Biennial Oscillation, Stratospheric Sudden Warmings and the evo-9

lution of Southern Hemisphere springtime ozone depletion over the 20th cen-10

tury. WACCM6 can also reproduce the climate and temperature trends of11

the 20th century throughout the atmospheric column. The representation12

of the climate has improved in WACCM6, relative to WACCM4. In addi-13

tion, there are improvements in high latitude climate variability at the sur-14

face and sea ice extent in WACCM6 over the lower top version of the model15

(CAM6) that come from the extended vertical domain and expanded aerosol16

chemistry in WACCM6, highlighting the importance of the stratosphere and17

tropospheric chemistry for high latitude climate variability.18


GETTELMAN ET AL.: WACCM6 X - 3

1. Introduction

Simulating the climate system means more than just simulation of the surface climate19

and the troposphere. For example, the climate and chemistry of the stratosphere may20

also affect the surface, by changing surface forcing through the climatology of water vapor21

and ozone [Solomon et al., 2010], by stratospheric aerosol loading [Mills et al., 2016], or22

dynamical interactions between the stratosphere and troposphere with impacts all the way23

to the surface [Baldwin and Dunkerton, 2001]. In addition to surface climate variables24

such as temperature and precipitation, the climate system includes chemical reactions25

that impact humans and ecosystems through the oxidizing capacity of the troposphere,26

including near surface and tropopsheric ozone [e.g., Lefohn et al., 2018]. Changes in chem-27

istry and dynamics also affect absorbed radiation through the stratospheric ozone layer28

[World Meteorological Organization, 2010]. Stratospheric ozone changes are the premier29

example of the influence of stratospheric composition on climate [e.g., Son et al., 2010]30

and air quality [Hodzic and Madronich, 2018]. Finally, the dynamics of the sun may affect31

the atmosphere, particularly the upper atmosphere (from the mesosphere to thermosphere32

to ionosphere). Periods of extreme solar activity can affect the near space environment33

and impact electrically sensitive human structures from power grids to electronic devices,34

mediated through solar impacts with the upper atmosphere.35

Simulating the earth system and its impacts on human health and ecosystems requires36

the use of comprehensive simulations that can represent the physical, dynamical and37

chemical climate of the whole atmosphere. Such models are now becoming commonplace38

in climate simulations [Marsh et al., 2013], and may be necessary to improve under-39



standing and predictability of the lower atmosphere [Thompson et al., 2002; Charlton-40

Perez et al., 2013]. This work documents a major upgrade to the Whole Atmosphere41

Community Climate Model (WACCM) in the Community Earth System Model version42

2 (CESM2), or CESM2-WACCM. Section 2 describes CESM2-WACCM, which is also43

known as WACCM6. Section 3 describes the available model configurations, and Section 444

the specific simulations used in this study. Section 5 presents WACCM6 simulations, Sec-45

tion 6 describes differences between WACCM and CAM versions and Section 7 presents46

conclusions.47

2. Model Description

This section documents the important features and changes to WACCM since WACCM448

[Marsh et al., 2013].49

2.1. Physical Atmosphere

WACCM6 is the whole atmosphere version of the Community Atmosphere Model ver-50

sion 6 (CAM6), described by Neale et al. [2019]. Unlike in previous versions of CESM,51

WACCM6 is identical to CESM2-CAM (also known as CAM6) in the range of processes52

that are parameterized. The only exception is the representation of parameterized gravity53

waves (see below). A summary of the current parameterizations for CAM6 and WACCM654

clouds and aerosols is contained in Table 1. These are detailed further below as applied55

to WACCM6, and also in Neale et al. [2019] as applied to CAM6.56

Table 1 describes the different versions of WACCM starting with WACCM4, described57

by Marsh et al. [2013]. WACCM-CCMI was developed for the Chemistry Climate Mod-58

eling Iniative (CCMI) and features updated tropospheric and stratospheric chemistry.59



WACCM5 [Mills et al., 2017] is based on WACCM4 chemistry, but with updated physical60

parameterizations following Neale et al. [2010], and features adjustments to the physical61

parameterizations and gravity wave schemes (see below), as well as updated stratospheric62

prognostic aerosols [Mills et al., 2016]. WACCM5 also transitions to higher horizon-63

tal and vertical resolution. WACCM-CCMI emphasized the evolution of chemistry, and64

WACCM5 the evolution of dynamics. WACCM6 combines the new aspects of both models65

with additional updates for CESM2.66

2.2. Gravity Wave Drag

In addition to the orographic gravity wave drag parameterization that is identical be-67

tween CAM6 and WACCM6, WACCM6 includes a non-orographic gravity wave drag68

parameterization following Richter et al. [2010] with separate specification of frontal and69

convective gravity wave sources. Two main adjustable parameters in the frontal gravity70

wave source specification have been changed since WACCM4 due to the increased hori-71

zontal resolution in WACCM6: the frontogenesis threshold in WACCM6 is set to 0.10872

K2 (100 km)−2 h−1 and the source stress of frontally generated waves is set to τb = 3 x73

10−3 Pa. These parameters are the same as those used in Mills et al. [2017] for WACCM5.74

In order to obtain an internally generated QBO with a reasonable period, a scaling fac-75

tor of 0.25 was applied to the depth of heating in the deep convective parameterization76

(which reduces the effective phase speeds of convectively generated gravity waves), and77

the efficiency of convectively generated waves was changed to 0.4 [Mills et al., 2017].78

Updated orographic schemes were implemented in WACCM6 for Planetary Boundary79

Layer (PBL) form drag and Orographic Gravity Waves (OGW). The updated PBL form80

drag scheme is that of Beljaars et al. [2004]. The updated OGW scheme incorporates81



near surface nonlinear drag processes following Scinocca and McFarlane [2000] and uses82

a feature-based algorithm to derive forcing data based on Bacmeister et al. [1994].83

2.3. Solar and Geomagnetic Forcing

For almost all model configurations, WACCM6 uses the recommended CMIP6 so-84

lar and geomagnetic forcing as described in Matthes et al. [2017] and available via85

http://solarisheppa.geomar.de/cmip6. The solar spectral irradiance used to calculate86

heating and photolysis rates are averages from two semi-empirical models: version 2 of the87

Naval Research Laboratory model (NRLSSI2, Coddington et al. [2015]) and a composite88

of two Spectral And Total Irradiance REconstruction (SATIRE) models [Yeo et al., 2015].89

For photoionization and heating rates at wavelengths shorter than Lyman-α, WACCM690

uses the parameterization of Solomon and Qian [2005], that takes as input the F10.7 in-91

dex. Geomagnetic variability affects the flux of energetic particles that precipitate into92

the atmosphere, ionizing and possibly dissociating major species. Ion-pair production93

rates (IPR) by galactic cosmic rays, solar protons and medium-energy electrons are also94

prescribed following Matthes et al. [2017]. IPRs are specified on pressure levels and geo-95

magnetic L-shell, which are interpolated to the WACCM6 geographic grid. For all schemes96

other than the MAD scheme described below, IPR rates are converted into rates of pro-97

duction for odd-hydrogen and odd-nitrogen species following Jackman et al. [2009].98

For lower-energy electrons that precipitate in the auroral regions, WACCM6 continues to99

use the parameterized auroral oval model of Roble and Ridley [1994], the implementation100

of which in WACCM is described in Marsh et al. [2007]. This model takes as input101

hemispheric power (HP ) that is assumed to be related to the Kp geomagnetic index102



through the following revised relationship:103

HP (GW ) = 16.82e0.32Kp − 4.86 Kp ≤ 7

= 153.13 + 73.5(Kp− 7.0) Kp > 7

In the pre-industrial control simulations, the solar and geomagnetic forcing are an aver-104

age over the period 1850 to 1873. For transient simulations, solar and geomagnetic forcing105

are linearly interpolated at each model time step from daily averaged data. The model is106

capable of taking higher frequency forcing files, should the scientific application demand107

it (e.g., the modeling of solar storms).108

It should be noted that beginning January 1, 2015, solar forcing data are projections109

based on historical solar cycles rather than from observations. It is for this reason that110

in non-CMIP6 simulations that are nudged to reanalysis (see Section 3), WACCM6 uses111

only irradiance fluxes from NRLSSI2, which are updated routinely and available from112

National Centers for Environmental Information (dataset doi:10.7289/V51J97P6).113

2.4. Chemistry

WACCM6 is designed to represent a full suite of chemical constituents. It shares the114

4 mode Modal Aerosol Model (MAM4, Liu et al. [2016]; Mills et al. [2016]) with CAM6,115

but adds chemistry (explicitly calculating the oxidants which are specified in CAM6).116

The baseline chemical mechanism contains reactions relevant for the whole atmosphere:117

Troposphere, Stratosphere, Mesosphere and Lower Thermosphere (TSMLT), described by118

Emmons et al. [2019]. Three additional chemistry schemes are available: a Tropopshere119

and Stratosphere (TS) scheme, a Middle Atmosphere (MA) scheme with a reduced set120



of tropospheric reactions, and the same MA scheme with the addition of D-region ion121

chemistry (MAD).122

Here we describe the TSMLT mechanism used in WACCM. This is a superset of all123

the other mechanisms, except that it does not contain detailed D-region ion chemistry124

(see below). The chemical species within this mechanism include the extended Ox, NOx,125

HOx, ClOx, and BrOx chemical families, along with CH4 and its degradation products.126

In additon to CH4, we also include N2O (major source of NOx), H2O (major source of127

HOx), plus various natural and anthropogenic precursors of the ClOx and BrOx families.128

This mechanism also includes primary non-methane hydrocarbons and related oxygenated129

organic compounds. The chemical processes have evolved from previous versions [e.g.,130

Kinnison et al., 2007; Emmons et al., 2010; Lamarque et al., 2012; Marsh et al., 2013;131

Tilmes et al., 2016], and are summarized in detail in Emmons et al. [2019]. Reaction132

rates are updated following JPL 2015 recommendations [Burkholder et al., 2015]. The133

current mechanism includes a new detailed representation of secondary organic aerosols134

(SOAs) based on the Volatility Basis Set (VBS) approach from mahor anthropogenic135

and biogenic VOC precursors [Tilmes et al., 2019; Hodzic et al., 2016]. The WACCM136

mechanism includes a total of 231 solution species, 583 chemical reactions broken down137

into 150 photolysis reactions, 403 gas-phase reactions, 13 tropospheric and 17 stratospheric138

heterogeneous reactions. The photolytic calculations are based on both inline chemical139

modules and a lookup table approach [Kinnison et al., 2007]. The chemical mechanism140

includes two very short-lived halogens: CHBr3 and CH2Br2. The surface mole fraction141

for these two species is set to 1.2 pptv (i.e., 6 pptv of total bromine). This approach142

adds an additional ∼5pptv of inorganic bromine to the stratosphere. The heterogeneous143



reactions use aerosol surface area density (SAD) derived from MAM4 [Mills et al., 2016].144

The tropospheric heterogeneous reactions take four aerosol types into account (i.e., sulfate,145

black carbon, particulate organic matter and secondary organic aerosol). The stratosphere146

heterogeneous reactions occur on three aerosol types (i.e., sulfate, nitric acid trihydrate,147

and water-ice). The liquid binary sulfate aerosol SAD is derived from MAM4, but modified148

in very cold regions (<200K) using the Aerosol Physical Chemistry Model [Tabazadeh149

et al., 1994] to represent supercooled ternary solution (STS) aerosols. WACCM6 uses150

CMIP6 specified mixing ratios for greenhouse gases [Meinshausen et al., 2017], reactive151

gases and aerosols from anthropogenic sources, including aircraft NOx [Hoesly et al., 2018]152

and biomass burning [vanMarle et al., 2017]. Biogenic emissions are calculated online in153

CLM with MEGANv2.1 [Guenther et al., 2012]. Lightning NOx emissions are interactive,154

and total ∼3-4 TgN/year.155

The water-ICE and nitric acid trihydrate (NAT) SAD approach is described in Kinni-156

son et al. [2007] with updates listed in Solomon et al. [2015]. The chemical mechanism157

includes 9 chemical tracers (SF6, O3S, E90, AOA NH, AOA1, AOA2, ST80 25, NH5,158

NH50). These tracers are described in Tilmes et al. [2016]. The mesosphere and lower159

thermosphere (MLT) chemistry component of WACCM6 includes the species and reac-160

tions described in Marsh et al. [2007]. The radiatively active gases in WACCM include161

