THE COMMUNITY EARTHSYSTEM MODEL (CESM) LARGEENSEMBLE PROJECTA Community Resource for Studying Climate Change
in the Presence of Internal Climate Variability
by J. E. K ay, C. Deser, A. Phillips, A. Mai, C. Hannay, G. Strand, J. M. Arblaster, S. C. Bates,
G. Danabasoglu, J. Edwards, M. Holland, P. Kushner, J.-F. L amarque, D. L awrence, K. Lindsay,
A. Middleton, E. Munoz, R. Neale, K. Oleson, L. Polvani, and M. Vertenstein

By simulating climate trajectories over the period 1920–2100 multiple times with small atmospheric initialization differences, but using the same model and external forcing, this community project provides a comprehensive resource for studying climate change in the presenceof internal climate variability.

I

nternal climate variability, by which we mean unforced climate variability intrinsic to a given climate
state, arises from atmospheric, oceanic, land, andcryospheric processes and their coupled interactions.
Internal climate variability is known to have important effects on climate change projections, especiallyat regional spatial scales and subdecadal time scales
AFFILIATIONS: Kay—Department of Atmospheric and Oceanic

Sciences, University of Colorado Boulder, Boulder, Colorado; Deser,
Phillips, Mai, Hannay, Strand, Bates, Danabasoglu, Edwards, Holland,
Lamarque, Lawrence, Lindsay, Middleton, Munoz, Neale, Oleson, andVertenstein—Climate and Global Dynamics Division, National Center
for Atmospheric Research, Boulder, Colorado; Arblaster—Climate
and Global Dynamics Division, National Center for AtmosphericResearch, Boulder, Colorado, and Bureau of Meteorology, Melbourne,
Victoria, Australia; Kushner—Department of Physics, University ofToronto, Toronto, Ontario, Canada; Polvani—Department of Applied
Physics and Applied Math, and Department of Earth and Environmental Sciences, Columbia University, New York, New YorkAMERICAN METEOROLOGICAL SOCIETY(e.g., Hawkins and Sutton 2009; Deser et al. 2014).
Nevertheless, internal climate variability is often
underappreciated and confused with model error[e.g., as discussed in Tebaldi et al. (2011)]. Why? In
general, modeling centers contribute a small numberof realizations to international climate change projection assessments [e.g., phase 5 of the Coupled Model
CORRESPONDING AUTHOR: Jennifer E. Kay, Cooperative
Institute for Research in Environmental Sciences, University ofColorado Boulder, 216 UCB, Boulder, CO 80309
E-mail: jennifer.e.kay@colorado.edu
The abstract for this article can be found in this issue, following the tableof contents.
DOI:10.1175/BAMS-D-13-00255.1
In final form 25 September 2014©2015 American Meteorological Society

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Intercomparison Project (CMIP5; Taylor et al. 2012)].
As a result, model error and internal climate variability
are difficult, and at times impossible, to disentangle.
In response, we designed the Community Earth System
Model Large Ensemble (CESM-LE) with the explicit
goal of enabling assessment of recent past and nearfuture climate change (1920–2100) in the presence ofinternal climate variability (Table 1). Two companion,
1000+-yr-long, preindustrial, control simulations(fully coupled, prognostic atmosphere and land only)
enable assessment of internal climate variability inthe absence of climate change.
Unlike perturbed physics ensembles (e.g., Murphy
et al. 2004) or multimodel ensembles of opportunity(e.g., CMIP5), all 30 CESM-LE members use the samemodel and the same external forcing. Each CESM-LE
ensemble member has a unique climate trajectory
because of small round-off level differences in theiratmospheric initial conditions. Simply put, the CESMLE ensemble spread results from internally generated
climate variability alone.
The influence of small, initial condition differences on climate projections in the CESM-LE parallels
initial condition impacts on weather forecasts (Lorenz
1963). After initial condition memory is lost, which
occurs within weeks in the atmosphere, each ensemble
member evolves chaotically, affected by atmosphericcirculation fluctuations characteristic of a random,
stochastic process (e.g., Lorenz 1963; Deser et al.
2012b). As we will show, internal climate variability
has a substantial influence on climate trajectories, aninfluence that merits further investigation, comparison with available observations, and communication.
Evaluating the realism of internal climate variability
simulated by the CESM-LE is challenging, especiallyon decadal time scales, but vital (e.g., Goddard et al.
2013), especially given differences in model variability(e.g., Knutson et al. 2013). Model biases can degrade
the realism of simulated internal variability and forced
climate responses and we therefore encourage usersof the CESM-LE to understand relevant model biases
and their potential ramifications.
The CESM-LE builds upon previous large ensemble projects (e.g., Roeckner et al. 2003; Deser et al.
2012b; Mudryk et al. 2014; Fischer et al. 2013) with its(i) comprehensive, community-selected, freely available, and easily accessed outputs; (ii) well-documented
experimental design that enables future contributionsof additional ensemble members and off-shoot experiments; (iii) simulation length from the early twentiethcentury to the late twenty-first century; and (iv)long (1000+ yr) companion preindustrial control
simulations. Given these attributes, the CESM-LE
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experiment uniquely enables a diverse community toassess the influence of internal climate variability andforced climate change on the climate system.
The purpose of this article is two-fold. First, we
document the CESM-LE experimental design, including model version, control run and ensemble generation, external forcing, and outputs saved. Second, we
provide thought-provoking examples and promising
directions for use of the CESM-LE by climate scientists
and stakeholders.
EXPERIMENTAL DESIGN. All CESM-LE
simulations use a single CMIP5 coupled climate
model: the Community Earth System Model, version 1, with the Community Atmosphere Model,
version 5 [CESM1(CAM5); Hurrell et al. 2013] atapproximately 1° horizontal resolution in all modelcomponents. Like most state-of-the-art global coupledclimate models, CESM1(CAM5) consists of coupledatmosphere, ocean, land, and sea ice component
models (Fig. 1). In addition to land carbon cycle
calculations, the CESM-LE simulations also include
diagnostic biogeochemistry calculations for the ocean
ecosystem and the atmospheric carbon dioxide
cycle (Lawrence et al. 2012; Long et al. 2013; Moore
et al. 2013; Lindsay et al. 2014). Unlike the landcarbon cycle calculations, which affect local energy
and water cycles, the ocean biogeochemistry andatmospheric carbon dioxide tracer calculations do notaffect the climate of the CESM-LE simulations. Many
aspects of fully coupled simulations performed with
CESM1(CAM5) are documented in a special collectionof the Journal of Climate (see http://journals.ametsoc.org/page/CCSM4/CESM1).
We began the CESM-LE with a multicentury
1850 control simulation with constant preindustrial
forcing (Table 1; Fig. 2). While the ocean model was
initialized from observations, the atmosphere, land,and sea ice models were initialized using previous
CESM1(CAM5) simulations. Atmosphere, land, andsea ice processes have memory on short time scales(weeks to years), so the influence of their initial conditions on the coupled climate state in a multicenturylong control simulation is negligible. In contrast, theocean has memory on long time scales (up to several
thousand years in the abyssal ocean). As a result, ocean
initial conditions can influence multicentury coupledclimate model runs. Because global ocean observations are not available for 1850, we initialized the ocean
from a state of rest (Danabasoglu et al. 2012) using
modern observations. Specifically, we initialized theocean model with January mean climatological Polar
Science Center Hydrographic Climatology (PHC2)

