Shigemitsu et al.
Progress in Earth and Planetary Science(2024) 11:22
https://doi.org/10.1186/s40645-024-00623-0

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open AccessControlling factors of latitudinal distributionof dissolved organic matter in the upper layersof the Indian Ocean
M. Shigemitsu1* , K. Sasaoka1, M. Wakita2, T. Yokokawa3, T. Hashioka4, K. Arulananthan5 and A. Murata1

Abstract
We studied chromophoric (CDOM) and fluorescent (FDOM) dissolved organic matter (DOM) and dissolved organiccarbon in surface waters to determine the factors controlling the geographical distribution of DOM along two
meridional transects in the Indian Ocean. For CDOM, we calculated the absorption coefficients, spectral slope,and absorption coefficient ratio from the observed absorption spectra. For FDOM, we calculated the biological (BIX)
and humification (HIX) indices from the excitation emission matrices (EEMs); parallel factor analysis of the EEMs
identified three fluorescent components, i.e., two humic-like and one protein-like. Using these DOM parameters,a factor analysis extracted fewer latent variables than the observed variables to account for the geographical distributions. We obtained three factors (F1, F2, and F3), which explained ~ 84% of the variance in the observed data. From
the factor loadings, F1, F2, and F3 were interpreted as the effects of net primary production-derived DOM and its
horizontal transport, photodegradation, and vertical transport by physical processes. We characterized seven marine
biogeochemical provinces by factor scores. F1 scores gradually decreased from the northernmost to the Antarctic
province, with a small maximum around the subtropical front. F2 scores were highest in the subtropical provinceand decreased in both the northward and southward directions. F3 scores were high in the Antarctic and northernmost provinces, and lowest in the subtropical province. Only BIX was insufficiently explained by these factors. BIX
was highest in the northern part of the subtropical province, where photodegradation of DOM was the most intense.
This suggests that the possible interaction between photodegradation, autochthonous production, and reworking
by heterotrophic bacteria of DOM occurs in the subtropical province.
Keywords FDOM, CDOM, DOC, Surface mixed layer, Indian Ocean*Correspondence:
M. Shigemitsu
ma-shige@jamstec.go.jp
1
Physical and Chemical Oceanography Research Group, Global Ocean
Observation Research Center, Research Institute for Global Change,
Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 2‑15
Natsushima‑cho, Yokosuka, Kanagawa 237‑0061, Japan2
Mutsu Institute for Oceanography, Research Institute for Global Change,
Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 690
Kitasekine Sekine, Mutsu, Aomori 035‑0022, Japan
3
Super‑Cutting‑Edge Grand and Advanced Research Program, Institutefor Extra‑Cutting‑Edge Science and Technology Avant‑Garde Research,
Japan Agency for Marine-Earth Science and Technology (JAMSTEC),
Yokosuka, Japan
4
Marine Ecosystem Research Group, Earth Surface System ResearchCenter, Japan Agency for Marine-Earth Science and Technology(JAMSTEC), Yokosuka, Japan

5
National Institute of Oceanography and Marine Sciences, National
Aquatic Resources Research and Development Agency, Colombo, Sri
Lanka© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/.

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1 Introduction
Globally, the export of dissolved organic matter (DOM)
from the ocean surface to the interior accounts forapproximately 20% of the biological pump (Roshan and
DeVris 2017). However, very few current climate models consider marine DOM dynamics. This is most likely
because information regarding DOM dynamics in surface waters is scarce, and most marine DOM is refractory
to centennial-to-millennial turnover time (Hansell 2013).
The contribution of DOM to biological pumps varies
regionally (Roshan and DeVries 2017) and is expected
to change with global warming (Lønborg et al. 2020).
Thus, large-scale investigations of DOM in the ocean are
becoming increasingly important to thoroughly understand the mechanisms controlling the surface DOM
distribution and to properly incorporate DOM dynamics into climate models. Various biogeochemical sources
and sinks of DOM exist in surface waters. Autochthonous DOM sources are primarily derived from extracellular release (Wood and Van Valen 1990) and autolysis(Veldhuis et al. 2001) by phytoplankton. This is followedby other processes such as sloppy feeding (Møller
2007), excretion (Thibodeau et al. 2020), and defecation(Lampert 1978) by zooplankton; bacterial production(Kawasaki and Benner 2006); and viral lysis (Middelboeand Lyck 2002). DOM sinks are mainly biodegradation byheterotrophic prokaryotes (del Giogio and Duarte 2002)
and photodegradation (Andrews et al. 2000). The net balance between the sources and sinks, as well as physicalocean processes, including advection and mixing determines the local tendencies of DOM in the surface waterand its spatial distribution.
Dissolved organic carbon (DOC) is typically measured
to quantify the amount of DOM. However, DOC does
not provide specific information regarding the DOM
quality. Considering the above-mentioned processes
related to DOM dynamics, measuring DOC alone isinsufficient to describe the dynamics of DOM in surface
water; therefore, information regarding DOM quality isneeded. To this end, measuring the optical properties(i.e., absorbance and fluorescence) of DOM is the mostsuitable for large-scale DOM observations because of
their high-throughput and low-cost advantages overother measurements for determining molecular, elemental, and/or isotopic properties (Medeiros et al. 2015;
Broek et al. 2020). Chromophoric dissolved organic matter (CDOM) is a DOM fraction that absorbs light in theultraviolet (UV) and visible regions. The absorption coefficient, spectral slope, and absorption coefficient ratio atdifferent wavelengths can be used to infer the processes
that affect the surface DOM composition (Iuculano et al.
2019). A fraction of CDOM fluoresces and is called fluorescent dissolved organic matter (FDOM), and treating

the excitation‒emission matrix (EEM) with parallel factor analysis (PARAFAC), as well as the ratio of fluorescent intensity of some pairs of excitation and emission
wavelengths, can be used to clarify the variability in
DOM composition in surface waters (Heller et al. 2013;
Kowalczuk et al. 2013; Catalá et al. 2016; Loginova et al.
2016; Yamashita et al. 2017). The above studies verified
that CDOM and FDOM-related parameters are useful for
obtaining information regarding the DOM composition.
To date, several studies have addressed the opticalproperties of DOM or DOC in surface water at basin or
global scales (Hansell 2009; Heller et al. 2013; Kowalczuk
et al. 2013; Catalá et al. 2016; Yamashita et al. 2017; Iuculano et al. 2019). Kowalczuk et al. (2013) and Heller et al.
(2013) examined CDOM and FDOM distributions along
meridional transects in the Atlantic; Yamashita et al.
(2017) recorded observations of FDOM within wide latitudinal and longitudinal ranges in the Pacific. Although
Catalá et al. (2016) and Iuculano et al. (2019) used global
observations of FDOM and CDOM, respectively, theobservations in the Indian Ocean were confined to thesubtropical region. Kim et al. (2020) and Shigemitsu et al.
(2020) observed the meridional distributions of FDOMin the Indian Ocean but mainly targeted processes occurring in deep (> 250 m) rather than upper waters. Hansell(2009) reported DOC distributions along the transectsfrom the northern end to ~ 40° S in the Central Indian
Ocean, in the Arabian Sea (AS), and in the Bay of Bengal (BoB). Based on DOC measurements along thetransects, they arrived at the following conclusion withrespect to the surface waters: (1) In the Central Indian
Ocean, DOC concentrations are high in warm and lowlatitude waters but low in cold and high-latitude waters.
They suggested that the former is due to reduced verticalmixing and the latter due to greater vertical mixing. (2)In the AS surface waters, DOC concentrations were in arange of ~ 70–80 μmol/kg in summer and ~ 75–90 μmol/
kg in winter, showing seasonal variabilities in DOC budgets. (3) In the BoB, high DOC concentrations (> 75 μmol/kg) were found in the surface waters of the northernmost
part of the section, probably affected by rivers and tending to decrease with decreasing latitude. Regardless, theIndian Ocean is the least-examined basin in terms of
surface DOM quality, although the DOC distributions as
DOM quantity have been determined to some extent.
In this study, we obtained large-scale observationsof the optical properties of DOM (CDOM and FDOM)
and DOC along the meridional transects in the Indian
Ocean. The meridional transects range from the northern to the southern end of the Indian Ocean (Fig. 1),
where basin-scale information on surface DOM in termsof both amount and composition is scarce. As such, ourobjectives in this study were to: (1) clarify the meridional