H2O, O2, CO2, O3, N2O, CH4, CFC12, aerosols, and an ‘equivalent CFC11’ (CFC11eq),162

which includes radiative effects of CFC11 along with a scaling to reflect other CFCs and163

HFCs [Meinshausen et al., 2017].164

The gas phase chemistry of the MA chemical scheme is closest to the chemical scheme165

used in WACCM4. It includes two additional species not present in WACCM4. These166



species are the metastable states O+(2D) and O+(2P), which are important for the ener-167

getics of the thermosphere but do not have any impact on composition or chemistry in168

the lower atmosphere. As with all schemes, the rate coefficients have been updated to169

JPL-2015.170

The middle atmosphere D-region (MAD) chemical scheme, that adds negative ion and171

cluster ion chemistry, was first implemented in WACCM4 and is described in Verronen172

et al. [2016]. Since version 3 of WACCM, the chemical scheme has solved for the densities173

of 5 ions and electrons that make up the E-region ionosphere [Marsh et al., 2007]. The174

MAD scheme adds 15 positive and 21 negative ions, and addresses several deficiencies in175

the mesosphere that occur when only E-region ions are considered. The first is that the176

dominant negative charge carriers are no longer electrons below 75 km, but negative ions177

such as Cl−(HCl), HCO−3 and NO−

3 (HNO3). The negative charge is balanced not by O+2178

and NO+, as in the E-region, but by proton hydrates (H+(H2O)n=3,4,5). This, in theory,179

should yield a better representation of electron density throughout the mesosphere. The180

second improvement is that production of odd-hydrogen (H, OH) and odd-nitrogen (N,181

NO) from ionization by energetic particles is no longer parameterized but flows through182

the complex chemistry following the ionization of the major species (N2, O2). It includes183

the chemistry that produces the enhanced levels of HNO3 observed in the polar middle184

stratosphere following a solar storm that are not seen in simulations with the MA scheme185

[e.g., Orsolini et al., 2018].186

2.5. Prognostic stratospheric aerosols

WACCM6 features prognostic stratospheric aerosols. As described by Mills et al. [2016],187

the modal aerosol model, MAM [Liu et al., 2012, 2016] has been modified to change188



the mode widths and allow growth of sulfate aerosol into the coarse mode (MAM4).189

This is important to properly represent aerosol sources in the stratosphere, including190

volcanic emissions, and natural background emissions of carbonyl sulfide (OCS), which191

form the stratospheric aerosol layer. Historical variability in OCS is inlcuded as a time-192

varying lower boundary condition [Montzka et al., 2004]. SO2 emissions from volcanic193

eruptions are derived from version 3.11 of Volcanic Emissions for Earth System Models194

[VolcanEESM, Neely and Schmidt , 2016, ]. To account for the self-lofting of aerosols due195

to in situ absorption of longwave radiation, we impose a maximum altitude of 20 km for196

eruptions inputting more than 3.5 Tg of SO2. This adjustment affects 4 eruptions in the197

historical period of simulation (1850-2014): Krakatau (1883), Agung (1963), El Chichon198

(1982), and Pinatubo (1991). To account for the loss of sulfur on ice and ash as observed199

in the initial 10 days following the 1991 Pinatubo eruption, we scale the mass input to200

the model by a factor of 5/9 from eruptions estimated to have emitted more than 15201

Tg of SO2. This adjustment affects 2 eruptions: Krakatau (1883), and Pinatubo (1991).202

The rationale for these altitude and mass adjustments is discussed further in Mills et al.203

[2016]. For each day of eruption, SO2 emissions occurs over 6 hours from 1200 to 1800UT.204

A complete table of volcanic eruptions and emissions parameters is included as metadata205

in the VolcanEESM netCDF file used for historical simulations, which is publicly available206

from the CESM2 inputdata repository.207

Following CMIP6 guidance [Eyring et al., 2016], we include in pre-industrial control208

simulations background volcanic aerosol at a level to match average radiative forcing over209

the historical simulation (i.e. 1850-2014 mean). Our pre-industrial control simulations210

therefore include constant SO2 emission rates time-averaged from all eruptions over the211



historical period. SO2 emissions from volcanic eruptions are zeroed below the tropopause212

in pre-industrial simulations.213

2.6. Secondary Organic Aerosols

In addition to the updated prognostic stratospheric aerosol scheme in WACCM6,214

for configurations that include comprehensive tropospheric and stratospheric chemistry,215

WACCM6 includes an interactive Secondary Organic Aerosol (SOA) approach based on216

the Volatility Bin Set (VBS), following the approach described by Hodzic et al. [2016].217

Gas phase semi-volatile SOA components (SOAG) are formed from anthropogenic and218

biomass burning precursor emissions at the surface, as well as from biogenic emissions219

from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.1220

[Guenther et al., 2012]. In addition to the traditional SOA precursors such as isoprene,221

monoterpenes, aromatics and short-chain VOCs, the updated mechanism also includes the222

long-chain n-alkanes (C ¿ 12) that are not included in the standard emission inventories,223

and that were added as co-emitted species with primary organic carbon emissions from224

fossil-fuel and biomass burning sources. This approach is more interactive than earlier225

approaches since the formation of organic SOA interacts with changes in the land model226

and climate variables. In addition, the new SOA approach includes a more comprehen-227

sive description of processes including the water solubility of intermediate organic vapors228

that determines their dry and wet deposition, formation of SOA by the uptake of glyoxal229

SOAGs into aqueous aerosols [Knote et al., 2014], and photolytic removal of particulate230

SOA Hodzic et al. [2015]. In addition to the basic scheme in WACCM6, the model can be231

run with an extended scheme, where source contribution of different precursor emissions232

from biomass burning, fossil fuel and biogenic emissions to SOA can be identified.233



3. Configurations

The basic configuration of WACCM6 features ∼1◦ (0.9◦ latitude x 1.25◦ longitude) hori-234

zontal resolution using the Finite Volume dynamical core [Lin and Rood , 1997]. WACCM6235

has 70 levels in the vertical from the surface to 6x10−6 hPa (∼140km). By contrast, CAM6236

has 32 levels with a top at 3.6 hPa. Notably, CAM6 and WACCM6 share the same vertical237

level structure up to the 87 hPa level.238

WACCM6 has several different baseline configurations as illustrated in Table 2. The239

Free Running (FR) configuration features one of the chemistry packages (TSMLT,TS,MA,240

MAD) described in Section 2.4. The baseline is TSMLT: full chemistry from the tropo-241

sphere through the lower thermosphere. Free Running WACCM6 configurations can be242

run with specified sea surface temperatures (SST) and sea ice distribution (called ’FW’243

case simulations) or fully coupled to active ocean and sea ice model components (’BW’244

case) [Marsh et al., 2013]. These simulations can be configured for specific years with245

annually repeating boundary conditions, such as the BW1850 configuration, or using246

time-dependent boundary conditions for greenhouse gas concentrations, aerosols, volcanic247

eruptions (BWHIST), solar variability and in the case of FW simulations, specified his-248

torical sea surface temperatures (FWHIST). WACCM6 always runs with an interactive249

land surface model (the Community Land Model version 5, or CLM5).250

WACCM6 can also be run in a nudged, or Specified Dynamics (SD) configuration251

(FWSD). For SD simulations, winds and temperatures are relaxed to a specified set of252

data, typically another model or a reanalysis system. Commonly, the NASA Goddard253

Earth Observation System (GEOS) model analyses, Modern-Era Retrospective analysis254

for Research and Applications (MERRA) [Rienecker et al., 2011] or MERRA2 Molod et al.255



[2015] are used. This SD version is effective for reducing climate noise, eliminating biases256

in winds and/or temperatures and reproducing the chemical response to specific events or257

for specific times, such as comparison to specific field programs or individual observations.258

WACCM6-SD configurations typically use a vertical level structure taken from the source259

nudging data.260

As described in Smith et al. [2014], WACCM can also be run with Specified Chem-261

istry (SC). In WACCM6-SC simulations (compset FWscHIST), radiatively active chem-262

ical species (like Ozone) are prescribed, typically from a WACCM6 run with interactive263

chemistry, while all other CAM6 and WACCM6 parameterizations for clouds are inter-264

active. The aerosol model is also reduced in complexity for WACCM6-SC, using CAM6265

aerosols (simplified SOA and prescribed stratospheric aerosols). WACCM6-SC is an ef-266

ficient model useful for dynamical studies. An example of the use of WACCM6-SC for267

understanding dynamical and chemical effects are provided in Section 6.268

For comparison, we also perform CAM6 simulations with specified SST and ice (FHIST).269

CAM6 has the same physical parameterization suite as WACCM6 minus the frontal and270

convective gravity wave drag schemes, with a lower top, loss of several upper atmosphere271

processes, fewer levels, and no chemistry. CAM6 is essentially WACCM6-SC with a272

lower lid (and without the convective and frontal gravity waves). Comparisons between273

WACCM6, WACCM6-SC and CAM6 can illustrate the impact of chemistry-climate cou-274

pling (WACCM6 v. WACCM6-SC), the impact of the broader vertical range (WACCM6-275

SC v. CAM6) or both (WACCM6 v. CAM6).276



Finally, WACCM6 now has the option to run with a more complete thermosphere and277

ionosphere, with a lid near 500km. This extended version, or WACCM-X, currently is278

coupled to WACCM4 physics and is described in a companion paper by Liu et al. [2018].279

4. Simulation Description

Results documented below (Section 5) are based on the simulations with WACCM6280

listed in Table 2. All are at 0.9◦x1.25◦ horizontal resolution and 70 vertical levels, with281

TSMLT chemistry, and all are coupled actively to the CLM5 land model. Fully cou-282

pled WACCM6 in CESM2 (BW1850) is run first for a long control run representing283

pre-industrial conditions and starting from a 200 year control simulation with CAM6.284

Then three ‘BWHIST’ simulations with transient forcing from the Coupled Model Inter-285

comparison Project round 6 (CMIP6) [Eyring et al., 2016] from 1850–2014 are initialized286

from year 56, 61 and 70 of this run. WACCM6 is also run with prescribed SST and sea ice287

distribution and CMIP6 forcing for greenhouse gases and aerosols over the period from288

1950-2014, initialized with land and atmospheric initial conditions from BHIST simula-289

tions (FWHIST). WACCM6-SD (FWSD) is a specified dynamics simulation forced with290

MERRA2 analysis over the more recent period when comprehensive satellite data is avail-291

able (1980 to 2014) to examine chemistry with dynamic variability prescribed from the292

historical atmosphere (as conditioned by a reanalysis system). WACCM6-SC (FWscHIST)293

is run with specified SSTs and chemical fields from the FWHIST simulations.294

5. Results



Here we present key results from WACCM6, focusing on the stratosphere. The over-295

all WACCM6 tropospheric climatology and climate are described in Section 6 with a296

comparison to CAM6. CAM6 climatology is more fully described by Neale et al. [2019].297

5.1. Climatology/Annual cycle

WACCM6 is able to effectively simulate the zonal winds from the surface through298

the mesosphere. Figure 1 illustrates the December, January, February (DJF) and June,299

July, August (JJA) WACCM6 FWHIST climatological zonal wind, and compares it to300

European Center Interim (ERAI) reanalysis [Dee et al., 2011]. There are ∼5 m s−1 biases301

in S. Hemisphere winter zonal wind in the lower stratosphere, but, in general, the mean302

wind speeds compare well to reanalyses. Biases would be expected to become larger with303

altitude. WACCM6 is able to simulate the seasonal variation of the stratospheric polar304

jets.305

WACCM6 is also able to effectively simulate the temperature structure from the sur-306

face through the mesosphere. Figure 2 illustrates DJF and JJA WACCM6 FWHIST307

climatological temperature compared to ERAI [Dee et al., 2011]. Seasonal mean tropical308

tropopause temperatures are within ±2K of the reanalysis. There are winter time biases309

in the S. Hemisphere temperature in the lower stratosphere, consistent with the zonal310

wind biases (Figure 1). WACCM6 also has a cold summer mesopause at ∼85km with311

temperatures close to 140K, similar to observations.312

The winter (JJA) seasonal temperature biases over the South Pole in Figure 2d increase313

and propagate downward in spring, as illustrated in Figure 3. These biases result from314

remnants of the winter jet in the lower stratosphere persisting too late. The biases occur315

late enough in the spring that they do not impact lower stratosphere southern polar316



springtime ozone depletion (see below). This bias reflects a delayed breakdown of the317

southern hemisphere polar vortex relative to observations [Butchart et al., 2011].318

