the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Oct 2025
Phytoplankton blooms affect microscale gradients of oxygen and temperature across the sea surface microlayer
Abstract. The sea surface microlayer (SML) is the thin layer on top of the ocean that is in direct contact with the atmosphere and is crucial for air–sea interactions. Its properties are influenced in particular by surface-active substances (surfactants), mainly produced by phytoplankton and bacteria. Thus, phytoplankton blooms and their decay can have a considerable influence on the SML. A mesocosm study was conducted to assess the impact of a phytoplankton bloom on the SML using a multidisciplinary approach, which enabled in situ measurements under controlled yet natural conditions. A phytoplankton bloom was induced within a mesocosm facility filled with seawater, resulting in three phases of the study: the pre-bloom, bloom, and post-bloom phases. During all phases, microsensors measured in situ microprofiles of oxygen and temperature with a 125 µm vertical resolution through the air, SML, and underlying water. Oxygen and temperature gradients were determined from the profiles, as well as the thicknesses of the oxygen diffusion boundary layer (DBL) and thermal boundary layer (TBL). The night-time oxygen gradients (ØΔO2, pre-bloom = –2.16 ± 5.53 µmol L–1, ØΔO2, bloom = +24.90 ± 14.51 µmol L–1, ØΔO2, post-bloom = +2.07 ± 4.82 µmol L–1) correlated highly with the chlorophyll a concentration (r = 0.755, p < 0.001), while the DBL thickness (ØDBL, overall = 937 ± 369 µm) showed a moderate correlation to the SML surfactant concentration (r = 0.490, p = 0.014). Both indicate the phytoplankton bloom's influence on oxygen gradients across the SML. Night-time temperature gradients (ØΔT, overall = –0.133 ± 0.079 °C) and the TBL thickness (ØTBL, overall = 1300 ± 392 µm) were not correlated to the chlorophyll a or surfactant concentration. The mesocosm study and the microprofiling approach provide in situ data on the air–sea exchange processes in the SML, reflecting the distinct interplay of the SML and phytoplankton blooms in the exchange of oxygen and heat. This has implications for future studies on air–sea gas and heat exchange between the ocean and the atmosphere.
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Status: open (until 19 Jan 2026)
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RC1: 'Comment on egusphere-2025-4833', Angelika Renner, 17 Nov 2025
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AC1: 'Reply on RC1', Carsten Rauch, 16 Dec 2025
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Answer to the review of Angelika Renner from the 17. November 2025
We like to extend our thanks to reviewer Angelika Renner for her positive comments and constructive criticism. We reviewed the manuscript and delved deeper into the data to address her comments and suggestions. Following are her comments, our response, and excerpts from the manuscript with the changes in italics. The updated Figures are attached as a supplement.
Comment Response Changes in manuscript - Line 96 and following lines were discrete samples are mentioned: how did you take these samples?
The SML samples were collected using the glass plate method (Harvey and Burzell, 1972). The ULW samples were collected using a syringe with a tube attached. Subsamples of these samples were then analysed for different parameters. More details about the discrete sampling procedure can also be found in Bibi et al. (2025a). At line 93, a brief paragraph was inserted to describe the sampling procedure. Line 93: […] 2 cm and 40 cm below the surface. Discrete water samples from the SML and ULW were collected daily. To account for diurnal changes, sampling alternated between one hour after sunrise and ten hours after sunrise (Bibi et al., 2025a). SML samples were collected using the glass plate method of Harvey and Burzell (1972), while ULW samples at 40 cm depth were collected with a syringe attached to a tube (Bibi et al., 2025a). Subsamples of these samples were subsequently analysed for different parameters. Surfactant concentrations in the SML and ULW […]
- Line 99: I immediately wondered about calibration samples for oxygen and only discovered the answer to that when I got to the appendix – add a reference to the relevant section in the appendix somewhere here?
A short sentence about the correction of the FerryBox oxygen measurements was inserted in the method section (line 100) and in Appendix B.
Line 100: […] measured oxygen concentration, temperature, and salinity. The FerryBox oxygen concentration was corrected with daily discrete samples analysed with the Winkler method (see Appendix B). Chlorophyll a measurements […]
- Line 114: did you allow for the water to settle again after moving the microsensors to a new depth? With the entire plate that the sensors are mounted on moving through the water, that generates quite a disturbance?
The water settled during the measurements, which took about 30 s per vertical step. With a small step size of 125 µm, minimal turbulence during sensor movement can be assumed, and enough time for settling is ensured. The sentence in line 115 was changed accordingly.
Furthermore, since the sensors were mounted as far away as possible from the centre of the holder and the tips of the microsensors were positioned a few centimetres above the holder, any micro-scale turbulence generated by the holder's movement should not reach the sensors and therefore not influence the measurements.
