# Comparing satellite and BGC-Argo chlorophyll estimation: A phenological study

Alberto Baudena, Wilhem Riom, Vincent Taillandier, Nicolas Mayot, Alexandre Mignot, Fabrizio D'ortenzio

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Alberto Baudena, Wilhem Riom, Vincent Taillandier, Nicolas Mayot, Alexandre Mignot, et al. Comparing satellite and BGC-Argo chlorophyll estimation: A phenological study. Remote Sensing of Environment, 2025, 326, 10.1016/j.rse.2025.114743. hal-05067331

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# Comparing satellite and BGC-Argo chlorophyll estimation: A phenological study

Alberto Baudena<sup>a,b,●,*</sup>, Wilhem Riom<sup>a,1</sup>, Vincent Taillandier<sup>a,1</sup>, Nicolas Mayot<sup>a,●</sup>,
Alexandre Mignot<sup>c</sup>, Fabrizio D'Ortenzio<sup>a</sup>

## ARTICLE INFO

Edited by Menghua Wang

**Keywords:**

Marine chlorophyll

Satellite-derived chlorophyll

BGC-Argo drifter

Time series analysis

Bioregionalisation

Phenology

Satellite BGC-Argo comparison

Satellite BGC-Argo chlorophyll differences

## ABSTRACT

Ocean primary production is a key process that regulates marine ecosystems and the global climate, but its estimation is still affected by multiple uncertainties. Typically, the chlorophyll-a concentration (CHL) is used to characterise this process, as it is considered as a proxy of phytoplankton biomass. To date, the most common observing systems for studying CHL are ocean colour satellites and Biogeochemical-Argo (BGC-Argo) floats. These are complementary systems: satellite observations provide global coverage but are limited to the ocean surface, while BGC-Argo floats provide punctual observations along the whole water column. Quantitative matching of these two observing systems has been obtained only at regional or single-float scales, while at a global scale the relatively low and irregular BGC-Argo coverage results in large uncertainties. Here, we propose a different method, by comparing satellite and BGC-Argo climatological annual time series within seven different bioregions, each characterised by a homogeneous phytoplankton phenology, allowing us to smooth the uncertainties. By comparing the mean values, amplitudes, and shapes of the two time series, we identify regions (a) where they agree (58%-61% of the ocean surface area); (b) regions undersampled by the BGC-Argo float network (particularly in the Arabian Sea and near the Amazon delta); (c) where the discrepancy may stem from satellite or (d) BGC-Argo performance (mainly found at subtropical and high latitudes, respectively). Caution is required when using BGC-Argo and satellite data in regions b-d, and, for each region, we provide suggestions on which system could be affected by the largest uncertainties.

## 1. Introduction

Ocean primary production is considered to contribute to 50% of the global net primary production (Field et al., 1998). One of the main sources of error in this estimation is the extreme spatial and temporal variability of the global ocean distribution of phytoplankton. This variability, which is required to evaluate and predict the consequent primary production, is not yet completely characterised and understood.

To date, mapping the global ocean phytoplankton distribution is mainly assessed through the systematic observation of the chlorophyll-a concentration (CHL), which is the main algal pigment responsible for the photosynthetic process in phytoplankton cells. CHL is generally considered a good, although not perfect, proxy of phytoplankton biomass concentration in the ocean (see for example discussion in Huot et al.

2007). Therefore, estimates of ocean primary productivity strongly rely on the availability of CHL measurements.

CHL is estimated by several methods. The most efficient in terms of spatial and temporal resolution is satellite ocean colour remote sensing. Since 1997, systematic and synoptic databases of remotely sensed CHL, at a spatial resolution of a few kilometers, have been obtained, thanks to an uninterrupted series of ocean colour missions: Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate and Medium resolution Imaging Spectroradiometers (MODIS and MERIS, respectively), Visible Infrared Imaging Radiometer Suite (VIIRS), and Ocean and Land Colour Instrument (OLCI, McClain et al. 2022). These observations are widely used to identify global or regional patterns in the CHL distribution. In addition, they are used to characterise specific phytoplankton dynamics as well as seasonal, frontal, or mesoscale blooms (see a recent review of ocean colour applications in Sathyendranath et al. 2023). Ocean colour

<footnote><sup>a</sup> Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, UMR 7093 LOV, Villefranche-sur-Mer, France</footnote>

<footnote><sup>b</sup> Consiglio Nazionale Delle Ricerche- Istituto di Scienze Marine (CNR-ISMAR), Lerici (SP), Italy</footnote>

<footnote><sup>c</sup> Mercator Ocean International, 31400 Toulouse, France</footnote>

<footnote>* Corresponding author at: Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, UMR 7093 LOV, Villefranche-sur-Mer, France.
E-mail address: alberto.baudena@gmail.com (A. Baudena).</footnote>

<footnote n="1">¹ These authors contributed equally to this work.</footnote>
CHL is also the primary data source for validating phytoplankton con-
centration simulated by global ocean biogeochemistry models (Gregg,
2008; Pradhan et al., 2019) and, more recently, it has been successfully
assimilated in operational models (e.g. in the global Copernicus marine
service biogeochemical forecasting system; Mignot et al. (2023)). How-
ever, ocean colour satellites are unable to sense the ocean surface in
the presence of clouds, strong winds, or at high latitudes in winter,
resulting in large portions of unobserved ocean areas (Stock et al.,
2020). Products generated by using observations obtained by different
satellite sensors could partially palliate this problem by increasing the
spatial and temporal coverage of CHL satellite maps, although they
could introduce additional errors (Garnesson et al., 2019). Further-
more, although uncertainty of global satellite CHL retrieval is estimated
to range between ~ 16.5% and 35% for low CHL concentrations (Hu
et al., 2012), regional characteristics and large CHL concentrations
could strongly degrade satellite-derived estimations (e.g. Chen et al.
2021).

Another diffused method to estimate CHL is through fluorescence
measurements: the excitation of a water parcel containing algal pig-
ments with blue/green light generates an emission of light in the
red wavelengths, which is proportional to CHL. Fluorimeters (i.e. sen-
sors measuring fluorescence) are routinely used to determine in situ
CHL, providing then a method to obtain depth profiles of CHL (see
for example Petit et al. 2022 and references therein). Fluorimeters
are usually calibrated by high performance liquid chromatography
(HPLC) or spectrofluorometers. To date, fluorescence-based CHL es-
timations are undoubtedly the largest source of in situ CHL obser-
vations. More recently, the number of available fluorescence-based
CHL profiles has dramatically increased thanks to the massive de-
ployment of Biogeochemical-Argo profiling floats (BGC-Argo, Claustre
et al. 2020) equipped with fluorometers. A BGC-Argo float is a robotic
drifter, equipped with physical and biogeochemical sensors, able to
autonomously sample the first 2 kms of the water column and to
transmit the acquired data in real-time. To date, the BGC-Argo floats
have acquired more than 200,000 CHL profiles (Stoer et al., 2023) at
locations and seasons generally undersampled by traditional research
vessels. However, the calibration and validation of CHL data from BGC-
Argo floats is not straightforward (see Roesler et al. 2017, Schmechtig
et al. 2023, Lavigne et al. 2012), mainly because the HPLC data
obtained simultaneously to BGC-Argo profiles are mainly limited at the
float deployment or at the recovery (Johnson et al., 2017; Taillandier
et al., 2018; D'Ortenzio et al., 2020). In addition, the BGC-Argo network
has limited spatial coverage, and is still under development (BGC-Argo
implementation plan, 2016).

