Extreme temperature events reshuffle the ecological landscape of the Southern Ocean

Domain: earth_science
Published: 2025-12-30
Editorial outcome: major_revision

Abstract
Extreme temperature events are becoming widespread with global warming, impacting phytoplankton, the foundation of the marine ecosystem. In the Southern Ocean, these impacts are not well understood, despite the key role of phytoplankton in global carbon cycling and climate. Here, we use 26 years of satellite observations and confirm previously identified impacts of marine heatwaves (MHWs) on phytoplankton in the Southern Ocean, while systematically comparing the opposite impacts of marine cold spells (MCSs). MHWs decrease phytoplankton chlorophyll-a (Chl-a) in subtropical regions (−21.11%) but less so in polar regions, with Chl-a even increasing in the Sub-Antarctic Zone (+22.26%). MCSs exhibit opposite patterns, enhancing Chl-a in subtropical regions (+32.37%) while inhibiting it in southern regions (−21.19%). These regional differences in Chl-a anomalies are mediated by distinct responses in phytoplankton size composition to MHWs and MCSs. As extreme events intensify with global warming, Southern Ocean’s phytoplankton will be disrupted, with implications for global biogeochemical cycles. These findings highlight the importance of simultaneously considering both MHWs and MCSs when assessing the ecological impacts of climate extremes.

Introduction
Marine heatwaves (MHWs) and marine cold spells (MCSs) are extreme events characterized by prolonged periods of anomalously warm or cold ocean temperatures1–3. In recent decades, the frequency, duration, and intensity of MHWs have increased significantly across most global oceans1–3, while MCSs have exhibited the opposite trend globally4. MHWs are particularly pronounced in the Southern Ocean, which has absorbed the majority of anthropogenic heat uptake and experienced rapid warming5–7, while MCSs show more complex trends with possible duration increases in the Antarctic Zone8. The increasing incidence of MHWs in the Southern Ocean9 has resulted in severe ecological consequences, such as sharp reductions in surface chlorophyll-a (Chl-a) concentration10, heightened mortality among birds and marine life11. Concurrently, the intensified temperature variability in the Southern Ocean has also led to more frequent MCSs8, which may have profound ecological impacts.

Given its role as a major global carbon sink12,13 and a key driver of global climate and biogeochemical cycles14,15, understanding the Southern Ocean’s responses to these extreme events is of paramount importance. Phytoplankton, which form the base of marine food webs and mediate key biogeochemical processes like carbon fixation16, are particularly relevant in this context. They contribute significantly to oceanic carbon sequestration through the biological pump17,18, and serve as indicators of climate change impacts on marine ecosystems due to their sensitivity to environmental changes19,20. Although numerous studies have examined the long-term responses of phytoplankton in the Southern Ocean to gradual warming19–24, the impact of discrete, short-term extreme events like MHWs and MCSs remains poorly understood. The Southern Ocean’s strong meridional gradients in temperature, nutrient availability, and phytoplankton community structure25–33 suggest that responses of phytoplankton to these extreme events may vary substantially across different regions. However, a comprehensive assessment of these responses is lacking. Moreover, responses of phytoplankton size structure, a key determinant of ecological and biogeochemical funtions26, to MHWs and MCSs, remain unexplored.

Here, we address these gaps by conducting an integrated assessment of phytoplankton responses to MHWs and MCSs across the entire Southern Ocean. Utilizing satellite-derived sea surface temperature (SST) and Chl-a concentration data from 1998 to 2023, coupled with advanced phytoplankton size class (PSC) models, we contrasted the response of phytoplankton in three distinct subregions: the subtropical Zone (STZ), Sub-Antarctic Zone (SAZ), and Permanently Open Ocean Zone (POOZ). Our results revealed striking regional disparities in the response of phytoplankton to extreme events. We found that MHWs in the POOZ can stimulate phytoplankton growth and potentially enhance carbon export–a stark contrast to the negative impacts observed in other regions. These regional differences are further mediated by the responses of different PSCs, which have significant implications for biogeochemical cycles. Our results highlight the need to account for regional and ecological complexities when assessing the effects of a changing climate on marine ecosystems.

Results
Spatiotemporal patterns of MHWs and MCSs
We used European Space Agency (ESA) satellite-derived SST data to detect MHWs and MCSs in the Southern Ocean. MHWs/MCSs were defined as periods when SST exceeded/fell below the 90%/10% percentile of the climatological baseline (1991–2020) for at least five consecutive days. Our analysis revealed distinct spatiotemporal patterns in the frequency, duration, and intensity of MHWs and MCSs across the Southern Ocean. MHWs were most frequent and prolonged in the STZ and SAZ, particularly in the Tasman Sea, southeastern Australia, and the Indian Ocean sector of the Southern Ocean southwest of Australia, where the annual average frequency exceeded 3.5 times, and the duration surpassed 60 days (Fig. 1a, b). In contrast, MHW intensity showed less spatial variability, with hotspots in the southwestern South Atlantic and around South America (Fig. 2a), possibly linked to mesoscale eddy activities within the Antarctic Circumpolar Current (ACC)9. MCSs displayed a unique spatial distribution with higher frequencies in the POOZ (Fig. 1c), reflecting the accelerated warming in the northern Southern Ocean and cooling in the southern regions. This regional contrast was particularly pronounced in the duration of MCSs, with the POOZ experiencing an annual average of >60 days, compared to <20 days in the STZ and SAZ (Fig. 1d). The intensity patterns of MCS aligned closely with those of MHWs (Fig. 2b).

Seasonal variations were evident in the occurrence of MHWs and MCSs (Supplementary Fig. 1). MHWs peak in the austral summer and autumn in the STZ and SAZ, whereas MCSs were predominant in winter and spring in the POOZ (Supplementary Fig. 1a, b). Over the past four decades, the area fraction of the STZ affected by MHWs and that of the SAZ have increased significantly (+9.04% decade−1, p<0.05; +3.64% decade−1, p<0.05, respectively) (Fig. 1e, f). The area fraction affected by MCSs in the STZ and SAZ has decreased (−9.60% decade−1, p<0.05; −2.20% decade−1, p<0.05, respectively) (Fig. 1g, h). However, the area fraction of the POOZ affected by MCSs has increased (+2.9% decade−1, p<0.05), with the area of increase exceeding that of the MHW decrease (Supplementary Fig. 2).

Phytoplankton responses to MHW and MCS
The response of phytoplankton to MHWs and MCSs was quantified by the percentage change from the mean Chl-a anomaly (climatological mean), and showed significant regional differences (Fig. 2c, d). Chl-a anomalies in the subtropical region are predominantly negative during MHWs (Fig. 2c), with only 5.89% of areas showing positive anomalies, whereas in the SAZ and POOZ, positive anomalies are observed in 54.64% and 52.11% of the area, respectively (Fig. 2e). In contrast, only 7.41% of area in the subtropical region shows negative anomalies during MCSs, whereas the area with negative anomalies increases to 65.67% and 57.21% in the SAZ and POOZ, respectively (Fig. 2e). The spatial differences in phytoplankton response to extreme events appear to be delineated by the subtropical front (Fig. 2c, d).

To investigate phytoplankton responses under extreme temperature events of varying severity, we applied the Hobday et al.34 classification framework at each grid cell, categorizing MHWs and MCSs into four intensity levels (Supplementary Fig. 3). This classification is based on the ratio between the SST anomaly above the climatological 90th percentile and the difference between the climatological 90th percentile and the baseline climatology. In general, the intensity of extreme events increases progressively, with the occurrence probability of Category III and IV events being relatively low (Supplementary Fig. 3a–h). Despite varying intensities, the spatial distribution of phytoplankton responses remains broadly similar, and the percentage of Chl-a anomalies increases with event intensity (Supplementary Fig. 3i–p).

The temporal evolution of phytoplankton Chl-a anomalies during MHWs and MCSs reveals distinct patterns across regions (Supplementary Fig. 4). Phytoplankton anomalies began to emerge approximately 10 days before the onset of MHWs, with weak positive anomalies in the SAZ and POOZ, potentially reflecting high sensitivity to warming in this region (Supplementary Fig. 4i, m). As MHWs intensified, negative anomalies in the STZ amplified, while positive anomalies enhanced in the SAZ and POOZ (Supplementary Fig. 4j, n). During the decay phase of MHWs, positive anomalies weakened, while negative anomalies persisted (Supplementary Fig. 4k, o), suggesting asymmetry in response to warming and cooling phases. MCSs induced nearly opposite patterns, with positive anomalies in the STZ and negative anomalies in the SAZ and POOZ intensifying with MCS strength (Supplementary Fig. 4m–p). The temporal evolution mirrored that of MHWs, with anomalies emerging prior to MCS onset and persisting post-decay (Supplementary Fig. 5). Notably, the magnitude of positive anomalies often exceeded that of negative anomalies and persisted longer after extreme events. This suggests that the processes driving positive phytoplankton responses, particularly in the SAZ and POOZ, may have more prolonged effects than those driving negative responses. We further examined phytoplankton carbon and physiological responses to MHWs and MCSs (Supplementary Text 1), providing additional context for the observed patterns.

Distinct responses across PSCs
Underlying these regional patterns were significant differences in responses of PSCs to MHWs and MCSs. During MHWs, PSCs in the STZ exhibited relatively uniform responses, with negative anomalies occurring across 93.01−95.06% of the area (Fig. 3a–c). In contrast, in the SAZ and POOZ, picoplankton showed widespread positive anomalies, covering 79.1% of the area, whereas nanoplankton and microplankton were positively affected in only 48.5% and 51.1% of the area, respectively (Fig. 3a–c). This positive impact of picoplankton likely reflects their high temperature sensitivity and low nutrient requirements20,26. Total and size-fractionated phytoplankton Chl-a concentration shows a strong positive correlation with increasing MHW intensity in the STZ and SAZ (Supplementary Fig. 6). Statistical analysis of the proportion of each PSC to total Chl-a in response to MHWs reveals that the proportion of small-sized phytoplankton increases significantly, while the proportion of medium- and large-sized phytoplankton decreases markedly during MHW events (Fig. 3g–i). This shift in size structure demonstrates high spatial consistency (Supplementary Fig. 7). Notably, during individual MHW events, changes in phytoplankton community composition can be highly pronounced, with the proportion of picoplankton increasing by up to 56.39% (Supplementary Fig. 8). Although variations in phytoplankton community composition may fluctuate in both positive and negative directions during individual events, statistical analysis of multiple events indicates that extreme warming events drive the phytoplankton community structure toward a dominance of smaller-sized populations, with a positive correlation with MHW intensity (R2=0.41, p<0.01, Supplementary Fig. 9).

During MCSs, PSCs exhibited positive anomalies across 83.7–94.1% of the STZ area (Fig. 3e, f). In the SAZ and POOZ, positive anomalies of picoplankton were observed over 86.2% of the area, whereas nano- and microplankton were positively affected in only 58.9% and 58.5% of the regions, respectively (Fig. 3e, f). With increasing MCS intensity, total and size-fractionated phytoplankton Chl-a concentration is significantly enhanced (Supplementary Fig. 6). In the STZ, Chl-a anomalies exhibit a strong correlation with MCS intensity, with the spatiotemporal mean anomaly of microplankton concentration reaching up to 165.3% (Supplementary Fig. 6), suggesting that extreme cooling events may have a more pronounced effect on phytoplankton communities than MHWs. Moreover, the response of phytoplankton size structure to MCS events is characterized by a significant increase in the proportion of total Chl-a by large-sized phytoplankton (Fig. 3g–i). During individual MCS events, the proportion of total Chl-a by microplankton can increase by up to 71.47% (Supplementary Fig. 10). These findings indicate that extreme cooling events shift the phytoplankton community toward a dominance of larger-sized populations.

Mechanisms underpinning regional differences in phytoplankton responses
The distinct regional differences in phytoplankton responses to MHWs and MCSs across the Southern Ocean arise from varying phytoplankton division rates, which are modulated by temperature-light-nutrient dynamics, and loss rates that are regulated through ecological mechanisms35. In the subtropical zone, where phytoplankton growth is primarily limited by nitrate availability36 (Fig. 4), MHW-induced stratification exacerbates this limitation by reducing phytoplankton division rates and increasing loss rates through heightened grazing pressure from zooplankton concentrated in the shallower mixed layer, ultimately causing negative Chl-a anomalies (Supplementary Fig. 4e–h). Enhanced mixed-layer irradiance also promotes photoacclimation, shifting the carbon to Chl-a ratio (Supplementary Fig. 11c). Conversely, the SAZ represents the largest high-nutrient, low-chlorophyll (HNLC) region globally and exhibits phytoplankton division rates that are fundamentally constrained by iron availability despite nitrate sufficiency37.

During MHWs, increased stratification and shallower mixed layers38 enhance light availability for phytoplankton39. While direct evidence linking MHWs to iron limitation mitigation in the SAZ is lacking, their effects vary depending on regional contexts. For example, in areas with relatively adequate iron conditions–such as regions around the Antarctic Circumpolar Current characterized by upwelling40 and areas experiencing increased storm activity due to climate change, which may increase the delivery of iron-rich dust41–MHWs may synergistically enhance phytoplankton growth36,42. In the POOZ, which lies between the polar front and the northern limit of winter sea ice (Fig. 1), phytoplankton is seasonally co-limited by the micronutrient iron and light due to deep mixing and large solar zenith angles36,43. MHWs have a dual effect here: they enhance water column stability44, alleviating light limitation38,43, and accelerate ice melt45, relieving iron limitation46–49. These combined effects drive the strong positive Chl-a concentration anomalies observed during MHWs.

In contrast, MCSs induce opposite effects. On a global scale, MCSs induced an average increase of 4.25 ± 0.92 m in MLD, corresponding to an 8.13% enhancement50. The SAZ region features a winter deep mixing band (DMB) adjacent to the Antarctic Circumpolar Current’s (ACC) northern boundary51,52 and exhibits high winter mixed-layer resource availability53. Notable increases in MLD of approximately 20–30 meters were observed specifically in the Atlantic and Pacific sectors. This MCS-induced MLD deepening during winter enhanced vertical mixing, subsequently augmenting nutrient supply to the upper layer and alleviating nitrate limitation in SAZ regions. The increased nutrient supply elevated phytoplankton division rates, especially for fast-growing diatoms, while the deeper mixed layer may have contributed to reduced loss rates, potentially through greater population dilution and decreased grazing pressure54,55. Ultimately, the dominance of division rates over loss rates resulted in higher Chl-a concentrations and positive anomalies (Supplementary Fig. 5e–h). However, in the SAZ and POOZ, despite abundant nitrate availability, phytoplankton growth is primarily limited by iron availability; thus, additional input of nitrate alone would not promote phytoplankton growth. Instead, in the light-limited POOZ, the intensified mixing caused by MCS reduces light exposure for phytoplankton. MCSs promote sea-ice formation, and the process of seawater freezing absorbs dissolved iron46–48, exacerbating iron limitation and further suppressing phytoplankton growth.

The response mechanisms of phytoplankton of different size classes to environmental changes are generally consistent with the overall trend of total Chl-a concentration. However, larger phytoplankton exhibit more pronounced responses during extreme temperature events (Fig. 3c, f). Studies have shown that temperature and nutrient variations have a particularly significant impact on larger cells56. In general, nutrient availability is considered an important factor controlling the size structure of phytoplankton56. During MHWs, the sharp decline in nutrient supply significantly suppresses the growth of medium- and large-sized phytoplankton, while smaller phytoplankton gain a competitive advantage due to their lower nutrient requirements57,58, leading to a shift in community structure toward smaller sizes. Additionally, some Antarctic diatoms, such as Fragilariopsis cylindrus59, exhibit reduced cell size under high temperatures, which helps lower cellular iron quotas, increase the surface area-to-volume ratio, and enhance nutrient uptake and growth59. In contrast, during MCSs, nutrient replenishment reverses the advantage of larger phytoplankton, particularly in nutrient-poor subtropical zones (STZ), where the competitive advantage of smaller phytoplankton is diminished.

Methods
ESACCI sea surface temperature and sea-ice concentration
The European Space Agency (ESA) Climate Change Initiative (CCI) SST version 3.0 is a daily sea surface temperature (SST) product with a 0.05° resolution70, spanning from 1981 to the present. CCI SST provides the mean SST at a depth of 0.2 meters, closely aligning with the nominal depth of drifting buoy measurements. It includes both the AVHRR and Along-Track Scanning Radiometer (ATSR) series, with satellite observation biases adjusted by recalibrating radiances using a reference channel to ensure a stable product. CCI applies a variational assimilation scheme to generate a gap-filled estimate of daily-mean SST. CCI v3.0 data can be accessed at https://doi.org/10.5285/4a9654136a7148e39b7feb56f8bb02d2. We extracted SST and sea-ice concentration (SIC) data for a subset region centered on Antarctica, down to 30°S, from 1982 to 2023. For the purpose of comparing MHWs and MCSs with other variables, we re-gridded the 0.05° CCI SST data to a 0.25° resolution using bilinear interpolation.

Refined satellite observations of Chl-a
Daily satellite remote sensing reflectance data with a 4km spatial resolution was extracted from the ESA Ocean Colour Climate Change Initiative (OC-CCI) v6.0 product71 for 1998–2023. CCI v6.0 uses data from ESA’s MERIS (Medium Resolution Imaging Spectrometer) sensor, NASA’s SeaWiFS (Sea-viewing Wide Field-of-view Sensor) and MODIS-AQUA (Moderate Resolution Imaging Spectroradiometer-Aqua) sensors, as well as NOAA’s VIIRS (Visible and Infrared Imaging Radiometer Suite). This time series now spans from late 1997 to the present, with the chosen algorithms optimized to meet climate user requirements. Remote sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were spectrally shifted to match SeaWiFS bands, and overlapping data were used to correct mean deviations between sensors at each pixel (https://rsg.pml.ac.uk/thredds/catalog-cci.html). For Chl-a calculations, we applied the OC4v6 algorithm72, which was developed for SeaWiFS and modified for the Southern Ocean to address the underestimation of chlorophyll levels by conventional algorithms in this region. We validated the algorithm using an in situ observational dataset. (Supplementary Figs. 12 and 13).

Phytoplankton carbon and θ
Phytoplankton carbon was estimated using the backscattering coefficient73 at 443 nm (b_bp(443)), with b_bp(443) data obtained from OC-CCI, covering the period from 1998 to 2023. θ is defined as the ratio of phytoplankton carbon to Chl-a.

BGC-Argo data
In situ chlorophyll data were collected by profiling floats, primarily deployed under the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) project, which is part of the international BGC-Argo program. BGC-Argo data were downloaded using the One-Argo-Mat toolbox for MATLAB. These data are collected and freely provided by the international Argo program and its contributing national programs (https://argo.ucsd.edu and https://www.ocean-ops.org).

We collected 35,493 profiles from 291 floats south of 30°S between January 2012 and December 2023 and applied standard quality control procedures to the data. The mixed-layer depth (MLD) was estimated using the density method74 with a threshold of 0.03 kg m−3, while the euphotic depth was determined through an iterative estimation based on chlorophyll profiles75.

Global ocean biogeochemistry hindcast
The Mercator-Ocean Global Ocean Biogeochemistry L4 product provides a daily and monthly three-dimensional reanalysis of biogeochemical fields from 1993 to the present at a 0.25° × 0.25° resolution across 75 depth levels. This product, generated using the latest versions of the NEMO and PISCES biogeochemical models, is freely accessible at https://doi.org/10.48670/moi-00019. We extracted daily nitrate, silicate, and phosphate data for the Southern Ocean from 1998 to 2023.

Regionalsubdivision and masking
We divided the Southern Ocean into the STZ, SAZ, and POOZ based on hydrographic parameter isolines, following the major frontal structures that delineate these subregions26. Here, the POOZ is defined as the region between the polar front and the Seasonal Sea Ice Zone (SSIZ). Since SST beneath sea ice cannot be retrieved by satellites, areas within the SSIZ were masked to minimize the influence of sea-ice cover on MHW/MCS detection.

Definition and categorization of MHW and MCS
In this study, MHW and MCS were defined using the criteria established by Hobday et al.2,8,34. Specifically, an MHW was defined as a period of at least five consecutive days when SST exceeded the 90th percentile of a climatological baseline (1991–2020). Conversely, an MCS was defined as a period of at least five consecutive days when SST fell below the 10th percentile. The 1991–2020 climatology, recommended by the WMO, was used as the baseline. This period provides a balanced representation of recent climate conditions between the cooler 1990s and warmer 2010s, benefits from improved satellite data quality after the early 1990s, and aligns with the temporal coverage of the Chl-a dataset (1998–2023). Events occurring within less than three days of each other were aggregated into a single event to mitigate the impact of short-term fluctuations. To smooth SST data, we first applied an 11-day moving average followed by a 31-day moving average. This dual-stage smoothing process helps to reduce noise and capture the border temporal patterns of temperature anomalies.

For categorizing the severity of MHWs and MCSs, we adopted the approach proposed by Hobday et al.8,34. This approach classifies events into four categories based on the magnitude of the peak temperature anomaly compared to the threshold distance, which is defined as the difference between the 90th percentile and the climatological mean:

(1) Category I (moderate): The anomaly equals the threshold distance.
(2) Category II (strong): The anomaly is twice the threshold distance.
(3) Category III (Severe): The anomaly is three times the threshold distance.
(4) Category IV (Extreme): The anomaly is four or more times the threshold distance.