The holder for the microsensors shown in the photo was used during the pre-experiment phase but was replaced during the main experiment with another holder with a smaller surface area, reducing turbulence. To avoid confusion, the photo was replaced with the correct holder, and a second photo showing a detailed view of the microsensors in the water was added. Furthermore, the microsensor measurement rate of 50 Hz was added in line 114.
Line 114: […] the microsensors took three measurements with 50 Hz, each lasting 10 seconds, […]
Line 115: […] The slow movement of the microprofiler ensured minimal artificial water flow and turbulence in proximity to the microsensors. […]
See Figure 1a and 1b in supplement
Figure 1 (a): Microprofiler setup. Microsensors mounted on a microprofiler above the SURF water basin. (b): Close-up image of the microsensors inside the SURF water basin.
- Lines 119-121: was the room kept dark over night as well?
No, as SURF is located outside and has a transparent roof, it was exposed to natural diurnal variations in light. Artificial light or moonlight at night could not be excluded entirely. A short sentence was added in line 117. More pictures of the mesocosm facility can also be found in Bibi et al. (2025a).
Line 117: […] from 22 May onward were further analysed. The transparent roof of SURF allowed the water to be exposed to natural diurnal variations in solar radiation. To reduce the effects of solar radiation and diurnal heating […]
- Section 2.2: what are accuracy and precision of the microsensors?
According to the company, no official accuracy or precision for the microsensor can be provided, as these depend on many factors, including calibration frequency, lab electrical noise, and temperature stability. However, the sensors are fast-responding, exhibit minor drift over time, and have a low stirring sensitivity (Unisense.com). For the oxygen microsensor, only a detection limit of 0.3 µmol L–1 can be given, which is well exceeded in our study. The temperature microsensor has a resolution of 0.1 °C. These specifications of the microsensors were added line 110. In other literature, no accuracy or precision data for the Unisense microsensors could be found. Accuracies for other, larger Clark-type oxygen sensors are provided as ± 3 % (e.g. https://www.sea-sun-tech.com/product/clark-oxygen/).
In general, our focus does not lie on the absolute values of oxygen concentration or temperature, but on the profiles and anomalies in the DBL/TBL. These anomalies were clearly identifiable in of the majority of the 141 profiles. Due to large anomalies being observed in our study, slightly larger inaccuracies – even if assumed doubles compared to other larger Clark-type sensors – are of less concern.
Line 110: […] on a MicroProfiling System (UNISENSE, Denmark) (Fig. 1). The microsensors were fast responding, exhibited minor drift over time and had a low stirring sensitivity. The oxygen microsensor possessed a detection limit of is 0.3 µmol L–1, and the resolution of the temperature microsensor was 0.1 °C. The microsensors were mounted […]
- Line 140: do you have an estimate how much of an offset there was?
The offsets in microsensor height relative to the water surface differed slightly between the nights and laid between -2500 µm and 3375 µm (mean –371 ± 1292 µm). The estimate was inserted in line 140. The microsensors were mounted by hand as close as possible to the water surface. Together with changes in the water level through evaporation, this manual sensor adjustment introduced the offsets. These effects could not be completely avoided. For most profiles, the offsets did not affect the analysis, as a complete profile from the air into the water was present. Only on 26 May for temperature sensor 2 and 30 May for temperature sensor 1, did they prevent the profiles from being analysed.
Line 140: […] introduced minor vertical offsets (–371 ± 1292 µm), which were corrected […]
- Lines 146-149: I can’t quite follow here – you used only the measured values at points P1-6 for the regressions, not the measurements above/below/in between? Why?
No, for the air regression line, all measurements between P1 and P2 were used; the same for all measurements between P3 and P4 for the DBL regression line, and all measurements between P5 and P6 for the ULW regression line. Line 146 was changed for clarification.
Line 146: […] three linear regressions were computed with all measurements between the pairs P1 and P2, P3 and P4, and P5 and P6. […]
- Figures 2 & 3 and respective parts in the main text: What you call «oxygen gradient» and «temperature gradient» is just a difference as you actually also say in the captions. The gradient would be the slope of the curve. To derive the gradient (with depth), you need to divide this difference by the vertical distance between the two points as well, i.e. by the DBL and TBL thickness, respectively. Or refer to the differences in the text, not gradients.