The annual cycle of tropical stratospheric water vapor is illustrated in Figure 4, with a319

representation of the water vapor ‘tape-recorder’ [Mote et al., 1996]. WACCM6 is com-320

pared to an observational climatology from the AURA Microwave Limb Sounder (MLS,321

[Waters et al., 2006; Read et al., 2007]), illustrated in Figure 4B. The tape recorder ver-322

tical propagation speed based on tracer contour advection is slightly faster and shifted323

slightly upward relative to observations (Figure 4A). The amplitude of the tape recorder is324

well represented, with a 0.25–0.5ppm high (moist) bias in WACCM6. Specified Dynamics325

(SD) simulations (Figure 4C) have a slightly lower velocity but higher bias than either326

free running fixed SST (FR AMIP, Figure 4D) or free running coupled (FR COUPLED,327

Figure 4E) simulations. Free running coupled simulations (Figure 4E) have small biases in328

value or amplitude relative to MLS. We will discuss differences between WACCM6 (solid329

lines in Figure 4A) and WACCM4 (dashed lines in Figure 4A) in Section 6.330

WACCM6 also does a good job at reproducing the distribution of ozone in the strato-331

sphere. This will be discussed in more detail in analysis of the evolution of stratospheric332

ozone over the late 20th century in Section 5.3.333

5.2. Variability

One of the major features of intraseasonal variability in the stratosphere is Northern334

Hemisphere Stratospheric Sudden Warmings (SSWs) which can have impacts all the way335

down to the surface [Baldwin and Dunkerton, 2001]. In this case we define an SSW by336

a period when the 10 hPa zonal mean zonal wind at 60◦N is < 0 m/s (i.e. eastward)337

following Charlton and Polvani [2007]. Figure 5 indicates that the coupled (Figure 5A)338



and historical fixed SST or AMIP (Figure 5B) WACCM6 ensembles are able to capture339

the frequency of SSWs averaged over 40 years (1975–2014). The ensemble mean frequency340

(black bars) is close to observations. The AMIP simulations have too few SSWs in Febru-341

ary, and too many in March (Figure 5B) while coupled cases (Figure 5A) also have too342

few SSWs in January and February. The high March frequency may be a result of a343

late bias in the spring polar vortex break up, but it is also clear from the spread among344

WACCM ensemble members that the observed record is likely subject to considerable345

sampling uncertainty. The total winter frequency is 0.60 warmings per year in NCEP,346

with 0.52 per year for the historical SST and 0.72 per year for the coupled model, neatly347

bracketing the observations. Fow WACCM4, the frequency of occurrence of SSWs was348

0.46 per year with a range across ensembles of 0.33 to 0.53 per year [Marsh et al., 2013].349

WACCM6 is also able to simulate, albeit imperfectly, the Quasi-Biennial Oscillation350

(QBO) (Figure 6), the equatorial zonal wind propagation which in observations has an351

average period of ∼28 months. Similar to WACCM5 [Mills et al., 2017], WACCM6 simu-352

lates a reasonable QBO with 70 levels in a free running configuration, but the amplitude353

in the lower stratosphere is too weak and disappears completely below 20km. The average354

QBO period calculated using fourier analysis is 29 months for WACCM6 (fixed SST), 27355

months for WACCM6 (coupled ocean) compared to 28 months for ERAI reanalysis. The356

QBO does not extend low enough in the stratosphere (Figure 6B and C) compared to ob-357

servations (Figure 6A). This deficiency is due to the relatively coarse vertical resolution in358

the lower stratosphere in this version of the model. More realistic downward propagation359

of the QBO can be obtained in WACCM with increased vertical resolution [Garcia and360

Richter , 2018].361



WACCM6 does not perform as well in reproducing the semi-annual oscillation in strato-362

spheric and lower mesospheric zonal wind at higher altitudes except at the stratopause,363

as illustrated in Figure 7, which compares model winds at the Equator with winds de-364

rived from SABER satellite data [Smith et al., 2017]. The timing of the SAO minimum365

winds at solstices in the upper stratosphere is well produced, and is driven primarily by366

horizontal advection of zonal mean momentum that varies with seasonal changes in the367

Brewer-Dobson circulation. However, wind variations in the mesosphere simulated by368

WACCM6 are overall much weaker than those deduced from observations. This may be369

due to a deficiency in forcing by tropical waves, and could also be a result of coarse vertical370

resolution. There is not currently an observational base to determine whether the waves371

driving winds in the tropical upper stratosphere and lower mesosphere are small-scale372

gravity waves or larger-scale waves, such as equatorially trapped Kelvin waves.373

WACCM6 represents impacts of both large (e.g. Pinatubo, 1991) and many small-to-374

moderate (e.g. 2005–2014) eruptions. Figure 8 shows simulated and observed Strato-375

spheric Aerosol Optical Depth (SAOD) from 1980-2015 at different locations correspond-376

ing to ground-based lidar observations using a backscatter-to-extinction ratio of 50.377

WACCM6 with prognostic stratospheric sulfur is able to produce the correct SAOD from378

volcanic eruptions over most latitudes. There are some differences in the tropics (Mauna379

Loa) during the volcanically quiescent period of 1997-2004, and in the Southern Hemi-380

sphere (Lauder) during the recent period of small-to-moderate eruptions.381

5.3. Trends

WACCM6 is able to reproduce the evolution of the ozone layer, including the Southern382

Hemisphere polar ‘ozone hole’ [World Meteorological Organization, 2010], as illustrated383



in Figure 9. The observations lie within the range of variability spanned by the historical384

coupled and fixed SST (AMIP) simulations. The SD simulations with imposed variability385

(purple in Figure 9) are able to reproduce the interannual variability seen in observations386

(black in Figure 9). Variability at both poles (Figure 9A and B) is reproduced, including387

low ozone events in the N. Hemisphere (Figure 9B). There are some biases in mid-latitudes388

in free running simulations (Figure 9C and D), which are not seen in SD simulations.389

Larger biases occur in the tropics (Figure 9E).390

The reason for the tropical differences is related to tropical upwelling, illustrated in391

Figure 10, and consistent with Figure 4. In the 20◦S-20◦N region, the vertical velocity is392

larger for pressures greater than 50 hPa for the coupled case, the free running (AMIP)393

case than the SD case. Since the lower stratosphere is dynamically controlled, the vertical394

velocity will impact the Total Column Ozone (TCO). The larger vertical velocities in395

the lower stratosphere are expected to be associated with reduced ozone due to vertical396

advection of ozone poor air from the troposphere. The difference in vertical velocity397

would result in more O3 in the SD case than free running for pressures greater than 50398

hPa. The opposite is the case for pressures between 50 and 10 hPa, broadly consistent399

with the representation of tropical upwelling between the two cases. Tropical anomalies400

(Figure 9E) will affect the mid-latitudes (Figure 9C and D) as well as the near global401

(60◦S–60◦N) TCO (Figure 9F).402

Globally averaged surface temperature in the WACCM6 fully coupled simulations for403

the historical period (1850-2014) are compared to observations and CAM6 simulations404

in Figure 11. Both WACCM6 and CAM6 are able to reproduce the observed historical405

evolution of global mean surface temperature anomalies. Notably, WACCM6 global mean406



surface temperature does not have a different mean (not shown) or global variability than407

CAM6 simulations. Note how in the first 80 years of this record from 1850-1930 or so, when408

radiative forcing was dominated by volcanic eruptions, both CAM6 and WACCM6 track409

much of the observed decadal variability, indicating that it was likely forced variability,410

and associated with volcanoes. Also note that there is a spread of variation of volcanic411

response in WACCM6 to large volcanoes such as Krakatoa in 1883. Volcanic SAOD is412

shown on the top of Figure 11. The CESM2 historical ensemble used volcanic forcing413

equal to the average of the 3 CESM2-WACCM ensemble members.414

Figure 12 illustrates stratospheric temperature trends from WACCM6 historical AMIP415

simulations compared to Stratospheric Sounding Unit (SSU) and the advanced Microwave416

Sounding Unit (MSU) temperatures [Randel et al., 2017]. As with WACCM4 [Randel417

et al., 2017, Fig. 1], WACCM6 is able to capture stratospheric temperature trends from418

1980–2014. WACCM6 has a slightly better representation of the temperature response to419

the 1991 Mount Pinatubo eruption. As noted by Randel et al. [2017], temperature trends420

are a combination of effects from ozone depletion and recovery, and increasing greenhouse421

gases, which WACCM6 captures well. Variability is forced from volcanic eruptions and422

the tropospheric El Nino Southern Oscillation (ENSO), which is imposed on the WACCM423

historical AMIP simulations with observed SSTs.424

6. Difference between WACCM Versions and Configurations

Figure 11 illustrates transient differences in global mean temperature anomalies between425

CAM6 and WACCM6 simulations. Table 3 illustrates differences in global mean climate426

metrics between different configurations for the historical period (2000–2014) as well as427

overlapping observations. Here we compare both CAM6 and WACCM6, as well as the428



previous version of WACCM, WACCM4-CCMI. Table 3 also shows pre-industrial (PI,429

from B1850 simulations) values from WACCM6, CAM6 and WACCM4.430

6.1. WACCM6 v. CAM6

WACCM6 and CAM6 have very similar climates defined by their top of atmosphere431

energy budget and cloud radiative effects. WACCM6 of course provides significantly more432

fidelity in the stratosphere as it represents the meridional overturning circulation of the433

stratosphere fully, as well as full tropospheric and stratospheric chemistry, with interactive434

oxidants and ozone. Differences between WACCM6 and CAM6 are thus a combination of435

differences due to the different lid and upper atmospheric processes with the differences in436

chemistry and aerosols [Emmons et al., 2019]. The tropospheric physical processes are the437

same between WACCM6 and CAM6. We discuss these differences below, and also refer438

to a WACCM-SC (Specified Chemistry) simulation where appropriate to help distinguish439

dynamical from chemical and aerosol processes.440

There are several climate differences between CAM6 and WACCM6. In the clear sky,441

there is a -2Wm−2 top of model difference in both the SW net flux (FSNTC) and LW442

net flux (FLNTC) in WACCM6 v. CAM6. This is likely due to absorption of radiation443

occurring above the model top of CAM, despite an effort to parameterize this absorption444

in CAM. Additional discussion is found in Tilmes et al. [2019].445

Table 3 also indicates a 1 Wm−2 magnitude increase in SW (negative) and LW (positive)446

cloud forcing (SWCRE and LWCRE) in WACCM. This comes from high clouds in the447

tropics, where there is an increase in high cloudiness (CLDHGH) at the edges of the448

tropics, and an increase in ice mass (TGCLDIWP) and overall ice crystal number. This449

would appear to be a result of an increase in accumulation mode sulfate in the tropics450



causing increased homogeneous nucleation of cirrus ice crystals. The increased sulfate is451

from (a) DMS (dimethylsulfide) sources and (b) stratospheric volcanic sulfate in WACCM6452

which are not present in CAM6. CAM6 takes in prescribed surface area densities of sulfate453

from WACCM6, but not the actual sulfate which can impact tropospheric clouds.454

There are also small increases in high latitude Northern Hemisphere shortwave cloud455

forcing (SWCRE) in WACCM6 over CAM6, due to increases in cloud droplet number456

from increased East Asian aerosol burdens which extend into the Arctic (see Tilmes et al.457

[2019] for details).Aerosol lifetimes also differ in the tropics in WACCM6 due to differences458

in tropospheric chemistry and removal of oxygenated organics.459

CAM6 and WACCM6 have similar pre-industrial (1850) annual mean Sea Ice Extent460

(SIE), but the SIE is much less than WACCM4 (Table 3). The main difference in SIE461

between CAM6 and WACCM6 is in summer when the WACCM6 simulations have much462

less melt, but the annual mean SIE is not very different. As a result, there are larger dif-463

ferences between CAM6 and WACCM6 in pre-industrial Sea Ice Volume (SIV) in Table 3.464