Nonetheless, by providing CHL at depth, under the clouds and at
high latitudes (i.e. where and when the ocean colour satellites cannot
provide data), autonomously and in real time, the BGC-Argo network
is the ideal observing system to complement ocean colour satellites.
The combined use of the two observing systems offers extraordinary
opportunities to characterise the spatio-temporal distribution of CHL at
a global scale. Several studies have focused on the combination of the
two data sets to validate and assimilate ocean biogeochemistry models
(e.g. Mignot et al. 2023, Cossarini et al. 2019, Teruzzi et al. 2021).
Other studies have compared satellite and BGC-Argo CHL observations,
either at the scale of individual BGC-Argo missions (i.e. Chiswell et al.
2022, Boss et al. 2008, D'Ortenzio et al. 2021), or focusing on specific
regions (i.e. D'Ortenzio et al. 2020, Haëntjens et al. 2017, Wojtasiewicz
et al. 2018). To date, however, very few studies have compared the
two data systems with a strict matchup approach at a global scale as
generally required for satellite calibration/validation exercise (Werdell
and Bailey, 2005).

Independently to the intrinsic uncertainties related to the two ob-
serving systems, a matchup analysis performed at a global scale is
not trivial. The stringent criteria necessary for conducting calibration
and validation exercises for ocean colour (as described in Werdell and
Bailey 2005) are often too severe to obtain a sufficient number of

matchups. These criteria are then weakened, for example, by enlarging
the time window (from ±3 h to ±12 h, as in Wojtasiewicz et al. 2018).
In addition, the irregular distribution of the BGC-Argo profiles, with
regions of strong density and others completely unobserved by floats,
could generate bias in the matchup analysis, pushing towards regional
approaches (as in D'Ortenzio et al. 2021).

In this context, we propose an alternative method to compare
these two datasets. Rather than comparing BGC-Argo and satellite CHL
at the locations where BGC-Argo profiles are obtained, we compare
their climatological annual time series within defined regions. CHL
time series represent a pivotal information to characterise the phenol-
ogy of phytoplankton, i.e., the identification of key moments in the
phytoplankton annual cycle, such as bloom onset or decay. Studying
phytoplankton phenology is considered crucial within the framework
of climate change: a modification in the timing of the main phyto-
plankton dynamics, induced by alterations in its environment, could
have significant repercussions on the entire marine food web (Ji et al.,
2010). In this sense, by using phenology as a comparison metric, we
explore how the two datasets perform in giving a global picture of the
CHL distribution and dynamics, and how they could simultaneously be
used to compensate for their intrinsic limitations (see also Gittings et al.
2019).

Comparing time series obtained from the two observing systems,
however, is not completely trivial. Although for each location (i.e. a
pixel) a CHL time series could be generated from a satellite data set,
this is not the case for BGC-Argo floats, which could change positions
at each time step. In such a sense, satellite time series could be
considered as an Eulerian observation, whereas BGC-Argo times series
are closer to a Lagrangian data frame. To overcome this issue, we
use a method to aggregate multiple individual (i.e. satellite and BGC-
Argo) time series, within large ocean regions. For this, we use the
concept of “bioregion”, which could be defined as an oceanic region
characterised by homogeneous biogeochemical characteristics. In our
case, the bioregions are defined as oceanic areas that exhibit similar
phytoplankton phenological characteristics and, more precisely, CHL
time series with similar shapes. This approach has already been applied
to ocean colour time series and used to identify phenological patterns
in the Mediterranean Sea (D'Ortenzio and Ribera d'Alcalà, 2009; Mayot
et al., 2016), in the Red Sea (Kheireddine et al., 2021), in the Labrador
Sea (Marchese et al., 2019), and in the Southern Ocean (Ardyna et al.,
2017). It was also used at a global scale to compare two different
ocean colour data sets obtained at 20 years of interval (D'Ortenzio
et al., 2012). Finally, and more importantly for the present study,
maps of satellite-derived phenological bioregions have been used as
masks to aggregate sparse in situ data and to generate CHL time series
comparable to those obtained from satellite (Lacour et al., 2015; Mayot
et al., 2017; D'Ortenzio et al., 2021).

Here, we apply this approach with the specific aim of comparing the
capability of ocean colour satellites and BGC-Argo observing systems
to evaluate CHL seasonality at global scale. The advantage of this
approach is that it allows us (i) to identify ocean regions that share
the same phytoplankton phenology in a robust way; (ii) to smooth the
uncertainties, by regrouping and averaging satellite and BGC-Argo data
within each bioregion; (iii) to conduct the comparison in phenologically
homogeneous regions, avoiding potential biases.

## 2. Materials and methods

This paper is based on the analysis of time series of CHL computed
in a climatological way, i.e. averaged over several years. The key
concept is that, for a given location, we build a time series which
reveals how the CHL changes, on average, along a typical year. This
section is organised as follows.

In Sections 2.1–2.4, we describe the calculation of time series of
satellite CHL covering the global ocean (referred to as a *climatology*),
and how they are clustered in bioregions using the phenological approach of D'Ortenzio and Ribera d'Alcalà (2009). In Section 2.5, we illustrate how the phenological characteristics of each bioregion are calculated.

In Section 2.6, we describe the processing of BGC-Argo data and the calculation of BGC-Argo-derived seasonal CHL time series specific to each bioregion.

In Section 2.7, we expose how satellite and BGC-Argo CHL time series are statistically compared within each bioregion.

## 2.1. Satellite data

The surface CHL satellite data set used is the OCEANCOLOUR_GLO_BGC_L3_MY_009_103 product which is provided by the Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu.eu/) platform (product version cmemsobs-oc_glo_bgc-plankton_my_l3-multi-4 km_P1D). This L3 product has a 4 km spatial resolution, is provided daily from 1998 to present day and merges observations from SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3 A & S3B missions. Consequently, the accuracy and coverage of the satellite CHL used here are dependent on the number and quality of the in orbit sensors (increasing in the last years when the number of simultaneous sensors was higher).