As the focus lies on absolute air-sea differences, e.g., to compare the cool skin effect, the wording was changed throughout the text from oxygen/temperature gradients to anomalies. The choice of the word “anomaly”, rather than differences, should more strongly underscore the differences in oxygen and temperature conditions at the surface compared to the ULW, and is used in the literature (i.e. skin temperature anomalies, Jaeger at al., 2025). The gradient, as a difference per layer thickness, was not calculated. Changes from oxygen/temperature gradient to oxygen/temperature anomaly were made in Figures 2, 3, 5, and 8 and throughout the manuscript when not explicitly referring to a gradient.
Changes from 'oxygen gradient' to 'oxygen anomaly' throughout the text and Figures.
See Figure 2 in supplement
Figure 2: Microprofile of the oxygen concentration (31 May 2023, Profile 2). The oxygen concentration is accurate for the water profile but not for the air profile; the oxygen partial pressure is accurate for both profiles. Depth corrected for the proper sensor position; 0 µm indicates the air–water interface; points P1–P6 were used to calculate regressions for air, DBL, and ULW. The intersection of air and DBL regression H0 is the upper position of the water surface, and the intersection of DBL and ULW regression HDBL is the lower DBL boundary. The vertical difference between HDBL and H0 represents the DBL thickness, and the horizontal difference represents the oxygen anomaly.
See Figure 3 in supplement
Figure 3: Microprofile of the temperature (31 May 2023, Profile 1). Depth corrected for the proper sensor position; 0 µm indicates the air–water interface; points P1–P6 were used to calculate regressions for air, TBL, and ULW. The intersection of air and TBL regression H0 is the upper position of the water surface, and the intersection of TBL and ULW regression HTBL is the lower TBL boundary. The vertical difference between HTBL and H0 represents the TBL thickness, and the horizontal difference represents the temperature anomaly.
See Figure 5 in supplement
Figure 5 (a): ULW chlorophyll a concentration (green) and SML surfactant concentration (blue) between 22 May and 16 June, (b): Oxygen anomalies in the SML during the nights between 22 May and 16 June, 4–10 profiles per night, box: 25 % to 75 % quartile, horizontal line: median, whiskers: largest and smallest value.
See Figure 8 in supplement
Figure 8 (a): ULW chlorophyll a concentration (green) and SML surfactant concentration (blue) between 22 May and 16 June, (b): Temperature anomalies from Sensor 1 (blue) and Sensor 2 (red) during the nights between 22 May and 16 June, 4–10 profiles per night, box: 25 % to 75 % quartile, horizontal line: median, whiskers: largest and smallest nonoutlier value, open circles: outliers (difference to next value > 1.5 times interquartile range).
- Line 197-198: I don’t understand what you mean by «the smallest T gradients could be detected»? Do you mean that the differences in temperature were greater than the noise floor of 35 mK?
Yes, the temperature differences exceeded 35 mK. But the sentence means that temperature differences as low as 35 mK were detectable due to the slight noise-equivalent temperature difference. The sentence in line 198 was rephrased for clarification.
Line 197: […] While the absolute value had an accuracy of ±1 °C, horizontal temperature differences larger than 35 mK could be detected, as the noise equivalent temperature difference was greater than 35 mK. […]
- Line 213: how did you observe/asses the «slick-like properties»?
The slick-like properties were not only visually apparent but also evident in the surfactant concentration, which exceeded the threshold for slick conditions of 1000 µg L–1 (Bibi et al., 2025; Wurl et al., 2011) in large parts of the bloom and post-bloom phases. A sentence was added in line 213.
Line 213: […] During the bloom and post-bloom phases, a slick was visually observed at the water surface. The presence of a slick was confirmed, as the surfactant concentration exceeded the slick threshold of 1000 µg L–1 during the bloom and post-bloom phases (Bibi et al., 2025a; Wurl et al., 2011). For the oxygen microprofiles […]
- Line 215-216: How much of the decrease with depth is due to the time it took to complete the profiles? Looking at the temporal progression between profiles, this could have been a major factor, not that there was such a pronounced vertical gradient if you could have measured each profile instantaneously?
We calculated the vertical rate similarly to the gas exchange rate (Eq. 1), with C0 and Ct representing the oxygen concentrations in the ULW at P5 and P6, respectively, and Δt the time difference between those measurements. The results show that this vertical rate is typically similar to the gas exchange rate, with differences between the two rates around 0.038 ± 0.157 cm h-1 for most of our observations. This means that the vertical gradients observed are, to a large extent, caused by a decrease in oxygen over the time between the measurements. Only after 07 June, in the post-bloom phase, did the vertical rate consistently exceed the temporal gas exchange rate (mean difference of 0.538 ± 0.375 cm h–1). This meant that, in this phase, the vertical rate was not entirely due to the decrease in oxygen over time, but also due to a vertical gradient in the ULW. A paragraph describing this was added in section 3.1. A sentence in the discussion in line 355 was also changed to account for this.