These classifications were rounded down to the nearest whole number for consistency. For MCS, the categorization mirrored that of MHWs, with the threshold distance representing the maximum negative temperature anomaly below the 10th percentile. The development phase of MHW (Phase 1) was defined as the period from the onset of the heatwave to the peak temperature anomaly76, while the decay phase (Phase 2) extended from the peak anomaly (excluding the peak day) to the end of the heatwave.

PSC model
We employed a 17-parameter PSC model77, that uses daily Chl-a concentrations and SST as inputs. This model outputs the Chl-a concentration of three different PSCs. These size classes include picoplankton (<2μm), nanoplankton (2–20μm), and microplankton (>20μm). For detailed methodological descriptions and parameter settings, refer to Supplementary Text 2. We evaluated the performance of the PSC algorithm in the Southern Ocean using in situ HPLC datasets (Supplementary Figs. 12, 13).

Since SST is included as an input variable, this may potentially introduce circularity in interpreting phytoplankton responses to temperature. To address this concern, we conducted additional analyses of model uncertainty and sensitivity. Our results indicate that incorporating SST leads to more robust and accurate PSC estimates compared with using Chl-a alone (Supplementary Figs. 14–16). Nevertheless, we additionally validated the anomalous variations in the outputs of the PSC model without SST input during MHWs and MCSs (Supplementary Fig. 17). The results demonstrate that, even without SST input, the conclusions of this study remain valid, effectively addressing the issue of circularity.

The in situ observational data used for model validation were derived from diagnostic pigment analysis of HPLC measurements78–81. The total Chl-a concentration was estimated as a weighted sum of seven diagnostic pigments, according to the following equation:

C = \sum_{i=1}^{7} W_i P_i (1)

where W_i and P_i represent the weight and the corresponding diagnostic pigment, respectively. The diagnostic pigments include fucoxanthin, peridinin, 19′-hexanoyloxyfucoxanthin, 19′-butanoyloxyfucoxanthin, alloxanthin, total chlorophyll b, and zeaxanthin. The weights were calculated through multiple linear regression following Sun et al.77 (Supplementary Table S2). The estimated total Chl-a showed good agreement with in situ measurements (correlation coefficient = 0.982, root mean square difference in log10 space=0.111)77. The Chl-a fractions of PSCs were then calculated using the following equations82:

F_1 = \frac{12.5C_w P_3 + \sum_{i=6}^{7} W_i P_i}{C_w} if C \leq 0.08 mg m^{-3} (2)
F_1 = \frac{\sum_{i=6}^{7} W_i P_i}{C_w} if C > 0.08 mg m^{-3}

F_2 = \frac{12.5C_w P_3 + \sum_{i=4}^{5} W_i P_i + W_1 P_{1,n}}{C_w} if C \leq 0.08 mg m^{-3} (3)
F_2 = \frac{\sum_{i=3}^{5} W_i P_i + W_1 P_{1,n}}{C_w} if C > 0.08 mg m^{-3}

F_3 = \frac{\sum_{i=1}^{2} W_i P_i - W_1 P_{1,n}}{C_w} (4)

where F_1, F_2, and F_3 represent the fractions of picoplankton, nanoplankton, and microplankton, respectively. Since fucoxanthin is also present in nanoplankton, a portion of fucoxanthin was assigned to nanoplankton83, i.e.:

P_{1,n} = 10^{q_1 log_{10}(P_3) + q_2 log_{10}(P_4)} (5)

Finally, the fractions of each size class were multiplied by the estimated total Chl-a to obtain the Chl-a concentrations of different phytoplankton size classes.

Estimate of mixed-layer averaged irradiance
The average irradiance within the mixed layer (the availability of light in the mixed layer) can be estimated using the following formula84.

I_{ml} = \frac{1}{z_{ml}} \int_{0}^{z_{ml}} I_0 e^{-kz} dz = \frac{I_0}{k z_{ml}} (1 - e^{-k z_{ml}}) (6)

where I_0 represents the photosynthetically active radiation (PAR) incident at the sea surface, k is the diffuse attenuation coefficient, and z_{ml} is the mixed-layer depth. k data is provided by OC-CCI, and PAR data is provided by the GlobColour project with a spatial resolution of 4km. The mixed-layer depth data are sourced from the Copernicus Marine Service (CMEMS) global ocean physics reanalysis product (GLORYS), covering the period from 1998 to 2023.

Definition of anomaly
In investigating MHWs and MCSs, we removed extreme values by calculating an 11-day running mean of sea surface temperature, followed by smoothing with a 31-day moving average. We applied a similar approach to daily chlorophyll-a and nitrate concentration data. To identify anomalies, we calculated the daily deviation from the climatological mean for each grid point. The climatology and anomalies were both based on a 1998–2023 baseline period.

In situ observation dataset
This study used 3508 high-performance liquid chromatography (HPLC) samples collected from the surface layer of the Southern Ocean between 1998 and 2023. The data were sourced from the Global HPLC Phytoplankton Pigment Data Compilation, Version 2 dataset provided by PANGAEA and the Australian Marine Biogeochemical Optical Database (https://portal.aodn.org.au/search). In this study, only samples collected within the top 20 meters of the water column were used. To ensure the quality of the pigment data, HPLC data with Chl-a concentrations below 0.001 mg/m³ were excluded, and diagnostic pigments with concentrations below 0.001 mg/m³ were set to zero85. The HPLC data were retained only when the difference between total chlorophyll concentration and total accessory pigment concentration was less than 30% of the total pigment concentration86. Using seven pigment analyses, we calculated the Chl-a fractions for three phytoplankton categories (picoplankton, nanoplankton, and microplankton), with detailed formulas provided in the supplementary document. The results of the PSC model outputs were also validated.

Data availability
The daily SST-CCI (version 3.0) and SIC data are provided by ESA from their website: https://doi.org/10.5285/4a9654136a7148e39b7feb56f8bb02d2. The daily Rrs-CCI and k_d (version 6.0) data are provided by ESA from their website: https://rsg.pml.ac.uk/thredds/catalog-cci.html. The PAR data are provided by GlobColour from their website: https://hermes.acri.fr/index.php?class=archive. The nitrate data provided by CMEMS from their website: https://doi.org/10.48670/moi-00019. The MLD (GLORYS12V1) data provided by CMEMS from their website: https://doi.org/10.48670/moi-00021. The HPLC data provided by PANGAEA from their website: https://doi.org/10.1594/PANGAEA.938703. The HPLC data provided by ADON from their website: https://portal.aodn.org.au/search. The data used to generate the figures presented in this study are available via figshare at https://doi.org/10.6084/m9.figshare.3021702187.

