Underestimated accelerated Antarctic phytoplankton net primary production in winter over past decade from spaceborne LiDAR

Domain: earth_science
Published: 2025-12-12
Editorial outcome: minor_revision

Abstract
The Southern Ocean’s vital carbon sink is driven by phytoplankton Net Primary Production (NPP). Winter phytoplankton seed spring algal blooms and regulate nutrient cycling and ecosystem dynamics, yet Antarctic winter NPP remains poorly constrained due to limited in situ data and passive satellite sensor challenges in low-light, ice-covered conditions. Here, we leverage spaceborne LiDAR (CALIOP), analyzing 16 years of data (16,236 tracks spanning 2006-2023), to reveal a significant, previously underestimated rise in winter NPP, increasing by ~2.2 Tg C yr⁻¹ (P<0.01). Enhanced coverage with CALIOP boosts ice-free sea observations from 12.5% to 80.7%, exposing pronounced NPP gains in the seasonal sea-ice zone, notably the Weddell and Ross Seas, driven by declining sea ice, greater light penetration, and nutrient mixing. These shifts, modulated by climate modes like the Southern Annular Mode and El Niño-Southern Oscillation, highlight the Southern Ocean’s escalating role in global carbon dynamics. Integrating winter NPP into carbon models is essential to refine projections of polar carbon sequestration under climate change.

Introduction
The Southern Ocean plays a pivotal role in Earth’s climate system, acting as a major carbon sink1–3 because of its high rates of phytoplankton net primary production (NPP). NPP refers to the conversion of carbon dioxide (CO2) into organic carbon through photosynthesis, which is then transferred through the marine food web or sequestered in deep ocean waters, helping to store carbon and mitigate climate change4. The sensitivity of Southern Ocean phytoplankton to environmental changes, including ocean warming, wind speeds, iron contents, and sea ice melting, makes the Southern Ocean a critical area for studying the impacts of climate change on marine ecosystems and global biogeochemistry5–7.

Recent studies have highlighted the significant influence of climate change on Southern Ocean phytoplankton, with rising temperatures, shrinking sea ice, and alterations in ocean circulation affecting their distribution and production. Specifically, a warmer ocean and atmosphere8,9 and sea ice retreat10–12 are reshaping the timing and magnitude of phytoplankton blooms by influencing the wind strength, nutrient availability, light conditions13,14 and mixed layer depth15–17, which are factors that affect phytoplankton blooms. These regional shifts are often modulated by the Southern Annular Mode (SAM), the dominant climate pattern in the Southern Hemisphere. A positive SAM trend has been shown to influence phytoplankton communities and production by altering physical drivers like mixed layer depth, light availability, and sea ice cover18–20. Notably, a recent intensification of the SAM since 2010 has been linked to increased phytoplankton biomass and longer blooms in the West Antarctic Peninsula21. These shifts in the timing and magnitude of phytoplankton blooms are particularly evident in austral summer, when phytoplankton blooms grow in magnitude but terminate earlier compared to previous decades, with significant trends in bloom initiation and termination observed over the past 25 years (1998–2022)5. In the marginal ice zone to the west of the Antarctic Peninsula, climate change is causing the timing of phytoplankton blooms to shift, with increased chlorophyll-a concentrations (Chla) in fall and a decrease in bloom intensity in spring22. A “greening” of Chla in the Southern Ocean has been observed23, similar to the climate-driven trends observed in low-latitude regions24, where the timing and intensity of phytoplankton blooms have shifted as the ocean has been subjected to warming, anthropogenic nutrient inputs25, and acidification26. Over the past two to three decades, long-term increases in Chla, with regional differences in phytoplankton blooms, have also been observed via satellite-based observations5,27. However, in these observations, the critical role of wintertime production, which enables early-season phytoplankton growth under low-light conditions that support biomass buildup and sustain the spring bloom28,29, is often overlooked. This is primarily because the passive ocean color remote sensing techniques depend on sunlight, and existing approaches typically cannot capture data under low-light conditions30,31. Additionally, the presence of whitecaps—foam-covered wave crests formed by wind—significantly increases surface reflectance, particularly during winter storms, further complicating satellite observations32.

A significant gap in the current understanding of NPP in the Southern Ocean arises from this seasonal data limitation. Satellite-based studies and shipborne observations have focused mostly on summer33,34, leaving the Antarctic winter months (June to August) underrepresented. While biogeochemical (BGC)-Argo floats collect some data in winter29, their sparse coverage makes continuous long-term observations challenging. The coverage rate of ocean color remote sensing data, which are commonly used to estimate NPP35,36, in ice-free sea areas of the Antarctic in winter is less than 15%, especially in June and July, when the coverage rate is less than 1% (Fig. S1). However, phytoplankton can still survive underneath sea ice even under extremely low-light conditions, which are typical of the polar night29,37–39—a period in polar regions when the sun does not rise above the horizon for weeks or months.

To address this critical gap, we use spaceborne light detection and ranging (LiDAR) active remote sensing technology, which has already proven powerful in polar ocean detection because this technology does not require sunlight and can provide extensive spatial coverage31,40–44. The effective coverage rate of cloud-aerosol LiDAR with orthogonal polarization (CALIOP) in Antarctic winter is 6 to 7 times greater than that of moderate resolution imaging spectroradiometer (MODIS), and the use of CALIOP can increase the effective coverage area from 12.5% to 80.7% (Fig. S1). Even in June, the coverage rate of CALIOP is more than half of the sea ice-free ocean area, and the coverage rates in July and August are both greater than 80%. However, few studies used the spaceborne LiDAR data to retrieve chlorophyll concentration or explore the dynamics of polar primary production. In this study, CALIOP technology is utilized to retrieve winter phytoplankton data, offering a more complete picture of long-term trends, full seasonal cycles and potential drivers of Antarctic phytoplankton production.

The aim of this study is to address the critical gap in Antarctic NPP by utilizing enhanced satellite-based techniques, filling the void of winter observations. By incorporating CALIOP data from both the winter and summer months, we generate a comprehensive monthly dataset using spaceborne LiDAR, revealing a significant increase in Antarctic winter NPP over the past decade. The study area covers the Antarctic continental shelf and the seasonal sea-ice zone (SSIZ), the area within the temporally average extent of sea ice, which is bounded by the gray solid line in Fig. S1. This area is chosen as the focus of this study because the Antarctic shelf and its surrounding coastal areas are highly ecologically important, serving as habitats for all penguin species in Antarctica, and this area is characterized by high phytoplankton production45. In this work, we present insights into the seasonal variations in primary production, identify long-term trends and key drivers, and attain a more complete understanding of the mechanisms shaping NPP in this critical region. The results of our research complement existing data by covering previously unobserved NPP in winter months, and the drivers of observed changes are quantitatively analyzed, illuminating how climate change impacts the Southern Ocean’s primary production. By filling a significant gap in wintertime Antarctic NPP data and analyzing the drivers of observed changes, this study offers valuable insights into how climate change impacts primary production in the Southern Ocean.

Results
Underestimated substantial increase in Antarctic winter NPP
To determine the Antarctic winter NPP, we first compared the variability in NPP from CALIOP with that from MODIS. To derive NPP from CALIOP data, we used the two-branch-two-step (TBTS) deep learning model to retrieve Chla, backscattering coefficient (bbp), and attenuation coefficient (Kd), followed by the application of the carbon-based productivity model (CbPM) to estimate NPP, whereas MODIS-derived NPP data were accessible from http://orca.science.oregonstate.edu/1080.by.2160.monthly.hdf.cbpm2.m.php. Antarctic winter NPP, defined as the sum of June, July, and August NPP, has exhibited significant spatial variability and a notable increase from 2007 to 2022 (Fig. 1). The CALIOP-derived climatological winter NPP shows high production concentrated along the edge of the ice in the SSIZ (Fig. 1a), where sea-ice retreat creates favorable conditions for the growth of phytoplankton46 by allowing more sunlight to penetrate the surface waters and increasing the availability of nutrients. In comparison, although the MODIS-derived NPP (Fig. 1b), captures similar spatial patterns, production levels are underestimated because of the limitations of this passive remote sensing technique under low-light winter conditions.

To further explore the patterns of NPP change, we examined the spatial distribution of NPP variations across different regions (Fig. 1c, d). This analysis reveals distinct patterns of both increases and decreases in winter NPP. CALIOP-derived rates of NPP change (Fig. 1c) show widespread increases in production along the SSIZ, particularly in the Weddell Sea, Ross Sea, and parts of the Indian Ocean sector. Consistent increases in production are observed in these regions, likely driven by thinning sea ice, as shown in Figs. S2 and S3, as well as increased light penetration and episodic nutrient mixing. However, areas of NPP decline are also evident, such as in the Bellingshausen Sea and the eastern Amundsen Sea, where environmental factors such as unfavorable ice dynamics—like increased sea ice concentration, which can limit light penetration and hinder phytoplankton growth—along with limited nutrient access, as shown in Fig. S3, likely contributed to these reductions. Compared with the CALIOP observations, the MODIS-derived rates of NPP change (Fig. 1d) reveal weaker and more spatially limited trends, with substantial increases and decreases observed in fewer areas. These differences highlight the advantages of using CALIOP data for a more detailed and comprehensive assessment of NPP changes, especially in regions where sea-ice dynamics play a critical role in primary production. The ability to capture finer spatial variability in NPP is a key strength of the CALIOP-based approach over the MODIS data.

Temporal trends reveal a marked increase in winter NPP over the study period (Fig. 1e). From 2007 to 2022, the annual mean CALIOP-derived winter NPP is 54.5 ± 8.9 Tg C, which is six times greater than the MODIS-derived NPP (8.5 ± 1.4 Tg C). A sharp acceleration in NPP is observed in the later years (2012–2022), with an annual increase of 2.24 Tg C yr⁻¹ (R=0.84, P<0.01). This acceleration corresponds to ongoing reductions in the extent of winter sea ice, as shown by the steady decline in winter sea ice extent (Fig. 1e, red circles). In contrast, the MODIS-derived NPP shows minimal changes over the same period, underscoring the importance of active remote sensing technologies for observing winter phytoplankton dynamics. Moreover, with the completion of CALIOP data collection, the annual pan-Antarctic (i.e, the entire Antarctic SSIZ) phytoplankton NPP calculated from 2007 to 2022 was 1022.8 ± 136.8 Tg C; without these data, the NPP estimated on the basis of MODIS data alone would be underestimated by nearly 15%, a substantial difference that suggests the importance of CALIOP (Fig. S4).

Next, in an effort to verify the increasing trend, several additional NPP models were investigated: the vertically generalized production model (VGPM), carbon, absorption, and fluorescence euphotic-resolving (CAFE) model, and Eppley model. These models were selected based on their ability to estimate NPP across different oceanographic conditions. Evaluating multiple models allows for a more robust validation of the increasing trend observed in winter NPP. The data from all four models VGPM, CbPM, CAFE, and Eppley indicate a clear increase in polar winter NPP, with the most significant increase observed between 2012 and 2022. The CAFE model consistently estimates the highest NPP, followed by the VGPM and CbPM, whereas a more modest increase is obtained with the Eppley model (Fig. S5). These trends highlight the increasing production in the polar regions, particularly in winter. This finding aligns with the findings shown in Fig. 1e, emphasizing that the winter NPP in the Southern Ocean has increased substantially over the last decade.

Given that the CALIOP-derived results utilizes MODIS data for training, biases present in the MODIS data47,48 are likely to be propagated in the ensuing CALIOP outputs and affect the resulting CALIOP bbp, Chla, and Kd predictions, which in turn affects the accuracy of NPP calculations. In addition, in-situ BGC-Argo data were used to validate CALIOP-derived NPP. A total of 125 Argo floats and 5131 observation profiles were deployed in the SSIZ from 2012 to 2023, with a seasonal distribution of 583 profiles in winter (June-August), 579 in spring, 1968 in summer, and 2001 in autumn. Fig. 2a illustrates the spatial distribution of these profiles by season. The Argo NPP rate values were derived by inputting the water parameters observed by the Argo floats into the CbPM model49. To specifically assess our model’s performance during the critical, under-sampled winter period, we compared our CALIOP NPP estimates exclusively against the wintertime Argo profiles (Fig. 2b). Despite the relative sparsity of in-situ winter data, this targeted validation reveals a good correlation providing confidence in our model’s ability to retrieve NPP in the challenging low-light conditions of the polar winter. A comprehensive validation across all seasons, including individual plots for spring, summer, and autumn, is provided in Supplementary Fig. S11 for a complete performance assessment. Although there is some margin of error, the overall agreement validates the use of CALIOP data for monitoring primary production in polar regions, where in situ data are often limited.