Line 217: […] over a few millimetres. In addition, the partial pressure decreased with depth in the ULW. To test whether this decrease was due to depth or time, vertical rates were calculated, similar to the gas exchange rate in section 2.5. Equation (1) was used, now with C0 being the oxygen concentration at P5 and Ct the concentration at P6. Δt was the time difference between the measurements at P5 and P6. Until 06 June, the mean vertical rate for each night was similar to the overall gas exchange rate (Section 3.4) (differences of 0.038 ± 0.157 cm h–1), indicating that a decline over time mainly caused the decrease in oxygen concentration with depth in the ULW. However, in the post-bloom phase from 07 June onward did the vertical rate consistently exceed the gas exchange rate (difference of 0.538 ± 0.375 cm h–1), indicating a vertical oxygen gradient in the ULW that was not solely due to temporal decreases. On 22 May, before the bloom, […]
Line 355: […] two millimetre-thick layers in the air and ULW with notable oxygen gradients in the air, and one sharp diffusion boundary layer […]
- Line 216: did you do a reference measurement in the air in the room at large?
No, there was no reference measurement in the air. The oxygen microsensor measured the partial pressure of oxygen and then calculated the oxygen concentration based on its solubility in water. Consequently, the measurements in the air did not have the correct concentration because the solubility in water was assumed. Thus, no reference measurement in air was needed, as no accurate oxygen concentrations in the air portion of the profiles were measured; only partial pressure was measured. A second x-axis was added to Figures 2 and 4 to show the partial pressure measurement. This represents accurate values across all parts of the profiles, while the oxygen concentration values are correct for the water (DBL and ULW). Since the concentrations in the DBL are later used to calculate oxygen anomalies, we decided to include both units on the axis.
With the measurement in partial pressure, the shape of the profiles did not change, as the conversion from concentration to partial pressure is a division by the solubility of oxygen in water. Thus, H0 and HDBL can still be accurately determined, and the method for determining the DBL thickness and oxygen anomaly from section 2.3 remains accurate. The descriptions in Section 3.1 of the profile in the air were changed from describing the changes in oxygen concentration to changes in partial pressure. Furthermore, a sentence in the discussion was changed accordingly.
Line 214: […] For the oxygen microprofiles during the nights of all bloom phases, the oxygen partial pressure in both the air and the water decreased from one profile to the next, with the larger decrease in the water (Fig. 4). During all nights, a gradient in oxygen partial pressure existed in the air, with higher partial pressures near the water surface. Typically, air gradients exceeded 0.02 atm over a few millimetres. […]
Line 358: […] Our measurements indicate that significant gradients in partial pressure are present in these layers. […]
- Figure 4: the colours are neither colour-blind nor greyscale friendly, please edit.
The colours in Figure 4, A1 and E1 were changed to a more colour-blind and grey-scale-friendly palette.
See Figure 4 in supplement
Figure 4: Oxygen microprofiles throughout the night of 22 May in the pre-bloom phase (a), 31 May in the bloom phase (b), and 11 June in the post-bloom phase (c), depth corrected for the proper sensor position. The oxygen concentration is accurate for the water profile but not for the air profile; the oxygen partial pressure is accurate for both profiles. 0 µm indicates the air–water interface. Times are the mean times of the profile; one profile took between 40 and 50 minutes to complete.
- Line 243: what do you mean by «them»?
We meant the correlation between the oxygen anomaly and the surfactant concentration. Line 243 was rephrased for clarification.
Line 243: […] The oxygen anomalies were not significantly correlated with the surfactant concentration (r = 0.215, p = 0.300), which started to increase at the bloom’s peak and reached its maximum after the chlorophyll a and oxygen gradient maxima. […]
- Line 256: did you test lagged correlations? Wouldn’t that be something to consider?
We now tested for lagged correlations. For the oxygen anomaly, a 3-day lag increased the correlation with surfactant concentration to a strong one (r = 0.703, p < 0.001). The same was true for the diffusive rates; with a 3-day lag, the correlation with surfactant concentration increased to a moderate level (r = 0.668, p < 0.001). The gas exchange rate showed an increased correlation with the surfactant concentration, with a 2-day lag to a strong correlation (r = 0.788, p < 0.001), and it showed an increased correlation with the zooplankton abundance, with a 4-day lag to a strong correlation (r = 0.857, p < 0.001). All other parameters showed no significant increase in correlation with other parameters when calculated with a time lag. We added these information in Sections 3.2, 3.4, and Appendix D.