Ice sticks around for the subsequent winter and then the ice is thicker.465

In coupled WACCM6 historical simulations (BWHIST), the recent 20th Century warm-466

ing makes these differences more apparent (Figure 13). WACCM6 has higher September467

NH SIE than CAM6, in better agreement with observations (Figure 13A). WACCM and468

CAM NH SIE are close in March (the month of maximum sea ice, Figure 13A). WACCM469

annual NH sea ice volume (Figure 13B) is dropping slight faster than observations, while470

CAM6 has lower ice volume, but a decline rate is similar to observed. WACCM6 is a bit471

colder than CAM in the 1960-1980 period, and warming up faster. Analysis (not shown)472

indicates that in 2000-2014, there is less downward surface SW and LW in WACCM6,473



and that this happens because of slightly higher LWP (in winter around the ice edge, in474

summer over the ice). Higher LWP in WACCM6 than CAM6 results from higher aerosol475

number. The higher aerosol number increases cloud condensation nuclei and cloud drop476

number, resulting in smaller drops that do not precipitate as readily. Thus the tropo-477

spheric aerosol chemistry impacts Arctic sea ice.478

WACCM6 and CAM6 climate variability metrics are also very similar, with very few479

statistical differences. WACCM6 however does have better high latitude surface pressure480

variability, as illustrated in Figure 14. Other ensemble members are similar. WACCM6481

has imporoved Northern Hemisphere high latitude surface variability by this metric com-482

pared to CAM6, despite the same vertical resolution in the troposphere. Area-weighted483

pattern correlations and Root Mean Square (RMS) differences for the standard deviation484

of sea level pressure were calculated between observations (ERA Interim and 20th Cen-485

tury reanalyses) and 3 ensemble members for WACCM6 and CAM6 historical coupled486

experiments. December – February pattern correlations are as high for WACCM6 (0.96)487

as for other reanalyses (0.95-0.96) and higher than for CAM6 (0.95-0.93). RMS differences488

are 0.10-0.12 for other reanalyses relative to ERA, 0.11-0.12 for WACCM6 and 0.12-0.14489

for CAM6 ensemble members. This variability improvement is present across 3 WACCM6490

20th Century ensemble members compared to 9 CAM6 ensemble members. The differ-491

ence is likely related to slight changes in the Northern Annular Mode (NAM) pattern492

in the stratosphere, and indicates the importance of resolving stratospheric variability.493

Experiments with WACCM-SC (no chemistry) look similar to WACCM6, indicating that494

chemical-climate interactions and aerosol differences are not the cause. However, in a495

WACCM6 experiment without convective gravity waves, high latitude variability looks496



more like CAM6, indicating that momentum forcing of the stratosphere is likely impor-497

tant.498

Improvements in tropospheric variability can also be seen in a metric of atmospheric499

blocking. Figure 15 illustrates a longitudinal index of blocking frequency as determined500

using the method of D’Andrea et al. [1998] which considers reductions in the meridional501

gradient of 500-hPa geopotential height below a threshold (-5m deg−1) to indicate block-502

ing. Figure 15 illustrates the blocking frequency from CESM1 (LENS, 35 simulations),503

CESM2-CAM6 (5 simulations) and CESM2-WACCM6 (3 simulations). CESM2 is better504

at many locations than CESM1. March–May (MAM) is better than December–February505

(DJF) for all versions. In common with many CMIP5 models [Dunn-Sigouin and Son,506

2013], CESM2 still has a DJF bias over the Atlantic, but there are improvements in507

CESM2 near the Greenland blocking ’bump’ at 30◦W in March–May, particularly in508

WACCM6. WACCM6 is also considerably better than CAM6 in the Pacific sector during509

DJF. Internal variability however is significantly greater than in the Atlantic sector.510

6.2. WACCM6 v. WACCM4

Table 3 highlights several important differences in the climate of WACCM6 v.511

WACCM4. The tropospheric cloud radiative forcing (SWCRE and LWCRE) is lower,512

and closer to observations (CERES-EBAF). WACCM6 has higher cloud fraction (closer513

to obs) with reduced LWP, which is also more in line with observations where available514

(global values are not available). These results are consistent with improvements in CAM6515

over CAM4 (see Neale et al. [2019] for details).516



For Pre-Industrial (1850) control climates, WACCM6 has higher global mean precip-517

itation, but very similar surface Temperature (Table 3). The global sea ice extent in518

WACCM4 [Marsh et al., 2013] is 20% higher than WACCM6 or CAM6.519

WACCM6 and WACCM4 do a good job of reproducing tropical tropopause tempera-520

tures, but WACCM6 has a warmer summer tropopause temperature and increased annual521

cycle amplitude. WACCM4 (dashed lines in Figure 4A) has a faster tape recorder prop-522

agation speed in the mid stratosphere for free running simulations than WACCM6 (solid523

lines in Figure 4A). WACCM6 does a good job of reproducing the speed of the tape524

recorder compared to observations, with some vertical shifts. However, WACCM6 has525

a 0.5 ppm positive bias in summer, depending on the simulation (a coupled case looks526

better than the fixed SST case in Figure 4).527

As noted in discussion of Figure 5, WACCM6 has slightly higher frequency of occurrence528

of SSWs than WACCM4, due to more late winter (March) warmings when compared to529

[Marsh et al., 2013, , Figure 3],530

Finally, WACCM4 did not have an internally generated QBO, but it was forced to531

observations, while WACCM6 has an internally generated QBO (Figure 6).532

7. Summary and Conclusions

WACCM6 represents the state of the art in simulation of the middle atmosphere up533

through the mesosphere. WACCM6 is available in several different configurations, and534

can be run fully coupled in CESM2, with fixed SSTs, or even with specified dynamics to535

produce observed dynamical variability. WACCM6 can also be run without full chemistry536

if just dynamical interactions are desired. WACCM-X [Liu et al., 2018] is a variant of537

WACCM that goes all the way to 500km, and represents additional thermosphere and538



ionospheric processes, with physical parameterizations for the lower atmosphere lagging539

one generation behind WACCM6.540

WACCM6 is able to reproduce the observed climatology of temperatures, winds and541

trace constituents such as water vapor and ozone in the middle atmosphere. WACCM6 is542

able to reproduce stratospheric variability from SSWs, the response to volcanic eruptions,543

the QBO and long term secular trends in the middle atmosphere. Biases in temperatures,544

winds and water vapor are small, and smaller than previous versions of WACCM. Volcanic545

emissions are fully prognostic from gas phase through stratospheric aerosol, improving546

temperature response to volcanic eruptions. Despite temperature biases in spring and547

early summer in the S. Hemisphere polar stratosphere, WACCM6 is able to reproduce the548

evolution of 20th and 21st century ozone. There are some biases in the tropics likely due549

to the speed of tropical upwelling.550

WACCM6 features exactly the same lower atmosphere physical parameterizations as551

CAM6, making it very useful for comparable studies of the model top, as well as the552

impact of chemistry/aerosols in the troposphere and stratosphere. CAM6 uses specified553

stratospheric aerosol from WACCM6, as it was found that without interactive oxidants554

(OH), the lifetime of stratospheric SO2 under high loading was different (and not cor-555

rect). The present day climate of WACCM6 is nearly identical to CAM6 global mean556

variables, with similar trends over the 20th century in surface temperature. There are557

some differences due to absorbtion and scattering above the CAM6 top in WACCM6, and558

due to different evolution of tropospheric aerosols with the full tropospheric chemistry of559

WACCM6, that alters aerosol lifetime, particularly for organic aerosols. This can impact560

regional climate [Tilmes et al., 2019] as well as sea ice. Thus tropospheric chemistry and561



aerosols can significantly impact Arctic climate, improving sea ice extent and volume in562

WACCM6 over CAM6.563

Finally, there are some indications that the stratosphere can improve climate variability,564

even at the surface. High latitude variability in WACCM6, in particular the standard565

deviation of winter sea level pressure, is lower in WACCM6 than in CAM6, in better566

agreement with observations. This extends to WACCM-SC without chemistry, and seems567

to be related to gravity wave momentum in WACCM6. Blocking frequency is also closer to568

observed in WACCM6 than CAM6. This indicates that stratospheric dynamical processes569

are important for high latitude tropospheric variability. Thus stratospheric dynamics can570

improve high latitude climate variability.571

Acknowledgments. The CESM project is supported primarily by the National Sci-572

ence Foundation (NSF). This material is based upon work supported by the Na-573

tional Center for Atmospheric Research, which is a major facility sponsored by the574

NSF under Cooperative Agreement No. 1852977. Computing and data storage re-575

sources, including the Cheyenne supercomputer (doi:10.5065/D6RX99HX), were pro-576

vided by the Computational and Information Systems Laboratory (CISL) at NCAR.577

Bardeen was supported by NASA Grant for the ATTREX project. WACCM6578

code is available as part of the CESM2 release via github. Instructions are at579

http://www.cesm.ucar.edu/models/cesm2/release download.html. Simulations shown in580

this work for WACCM6 historical and coupled experiments are available as part of the581

Coupled Model Intercomparison Project round 6 (CMIP6) on the Earth System Grid.582

Specified Dynamics simulations are available from the NCAR ESG node. We thank Tetsu583



Sakai, Vladislav V. Gerasimov, Aleksei V. Nevzorov, and David Ridley for providing lidar584

data.585

.586

References

Ansmann, A., I. Mattis, U. Wandinger, F. Wagner, J. Reichardt, and T. Desh-587

ler (1997), Evolution of the Pinatubo Aerosol: Raman Lidar Observations of588

Particle Optical Depth, Effective Radius, Mass, and Surface Area over Cen-589

tral Europe at 53.4N, J. Atmos. Sci., 54 (22), 2630–2641, doi:10.1175/1520-590

0469(1997)054¡2630:EOTPAR¿2.0.CO;2.591

Bacmeister, J. T., P. A. Newman, B. L. Gary, and K. R. Chan (1994), An Algorithm for592

Forecasting Mountain Wave–Related Turbulence in the Stratosphere, Wea. Forecasting,593

9 (2), 241–253, doi:10.1175/1520-0434(1994)009¡0241:AAFFMW¿2.0.CO;2.594

Baldwin, M. P., and T. J. Dunkerton (2001), Stratospheric Harbingers of Anomalous595

weather Regimes, Science, 294, 581–4.596

Beljaars, A. C. M., A. R. Brown, and N. Wood (2004), A new parametrization of turbulent597

orographic form drag, Quarterly Journal of the Royal Meteorological Society, 130 (599),598

1327–1347, doi:10.1256/qj.03.73.599

Bogenschutz, P. A., A. Gettelman, H. Morrison, V. E. Larson, C. Craig, and D. P.600

Schanen (2013), Higher-order turbulence closure and its impact on Climate Simula-601

tion in the Community Atmosphere Model, Journal of Climate, 26 (23), 9655–9676,602

doi:10.1175/JCLI-D-13-00075.1.603



Boville, B. A., P. J. Rasch, J. J. Hack, and J. R. McCaa (2006), Represenation of Clouds604

and Precipitation in the Community Atmosphere Model Version 3 (CAM3), joc, 19 (11),605

2184–2198.606

Burkholder, J. B., S. P. Sander, J. P. D. Abbatt, J. R. Barker, R. E. Huie, C. E. Kolb, M. J.607

Kurylo, V. L. Orkin, D. M. Wilmouth, and P. H. Wine (2015), Chemical kinetics and608

photochemical data for use in atmospheric studies: Evaluation number 18, Technical609

Report, Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space610

Administration, 2015.611

Butchart, N., A. J. Charlton-Perez, I. Cionni, S. C. Hardiman, P. H. Haynes, K. Kruger,612

P. J. Kushner, P. A. Newman, S. M. Osprey, J. Perlwitz, M. Sigmond, L. Wang,613

H. Akiyoshi, J. Austin, S. Bekki, A. Baumgaertner, P. Braesicke, C. Bruhl, M. Chip-614

perfield, M. Dameris, S. Dhomse, V. Eyring, R. Garcia, H. Garny, P. Jockel, J.-F.615