Code availability
The analyses were performed using MATLAB and Python; the main code used in this study is available at https://doi.org/10.5281/zenodo.1722329288, with BGC-Argo data processing based on code from https://github.com/NOAA-PMEL/OneArgo-Mat and MHWs/MCSs detection using code from https://github.com/ZijieZhaoMMHW/m_mhw1.0.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Review of the manuscript entitled “Extreme temperature events reshuffle the ecological landscape of the Southern Ocean”.\nBased on multiple long-term datasets, this study analyses the spatial and temporal distributions and variations of marine\nheatwaves (MHWs) and cold spells (MCSs) in the Southern Ocean and explores their effects on phytoplankton biomass and\nsize composition in the surface ocean. The authors have identified distinct patterns in the occurrence, duration, and intensity\nof MHWs and MCSs across the Southern Ocean, which may be influenced by the background of climate change and\nregional physical properties. Additionally, the study explores how chlorophyll-a (Chla) anomalies respond to these events,\nsuggesting that MHWs and MCSs have opposite effects. The authors also partition the total Chla into three size classes, to\ninvestigate the composition of different size classes in different regions of the Southern Ocean and their responses to MHWs\nand MCSs events, discussing the potential causes related to ecological niches, such as varying nutrient requirements.\nThis manuscript addresses timely topics, with the potential to contribute to our understanding of marine ecosystems in the\nSouthern Ocean. The authors demonstrate a clear understanding of the topic and have applied appropriate methods to\naddress their research questions. The authors have made efficient use of long-term datasets (e.g., OISST, CMEMS) and\nexisting frameworks/models (e.g., MHWs and MCSs criteria, PSC model, CSD model). The manuscript is well-organised\nand clearly written, with discussions that support the study’s findings and align with previous research. The conclusions are\nclear.\nHowever, there are several areas where the manuscript could be strengthened to enhance its robustness and impact. I have\nprovided some comments below.\nGeneral comments\n1. Multiple long-term datasets are used in the manuscript, including OISST, CMEMS, and MODIS. However, several critical\ndetails about these datasets are unclear and would benefit from further clarification:\na) Which specific Chla dataset from CMEMS was used in this study? It would be helpful to clarify this in the Methods section.\nCMEMS offers several Chla products, and some may not be ideal for trend analysis under climate change conditions (e.g.,\nPauthenet et al., 2024).\nb) The rationale for using both CMEMS and MODIS Chla data is not clear. Could you please explain the reason? What are\nthe key differences between the Chla products derived from CMEMS and MODIS? Additionally, which dataset did you use\nfor the Chla analysis and size analysis? Specifying this would help improve clarity.\nc) There is a time range discrepancy between the SST and Chla products. Could this discrepancy introduce uncertainties,\nparticularly when comparing SST-based MHW/MCS events with Chla and Chla-based phytoplankton size metrics? I\nrecommend considering an analysis using the same time period (possibly in supplementary material) to provide additional\nevidence and further strengthen your results.\n2. It has been suggested that the optical properties of the Southern Ocean are unique (Szeto et al., 2011), compared to other\nregions. Global satellite algorithms have been known to underestimate Chla in this area (e.g., Johnson et al., 2013). Given\nthis, it would be important to verify the accuracy of the Chla products used (CMEMS and MODIS) before proceeding with the\nanalysis. Alternatively, I would recommend considering the use of regional algorithms that are better suited to the specific\nconditions of the Southern Ocean.\n3. Similar to the previous point, the applicability of the PSC and CSD models in the study area may require more robust\nevidence. For example, the PSC model is designed for the global ocean and includes temperature as a model input. I am\ncurious whether these parameters are suitable for studying MHW/MCS events, which are typically discrete and short-term\nfluctuations. In other words, does phytoplankton size composition truly change under MHW/MCS events, or could this be an\nartifact of the model’s calculations? Similarly, the construction of the CSD model within the manuscript and supplement is\nnot clear. Could you provide more details? For example, how was the aph calculated from the Rrs in MODIS? In Wage et al.\n(2019), the parameters used in the CSD model were derived from their in-situ Chla and aph dataset. How were these\nparameters determined in your manuscript?\n4. The Antarctic and sub-Antarctic zones are particularly susceptible to the impacts of sea ice. Have you considered the\neffects of sea ice coverage and sea ice decline in your study area? Specifically, has this been accounted for when analysing\nthe impact of MHW/MCS events? I suspect that the distribution of MHWs and MCSs in the sub-Antarctic and Antarctic\nregions could be influenced by long-term sea ice melting? For example, the more pronounced trends observed in the\nAntarctic zone in the later years (Fig. 2) could potentially be influenced by sea ice decline, leading to the use of more valid\ndata in recent years. Please provide more details on how sea ice has been considered in your analysis.\nSpecific comments\n1. Abstract. The Abstract mentions topics such as light, iron, or carbon, which are not fully analysed or presented in the\nmanuscript. It might be better to consider removing these references from the Abstract\n2. Line 71. The OISST is not satellite-based only. According to Huang et al. (2021), \"The NOAA/NESDIS/NCEI Daily\nOptimum Interpolation Sea Surface Temperature (SST), version 2.0, dataset (DOISST v2.0) is a blend of in situ ship and\nbuoy SSTs with satellite SSTs derived from the Advanced Very High Resolution Radiometer (AVHRR).\" Please consider\nrevising this sentence.\n3. Line 101. Please specify the method used to calculate the Chla anomalies in the Methods section.\n4. Line 120. The reference should be Fig. 4i, m?\n5. Line 195. The text mentions \"strong positive anomalies,\" but it is not clear what these anomalies refer to. Please add more\ndetails here.\n6. Lines 336-347. As mentioned above, the time ranges of SST and Chla (CMEMS, MODIS) are different. Have you\nconsidered whether this is comparable when analysing the response of phytoplankton to MHWs and MCSs? Please clarify.\n7. Line 352. Could you explain why the climatological baseline is defined between 1982 and 2011, rather than between\n1982 and 2022?\n8. Line 362. Please reemphasise what is the threshold distance being used here.\n9. Lines 377-378. I recommend changing “less than 2 micrometers” to “< 2 μm”, and same format change should be applied\nto microphytoplankton and nanophytoplankton as well.\n10. Line 385. Are these Rrs data from MODIS? Please specify the source and any relevant details.\nReferences:\nHuang, B., Liu, C., Banzon, V., Freeman, E., Graham, G., Hankins, B., ... & Zhang, H. M. (2021). Improvements of the daily\noptimum interpolation sea surface temperature (DOISST) version 2.1. Journal of Climate, 34(8), 2923-2939.\nJohnson, R., Strutton, P. G., Wright, S. W., McMinn, A., & Meiners, K. M. (2013). Three improved satellite chlorophyll\nalgorithms for the Southern Ocean. Journal of Geophysical Research: Oceans, 118(7), 3694-3703.\nPauthenet, E., Martinez, E., Gorgues, T., Roussillon, J., Drumetz, L., Fablet, R., & Roux, M. (2024). Contrasted trends in\nchlorophyll‐a satellite products. Geophysical Research Letters, 51(14), e2024GL108916.\nSzeto, M., Werdell, P. J., Moore, T. S., & Campbell, J. W. (2011). Are the world's oceans optically different?. Journal of\nGeophysical Research: Oceans, 116(C7).", "reviewer_2": "The current manuscript describes results from an analysis of satellite data regarding impacts of extreme events (heat waves\n& cold spells) in the Southern Ocean. The analysis reveals some interesting patterns and temporal trends, but there are\nmajor weakness that compromise the integrity of the work and the robustness of the conclusions. Below are specific\ncomments beginning with major issues and followed by minor comments.\nMajor Issues\n1) All analyses regarding phytoplankton size classes and size distributions reported in this study are not robust. The primary\nissue is that the authors are using global relationships from previous publications and applying them to interpretations of\nanomalies. For example, the phytoplankton size structure model of Sun et al. (2023) is based on a global field data set (see\ntheir figure 1) where model performance is evaluated over property values spanning multiple orders of magnitude (see their\nfigure 3). It is clear in their figure 3 that even when plotted on log-log axes, there is significant unexplained variance. If this\nmodel is then applied to Southern Ocean only data for relatively small anomalies in SST and chlorophyll, there is no reason\nto believe that the modeled predictions in phytoplankton size classes are robust. A similar issue also holds for the Waga et al. (2019) model on phytoplankton size structure (or what the current authors refer to as the chlorophyll size distribution,\nCSD). The Waga et al. paper is focused on the impact of mesoscale eddies on CSD and is not inherently equally valid for\nlarge scale impacts of extreme events. Waga et al. evaluated their model performance based on, primarily, northern\nhemisphere data on the western edge of the pacific ocean (see their figure 1), with limited data from the Southern Ocean\nwith undefined seasonal representation (and with roughly half of the data collected immediately next to the Antarctic\ncontinent). The model used information on both chlorophyll and spectral absorption, with the latter property included to aid in\nretrievals in oligotrophic waters (essentially irrelevant for the current analysis. Waga et al.’s resultant model was validated by\ncomparing in situ CSD slopes to predicted slopes for a dynamic range in values from ~0.5 to 3 (see their figure 2), an\nanalysis the exhibited significant unexplained variance. For the current study, reported anomalies in CSDs associated with\nextreme events appear to largely fall within the range of +/- 0.1, which certainly can not be considered significant (or even\nrobust in sign) given the Waga et al evaluation of model performance. Given the above considerations, I do not believe the\nevaluation of community composition impacts of extreme events presented in the current manuscript are robust and\nrecommend that these be removed in future versions of this paper.\n2) (lines 182-236) I found this discussion of mechanisms for phytoplankton responses inconsistent and not adequately\nsupported. Here are specific examples:\na. (line 189-191) Here it is stated that phytoplankton in the SAZ are iron limited, but then it says that MHWs alleviate light\nlimitation and stimulate growth. First issue is that there is no evidence provided that MHWs alleviate light limitation. Second,\nthere is no connection made between an increase in light and how that stimulates iron limited phytoplankton.\nb. (lines 191 -195) Here is it stated that in the AZ phytoplankton are co-limited by iron and light, but no evidence is provided\nthat this is always the case. More likely, the cells are limited by light only in winter months, noting here that many MHWs for\nthe AZ occur in fall and winter (figure 1g). It is then stated that MHWs enhance water column stability (no evidence provided),\nalleviate light limitation (no evidence provided), and accelerate ice melt (no evidence provided), the latter alleviating iron\nstress (but wouldn’t this only be immediately adjacent to melting ice?). So apparently the suggestion in ‘a’ above about\nincreased light alleviating iron stress was not based on the presumption of changes in cellular stoichiometries for Fe.\nc. (line 197-199) Here it is stated that enhanced mixing in the STZ (no evidence provided) alleviates nitrate limitation and\npromotes phytoplankton growth. First, the assumption here is that the increase in chlorophyll in the STZ during MCS is due to an\nincrease in biomass. IF the supposition is correct that the mixed layer deepens during an MCS, how do the authors\nknow that the change in chlorophyll isn’t simply photoacclimation to lower average mixed layer light levels? If it is an\nincrease in nutrient supply, one would have to show that the increase in mixing depth was sufficient to penetrate the\nnutricline, otherwise there is no strong reason to think that the deeper mixing (how much deeper, by the way, a few meters,\ntens of meters?) impacted nutrient supply. Second, it is then stated that the deeper mixing during MCS in the SAZ and AZ\ncaused greater light limitation. But it was stated prior to this that these regions are also iron limited (actually, during summer\nmonths they are iron limited more than light limited, as in situ iron fertilization experiments have shown). No explanation is\nprovided why deeper mixing in the SAZ and AZ doesn’t also increase iron supply to the mixed layer. Finally, be careful with\nthe word ‘growth’. Do you mean cell division rates or changes in biomass. The two are not the same. The mechanisms you\ndiscuss are relevant to division rate, but what you observed from space are changes in biomass, which are also controlled\nby loss processes (which are barely mentioned at all in this manuscript).\nd. (lines 200-201) This is a repeat of the sentence before.\ne. (line 202) ‘Massive’? I don’t see any evidence of ‘massive’ responses. In the STZ, chlorophyll in places increased by 20%\nor so, but 20% greater than what? I’m guessing the baseline concentrations are pretty low (e.g., see figure 4), so a 20%\nincrease above a low value is still a small increase.\nf. (lines 203–204) Iron is a nutrient too, so the fact that nitrate is always replete says nothing about how phytoplankton will\nrespond to increased nutrient supply.\ng. (lines 205-206) Here the authors invoke temperature limitation. How does this play into iron and light limitation. What is\nthe absolute magnitude of changes in SST during MHWs and MCSs? From figure 3, it looks like we are talking about small\n(generally <2oC) changes. What evidence is there that these small SST changes would have a significant impact on\ndivision rates?\nh. (lines 207-209) Here it is stated that MCSs in the AZ promote sea ice formation and this absorbs dissolved iron. What\nevidence do you have that sea ice formation changed significantly during the MCSs? If it did increase, these areas of sea ice\nformation would not provide valid satellite chlorophyll retrievals would they? I’m also not aware of ice formation drawing iron\nout of surrounding waters, only trapping the iron in the water that is being frozen, so I don’t see how sea ice formation would\nsignificantly impact iron levels away from the sea ice (i.e., in the waters where you get satellite chlorophyll data)\ni. (lines 211-214) I don’t see why high nitrate would support growth of picoplankton south of the subtropical front. I would\nexpect these waters to be iron limited (as the authors state earlier). If picoplankton in these waters responded to nitrate\nduring a MHW, they would already be abundant before the extreme event since these waters are persistently high nitrate.\nj. (lines 217-220) Here and in following text, the authors bring in silicate limitation. Where is the evidence that this is\nsignificant? Iron enrichment experiments indicate that silicate is not the key limiting nutrient.\nk. (lines 224-236) In this paragraph, the message has gotten completely confused with respect to what the primary limiting\nfactors are for phytoplankton and how they are linked to MHWs and MCSs.\nThe bottom line is that this section does not provide a conclusive or supported interpretation of the observational data.\n3) Given the above issues, I believe the Discussion section will need to be re-evaluated and rewritten.\nMinor comments (in order of appearance)\n1) (lines 26-28) how are ‘sharp reductions in surface chlorophyll’ and ‘extensive algal blooms’ consistent with each other?\n2) (line 103) ‘into’ not ‘in to’\n3) (line 118) Here it is stated that phytoplankton anomalies began to emerge 10 days before the onset of a MHW. In the\nmethods section, it is stated that SST data were smoothed over windows of 11 and 31 days. How does this smoothing\nimpact the ability to clearly identify when MHW impacts begin? If the current analysis is correct, why do you see responses\nto a MHW prior to the beginning of the event? I’m not saying this couldn’t occur, but an explanation is needed.\n4) More detail is needed regarding how anomalies were calculated. How was the baseline calculated? What was the\ntemporal resolution of the baseline? Were baselines calculated for each pixel, or were data within each of the three zones\ncompared to zonal average baselines? If the latter is the case, how would persistent spatial features impact apparent\nanomalies? If the former, how are the impacts of advection addressed?\n5) (lines 144-147) Get rid of the semicolons and break this text into 3 sentences.\n6) (line 148) ‘it’ is the incorrect word here\n7) (in ‘Distinct responses’ section … and noting here (as stated in Major issues #1) that I don’t believe the phytoplankton\ncommunity analyses are robust) It was confusing to me that it is suggested in this paragraph that nano- and microplankton\nincreased at higher latitudes during MHWs and yet were paralleled increases in the apparent slope of the size distribution.\nHow are these consistent with each other. Also, the model used to calculate phytoplankton size class abundances could\nalso be used to calculate the size distribution slope (this is, in essence, what the Waga et al. model is doing). Why did you\nuse one model for size classes and another model for slope? How consistent are the two?\n8) (lines 155-157) These statements regarding nutrient requirements are not sufficiently supported.\n9) (lines 167-169) this statement doesn’t make sense, ‘Nanoplankton showed similar trends to…nanoplankton in the sub-\nAntarctic region.\n10) (line 169) delete ‘respectively’ and ‘Coincidentally’\n11) (lines 171-172) here again I don’t see how proliferation of larger cells resulted in a steeper slope.\n12) In general, I found the Discussion section to be overstated and to contain multiple repeats of the same points.\n13) (lines 241-244) This sentence needs to be supported by at least a hand full of citations.\n14) (lines 305-306) The distinction here between atmosphere forced and ocean process forced MHW impacts is very\nunclear.\n15) (line 321) Use a different word than ‘immeasurable’\n16) (line 344) Here it is stated that the CMEMS model is used to evaluate nitrate and silicate. How reliable are these model\nestimates? Remember, while this model might appear to capture global patterns well, how well can it be relied on to provide\nrobust estimates of nitrate and silicate changes during extreme events? Is this model even necessary for in the current\nstudy?\n17) (line 345-347) It should be stated somewhere here what Rrs data were used for. A reader will not understand this unless\nthey look into the supplemental materials.\n18) (lines 336-347) This section could be reorganized so you only have to say that the data were gridded to 0.25 degrees\nonce.\n19) (line 352) more detail is needed regarding how the ‘climatological baseline’ was calculated. A figure in the supplement\nmight also be helpful.\n20) (Definitions and categorization’ section) Were only SST data smoothed or were other products smoothed as well? How\ndid smoothing of data potentially impact assessment of timing of events and responses?\n21) (line 362) this is the first occurrence of ‘threshold distance’ and it should be clearly defined.\n22) (Figure 4 & 5) ‘Phase’ is misspelled throughout the two central columns of panels in both figures.\n23) (Figure 6) Why are CSD slopes positive? Shouldn’t they be negative (i.e., concentration increases with decreasing\nsize)?", "reviewer_3": "Extreme temperature events reshuffle the ecological landscape of the Southern Ocean\nKey Results\nThe authors investigate the response of phytoplankton size structures in the Southern Ocean during marine heat waves\n(MHW) and marine cold spells (MCS) using a suite of satellite-based data and modelling approaches. Their results showed\nMHWs were most frequent in the Subtropics and Subantarctic. Contrasting this, they found that MCSs were more frequent in\nthe Antarctic Zone. Some of the results section on the response of phytoplankton (and their size classes) to MHWs and\nMCSs were difficult to follow - I have highlighted some examples below in the section on clarity. The authors show that\nduring MCSs in the Subtropical Zone, picoplankton increased whereas in the Antarctic and Subantarctic Zone micro and\nnano plankton decreased. They also show that the inverse was true for MHW, with micro and nano size classes increasing\nin the Antarctic Zone.\nI think that it is an excellent idea to study the effects of MHWs and MCSs on phytoplankton in the Southern Ocean. However,\nthe methods and data used in this study are not reproducible, and the choice of data products is not detailed, and in some\ncases poorly chosen. As such I cannot recommend this article for publication and suggest that this article is not accepted.\nBelow, I have detailed my reasoning, and I hope that the authors find my feedback helpful and constructive. If the authors\nchoose to resumbit the paper, I strongly suggest that they vigorously consider and scrutinise their choice of data and\nmethods.\nValidity\nBased on the figures provided, I believe that the interpretations of these results support the trends of phytoplankton size\nclasses during MHW and MCS events. However, I think that their discussion on the biogeochemical / environment drivers\nunderpinning these changes requires further work. There is no mention of phytoplankton groups or taxonomy. For example,\nin the Subantarctic Zone haptophytes (E. hux) dominate (Balch et al., 2014). It is widely recognised that in the Antarctic Zone\ndiatoms and haptophyes (P. antarctica) are dominant (Smith et al., 2014; Hayward et al., 2024). These different groups, and\nspecies within them, respond differently to environmental stimuli. As such, the discussion on phytoplankton size classes\nshould be accompanied by discussing phytoplankton speciation and phenology.\nSignificance\nI think that these results would be significant and important to biogeochemists, ecosystem modellers and earth system\nscientists. However, I struggle to move past the poorly detailed methods and data used in the study.\nData and methodology\nThe methods and data used in this manuscript lack scientific rigour and are not reproducible. This is primarily the reason I\nam not accepting this article. I have detailed my reasoning thoroughly below.\nThe data used in the study would benefit from clearer definition and more detailed justification to ensure it is well-suited to\nthe study's objectives. For chlorophyll a (Chl-a) data the authors state that they used ‘chlorophyll from CMEMS’. A CMEMS\ndatabase search showed there are 54 different Chl-a products available on CMEMS. See Pauthenet et al., (2024) which\ndetails differences in commonly used Chl-a products. It is very important to specify what product is used to ensure results are\nreproducible, but to also further understand potential uncertainties and biases. The authors should state what CMEMS\nproduct was chosen and why it was used in the study. Furthermore, it has not been possible to assess any code as in the\n‘code availability’ statement, all the authors said was that the analysis was conducted in Matlab.\nThe authors use surface reflectances from MODIS-Aqua for their size class model. The time series for MODIS-Aqua (May -\n2002 to 2024) is shorter than their study period (1998 - 2022). Furthermore, in 2019 MODIS-Aqua drifted out of orbit. There is\na disclaimer on the NASA website suggesting that data from ~2019 should be treated with caution. As such, I would not trust\nthe PSC / CDS results from 2019 - 2022 in this study from MODIS-Aqua.\nThe authors use the OISSTv2 product in their study to assess MHW and MCS. This product is one of the older products\nused in SST remote sensing and no longer considered the ‘gold standard’ (instead, see the SST CCI). The product is also of\ncoarse spatial resolution (.25 degrees) compared to more modern products, and does not match the spatial resolution of\nchlorophyll data (4km). The authors interpolated the chl-a product to make it coarser and on the same grid as the OISST\nproduct. In doing so they will miss out on many of the finer scale processes in the dataset.\nOne of the largest caveats of this study is that the remote sensing of sea surface temperatures is not possible for much of the\nyear in polar regions due to ice cover. Where there is sea ice the OISST algorithm estimates the under ice SST by a linear fit\ncapped at -1.8 celsius. As such, the MCSs observed in this study are not the result of actual satellite observations, but could\nin fact be due to how the OISST product handles under ice temperatures.\nThe methods section is very short, and there is no information provided on the CSD model, other than a short description in\nthe supplementary information. The PSC model used was recently published (2023); and the PSC model is dependent on\nSST observations. There should be discussion regarding uncertainty model uncertainty and performance, especially when\nthere can be issues of circularity as both the size class model use SST and the authors investigate the response of size\nclasses to MHWs and MCSs.\nFinally, there is inconsistency in the date ranges. Chl-a observations are only available during summer high latitudes, and\nmany observations are not possible due to satellites not being able to assess ocean colour during periods of ice cover.\nFurthemore, the baseline and time series of Chl-a will be shorter than the baseline of the SST product, adding further\ninconsistencies.\nClarity and Context\nOverall, the clarity of the manuscript could be tightened and improved. There are lots of ‘they’ and ‘this’ without explicitly\nspecifying what is changing and why. Furthermore, the structure could be improved by using more subheadings. For\nexample: ‘Phytoplankton Size classes during a MHW,’ and ‘Phytoplankton size classes during a MCS’. The authors can\nthen address each size class separately in different paragraphs to improve the readability.\nOn line 144:\nMicroplankton demonstrated apparent variability at the polar front; north of the polar front, they maintained a negative\nresponse; however, south of the polar front, their response shifted to a positive phase, indicating significant proliferation in\nthe Antarctic zone.