It is, however, unlikely that these stronger lagged correlations, mostly with surfactants, have a direct causal relationship with the parameters, since the surfactant maxima occur after the peaks, e.g., in oxygen anomalies. The higher correlation is somewhat due to the relationship between chlorophyll a and surfactants, and to the correlations of the different parameters with chlorophyll a. When the chlorophyll a concentration starts to decrease towards the end of the bloom, surfactants are released by the phytoplankton. Thus, the surfactant peak follows the chlorophyll a peak. Parameters strongly correlated with chlorophyll a also show a strong correlation with surfactants when the lag between the end of the bloom and the release of surfactants is accounted for. It is, however, doubtful, that a peak in oxygen anomalies or gas exchange rate is the cause for the peak in surfactant concentration.
Line 243: […] When a lagged correlation was calculated between oxygen anomalies and surfactant concentration with a 3-day lag, the correlation strengthened significantly (r = 0.703, p < 0.001). Unless otherwise noted, other correlations did not significantly increase when lagged. […]
Line 282: […] The gas exchange rate showed a moderate correlation with the phytoplankton abundance (r = 0.559, p = 0.004) and the surfactant concentration (r = 0.588, p = 0.002), but not with zooplankton abundance (r = 0.324, p = 0.114) or chlorophyll a concentration (r = 0.384, p = 0.059). With 2-day lag, the correlation with the surfactant concentration increased to a strong and significant correlation (r = 0.788, p < 0.001), while with a 4-day lag, the correlation with the zooplankton abundance increased to a strong and significant correlation (r = 0.857, p < 0.001). The oxygen saturation followed a similar trend […]
Line 537: […] but not with the surfactant concentration (r = 0.239, p = 0.250) (Fig. D1). With a 3-day lag, the correlation between the diffusive rate and surfactant concentration increased to a moderate and significant level (r = 0.668, p < 0.001). The mean diffusive rate […]
- Figure 7: what are the units for plankton and bacterial abundance? It’s interesting that the increase in bacterial abundance in the SML seems delayed compared to the increase in the ULW. Any suggestions as to why?
The units for plankton and bacterial abundance are cells L–1. This was added accordingly in Figure 7 and Figure C1. The increase in bacterial abundance in the post-bloom phase might be due to the release of DOM at the end of the phytoplankton bloom (see also Bibi et al., 2025a). Because most phytoplankton reside in the ULW, more DOM is released there, and the increase in bacteria taking up the DOM occurs first in the ULW.
Other colleagues are focusing on bacterial processes during the mesocosm experiment. Their first results show, that factors affecting the increase in cell abundance are different in SML and ULW, as the presence and proportion of the bacterial groups are different (Athale et al., in preparation). The increase of ULW bacteria from 06 to 09 June was due to an increase in bacterial groups already prevalent during the bloom phase. The increase in SML bacteria from 09 to 12 June was due to the stark increase in an order of Gammaproteobacteria, which was related to the biofilm-like nature of the SML in the post-bloom phase (Athale et al., in preparation; Rahlff et al., 2023). A short explanation was added in line 274, and the references were added accordingly.
Line 274: […] and 1.68 x 109 cells L–1 (ULW). The earlier increase in the ULW was partly caused by the release of dissolved organic matter in the ULW at the end of the phytoplankton bloom, which was consumed by the bacteria (Bibi et al. 2025a). The shift between SML and ULW can be explained by the presence and proportion of different bacterial groups. In the ULW, bacterial communities were already prevalent during the bloom phase and increased in abundance. In contrast, in the SML Gammaproteobacteria associated with the biofilm-like properties of the SML lead to a later increase in bacterial abundance (Athale et al., in preparation; Rahlff et al., 2023). Before the bloom phase […]
Line 605: Athale, I., Spriahailo, D., Bowen, S., Singh, G., Poehlein, A., Daniel, R., Reinthaler, T., and Brinkhoff, T.: Microbial community dynamics in the sea-surface microlayer of a mesocosm during an induced phytoplankton bloom, in preparation.
Line 706: Rahlff, J., Wietz, M., Giebel, H.-A., Bayfield, O., Nilsson, E., Bergström, K., Kieft, K., Anantharaman, K., Ribas-Ribas, M., Schweitzer, H. D., Wurl, O., Hoetzinger, M., Antson, A., and Holmfeldt, K.: Ecogenomics and cultivation reveal distinctive viral-bacterial communities in the surface microlayer of a Baltic Sea slick, ISME Commun., 3, 1, https://doi.org/10.1038/s43705-023-00307-8, 2023.
See Figure 7 in supplement
See Figure C1 in supplement
- Section 3.5: see comment above regarding gradient versus difference.
Temperature gradients were also changed to temperature anomalies throughout the text.
Changes from ‘temperature gradient’ to ‘temperature anomaly’ throughout the text and Figures.