Lamarque, M. Marchand, M. Michou, O. Morgenstern, T. Nakamura, S. Pawson,616

D. Plummer, J. Pyle, E. Rozanov, J. Scinocca, T. G. Shepherd, K. Shibata, D. Smale,617

H. Teyssedre, W. Tian, D. Waugh, and Y. Yamashita (2011), Multimodel climate and618

variability of the stratosphere, Journal of Geophysical Research: Atmospheres, 116 (D5),619

doi:10.1029/2010JD014995.620

Charlton, A. J., and L. M. Polvani (2007), A New Look at Stratospheric Sudden Warm-621

ings. Part I: Climatology and Modeling Benchmarks, J. Climate, 20 (3), 449–469, doi:622

10.1175/JCLI3996.1.623

Charlton-Perez, A. J., M. P. Baldwin, T. Birner, R. X. Black, A. H. Butler, N. Calvo,624

N. A. Davis, E. P. Gerber, N. Gillett, S. Hardiman, J. Kim, K. Kruger, Y.-Y. Lee,625

E. Manzini, B. A. McDaniel, L. Polvani, T. Reichler, T. A. Shaw, M. Sigmond, S.-W.626



Son, M. Toohey, L. Wilcox, S. Yoden, B. Christiansen, F. Lott, D. Shindell, S. Yukimoto,627

and S. Watanabe (2013), On the lack of stratospheric dynamical variability in low-top628

versions of the CMIP5 models, Journal of Geophysical Research: Atmospheres, 118 (6),629

2494–2505, doi:10.1002/jgrd.50125.630

Coddington, O., J. L. Lean, P. Pilewskie, M. Snow, and D. Lindholm (2015), A Solar631

Irradiance Climate Data Record, Bull. Amer. Meteor. Soc., 97 (7), 1265–1282, doi:632

10.1175/BAMS-D-14-00265.1.633

Collins, W. D., J. K. Hackney, and D. P. Edwards (2002), An updated parameteri-634

zation for infrared emission and absorption by water vapor in the National Center635

for Atmospheric Research Community Atmosphere Model, jgr, 107 (D22), 17, doi:636

10.1029/2001JD001365.637

Cowtan, K., and R. G. Way (2014), Coverage bias in the HadCRUT4 temperature series638

and its impact on recent temperature trends, qjrms, doi:10.1002/qj.2297.639

D’Andrea, F., S. Tibaldi, M. Blackburn, G. Boer, M. Deque, M. R. Dix, B. Dugas, L. Fer-640

ranti, T. Iwasaki, A. Kitoh, V. Pope, D. Randall, E. Roeckner, D. Strauss, W. Stern,641

H. Van den Dool, and D. Williamson (1998), Northern Hemisphere atmospheric block-642

ing as simulated by 15 atmospheric general circulation models in the period 1979–1988,643

Climate Dynamics, 14 (6), 385–407, doi:10.1007/s003820050230.644

Dee, D. P., S. M. Uppala, A. J. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae,645

M. A. Balmaseda, G. Balsamo, P. Bauer, P. Bechtold, A. C. M. Beljaars, L. van de Berg,646

J. Bidlot, N. Bormann, C. Delsol, R. Dragani, M. Fuentes, A. J. Geer, L. Haimberger,647

S. B. Healy, H. Hersbach, E. V. Holm, L. Isaksen, P. K\rallberg, M. Kohler, M. Matri-648

cardi, A. P. McNally, B. M. Monge-Sanz, J.-J. Morcrette, B.-K. Park, C. Peubey, P. de649



Rosnay, C. Tavolato, J.-N. Thepaut, and F. Vitart (2011), The ERA-Interim reanalysis:650

Configuration and performance of the data assimilation system, Quarterly Journal of651

the Royal Meteorological Society, 137 (656), 553–597, doi:10.1002/qj.828.652

Dunn-Sigouin, E., and S.-W. Son (2013), Northern Hemisphere blocking frequency and653

duration in the CMIP5 models, Journal of Geophysical Research: Atmospheres, 118 (3),654

1179–1188, doi:10.1002/jgrd.50143.655

Emmons, L. K., S. Walters, P. G. Hess, J.-F. Lamarque, G. G. Pfister, D. Fillmore,656

C. Granier, A. Guenther, D. Kinnison, T. Laepple, J. Orlando, X. Tie, G. Tyndall,657

C. Wiedinmyer, S. L. Baughcum, and S. Kloster (2010), Description and evaluation of658

the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4), Geoscien-659

tific Model Development, 3 (1), 43–67, doi:https://doi.org/10.5194/gmd-3-43-2010.660

Emmons, L. K., J. J. Orlando, G. Tyndall, R. H. Schwantes, D. E. Kinnison, D. R.661

Marsh, M. J. Mills, S. Tilmes, and J.-F. Lamarque (2019), The MOZART Chemistry662

Mechanism in the Community Earth System Model version 2 (CESM2), to be Submitted663

to J. Adv. Modeling Earth Systems.664

Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and665

K. E. Taylor (2016), Overview of the Coupled Model Intercomparison Project Phase 6666

(CMIP6) experimental design and organization, Geosci. Model Dev., 9 (5), 1937–1958,667

doi:10.5194/gmd-9-1937-2016.668

Fetterer, F., K. Knowles, W. N. Meier, M. Savoie, and A. K. Windnagel (2017), Sea Ice669

Index, Version 3, doi:10.7265/n5k072f8.670

Garcia, R. R., and J. H. Richter (2018), On the Momentum Budget of the Quasi-Biennial671

Oscillation in the Whole Atmosphere Community Climate Model, J. Atmos. Sci., 76 (1),672



69–87, doi:10.1175/JAS-D-18-0088.1.673

Gettelman, A., and H. Morrison (2015), Advanced Two-Moment Bulk Microphysics for674

Global Models. Part I: Off-Line Tests and Comparison with Other Schemes, J. Climate,675

28 (3), 1268–1287, doi:10.1175/JCLI-D-14-00102.1.676

Guenther, A. B., X. Jiang, C. L. Heald, T. Sakulyanontvittaya, T. Duhl, L. K. Em-677

mons, and X. Wang (2012), The Model of Emissions of Gases and Aerosols from678

Nature version 2.1 (MEGAN2.1): An extended and updated framework for mod-679

eling biogenic emissions, Geoscientific Model Development, 5 (6), 1471–1492, doi:680

https://doi.org/10.5194/gmd-5-1471-2012.681

Hack, J. J. (1994), Parameterization of moist convection in the National Center for Atmo-682

spheric Research community climate model (CCM2), J. Geophys. Res., 99 (D3), 5551–683

5568, doi:10.1029/93JD03478.684

Hansen, J., R. Ruedy, M. Sato, and K. Lo (2010), Global surface temperature change,685

Rev. Geophys., 48 (RG4004), doi:10.1029/2010RG000345.686

Heald, C. L., D. K. Henze, L. W. Horowitz, J. Feddema, J.-F. Lamarque, A. Guenther,687

P. G. Hess, F. Vitt, J. H. Seinfeld, A. H. Goldstein, and I. Fung (2008), Predicted change688

in global secondary organic aerosol concentrations in response to future climate, emis-689

sions, and land use change, Journal of Geophysical Research: Atmospheres, 113 (D5),690

doi:10.1029/2007JD009092.691

Hodzic, A., and S. Madronich (2018), Response of surface ozone over the continental692

United States to UV radiation declines from the expected recovery of stratospheric693

ozone, npj Climate and Atmospheric Science, 1 (1), 35, doi:10.1038/s41612-018-0045-5.694



Hodzic, A., S. Madronich, P. S. Kasibhatla, G. Tyndall, B. Aumont, J. L. Jimenez, J. Lee-695

Taylor, and J. Orlando (2015), Organic photolysis reactions in tropospheric aerosols:696

Effect on secondary organic aerosol formation and lifetime, Atmospheric Chemistry and697

Physics, 15 (16), 9253–9269, doi:https://doi.org/10.5194/acp-15-9253-2015.698

Hodzic, A., P. S. Kasibhatla, D. S. Jo, C. D. Cappa, J. L. Jimenez, S. Madronich,699

and R. J. Park (2016), Rethinking the global secondary organic aerosol (SOA) bud-700

get: Stronger production, faster removal, shorter lifetime, Atmospheric Chemistry and701

Physics, 16 (12), 7917–7941, doi:https://doi.org/10.5194/acp-16-7917-2016.702

Hoesly, R. M., S. J. Smith, L. Feng, Z. Klimont, G. Janssens-Maenhout, T. Pitkanen, J. J.703

Seibert, L. Vu, R. J. Andres, R. M. Bolt, T. C. Bond, L. Dawidowski, N. Kholod, J.-i.704

Kurokawa, M. Li, L. Liu, Z. Lu, M. C. P. Moura, P. R. O’Rourke, and Q. Zhang (2018),705

Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the706

Community Emissions Data System (CEDS), Geoscientific Model Development, 11 (1),707

369–408, doi:https://doi.org/10.5194/gmd-11-369-2018.708

Hofmann, D., J. Barnes, M. O’Neill, M. Trudeau, and R. Neely (2009), Increase in back-709

ground stratospheric aerosol observed with lidar at Mauna Loa Observatory and Boul-710

der, Colorado, Geophysical Research Letters, 36 (15), doi:10.1029/2009GL039008.711

Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D.712

Collins (2008), Radiative forcing by long-lived greenhouse gases: Calculations with the713

AER radiative transfer models, jgr, 113 (D13103), doi:10.1029/2008JD009944.714

Jackman, C. H., D. R. Marsh, F. M. Vitt, R. R. Garcia, C. E. Randall, E. L. Flem-715

ing, and S. M. Frith (2009), Long-term middle atmospheric influence of very large716

solar proton events, Journal of Geophysical Research: Atmospheres, 114 (D11), doi:717



10.1029/2008JD011415.718

Kinnison, D. E., et al. (2007), Sensitivity of Chemical Tracers to Meteorological Pa-719

rameters in the MOZART-3 Chemical Transport Model, jgr, 112 (D20302), doi:720

10.1029/2006JD007879.721

Knote, C., A. Hodzic, J. L. Jimenez, R. Volkamer, J. J. Orlando, S. Baidar, J. Brioude,722

J. Fast, D. R. Gentner, A. H. Goldstein, P. L. Hayes, W. B. Knighton, H. Oetjen,723

A. Setyan, H. Stark, R. Thalman, G. Tyndall, R. Washenfelder, E. Waxman, and724

Q. Zhang (2014), Simulation of semi-explicit mechanisms of SOA formation from glyoxal725

in aerosol in a 3-D model, Atmospheric Chemistry and Physics, 14 (12), 6213–6239, doi:726

https://doi.org/10.5194/acp-14-6213-2014.727

Lamarque, J.-F., L. K. Emmons, P. G. Hess, D. E. Kinnison, S. Tilmes, F. Vitt, C. L.728

Heald, E. A. Holland, P. H. Lauritzen, J. Neu, J. J. Orlando, P. J. Rasch, and G. K. Tyn-729

dall (2012), CAM-chem: Description and evaluation of interactive atmospheric chem-730

istry in the Community Earth System Model, Geosci. Model Dev., 5 (2), 369–411, doi:731

10.5194/gmd-5-369-2012.732

Larson, V. E., J.-C. Golaz, and W. R. Cotton (2002), Small-Scale and Mesoscale Variabil-733

ity in Cloudy Boundary Layers: Joint Probability Density Functions, J. Atmos. Sci.,734

59 (24), 3519–3539, doi:10.1175/1520-0469(2002)059¡3519:SSAMVI¿2.0.CO;2.735

Lefohn, A. S., C. S. Malley, L. Smith, B. Wells, M. Hazucha, H. Simon, V. Naik, G. Mills,736

M. G. Schultz, E. Paoletti, A. D. Marco, X. Xu, L. Zhang, T. Wang, H. S. Neufeld,737

R. C. Musselman, D. Tarasick, M. Brauer, Z. Feng, H. Tang, K. Kobayashi, P. Sicard,738

S. Solberg, and G. Gerosa (2018), Tropospheric ozone assessment report: Global ozone739

metrics for climate change, human health, and crop/ecosystem research, Elem Sci Anth,740