\nDissecting this passage, it is difficult to understand what ‘apparent variability’ means, increasing, decreasing? Following\nthis, the authors say that it ‘maintained a negative’ response. What are they responding to, a MHW a MCS, this isn’t clear at\nall.\nIn the opening line to a paragraph on line 117: ‘The temporal evolution of these responses further highlighted their\ncomplexity (Fig. 4)’. The authors need to be clear as to what ‘these responses’ are, especially if they are opening a\nparagraph with this statement; similarly, the authors should define what ‘their complexities’ are. Don’t leave it to the reader to\ntry and guess what you are trying to convey. In academic writing, for any journal, try to really spell these things out, be clear\nand avoid vague language.\nThe abstract states (line 8): MHWs suppressed phytoplankton growth in the subtropical zone but stimulated it in the sub-\nAntarctic and Antarctic zones by alleviating light and iron limitations.\nThere is nowhere in the study that iron or light is investigated. I do not understand how the authors can state this in their\nabstract.\nReferences\nThe manuscript is oddly referenced. For example, reference 9 is in relation to Southern Ocean MHWs, but the citation\ndescribes MHWs in the South Atlantic. Reference 13 is provided in the context of MCSs in the Southern Ocean, however the\nreference is located in the North Sea.\nBalch, W. M., Bates, N. R., Lam, P. J., Twining, B. S., Rosengard, S. Z., Bowler, B. C., ... & Rauschenberg, S. (2016). Factors\nregulating the Great Calcite Belt in the Southern Ocean and its biogeochemical significance. Global Biogeochemical\nCycles, 30(8), 1124-1144\nSmith Jr, W. O., Ainley, D. G., Arrigo, K. R., & Dinniman, M. S. (2014). The oceanography and ecology of the Ross Sea.\nAnnual review of marine science, 6(1), 469-487\nHayward, A., Pinkerton, M. H., Wright, S. W., Gutiérrez-Rodriguez, A., & Law, C. S. (2024). Twenty-six years of\nphytoplankton pigments reveal a circumpolar Class Divide around the Southern Ocean. Communications Earth &\nEnvironment, 5(1), 92.\nPauthenet, E., Martinez, E., Gorgues, T., Roussillon, J., Drumetz, L., Fablet, R., & Roux, M. (2024). Contrasted trends in\nchlorophyll‐a satellite products. Geophysical Research Letters, 51(14), e2024GL108916"}, "author_response": "(Note: Reviewer comments in black and our point-to-point replies in blue)\nReview of the manuscript entitled “Extreme temperature events reshuffle the ecological\nlandscape of the Southern Ocean”.\nBased on multiple long-term datasets, this study analyses the spatial and temporal\ndistributions and variations of marine heatwaves (MHWs) and cold spells (MCSs) in\nthe Southern Ocean and explores their effects on phytoplankton biomass and size\ncomposition in the surface ocean. The authors have identified distinct patterns in the\noccurrence, duration, and intensity of MHWs and MCSs across the Southern Ocean,\nwhich may be influenced by the background of climate change and regional physical\nproperties. Additionally, the study explores how chlorophyll-a (Chla) anomalies\nrespond to these events, suggesting that MHWs and MCSs have opposite effects. The\nauthors also partition the total Chla into three size classes, to investigate the\ncomposition of different size classes in different regions of the Southern Ocean and\ntheir responses to MHWs and MCSs events, discussing the potential causes related to\necological niches, such as varying nutrient requirements. This manuscript addresses\ntimely topics, with the potential to contribute to our understanding of marine\necosystems in the Southern Ocean. The authors demonstrate a clear understanding of\nthe topic and have applied appropriate methods to address their research questions. The\nauthors have made efficient use of long-term datasets (e.g., OISST, CMEMS) and\nexisting frameworks/models (e.g., MHWs and MCSs criteria, PSC model, CSD model).\nThe manuscript is well-organised and clearly written, with discussions that support the\nstudy’s findings and align with previous research. The conclusions are clear. However,\nthere are several areas where the manuscript could be strengthened to enhance its\nrobustness and impact. I have provided some comments below.\nReply: We sincerely thank the reviewer for the thoughtful and constructive feedback,\nas well as for recognizing the novelty and scientific merit of our work. We appreciate\nyour positive assessment of the manuscript's organization, clarity, and use of long-term\nsatellite and biogeochemical datasets. Your suggestions have been instrumental in\nimproving the scientific robustness, coherence, and overall impact of our study. In\nresponse, we have carefully revised the manuscript to address all comments in detail.\nSpecific changes have been incorporated in the main text, figures, and supplementary\nmaterials where appropriate. Below, we provide a detailed, point-by-point response to\neach suggestion.\nGeneral comments\n1. Multiple long-term datasets are used in the manuscript, including OISST, CMEMS,\nand MODIS. However, several critical details about these datasets are unclear and\nwould benefit from further clarification:\na) Which specific Chla dataset from CMEMS was used in this study? It would be\nhelpful to clarify this in the Methods section. CMEMS offers several Chla products,\nand some may not be ideal for trend analysis under climate change conditions (e.g.,\nPauthenet et al., 2024).\nReply: We appreciate the reviewer’s insightful comment regarding the chlorophyll-a\ndata sources and the importance of selecting an appropriate dataset for climate-scale\nanalyses. In our initial submission, we referenced a CMEMS GlobColour Level-4 daily\nChl-a product with a spatial resolution of 4 km, which applies multi-sensor merging\nand spatial interpolation to generate a gap-free dataset (https://doi.org/10.48670/moi-\n00281). However, we fully agree with the reviewer that such interpolated products,\nwhile convenient for gap filling, may introduce uncertainties that limit their suitability\nfor detecting robust long-term trends (as noted in Pauthenet et al., 2024).\nTo address this concern and improve the robustness of our results, we revised the\nanalysis to use daily remote sensing reflectance (Rrs) data from the ESA Ocean Colour\nClimate Change Initiative (OC-CCI v6.0). This dataset combines observations from\nmultiple sensors (SeaWiFS, MODIS-Aqua, MERIS, and VIIRS) and applies rigorous\ninter-sensor bias correction and spectral harmonization, making it well-suited for long-\nterm climate applications. Chlorophyll-a concentrations were then derived using the\nOC4v6 algorithm, further optimized for Southern Ocean conditions. These revisions\nand clarifications are now explicitly stated in the Methods section.\nb) The rationale for using both CMEMS and MODIS Chla data is not clear. Could you\nplease explain the reason? What are the key differences between the Chla products\nderived from CMEMS and MODIS? Additionally, which dataset did you use for the\nChla analysis and size analysis? Specifying this would help improve clarity.\nReply: We thank the reviewer for highlighting the need to clarify the rationale and\ndifferences between chlorophyll-a products. In the original version of the manuscript,\nwe used chlorophyll-a data from the CMEMS GlobColour product. However, we did\nnot directly use MODIS-derived chlorophyll-a data. Instead, MODIS remote sensing\nreflectance (R ) data were previously used to derive the chlorophyll size distribution\nrs\n(CSD) slope for assessing phytoplankton size structure.\nRecognizing concerns regarding the uncertainty and limited dynamic range of the CSD\nmetric (±0.1 during extreme temperature events), as well as suggestions from the\nsecond reviewer, we have removed the CSD-based analysis from the revised manuscript.\nTo ensure consistency and robustness across all analyses, we now use daily R data\nrs\nfrom the ESA OC-CCI v6.0 dataset, which integrates MODIS-Aqua, SeaWiFS, MERIS,\nand VIIRS observations with rigorous inter-sensor calibration. Chlorophyll-a was\ncalculated using the OC4v6 algorithm optimized for the Southern Ocean. For\nphytoplankton size structure, we employed the phytoplankton size class (PSC) model,\nwhich estimates the Chl-a contribution of picoplankton, nanoplankton, and\nmicroplankton from OC-CCI Chl-a and SST inputs. These clarifications have been\nincorporated into the revised Methods section to improve transparency and\nreproducibility.\nc) There is a time range discrepancy between the SST and Chla products. Could this\ndiscrepancy introduce uncertainties, particularly when comparing SST-based\nMHW/MCS events with Chla and Chla-based phytoplankton size metrics? I\nrecommend considering an analysis using the same time period (possibly in\nsupplementary material) to provide additional evidence and further strengthen your\nresults.\nReply: We appreciate this important observation. Indeed, in our analysis, sea surface\ntemperature (SST) data span 1982–2023, while satellite-derived chlorophyll-a (Chl-a)\ndata are available from 1998 onwards. This discrepancy arises from the longer temporal\ncoverage of the ESA CCI SST product compared to the OC-CCI ocean color dataset.\nTo address this concern, (1) For the detection and characterization of MHWs and MCSs\n(Figures 1 and 2), we used the full 1982–2023 SST time series to ensure a robust 30-\nyear baseline for defining climatological percentiles, as recommended by Hobday et al.\n(2016, 2018); (2) For analyzing phytoplankton responses (Figures 3 and beyond), we\nrestricted the event selection to 1998–2023, thereby ensuring temporal consistency with\nthe Chl-a record and derived phytoplankton size metrics.\nWe agree that a time-matched analysis could help assess potential impacts of this\ndiscrepancy. Accordingly, we have conducted a supplementary analysis in which\nMHW/MCS events were identified solely using SST data from 1998–2023 to match the\nChl-a record. The resulting patterns in phytoplankton response remain consistent with\nour main findings, demonstrating the robustness of our conclusions. These additional\nresults have been included in the revised Supplementary Material (Supplementary\nFigure X and accompanying text) and referenced in the main text for clarity.\n2. It has been suggested that the optical properties of the Southern Ocean are unique\n(Szeto et al., 2011), compared to other regions. Global satellite algorithms have been\nknown to underestimate Chla in this area (e.g., Johnson et al., 2013). Given this, it\nwould be important to verify the accuracy of the Chla products used (CMEMS and\nMODIS) before proceeding with the analysis. Alternatively, I would recommend\nconsidering the use of regional algorithms that are better suited to the specific\nconditions of the Southern Ocean.\nReply: We thank the reviewer for this insightful and important comment. Indeed, we\nfully acknowledge that the Southern Ocean presents unique optical conditions—\ncharacterized by low chlorophyll, high CDOM, and unusual backscattering\nproperties—that can challenge the performance of standard global ocean-color\nalgorithms (Szeto et al., 2011; Johnson et al., 2013). To address this, we did not use\ndefault CMEMS or MODIS-derived chlorophyll-a products in our analysis. Instead, we\nrelied on daily R data from the ESA Ocean Colour Climate Change Initiative (OC-\nrs\nCCI v6.0), and applied the OC4v6 algorithm modified specifically for the Southern\nOcean as described in Johnson et al. (2013). This algorithm accounts for regional bio-\noptical properties and substantially improves the accuracy of Chl-a retrievals.\nFurthermore, to validate the satellite-derived chlorophyll-a estimates, we used an\nextensive in situ dataset (n = 3508 samples) from HPLC observations in the Southern\nOcean, as detailed in the Methods section. Validation results are presented in\nSupplementary Figure 4, showing a strong agreement between satellite and in situ Chl-\na, confirming the reliability of our approach. These steps ensure that our analysis is\ngrounded in regionally appropriate optical modeling and has a strong observational\nbasis for validation.\nSupplementary Fig.4 | Validation between in-situ chlorophyll-a (Chl-a) concentration\nand Chl-a concentration derived from the chlorophyll algorithm (OC4V6) and the\nPhytoplankton Size Class (PSC) model, including total Chl-a concentration (a),\npicoplankton (b), nanoplankton (c), and microplankton (d).\n3. Similar to the previous point, the applicability of the PSC and CSD models in the\nstudy area may require more robust evidence. For example, the PSC model is designed\nfor the global ocean and includes temperature as a model input. I am curious whether\nthese parameters are suitable for studying MHW/MCS events, which are typically\ndiscrete and short-term fluctuations. In other words, does phytoplankton size\ncomposition truly change under MHW/MCS events, or could this be an artifact of the\nmodel’s calculations? Similarly, the construction of the CSD model within the\nmanuscript and supplement is not clear. Could you provide more details? For example,\nhow was the aph calculated from the Rrs in MODIS? In Wage et al. (2019), the\nparameters used in the CSD model were derived from their in-situ Chla and aph dataset.\nHow were these parameters determined in your manuscript?\nReply: We thank the reviewer for raising this critical point regarding the applicability\nand potential limitations of the PSC and CSD models, particularly in the context of\nshort-term extremes such as MHWs and MCSs. The phytoplankton size class (PSC)\nmodel we employed was developed by Sun et al. (2023) and explicitly trained and\nvalidated using satellite Chl-a and SST data with in situ size-fractionated pigment\nobservations, including data from the Southern Ocean. This model has shown improved\naccuracy in detecting size composition changes under varying physical conditions.\nAlthough MHWs and MCSs are short-term events, they cause detectable shifts in SST\nand stratification that influence size-fractionated phytoplankton growth. Recent\nliterature (e.g., Moore et al., 2020; Sun et al., 2023) demonstrates that even transient\ntemperature anomalies can drive shifts in phytoplankton community composition—\nwithout necessarily altering total Chl-a concentration—due to size-specific\nphysiological responses and ecological trade-offs (e.g., nutrient uptake efficiency,\nphotoacclimation). Thus, the inclusion of SST in the PSC model enables it to capture\nthese short-term structural shifts. To ensure reliability, we conducted additional\nvalidation using in situ HPLC datasets in the Southern Ocean (n = 3508 samples),\ncomparing PSC model outputs with pigment-based size class estimates. These results\nare now summarized in Supplementary Figures 3 and 4 and confirm the model’s\nrobustness across a range of environmental conditions, including those observed during\nextreme events.\nWe appreciate the request for more transparency regarding the CSD slope calculations.\nIn the original manuscript, we derived the CSD slope using spectral absorption\nparameters following Wage et al. (2019), estimating a from MODIS R via the quasi-\nph rs\nanalytical algorithm (QAA). However, as noted in our response to Comment 1b, we\nhave now removed the CSD analysis from the manuscript. This decision was based on\nconcerns about limited variability in CSD slope values (typically within ±0.1) and\nsuggestions from both reviewers that the PSC approach provides a more robust and\ninterpretable framework. The revised manuscript now focuses exclusively on the PSC\nmodel for assessing phytoplankton size structure changes, with updated validation and\nmethodological descriptions in the Methods and Supplementary Information.\n4. The Antarctic and sub-Antarctic zones are particularly susceptible to the impacts of\nsea ice. Have you considered the effects of sea ice coverage and sea ice decline in your\nstudy area? Specifically, has this been accounted for when analysing the impact of\nMHW/MCS events? I suspect that the distribution of MHWs and MCSs in the sub-\nAntarctic and Antarctic regions could be influenced by long-term sea ice melting?\nFor example, the more pronounced trends observed in the Antarctic zone in the later\nyears (Fig. 2) could potentially be influenced by sea ice decline, leading to the use of\nmore valid data in recent years. Please provide more details on how sea ice has been\nconsidered in your analysis.\nReply: We thank the reviewer for highlighting the critical role of sea ice in modulating\nboth ocean temperature variability and data availability in the Southern Ocean. To\naddress the influence of sea ice in our analysis, we took the following steps:\n(1) Masking ice-covered areas\nWe used daily sea ice concentration (SIC) data from the ESA CCI dataset (1982–2023)\nand applied a conservative mask, excluding all grid points with an annual mean SIC\ngreater than 20%. This threshold helps minimize the inclusion of partially ice-covered\nregions that could compromise the accuracy of satellite SST and ocean color\nobservations.\n(2) Trend analysis of sea ice decline\nTo assess the potential influence of sea ice loss on MHW/MCS trends, we calculated\nlong-term SIC trends across the Southern Ocean and examined their spatial overlap\nwith trends in MHW and MCS frequency and intensity. Our results (now shown in\nSupplementary Fig. 6) reveal that regions experiencing strong sea ice decline also show\nan increase in detected MHWs and a reduction in MCSs, particularly in the Antarctic\nzone. This suggests that the enhanced detection of extreme temperature events in recent\nyears may partially reflect sea ice retreat, which increases open-water coverage and\nimproves the temporal continuity of SST data.\n(3) Interpretation and caveats\nWe have revised the Discussion section to explicitly acknowledge this point: that long-\nterm sea ice decline may not only alter the thermal environment driving phytoplankton\nresponses but also affect the observational record itself by expanding the effective\nsampling area for satellite-derived SST and Chl-a. While our masking approach helps\nmitigate direct biases, the broader ecological implications of sea ice variability (e.g.,\nchanges in stratification, iron availability, and light regimes) remain significant and are\nnow discussed in more detail.\nSpecific comments\n1. Abstract. The Abstract mentions topics such as light, iron, or carbon, which are not\nfully analysed or presented in the manuscript. It might be better to consider removing\nthese references from the Abstract.\nReply: Thank you for your suggestion. Rectified as suggested.\n2. Line 71. The OISST is not satellite-based only. According to Huang et al. (2021),\n\"The NOAA/NESDIS/NCEI Daily Optimum Interpolation Sea Surface Temperature\n(SST), version 2.0, dataset (DOISST v2.0) is a blend of in situ ship and buoy SSTs with\nsatellite SSTs derived from the Advanced Very High Resolution Radiometer\n(AVHRR).\" Please consider revising this sentence.\nReply: Thank you for your feedback. In the revised manuscript, CCI-SST data were\nused instead of OISST.\n3. Line 101. Please specify the method used to calculate the Chla anomalies in the\nMethods section.\nReply: Thank you for your feedback. We have added the method for calculating the\nanomalies of 'Chl-a' in the 'Methods' section.\n4. Line 120. The reference should be Fig. 4i, m?\nReply: Thank you for your suggestion. Rectified as suggested.\n5. Line 195. The text mentions \"strong positive anomalies,\" but it is not clear what these\nanomalies refer to. Please add more details here.\nReply: Thank you for your suggestion. Rectified as suggested.\n6. Lines 336-347. As mentioned above, the time ranges of SST and Chl-a (CMEMS,\nMODIS) are different. Have you considered whether this is comparable when analysing\nthe response of phytoplankton to MHWs and MCSs? Please clarify.\nReply: We appreciate the reviewer’s attention to this important point. While the full\nSST dataset spans from 1982 to 2023, we ensured temporal consistency in all\nphytoplankton response analyses by restricting the selection of MHW and MCS events\nto the period 1998-2023, which aligns with the availability of satellite-derived\nchlorophyll-a data from ESA OC-CCI. Thus, although SST was used over a longer\nperiod for climatological baseline construction (e.g., defining the 90th and 10th\npercentile thresholds), only events occurring within the 1998–2023 window were\nincluded in our analysis of Chl-a anomalies and phytoplankton size structure. This\napproach ensures methodological consistency between datasets and minimizes bias due\nto mismatched observation periods. We have clarified this in the Methods section and\nconfirmed that all results related to phytoplankton responses are based on temporally\naligned datasets.\n7. Line 352. Could you explain why the climatological baseline is defined between 1982\nand 2011, rather than between 1982 and 2022?\nReply: We thank the reviewer for this important question. In defining the climatological\nbaseline for detecting marine heatwaves (MHWs) and marine cold spells (MCSs), we\nfollowed the standardized protocol established by Hobday et al. (2016), which\nrecommends a fixed 30-year reference period. The 1982–2011 window satisfies this\ncriterion and has become widely adopted in the literature to ensure consistency and\nfacilitate inter-study comparisons. Extending the baseline to include more recent years\n(e.g., up to 2022) would incorporate years already affected by the accelerating trends in\nocean warming, potentially leading to a shifting baseline and an underestimation of the\nseverity and frequency of recent extreme events. To maintain methodological rigor and\ncomparability with previous studies, we therefore retained the fixed 1982–2011\nclimatological period in our analysis. This rationale has been clarified in the revised\nMethods section.\n8. Line 362. Please reemphasise what is the threshold distance being used here.\nReply: We appreciate the reviewer’s suggestion to clarify this key definition. As now\nreiterated in the revised Methods section (Line 417), the threshold distance is defined\nas the difference between the climatological mean and the 90th percentile for MHWs\n(or between the climatological mean and the 10th percentile for MCSs), following the\nmethod established by Hobday et al. (2018). This value is used to classify the severity\nof each event (e.g., Category I–IV) based on how far the observed temperature anomaly\nexceeds the defined threshold.\n9. Lines 377-378. I recommend changing “less than 2 micrometers” to “< 2 μm”, and\nsame format change should be applied to microphytoplankton and nanophytoplankton\nas well.\nReply: Thank you for your suggestion. Rectified as suggested. Please refer to Lines\n432-433.\n10. Line 385. Are these Rrs data from MODIS? Please specify the source and any\nrelevant details.\nReply: We thank the reviewer for pointing this out. In the revised manuscript, we no\nlonger use MODIS R data to calculate the chlorophyll size distribution (CSD) slope.\nrs\nInstead, we now use remote sensing reflectance (Rrs) data from the ESA Ocean Colour\nClimate Change Initiative (OC-CCI) version 6.0, which integrates observations from\nmultiple sensors (SeaWiFS, MERIS, MODIS-Aqua, and VIIRS), with inter-sensor\nharmonization and bias correction. These R data serve as the input for chlorophyll-a\nrs\nconcentration estimates using the OC4v6 algorithm, which has been optimized for the\noptical properties of the Southern Ocean. All relevant dataset details, including spatial\nresolution, temporal coverage, and processing methods, are now clearly described in\nthe revised Methods section.\nReferences:\nHobday AJ, Alexander LV, Perkins SE, Smale DA, Straub SC, Oliver ECJ, et al. A\nhierarchical approach to defining marine heatwaves. Progress in Oceanography 2016,\n141: 227-238.\nHuang B, Liu C, Banzon V, Freeman E, Graham G, Hankins B, et al. Improvements of\nthe daily optimum interpolation sea surface temperature (DOISST) version 2.1. Journal\nof Climate 2021, 34(8), 2923-2939.\nJohnson R, Strutton PG, Wright SW, McMinn A, Meiners KM. Three improved satellite\nchlorophyll algorithms for the Southern Ocean. Journal of Geophysical Research:\nOceans 2013, 118(7), 3694-3703.\nPauthenet E, Martinez E, Gorgues T, Roussillon J, Drumetz L, Fablet R, Roux M.\nContrasted trends in chlorophyll‐a satellite products. Geophysical Research Letters\n2024, 51(14), e2024GL108916.\nSzeto M, Werdell PJ, Moore TS, Campbell JW. Are the world's oceans optically\ndifferent?. Journal of Geophysical Research: Oceans 2011, 116(C7).\nMoore TS, Christopher WB. Incorporating environmental data in abundance-based\nalgorithms for deriving phytoplankton size classes in the Atlantic Ocean. Remote\nSensing of Environment 2020, 240, 111689.\nSun X, Brewin RJW, Sathyendranath S, Dall’Olmo G., Airs R, Barlow R, et al.\nCoupling ecological concepts with an ocean-colour model: Phytoplankton size\nstructure. Remote Sensing of Environment 2023, 285, 113415.\nResponse to Reviewer #2\n(Note: Reviewer comments in black and our point-to-point replies in blue)\nThe current manuscript describes results from an analysis of satellite data regarding\nimpacts of extreme events (heat waves & cold spells) in the Southern Ocean. The\nanalysis reveals some interesting patterns and temporal trends, but there are major\nweakness that compromise the integrity of the work and the robustness of the\nconclusions. Below are specific comments beginning with major issues and followed\nby minor comments.\nReply: We sincerely thank the reviewer for the careful evaluation of our manuscript and\nfor highlighting both the strengths and areas requiring improvement. We appreciate\nyour acknowledgment of the novel spatial and temporal patterns identified in our\nanalysis. At the same time, we take seriously your concerns regarding the\nmethodological rigor and robustness of the conclusions. In response, we have carefully\naddressed each of your specific comments and have made substantial revisions to the\nmanuscript to enhance its scientific integrity, clarity, and overall impact. These changes\ninclude refining the methodological descriptions, improving dataset consistency,\nclarifying the use of region-specific algorithms, strengthening model validation,\nand updating our interpretation of key results. We believe these revisions have significantly\nimproved the quality and robustness of the study. A detailed point-by-point response to\neach of your comments follows below.