15 SEPTEMBER 2020

DESER ET AL.

7835

Isolating the Evolving Contributions of Anthropogenic Aerosols and Greenhouse Gases:
A New CESM1 Large Ensemble Community Resource
CLARA DESER, ADAM S. PHILLIPS, ISLA R. SIMPSON, NAN ROSENBLOOM, DANI COLEMAN,FLAVIO LEHNER, AND ANGELINE G. PENDERGRASSClimate and Global Dynamics Division, NCAR, Boulder, Colorado

SAMANTHA STEVENSON
University of California at Santa Barbara, Santa Barbara, California(Manuscript received 24 February 2020, in final form 26 June 2020)
ABSTRACTThe evolving roles of anthropogenic aerosols (AER) and greenhouse gases (GHG) in driving large-scalepatterns of precipitation and SST trends during 1920–2080 are studied using a new set of ‘‘all-but-one-forcing’’
initial-condition large ensembles (LEs) with the Community Earth System Model version 1 (CESM1), which
complement the original ‘‘all-forcing’’ CESM1 LE (ALL). The large number of ensemble members (15–20) in
each of the new LEs enables regional impacts of AER and GHG to be isolated from the noise of the model’sinternal variability. Our analysis approach, based on running 50-yr trends, accommodates geographical andtemporal changes in patterns of forcing and response. AER are shown to be the primary driver of large-scalepatterns of externally forced trends in ALL before the late 1970s, and GHG to dominate thereafter. The AER
and GHG forced trends are spatially distinct except during the 1970s transition phase when aerosol changes
are mainly confined to lower latitudes. The transition phase is also characterized by a relative minimum in theamplitude of forced trend patterns in ALL, due to a combination of reduced AER and partially offsetting
effects of AER and GHG. Internal variability greatly limits the detectability of AER- and GHG-forced trend
patterns in individual realizations based on pattern correlation metrics, especially during the historical period,
highlighting the need for LEs. We estimate that ,20% of the spatial variances of observed precipitation and
SST trends are attributable to AER and GHG forcing, although model biases in patterns of forced response
and signal-to-noise may affect this estimate.

1. Introduction
Anthropogenic aerosols (AER) and greenhouse gases(GHG) are the dominant drivers of forced climatechange on a global scale over the past century, with
opposing effects on the planetary radiative energy
Denotes content that is immediately available upon publication as open access.
Supplemental information related to this paper is available atthe Journals Online website: https://doi.org/10.1175/JCLI-D-200123.s1.
Corresponding author: Dr. Clara Deser, cdeser@ucar.edu

balance (Myhre et al. 2013; Zhao et al. 2019). AER
cause a net cooling through increased absorption andscattering of shortwave radiation (direct effects) andchanges in cloud microphysical properties (indirect effects, also referred to as aerosol–cloud interactions) [seethe recent review by Bellouin et al. (2020)], while GHG
induce warming through enhanced absorption of longwave radiation. AER and GHG differ not only in theirradiative impacts, but also in their geographical distributions and temporal evolutions. AER are spatiallyheterogeneous, originating primarily over the majorindustrial centers of the Northern Hemisphere (NH)and Southeast Asia, with additional sources from biomass burning, while GHG are globally predominantly
well mixed. Temporal changes in AER are regionally

DOI: 10.1175/JCLI-D-20-0123.1
Ó 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright
Policy (www.ametsoc.org/PUBSReuseLicenses).

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

PEDRO DINEZIOThe University of Texas at Austin, Austin, Texas

7836

JOURNAL OF CLIMATEet al. 2014; Giannini and Kaplan 2019; Hirasawa et al. 2020,
manuscript submitted to J. Climate, the Asian monsoon(Ganguly et al. 2012; Undorf et al. 2018a; Li et al. 2018;
Zhao et al. 2019), Eurasian climate (Undorf et al. 2018b),
and regional patterns of SST change (Xie et al. 2010, 2013;
Wang et al. 2016). Air–sea interactions are important
modulators of these responses, with both thermodynamic
and dynamic processes contributing (Li et al. 2018; Kang
et al. 2018, 2019). For example, the wind–evaporation–SST(WES) feedback mechanism, SST–cloud interactions,and wind-driven changes in ocean gyre-scale circulations
contribute to the formation of regional patterns of SST
response (Xie et al. 2010). These air–sea feedback
mechanisms operate under both AER and GHG forcing, and thus impart a degree of commonality to theregional patterns of SST response to AER and GHGchanges (Xie et al. 2013). Aerosol–cloud interactions
also play an important role in the response of the climate
system to AER forcing (Chung and Soden 2017; Soden
and Chung 2017), with impacts on climate feedbacks andclimate sensitivity (Gettelman et al. 2016; Ghan et al.
2016; Gryspeerdt et al. 2020).
A majority of the studies cited above adopt a ‘‘time
slice’’ approach to their analysis of climate model simulations by contrasting two different periods, for example preindustrial vs. present-day (e.g., Li et al. 2018;
Chung and Soden 2017; Soden and Chung 2017; Denget al. 2020), early vs. late twentieth century (Hwang et al.
2013), 1970s vs. 2010s (Zhao et al. 2019), and presentday vs. late twenty-first century (Xu et al. 2018). Othersprescribe idealized time-invariant radiative perturbations in a hierarchy of models to probe dependencies
upon amplitude, geographical location, and degree of
air–sea coupling (Seo et al. 2014; Persad and Caldeira2018; White et al. 2018; Kang et al. 2018, 2019). One
recent multimodel ensemble compared two different
greenhouse gas and two different aerosol forcings (Myhreet al. 2017). Such idealized and time-slice approaches arefruitful for establishing a mechanistic understanding of
the response to a particular configuration of radiative
forcing, which may or may not lend itself to ‘‘patternscaling’’ for future projections (Tebaldi and Arblaster2014; Pendergrass et al. 2015). However, by design, thesestudies do not take into account the full complexity of theobserved spatiotemporal evolution of AER over thehistorical period, nor do they consider how the changing
geographical distribution of AER may interact with rising
GHGs as a function of time, nor to what extent theevolving forced responses may be obscured by multidecadal trends driven by internal variability. Nonlinear
interactions between AER and GHG were investigatedin Gettelman et al. (2016), Deng et al. (2020), and Zhao
et al. (2019), but only for a particular time period.

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

specific, whereas GHG concentrations have risen approximately monotonically in all locations. For example, AER over North America and western Europe
increased rapidly from the 1940s to 1970s followed by agradual decline, while those over Southeast Asia haverisen continuously since the late 1940s (Lamarque et al.
2010; Stevens 2015; Persad and Caldeira 2018; Samset
et al. 2019). These contrasting aspects of AER and GHG
complicate the study of anthropogenic climate change
on regional scales, including physical understanding,detection and attribution, and model projections. In
addition, the magnitude of AER forcing remains uncertain (Wilcox et al. 2015; Samset et al. 2019; Wilcox
et al. 2020; Bellouin et al. 2020).
The physical mechanisms underpinning the largescale response of the climate system to radiative perturbations associated with AER and GHG have been
investigated using a range of approaches, including
theoretical considerations, simplified modeling experiments with idealized forcings, and full complexity Earthsystem model (ESM) simulations with realistic radiative
forcing scenarios. Collectively, these approaches have
led to the following insights and understanding. On a
global scale, energetic constraints dictate that enhanced
NH cooling induced by AER sources in Eurasia andNorth America will cause an interhemispheric climate
shift, including a southward displacement of the zonal-mean
intertropical convergence zone (ITCZ) (e.g., Rotstayn andLohmann 2002; Ming and Ramaswamy 2009; Hwang et al.
2013; Wang et al. 2016; Chung and Soden 2017; Soden and
Chung 2017; Wang et al. 2019). In contrast, GHG-induced
global warming will produce a zonal mean response thatis largely symmetric about the equator, with additional
asymmetries introduced by the mean state and continental
configuration. In general, the zonal-mean response to GHG
is characterized by enhanced rainfall in the deep tropics anddrying in the subtropics, following the ‘‘wet-get-wetter’’ and‘‘upped ante’’ paradigms (e.g., Held and Soden 2006; Xieet al. 2010, 2013; Wang et al. 2016; Neelin et al. 2003). The
zonal-mean responses to both AER and GHG are strongly
mediated by dynamical ocean–atmosphere interactions, inparticular oceanic meridional heat transports associatedwith the wind-driven subtropical overturning cells and theAtlantic meridional overturning circulation (Kang et al.
2018, 2019; Tomas et al. 2016; Wang et al. 2018). In addition,
both types of radiative forcing induce amplified temperature(and associated precipitation) responses in the Arctic as aresult of positive ice-albedo feedback (Deng et al. 2020;
Zhao et al. 2019).
Regional aspects of the precipitation and sea surface
temperature (SST) responses to AER and GHGchanges have been widely studied. These include impacts on Sahel rainfall (e.g., Haywood et al. 2013; Dong

VOLUME 33

15 SEPTEMBER 2020

DESER ET AL.

over the period 1920–2080; 2) to quantify as a function oftime the relative contributions of AER and GHG forcing to externally driven climate trends; and 3) to assessthe degree to which AER- and GHG-induced effects
can be detected in a single ensemble member and observations. Our simple analysis approach, based onrunning 50-yr trends, accommodates temporal changes
in the spatial patterns of forcing and response, an aspectthat has been largely neglected in previous studies.
Such a perspective is particularly advantageous for AER(and its interactions with GHG) due to pronounced
changes in the geographical distribution of emissions
over time. The rest of this paper is organized as follows.
The model simulations, observational datasets, and analysis approach are described in section 2. Results are presented in section 3, summarized in section 4, and discussed
in section 5.

2. Data and methods
a. Model simulations
All experiments use the same model version andnominal 18 spatial resolution as the 40-member fully
coupled global CESM1 LE described in Kay et al. (2015).
CESM1 consists of interactive atmosphere, ocean, land,and sea ice component models, as well as a terrestrial
carbon cycle, and diagnostic biogeochemistry calculations for the ocean ecosystem and the atmospheric carbon
dioxide cycle, which do not feed back on the model’s
physical climate. The atmospheric model component is
Community Atmosphere Model version 5 (CAM5; Nealeet al. 2012), which includes a three-mode modal aerosol
scheme (Liu et al. 2012) with prognostic aerosols andindirect effects (aerosol–cloud interactions) for both liquid and ice phase clouds in the ‘‘stratiform regime’’(Morrison and Gettelman 2008; Gettelman et al. 2010).
CESM1 has a present-day value of aerosol effective radiative forcing of 21.37 W m22 (Zelinka et al. 2014),
which falls within the one standard deviation range ofplausible estimates (from 21.6 to 20.6 W m22; Bellouin
et al. 2020). The 40-member CESM1 LE (Kay et al. 2015)
is configured as follows. The first ensemble member begins in 1850 from year 401 of a long preindustrial control
simulation, and is integrated forward under historical(prior to 2006) and future (RCP8.5 for 2006–2100) emissions scenarios. All other members are branched from thefirst member in 1920 and are subject to the same forcing
protocol, but differ by a small (order 10214 K, the level of‘‘round-off’’ error in CAM5) random noise perturbation totheir initial atmospheric temperature fields. The CESM1‘‘all forcing’’ LE (hereafter referred to as ‘‘ALL’’) realistically simulates the evolution of global-mean surface air