Owing to the sparsity of the Argo data, we employ a 5°×4° grid system to organize the Argo data on a monthly basis spatially. We then analyzed the winter NPP trends in the Antarctic using these sparse BGC-Argo data. Fig. 2c shows a map of the spatial distribution of winter observation frequencies within a 5°×4° grid system, revealing stark disparities in data coverage across the Southern Ocean. While up to eight observations were obtained in some grids, vast areas remain poorly sampled, reflecting the logistical challenges of polar data collection. Fig. 2d quantifies this sparsity, showing that fewer than five winter observations were obtained in the majority of grids (5°×4°) in the 2012–2023 study period. Such limited coverage complicates robust temporal or spatial analyses of NPP. Fig. 2e shows the winter NPP calculated in the grid with the highest observation frequency, displaying a positive trend of winter NPP rate. Notably, this trend mirrors CALIOP-LiDAR satellite-derived NPP estimates, validating the consistency between in situ and remote sensing datasets. The alignment underscores the critical role of satellite technologies like CALIOP in bridging observational gaps, particularly in remote polar regions where traditional methods face limitations. Together, these results highlight both the challenges of relying on sparse Argo data and the value of integrating multiplatform approaches to advance understanding of Antarctic primary production dynamics.

Given that our CALIOP retrieval model was trained using MODIS data, it is important to address the potential for data dependency and validate our findings with an independent satellite sensor. To this end, we conducted an independent analysis using data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor for the 2012–2022 period, applying the same CbPM model to ensure a consistent comparison (see Supplementary Fig. S10 for the full seasonal trend comparison). The results show that the winter NPP trend derived from VIIRS (0.4 Tg C yr⁻¹, P<0.05) is highly consistent with the trend observed from MODIS (0.3 Tg C yr⁻¹, P<0.05). While the absolute NPP values from VIIRS are slightly lower than those from MODIS, the remarkable consistency in the trends between these two independent passive sensors confirms the result obtained by this type of instrument across all seasons. Crucially, both of these passive sensor trends are more than five times smaller than the trend derived from CALIOP (2.2 Tg C yr⁻¹, P<0.05), providing strong independent evidence that passive ocean color remote sensing significantly underestimates the recent acceleration of Antarctic winter NPP. This underscores the necessity of active LiDAR measurements for capturing these critical polar dynamics.

Distinct subregional trends and seasonal cycles
Building upon the overall analysis of polar winter NPP trends, subregional analyses were performed to investigate seasonal NPP dynamics across key sectors of the SSIZ.

The Pan-Antarctic SSIZ was divided into five key subregions including the Weddell Sea (WS), south Indian Ocean (SIO), southwestern Pacific Ocean (SPO), Ross Sea (RS), and Bellingshausen–Amundsen Sea (BAS). Specifically, the WS and RS are known for their high nutrient availability and increased light penetration as sea ice retreats, while the SIO and BAS are more constrained by lower nutrient concentrations and light availability. The SPO, with its persistent sea ice cover, presents a unique case for examining the effects of prolonged ice cover on NPP.

The NPP from 2007 to 2022 (Fig. 3a) markedly increases across most subregions, particularly in the WS and RS. The steepest annual increase of 7 Tg C yr⁻¹ is observed in the WS, followed by the RS with an increase of 5 Tg C yr⁻¹. These robust increases align with reduced sea ice concentrations50, which have increased light penetration and thus facilitated greater production in these regions. Moderate increases are observed in the SIO (4 Tg C yr⁻¹) and BAS (2 Tg C yr⁻¹). A smaller increase of 1 Tg C yr⁻¹ is found in the BAS, reflecting persistent environmental constraints, such as lower light availability and nutrient deficiencies, in this region. These trends highlight the significant role of changing sea ice conditions in driving primary production across the SSIZ, with notable regional differences in NPP responses.

On the basis of the more complete wintertime data obtained with CALIOP, the full seasonal cycles, referring to the entire annual cycle of primary production (Fig. 3), clearly reveal differences in the timing and magnitude of production across subregions. Monthly variations contribute to the seasonal cycle, especially production peaks, which complements these differences across subregions (Fig. S6). Notable seasonal cycles are observed in the WS (Fig. 3b) and RS (Fig. 3f), with significant summer production peaks occurring in January. The CALIOP-derived NPP data (solid lines with circles) capture these seasonal dynamics more effectively than the MODIS-derived NPP data (dashed lines with triangles), as they provide continuous measurements during the winter months, including periods of reduced sunlight.

Notably, in the BAS, RS, and SPO (Fig. 3e–g), NPP begins to increase as early as June during austral winter. The data presented in Fig. S6 reflect this early winter increase, particularly in regions where sea ice is thinning or seasonally retreating. This pattern suggests that reduced ice cover may allow more light penetration, supporting phytoplankton growth despite overall low-light conditions. Increased primary production during these winter months contributes to the uptake of carbon in the Southern Ocean, which plays a crucial role in the global carbon cycle. This finding also has important implications for understanding changes in the Southern Ocean’s carbon cycle, which may affect its role in global carbon sequestration. The inability of MODIS to capture this phenomenon further underscores the need for year-round monitoring using active remote sensing methods, such as CALIOP.

The contrasting trends and seasonal cycles emphasize the spatial heterogeneity of Antarctic production. The strong increases in NPP in the WS and RS reflect the critical role of increased light penetration under thinning sea ice, whereas the limited changes in NPP in the SPO highlight the ongoing influence of increased ice concentration and prolonged ice cover as shown in Fig. S3. These results underscore the importance of active remote sensing techniques, such as CALIOP, in capturing production trends during the polar night that are otherwise underestimated by passive remote sensing instruments, such as MODIS.

Shifts in seasonal driver of Antarctic subregional NPP
To better understand the environmental drivers of NPP across Antarctic subregions, we examined key oceanographic parameters that influence primary production. Generally, sea surface temperature (SST) plays a key role in regulating metabolic rates, which refer to the rate at which organisms like phytoplankton grow and respire, and stratification, which is the layering of water masses in the ocean that affects the mixing of nutrients between surface and deeper waters. These factors influence the timing and magnitude of phytoplankton production peaks51–53. Sea surface salinity (SSS) is another important factor that influences production by affecting water column stability and nutrient mixing54. Low SSS can cause stronger stratification, limiting vertical mixing and nutrient transport to the surface, which reduces production. Moreover, sea surface wind (SSW)-induced mixing is a critical physical process that enhances vertical nutrient transport, particularly in regions where upwelling occurs, and this process contributes to the availability of nutrients essential for phytoplankton growth55. A greater mixed layer depth (MLD) enhances the supply of nutrients from deeper waters to the surface, playing a vital role in sustaining production in the Southern Ocean56. Furthermore, sea ice dynamics indicated by sea ice concentration (SIC), which refer to the fraction of the ocean surface covered by sea ice, influence light availability and nutrient mixing57,58. The extent of sea ice significantly affects the timing of phytoplankton blooms, as thinner ice allows more light to penetrate, thus increasing production53. In addition, key nutrients, including nitrate (NO3), phosphate (PO4), dissolved silica (DSi), and dissolved iron (DFe), are essential for phytoplankton growth, and their availability drives production in the Southern Ocean59.

Here we use random forest (RF) models to capture the contributions of these environmental parameters and nutrients to the NPP in the above-outlined Antarctic subregions. Through this model, the seasonal variations in the contributions of the abovementioned environmental parameters to NPP across Antarctic subregions were investigated. We first investigated the contributions of these parameters during austral winter, a period that was previously unexplored due to the lack of winter NPP data in the Antarctic region. In the BAS (Fig. 4a), the key contributors to NPP are NO3 (16%), SST (15%), and PO4 (15%), reflecting the critical role of nutrients and temperature in supporting limited production under thick ice cover, which supports the moderate increase in NPP (2 Tg C yr⁻¹) in the BAS. On the other hand, in the RS (Fig. 4b), SIC (25%) is the dominant driver, along with NO3 (13%) and DFe (13%), suggesting that the significant increase in NPP (5 Tg C yr⁻¹) in the RS is driven by reduced SIC, which enhances light penetration and improves light conditions. Similarly, in the SIO (Fig. 4c), SIC (32%) and SSS (16%) are the primary contributors, with NO3 (10%) also playing a notable role. This underscores that the moderate increase in NPP (4 Tg C yr⁻¹) in the SIO is driven by SIC and SSS, which influence water column stability and nutrient availability. In the WS (Fig. 4d), DFe (33%), NO3 (16%), and PO4 (15%) are the dominant drivers, suggesting that despite the limited light in this region, the supply of essential nutrients is critical for sustaining low but persistent phytoplankton growth in this iron-limited region60. However, a different pattern is observed in the SPO (Fig. 4e), where the SIC (23%), SST (13%), SSS (19%) and MLD (11%) are the leading contributors. This highlights the roles of physical mixing and heat flux in driving productivity during the polar night. SIC and SSS influence the stratification and stability of the water column, while SST and MLD are indicators of the thermal and mixing conditions that affect nutrient availability and light penetration. These factors, together, drive the production despite the low light conditions in winter, which influence the weaker increase in NPP (1 Tg C yr⁻¹) in the SPO. Thus, during the austral winter, physical factors are dominant because of limited sunlight and extensive sea ice cover.

We next investigated the trends in these parameters during austral summer. In the BAS (Fig. 4a), DFe (37%), DSi (19%), and NO3 (13%) dominate, underscoring the importance of nutrient synergy (i.e., the combined availability of multiple nutrients) in supporting high production during this period. Similarly, in the RS (Fig. 4b), DFe (22%), NO3 (14%), and SIC (21%) are the major drivers, reflecting the combined influence of nutrient availability and sea ice-driven nutrient resupply. DFe and NO3 directly contribute to phytoplankton growth by providing essential nutrients, whereas SIC plays a role in the seasonal resupply of nutrients by influencing the movement and melting of sea ice, which brings nutrients from deeper layers of the ocean to the surface, enhancing nutrient availability during critical periods of primary production. In the SIO (Fig. 4c), the leading contributors are DSi (23%), DFe (22%), NO3 (20%), and PO4 (20%), emphasizing the central role of nutrient and trace metal availability. The availability of these key nutrients, particularly DSi, DFe, and NO3, supports phytoplankton growth by providing essential resources for cellular processes. The WS (Fig. 4d) shows a relatively balanced contribution from DFe (21%), NO3 (20%), and PO4 (18%), with DFe being the dominant driver. This suggests that while iron plays a significant role, the similar contributions of nitrate and phosphate indicate that multiple nutrients are limiting primary productivity in the region. In the SPO (Fig. 4e), SIC (38%), DFe (20%), and MLD (13%) emerge as the key drivers, with sea ice coverage and vertical mixing playing critical roles in enhancing nutrient availability during this period of elevated production. Thus, during austral summer, nutrient availability is the predominant controller of NPP across all regions, reflecting heightened biological demand for nutrients during the peak growing season. These nutrients provide the essential resources for phytoplankton growth, and their availability directly influences NPP. The combined effect of DFe, nitrate, and phosphate determines the overall nutrient supply, which is critical for sustaining primary production during the peak growing season.

A clear seasonal contrast emerges from this analysis. In particular, the results of the RF model underscore the distinct differences in the importance of these drivers (environmental parameters) between austral winter and summer, reflecting the interplay of light, nutrient availability, and sea ice dynamics. During winter, physical parameters such as the SIC, SSS, SST, and MLD dominate by regulating light availability and stratification under harsh conditions. When water temperature rises, the rate of photosynthesis may increase, which could enhance the overall NPP in these areas61. Additionally, ice melt or extended melting periods lead to increased daylight hours, directly stimulating phytoplankton growth62. In summer, the biological demand for nutrients such as DFe and NO3, becomes the primary control, reflecting a seasonal shift from physical to biological regulation of production. This seasonal shift can lead to a negative correlation between NPP and nutrient concentrations. As NPP increases rapidly during summer blooms, phytoplankton efficiently utilize available nutrients, potentially leading to a drawdown in dissolved nutrient concentrations13. The temporal trends in NPP and the environmental drivers reveal remarkable increases in production between 2007 and 2022 in regions such as the RS and WS, and these increases are closely linked to reductions in SIC during winter (Fig. S3). As SIC decreases, more sunlight penetrates the water, enhancing light availability for phytoplankton growth. In contrast, more stable NPP is observed in the BAS, suggesting persistent environmental constraints such as increasing sea ice concentration (Fig. S3) despite seasonal shifts in the importance of drivers.

