1

Critical role of biomass burning aerosols in enhanced historical2

Indian Ocean warming

3
4
5

Yiqun Tian1, Shineng Hu1*, Clara Deser2

6
7
8

1

9
10

Division of Earth and Climate Sciences, Nicholas School of the Environment, Duke University,
Durham, NC, USA

2

Climate and Global Dynamics, National Center for Atmospheric Research, Boulder, CO, USA

11
12
13
14

Revised, 2 April 2023

15
16
17
18
19

*

20

Division of Earth and Climate Sciences, Nicholas School of the Environment, Duke University

Corresponding author: Shineng Hu (shineng.hu@duke.edu)

21

Abstract

22
23

The tropical Indian Ocean (TIO) has experienced enhanced surface warming relative to the24tropical mean during the past century, but the underlying mechanisms remain unclear.

25

Here we use single-forcing, large-ensemble coupled model simulations to demonstrate that

26

changes of biomass burning (BMB) aerosols have played a critical role in this TIO relative

27

warming. Although the BMB aerosol changes have little effect on global mean

28

temperatures due to regional cancellation, they significantly influence the pattern of29

warming over the tropical oceans. In particular, the reduction of BMB aerosols over the

30

Indian subcontinent induces a TIO warming, while the increase of BMB aerosols over

31

South America and Africa causes a cooling of the tropical Pacific and Atlantic,

32

respectively. The resultant TIO relative warming leads to prominent global climate

33

changes, including a westward expanded Indo-Pacific warm pool, a fresher TIO due to34enhanced rainfall, and an intensified North Atlantic jet stream affecting European

35hydroclimate.

36
37

The tropical oceans have warmed substantially over the past century, with pronounced regional

38

structures (Fig. 1a). Relative to the tropical mean, sea surface temperature (SST) warming is39enhanced in the TIO, western Pacific and eastern Atlantic, and suppressed in the central-eastern

40

Pacific. The enhanced warming in the climatologically warm TIO has attracted much41

attention1,2,3as it has been suggested to cause a wide range of climate impacts via

42

teleconnections, including a strengthened positive phase of North Atlantic Oscillation (NAO)4,43

reduced Sahel rainfall5, an enhanced Pacific Walker Circulation6, the occurrence of a North

44

Atlantic “warming hole”7, and an intensified Atlantic Meridional Overturning Circulation8.

45
46

The enhanced TIO warming relative to the tropical mean is a robust feature across different

47

datasets (Supplementary Figs. 1&2) and is particularly evident after the 1950s (Supplementary48

Fig. 3). We find that the observed absolute TIO warming rate is subject to large data

49

uncertainties, but is positively correlated with the tropical mean warming rate (Fig. 1c). One

50

common feature to all the observational datasets investigated here is that the TIO warming is

51

greater than the tropical-mean warming (Fig. 1d). Our analysis suggests that this TIO “relative

52

warming” trend remains a prominent feature regardless of the time period chosen for analysis

53(Supplementary Fig. 2). The fact that the TIO has warmed faster than the tropical oceans on

54

average may imply an overall increase of TIO rainfall, therefore driving the TIO-induced global

55

teleconnections mentioned above, but whether TIO rainfall has indeed increased over the past

56

century remains under debate due partly to the lack of direct observations9,10,11.

57
58

The physical mechanisms underlying the observed TIO relative warming remain unclear. Several

59

mechanisms have been proposed to explain the TIO absolute warming, including, for example,

60

the increase in greenhouse gas concentration1,3 or changes in El Niño-Southern Oscillation

61

properties2. These mechanisms may not necessarily be applicable to TIO relative warming,

62

however. Given the critical role of relative SST changes in shaping the tropical rainfall pattern12,63

it is therefore important to address the following questions. Is the observed TIO relative warming64

a response to external radiative forcing? Does internal climate variability play a role? If

65externally forced, which radiative forcing agent (or agents) is responsible for the TIO relative

66

warming? These questions are difficult to tackle by relying solely on observations. To that end,67

we analyze single-forcing, large-ensemble coupled model simulations together with available

68

observations, which has led us to identify a previously overlooked role for BMB aerosol forcing

69

in the historical TIO relative warming.

70
71

Tropical SST response to historical BMB changes

72

To explore the mechanisms of TIO warming, we use the set of initial-condition Large Ensembles

73(LEs) conducted with Community Earth System Model version 1 (CESM1) (Methods). CESM1

74

is a widely used, fully coupled general circulation model (GCM) that participates in the Coupled75

Model Intercomparison Project Phase 5 (CMIP5). The LE setting helps to separate the externally76

forced climate response from internal climate variability13. In addition to the standard all-forcing

77(ALL) historical LE simulations14, we use the “all-but-one-forcing” LE simulations15 to isolate78

the impacts of greenhouse gases (GHG), anthropogenic aerosols (AAER), and biomass burning

79

aerosols (BMB). The historical portion of the simulations spans the period 1920-2005, and we80

use the period 1925-2005 for analysis to remove some of the potential influence of ocean initial81

condition memory. The main conclusions presented do not change if a different period (e.g.,82

1930-2005) is used for analysis.