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measured their DOM optical properties and DOC forthem at the stations (Fig. 1).
2.2 CDOM

Fig. 1 Map of sampling sites. The northern transect is Leg 2
and the southern transect is Leg 3. According to Longhurst (1998),
the seven biogeochemical provinces encountered in this study
are shown in colors: INDW (steel blue), Western India Coastal;
MONS (blue), Indian Monsoon Gyres; ISSG (light blue), IndianSouth Subtropical Gyre; SSTC (yellow green), South Subtropical
Convergence; SANT (yellow), Subantarctic Water Ring; ANTA (orange),
Antarctic; APLR (brown), Austral Polar provinces. Gray circles represent
sites where CDOM measurements were obtained, black triangles
represent sites for FDOM measurements, and green crosses represent
DOC measurements

distributions of the optical properties of DOM and DOC
in the surface waters of the Indian Ocean and (2) obtaina synoptic view of the factors controlling the meridional
distributions of DOM using the observations.

2 Materials and Methods
2.1 Sampling

We obtained observations and samples through global
ocean ship-based hydrographic investigations program(GO-SHIP) cruises aboard the R/V Mirai (MR19-04,
Legs 2 and 3) (Fig. 1). Leg 2 covered the northern transect (December 5–27, 2019) and Leg 3 covered the southern transect (December 29, 2019–January 22, 2020). The
cruises for both legs were conducted from north to south.
Both legs covered the seven biogeochemical provinces(BGCPs) proposed by Longhurst (1998): the Western
India Coastal (INDW), Indian Monsoon Gyres (MONS),
Indian South Subtropical Gyre (ISSG), South Subtropical
Convergence (SSTC), Subantarctic Water Ring (SANT),
Antarctic (ANTA), and Austral Polar (APLR) provinces.
In this study, we used measurements obtained above a
depth of 250 m, although we collected seawater samples
from depths between 10 and 10 m above the bottom andWe transferred each sample from a spigot of a Niskin bottle to acid and detergent-cleaned 250-mL plastic bottles
after three rinses. We then filtered the CDOM samples
using 0.2-μm Nuclepore polycarbonate filters, which werinsed with Milli-Q water before use. The samples werethen stored in the dark in a refrigerator until analysis.
After the samples were acclimated to the ambient laboratory temperature in the dark, we measured the absorbance spectra of the filtrates onboard between 190 and
600 nm at 0.5-nm intervals in 10-cm pathlength quartz
cuvettes using a UV–Vis recording spectrophotometer(UV-2600, Shimadzu Co.). The measurements were taken
within one day of sampling. We measured absorbance
spectra of samples and blanks (Milli-Q water) against thereference Milli-Q water. To correct baseline offsets in themeasurements, we subtracted the value at 600 nm fromthe entire absorbance spectrum of each sample (Yamashita et al. 2013).
2.3 FDOM

We transferred each sample from a spigot of a Niskinbottle through a pre-combusted GF/F filter into precombusted glass vials with acid-washed Teflon-lined
caps after triple rinsing. We stored the samples in thedark in a refrigerator until analysis. We measured thespectra of EEM fluorescence onboard using the method
described by Shigemitsu et al. (2020). Briefly, emission
scans at 248–829 nm were obtained at 2.33-nm intervals
for sequential excitations at 240–560 nm at 5-nm intervals, with an integration time of 12 s and using the highcharge-coupled device (CCD) gain mode. The measurements were taken within two days of sampling. Furthermore, we converted the spectra to Raman units (RU)
using the Milli-Q water spectra obtained on every EEM
measurement day (Lawaetz and Stedmon 2009). Here,
we did not apply inner filter effect correction because theabsorption coefficients of seawater samples in this study
were much lower (i.e., 1.00 ± 0.16 ­m−1 at 250 nm) than
the threshold of 10 ­m−1 for the correction (Stedmon and
Bro 2008).
2.4 DOC

We obtained seawater samples for DOC analyses following the same method as used for FDOM, except for the
sample collection bottles. For DOC, we used acid-washed
60-mL high-density polyethylene bottles. Upon sample
collection, we immediately froze the bottles until analysis. In the land laboratory at JAMSTEC, we thawed thefrozen samples at room temperature and acidified them

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to pH < 2 with hydrochloric acid following the method of
Wakita et al. (2016). We then measured DOC using a Shimadzu TOC-L system coupled with a Shimadzu Total N
analyzer. We checked the accuracy of the DOC measurements on each DOC measurement day using referencematerials provided by D. A. Hansell (University of Miami,
USA). All the measurements of Florida Strait reference
waters (700 m) yielded results of 42.4 ± 2.7 μmol ­kg−1(n = 66) across all sample measurements. The precision
was estimated from the standard deviation of duplicate
measurements and was within 2.4 μmol ­kg−1.

(BIX) (Huguet et al. 2009) and used them as proxies of
the FDOM quality. We calculated HIX and BIX by dividing a peak area between 435 and 480 nm by that between
300 and 345 nm (both at excitation 254 nm) and by dividing fluorescence intensity at 380 nm by that at 430 nm(both at excitation 310 nm), respectively. The precision
estimated from the standard deviation of duplicate measurements for C1, C2, C3, BIX, and HIX was within 0.0002
RU, 0.0002 RU, 0.0007 RU, 0.16, and 0.10, respectively. At
all the stations, duplicate samples for FDOM were collected at 50 m depth.