Mechanistic explanations of Antarctic NPP variation
Understanding the mechanisms underlying Antarctic NPP variation is critical for exploring how environmental and climatic factors drive primary production in the Southern Ocean. With the above results in seasonal data analysis, including the incorporation of winter data, a more complete examination of NPP variations over a full seasonal cycle is now possible. By employing the partial correlation and multiple conditional independence (PCMCI+) algorithm, in this study, the causal relationships between Antarctic NPP and key climatic and environmental factors, such as SIC, SST, DFe, and the southern annual mode (SAM), are quantitatively assessed. The SAM describes the pressure difference between the middle and high latitudes of the Southern Hemisphere and is closely related to Antarctic climate dynamics63. PCMCI+ algorithm was chosen due to its ability to effectively capture complex, non-linear causal relationships and interactions between multiple variables64,65. This approach provides a deeper understanding of how local environmental conditions and remote climate systems, including phenomena such as the SAM, El Niño-Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD) interact with Antarctic ecosystems, highlighting the potential impacts of climate change on NPP dynamics. The PCMCI+ analysis reveals how the temporal lags and feedback mechanisms between environmental factors shape subregional variations in NPP and its seasonal cycles. As illustrated in Fig. 5, the causal network, which represents the relationships between different environmental and climatic factors and NPP, visually shows interactions between NPP and various drivers, including climate indices, environmental factors, and nutrients indices. The blue and red lines indicate positive and negative causal influences, respectively, with the PCMCI+ algorithm quantifying the strength of these causal relationships. This network underscores the complexity of Antarctic NPP variability, which is shaped directly by local conditions, remote climate events, and their temporal lags and feedbacks across seasons.

The SAM has emerged as the dominant mode of atmospheric variability influencing Antarctic climate and ecological systems66. Acting over monthly to decadal timescales, the SAM modulates atmospheric circulation, shaping sea ice distributions, temperature gradients, and wind patterns in the Southern Ocean67. Analysis with the PCMCI+ algorithm highlights the pivotal role of the SAM, revealing strong causal links between the SAM and NPP variability across subregions (Fig. 5). Persistent positive SAM phases67, characterized by poleward shifts in westerly winds, typically reduce the extent of sea ice, increase light penetration and promote phytoplankton growth68,69. These effects are particularly pronounced in regions such as the WS (Fig. 5b) and RS (Fig. 5e), where NPP peaks often coincide with the timing of ice retreat. In general, the positive phase of SAM induces stronger westerly winds, which promotes the upwelling of warmer subsurface waters, increases the salinity of surface waters, and weakens stratification. This process triggers deep convection, bringing more subsurface warm, saline water to the surface, which leads to a reduction in sea ice70. This phenomenon aligns with the sharp increase in NPP after 2014. Moreover, the SAM plays a critical role in driving changes in the MLD and mesoscale eddy activity71,72, which in turn affect nutrient distributions and fluctuations in phytoplankton20.

In addition to the SAM, other climatic factors, such as El Niño–Southern Oscillation (ENSO), significantly influence the Antarctic marine environment through teleconnections, which refer to the long-distance connections between climate phenomena in different regions, and lagged effects73, which describe the delayed response of one variable to changes in another. The PCMCI+ algorithm results reveal that tropical climate phenomena, such as the southern oscillation index (SOI) and ENSO, impact NPP indirectly through changes in atmospheric and oceanic circulation (Fig. 5). ENSO events can alter Southern Hemisphere wind patterns, affecting sea ice dynamics73 in Antarctic waters. ENSO generally influences the Antarctic climate and sea ice distribution in two ways: (1) ENSO events, especially El Niño, enhance convective activity in the tropical Pacific, triggering Rossby waves in the atmosphere. These waves propagate toward the poles and ultimately affect atmospheric circulation in the Antarctic region; (2) ENSO events also excite the Pacific-South America (PSA) teleconnection pattern, an anomalous atmospheric circulation mode that causes pressure anomalies in the South Pacific and Antarctic regions74,75. These lagged relationships, revealed by PCMCI+, suggest that tropical climate anomalies can influence Antarctic production several months later.

As global climate warming progresses, its influence on the Antarctic ecosystem is becoming increasingly evident. The ongoing reduction in sea ice extent, as seen in recent decades, has amplified the role of winter NPP in contributing to overall carbon sequestration. Fig. 5g illustrates the mechanisms underlying the trends in Antarctic NPP in response to climate change. During the winter months, reductions in SIC lead to increased photosynthetically active radiation (PAR) that refer to the portion of sunlight that plants use for photosynthesis, which in turn enhances Chla and subsequently increases NPP. In summer, a greater MLD provides greater access to nutrients, fostering Chla growth and increasing NPP76,77. Over long-term scales, which in this study refer to periods greater than six months, changes such as increases in Niño 3.4 and SST in the SIO further increase NPP (Fig. 5c). These findings collectively highlight the complex interplay between local environmental conditions and remote climate systems in regulating Antarctic NPP, providing a crucial foundation for understanding future changes in primary productivity under ongoing climate change.

Ecological implications
The observed considerable increase in Antarctic winter NPP fundamentally alters our understanding of the role of the Southern Ocean in global carbon cycling and ecosystem dynamics. Traditionally, Antarctic winter has been considered a period of biological dormancy due to extreme light limitations and extensive sea ice cover29. However, emerging evidence highlights winter NPP as a critical contributor to carbon sequestration, ocean‒atmosphere interactions, and biogeochemical processes, thereby redefining the Southern Ocean’s role as a year-round carbon sink78. This finding aligns with recent studies, which demonstrate that photosynthesis can occur at extreme low light levels28. Enhanced winter NPP drives substantial production of particulate organic carbon (POC), which is exported below the euphotic zone via a biological pump, enabling long-term storage of atmospheric CO2 in the deep ocean79. This process bolsters the Southern Ocean’s efficiency in offsetting anthropogenic CO2 emissions, transforming it into a more effective buffer against global climate change80. The concomitant reduction in the surface water partial pressure of CO2 (pCO2) strengthens the air‒sea CO2 gradient, especially in ice‒edge regions where diminishing sea ice promotes greater gas exchange81. These findings underscore the previously underappreciated importance of winter processes in shaping global carbon fluxes, highlighting the crucial role of Antarctic winter NPP in carbon sequestration and its potential implications for future climate modeling and ecosystem dynamics.

The ecological ramifications of increased winter NPP observed in this study extend beyond carbon cycling, with the potential to reshape the phytoplankton community structure and ecosystem impacts on higher trophic levels. A sustained increase in winter biomass has the potential to reshape the phytoplankton community structure that overwinters and initiates the subsequent spring bloom. A shift in the dominant cell size or species could alter trophic transfer efficiency and hence higher trophic production and the community composition of zooplankton and other predators82,83. Increased winter production may favor diatom propagation. While these cells contribute disproportionately to POC export and biological pump efficiency84,85, they are also a critical, high-quality food source for grazers like Antarctic krill. Therefore, any change in the abundance or timing of these diatom populations could have cascading impacts on the food web, affecting the krill that are a vital link to higher trophic levels such as penguins, seals, and whales86. These changes in primary production could enhance the ocean’s ability to sequester carbon by promoting the export of particulate organic carbon to deeper waters. Conversely, if increased winter NPP leads to greater activity of coccolithophores, which are producers of particulate inorganic carbon (PIC), this could reduce the oceanic sink for atmospheric CO2 in the short-term by increasing near-surface pCO2, with cascading effects on nutrient cycling and ecosystem functioning87–90. The role of sea ice dynamics is pivotal: reduced ice cover increases light penetration91 and nutrient availability, whereas episodic mixing events resupply macronutrients and trace metals, such as nitrate and dissolved iron, respectively, to surface waters92,93. Our results indicate that the interplay among physical and biological processes sustains winter production even under low-light conditions, challenging long-held assumptions about polar ecosystem dormancy during the austral winter. These dynamics also amplify ocean‒atmosphere feedbacks, modifying local heat fluxes, wind-driven mixing, and nutrient transport, with far-reaching consequences for regional climate regulation and energy balance19.

Our results show that Antarctic winter NPP has rapidly increased over the past decade, with an annual growth rate of 2.24 Tg C/year in the past decade, making it a significant component of the Southern Ocean’s carbon storage. Given this increasing trend, it is essential to include winter NPP in global carbon models, as its contributions have historically been underestimated or omitted. While global ocean NPP trends show overall decreases35,36, Antarctic winter NPP shows a particularly strong increasing trend, highlighting its increasing role in the Southern Ocean’s carbon dynamics. Its potential impact on POC export and surface pCO2 dynamics positions it as a critical driver of global carbon fluxes, necessitating its integration into biogeochemical and Earth system models. These shifts are particularly relevant given the Southern Ocean’s disproportionate contribution to global carbon uptake1–3 and its sensitivity to anthropogenic climate forcing. The continued increase in Antarctic winter NPP is a key trend that could offset or mitigate declines in NPP observed in other oceanic regions35. Furthermore, this growing recognition of Antarctic winter NPP underscores a revelation of previously unrecognized potential for carbon fixation, suggesting that this previously overlooked component could significantly influence global carbon dynamics28.

The drivers of this unexpected increase in winter production reflect the interplay of regional and global climatic factors. The SAM is shown as a primary regulator, influencing the SIC, mixed layer dynamics, and light availability. The positive phases of the SAM, characterized by poleward-shifted westerlies, reduce SIC and increased light penetration, creating favorable conditions for enhanced NPP in high-latitude regions68,69,71,72. Our results provide evidence that these processes are driving significant NPP increases specifically in the WS and RS, highlighting the regional variability in the response of primary production to SAM fluctuations. Concurrently, teleconnections with tropical climate phenomena, such as ENSO, introduce additional complexity. For example, ENSO-induced atmospheric anomalies influence zonal sea ice drift, nutrient resupply and stratification in the Southern Ocean94,95, with lagged effects on production96. These findings emphasize the interconnectedness of global climate systems and the need for a holistic understanding of how remote drivers interact with local processes to shape Antarctic ecosystems.

As the Antarctic ecosystem has undergone rapid transformation due to climate warming and sea ice loss97, understanding the mechanisms underlying winter NPP is essential for predicting future changes in the global carbon cycle. The interplay of increased light availability, nutrient dynamics, and community shifts highlights the dynamic and evolving nature of Antarctic production98. By utilizing CALIOP for obtaining NPP data in winter and integrating full seasonal cycle analyses into existing frameworks, this study provides a paradigm for understanding Southern Ocean ecosystems under changing climatic conditions. Future research should prioritize extreme climatic events, such as marine heatwaves and anomalous wind patterns, which could exacerbate or mitigate the observed trends. This knowledge is critical not only for refining predictions of Antarctic ecosystem resilience but also for assessing the Southern Ocean’s role in regulating the global climate and supporting biodiversity in an era of unprecedented environmental change.

Methods
Data acquisition
Multiple data sources, including MODIS, CALIOP, and BGC-Argo datasets, are used to investigate Antarctic NPP dynamics. The MODIS-Aqua Level 3 9-km daily products including bbp, Chla, and Kd available at https://oceancolor.gsfc.nasa.gov/, serve as key predictors. MODIS data were strategically chosen to train our TBTS model because both MODIS (on the Aqua satellite) and CALIOP (on the CALIPSO satellite) are part of the A-Train constellation, with observation times separated by only ~90 seconds. This near-simultaneous data acquisition is crucial for machine learning as it minimizes the temporal mismatch between the LiDAR signal and the ocean color properties, thereby reducing a significant source of uncertainty in model training. CALIOP Level-1B V4.199 and Level-2 Merged Layer V4.20 products are used to acquire LiDAR-derived oceanographic parameters from 16,236 tracks spanning from 2006 to 2023. These data include backscatter measurements and the depolarization ratio, which are used to calculate Kd, Chla, and bbp, which are related to the distribution of phytoplankton in the ocean and the photosynthesis in the NPP model. The biogeochemical Argo (BGC-Argo) dataset100–102, which provides in situ measurements, is used for model validation. This dataset includes direct measurements of Chla and other biogeochemical parameters accessible via the Argo Global Data Assembly Center (GDACs) at Ifremer (ftp://ftp.ifremer.fr/ifremer/argo/).

The data sources for the evaluated parameters are as follows.
The Global Ocean OSTIA Sea Surface Temperature and Sea Ice Reprocessed product (SST-GLO-SST-L4-REP-OBSERVATIONS-010-011)103 provides SST Level-4 data. The Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density product (MULTIOBS_GLO_PHY_S_SURFACE_MYNRT_015_013)104–106 provides SSS Level-4 data. The Global Ocean Monthly Mean Sea Surface Wind and Stress from Scatterometer and Model product (WIND_GLO_PHY_CLIMATE_L4_MY_012_003) provides SSW Level-4 data. The Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD product (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012)107,108 provides MLD Level-4 data. Data on sea ice extent and concentration are accessed from the Sea Ice Index extent data obtained from the National Snow and Ice Data Center (NSIDC)109. The Global Ocean Biogeochemistry Hindcast product from the Copernicus Marine Environment Monitoring Service (CMEMS) provides Level-4 data for key nutrients, including nitrate (NO3), phosphate (PO4), dissolved silica (DSi), and iron (DFe) (GLOBAL_MULTIYEAR_BGC_001_029). Data on the SAM are available from Marshall’s SAM Index page (https://legacy.bas.ac.uk/met/gjma/sam.html). Data on other climate indices in anomaly format for the period of 1978–2022 are available from the Global Climate Observing System website (https://psl.noaa.gov/data/timeseries/month/). These indices include Niño 3.4, which tracks SST anomalies in the equatorial Pacific region (5°S to 5°N, 170°W to 120°W)110; the SOI, which represents the normalized pressure difference between Tahiti and Darwin111; the Pacific Decadal Oscillation (PDO), which describes a long-term El Niño-like climate variability pattern in the Pacific at 20°N112; and the dipole mode index (DMI), which is defined by the anomalous SST gradient between the western equatorial Indian Ocean (50°E to 70°E, 10°S to 10°N) and the southeastern equatorial Indian Ocean (90°E to 110°E, 10°S to 0°N)113.