83
84

A recent study suggests that CMIP5 coupled climate models on average simulate largely uniform

85

surface warming over the tropical oceans and thus underestimate the historical TIO relative86

warming rate compared to observations16. Although the ensemble-mean of the CESM1 ALL LE

87

underestimates the observed tropical-mean ocean warming rate (Fig. 1c), it captures the TIO

88relative warming with good fidelity (Fig. 1b). This implies that the observed TIO relative

89

warming may be partly attributable to external forcing, according to CESM1. The observation-

90

CESM1 mismatches (e.g., the enhanced western Pacific warming) can result from multidecadal

91

internal variability, among other factors such as uncertainties in radiative forcing and model92

mean-state biases. We note that the LE ensemble spread in simulated TIO SST variations, both93

absolute and relative, encompasses the observed variations (Supplementary Fig. 3). Although the94

LE ensemble seems to slightly underestimate the TIO absolute warming since 1925, its spread95

well encompasses the observed TIO relative warming (Fig. 1d). These results indicate that the96

model has a realistic depiction of the combined influences of natural variability and forced

97

response in TIO trends. Interestingly, we find that ensemble members that simulate stronger TIO

98relative warming rates tend to show better agreement with the observed pattern of tropical99

warming, quantified in terms of spatial correlation (Supplementary Fig. 4).

100
101

Given the complex set of radiative forcing changes over the past century, is there a primary102

factor that drives the forced component of TIO relative warming in CESM1? To answer this103

question, we investigate the impacts of individual forcing agents, including GHG, AAER, and104

BMB using the “all-but-one-forcing” LEs. As expected, GHG and AAER are the two dominant

105

and partially offsetting drivers of tropical mean surface temperature trends during the past106

century, while BMB plays a minor role (Fig. 2a). Next, we compute the standard deviation of all

107

the grid points in the ensemble-mean SST trend field and use it as a measure of spatial variance.

108

Interestingly, we find that BMB contributes significantly to the spatial variability of tropical SST

109

warming and is comparable to the contributions from GHG or AAER (Fig. 2b). As far as we

110know, the impact of BMB on the spatial variability of tropical SST trends (i.e., the pattern) has

111

been overlooked in previous studies. More specifically, we find that BMB is the main driver of112

the TIO relative warming in the historical period, as we elaborate below.

113

114

Fig. 3b-f shows the ensemble-mean 1925-2005 trends of tropical relative SST (i.e., absolute SST

115

minus tropical-mean SST; see Methods), associated with each specific radiative forcing. The

116

standard, all-forcing historical simulations exhibit a complex spatial structure with relative

117

warming located in the central equatorial Pacific, the southeastern tropical Pacific, the central-118

eastern equatorial Atlantic, and almost the entire TIO (Fig. 3b). Our forcing decomposition

119

analysis suggests that although BMB contributes little to the absolute TIO SST trend, it acts as120

the most important radiative forcing for the TIO relative warming (Fig. 3a). In contrast, GHG

121

and AAER are two leading factors for TIO absolute SST changes, consistent with previous

122

studies3, but not for TIO relative SST. Within the TIO, both GHG and AAER induce a123

hemispheric asymmetry in relative SST trends, but these patterns largely cancel out leaving only124

weak net changes (Fig. 3d-f). In the Pacific and Atlantic, GHG leads to an equatorially enhanced

125

warming, a common feature identified in coupled GCMs17,18. The southeastern tropical Pacific126

relative warming is mainly induced by AAER, which is not unexpected given the greater

127

anthropogenic aerosol loading in the Northern Hemisphere19,20. Below we will focus primarily

128

on the mechanisms and impacts of BMB-induced TIO relative warming.

129
130

Mechanisms of BMB-induced TIO relative warming

131

How do the historical BMB changes affect the tropical SST warming pattern? BMB alters the132

radiative budget both at the top-of-atmosphere and the surface by absorbing and scattering solar

133

radiation directly and by influencing cloud properties via indirect and semi-direct effects21. The

134

net radiative forcing of BMB aerosols at the ocean surface is suggested to be negative, albeit

135

bearing large uncertainties, and it acts to cool the ocean. During 1925-2005, BMB aerosols have136

significantly increased over Equatorial Asia, South America and Central Africa, but have greatly

137

decreased over the Indian subcontinent, primarily in the 1950s (Supplementary Fig. 5b)22. These138

BMB aerosol changes which originate over land in turn affect the surrounding tropical oceans,

139

leading to an increase of aerosol optical depth (AOD) over the tropical Atlantic, the tropical

140

Pacific, and the far eastern TIO, and a decrease of AOD over most of the TIO (Supplementary

141

Fig. 5a). This spatial heterogeneity in BMB emissions gives rise to a pronounced structure in the142

SST response, with relative warming over the TIO and relative cooling over the tropical Pacific143

and Atlantic (Fig. 4b).