Table 1. CESM-LE simulations. Additional information about the simulations including all of the saved
variables, diagnostics, model support, and known issues can be found at the CESM-LE web page (www2.cesm.ucar.edu/models/experiments/LENS).

1850 fully coupledcontrol
Case nameb.e11.B1850C5CN.
f09_g16.005

Yearsb1,500 years, years 400–
1500 released
Atmosphere, ocean,land, sea ice

aPrognostic
model
components
Forcing

Initialization

Preindustrial (1850),
Whole Atmosphere
Community Climate
Model (WACCM)
ozone forcingc

1850 atmosphereand land control

Ensemble member 1

f.e11.F1850C5CN. b.e11.
f09_f09.001
B20TRC5CNBDRD.
f09_g16.001, b.e11.
BRCP85C5CNBDRD.
f09_g16.001
2,000 years, years 1850–2100
1–1999 released
Atmosphere, land Atmosphere, ocean,land, sea ice

Preindustrial(1850) withprescribed monthly
mean sea surface
temperature andsea ice averaged
over years 402–
1510 of the 1850
control,WACCM
ozone forcingc
Jan mean present-day
1 Jan, year 402
potential temperatureof 1850 coupled
and salinity from PHC2 control fordataset for ocean,
atmosphere andprevious CESM1(CAM5) land.
1850 control run for
For ocean/ice,
atmosphere, land, andN/A.
sea ice

Ensemble member 2–N
b.e11.
B20TRC5CNBDRD.
f09_g16.00N, b.e11.
BRCP85C5CNBDRD.
f09_g16.00N
1920–2100
Atmosphere, ocean, land,sea ice

1850–2005 historical,
Same as ensemble
2006–2100 RCP8.5
member 1 forwell-mixed greenhouse overlapping years
gases (Meinshausen etal. 2011) and shortlived gases and aerosols(Lamarque et al. 2011),
WACCM ozone
forcingc
1 Jan, year 402 of 1850
coupled control for
all model components(atmosphere, land,
ocean, sea ice)

Ensemble member 2:
1 Jan 1920 of ensemble
member 1 started with
1-day lagged ocean
temperatures
Ensemble members 3–N:
1 Jan 1920 of ensemble
member 1 for all modelcomponents; atmospherewith round-off (order of
10–14 K) differences in air
temperature