CALIOP data preprocessing
For CALIOP data, the 532 nm echo signal is split into vertical and parallel components using a polarization beam splitter (PBS), and these components are detected by photomultiplier tubes (PMTs)114. However, the transient response of the detector causes the recorded signal intensity to exceed the actual backscatter intensity after strong signals from sources such as the Earth’s surface or dense clouds are detected. Initially, the signal decreases as expected, but it eventually decays at a slower rate resembling an exponential function115. To correct for this, a deconvolution process is applied to the measured signals116:

β0(z) = [F]-1β(z) (1)

where β0(z) is the corrected backscatter signal, β(z) is the measured signal, and [F]-1 is the matrix form of the transient function.
Owing to the imperfect PBS, part of the parallel polarization component leaks into the vertical polarization component, which is a phenomenon known as polarization crosstalk. To eliminate this effect, the following corrections are made117:

βk,c = 1 βk,m - CT β?,m (2)

β?,c = β?,m - CT × βk,c (3)

where βk,c and β?,c are the corrected parallel and vertical signals, respectively. βk,m and β?,m are the parallel and vertical signals corrected for the transient response. CT represents the crosstalk.
Next, the total column-integrated depolarization ratio δT which includes both surface backscatter and subsurface backscatter, is computed as follows:

δT = Pi=p+2 β?,c / Pi=p+2 βk,c (4)

where p represents the range bin of the peak surface return118.
Finally, to calibrate the ocean surface backscatter, the subsurface cross-polarized component of the column-integrated backscatter βW is calculated as follows44:

βW = δT / (1 - 10δT) βS (5)

where βS denotes the LiDAR surface backscatter, which is estimated using colocated sea surface wind speed data119.
To eliminate the impact of sea ice on CALIOP observations in polar regions, we selectively included data where δT less than 0.05, effectively excluding areas with full or partial sea ice coverage, given that sea ice tends to have a high depolarization ratio44. We further ensured data integrity by retaining only those measurements with integrated attenuated backscatter values less than 0.017 sr⁻¹, which indicates clear atmospheric conditions118. Additionally, to reduce interference from ocean surface phenomena such as foam and whitecaps, which are prevalent at high wind speeds, and to prevent signal saturation at low wind speeds, we included only data captured at wind speeds between 2 m/s and 9 m/s44,116.

Antarctic phytoplankton NPP derived from CALIOP
A two-branch-two-step (TBTS) model is used to derive Chla, bbp, Kd from the CALIOP data (Fig. S7). The deep learning model developed in this study consists of two branches: a waveform branch and a physical parameter branch. This structure leverages both deep learning techniques and physical parameter inputs120. Unlike traditional deep learning methods that rely solely on waveform data, this model incorporates data on additional physical parameters, i.e., δT and βW, allowing for more seamless and effective integration of physical and observational data. By incorporating physical parameters as a second branch, the model can better account for the underlying physical processes, thereby improving its performance in oceanic LiDAR applications40.

The waveform deep learning branch takes as input the corrected parallel and vertical backscatter signals within a range of 5 bins above and below the sea surface40. Specifically, the input includes 22 variables derived from the CALIOP measurements. In the model, a convolutional neural network (CNN)121 is used to automatically extract features from these corrected signals. After processing through the CNN layers, the data are flattened into 128 features via fully connected (FC) layers. The second branch of the model addresses physical parameters, which form an integral part of the input for the network. The physical parameters include δT and βW. These physical parameters are processed through FC layers for feature extraction. The features from both branches are then combined through a concatenation layer and passed through another FC layer before performing regression. The model uses Rectified Linear Units (ReLU) activation functions throughout, except for the final layer with linear activation functions to retrieve the values of bbp, Chla, and Kd. The model is trained using the RMSprop optimization algorithm, which divides the gradient by the average of its recent magnitudes122.

To increase the accuracy of the model, a two-step approach is applied to reconstruct bbp, Chla, and Kd based on the MODIS data. The first step involves the reconstruction of climatologies, which refer to the monthly climatological mean values averaged over the period 2006–2023. The second step focuses on reconstructing anomalies relative to the climatologies. This two-step approach is particularly advantageous because it mitigates issues related to sparse observational coverage and accounts for seasonal variations driven by climate change123. The two steps are as follows.

Step 1: Reconstruction of climatologies: In this step, the two-branch model reconstructs the normalized monthly climatologies for bbp, Chla, and Kd as a nonlinear function of the normalized CALIOP waveform data (βn), δT,n, and βW,n:

(bbp,n, Chlan, Kd,n) = TBN(βn, δT,n, βW,n) (6)

Step 2: Reconstruction of anomalies: In the second step, the model reconstructs anomalies in bbp, Chla, and Kd as a nonlinear function of the normalized waveform data and their respective anomalies:

(bbp,anom, Chlaanom, Kd,anom) = TBN(β, δT, βW, βanom, δT,anom, βW,anom) (7)

The anomalies are calculated as the difference between colocated MODIS bbp, Chla, and Kd measurements and the climatologies reconstructed in Step 1.

To increase model accuracy, all datasets are normalized to zero mean and a unit standard deviation before training. This normalization ensures that the ranges of all the predictors are comparable and prevents predictors with greater variability from dominating the model124. During the training, MODIS bbp, Chla, and Kd data and CALIOP measurements from 2006 to 2023 are used. The data are averaged along 9 km segments to align with the MODIS observations. The training dataset includes approximately 106 match points per month, with 105 points in high-latitude regions, ensuring robust model performance in polar areas. The data are randomly split, with 70% used for training, 15% for validation, and 15% for evaluation. This division ensures that the data for evaluation remain separate from the training process, thereby providing an unbiased assessment of the model’s performance.

The accuracy of the model is then validated by comparing its results with in-situ BGC-Argo data from 2006 to 2023. The data are matched within a 25 km2 area and 24-hour window studies125,126. This ensures proper temporal and spatial alignment, providing a robust evaluation of the model’s performance. As shown in Fig. S8, the model demonstrates strong performance in predicting oceanographic parameters, as evidenced by the high correlation coefficients (R values) and low root mean square deviations (RMSDs) across all the validation comparisons. For the CALIOP and MODIS data, the model achieves R values ranging from 0.85 to 0.95, indicating a robust linear relationship between the satellite-derived and observed values. The low RMSD values further highlight the model’s accuracy, with only minimal deviation from the true measurements. Additionally, the mean absolute percentage deviation (MAPD) is relatively low, underscoring the model’s precision and reliability in predicting bbp, Chla, and Kd. Overall, the model’s strong predictive capabilities, as demonstrated through the validation results, suggest its potential for accurate oceanographic monitoring and analysis.

After validation, the trained model is used to reconstruct bbp, Chla, and Kd from 2006 to 2023, specifically focusing on polar regions, where traditional passive sensors like MODIS often fail to provide data. The carbon-based productivity model (CbPM)127,128, a method that estimates net primary productivity from phytoplankton carbon biomass and growth rates, is subsequently used to derive net primary productivity (NPP rate) in polar regions. Notably, the CbPM accounts for photoacclimation by calculating a dynamic chlorophyll-to-carbon ratio that varies with light and temperature, a critical feature for accurately assessing productivity in variable polar environments.

The NPP rate is derived based on CALIOP-derived parameters:

NPP rate (mg C m-2 d-1) = carbon × growth rate × volume function (8)

where carbon is the phytoplankton carbon biomass that is estimated from bbp128, growth rate is derived from chlorophyll-to-carbon128, and volume function is expressed as a function of depth-dependent photoacclimation and vertical variations in nutrient limitations127. To calculate the net primary production (NPP) for a specific region and period (such as for one winter or year), we accumulate NPP * cell area * number of days, at which point the unit becomes mg C or Tg C.

Figure S9 shows the comparison of monthly phytoplankton NPP records derived from CALIOP and MODIS for the southern polar zones. Panels (a) to (e) depict NPP values for different latitudinal bands (40°S–55°S, 55°S–65°S, 65°S–75°S, 75°S–81.5°S, and 81.5°S–45°S). The data demonstrates that CALIOP (blue lines) and MODIS (orange lines) show strong agreement during austral summer, suggesting high consistency between the two datasets. Besides, CALIOP effectively compensates for missing MODIS data during the austral winter, providing a more continuous and comprehensive NPP record across the entire year.

It is important to acknowledge that by training our model with MODIS data, its inherent biases are propagated into our results. To formally assess this, we identified three primary sources of error contributing to the total uncertainty (E) of our CALIOP-derived products: measurement (M), representation (R), and prediction (P) errors129. The total uncertainty can be expressed as the root of the sum of squares of these components: E = sqrt(M2+R2+P2). The measurement error (M) includes the combined uncertainties from the CALIOP input variables and the MODIS referenced data used for model training. The representation error (R) arises from spatio-temporal mismatches between observations and the model grid. Following established methods, this error is assumed to be normally distributed with a bias of 0 on a large scale and is not included in the final error budget. The prediction error (P) is the inherent error of our TBTS model, as determined from the validation dataset. Combining these components, the total estimated uncertainty (E) for our CALIOP-derived products (Chla, bbp and Kd) is approximately 30~35%43,130. These uncertainties propagate to the final NPP estimates. As the CbPM is a multiplicative model, the total uncertainty in our NPP estimates (ENPP) can be approximated by the root sum of squares of the uncertainties from our key inputs: ENPP = sqrt(E2Chla + E2bbp + E2Kd). This results in an uncertainty for the final NPP estimate of approximately 55%. It is critical to note that this substantial uncertainty primarily affects the absolute magnitude of our NPP estimates. The central conclusion of our study, however, is the accelerating trend in winter NPP. The calculation of a linear trend is statistically robust against such random errors in individual data points. Our confidence in the positive and significant trend is further bolstered by the consistent increasing trends observed across four different NPP models (Supplementary Fig. S5) and our independent validation using VIIRS data (Supplementary Fig. S10).

Statistical analysis of potential factors and quantitative causality analysis
For the statistical analysis of potential factors influencing Antarctic NPP, we employed a random forest (RF) regression model to quantify the relative contributions of different environmental factors, a method successfully applied for analyzing drivers in environmental science131. This method allows for the assessment of nonlinear relationships between predictor variables and NPP, providing a robust framework for understanding how various factors such as temperature, salinity, and nutrient availability, influence primary production in the Southern Ocean. To provide a robust and unbiased assessment of feature importance, we used the permutation-based method132. This method directly measures a feature’s impact on the model’s performance on an unseen validation dataset, avoiding the potential biases of standard impurity-based metrics. As originally described by ref. 133, the importance of a single feature is calculated as the average decrease in the model’s performance score when the values of that feature are randomly shuffled. This process was repeated multiple times for each feature to ensure statistical stability. The resulting importance values are normalized to ensure that they sum to 100%.

To further explore the causal relationships between Antarctic NPP and various environmental factors, we utilized the PCMCI+ algorithm, which is a powerful statistical method for detecting causal interactions in multivariate time series data64,65. PCMCI+ is a time series causal discovery approach that efficiently identifies causal links by testing for conditional independence between variables134. In the method, first, a condition-selection step (PC) is applied to iteratively test for conditional independence and identify the relevant conditions necessary for establishing causal relationships. This step helps reduce the complexity of the model, especially when handling many variables. In the second step, momentary conditional independence (MCI) tests are performed using a smaller set of selected conditions, helping to address issues such as strong autocorrelation in time series data. Partial correlation (ParCorr) calculations are used for conditional independence testing in this analysis, focusing on linear relationships between variables. The maximum time delay (τmax = 12) is chosen to reflect teleconnection. The pruning hyperparameter (pc-α) is optimized using the Akaike information criterion (AIC). PCMCI+ produces p values for causal links between variables at different time lags, and in this study, links were defined at a strict significance level of 0.05. This approach offers a rigorous way to understand the complex interactions in Antarctic ecosystems and provides insights into how changes in environmental factors influence NPP through direct or mediated pathways. By using PCMCI+, we can identify key drivers of NPP variations that are not merely correlated but have a causal influence, thereby helping to improve our understanding of the underlying mechanisms that regulate primary production in the Southern Ocean.