## 2.2. Climatology calculation

The clusterization method used requires, for each satellite pixel, a complete time series (i.e. without temporal gaps). Observations missing due e.g. to clouds or algorithmic errors are solved by interpolation (Subsec. S.1). However, polar regions require a specific processing, mainly because they cannot be observed during winter months. This reduces the number of observations available and results in relatively long temporal gaps, which need to be interpolated, introducing then a high level of uncertainty. Although uncertainty is not an intrinsic problem, the application of the partition method to such time series prevents a rapid convergence of the clustering and the identification of an optimal number of clusters, resulting in a numerical failure of the method.

To overcome this issue, a possibility would be to exclude polar regions from our analyses. However, a large number of BGC-Argo profiles (used to compare with satellite data) are in polar regions. In addition, the polar regions are crucial locations regulating the global primary productivity and being importantly affected by climate change (Post et al., 2019). Therefore, we proceed as follows.

### 2.2.1. Reference climatology

First, we calculate a climatology (referred to as reference climatology) adopting an approach already used in previous works (D'Ortenzio and Ribera d'Alcalà, 2009; Marchese et al., 2019; Mayot et al., 2016; Kheireddine et al., 2021; Ardyna et al., 2017; D'Ortenzio et al., 2012), and readapting it to study the global ocean (Supplementary Material S.1). The reference climatology is defined over a regular grid of 1° cell size. Coastal regions (defined as locations less than 1° from the shore) are excluded as they are affected by larger CHL uncertainties (Van Oostende et al., 2018; Zeng et al., 2023; McCluskey et al., 2022), as well as locations without enough temporal and spatial coverage. In addition, only the last 5 years of data (2018–2022), those with larger accuracy, are used. Other criteria are applied in order to reduce the uncertainty in the time series (see details in Supplementary Material S.1). To characterise the phenology of each CHL time series, we normalise it by dividing it by its maximum. We will refer to normalised CHL as nCHL (with values from 0 to 1), as opposed to CHL (in mg/m³). The resulting reference climatology covers the 57°S-57°N band (Supplementary Fig. S.1). These strict criteria allow us to obtain less noisy time series, and permits the partition method to converge and identify an optimal number of clusters and reference time series (Sections 2.3–2.4). However, this partition does not cover polar and coastal regions, where a large number of BGC-Argo profiles, necessary for our comparison, are found.

### 2.2.2. Extended climatology

To extend our coverage to polar zones and coastal regions, a second climatology is calculated, which we refer to as extended climatology. To calculate it, we include coastal regions, and we use of the entirety of the chlorophyll observations over the last 25 years. Furthermore, we use less strict criteria when excluding locations without enough temporal and spatial coverage (see details in Supplementary Material S.2).

## 2.3. Partition methods

First, we partition the reference climatology by assigning each time series to a specific cluster. To this aim, we use the *k*-means method (Hartigan and Wong, 1979; Ahmed et al., 2020), chosen as it has already been successfully applied to a variety of case studies including clustering of time series of satellite CHL (D'Ortenzio and Ribera d'Alcalà, 2009; Marchese et al., 2019; Mayot et al., 2016; Kheireddine et al., 2021; Ardyna et al., 2017; D'Ortenzio et al., 2012). The *k*-means is an unsupervised machine learning technique which groups together time series so that the Euclidean distance (Supplementary Material S.3) among them is minimum, while maximising the distance between different groups. At the end of the *k*-means process, each time series (i.e. each grid cell) of the reference climatology is assigned to one of the 7 clusters (see Sub Section 2.4 for the determination of the optimal number of clusters). The average of all the time series of the reference climatology belonging to a same cluster is called hereafter reference time series. In summary, for each of the *k* clusters obtained from the *k*-means partition, we obtain a spatial distribution (that we call bioregion sensu D'Ortenzio and Ribera d'Alcalà 2009) and a reference time series (Supplementary Fig. S.1).

Second, we use the reference time series to partition the extended climatology. Note that, given the larger noise and uncertainties, the *k*-means method fails to converge and identify an optimal number of clusters when applied directly to the extended climatology. Therefore, we identify, for each time series of the extended climatology, the reference time series (among the 7) with the lowest Euclidean distance from it. Here, the Euclidean distance is considered as an estimate of the similarity between two time series, analogously to the principle used in the *k*-means method. In this way, each time series of the extended climatology is assigned to a bioregion (further details in Supplementary Material S.4).

Third, we then verify the resemblance of the two partitions (one obtained from the reference climatology, the other from the extended one) to assure they are consistent (Supplementary Table S.1 and Supplementary Fig. S.2). To this aim, for each bioregion, we identify a threshold of acceptance. Supplementary analyses show that the pattern of the bioregions obtained from the reference and extended climatology are similar (Supplementary Material S.5). In addition, the large majority (97.3%) of the time series of the extended climatology are within prescribed distance thresholds from the time series of the reference climatology. Finally, the mean time series of the extended climatology are significantly similar to the reference time series (Supplementary Material S.5).

## 2.4. Determination of the optimal number of clusters

Different tests (Elbow, Silhouette, Calinski-Harabasz) are used to determine the optimal number of clusters of the reference climatology (D'Ortenzio and Ribera d'Alcalà 2009; Supplementary Material S.6 and S.7). We identify the number of optimal clusters applying these techniques to the reference climatology (rather than to the extended one) due to the higher accuracy of this climatology. Indeed, the stricter criteria used to calculate it (compared to the criteria used for the extended climatology, Supplementary Table S.2) make its time series less noisy and more robust.

The results of the tests vary between 5 and 9, although all (except Caliński and Harabasz (1974) which indicates 3 clusters and was
discarded as its partition resulted too broad for the purposes of this paper, Supplementary Fig. S.3) converged on 7 and 8. We finally decided on 7 after visual inspection of the time series, which suggests that two of the 8 times series, although considered statistically different, show very close seasonal traits (see Supplementary Material S.7 and Supplementary Fig. S.4).

We also stress that this number is consistent (i) with the study of D'Ortenzio and Ribera d'Alcalà (2009, 7 clusters), Mignot et al. (2023, 8 clusters), which was based on a *k*-means partition as well, and with the global study of Bock et al. (2022, 6 clusters); (ii) when changing the resolution of the grid over which the climatology is calculated (Supplementary Fig. S.5); (iii) with the result obtained without shifting time series in the Southern hemisphere of 6 months (Supplementary Fig. S.6).