2.5 Chlorophyll a

2.7 Factor analysis

We obtained seawater samples for chlorophyll a (Chl a)from the surface to 250 m. We transferred each sample
from a spigot of a Niskin bottle to a detergent-washed
250 or 500-mL brown plastic bottle without headspace.
Upon collection, we gently filtered the samples under low
vacuum pressure (< 0.02 MPa) through a GF/F filter ina dark room, followed by pigment extraction in 7 mL of
N,N-dimethylformamide in the dark at − 20 ℃ for > 24 h.
We measured Chl a concentrations using an onboard
Turner fluorometer (10-AU-005, TURNER DESIGNS).
2.6 Data treatment

We calculated the CDOM absorption coefficient at wavelength λ (aλ ­(m−1)) from the measured absorbance spectrum (Aλ): aλ = 2.303 × Aλ/l, where l is the path length ofthe cuvette (0.1 m). We used four optical parameters toinvestigate the CDOM quantity and quality. We used a254
and a325 as proxies for CDOM quantity (Loginova et al.
2016; Iuculano et al. 2019) and calculated the spectral
slope coefficient between 275 and 295 nm (S275–295) andthe ratio of absorption coefficients at 254 nm and 365 nm(a254/365) according to Helms et al. (2008) and Engelhaupt
et al. (2003), respectively, as proxies of the CDOM quality. The precision estimated from the standard deviation of duplicate measurements for each parameter, a254,
a325, S275–295, and a254/365, was within 0.02 ­m−1, 0.01 ­m−1,
0.001 ­nm−1, and 0.62, respectively.
We analyzed the obtained EEMs (n = 946) using PARAFAC. The PARAFAC modeling was performed using theeem_parafac function in the staRdom library for R (version 3.6.1). For the PARAFAC analysis, we used the EEMs
for excitation wavelengths ranging from 260 to 450 nm
and emission wavelengths ranging from 300 to 550 nm.
Each EEM was normalized to its total signal according
to the method described by Murphy et al. (2013). The
three-component model (C1–C3) was validated by splithalf analysis (Murphy et al. 2013). We used the PARAFAC analysis results as proxies for FDOM quantity. In
addition, we calculated the humification index (HIX)(Zsolnay et al. 1999) and biological/autochthonous index

Factor analysis is a statistical method used to obtain asmaller number of latent variables (i.e., common factors)
to explain the variability in observed variables, rather
than the total number of observed variables, which facilitates the interpretation of observed data (Davis 1973;
Glover et al. 2011). The factor model is expressed asfollows:

Z = AF + E,

(1)

where Z is an n × m data matrix (where n is the numberof variables, and m is the number of samples); A is an
n × p matrix of factor loadings (where p is the number offactors); F is a p × m matrix of factor scores; and E is an
n × m error matrix. Assuming that no correlations exist
between the factor scores for each factor, between errors
for each variable, or between factor scores and errors, thefollowing equation can be introduced:′

R = AA + V,

(2)

where R is the variance–covariance matrix for variables;
A’ is the transposed A; and V is the variance–covariancematrix for errors. A is estimated to reproduce R as muchas possible, and the factor scores, F, can be estimated asfollows: F = Z’ R−1A, where R−1 is the inverse of R. Factor loading represents the extent to which each variable
is associated with each factor, and the factor score is theamount of each factor that is specific to each sample.
For factor analysis, we used the quantity and quality
parameters of CDOM, FDOM (a254, a325, S275–295, a254/365,
the sum of C1 and C2, C3, BIX, and HIX), and DOC
obtained within the surface mixed layer (n = 56) after all
the parameters were standardized. Factor analysis was
conducted with the varimax rotation of factors using R(version 4.0.3).
2.8 Other relevant data

We determined three factors via factor analysis. To gain
new insights from the results, we obtained and used themonthly mean net primary production (NPP) from theShigemitsu et al. Progress in Earth and Planetary Science(2024) 11:22

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Oregon State Ocean Productivity Website (http://​orca.​
scien​ce.​orego​nstate.​edu/​npp_​produ​cts.​php),
monthlymeans of ultraviolet (UV) radiation (specific output
called “downward UV radiation at the surface”) from the
ERA5 reanalysis (Hersbach et al. 2020), monthly climatological temperature and salinity data from the World
Ocean Atlas 2013 (Locarnini et al 2013; Zweng et al.
2013), and daily sea ice concentration (AMSR-2) from the
EUMETSAT website (https://​osi-​saf.​eumet​sat.​int/​produ​
cts/​osi-​408). For NPP, we used the averages of the four
satellite-derived NPP products: (1) vertically generalized
productivity model (VGPM) (Behrenfeld and Falkowski
1997); (2) Eppley-VGPM model (Eppley 1972; Behrenfeld and Falkowski 1997); (3) carbon, absorption, andfluorescence euphotic-resolving model (CAFE) (Silsbe
et al. 2016); and (4) carbon-based productivity model(CbPM) (Westberry et al 2008). For the temperature andsalinity data, we calculated the monthly climatologicalmixed layer depth, which is the depth at which a density
increases from the sea surface value and exceeds 0.125 σθ.

The Chl a concentrations in the surface mixed layer in
INDW, MONS, and ISSG decreased from ~ 1 to ~ 0.04 μg­L−1 with decreasing latitude and were generally lower
than the subsurface maxima of Chl a below the surface
mixed layer (Additional fig 1: Fig. S1c). In the SSTC to
ANTA provinces, the Chl a concentrations in the surface mixed layer decreased from ~ 1.5 to ~ 0.2 μg ­L−1 withdecreasing latitude. We found higher values in the provinces but not below the surface mixed layer, which wasdifferent to that in INDW, MONS, and ISSG. In APLR,
the Chl a concentrations in the surface mixed layer
were much lower than those in SANT and ANTA, andthe subsurface Chl a maxima were present as in INDW,MONS, and ISSG.

3 Results and discussion
3.1 Meridional distributions of potential temperature,salinity, and Chl a

From INDW to SSTC, potential temperature and salinity
in the surface mixed layer generally decreased from ~ 32
to ~ 13 °C and increased from ~ 32.5 to ~ 35.5, respectively, with decreasing latitude (Additional fig 1: Figs.
S1a and b). In INDW and northern MONS, low-salinity
water (~ 32.5 to ~ 34.0) was prominent, and in the season when the observations were obtained, these areas
would be influenced by the Northeast Monsoon Current(NMC) and North Equatorial Current (NEC), which has
low salinity, being derived from BoB and waters transported westward through the Indonesian archipelago(Longhurst 1998). In southern MONS and northern
ISSG, salinity was lower and the potential temperature
was higher than that in the southern ISSG. The waterin the southern MONS and northern ISSG provinces isaffected by the South Equatorial Current (SEC), which
has relatively low salinity (Longhurst 1998; Talley and
Sprintall 2005). SSTC lies between the subtropical andsubantarctic waters; consequently, the potential temperature and salinity in this province are between the valuesof the ISSG and SANT. From SANT to APLR, potential temperature and salinity in the surface mixed layer
decreased from ~ 13 to − 2 °C and remained relatively
constant (~ 33.5–34), respectively. SANT is located in the
Antarctic Circumpolar Current (ACC), the ANTA province is located between the polar front and the Antarctic
divergence, and the APLR province is in the most polar
region from the coast of Antarctica to the north of the
Antarctic divergence (Longhurst 1998).

