Climate change has disproportionately affected the Arctic, shown by drastic increases in temperature and ongoing sea–ice decline (England et al., 2021; Stroeve and Notz, 2018). Changing conditions in the Arctic have already altered the food web, with studies indicating further impacts in the near future (Ardyna and Arrigo, 2020; Arrigo and van Dijken, 2015; Flores et al., 2023; Freer et al., 2022; Quinlan et al., 2005). As the foundation of marine food webs, phytoplankton and sea–ice algae are critical for sustaining ecosystems and contribute to large parts of global primary production, leading to energy transfer across trophic levels and ultimately carbon sequestration (Falkowski, 1994; Serra-Pompei et al., 2022). Environmental heterogeneity and changes may alter community composition, with the potential to impact large-scale ecological processes. For example, in the Arctic and sub-Arctic regions, increased open water areas has led to a longer phytoplankton growing season likely associated with changes in community composition. Studies have demonstrated that climate change may favor increased biomass of smaller flagellated phytoplankton species, leading to reduced dominance of large diatoms species (Blais et al., 2017; Coupel et al., 2012; Vonnahme et al., 2025). This can impact the structure of the food web and overall carbon cycling by having an immediate effect on higher trophic levels, potentially influencing the abundance, distribution and feeding preferences of zooplankton (Campbell et al., 2009; Negrete-García et al., 2024).

Characterising phytoplankton community composition is therefore essential for better understanding ecosystem structure, dynamics and services, especially in the under-sampled Arctic seas, which have experienced the greatest impact of climate change in recent years (England et al., 2021). It is clear that long-term data on microalgae physiology and biomass are vital to enhance our ability to assess and detect spatial and temporal patterns in phytoplankton and sea–ice algae community structure and physiology in response to environmental change. Many studies have used pigment-based measurements, but datasets are often scattered across many sources. This makes it difficult to obtain, access and synthesize into a larger spatial and temporal scale. To address this gap, our paper provides baseline information regarding phytoplankton and sea–ice algae biomass and photosynthetic pigments in the Arctic Ocean aiming at providing a framework to support such large-scale Arctic algae studies across two decades of data.

Phytoplankton pigments, particularly chlorophyll a (Chl a), drives photosynthesis by harvesting light and protecting the cells (Falkowski and Kiefer, 1985; Pereira and Gonçalves, 2022). Advances in pigments analysis through High-Performance Liquid Chromatography (HPLC) and ultra HPLC (UHPLC), have enabled precise identification and quantification of a suite of photosynthetic and photoprotective microalgal pigments. These pigments can serve as chemotaxonomic markers for major functional and taxonomic phytoplankton groups, including the commonly used diagnostic pigments, Alloxanthin (Allo), 19'-Butanoyloxyfucoxanthin (But-fuco), Chlorophyll b (Chl b), Fucoxanthin (Fuco), 19'-Hexanoyloxyfucoxanthin (Hex-fuco), Peridinin (Peri), and Zeaxanthin (Zea) used in this study. Additionally and when available, the database presented in this paper also includes α-/β-carotene (αβ-Car), Bacteriochlorophyll a, Chlorophyllide a (Chlide a), Chl c1, Chl c1+c2, Chl-c3, Diadinoxanthin (Diadino), Diatoxanthin (Diato), Divinyl Chlorophyll a and b (DV-Chl a and DV-Chl b), Lutein (Lut), Neoxanthin (Neo), Pheophorbide a (Phide a), Pheophytin a (Phytin a), Prasinoxanthin (Pras) and Violaxanthin (Viola) (Jeffrey et al., 1999) (Table 1). These additional accessory pigments, some of which are also commonly used as marker pigments (Coupel et al., 2015; Fragoso et al., 2017a), enable phytoplankton to adapt and/or acclimate to various light regime changes that occur in the environment (Brunet et al., 2011). Variability in pigment composition can reflect strategies such as photoprotection, which involves the production of pigments that mitigate damage from excessive light and photo acclimation that refers to the ability of algae to adjust their pigment composition in response to light intensity changes that hereby can optimise photosynthetic efficiency (Bonilla et al., 2009; Gosselin et al., 2017).

Absorption and scattering of light by phytoplankton pigments and other cellular constituents influence the reflectance, which allow their detection by radiometers, making them an invaluable source for remote sensing applications (see overview in Bracher et al., 2017). Multispectral satellite sensors such as SeaWiFS, MODIS, MERIS and OLCI have paved the way and demonstrated the ability to distinguish phytoplankton functional types on large scales (Miller, 2004; Nieke et al., 2015). Recent development have focused on hyperspectral sensors, capable of measuring more narrow absorption and reflectance propertied, that can be missed by multispectral sensors (Hu, 2022). Successful applications include ENVISAT's Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) using the PhytoDOAS method (Bracher et al., 2009; Sadeghi et al., 2012) and the DLR Earth Sensing Imaging Spectrometer Mission (DESIS) using the WebAssembly System Interface (WASI) (Gege, 2004, 2014) by Bracher et al. (2021). Most recently NASA's Plankton, Aerosol, Clouds, ocean Ecosystem (PACE) mission have been launched (Werdell et al., 2024) and the European Space Agency (ESA) is preparing the CHIME hyperspectral to be launched in the coming decade (Nieke et al., 2023).