## 2.5. Phenological indices of the bioregions

For each bioregion, we identify the dates of bloom initiation, peak, and termination, following the methodology from Thomalla et al. (2023) and Nicholson et al. (2024). For each time series, the timing of the bloom peak is identified as the day of the year corresponding to the maximum CHL. The bloom initiation date is defined as the day when the CHL concentration, within the six months preceding the bloom peak, exceeds the annual CHL minimum by 5% of the annual CHL range. The latter is the difference between the annual CHL maximum and CHL minimum in the 6 months preceding the bloom peak. The bloom termination date is defined as the day when the CHL concentration, within the six months following the bloom peak, drops below the annual CHL minimum plus 5% of the annual CHL range. In this case, the annual CHL range is the difference between the annual CHL maximum and minimum in the 6 months following the bloom peak. For a given bioregion, bloom timing metrics (initiation, peak, and termination) are calculated for each time series within that bioregion. The average values, along with the 25th and 75th percentiles (used to assess dispersion), are reported.

## 2.6. BGC-Argo data

### 2.6.1. BGC-Argo data processing

BGC-Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo.ucsd.edu, https://www.ocean-ops.org; Argo (2020)). The Argo Program is part of the Global Ocean Observing System.

BGC-Argo are autonomous drifters which sample the water column and measure state variables including temperature, pressure, and salinity. In addition, different sensors allow the drifter to measure biogeochemical variables such as CHL, oxygen, particulate backscattering ($b_{bp}$), nitrates, pH, and downwelling irradiance. BGC-Argo data are subjected to several quality control procedures (Schmechtig et al., 2023, 2015; Thierry et al., 2022; Johnson et al., 2023). First, raw data are converted to “real time” state variables and outliers are identified and flagged. Second, automated algorithms calibrate real time data, providing “adjusted” data. Finally, adjusted data are verified and validated by an expert, providing “delayed” mode data. As the latter data have only a limited coverage, we considered data both in “real time”, “adjusted”, and “delayed” mode, following the methodological approach of Mignot et al. (2023). However, we exclude from each profile all the values classified as “probably bad data”, “bad data”, or “missing value” (~30% of the profiles were integrally excluded in this way).

We only select BGC-Argo floats which measured CHL, excluding CHL values larger than 40 mg/m³ from that profile. Values larger than this threshold were considered as outliers; overall, 94 profiles from 4 of the 892 analysed drifters were excluded in this way, obtaining a total of 84186 profiles across the global ocean. The use of “real time” data allowed us to augment the spatial and temporal coverage,

although the quality of the CHL evaluation will certainly be impacted. However, supplementary analyses show that this does not impact our results (Supplementary Fig. S.7). In any case, these data only represent ~11% of the profiles for CHL (Stoer et al., 2023).

For each BGC-Argo profile, we calculate the CHL averaged in the first optical depth, which is usually considered as representative of the CHL measured by satellite observations (Morel and Berthon, 1989). The first optical depth was calculated as in Morel and Berthon (1989).

### 2.6.2. BGC-Argo time series

In order to compare CHL observations from BGC-Argo with the CHL time series obtained from satellite data, we define a BGC-Argo time series as follows:

* For each bioregion, we select all the BGC-Argo profiles which are collected within that bioregion.

* For each 8-day time step (e.g. 1–8 January, or 9–16 January, etc.), we select all the BGC-Argo profiles measured in correspondence of that time step and we consider their CHL averaged in the first optical depth. We calculate the median of these values and we repeat this for each time step, generating a final time series of 46 values for each bioregion.

* A running average over a 40 day window (i.e. 5 time steps), is finally carried out.

Furthermore, for each bioregion, we count the mean number of BGC Argo profiles per 100 × 100 km grid cell in that bioregion (referred to as “# BGC prof/cell (mean)” in Table 3), as well as the percentage of BGC-Argo profiles which are in that bioregion compared to all the BGC-Argo profiles available (“Percentage of all BGC profiles” in Table 3). Other metrics are described in Supplementary Material S.8.

## 2.7. Comparison of BGC-Argo and satellite time series

The similarity between the mean time series of the cluster and the BGC-Argo time series is tested in two ways:

* We verify that both time series are normally distributed, then we perform a t-test (Supplementary Material S.9; Student 1908)

* We calculate the Euclidean distance between the two time series and we compare it with a prescribed threshold of acceptance of the reference cluster (defined in Supplementary Material S.4). If the Euclidean distance is lower, then we consider the BGC time series as significantly similar to the mean time series of the cluster. This second test is applied only to compare normalised time series. In this case, before applying the test, the BGC time series are normalised by dividing them by their maximum.

# 3. Results

## 3.1. Partition of the global ocean

The global ocean is partitioned in *k* = 7 bioregions (Fig. 1). The number of bioregions is chosen using multiple statistical tests (Methods and Supplementary Material S.6 and S.7).

The bioregions obtained from the clustering of the satellite derived nCHL extended climatology shows a distribution broadly organised along latitudinal bands. A first bioregion is identified in the Equatorial band and in the Southern Pacific (bioregion 1), and is characterised by rather constant nCHL values along the year. This lack of seasonality is confirmed by the bloom initiation and termination periods, which superpose. Two bioregions are obtained at low to mid latitudes (bioregions 2 and 3), and are characterised by an enhancement in nCHL which initiates in the beginning of mid autumn and with an annual maximum value observed in mid and late winter, respectively (Table 1). In particular, nCHL time series of bioregion 3 presents a large amplitude (normalised values ranging from 0.35–0.95, compared to a
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Regionalisation of the global ocean in *k* = 7 bioregions (upper panel) obtained from the 1998–2022 extended climatology. In the lower panels, each plot represents the mean normalised nCHL time series of a specific bioregion (i.e. average of all the time series belonging to that bioregion, thick solid lines) along with their uncertainty (thin solid lines, standard deviation) identified by a different colour (right-hand legend in the upper panel). The grey and blue shadowed regions in each panel identify the bloom initiation and termination periods (identified by the 25 and 75 percentiles of the bloom initiation and termination of all the time series belonging to a given bioregion; Methods). Please note that the bloom initiation and termination months, reported in Table 1, correspond to the middle of the bloom initiation and termination periods, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

0.60–0.95 range for bioregion 2). Moving further from the Equatorial and Tropical regions, bioregion 4 is mainly found at ~40°N and ~40°S and is characterised by a bloom initiation in early winter with an annual maximum of nCHL in mid spring, followed by an intense decline in mid summer and a minor increase in autumn. The last 3 bioregions (bioregions 5, 6, and 7) are mainly found at high latitudes and show the largest amplitudes in nCHL seasonality. Bioregion 5 is principally located in the Southern Ocean, while bioregion 7 is in the Atlantic Ocean. Both are characterised by an enhancement in CHL which starts at the beginning of spring and reaches its annual maximum in summer. In bioregion 7, however, the bloom has a larger amplitude (values ranging from its seasonal baseline of 0.4 to a maximum of 0.8) and

lasts until the end of summer, while in bioregion 5 it gradually declines until the end of autumn. Conversely, bioregion 6 presents an important increase of CHL from the beginning to the end of spring (values from 0.4 to 0.8), followed by a rapid decline in summer.