3.2 Meridional distributions of DOM quantity proxies

We obtained three components using PARAFAC analysisof the EEMs (Additional fig 1: Fig. S2). The two humiclike components (C1 and C2) and one protein-like component (C3) are comparable to those reported by Kim
et al. (2020) in the whole water column along the transect of 67°E, covering 5° N to 16° S in the Indian Ocean.
Our results are also comparable to those of Yamashita
et al. (2017) for the Pacific Ocean and of Catalá et al.
(2015a, b) and Jørgensen et al. (2011) for the global
ocean. C1 showed peaks at the excitation/emission pairof 260/474 nm and 365/474 nm for the primary and secondary peaks, respectively; C2 had peaks at the pair of
260/393 nm and 320/393 nm for the primary and secondary peaks, respectively. C3 had peaks at the excitation/
emission pair of 265/352 nm as the primary peak and isconsidered a tryptophan-like component.
DOC, a254, and C3 in the surface mixed layer showedsimilar spatial distributions (Fig. 2a, b, f; Table 1). From
the northernmost INDW to the southernmost APLR
provinces, these levels gradually decreased, with small
maxima in SSTC and around its borders (ISSG to thenorth and SANT to the south). These parameters ranged
from ~ 88 μmol ­kg−1 to ~ 38 μmol ­kg−1, from ~ 1.6 ­m−1
to ~ 0.8 ­m−1, and from 0.023 RU to 0.005 RU, respectively. The vertical aspects of DOC, a254, and C3 generally tended to decrease with depth (Figs. 3a, b, and 4c),
except that C3 tended to have maxima below the surface
mixed layer from INDW to ISSG, the tendency of which
was similar to that of Chl a. The vertical trends in DOC
and a254 were similar to those reported in previous studies (Hansell 2009; Iuculano et al. 2019), and those of C3,
including the subsurface maxima, are also similar to previous observations (Catalá et al. 2016; Makarewicz et al.
2018).
The distribution of C1 in the surface mixed layer was
similar to that of C2, as also indicated in previous studies (Fig. 2d, e; Table 1) (Catalá et al. 2016; Yamashita

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Fig. 2 Meridional distributions in the surface mixed layer of a DOC (μmol ­kg−1), b a254 ­(m−1), c a325 ­(m−1), d FDOM-C1 (RU), e FDOM-C2 (RU), f
FDOM-C3 (RU), g S275–295 ­(nm−1), h a254/365, i BIX, and j HIX. All data from the surface mixed layer are shown. Large variations in some variables
at individual stations are due to (1) relatively close distance between stations (instances in INDW and APLR) or (2) relatively low verticalsampling resolution (10, 50, 100, 150, 200, and 250 m as well as Chl a maximum depth), resulting in some sampling depths being positioned
near the bottom of the surface mixed layer and a Chl a maximum depth potentially being included in the layer. Vertical lines show each boundary
between the biogeochemical provinces

et al. 2017). Both distributions showed the highest values (~ 0.010 RU for C1 and ~ 0.007 RU for C2) in INDW
and decreased to the lowest values (~ 0.002 RU for C1
and ~ 0.001 RU for C2) in ISSG, with the small maximain MONS. From ISSG to the south, the values again
increased to the second highest values (~ 0.007 RU for C1
and ~ 0.006 RU for C2) around the border between SSTC
and SANT, followed by a gradual decrease (to ~ 0.004
RU for C1 and ~ 0.003 RU for C2) to ANTA, and sharp
increases (to ~ 0.006 RU for C1 and ~ 0.005 RU for C2)

from ANTA to APLR. The C1 and C2 levels generally
tended to increase with depth (Fig. 4a, b), which is consistent with the values reported in previous studies (Jørgensen et al. 2011; Catalá et al. 2016; Shigemitsu et al.
2021).
The distribution of a325 in the surface mixed layer
was also similar to that of C1 and C2 (Fig. 2c; Table 1),
with values ranging from ~ 0.08 ­m−1 to 0.20 ­m−1, except
for an extremely high value of ~ 0.32 ­m−1. a325 generally
increased with depth, and the depth distribution was also

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Table 1 Correlation coefficients between each pair of observed variables in the surface mixed layer

DOC
a254
a325
C1

a254

a325

C1

0.86***

0.27*− 0.08

***

0.58

*

0.27

***

0.61

C2

C3

− 0.090.21***

S275–295

a254/365

0.74***

0.70***

0.32*

0.36**− 0.72***

***

***

− 0.010.12− 0.47***

0.84

***

0.43

**

***

BIX

HIX

*

0.56

0.43

− 0.40− 0.70− 0.28− 0.200.88***

0.35**− 0.58***− 0.67***− 0.40**0.54***

**

***

***

− 0.61*− 0.290.51***

**

0.05

0.17

− 0.51***

C2
C3
S275–295
a254/3650.36− 0.54

0.39

***

0.78

***

− 0.87******− 0.65***

0.54
0.47

− 0.41**

BIX

Absolute values of correlation coefficient greater than or equal to 0.7 are indicated in bold. * p < 0.05, ** p < 0.01, and *** p < 0.001. Dissolved organic carbon (DOC),
CDOM absorption coefficient at wavelength λ (aλ ­(m−1)), components determined using PARAFAC analysis (C1–C3), spectral slope coefficient between 275 and 295 nm(S275–295), absorption coefficient ratio at 254 and 365 nm (a254/365), biological index (BIX), and humification index (HIX)

similar to that of C1 and C2 (Figs. 3c, 4a, b) and to thetrend reported in previous studies (Catalá et al. 2016;
Iuculano et al. 2019).
3.3 Meridional distributions of DOM quality proxies

The distribution of S275–295 in the surface mixed layer
was similar to that of a254/325 (Fig. 2g, h; Table 1). Bothparameters had the highest values in the northern ISSG(~ 0.050 ­nm−1 for S275–295 and ~ 27 for a254/325), which
decreased in both the northward and southward directions. In the southward direction, they reached minimum
values (~ 0.030 ­nm−1 for S275–295 and ~ 11 for a254/325)
around the border between SANT and ANTA, followed
by increases to around the border between ANTA and
APLR and sharp decreases in APLR. They generally
tended to decrease with depth (Fig. 5a, b), which is consistent with the findings of a previous study (Iuculano
et al. 2019).
To the best of our knowledge, BIX has not been previously reported in basin-scale observations. BIX in thesurface mixed layer generally showed a similar trend to
S275–295 and a254/365, except for the absence of a gradualdecrease from ISSG to MONS and a sharp decreasein APLR (Fig. 2g‒i; Table 1). This reflects a weaker correlation between BIX and S275–295 or between BIX anda254/325 compared to that between S275–295 and a254/325.
The BIX values ranged from approximately 0.8‒1.8. The
vertical distributions of BIX were complicated (Fig. 5c).
In INDW, northern MONS, and ISSG, BIX decreased
with depth with subsurface maxima similar to that of
Chl a concentrations; whereas, in the other BGCPs, BIX
increased or remained relatively constant with depth.
The HIX in the surface mixed layer showed opposite
trends in S275–295, a254/365, and BIX, i.e., HIX had the lowest values of ~ 0.5 in the northern ISSG, which increased
to ~ 1.1 and ~ 2.2 in both the northward and southward

directions, respectively (Fig. 2g‒j; Table 1). The southward increase involved a small maximum of ~ 1.7 aroundthe border between SANT and ANTA. The vertical
distribution was opposite to those of S275–295, a254/365,
and BIX and generally increased with increasing depth(Fig. 5d), which is consistent with observations in the
Atlantic (Kowalczuk et al. 2013).
3.4 Interpretation of factor analysis results

When conducting factor analysis, we used the sumof the humic-like components C1 and C2 as an original variable because both the spatial distributions weresimilar, as stated above. In terms of determining thenumber of factors in this study, we selected the factors
which explained > 10% of the variance. We identified
three factors in this study: the first, second, and third
factors explained 37.9%, 36.0%, and 10.1% of the variance, respectively, with a total of approximately 84%. The
uniqueness of each variable, showing the proportion ofvariance that could not be explained by the three factors,
was 8.9% for DOC, 6.4% for a254, 16.7% for C3, 1.3% for
a325, 0.5% for the sum of humic-like components, 3.6%
for S275–295, 19.2% for a254/365, 71.1% for BIX, and 16.7%
for HIX. From the results, BIX was the least explained by
the three factors; hence, we found no strong correlations
between BIX and other variables (Table 1).
In a factor analysis, the factor loadings show the extent
to which each observed variable is related with each factor (Fig. 6). Factor 1 (F1) showed large positive loadings
for DOC, a254, and C3, and a negative loading for HIX.
Factor 2 (F2) showed large positive loadings for a254/365
and S275-295 and large negative loadings for a325, HIX, andthe sum of the humic-like components (T-humic). Factor
3 (F3) showed a large positive and negative loading for
T-humic and S275–295, respectively.