144

145

To uncover the atmospheric and oceanic adjustments to the BMB forcing, we conduct an ocean

146

mixed layer heat budget analysis following ref. 23 (Methods). Below we summarize the key

147

points of the methodology. The SST changes induced by external radiative forcing (here BMB)

148

can be decomposed as:
!
!
!
!
!
!
!
𝑇 ! ≈ 𝑇"#
+ 𝑇$#
+ 𝑇"%
+ 𝑇&'(+ 𝑇$%,*+ 𝑇$%,+%+ 𝑇$%,∆-

149

(1)

150

In Eq. (1), the simulated, ensemble-mean SST trend, denoted as 𝑇 ! , is broken down

151

approximately into 7 components. It is worth noting that the sum of the 7 sub-terms (denoted as

152

!
𝑇./0
) may not be expected to exactly match the model simulated 𝑇 ! due to the assumptions

153

made through the derivation, e.g., a linearized bulk formula of latent heat flux (Methods). The

154

first 4 components represent the contributions from the long-term trends in shortwave radiative

155

!
!
!
!
flux 𝑇"#, longwave radiative flux 𝑇$#, sensible heat flux 𝑇"%, and ocean dynamics 𝑇&'(,

156

respectively. The last three components represent the contributions from the trends in surface

157

!
!
!
wind speed 𝑇$%,*, relative humidity 𝑇$%,+%, and air-sea temperature gradient 𝑇$%,∆, respectively,

158

via latent heat flux changes. The full derivations and the expression of each component are

159

shown in the Methods.

160
161

!
The sum of the 7 SST trend components (𝑇./0) agrees quite well with the tropical SST trend

162

response to BMB forcing (i.e., 𝑇 ! ) with a pattern correlation of 0.88, validating our methodology

163!
(Fig. 4b, c). The biggest contributors to the BMB-induced TIO relative warming are 𝑇"#
and

164

!
!
𝑇$%,*
, while 𝑇&'(
contributes to the TIO absolute but not relative warming (Fig. 4a), as we will165

further discuss in detail below.

166

•

!
!
𝑇"#
: To separate the BMB direct and indirect/semidirect effects, we split 𝑇"#
into the167

clear-sky and cloud components (Fig. 4f; Supplementary Fig. 6). The BMB reduction

168

over the TIO increases the clear-sky shortwave radiation reaching the surface and induces

169

a local warming especially around the Indian subcontinent (cf. Supplementary Fig. 5),170

while cloud changes contribute to the warming primarily near the equator. At the same171

time, the BMB increase over the eastern Pacific and Atlantic cools these areas primarily

172

via clear-sky shortwave radiation, amplified by SW cloud feedbacks in the eastern

173!
Pacific. Altogether, 𝑇"#
becomes the primary driver of the BMB-induced TIO relative174

warming (Fig. 4a).

175

•

!
𝑇$%,*
: The anomalous easterly winds in the northern TIO and northwesterly winds in the176

southern TIO oppose the background wind and cause decreases in surface wind speed

177

and thus latent heat flux, acting as a warming effect (Fig. 4h). In addition, a considerable

178

cooling due to the increase of wind speed is identified over the southeast subtropical179!
Pacific. In terms of TIO relative warming, 𝑇$%,*
serves as the second largest contributor

180(Fig. 4a).

181

•

!
𝑇&'(
: The change in ocean heat transport induces a relatively complex warming/cooling182

structure (Fig. 4d). The prominent warming in the western TIO is consistent with the183

anomalous alongshore northerly winds that are expected to induce Ekman convergence184

and downwelling near the coast. The equatorial Atlantic warming is associated with an185

anomalous Ekman convergence due to the westerly wind anomalies, while the eastern

186

equatorial Pacific warming can potentially be explained by a weaker poleward heat

187

transport by the climatological Ekman divergence due to the cooling SSTs (Fig. 4b). As a

188

result, ocean heat transport changes only play a minor role in the TIO relative SST

189changes (Fig. 4a).

190
191

The changes in air-sea temperature gradient generally act as a damping effect to the tropical SST

192

changes with a pattern correlation of -0.67 (Fig. 4i; cf. Fig. 4b), since a warmer SST tends to193

enhance the air-sea temperature contrast favoring an enhanced latent heat release. Other terms in194

the heat budget are relatively small in terms of TIO basin averaged SST changes, although they195

can be non-negligible locally.

196
197

Potential impacts of BMB-induced TIO relative warming

198

The BMB-induced TIO relative warming can potentially have important impacts on regional and199

global climate. First, the TIO relative warming leads to an increase of rainfall over most of the

200

TIO, while in contrast, the western Pacific cooling in response to BMB increases over Equatorial

201

Asia induces a local reduction in precipitation (Fig. 5a). Such a zonal dipole in rainfall anomalies

202

suggests a westward displacement of the Indo-western Pacific warm pool convective center,203

although the magnitude of rainfall change is rather weak (~10% of the climatological rate over a

204

century). Consistent with the precipitation change dipole, the TIO becomes fresher while the205western Pacific becomes saltier (Fig. 5b). These changes are also seen in the all-forcing

206

experiments but with a greater amplitude (Supplementary Fig. 7), implying that BMB changes

207

may play a critical role in shaping the Indo-western Pacific warm pool rainfall change pattern in208

the past century.