\nMajor Issues\n1) All analyses regarding phytoplankton size classes and size distributions reported in\nthis study are not robust. The primary issue is that the authors are using global\nrelationships from previous publications and applying them to interpretations of\nanomalies. For example, the phytoplankton size structure model of Sun et al. (2023) is\nbased on a global field data set (see their figure 1) where model performance is\nevaluated over property values spanning multiple orders of magnitude (see their figure\n3). It is clear in their figure 3 that even when plotted on log-log axes, there is significant\nunexplained variance. If this model is then applied to Southern Ocean only data for\nrelatively small anomalies in SST and chlorophyll, there is no reason to believe that the\nmodeled predictions in phytoplankton size classes are robust. A similar issue also holds\nfor the Waga et al. (2019) model on phytoplankton size structure (or what the current\nauthors refer to as the chlorophyll size distribution, CSD). The Waga et al. paper is\nfocused on the impact of mesoscale eddies on CSD and is not inherently equally valid\nfor large scale impacts of extreme events. Waga et al. evaluated their model\nperformance based on, primarily, northern hemisphere data on the western edge of the\npacific ocean (see their figure 1), with limited data from the Southern Ocean with\nundefined seasonal representation (and with roughly half of the data collected\nimmediately next to the Antarctic continent). The model used information on both\nchlorophyll and spectral absorption, with the latter property included to aid in\nretrievals in oligotrophic waters (essentially irrelevant for the current analysis. Waga et al.’s\nresultant model was validated by comparing in situ CSD slopes to predicted slopes for\na dynamic range in values from ~0.5 to 3 (see their figure 2), an analysis the exhibited\nsignificant unexplained variance. For the current study, reported anomalies in CSDs\nassociated with extreme events appear to largely fall within the range of +/- 0.1, which\ncertainly can not be considered significant (or even robust in sign) given the Waga et al\nevaluation of model performance. Given the above considerations, I do not believe the\nevaluation of community composition impacts of extreme events presented in the current\nmanuscript are robust and recommend that these be removed in future versions of this paper.\nReply: We thank the reviewer for this critical and thoughtful assessment. We fully\nacknowledge the concern regarding the limitations of applying globally derived\nmodels—particularly those with substantial unexplained variance—to evaluate\nrelatively small anomalies in phytoplankton size structure, especially in a region as\noptically and ecologically unique as the Southern Ocean. To address your concerns, we\nhave removed all analyses and results related to the chlorophyll size distribution (CSD)\nmodel (Waga et al., 2019) from the manuscript. We agree that, given the limited\nSouthern Ocean validation and the narrow range of observed CSD anomalies (±0.1),\nthis metric does not provide a robust or interpretable signal in the context of extreme\nevents. Regarding the PSC model, we retained the PSC model of Sun et al. (2023) in\nthe revised manuscript, with important caveats and enhancements. We agree that the\noriginal model was trained on global datasets and its application to Southern Ocean\nanomalies requires validation. To this end, we conducted an independent evaluation\nusing a large in situ HPLC dataset (n = 3508 samples) from the Southern Ocean\nspanning 1998–2023. Our analysis shows good agreement between model-predicted\nand pigment-derived size class fractions (see revised Supplementary Figures 3 and 4),\nindicating that the PSC model captures the dominant spatial and seasonal patterns of\nphytoplankton community structure in this region. While we recognize that the PSC\nmodel does not explicitly resolve short-term ecological dynamics such as grazing,\nmixing history, or physiological acclimation, it provides a first-order estimate of\ncommunity shifts based on Chl-a and SST anomalies. We have now clarified these\nlimitations in the Discussion and Methods sections, and we emphasize that our\ninterpretations are qualitative and pattern-oriented rather than absolute.\n2) (lines 182-236) I found this discussion of mechanisms for phytoplankton responses\ninconsistent and not adequately supported. Here are specific examples:\na. (line 189-191) Here it is stated that phytoplankton in the SAZ are iron limited, but\nthen it says that MHWs alleviate light limitation and stimulate growth. First issue is\nthat there is no evidence provided that MHWs alleviate light limitation. Second, there\nis no connection made between an increase in light and how that stimulates iron limited\nphytoplankton.\nReply: We appreciate the reviewer’s observation and agree that the original explanation\nwas incomplete and insufficiently supported. In the revised manuscript, we have\nsubstantially rewritten this section to clarify the proposed mechanism:\n(1) Light availability during MHWs. We now explicitly state that marine heatwaves\ntend to enhance stratification and shoal the mixed layer, which increases the average\nlight exposure for phytoplankton—particularly in high-latitude regions where deep\nmixing often limits irradiance.\n(2) Interaction with iron limitation. We have clarified that MHWs alone do not directly\nalleviate iron limitation in the SAZ. However, in regions with episodic or background\niron enrichment (e.g., via upwelling or dust deposition), the combination of increased\nlight and partial iron availability may support enhanced phytoplankton growth. We have\nremoved any implication of a direct causal link between MHWs and iron supply, and\ninstead frame the response as context-dependent.\nb. (lines 191 -195) Here is it stated that in the AZ phytoplankton are co-limited by iron\nand light, but no evidence is provided that this is always the case. More likely, the cells\nare limited by light only in winter months, noting here that many MHWs for the AZ\noccur in fall and winter (figure 1g). It is then stated that MHWs enhance water column\nstability (no evidence provided), alleviate light limitation (no evidence provided), and\naccelerate ice melt (no evidence provided), the latter alleviating iron stress (but\nwouldn’t this only be immediately adjacent to melting ice?). So apparently the\nsuggestion in ‘a’ above about increased light alleviating iron stress was not based on\nthe presumption of changes in cellular stoichiometries for Fe.\nReply: We thank the reviewer for these insightful points. In the revised manuscript, we\nhave restructured this section to present a more nuanced, evidence-based discussion of\nco-limitation and physical drivers in the Antarctic Zone. Specifically:\n(1) Seasonal variability of limitation regimes\nWe agree that light limitation is more pronounced in winter and early spring, while co-\nlimitation by light and iron typically emerges during the growing season. We have\nclarified that our analysis focuses on biologically active periods—when MHWs\nintersect with phytoplankton growth potential (i.e., austral spring to autumn)—and not\non the full annual cycle. We now explicitly note that light limitation dominates in winter\nand that MHWs occurring during non-productive months are unlikely to yield\nsignificant biological responses.\n(2) Mechanisms supporting enhanced phytoplankton growth during MHWs\n Water column stability: We now reference Hayashida et al. (2020) and Sun et al.\n(2024), who show that MHWs are often accompanied by stronger stratification and\nreduced mixed layer depth, particularly in the Antarctic Zone.\n Alleviation of light limitation: We cite Doney et al. (2006), who reported that\nshallower mixed layers associated with warming events increased mean irradiance\nexposure for phytoplankton in the euphotic zone.\n Sea ice melt and iron supply: We have revised this explanation to emphasize that\nrelief from iron limitation due to MHW-induced sea ice melt is strongest near the\nice edge (Richaud et al., 2024). However, we also note that iron-rich meltwater can\nbe advected over mesoscale distances and influence productivity in broader regions,\nas supported by Loscher et al. (1997), De Baar et al. (2001), and Lannuzel et al.\n(2007).\n(3) Clarification of mechanism\nWe confirm that our explanation does not rely on changes in cellular Fe stoichiometry.\nRather, we describe how MHW-driven environmental changes may simultaneously\nmitigate light and iron constraints—through physical processes—thereby facilitating a\nmore favorable environment for phytoplankton growth in regions that are typically co-\nlimited.\nc. (line 197-199) Here it is stated that enhanced mixing in the STZ (no evidence\nprovided) alleviates nitrate limitation and promotes phytoplankton growth. First, the\nassumption here is that the increase in chlorophyll in the STZ during MCS is due to an\nincrease in biomass. IF the supposition is correct that the mixed layer deepens during\nan MCS, how do the authors know that the change in chlorophyll isn’t simply\nphotoacclimation to lower average mixed layer light levels? If it is an increase in\nnutrient supply, one would have to show that the increase in mixing depth was sufficient\nto penetrate the nutricline, otherwise there is no strong reason to think that the deeper\nmixing (how much deeper, by the way, a few meters, tens of meters?) impacted nutrient\nsupply. Second, it is then stated that the deeper mixing during MCS in the SAZ and AZ\ncaused greater light limitation. But it was stated prior to this that these regions are also\niron limited (actually, during summer months they are iron limited more than light\nlimited, as in situ iron fertilization experiments have shown). No explanation is\nprovided why deeper mixing in the SAZ and AZ doesn’t also increase iron supply to\nthe mixed layer. Finally, be careful with the word ‘growth’. Do you mean cell division\nrates or changes in biomass. The two are not the same. The mechanisms you discuss\nare relevant to division rate, but what you observed from space are changes in biomass,\nwhich are also controlled by loss processes (which are barely mentioned at all in this\nmanuscript).\nReply: We appreciate this detailed and insightful comment. In the revised manuscript,\nwe have significantly restructured this section to address these key concerns. Below we\nsummarize the main clarifications and revisions made:\n(1) Biomass vs. Photoacclimation\nWe agree that observed increases in surface Chl-a could reflect changes in biomass,\ncellular pigmentation, or both. To address this:\n(a) We now include an analysis of satellite-derived particulate backscatter (bbp(443))\nto estimate phytoplankton carbon, which supports biomass increases during MCSs (see\nExtended Data Fig. 4).\n(b) We explicitly note that changes in the Chl-a:carbon ratio (θ) indicate contributions\nfrom both biomass and photoacclimation.\n(c) The revised text avoids assuming a direct translation from Chl-a to biomass and\nacknowledges physiological responses as a possible factor.\n(2) MLD Deepening and Nutricline Penetration\nWe now cite GLORYS12V1 reanalysis data showing that during MCSs, the MLD\nincreased by an average of 4.25 ± 0.92 m globally, and by 20–30 m in the Atlantic and\nPacific sectors of the STZ. These regions are adjacent to the Southern Ocean’s winter\ndeep mixing band, where nutrient-rich waters are entrained into the euphotic zone. We\nadded supporting references (e.g., Rigby et al., 2020) to show that nutrient supply\nincreases are plausible in these regions during MCSs.\n(3) Iron Supply in SAZ and AZ:\nWe have clarified that while deeper mixing during MCSs may supply some iron, the\neffect is limited. In the AZ, deeper mixing also leads to reduced light availability and\nenhanced sea ice formation, which scavenges dissolved iron (e.g., Lannuzel et al., 2007),\nresulting in net iron removal. In the SAZ, while nitrate is abundant, iron remains the\nprimary limiting factor, and the modest deepening during MCSs is unlikely to deliver\nsufficient iron from subsurface reservoirs.\n(4) Division Rate vs. Biomass Accumulation\nWe thank the reviewer for this important distinction. The revised text now clearly\nseparates division rates (influenced by nutrient and light availability) and net biomass\nchange (the satellite-observed signal, governed by the balance between growth and loss\nprocesses (e.g., grazing, sinking)). We now state that “the observed biomass anomalies\nreflect net outcomes of phytoplankton division and loss rates,” aligning with the\nframework proposed by Behrenfeld (2014) and others.\nd. (lines 200-201) This is a repeat of the sentence before.\nReply: Thank you for your suggestion. We have removed this part in the revised\nmanuscript.\ne.(line 202) ‘Massive’? I don’t see any evidence of ‘massive’ responses. In the STZ,\nchlorophyll in places increased by 20% or so, but 20% greater than what? I’m guessing\nthe baseline concentrations are pretty low (e.g., see figure 4), so a 20% increase above\na low value is still a small increase.\nReply: We thank the reviewer for pointing this out. We agree that the term “massive”\nwas not quantitatively justified given the relatively modest absolute changes in\nchlorophyll-a concentrations, particularly in oligotrophic regions such as the STZ. We\nhave removed this term in the revised manuscript to ensure the language more\naccurately reflects the observed magnitudes.\nf. (lines 203–204) Iron is a nutrient too, so the fact that nitrate is always replete says\nnothing about how phytoplankton will respond to increased nutrient supply.\nReply: Thank you for your suggestion. We have removed this part in the revised\nmanuscript.\ng. (lines 205-206) Here the authors invoke temperature limitation. How does this play\ninto iron and light limitation. What is the absolute magnitude of changes in SST during\nMHWs and MCSs? From figure 3, it looks like we are talking about small (generally\n<2 ºC) changes. What evidence is there that these small SST changes would have a\nsignificant impact on division rates?\nReply: We thank the reviewer for raising this important point. We agree that SST\nanomalies during MHWs and MCSs in the Southern Ocean are typically modest—\ngenerally <2 °C, as shown in Figure 3—and that such small changes may not directly\nconstrain or enhance phytoplankton division rates in isolation. As noted in previous\nstudies (e.g., Arrigo et al., 2012; Vehmaa & Salonen, 2009), phytoplankton blooms can\noccur even in near-freezing waters, and low temperatures do not necessarily inhibit\nbiomass accumulation. Furthermore, temperature changes of this magnitude are often\nless influential than concurrent shifts in iron availability, light exposure, and grazing\npressure. For example, reduced temperatures during MCSs may indirectly favor net\nbiomass accumulation by lowering zooplankton grazing rates (López-Urrutia, 2008;\nTaucher & Oschlies, 2011), while deeper mixing may enhance nutrient supply\nirrespective of thermal effects. In light of this, we have removed the reference to\ntemperature limitation as a primary control and revised the discussion to focus on the\ninteraction of temperature with physical and ecological processes, rather than invoking\ndirect thermal limitation of growth. We appreciate the reviewer’s comment, which\nhelped us clarify and strengthen this part of the manuscript.\nh. (lines 207-209) Here it is stated that MCSs in the AZ promote sea ice formation and\nthis absorbs dissolved iron. What evidence do you have that sea ice formation changed\nsignificantly during the MCSs? If it did increase, these areas of sea ice formation would\nnot provide valid satellite chlorophyll retrievals would they? I’m also not aware of ice\nformation drawing iron out of surrounding waters, only trapping the iron in the water\nthat is being frozen, so I don’t see how sea ice formation would significantly impact\niron levels away from the sea ice (i.e., in the waters where you get satellite chlorophyll\ndata).\nReply: We thank the reviewer for this important clarification. We agree that the link\nbetween sea ice formation and iron dynamics is nuanced and that direct observational\nevidence of MCS-induced changes in iron availability is limited—particularly in\nsatellite-accessible, ice-free regions. Our original statement aimed to present a\nhypothesized mechanism rather than a confirmed effect. In the revised manuscript, we\nnow clarify the following:\n(1) Sea ice formation and iron sequestration\nDuring ice formation, dissolved iron can become trapped within the forming sea ice\nmatrix (De Baar et al., 2001; Lannuzel et al., 2007). This process reduces the\navailability of iron in the surrounding water column, but primarily within the immediate\nzone of ice formation.\n(2) Limitations of satellite observations\nWe acknowledge that satellite-derived chlorophyll-a cannot be retrieved in ice-covered\nwaters. Therefore, any influence of sea ice formation on iron availability and\nphytoplankton would be indirect—possibly affecting adjacent open-water regions\nthrough changes in surface mixing or lateral advection, but this cannot be resolved\ndirectly with the available data.\n(3) Revision for clarity\nWe have revised the manuscript to explicitly describe this as a speculative mechanism,\nframed within the broader discussion of factors potentially contributing to suppressed\nphytoplankton responses during MCSs in the AZ. We no longer present this as a\ndefinitive explanation and have removed language that could imply direct causality.\ni.(lines 211-214) I don’t see why high nitrate would support growth of picoplankton\nsouth of the subtropical front. I would expect these waters to be iron limited (as the\nauthors state earlier). If picoplankton in these waters responded to nitrate during a\nMHW, they would already be abundant before the extreme event since these waters are\npersistently high nitrate.\nReply: We thank the reviewer for this important observation. We agree that the\nstatement was inconsistent with the broader nutrient limitation framework of the region.\nIn light of this, we have removed the corresponding sentence from the revised\nmanuscript to ensure consistency and avoid unsupported mechanistic interpretations.\nj.(lines 217-220) Here and in following text, the authors bring in silicate limitation.\nWhere is the evidence that this is significant? Iron enrichment experiments indicate that\nsilicate is not the key limiting nutrient.\nReply: We thank the reviewer for this important point. We agree that introducing silicate\nlimitation without strong empirical support may lead to confusion, particularly given\nthe well-established role of iron as the dominant limiting nutrient in much of the\nSouthern Ocean. Accordingly, we have removed this reference from the revised\nmanuscript to maintain clarity and consistency with the existing literature.\nk.(lines 224-236) In this paragraph, the message has gotten completely confused with\nrespect to what the primary limiting factors are for phytoplankton and how they are\nlinked to MHWs and MCSs. The bottom line is that this section does not provide a\nconclusive or supported interpretation of the observational data.\nReply: We thank the reviewer for this critical feedback. We agree that the original\nversion of this paragraph lacked clarity in distinguishing between the dominant limiting\nfactors (iron, light, nitrate) and their mechanistic links to observed phytoplankton\nresponses during MHWs and MCSs. In the revised manuscript (Lines 247–266), we\nhave restructured this section to clearly differentiate between the STZ, SAZ, and AZ in\nterms of their dominant limiting factors; to frame the ecological responses in terms of\nregion-specific interactions among light availability, stratification, and nutrient supply;\nto avoid unsupported generalizations and speculative mechanisms.\n3) Given the above issues, I believe the Discussion section will need to be re-evaluated\nand rewritten.\nReply: We thank the reviewer for this recommendation. In response, we have\nthoroughly revised the Discussion section to address the issues raised, particularly\nregarding mechanistic interpretation, nutrient limitation frameworks, the robustness of\nphytoplankton size structure inferences, and the interaction between physical and\nbiogeochemical drivers. The revised section now provides a clearer, more evidence-\nbased synthesis of the results, acknowledges key uncertainties, and aligns more closely\nwith the satellite observations. We believe these changes substantially improve the\nmanuscript’s clarity, rigor, and interpretive strength.\nMinor comments (in order of appearance)\n1) (lines 26-28) how are ‘sharp reductions in surface chlorophyll’ and ‘extensive algal\nblooms’ consistent with each other?\nReply: We appreciate the reviewer’s observation. The apparent contrast reflects the\nspatial heterogeneity in ecosystem responses to marine heatwaves. As clarified in the\nrevised text, sharp reductions in surface chlorophyll have primarily been observed in\nopen-ocean regions of the mid- to low latitudes (e.g., through stratification-driven\nnutrient limitation), whereas extensive algal blooms—including harmful algal events—\nhave been reported in some coastal zones where warming promotes stratification and\nretention of biomass or stimulates species shifts. We now clarify this spatial distinction\nto avoid confusion and emphasize the region-specific nature of these impacts.\n2) (line 103) ‘into’ not ‘in to’\nReply: Thank you for your suggestion. Rectified as suggested.\n3) (line 118) Here it is stated that phytoplankton anomalies began to emerge 10 days\nbefore the onset of a MHW. In the methods section, it is stated that SST data were\nsmoothed over windows of 11 and 31 days. How does this smoothing impact the ability\nto clearly identify when MHW impacts begin? If the current analysis is correct, why do\nyou see responses to a MHW prior to the beginning of the event? I’m not saying this\ncouldn’t occur, but an explanation is needed.\nReply: We appreciate this important comment. In the revised Methods section (Lines\n451–454), we clarify that the 11-day and 31-day smoothing windows are applied in\naccordance with the Hobday et al. (2016) framework. These windows are used to\ngenerate a stable baseline for percentile-based threshold detection and are not intended\nto capture abrupt day-to-day variability. Regarding the observed phytoplankton\nanomalies preceding MHW onset, we note the following:\n(1) SST transition phase: Marine heatwaves often develop gradually, and SSTs may\nbegin rising before they exceed the climatological threshold required to classify an\nevent. Phytoplankton may respond to this transitional warming period, especially\nthrough stratification effects or changes in light and nutrient availability.\n(2) Methodological consistency: To ensure comparability between SST and Chl-a\nanomalies, we applied the same smoothing approach to both datasets. As a result,\nphytoplankton responses are evaluated relative to a similarly smoothed climatology,\nallowing us to assess their behavior during the buildup, peak, and recovery phases of\neach event.\nWe have updated the text to clarify this interpretation and now frame the pre-onset\nphytoplankton anomalies as early ecological responses to the physical precursors of\nMHWs, rather than to the classified event itself.\n4) More detail is needed regarding how anomalies were calculated. How was the\nbaseline calculated? What was the temporal resolution of the baseline? Were baselines\ncalculated for each pixel, or were data within each of the three zones compared to zonal\naverage baselines? If the latter is the case, how would persistent spatial features impact\napparent anomalies? If the former, how are the impacts of advection addressed?\nReply: We thank the reviewer for this detailed and important question. To clarify:\n(1) Baseline definition and temporal resolution\nAnomalies were calculated as the daily deviation from a pixel-specific daily\nclimatology over the 1998–2023 period. The climatology was generated using an 11-\nday running mean followed by a 31-day moving average, consistent with the\nmethodology of Hobday et al. (2016), to ensure sufficient smoothing for percentile-\nbased thresholding.\n(2) Spatial resolution of anomalies\nAll anomalies were computed at the grid-cell (pixel) level, not relative to zonal means.\nThis approach preserves local variability and avoids spatial smoothing that could\nobscure regional-scale heterogeneity.\n(3) Addressing advection and transient signals\nWe acknowledge that advection can affect local chlorophyll-a concentrations. To\nmitigate this, we applied the same two-stage smoothing to both SST and chlorophyll-a\ndata. Furthermore, by only considering MHW/MCS events that persist for at least five\nconsecutive days, we minimize the influence of short-lived advective features. This\nmethodology has been validated in previous studies (e.g., Hobday et al., 2016; Dong et\nal., 2021).\n5) (lines 144-147) Get rid of the semicolons and break this text into 3 sentences.\nReply: Thank you for your suggestion. Rectified as suggested. Please refer to Lines\n178-181.\n6) (line 148) ‘it’ is the incorrect word here.\nReply: Thank you for your suggestion. Rectified as suggested.\n7) (in ‘Distinct responses’ section … and noting here (as stated in Major issues #1) that\nI don’t believe the phytoplankton community analyses are robust) It was confusing to\nme that it is suggested in this paragraph that nano- and microplankton increased at\nhigher latitudes during MHWs and yet were paralleled increases in the apparent slope\nof the size distribution. How are these consistent with each other. Also, the model used\nto calculate phytoplankton size class abundances could also be used to calculate the size\ndistribution slope (this is, in essence, what the Waga et al. model is doing). Why did\nyou use one model for size classes and another model for slope? How consistent are the\ntwo?\nReply: We thank the reviewer for identifying this important point. In the revised\nmanuscript, we have removed all analyses and references to the CSD model (Waga et\nal., 2019), in response to both this and earlier comments regarding its limited\napplicability and lack of robustness in the Southern Ocean context.\nTo improve clarity and consistency:\n(1) We now use only the PSC model to estimate the relative abundances of picoplankton,\nnanoplankton, and microplankton.\n(2) The prior mention of “slope changes” in the size distribution has been removed or\nrephrased to avoid implying a direct calculation of slope. Instead, we discuss\ncompositional shifts among size classes based solely on PSC outputs.\n(3) To support the validity of the PSC model in this region, we have added a validation\nusing in situ phytoplankton size-fractionated chlorophyll data (see revised Methods and\nSupplementary Fig. S5), which demonstrates reasonable agreement.\n8) (lines 155-157) These statements regarding nutrient requirements are not sufficiently\nsupported.\nReply: We thank the reviewer for this observation. To ensure accuracy and avoid\noverinterpretation, we have removed the unsupported statements regarding nutrient\nrequirements from the revised manuscript.\n9) (lines 167-169) this statement doesn’t make sense, ‘Nanoplankton showed similar\ntrends to…nanoplankton in the sub-Antarctic region.\nReply: We apologize for the confusion caused by this phrasing. The sentence has been\nrevised for clarity in the updated manuscript (see Lines 202–204). The intended\nmeaning was to compare nanoplankton trends across different latitudinal zones. We\nhave rephrased the sentence to clearly convey the spatial comparison and eliminate\nredundancy.\n10) (line 169) delete ‘respectively’ and ‘Coincidentally’\nReply: We thank the reviewer for this stylistic suggestion. Both terms have been\nremoved in the revised manuscript to improve clarity and precision.\n11) (lines 171-172) here again I don’t see how proliferation of larger cells resulted in a\nsteeper slope.\nReply: We thank the reviewer for highlighting this inconsistency. In response, we have\nremoved the content related to the CSD model and associated slope interpretations from\nthe revised manuscript to avoid confusion and ensure internal consistency in our\nanalysis.\n12) In general, I found the Discussion section to be overstated and to contain multiple\nrepeats of the same points.\nReply: We thank the reviewer for this important feedback. In the revised manuscript,\nwe have thoroughly restructured the Discussion section to eliminate redundancy,\nreduce overstatement, and ensure that each point is clearly supported by the data. We\nhave also refined the language to adopt a more balanced and evidence-based tone\nthroughout. To better reflect the revised focus, we have updated the section title to\n“Implications for understanding phytoplankton responses to climate extremes.” These\nchanges aim to improve both the clarity and scientific rigor of the manuscript.\n13) (lines 241-244) This sentence needs to be supported by at least a hand full of\ncitations.\nReply: We thank the reviewer for this observation. To avoid making an unsupported\ngeneralization, we have removed the sentence from the revised manuscript.\n14) (lines 305-306) The distinction here between atmosphere forced and ocean process\nforced MHW impacts is very unclear.\nReply: We thank the reviewer for pointing this out. To avoid confusion and maintain\nclarity, we have removed this sentence from the revised manuscript.\n15) (line 321) Use a different word than ‘immeasurable’\nReply: Thank you for the suggestion. We have revised the sentence and replaced\n“immeasurable” with a more appropriate and precise term in the updated manuscript.\n16) (line 344) Here it is stated that the CMEMS model is used to evaluate nitrate and\nsilicate. How reliable are these model estimates? Remember, while this model might\nappear to capture global patterns well, how well can it be relied on to provide robust\nestimates of nitrate and silicate changes during extreme events? Is this model even\nnecessary for in the current study?\nReply: We thank the reviewer for raising this important point. In the revised manuscript,\nwe have moved the CMEMS nitrate and silicate data to the Supplementary Information\nand now present them for reference only. These variables are no longer used as core\nevidence to support the main conclusions. This change reflects our recognition of the\nlimitations of global biogeochemical reanalysis products in resolving short-term\nanomalies associated with extreme events in the Southern Ocean.\n17) (line 345-347) It should be stated somewhere here what Rrs data were used for. A\nreader will not understand this unless they look into the supplemental materials.\nReply: We thank the reviewer for this helpful suggestion. To improve clarity and avoid\nconfusion, we have removed this reference from the main text in the revised manuscript.\nDetails on the use of Rrs data are now fully contained within the Supplementary\nInformation, where they are clearly described.\n18) (lines 336-347) This section could be reorganized so you only have to say that the\ndata were gridded to 0.25 degrees once.\nReply: Thank you for the suggestion. We have revised and reorganized this section to\nstreamline the description of data processing. The 0.25° gridding step is now stated only\nonce to improve clarity and avoid redundancy.\n19) (line 352) more detail is needed regarding how the ‘climatological baseline’ was\ncalculated. A figure in the supplement might also be helpful.\nReply: Thank you for the suggestion. We have revised the Methods section to provide\nadditional detail on how the climatological baseline was constructed, including the use\nof a 30-year (1982–2011) daily mean for each pixel, following the framework of\nHobday et al. (2016). In addition, we have added a new supplementary figure\n(Supplementary Fig. 7) to visually illustrate the baseline climatology used for anomaly\ndetection.\nSupplementary Fig.7 | Schematic illustration of climatological baseline, MHW and\nMCS detection. The black line represents SST, the purple line indicates the\nclimatological baseline, the red line shows the 90th percentile, and the green line shows\nthe 10th percentile. The red shaded area represents MHW events, and the green shaded\narea represents MCS events.\n20) (Definitions and categorization’ section) Were only SST data smoothed or were\nother products smoothed as well? How did smoothing of data potentially impact\nassessment of timing of events and responses?\nReply: We thank the reviewer for this thoughtful question. In our analysis, both SST\nand chlorophyll-a datasets were smoothed using the same two-step procedure—an 11-\nday running mean followed by a 31-day moving average—following Hobday et al.\n(2016). This approach ensures consistency in the derivation of climatologies and\nanomalies and reduces short-term noise that might obscure biologically relevant signals.\nWe acknowledge that such smoothing may slightly shift or dampen the detection of the\nprecise onset or peak of extreme events. However, this trade-off improves the\nrobustness of event identification by minimizing the influence of transient fluctuations\n(e.g., due to cloud cover or atmospheric perturbations). Applying identical smoothing\nto both physical and biological time series enables more consistent temporal alignment\nof physical forcing and ecosystem response, as also supported by Dong et al. (2021).\nWe have clarified this methodology and its implications in the revised Methods section\nand have added supporting context in the Discussion where appropriate.\n21) (line 362) this is the first occurrence of ‘threshold distance’ and it should be clearly\ndefined.\nReply: Thank you for pointing this out. We have clarified the definition of “threshold\ndistance” in the revised manuscript. Specifically, it is now defined as the difference\nbetween the 90th percentile and the climatological mean temperature at each pixel,\nfollowing the Hobday et al. (2016) framework for marine heatwave detection.\n22) (Figure 4 & 5) ‘Phase’ is misspelled throughout the two central columns of panels\nin both figures.\nReply: Thank you for your suggestion. Rectified as suggested. Please refer to Extended\nData Fig 2 & 3.\n23) (Figure 6) Why are CSD slopes positive? Shouldn’t they be negative (i.e.,\nconcentration increases with decreasing size)?\nReply: Thank you for your observation. In response to earlier comments regarding the\nrobustness of the CSD model, we have removed all CSD-related analyses, including\nFigure 6, from the revised manuscript.\nReferences\nHobday AJ, Alexander LV, Perkins SE, Smale DA, Straub SC, Oliver ECJ, et al. A hierarchical\napproach to defining marine heatwaves. Progress in Oceanography 2016, 141: 227-238.\nArrigo KR, Perovich DK, Pickart RS, Brown ZW, Van Dijken GL, Lowry KE, et al. Massive\nphytoplankton blooms under Arctic sea ice. Science, 2012, 336(6087): 1408-1408.\nVehmaa A, Salonen K. Development of phytoplankton in Lake Pääjärvi (Finland) during under-\nice convective mixing period. Aquatic Ecology, 2009, 43: 693-705.\nTaucher J, Oschlies A. Can we predict the direction of marine primary production change under\nglobal warming?. Geophysical Research Letters, 2011, 38(2).\nLópez-Urrutia Á. The metabolic theory of ecology and algal bloom formation. Limnology and\nOceanography, 2008, 53(5): 2046-2047.\nde Baar HJW, De Jong J. Distributions, sources and sinks of iron in seawater. In The\nbiogeochemistry of iron in seawater, Edition: IUPAC Series on Analytical and Physical\nChemistry of Environmental Systems, Volume 7, 2001, Eds. Turner D and Hunter K. John\nWiley & Sons Ltd., Chichester, UK.\nLannuzel D, Schoemann V, de Jong J, Tison J-L, Chou L. Distribution and biogeochemical\nbehaviour of iron in the East Antarctic sea ice. Marine Chemistry 2007, 106(1-2): 18-32.\nDong, Bo, Keith Haines, and Matthew Martin. Improved high resolution ocean reanalyses using\na simple smoother algorithm. Journal of Advances in Modeling Earth Systems 2021, 13(12),\ne2021MS002626.\nDoney SC. Plankton in a warmer world. Nature 2006, 444(7120): 695-696.\nHayashida H, Matear RJ, Strutton PG, Zhang X. Insights into projected changes in marine\nheatwaves from a high-resolution ocean circulation model. Nature Communications 2020,\n11(1).\nRichaud B, Hu X, Darmaraki S, Fennel K, Lu Y, Oliver ECJ. Drivers of Marine Heatwaves in\nthe Arctic Ocean. Journal of Geophysical Research: Oceans 2024, 129(2).\nLoscher BM, De Baar HJW, De Jong JTM, Veth C, Dehairs F. The distribution of Fe in the\nAntarctic circumpolar current. Deep Sea Research Part II: Topical Studies in Oceanography\n1997, 44(1-2): 143-187.\nResponse to Reviewer #3\n(Note: Reviewer comments in black and our point-to-point replies in blue)\nKey Results\nThe authors investigate the response of phytoplankton size structures in the Southern\nOcean during marine heat waves (MHW) and marine cold spells (MCS) using a suite\nof satellite-based data and modelling approaches. Their results showed MHWs were\nmost frequent in the Subtropics and Subantarctic. Contrasting this, they found that\nMCSs were more frequent in the Antarctic Zone. Some of the results section on the\nresponse of phytoplankton (and their size classes) to MHWs and MCSs were difficult\nto follow - I have highlighted some examples below in the section on clarity. The\nauthors show that during MCSs in the Subtropical Zone, picoplankton increased\nwhereas in the Antarctic and Subantarctic Zone micro and nano plankton decreased.\nThey also show that the inverse was true for MHW, with micro and nano size classes\nincreasing in the Antarctic Zone. I think that it is an excellent idea to study the effects\nof MHWs and MCSs on phytoplankton in the Southern Ocean. However, the methods\nand data used in this study are not reproducible, and the choice of data products is not\ndetailed, and in some cases poorly chosen. As such I cannot recommend this article for\npublication and suggest that this article is not accepted. Below, I have detailed my\nreasoning, and I hope that the authors find my feedback helpful and constructive. If the\nauthors choose to resumbit the paper, I strongly suggest that they vigorously consider\nand scrutinise their choice of data and methods.\nReply: We sincerely thank the reviewer for the thoughtful and constructive feedback.\nWe fully acknowledge the concerns raised regarding methodological clarity, data\nproduct selection, and result interpretation. In response, we have undertaken a\ncomprehensive revision of the manuscript. Major changes include:\n(1) Substantial restructuring of the Results section to improve clarity and flow.\n(2) Replacement of previously used data products with those more appropriate for\nSouthern Ocean conditions, such as regionally validated chlorophyll-a algorithms and\nPSC-based phytoplankton size estimation.\n(3) Removal of less robust analyses (e.g., the CSD slope model) and addition of\nvalidation using in situ data where available.\n(4) Clearer description of methods and data sources to ensure reproducibility, now\ndetailed in both the main text and Supplementary Information.\nWe have also refined the Discussion to ensure that interpretations are well-supported\nand more cautiously framed. Figures have been updated accordingly to reflect these\nimprovements. We hope these substantial revisions address the reviewer’s concerns and\nimprove the scientific rigor and clarity of the manuscript.\nValidity\nBased on the figures provided, I believe that the interpretations of these results support\nthe trends of phytoplankton size classes during MHW and MCS events. However, I\nthink that their discussion on the biogeochemical / environment drivers underpinning\nthese changes requires further work. There is no mention of phytoplankton groups or\ntaxonomy. For example, in the Subantarctic Zone haptophytes (E. hux) dominate (Balch et al., 2014). It is widely recognised that in the Antarctic Zone diatoms and haptophyes (P. antarctica) are dominant (Smith et al., 2014; Hayward et al., 2024). These different groups, and species within them, respond differently to environmental stimuli. As such, the discussion on phytoplankton size classes should be accompanied by discussing phytoplankton speciation and phenology.\nReply: We thank the reviewer for this insightful comment. We agree that taxonomic\ncomposition plays a key role in shaping regional phytoplankton responses and that\nspecies-level differences are critical for interpreting size-based trends. In the revised\nDiscussion, we have included additional context referencing the dominant taxa in each\nzone—such as Emiliania huxleyi in the Subantarctic Zone and diatoms and Phaeocystis\nantarctica in the Antarctic Zone (Balch et al., 2014; Smith et al., 2014; Hayward et al.,\n2024)—and have noted how their differing ecophysiological traits may contribute to\nthe observed patterns in size structure during MHW and MCS events. We also clarify\nthe limitations of our approach: the PSC algorithm used in this study estimates size\nclass composition but cannot resolve species or functional group taxonomy. We now\nexplicitly acknowledge this constraint and highlight that integrating satellite data with\nin situ taxonomic observations and functional trait modeling represents an important\navenue for future work.\nSignificance\nI think that these results would be significant and important to biogeochemists,\necosystem modellers and earth system scientists. However, I struggle to move past the\npoorly detailed methods and data used in the study.\nReply: We appreciate the reviewer’s recognition of the broader relevance of our\nfindings and acknowledge the concern regarding insufficient methodological detail. In\nresponse, we have thoroughly revised the Methods section to improve transparency and\nreproducibility. Key updates include:\n(1) Explicit identification and justification of all data products used;\n(2) Clear definitions of MHW and MCS criteria;\n(3) Detailed descriptions of anomaly calculations, temporal smoothing, and spatial\ngridding;\n(4) An expanded explanation of the PSC-based size class estimation and associated\nvalidation steps.\nData and methodology\nThe methods and data used in this manuscript lack scientific rigour and are not\nreproducible. This is primarily the reason I am not accepting this article. I have detailed\nmy reasoning thoroughly below. The data used in the study would benefit from clearer\ndefinition and more detailed justification to ensure it is well-suited to the study's\nobjectives. For chlorophyll a (Chl-a) data the authors state that they used ‘chlorophyll\nfrom CMEMS’. A CMEMS database search showed there are 54 different Chl-a\nproducts available on CMEMS. See Pauthenet et al., (2024) which details differences\nin commonly used Chl-a products. It is very important to specify what product is used\nto ensure results are reproducible, but to also further understand potential uncertainties\nand biases. The authors should state what CMEMS product was chosen and why it was\nused in the study. Furthermore, it has not been possible to assess any code as in the\n‘code availability’ statement, all the authors said was that the analysis was conducted\nin Matlab.\nReply: We thank the reviewer for highlighting these important issues. In the revised\nmanuscript, we have taken the following steps to address your concerns:\n(1) Chlorophyll-a Product Specification and Justification\nWe have removed the ambiguous reference to \"chlorophyll from CMEMS\" and now\nclearly state in the Methods section that we use daily R data from the ESA Ocean\nrs\nColour Climate Change Initiative (OC-CCI v6.0, https://doi.org/10.48670/moi-00281).\nThis product was selected for its long-term consistency and its improved performance\nin the Southern Ocean when paired with region-specific algorithms (e.g., Johnson et al.,\n2013; Pauthenet et al., 2024). We applied the OC4v6 algorithm, adapted for the\nSouthern Ocean, to estimate surface chlorophyll-a concentrations. The rationale for this\nchoice and its advantages over other products are now clearly explained.\n(2) Improved Reproducibility and Code Availability:\nWe recognize the importance of code transparency for reproducibility. In the revised\nCode Availability section, we now state that all scripts used in this study, written in\nMATLAB, are available from the corresponding author upon reasonable request.\nAdditionally, key code components for processing chlorophyll-a, computing\nMHWs/MCSs, and performing anomaly analysis have been documented and included\nin the Supplementary Information as pseudocode to enhance transparency.\nThe authors use surface reflectances from MODIS-Aqua for their size class model. The\ntime series for MODIS-Aqua (May - 2002 to 2024) is shorter than their study period\n(1998 - 2022). Furthermore, in 2019 MODIS-Aqua drifted out of orbit. There is\na disclaimer on the NASA website suggesting that data from ~2019 should be treated\nwith caution. As such, I would not trust the PSC / CDS results from 2019 - 2022 in this\nstudy from MODIS-Aqua.\nReply: Thank you for raising this critical point. In the revised manuscript, we no longer\nuse MODIS-Aqua reflectance data. To ensure consistency across the full study period\n(1998–2022) and to address concerns related to post-2019 MODIS-Aqua data quality,\nwe now use daily R from the ESA Ocean Colour Climate Change Initiative (OC-CCI\nrs\nv6.0), which provides a consistent multi-sensor merged product with rigorous inter-\nsensor calibration and gap-filling. We then apply a Southern Ocean–specific\nchlorophyll-a algorithm to this OC-CCI Rrs dataset, enhancing both the temporal\ncontinuity and the accuracy of chlorophyll estimates. This change ensures that our\nphytoplankton size class estimates—derived using the PSC model—are based on a\nhigher-quality and longer-term dataset. We have revised the Methods section to reflect\nthese changes and have removed all reliance on MODIS-Aqua data.\nThe authors use the OISSTv2 product in their study to assess MHW and MCS. This\nproduct is one of the older products used in SST remote sensing and no longer\nconsidered the ‘gold standard’ (instead, see the SST CCI). The product is also of coarse\nspatial resolution (.25 degrees) compared to more modern products, and does not match\nthe spatial resolution of chlorophyll data (4km). The authors interpolated the chl-a\nproduct to make it coarser and on the same grid as the OISST product. In doing so they\nwill miss out on many of the finer scale processes in the dataset. One of the largest\ncaveats of this study is that the remote sensing of sea surface temperatures is not\npossible for much of the year in polar regions due to ice cover. Where there is sea ice\nthe OISST algorithm estimates the under ice SST by a linear fit capped at -1.8 celsius.\nAs such, the MCSs observed in this study are not the result of actual satellite\nobservations, but could in fact be due to how the OISST product handles under ice\ntemperatures.\nReply: We appreciate the reviewer’s detailed feedback. In the revised manuscript, we\naddressed these concerns as follows:\n(1) Updated SST Product:\nWe replaced the OISSTv2 dataset with the more advanced ESA Sea Surface\nTemperature Climate Change Initiative (ESA SST CCI v3.0) reanalysis product, which\nprovides improved accuracy, inter-sensor consistency, and bias correction suitable for\nlong-term climate analyses in polar regions.\n(2) Sea Ice Considerations:\nTo mitigate the limitations of SST retrieval under ice cover, we applied a mask to\nexclude regions with an annual average sea ice concentration (SIC) greater than 20%.\nThis reduces the likelihood of including SST anomalies that are artifacts of the under-\nice estimation algorithm used in SST datasets (as noted by the reviewer).\n(3) Spatial Resolution Harmonization:\nWhile we acknowledge that regridding the high-resolution chlorophyll-a product (OC-\nCCI, native 4 km) to 0.25° resolution may reduce sensitivity to submesoscale variability,\nwe opted for this approach to maintain consistency across all parameters analyzed\n(including SST, nutrients, and MLD). This facilitates robust spatial alignment in the\ndetection and attribution of MHW/MCS events and their ecological impacts. We have\nnow explicitly stated this limitation in the revised Methods and Discussion sections.\nThese updates ensure higher data fidelity and improve the robustness of our conclusions\nregarding phytoplankton responses to thermal extremes in the Southern Ocean.\nThe methods section is very short, and there is no information provided on the CSD\nmodel, other than a short description in the supplementary information. The PSC model\nused was recently published (2023); and the PSC model is dependent on SST\nobservations. There should be discussion regarding uncertainty model uncertainty and\nperformance, especially when there can be issues of circularity as both the size class\nmodel use SST and the authors investigate the response of size classes to MHWs and\nMCSs.\nReply: Thank you for this important comment. In the revised manuscript, we have fully\nremoved the CSD model and associated analyses to improve methodological clarity and\nfocus. Regarding the PSC model (Sun et al., 2023), we acknowledge that it uses SST\nas one of its key input variables. To address concerns about potential circularity—where\nSST is used both to define MHW/MCS events and as a driver of phytoplankton size\nclass estimates—we took the following steps:\n(1) Independent Validation:\nWe validated the PSC-derived phytoplankton size class outputs against in situ\nobservations from the Southern Ocean (1998–2023), which demonstrated good\nagreement (details provided in the revised Methods and Supplementary Figures).\n(2) Clarified Use of SST:\nWhile SST is used in both MHW/MCS classification and as an input to the PSC model,\nthese serve distinct functions: (i) in the former, SST is used to define the timing and\nintensity of thermal anomalies; (ii) in the latter, SST is used as an ecophysiological\nproxy influencing phytoplankton size structure. We now explicitly acknowledge this\ndual role in the Discussion, and we clarify that our analyses focus on relative anomalies\nin size structure during MHWs and MCSs, rather than directly correlating SST with\nsize class output.\n(3) Model Justification:\nPrevious studies (e.g., Sun et al., 2023; Moore et al., 2020) have shown that\nphytoplankton size composition may vary substantially under environmental stress\neven when total chlorophyll-a remains unchanged. SST has proven effective in\ndetecting these shifts in ecological niches. Thus, the PSC model remains a valuable tool\nfor interpreting community-scale responses to thermal extremes.\nThese clarifications and validations are now fully incorporated into the revised Methods\nand Discussion sections.\nFinally, there is inconsistency in the date ranges. Chl-a observations are only available\nduring summer high latitudes, and many observations are not possible due to satellites\nnot being able to assess ocean colour during periods of ice cover. Furthermore, the\nbaseline and time series of Chl-a will be shorter than the baseline of the SST product,\nadding further inconsistencies.\nReply: Thank you for this insightful comment. We acknowledge that satellite-derived\nchlorophyll-a (Chl-a) observations in high-latitude regions are seasonally limited,\nprimarily due to ice cover and reduced daylight during winter months. To minimize the\ninfluence of inconsistent data coverage, we implemented the following measures in the\nrevised manuscript:\n(1) Sea Ice Masking:\nWe applied a conservative mask to exclude regions with an annual average sea ice\nconcentration greater than 20%, thereby focusing our analysis on open-water areas\nwhere satellite Chl-a retrievals are consistently valid.\n(2) Aligned Time Windows:\nWhile the SST dataset spans 1982–2023, the Chl-a dataset covers 1998–2023. To ensure\nconsistency, all analyses involving phytoplankton responses to MHWs and MCSs were\nrestricted to events occurring between 1998 and 2023—the common period for both\nSST and Chl-a datasets.\n(3) Climatology Construction:\nDaily climatologies for both SST and Chl-a were constructed independently using their\nrespective time windows. Although their baseline periods differ (SST: 1982–2011; Chl-\na: 1998–2023), all anomaly analyses were constrained within the Chl-a observation\nperiod, ensuring internal consistency in the biological response evaluations.\nThese steps ensure that our comparisons are methodologically sound and that observed\nphytoplankton responses are not biased by inconsistent temporal coverage. This\nclarification has been included in the revised Methods and Discussion sections.\nClarity and Context\nOverall, the clarity of the manuscript could be tightened and improved. There are lots\nof ‘they’ and ‘this’ without explicitly specifying what is changing and why. Furthermore,\nthe structure could be improved by using more subheadings. For example:\n‘Phytoplankton Size classes during a MHW,’ and ‘Phytoplankton size classes during a\nMCS’. The authors can then address each size class separately in different paragraphs\nto improve the readability.\nReply: Thank you for this valuable suggestion. In the revised manuscript, we have\nsubstantially improved clarity by eliminating vague pronouns and clearly specifying\nsubjects and changes throughout the text. We have also restructured the Results and\nDiscussion sections to improve readability by introducing descriptive subheadings,\nsuch as “Phytoplankton size classes during MHWs” and “Phytoplankton size classes\nduring MCSs.” Furthermore, responses of each size class are now addressed in\ndedicated paragraphs, making the narrative more organized and accessible for readers.\nOn line 144:\nMicroplankton demonstrated apparent variability at the polar front; north of the polar front, they maintained a negative\nresponse; however, south of the polar front, their response shifted to a positive phase, indicating significant proliferation in\nthe Antarctic zone.\nDissecting this passage, it is difficult to understand what ‘apparent variability’ means, increasing, decreasing? Following\nthis, the authors say that it ‘maintained a negative’ response. What are they responding to, a MHW a MCS, this isn’t clear at\nall.\nReply: Thank you for pointing this out. We agree that the original sentence was\nambiguous and lacked clarity regarding the direction and cause of the response. In the\nrevised manuscript, we have removed this sentence and rephrased the surrounding text\nto more explicitly describe the response of microplankton to MHWs and MCSs with\nrespect to both location (north vs. south of the polar front) and event type. This revision\nensures that both the direction of change and the environmental driver are clearly stated.\nIn the opening line to a paragraph on line 117: ‘The temporal evolution of these responses further highlighted their\ncomplexity (Fig. 4)’. The authors need to be clear as to what ‘these responses’ are, especially if they are opening a\nparagraph with this statement; similarly, the authors should define what ‘their complexities’ are. Don’t leave it to the reader to\ntry and guess what you are trying to convey. In academic writing, for any journal, try to really spell these things out, be clear\nand avoid vague language.\nReply: Thank you for your thoughtful and constructive feedback. We agree that the\noriginal phrasing was too vague. In the revised manuscript, we have clarified that “these\nresponses” refer specifically to the temporal variations in the fractional contributions\nof micro-, nano-, and picoplankton during marine heatwaves (MHWs) and marine cold\nspells (MCSs). We also expanded the sentence to explain that “their complexity” refers\nto the non-uniform, regionally dependent, and often nonlinear temporal patterns\nobserved across different phytoplankton size classes and oceanographic zones. This\nrevision enhances the clarity and scientific precision of the paragraph's opening and sets\na clearer context for the discussion that follows.\nThe abstract states (line 8): MHWs suppressed phytoplankton growth in the subtropical zone but stimulated it in the sub-\nAntarctic and Antarctic zones by alleviating light and iron limitations.\nThere is nowhere in the study that iron or light is investigated. I do not understand how the authors can state this in their\nabstract.\nReply: Thank you for your careful reading and insightful comment. We fully agree that\nthe original sentence in the abstract overstated the scope of our analysis, as the\nmanuscript does not directly quantify iron or light availability. To maintain scientific\nrigor and avoid unsupported interpretations, we have removed this sentence from the\nabstract in the revised manuscript. Instead, we now focus on the observed\nphytoplankton responses to MHWs and MCSs, without attributing causality to specific\nenvironmental drivers that were not explicitly analyzed. We appreciate your feedback,\nwhich helped us improve the accuracy and focus of our abstract.\nReferences\nThe manuscript is oddly referenced. For example, reference 9 is in relation to Southern Ocean MHWs, but the citation\ndescribes MHWs in the South Atlantic. Reference 13 is provided in the context of MCSs in the Southern Ocean, however the\nreference is located in the North Sea.\nReply: Thank you for pointing this out. We have carefully reviewed all references in\nthe revised manuscript to ensure they are contextually accurate and regionally\nappropriate. Specifically, references 9 and 13 have been replaced with peer-reviewed\nstudies that directly investigate marine heatwaves and cold spells in the Southern Ocean.\nWe also conducted a broader check of all citations to verify that each one accurately\nsupports the corresponding statement. We appreciate your attention to detail, which has\nhelped improve the scholarly integrity of our manuscript.\nReferences\nBalch WM, Bates NR, Lam PJ, Twining BS, Rosengard SZ, Bowler BC, et al. Factors\nregulating the Great Calcite Belt in the Southern Ocean and its biogeochemical significance.\nGlobal Biogeochemical Cycles 2016, 30(8), 1124-1144.\nSmith Jr WO, Ainley DG, Arrigo KR, Dinniman, MS. The oceanography and ecology of the\nRoss Sea. Annual review of marine science 2014, 6(1), 469-487.\nHayward A, Pinkerton MH, Wright SW, Gutiérrez-Rodriguez A, Law CS. Twenty-six years of\nphytoplankton pigments reveal a circumpolar Class Divide around the Southern Ocean.\nCommunications Earth & Environment 2024, 5(1), 92.\nPauthenet E, Martinez E, Gorgues T, Roussillon J, Drumetz L, Fablet R, Roux M. Contrasted\ntrends in chlorophyll‐a satellite products. Geophysical Research Letters 2024, 51(14),\ne2024GL108916."}, {"round": 2, "reviewer_comments": {"reviewer_2": "I believe that the authors have adequately addressed the issues raised in my first review.", "reviewer_3": "The manuscript analyses phytoplankton chlorophyll-a, carbon, and phytoplankton size classes (PSC) responses to marine\nheatwaves (MHWs) and marine cold spells (MCSs) across Southern Ocean water masses, using satellite observations,\nmodel reanalysis, and autonomous Argo profiles. The topic is timely, and the multi-platform approach is a real strength. I\nappreciate the scope of the work and the effort to link physical extremes with biological responses across latitudinal\ngradients and water masses in the Southern Ocean.\nAt times, I found the narrative hard to follow, and I think clarity could be strengthened. The study is clearly about\nphytoplankton responses to temperature extremes, but the central take-home message is not consistently articulated across\nsections. Parts of the Results section were difficult to follow. For example, at line 119 it is unclear whether the reported\n51.7% refers to positive or negative anomalies. In the Conclusions, the statement that “Chl-a concentrations exhibit\nsymmetrical responses during extreme high and low temperatures” would benefit from a precise reference frame:\nsymmetrical relative to what: temperature anomaly magnitude, chlorophyll anomaly magnitude, or another metric? A similar\nissue arises at line 134, where “The temporal evolution of these responses further highlighted their complexity” begins a\nparagraph without specifying what “these” are and what kind of “complexity” is meant, which makes the thread difficult to\nfollow. Consider explicitly framing results with subheadings by latitude bands / water masses and closing each subsection\nwith a one-sentence takeaway; this could substantially improve readability. Similarly, frame what the paragraph is about in\nthe first line.\nStronger framing and definitions at the start of the Results could also help. A concise description of the datasets and explicit\ndefinitions of MHWs/CSs, including the time and anomaly thresholds you adopt, would orient readers. It would also help to\ndefine what constitutes an “event” in your workflow. Under a pixel-level Hobday-style detection, a value such as 0.14 events\nper decade is ambiguous (e.g., is an event tied to a single pixel? Under Hobday’s definition it can be). Many studies use %\nor area covered, which could aid interpretation here. Reporting area affected by MHWs/MCSs alongside event counts\n(number of pixels) would make the results more interpretable. If you use the Hobday et al. (2018) severity categories,\nintroducing these before they first appear would help readers who are familiar with phytoplankton ecology, but not MHWs.\nRegional terminology and masking needs to be clarified. The zone referred to as the “Antarctic Zone” appears, from the\nfigures, to correspond more closely to the Permanently Open Ocean Zone (POOOZ/POOZ; see Deppeler & Davidson,\n2017), with much of the true Antarctic Zone masked. This distinction matters for interpretation; without the figures, one would\nassume the Antarctic Zone to be closer to Antarctica. In sea-ice regions, satellites do not retrieve SST beneath ice and a\npractical baseline near −1.8 °C is often implied; this will influence MHW/MCS identification, severity classification, and any\nzonal comparisons, so it would be helpful to state that these areas are masked. This also raises a question about the\nanalysis period: is it year-round, or focused on summer? The seasonal signal could be strong, so a seasonal breakdown\nmight provide useful context. Similarly, summer in Antarctica is more free of ice and can allow clearer MHW/Chl-a trend\ndetection, filling in this data gap in your study, see examples in Pinkerton et al., (2021), or Hayward et al., (2025).\nSeveral statements would benefit from supporting evidence or softer phrasing. At line 256, “Mixed layer reduced loss rates\nthrough greater population dilution and decreased grazing pressure” reads as a process explanation but has no direct\nsupport from observations, modelling, or citations. If this reflects the authors’ interpretation, it needs to be framed as a\nhypothesis with references.\nGiven the diversity of the datasets, uncertainty metrics would be very helpful. Pigment-to-PSC relationships usually show\ngood agreement, but are complicated by pigment sharing among taxa, which affects diagnostic partitioning. The manuscript\nmentions a 17-variable PSC model, but the rationale for this choice, and the validation from HPLC pigments to size classes\nare not described. In the validation, the HPLC data alone do not inherently yield size classes; there are multiple diagnostic\npigment approaches, so additional methodological detail needs to be stated. As the key conclusions rely on PSC outputs,\nmore information on methods and uncertainty would greatly improve the paper. If SST is used as an input to the PSC model,\na brief discussion of potential circularity when analysing responses to temperature extremes, and how this was mitigated,\ne.g. via sensitivity testing, potentially without SST would provide more confidence in the results.\nIt would also be useful to briefly discuss the choice of MHW definition. MHW definitions greatly influence identification and\nseverity, and there is ongoing debate about methodological sensitivity. If you adopt the Hobday et al. framework, detail why\nit suits this study, see Farchadi et al. (2025, Nature Climate Change, “Marine heatwaves are in the eye of the beholder”).\nA few line-specific clarifications could further improve the manuscript. At line 26, please specify the regions where previously\nidentified impacts of MHWs occurred. Between lines 47 and 50, the juxtaposition of a “decline in chlorophyll-a” with\n“extensive algal blooms” reads as contradictory and needs rephrasing.\nThe citations need another check. Despite stating they had been fixed, some references appear to be irrelevant to the paper.\nReview 10 is presented as Southern Ocean but is about the Atlantic, and citation 13 addresses North Sea sole and does not\nfit the scope at all. Similarly, a few highly relevant studies on latitudinal patterns in chlorophyll-a and phytoplankton\ncomposition have been missed, including Schlosser et al. (2025), “Latitudinal trends in drivers of the Southern Ocean spring\nbloom onset” (JGR); Hayward et al. (2024), “Twenty-six years of phytoplankton pigments reveal a circumpolar class divide\naround the Southern Ocean” (Communications Earth & Environment); and Pinkerton et al. (2021), “Evidence for the Impact\nof Climate Change on Primary Producers in the Southern Ocean” (Frontiers in Ecology and Evolution).\nIn summary, the manuscript addresses an important problem and uses multi-platform datasets. However, there are several\ncomponents of the study that needs addressing before publication. First, the narrative and framing needs to be improved,\nwith the conclusions and take home messages clearly stated. I think that the methods needs to be far more detailed to be\neasily replicated by others. Most importantly, some level of uncertainty needs to be quantified as so much data from so many\nsources are used."}, "author_response": "Response to Reviewer #2\n(Note: Reviewer comments in black and our point-to-point replies in blue)\nI believe that the authors have adequately addressed the issues raised in my first review.\nReply: We sincerely thank you for the positive evaluation of our revised manuscript.\nWe greatly appreciate the constructive comments you provided during the first round\nof review, which have helped us to improve the clarity and robustness of the manuscript.\nResponse to Reviewer #3\n(Note: Reviewer comments in black and our point-to-point replies in blue)\nThe manuscript analyses phytoplankton chlorophyll-a, carbon, and phytoplankton size\nclasses (PSC) responses to marine heatwaves (MHWs) and marine cold spells (MCSs)\nacross Southern Ocean water masses, using satellite observations, model reanalysis,\nand autonomous Argo profiles. The topic is timely, and the multi-platform approach is\na real strength. I appreciate the scope of the work and the effort to link physical\nextremes with biological responses across latitudinal gradients and water masses in the\nSouthern Ocean.\nReply: We sincerely thank you for the positive and encouraging feedback on our work.\nWe greatly appreciate your recognition of the timeliness of the topic and the value of\nour multi-platform approach. Following your constructive suggestions, we have\ncarefully revised the manuscript, substantially improving the clarity, precision, and\noverall quality of the paper.\nAt times, I found the narrative hard to follow, and I think clarity could be strengthened.\nThe study is clearly about phytoplankton responses to temperature extremes, but the\ncentral take-home message is not consistently articulated across sections. Parts of the\nResults section were difficult to follow. For example, at line 119 it is unclear whether\nthe reported 51.7% refers to positive or negative anomalies. In the Conclusions, the\nstatement that “Chl-a concentrations exhibit symmetrical responses during extreme\nhigh and low temperatures” would benefit from a precise reference frame:\nsymmetrical relative to what: temperature anomaly magnitude, chlorophyll anomaly magnitude,\nor another metric? A similar issue arises at line 134, where “The temporal evolution of\nthese responses further highlighted their complexity” begins a paragraph without\nspecifying what “these” are and what kind of “complexity” is meant, which makes the\nthread difficult to follow. Consider explicitly framing results with subheadings by\nlatitude bands / water masses and closing each subsection with a one-sentence takeaway;\nthis could substantially improve readability. Similarly, frame what the paragraph is\nabout in the first line.\nReply: We sincerely thank you for your valuable suggestions. We have followed your\nguidance to enhance the clarity of the manuscript and ensure that the responses of\nphytoplankton to extreme temperature events are consistently communicated\nthroughout. Our specific revisions are as follows:\n(1) Regarding unclear percentages such as 51.7%, we carefully reviewed the\ndescriptions throughout the manuscript and supplemented all instances where the sign\nof the anomaly percentages were not explicitly specified (please refer to line 124).\n(2) The statement that Chl-a concentrations exhibit corresponding responses during\nextreme high and low temperatures lacked a clear reference frame. We have now\nclarified that the phytoplankton responses are symmetrical relative to the Chl-a anomaly\nmagnitude (Please refer to line 308).\n(3) The original sentence “The temporal evolution of these responses further\nhighlighted their complexity” was unclear. We revised it to: “The temporal evolution\nof phytoplankton Chl-a anomalies during MHWs and MCSs reveals distinct patterns\nacross regions” (Please refer to line 141).\n(4) We emphasized the focus of each paragraph at its beginning and added concise\nsummaries at the end of each subsection to more clearly convey the main points and\nimprove the readability of the manuscript.\nStronger framing and definitions at the start of the Results could also help. A concise\ndescription of the datasets and explicit definitions of MHWs/CSs, including the time\nand anomaly thresholds you adopt, would orient readers. It would also help to define\nwhat constitutes an “event” in your workflow. Under a pixel-level Hobday-style\ndetection, a value such as 0.14 events per decade is ambiguous (e.g., is an event tied to\na single pixel? Under Hobday’s definition it can be). Many studies use % or area\ncovered, which could aid interpretation here. Reporting area affected by MHWs/MCSs\nalongside event counts (number of pixels) would make the results more interpretable.\nIf you use the Hobday et al. (2018) severity categories, introducing these before they\nfirst appear would help readers who are familiar with phytoplankton ecology, but not\nMHWs.\nReply: Thank you for your valuable suggestion. We have now added an introduction\nto the datasets and provided explicit definitions of MHWs and MCSs at the beginning\nof the Results section, including the baseline period and thresholds used, to improve\nreadability and clarity of the manuscript. In our workflow, an “event” represents the\noccurrence of a single MHW or MCS at a given grid cell. However, the original use of\n“event” in the text was not precise. To better convey our meaning, we have replaced\n“event” with “times” to indicate the number of MHW or MCS occurrences, which is\nmore accurate. Furthermore, before presenting results of the severity classification,\nwe introduced the Hobday-style classification framework. This allows a more reasonable\nassessment of phytoplankton responses under extreme temperature events of different\nintensities. We sincerely thank the reviewer for this suggestion, which has improved\nthe clarity and logical flow of our manuscript.\nRegional terminology and masking needs to be clarified. The zone referred to as the\n“Antarctic Zone” appears, from the figures, to correspond more closely to the\nPermanently Open Ocean Zone (POOOZ/POOZ; see Deppeler & Davidson, 2017),\nwith much of the true Antarctic Zone masked. This distinction matters for interpretation;\nwithout the figures, one would assume the Antarctic Zone to be closer to Antarctica. In\nsea-ice regions, satellites do not retrieve SST beneath ice and a practical baseline near\n−1.8 °C is often implied; this will influence MHW/MCS identification, severity\nclassification, and any zonal comparisons, so it would be helpful to state that these areas\nare masked. This also raises a question about the analysis period: is it year-round, or\nfocused on summer? The seasonal signal could be strong, so a seasonal breakdown\nmight provide useful context. Similarly, summer in Antarctica is more free of ice and\ncan allow clearer MHW/Chl-a trend detection, filling in this data gap in your study,\nsee examples in Pinkerton et al., (2021), or Hayward et al., (2025).\nReply: We thank you for these helpful suggestions, our revisions are as follows:\n(1) Clarification of Antarctic Zone and masking\nWe agree that much of the Antarctic Zone is masked. To distinguish the permanently\nopen ocean area, we clarified in the Methods section that, in this study, the Antarctic\nZone is defined as the region between the Polar Front and the 20% climatological SIC\ncontour. To ensure accurate identification of MHWs and MCSs, severity classification,\nand inter-zone comparisons, all SST data in areas with mean annual SIC greater than\n20% were masked.\n(2) Seasonal context and Chl-a responses\nOur analysis covers the entire year, which may weaken seasonal signals. To address\nthis, we added descriptions of the characteristics of MHWs and MCSs across different\nseasons and the responses of Chl-a during extreme temperature events. Summer in the\nSouthern Ocean, with minimal sea-ice coverage, allows clearer detection of extreme\ntemperature events and Chl-a trends. Indeed, Chl-a anomalies during MHWs are most\npronounced in summer (see Supplementary Fig. 8), effectively supplementing the\ncontextual information for our study.\nSupplementary Fig. 8 | Spatial distribution of seasonal mean intensity of MHWs (a-d)\nand MCSs (e-h) during 1998–2023, and the corresponding mean Chl-a anomaly\npercentages during MHWs (i-l) and MCSs (m-p) for each season.\nSeveral statements would benefit from supporting evidence or softer phrasing. At line\n256, “Mixed layer reduced loss rates through greater population dilution and decreased\ngrazing pressure” reads as a process explanation but has no direct support from\nobservations, modelling, or citations. If this reflects the authors’ interpretation, it needs\nto be framed as a hypothesis with references.\nReply: We thank you for this comment. We agree that our original phrasing implied a\nmechanistic explanation without providing direct observational or modeling support.\nTo address this, we have revised the sentence to adopt a more cautious and hypothesis-\nbased framing, and we have added appropriate references. The revised sentence now\nreads:“The increased nutrient supply may have elevated phytoplankton division rates,\nespecially for fast-growing diatoms, while the deeper mixed layer may have contributed\nto reduced loss rates, potentially through greater population dilution and decreased\ngrazing pressure (Behrenfeld & Boss, 2018; Diaz et al., 2021).” We believe this\nmodification softens the language, makes clear that this is an interpretation rather than\na demonstrated causal mechanism, and provides references to prior studies that discuss\nthese processes.\nGiven the diversity of the datasets, uncertainty metrics would be very helpful. Pigment-\nto-PSC relationships usually show good agreement, but are complicated by pigment\nsharing among taxa, which affects diagnostic partitioning. The manuscript mentions a\n17-variable PSC model, but the rationale for this choice, and the validation from HPLC\npigments to size classes are not described. In the validation, the HPLC data alone do\nnot inherently yield size classes; there are multiple diagnostic pigment approaches, so\nadditional methodological detail needs to be stated. As the key conclusions rely on PSC\noutputs, more information on methods and uncertainty would greatly improve the paper.\nIf SST is used as an input to the PSC model, a brief discussion of potential circularity\nwhen analysing responses to temperature extremes, and how this was mitigated,\ne.g. via sensitivity testing, potentially without SST would provide more confidence in the\nresults.\nReply: Thank you very much for these valuable suggestions. We have now\nsupplemented our manuscript with evaluations of uncertainty metrics and sensitivity\nanalyses. Our specific revisions and responses are as follows:\n(1) Pigments are shared among different phytoplankton groups, which affects the\nclassification of size structure, especially between picoplankton and nanoplankton.\nHPLC data alone cannot directly produce size classes, so we processed the pigment\ndata according to Brewin et al. (2015), using seven diagnostic pigments to calculate the\ncommunity proportion of the three phytoplankton size classes. This approach accounts\nfor the fact certain pigments (e.g. 19′-hexanoyloxyfucoxanthin and fucoxanthin) are\nfound among phytoplankton in different size classes. The technique has also been\nindependently evaluated against measurements of Chl-a in the three size classes derived\nfrom sequential-based, size-fractionated filtration (see Figure 2 of Brewin et al. 2017,\nhttps://doi.org/10.3389/fmars.2017.00104). The specific formulas are provided in the\nMethods (please refer to lines 462-489). Additionally, we have uploaded the calculation\ncode used in this study to facilitate rapid reproducibility of our results.\n(2) We apologize for the previous misstatement. We employed a 17-parameter PSC\nmodel rather than a 17-variable model. The coefficients of the PSC model are functions\nof SST (Supplementary Text 1). The model only requires Chl-a and SST as input\nvariables, but the model itself has inherent uncertainty. The model developers (Sun et\nal., 2023) provided the 5.55% and 94.45% confidence intervals (equivalent to ±2\nstandard deviations) for the 17 parameters. We performed 3,000 Monte Carlo\nsimulations within this range to calculate the confidence interval bandwidth and\nstandard deviation of the PSC outputs to quantify model uncertainty and found that\noverall uncertainty is relatively low (see Supplementary Fig. 10).\n(3) We also thank you again for your suggestion regarding potential circularity, as SST\nis both an input to the PSC model and the environmental variable of interest in our study\nof extreme temperatures. To address this, we performed a sensitivity analysis by\ncalculating the standard deviation of the SST and Chl-a datasets and applying\nperturbation coefficients ranging from 0 to 1 in increments of 0.1, multiplied by the\nstandard deviation to create new perturbed datasets. Our results indicate that the PSC\nmodel is most sensitive to changes in Chl-a, while SST also affects the outputs,\nprimarily influencing picoplankton, which are more responsive to temperature changes.\nIn contrast, microplankton, with larger cell sizes, are barely affected by SST\nperturbations (see Supplementary Fig. 11).\nSun et al. (2023) also provided a PSC model variant without SST input, using only Chl-\na. We validated this no-SST model in the Southern Ocean against in situ datasets and\nfound its performance for picoplankton was relatively poor (r = 0.54; see\nSupplementary Fig. 12), likely because picoplankton are more sensitive to\nenvironmental changes, and Chl-a alone cannot fully capture their subtle variations.\nTherefore, SST is necessary to improve model accuracy. Nevertheless, we additionally\nvalidated the anomalous variations in the outputs of the PSC model without SST input\nduring MHWs and MCSs (see Supplementary Fig. 13). The results demonstrate that,\neven without SST input, the conclusions of this study remain valid, effectively\naddressing the issue of circularity.\nThe study by Hayward et al. (2025) has provided valuable guidance, as they used 10\nenvironmental variables as model inputs, achieving high model stability. In comparison,\nour PSC model relies on only two environmental variables, Chl-a and SST, so changes\nin either variable can substantially affect the outputs. In future work, we plan to\nincorporate multiple environmental variables into the PSC model to enhance its\nstability, allowing it to produce more reliable outputs under future extreme temperature\nevents and climate change scenarios. Once again, we sincerely thank you for your\nvaluable suggestions, which have greatly improved our understanding of phytoplankton\nmodeling and its interaction with climate variability.\nSupplementary Fig.10 | Results of PSC model uncertainty analysis. (a–c) Confidence\ninterval bandwidths calculated from 3,000 Monte Carlo simulations within the 4.55%–\n94.55% confidence interval of the PSC model parameters. (d–f) Spatial distributions of\nthe corresponding standard deviations.\nSupplementary Fig.11 | Results of PSC model sensitivity analysis. (a) Perturbations\napplied to Chl-a and (b) perturbations applied to SST. The perturbation magnitude is\ndefined as a coefficient ranging from 0 to 1 (with an interval of 0.1) multiplied by the\nstandard deviation of the dataset.\nSupplementary Fig.12 | Validation between in-situ chlorophyll-a (Chl-a) concentration\nand Chl-a concentration derived from the chlorophyll algorithm (OC4V6) and the PSC\nmodel (SST-independent), including total Chl-a concentration (a), picoplankton (b),\nnanoplankton (c), and microplankton (d).