Shigemitsu et al. Progress in Earth and Planetary Science(2024) 11:22

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Fig. 3 Meridional distributions of a DOC (μmol ­kg−1), b a254 ­(m−1), and c a325 ­(m−1). Right and left columns represent Leg 2 and 3 transects,
respectively. White plus signs represent the mixed layer depths, which we calculated as the depth at which a density increase from the sea surface
value was > 0.125 σθ

F1 accounted for the biogeochemical process that had
a stronger influence on DOC, a254, and C3 than HIX(Fig. 6a). DOC and protein-like C3 in surface waters
are mainly derived from primary production in surface
waters (Castillo et al. 2010; Brym et al. 2014; Makarewicz et al. 2018; Moran et al. 2022). In addition, thoseparameters tend to have the similar vertical distributions,
i.e., high levels in the surface water and decreasing withdepth (Hansell et al. 2009; Kowalczuk et al. 2013), whichis consistent with our findings (Figs. 3a and 4c). a254 is
considered to be a proxy for DOC (Catalá et al. 2018;
Iuculano et al. 2019). a254 in the surface mixed layer was
significantly correlated with the DOC concentrations inthis study (Table 1) and showed similar vertical distributions to those of DOC (Fig. 3a, b). Conversely, according
to Zsolnay et al. (1999), HIX increases with humification,i.e., the higher the HIX, the older the DOM. If primary
production is closely related to F1, autochthonouslyShigemitsu et al. Progress in Earth and Planetary Science(2024) 11:22

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Fig. 4 Meridional distributions of a FDOM-C1 (RU), b FDOM-C2 (RU), and c FDOM-C3 (RU). The definitions of the right and left columns and whiteplus signs are the same as in Fig. 3

produced DOM in the surface mixed layer should be
fresher, resulting in low HIX values (or high DOC, a254,
and C3 levels). One caveat should be observed in thisinterpretation. HIX calculation used the areas under the
curve of emission 300–345 nm at excitation 254 nm, andthe C3 levels might have affected the HIX levels (Fig. S2).
However, the correlation coefficient between HIX andC3 was weaker than that between HIX and DOC andbetween HIX and S275–295 (Table 1). Thus, the effect of C3
on HIX was minimal. In this context, F1 was interpreted

as the effect of surface primary production on DOM.
This conclusion is corroborated by the following aspects.
DOM in surface waters can be decomposed by solar
radiation (Catalá et al. 2016), which is called photodegradation. However, solar radiation has less influence on
DOC and protein-like C3 than on humic-like components (Mostofa et al. 2007). a254 is also less influenced
by photodegradation than a325 (Iuculano et al. 2019)because a325 is a proxy for aromatic substances producedby heterotrophic prokaryotes that absorb UVA radiation

Shigemitsu et al. Progress in Earth and Planetary Science(2024) 11:22

Fig. 5 Meridional distributions of a S275–295 ­(nm−1), b a254/365, c BIX, and d HIX. The definitions are the same as in Fig. 3

Page 10 of 19

Shigemitsu et al. Progress in Earth and Planetary Science(2024) 11:22

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Fig. 6 Scatter plots of a factor loadings of F1 vs. F2 and b of F1 vs. F3. T-humic, the sum of humic-like C1 and C2

and are subject to photodegradation (Nelson et al. 2004;
Catalá et al. 2018). This results in the rarity of significant
positive correlations between a325 and DOC (Nelson andSiegel 2013), which applied to our findings (Fig. 3a, c;Table 1). Although primary producers generate humiclike components (Castillo et al. 2010), photodegradationinfluences humic-like components, resulting in low levels
in surface waters (Omori et al. 2010). Thus, considering
the extent to which photodegradation affect the DOM
parameters, the result whereby T-humic and a325 showed
the smaller positive loadings compared to DOC, a254, andC3 is reasonable.
Regarding F2, a254/365, and S275–295, which showed large
positive loadings, are related to the effect of photodegradation and the molecular weight of DOM (Dahlén et al.
1996; Helms et al. 2008). Their parameter values increaseas photodegradation affects DOM (Obernosterer and
Herndl 2000; Helms et al. 2008), whereas the lower themolecular weight of the DOM, the higher the values ofboth parameters, and vice versa. As stated above, a325
and humic-like components are subject to photodegradation. Thus, when DOM undergoes photodegradation,
the a325 and T-humic values decrease and the a254/365
and S275–295 values increase. Furthermore, Helms et al.
(2013) and Hansen et al. (2016) indicated that HIX andBIX decreased and increased, respectively, with photodegradation. From these perspectives, it is evident that
F2 can be interpreted as the effect of photodegradation
on DOM.
Regarding F3, the vertical distributions of humiclike components with a large positive loading generally

increased with depth, whereas those of S275–295 with alarge negative loading generally decreased with depth(Figs. 4a, b, and 5a). Especially in INDW, APLR, aroundthe border between SSTC and SANT, and near the equator, the levels of both C1 and C2 in and just below thesurface mixed layer were higher than those in the neighboring regions (Figs. 2d, e, 4a, b). In contrast, the levelsof S275–295 for APLR in and below the surface mixed layer
were lower than those in the other BGCPs, and the values
in INDW, around the border between SANT and ANTA,
and near the equator, had local minimum values (Figs. 2g
and 5a). If T-humic and S275–295 in the surface mixed
layer are affected by vertical physical processes, such as
advection, mixing, and entrainment, the former tends toincrease and the latter tends to decrease. Thus, F3 wasinterpreted as the effect of vertical physical processes on
DOM.
One caveat should be observed in interpretations ofF2 and F3. The factor loadings of both, except those of
C3 and a325, generally showed mutually opposite trends(Fig. 6), obscuring their separability. Variables that weremore likely to be affected positively by photodegradation, i.e., a254/365 and S275–295, tended to decrease withdepth (Fig. 5), whereas those that seemed to be predominantly influenced in the direction by vertical physicalprocesses, i.e., T-humic, were apt to increase with depth(Fig. 4). Thus, vertical physical processes might determine the factor loadings of F2 and F3. However, as shownin Sects. 3.5.2 and 3.5.3, the photodegradation degreeof DOM in the surface mixed layer is expected to besmall in BGCPs, where vertical physical processes exert

Shigemitsu et al. Progress in Earth and Planetary Science(2024) 11:22

Page 12 of 19

a dominant influence on the surface DOM, but large in
BGCPs, where this influence is not predominant. The
geographically based difference in magnitude between
possible effects of photodegradation and vertical physical processes on the surface DOM probably enables us
to separate F2 from F3. In addition, as noted in Sect. 3.2,C3 generally decreased with depth but showed subsurface maxima from INDW to ISSG, making it possible forC3 to positively contribute to F3; in contrast, C3 had noinfluence on F2 at all (Fig. 6). These results also corroborate a possible separation of F2 from F3 based on the present observations. Although a325 generally increases withdepth, the variable does not contribute to F3 at all, which
is probably due to its relatively noisy geographical distribution in the surface mixed layer.