Data availability
All third-party data used in this study are publicly available from their respective archives. Specifically, MODIS-Aqua Level 3 data were obtained from the NASA Ocean Color Web (https://oceandata.sci.gsfc.nasa.gov/directdataaccess/Level-3%20Mapped/Aqua-MODIS/ with an alternative access point at https://oceandata.sci.gsfc.nasa.gov/l3/). CALIOP Level-1B and Level-2 products were sourced from the NASA Atmospheric Science Data Center (ASDC) at https://asdc.larc.nasa.gov/data/CALIPSO/. In-situ biogeochemical data from BGC-Argo floats are available from the Argo Global Data Assembly Center (ftp://ftp.ifremer.fr/ifremer/argo/). Data for sea surface temperature (https://doi.org/10.48670/moi-00168), sea surface salinity (https://doi.org/10.48670/moi-00181), sea surface wind (https://doi.org/10.48670/moi-00051), mixed layer depth (https://doi.org/10.48670/moi-00052), and nutrients (https://doi.org/10.48670/moi-00019) were obtained from the Copernicus Marine Environment Monitoring Service (CMEMS). Sea ice index data (Version 3) were sourced from the National Snow and Ice Data Center (NSIDC) at https://doi.org/10.7265/N5K072F8. The Southern Annular Mode (SAM) index was obtained from the British Antarctic Survey (https://legacy.bas.ac.uk/met/gjma/sam.html), and other climate indices were from the NOAA Physical Sciences Laboratory (https://psl.noaa.gov/data/timeseries/month/). The processed data generated in this study, including the source data underlying the figures, have been deposited in the Code Ocean database under the identifier https://doi.org/10.24433/CO.9252735.v1.

Code availability
The code used for all data processing, analysis, and figure generation in this study is publicly available and fully executable within a Code Ocean capsule, which has been deposited in the Code Ocean repository under accession code 10.24433/CO.9252735.v1 [https://doi.org/10.24433/CO.9252735.v1].