- Line 298: a difference in temperatures between sensors of 0.142 deg C is quite a lot – or is this actually the rate? Any comment on that?
The differences between the sensors were not due to temperature differences between the two sensors measuring the same water mass. They were the difference between the measured temperature anomalies across the TBL. The 0.142 °C difference was an extreme case, possibly due to outliers, while the mean difference in temperature anomalies between the two sensors was only 0.048 ± 0.036 °C.
There were several reasons for the differences, other than measurement inaccuracies. The sensors were positioned a few centimetres apart, and, as explored in Section 3.7, over this distance, large surface temperature differences could occur, which were well within the range observed between the sensors. Small-scale processes such as buoyancy fluxes could change the shape of the profile very locally and thus the measured temperature anomalies. To investigate these processes, our group is currently conducting further experiments with an optical schlieren system. Such a system shows small-scale convection near the surface and demonstrates on which scales they occur.
The microsensors are handmade due to their size and fragility. While they do not match the accuracy of conventional temperature sensors used in oceanography, they are very suited for studying micro-scale surface temperature profiles. Our study would not have been possible with larger, possibly more accurate temperature sensors. We added a sentence in line 299 for clarification.
Line 299: […] with a mean of 0.048 ± 0.036 °C. These differences were not due to inaccuracies in the measurements, but instead caused by small scale processes such as buoyancy fluxes, changing the shape of the profiles very locally. The mean temperature [...]
- Figure 8: see comment above regarding gradient versus difference. I find the difference between the sensors quite large. How do you assess the reliability of the sensors when they actually diverge so much?
The divergence between the sensors was not necessarily due to sensor offsets; it could have been caused by small-scale processes that influenced the profiles locally, as the sensors were positioned few centimetres apart. The use of multiple sensors provided more representative measurements over a larger area of several cm2. It ensured continuous data acquisition if a sensor failed or was mounted too deep to reach the water surface. Minor differences between sensors are not among our criteria for assessing sensor reliability; instead, we focus on relatively stable measurements, few outliers, and a well-positioned sensor tip close to the water surface. Of course, all microsensors were tested for functionality before use in our study. At the observed microscales, we measure significant differences that cannot be explained by the lack of sensor performance, but due to dynamic processed across the air-sea boundary layer.
No changes in text.
- Section 3.7 and Figure 10: This pattern is at the surface. Do you expect similar patterns and horizontal gradients of similar magnitude at «depth», e.g. within the surface microlayer, at the bottom of the TBL, or in the ULW?
The occurrence of these patterns is more likely at the surface, as material accumulated, broke up, and lead to distinct patterns in surface temperatures. These patterns likely occur throughout the SML/TBL. Going deeper, it becomes less likely that these sharp patterns and strong gradients existed because the water flow by the pumps lead to a more uniform water mass in the ULW. However, temperature gradients in the ULW across a larger area within the mesocosm facility cannot be ruled out entirely. Temperature measurements with a similarly high horizontal resolution are currently only possible at the surface, not at deeper depths, so this remains speculation. A sentence concerning this uncertainty was added in the discussion in line 388.
Line 388: […] but on the mean from a larger area. The extent, to which these distinct horizontal temperature distributions continue beyond the sea surface into the ULW remains unclear, as high-resolution thermal images are limited to the water surface. […]
- Line 348: The dissolved oxygen concentration profiles in Figure 4 and the temperature profiles in Figure E1 suggest that you did not fully exclude diurnal warming.
Yes, as the facility is outside, diurnal warming could not be entirely excluded, but using night-time profiles at least reduced this effect. Only cooling down throughout the night is included in the profiles; heating up from solar radiation is not. Line 348 was rephrased accordingly.
Line 348: […] night-time microprofiles were analysed. This excluded atmospheric influences, such as solar radiation and wind forcing at the water surface, and reduced the effect of diurnal warming. Both oxygen […]
- Line 364: add «that» before «Rahlff et al».
Line 364 was changed accordingly.
Line 364: […] 1100 µm thick DBL, that Rahlff et al. (2019) […]
- Line 378: you did not show/present anything about skin layer temperatures before this sentence, this statement comes a bit out of the blue.
The sentence was rephrased to introduce the cool skin before the comparison with our data.
Line 377: […] reported by GESAMP (1995). A colder water surface compared to subsurface water, called “cool skin layer”, is commonly observed. with temperature anomalies between –0.1 °C and –0.2 °C (Donlon et al., 1999; Murray et al., 2000; Donlon et al., 2002; Minnett et al., 2011; Jaeger et al, 2025). Our in situ measurements of the mean temperature anomaly of –0.133 ± 0.079 °C can confirm these observations. Most of these observations, […]
- Line 381: what kind of values?