satellite NPP products to be the representative NPP in
the Indian Ocean (Fig. 8a).
The monthly averages of local NPP were relatively similar in spatial distribution to the F1 scores (r = 0.80, n = 52,
p < 0.001; using the NPP data in December for Leg 2 andin January for Leg 3; in the calculation, NPP of each station was assigned to all associated samples), except that
the NPP values in MONS and INDW were lower thanthose from the northern SANT to SSTC (Figs. 7a and8a). Although the trends in the annual mean of local NPP
and zonally and annually averaged NPP (20°‒100° E) weresimilar to those of the monthly averages, the NPP values
for the formers were more similar to the distribution of
F1 scores than the monthly averaged local NPP (r = 0.92,
n = 52, p < 0.001; r = 0.89, n = 56, p < 0.001; in the calculation, NPP of each station was assigned to all associatedsamples) (Figs. 7a and 8a). Notably, the annual meanof local NPP, and zonally and annually averaged NPP(20°‒100° E) in MONS and INDW showed higher or similar values than or to those from the northern SANT to
SSTC, respectively. From the results, we concluded thatthe annually averaged local NPP or zonally and annually
averaged NPP represented the F1 scores. This may meanthat the meridional distribution of DOM in the surface
mixed layers, which is related to surface NPP, is determined on an annual time scale.
In addition to NPP, horizontal transport of NPPderived DOM may also contribute to the F1 score distribution. In the season when the MR19-04 cruise wasconducted, the northeast (NE) monsoon, which occurs
from December‒April, is conventionally predominant.
The NE monsoon generates the Northeast Monsoon
Current (NMC), which flows from the BoB through
INDW and northern MONS to AS (Schott et al. 2009;
De Vos et al. 2014). Because the surface salinities in the
BoB are lower than those in the INDW and northern
MONS on the transect considered in this study (Akhil
et al. 2016), the observed low salinities in INDW andnorthern MONS would be affected by NMC (Additional fig 1: Fig. S1b). This suggested that DOM which

3.5 Comparison of each factor score and relevant
environmental parameterWe determined three factors using factor analysis andinterpreted each factor based on both the factor loadings
assigned to the observed variables and the characteristics of the variables (Fig. 6). F1 was the effect of primary
production on DOM, and F2 and F3 were the effects ofphotodegradation and vertical physical processes (i.e.,
advection, mixing, and entrainment) on DOM, respectively. Here, we compared the spatial distribution ofeach factor score (Fig. 7) with the related environmental
parameter (Fig. 8) to gain deeper insights into the DOM
dynamics in the surface waters.
3.5.1 F1: Effect of primary production on DOM

The monthly averaged NPP interpolated to each stationfor each satellite-derived NPP product (i.e., local NPP)in November 2019, December 2019, and January 2020
was similar in spatial distribution and magnitude; theexception was CAFE-based NPP in BoB, which showed
the opposite trend (Additional fig 1: Fig. S3). The similarities among the products applied to the annually averaged local NPP and zonally and annually averaged NPP(20°‒100° E). Thus, we considered the average of fourFig. 7 Meridional distributions of factor scores of a F1, b F2, and c F3. As in Fig. 2, all data in the surface mixed layer are shown. Vertical lines show
the boundaries between the biogeochemical provincesShigemitsu et al. Progress in Earth and Planetary Science(2024) 11:22

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Fig. 8 Meridional distributions of a NPP (mol ­m−2 ­year−1), b UV
radiation (MJ ­m−2), and c FDOM-C2 (RU). a Red triangles, green
plus signs, and blue crosses represent the monthly averaged NPP
of four NPP model outputs (VGPM, Eppley-VGPM, CAFÉ, and CbPM)
interpolated for each station in November 2019, December 2019,
January 2020, respectively; black circles are annually averaged
NPP of four NPP model outputs interpolated for each station
during period from February 2019 to January 2020. Red, green,
and blue lines are the monthly and zonally averaged NPP of four NPP
models in the Bay of Bengal in November 2019, December 2019,
and January 2021, respectively; black line is the zonally (20° E–100°E) and annually averaged NPP of four NPP models from February
2019 to January 2020. b Colored symbols have the same meaning
as in a except that they represent the UV radiation data. c Blue solid
triangles and black open circles represent C2 levels just below and in
the surface mixed layer, respectively. Vertical lines in each panel
show the boundaries between the biogeochemical provinces. MLD:
mixed layer depth. Note that the x-axis of each panel is expanded
to the north in a different manner than in Figs. 2 and 7 and that theboundary of INDW is indicated by a vertical line as in those figures

was produced and/or existed in the BoB was transported to the regions of the transect. In the BoB, NPP
was much higher than that in INDW and northern
MONS throughout a year (Fig. 8a) and the DOC concentrations in the surface waters of the BoB would also
be higher than those in INDW and northern MONS.
Thus, the horizontal transport of DOM via NMC would
also contribute to shaping the F1 score distribution
in INDW and northern MONS. Considering that theannually averaged NPP is more influential in the distribution of F1 scores than monthly mean NPP, the southwest monsoon current (SMC), which flows from the AS
through INDW and northern MONS into the BoB during the southwest (SW) monsoon season (June‒October), may also affect the DOM in the surface watersof INDW and northern MONS (Schott et al. 2009; De
Vos et al. 2014). That is because the zonally and annually averaged NPP in AS was much higher than that in
INDW and northern MONS (Fig. 8a). Finally, as statedin Sect. 3.1, the zonal currents, such as NEC and SEC,
would also affect the F1 score distribution. However,taking it into account that “annually averaged local
NPP” and “zonally and annually averaged NPP” weresimilar in spatial distribution to each other, the effectwould be relatively small.
Taken together, we found that F1 represented theeffect of “annually averaged local NPP” or “zonally andannually averaged NPP” and its horizontal transport
on DOM. The effect of horizontal transport of DOM
on the meridional distribution in the surface waters of
the Indian Ocean should be quantitatively confirmed infuture studies.