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Chen et al. use +15 years of CALIOP LiDAR to overcome the significant lack of winter data that impacts assessments of primary production in the Southern Ocean, which are mainly dependent on passive ocean colour sensors. The authors use a two-branch two step (TBTS) deep-learning model that converts LiDAR backscatter into Chl-a, backscatter and Kd, which subsequently feeds an NPP model.\nUsing CALIOP LiDAR, the authors managed to substantially increase data coverage during the winter and showed that not only MODIS underestimates basin-wide NPP but also there is an increasing trend in NPP since 2012. They attribute this trend to declining sea-ice concentration and persistent positive SAM.\nI really enjoyed reading this study as it targets a very important knowledge gap. The methods are also generally sound.\nHowever, I still have some doubts that I think are important to clarify prior to acceptance.\nMajor comments:\n1) Everything is a little too dependent on MODIS. For instance, the TBTS model is based on MODIS data. Then MODIS is the only satellite-based source used to compare the findings from the model, including the trends. Since the TBTS model was trained with millions of data from MODIS climatologies, it is likely that there is a degree of dependence that would be better to clarify. For instance, comparing the trend from 2012 onwards with an NPP model applied to VIIRS data, which was launched in 2012.\n2) Additionally, while the authors acknowledge that training the model with MODIS data propagates the bias present in MODIS, they do not quantify or discuss these biases’ potential impact on winter NPP (where MODIS data is especially limited).\n3) Apart from validating the CALIOPS-derived results with MODIS (dependent), the authors also use Argo floats data. However, most grid cells contain less than 5 winter Argo profiles during 2012-2023, if I am correct (or is it the number of winters which there is data available?). Nevertheless, this a very low observation number and even in Figure 2e, the authors only show the trend for a single grid cell where the frequency was higher. Furthermore, the authors mention in lines 201-203 that +5000 ARGO profiles but what was the number of winter profiles used? Is the scatter plot in Figure 2b not including all correspondence between CALIOPS and ARGO profiles, including summer? If so, the ideal validation should use only winter ARGO profiles, even if few.\n4) The carbon-based model (CbPM) used to calculate NPP assumes constant chlorophyll-to-carbon ratios. First, these, which may not be true for polar regions, especially for winters, when the very low PAR can significantly affect the C:Chl-a ration. Clarify or ensure that.\n5) How were the Random Forest importances calculated? The authors do not mention the methods, e.g. it was calculated using SHAP importances, permutation-based or drop-column importances. Note that often using the default Random Forest importances (without permutations) can often lead to biases in the results.\nMinor comments:\nOne of the authors’ key conclusions is that SAM is a key factor in rising NPP in recent years due to its effect on reducing sea ice and promoting phytoplankton growth. This has already been shown by other papers for Antarctica(e.g. Ferreira et al. 2024 using satellite chlorophyll-a for the Antarctic Peninsula, included in the WS subregion), yet the introduction does not cover these findings. Given the importance of SAM for the observed trends in NPP, I think that this link between SAM and PP should be discussed in the introductory section of the main text.\nFigure 1: The permanent ice zone should not be coloured in dark blue since that colour is part of the colorbar. I suggest changing to gray.\nLine 405: double ..\nFigure 4: some of the labels are too small and thus not legible. Also, I would suggest opting for a different colour scheme, one that might be easier to distinguish the variables (or using both colours and hatches). Also need more info on the random forests.\nFigure 5: the initial panels (a-f) are not legible. Consider increasing the font size.\nLines 672: two-branch-two-step\nLine 1206-onwards: references with duplicated DOI link.", "reviewer_2": "Review of manuscript NCOMMS-25-35467-T, “Underestimated Accelerated Antarctic Phytoplankton NPP in Winter Over the Past Decade Revealed by Spaceborne LiDAR” by Peng Chen et al.\nGeneral Comments\nThis well written manuscript presents a thorough study of spatio-temporal patterns of Net Primary Production (NPP) in the Antarctic ocean based on spaceborne LiDAR. The emphasis is on identifying previously unknown temporal trends, spatial patterns, and underlying mechanisms of wintertime NPP, which is made possible for the first time using LiDAR. The analysis of causality using the partial correlation and multiple conditional independence (PCMCI+) algorithm is also interesting and informative.\nMost of the results are based on Carbon-based Productivity Model (CbPM), which is one prominent model for estimating NPP based on remote sensing data. These results are bolstered by a comparison of the results from multiple production models, presented in the Supplementary, which confirms that the results do not depend crtically on the choice of the CbPM model for estimating NPP from remote sensing observations.\nOverall, I find the manuscript well written, the analyses sound, and the conclusions both well supported and likely to be of interest to many readers. The authors are correct in noting that their findings will provide much needed observation-based data for improving models of the annual cycle of phytoplankton growth and NPP in the Antarctic. These results may further lead to future studies applying space-based LiDAR to the sub-arctic and arctic oceans, which would be beneficial.\nBelow please find a few specific comments and suggestions, which are intended to improve the manuscript by enhancing clarity and adding a bit more detail in the Discussion.\nSpecific Comments:\nLines 23-24 (Abstract, first sentence): how about revising to something like : “The Southern Ocean’s vital carbon sink is driven by phytoplankton Net Primary Production (NPP).”\nLines 42-43: how about revising to: “NPP refers to the conversion of carbon dioxide (CO₂) into organic carbon through photosynthesis, …”\nLines 63-64: Although it seems most likely that warming has played an important role as a cause of observed changes, other concomitant changes (e.g., atmospheric deposition of nutrients from anthropogenic sources, as well as riverine inputs, and ocean acidification) have also contributed to large-scale changes. For example, see\nDuce, Robert A., et al. \"Impacts of atmospheric anthropogenic nitrogen on the open ocean.\" science 320.5878 (2008): 893-897.\nTherefore, I suggest re-wording to something like: “where the timing and intensity of phytoplankton blooms has shifted as the ocean has been subjected to warming, anthropogenic nutrient inputs, and acidification.”\nLine 70: add a comma after “sunlight”\nLine 87: “because of this” -> “because this”\nLine 105: add “the” before “temporally\nLine 378: “influencing” -> “limiting”\nFigure 4. Maybe add small vertical labels (maybly only on the left-most side?) for the summer and winter plots, respectively? That could make it easier to read the figure at a glance.\nLines 448-450: Delete this sentence, which repeats the previous one.\nLine 507 onwards, Ecological Implications”:\nThis section only touches on implications for phytoplankton community composition, with emphasis on their expected biogeochemical impacts. That is fine, but more could be said.\nHow about also mentioning that shifts in the phytoplankton community composition could alter trophic transfer efficiency and hence higher trophic production and the community composition of zooplankton and other predators?\nLines 533-536: “alter” does not specify the direction of the change (precise impact) expected.\nHow about specifying the expected consequence of increasing calcification by coccolithophorids, which would (in the short term) be a reduction in the oceanic sink (uptake) of atmospheric CO2, as a result of an increasing near surface pCO2."}, "author_response": "Response to Reviewers\nWe sincerely thank all reviewers for their constructive and insightful comments, which have\ngreatly helped us improve the quality and clarity of our manuscript. We have carefully\nrevised the manuscript according to the suggestions.\nReviewer 1\nChen et al. use +15 years of CALIOP LiDAR to overcome the significant lack of winter\ndata that impacts assessments of primary production in the Southern Ocean, which are\nmainly dependent on passive ocean colour sensors. The authors use a two-branch two\nstep (TBTS) deep-learning model that converts LiDAR backscatter into Chl-a,\nbackscatter and Kd, which subsequently feeds an NPP model.\nUsing CALIOP LiDAR, the authors managed to substantially increase data coverage\nduring the winter and showed that not only MODIS underestimates basin-wide NPP but\nalso there is an increasing trend in NPP since 2012. They attribute this trend to declining\nsea-ice concentration and persistent positive SAM.\nI really enjoyed reading this study as it targets a very important knowledge gap. The\nmethods are also generally sound. However, I still have some doubts that I think are\nimportant to clarify prior to acceptance.\nResponse: Thank you for your positive feedback and for enjoying our manuscript. We\nappreciate your recognition that our study targets a very important knowledge gap by using\n+15 years of CALIOP LiDAR data to overcome the lack of winter data in the Southern\nOcean. We are also grateful for your assessment that our methods are generally sound.\nWe have taken the doubts you raised seriously, as they are important to clarify prior to\nacceptance. Below, we address each of your concerns with detailed explanations and the\ncorresponding changes made in the manuscript.\nMajor comments:\n1) Everything is a little too dependent on MODIS. For instance, the TBTS model is\nbased on MODIS data. Then MODIS is the only satellite-based source used to compare\nthe findings from the model, including the trends. Since the TBTS model was trained\nwith millions of data from MODIS climatologies, it is likely that there is a degree of\ndependence that would be better to clarify. For instance, comparing the trend from 2012\nonwards with an NPP model applied to VIIRS data, which was launched in 2012.\nResponse: We sincerely thank the reviewer for this critical and highly constructive\ncomment. We agree that the dependence on MODIS for both training our model and for\ntrend comparison is a valid concern that requires thorough clarification and independent\nvalidation.\nFirst, we would like to briefly clarify our rationale for initially using MODIS. We strategically\nchose MODIS to train our TBTS model because both MODIS (on the Aqua satellite) and\nCALIOP (on the CALIPSO satellite) are part of the A-Train constellation, with observation\ntimes separated by only about 90 seconds. This near-simultaneous data acquisition is\ncrucial for machine learning as it minimizes the temporal mismatch between the LiDAR\nsignal and the ocean color properties, thereby reducing a significant source of uncertainty\nin model training. This has been pointed out in the method as follows:\n“Multiple data sources, including MODIS, CALIOP, and BGC-Argo datasets, are used to\ninvestigate Antarctic NPP dynamics. The MODIS-Aqua Level 3 9-km daily products\nincluding 𝑏 , 𝐶ℎ𝑙𝑎, and 𝐾 available at https://oceancolor.gsfc.nasa.gov/, serve as key\n𝑏𝑝 𝑑\npredictors. MODIS data were strategically chosen to train our TBTS model because both\nMODIS (on the Aqua satellite) and CALIOP (on the CALIPSO satellite) are part of the A-\nTrain constellation, with observation times separated by only about 90 seconds. This near-\nsimultaneous data acquisition is crucial for machine learning as it minimizes the temporal\nmismatch between the LiDAR signal and the ocean color properties, thereby reducing a\nsignificant source of uncertainty in model training. CALIOP Level-1B V4.199 and Level-2\nMerged Layer V4.20 products are used to acquire LiDAR-derived oceanographic\nparameters from 16,236 tracks spanning from 2006 to 2023.”\nTo address the core of your concern about data dependency, we have followed your\nexcellent suggestion and conducted a new, independent analysis using data from the\nVIIRS sensor for the 2012–2022 period. We applied the identical CbPM model to VIIRS\ndata to ensure a fair comparison. The results, which we have added as a new figure in the\nSupplementary Information, are revealing. Quantitatively, we note that the absolute NPP\nvalues derived from VIIRS are slightly lower than those from MODIS. However, the trends\nderived from these two independent passive sensors show remarkable consistency across\nall analyzed seasons (annual, winter, spring, summer, and autumn). Specifically for the\nwinter season, the trend from VIIRS is 0.4 TgC yr⁻¹ (P < 0.05), which is nearly identical to\nthe trend from MODIS at 0.3 TgC yr⁻¹ (P < 0.05). This consistency confirms that the weak\ntrend is a robust feature of passive ocean color measurements in this challenging region,\nnot an artifact of a single sensor. Crucially, both of these passive sensor trends are more\nthan five times smaller than the trend we derived from the active CALIOP LiDAR sensor\n(2.2 TgC yr⁻¹, P < 0.05). This new analysis provides powerful, independent evidence that\nthe underestimation of the winter NPP trend is a systemic issue for passive ocean color\nsensors, not an artifact of a single instrument, thereby highlighting the unique and essential\ncapability of active LiDAR for polar research. This new analysis serves two vital purposes:\n1) It directly resolves the concern about over-reliance on MODIS by demonstrating\nconsistency with another state-of-the-art passive sensor.\n2) More importantly, it provides powerful, independent evidence for our manuscript's\ncentral conclusion: that passive sensors as a class, regardless of the specific\ninstrument, substantially underestimate the recent, rapid acceleration of Antarctic\nwinter NPP. This reinforces our argument that active remote sensing from LiDAR is\nessential for capturing these critical polar dynamics.\nFinally, we wish to frankly acknowledge the inherent limitations of this approach. As the\nreviewer rightly pointed out, by training our model on MODIS data, we are effectively\nconsidering it as a reference, even though we know MODIS products have their own\nsignificant uncertainties. Therefore, the biases present in MODIS are inevitably propagated\ninto our CALIOP-derived products. While our extensive validation provides confidence in\nour current findings, we see this work as a crucial step in an evolving field. We believe that\nin the future, as the volume of high-quality in-situ data from platforms like BGC-Argo\ncontinues to grow, it will become feasible to train these deep learning models directly on\nin-situ measurements, thereby reducing the dependency on passive satellite products and\nfurther improving the accuracy of LiDAR-based retrievals.\nThe related statements have been added in the Section 1 in the revised manuscript as\nfollows:\n“Given that our CALIOP retrieval model was trained using MODIS data, it is important to\naddress the potential for data dependency and validate our findings with an independent\nsatellite sensor. To this end, we conducted an independent analysis using data from the\nVisible Infrared Imaging Radiometer Suite (VIIRS) sensor for the 2012–2022 period,\napplying the same CbPM model to ensure a consistent comparison (see Supplementary\nFigure S10 for the full seasonal trend comparison). The results show that the winter NPP\ntrend derived from VIIRS (0.4 TgC yr⁻¹, P < 0.05) is highly consistent with the trend\nobserved from MODIS (0.3 TgC yr⁻¹, P < 0.05). While the absolute NPP values from VIIRS\nare slightly lower than those from MODIS, the remarkable consistency in the trends\nbetween these two independent passive sensors confirms the result obtained by this type\nof instrument across all seasons. Crucially, both of these passive sensor trends are more\nthan five times smaller than the trend derived from CALIOP (2.2 TgC yr⁻¹, P < 0.05),\nproviding strong independent evidence that passive ocean color remote sensing\nsignificantly underestimates the recent acceleration of Antarctic winter NPP. This\nunderscores the necessity of active LiDAR measurements for capturing these critical polar\ndynamics.\nFigure S10. Comparison of seasonal and annual NPP trends derived from CALIOP, MODIS,\nand VIIRS (2012–2022). Interannual trends in Net Primary Production (NPP) for the\nAntarctic Seasonal Sea-Ice Zone (SSIZ) from 2012 to 2022, calculated independently from\nCALIOP (active LiDAR sensor), MODIS (passive sensor), and VIIRS (passive sensor) data.\nPanels show trends for (a) Annual, (b) Winter, (c) Spring, (d) Summer, and (e) Autumn NPP.\nSolid lines represent the linear regression fits, with corresponding trend values (slope in\nTgC yr⁻¹) and statistical significance (P-value) annotated. Shaded areas indicate the 95%\nconfidence intervals. Note the strong consistency between the two passive sensors\n(MODIS and VIIRS), which both show a much weaker winter trend compared to the\nsignificant, accelerated trend captured by CALIOP. This provides independent evidence\nthat passive sensors underestimate the recent increase in winter NPP.\n”\n2) Additionally, while the authors acknowledge that training the model with MODIS\ndata propagates the bias present in MODIS, they do not quantify or discuss these biases’\npotential impact on winter NPP (where MODIS data is especially limited).\nResponse: We thank the reviewer for raising this very important point. We agree that a\nquantitative discussion of the potential impact of MODIS-propagated biases is essential for\na robust analysis, especially for the winter NPP estimates where MODIS data is limited.