209
210

The BMB-induced local rainfall changes over the TIO and western Pacific can potentially lead to211

far-reaching impacts via global teleconnections. For example, we find that the North Atlantic jet

212

stream is significantly enhanced and shifted northward by the BMB forcing (Fig. 5c), similar to213

the jet strengthening seen in the all-forcing experiments (Supplementary Fig. 7). The enhanced

214

North Atlantic jet stream transports more water vapor towards northwestern Europe, enhancing

215

rainfall over the British Isles and Scandinavia and reducing it over southern Europe and the216

Mediterranean (Fig. 5d; Supplementary Fig. 7). The BMB-induced changes in the North Atlantic

217

jet and the associated European rainfall pattern all suggest a shift towards the positive phase of

218

NAO, as observed in the past century24. The BMB-induced changes can explain about a half of219

the NAO positive trend seen in the all-forcing experiments, while the other half comes mainly

220from GHG-induced changes (Supplementary Fig. 8). Our results are broadly consistent with the221previous studies that argue a TIO warming can induce a positive NAO4,7,8. A more definitive222

demonstration of the BMB-TIO-NAO linkage, particularly whether the BMB-induced Atlantic

223

jet strengthening is explained solely by the TIO relative warming or partially by other factors,

224

requires further investigation.

225
226

Discussion

227

Our findings with CESM1 “all-but-one-forcing” large ensembles imply that BMB may play a228

more important role in historical climate change than previously thought. Although the BMB

229

impact on global mean temperature change may be secondary as compared with GHG or AAER,

230

it does significantly influence the pattern of warming. In fact, over 80% of global BMB

231

emissions have occurred in tropical regions25, and we indeed find that the change in BMB is

232

critical in shaping the tropical SST warming pattern in the past century. In particular, BMB is the233

dominant contributor to the forced component of TIO relative warming, at least compared to234

GHG and AAER (Fig. 3; Supplementary Fig. 9). While observational data products also show

235

relative TIO warming, most of this warming occurs after 1960 with considerable uncertainty in

236

terms of magnitude (Supplementary Fig. 9). It is likely that internal variability also contributes

237

to the observed relative TIO SST record. The TIO relative warming induced by the BMB

238

aerosol changes leads to locally enhanced rainfall, which together with the BMB-induced

239

cooling of the western Pacific, potentially acts to shift the Indo-western Pacific warm pool

240

convective center westward. The BMB-induced tropical rainfall changes may in turn cause a241

positive NAO phase and influence North Atlantic/European climate via atmospheric242

teleconnections. To what extent the results presented in this study are model dependent needs to243

be tested by other climate models conducting similar types of historical experiments isolating the244

contribution of BMB aerosol changes, which is currently not a protocol for most models.

245
246

Our study points to an urgent need to accurately represent the chemical, microphysical, and247

radiative properties of BMB aerosols in GCMs. In the past decades, enormous progress has been248

made in BMB-related studies, including both field campaign observations26-31 and the associated249

modeling efforts32,33,34. Despite these tremendous advances, an accurate representation of the

250

influence of BMB aerosols in climate models remains challenging. For example, a recent study

251

suggests that BMB aerosols in most climate models are too absorbing, leading to a potential bias

252

in radiative forcing35. In contrast, another study argues that the absorbing nature of BMB

253

aerosols over the Southeast Atlantic is underestimated in most climate models36. Such254

discrepancies have yet to be reconciled. Another recent study reports that a discontinuity in the255variability of prescribed BMB emissions between the satellite era and the prior period in some256

climate models can rectify the modeled mean climate and generate a spurious warming37. As

257continuous observational efforts remain necessary in the coming decades, developing proxies of258

historical BMB aerosols38 and modeling their physical and chemical properties, transport within259

the atmosphere, interactions with clouds, and role in carbon cycle dynamics all need to be

260

refined. In that regard, modeling efforts like the Fire Modeling Intercomparison Project34 will be

261

particularly helpful in interpreting and potentially reducing inter-model spread. Improving the262

understanding and simulation of BMB aerosols thus requires the integrated efforts of worldwide

263

observational and laboratory communities, Earth science modeling communities, and more

264

importantly, the collaboration between those communities.

265

Methods

266
267

Observational datasets

268

We use four monthly observational SST datasets: 1. National Oceanic and Atmospheric

269

Administration Extended Reconstructed Sea Surface Temperature Version 5 (ERSSTv5) with270

resolution 2°×2°39. 2. Hadley Centre Sea Ice and SST v.1.1 (HadISST 1.1) with resolution

271

1°×1°40. 3. Centennial In Situ Observation-Based Estimates of the Variability of SST and Marine

272

Meteorological Variables (COBE) with resolution 1°×1°41. 4. Kaplan Extended SST v2 with273

resolution 5°×5°42.

274
275

CESM1 Large-Ensemble simulations

276

We use four sets of simulations from the Community Earth System Model version 1 Large

277

Ensemble (CESM1-LE)14 produced by the National Center for Atmospheric Research (NCAR).

278

The first set is the standard all-forcing (ALL) historical simulations which contains all historical

279

radiative forcings; it has 40 ensemble members. The other three sets are the CESM1-LE ‘‘all-280

but-one-forcing’’ experiments15, which are designed to isolate the influence of individual forcing

281

agents, including anthropogenic aerosols (AAER), greenhouse gases (GHG), and biomass

282

burning aerosols (BMB). They are identical to the all-forcing historical simulations (i.e., ALL)283

except that one forcing agent is fixed at its 1920 level. More specifically, the XGHG (where “X”

284

refers to the fact that GHG is held fixed) set has 20 ensemble members with fixed GHG; the285

XAAER set has 20 ensemble members with fixed AAER; the XBMB set has 15 ensemble

286

members with fixed BMB. Each ensemble member within each set starts from slightly different

287

atmospheric initial conditions on the order of 10-14K. The influence of each individual forcing

288

agent can therefore be determined by subtracting ‘‘all-but-one-forcing’’ member-mean from289

ALL member-mean, that is,290

GHG = ALL ensemble mean – XGHG ensemble mean

291

AAER = ALL ensemble mean – XAAER ensemble mean

292

BMB = ALL ensemble mean – XBMB ensemble mean

293

The full time period for all the experiments under historical forcing is 1920-2005. To avoid the294

potential influence of ocean initial conditions, we use the period 1925-2005.