Since satellites provide long-term synoptic observations of phytoplankton groups high-quality in situ pigment data is invaluable for product validation and algorithm refinement (Cetinić et al., 2024). Similarly, several studies based on self-organizing maps, spectral composition or machine-learning-based methods applied to satellite data have leveraged phytoplankton pigments to understand taxonomic shifts in phytoplankton communities (El Hourany et al., 2019, 2024; Hayward et al., 2025; Xi et al., 2020).

While satellite data is becoming increasingly important, it remains limited by its ability to capture only the first optical depth under cloud- and ice-free conditions.

In situ pigment data from the Arctic have compared to other parts of the world historically been limited due to the region's remoteness, harsh weather conditions and logistical challenges of conducting fieldwork in such remote regions. Advances in HPLC technology, together with standardised protocols (Hooker et al., 2005) and round-robins intercomparison of methodologies (Canuti, 2023), have contributed to the growing availability of pigment data globally, which also includes data from the Arctic and Sub-arctic regions (Kramer et al., 2022; Losa et al., 2017; Mattei and Scardi, 2021; Swan et al., 2016; Xi et al., 2023). Related efforts to obtain pigment data, such as those using UPLC methods (Hwang, 2025), also represent a valuable contribution, though they cannot be incorporated into the current database for inter-comparability reasons with HPLC-based measurements. Despite advances, spatial and temporal coverage across the Arctic remains uneven, underscoring persistent knowledge gaps, highlighting the need for coordinated, collective efforts to compile and expand existing data in this rapidly changing region.

To address this gap, we present the first pan-Arctic overview of phytoplankton and sea–ice algae pigment distributions, based on a comprehensive database of Arctic and sub-Arctic in situ pigment measurements collected between 2000 and 2024. The database is compiled from 77 cruises conducted across the Arctic Ocean, covering coastal, shelf, open-ocean and sea–ice domains (see Table 2). Importantly, this database represents a collective effort involving multiple institutions, research teams and international collaborations. By consolidating data from diverse sources, it provides a baseline for assessing long-term trends in phytoplankton pigments, communities and their ecological responses to environmental change. Additionally, such a database can support a pan-Arctic taxonomic characterisation of phytoplankton communities by employing pigment-based taxonomy techniques (Hayward et al., 2023, 2024; Vidussi et al., 2001; Wright and Jeffrey, 2006).

All datasets used in this paper have been compiled into a single repository, accessible using 10.11583/DTU.29445104 (Heidemann et al., 2026).

Photosynthetic and accessory pigments serve as indicators of community structure and biomass, and are important oceanographic variables for understanding ocean ecology, as they can also support assessment of biogeochemical cycling. Yet, datasets reporting concentrations of those pigments remain sparse in the Arctic region. To address this gap, we compiled, filtered, standardised and harmonised 10 798 pigment samples from HPLC, including water (n=10095) and sea–ice samples (n=703), collected during 77 research cruises spanning from 2000 to 2024 across 15 defined Arctic and sub-Arctic regions. Sampling frequency varied by year, with peak efforts in 2002, 2009, 2016 and 2020. These years correspond to major multidisciplinary research projects and international initiatives such as the International Polar Year (IPY). Seasonally, most samples were collected during spring and summer months in the upper water column.

Within the available dataset, Chl a was found in higher concentrations in sea–ice samples compared to water samples, on average. However, for accessory pigments, concentrations were generally found to be lower in sea–ice samples. Despite this, the highest concentration of Chl a across the dataset, 32.76 µg L⁻¹, was recorded for a water sample in the Chukchi Sea. Other areas that exhibited elevated Chl a concentrations were spread across the Arctic marginal seas such as the Fram Strait, Barents Sea, Beaufort Sea and Labrador Sea. Interestingly, among the eight marker pigments, Fuco consistently had the highest pigment to Chl a ratios, particularly during winter months, followed by a decline through summer and early autumn. For the sea–ice samples, the only accessory pigments with high ratios were Fuco and Chl b.

Our comprehensive phytoplankton and sea–ice algal HPLC-based pigment dataset provides valuable insights into the research effort in the Arctic Ocean. Furthermore, it offers important observations on spatial and temporal patterns of primary producers supporting large-scale environmental research in the Arctic across over two decades of sampling efforts.