Simulation output using the case names listed in this table are available to download from the ESG (www.earthsystemgrid.org).
b
Simulations may be extended and additional years will be posted on the ESG.
c
We elected to use ozone concentrations produced by CESM1(WACCM) for the CESM-LE because the CMIP5 CESM
ozone forcing underestimates the strength of stratospheric ozone depletion over Antarctica (Eyring et al. 2013). Indeed,
the ozone hole is more pronounced, realistic, and internally consistent in CESM1(WACCM) than it is in the CESM
CMIP5 simulations.The CESM-LE 1850 control run was forced by the average seasonal cycle of ozone concentrations
from 200 years of a CESM1(WACCM) 1850 control run. For the transient ensemble members,WACCM ozone
concentrations were taken from available CESM1(WACCM) CMIP5 simulations (average of two ensemble members for
1955–2055, single ensemble member for 1850–1954 and 2056–2100). Following CMIP5 external forcing protocol, we
wanted the applied WACCM ozone forcing to emphasize externally forced ozone variations and deemphasize internal
climate variability in the CESM1(WACM) CMIP5 simulations. As such, we applied a 10-yr running mean to each monthof ozone forcing separately. This smoothing reduces the impact of CESM1(WACM)-specific internal climate variability(e.g., ENSO variability and sudden stratospheric warmings) on the ozone concentrations that are applied to the CESMLE. The smoothing also minimizes the impact of the 11-yr solar cycle on stratospheric ozone concentrations.
a

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Table 2. CMIP5 models and expansions.

Model nameACCESS1.0
ACCESS1.3
BCC_CSM1.1
BCC_CSM1.1(m)BNU-ESM
CanESM2
CCSM4
CESM1(BGC)
CESM1(CAM5)
CESM1(WACCM)
CMCC-CMS
CNRM-CM5CSIRO-Mk3.6.0
EC-EARTH
FGOALS-g2
FIO-ESMGFDL-CM3
GFDL-ESM2G