The temperature anomaly across the TBL. The sentence was changed for clarification.
Line 381: […] a cool skin layer is always present, reaching temperature anomalies across the TBL of more than –0.3 °C due to net heat loss. […]
- Line 394: Such an impact would not be expected, would it?
No, a direct impact of the bloom on the temperature was not expected. However, these data show that there is also no indirect effect of the bloom on the temperature, e.g., through surfactant production. A sentence was added in Line 395.
Line 395: […] no direct impact of the bloom. While such a direct impact of the bloom on the temperature was not expected, these results show that there is no indirect impact of the bloom on the water temperature, e.g., through surfactant production. We suggest that the temperature gradients […]
- Line 408: Suggest to delete «energy exchanges, like» since you suggested in the introduction that surfactants do have an effect on momentum exchange.
Here, energy exchanges were used to underscore the contrast with gas exchanges, which are exchanges of matter, not energy. It was deleted to simplify the sentence.
Line 408: […] but less effective for heat exchange. […]
- Line 410: faster than what?
Faster than oxygen. Sentence changed accordingly
Line 410: […] heat equilibrates much faster than oxygen, because […]
- Lines 411-413: But the temperature gradient did not change with surfactant concentrations – I’m not sure what you are trying to argue here?
We try to explain why heat transfer is less affected by surfactants than oxygen transfer. The reason is the higher heat-diffusion coefficient in air compared to water, which leads to faster exchange between air and water. Due to these much faster exchanges, the effect of the surfactants in reducing heat transfer is much less apparent than that in reducing oxygen transfer. We rephrased lines 410-413 for more clarity.
Line 410: […] gradients (Goldman et al., 1988; Wurl et al., 2011; Rahlff et al., 2019). Heat, however, is exchanged much faster than oxygen, because the thermal diffusion coefficient in water is by two magnitudes greater than the oxygen diffusion coefficient (Bindhu et al., 1998; Ambari et al., 2022). Due to the faster heat exchanges, surfactants might be less effective in reducing air-sea heat exchanges than oxygen exchanges. The high gas exchange rate […]
- Figure A1: There is quite a large spread in values for each sensor on short and slightly longer timescales. It’s quite difficult to see and assess how they compare with all these different patterns. Maybe plot the deviation from the Ferrybox measurements to at least reduce the daily cycle?
The microsensor data from this experiment were now filtered using a Hampel filter with a 10-minute window, which replaces outlier values that differ from the median in that window by more than three standard deviations with the median. This way, the data appear smoother, making comparisons with the FerryBox data easier. We decided against plotting the differences from the FerryBox to show better the jumps in oxygen concentration that occurred during calibration. Figure A1 was updated, including a new more grey-scale-friendly colour scale, and a brief description of the filter was added in line 476.
Line 476: […] and water temperature. The microsensor data were filtered for clarity using a Hampel filter, replacing outliers within a 10-minute window that differed from the median by more than three standard deviations with the median value. The results showed […]
See Figure A1 in supplement
Figure A1: Comparison of the oxygen concentration measured by an oxygen microsensor calibrated at the start of the experiment and not corrected for temperature (black), one microsensor calibrated daily and corrected for temperature (blue), and corrected oxygen concentration measured by a FerryBox (red), experiment conducted in May 2024 in SURF. Microsensor data are filtered using a Hampel filter with a 10-minute window.
- Lines 489-490: how often did you take the discrete samples? Would they resolve changes due to diurnal variations?
The discrete samples were collected daily, alternating between early morning and afternoon, to account for diurnal changes. Thus, water was either exposed to darkness or to light before sampling. A more frequent sampling was not performed to keep the integrity of the SML. For this reason, no diurnal changes on single days were resolved. However, the alternation between early morning one day and late afternoon the next day helped to resolve general diurnal changes between these two conditions. A sentence was inserted in line 490.
Line 490: […] analysed with the Winkler method. These discrete samples were taken daily at alternating times (one day early morning, the next day afternoon) to account for diurnal changes in oxygen concentration. The inlet of the FerryBox […]
- Line 491: Which depth range in the ULW?
The mean of the data from HDBL to the end of the profile was taken, so between approximately 1 mm and 7 mm (depending on sensor positioning and profile depth).
Line 491: […] The comparison of the mean microsensor measurements from the ULW (ranging approximately between 1 mm and 7 mm) and FerryBox shows […]
- Line 504: did you take samples for calibration of the salinity sensor? Could evaporation affect the accuracy of your salinity measurements?
The evaporation increased the water's salinity by 3.1 g kg–1 throughout the experiment (Bibi et al., 2025a). This increase, however, had no direct effect on the measurement's accuracy. No salinometer was available, but conductivity sensors were calibrated by the manufacturer according to the specifications prior the mesocosm experiment.