\nTo formally address this, we have added a new discussion about Uncertainty Analysis to\nour Methods section. In this section, we adopt an established framework to quantify the\ntotal uncertainty (E) of our CALIOP-derived products by breaking it down into three\ncomponents: Measurement error (M), Representation error (R), and Prediction error (P),\nsuch that 𝐸 =√𝑀2+𝑅2+𝑃2. Our approach and findings are summarized as follows:\nThe Measurement error (M) component explicitly accounts for the propagation of\nuncertainty from the reference MODIS data used in our model training. Based on literature\nvalues, we used uncertainties of ~35% for MODIS Chla, ~30% for MODIS bbp43,130 and a\nsimilar value for Kd. By combining these with the much smaller instrument uncertainties of\nCALIOP, we calculated the total measurement error for our key retrieved parameters. This\nanalysis shows that the uncertainty is dominated by the reference MODIS data. We then\ndiscuss how the uncertainties in these retrieved parameters (Chla, bbp, and Kd) propagate\nto the final NPP estimates. Since the CbPM is a multiplicative model, we approximated the\ntotal uncertainty in our NPP estimates (𝐸 ) using the root sum of squares method\n𝑁𝑃𝑃\n(𝐸 =√𝐸2 +𝐸2 +𝐸2 ). Our analysis indicates that the total uncertainty on the\n𝑁𝑃𝑃 𝐶ℎ𝑙𝑎 𝑏𝑏𝑝 𝐾𝑑\nabsolute magnitude of our final NPP estimates is approximately 55%. Most importantly, we\nargue that this substantial uncertainty affects the absolute magnitude of NPP, but has a\nmuch smaller impact on the robustness of our central finding — the accelerating trend in\nwinter NPP. We support this argument with two independent lines of evidence. As detailed\nin our response to your next comment, we conducted a new winter-only validation. Despite\nthe high theoretical uncertainty, our CALIOP-derived winter NPP shows a strong\ncorrelation (R = 0.79) with the sparse in-situ BGC-Argo data, providing the best available\nground truth and confirming our model's credibility during winter. Our independent analysis\nusing the VIIRS sensor shows that it reports a weak winter NPP trend that is remarkably\nsimilar to MODIS. This demonstrates that the much stronger trend seen by CALIOP is a\ngenuine observation resulting from the use of active LiDAR, rather than an artifact of a bias\npropagated from a single sensor (MODIS). Our confidence in the positive and significant\ntrend is further bolstered by the consistent increasing trends observed across four different\nNPP models (Supplementary Figure S5) and our independent validation using VIIRS data\n(Supplementary Figure S10).\nIn summary, while we have now quantified the uncertainty in the absolute NPP values,\nmultiple independent checks confirm that our main conclusion about the accelerating\nwinter trend is robust and not an artifact of the model's training data. We have added a\ncomprehensive discussion of this entire analysis to the manuscript.\nThe related statements have been added in the revised manuscript as follows:\n“\nIt is important to acknowledge that by training our model with MODIS data, its inherent\nbiases are propagated into our results. To formally assess this, we identified three primary\nsources of error contributing to the total uncertainty (E) of our CALIOP-derived products:\nmeasurement (M), representation (R), and prediction (P) errors129. The total uncertainty\ncan be expressed as the root of the sum of squares of these components: 𝐸 =\n√𝑀2+𝑅2+𝑃2. The measurement error (M) includes the combined uncertainties from the\nCALIOP input variables and the MODIS reference data used for model training. The\nrepresentation error (R) arises from spatio-temporal mismatches between observations\nand the model grid. Following established methods, this error is assumed to be normally\ndistributed with a bias of 0 on a large scale and is not included in the final error budget.\nThe prediction error (P) is the inherent error of our TBTS model, as determined from the\nvalidation dataset. Combining these components, the total estimated uncertainty (E) for\nour CALIOP-derived products (Chla, bbp and Kd) is approximately 30~35%43,130. These\nuncertainties propagate to the final NPP estimates. As the CbPM is a multiplicative model,\nthe total uncertainty in our NPP estimates (𝐸 ) can be approximated by the root sum of\n𝑁𝑃𝑃\nsquares of the uncertainties from our key inputs: 𝐸 =√𝐸2 +𝐸2 +𝐸2 . This results\n𝑁𝑃𝑃 𝐶ℎ𝑙𝑎 𝑏𝑏𝑝 𝐾𝑑\nin an uncertainty for the final NPP estimate of approximately 55%. It is critical to note that\nthis substantial uncertainty primarily affects the absolute magnitude of our NPP estimates.\nThe central conclusion of our study, however, is the accelerating trend in winter NPP. The\ncalculation of a linear trend is statistically robust against such random errors in individual\ndata points. Our confidence in the positive and significant trend is further bolstered by the\nconsistent increasing trends observed across four different NPP models (Supplementary\nFigure S5) and our independent validation using VIIRS data (Supplementary Figure S10).\n”\napproach. Remote Sens Environ 287, 113482, doi:10.1016/j.rse.2023.113482 (2023).\nocean carbonate system for seasonal to decadal studies of ocean acidification. Earth Syst.\nSci. Data 13, 777-808, doi:10.5194/essd-13-777-2021 (2021).\nFront Mar Sci Volume 10 - 2023, doi:10.3389/fmars.2023.1050087 (2023).\n3) Apart from validating the CALIOPS-derived results with MODIS (dependent), the\nauthors also use Argo floats data. However, most grid cells contain less than 5 winter\nArgo profiles during 2012-2023, if I am correct (or is it the number of winters which\nthere is data available?). Nevertheless, this a very low observation number and even in\nFigure 2e, the authors only show the trend for a single grid cell where the frequency\nwas higher. Furthermore, the authors mention in lines 201-203 that +5000 ARGO\nprofiles but what was the number of winter profiles used? Is the scatter plot in Figure\n2b not including all correspondence between CALIOPS and ARGO profiles, including\nsummer? If so, the ideal validation should use only winter ARGO profiles, even if few.\nResponse: Thank you for these meticulous and very important questions regarding our\nvalidation with BGC-Argo data. These points have helped us significantly clarify our\nmethodology and strengthen our validation. We address each of your points below:\nAs for the sparsity of winter data, winter Argo data is extremely sparse, with most grid cells\ncontaining fewer than five winter profiles over the entire 2012–2023 period, as we show in\nFigure 2d. This scarcity is a challenge in polar research and is precisely why we presented\nthe trend analysis in Figure 2e for the single grid cell with the highest observation frequency,\nas it was the location with sufficient data to illustrate a meaningful temporal trend. We have\nnow quantified the exact number of profiles for each season. Of the 5,131 total profiles in\nthe SSIZ from 2012–2023, the seasonal breakdown is as follows: 583 for Winter, 579 for\nSpring, 1968 for Summer, and 2001 for Autumn. We have added this detailed breakdown\nto the revised manuscript. You are also correct that the scatter plot in the original Figure\n2b included data from all seasons. The purpose of this plot was to provide a general\nvalidation of our NPP retrieval algorithm's performance across the full range of conditions\nencountered throughout the year. Following your excellent suggestion, we have completely\nrevised our validation approach to be more targeted and transparent:\nIn the main manuscript, Figure 2b has been replaced with a new winter-only validation plot.\nThis plot now directly compares our CALIOP NPP estimates against the 583 winter Argo\nprofiles. The result shows a good correlation (R = 0.79, MAPD = 15.6%), giving us high\nconfidence in our model's performance specifically for the winter season.\nFor full transparency, we have moved the all-season comparison to a new Supplementary\nFigure. This new figure also includes separate validation plots for Spring, Summer, and\nAutumn, allowing for a complete seasonal performance assessment.\nWe have also updated Figure 2a with seasonal markers to better visualize the data\ndistribution.\nWe hope this new, two-tiered validation structure—a specific winter-focused validation in\nthe main text and a comprehensive seasonal breakdown in the supplement—fully\naddresses your concerns and provides a much more robust assessment of our model's\ncapabilities.\nThe related statements have been revised as follows:\n“\nGiven that the CALIOP-derived results utilizes MODIS data for training, biases present in\nthe MODIS data47,48 are likely to be propagated in the ensuing CALIOP outputs and affect\nthe resulting CALIOP 𝑏 , 𝐶ℎ𝑙𝑎, and 𝐾 predictions, which in turn affects the accuracy of\n𝑏𝑝 𝑑\nNPP calculations. In addition, in-situ BGC-Argo data were used to validate CALIOP-\nderived NPP. A total of 125 Argo floats and 5131 observation profiles were deployed in the\nSSIZ from 2012 to 2023, with a seasonal distribution of 583 profiles in winter (June-August),\n579 in spring, 1968 in summer, and 2001 in autumn. Figure 2a illustrates the spatial\ndistribution of these profiles by season. The Argo NPP rate values were derived by inputting\nthe water parameters observed by the Argo floats into the CbPM model49. To specifically\nassess our model's performance during the critical, under-sampled winter period, we\ncompared our CALIOP NPP estimates exclusively against the wintertime Argo profiles\n(Figure 2b). Despite the relative sparsity of in-situ winter data, this targeted validation\nreveals a good correlation providing confidence in our model's ability to retrieve NPP in the\nchallenging low-light conditions of the polar winter. A comprehensive validation across all\nseasons, including individual plots for spring, summer, and autumn, is provided in\nSupplementary Figure S11 for a complete performance assessment. Although there is\nsome margin of error, the overall agreement validates the use of CALIOP data for\nmonitoring primary production in polar regions, where in situ data are often limited.\nFigure 2. (a) Spatial distribution of NPP derived from BGC-Argo data in the SSIZ from 2012\nto 2023. (b) Scatter plot comparing NPP estimates from BGC-Argo and CALIOP in winter.\nThe black solid line represents the linear fit, and the red dashed lines indicate the 95%\nprediction band. (c) Spatial distribution of winter observation frequencies in a 5°×4° grid\nderived from BGC-Argo data (2012–2023). (d) Histogram of grid counts showing winter\nobservation frequencies, where most grids (5°×4°) contain fewer than five observations,\nunderscoring data scarcity. (e) NPP trend (mgC m-² d⁻¹) for the grid with the highest\nobservation frequency, revealing an upward trend.\nFigure S11. Seasonal and all-season validation of CALIOP-derived NPP with BGC-Argo\ndata. Scatter plots comparing CALIOP-derived Net Primary Production (NPP) rate with in-\nsitu estimates from BGC-Argo floats from 2012-2023. Panel (a) shows the comparison for\nall available data across all seasons. Panels (b), (c), and (d) show the validation specifically\nfor spring, summer, and autumn, respectively. The solid blue line represents the linear\nregression fit, and the red dashed lines indicate the 95% prediction band. Correlation\ncoefficient (R) and Mean Absolute Percentage Deviation (MAPD) are shown for each\ncomparison.\n”\n4) The carbon-based model (CbPM) used to calculate NPP assumes constant\nchlorophyll-to-carbon ratios. First, these, which may not be true for polar regions,\nespecially for winters, when the very low PAR can significantly affect the C:Chl-a\nration. Clarify or ensure that.\nResponse: We thank the reviewer for raising this crucial point about a key assumption in\nprimary production modeling. We agree that the chlorophyll-to-carbon (C:Chl-a) ratio is a\ncritical parameter that is known to be highly variable, especially under the low-light (PAR)\nconditions of polar winters. We wish to clarify a key feature of the Carbon-based\nProductivity Model (CbPM). Unlike simpler models that assume a fixed C:Chl-a ratio, the\nCbPM is specifically designed to account for photoacclimation by calculating a dynamic\nC:Chl-a ratio that varies as a function of light and temperature127,128. This built-in\nphysiological response partly addresses the valid concern about the significant effect of\nlow winter PAR. However, we acknowledge that any model's parameterization has inherent\nuncertainties. To ensure that our primary conclusion—the accelerating trend in winter\nNPP—is not an artifact of the CbPM's specific assumptions, we compared its results with\nthree other widely used NPP models (VGPM, CAFE, and Eppley), each with different\nunderlying structures.\nAs shown in Supplementary Figure S5, all four models, despite their different approaches\nto phytoplankton physiology, show a consistent and significant increasing trend in winter\nNPP, particularly from 2012–2022. This strong agreement across different models provides\nhigh confidence that our central finding is robust and not dependent on the specific C:Chl-\na parameterization of a single model.\nWe have added text to the manuscript to clarify that the CbPM accounts for\nphotoacclimation to make this clear to the reader as follows:\n“After validation, the trained model is used to reconstruct 𝑏 , 𝐶ℎ𝑙𝑎, and 𝐾 from 2006 to\n𝑏𝑝 𝑑\n2023, specifically focusing on polar regions, where traditional passive sensors like MODIS\noften fail to provide data. The carbon-based productivity model (CbPM)127,128, a method\nthat estimates net primary productivity from phytoplankton carbon biomass and growth\nrates, is subsequently used to derive net primary productivity (NPP rate) in polar regions.\nNotably, the CbPM accounts for photoacclimation by calculating a dynamic chlorophyll-to-\ncarbon ratio that varies with light and temperature, a critical feature for accurately\nassessing productivity in variable polar environments. The NPP rate is derived based on\nCALIOP-derived parameters:\n”\nproductivity modeling with vertically resolved photoacclimation. Global Biogeochem Cycle\n22, doi:10.1029/2007GB003078 (2008).\nproductivity and phytoplankton physiology from space. Global Biogeochem Cycle 19,\ndoi:10.1029/2004GB002299 (2005).\n5) How were the Random Forest importances calculated? The authors do not mention\nthe methods, e.g. it was calculated using SHAP importances, permutation-based or\ndrop-column importances. Note that often using the default Random Forest\nimportances (without permutations) can often lead to biases in the results.\nResponse: Thank you for this important question regarding our methodology. We\napologize for not specifying the method in the original manuscript. To calculate the feature\nimportances, we used the permutation-based importance method, which you correctly\nidentified as a more robust approach. This was implemented using the standard\npermutation_importance function from the scikit-learn library.\nWe chose this method specifically to avoid the potential biases associated with default\nimpurity-based metrics. Permutation importance directly measures a feature's impact on\nthe model's performance by evaluating how much the prediction error increases when that\nfeature's values are randomly shuffled on an unseen validation set. This provides a more\nreliable and less biased estimate of a feature's true predictive power. We have now added\na detailed description of this methodology to the \"Materials and Methods\" section to ensure\nour approach is transparent and reproducible as follows:\n“For the statistical analysis of potential factors influencing Antarctic NPP, we employed a\nrandom forest (RF) regression model to quantify the relative contributions of different\nenvironmental factors, a method successfully applied for analyzing drivers in\nenvironmental science131. This method allows for the assessment of nonlinear\nrelationships between predictor variables and NPP, providing a robust framework for\nunderstanding how various factors such as temperature, salinity, and nutrient availability,\ninfluence primary production in the Southern Ocean. To provide a robust and unbiased\nassessment of feature importance, we used the permutation-based method132. This\nmethod directly measures a feature's impact on the model's performance on an unseen\nvalidation dataset, avoiding the potential biases of standard impurity-based metrics. As\noriginally described by133, the importance of a single feature is calculated as the average\ndecrease in the model's performance score when the values of that feature are randomly\nshuffled. This process was repeated multiple times for each feature to ensure statistical\nstability. The resulting importance values are normalized to ensure that they sum to 100%.\n”\nCommunications 15, 8049, doi:10.1038/s41467-024-52387-2 (2024).\nrandom forests. Statistics and Computing 27, 659-678, doi:10.1007/s11222-016-9646-1\n(2017).\ndoi:10.1023/A:1010933404324 (2001).\nMinor comments:\nOne of the authors’ key conclusions is that SAM is a key factor in rising NPP in recent\nyears due to its effect on reducing sea ice and promoting phytoplankton growth. This\nhas already been shown by other papers for Antarctica(e.g. Ferreira et al. 2024 using\nsatellite chlorophyll-a for the Antarctic Peninsula, included in the WS subregion), yet\nthe introduction does not cover these findings. Given the importance of SAM for the\nobserved trends in NPP, I think that this link between SAM and PP should be discussed\nin the introductory section of the main text.\nResponse: Thank you for pointing this out. We quite agree with your comments. We thank\nyou for this excellent suggestion and for highlighting the highly relevant work by Ferreira\net al. (2024). We agree that our original introduction did not sufficiently cover the\nestablished links between SAM and primary production. Including this provides crucial\ncontext for our results. Following your advice, we have revised the introduction to discuss\nhow the SAM, as a key climate mode, influences environmental factors that regulate\nphytoplankton growth. We have now incorporated findings from Ferreira et al. (2024) and\nother relevant research, which showed that a recent intensification of SAM since 2010 was\na key driver of regional phytoplankton variability, to better frame our own investigation into\nthese large-scale drivers.\nWe believe this addition significantly strengthens the manuscript's introduction and better\npositions our findings within the current state of research. The related statements have\nbeen revised as follows:\n“Recent studies have highlighted the significant influence of climate change on Southern\nOcean phytoplankton, with rising temperatures, shrinking sea ice, and alterations in ocean\ncirculation affecting their distribution and production. Specifically, a warmer ocean and\natmosphere8,9 and sea ice retreat10-12 are reshaping the timing and magnitude of\nphytoplankton blooms by influencing the wind strength, nutrient availability, light\nconditions13,14 and mixed layer depth15-17, which are factors that affect phytoplankton\nblooms. These regional shifts are often modulated by the Southern Annular Mode (SAM),\nthe dominant climate pattern in the Southern Hemisphere. A positive SAM trend has been\nshown to influence phytoplankton communities and production by altering physical drivers\nlike mixed layer depth, light availability, and sea ice cover18-20. Notably, a recent\nintensification of the SAM since 2010 has been linked to increased phytoplankton biomass\nand longer blooms in the West Antarctic Peninsula21. These shifts in the timing and\nmagnitude of phytoplankton blooms are particularly evident in austral summer, when\nphytoplankton blooms grow in magnitude but terminate earlier compared to previous\ndecades, with significant trends in bloom initiation and termination observed over the past\n25 years (1998–2022)5.”