Shigemitsu et al. Progress in Earth and Planetary Science(2024) 11:22

3.5.2 F2: Effect of photodegradation on DOM

The UV radiation (UVR) interpolated to each station(i.e., local UVR) in November 2019, December 2019,
and January 2020 had maximum values in the northern
ISSG, which decreased from there in both northwardand southward directions. In the southward direction,
the UVR had minimum values in SANT to ANTA andincreased values from ANTA to APLR. The UVR distribution was significantly correlated with that of the F2scores (r = 0.62, n = 49, p < 0.001; using the UVR data in
December for Leg 2 and in January for Leg 3; in the calculation, UVR of each station was assigned to all associated samples) except for stations in APLR and with anextremely negative F2 score in ISSG (Figs. 7b and 8b).
The reason behind the weak correlation between UVR
and F2 scores would derive from the fact that UVR isknown to have a small e-folding depth for the attenuation
with depth in water and the e-folding depth varies fromclear to turbid waters (Tedetti and Sempere 2006). The
uppermost samples for DOM in this study were obtained
at approximately 10 m depth along the two transects,
and the difference in UVR e-folding depth between stations is considered to be important for determining thecorrelation.
In APLR, F2 scores decreased and UVR increased.
In APLR, even in January 2020 when the observations
were obtained, sea ice covered the province to someextent (Fig. 9). Thus, the effect of UVR should be attenuated by the sea ice cover and the trends in F2 score andUVR in APLR would be the opposite to each other. In
addition, APLR includes the Antarctic divergence, andthe residence time of the surface waters would be short,which is considered to be the cause of the locally lower

Fig. 9 Averaged sea ice concentration (%) in the vicinity of Antarctica
from January 1, 2020, to January 22, 2020, on which sampling stations(white open circles) are superimposed

Page 14 of 19

anthropogenic ­CO2 content (Sarmiento and Gruber
2006). If the residence time of the surface water is short,the period of DOM exposure to sunlight is also short.
Thus, the short residence time of surface water also influenced the difference between the F2 scores and UVR in
APLR. The timescale on which photodegradation influences the surface DOM distribution is unclear in thisstudy and should be studied in future.
3.5.3 F3: Effect of vertical physical transport processes
on DOMWe obtained the optical properties of DOM and DOC
during a single cruise; therefore, estimating the contribution of each vertical physical transport process to surface DOM optical properties and DOC was difficult. As
stated above, the variable with a large positive loading in
F3 was T-humic, and the variable with a large negative
loading was S275–295 (Fig. 6). Thus, for humic-like components, the transport into the surface mixed layer by vertical mixing, advection, and entrainment would tend toincrease the levels of humic-like components in this layer.
The humic-like C2 levels just below the surface mixed
layer (C1 was similar to C2 and is not shown here) weregenerally higher than those within the mixed layer depth,
and the latitudinal distributions just below and within thesurface mixed layer were similar (Fig. 8c). Thus, if humiclike components are supplied into the surface mixed layerfrom below, the levels in the surface mixed layer increase.
A picture of vertical advection driven by divergent
Ekman flow has been shown in the previous studies(Liang et al. 2017; Nagura and McPhaden 2018). The vertical advection by Ekman pumping was upward in INDW,MONS and APLR, but was downward in ISSG through
SSTC to SANT. These results are consistent with the F3score distribution (Fig. 7c). In addition, to obtain information regarding the effect of entrainment, we investigated the climatological mixed layer depths along theoccupied transects in this study. The climatologicalmixed layer depths in INDW and MONS deepen twicea year, i.e., from November‒December to January‒February and from March‒April to June‒September (Fig. 10).
The climatological mixed layer depths in the SW monsoon (June‒October) are deeper than those in the NE
monsoon (December‒April). In INDW and MONS, thedeepest climatological mixed layer depth was approximately 40 m in depth and the effect of entrainment on
DOM in the surface mixed layer would be the largest in
INDW and around the equator because the C1 and C2
levels at this depth were the highest in INDW and MONS
(Fig. 4a, b). This is also consistent with the F3 score distribution. In ISSG to APLR, the climatological mixed layer
depths monotonically increased from December‒February (austral summer) to July‒September (austral winter).

Shigemitsu et al. Progress in Earth and Planetary Science(2024) 11:22

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Fig. 10 a, c Climatological monthly mixed layer depth (m) calculated from temperature and salinity of World Ocean Atlas 2013 for the periodsof January–June and for July–December, respectively. We simply calculated mixed layer depth as the depth at which a density increase from the sea
surface value was > 0.125 σθ. Red solid circles represent observations in this study. b, d As in (a, c), except for along Leg 2 transect and expanded
y-axis

In particular, in SSTC, around the border between SANT
and ANTA, and APLR, the climatological mixed layers
become deepest in austral winter. From SANT to APLR,
the isolines of high humic-like component levels (C1 andC2) below the mixed layer depth in austral summer tilted
toward the south (Fig. 4a, b). Thus, the effect of entrainment would become higher from SSTC through aroundthe border between SANT and ANTA toward APLR,
which is also consistent with the F3 scores. Althoughestimating the effect of mixing from the results from asingle cruise was difficult, the qualitative information
regarding the effects of advection and entrainment wasconsistent with the F3 score distribution. One caution
must be raised again. In Sect. 3.4, separating F2 from F3
in our analysis may have been difficult. We calculated thecorrelation coefficient between the UVR and F3 score
distributions under the same conditions as for between
the UVR and F2 score distributions in Sect. 3.5.2. The
result was r = − 0.58; the absolute value was weaker thanthat between the UVR and F2 score distributions (0.62).
Although this is consistent with our interpretation of F2and F3, the slight difference cannot completely prove theseparation of F2 and F3. Thus, quantitative estimation ofvertical physical transport of DOM by each process into

Shigemitsu et al. Progress in Earth and Planetary Science(2024) 11:22

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the surface mixed layer as well as the time scale on which
the DOM level in the surface mixed layer is influenced
by each physical process are required in future studies. Finally, when we conducted the observations in thisstudy, a positive Indian Ocean Dipole (IOD) event had
occurred (Kouketsu et al. 2022). The observed mixed
layer depths in INDW, MONS and ISSG were much
deeper than the climatological maximum mixed layer
depths at many stations (Fig. 10). Thus, it is notable that
the F3 contributions in INDW, MONS, and ISSG may behigher than those in normal conditions or during a negative IOD event.

heterotrophic prokaryotes, resulting in younger DOM,
which is consistent with fresher DOM as inferred fromthe low HIX, humic-like components, and a325 values.
Coupling between photodegradation, autochthonous
production, and bacterial reworking of DOM is important for understanding the fate of surface DOM because
DOM can be converted to C
­ O2 by these processes, andthe generated C­ O2 influences the air‒sea gas exchange of­CO2 (Kieber et al. 1997; Cory and Kling 2018). However,
the interactions in the open ocean are not well known.
Our findings indicate the possibility that large-scale,simultaneous observations of the optical properties of
DOM and DOC are useful for understanding the interaction between the photodegradation, autochthonous production, and bacterial reworking of DOM.

3.6 Potential interaction between photodegradation,
autochthonous production, and bacterial reworkingof DOM

We used the correlation structure of the observed variables to obtain common factors with factor analysis.
Unlike principal component analysis, contributions thatare not explained by common factors are allowed and arecalled uniqueness (as shown above). Thus, variables withunusual variability are not included as common factors.
Among the observed variables, the uniqueness of BIX
was the highest (71.1%); in other words, BIX could not
be explained by the common factors, which could beunderstood from the spatial distribution being dissimilar to those of the common factors (Figs. 2i and 7; andTable 1). We observed high BIX values in the northern
ISSG, together with high values of S275–295 and a254/365
and lower values of HIX, humic-like components, anda325 (Fig. 2). Helms et al. (2013) and Hansen et al. (2016)
demonstrated that BIX values of subtropical Pacific watercollected at ~ 700 m depth and algae-leachate increased
with photo-exposure times of 68 days and 111 days,
respectively. The high BIX values in the northern ISSG,
where the F2 effect was highest, and the BIX contribution
to F2 loading are, therefore, in line with previous studies
to some extent. However, both of the long-term photoexposure experiments showed BIX value increases ofapproximately 0.2. This extent is much smaller than thelatitudinal variation range (0.4–1.0) observed in the surface mixed layer in this study across all the BGCPs, eventaking the measurement precision of BIX into account.
Additional mechanisms might, therefore, be needed to
explain these observations. A higher BIX indicates anincrease in DOM levels which have recently been autochthonously produced and reworked by heterotrophic
prokaryotes (Huguet et al. 2009; Loginova et al. 2016).
Our findings might, therefore, indicate that photodegradation of both of freshly produced and aged DOM wasoccurring, high-molecular-weight DOM was converted
to low-molecular-weight DOM or less aromatic forms,
and the low-molecular-weight DOM was reworked by

3.7 Characterizing each biogeochemical province
with score of each factor

Three major factors were responsible for most of thevariability in optical properties of DOM and DOC: annually averaged local NPP or zonally and annually averaged NPP-derived DOM and its horizontal transport(F1) explained 37.9% of the variance; DOM consumption
by photodegradation (F2) explained 36.0%; and verticaltransport of DOM by physical processes (F3) explained
10.1%. Each BGCP was characterized by the factor scores(Fig. 7; Table 2).
F1 scores gradually decreased from INDW to APLR,
with a local maximum in SSTC and around the border between SSTC and SANT (Fig. 7a; Table 2). SANT
is located in the circumpolar ACC, which is bounded
to the north by the subtropical front and to the southby the polar front and includes two different ecologicalzones (Longhurst 1998). Thus, F1 scores in the northernparts of the SANT were high and those in the southernpart were low, which resulted in low average F1 scores in
the BGCP (Fig. 7a; Table 2). F2 scores were high in ISSG

Table 2 Averaged factor scores with standard deviations foreach biogeochemical province
Province

F1

F2

F3

INDW

1.99 ± 0.54

− 0.46 ± 0.83

MONS

0.91 ± 0.29

0.56 ± 0.47

0.45 ± 0.60

ISSG

0.48 ± 0.62

0.87 ± 1.39

− 1.02 ± 0.95

SSTC

0.87

− 1.242.00 ± 0.86

− 0.94

SANT− 0.29 ± 0.70

− 0.57 ± 0.45

0.26 ± 0.61

ANTA

− 1.01 ± 0.09

− 0.33 ± 0.18

− 0.03 ± 0.32

APLR

− 1.10 ± 0.14

− 0.44 ± 0.45

0.38 ± 0.54

Western India Coastal (INDW), Indian Monsoon Gyres (MONS), Indian South
Subtropical Gyre (ISSG), South Subtropical Convergence (SSTC), Subantarctic
Water Ring (SANT), Antarctic (ANTA), Austral Polar (APLR), and factor analysis
factors 1–3 (F1–F3)

Shigemitsu et al. Progress in Earth and Planetary Science(2024) 11:22

and MONS, with decreasing trends from there in boththe northward and southward directions. The high photodegradation of DOM in the subtropical region, ISSG,
is in line with that previously observed in the Atlantic(Kowalczuk et al. 2013) (Fig. 7b; Table 2). F3 scores werehigh in INDW, MONS, and APLR, in agreement withupward Ekman pumping (Liang et al. 2017; Nagura and
McPhaden 2018) and the presumed entrainment discussed above (Fig. 7c). F3 scores were low in ISSG, whichis also consistent with downward Ekman pumping (Lianget al. 2017; Nagura and McPhaden 2018).
Overall, each BGCP was characterized by the threefactors that were determined by factor analysis. This
indicated that the effects of various biogeochemical
processes, including physical processes, on DOM in thesurface mixed layer are closely related to the BGCPs
determined based on the geographical patterns of phytoplankton ecology and biogeochemical characteristics(Longhurst 1998).

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BIX	Biological index
CDOM	Chromophoric dissolved organic matter
DOC	Dissolved organic carbon
DOM	Dissolved organic matter
EEM	Excitation emission matrices
FDOM	Fluorescent dissolved organic matter
HIX	Humification index
INDW	Western India Coastal
ISSG	Indian South Subtropical Gyre
MONS	Indian Monsoon Gyres
NEC	North equatorial current
NMC	Northeast monsoon current
NPP	Net primary productivity
PARAFAC	Parallel factor analysis
RU	Raman units
SANT	Subantarctic water ring
SEC	South equatorial current
SSTC	South subtropical convergence
UV	Ultraviolet
UVR	Ultraviolet radiation

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40645-​024-​00623-0.
Additional file 1. Supplementary figures.

4 Conclusions
In this study, we investigated the meridional distributionsof the optical properties of DOM and DOC in the Indian
Ocean. Using these observations, we conducted factor
analysis to extract the common factors that explained
the distributions. Consequently, we identified three factors: annually averaged local NPP or zonally and annually
averaged NPP-derived DOM and its horizontal transport(F1); DOM consumption by photodegradation (F2); andvertical transport of DOM by physical processes (F3).
The observed variables are generally related to primary
production, except for a325 that is related to heterotrophic prokaryote production. The extent to which theseparameters were affected by photodegradation differed.
The difference in photoreactivity dependence and origin
among the observed variables led to differences in thespatial distributions of the observed variables and helpedwith extracting the three common factors. Although BIX
could not be explained by these factors, their spatial distributions led to the additional information that DOM
reworked by heterotrophic prokaryotes and autochthonously produced existed in the northern part of ISSG andthat the photo- and biodegradation of DOM interacted.
In addition, the difference in spatial distribution between
F2 scores and UVR in APLR indicated that DOM photodegradation should be prevented by sea ice cover. Ourfindings demonstrated that the multiproxy approachbased on the optical properties of DOM and DOC is a
powerful tool for examining marine DOM dynamics.
Abbreviations
ANTA	Antarctic
APLR	Austral polar
BGCP	Biogeochemical province

Acknowledgements
We express our gratitude to the physical and chemical oceanography marine
technicians of Marine Works Japan during the MR19-04 cruise. We also thankMs. Yamauchi for helping us with the shipboard FDOM measurements.
Author contributions
MS and KS measured FDOM, DOC, and CDOM. MS conducted the analyses forthe results and prepared the manuscript based on advice from KS, MW, TY, TH,
KA, and AM. All authors read and approved the final manuscript.
Funding
This study was partly supported by a grant-in-aid for Scientific Research(B) (19H04246) and World Premier International Research Center Initiative(WPI-AIMEC) from the Ministry of Education, Culture, Sports, Science, andTechnology, Japan.
Availability of data and materials
Monthly net primary production data were available from the Oregon State
Ocean Productivity website (http://​orca.​scien​ce.​orego​nstate.​edu/​npp_​produ​cts.​php). Monthly means of UV radiation are available from the ERA5
reanalysis data (https://​cds.​clima​te.​coper​nicus.​eu/​cdsapp#​!/​datas​et/​reana​
lysis-​era5-​single-​levels?​tab=​form). Monthly climatological temperature andsalinity data are from the World Ocean Atlas 2013 (https://​www.​nodc.​noaa.​
gov/​OC5/​SELECT/​woase​lect/​woase​lect.​html), and daily sea ice concentration(AMSR-2) are from the EUMETSAT website (https://​osi-​saf.​eumet​sat.​int/​produ​
cts/​osi-​408). The data of DOM presented in this study are available from thecorresponding author upon reasonable request.

Declarations
Competing interests
The authors declare that they have no competing interest.
Received: 5 February 2024 Accepted: 1 April 2024

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