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

Apart from the idealized modeling studies, the majority of aforementioned studies were based on a limited
collection of ‘‘single forcing’’ AER and GHG historicalsimulations conducted with a subset of models participating in phase 5 of the Coupled Model Intercomparison
Project (CMIP5; Taylor et al. 2012). Because of thelimited number (1–5) of simulations for each of the eightmodels in this single-forcing archive, the analyses reliedon the multimodel mean to estimate the forced response. However, averaging across models confounds
model structural uncertainty and uncertainty due to internal variability (i.e., Hawkins and Sutton 2009; Deseret al. 2012). This may be particularly problematic in thecase of AER, since each model differs in its representation of aerosol direct and indirect effects (e.g., Zelinka
et al. 2014; Bellouin et al. 2020). In addition, structural
uncertainty may be an issue for responses that areclosely tied to the mean state, as is the case for tropicalprecipitation (Xie et al. 2010).
To circumvent the issue of structural uncertainty,
initial-condition large ensembles (LEs) have been used
to determine the forced response in a given climate
model (e.g., Deser et al. 2020). In such a LE, each simulation is conducted with the same model and radiative
forcing scenario but begins from a different initial state;thus, ensemble spread is solely attributable to unpredictable internal variability and the forced response canbe estimated from the ensemble mean (e.g., Deser et al.
2012; Kay et al. 2015; Maher et al. 2019; Lehner et al.
2020). Almost all LEs to date prescribe the full suite ofanthropogenic and natural radiative forcing factors (seeTable 1 in Deser et al. 2020), including the widely used
40-member Community Earth System Model version 1(CESM1) LE (Kay et al. 2015). Here, we introduce a
new set of CESM1 LEs in which industrial AER, biomass burning AER, or GHG is kept fixed at 1920 conditions, while all other external anthropogenic andnatural forcings evolve following those in the original‘‘all-forcing’’ CESM1 LE (i.e., historical and future
RCP8.5 scenarios over the period 1920–2080). These
new LEs provide a unique community resource forstudying the regional impacts of AER and GHG over
the twentieth and twenty-first centuries. To the best of
our knowledge, the only other comprehensive models
with complementary historical and future all-forcing
and single-forcing LEs are the Canadian ESM version
2, which begins in 1950 and also employs CMIP5 forcings (Santer et al. 2019; Bonfils et al. 2020), and theCanadian ESM version 5, which begins in 1850 and
employs CMIP6 forcings (Swart et al. 2019).
The purpose of this study is to use the new set of
CESM1 LEs 1) to isolate the evolving patterns of precipitation and SST response to AER and GHG forcing

7837

7838

JOURNAL OF CLIMATE

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

temperature (GMST) during 1920–2019, with observations
lying within the ensemble spread nearly every year
(Fig. 1a). The observed linear trend in GMST during thisperiod [0.898C (100 yr)21] is also within the ensemble
spread [0.648–0.958C (100 yr)21].
The new set of CESM1 LEs conducted for this study
use the same forcing protocol as ALL, except that one
forcing agent is held fixed at 1920 conditions as follows:
1) a 20-member ensemble with fixed GHGs (hereafter‘‘XGHG’’); 2) a 20-member ensemble with fixed industrial AER (hereafter ‘‘XAERindus’’); and 3) a 15member ensemble with fixed biomass-burning AER(hereafter ‘‘XAERbmb’’). Each member of the ‘‘X’’ensembles begins from the same state as the first member of ALL in 1920 and is initialized in the same manneras described above for ALL. The XAERindus and
XGHG ensembles end in 2080, while the XAERbmb
ends in 2030 (note that biomass-burning aerosols in
RCP8.5 do not change appreciably after 2030).
To determine the effect of the forcing factor that washeld fixed in the ‘‘X’’ ensembles, we subtract the ensemble mean of the ‘‘X’’ ensemble from the ensemble
mean of ALL and term these residuals AERindus,
AERbmb, and GHG. Note that these residuals include any nonlinear interactions among AERindus,
AERbmb, and GHG that are present in ALL. For example, AERindus may interact nonlinearly with GHG
in the ALL ensemble, but this interaction is absent in
XAERindus. The conversions from XAERindus to
AERindus, and from XGHG to GHG, are illustrated
for GMST in Figs. 1b and 1c (the effect of AERbmb on
GMST is negligible; not shown). GMST in XGHG (redcurve and pink shading, Fig. 1b) decreases by approximately 0.58C from about 1940–90, while in XAERindus(blue curve and shading, Fig. 1b) it increases by approximately 1.48C from about 1930 to the present, which
is nearly 0.48C more than the increase in ALL (black
curve and gray shading, Fig. 1b). Synchronous episodic
cooling associated with volcanic eruptions in 1963, 1982,
and 1991 is evident in all three records. The GMST timeseries in GHG (red curve and pink shading, Fig. 1c) isvery similar to that in XAERindus, and GMST in
AERindus (blue curve and shading, Fig. 1c) is very
similar to that in XGHG except for the lack of volcanic
signatures. This close resemblance attests to the dominant influence of AERindus and GHG on ensemblemean GMST compared to other anthropogenic andnatural forcings (with the notable exception of volcanic
eruptions).
The combined effects of internal variability andforced response in any individual member of the GHG,
AERindus, and AERbmb ensembles can be calculated
as follows:

VOLUME 33

FIG. 1. Time series of annual global-mean surface air temperature (GMST) anomalies (8C) during 1920–2020 in (a) CESM1
ALL (black curve shows the ensemble mean and gray shadingshows the ensemble spread) and observations (1920–2019 fromBerkeley Earth Surface Temperature; Rohde et al. 2013; blue
curve); (b) CESM1 ALL (black curve and gray shading),
CESM1 XGHG (red curve and pink shading), and CESM1
XAERindus (light blue curve and shading); and (c) CESM1
ALL (black curve and gray shading), GHG (red curve and pinkshading), and AERindus (light blue curve and shading).
Anomalies are defined relative to the 1920–70 long-termmeans. See text for details.

15 SEPTEMBER 2020

GHGi 5 XGHGi 2 2XGHGem 1 ALLem ,DESER ET AL.

7839

annual means. Henceforth, for convenience we shall use
the term ‘‘historical period’’ to refer to the interval from
AERindusi 5 XAERindusi 2 2XAERindusem 1 ALLem , 1920–2019, with the understanding that all simulations(2) are subject to RCP8.5 forcing protocols after 2005 (recall section 2a).
AERbmbi 5 XAERbmbi 2 2XAERbmbem 1 ALLem ,
(1)

(3)

3. Resultsb. Observational datasets
We make use of the following observational datasets
over the period 1920–2018, all at monthly resolution: 1)
precipitation from the Global Precipitation Climatology
Centre (GPCC) version 7, a gridded gauge-analysis
product with a spatial resolution of 2.58 latitude 3 2.58
longitude (Schneider et al. 2015); 2) near-surface airtemperatures (SAT) from the Berkeley Earth Surface
Temperature (BEST) station-based dataset at 18 spatial
resolution (Rohde et al. 2013); and 3) SSTs fromthe NOAA Extended Reconstruction Sea Surface
Temperature version 5 (ERSSTv5) dataset at 58 spatial
resolution (Huang et al. 2017). We also include comparison to the Global Precipitation Climatology Product(GPCP) version 2.3 (Adler et al. 2018), which provides
near-global coverage by incorporating satellite data andis available for the period 1979–2018, as well as to the
Hadley Centre Sea Ice and Sea Surface Temperature(HadISST) version 1 dataset (Rayner et al. 2003).

c. Analysis methods
We compute linear trends using least squares regression analysis and assess statistical significance at the
90% confidence level using a two-tailed Student’s t test.
These trends are calculated over 50-yr periods, but our
results are insensitive to small changes in trend length(i.e., 40- and 60-yr trends yield similar results). All observational data are regridded to match the spatial resolution of CESM1 using bilinear interpolation. Area
weighting is used for constructing all regional and global
time series, as well as for computing spatial correlation
coefficients (the latter are computed after removing the
spatial mean of each field). All results are based on

a. Evolution of aerosol optical depth
Aerosol optical depth (AOD) at a wavelength of
550 nm provides a proxy for the magnitude of AER
forcing. Over the historical period (1920–2019), the ensemble mean AOD in AER shows distinctive behavior
over key regions (Fig. 2). In particular, North America
and Europe combined exhibit a slight increase from1920 to 1950, followed by a rapid rise from about 1955–
75 and a subsequent and similarly rapid decline to thepresent (Fig. 2a). In contrast, the southeastern portion of
the Asian continent (hereafter referred to as ‘‘Southeast
Asia’’ for convenience, although our usage differs fromgeographical convention) shows a steady increase in
AOD from the 1930s until the present (Fig. 2b), while
tropical South America and Africa show a pronounced
rise from 1945 to 1990 and remain relatively steadythereafter (Fig. 2c).
The evolving spatial patterns of AOD changes in
AER over the historical period are portrayed in Figs. 2d–fbased on ensemble-mean trends for three overlapping 50yr intervals: 1930–79, 1950–99, and 1970–2019 (these intervals, henceforth referred to respectively as ‘‘early,’’‘‘middle,’’ and ‘‘late,’’ provide a succinct way of summarizing the evolution of slow changes in AOD during 1930–2019). Note that we omit the first 10 years of the model
simulations from our analysis to guard against potential
memory from the single ocean initial condition used for
the LEs (Yeager et al. 2018). Consistent with the regional
AOD time series, the early period shows positive AOD
trends in the NH, South America, and Africa, with maximum values in the main AER source regions (Europe,
Southeast Asia, and tropical Africa and South America),
and slight declines in parts of the SH (Fig. 2d). The AOD
increases over tropical Africa and South America arelargely a result of biomass burning AER precursor emissions resulting from human interference (van Marle et al.
2017), while those over Southeast Asia, Europe, and North
America are primarily due to industrial AER precursor
emissions (note that biomass burning decreases offset
to a large extent industrial emissions over eastern North
America during this early period; see Fig. S1 in the online
supplemental material). In the middle period, the main
regions of AOD increase shift to the tropics, with maximum trends over Southeast Asia and weaker values overDownloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

where the subscript ‘‘i’’refers to an individual ensemble
member, and the subscript ‘‘em’’ refers to the ensemble
mean. These equations follow from the definition of internal variability in member i of the ‘‘X’’ ensemble (i.e.,
XGHGi 2 XGHGem), and the definition of the responseto the withheld forcing (i.e., ALL em 2 XGHG em).
Hereafter, we focus on the combined effects of AERindus
and AERbmb (termed AER) by summing their contributions. Data from the new CESM1 LEs are publicly
available at http://www.cesm.ucar.edu/working_groups/
CVC/simulations/cesm1-single_forcing_le.html.

7840

JOURNAL OF CLIMATEVOLUME 33

tropical Africa and South America (Fig. 2e). The late period exhibits pronounced declines in AOD over Europe
and eastern North America, and strong increases overIndia and Southeast Asia (Fig. 2f). The striking differencesin the spatial distribution of aerosol-induced AOD trends
among the three periods has important implications for the
evolving large-scale patterns of SST and precipitation response discussed next.

b. Evolution of forced SST trends
In this section, we present the evolution of forcedchanges in SST in the AER, GHG, and ALL ensembles
using the same framework of overlapping 50-yr trends as
for AOD above. Global trend maps and associated zonal
mean trends for each period are shown in Fig. 3; resultsfor SAT (with coverage over ocean, land, and sea ice) are

shown in Fig. S2. During the early period (1930–79), SST
trends in ALL are dominated by an interhemisphericstructure, with generally negative values over the NH
and positive values over the SH (Fig. 3a), consistent withthe lack of an appreciable global-mean trend (recall
Fig. 1a). Notable regional features include enhanced
warming over the southeastern tropical Pacific andIndian Oceans, and enhanced cooling over the northwestern subtropical Pacific and subpolar North Atlantic.
In the middle period (1950–99), SST trends in ALL arepositive in both hemispheres (except near Nova Scotia),
resulting in a more spatially homogenous pattern thanthe early period, although a remnant of interhemispheric asymmetry remains (more warming in the SH
compared to the NH; Fig. 3b). The late period (1970–2019) in ALL is characterized by amplified warming

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

FIG. 2. Aerosol optical depth (AOD) at a wavelength of 550 nm (no units, multiplied by 100) from the AER
ensemble mean. Regional anomaly time series relative to the 1920–2020 base period for (a) eastern North America
and Europe, (b) Southeast Asia, and (c) tropical South America and Africa [regions identified in (d)–(f)]. Spatial
distribution of linear trends in AOD during (d) 1930–79, (e) 1950–99, and (f) 1970–2019. AOD values in polar
regions (hatching) are omitted for plotting purposes only.

15 SEPTEMBER 2020

DESER ET AL.

7841

and a large degree of equatorial symmetry (Fig. 3c). In
particular, maximum warming occurs along the equator in the Pacific and Atlantic, with reduced warming
over the subtropical eastern basins of both hemispheres; cooling is seen over the central subpolar North
Atlantic.
The large-scale SST trend pattern driven by AER in
the early period is similar to that in ALL (with someregional exceptions such as the subpolar North Atlantic),
but with an overall offset due to global cooling (Fig. 3d).
The spatial correlation coefficient between the SST trends
in ALL and AER [denoted r(ALL, AER)] is 0.64 basedon the global domain and 0.86 for the tropics (308N–308S).
In the middle period, AER continues to cool most of the
global oceans, with the most pronounced SST declines
along the northern and eastern margins of the North
Pacific; a localized center of warming occurs to the southof Greenland (Fig. 3e). The imprint of the AER-induced
pattern is evident over the Pacific sector in ALL, but otherregions such as the subpolar North Atlantic show differences [r(ALL, AER) 5 0.40 global, 0.48 tropics]. In thelate period, the NH cooling due to AER weakens, and is
replaced by warming over the entire North Atlantic; thispattern bears little resemblance to that in ALL [r(ALL,
AER) 5 0.27 global, 0.37 tropics] (Fig. 3f). The reversal of

the cooling trends in the North Atlantic sector and theMediterranean Sea in the late period is likely a result ofconcurrent AOD declines over North America andEurope. The localized warming of the subpolar North
Atlantic, which develops in the early period and subsequently intensifies and persists, may be associated with a
gradual strengthening of AMOC in response to global
cooling. The decreasing resemblance between AER and
ALL with time is also evident in the zonal mean trend
profiles.
The pattern of GHG-induced SST trends is largely
static, with an increase in magnitude over the historical period (Figs. 3g–i). This pattern consists ofamplified warming over the equatorial Pacific andAtlantic, the western Indian Ocean, the North Pacific,
and portions of the Southern Ocean, and cooling overthe subpolar North Atlantic, consistent with previous
studies (e.g., Manabe and Stouffer 1993; Xie et al.
2010; DiNezio et al. 2009). Pattern correlations between SST trends in ALL and GHG [r(ALL, GHG)]
increase with time, from 0.26 and 0.24 in the early andmiddle periods, respectively, to 0.74 in the late period
based on the global domain (similar values are found
for the tropical domain: 0.24, 0.26, and 0.74, respectively). The increasing resemblance between GHG

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

FIG. 3. Linear trends in CESM1 ensemble-mean SST [K (50 yr)21] based on (a) ALL 1930–79; (b) ALL 1950–99; (c) ALL 1970–2019;(d) AER 1930–79; (e) AER 1950–99; (f) AER 1970–2019; (g) GHG 1930–79; (h) GHG 1950–99; and (i) GHG 1970–2019. Stippled regions
indicate insignificant values at the 90% confidence level based on a two-sided t test. Gray shading at the poles denotes regions with sea ice.
Panels on the right show zonal-mean profiles (608S–658N) of the trend maps in each row: ALL (black), AER (blue), and GHG (red).

7842

JOURNAL OF CLIMATEVOLUME 33

and ALL with time is also evident in the zonal mean
trend profiles.

c. Evolution of forced precipitation trends
Global trend maps and associated zonal mean trends
of precipitation for each period are shown in Fig. 4(precipitation trends as a percentage of the climatology
in each period are shown in Fig. S3). Like SST, the largescale pattern of forced precipitation trends in ALLchanges markedly over the course of the historical record, from a predominantly interhemispheric contrast in
the early period (Fig. 4a and associated zonal-mean
profile) to a more equatorially symmetric structure in
the late period (Fig. 4c and associated zonal-mean profile). While there are many regional exceptions to how
these large-scale patterns are manifest, the early period
has preferentially more drying north of the equator,
particularly over Southeast Asia, the Maritime Continent,
the western North Pacific, and northern South America,
and more wetting south of the equator, especially over the
Indian Ocean, central Pacific, and Australia (Fig. 4a),while the late period is dominated by precipitation increases along the equatorial Pacific in addition to theequatorial Atlantic and central Africa, drying over portions of the subtropics, and wetting at middle and highlatitudes (Fig. 4c). In relation to the model’s climatology,
the early period trends represent an eastward extension of

the South Pacific convergence zone (SPCZ), a southward
shift of the Indian Ocean convergence zone (IOCZ), and aweakening of the rainfall maximum over the western
tropical Pacific (Fig. 4a). In contrast, the late period trends
represent an equatorward intensification of the Pacific(and Atlantic) ITCZ, a retraction of the SPCZ, and aweakening of the IOCZ (Fig. 4c). Precipitation trends inthe middle period resemble those in the early period butwith reduced amplitude over the Pacific sector (Fig. 4b).
Regional exceptions to the large-scale patterns describedabove include drying trends over South Africa in the earlyand middle periods and wetting trends over the northwestern Indian Ocean in all three periods. The Southern
Ocean shows positive precipitation trends in all three periods, which amplify and expand with time.
Like SST, the resemblance between the large-scalepatterns of precipitation trends in AER (Figs. 4d,e) andALL (Figs. 4a–c) is highest in the early period [r(ALL,
AER) 5 0.72 global, 0.84 tropics] and gradually diminishes over time [middle period r(ALL, AER) 5 0.56
global, 0.55 tropics; late period r(ALL, AER) 5 0.23
global, 0.21 tropics]. This behavior is also evident in thezonal-mean trend profiles. In the early period, the spatial trend amplitudes are generally larger in AER compared to ALL (Figs. 4d,a). In the middle period, the
AER-induced drying over the equatorial Pacific andweak wetting within the ITCZ to the north are in

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

FIG. 4. As in Fig. 3, but for precipitation [mm day21 (50 yr)21]. The contours show the 6 mm day21 isopleth of the precipitation climatology(1930–2019) from ALL.

15 SEPTEMBER 2020

DESER ET AL.

just in the tropics but also in the extratropics [r(AER,
GHG) 5 20.62; Figs. 3e,h]. The structural similarity
between AER and GHG in the middle period is also
clear from the zonal-mean profiles for both SST (Fig. 3)
and precipitation (Fig. 4). We speculate that the common structure during the middle period results from thepredominance of tropical AER forcing, which may
mimic the way well-mixed GHGs affect the climate
system (albeit with opposite sign).

d. Evolving pattern correlations of forced trends:
1930–2080
Here, we provide a more systematic evaluation of theevolving contributions of AER and GHG to large-scalepatterns of forced trends in ALL by calculating r(AER,
ALL) and r(GHG, ALL) for running 50-yr trends during 1930–2080 (note the extension beyond the historicalperiod for this analysis). The pattern correlations arecomputed for both global and tropical domains. Resultsfor precipitation are insensitive to the domain chosen,
since precipitation trend amplitudes are much larger inthe tropics than the extratropics; hence, we report only
the tropical values. For precipitation, r(ALL, AER) is
highest (.0.8) for trends beginning in the 1930s, dropping rapidly to between 0.2 and 0.3 for trends starting in
the 1960s–90s, followed by a slight rise to around 0.5 fortrends beginning after 2010 (Fig. 5a). The increase at theend of the record is associated with a projected reversalof AER-induced AOD trends over Southeast Asia in
the RCP8.5 scenario (Fig. S4). The opposite behavior isseen for r(ALL, GHG), with minimum values (,0.3) for
trends beginning before 1950, rising rapidly to a maximum of about 0.9 for trends starting in the mid-1980s
and thereafter (Fig. 5a). The two pattern correlation
curves cross in 1954 (i.e., the 1954–2003 trend period)at a value of 0.42. This 50-yr period, centered in the late1970s, marks the transition between an aerosol-dominatedpattern of forced trends in ALL to a GHG-dominated one.
Similar results are found for SST in terms of r(AER, ALL)and r(GHG, ALL), with nearly identical timing andcomparable magnitude of the crossover point (Figs. 5b,c).
Compared to precipitation and tropical SST, global SST
shows a more abrupt rise of r(AER, ALL) around 1980 topeak values of about 0.8 thereafter (Fig. 5c). This sharptransition is coincident with the onset and projected continuation of declining AOD over North America andEurasia, and is manifest as a reversal in the sign of SST
anomalies in both the North Pacific and North Atlantic(not shown).
It is also interesting to compare the evolving patterncorrelations between AER and GHG (Figs. 5d–f). Bothprecipitation and SST show sizeable negative r(AER,
GHG) for trends beginning in the late 1940s to mid-1950s

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

striking contrast to the features in ALL, as are theopposite-signed trends over the Southern Ocean (cf.
Figs. 4e,b). Although the pattern correlation is low in thelate period, AER and ALL share certain regional features, for example wetting over eastern North America,
Europe, and Africa, and drying over Southeast Asia
(Figs. 4f,c).
It is interesting to note the differences in AERinduced precipitation trends over the tropical Pacificbetween the early and middle periods (Figs. 4d,e). In
particular, there is a large degree of equatorial symmetry within the tropical Pacific in the middle period, withdrying along the equator and wetting within the ITCZ
and SPCZ, in contrast to the antisymmetric pattern thatprevails in the early period. A similar structural change
is also apparent in the SST trends (Figs. 3d,e). We
speculate that this structural change results from thealtered distribution of regional AOD trends in the AER
simulations, in particular the fact that AOD trends in the
middle period are predominantly located in the tropics,
whereas those in the early period also include a NH
midlatitude component which would drive an interhemispheric shift (recall Figs. 2a,b). The negative AOD
trends over Europe and eastern North America in thelate period are likely responsible for inducing positive
precipitation trends north of the equator in the tropicalAtlantic and Africa, as well as local precipitation increases over Europe and eastern North America (Fig. 4f).
Like SST, the patterns of GHG-induced precipitation
trends are similar in all three periods, with generally
increasing amplitudes over time (Figs. 4g–i and accompanying zonal-mean profiles). GHGs produce an increase in precipitation along the equator in the Pacificand Atlantic, with compensating drying directly to thenorth and south. In addition, wetting occurs in the extratropics of both hemispheres and over the northern
Indian Ocean and Southeast Asia, while bands of drying
occur over the equatorial Indian Ocean and the subtropical North Atlantic. These signals become morestatistically significant over time as their amplitudes increase. Consistent with SST, the pattern correlations
between precipitation trends in ALL and GHG
strengthen over time, with low values in the early period(0.16 global, 0.15 tropics), increasing slightly in the
middle period (0.28 global, 0.24 tropics) and attaining
maximum values in the late period (0.78 global, 0.79
tropics). This behavior is also seen for the zonal-mean
trend profiles.
Finally, it is interesting to note the structural similarity(but opposing sign) of the AER and GHG induced
precipitation trends during the middle period, especiallyin the tropics [r(AER, GHG) 5 20.40; Figs. 4e,h]. Thisis also apparent for the accompanying SST trends, not

7843

7844

JOURNAL OF CLIMATEVOLUME 33(minimum values of 20.41 for precipitation; 20.63
and 20.65 for tropical and global SST, respectively),consistent with the middle period trend maps shown in
Figs. 3 and 4. As discussed earlier, these anticorrelations
may be related to a geographical shift in aerosol emissions, in particular strong tropical AOD increases in
the absence of extratropical AOD changes (recall
Fig. 2e). It is noteworthy that the strongest anticorrelations occur at about the same time as the transition between an aerosol-dominated pattern of forced
trends in ALL to a GHG-dominated one, which has
implications for the amplitude of forced trends in ALL(see section 3e). Global SST also shows a sharp transition to positive r(AER, GHG) values (0.4–0.6) for
trends beginning after 1980 (Fig. 5f), coincident withthe onset of AOD reductions over the northern continents mentioned above.

e. Evolving magnitudes of forced trend patterns:
1930–2080
Pattern correlations convey the degree of structuralsimilarity between pairs of trend maps, but do not indicate their relative magnitudes. Here we use a spatial‘‘root-mean-square’’ (rms) measure for quantifying theamplitude of a trend pattern. To compute the spatial
rms, we square the ensemble mean trend values at eachgrid box, take their spatial average over the domain inquestion (global or tropical) using area-weighting, andthen take the square root. This procedure is repeated foreach running 50-yr trend interval. For SST, we first
subtract the domain-mean trend from the individual
trends at each grid box in order to isolate the spatiallyvarying component (i.e., pattern) of the trends (this step
is not needed for precipitation trends, which are dominated by spatial variations). Consistent with the trend
maps shown in Figs. 3 and 4, the spatial rms of AERforced tropical precipitation and SST trends maximize atthe beginning of the analysis period (i.e., 50-yr trends
starting in the 1930s) whereas those of GHG-induced
tropical trends increase steadily with time (Figs. 6a,b).
The spatial rms magnitudes of the AER and GHG induced tropical trends are comparable for trend start
years ranging from 1955 to 1970 (Figs. 6a,b). The spatial
rms characteristics in ALL represent a hybrid between
those in AER and GHG, with a relative maximum fortropical trends beginning in the 1930s, a minimum forthose beginning in the 1950s, and a monotonic increase
thereafter (Figs. 6a,b). The minimum spatial rms in ALL

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

FIG. 5. Pattern correlations between ensemble-mean running 50-yr trends in ALL and AER [r(ALL, AER); blue curves] and between
ALL and GHG [r(ALL, GHG); red curves] for (a) tropical precipitation, (b) tropical SST, and (c) global SST. Pattern correlations
between ensemble-mean running 50-yr trends in GHG and AER [r(GHG, AER); green curves] for (d) tropical precipitation, (e) tropical
SST, and (f) global SST. The time axis denotes the start year of each 50-yr trend. Note that (a)–(c) share the same y-axis labels, and (d)–(f)share the same y-axis labels. All pattern correlation curves have been smoothed with a three-point average for clarity. Dashed curves are
based on industrial AER only. Results for global precipitation are nearly identical to those for tropical precipitation.

15 SEPTEMBER 2020

DESER ET AL.

7845

results from a combination of reduced AER-driven
magnitudes and anticorrelated r(AER, GHG). That is,
the minimum amplitude of forced patterns of trends in
ALL during the second half of the twentieth century(i.e., 50-yr trends beginning in the 1950s) compared toearlier and later periods is a consequence of reduced
aerosol forcing in the NH extratropics combined withsimilar patterns of response but opposite sign for AER
and GHG. The global and tropical SST spatial rms
curves are grossly similar, with a more well-definedcrossover point (in 1953) between AER and GHG for
global SST (Fig. 6c).

f. Additivity of individual forced responses
The comparisons shown above raise the question: To
what extent do the forced responses in AER and GHG
dominate the forced response in ALL? Recall that
ALL includes additional anthropogenic forcings beyond
AER and GHG (i.e., stratospheric and troposphericozone and land use/land cover changes) as well as natural radiative forcings (i.e., solar and volcanic) and potential nonlinear interactions between the AER and
GHG influences. The forced trends in ALL are compared with the sum of the forced trends in AER and
GHG (denoted SUM) for each historical period (1930–
79, 1950–99, and 1970–2019) in Fig. 7 for SST and Fig. 8
for precipitation (Fig. S5 for precipitation trends as apercentage of the climatology in each period). The
large-scale patterns of trends in ALL (panels a–c) andSUM (panels d–f) are very similar, with pattern correlations in excess of 0.9 for all three periods and bothvariables. Indeed, the differences between ALL andSUM are statistically insignificant over most of the globe(differences shown in panels g–i of Figs. 7 and 8), withsome regional exceptions as follows. SST trends in theearly period show significantly larger cooling over thewestern North Pacific and subpolar North Atlantic(Fig. 7g) while those in the late period show significantly
stronger warming over the sub-Arctic North Pacific andAtlantic (Fig. 7i) in SUM compared to ALL. Portions of
the Southern Ocean also show significant differences in
SST trends in the middle and late periods, in addition
to the Gulf Stream Extension in the middle period(Fig. 7h). Precipitation trends over the central tropical
Pacific in the early period and the northeastern North
Atlantic in the middle period are significantly greater in
SUM compared to ALL (Figs. 8g,h, and accompanying
zonal means), whereas those over the Southern Ocean
in the late period are significantly weaker (Fig. 8i andaccompanying zonal means). The physical origins ofthese statistically significant regional differences meritfurther investigation, including the roles of natural radiative forcings (Stevenson et al. 2019), southern polar
stratospheric ozone changes (Solomon et al. 2015), andnonlinear interactions between AER- and GHG-inducedchanges in ocean circulation, sea ice, and terrestrial snow
cover (Deng et al. 2020; Zhao et al. 2019). Over most of
the globe, however, the combined effects of AER and
GHG dominate the forced trends in temperature andprecipitation in ALL during the historical period.

g. How detectable is the forced trend in a single
realization?
Up to now, we have focused on the evolution of forced(i.e., ensemble mean) trends in the ALL, AER, and
GHG LEs. How detectable are these forced responses
in any single ensemble member? The answer to thisquestion has direct implications for the attribution
trends in the observational record (see section 3h). To
illustrate the diversity of 50-yr trends across individual

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

FIG. 6. Spatial RMS of ensemble-mean running 50-yr trends in ALL (black curves), AER (blue curves), and GHG (red curves) for(a) tropical precipitation, (b) tropical SST*, and (c) global SST*, where the asterisk denotes that the domain-mean SST trend has been
removed before computing the spatial RMS. The time axis denotes the start year of each 50-yr trend. All curves have been smoothed witha three-point average for clarity. Dashed blue curves are based on industrial AER only. Results for global precipitation are nearly
identical to those for tropical precipitation.

7846

JOURNAL OF CLIMATEVOLUME 33

members of a given LE (see also Deser et al. 2016), we
select two members of ALL with contrasting trends inthe early period (Figs. 9a–f) and two members with
contrasting trends in the late period (Figs. 9g–l). In theearly period, member A shows positive precipitation
trends in the eastern tropical Pacific and negative trends
in the west (Fig. 9e), similar to the pattern of the forced
trend but with much larger amplitude (Fig. 9d). In
contrast, member B shows a pronounced drying trend
across the entire equatorial Pacific, accompanied byweaker wetting trends to the north and south, a patternthat bears almost no resemblance to the forced trend(Fig. 9f). The associated tropical Pacific SST trends also
show distinctive patterns that are physically consistent
with the precipitation trends: member A shows warmingin the east and cooling in the west, while member B
shows cooling that extends across most of the equatorial
Pacific (Fig. 9b). The late period shows a similar diversity as the early period, despite the larger global warming signal (Figs. 9h,i,k,l). Indeed, precipitation trends inmembers A and B during 1970–2019 are of opposite sign
over the tropical Pacific, consistent with their contrasting trends in zonal SST gradient. The fact that 50-yr
trends in individual ensemble members can be sodifferent, and can contrast so markedly from the forced
trends, attests to the large contribution from internalvariability. Such internal variability must be taken into
account when interpreting transient and inferred equilibrium climate sensitivity estimates based on observations during the last 50 years, particularly in light ofrecent work that highlights the sensitivity of thesequantities to patterns of SST trends in the tropical IndoPacific as a result of their outsized effects on global-scale
radiative feedbacks (Jiménez-de-la-Cuesta and Mauritsen
2019; Dong et al. 2019).
We now generalize these anecdotal results on therelative contributions of internal variability and external
forcing in individual realizations to the full set of ensemble members and all 50-yr running trend periods
during 1930–2080. We use pattern correlations as a
metric to summarize the degree of structural resemblance between each individual member’s trend and theensemble-mean trend. For the ALL ensemble, thispattern correlation is denoted r(ALLi, ALLem), where iis the trend in ensemble member i and em is the ensemble mean trend. Similar definitions are used for the
AER and GHG ensembles [i.e., r(AERi, AERem) andr(GHGi, GHGem)]. We also assess the likelihood that an

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

FIG. 7. Linear trends in CESM1 ensemble-mean SST [K (50 yr)21] based on (a) ALL 1930–79; (b) ALL 1950–99; (c) ALL 1970–2019;(d) AER 1 GHG 1930–79; (e) AER 1 GHG 1950–99; (f) AER 1 GHG 1970–2019; (g) ALL minus AER 1 GHG 1930–79; (h) ALL
minus AER 1 GHG 1950–99; and (i) ALL minus AER 1 GHG 1970–2019. Stippled regions indicate insignificant values at the 90%
confidence level based on a two-sided t test. Gray shading at the poles denotes regions with sea ice. Panels on the right show zonal-mean
profiles (608S–658N) of the trend maps in each row: ALL (black), AER 1 GHG (purple), and their difference (green).

15 SEPTEMBER 2020

DESER ET AL.

7847

individual member’s trend pattern could have occurredin the absence of external forcing by making use of the1800-yr CESM1 preindustrial control simulation (Kayet al. 2015). Specifically, we compute trends for 72
overlapping 50-yr segments of the control simulation(i.e., years 401–450, 426–475, 451–501, etc.), and thencompute pattern correlations between each of thesecontrol trends and the trends in ALLem, AERem, andGHGem. If less than 5% of these control pattern correlations exceeds the value of r(ALLi, ALLem), then thetrend in member i of the ALL ensemble is unlikely tohave occurred without external forcing. Similar logic is
applied to the AER and GHG ensembles.
Individual members of ALL show a wide range ofpattern correlations with the forced response, especiallyfor trends starting before 2000 (Figs. 10a,d,g). For example, r(ALLi, ALLem) for precipitation and tropical
SST ranges from about 0.2 to 0.85 for trends beginning in
the 1930s and from about 0.1 to 0.7 for trends beginningin the 1950s–early 1960s (Figs. 10a,d), with a slightly
narrower spread (approximately 0.3–0.8) for global SST
during these periods (Fig. 10g). These ranges narrowconsiderably for future trends beginning after 2010, toabout 0.6–0.9 for precipitation and tropical SST, andabout 0.8–0.95 for global SST. The pattern correlationsr(ALLi, ALLem) are unlikely to have occurred bychance without external forcing, except for precipitation
trends beginning between 1940–74, tropical SST trends
beginning in the 1930s and 1950s–60s, and global SST
trends beginning in the late 1950s (with some intermittent exceptions; maroon bars along the bottom of
Figs. 10a,d,g).
The average r(ALLi, ALLem) across the individual
ensemble members indicates the degree to which theforced trend pattern is detectable in a single realization,
on average. For precipitation and tropical SST, theaverage r(ALLi, ALLem) is about 0.60–0.65 for trends
starting in the 1930s, falling to about 0.45 for trends
starting in the mid-1950s; the ‘‘1930s’’ value is notreached again until trend start years in the early 1990s,
and rises thereafter to a maximum value of 0.80–0.85 fortrends starting after 2015 (thick black curves in Figs. 10aand 10d). The average r(ALLi, ALLem) is slightly higher
for global SST than the other variables, with values
around 0.6 for trends starting before about 1960 andincreasing to 0.9 for trends beginning after 2000 (thickblack curve in Fig. 10g). The temporal character of theaverage r(ALLi, ALLem) curves is consistent with thechanging amplitudes of the forced trend patterns (recall Fig. 6).
Another useful metric of detectability of forced trend
patterns in individual realizations is the percentage of

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

FIG. 8. As in Fig. 7, but for precipitation [mm day21 (50 yr)21]. The contours show the 6 mm day21 isopleth of the precipitation climatology (1930–2019) from ALL. Panels on the right show zonal-mean profiles of the trend maps in each row: ALL (black), AER 1 GHG(purple), and their difference (green).

7848

JOURNAL OF CLIMATEVOLUME 33

ensemble members with r(ALLi, ALLem) . 0.71 (.0.86),
corresponding to .50% (.75%) of shared spatial variance with the forced response. This metric is displayed by
the horizontal colored bars centered at the two patterncorrelation thresholds in Fig. 10. For precipitation, ,10%of members exceed the 50% threshold for trends

beginning earlier than the mid-1990s, while .70% of themembers do so for trends beginning after the early2010s; less than 10% of members exceed the 75%
threshold for all trends regardless of start year (Fig. 10a).
Similar percentages are found for tropical SST (Fig. 10d).
For global SST, more than 90% of members exceed the

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

FIG. 9. Linear trends from CESM1 ALL for (a) ensemble-mean SST 1930–79; (b) member A SST 1930–79; (c) member B SST 1930–79;(d) ensemble-mean precipitation 1930–79; (e) member A precipitation 1930–79; (f) member B precipitation 1930–79; (g) ensemble-mean
SST 1970–2019; (h) member A SST 1970–2019; (i) member B SST 1970–2019; (j) ensemble-mean precipitation 1970–2019; (k) member A
precipitation 1970–2019; and (l) member B precipitation 1970–2019. SST is in units of K (50 yr)21, and precipitation is in units of mm day21(50 yr)21. Stippled regions indicate insignificant values at the 90% confidence level based on a two-sided t test. Gray shading at the poles in
SST panels denotes regions with sea ice. The contours on the precipitation panels show the 6 mm day21 isopleth of the precipitation
climatology (1930–2019) from ALL.

15 SEPTEMBER 2020

DESER ET AL.

7849

50% variance threshold for trends beginning after the
mid-1970s, and about 50% of members exceed the 75%
threshold for trends starting after 2000 (Fig. 10g). These
results are a sobering reminder that the chances ofobtaining a trend pattern that closely resembles theforced trend pattern in a single ensemble member are low
for any 50-yr interval during the historical period, withclear implications for interpreting the observational record (model biases aside).
The AER (Figs. 10b,e,h) and GHG (Figs. 10c,f,i) ensembles also show a large range of pattern correlations
with the forced response across individual members,

typically about 0.4 units for precipitation and tropical
SST, and 0.3 units for global SST. Like ALL, the average r(AERi, AERem) and r(GHGi, GHGem) evolve inconcert with the amplitude of the forced trend patterns,
decreasing over time for AER (from about 0.6–0.8 fortrends starting in the 1930s to about 0.4–0.6 for trends
starting in the 2030s: thick curves in Figs. 10b,e,h) andincreasing over time for GHG (from about 0.3–0.4 fortrends starting in the 1930s to about 0.8 for trends
starting in the 2030s: thick curves in Figs. 10c,f,i). Thereis also a conspicuous minimum in average r(AERi,
AERem) for tropical and global SST trends starting in

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

FIG. 10. Pattern correlations (gray curves) of running 50-yr trends between each individual ensemble member and the ensemble mean of(a) ALL tropical precipitation; (b) AER tropical precipitation; (c) GHG tropical precipitation; (d) ALL tropical SST; (e) AER tropical
SST; (f) GHG tropical SST; (g) ALL global SST; (h) AER global SST; and (i) GHG global SST. Thick black curves are the average of thegray curves. Lower and upper horizontal colored bars across the top of each panel show the percentage of ensemble members with apattern correlation exceeding 0.71 and 0.86, corresponding to 50% and 75% variance explained, respectively. Maroon line segments at thebottom of each panel indicate the trend intervals that are significant with respect to the control simulation (see text for details). The time
axis denotes the start year of each 50-yr trend. Results for global precipitation are nearly identical to those for tropical precipitation.

7850

JOURNAL OF CLIMATE

h. Interpretation of observed trends in the contextof CESM1
The model results shown above have implications forthe interpretation of trends in observations, model biases
notwithstanding. In particular, if the model’s signal-tonoise ratio is realistic, then the prominence of internalvariability relative to the forced response in any single
ensemble member suggests that it is challenging to extract the forced response in observed patterns of 50-yr
SST and precipitation trends (see also Deser et al. 2016).
In addition, analysis of observations is hampered by issues of data quality, homogeneity, and spatial andtemporal coverage. Although it is beyond the scope of
this study to conduct a formal ‘‘detection and attribution’’ analysis (e.g., Santer et al. 2019; Bonfils et al.
2020), we examine the observed evolution of 50-yr SST
and precipitation trend patterns within the context of
the CESM1 ALL, AER, and GHG LEs whose sizes
allow for the first time a quantitative assessment of AER
and GHG effects on regional scales during 1930–2018.
Maps of observed SST and precipitation trends for thethree overlapping 50-yr periods are shown in Fig. 11,
along with ensemble-mean trends from ALL (ALLem)
for direct comparison. On a global scale, observed SST
trends show an acceleration of warming over the historical period (Figs. 11a–c), consistent with the forced
signal in ALL (Figs. 11c–f; see also Fig. 1a for GMST).
The spatial patterns of observed SST trends, as expectedfrom the internal variability inherent in the single observed realization, show similarities and differences withthe forced trends in ALL. In the early period and similar
to ALL, observed SSTs cool over most of the North
Atlantic and eastern North Pacific, and warm over muchof the Indian Ocean, South Atlantic, and South Pacific,
although their amplitudes are considerably larger thanthose of the model’s forced response (Figs. 11a,d). Other
areas show opposite sign trends between observations
and ALLem in the early period, such as the western halfof the North Pacific and the southeastern tropical Pacificand Atlantic. Note that data coverage over the Southern
Ocean and southeast tropical Pacific is very limited(Fig. S7), so that uncertainty in SST trends in these regions is higher than elsewhere. The pattern correlation
between SST trends in observations and ALLem [r(OBS,
ALLem)] for the early period is 0.03 for the tropics and0.39 for the global domain. The large-scale structure ofobserved SST trends evolves in the middle period, with
an amplification of warming over the Indian Ocean andeastern tropical Pacific and Atlantic, a reduction incooling over the North Atlantic and an opposite-signed
pattern over the North Pacific compared to the early
period (Fig. 11b). The observed trend magnitudes in the
middle period are considerably larger than those in
ALLem (Fig. 11e), and the pattern correlation with
ALLem is modest (0.23 for the tropics and 0.25 for the
global domain). The observed warming pattern continues to evolve in the late period, with relative maxima
near the equator in all three ocean basins as well as the
subpolar North Pacific and most of the North Atlantic,
and relative minima south of Iceland, the tropical South
Atlantic and southeast Pacific, and the Southern Ocean,which exhibit cooling (Fig. 11c). Some of these features
are evident in ALLem (Fig. 11f), but the pattern correlations remain modest (0.28 for the tropics and r 5 0.31
for the global domain).
A comparison of precipitation trends in observations(GPCC) and ALLem is limited to land areas before thesatellite era (Figs. 11g–i). Overall, the resemblance oflarge-scale spatial patterns of terrestrial precipitation
trends between observations and ALLem is low, as evidenced by the weak pattern correlations: 0.16 for 1930–
79, 0.07 for 1950–99, and 0.04 for 1970–2018 in thetropics, with similar values for the global domain. In theearly period (Figs. 11g,j), similarities between GPCC
and ALLem include drying of the Sahel, the Caribbean,
and the Maritime Continent, and wetting over Australia,
northeast Brazil, and parts of the United States andeastern Eurasia (although GPCC shows generally larger
amplitudes than ALLem). In addition, the pronounced
early-period drying over Southeast Asia in ALLem isconfined to coastal locations in GPCC, and early-period
trends over South Africa and Argentina are opposite in
GPCC and ALLem. Precipitation trends in the middle
period (Figs. 11h,k) are similar to those in the early period,
except for the increased wetting over northern Europe andthe reversal to drying over the Mediterranean in both
GPCC and ALLem; there is also increased drying over
the Sahel and South Africa in GPCC, opposite to that in

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

the 1950s (values around 0.45–0.50; Figs. 10e,h). In both
the AER and GHG ensembles, ,10% of members exceed the 75% variance threshold throughout the analysis period for all three quantities (upper horizontal bar
in Figs. 10b,c,e,f,h,i). The 50% variance threshold isexceeded by 70%–80% of AER members for global SST
trends starting in the 1930s, and by 70%–80% of GHG
members for trends in all three quantities starting later thanthe mid-2010s (lower horizontal bar in Figs. 10b,c,e,f,h,i).
These results indicate that even for the AER and GHG
ensembles, the chances of obtaining a trend pattern thatclosely resembles the forced trend pattern in a single ensemble member are low, except at the very end of the record
for GHG and the very beginning of the record for AER
global SST. A complementary analysis of the ensemble
spread in the amplitude of large-scale trend patterns is given
in Fig. S6.

VOLUME 33

15 SEPTEMBER 2020

DESER ET AL.

7851

ALLem. In the late period (Fig. 11i), observed trends over
the Maritime Continent, eastern Australia, Brazil, and theSahel have reversed sign from the earlier periods; of these,
only the wetting trend over the Sahel is consistent with thesign of the trends in ALLem (Fig. 11l).
Without better spatial coverage over the oceans, it isdifficult to make any statements about observed precipitation trends in marine areas including the tropicalconvergence zones. However, precipitation trends during 1979–2018 from GPCP, which provides near-global

coverage by incorporating satellite data, also show
limited spatial correspondence with ALLem (patterncorrelation 5 0.17; see maps in Fig. S8).
Last, we provide an integrated perspective on therelative roles of internal variability, GHG, and AER in
the evolution of large-scale patterns of 50-yr precipitationand SST trends in observations and ALL. Figures 12a,
12d, and 12g show r(OBS, ALLem) for running 50-yr
trends during 1930–2018 (blue curves), superimposed
upon r(ALLi, ALLem) over the extended period 1930–

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

FIG. 11. Linear SST trends [K (50 yr)21] for (a) observations 1930–79; (b) observations 1950–99; (c) observations 1970–2018; (d) ALL
ensemble-mean 1930–79; (e) ALL ensemble-mean 1950–99; and (f) ALL ensemble-mean 1970–2019. Also shown are linear precipitation
trends [mm day21 (50 yr)21] for (g) observations 1930–79; (h) observations 1950–99; (i) observations 1970–2018; (j) ALL ensemble-mean
1930–79; (k) ALL ensemble-mean 1950–99; and (l) ALL ensemble-mean 1970–2019. Gray shading at the poles in SST panels denotes
regions with sea ice. The contours on the precipitation panels show the 6 mm day21 isopleth of the precipitation climatology. ERSSTv5
and GPCC are used for observations of SST and precipitation, respectively. White regions over the oceans in the observed precipitation
panels indicate missing data.

7852

JOURNAL OF CLIMATEVOLUME 33

2080 (repeated from Fig. 10). The r(OBS, ALLem) ranks
near the bottom or below the ensemble spread of r(ALLi,
ALLem) regardless of trend start year for precipitationand SST. Masking the simulated precipitation trends according to the evolving spatial coverage in GPCC increases r(ALLi, ALLem) by about 0.15, on average, fortrends starting before about 1980, widening the gap withr(OBS, ALLem) (Fig. 13a). Many factors may play a rolein the discrepancy between r(OBS, ALLem) and r(ALLi,
ALLem), including some combination of the following: 1)

uncertainty in anthropogenic aerosol emissions; 2) biases
in the model’s pattern and/or amplitude of forced response; 3) biases in the model’s patterns of internal variability; 4) biases in the model’s signal-to-noise of forced
response relative to internal variability; 5) a highly unusual occurrence of internal variability in observations;and 6) observational error. Further investigation isneeded to address the relative importance of each ofthese factors; additional discussion is provided in
section 5.

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

FIG. 12. Pattern correlations (gray curves) of running 50-yr trends between the individual ensemble members (i) of ALL (ALLi) and theensemble mean (em) of (a) ALL, tropical precipitation; (b) AER, tropical precipitation; (c) GHG, tropical precipitation; (d) ALL,tropical SST; (e) AER, tropical SST; (f) GHG tropical SST; (g) ALL, global SST; (h) AER, global SST; and (i) GHG, global SST. Thickblack curves are the average of the gray curves. Lower and upper horizontal colored bars across the top of each panel show the percentage
of ensemble members with a pattern correlation exceeding 0.71 and 0.86, corresponding to 50% and 75% variance explained, respectively.
Maroon line segments at the bottom of each panel indicate the trend intervals that are significant with respect to the control simulation(see text for details). Blue curves show the pattern correlations between observations and the ensemble means from ALL, AER, andGHG. GPCC and ERSSTv5 are used for observations of precipitation and SST, respectively (results based on HadISST SST are similar;
not shown). The time axis denotes the start year of each 50-yr trend. Results for global precipitation are nearly identical to those fortropical precipitation.

15 SEPTEMBER 2020

DESER ET AL.

7853

To determine the individual contributions of AER
and GHG to the trend patterns in each member of ALL,
we compute r(ALLi, AERem) and r(ALLi, GHGem).
The large spread in r(ALLi, AERem) and r(ALLi,
GHGem) across the ensemble members indicates substantial uncertainty in the detectability of AER- and
GHG-driven trend patterns in any single realizationaccording to our metrics (gray curves in Figs. 12b,c,e,f,h,i).
Further, the average r(ALLi, AERem) and average r(ALLi,
GHGem) values (thick black curves in Figs. 12b,c,e,f,h,i) fall
well below the 50% variance threshold (pattern correlationof 0.71) throughout the historical period. Indeed, thisthreshold is only met or exceeded for GHG-induced precipitation and tropical SST trends beginning after 2010, andfor AER- and GHG-induced global SST trends beginning
after 1990. Of these, only the future GHG-induced SST
trends exceed the 50% threshold in a majority of ensemble
members (colored horizontal bars in Figs. 12f and 12i).
Turning to the observations, we compute r(OBS,
AERem) and r(OBS, GHGem) in analogy with r(ALLi,
AERem) and r(ALLi, GHGem) and superimpose theresults in Fig. 12 (blue curves). Note that masking thesimulated precipitation trends by the evolving spatial
coverage in GPCC does not substantially alter the average values of r(ALLi, AERem) and r(ALLi, GHGem),
although it narrows their ensemble spread (Figs. 13b,c).
r(OBS, AERem) mostly falls within the ensemble spreadof r(ALLi, AERem), although generally below the average value of r(ALLi, AERem): Figs. 12b,e,h and 13b.
Similarly, r(OBS, GHGem) lies near the middle of the
distribution of r(ALLi, GHGem) for trends starting before about 1950 and within the lower end of the distribution thereafter (Figs. 12c,f,i and 13c). While theobserved pattern correlations with the forced trends in
AER and GHG are thus generally within the distributions expected from the combined influence of external
forcing and internal variability (according to CESM1),

their weak amplitudes (,0.4) indicate that ,20% of thespatial variance in the observed trend patterns is driven
by AER or GHG. This estimate pertains only to thepattern correlation metrics used here, and is subject toall of the caveats mentioned above (see further discussion in section 5).

4. Summary
We have studied the evolving roles of anthropogenic
aerosols (AER) and greenhouse gases (GHG) in driving
large-scale patterns of forced precipitation and SST
trends during 1920–2080 using a new set of CESM1
initial-condition large ensembles (LEs). In these newLEs, either industrial aerosols, biomass burning aerosols, or greenhouse gases are kept fixed at earlytwentieth-century conditions while all other external
anthropogenic and natural forcings vary in time according to historical and future (RCP8.5) scenarios.
These new LEs use the same model version and experimental design as the ‘‘all forcing’’ (ALL) CESM1 LE(Kay et al. 2015), allowing a direct assessment of theindividual roles of each forcing factor within the context
of a background changing climate, obtained by differencing the new LEs from the all-forcing LE. The large
number of ensemble members (15–20) in each of the
new LEs enables regional impacts of AER and GHG
forcing to be isolated from the noise of internal variability. Conversely, the LEs provide a unique perspective on the role of internal variability in limiting
detection and attribution of AER and GHG effects inany single realization, and by extension the observational record. Our analysis approach, based on running
50-yr trends, accommodates geographical and temporalchanges in both the patterns of forcing and response.
The main findings of our study are summarized asfollows.

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

FIG. 13. As in Figs. 12a–c, but for precipitation masked according to the evolving spatial coverage in observations (GPCC).

7854

JOURNAL OF CLIMATEThe low (generally ,0.4) pattern correlations withobservations imply that ,20% of the spatial variancesof observed SST and precipitation trend patterns areforced, regardless of which 50-yr interval during 1930–2018 is considered, but model biases in addition to
uncertainty in the magnitude of anthropogenic aerosol forcing will undoubtedly affect this estimate.

5. Discussion
Our findings are based on a single model with a single
scenario of anthropogenic and natural radiative forcings. Structural model uncertainty related to physics andparameterizations as well as uncertainty in radiative
forcings, especially those associated with anthropogenic
aerosols, remain important caveats to this work. In
particular, there is also no consensus to date on how
strong aerosol forcing is in the real world (Wilcox et al.
2015), although we note that the magnitude of effective
aerosol radiative forcing in CESM1 (21.37 W m22: Zelinka
et al. 2014) lies within the one-standard-deviation range ofplausible estimates (from 21.6 to 20.6 W m22) according
to a recent comprehensive review (Bellouin et al. 2020).
Another important caveat is the degree to which
CESM1 realistically simulates both the patterns andmagnitudes of internal variability and forced responses,
with implications for the signal-to-noise ration and thedetectability of forced trends in individual realizations
and in the real world. Evaluating internal variability inmodels is a challenging endeavor, particularly with respect to multidecadal trends given the brevity of theobservational record (McKinnon et al. 2017; McKinnon
and Deser 2018; Deser et al. 2020). Existing efforts havefocused on evaluation of internal variability of trends
in terrestrial air temperatures and precipitation (e.g.,Thompson et al. 2015; McKinnon and Deser 2018;
Beusch et al. 2020. These studies indicate that CESM1
overestimates internal variability of 50-yr trends in terrestrial air temperature at many locations; however,precipitation trend variability shows regions of bothunderestimation and overestimation. The extent to whichthese biases in internal trend variability over land extend tomarine areas remains to be assessed, along with implications for signal-to-noise and detectability of forced trends.
We have emphasized a large-scale pattern perspective
in our analysis, using spatial correlations—over tropicaland global domains—as a metric. Our conclusions regarding detectability of forced trends in observations
based on this metric may be compromised by model
biases in the patterns of internal variability and forced
response. Alternate metrics that place less emphasis on
regional detail, such as spatially aggregated quantities(Fischer and Knutti 2015), interhemispheric contrasts

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

1) Large-scale patterns of forced precipitation and SST
trends in ALL evolve over the historical period
1930–2019 from an interhemispheric structure dominated by aerosol effects to a more equatoriallysymmetric pattern dominated by greenhouse gas
influences, with the transition occurring in the late1970s. The transition phase is characterized by arelative minimum in trend pattern amplitudes in
ALL, due to a combination of reduced AER forcing
and offsetting effects from AER and GHG. During
this phase, AER and GHG drive similar patterns ofresponse, albeit of opposite polarity.
2) Over most of the globe, the sum of the forced trends
in AER and GHG is statistically indistinguishable
from the forced trends in ALL for both precipitationand SST throughout the historical period. This confirms the dominant roles of AER and GHG externally forced climate change over the historical period.
Those areas that do show significant differences aremainly associated with strong ocean currents, deep
water formation and sea ice, which could arise from
nonlinear interactions between AER and GHG, orfrom additional (natural and anthropogenic) external
influences (e.g., stratospheric ozone depletion, volcanic eruptions, etc.).
3) Internal variability greatly limits the detectability offorced trend patterns in individual realizations of the
AER, GHG, and ALL ensembles, especially during
the historical period, according to our pattern correlation metric. The average pattern correlation between an individual member’s trend and the forced
trend in ALL ranges from approximately 0.4 to 0.7
over the course of the historical period, rising to 0.8–
0.9 in the future. However, a single member can show
pattern correlations as low as 0.2 or as high as 0.8 forthe same 50-yr trend interval during the historicalperiod. Qualitatively similar ranges are found for the
AER and GHG ensembles. It should also be notedthat the forced trend patterns themselves are subject
to uncertainty due to the finite number of ensemble
members used for estimating them.
4) The model results summarized above highlight anumber of challenges for attributing trends in theobservational record, an endeavor that is further
hampered by data coverage and quality issues aswell as model bias. Pattern correlations between
observed trends and forced trends in the ALL,
AER, and GHG ensembles rank near the bottomof (or in some cases below) the model distributions.
This implies likely model biases in one or more of thefollowing: patterns of internal variability; patterns offorced response; and relative magnitudes of forced
response and internal variability (i.e., signal-to-noise).

VOLUME 33

15 SEPTEMBER 2020

DESER ET AL.

are dissimilar in earlier and later periods of the historical
record. We hypothesize their common structure may be
due to the fact that trends in regional aerosol emissions
during the period centered in the late 1970s are locatedprimarily within the tropics, unlike earlier and later
periods when there are additional prominent sources
and sinks in the northern extratropics, respectively.
While additional targeted experiments are needed totest our hypothesis, the results reported by Stuecker
et al. (2020) may provide circumstantial evidence for therole of tropical aerosol forcing. In particular, Stuecker
et al. (2020) show that when GHG increases are artificially confined to the tropics in CESM1, the structure of
the zonal-mean response within the tropics is very similar (albeit of opposite sign) to those of our AERinduced trends during 1950–99 for both SST and precipitation. More generally, additional research is needed
to understand the physical mechanisms governing the
evolving forced responses to AER and GHG in CESM1,
including the potential role of nonlinear interactions
between these forcing agents.
Other outstanding issues raised by our findings remain
to be addressed in future work. For example, what roledo CESM1 biases play in limiting detectability of AER
and GHG influences on observed large-scale patterns ofprecipitation and SST, and what particular aspects ofmodel bias are most important (e.g., patterns versus
amplitudes, forced responses versus internal variability,
and signal-to-noise ratio)? How much can the signal-tonoise ration of forced climate trends in the CESM1
ALL, AER, and GHG ensembles be enhanced throughthe application of more advanced statistical approaches,and how does this affect our conclusions regarding detectability of AER and GHG influences on observed
large-scale patterns of precipitation and SST trends?
Finally, there is scope for repeating our analysis on the
all-forcing and single-forcing LEs available from CanESM2(Bonfils et al. 2020) and CanESM5 (Swart et al. 2019), andfrom other models as they become available.
Acknowledgments. We are indebted to Gary Strand
for post-processing the single-forcing simulations andplacing them on the Earth System Grid. We appreciateconversations with Dr. Laurent Terray and Dr. Jie He
during the course of this work, and thank the threeanonymous reviewers for their constructive comments
and suggestions. The CESM project is supported primarily by the National Science Foundation (NSF). This
material is based upon work supported by the National
Center for Atmospheric Research, which is a major facility
sponsored by the NSF under Cooperative Agreement
1852977. Portions of this study were supported by the
Regional and Global Model Analysis (RGMA) component

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

(Friedman et al. 2013, 2020), or the use of a limited
number of empirical orthogonal functions (EOFs; Bonfils
et al. 2020), may result in a higher level of detectability offorced signals in the real world. Conversely, specific regions with greater sensitivity to anthropogenic aerosols,
such as the Sahel, Southeast Asia, and Europe, may also
have higher and/or more accurate detectability. Finally,
application of more sophisticated statistical approaches
such as optimal fingerprinting (e.g., Hegerl et al. 1996;
Ting et al. 2009), formal detection and attribution (Santer
et al. 2019; Bonfils et al. 2020), and novel pattern recognition methods (Barnes et al. 2019; Sippel et al. 2019;
Wills et al. 2020) to this new collection of CESM1 LEs
may be promising avenues for future work aimed at assessing detectability of trends forced by anthropogenic
aerosols and GHGs. Adopting a framework that allows
for temporal changes in both the patterns of forcing andof climate response, especially as they relate to regional
aerosol emissions, will be important regardless of theapproach used. In particular, using a single EOF to capture the evolving impacts of anthropogenic aerosol effects may be inadequate.
The above caveats notwithstanding, our results emphasize the need for caution when using individual realizations from a given model to assess climatic impactsof anthropogenic aerosols and GHGs during the historical period due to confounding effects from internalvariability. Combining individual simulations from different models (such as those in the CMIP5 and CMIP6
archives) to reduce internal variability may also be
problematic due to the models’ diverse representationof aerosol direct and indirect effects (e.g., Bellouin et al.
2020). Further, anthropogenic aerosol precursor emissions differ between CMIP5 and CMIP6, both over thehistorical period and in future projections, thereby introducing additional uncertainty into their climatic impacts (Samset et al. 2019; Wilcox et al. 2020). Theseissues emphasize the need for additional ‘‘single-forcing’’ large ensembles with other comprehensive Earthsystem models to obtain a more robust assessment of theclimate impacts of anthropogenic aerosols and associated uncertainties (Bonfils et al. 2020; Swart et al. 2019;Dittus et al. 2020). The ensemble size requirements forsuch single-forcing LEs remain to be determined andwill depend on the particular application and model(Deser et al. 2012; Milinski et al. 2019).
A number of outstanding issues are raised by ourfindings and remain to be addressed in future work. For
example, additional research is needed to understand
why the CESM1-simulated patterns of precipitation and
SST trends forced by AER and GHG share a common
structure during the latter half of the twentieth century(50-yr trends centered in the late 1970s), whereas they

7855

7856

JOURNAL OF CLIMATEof the Earth and Environmental System Modeling
Program of the U.S. Department of Energy’s Office of
Biological and Environmental Research (BER) via NSF
IA 1844590. Computing and data storage resources,
including the Cheyenne supercomputer (doi:10.5065/
D6RX99HX), were provided by the Computational andInformation Systems Laboratory (CISL) at NCAR. We
thank all the scientists, software engineers, and administrators who contributed to the development of CESM1.

REFERENCES
Adler, R. F., and Coauthors, 2018: The Global Precipitation
Climatology Project (GPCP) monthly analysis (new version2.3) and a review of 2017 global precipitation. Atmosphere, 9,138, https://doi.org/10.3390/atmos9040138.
Barnes, E. A., J. W. Hurrell, I. Ebert-Uphoff, C. Anderson, and
D. Anderson, 2019: Viewing forced climate patterns throughan AI lens. Geophys. Res. Lett., 46, 13 389– 13 398, https://doi.org/10.1029/2019GL084944.
Bellouin, N., and Coauthors, 2020: Bounding global aerosol radiative
forcing of climate change. Rev. Geophys., 58, e2019RG000660,
https://doi.org/10.1029/2019RG000660.
Beusch, L., L. Gudmundsson, and S. I. Seneviratne, 2020: Emulating
Earth system model temperatures with MESMER: From global
mean temperature trajectories to grid-point-level realizations
on land. Earth Syst. Dyn., 11, 139–159, https://doi.org/10.5194/
esd-11-139-2020.
Bonfils, C., B. D. Santer, J. C. Fyfe, K. Marvel, T. J. Phillips, andS. Zimmerman, 2020: Human influence on joint changes intemperature, rainfall and continental aridity. Nat. Climate
Change, https://doi.org/10.1038/s41558-020-0821-1, in press.
Chung, E., and B. Soden, 2017: Hemispheric climate shifts driven
by anthropogenic aerosol–cloud interactions. Nat. Geosci., 10,
566–571, https://doi.org/10.1038/ngeo2988.
Deng, J., A. Dai, and H. Xu, 2020: Nonlinear climate responses to increasing CO2 and anthropogenic aerosols simulated by CESM1.
J. Climate, 33, 281–301, https://doi.org/10.1175/JCLI-D-19-0195.1.
Deser, C., A. S. Phillips, V. Bourdette, and H. Teng, 2012:
Uncertainty in climate change projections: The role of internalvariability. Climate Dyn., 38, 527–546, https://doi.org/10.1007/
s00382-010-0977-x.
——, L. Terray, and A. S. Phillips, 2016: Forced and internal
components of winter air temperature trends over North
America during the past 50 years: Mechanisms and implications. J. Climate, 29, 2237–2258, https://doi.org/10.1175/JCLID-15-0304.1.
——, and Coauthors, 2020: Insights from Earth system model
initial-condition large ensembles and future prospects. Nat.
Climate Change, 10, 277–286, https://doi.org/10.1038/s41558020-0731-2.
DiNezio, P. N., A. C. Clement, G. A. Vecchi, B. J. Soden, B. P.
Kirtman, and S. Lee, 2009: Climate response of the equatorial

Pacific to global warming. J. Climate, 22, 4873–4892, https://doi.org/10.1175/2009JCLI2982.1.
Dittus, A. J., E. Hawkins, L. J. Wilcox, R. Sutton, C. J. Smith, M. B.
Andrews, and P. M. Forster, 2020: Sensitivity of historical climate
simulations to uncertain aerosol forcing. Geophys. Res. Lett., 47,e2019GL085806, https://doi.org/10.1029/2019GL085806.
Dong, B., R. T. Sutton, E. Highwood, and L. Wilcox, 2014: The
impacts of European and Asian anthropogenic sulfur dioxide
emissions on Sahel rainfall. J. Climate, 27, 7000–7017, https://doi.org/10.1175/JCLI-D-13-00769.1.
Dong, Y., C. Proistosescu, K. C. Armour, and D. S. Battisti, 2019:
Attributing historical and future evolution of radiative feedbacks to regional warming patterns using a Green’s function
approach: The preeminence of the western Pacific. J. Climate,32, 5471–5491, https://doi.org/10.1175/JCLI-D-18-0843.1.
Fischer, E., and R. Knutti, 2015: Anthropogenic contribution to
global occurrence of heavy-precipitation and high-temperature
extremes. Nat. Climate Change, 5, 560–564, https://doi.org/
10.1038/nclimate2617.
Friedman, A. R., Y. Hwang, J. C. H. Chiang, and D. M. W.
Frierson, 2013: Interhemispheric temperature asymmetry over
the twentieth century and in future projections. J. Climate, 26,
5419–5433, https://doi.org/10.1175/JCLI-D-12-00525.1.
——, G. C. Hegerl, A. P. Schurer, S. Lee, W. Kong, W. Cheng, andJ. C. H. Chiang, 2020: Forced and unforced decadal behaviorof the interhemispheric SST contrast during the instrumental
period (1881–2012): Contextualizing the late 1960s–early
1970s shift. J. Climate, 33, 3487–3509, https://doi.org/10.1175/
JCLI-D-19-0102.1.
Ganguly, D., P. J. Rasch, H. Wang, and J.-H. Yoon, 2012: Climate
response of the South Asian monsoon system to anthropogenic aerosols. J. Geophys. Res., 117, D13209, https://doi.org/
10.1029/2012JD017508.
Gettelman, A., and Coauthors, 2010: Global simulations of ice
nucleation and ice supersaturation with an improved cloud
scheme in the Community Atmosphere Model. J. Geophys.
Res., 115, D18216, https://doi.org/10.1029/2009JD013797.
——, L. Lin, B. Medeiros, and J. Olson, 2016: Climate feedback
variance and the interaction of aerosol forcing and feedbacks.
J. Climate, 29, 6659–6675, https://doi.org/10.1175/JCLI-D-160151.1.
Ghan, S., and Coauthors, 2016: Challenges in constraining anthropogenic aerosol effects on cloud radiative forcing using presentday spatiotemporal variability. Proc. Natl. Acad. Sci. USA, 113,
5804–5811, https://doi.org/10.1073/pnas.1514036113.
Giannini, A., and A. Kaplan, 2019: The role of aerosols and greenhouse gases in Sahel drought and recovery. Climatic Change,
152, 449–466, https://doi.org/10.1007/s10584-018-2341-9.
Gryspeerdt, E., and Coauthors, 2020: Surprising similarities inmodel and observational aerosol radiative forcing estimates.
Atmos. Chem. Phys., 20, 613–623, https://doi.org/10.5194/acp20-613-2020.
Hawkins, E., and R. Sutton, 2009: The potential to narrow uncertainty in regional climate predictions. Bull. Amer. Meteor.
Soc., 90, 1095–1108, https://doi.org/10.1175/2009BAMS2607.1.
Haywood, J. M., A. Jones, N. Bellouin, and D. Stephenson, 2013:
Asymmetric forcing from stratospheric aerosols impacts Sahelian
rainfall. Nat. Climate Change, 3, 660–665, https://doi.org/10.1038/
nclimate1857.
Hegerl, G. C., H. von Storch, K. Hasselmann, B. D. Santer, U. Cubasch, andP. D. Jones, 1996: Detecting greenhouse-gas-induced climate changewith an optimal fingerprint method. J. Climate, 9, 2281–2306, https://doi.org/10.1175/1520-0442(1996)009,2281:DGGICC.2.0.CO;2.

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

Data availability statement. Observational datasets usedin this study are available from https://www.esrl.noaa.gov/
psd. The CESM1 Large Ensemble simulations used inthis study are available from http://www.cesm.ucar.edu/
experiments/cesm1.1/LE/#single-forcing and http://www.
cesm.ucar.edu/projects/community-projects/LENS.

VOLUME 33

15 SEPTEMBER 2020

DESER ET AL.

Morrison, H., and A. Gettelman, 2008: A new two-moment bulk
stratiform cloud microphysics scheme in the NCAR
Community Atmosphere Model (CAM3), Part I: Description
and numerical tests. J. Climate, 21, 3642–3659, https://doi.org/
10.1175/2008JCLI2105.1.
Myhre, G., and Coauthors, 2013: Anthropogenic and natural radiative forcing. Climate Change 2013: The Physical Science Basis,
T. F. Stocker et al., Eds., Cambridge University Press, 659–740.
——, and Coauthors, 2017: PDRMIP: A precipitation driver andresponse model intercomparison project—Protocol and preliminary results. Bull. Amer. Meteor. Soc., 98, 1185–1198,
https://doi.org/10.1175/BAMS-D-16-0019.1.
Neale, R. B., and Coauthors, 2012: Description of the NCAR
Community Atmosphere Model (CAM 5.0). NCAR Tech. Note
NCAR/TN-4861STR, 268 pp., https://doi.org/10.5065/D6N877R0.
Neelin, J. D., C. Chou, and H. Su, 2003: Tropical drought regions in
global warming and El Niño teleconnections. Geophys. Res.
Lett., 30, 2275, https://doi.org/10.1029/2003GL018625.
Pendergrass, A. G., F. Lehner, B. M. Sanderson, and Y. Xu, 2015:
Does extreme precipitation intensity depend on the emissions
scenario? Geophys. Res. Lett., 42, 8767–8774, https://doi.org/
10.1002/2015GL065854.
Persad, G. G., and K. Caldeira, 2018: Divergent global-scale temperature effects from identical aerosols emitted in different
regions. Nat. Commun., 9, 3289, https://doi.org/10.1038/s41467018-05838-6.
Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V.
Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003:
Global analyses of sea surface temperature, sea ice, and nightmarine air temperature since the late nineteenth century. J. Geophys.
Res., 108, 4407, https://doi.org/10.1029/2002JD002670.
Rohde, R., and Coauthors, 2013: Berkeley Earth temperature
averaging process. Geoinfor. Geostat.: Overview, 1, 20–100,
https://doi.org/10.4172/2327-4581.1000103.
Rotstayn, L. D., and U. Lohmann, 2002: Tropical rainfall trends andthe indirect aerosol effect. J. Climate, 15, 2103–2116, https://doi.org/10.1175/1520-0442(2002)015,2103:TRTATI.2.0.CO;2.
Samset, B. H., M. T. Lund, M. Bollasina, G. Myhre, and L. Wilcox,
2019: Emerging Asian aerosol patterns. Nat. Geosci., 12, 582–
584, https://doi.org/10.1038/s41561-019-0424-5.
Santer, B., J. C. Fyfe, S. Solomon, J. F. Painter, C. Bonfils,
G. Pallotta, and M. D. Zelinka, 2019: Quantifying stochastic
uncertainty in detection time of human-caused climate signals.
Proc. Natl. Acad. Sci., 116, 19 821–19 827, https://doi.org/
10.1073/pnas.1904586116.
Schneider, U., A. Becker, P. Finger, A. Meyer-Christoffer, B. Rudolf,and M. Ziese, 2015: GPCC full data reanalysis version 6.0 at 0.58:
Monthly land-surface precipitation from rain-gauges built on
GTS-based and historic data. Global Precipitation Climatology
Centre, accessed October 2019, https://doi.org/10.5676/DWD_
GPCC/FD_M_V7_050.
Seo, J., S. M. Kang, and D. M. Frierson, 2014: Sensitivity of intertropical convergence zone movement to the latitudinal position of thermal forcing. J. Climate, 27, 3035–3042, https://doi.org/10.1175/JCLI-D-13-00691.1.
Sippel, S., N. Meinshausen, A. Merrifield, F. Lehner, A. G.
Pendergrass, E. Fischer, and R. Knutti, 2019: Uncovering theforced climate response from a single ensemble member using
statistical learning. J. Climate, 32, 5677–5699, https://doi.org/
10.1175/JCLI-D-18-0882.1.
Soden, B., and E. Chung, 2017: The large-scale dynamical responseof clouds to aerosol forcing. J. Climate, 30, 8783–8794, https://doi.org/10.1175/JCLI-D-17-0050.1.

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

Held, I. M., and B. J. Soden, 2006: Robust responses of the hydrological cycle to global warming. J. Climate, 19, 5686–5699,
https://doi.org/10.1175/JCLI3990.1.
Huang, B., and Coauthors, 2017: Extended Reconstructed Sea
Surface Temperature, version 5 (ERSSTv5): Upgrades, validations, and intercomparisons. J. Climate, 30, 8179–8205,
https://doi.org/10.1175/JCLI-D-16-0836.1.
Hwang, Y. T., D. M. Frierson, and S. M. Kang, 2013:
Anthropogenic sulfate aerosol and the southward shift oftropical precipitation in the late 20th century. Geophys. Res.
Lett., 40, 2845–2850, https://doi.org/10.1002/grl.50502.
Jiménez-de-la-Cuesta, D., and T. Mauritsen, 2009: Emergent
constraints on Earth’s transient and equilibrium response to
doubled CO2 from post-1970s global warming. Nat. Geosci.,
12, 902–905, https://doi.org/10.1038/s41561-019-0463-y.
Kang, S. M., Y. Shin, and S.-P. Xie, 2018: Extratropical forcing andtropical rainfall distribution: Energetics framework and ocean
Ekman advection. npj Climate Atmos. Sci., 1, 20172, https://doi.org/10.1038/s41612-017-0004-6.
——, and Coauthors, 2019: Extratropical–Tropical Interaction
Model Intercomparison Project (ETIN-MIP): Protocol andinitial results. Bull. Amer. Meteor. Soc., 100, 2589–2606,
https://doi.org/10.1175/BAMS-D-18-0301.1.
Kay, J. E., and Coauthors, 2015: The Community Earth System
Model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal
climate variability. Bull. Amer. Meteor. Soc., 96, 1333–1349,
https://doi.org/10.1175/BAMS-D-13-00255.1.
Lamarque, J.-F., and Coauthors, 2010: Historical (1850–2000) gridded
anthropogenic and biomass burning emissions of reactive gases
and aerosols: Methodology and application. Atmos. Chem. Phys.,
10, 7017–7039, https://doi.org/10.5194/acp-10-7017-2010.
Lehner, F., C. Deser, N. Maher, J. Marotzke, E. Fischer, L. Brunner,
R. Knutti, and E. Hawkins, 2020: Partitioning climate projection
uncertainty with multiple large ensembles and CMIP5/6. Earth
Syst. Dyn., 11, 491–508, https://doi.org/10.5194/esd-11-491-2020.
Li, X., M. Ting, and D. E. Lee, 2018: Fast adjustments of the Asiansummer monsoon to anthropogenic aerosols. Geophys. Res.
Lett., 45, 1001–1010, https://doi.org/10.1002/2017GL076667.
Liu, X., and Coauthors, 2012: Toward a minimal representation ofaerosols in climate models: Description and evaluation in theCommunity Atmosphere Model CAM5. Geosci. Model Dev.,
5, 709–739, https://doi.org/10.5194/gmd-5-709-2012.
Maher, N., and Coauthors, 2019: The Max Planck Institute Grand
Ensemble: Enabling the exploration of climate system variability. J. Adv. Model. Earth Syst., 11, 2050–2069, https://doi.org/10.1029/2019MS001639.
Manabe, S., and R. Stouffer, 1993: Century-scale effects of increased atmospheric CO2 on the ocean–atmosphere system.
Nature, 364, 215–218, https://doi.org/10.1038/364215a0.
McKinnon, K. A., and C. Deser, 2018: Internal variability and regional
climate trends in an observational large ensemble. J. Climate, 31,
6783–6802, https://doi.org/10.1175/JCLI-D-17-0901.1.
——, A. Poppick, E. Dunn-Sigouin, and C. Deser, 2017: An ‘‘observational large ensemble’’ to compare observed and modeled temperature trend uncertainty due to internal variability. J. Climate, 30,7585–7598, https://doi.org/10.1175/JCLI-D-16-0905.1.
Milinski, S., N. Maher, and D. Olonscheck, 2019: How large does alarge ensemble need to be? Earth Syst. Dyn. Discuss., https://doi.org/10.5194/esd-2019-70.
Ming, Y., and V. Ramaswamy, 2009: Nonlinear climate and hydrological responses to aerosol effects. J. Climate, 22, 1329–
1339, https://doi.org/10.1175/2008JCLI2362.1.

7857

7858

JOURNAL OF CLIMATE

culations. J. Climate, 32, 5275–5287, https://doi.org/10.1175/
JCLI-D-18-0641.1.
Wang, H., S. Xie, and Q. Liu, 2016: Comparison of climate response to anthropogenic aerosol versus greenhouse gas forcing: Distinct patterns. J. Climate, 29, 5175–5188, https://doi.org/10.1175/JCLI-D-16-0106.1.
Wang, K., C. Deser, L. Sun, and R. A. Tomas, 2018: Fast responseof the tropics to an abrupt loss of Arctic sea ice via ocean
dynamics. Geophys. Res. Lett., 45, 4264–4272, https://doi.org/
10.1029/2018gl077325.
White, R. H., A. A. McFarlane, D. M. W. Frierson, S. M. Kang,
Y. Shin, and M. Friedman, 2018: Tropical precipitation andcross-equatorial heat transport in response to localized heating: Basin and hemisphere dependence. Geophys. Res. Lett.,
45, 11 949–11 958, https://doi.org/10.1029/2018GL078781.
Wilcox, L. J., E. J. Highwood, B. B. B. Booth, and K. S. Carslaw,
2015: Quantifying sources of inter-model diversity in the cloud
albedo effect. Geophys. Res. Lett., 42, 1568–1575, https://doi.org/10.1002/2015GL063301.
——, and Coauthors, 2020: Accelerated increases in global andAsian summer monsoon precipitation from future aerosol
reductions. Atmos. Chem. Phys. Discuss., https://doi.org/
10.5194/acp-2019-1188.
Wills, R. C. J., D. S. Battisti, K. C. Armour, T. Schneider, and
C. Deser, 2020: Pattern recognition methods to separate
forced responses from internal variability in climate model
ensembles and observations. J. Climate, https://doi.org/
10.1175/JCLI-D-19-0855.1, in press.
Xie, S.-P., C. Deser, G. A. Vecchi, J. Ma, H. Teng, and A. T.
Wittenberg, 2010: Global warming pattern formation: Sea
surface temperature and rainfall. J. Climate, 23, 966–986,
https://doi.org/10.1175/2009JCLI3329.1.
——, B. Lu, and B. Xiang, 2013: Similar spatial patterns of climate
responses to aerosol and greenhouse gas changes. Nat. Geosci.,
6, 828–832, https://doi.org/10.1038/ngeo1931.
Xu, Y., J. Lamarque, and B. M. Sanderson, 2018: The importanceof aerosol scenarios in projections of future heat extremes.
Climatic Change, 146, 393–406, https://doi.org/10.1007/s10584015-1565-1.
Yeager, S. G., and Coauthors, 2018: Predicting near-term changes
in the Earth system: A large ensemble of initialized decadal
prediction simulations using the Community Earth System
Model. Bull. Amer. Meteor. Soc., 99, 1867–1886, https://doi.org/10.1175/BAMS-D-17-0098.1.
Zelinka, M. D., T. Andrews, P. M. Forster, and K. E. Taylor, 2014:
Quantifying components of aerosol–cloud–radiation interactions in climate models. J. Geophys. Res. Atmos., 119, 7599–
7615, https://doi.org/10.1002/2014jd021710.
Zhao, A., D. S. Stevenson, and M. A. Bollasina, 2019: Climate
forcing and response to greenhouse gases, aerosols, and ozone
in CESM1. J. Geophys. Res. Atmos., 124, 13 876–13 894,
https://doi.org/10.1029/2019JD030769.

Downloaded from http://journals.ametsoc.org/jcli/article-pdf/33/18/7835/4988417/jclid200123.pdf by guest on 14 August 2020

Solomon, A., L. M. Polvani, K. L. Smith, and R. P. Abernathey,
2015: The impact of ozone depleting substances on the circulation, temperature, and salinity of the Southern Ocean: An
attribution study with CESM1(WACCM). Geophys. Res.
Lett., 42, 5547– 5555, https://doi.org/10.1002/2015GL064744.
Stevens, B., 2015: Rethinking the lower bound on aerosol radiative
forcing. J. Climate, 28, 4794–4819, https://doi.org/10.1175/
JCLI-D-14-00656.1.
Stevenson, S., A. Capotondi, J. Fasullo, and B. Otto-Bliesner, 2019:
Forced changes to twentieth century ENSO diversity in a last
millennium context. Climate Dyn., 52, 7359–7374, https://doi.org/10.1007/s00382-017-3573-5.
Stuecker, M. F., and Coauthors, 2020: Strong remote control of
future equatorial warming by off-equatorial forcing. Nat.
Climate Change, 10, 124–129, https://doi.org/10.1038/s41558019-0667-6.
Swart, N. C., and Coauthors, 2019: The Canadian Earth System
Model version 5 (CanESM5.0.3). Geosci. Model Dev., 12,4823–4873, https://doi.org/10.5194/gmd-12-4823-2019.
Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of
CMIP5 and the experiment design. Bull. Amer. Meteor. Soc.,
93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1.
Tebaldi, C., and J. M. Arblaster, 2014: Pattern scaling: Its strengths andlimitations, and an update on the latest model simulations. Climatic
Change, 122, 459–471, https://doi.org/10.1007/s10584-013-1032-9.
Thompson, D. W. J., E. A. Barnes, C. Deser, W. E. Foust, and A. S.
Phillips, 2015: Quantifying the role of internal climate variability in future climate trends. J. Climate, 28, 6443–6456,
https://doi.org/10.1175/JCLI-D-14-00830.1.
Ting, M., Y. Kushnir, R. Seager, and C. Li, 2009: Forced and internal
twentieth-century SST trends in the North Atlantic. J. Climate,22, 1469–1481, https://doi.org/10.1175/2008JCLI2561.1.
Tomas, R. A., C. Deser, and L. Sun, 2016: The role of ocean heattransport in the global climate response to projected Arctic
sea ice loss. J. Climate, 29, 6841–6859, https://doi.org/10.1175/
JCLI-D-15-0651.1.
Undorf, S., D. Polson, M. A. Bollasina, Y. Ming, A. Schurer, and G. C.
Hegerl, 2018a: Detectable impact of local and remote anthropogenic aerosols on the 20th century changes of West African andSouth Asian monsoon precipitation. J. Geophys. Res. Atmos.,
123, 4871–4889, https://doi.org/10.1029/2017jd027711.
——, M. A. Bollasina, and G. C. Hegerl, 2018b: Impacts of the 1900–
1974 increase in anthropogenic aerosol emissions from North
America and Europe on Eurasian summer climate. J. Climate, 31,
8381–8399, https://doi.org/10.1175/JCLI-D-17-0850.1.
van Marle, M. J. E., and Coauthors, 2017: Historic global biomassburning emissions for CMIP6 (BB4CMIP) based on merging
satellite observations with proxies and fire models (1750–
2015). Geosci. Model Dev., 10, 3329–3357, https://doi.org/
10.5194/gmd-10-3329-2017.
Wang, C.-C., W.-L. Lee and C. Chou, 2019: Climate effects of anthropogenic aerosol forcing on tropical precipitation and cir-

VOLUME 33