## 3.2. Global comparison with BGC-Argo observations

Fig. 2 shows the positions of the available BGC-Argo profiles. The CHL time series obtained from the two data sets are represented separately for each bioregion in the lower panels. Note that satellite data are re-transformed from nCHL to CHL, implying a larger variability: the standard deviation is 2.7 times larger than in the normalised case
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Comparison of CHL time series from satellite and BGC-Argo. The CHL time series are non-normalised (i.e. real values). The upper panel shows the partition of the global ocean in *k* = 7 bioregions as reported in Fig. 1 with the black dots indicating the position of the 84186 BGC profiles used in the present study (Methods). In the lower panels, each plot represents (i) the mean CHL time series of the bioregion from satellite data as coloured solid lines (thicker coloured solid line: average time series; thinner coloured solid line: uncertainty, expressed as standard deviation; colours in the right-hand legend of the upper panel); and (ii) the BGC-Argo time series (black dots: median of chlorophyll of that time step; black solid line: 40-days running average of the black dots, Methods). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

and represents 53% of the mean values (for the normalised case, it represents 20%; thin solid coloured lines in Fig. 1 and Fig. 2). Table 2 also summarises statistics of the different bioregions for the two data sets.

The spatial distribution of BGC-Argo profiles is not homogeneous, resulting in a number of BGC-Argo profiles per bioregion that is highly variable (both considering the total number of profiles, which ranged from 5636 profiles in bioregion 4 to 15994 profiles in bioregion 7, and considering the average number of profiles per cell, which ranged from 2.1 in bioregion 1 to 8.8 in bioregion 6). Overall, satellite and BGC-Argo mean CHL values are close except for bioregion 1, which exhibits BGC-Argo CHL almost double that of satellite CHL.

According to t-tests, satellite and BGC-Argo time series are similar in six out of seven bioregions, while the shapes of the time series are similar in all the bioregions considered (see also the comparison of normalised time series in Supplementary Fig. S.8). The largest differences are present in bioregion 1, while some differences are observed in bioregion 5, which shows a CHL increase observed by BGC-Argo time series in summer but not in satellite data. In bioregion 6, the bloom initiation detected in satellite and BGC-Argo data temporally matches, while the annual maximum is delayed by about one month in BGC-Argo time series. In bioregion 7, the mid-summer increase in CHL is smaller in the satellite time series than in the BGC-Argo data.
<caption>**Table 1**</caption>

Mean phenological indices (columns) of each bioregion (rows), calculated as in SubSection 2.5. As the time series in the Southern hemisphere are shifted of six months, we use phenological months rather than their names. Thus, the first month of Spring will be March in the Northern hemisphere, and September in the Southern one, and so on.

<table><thead><tr><th>Bioregion</th><th>Timing of<br>bloom max</th><th>Bloom<br>initiation</th><th>Bloom<br>termination</th></tr></thead><tbody><tr><td>1</td><td>First month<br>of spring</td><td>Last month<br>of summer</td><td>First month<br>of summer</td></tr><tr><td>2</td><td>Second month<br>of winter</td><td>First month<br>of fall</td><td>Last month<br>of spring</td></tr><tr><td>3</td><td>Last month<br>of winter</td><td>First month<br>of fall</td><td>First month<br>of summer</td></tr><tr><td>4</td><td>Second month<br>of spring</td><td>First month<br>of winter</td><td>Second month<br>of summer</td></tr><tr><td>5</td><td>Second month<br>of summer</td><td>First month<br>of spring</td><td>Second month<br>of fall</td></tr><tr><td>6</td><td>Last month<br>of spring</td><td>First month<br>of spring</td><td>Last month<br>of summer</td></tr><tr><td>7</td><td>Second month<br>of summer</td><td>First month<br>of spring</td><td>Third month<br>of fall</td></tr></tbody></table>

### 3.3. Analysis of variability of satellite time series

Here, we deepen the comparison between the satellite and BGC-Argo time series, in particular focusing on the variability of the time series.

First, we hypothesised that the high seasonal variability (a large standard deviation of the time series), is the result of grouping time series which are geographically distant. For example, bioregion 5 is observed in equatorial and polar regions, with seasonal mean values that are potentially different. Indeed, our partition method, which aims to identify different phenological regimes, is based on the use of nCHL time series. This can result in bioregions that, while sharing the same shape of the nCHL, are located in regions known to have mean values of CHL largely different.

To solve this point, each bioregion is split in a sub-bioregion at high latitudes (HL) by considering only time series obtained in grid cells located poleward from 38° latitude, and in a sub-bioregion at low latitudes (LL; points located around the equator lower than 38° latitude). In that way, bioregion 1 is split into sub-bioregion 1 LL and sub-bioregion 1 HL; the same for bioregion 2, 3, etc.

Another source of the variability, likely artifactual, derives from the irregular distribution of BGC-Argo profiles, which do not cover the global ocean homogeneously (Table 2), potentially inducing biases in the satellite BGC-Argo comparison. Therefore, to calculate the mean satellite time series for each sub-bioregion, we consider only grid cells located less than 1° from a BGC-Argo profile.

The results confirm that the agreement between satellite and BGC-Argo time series is good, but not valid everywhere (time series in Fig. 3 and metrics in Table 3). In particular, 8 out of 14 sub-bioregions show a significant agreement between satellite and BGC-Argo time series (t-test; sub-bioregions 1, 3, 4, and 5 at low latitudes, and sub-bioregions 2, 3, 4, and 7 at high latitudes), while 6 do not (sub-bioregions 2, 6, and 7 LL, and sub-bioregions 1, 5, and 6 HL).

Subsequently, we calculate different properties for each LL and HL sub-bioregion (Table 3 and Sub Section 2.6.2). In general, satellite and BGC-Argo time series are significantly different in sub-bioregions where the BGC-Argo sampling density is low (less than 5 BGC-Argo profiles per grid cell; Supplementary Fig. S.9), while they agree where the BGC-Argo sampling density is large (more than 8 BGC-Argo profiles per cell). This is particularly visible in sub-bioregion 6 LL, where a potentially erroneous double peak in the BGC time series can be explained by one of the lowest density of BGC profiles there (4.4). On the other hand,

the remarkable agreement found in sub-bioregion 3 HL can be due to the large BGC-Argo sampling density there (37.0, the highest).