295

296

Definitions of indices297

For both observations and simulations, the tropics refers to the latitudinal band 20°S – 20°N, and298

the TIO refers to the region 20°S-20°N, 40°E-100°E. Relative SST is defined as the difference299

between local SST and tropical mean SST.

300
301

The NAO pattern and index used here are computed as the first EOF and PC of annual mean sea

302

level pressure anomalies within the region of 20°N-80°N, 90°W-40°E, after concatenating all the303

members from ALL, XBMB, XAER and XGHG.

304
305

Ocean mixed layer heat budget analysis

306

To further understand the mechanisms of BMB-induced TIO warming, we conducted ocean

307

mixed layer heat budget analysis following the methodology in ref. 23 and the references therein.

308

The ocean surface mixed layer heat budget can be written as:
2""-

309

𝜌𝐶1 𝐻 2! = 𝑆𝑊 + 𝐿𝑊 + 𝑆𝐻 + 𝐿𝐻 + 𝑂𝑐𝑛.

310

The left-hand side represents mixed layer heat storage, where 𝜌 is density of sea water, 𝐶1 is311

specific heat of sea water, H is depth of ocean mixed layer and SST is temperature of ocean

312

mixed layer. On the right-hand side, SW is surface net shortwave radiative flux, LW is surface

313

net longwave flux, SH is surface sensible heat flux, LH is surface latent heat flux and Ocn is the314heat flux due to ocean dynamics. We then do the linear regression to Eq. (1). Since the trend of315

ocean mixed layer heat storage can be treated as negligible12,43, the left-hand side becomes zero316

and we have:317

0 = 𝑆𝑊 ! + 𝐿𝑊 ! + 𝑆𝐻! + 𝐿𝐻! + 𝑂𝑐𝑛!

(1)

(2)

318

We define downward as positive and the superscript t represents BMB induced ensemble mean319

trend during 1925-2005. Latent heat flux term is directly related to SST and can be written as:

320

𝐿𝐻 = −𝜌345 𝐿6 𝐶7 𝑊 [𝑞∗ (𝑆𝑆𝑇) − 𝑞345 ]

321
322

(3)

𝑞345 in Eq. (3) is specific humidity of air above sea surface. It can be expressed as:
𝑞345 = 𝑅𝐻𝑞∗ (𝑆𝑆𝑇 − ∆𝑇)

(4)

323

Where RH is relative humidity at sea surface, ∆𝑇 is temperature gradient near sea surface and we

324

define it as (𝑆𝑆𝑇 − 𝑇345 ). We further rewrite 𝑞345 by using Clausius-Clapeyron equation:325

𝑞345 = 𝑅𝐻𝑞∗ (𝑆𝑆𝑇)𝑒 9:∆-326

Where 𝛼 ≈ 0.06/𝐾. We plug Eq. (5) into Eq. (3) and get new latent heat flux expression:(5)𝐿𝐻 = −𝜌345 𝐿6 𝐶7 𝑊𝑞∗ (𝑆𝑆𝑇)(1 − 𝑅𝐻𝑒 9:∆- )

327
328

Next, we use linear regression to get 𝐿𝐻! from Eq. (6):
𝐿𝐻! =

329
330

(6)

𝜕𝐿𝐻 ! 𝜕𝐿𝐻
𝜕𝐿𝐻
𝜕𝐿𝐻 !
𝑊 +
𝑆𝑆𝑇 ! +
𝑅𝐻! +
∆𝑇
𝜕𝑊
𝜕𝑆𝑆𝑇
𝜕𝑅𝐻
𝜕∆𝑇

;;;;

;;;;

;;;; ;;;;$%$%:$% +%!
!
AAAA 𝑆𝑆𝑇 ! − !∆#=#𝑊 ! + 𝛼𝐿𝐻$$$$ ;;;; 𝑅𝐻 + !∆#$$$$ ;;;; ∆𝑇
<
=

9+%

=

(7)

9+%

331

B , AAAAAAAA is climatology computed using the period of 1925-1944.
where the overbar for AAAA
𝐿𝐻, 𝑊
∆𝑇, 𝑅𝐻

332

Plugging in Eq. (7), we can then rewrite Eq. (2) as:

333
334

𝑇! ≈ −;;;; ∆- %"# % ?$# % ?"% % ?&'(%
#%
+%
+% %
−
−
+
$$$$
$$$$ ;;;;
;;;;
<
;;;;
:$%
:#
= !∆# 9+%:(= !∆#
9+% )
!
!
!
!
!
!
!
= 𝑇"#
+ 𝑇$#
+ 𝑇"%
+ 𝑇&'(+ 𝑇$%,*+ 𝑇$%,+%+ 𝑇$%,∆-(8)335

where 𝑆𝑆𝑇 ! is written as 𝑇 ! for brevity. The last three terms are all related to latent heat flux

336

!
!
changes via the changes in surface wind speed 𝑇$%,*, relative humidity 𝑇$%,+%and temperature337!
gradient 𝑇$%,∆, respectively. In summary, 𝑇 ! can be split into seven sub-terms as follow:
"# %

338

!
𝑇"#
= − :$%
;;;;

339

!
𝑇$#
= − :$%
;;;;

340

!
𝑇"%
= − :$%
;;;;

341

!
𝑇&'(
= − :$%
;;;;

342

!
𝑇$%,*
= − :#<

343!
𝑇$%,∆= − !∆#$$$$ ;;;;

344!
𝑇$%,+%
=

$# %
"% %

&'(%
#%

;;;;
+% ∆- %

=

9+%

+% %
$$$$ ;;;;:(= !∆#
9+% )

345

!
It is worth noting that the sum of the seven sub-terms (𝑇./0) may not be expected to exactly

346

match the model simulated 𝑇 ! due to the assumptions made through the derivation, e.g., a347

linearized bulk formula of latent heat flux.

(9)
(10)
(11)
(12)
(13)
(14)
(15)

348

Data availability349

All data used in this study are publicly available. For observational datasets, the NOAA’s

350

ERSSTv5 data are available at https://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.html;

351

HadISST 1.1 data at https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html; COBE352

SST at https://psl.noaa.gov/data/gridded/data.cobe.html; Kaplan Extended SST v2 at353

https://psl.noaa.gov/data/gridded/data.kaplan_sst.html. CESM1-LE data are available through the354

Casper cluster at /glade/campaign/cesm/collections/cesmLE/CESM-CAM5-BGC-LE/.

355
356

Code availability357

All codes are available upon request.

358
359

Acknowledgement360

We thank the three anonymous reviewers for their constructive comments and suggestions. S.H.

361

is supported by NASA Award 80NSSC22K1025. We acknowledge computational support from362

the NCAR-Wyoming Supercomputing Center. We acknowledge Isla Simpson for the help with363

the analysis of aerosol forcing data for CESM1 LE. NCAR is sponsored by the National Science

364

Foundation.

365
366

Author contributions

367

Y. T. and S.H. conceived the original idea and wrote the first draft of the manuscript. Y.T.

368

performed the analysis. Y.T., S.H., and C.D. contributed to the interpretation of results and369

edited the manuscript.

370
371

Competing interest

372

The authors declare that they have no competing interests.

373
374

Materials & Correspondence

375

Correspondence and requests for materials should be addressed to S. H.

376
377

References
1. Du, Y., & Xie, S. P. Role of atmospheric adjustments in the tropical Indian Ocean

378

warming during the 20th century in climate models. Geophys. Res. Lett.

379

https://doi.org/10.1029/2008GL033631 (2008).

380
381

2. Roxy, M. K., Ritika, K., Terray, P., & Masson, S. The curious case of Indian Ocean
warming. J. Clim. 27, 8501-8509 (2014).

382

3. Dong, L., & Zhou, T. The Indian Ocean sea surface temperature warming simulated by383

CMIP5 models during the twentieth century: Competing forcing roles of GHGs and384

anthropogenic aerosols. J. Clim. 27, 3348-3362 (2014).

385
386
387
388

4. Hoerling, M. P., Hurrell, J. W., & Xu, T. Tropical origins for recent North Atlantic
climate change. Science 292, 90-92 (2001).
5. Lu, J., & Delworth, T. L. Oceanic forcing of the late 20th century Sahel drought.
Geophys. Res. Lett. https://doi.org/10.1029/2005GL023316 (2005).

389

6. Luo, J. J., Sasaki, W., & Masumoto, Y. Indian Ocean warming modulates Pacific climate

390

change. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1210239109 (2012).

391
392
393
394
395

7. Hu, S., & Fedorov, A. V. Indian Ocean warming as a driver of the North Atlantic
warming hole. Nat. Commun. 11, 1-11 (2020).
8. Hu, S., & Fedorov, A. V. Indian Ocean warming can strengthen the Atlantic meridional
overturning circulation. Nat. Clim. Change 9, 747-751 (2019).
9. Hoerling, M. P., Hurrell, J. W., Xu, T., Bates, G. T., & Phillips, A. S. Twentieth century

396

North Atlantic climate change. Part II: Understanding the effect of Indian Ocean

397

warming. Clim. Dyn. 23, 391-405 (2004).

398
399
400
401
402
403
404
405

10. Deser, C., Phillips, A. S., & Alexander, M. A. Twentieth century tropical sea surface
temperature trends revisited. Geophys. Res. Lett. 37, (2010).
11. Hu, S., & Seager, R. Revisiting the enhanced Indian Ocean warming since 1950: A major
point of disagreement between observations and CMIP6 models, submitted.
12. Xie, S. P. et al. Global warming pattern formation: Sea surface temperature and rainfall.
J. Clim. 23, 966-986 (2010).
13. Deser, C. et al. Insights from Earth system model initial-condition large ensembles andfuture prospects. Nat. Clim. Change 10, 277-286 (2020).

406

14. Kay, J. E. et al. The Community Earth System Model (CESM) large ensemble project: A

407

community resource for studying climate change in the presence of internal climate

408

variability. Bull. Am. Meteorol. Soc. 96, 1333-1349 (2015).

409

15. Deser, C. et al. Isolating the evolving contributions of anthropogenic aerosols and410

greenhouse gases: a new CESM1 large ensemble community resource. J. Clim. 33, 7835-

411

7858 (2020).

412

16. Zhang, L. et al. Indian Ocean warming trend reduces Pacific warming response to413

anthropogenic greenhouse gases: An interbasin thermostat mechanism. Geophys. Res.

414

Lett. https://doi.org/10.1029/2019GL084088 (2019).

415

17. Liu, Z., Vavrus, S., He, F., Wen, N., & Zhong, Y. Rethinking tropical ocean response to416

global warming: The enhanced equatorial warming. J. Clim. 18, 4684-4700 (2005).

417

18. Heede, U. K., Fedorov, A. V., & Burls, N. J. Time scales and mechanisms for the tropical

418

Pacific response to global warming: A tug of war between the ocean thermostat and419

weaker walker. J. Clim. 33, 6101-6118 (2020).

420

19. Wang, H., Xie, S. P., & Liu, Q. Comparison of climate response to anthropogenic aerosol

421

versus greenhouse gas forcing: Distinct patterns. J. Clim. 29, 5175-5188 (2016).

422

20. Shi, J. R., Kwon, Y. O., & Wijffels, S. E. Two Distinct Modes of Climate Responses to

423
424

the Anthropogenic Aerosol Forcing Changes. J. Clim. 35, 3445-3457 (2022).
21. Liu, L. et al. Impact of biomass burning aerosols on radiation, clouds, and precipitation

425

over the Amazon: relative importance of aerosol–cloud and aerosol–radiation

426

interactions. Atmos. Chem. Phys. 20, 13283-13301 (2020).

427

22. Lamarque, J. F. et al. Historical (1850–2000) gridded anthropogenic and biomass burning428

emissions of reactive gases and aerosols: methodology and application. Atmos. Chem.

429

Phys. 10, 7017-7039 (2010).

430

23. Zhang, X., Deser, C., & Sun, L. Is there a tropical response to recent observed Southern

431

Ocean cooling? Geophys. Res. Lett. https://doi.org/10.1029/2020GL091235 (2021).

432

24. Hurrell, J. W. Decadal trends in the North Atlantic Oscillation: Regional temperatures

433
434

and precipitation. Science 269, 676-679 (1995).
25. Van Marle, M. J. et al. Historic global biomass burning emissions for CMIP6435(BB4CMIP) based on merging satellite observations with proxies and fire models (1750–

436

2015). Geosci. Model Dev. 10, 3329-3357 (2017).

437
438
439

26. Kaufman, Y. J. et al. Smoke, clouds, and radiation‐Brazil (SCAR‐B) experiment. J.
Geophys. Res. Atmos. 103, 31783-31808 (1998).
27. Delmas, R. A. et al. Experiment for Regional Sources and Sinks of Oxidants

440(EXPRESSO): An overview, J. Geophys. Res. Atmos. 104, 30609-30624 (1999).

441

28. Swap, R. J. et al. Africa burning: A thematic analysis of the Southern African Regional

442

Science Initiative (SAFARI 2000), J. Geophys. Res. Atmos. 108, 8465 (2003).

443

29. Janicot, S. et al. Large-scale overview of the summer monsoon over West Africa during

444

the AMMA field experiment in 2006. Annales Geophysicae 26, 2569-2595 (2008).

445

30. Abel, S. J. et al. Open cells exhibit weaker entrainment of free-tropospheric biomass

446

burning aerosol into the south-east Atlantic boundary layer. Atmos. Chem. Phys. 20,447

4059-4084 (202).

448

31. Redemann, J. et al. An overview of the ORACLES (ObseRvations of Aerosols above

449

CLouds and their intEractionS) project: aerosol–cloud–radiation interactions in the450southeast Atlantic basin. Atmos. Chem. Phys. 21, 1507-1563 (2021).

451

32. Keil, A., & Haywood, J. M. Solar radiative forcing by biomass burning aerosol particles

452

during SAFARI 2000: A case study based on measured aerosol and cloud properties. J.

453

Geophys. Res. Atmos. 108, 8467 (2003).

454

33. Haywood, J. M., Osborne, S. R., & Abel, S. J. The effect of overlying absorbing aerosol

455

layers on remote sensing retrievals of cloud effective radius and cloud optical depth. Q. J.

456

R. Meteorol. Soc. https://doi.org/10.1256/qj.03.100 (2004)

457

34. Rabin, S. S. et al. The Fire Modeling Intercomparison Project (FireMIP), phase 1:

458

experimental and analytical protocols with detailed model descriptions. Geosci. Model

459

Dev. 10, 1175-1197 (2017).

460
461

35. Brown, H. et al. Biomass burning aerosols in most climate models are too absorbing. Nat.
Commun. 12, 1-15 (2021).

462

36. Mallet, M. et al. Climate models generally underrepresent the warming by Central Africa

463

biomass-burning aerosols over the Southeast Atlantic. Sci. Adv. 7, eabg9998 (2021).

464

37. Fasullo, J. T. et al. Spurious Late Historical‐Era Warming in CESM2 Driven by

465

Prescribed Biomass Burning Emissions. Geophys. Res. Lett.

466

https://doi.org/10.1029/2021GL097420 (2022).

467
468
469
470

38. Marlon, J. R. et al. Reconstructions of biomass burning from sediment-charcoal records
to improve data–model comparisons. Biogeosciences 13, 3225-3244 (2016).
39. Huang, B. et al. Extended reconstructed sea surface temperature, version 5 (ERSSTv5):
upgrades, validations, and intercomparisons. J. Clim. 30, 8179-8205 (2017).

471

40. Rayner, N. A. A. et al. Global analyses of sea surface temperature, sea ice, and night472

marine air temperature since the late nineteenth century. J. Geophys. Res. Atmos.

473

https://doi.org/10.1029/2002JD002670 (2003).

474

41. Ishii, M., Shouji, A., Sugimoto, S., & Matsumoto, T. Objective analyses of sea‐surface

475

temperature and marine meteorological variables for the 20th century using ICOADS and476

the Kobe collection. Int. J. Climatol. 25, 865-879 (2005).

477
478

42. Kaplan, A. et al. Analyses of global sea surface temperature 1856–1991. J. Geophys Res.
Oceans, 103, 18567-18589 (1998).

479

43. Cook, K. H., Vizy, E. K., & Sun, X. Multidecadal-scale adjustment of the ocean mixed

480

layer heat budget in the tropics: examining ocean reanalyses. Clim. Dyn. 50, 1513-1532

481(2018).

482
483

Fig. 1 | Observed and model simulated tropical SST trends since 1925. a, Observed tropical484

SST trends (K/century) during 1925-2021, averaged across four SST datasets (ERSSTv5,

485

HadISST, COBE-SST, Kaplan SST). b, CESM1-LE ensemble-mean tropical SST trends

486(K/century) during 1925-2005 from the all-forcing experiments. In panels a and b, the black

487

contour line represents the isopleth of the tropical-mean SST trend (values shown on the top-488

right corner). c, Simulated and observed TIO SST trends versus tropical-mean SST trends during489

1925-2005. The observed trends during 1925-2021 are also shown for comparison. d, Similar to490

panel c but for TIO SST trends vs. TIO relative SST trends. Relative SST is defined as absolute

491

SST minus tropical-mean SST. White hatches in a and b represent the regions that are 99%492

significant based on a t-test.

493
494

Fig. 2 | Attribution of tropical warming to individual radiative forcing agents in 1925-2005.

495

a, Ensemble-mean, tropical-mean surface temperature trends (K/year) attributed to each radiative

496

forcing: all-forcing (ALL), greenhouse gas (GHG), anthropogenic aerosols (AAER), biomass

497

burning aerosols (BMB), anthropogenic aerosols and greenhouse gas combined (AAER+GHG).

498

For each forcing, calculations have been performed for land only, ocean only, and land-ocean

499

combined. b, Similar to panel a but for spatial standard deviation of ensemble-mean SST trends

500

in the tropics. For both panels, the tropics is defined as 20°S-20°N.

501
502

Fig. 3 | Critical role of BMB in the historical TIO relative warming. a, Absolute and relative

503

TIO SST trends (K/century) during 1925-2005 attributed to individual radiative forcing agents,

504

similar to Fig. 2. Note the two different vertical axes. b-f, Tropical relative SST trends

505(K/century) during 1925-2005 in all-forcing experiments and attributed to each radiative forcing.

506

Relative SST is defined as absolute SST minus tropical-mean SST. The value on the top right507

corner represents the TIO-mean relative SST trend. White hatches in b-f represent the regions

508

that are 99% significant based on a t-test.

509
510

Fig. 4 | Heat budget analysis for BMB-induced TIO warming. a, TIO SST trends (K/century)

511

during 1925-2005 attributed to each component in the ocean-mixed layer heat budget (Methods).

512

The TIO absolute trends are further split into two components, tropical-mean SST trends and513

TIO relative SST trends. b, Simulated tropical SST trends during 1925-2005 (K/century) induced514

by BMB changes. c-j, BMB-induced tropical SST trends during 1925-2005 attributed to c sum of515

the following seven components (𝑇./0 ! ), the changes in d ocean heat transport convergence

516

!
!
!
(𝑇&'(
), e longwave radiative flux (𝑇$#), f shortwave radiative flux (𝑇"#), g sensible heat flux

517

!
!
(𝑇"%
), and latent heat flux due to the changes in h surface wind speed (𝑇$%,*), i air-sea

518

!
!
temperature difference (𝑇$%,∆), and j surface relative humidity (𝑇$%,+%
).

519
520

Fig. 5 | Potential climate impacts associated with BMB-induced TIO relative warming. a,

521

BMB-induced rainfall trends (colors; mm/d/century) overlaid by climatological rainfall

522(contours; mm/d) and b, BMB-induced ocean salinity trends (g/kg/century) during 1925-2005. c,

523

BMB-induced 850 mb zonal wind trends (colors; m/s/century) overlaid by climatological 850 mb

524

zonal wind (contours; m/s) and d, BMB-induced rainfall trends (mm/d/century) during 1925-

525

2005. White hatches in a-d represent the regions that are 99% significant based on a t-test.