\nPeninsula region to the Southern Annular Mode and the implications for the\nphytoplankton production. Progress in Oceanography 188, 102416,\ndoi:https://doi.org/10.1016/j.pocean.2020.102416 (2020).\nphytoplankton communities in the seasonal ice zone of the Southern Ocean.\nBiogeosciences 17, 3815-3835, doi:10.5194/bg-17-3815-2020 (2020).\nresponse to the Southern annular mode in the marginal sea of the Southern ocean.\nSci Rep 11, 10266, doi:10.1038/s41598-021-89720-4 (2021).\nbiomass and longer blooms in the West Antarctic Peninsula. Nature\nCommunications 15, 6536, doi:10.1038/s41467-024-50381-2 (2024).\nFigure 1: The permanent ice zone should not be coloured in dark blue since that colour\nis part of the colorbar. I suggest changing to gray.\nResponse: Thank you for pointing this out. We quite agree that using a color from the color\nbar for the permanent ice zone was potentially confusing. Accordingly, we have revised\nFigure 1. The color of the permanent ice zone in panels (a-d) has now been changed from\ndark blue to gray, making it distinct from the data represented by the color bar as follows:\nFigure 1. Spatial and temporal variability in Antarctic winter NPP. (a) and (c) CALIOP-\nderived annual mean winter NPP distributions and winter rates of NPP change (2007–\n2022), respectively. (b) and (d) MODIS-derived annual mean winter (June–July–August\n(JJA)) NPP distributions and winter rates of NPP change (2007–2022), respectively. Gray\nrepresents the permanent ice zone in winter. (e) Interannual trends in winter Antarctic NPP.\nThe green squares and yellow–orange stars represent the CALIOP and MODIS-Aqua\nresults, respectively. The solid green and yellow-orange lines represent the LiDAR and\nMODIS linear fits from 2007 to 2020, respectively. The dotted green line represents LiDAR\nfit from 2012 to 2022. The green and yellow–orange data and associated lines correspond\nto the left vertical axis. The red circles show the average extent of sea ice in winter,\ncorresponding to the right vertical axis.\nLine 405: double ..\nResponse: Thank you for pointing this out. The related statements have been revised as\nfollows:\n“As SIC decreases, more sunlight penetrates the water, enhancing light availability for\nphytoplankton growth.”\nFigure 4: some of the labels are too small and thus not legible. Also, I would suggest\nopting for a different colour scheme, one that might be easier to distinguish the variables\n(or using both colours and hatches). Also need more info on the random forests.\nResponse: We thank the reviewer for these valuable and detailed suggestions, which have\nsignificantly improved the clarity and professionalism of Figure 4. We agree that the labels\nin the original figure were too small. We have revised the figure and increased the font size\nof all text, including the axis labels, numerical values, and trend annotations, to ensure they\nare all clearly legible. Following your excellent suggestion, we have completely updated\nthe color scheme. We have implemented the \"Muted\" qualitative color scheme designed\nby Paul Tol (https://sronpersonalpages.nl/~pault/#sec:qualitative), which is specifically\ndeveloped to be colorblind-safe and provides excellent distinction between the nine\nvariables. Furthermore, we have ensured that the colors for each variable are now\nconsistent between the pie charts on the left and the corresponding time-series line graphs\non the right, which greatly improves the figure's readability and makes it easier to interpret\n\"at a glance\". We also agree that more information was needed on the Random Forest\nmethodology. As detailed in our response to another comment, we have now added a\ncomprehensive description of our feature importance calculation method (Permutation\nImportance) to the \"Materials and Methods\" section to ensure our analysis is fully\ntransparent and reproducible. We believe these combined revisions have fully addressed\nall the concerns raised and have substantially improved the quality and clarity of Figure 4.\nThe revised figure is as follows:\nFigure 5: the initial panels (a-f) are not legible. Consider increasing the font size.\nResponse: Thank you for pointing this out. We quite agree that the font size was too small,\nmaking the details difficult to discern. Following your suggestion, we have revised Figure\n5 by significantly increasing the font size for all labels and numbers within panels (a-f). We\nbelieve the revised figure is now clear, and all details are fully legible. Thank you for helping\nus improve the presentation of our results. The revised figure is as follows:\nLines 672: two-branch-two-step\nResponse: Thank you for pointing this out. We have corrected \"tow-branch-two-step\" to\n\"two-branch-two-step\" in the manuscript as follows:\n“A two-branch-two-step (TBTS) model is used to derive 𝐶ℎ𝑙𝑎, 𝑏 , 𝐾 from the CALIOP\n𝑏𝑝 𝑑\ndata (Figure S7).”\nLine 1206-onwards: references with duplicated DOI link.\nResponse: Thank you for your careful reading and for catching the formatting error in our\nreference list. You are correct; several references had duplicated DOI links. We have\nthoroughly reviewed and corrected the entire bibliography to fix this issue. All DOIs now\nfollow the standard format, and the duplicated links have been removed.\nReviewer #1 (Remarks on code availability):\nNot available.\nResponse: Thank you for pointing this out. We have already shared the code and data via\nthe CodeOcean system of Nature when we submitted the manuscript\n(https://codeocean.com/capsule/9252735/tree). Now we have updated and shared all the\ncode and data again.\n[FIGURE REDACTED]\n[FIGURE REDACTED]\n[FIGURE REDACTED]\nReviewer #2 (Remarks to the Author):\nReview of manuscript NCOMMS-25-35467-T, “Underestimated Accelerated Antarctic\nPhytoplankton NPP in Winter Over the Past Decade Revealed by Spaceborne LiDAR”\nby Peng Chen et al.\nGeneral Comments\nThis well written manuscript presents a thorough study of spatio-temporal patterns of\nNet Primary Production (NPP) in the Antarctic ocean based on spaceborne LiDAR. The\nemphasis is on identifying previously unknown temporal trends, spatial patterns, and\nunderlying mechanisms of wintertime NPP, which is made possible for the first time\nusing LiDAR. The analysis of causality using the partial correlation and multiple\nconditional independence (PCMCI+) algorithm is also interesting and informative.\nMost of the results are based on Carbon-based Productivity Model (CbPM), which is\none prominent model for estimating NPP based on remote sensing data. These results\nare bolstered by a comparison of the results from multiple production models, presented\nin the Supplementary, which confirms that the results do not depend crtically on the\nchoice of the CbPM model for estimating NPP from remote sensing observations.\nOverall, I find the manuscript well written, the analyses sound, and the conclusions\nboth well supported and likely to be of interest to many readers. The authors are correct\nin noting that their findings will provide much needed observation-based data for\nimproving models of the annual cycle of phytoplankton growth and NPP in the\nAntarctic. These results may further lead to future studies applying space-based LiDAR\nto the sub-arctic and arctic oceans, which would be beneficial.\nBelow please find a few specific comments and suggestions, which are intended to\nimprove the manuscript by enhancing clarity and adding a bit more detail in the\nDiscussion.\nResponse: We are very grateful for your overwhelmingly positive and encouraging\nfeedback. We sincerely appreciate your recognition of the study's key strengths and\nsignificant potential for future research in other polar regions. We welcome the specific\ncomments and suggestions that follow, and we have carefully addressed each one to\nenhance the clarity and detail of our manuscript as requested.\nSpecific Comments:\nLines 23-24 (Abstract, first sentence): how about revising to something like : “The\nSouthern Ocean’s vital carbon sink is driven by phytoplankton Net Primary Production\n(NPP).”\nResponse: Thank you for pointing this out. We quite agree with your comments. The\nrelated statements have been revised as follows:\n“The Southern Ocean’s vital carbon sink is driven by phytoplankton Net Primary Production\n(NPP). Winter phytoplankton seed spring algal blooms and regulate nutrient cycling and\necosystem dynamics, yet Antarctic winter NPP remains poorly constrained due to limited\nin situ data and passive satellite sensor challenges in low-light, ice-covered conditions.”\nLines 42-43: how about revising to: “NPP refers to the conversion of carbon dioxide\n(CO₂) into organic carbon through photosynthesis, …”\nResponse: Thank you for pointing this out. We quite agree with your comments. The\nrelated statements have been revised as follows:\n“NPP refers to the conversion of carbon dioxide (CO₂) into organic carbon through\nphotosynthesis, which is then transferred through the marine food web or sequestered in\ndeep ocean waters, helping to store carbon and mitigate climate change4.”\nLines 63-64: Although it seems most likely that warming has played an important role\nas a cause of observed changes, other concomitant changes (e.g., atmospheric\ndeposition of nutrients from anthropogenic sources, as well as riverine inputs, and\nocean acidification) have also contributed to large-scale changes. For example, see\nDuce, Robert A., et al. \"Impacts of atmospheric anthropogenic nitrogen on the open\nocean.\" science 320.5878 (2008): 893-897.\nTherefore, I suggest re-wording to something like: “where the timing and intensity of\nphytoplankton blooms has shifted as the ocean has been subjected to warming,\nanthropogenic nutrient inputs, and acidification.”\nResponse: Thank you for pointing this out. We completely agree that factors beyond\nwarming, such as anthropogenic nutrient inputs and acidification, contribute to the\nobserved large-scale changes in phytoplankton. Following your recommendation, we have\nrevised the sentence in the manuscript to better reflect these multiple drivers as follows:\n“A \"greening\" of 𝐶ℎ𝑙𝑎 in the Southern Ocean has been observed23, similar to the climate-\ndriven trends observed in low-latitude regions24, where the timing and intensity of\nphytoplankton blooms has shifted as the ocean has been subjected to warming,\nanthropogenic nutrient inputs25, and acidification26.”\nSouthern Ocean Getting Greener? Geophys Res Lett 46, 6034-6040,\ndoi:10.1029/2019GL083163 (2019).\nchange trends detected in indicators of ocean ecology. Nature 619, 551-554,\ndoi:10.1038/s41586-023-06321-z (2023).\nOcean. Science 320, 893-897, doi:10.1126/science.1150369 (2008).\nNature Climate Change 5, 12-14, doi:10.1038/nclimate2456 (2015).\nLine 70: add a comma after “sunlight”\nResponse: Thank you for pointing this out. We quite agree with your comments. The\nrelated statements have been revised as follows:\n“This is primarily because the passive ocean color remote sensing techniques depend on\nsunlight, and existing approaches typically cannot capture data under low-light\nconditions30,31.”\nLine 87: “because of this” -> “because this”\nResponse: Thank you for pointing this out. We quite agree with your comments. The\nrelated statements have been revised as follows:\n“To address this critical gap, we use spaceborne light detection and ranging (LiDAR) active\nremote sensing technology, which has already proven powerful in polar ocean detection\nbecause this technology does not require sunlight and can provide extensive spatial\ncoverage31,40-44.”\nLine 105: add “the” before “temporally\nResponse: Thank you for pointing this out. We quite agree with your comments. The\nrelated statements have been revised as follows:\n“The study area covers the Antarctic continental shelf and the seasonal sea-ice zone\n(SSIZ), the area within the temporally average extent of sea ice, which is bounded by the\ngray solid line in Figure S1.”\nLine 378: “influencing” -> “limiting”\nResponse: Thank you for pointing this out. We quite agree with your comments. The\nrelated statements have been revised as follows:\n“the similar contributions of nitrate and phosphate indicate that multiple nutrients are\nlimiting primary productivity in the region.”\nFigure 4. Maybe add small vertical labels (maybly only on the left-most side?) for the\nsummer and winter plots, respectively? That could make it easier to read the figure at a\nglance.\nResponse: Thank you for this helpful suggestion to improve the clarity of Figure 4. We\nagree that adding vertical labels to distinguish between the winter and summer pie charts\nmakes the figure much easier to interpret at a glance. We have revised Figure 4 accordingly,\nadding vertical \"Winter\" and \"Summer\" labels to the left-hand side of the figure to clearly\nidentify the corresponding plots, just as you suggested. The revised figure is as follows:\nLines 448-450: Delete this sentence, which repeats the previous one.\nResponse: Thank you for pointing this out. We quite agree with your comments. As\nsuggested, we have deleted the repetitive sentence “This network underscores the\ncomplexity of Antarctic NPP variability, shaped by both direct local conditions and distant\nclimate events, with varying impacts across different seasons and time scales.” from the\nmanuscript to improve the text's conciseness.\nLine 507 onwards, Ecological Implications”:\nThis section only touches on implications for phytoplankton community composition,\nwith emphasis on their expected biogeochemical impacts. That is fine, but more could\nbe said.\nHow about also mentioning that shifts in the phytoplankton community composition\ncould alter trophic transfer efficiency and hence higher trophic production and the\ncommunity composition of zooplankton and other predators?\nResponse: Thank you for this excellent suggestion to broaden our discussion. We agree\nthat the ecological implications of our findings extend beyond biogeochemical cycling and\nthat discussing the potential impacts on the broader food web is a crucial addition.\nFollowing your advice, we have expanded the \"Ecological Implications\" section. The\nrevised text now discusses how shifts in the phytoplankton community composition could\nalter trophic transfer efficiency and subsequently impact higher trophic levels, such as\nzooplankton and other predators. This addition provides a more holistic view of the\npotential consequences of our findings and significantly strengthens the manuscript.\n“The ecological ramifications of increased winter NPP observed in this study extend\nbeyond carbon cycling, with the potential to reshape the phytoplankton community\nstructure and ecosystem impacts on higher trophic levels. A sustained increase in winter\nbiomass has the potential to reshape the phytoplankton community structure that\noverwinters and initiates the subsequent spring bloom. A shift in the dominant cell size or\nspecies could alter trophic transfer efficiency and hence higher trophic production and the\ncommunity composition of zooplankton and other predators82,83. Increased winter\nproduction may favor diatom propagation. While these cells contribute disproportionately\nto POC export and biological pump efficiency84,85, they are also a critical, high-quality food\nsource for grazers like Antarctic krill. Therefore, any change in the abundance or timing of\nthese diatom populations could have cascading impacts on the food web, affecting the krill\nthat are a vital link to higher trophic levels such as penguins, seals, and whales86.”\nAntarctic Peninsula: Implications For The Evolving Southern Ocean Carbon\nSink. Geophys Res Lett 48, e2020GL091835, doi:10.1029/2020GL091835\n(2021).\nblooms, algal food quality and Calanus glacialis reproduction and growth in a\nchanging Arctic. Global change biology 16, 3154-3163, doi:10.1111/j.1365-\n2486.2010.02175.x (2010).\nfeeding ecology of two keystone Arctic marine predators. Sci Rep 13, 14056,\ndoi:10.1038/s41598-023-39091-9 (2023).\npump. Nat Geosci 11, 27-37, doi:10.1038/s41561-017-0028-x (2018).\ncycle. Proceedings of the National Academy of Sciences 105, 20344-20349,\ndoi:10.1073/pnas.0811302106 (2008).\nRegenerated Primary Production in the Atlantic Inflow North of Svalbard. Front\nMar Sci Volume 6 - 2019, doi:10.3389/fmars.2019.00293 (2019).\nLines 533-536: “alter” does not specify the direction of the change (precise impact)\nexpected.\nHow about specifying the expected consequence of increasing calcification by\ncoccolithophorids, which would (in the short term) be a reduction in the oceanic sink\n(uptake) of atmospheric CO2, as a result of an increasing near surface pCO2.\nResponse: Thank you for this very helpful suggestion. You are correct that the word “alter”\nwas too vague, and specifying the precise biogeochemical impact provides much more\nclarity. We have revised this section of the manuscript as you recommended. The text now\nexplicitly states that increased calcification by coccolithophores could, in the short term,\nlead to a reduction in the oceanic sink for atmospheric CO2 as a result of an increase in\nnear-surface pCO2. This change significantly improves the precision of our \"Ecological\nImplications\" discussion as follows:\n“Conversely, if increased winter NPP leads to greater activity of coccolithophores, which\nare producers of particulate inorganic carbon (PIC), this could reduce the oceanic sink for\natmospheric CO₂ in the short-term by increasing near-surface pCO₂, with cascading effects\non nutrient cycling and ecosystem functioning84-87.”"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have made an excellent effort in addressing the reviewers’ suggestions and questions, including adding new analyses, clarifying methods, and revising figures. These improvements have strengthened a paper that was already very well written.\nI am satisfied with the authors’ thorough and thoughtful response and believe that the manuscript is now suitable for publication as is."}, "author_response": ""}]

Decision letter