No changes in text.
- Line 511: Yes, but should bacterial abundance lead to a decrease in oxygen saturation?
An increase in heterotrophic bacteria would lead to a decrease in oxygen saturation; an increase in phototrophic bacteria like cyanobacteria, however, would lead to an increase in oxygen saturation. The later increase in oxygen saturation was likely due to the formation of algal and bacterial biofilms on the floor and walls of SURF and on the submerged equipment during the later post-bloom phase. These biofilms likely contained a lot of phototrophic organisms, which lead to an increase in oxygen production, even after the main phytoplankton bloom in the ULW declined. While we did not further analyse these biofilms, other research showed a strong oxygen production in biofilms by e.g. cyanobacteria (Kühl et al., 1996; Pringault and Garcia-Pichel, 2000). The sentence in line 511 was changed and references added accordingly.
Line 511: […] This also coincided with the post-bloom increase in bacterial abundance. The increased post-bloom oxygen saturation can be attributed to the formation of biofilms on the floor, walls, and submerged equipment of SURF. These biofilms have been shown to contain cyanobacteria, which contribute to increased oxygen production (Kühl et al., 1996; Pringault and Garcia-Pichel, 2000). The mean oxygen saturation […]
Line 683: Kühl, M., Glud, R. N., Ploug, H., and Ramsing, N. B.: Microenvironmental control of photosynthesis and photosynthesis-coupled respiration in an epilithic cyanobacterial biofilm, J. Phycol., 32, 799-812, https://doi.org/10.1111/j.0022-3646.1996.00799.x, 1996.
Line 703: Pringault, O., and Garcia-Pichel, F.: Monitoring of oxygenic and anoxygenic photosynthesis in a unicyanobacterial biofilm, grown in benthic gradient chamber, FEMS Microbiol. Ecol., 33, 251-258, https://doi.org/10.1111/j.1574-6941.2000.tb00747.x, 2000.
- Appendix E: with the above noted discrepancies between the temperature sensors, how do the profiles with depth compare?
In Figure E1, a subplot showing the temperature sensor 2 profiles was added. Overall, the profiles of both sensors appeared very similar. The only notable differences were a slightly different mounting height and approximately 0.1 °C difference in the measured temperature, which was within the resolution of the sensors. Appendix E was expanded, and Figure E1 was changed to show the profiles of both sensors.
Line 551: The temperature microprofiles on 22 May give a typical example of the profiles from both temperature sensors during all nights (Fig. E1). The warmest profile was the first of the night, and all the subsequent profiles were approximately 0.02 °C to 0.05 °C cooler than the preceding profile. The air temperature decreased as it moved farther from the water surface; it also decreased from profile to profile during the night and was always lower than the water temperature. A cooler thermal boundary layer was present in all profiles. Sensor 2 measured up to 0.1 °C higher temperatures and was mounted approximately 750 µm deeper than sensor 1. These slight differences in mounting height and measured temperature were also observed in profiles on other nights, while the overall shape of the profiles remained very similar between both sensors. The only significant differences between nights for each sensor were the mounting height and the absolute temperature, which typically rose from night to night, starting at approximately 18.9 °C on 22 May and reaching around 22.6 °C on 15 June.
See Figure E1 in supplement
Figure E1: Temperature microprofiles of Sensor 1 (a) and Sensor 2 (b) throughout the night of 22 May, in the pre-bloom phase, depth corrected for the proper sensor position. 0 µm indicates the air–water interface. Times are the mean times of the profile; one profile takes approximately 40 minutes to complete.
- Figure E1: as for Figure 4, please choose a better colour scheme.
The colours in Figure E1 were changed to a more colour-blind and grey-scale-friendly palette.
See Figure E1 in supplement
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AC1: 'Reply on RC1', Carsten Rauch, 16 Dec 2025
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Rauch et al.: Phytoplankton blooms affect microscale gradients of oxygen and temperature across the sea surface microlayer
The authors present a mesocosm study of the sea surface microlayer. Using microsensor profiles at high vertical resolution, they investigate dissolved oxygen and temperature gradients from the air, through the sea surface microlayer and into the water below. They find that phytoplankton bloom dynamics and surfactant concentration influence oxygen concentrations and diffusive layer depth but not temperature gradients, and suggest implications for air-sea exchanges across the sea surface microlayer.
The manuscript is very well written and structured. Results are interesting and clearly presented. Short-comings, challenges and implications are nicely discussed. I only have minor comments where some clarification could be beneficial, otherwise I really enjoyed reading this manuscript.
Detailed comments: