manuscript submitted to Geophysical Research Letters

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Distilling the evolving contributions of anthropogenic
aerosols and greenhouse gases to historicallow-frequency surface ocean changes

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Yue Dong1,2 , Jennifer E. Kay1,3 , Clara Deser4 , Antonietta Capotondi1,5 , Sara
C. Sanchez3

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1 Cooperative Institute For Research in Environmental Sciences, University of Colorado Boulder, Boulder,

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2 Department of Atmospheric and Oceanic Sciences, University of California Los Angeles, Los Angeles, CA
3 Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO
4 Climate and Global Dynamics Division, NCAR, Boulder, CO
5 Physical Sciences Laboratory, NOAA, Boulder, CO

Key Points:

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Over the past century, GHG forced response is characterized by a single dominantmode while AER response consists of two distinct modes.
• AER’s first mode features a monotonic increase in global AER and global cooling pattern opposite to the GHG warming pattern• AER’s second mode features the recent shift in AER emissions, which drives regional warming amplifying the GHG response•

Corresponding author: Yue Dong, ydong@atmos.ucla.edu–1–manuscript submitted to Geophysical Research Letters

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Abstract
Anthropogenic aerosols (AER) and greenhouse gases (GHG) – the leading drivers of theforced historical change – produce different large-scale climate response patterns, withvarying trend pattern correlations from negative to positive over the past century. To
understand what caused the time-evolving comparison between GHG and AER responses,
we apply a joint low-frequency component analysis on global sea-surface temperature andsea-surface salinity response over 1921-2020 from CESM1 single-forcing large ensemble
simulations. While GHG response is well-described by its first leading mode, AER response consists of two distinct modes. The first one features global AER increase andglobal cooling, opposite to GHG-induced warming. The second mode features multidecadal
variations in AER distributions, where the recent shift from North America/western Europe to southeast Asia emissions drives regional changes enhancing the GHG effect. We
argue that AER can have both competing and synergistic effects with GHG, as their emissions change temporally and spatially.

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Plain Language Summary

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Anthropogenically forced climate change over the past century has been mainly causedby two types of emissions: greenhouse gases (GHG) and aerosols (AER). In general, sulfate aerosols from industrial sources can reflect shortwave radiation to yield a cooling
effect opposite to the GHG warming effect. However, model simulations isolating GHG
and AER forcings show that the large-scale climate effect of AER does not always dampen
the GHG effect. Instead, over recent decades, AER have produced surface ocean response
patterns more like the GHG response. Using a novel principle component analysis, wefind that aerosols have driven two distinct modes of climate change patterns over the historical period. The first mode is associated with global aerosol increase, resulting in globalwide cooling damping the GHG-induced warming. The second mode is associated withthe shift in aerosol emissions from north America/western Europe to southeast Asia, which
drives regional changes enhancing the GHG effect. Our results highlight the importanceof considering the temporal and spatial evolutions of AER emissions in assessing GHG
and AER climate effects and attributing historical anthropogenic climate changes to GHG
and AER forcings.

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1 Introduction

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Anthropogenically forced climate change over the past century has been primarily driven by two components: greenhouse gases (GHG) and anthropogenic aerosols (AER).
These components modulate the global-mean surface temperature through distinct radiative effects (Myhre et al., 2014; Forster et al., 2021) - GHG cause surface warming
due to absorption and re-emission of longwave radiation, while AER change energy budget through reflection or absorption of shortwave radiation by scattering (e.g., sulfate)or absorbing species (e.g., black carbon). Additionally, AER have indirect effects on climate through cloud-aerosol interactions, where aerosols can serve as cloud condensation
nuclei affecting clouds’ albedo, lifetime, and properties (Twomey, 1977; Ackerman et al.,2004). Over the past century, long-term increases in global-mean GHG and AER have
led to a large cancellation between GHG-induced warming and AER-induced cooling effects (Deser et al., 2020).
While a clear opposing effect from GHG and AER on global-mean surface temperature has been found, comparing the spatial patterns of their climate responses has beenless straightforward. Focusing on global GHG and AER forcings in the 20th century, Xieet al. (2013) found that the first leading modes of climate response patterns to GHG and
AER bear a great resemblance, suggesting that large-scale climate responses are governedby the same ocean-atmosphere feedbacks intrinsic to the climate system. Wang et al. (2016)
further examined the differences in those leading modes, highlighting the unique features–2–