Model expansion

Australian Community Climate and Earth-System Simulator, version 1.0
Australian Community Climate and Earth-System Simulator, version 1.3
Beijing Climate Center, Climate System Model, version 1.1
Beijing Climate Center, Climate System Model, version 1.1 (moderate resolution)
Beijing Normal University–Earth System Model
Second Generation Canadian Earth System Model
Community Climate System Model, version 4
Community Earth System Model, version 1 (biogeochemical)
Community Earth System Model, version 1 (Community Atmosphere Model, version 5)
Community Earth System Model, version 1 (Whole Atmosphere Community Climate Model)
Centro Euro-Mediterraneo per I Cambiamenti Climatici Stratosphere-resolving Climate Model
Centre National de Recherches Météorologiques Coupled Global Climate Model, version 5
Commonwealth Scientific and Industrial Research Organisation Mark 3.6.0
European Consortium Earth System Model
Flexible Global Ocean–Atmosphere–Land System Model gridpoint, version 2.0
First Institute of Oceanography (FIO) Earth System Model (ESM)
Geophysical Fluid Dynamics Laboratory Climate Model, version 3
Geophysical Fluid Dynamics Laboratory Earth System Model with Generalized Ocean
Layer Dynamics (GOLD) component
GFDL-ESM2M
Geophysical Fluid Dynamics Laboratory Earth System Model with Modular Ocean Model(MOM), version 4, component
GISS-E2H
Goddard Institute for Space Studies Model E, coupled with the Hybrid Coordinate Ocean
Model (HYCOM) ocean model
GISS-E2-H-CC
Goddard Institute for Space Studies Model E2, coupled with HYCOM and interactive
terrestrial carbon cycle (and oceanic biogeochemistry)
GISS-E2-R
Goddard Institute for Space Studies Model E2, coupled with the Russell ocean model
GISS-E2-R-CC
Goddard Institute for Space Studies Model E2, coupled with Russell and interactive
terrestrial carbon cycle (and oceanic biogeochemistry)
HadGEM2-AO
Hadley Centre Global Environment Model, version 2—Atmosphere and Ocean
HadGEM2-ES
Hadley Centre Global Environment Model, version 2—Earth System
INM-CM4.0
Institute of Numerical Mathematics Coupled Model, version 4.0
INGV-SXG
Istituto Nazionale di Geofisica e Vulcanologia, SINTEX-G
IPSL-CM4
L’Institut Pierre-Simon Laplace Coupled Model, version 4
IPSL-CM5A-LR
L’Institut Pierre-Simon Laplace Coupled Model, version 5A, low resolution
IPSL-CM5A-MR
L’Institut Pierre-Simon Laplace Coupled Model, version 5A, mid resolution
IPSL-CM5B-LR
L’Institut Pierre-Simon Laplace Coupled Model, version 5B, low resolution
MIROC5Model for Interdisciplinary Research on Climate, version 5
MIROC-ESM
Model for Interdisciplinary Research on Climate, Earth System Model
MIROC-ESM-CHEM Model for Interdisciplinary Research on Climate, Earth System Model, Chemistry Coupled
MPI-ESM-LR
Max Planck Institute Earth System Model, low resolution
MRI-CGCM3
Meteorological Research Institute Coupled Atmosphere–Ocean GCM, version 3
NorESM1-M
Norwegian Earth System Model, version 1 (intermediate resolution)
NorESM1-ME
NorESM1-M with carbon cycling (and biogeochemistry)
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in the deep ocean (global ocean temperature drift of~0.005 K century–1 for years 400–1000).
After a few centuries, the control run climate was inquasi equilibrium with the 1850 forcing. At this point,
we started the first ensemble member using initialconditions from a randomly selected date in the 1850
control run: 1 January, year 402 (Table 1). Ensemble
member 1 was integrated forward from 1850 to 2100
(Fig. 2). Ensemble members 2–30 were all started on
1 January 1920 using slightly different initial conditions (Table 1). Spread in ensemble members 3–30
was generated by round-off level differences in their
Fig . 1. CESM1(CAM5) component models and coupling (Hurrell et al. 2013). All components were run initial air temperature fields. Specifically, we applied–14
at ~1° horizontal resolution. CESM1(CAM5) consists random round-off level (order of 10 K) differencesof coupled atmosphere (CAM5, 30 vertical levels), to the air temperature field of ensemble member 1 toocean [Parallel Ocean Program, version 2 (POP), 60 generate atmospheric initial conditions for ensemble
vertical levels], land [Community Land Model, version members 3–30. With the exception of their initial air
4 (CLM4)], and sea ice [Los Alamos Sea Ice Model temperature field, ensemble members 3–30 all had the(CICE)] component models.
same initial conditions. For technical reasons, ensemble member 2 was started using a 1-day lagged ocean
potential temperature and salinity data. The PHC2 da- initial condition. Because all 30 CESM-LE members
taset represents a blending of the Levitus et al. (1998) share essentially the same ocean initial conditions, thewith Steele et al. (2001) data for the Arctic Ocean. CESM-LE does not sample internal climate variability
Ocean biogeochemical tracers were initialized from resulting from differing ocean states.
a separate 600-yr spinup.
All CESM-LE ensemble members have the same
Our 1850 ocean initialization strategy leverages specified external forcing. Following the CMIP5 design
two assumptions. First, the upper ocean equilibrates protocol, we applied historical forcing from 1920 toon much shorter time scales than the deep ocean. 2005 (Lamarque et al. 2010) and representative conTherefore, the upper ocean adjusts to a preindustrial centration pathway 8.5 (RCP8.5) forcing (Meinshausen
state after several decades under constant forcing. et al. 2011; Lamarque et al. 2011) from 2006 to 2100.
Second, modern observations still reflect preindus- Unlike the CMIP5 CESM runs, which specified ozone
trial conditions at depth because of the long abyssal forcing from the CAM-Chem model (“CMIP5 CESM
ocean equilibrium time scales. After an expected ozone”; Lamarque et al. 2010, 2011; Meehl et al. 2012),
initial surface ocean cooling, the 1850 control arrived the CESM-LE simulations use ozone concentrations
at a balanced coupled state with climate drift only calculated by a high-top coupled chemistry–climate
model {CESM1[Whole
Atmosphere Community
Climate Model (WACCM)];
Marsh et al. 2013} withspecified ozone depleting
substances (Table 1).
In response to the applied historical and RCP8.5
external forcing from 1920
to 2100, the global surface
temperature increases byapproximately 5 K in all
ensemble members (Fig.
2). This consistent ~5-K
global warming signal in all
ensemble members by year
Fig. 2. Global surface temperature anomaly (1961–90 base period) for the2100 reflects the climate
1850 control, individual ensemble members, and observations (HadCRUT4;
response to forcing andMorice et al. 2012).
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feedback processes as represented by this particularclimate model. In contrast, internal variability causes
a relatively modest 0.4-K spread in warming acrossensemble members. The global surface air temperature
evolution in the CESM-LE simulations is similar tothat in CMIP5 CESM1(CAM5) experiments contributed to CMIP5 (Meehl et al. 2013).
Completing the CESM-LE simulations required
substantial computational, storage, and humanresources. The CESM-LE is a “big data” project forthe current generation of computing. All CESM-LE
simulations were run on Yellowstone, NCAR’s highperformance computing resource. Each CESM-LE
member (1920–2100) took ~3 weeks to complete andproduced 6.6 TB (15.3 TB) of postprocessed (raw)output. When combined with the preindustrial
controls, a total of over 17 million Yellowstone core
hours in over 1,500 individual jobs were required
to complete the CESM-LE simulations. The total
postprocessed archived data volume for the CESMLE simulations (200+ TB) exceeds the entire CESM
contribution to CMIP5 (~170 TB).
The CESM-LE outputs are publicly available via the
Earth System Grid (ESG; www.earthsystemgrid.org) as
single-variable time series in self-documenting losslesscompressed netCDF-4 format. All saved outputs—including variable names, units, and frequency—are detailed on the CESM-LE website. A unique aspect of the
CESM-LE is the large number of high-frequency fields
that have been saved. Continuous daily outputs will
be useful for studying extremes on socially relevanttime scales. The 6-hourly outputs during three 10-yr
periods (1996–2005, 2025–34, and 2071–80) enablecyclone tracking and can serve as boundary conditions
for regional climate model simulations, among otherapplications. Of interest to those planning offshootexperiments, the files required to restart CESM are
saved every five years in all ensemble members. The
experimental design for the CESM-LE project is reproducible. The code base and associated external forcing files are also available on the Earth System Grid.
ILLUSTRATIVE RESULTS AND THE QUESTIONS THEY INSPIRE. With the ensem­b leexperimental design described, we next present
thought-provoking initial results from the CESM-LE.
We begin with global-mean surface air temperature
trends. Global-mean trends are less affected byinternal climate variability than regional trends. Yet,the influence of internal climate variability on global
trends has recently emerged as an important research
topic because of the observed global surface warming “hiatus” or the reduction in the rate of global
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surface air temperature increase since the mid-1990s(Tollefson 2014; see Fig. 2).
One leading hypothesis for the global warming
hiatus implicates internally generated, deep ocean
heat uptake. While the surface ocean cools, thedeep ocean traps heat and thus warming decelerates(Meehl et al. 2011; Kosaka and Xie 2013; Trenberthand Fasullo 2013; England et al. 2014; Clement
and Dinezio 2014). Another hypothesis posits that
increased stratospheric aerosol from volcanic eruptions contributes to the hiatus (Solomon et al. 2011;
Neely et al. 2013; Santer et al. 2014). The CESM-LE
can help address the causes of global surface warming acceleration and deceleration. For example, how
do modes of coupled internal climate variability andexternal forcing jointly affect global warming trends,
and what mechanisms are involved?
To illustrate the influence of internal climate variability on global warming trends in the CESM-LE, Fig. 3
shows histograms of 10- and 20-yr global surface airtemperature trends for the preindustrial (1850), thepresent (1990–2009), and the near future (2030–49).
The preindustrial control run has constant 1850
forcing, a balanced top-of-atmosphere energy budget,and negligible climate drift. As a result, the 10- and20-yr preindustrial trend histograms are symmetric
about 0 (no trend). Even with a stable mean climate,
there is a range in preindustrial trend magnitudes(e.g., –0.2 to +0.2 K decade–1 for 20-yr trends). This
range is a measure of unforced internally generated
climate variability, and the size of this range depends
on the length of the interval over which trends arecomputed (e.g., Hunt and Elliot 2006). For example,
the largest 10-yr trends are approximately twice themagnitude of the largest 20-yr trends.
By the late twentieth century/early twenty-first
century, global warming has occurred in all ensemble
members (Fig. 2), and the majority of the 10-yr andall of the 20-yr trends are positive (Fig. 3). Like thepreindustrial, there is a considerable range of warming rates because of the internal climate variability.
Of interest to hiatus research, recently observed 10and 20-yr global surface air temperature trends arewithin the trend spread predicted by the CESM-LE
ensemble members. In other words, the observed
global warming hiatus is within the lower end of therange of plausible global warming responses to historical forcing as predicted by this particular climate
model. Looking to the future, cooling trends become
increasingly unlikely. Indeed, while a range in trend
magnitude remains, 10- and 20-yr cooling trends nolonger occur in the mid-twenty-first century under
RCP8.5 forcing. In summary, initial analysis of theFig. 3. Histograms of 10- and 20-yr trends in global surface air temperature for the 1850 control(black), starting from 1990 to 2009 (blue) and starting from 2030 to 2049 (red). Trends were
calculated using time series of monthly mean values. The number of trends contributing to eachhistogram is not identical. For the 1850 control, independent continuous 10- and 20-yr segments
were used to calculate trends. For the 1990–2009 and 2030–49 periods, trends were calculated
for every possible start year. For example, for the period 1990–2009, 10-yr trends were calculatedusing Jan 1990–Dec 1999, Jan 1991–Dec 2000, … , Jan 2009–Dec 2018, while the 20-yr trends were
calculated as Jan 1990–Dec 2009, Jan 1991–Dec 2010, … , Jan 2009–Dec 2028. Observed ranges
are from HadCRUT4 (Morice et al. 2012) and Goddard Institute for Space Studies Surface
Temperature Analysis (GISTEMP; Hansen et al. 2010). Observed trend minimum–maximum
ranges were based on 10-yr trends calculated from Jan 1990–Dec 1999, Jan 1991–Dec 2000, … ,Jan 1994–Dec 2013 and on 20-yr trends calculated from Jan 1990–Dec 2009, Jan 1991–Dec 2010,… , Jan 1994–Dec 2013.