6 (1), 28, doi:10.1525/elementa.279.741

Lin, S. J., and R. B. Rood (1997), An explicit Flux-Form Semi-Lagrangian shallow water742

model on the sphere, qjrms, 123, 2477–2498.743

Liu, H.-L., C. G. Bardeen, B. T. Foster, P. Lauritzen, J. Liu, G. Lu, D. R. Marsh,744

A. Maute, J. M. McInerney, N. M. Pedatella, L. Qian, A. D. Richmond, R. G. Roble,745

S. C. Solomon, F. M. Vitt, and W. Wang (2018), Development and Validation of the746

Whole Atmosphere Community Climate Model With Thermosphere and Ionosphere747

Extension (WACCM-X 2.0), Journal of Advances in Modeling Earth Systems, 10 (2),748

381–402, doi:10.1002/2017MS001232.749

Liu, X., R. C. Easter, S. J. Ghan, R. Zaveri, P. Rasch, X. Shi, J.-F. Lamarque, A. Gettel-750

man, H. Morrison, F. Vitt, A. Conley, S. Park, R. Neale, C. Hannay, A. M. L. Ekman,751

P. Hess, N. Mahowald, W. Collins, M. J. Iacono, C. S. Bretherton, M. G. Flanner, and752

D. Mitchell (2012), Toward a minimal representation of aerosols in climate models: De-753

scription and evaluation in the Community Atmosphere Model CAM5, Geosci. Model754

Dev., 5 (3), 709–739, doi:10.5194/gmd-5-709-2012.755

Liu, X., P.-L. Ma, H. Wang, S. Tilmes, B. Singh, R. C. Easter, S. J. Ghan, and P. J. Rasch756

(2016), Description and evaluation of a new four-mode version of the Modal Aerosol757

Module (MAM4) within version 5.3 of the Community Atmosphere Model, Geoscientific758

Model Development, 9 (2), 505–522, doi:https://doi.org/10.5194/gmd-9-505-2016.759

Loeb, N. G., B. A. Wielicki, D. R. Doelling, G. L. Smith, D. F. Keyes, S. Kato, N. M.760

Smith, and T. Wong (2009), Towards Optimal Closure of the Earth’s Top-of-Atmosphere761

Radiation Budget, joc, 22 (10.1175/2008JCLI2637.1), 748–766.762



Marsh, D. R., R. R. Garcia, D. E. Kinnison, B. A. Boville, F. Sassi, S. C. Solomon, and763

K. Matthes (2007), Modeling the whole atmosphere response to solar cycle changes764

in radiative and geomagnetic forcing, Journal of Geophysical Research: Atmospheres,765

112 (D23), doi:10.1029/2006JD008306.766

Marsh, D. R., M. J. Mills, D. E. Kinnison, J.-F. Lamarque, N. Calvo, and L. M. Polvani767

(2013), Climate Change from 1850 to 2005 Simulated in CESM1(WACCM), J. Climate,768

26 (19), 7372–7391, doi:10.1175/JCLI-D-12-00558.1.769

Matthes, K., B. Funke, M. E. Andersson, L. Barnard, J. Beer, P. Charbonneau, M. A.770

Clilverd, T. Dudok de Wit, M. Haberreiter, A. Hendry, C. H. Jackman, M. Kretzschmar,771

T. Kruschke, M. Kunze, U. Langematz, D. R. Marsh, A. C. Maycock, S. Misios, C. J.772

Rodger, A. A. Scaife, A. Seppala, M. Shangguan, M. Sinnhuber, K. Tourpali, I. Usoskin,773

M. van de Kamp, P. T. Verronen, and S. Versick (2017), Solar forcing for CMIP6 (v3.2),774

Geoscientific Model Development, 10 (6), 2247–2302, doi:https://doi.org/10.5194/gmd-775

10-2247-2017.776

Meinshausen, M., S. J. Smith, K. Calvin, J. S. Daniel, M. L. T. Kainuma, J.-F. Lamarque,777

K. Matsumoto, S. A. Montzka, S. C. B. Raper, K. Riahi, A. Thomson, G. J. M. Velders,778

and D. P. van Vuuren (2011), The RCP greenhouse gas concentrations and their exten-779

sions from 1765 to 2300, Climatic Change, 109 (1), 213, doi:10.1007/s10584-011-0156-z.780

Meinshausen, M., E. Vogel, A. Nauels, K. Lorbacher, N. Meinshausen, D. M. Etheridge,781

P. J. Fraser, S. A. Montzka, P. J. Rayner, C. M. Trudinger, P. B. Krummel,782

U. Beyerle, J. G. Canadell, J. S. Daniel, I. G. Enting, R. M. Law, C. R. Lunder,783

S. O’Doherty, R. G. Prinn, S. Reimann, M. Rubino, G. J. M. Velders, M. K. Vollmer,784

R. H. J. Wang, and R. Weiss (2017), Historical greenhouse gas concentrations for cli-785



mate modelling (CMIP6), Geoscientific Model Development, 10 (5), 2057–2116, doi:786

https://doi.org/10.5194/gmd-10-2057-2017.787

Mills, M. J., A. Schmidt, R. Easter, S. Solomon, D. E. Kinnison, S. J. Ghan, R. R.788

Neely, D. R. Marsh, A. Conley, C. G. Bardeen, and A. Gettelman (2016), Global vol-789

canic aerosol properties derived from emissions, 1990–2014, using CESM1(WACCM),790

J. Geophys. Res. Atmos., 121 (5), 2015JD024,290, doi:10.1002/2015JD024290.791

Mills, M. J., J. H. Richter, S. Tilmes, B. Kravitz, D. G. MacMartin, A. A. Glanville,792

J. J. Tribbia, J.-F. Lamarque, F. Vitt, A. Schmidt, A. Gettelman, C. Hannay, J. T.793

Bacmeister, and D. E. Kinnison (2017), Radiative and Chemical Response to Interactive794

Stratospheric Sulfate Aerosols in Fully Coupled CESM1(WACCM), J. Geophys. Res.795

Atmos., p. 2017JD027006, doi:10.1002/2017JD027006.796

Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough (1997),797

Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k798

model for the longwave, Journal of Geophysical Research: Atmospheres, 102 (D14),799

16,663–16,682, doi:10.1029/97JD00237.800

Molod, A., L. Takacs, M. Suarez, and J. Bacmeister (2015), Development of the GEOS-5801

atmospheric general circulation model: Evolution from MERRA to MERRA2, Geosci.802

Model Dev., 8 (5), 1339–1356, doi:10.5194/gmd-8-1339-2015.803

Montzka, S. A., M. Aydin, M. Battle, J. H. Butler, E. S. Saltzman, B. D. Hall, A. D.804

Clarke, D. Mondeel, and J. W. Elkins (2004), A 350-year atmospheric history for car-805

bonyl sulfide inferred from Antarctic firn air and air trapped in ice, Journal of Geo-806

physical Research: Atmospheres, 109 (D22), doi:10.1029/2004JD004686.807



Mote, P. W., et al. (1996), An atmospheric tape recorder: The imprint of tropical808

tropopause temperatures on stratospheric water vapor, J. Geophys. Res., 101 (D2),809

3989–4006.810

Neale, R. B., J. H. Richter, and M. Jochum (2008), The Impact of Convection on811

ENSO: From a Delayed Oscillator to a Series of Events, J. Climate, 21, 5904–+, doi:812

10.1175/2008JCLI2244.1.813

Neale, R. B., C. C. Chen, A. Gettelman, P. H. Lauritzen, S. Park, D. L. Williamson, A. J.814

Conley, R. Garcia, D. Kinnison, J. F. Lamarque, D. Marsh, M. Mills, A. K. Smith,815

S. Tilmes, F. Vitt, P. Cameron-Smith, W. D. Collins, M. J. Iacono, R. C. Easter,816

S. J. Ghan, X. Liu, P. J. Rasch, and M. A. Taylor (2010), Description of the NCAR817

Community Atmosphere Model (CAM5.0), Tech. Rep. NCAR/TN-486+STR, National818

Center for Atmospheric Research, Boulder, CO, USA.819

Neale, R. B., J. T. Bacmeister, C. Hannay, A. Gettelman, P. A. Bogenschutz, H. Morri-820

son, X. Liu, C. G. Bardeen, V. E. Larson, P. H. Lauritzen, M. Zhang, M. A. Taylor,821

C. Jablonowski, and P. M. Caldwell (2019), The NCAR/DOE Community Atmosphere822

Model, version 6 (CAM6): Scientific Configuration and Simulation Fidelity, to be Sub-823

mitted to J. Adv. Modeling Earth Systems.824

Neely, R. R., and A. Schmidt (2016), VolcanEESM: Global volcanic sulphur dioxide (SO2)825

emissions database from 1850 to present - Version 1.0, doi:10.5285/76ebdc0b-0eed-4f70-826

b89e-55e606bcd568.827

Orsolini, Y. J., C. Smith-Johnsen, D. R. Marsh, F. Stordal, C. J. Rodger, P. T. Verronen,828

and M. A. Clilverd (2018), Mesospheric Nitric Acid Enhancements During Energetic829

Electron Precipitation Events Simulated by WACCM-D, Journal of Geophysical Re-830



search: Atmospheres, 123 (13), 6984–6998, doi:10.1029/2017JD028211.831

Park, S., and C. S. Bretherton (2009), The University of Washington shallow convec-832

tion and moist turbulence schemes and their impact on climate simulations with the833

Community Atmosphere Model., J. Climate, 22, 3449–3469.834

Park, S., et al. (2011), Revised Stratiform Macrophysics in the Community Atmosphere835

Model, J. Climate, in preparation.836

Randel, W. J., L. Polvani, F. Wu, D. E. Kinnison, C.-Z. Zou, and C. Mears (2017),837

Troposphere-Stratosphere Temperature Trends Derived From Satellite Data Compared838

With Ensemble Simulations From WACCM, Journal of Geophysical Research: Atmo-839

spheres, 122 (18), 9651–9667, doi:10.1002/2017JD027158.840

Read, W. G., et al. (2007), Aura Microwave Limb Sounder upper tropospheric and841

lower stratospheric H 2 O and relative humidity with respect to ice validation, jgr,842

112 (D24S35), doi:10.1029/2007JD008752.843

Richter, J. H., F. Sassi, and R. R. Garcia (2010), Toward a Physically Based Gravity844

Wave Source Parameterization in a General Circulation Model, J. Atmos. Sci., 67 (1),845

136–156, doi:10.1175/2009JAS3112.1.846

Ridley, D. A., S. Solomon, J. E. Barnes, V. D. Burlakov, T. Deshler, S. I. Dolgii, A. B.847

Herber, T. Nagai, R. R. Neely, A. V. Nevzorov, C. Ritter, T. Sakai, B. D. Santer,848

M. Sato, A. Schmidt, O. Uchino, and J. P. Vernier (2014), Total volcanic stratospheric849

aerosol optical depths and implications for global climate change, Geophysical Research850

Letters, 41 (22), 7763–7769, doi:10.1002/2014GL061541.851

Rienecker, M. M., et al. (2011), MERRA: NASA’s modern-era retrospective analysis for852

research and applications., joc, 24, 3624–3648, doi:10.1175/JCLI-D-11-00015.1.853



Roble, R. G., and E. C. Ridley (1994), A thermosphere-ionosphere-mesosphere-854

electrodynamics general circulation model (time-GCM): Equinox solar cycle mini-855

mum simulations (30–500 km), Geophysical Research Letters, 21 (6), 417–420, doi:856

10.1029/93GL03391.857

Sakai, T., O. Uchino, T. Nagai, B. Liley, I. Morino, and T. Fujimoto (2016), Long-858

term variation of stratospheric aerosols observed with lidars over Tsukuba, Japan, from859

1982 and Lauder, New Zealand, from 1992 to 2015, Journal of Geophysical Research:860

Atmospheres, 121 (17), 10,283–10,293, doi:10.1002/2016JD025132.861

Schweiger, A., R. Lindsay, J. Zhang, M. Steele, H. Stern, and R. Kwok (2011), Uncer-862

tainty in modeled Arctic sea ice volume, Journal of Geophysical Research: Oceans,863

doi:10.1029/2011JC007084.864

Scinocca, J. F., and N. A. McFarlane (2000), The parametrization of drag induced by865

stratified flow over anisotropic orography, Quarterly Journal of the Royal Meteorological866

Society, 126 (568), 2353–2393, doi:10.1002/qj.49712656802.867

Smith, A. K., R. R. Garcia, A. C. Moss, and N. J. Mitchell (2017), The Semiannual868

Oscillation of the Tropical Zonal Wind in the Middle Atmosphere Derived from Satellite869

Geopotential Height Retrievals, J. Atmos. Sci., 74 (8), 2413–2425, doi:10.1175/JAS-D-870

17-0067.1.871

Smith, K. L., R. R. Neely, D. R. Marsh, and L. M. Polvani (2014), The Specified Chemistry872

Whole Atmosphere Community Climate Model (SC-WACCM), Journal of Advances in873

Modeling Earth Systems, 6 (3), 883–901, doi:10.1002/2014MS000346.874

Solomon, S., K. H. Rosenlof, R. W. Portmann, J. S. Daniel, S. M. Davis, T. J.875

Sanford, and G.-K. Plattner (2010), Contributions of Stratospheric Water Vapor to876



Decadal Changes in the Rate of Global Warming, Science, 327 (5970), 1219–1223, doi:877

10.1126/science.1182488.878

Solomon, S., D. Kinnison, J. Bandoro, and R. Garcia (2015), Simulation of polar ozone879

depletion: An update, Journal of Geophysical Research: Atmospheres, 120 (15), 7958–880

7974, doi:10.1002/2015JD023365.881

Solomon, S. C., and L. Qian (2005), Solar extreme-ultraviolet irradiance for general882

circulation models, Journal of Geophysical Research: Space Physics, 110 (A10), doi:883

10.1029/2005JA011160.884

Son, S. W., et al. (2010), Impact of stratospheric ozone on Southern Hemisphere circula-885

tion change: A multimodel assessment, jgr, 115 (D00M07), doi:10.1029/2010JD014271.886

Tabazadeh, A., R. P. Turco, and M. Z. Jacobson (1994), A model for studying the com-887

position and chemical effects of stratospheric aerosols, Journal of Geophysical Research:888

Atmospheres, 99 (D6), 12,897–12,914, doi:10.1029/94JD00820.889

Thompson, D. W. J., M. P. Baldwin, and J. M. Wallace (2002), Stratospheric Connection890

to Northern Hemisphere Wintertime Weather: Implications for Prediction, J. Climate,891

15 (12), 1421–1428, doi:10.1175/1520-0442(2002)015¡1421:SCTNHW¿2.0.CO;2.892

Tilmes, S., J.-F. Lamarque, L. K. Emmons, D. E. Kinnison, D. Marsh, R. R. Garcia,893

A. K. Smith, R. R. Neely, A. Conley, F. Vitt, M. Val Martin, H. Tanimoto, I. Simpson,894

D. R. Blake, and N. Blake (2016), Representation of the Community Earth System895

Model (CESM1) CAM4-chem within the Chemistry-Climate Model Initiative (CCMI),896

Geoscientific Model Development, 9 (5), 1853–1890, doi:https://doi.org/10.5194/gmd-897

9-1853-2016.898



Tilmes, S., A. Hozdic, L. K. Emmons, M. J. Mills, A. Gettelman, D. E. Kinnison, M. Park,899

J.-F. Lamarque, F. Vitt, M. Shrivastava, P. Campuzano, J. Jimenez, and X. Liu (2019),900

Climate forcing and trends of organic aerosols in the Community Earth System Model901

(CESM2), to be Submitted to J. Adv. Modeling Earth Systems.902

vanMarle, M. J. E., S. Kloster, B. I. Magi, J. R. Marlon, A.-L. Daniau, R. D. Field, A. Ar-903

neth, M. Forrest, S. Hantson, N. M. Kehrwald, W. Knorr, G. Lasslop, F. Li, S. Mangeon,904

C. Yue, J. W. Kaiser, and G. R. van der Werf (2017), Historic global biomass burning905

emissions for CMIP6 (BB4CMIP) based on merging satellite observations with prox-906

ies and fire models (1750–2015), Geoscientific Model Development, 10 (9), 3329–3357,907

doi:https://doi.org/10.5194/gmd-10-3329-2017.908

Verronen, P. T., M. E. Andersson, D. R. Marsh, T. Kovacs, and J. M. C. Plane909

(2016), WACCM-D—Whole Atmosphere Community Climate Model with D-region910

ion chemistry, Journal of Advances in Modeling Earth Systems, 8 (2), 954–975, doi:911

10.1002/2015MS000592.912

Waters, J. W., et al. (2006), The Earth Observing System Microwave Limb Sounder913

(EOS MLS) on the Aura Satellite, IEEE Trans. on Geosci. and Remote Sensing, 44,914

1075–1092, doi:10.1109/TGRS.2006.873771.915

World Meteorological Organization (2010), Scientific Assessment of Ozone Depletion:916

2010, WMO Report, World Meteorological Organization, Geneva.917

Yeo, K. L., W. T. Ball, N. A. Krivova, S. K. Solanki, Y. C. Unruh, and J. Morrill (2015),918

UV solar irradiance in observations and the NRLSSI and SATIRE-S models, Journal of919

Geophysical Research: Space Physics, 120 (8), 6055–6070, doi:10.1002/2015JA021277.920



Zhang, G. J., and N. A. McFarlane (1995), Sensitivity of climate simulations to the param-921

eterization of cumulus convection in the Canadian Climate Center general circulation922

model, Atmos. Ocean, 33, 407–446.923

Zuev, V. V., V. D. Burlakov, A. V. Nevzorov, V. L. Pravdin, E. S. Savelieva, and V. V.924

Gerasimov (2017), 30-year lidar observations of the stratospheric aerosol layer state925

over Tomsk (Western Siberia, Russia), Atmospheric Chemistry and Physics, 17 (4),926

3067–3081, doi:https://doi.org/10.5194/acp-17-3067-2017.927



Table 1. Parameterizations in different versions of WACCM. References are as follows:

HB: Boville et al. [2006], Hack: Hack [1994], MG2: Gettelman and Morrison [2015],

CLUBB: Bogenschutz et al. [2013]; Larson et al. [2002], ZM: Zhang and McFarlane [1995],

ZM*: Zhang and McFarlane [1995]; Neale et al. [2008], MAM3: Liu et al. [2012]; Mills

et al. [2016], MAM4: Liu et al. [2016]; Mills et al. [2016], RRTMG: Iacono et al. [2008];

Mlawer et al. [1997], CAMRT: Collins et al. [2002], UW: Park and Bretherton [2009],

Park: Park et al. [2011], WMO 2010 : World Meteorological Organization [2010], JPL-

06, JPL-11, JPL-15: Burkholder et al. [2015], CMIP5 RCPs: Meinshausen et al. [2011],

CMIP6: SSPs Meinshausen et al. [2017], 2-product: Heald et al. [2008], SOAG: Liu et al.

[2012], VBS: Tilmes et al. [2019].

Common Name WACCM4 WACCM-CCMI WACCM5 WACCM6Horizontal Resolution 1.9◦x2.5◦ 1.9◦x2.5◦ 0.95◦x1.25◦ 0.95◦x1.25◦

Vertical Levels 66 66 70 70Deep Convection ZM ZM ZM* ZM*Boundary Layer HB HB UW CLUBBShallow Convection Hack Hack UW CLUBBMacrophysics RK RK Park CLUBBMicrophysics RK RK MG2 MG2Radiation CAMRT CAMRT RRTMG RRTMGAerosols Bulk Bulk MAM3 MAM4QBO Nudged Nudged Interactive InteractiveChemical Mechanism MA(59) TSMLT (180) MA(59) TSMLT1 (228)Chemical Rates JPL-06 JPL-11 JPL-06 JPL-15SOA 2-product 2-product SOAG VBSSulfate SAD CCMVal2 CCMI Interactive InteractiveIce SAD Bulk Bulk Bulk MG2Solar Variability CMIP5-Solar CCMVal2-Solar CMIP5-Solar CMIP6-SolarGHG Abundances CMIP5 RCPs CMIP5 RCPs CMIP5 RCPs CMIP6 SSPsHalogens CMIP5 RCPs WMO 2010 CMIP5 RCPs CMIP6 SSPs



Table 2. WACCM/CAM Configurations used in this paper, including cost (CPU-hours)

of the model.

Component Set FWHIST FWSD BW1850 BWHIST FWscHIST

CAM Component Set FHIST FWSD B1850 BHIST FHIST

WACCM Ensembles 3 1 1 3 1

# Years or Dates 1950-2014 2005-2017 500 1850-2014 1979-2014

Coupled Ocean/Ice No No Yes Yes No

Specified Dynamics No Yes No No No

Chemistry TSMLT1 TSMLT1 TSMLT1 TSMLT1 None



-90 -60 -30 0 30 60 900

20

40

60

80

100

120

-90 -60 -30 0 30 60 90Latitude (deg)

0

20

40

60

80

100

120

Hei

ght (

km)

U DJF WACCM6

-40-30

-20

-20

-10

0

0

0

0

00 0

10

10

10

10

20

20

20

20 20

30

30

40

40

50

50

-90 -60 -30 0 30 60 900

20

40

60

80

100

120

102

100

10-2

10-4

Pre

ssur

e (h

Pa)

a)

-90 -60 -30 0 30 60 90Latitude (deg)

0

20

40

60

80

100

120

Hei

ght (

km)

U JJA WACCM6

-60-50-40-30

-30

-20

-20

-10

-10

-10

0 0

0

00

0

10 10

10

10

20

20

20

30

30

40

40

50

60

7080

90

-90 -60 -30 0 30 60 900

20

40

60

80

100

120

102

100

10-2

10-4

Pre

ssur

e (h

Pa)

b)

-90 -60 -30 0 30 60 90Latitude (deg)

0

20

40

60

80

100

120

Hei

ght (

km)

U DJF WACCM6-ERAI

-5.0

-2.5

-2.5

2.5 2.5

2.5

5.0

5.0

5.0

-90 -60 -30 0 30 60 900

20

40

60

80

100

120

102

100

10-2

10-4P

ress

ure

(hP

a)c)

-90 -60 -30 0 30 60 90Latitude (deg)

0

20

40

60

80

100

120H

eigh

t (km

)

U JJA WACCM6-ERAI

-40.0-20.0 -10.0-5.0

-5.0

-5.0

-2.5

-2.5

-2.5

2.5

2.5

5.0

-90 -60 -30 0 30 60 900

20

40

60

80

100

120

102

100

10-2

10-4

Pre

ssur

e (h

Pa)

d)

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

Figure 1. WACCM6 (FHIST) simulation zonal wind for (A) DJF and (B) JJA. Contour

interval of 10 m s−1. Differences between WACCM and ERA-Interim Reanalysis for (C)

DJF and (D) JJA. Contour interval of 2.5 m s−1.



-90 -60 -30 0 30 60 900

20

40

60

80

100

120

-90 -60 -30 0 30 60 90Latitude (deg)

0

20

40

60

80

100

120

Hei

ght (

km)

T DJF WACCM6

160170180

190190200

200

200

210

210

210

210

220

220

220220

230

230

230

230

240

240

240

240

250

250

250

250

260

260

260

270

270

270

280

280

290

290

300310320330

-90 -60 -30 0 30 60 900

20

40

60

80

100

120

102

100

10-2

10-4

Pre

ssur

e (h

Pa)

a)

-90 -60 -30 0 30 60 90Latitude (deg)

0

20

40

60

80

100

120

Hei

ght (

km)

T JJA WACCM6

160170180

190

190

200

200

200

200

210

210

210

210

220

220

220

230

230

230

230

240

240

240

240

250

250

250

250

260

260260

260

260

270

270

280

280

290

290

300 310 320330

-90 -60 -30 0 30 60 900

20

40

60

80

100

120

102

100

10-2

10-4

Pre

ssur

e (h

Pa)

b)

-90 -60 -30 0 30 60 90Latitude (deg)

0

20

40

60

80

100

120

Hei

ght (

km)

T DJF WACCM6-ERAI

-4

-4

-2

-2

-2

-2 -2

-2

2

2

-90 -60 -30 0 30 60 900

20

40

60

80

100

120

102

100

10-2

10-4

Pre

ssur

e (h

Pa)

c)

-90 -60 -30 0 30 60 90Latitude (deg)

0

20

40

60

80

100

120H

eigh

t (km

)T JJA WACCM6-ERAI

-8-4 -4-2

-2

-2 -2

-2

-2-2

-2

-2

2

2

2

4

48

-90 -60 -30 0 30 60 900

20

40

60

80

100

120

102

100

10-2

10-4

Pre

ssur

e (h

Pa)

d)

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

Figure 2. WACCM6 (FHIST) simulation temperatures for (A) DJF and (B) JJA.

Contour interval of 10K. Differences between WACCM and ERA-Interim Reanalysis for

(C) DJF and (D) JJA. Contour interval of 2K.



B) FWHIST - MERRA2 T Diff 1980-2010 (90˚S - 59˚S)

A) FWHIST Temp Climatology 1980-2010 (90˚S - 59˚S)

Figure 3. WACCM6 (FWHIST) simulation (A) Climatological (1980–2010) S. Hemi-

sphere Polar Cap (90-59◦S) temperature evolution. Contour interval of 10K. (B) Difference

v. MERRA2 reanalysis. Shaded differences are not significant at the 95% level based on

a t-test. Contour interval of 2K.



B) MLS

3

3.5

4

4

4

4

4

4.5

4.5

4.5

J F M A M J J A S O N DMonth

20

30

40

50

60708090

100

A) Tape Recorder Velocity

MLSSDFR AMIPFR COUPLED







0.2 0.3 0.4 0.5 0.6mm/s

20

30

40

50

60708090

100

hP

a

2.5

3

3.5

4

4.5

5

5.5

6

C) SD

3

3.5

4

4

4

4

4.5

4.5

4.5

J F M A M J J A S O N D

20

30

40

5060708090

100

hP

a

D) FR AMIP

3

3.5

4

44

4

4.5

4.5

4.5


E) FR COUPLED

3

3.5

4

4

4

4

4.5

4.5

4.5


Wat

er V

apo

r (p

pm

v)

Figure 4. A) Tape recorder velocity (mm/s) averaged over 2005-2014 from MLS Satellite

observations (black), Specified dynamics WACCM (SD-Red), Free running specified SST

WACCM6 (FR AMIP-blue) and fully coupled WACCM6 historical run (FR COUPLED-

Green). WACCM6 solid lines, WACCM4 dashed lines. B) Composite MLS water vapor

annual cycle showing the ’tape recorder’ of low and high water vapor being advected in the

vertical circulation of the stratosphere. C-E) composite water vapor annual cycles (contour

shading) with MLS annual cycle (white contours). C) Specified dynamics WACCM6 (SD),

D) Free running specified SST WACCM6 (FR AMIP) and E) Fully coupled WACCM6

historical run (FR COUPLED).



A) SSW Frequency: Coupled v. NCEP (1975-2014) B) SSW Frequency: AMIP v. NCEP (1975-2014)

Figure 5. N. Hemisphere Winter SSW frequency by month for A) Coupled (BWHIST)

and B) Historical specified SST or AMIP (FWHIST) WACCM6 simulations (gray shad-

ing=ensembles, black=mean), and NCEP reanalyses (red).



0 20 40 60 80 100 120 140 160 180 200 220 240100

10

1

0 20 40 60 80 100 120 140 160 180 200 220 240Time (months)

100

10

1Pr

essu

re (

hPa)

ERAI

0 20 40 60 80 100 120 140 160 180 200 220 240100

10

1

20

25

30

35

40

45

Hei

ght (

km)

a)

0 20 40 60 80 100 120 140 160 180 200 220 240Time (months)

100

10

1

Pres

sure

(hP

a)

WACCM6

0 20 40 60 80 100 120 140 160 180 200 220 240100

10

1

20

25

30

35

40

45

Hei

ght (

km)

b)

-40 -30 -20 -10 0 10 20 30 40

Zonal Wind (m s-1)

0 20 40 60 80 100 120 140 160 180 200 220 240Time (months)

100

10

1

Pres

sure

(hP

a)

CESM2(WACCM6)

0 20 40 60 80 100 120 140 160 180 200 220 240100

10

1

20

25

30

35

40

45H

eigh

t (km

)

c)

Figure 6. Monthly average zonal wind from 10◦S–10◦N in (A) European Center

Reanalysis (ERAI), (B) WACCM6 Historical simulation with historical SSTs and (C)

Fully Coupled WACCM6 historical simulation illustrating the Quasi-Biennial Oscillation.



A)

B)

C)

Figure 7. Monthly average zonal wind from 10◦S–10◦N in A) SABER Satellite obser-

vations, B) Fully Coupled WACCM6 historical simulation (BWHIST) and C) WACCM6

specified SST simulation (FWHIST) illustrating the Semi-Annual Oscillation.



Figure 8. 5-day averaged SAOD (above model tropopause) from coupled (blue) and

AMIP (red) ensembles compared to lidar observations (green, black, yellow). (a) Ny

Alesund (78.9◦N, 11.9◦W) above 10 km (black, Ridley et al. [2014]) (b) Tomsk (56.5◦N,

85.0◦E) 15-30 km 10-day averages from Jan 1986 to Dec 2014 (black, Zuev et al. [2017]),

and 12-30 km 10-day averages from Jan 2006 to Dec 2013 (green) (c) Geesthacht (53.4◦N,

10.4◦E) above the tropopause (black with 1-σ error bars, Ansmann et al. [1997]) (d)

Tsukuba (36.1◦N, 140.1◦E) 15-30 km monthly averages from Apr 1982 to Dec 2014 (yellow

circles), above the tropopause monthly averages from Nov 1988 to Dec 2014 (black, Sakai

et al. [2016]), and above the tropopause daily from Jan 2008 to Jul 2013 (green) (e) Mauna

Loa (19.5◦N, 155.6◦W) above the tropopause (black), Hofmann et al. [2009]] (f) Lauder

(45.0◦S, 169.7◦E) monthly averages from Nov 1992 to Dec 2014, 16.5-33 km (yellow) and

above the tropopause (black) Sakai et al. [2016].D R A F T May 2, 2019, 10:00pm D R A F T


1950 1960 1970 1980 1990 2000 2010 2020150

200

250

300

350

400October [90S-60S]

1950 1960 1970 1980 1990 2000 2010 2020

150

200

250

300

350

400TC

O (D

U)

SBUV-MODHistoricalAMIPSD-MERRA2

March [60N-90N]

1950 1960 1970 1980 1990 2000 2010 2020

340

380

420

460

500

SH Mid-Lat [60S-35S]

1950 1960 1970 1980 1990 2000 2010 2020

280

300

320

340

360

TCO

(DU

)

NH Mid-Lat [35N-60N]

1950 1960 1970 1980 1990 2000 2010 2020

300

310

320

330

340

350

360

Tropics [20S-20N]

1950 1960 1970 1980 1990 2000 2010 2020Year

220

230

240

250

260

270

280

TCO

(DU

)

Near Global [60S-60N]

1950 1960 1970 1980 1990 2000 2010 2020Year

250

260

270

280

290

300

310

A) B)

C) D)

E) F)

Figure 9. Total column ozone (in Dobson Units: DU) from WACCM6 coupled simula-

tions (blue), specified SST (FWHIST) simulations (green), specified dynamics simulation

(purple), and observations (black). Individual points are symbols, mean across ensem-

bles solid line. A) October 90-60◦S, B) March 60-90◦N, C) Annual 65-35◦S, D) Annual

35-60◦N, E) Annual 20◦S-20◦N and F) Annual 60◦S-60◦N.D R A F T May 2, 2019, 10:00pm D R A F T


Figure 10. Tropical (20S-20N) averaged upwelling (W*: TEM residual vertical ve-

locity in mm/s). WACCM6 coupled simulations (FRb, blue), specified SST simulations

(FRf, green), specified dynamics simulation with MERRA2 winds and temperatures (SD-

MERRA2, purple).



Figure 11. Global mean surface temperature anomalies with respect to the 1920-1980

average with 5 year smoothing. Blue: mean of 10-member CESM2 historical ensemble

(blue shading shows the ensemble spread). Three CESM2-WACCM historical ensemble

members are red (001), orange (002), and green (003) curves. Observed temperatures

from Hadley Centre-Climatic Research Unit Version 4 (HadCRUT4) infilled with kriging

[Cowtan and Way , 2014] (black solid). Goddard Institute for Space Studies (GISS) Surface

Temperature Analysis [Hansen et al., 2010] (black dashed). Top curves are a stratospheric

aerosol optical depth index (1.15 - 2 x global average) for each CESM2-WACCM ensemble

member.



Height(km)

Weighting

A)SSU/MSUWeightingFunctions B)GlobalAverageTemperatureAnomalies

Temp(K)

Year

Figure 12. A) MSU and SSU weighting functions from Randel et al. [2017] B) Global

average temperature timeseries from three WACCM6 specified SST (FWHIST) ensembles

convolved with SSU or MSU weigthing functions (red). SSU or MSU observations (black).



Table 3. WACCM and CAM Simulation global averages compared to observations from

historical (FHIST) simulations. Observations: CLDTOT (total cloud cover), CLDHGH

(cloud cover for p<400 hPa), CLDLOW (cloud cover for p>700 hPa) from CLOUD-

SAT+CALIPSO joint data product. Fluxes are compared to CERES EBAF 2.4 [Loeb

et al., 2009] for Shortwave/Longwave Net at Top of atmosphere for all sky (FSNT, FLNT),

clear sky (FSNTC, FNTC). Cloud Radiative Effects are the difference (LWCRE=FLNT-

FLNTC and SWCRE=FSNT-FSNTC). Liquid and Ice Water Path (LWP, IWP) are com-

pared to Microwave (liquid) and CloudSat (ice) observations. For Pre-Industrial (PI) 1850

control simulations, variables are surface temperature (Ts), preciptiation rate (Precip),

Sea Ice Extent (SIE) and Sea Ice Volume (SIV).

Historical SimulationsVariable Units WACCM6 WACCM4-CCMI CAM6 ObsFLNT Wm−2 237.4 233.3 236.6 239.7FSNT Wm−2 241.0 235.0 239.7 240.5FLNTC Wm−2 262.1 264.0 260.3 265.7FSNTC Wm−2 289.3 291.5 287.4 287.6SWCRE Wm−2 -48.4 -56.5 -47.7 -47.1LWCRe Wm−2 24.6 30.8 23.7 26.1CLDTOT % 69.5 55.4 69.1 66.8CLDHGH % 44.1 33.0 43.6 40.3CLDLOW % 40.9 35.4 40.8 43.1LWP gm−2 66.5 132.3 66.7IWP gm−2 13.4 16.3 12.8

1850 (PI) SimulationsVariable Units WACCM6 WACCM4 CAM6Ts K 288.0 287.6 288.2Precip mm d−1 2.93 2.92 2.83Global SIE 106 km2 25.9 30.4 25.4NH SIE 106 km2 11.5 14.0 11.2Global SIV 103 km3 41.2 36.5NH SIV 103 km3 26.2 21.8



A) Arctic Ice Extent B) Arctic Annual Mean Ice Volume

March

September

Figure 13. A) Arctic Sea Ice Extent in March (dashed) and September (solid). B)

Arctic annual mean Sea Ice Volume. WACCM6 (black), CAM6 (grey). National Snow and

Ice Data Center (NSIDC) ice extent satellite observations (red) [Fetterer et al., 2017] for

A) and Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) ice volume

estimate (red) [Schweiger et al., 2011] for B).

A) B) C)

Figure 14. Interannual standard deviation of December-February averaged Sea Level

Pressue (hPa) from A) ERA 20th Century Reanalysis B) WACCM6 coupled historical

simulation ensemble member and C) CAM6 coupled historical simulation ensemble mem-

ber.



A) B)

Figure 15. Northern Hemisphere Blocking Frequency defined following D’Andrea

et al. [1998]. Observations (black), CESM1 (blue), CESM2-CAM6 (Red) and CESM2-

WACCM6 (green) for (A)December–February (DJF), (B) March–May (MAM). Averages

are 1979-2005 from daily data and the shading is the full range of each ensemble set.


Documents

The Whole Atmosphere Community Climate Model · X - 2 GETTELMAN ET AL.: WACCM6 3 Abstract. The Whole Atmosphere Community Climate Model version 4 6 (WACCM6) is a major update of the