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of AER forced response associated with interhemispheric temperature asymmetry andcross-equatorial circulation change.
The spatial distributions of GHG and AER forcings add additional complexity to
the comparison. Unlike well-mixed GHG, AER emissions have much richer structuresin their spatial distributions and temporal evolution (Deser et al., 2020). Emissions from
North America (NA) and western Europe (EU) have dominated the global total AER
loading since the early decades of the 20th century until the 1970s, after which they havedeclined substantially following emission regulations. On the other hand, emissions fromsoutheast Asia (SA) have been increasing gradually since the 1950s, and more recently,
have surpassed the emissions from NA and EU since the 1990s. This transition of major AER sources has been found to cause large-scale climate changes in a different way
than global-mean AER change (Kang et al., 2021; Wang & Wen, 2022), and to some extent can compensate for the global mean AER effect (Shi et al., 2022).
To isolate and quantify the respective contributions of GHG and AER to forced
historical climate change, single-forcing large ensemble (SF-LE) simulations within fullycoupled global climate models (GCMs) have provided valuable insights. Using CESM1
SF-LE, Deser et al. (2020) found that the contributions of GHG and AER to the largescale patterns of total forced trends vary over time, with AER being the dominant driver
before the 1970s and GHG dominating thereafter. Wang and Wen (2022) further extended
the analysis to CMIP5 multi-model comparisons, highlighting both similarities and disparities in the spatial patterns of the trends driven by AER and GHG.
To recap the literature and to illustrate the evolving contributions of GHG and AER
to forced historical trends, we begin by showing the ensemble-mean response in CESM1
SF-LE for two 40-year periods, 1940-1980 and 1980-2020. Figure 1 shows the trend patterns for sea-surface temperature (SST) and sea-surface salinity (SSS), and key atmospheric variables coupled with them, sea level pressure (SLP) and surface water fluxes(i.e., precipitation minus evaporation, P-E). We compare these trend patterns forced by
all forcings (“ALL”; from the CESM1 LE project, Kay et al. 2015), GHG, and AER (fromDeser et al. 2020). As found in previous studies, during the early period (1940-1980; Fig.
1a), the total forced response is dominated by AER, featuring an inter-hemispheric contrast with pronounced cooling due to the SW absorption by AER and salinification trends
in the northern hemisphere (NH) driven by an enhancement of Atlantic Meridional Overturning Circulation (AMOC; Menary et al., 2020) and increased sea-ice formation in the
Arctic ocean. During recent decades (1980-2020; Fig. 1b), the total forced response has
been dominated by GHG, featuring broad global warming, a “wet-get-wetter” precipitation pattern, and a “salty-get-saltier” SSS pattern, consistent with the literature (Held& Soden, 2006; Xie et al., 2010; Durack et al., 2012; Capotondi et al., 2012).
Perhaps more interestingly, the climate response patterns driven by GHG and AER
largely oppose each other during the early period, but show some resemblance during
the later period at regional scales (e.g., NH warming and Arctic freshening). We further
compute the pattern correlations for running 40-yr trends in global SST and SSS between
the ALL and single forcing (GHG or AER) ensemble-means, following Deser et al. (2020).
As expected, the pattern correlations between ALL and GHG for both SST and SSS haveincreased steadily since the 1950s (red lines in Figs. 1c, d), suggesting the increasingly
dominant role of GHG in modulating global climate. However, the pattern correlations
between ALL and AER decreased only over the first half of the 20th century and have
gradually rebounded over recent decades (blue lines in Figs. 1c, d). The same non-monotonic
behavior is also found for the pattern correlation between GHG and AER (black lines
in Figs. 1 c, d), indicating that the surface ocean response patterns to AER have become
closer, not opposite, to those forced by GHG over recent decades.
This comparison between the evolving AER and GHG forced responses led us toask: what caused the AER response to change over time, and particularly, to amplify–3–manuscript submitted to Geophysical Research Letters

Figure 1.