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CESM-LE global temperature trends supports existing
research showing that global-mean surface air temperatures can show no trend or even slight cooling in thepresence of long-term warming (e.g., Easterling andWehner 2009), but unlike previous studies based on
ensembles of opportunity that mix together climate
model differences and internal variability, the CESMLE shows that internal variability alone can generate
substantial spread in global warming trends.
Having shown that internal climate variability canexert a substantial influence on global-mean temperature trends, we next look regionally, where we expect
the influence of internal climate variability to be evenlarger. In particular, we use trend maps to illustrate
the “single realization problem,” the fact that any individual climate trajectory deviates from the mean
forced response (e.g., Bengtsson et al. 2006; Easterlingand Wehner 2009). Figure 4 shows boreal winter[December–February (DJF)] surface air temperature
trends over the era with the best global observational
coverage (1979–2012). The ensemble mean, an estimate of the forced response, depicts the familiar patterns of Arctic amplification and more warming overland than over the ocean. While the ensemble meanis useful for identifying the forced climate response,
comparing across the individual ensemble members
illustrates that the forced response is rarely realized.
Why? The CESM-LE members show that regional
trends can vary dramatically in magnitude and sign
because of the internal climate variability. For example,
the East Coast of the United States warms by about
3 K over the period 1979–2012 in ensemble member
6 but cools by about 3 K over the same period in ensemble member 13. Similarly, much of Eurasia cools bynearly 2 K in member 7, while the same region warms
by about 3 K in member 17. The difference between
ensemble member 6 and 13, and between 7 and 17, isthe result of internal climate variability that cannot be
predicted from initial condition differences at 1920.
Like any individual model ensemble member, theobservations also represent one possible response of
the climate system to external forcing in the presenceof internal climate variability. As a consequence, comparing trends from a single ensemble member to theobserved 1979–2012 temperature trends to “validate”
the climate model simulation is problematic. Similarly problematic is comparing the ensemble-mean
trend to observations, as internal climate variabilityis (by construction) muted in the ensemble mean. To
confound matters even further, the available observations in some regions are too sparse to reliably detect
a trend from observations alone [e.g., mountainousand polar regions in the Hadley Centre Climatic
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Research Unit temperature (HadCRUT4) dataset(Morice et al. 2012)].
When no ensemble member is able to reproduce
the observed trend or when the trends in all ensemble
members look more similar to each other than they doto the observed trend, the question becomes have wedetected a model bias or have we inadequately sampled
the ensemble spread? For example, none of the CESMLE ensemble members replicate the magnitude of
the observed surface cooling in the Southern Oceanor the eastern tropical Pacific over recent decades. For
the Southern Ocean, the lack of surface cooling trends
in the CESM-LE is consistent with associated weakocean heat uptake in CESM1(CAM5). For the eastern
tropical Pacific, the lack of pronounced surface coolingin the CESM-LE is directly relevant to hiatus research.
Namely, the inability of any ensemble member toreplicate the observed magnitude of the tropical Pacific Ocean surface cooling suggests that the tropical
Pacific Ocean hypothesis [i.e., as tested by Kosaka andXie (2013)] is not the only mechanism contributing todecelerated warming rates in the CESM-LE.
While the comparison of models and observations provides important insights, climate change projections are made in part to plan for a future we cannot
observe. As such, we next examine the influence ofinternal climate variability on near-future trends,
looking forward as far as we looked backward (i.e.,
34 yr). Similar to Fig. 4, Fig. 5 shows that most CESMLE members exhibit 2013–46 warming trends in mostregions, with the exception of the North Atlantic.
Yet, in some ensemble members, the internal climate
variability is large enough to overwhelm the forcing
and result in cooling in some regions. For example,
ensemble member 20 shows pronounced cooling of
3 K over Asia that is in stark contrast to the appreciable warming of 6 K in that same region in ensemble
member 24. The take-home message is clear, consistent with previous studies analyzing large ensembles
with the same model and the same external forcing,and needs to be better communicated (e.g., Deser et al.
2012a): we need to plan for a range of future outcomes
not only because climate models imperfectly represent
the relevant processes, but also because there are inherent predictability limits in a climate system with large
internal climate variability.
COMPARISON TO CMIP5. CMIP5 is frequently
used to assess uncertainty in future climate projections. But CMIP5 is an ensemble of opportunity, andthe spread within the CMIP5 archive is not easy tointerpret. Individual CMIP5 ensemble members canhave differing physics, dynamical cores, resolutions,Fig. 4. Global maps of historical (1979–2012) boreal winter (DJF) surface air temperature trends for eachof the 30 individual CESM-LE members, the CESM-LE ensemble mean (denoted EM), and observations(denoted OBS based on GISTEMP; Hansen et al. 2010).