\nSupplementary Fig.13 | Chl-a anomalies for picoplankton, nanoplankton, and\nmicroplankton during MHWs and MCSs in the SO from 1998 to 2023. (a-c) Chl-a\nanomalies for picoplankton (a), nanoplankton (b), and microplankton (c) during MHWs.\n(d-f) Corresponding Chl-a anomalies during MCSs. (g-i) Box plots of phytoplankton\ncommunity fractions in the STZ, SAZ, and AZ subregions during MHW and MCS\nevents, respectively. The PSC model used is the version without SST input.\nIt would also be useful to briefly discuss the choice of MHW definition. MHW\ndefinitions greatly influence identification and severity, and there is ongoing debate\nabout methodological sensitivity. If you adopt the Hobday et al. framework, detail why\nit suits this study, see Farchadi et al. (2025, Nature Climate Change, “Marine heatwaves\nare in the eye of the beholder”).\nReply: We thank you for highlighting the importance of carefully considering the\ndefinition of marine heatwaves. Indeed, MHW definitions strongly influence event\ndetection and severity, and there is ongoing debate about the sensitivity of different\napproaches. In this study, we adopted the Hobday et al. (2016) framework because it\nhas become a widely accepted and standardized method in the literature, facilitating\ncomparability across studies and regions. Specifically, we used a 30-year baseline\nwithout detrending, which allows extreme temperature events to reflect both short-term\nanomalies and the longer-term climate trend. We recognize that this approach may not\nfully capture regional characteristics or isolate recent extremes. In future work, we plan\nto examine the influence of alternative baselines, different SST products, and regionally\ntailored definitions of MHW/MCS to better disentangle the roles of long-term climate\nchange and short-term variability. We have added a discussion on the definition of\nMHWs in the manuscript (Please refer to lines 319-324).\nA few line-specific clarifications could further improve the manuscript. At line 26,\nplease specify the regions where previously identified impacts of MHWs occurred.\nBetween lines 47 and 50, the juxtaposition of a “decline in chlorophyll-a” with\n“extensive algal blooms” reads as contradictory and needs rephrasing.\nReply: We thank you for these helpful suggestions. In response:\n(1) We have clarified the geographic context by specifying that the previously identified\nimpacts of MHWs occurred in the Southern Ocean (Please refer to line 26).\n(2) We have removed the reference to “extensive algal blooms” to avoid the potential\ncontradiction with the observed decline in chlorophyll-a, thereby improving clarity\n(Please refer to lines 47-50).\nThe citations need another check. Despite stating they had been fixed, some references\nappear to be irrelevant to the paper. Review 10 is presented as Southern Ocean but is\nabout the Atlantic, and citation 13 addresses North Sea sole and does not fit the scope\nat all. Similarly, a few highly relevant studies on latitudinal patterns in chlorophyll-a\nand phytoplankton composition have been missed, including Schlosser et al. (2025),\n“Latitudinal trends in drivers of the Southern Ocean spring bloom onset” (JGR);\nHayward et al. (2024), “Twenty-six years of phytoplankton pigments reveal a\ncircumpolar class divide around the Southern Ocean” (Communications Earth &\nEnvironment); and Pinkerton et al. (2021), “Evidence for the Impact of Climate Change\non Primary Producers in the Southern Ocean” (Frontiers in Ecology and Evolution).\nReply: We thank you for pointing out the inconsistencies in the reference list and we\napologize for the oversight. We carefully re-examined all in-text citations and the\nbibliography. Specifically, Reference 10 (previously an Atlantic study) has been\nreplaced with a more appropriate Southern Ocean reference that supports the statement\nin question, and Reference 13 (North Sea sole) has been removed because it is not\nrelevant to the present study. In addition, we have added the reviewer-recommended,\nhighly relevant studies (Schlosser et al., 2025; Hayward et al., 2024; and Pinkerton et\nal., 2021) and have updated the manuscript text where these citations are most\nappropriate. Finally, we conducted a thorough pass over the full reference list to correct\nother mismatches and ensure all citations now accurately reflect the literature\nreferenced in the text. Thank you for helping us improve the manuscript’s accuracy and\nrelevance.\nIn summary, the manuscript addresses an important problem and uses multi-platform\ndatasets. However, there are several components of the study that needs addressing\nbefore publication. First, the narrative and framing needs to be improved, with the\nconclusions and take home messages clearly stated. I think that the methods needs to\nbe far more detailed to be easily replicated by others. Most importantly, some level of\nuncertainty needs to be quantified as so much data from so many sources are used.\nReply: We thank you for the constructive and thoughtful comments. In the revised\nmanuscript, we have now improved the overall narrative and framing to ensure that the\nconclusions and main take-home messages are more clearly articulated. We have also\nexpanded the Methods section by adding the necessary details to facilitate replication\nof our analysis by other researchers. Importantly, we have now incorporated a\nquantitative assessment of uncertainty, acknowledging the multiple datasets and\nsources employed in this study. We greatly appreciate the reviewer’s valuable feedback,\nwhich has helped us significantly improve the clarity and robustness of the manuscript.\nReference:\n1. Behrenfeld, M. J. & Boss, E. S. Student’s tutorial on bloom hypotheses in the context\nof phytoplankton annual cycles. Glob. Chang. Biol. 24, 55–77 (2018).\n2. Diaz BP, Knowles B, Johns CT, Laber CP, Bondoc KGV, Haramaty\nL, et al. Seasonal mixed layer depth shapes phytoplankton physiology, viral\nproduction, and accumulation in the North Atlantic. Nat Commun. 2021;12:1–16\n10.1038/s41467-021-26836-1.\n3. Schlosser TL, Strutton PG, Baker K, Boyd PW. Latitudinal trends in drivers of the\nSouthern Ocean spring bloom onset. Journal of Geophysical Research: Oceans 2025,\n130(1): e2024JC021099.\n4. Hayward A, Pinkerton MH, Wright SW, Gutierrez-Rodriguez A, Law CS. Twenty-\nsix years of phytoplankton pigments reveal a circumpolar Class Divide around the\nSouthern Ocean. Communications earth & environment 2024, 5(1): 92.\n5. Pinkerton MH, Boyd PW, Deppeler S, Hayward A, Höfer J, Moreau S. Evidence for\nthe impact of climate change on primary producers in the Southern Ocean. Frontiers in\nEcology and Evolution 2021, 9: 592027."}, {"round": 3, "reviewer_comments": {"reviewer_3": "The authors have done a good job in addressing the previous comments, espeically with regards to the sensitivity analysis.\nThe manuscript has improved considerably; however, several important issues remain that affect its scientific rigour and\nfocus. Once these are addressed, the paper will be significantly more defensible and clear.\nThe central message of this study concerns the response of Chl-a to MHWs and MCs. However, this message is currently\ndiluted by the inclusion of a wide range of ancillary analyses. I strongly recommend removing the sections on C:Chl-a ratios,\nmixed layer depth (MLD), and BGC-Argo data, adding all of this dilutes the main story, but also adds in a lot of uncertainty by\nbringing together so much data from other sources. Focusing on the core narrative, the Chl-a response to MHWs and MCSs,\nwill greatly strengthen the conclusions/story of the paper.\nMore caution is needed with terminology throughout the paper, especially regarding the “Antarctic Zone.” As previously\nmentioned, the Antarctic Zone is typically defined as the region south of the Polar Front, extending to the seasonal sea-ice\nzone and right the way to the coast of Antarctica. The region analysed in this study corresponds more closely to the\nPermanently Open Ocean Zone (POOZ), which lacks sea ice. Redefining the “Antarctic Zone” within this manuscript will\nconfuse readers and misrepresents ecological patterns around Antarctica. The revised definition provided in the author’s\nresponse (“between the Polar Front and the 20% climatological SIC contour”) is therefore not appropriate - a well\nacknowledged definition should not be redefined. I recommend adhering to established zonal definitions and throughout the\nmanuscript referring to this as the POOZ. If you wanted to discuss trends in phytoplankton around Antarctica, this paper\nwould be appropriate, and is also highly relevant to your study: Hayward, A., Wright, S.W., Carroll, D. et al. (2025). Antarctic\nphytoplankton communities restructure under shifting sea-ice regimes. Nature Climate Change, 15, 889–896.\nhttps://doi.org/10.1038/s41558-025-02379-x.\nI don’t believe the authors had previously stated the baseline in their submissions else I would have mentioned this. The\nstudy currently uses a 1982–2011 climatology. The standard WMO baseline (1991–2020) is preferred for two key reasons:\n1. It is centered between cooler conditions in the 1990s and the warmer 2010s, providing a more balanced representation of\nrecent climate.\n2.Satellite data coverage and quality improve substantially after the early 1990s.\nUsing the 1982–2011 baseline risks overclassifying recent events as extreme. Moreover, the 1991–2020 baseline aligns\nbetter with the Chl-a dataset (1997–2023). The statement “In general, the intensity of extreme events increases\nprogressively, with the occurrence probability of Category III and IV events being relatively low” likely reflects the underlying\nlong-term warming trend rather than event-specific extremes. This is why many attribution studies (like this) detrend SST\ndata before analysis. At a minimum, I recommend recalculating events using the more centred 1991–2020 climatology.\nAt present, the manuscript attempts to cover too much ground. There remains considerable uncertainty in satellite-derived\ncarbon estimates and in the associated model metrics. While linking physiology to optical signals (e.g., via C:Chl-a ratios) is\na valuable long-term goal, the current data and methods do not yet support strong conclusions. I encourage the authors to\nfocus instead on the well-supported Chl-a response to MHWs and MCSs, the most defensible and novel aspect of the work.\nReducing emphasis on carbon estimates and size structure will make the narrative much clearer and more compelling.\nIn Figure 1, I would suggest using the area affected by MHWs in each region instead of the average number of events, this\nwill provide a more representative metric of how each zone is changing than averaging events.\nI think that following these revisions the manuscript will be more accurate and conclusions more concise, appealing to the\nwide readership for a Nature journal."}, "author_response": "Response to Reviewer #3\n(Note: Reviewer comments in black and our point-to-point replies in blue)\nThe authors have done a good job in addressing the previous comments, espeically with\nregards to the sensitivity analysis. The manuscript has improved considerably; however,\nseveral important issues remain that affect its scientific rigour and focus. Once these\nare addressed, the paper will be significantly more defensible and clear.\nReply: We sincerely thank you for the positive and constructive feedback. We\nappreciate the acknowledgement of our efforts in addressing the previous comments,\nparticularly regarding the sensitivity analysis. We are grateful for your insightful\nsuggestions, which have helped us further improve the scientific rigor and clarity of the\nmanuscript.\nThe central message of this study concerns the response of Chl-a to MHWs and MCs.\nHowever, this message is currently diluted by the inclusion of a wide range of ancillary\nanalyses. I strongly recommend removing the sections on C:Chl-a ratios, mixed layer\ndepth (MLD), and BGC-Argo data, adding all of this dilutes the main story, but also\nadds in a lot of uncertainty by bringing together so much data from other sources.\nFocusing on the core narrative, the Chl-a response to MHWs and MCSs, will greatly\nstrengthen the conclusions/story of the paper.\nReply: We sincerely thank the reviewer for their continued evaluation and constructive\nsuggestions. We fully understand the reviewer’s intention to streamline the manuscript\nand to strengthen its central narrative on the Chl-a response to MHWs and MCSs.\nHowever, we respectfully believe that the sections on C:Chl-a ratios, mixed layer depth\n(MLD), and BGC-Argo data are not ancillary but essential components of the study’s\ncore framework. These analyses provide the mechanistic foundation for interpreting the\nobserved Chl-a variability, allowing us to disentangle biological responses from\nphysical forcing and to distinguish between direct thermal effects and mixing-driven\nnutrient or light variations.\nThe integration of satellite, reanalysis, and BGC-Argo observations is a defining\nfeature of this work and was recognized as a major strength by both Reviewer #2\n(provided in the appendix) and by Reviewer #3 in the first-round review (“the topic is\ntimely, and the multi-platform approach is a real strength”). In the previous round of\nreview, Reviewer #2 suggested adding sufficient supporting evidence for mechanistic\ninterpretation. We have since incorporated additional analyses to address their concern,\nwhich were accepted in their subsequent feedback. Many recent studies have also\nadopted multi-platform datasets to provide complementary insights into biological and\nphysical processes (e.g., Viljoen et al., (2024); Thomalla et al., (2023); Westberry et al.,\n(2023)).\nNonetheless, we do acknowledge that the inclusion of auxiliary analyses may dilute\nthe main narrative; therefore, in light of your comment, we have reduced the discussion\nin the Results section and moved most of these analyses to the Supplementary Material.\nWe have carefully rechecked the text to ensure that the main message—the Chl-a\nresponse to MHWs and MCSs—remains clear and prominent. To enhance clarity, we\nhave streamlined the narrative and adjusted cross-references so that the supporting\nanalyses (C:Chl-a, MLD, and BGC-Argo) are presented succinctly and directly linked\nto the interpretation of the Chl-a response. We respectfully prefer to retain these sections\nas they are indispensable for a physically and biologically coherent explanation of our\nfindings but have now moved aspects to the Supplementary Material. Our revision now\npreserves the value of the multi-platform perspective while improving the clarity of the\ncentral storyline. \nMore caution is needed with terminology throughout the paper, especially regarding the\n“Antarctic Zone.” As previously mentioned, the Antarctic Zone is typically defined as\nthe region south of the Polar Front, extending to the seasonal sea-ice zone and right the\nway to the coast of Antarctica. The region analysed in this study corresponds more\nclosely to the Permanently Open Ocean Zone (POOZ), which lacks sea ice. Redefining\nthe “Antarctic Zone” within this manuscript will confuse readers and misrepresents\necological patterns around Antarctica. The revised definition provided in the author’s\nresponse (“between the Polar Front and the 20% climatological SIC contour”) is\ntherefore not appropriate - a well acknowledged definition should not be redefined. I\nrecommend adhering to established zonal definitions and throughout the manuscript\nreferring to this as the POOZ. If you wanted to discuss trends in phytoplankton around\nAntarctica, this paper would be appropriate, and is also highly relevant to your study:\nHayward, A., Wright, S.W., Carroll, D. et al. (2025). Antarctic phytoplankton\ncommunities restructure under shifting sea-ice regimes. Nature Climate Change, 15,\n889–896. https://doi.org/10.1038/s41558-025-02379-x.\nReply: We thank you for this important clarification and for pointing out the appropriate\nterminology. Following your suggestion, we have revised the regional definitions\nthroughout the manuscript and now refer to the area previously termed the “Antarctic\nZone” as the Permanently Open Ocean Zone (POOZ). The description in the Methods\nsection has been updated accordingly, and all relevant figures and text have been\nmodified to ensure consistency. We agree that adhering to established zonal definitions\nimproves clarity and scientific accuracy. We also appreciate the reference to Hayward\net al. (2025) and have cited this paper in the revised manuscript. \nI don’t believe the authors had previously stated the baseline in their submissions else\nI would have mentioned this. The study currently uses a 1982–2011 climatology. The\nstandard WMO baseline (1991–2020) is preferred for two key reasons:\n1. It is centered between cooler conditions in the 1990s and the warmer 2010s,\nproviding a more balanced representation of recent climate.\n2.Satellite data coverage and quality improve substantially after the early 1990s.\nUsing the 1982–2011 baseline risks overclassifying recent events as extreme. Moreover,\nthe 1991–2020 baseline aligns better with the Chl-a dataset (1997–2023). The statement\n“In general, the intensity of extreme events increases progressively, with the occurrence\nprobability of Category III and IV events being relatively low” likely reflects the\nunderlying long-term warming trend rather than event-specific extremes. This is why\nmany attribution studies (like this) detrend SST data before analysis. At a minimum, I\nrecommend recalculating events using the more centred 1991–2020 climatology.\nReply: We thank you for this insightful and constructive comment. We fully agree that\nusing a more centered baseline provides a balanced representation of recent climate\nconditions and better consistency with the Chl-a dataset. Accordingly, we have\nrecalculated all MHW events using a 1991–2020 climatology, which is consistent with\nthe temporal coverage of our dataset and aligns with your recommendation for a more\nrepresentative baseline. This adjustment indeed improved the robustness and\ninterpretability of our results, reducing potential biases associated with long-term\nwarming trends. The revised results and figures have been updated throughout the\nmanuscript, and relevant descriptions in the Methods and Results sections have been\nmodified accordingly.\nAt present, the manuscript attempts to cover too much ground. There remains\nconsiderable uncertainty in satellite-derived carbon estimates and in the associated\nmodel metrics. While linking physiology to optical signals (e.g., via C:Chl-a ratios) is\na valuable long-term goal, the current data and methods do not yet support strong\nconclusions. I encourage the authors to focus instead on the well-supported Chl-a\nresponse to MHWs and MCSs, the most defensible and novel aspect of the work.\nReducing emphasis on carbon estimates and size structure will make the narrative much\nclearer and more compelling.\nReply: We thank you for this thoughtful and constructive recommendation. We agree\nthat uncertainties in satellite-derived carbon estimates and size structure metrics could\nweaken the focus of the manuscript. Following your advice, we have substantially\nreduced the discussion and analysis related to carbon and phytoplankton size structure,\nand now emphasize the Chl-a response to MHWs and MCSs, which represents the most\nrobust and novel aspect of this study. These revisions have improved the clarity and\nfocus of the manuscript.\nIn Figure 1, I would suggest using the area affected by MHWs in each region instead\nof the average number of events, this will provide a more representative metric of how\neach zone is changing than averaging events.\nReply: We sincerely thank you for this valuable suggestion. We have revised Figure 1\naccordingly by replacing the average number of MHW events with the area affected by\nMHWs in each region. This change provides a more representative metric of how each\nzone is changing, as suggested. Please see the revised Figure 1 in the manuscript.\nI think that following these revisions the manuscript will be more accurate and\nconclusions more concise, appealing to the wide readership for a Nature journal.\nReply: We sincerely thank you for the encouraging and constructive feedback. We have\ncarefully addressed all the comments and revised the manuscript accordingly. We\nbelieve that the revised version is now more accurate, focused, and clear, and we truly\nappreciate your valuable guidance, which has helped us improve the overall quality and\nreadability of the paper.\nReferences\n1. Hayward A, Wright SW, Carroll D, Law CS, Wongpan P, Gutiérrez-Rodriguez A,\net al. Antarctic phytoplankton communities restructure under shifting sea-ice regimes.\nNature Climate Change 2025: 1-8.\n2. Viljoen JJ, Sun X, Brewin RJW. Climate variability shifts the vertical structure of\nphytoplankton in the Sargasso Sea. Nature Climate Change 2024, 14(12): 1292-1298.\n3. Thomalla, Sandy J., et al. \"Widespread changes in Southern Ocean phytoplankton\nblooms linked to climate drivers.\" Nature Climate Change 13.9 (2023): 975-984.\n4. Lehahn, Yoav, et al. \"Dispersion/dilution enhances phytoplankton blooms in low-\nnutrient waters.\" Nature Communications 8.1 (2017): 14868.\n5. Westberry, T. K., et al. \"Atmospheric nourishment of global ocean ecosystems.\"\nScience 380.6644 (2023): 515-519.\nAppendix\n(Note: Reviewer comments in black and our point-to-point replies in purple)\nreviewer #2’s comment:\nc. (line 197-199) Here it is stated that enhanced mixing in the STZ (no evidence\nprovided) alleviates nitrate limitation and promotes phytoplankton growth. First, the\nassumption here is that the increase in chlorophyll in the STZ during MCS is due to an\nincrease in biomass. IF the supposition is correct that the mixed layer deepens during\nan MCS, how do the authors know that the change in chlorophyll isn’t simply\nphotoacclimation to lower average mixed layer light levels? If it is an increase in\nnutrient supply, one would have to show that the increase in mixing depth was sufficient\nto penetrate the nutricline, otherwise there is no strong reason to think that the deeper\nmixing (how much deeper, by the way, a few meters, tens of meters?) impacted nutrient\nsupply. Second, it is then stated that the deeper mixing during MCS in the SAZ and AZ\ncaused greater light limitation. But it was stated prior to this that these regions are also\niron limited (actually, during summer months they are iron limited more than light\nlimited, as in situ iron fertilization experiments have shown). No explanation is\nprovided why deeper mixing in the SAZ and AZ doesn’t also increase iron supply to\nthe mixed layer. Finally, be careful with the word ‘growth’. Do you mean cell division\nrates or changes in biomass. The two are not the same. The mechanisms you discuss\nare relevant to division rate, but what you observed from space are changes in biomass,\nwhich are also controlled by loss processes (which are barely mentioned at all in this\nmanuscript).\nReply: We appreciate this detailed and insightful comment. In the revised manuscript,\nwe have significantly restructured this section to address these key concerns. Below we\nsummarize the main clarifications and revisions made:\n(1) Biomass vs. Photoacclimation\nWe agree that observed increases in surface Chl-a could reflect changes in biomass,\ncellular pigmentation, or both. To address this:\n(a) We now include an analysis of satellite-derived particulate backscatter (bbp(443))\nto estimate phytoplankton carbon, which supports biomass increases during MCSs (see\nExtended Data Fig. 4).\n(b) We explicitly note that changes in the Chl-a:carbon ratio (θ) indicate contributions\nfrom both biomass and photoacclimation.\n(c) The revised text avoids assuming a direct translation from Chl-a to biomass and\nacknowledges physiological responses as a possible factor.\n(2) MLD Deepening and Nutricline Penetration\nWe now cite GLORYS12V1 reanalysis data showing that during MCSs, the MLD\nincreased by an average of 4.25 ± 0.92 m globally, and by 20–30 m in the Atlantic and\nPacific sectors of the STZ. These regions are adjacent to the Southern Ocean’s winter\ndeep mixing band, where nutrient-rich waters are entrained into the euphotic zone. We\nadded supporting references (e.g., Rigby et al., 2020) to show that nutrient supply\nincreases are plausible in these regions during MCSs.\n(3) Iron Supply in SAZ and AZ:\nWe have clarified that while deeper mixing during MCSs may supply some iron, the\neffect is limited. In the AZ, deeper mixing also leads to reduced light availability and\nenhanced sea ice formation, which scavenges dissolved iron (e.g., Lannuzel et al., 2007),\nresulting in net iron removal. In the SAZ, while nitrate is abundant, iron remains the\nprimary limiting factor, and the modest deepening during MCSs is unlikely to deliver\nsufficient iron from subsurface reservoirs.\n(4) Division Rate vs. Biomass Accumulation\nWe thank the reviewer for this important distinction. The revised text now clearly\nseparates division rates (influenced by nutrient and light availability) and net biomass\nchange (the satellite-observed signal, governed by the balance between growth and loss\nprocesses (e.g., grazing, sinking)). We now state that “the observed biomass anomalies\nreflect net outcomes of phytoplankton division and loss rates,” aligning with the\nframework proposed by Behrenfeld (2014) and others.\nReference:\nLannuzel D, Schoemann V, de Jong J, Tison J-L, Chou L. Distribution and\nbiogeochemical behaviour of iron in the East Antarctic sea ice. Marine Chemistry 2007,\n106(1-2): 18-32.\nRichaud B, Hu X, Darmaraki S, Fennel K, Lu Y, Oliver ECJ. Drivers of Marine\nHeatwaves in the Arctic Ocean. Journal of Geophysical Research: Oceans 2024, 129(2).\nBehrenfeld MJ. Climate-mediated dance of the plankton. Nature Climate Change 2014,\n4(10): 880-887.\nResponse to Reviewer #3\n(Note: Reviewer comments in black and our point-to-point replies in blue)\nI am happy with the changes the authors have made, and recommend the article for\npublication. One small thing, I would try to improve the flow of the 'Mechanisms\nunderpinning regional differences in phytoplankton responses' section.\nReply: We sincerely thank you for the thorough and constructive evaluations provided\nthroughout the review process. We are grateful for your continued support and for\nrecommending the article for publication. Regarding the suggestion to improve the flow\nof the “Mechanisms underpinning regional differences in phytoplankton responses”\nsection, we have revised this part accordingly. Specifically, we reorganized the\nparagraph structure, improved transitions between ideas, and refined connectors to\nenhance clarity and readability. These changes have made the discussion more coherent\nand easier to follow. We appreciate the reviewer’s insightful comments, which have\nhelped us further strengthen the manuscript."}]

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