CESM1 ensemble-mean trend response to ALL, GHG and AER forcings, for (a)1940-1980 and (b) 1980-2020. Black contours overlaid on the SST panels are SLP trends (contour
interval is 0.16 hPa/40yr, zero contours are thickened, solid contours denote positive SLP trend,
dashed contours denote negative SLP trend). Colored contours overlaid on the SSS panels are
P-E trends (contour interval is 0.18 mm/day/40yr, zero contours are omitted; green denotes positive P-E and red denotes negative P-E). (c, d) Pattern correlations for 40-yr running trends inensemble-mean (d) global SST and (e) global SSS between ALL, GHG and AER.

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the GHG response over recent decades? Is it because of the decline of global AER emissions, or is it because of the change in the spatial distribution of those emissions, or both?
And what implications does this have for detecting and attributing historical low-frequency
surface ocean changes? Motivated by these questions, in this work we apply a principal
component analysis to investigate the leading modes of low-frequency historical surface
ocean changes driven by GHG and AER, and distill their evolving contributions to historical forced climate change.

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2 Low-frequency component anlaysis

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One of the caveats in the trend pattern analysis in Fig. 1 and other studies (e.g.
Deser et al., 2020; Kang et al., 2021) is that these trends are computed over arbitrary
time intervals, thus may not capture the whole series of the forced response and timeevolving forcing patterns. To achieve a more systematic assessment, other prior studies have utilized principal component (PC) analysis to linearly separate the total response
into several empirical orthogonal functions (EOFs) (e.g., Xie et al., 2013; Wang et al.,2016; Bonfils et al., 2020). However, the EOF results can be affected by high-frequency
natural variability (e.g., ENSO) that is not completely removed due to insufficient ensemble size. Therefore, to robustly examine the low-frequency forced response, we apply a low-frequency component analysis (LFCA; Wills et al. 2018) to the ensemble mean
GHG and AER forced response from CESM1 SF-LE. In the following sections, we first
introduce the LFCA method and the data analyzed (section. 2.1), and we next show theresults of the leading low-frequency modes for the GHG and AER forced response (section 2.2).
2.1 The LFCA method and data
Unlike conventional PC analysis which maximizes total variance, LFCA finds a linear combination of EOFs that maximize the ratio of low-pass filtered variance to total
variance, thereby isolating the leading modes of low-frequency variability (Wills et al.,2018). One can also apply LFCA to multiple spatial-temporal fields jointly, similar to
the joint EOF analysis. Previous studies (Wills et al., 2022; Dörr et al., 2023; Bonan etal., 2023) suggest that the use of joint LFCA to account for low-frequency co-variability
in multiple fields improves the isolation of long-term forced response.
Therefore, in this study we perform joint LFCA on global monthly SST and SSS
anomalies over 1921 – 2020 to study low-frequency modes of forced historical surface oceanchanges. The anomalies are relative to the 1921-2020 climatology. We take the SST andSSS response from the ensemble-mean of ALL, GHG and AER ensembles as part of CESM1
SF-LE (Deser et al. 2020), each containing 20 members. Note that original simulationsin CESM1 SF-LE used the “all-but-one” forcing scenario – that is, all historical radiative forcing agents are prescribed except their GHG or AER is fixed at the 1920 conditions. The net effect of GHG and AER can be then obtained by subtracting these “allbut-one” SF simulations from the standard ALL simulations. We also note that in thisset of LEs, AER specifically refers to industrial aerosols, not including biomass aerosols.
For the joint LFCA, we use a 15-year cut-off low-pass filter to isolate multidecadal
low-frequency variability, and we retain the 10 leading EOFs, which in total account for
99.9%, 99% and 97% of the joint low-frequency variance for ALL, GHG and AER, respectively. Additionally, to understand the dynamical processes associated with each mode,
we regress monthly SLP, precipitation (P), evaporation (E) and P-E anomalies onto thetimeseries of each of the PCs.
Although we take the ensemble mean response first to remove random internal variability before performing the joint LFCA, one can also perform LFCA on each ensemble member to obtain (the best estimate of) its low-frequency forced response and then–5–manuscript submitted to Geophysical Research Letters

Figure 2.

Leading low-frequency modes of GHG and AER ensemble-mean responses obtained

from joint SST/SSS LFCA. Numbers in the corner of the timeseries plots show the low-frequency
variance explained by the corresponding PC. SLP regressions are overlaid on the SST patterns(contour interval is 0.05 hPa, zero contours are thickened, solid contours denote positive SLP anddashed contours denote negative SLP); P-E regressions are overlaid on the SSS patterns (interval
is 0.06 mm/day, zero contours are omitted, green denotes positive P-E and red denotes negative
P-E). Regressions of aerosol optical depth at the 550nm (no unit, multiplied by 100) onto AER
PC1 and PC2 are shown in panels (g) and (k).

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average the leading modes across ensemble members (e.g., Wills et al., 2022; Kuo et al.,2023). We will show in the next section that the results remain the same regardless ofwhich method is used (cf. Fig. 2 and Fig. S2)
2.2 Low-frequency modes of GHG and AER forced responses
Figure 2 shows the leading joint LFCA modes of SST and SSS for the GHG and
AER forced responses. The GHG forced response is dominated by a single mode that
explains 97.4% of the low-frequency variance; we will denote its timeseries as “GHG PC1”
hereafter. The AER forced response consists of two leading modes, which both have strong
multi-decadal variability and explain 75.2% and 13.9% of the total low-frequency variance, respectively. We will denote their timeseries as “AER PC1” and “AER PC2” hereafter.
The GHG PC1 has increased monotonically throughout the past century, with apronounced positive trend starting from 1980 (Fig. 2a). The corresponding SST patternis characterized by broad global warming and enhanced tropical eastern Pacific warming (i.e., the El Niño-like SST pattern), accompanied by a reduced tropical zonal SLP
gradient (Fig. 2b). The global-scale warming causes precipitation to increase in the tropics and decrease in the broad subtropics (Fig. S1a), following the “wet-get-wetter” and“warmer get wetter” mechanism (Held & Soden, 2006; Xie et al., 2010). Associated withenhanced global-scale evaporation (Fig. S1b), the net P-E pattern links to a “salty-getsaltier” SSS pattern (Fig. 2c Durack et al., 2012; Sun et al., 2021), with an enhanced–6–

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SSS gradient between tropical and subtropical oceans as well as an amplified climatological contrast between the Pacific and Atlantic basins.
On the other hand, AER PC1 and PC2 feature distinct surface ocean patterns andtime evolutions (Figs. 2d-k). By regressing aerosol optical depth (AOD) onto PC1 andPC2, we find that the first mode is associated with a globally increasing AOD pattern,with the largest source in east Asia (Fig. 2g). The positive AOD anomalies cause globalscale SST cooling by reflecting SW radiation, which is most pronounced in the north Pacific downstream of the east Asian AOD source (Fig. 2e). The AOD-induced surface cooling further reduces precipitation in east Asia extending to the north Pacific (Fig. S1c),
along with the weakly decreased evaporation (Fig. S1d), leading to increased SSS in thenorth Pacific (Fig. 2f). Additionally, global cooling reduces runoff into the Arctic Ocean
from sea ice melting, thereby increasing its SSS. Overall, AER PC1 increases throughmost of the 20th century, with the strongest positive trend from 1940 to 1980 and a negligible trend after 1980 (Fig. 2d).
By contrast, AER PC2 is associated with the relative difference in AOD between
northeast America/western Europe and southeast Asia (Fig. 2k). This PC has a negative trend from the early 20th century to 1970 and a reversed (positive) trend from 1980
to present day (Fig. 2h), reflecting the transition of major AER emissions from NA/EU
to SA over the course of the 20th century. In the positive phase of PC2, the negative AOD
in NA/EU drives NH SST warming confined to mid-to-high latitudes (Fig. 2i) and Arctic freshening via increased runoff from sea-ice melting (Fig. 2j). The positive AOD in
SA drives weak cooling (Fig. 2i) and drying locally in the Indo-Pacific ocean (Fig. S1e),
which excites a Rossby wave response weakening the Aleutian Low (Fig. 2i; also see Smithet al. (2016); Dittus et al. (2021)). Due to the zonal-mean energy budget constraint, the
ITCZ shifts northward towards the warmer NH as required by cross-equatorial heat transport (Kang et al., 2008; Hwang et al., 2013), resulting in enhanced precipitation northof the equator and reduced precipitation in the south (Fig. S1e). This zonal-mean precipitation dipole pattern further links to a meridional SSS gradient in the tropical Pacific, with decreased (increased) SSS in the north (south) (Fig. 2j).
In summary, the leading low-frequency modes of GHG and AER forced responses
are diverse in both their spatial patterns and temporal evolutions. The GHG response
can be largely captured by a single leading mode, which has increased monotonically throughout the past century. The AER forced response, however, features two distinct modes.
AER PC1 is associated with increasing global AER emissions and resulting global cooling as well as enhanced regional responses in the NH western Pacific associated with east
Asian emissions. AER PC2 represents a multidecadal variation in AER distribution, highlighting the emission shift from high-latitude NA/EU to low-latitude SA over recent decades.
This mode features an inter-hemispheric SST gradient and a shift in zonal-mean precipitation and SSS anomalies. The spatial patterns of the two AER modes are consistent
with previous studies; however, their relative order and temporal characteristics may depend on the period analyzed. For example, when accounting for a longer period including the 21st century, Gu et al. (2024) find that the AER shift mode (our 2nd mode) isthe leading PC, followed by the AER global mode (our 1st mode).

3 Time-evolving contributions of the leading modes of AER and GHG
responses
Having quantified the leading modes of AER and GHG responses, in this section,
we aim to attribute the changes in the total forced response to these individual modes
to gain physical insights.
First, we approximate the time-varying forced responses using their leading LFCA
modes, and perform the pattern correlation analysis for running 40-yr trends in global–7–

manuscript submitted to Geophysical Research Letters

Figure 3.

Pattern correlations for running 40-yr trends in (left) global SST and global SSS(right) response reconstructed using the LFCA leading modes, between (a, b) ALL and GHG or
AER responses and (c, d) between GHG and AER responses.

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SST and SSS as in Fig. 1 but use the PC-reconstructed responses. We find that using
the first leading mode for GHG response and the leading two modes for AER and ALL
response can faithfully reproduce the results of the simulated total response (cf. Fig. 3
and Fig. 1 c, d). The PC-based pattern correlations capture the increasingly high values between ALL and GHG and the non-monotonic evolution of the correlations between
ALL and AER and between GHG and AER for both SST and SSS trend patterns.
Having verified that the LFCA modes can sufficiently reproduce the total response,
now we come back to the questions raised at the beginning: what caused the AER response to change over time and to amplify (rather than offset) the GHG response overrecent decades? More specifically, is it because of the change in AER PC1 associated with
global AER emissions or the change in PC2 associated with shifting AER emissions, orboth?
To answer these questions, we repeat the pattern correlation analysis with individual AER PCs. When only accounting for AER PC1, the pattern correlations between
ALL and AER decrease monotonically after the 1940s (Figs 3a and b, blue circled lines),
unlike the total AER response which bounces back after the 1960s (blue solid lines). Furthermore, the pattern correlations between AER PC1 and GHG PC1 stay at a constantnegative value of -0.88 for SST and -0.69 for SSS (Figs. 3c and d, black circled lines),
indicating that the response patterns associated with AER PC1 have continuously opposed those driven by GHG PC1. The high (albeit negative) pattern correlations between
GHG PC1 and AER PC1 can already be seen in Fig. 2: both modes feature a globalwide SST response driven by global forcing (with sign reversed), and a similar hydrological cycle response constrained by global warming or cooling, all consistent with earlier findings by Xie et al. (2013).
Turning to AER PC2, although this mode has a much weaker pattern correlation
with GHG PC1 overall, the correlation switches sign from negative to positive around
mid-century (Figs. 3c and d, black dashed lines), suggesting that it is AER PC2, not PC1,
that makes the total AER forced response patterns more similar to the GHG response
patterns (Figs. 3c and d, black solid lines). The abrupt change in the running trend pattern correlations arises from the phase transition in the AER PC2 timeseries around the1980s associated with the shift in major AER emissions from NA/EU to SA. As AERemissions increase over SA and decrease over NA/EU after the 1980s, this mode produce
north Pacific warming, Arctic freshening and SH subtropical drying, similar to the GHGinduced local changes (Fig. 2), thus making the AER forced response more like the GHG
response.
To further illustrate the different contributions of AER PC1 and PC2 to the total forced response, we show the trend patterns for AER and GHG PCs over 1940-1980
and 1980-2020 (Fig. 4). First, the PC-based trend patterns (Fig. 4, left two columns)
are remarkably consistent with the actual simulated trend patterns (Fig. 1), confirming that using the leading modes can reproduce the total forced response. Next, turning to the trend patterns for AER PC1 and PC2 individually, we find that AER forced
trends over 1940-1980 arise primarily from PC1 (Fig. 4a) and over 1980-2020 arise nearly
entirely from PC2 (Fig. 4b). Moreover, the increase in global AOD from the early 20th
century to the 1980s is predominately associated with PC1 and the moderate decline inglobal AOD afterward is caused mainly by PC2 (Fig. S3).
Overall, these findings suggest that both PC1 and PC2 make significant contributions to the AER response, but their roles vary in time. Over the first half of the 20th
century until the 1980s, PC1 dominates the total AER forcing and the forced response,with patterns largely opposite to those of GHG. If the spatial pattern of AER emissions
had remained unchanged from that of PC1 but continued to increase in magnitude overthe past 40 years, the GHG response would have been largely compensated for by AER,
resulting in a less detectable anthropogenically forced signal over the past century. How-–9–

manuscript submitted to Geophysical Research Letters

Figure 4.

Similar to Fig. 1, except for the linear trend patterns associated with GHG and

AER PCs for (a) 1940-1980, and (b) 1980-2020.

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ever, the geographical distribution of AER emissions did change, shifting from NA/EU
to SA, which led to the prominent phase transition in PC2 over recent decades. Although
PC2 is associated with small global-mean AOD anomalies, the dynamical response associated with this AER shift mode can be large at regional scales. Some of the regional
responses appear to enhance the GHG-induced changes, leading to a synergistic effectof AER with GHG over recent decades rather than a competing effect as in earlier periods.

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4 Implications for detection and attribution

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The time-evolving similarities and disparities between GHG and AER forced responses have important implications for detecting and attributing (D&A) historical climate change. Previous D&A studies, e.g. Bonfils et al. (2020), used an EOF approachwith historical simulations and identified two externally forced fingerprints. They argued
that the first one, featuring global warming and intensified wet-dry patterns, is driven
by GHG, and that the second one, featuring an inter-hemispheric temperature contrast
and meridional shift in ITCZ location, is driven by AER.
Using the SST/SSS joint LFCA, we also find two leading modes in the CESM1 ALL
ensemble-mean response (Fig. S4), similar to the results of Bonfils et al. (2020). Indeed,at first glance, ALL PC1 and PC2 seem to bear a strong resemblance to GHG PC1 and
AER PC1, respectively (Fig. 2). However, there are substantial differences between thepatterns of ALL PC1 and GHG PC1. For example, GHG PC1 is characterized by a strong
El Niño-like SST warming pattern (Fig. 2b), a zonal-mean wet-dry hydrological pattern(Fig. S1a) and a corresponding zonal SSS pattern (Fig. 2c). By contrast, ALL PC1 hasa more uniform tropical SST pattern (Fig. S4b), a zonally-asymmetric precipitation pattern with drying in the west Pacific and wetting in the central Pacific (Fig. S5a), anda resulting SSS dipole pattern with increased salinity in the western Pacific (Fig. S4e).
These mismatches, however, appear to be consistent with AER PC1, with a high pat-–10–

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tern correlation of -0.7 for global SST between ALL PC1 (Fig. S4b) and AER PC1 (Fig.
2e). Similarly, while the response patterns of ALL PC2 are overall anti-correlated withAER PC1, there are noticeable spatial features that cannot be explained by AER PCs
but rather resemble GHG PC1. Collectively, this suggests that the two modes of historical anthropogenic fingerprints obtained from CESM1 ALL simulations are forced by the
combined effects of GHG and AER, rather than by each forcing agent individually as
previously proposed (Bonfils et al., 2020).

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Thus, we argue that previous D&A approaches that separate the leading modesof historical fingerprints to GHG and AER may be biased, as they don’t account for thetwo distinct modes of AER response and their evolving synergistic and competing climate effects with GHG. Our CESM1 results suggest that robustly detecting and attributing historical forced climate change requires careful separation of GHG and AER responses,
which are not mutually independent.

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5 Summary

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In this study, we have analyzed the leading modes of low-frequency climate responses
to GHG and AER forcing and distilled their respective contributions to historical forced
climate change in CESM1. While the GHG response can be well represented by a single dominant mode, the AER response features two distinct modes. The first mode isassociated with an increase in global AER emissions over the past century, driving globalwide cooling and regional “wet-get-wetter” precipitation and “salty-get-saltier” salinity
response patterns. This AER mode is spatially anti-correlated with the leading GHG
mode, largely offsetting impacts from GHG, with some notable regional exceptions suchas the western Pacific close to the source of east Asian AER emissions. The second AER
mode is associated with a spatial redistribution of AER, featuring the shift of major emissions from north America/western Europe to southeast Asia over recent decades. This
zonally asymmetric AER forcing pattern, however, yields meridional shifts in the zonal
mean response of SST, hydrological and SSS, owing to large-scale energy budget constraints. Although this mode has a weaker correlation with the GHG response, the transition of this mode from a negative to positive phase over recent decades results in regional anomalies that can enhance the GHG-induced changes.

355

While our analysis has focused on the past century, the results and conclusions maychange for future forcing scenarios. As global AER emissions are projected to decrease
with clean-air efforts, AER PC1 will likely change sign in the future. Given the anti-correlation
between AER PC1 and GHG PC1, the future AER effect may exacerbate GHG-induced
climate change and enhance the detectability. Hence, it remains to be further investigated how our proposed framework will evolve in the future, accounting for various uncertainties arising from model structures, emission scenarios, and representations of AER(direct and indirect) forcings.

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Open Research

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The CESM1 Large Ensemble data (Kay et al., 2015) and single-forcing large ensemble data (Deser et al., 2020) are available from https://www.cesm.ucar.edu/community-projects/lens and https://www.cesm.ucar.edu/working-groups/climate/simulations/
cesm1-single-forcing-le. Code to perform LFCA (Wills et al., 2018) is available on
Github https://github.com/rcjwills/lfca.
Acknowledgments
YD, JEK and SCS are supported by NASA Oceanography Grant 80NSSC23K0358. The
National Center for Atmospheric Research (NCAR) is sponsored by the National Sci-

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manuscript submitted to Geophysical Research Letters366

ence Foundation under Cooperative Agreement 1852977. A.C. was supported by the NOAA
Climate Program Office’s CVP Program, and DOE Award # DE-SC0023228.

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GEOPHYSICAL RESEARCH LETTERS

Supporting Information for “Distilling the evolving
contributions of anthropogenic aerosols andgreenhouse gases to historical low-frequency surface
ocean changes”
Yue Dong1,2 , Jennifer E. Kay1,3 , Clara Deser4 , Antonietta Capotondi1,5 , Sara
C. Sanchez3
1 Cooperative Institute For Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO

2 Department of Atmospheric and Oceanic Sciences, University of California Los Angeles, Los Angeles, CA

3 Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO

4 Climate and Global Dynamics Division, NCAR, Boulder, CO

5 Physical Sciences Laboratory, NOAA, Boulder, COContents of this file
1. Figures S1 - S5

June 14, 2024, 4:31am

X-2

Figure S1.

:

Precipitation (left) and evaporation (right) regressions onto GHG PC1(top), AER PC1 (middle) and AER PC2 (bottom). Units: mm/day/std.

June 14, 2024, 4:31am

:Figure S2.

Results of LFCA applied to each of the individual ensemble members.

(left) PC time series. Grey lines are individual members; black lines and color filling
are the average of all members; red lines are the LFCA results applied to the ensemblemean response as in Fig. 2. (middle) SST and (right) SSS patterns are averaged over all
individual members’ PC patterns.

June 14, 2024, 4:31am

X-3

X-4

:

Figure S3. Global-mean AOD anomalies (relative to 1921-2020 climatology) from AER
simulations (black) and PC reconstructions (blue and orange). Red dashed line denotes
the sum of PC1 and PC2 associated AOD anomalies.

June 14, 2024, 4:31am

:Figure S4.

Same with Fig. 2 except for the two leading modes obtained from CESM1

ALL ensemble-mean responses.

June 14, 2024, 4:31am

X-5

X-6

Figure S5.

:Regressions of (left) precipitation and (right) evaporation onto ALL PC1

and PC2.

June 14, 2024, 4:31am