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Fig. 5. Global maps of near-future (2013–46) boreal winter (DJF) surface air temperature trends for eachof the 30 individual CESM-LE members and the CESM-LE ensemble mean (denoted EM).

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and initial conditions. To complicate matters, many
CMIP5 models share genes and are therefore not independent (Knutti et al. 2013). In sum, spread in CMIP5
climate projections results both from model formulation differences and from internal climate variability,
the relative importance of which is unknown. Unlike
the CMIP5 ensemble spread, CESM-LE ensemble
spread is generated by internal climate variability alone.
Given these points, a natural question becomes how
much of the spread in CMIP5 projections can be
explained by internal climate variability alone? We
can answer this question by using the CESM-LE toestimate the influence of internal climate variability
on ensemble spread.
Figure 6 shows spread in DJF surface air temperature trends during the preindustrial, historical,
and near-future periods. We quantify spread for both
the CESM-LE and CMIP5 using the standard deviation in 34-yr trends. Surprisingly, similar trend spreadpatterns and magnitudes are evident in the CESM-LE
regardless of the time period selected. Reinforcing thevisual similarity of the CESM-LE trend variability inall three periods (1850, 1979–2012, and 2013–46),
statistically different trend variability is rare (i.e., few
regions are stippled in the CESM-LE panels in Fig. 6).
These comparisons suggest that internal climate variability in this particular variable and time span islargely independent of forcing. In other words, theinfluence of forced climate change on internal climate variability in 34-yr DJF surface air temperatureis small. This result has practical relevance. Indeed,
when internal climate variability does not change
under climate forcing, long control runs and large
forced ensembles will provide similar estimates ofinternal climate variability. Of even greater interest in
Fig. 6, the trend spread generated by internal climate
variability alone—estimated using the CESM-LE—isoften statistically indistinguishable from the spreadin trends within CMIP5. At least for DJF surface
air temperature trends, the conclusion is stunning:
CMIP5 spread in many regions (i.e., regions that are
not stippled in the CMIP5 panels in Fig. 6) can be
explained by internal climate variability [as estimatedby CESM(CAM5)] alone.
While the trend variability comparisons in Fig. 6 areinteresting, the implications are not universal. The
contribution of internal climate variability to ensemble
spread depends on the climate variable, time period,
season, and location (e.g., Deser et al. 2012b). What is
the relative contribution of model formulation differences and internal climate variability to changes in otherclimate variables? Why are there differences acrossclimate variables, time periods, seasons, and locations?
AMERICAN METEOROLOGICAL SOCIETY

Where should we expect improved model formulations to reduce spread in future climate projections?
These are the types of important climate questions
that can be addressed by comparing CESM-LE andCMIP5 ensemble spread.
EXTREMES. Understanding extreme climate phenomena requires multiple realizations for adequate
statistical sampling. To illustrate the utility of the
CESM-LE for studying extreme climate phenomena,
we next focus on detecting changes in the climatologies of extreme events. In particular, we analyze atmospheric blocking changes produced by differences inmodel physics and heat stress changes resulting fromtwenty-first-century RCP8.5 forcing.
Atmospheric blocking is often associated withextreme events in the midlatitudes. Under blocked
atmospheric flow, persistent winter cold spells
and summer heat waves occur. On the peripheryof blocked regions, atypical weather patterns canalso lead to surface temperature and precipitation
extremes. Because blocking is a feature in the largescale atmospheric flow, it is a relatively reliable proxyfor extreme events in climate model simulations.
Yet, blocking statistics have appreciable year-to-year
variability and thus many years of data are needed
to attribute changes in blocking statistics to model
differences or to external forcing.
To illustrate the unique value of large ensembles
for evaluating blocking climatologies, Fig. 7 compares
Northern Hemisphere blocking frequency (D’Andrea
et al. 1998) in the 30-member CESM-LE; a 30-member Community Climate System Model, version 4(CCSM4) ensemble (Mudryk et al. 2014); and historical reanalysis. Substantial longitudinally dependent
ensemble spread in climatological blocking frequency
is evident in both 30-member ensembles, a finding that
demonstrates the importance of large ensembles forevaluating mean state model blocking climatologies.
Comparing the 30-member CCSM4 ensemble to the
30-member CESM-LE, we can confidently attributeblocking statistics differences to model physics differences. Specifically, the CESM-LE exhibits increased
and improved blocking frequency over the Atlantic(30°W–15°E) when compared to CCSM4. Interestingly, the ensemble spread in the CESM-LE is greaterthan that in the CCSM4 ensemble over the eastern
Atlantic (0°–30°E), hinting not only at differences inclimatological-mean blocking but also in blocking
variability.
Heat stress is a leading cause of weather-related
human mortality and morbidity (Guirguis et al. 2014;
CDC 2006). When the climate warms, heat stress
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Fig. 6. Global maps of standard deviation in 34-yr DJF surface air temperature trends for the (top) preindustrial (1850), (middle) historical (1979–2012), and (bottom) near-future (2013–46) periods. For the historical andnear-future periods, trends are shown for both the 30-member CESM-LE ensemble and the 38-member CMIP5
ensemble (Taylor et al. 2012). Stippling on the historical and near-future CESM-LE trend maps indicates standard
deviations that are statistically different than the CESM-LE preindustrial period. Stippling on the historical andnear-future CMIP5 maps indicates standard deviations that are statistically different than the CESM-LE forthe corresponding period. Stippling is based on an f test and a 95% confidence interval. For CMIP5, we used asingle (the first) ensemble member of the following models (see Table 2 for full list of expansions): ACCESS1.0,
ACCESS1.3, BCC_CSM1.1(m), BCC_CSM1.1, BNU-ESM, CanESM2, CCSM4, CESM1(BGC), CESM1(CAM5),
CESM1(WACCM), CMCC-CM, CMCC-CMS, CNRM-CM5, CSIRO Mk3.6.0, EC-EARTH, FGOALS-g2, FIOESM, GFDL CM3, GFDL-ESM2G, GFDL-ESM2M, GISS-E2-H, GISS-E2-H-CC, GISS-E2-R, GISS-E2-R-CC,
HadGEM2-AO, HadGEM2-CC, HadGEM2-ES, INM-CM4.0, IPSL-CM5A-LR, IPSL-CM5A-MR, IPSL-CM5B-LR,
MIROC5, MIROC-ESM, MIROC-ESM-CHEM, MPI-ESM-LR, MRI-CGCM3, NorESM1-M, and NorESM1-ME.