When focusing on the sub-bioregions where BGC-Argo and satellite time series do not match, different categories of discrepancy can be qualitatively identified (Table 3). (i) In some sub-bioregions, a strong peak is present in the BGC-Argo time series while, in the satellite time series, the amplitude is much less pronounced. Furthermore, on average, BGC-Argo time series shows values which are almost two-fold larger than satellite time series. It is the case of sub-bioregions 1, 5 HL and, notably, also of the portion of bioregion 1 in the Southern Pacific (Supplementary Material S.10 and Supplementary Fig. S.10). This may explain the mismatch between satellite and BGC-Argo CHL time series observed in bioregion 1. A similar discrepancy is found in sub-bioregion 6 HL with the difference that, there, the satellite time series shows a more visible peak. We define sub-bioregions with these characteristics as belonging to the category “similar shapes, different amplitudes and mean values” (SS, DA&MV). (ii) Conversely, in some regions, BGC-Argo time series show constantly lower values than satellite ones and similar shapes and amplitudes (sub-bioregion 2 LL). We define this category as “similar shapes and amplitudes, different mean values” (SS&A, DMV). Finally, (iii) in some sub-bioregions, the low number of BGC-Argo profiles is not sufficient to produce a reasonable BGC-Argo time series. It is the case of sub-bioregions 6 and 7 LL in which only 0.17% and 1.73% of all BGC profiles were carried, respectively. We define this category as “low BGC-Argo sampling” (LBGCS).

### 4. Discussion

The obtained regionalisation of the global ocean (Fig. 1) compares well with the regionalisation proposed by Longhurst (1995) and D'Ortenzio et al. (2012, Supplementary Material S.11). In particular, bioregion 1 corresponds to model number 4 of Longhurst (Tropics), while bioregion 2 closely matches Tropical bioregion of D'Ortenzio et al. (2012). Bioregion 3 corresponds to model 3 of Longhurst (Subtropical winter nutrient-limited), while bioregion 4 is similar to the Bloom bioregion of D'Ortenzio et al. (2012). Bioregion 5 shares characteristics of both model 1 (Polar irradiance-limited) and 2 (midlatitude nutrient-limited spring production peak) of Longhurst. Finally, both bioregions 6 and 7 correspond to model number 1 of Longhurst (1995), with bioregion 6 characterised by a stronger decline in CHL.

The pattern of our regionalisation compares also well with the one obtained by Fay and McKinley (2014) (Supplementary Material S.11), in particular concerning bioregions 2, 3, and 4. Differences are present in the Southern Ocean where the biomes found by Fay and McKinley (2014) are organised in latitudinal bands, while the locations of our bioregions are less regular. A good qualitative agreement is also found between our partition and the one by Bock et al. (2022, Supplementary Material S.11). Larger differences are found with the regionalisation of Mignot et al. (2023), possibly due to the very large number of parameters used in that work and to the use of model rather than satellite outputs.

The good qualitative matchup of our findings with previous works is important especially in the light of the fact that we base our regionalisation uniquely on the shape of the CHL time series, and not on a larger number of assumptions and variables. This highlights how CHL phenological cycles are the expression of multiple marine processes. More importantly, previous works were able to match the shape of the satellite and BGC-Argo time series, but they obtained very different mean CHL values (D'Ortenzio et al., 2020). Here, to our knowledge, we show for the first time an agreement not only from the point of view of the seasonal shape, but also with respect to the mean CHL values. Furthermore, the partition is robust with respect to changes in the size of the grid cell used to calculate the climatologies and to the use of the shift of 6 months applied to the Southern hemisphere time series.

To investigate the discrepancy between BGC-Argo and satellite CHL variability, we locally match individual BGC-Argo time series, which
<caption>**Table 2**</caption>

Number and density of BGC-Argo profiles in the 7 bioregions along with their mean satellite and BGC-Argo CHL values (and their respective standard deviation), and results of the comparison (t-test, Methods).

<table><thead><tr><th>Bioregion</th><th># BGC profiles (Total)</th><th># BGC profiles per cell</th><th>Satellite chl, mean [mg/m3]</th><th>Satellite chl, std [mg/m3]</th><th>BGC-Argo chl, mean [mg/m3]</th><th>BGC-Argo chl, std [mg/m3]</th><th>t test</th></tr></thead><tbody><tr><td>1</td><td>13 889</td><td>2.1</td><td>0,15</td><td>0,34</td><td>0,28</td><td>0,23</td><td>no</td></tr><tr><td>2</td><td>15 550</td><td>2.5</td><td>0,12</td><td>0,25</td><td>0,13</td><td>0,15</td><td>yes</td></tr><tr><td>3</td><td>15 758</td><td>2.8</td><td>0,13</td><td>0,20</td><td>0,17</td><td>0,16</td><td>yes</td></tr><tr><td>4</td><td>5636</td><td>3.0</td><td>0,27</td><td>0,35</td><td>0,33</td><td>0,37</td><td>yes</td></tr><tr><td>5</td><td>13 187</td><td>4.0</td><td>0,31</td><td>0,70</td><td>0,44</td><td>0,32</td><td>yes</td></tr><tr><td>6</td><td>9487</td><td>8.8</td><td>0,46</td><td>0,61</td><td>0,50</td><td>0,52</td><td>yes</td></tr><tr><td>7</td><td>15 994</td><td>6.2</td><td>0,47</td><td>0,82</td><td>0,50</td><td>0,65</td><td>yes</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Bioregions split into high and low latitude sub-bioregions. The upper panel shows the partition of the global ocean in $k=7$ bioregions, as reported in Fig. 1. The white lines show the 38 °N and S latitude used to split the bioregions. The 7 lower panels on the left (first two columns) show the low latitude sub-bioregions, while the 7 lower panels on the right (third and fourth columns) show the high latitude sub-bioregions. Each plot represents (i) the mean CHL time series of the sub-bioregion non-normalised calculated considering only grid cells located less than 1 ° from a BGC-Argo profile, as coloured solid lines (colours in the right-hand legend of the upper panel). (ii) The BGC-Argo time series (grass green dots: median of chlorophyll of that time step; grass green solid line: 40-days running average of the black dots, Methods). The title of each panel indicates the number of the bioregion, the percentage of cells belonging to it (in brackets), and whether the comparison between the satellite and BGC-Argo time series is significant or not (t-test="yes", Methods). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

stayed in the same cluster for at least one year, with satellite observations. Instrumental discrepancies between the two observational systems emerged as the main factor explaining the observed differences (Supplementary Fig. S.7 and Supplementary Material S.12). The conversion from fluorescence to chlorophyll-a concentration includes a slope factor, globally estimated to be 2. However, the relationship between fluorescence and chlorophyll-a concentration is influenced by factors such as species composition, light availability, nutrient levels, and physiological conditions. Consequently, the accuracy of chlorophyll-a concentration estimated from BGC-Argo float measurements can vary regionally (Roesler et al., 2017; Claustre et al., 2020). Similarly, it is well established that the performance of algorithms used to derive chlorophyll-a concentration from remote sensing reflectances also exhibits strong regional variations (IOCCG, 2019). These discrepancies explain some of the instrumental discrepancies reported here. Another factor contributing to explain this difference was the low availability of satellite data in some regions, in particular in high latitude regions. Conversely, the spatial and temporal averaging of the satellite time series and their grouping in bioregions do not affect the

discrepancy between BGC-Argo and satellite, further corroborating our bioregionalisation approach. As the number of BGC-Argo with at least one year of data that can be matched with satellite observations is low (34 drifters in total), and covers only a few regions (only the first 3 bioregions), these considerations cannot be quantitatively extended to all the bioregions and the global domain considered. However, with the expected densification of the BCG-Argo network, this approach could be used in a routine way to better evaluate uncertainties.

Overall, the general agreement between BGC-Argo and satellite CHL is partially explained by a large variability associated with the satellite time series. To investigate the processes underpinning this variability, we have split bioregions in high and low latitude sub-bioregions and considered only grid cells located in proximity (less than 1 °) of a BGC-Argo profile to calculate mean satellite time series. We find agreement in 8 out of 14 sub-bioregions, corresponding to 58% of our domain (reported in green in Fig. 4). In general, we find that for a BGC-Argo sampling density larger than 8 profiles per 100 × 100 km² grid cell, the likelihood of an agreement between satellite and BGC-Argo time series is larger. This implicitly suggests a way to determine whether
<caption>**Table 3**</caption>

Properties of the bioregions split in high and low latitudes sub-bioregions, results of the comparison between the BGC-Argo and satellite time series (t test), and classification of the sub-bioregions in four categories: (i) Similar Shape, Different Amplitude and Mean Value (SS, DA&MV); (ii) Similar Shape and Amplitude, Different Mean Value (SS&A, DMV); (iii) Low BGC-Argo sampling (LBGCS); (iv) Match (M). Sub-bioregion 6HL is classified as Match due to the good qualitative agreement between BGC-Argo and satellite chlorophyll measurements (Supplementary Table S.3). Note that satellite CHL (second and third column) and the density of BGC-Argo profiles ("# BGC prof/cell") are calculated considering only grid cells located less than 1° from a BGC-Argo profile.

<table><thead><tr><th>Bioregion</th><th>Satellite chl, mean [mg/m³]</th><th>Satellite chl, amplitude [mg/m³]</th><th>BGC-Argo chl, mean [mg/m³]</th><th>BGC-Argo chl, amplitude [mg/m³]</th><th>Surface [100*100 km²]</th><th># BGC prof/cell (mean)</th><th>Percentage cells covered [%]</th><th>Percentage of all BGC prof [%]</th><th>t test</th><th>Classification</th></tr></thead><tbody><tr><td>1LL</td><td>0.11</td><td>0.01</td><td>0.09</td><td>0.03</td><td>6293</td><td>5.3</td><td>15,64</td><td>10,22</td><td>yes</td><td>M</td></tr><tr><td>1HL</td><td>0.15</td><td>0.02</td><td>0.39</td><td>0.34</td><td>1830</td><td>4.5</td><td>6,77</td><td>5,33</td><td>no</td><td>SS, DA&MV</td></tr><tr><td>2LL</td><td>0.09</td><td>0.04</td><td>0.04</td><td>0.04</td><td>7148</td><td>6.2</td><td>18,5</td><td>13,41</td><td>no</td><td>SS&A, DMV</td></tr><tr><td>2HL</td><td>0.26</td><td>0.13</td><td>0.25</td><td>0.19</td><td>471</td><td>16.6</td><td>1,61</td><td>3,71</td><td>yes</td><td>M</td></tr><tr><td>3LL</td><td>0.11</td><td>0.11</td><td>0.09</td><td>0.20</td><td>6620</td><td>6.9</td><td>17,95</td><td>14,32</td><td>yes</td><td>M</td></tr><tr><td>3HL</td><td>0.18</td><td>0.20</td><td>0.19</td><td>0.34</td><td>201</td><td>37.0</td><td>0,64</td><td>2,87</td><td>yes</td><td>M</td></tr><tr><td>4LL</td><td>0.16</td><td>0.15</td><td>0.22</td><td>0.45</td><td>1338</td><td>3.5</td><td>3,69</td><td>2,51</td><td>yes</td><td>M</td></tr><tr><td>4HL</td><td>0.21</td><td>0.16</td><td>0.29</td><td>0.48</td><td>999</td><td>6.9</td><td>3,55</td><td>3,69</td><td>yes</td><td>M</td></tr><tr><td>5LL</td><td>0.20</td><td>0.04</td><td>0.10</td><td>0.10</td><td>1108</td><td>4.8</td><td>2,77</td><td>1,58</td><td>yes</td><td>M</td></tr><tr><td>5HL</td><td>0.19</td><td>0.08</td><td>0.45</td><td>0.60</td><td>2977</td><td>5.3</td><td>11,23</td><td>12,91</td><td>no</td><td>SS, DA&MV</td></tr><tr><td>6LL</td><td>0.55</td><td>0.77</td><td>0.30</td><td>0.64</td><td>148</td><td>4.4</td><td>0,39</td><td>0,17</td><td>no</td><td>LBGCS</td></tr><tr><td>6HL</td><td>0.32</td><td>0.41</td><td>0.53</td><td>1.21</td><td>1177</td><td>16.1</td><td>5,04</td><td>10,48</td><td>no</td><td>M*</td></tr><tr><td>7LL</td><td>0.36</td><td>0.44</td><td>0.22</td><td>0.37</td><td>816</td><td>5.4</td><td>2,02</td><td>1,73</td><td>no</td><td>LBGCS</td></tr><tr><td>7HL</td><td>0.30</td><td>0.26</td><td>0.49</td><td>1.04</td><td>2378</td><td>9.9</td><td>10,19</td><td>17,08</td><td>yes</td><td>M</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Summary of satellite and BGC-Argo time series agreement. Regions where both time series match (M) are shown in green. Regions where the density of BGC-Argo profiles is too low to permit comparison with satellite time series (LBGCS) are shown in light green. Regions where satellite and BGC-Argo time series present similar shapes and amplitudes, but different mean values, are shown in orange (SS&A, DMV). Regions where satellite and BGC-Argo time series share similar shapes but different amplitudes and mean values are shown in red (SS, DA&MV). Black dots show the sub-bioregion 6 HL, for which we found a good qualitative agreement between BGC-Argo and satellite chlorophyll measurements. However, attention must be paid when comparing BGC-Argo and satellite chlorophyll data in that region. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

the BGC-Argo sampling density in a given bioregion is, to date, good enough. An exception to this scenario is provided by bioregion 4 LL, which shows agreement of satellite and BGC-Argo time series despite having only 3.5 BGC-Argo profiles per grid cell. Conversely, further BGC-Argo float deployments are needed in bioregion 2 LL, despite a BGC-Argo sampling being quite remarkable (~6 BGC profiles per cell).

We qualitatively identify three types of discrepancy between BGC-Argo and satellite time series.

* Similar shapes, different amplitudes and mean values : These regions are characterised by a prominent CHL enhancement observed by BGC-Argo during summer, which is observed by satellite as well but with a lower amplitude. In addition, the CHL values observed by BGC-Argo floats are consistently larger than the ones observed by satellite across the year. These sub-bioregions are found at high latitudes (sub-bioregions 1, 5, 6 HL, and also bioregion 1 considered exclusively in the Southern Pacific, Supplementary Material S.10 and Supplementary Fig. S.10) and present a relatively large number of BGC-Argo profiles (5%, 13%, and 11% of all profiles, respectively). Thus, the mismatch is unlikely due to insufficient BGC-Argo sampling (in addition, in sub-bioregion 6 HL the density of BGC-Argo profiles is among the highest: 16

profiles per grid cell). We suggest that this mismatch could rather be due to well-known problems in the conversion of fluorescence measured by the floats into CHL (Behrenfeld et al., 2006, 2009; Bock et al., 2022). Notably, this discrepancy has already been reported in the region of sub-bioregion 1 HL, in particular in the Southern Pacific (Mignot et al. 2023, Bock et al. 2022, Supplementary Fig. S.10). This could explain as well why the BGC-Argo and satellite time series are different in bioregion 1, according to the t-test (Fig. 2 and Subsec 3.2). Another possibility is the scarce number of years for which observations are available in satellite CHL images at high latitudes (Supplementary Fig. S.11); hence, the climatological satellite time series may have missed important productivity events, which may explain the low value of the satellite-derived CHL during the annual maximum in summer. Hence, we stress that attention must be used when analysing CHL in these regions, particularly in the Southern Ocean during Austral summer. For these reasons, we report them in red in Fig. 4. When considering all the time series composing sub-bioregion 6 HL (and not only those closer than 1° to a BGC-Argo profile), satellite and BGC-Argo time series agree (Supplementary Table S.3). For this reason, Sub-bioregion 6 HL is shown in green (with
black dots), and leads to an agreement between satellite and BGC-Argo time series in 61% of our domain.

*   *Similar shapes and amplitudes, different mean values:* In these regions (sub-bioregion 2 LL), mainly located in a band around 25 °N and 25 °S, BGC-Argo and satellite time series show a similar phenology, both in terms of shape (t test significant, Supplementary Table S.3) and amplitude of the time series (Table 3). However, the chlorophyll reported by BGC-Argo floats is constantly lower than the one measured by satellite. This discrepancy could be due to the overestimation of CHL by satellite at low latitudes as reported in previous studies (Omta et al., 2009; Clow et al., 2024). Conversely, as the density and number of BGC-Argo profiles are among the largest there, the BGC-Argo coverage seems not to explain this difference. Hence, these regions are reported in orange in Fig. 4.

*   *Insufficient BGC-Argo sampling:* In sub-bioregions 6 and 7 LL, mainly located in the Arabian Sea and close to Amazon delta, the low number of BGC-Argo profiles did not allow us to calculate a robust BGC-Argo time series. This likely explains the mismatch with satellite measurements. These results suggest the need for further BGC-Argo deployments in these regions. Hence, sub-bioregions 6 and 7 LL are reported in yellow in Fig. 4.

## 5. Conclusions

Our results point to the fact that the agreement between CHL observed by satellite and BGC-Argo floats is valid in the majority of the global ocean (~58%–61% of the surface area), but not everywhere. We suggest that the discrepancies are mainly due to inherent differences between the two measuring systems. Our method provides a novel way to determine regions where satellite and BGC-Argo observations agree, and regions in which they do not, based on the phytoplankton phenology described with annual cycles of CHL. Our results suggest that the reasons for this mismatch are likely due to poor satellite performance (in particular in sub-bioregion 2 LL which mainly cover the tropics) or to problems in the relationship used to convert fluorescence to CHL in BGC-Argo floats (sub-bioregions 1, 5, and 6 HL which include the vast majority of the Southern Ocean, the Northern Atlantic close to the Scandinavian peninsula and south of Greenland, and the Northern Pacific). We also suggest that low satellite coverage in some regions may partially explain this mismatch, though more observations are necessary to corroborate this hypothesis. Our methods also allow us to identify regions lacking BGC-Argo data (sub-bioregions 6 and 7 LL, found in the Arabian Sea and in the Atlantic Ocean in front of Mauritania and Guiana). More generally, we suggest larger BGC-Argo float deployments in sub-bioregions showing discrepancy between satellite and BGC-Argo time series. In conclusion, we stress that attention must be paid when coupling together satellite and BGC-Argo CHL observations in these regions (e.g. to constrain biogeochemical models or for marine spatial planning), as they cover ~40% of the global ocean. The framework used in the present study can be applied in the comparison of other biogeochemical variables, or to validate and improve the accuracy of CHL estimates in global biogeochemical models.

### CRediT authorship contribution statement

**Alberto Baudena:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Wilhem Riom:** Writing – review & editing, Investigation, Data curation. **Vincent Taillandier:** Writing – review & editing, Validation, Supervision, Project administration, Funding acquisition, Conceptualization. **Nicolas Mayot:** Writing – review & editing, Software, Methodology, Formal analysis, Conceptualization. **Alexandre Mignot:** Writing – review & editing, Software, Methodology, Conceptualization. **Fabrizio D'Ortenzio:** Writing – review & editing, Writing – original draft, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

AB, VT, and FDO received support from the CNES/TOSCA project SEASONS. This study has been conducted using E.U. Copernicus Marine Service Information (product OCEANCOLOUR_GLO_BGC_L3_MY_009_103; https://doi.org/10.48670/moi-00280). The authors would like to thank the International Argo and BGC-Argo Program, as well as the NASA and Mercator Ocean agencies. AB furthermore acknowledge support from the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 – Call for tender No. 3138 of December 16, 2021, project “RAISE - Robotics and AI for Socio-economic Empowerment” (ECS00000035) rectified by Decree n.3175 of December 18, 2021 of the Italian Ministry of University and Research funded by the European Union – NextGenerationEU. The authors are grateful to Inés Jambou for her help in the design of Figs. 1–3 and the different tables, as well as Elodie Gutknecht, Coralie Perruche, and Giulio Tirabassi for their useful advise.

## Appendix A. Supplementary data

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.rse.2025.114743.

### Data availability

All the data necessary to reproduce all the plots shown in the present paper will be published in zenodo upon publication.

### References

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Further reading

Marutho, D., Hendra Handaka, S., Wijaya, E., Muljono, 2018. The determination of cluster number at k-mean using elbow method and purity evaluation on headline news. In: 2018 International Seminar on Application for Technology of Information and Communication. pp. 533–538. http://dx.doi.org/10.1109/ISEMANTIC.2018. 8549751.

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