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AUGUST 2015

corresponding changes in one randomly selected ensemble member(ensemble member 6). Over the
early twenty-first century, heat wave
intensity, duration, and frequency
increase everywhere, but with regional magnitude differences. When
all 30 ensemble members are used todetect heat stress changes, the results
are statistically significant nearly
everywhere. In contrast, the heatstress changes in ensemble member
6 are much less likely to be statistically significant and differ from the
30-member mean changes becauseof the unique expression of internal
climate variability in this particularensemble member.
NEW ANALYSES ENABLED.
Reflecting the demand for an openaccess large ensemble and community participation in the CESM-LE
experimental design, analysis ofFig. 7. Climatological meridional mean blocking frequency based ondaily 500-mb geopotential height fields (D’Andrea et al. 1998) for
CESM-LE simulations has already30 CESM-LE ensemble members, 30 CCSM4 ensemble members
begun and interest is expected to(Mudryk et al. 2014), and in observations [European Centre forgrow. Using the CESM-LE frameMedium-Range Weather Forecasts (ECMWF) Interim Re-Analysis
work, it is possible to cleanly sepa(ERA-Interim; Dee et al. 2011) and Modern-Era Retrospective Analyrate forced climate change fromsis for Research and Applications (MERRA; Rienecker et al. 2011)
internally generated variability, toreanalysis] during boreal spring [March–May (MAM)]. Lines showquantify model projection spread,mean values and shading shows plus or minus two standard deviationand to evaluate how variability
in the mean values. The periods are 1979–2010 (observations) and
1974–2005 (models). Note that differing periods were used for the
coevolves with a changing climate.
models and observations, but blocking statistics are not affected by
Research evaluating urban heat5-yr differences in averaging intervals.
waves, atmospheric circulation andblocking, precipitation characterworsens in severity (e.g., hotter days and nights), istics, snow cover, ozone hole impacts, air quality,
frequency (number of hot days and nights), and Greenland Ice Sheet surface mass balance, sea ice,
duration (heat waves) (Meehl and Tebaldi 2004). At and hurricanes has already begun. Biogeochemical
the same time, determining heat stress patterns and processes are being analyzed in a large ensemble
trends is not straightforward because of the wide framework for the first time including uptake andvariety of methodological approaches in defining a storage of anthropogenic carbon dioxide by the landheat wave (Smith et al. 2013) and because of the in- and ocean, primary productivity by biology in the
ternal climate variability (Perkins and Fischer 2013; land and ocean, and variation of atmospheric carbon
Fischer et al. 2013). Daily outputs from the CESM-LE dioxide on seasonal to decadal time scales. Offshootcan be used to identify the mechanisms underlying experiments have already started including experiregional differences in future heat stress projections ments to test the competing hypotheses for hiatus(e.g., connection to atmospheric blocking or land– periods, to evaluate hurricanes in CESM1(CAM5)
atmosphere feedbacks) and to assess the influence using high-resolution time slice experiments, toof internal climate variability on heat stress metrics. force regional downscaling simulations, and to run
To illustrate the value of the large ensemble for the a complementary ensemble under RCP4.5 forcing
latter, Fig. 8 contrasts early twenty-first-century heat to assess avoided impacts. To help coordinate comstress changes in all 30 CESM-LE members with munity analysis of the CESM-LE, we are compiling
AMERICAN METEOROLOGICAL SOCIETY

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| 1345

Fig. 8. Maps of early twenty-first-century change (2040s–2010s) in heatwave intensity, duration, and frequency
from the CESM-LE for (left) 30 ensemble members and for (right) 1 ensemble member. Heat waves are defined
following Meehl and Tebaldi (2004). Heat wave intensity, duration, and frequency were calculated for eachyear and then averaged. Hatching indicates differences are not statistically significant at the 95% level using aStudent’s t test.

a list of all ongoing projects at the CESM-LE website(www2.cesm.ucar.edu/models/experiments/LENS).
A new resource useful for analyzing the CESM-LE,
1346 |

AUGUST 2015

and for making comparisons to CMIP5, is the freely
available Climate Variability Diagnostics Package(Phillips et al. 2014).

SUMMARY AND BROADER IMPLICATIONS.
Understanding forced climate change in the presenceof internal climate variability is a major challenge forclimate prediction. To make progress, the scienceand stakeholder communities need relevant climate
model experiments with useful outputs. The CESMLE addresses this challenge through its transparent
experimental design and relevant accessible outputs.
Initial illustrative CESM-LE results affirm that becauseof the internal climate variability, single realizations
from climate models are often insufficient for modelcomparison to the observational record, model intercomparison, and future projections. A publicly
available ensemble with the scope, and the amountof community input as the CESM-LE, has never been
performed before. We anticipate analysis of this ensemble, alone or in combination with other ensembles
and/or regional climate simulations, will lead to noveland practical results that will inspire probabilistic
thinking and inform planning for climate change andclimate model development for many years to come.
ACKNOWLEDGMENTS. We thank the three reviewers who provided thoughtful comments and the scientists
and software engineers that develop CESM and support
Yellowstone. The National Science Foundation (NSF) andRegional and Global Climate Modeling Program (RGCM)
of the U.S. Department of Energy’s Office of Science (BER)
support the CESM project. The NSF-sponsored Climate
Simulation Laboratory at NCAR’s Computational and Information Systems Laboratory (CISL) provided computing
resources on Yellowstone.

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