The impact of synoptic meteorology on observed surface heat fluxes over the Southern Ocean

Avaronthan Veettil, Sreenath; Alinejadtabrizi, Tahereh; Siems, Steven; May, Peter; Zhang, Haifeng; Schulz, Eric

doi:https://doi.org/10.5194/egusphere-2025-3776

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https://doi.org/10.5194/egusphere-2025-3776

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15 Aug 2025

| 15 Aug 2025

The impact of synoptic meteorology on observed surface heat fluxes over the Southern Ocean

Sreenath Avaronthan Veettil, Tahereh Alinejadtabrizi, Steven Siems, Peter May, Haifeng Zhang, and Eric Schulz

Abstract. A 14-year climatology of the bulk sensible and latent heat fluxes (SHF and LHF) made from the Southern Ocean Flux Station (SOFS) is analyzed with respect to the synoptic meteorology and mesoscale cellular convection (MCC). A K-means clustering algorithm identified five synoptic regimes: High Pressure/Ridging (HPR), Tasman Blocking High (TBH), Zonal, Frontal, and Cold Air Advection (CAA). Among these, CAA showed the strongest air-sea coupling, with mean SHF of -40.4 W/m² and LHF of -131.0 W/m², which are 3.5 and 2 times greater than the overall mean, respectively. This striking increase in fluxes during CAA is associated with a high marine cold-air outbreak index (M-index) and weak inversion coupled with cold and dry air transport towards SOFS by the strong south-westerly wind. The SOFS measurements are also employed to evaluate ERA5 fluxes, finding that ERA5 accurately represents the observed bulk SHF and LHF, with a mean bias of 1.6 W/m² for SHF and -6.2 W/m² for LHF, along with significant correlation coefficients of r=0.9 and 0.92, respectively. Turning to open and closed MCC, relatively weak differences in the fluxes are observed between these two states, suggesting that the SHF and LHF are not the primary drivers in the transition between open and closed MCC. In open MCC, SHF and LHF show a strong correlation with the M-index, while closed MCC is associated with a stable atmosphere with a strong inversion, where the M-index relationship with surface fluxes is weak.

Received: 05 Aug 2025 – Discussion started: 15 Aug 2025

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Sreenath Avaronthan Veettil, Tahereh Alinejadtabrizi, Steven Siems, Peter May, Haifeng Zhang, and Eric Schulz

Status: final response (author comments only)

RC1:
'Comment on egusphere-2025-3776', Anonymous Referee #1, 01 Sep 2025
Using measurements performed with a buoy situated to the south west of Tasmania in the southern ocean, the authors explore the surface latent and sensible heat fluxes during five dominant weather regimes. These regimes are determined using a k-means clustering technique applied to a series of meteorological variables obtained with ERA5.
The goals are to first perform an evaluation of ERA5 surface fluxes, and they find a relatively good agreement between reanalysis and observations, albeit with the tendency for ERA5 to slightly overestimate the magnitude of the fluxes. Second they explore the occurrence of open and closed mesoscale cellular convection (MCC) clouds at that location and how they relate to their environment. They find little differences in surface fluxes between times when open cell MCCs dominate and those when closed cell MCCs do. They conclude that synoptic meteorology has a greater impact on the dominant MCC type than the air-sea contrast.

This is an interesting study on a topic that is undergoing active research, namely boundary layer clouds in the southern oceans, and how they interact with their environment, specifically in conditions of cold air outbreak. Having also an evaluation of ERA5, a widely used reanalysis, in a region with few observations is also of merit.
However, there are a number of weaknesses in the manuscript in its present form that call for major revisions before this can be accepted for publications. The main issues are:
The organization of the presentation is somewhat confusing. There are some results on an ERA5 evaluation, others regarding surface fluxes and weather regimes but then the focus becomes open versus closed mesoscale cellular clouds. At times it feels as if the results section is jumping back and forth through all three topics but never really reaches a conclusion for any of them. A decision is to be made on what the ultimate focus of the paper is, and a tighter organization of the various results leading to the main focus should be implemented. Based on the extensive discussion that finishes the paper on open versus closed MCCs versus meteorology (which is unexpected so probably needs to be better motivated), it would appear the motivation is to examine whether or not surface fluxes play a role or are impacted by cloud organization. In which case, the evaluation of ERA5 feels like a distraction. Whether ERA5 performs well or not does not seem to matter for the analysis. If it does, this is not clearly expressed. Furthermore, Seethala et al (2021) performed such a comparison in similar cold-air outbreak conditions, but a different location, i.e. the Gulf Stream region. It would make the ERA5 evaluation stronger if the results were compared to this other study. In any case, as currently presented, there are a lot of missing details and analysis to make this evaluation really interesting and solid. So I strongly encourage the authors to focus instead on the MCC versus surface fluxes analysis, and add to it. In particular, it is unclear at present if the two MCC types are examined separately in each weather regime during which they occur. Does the relationship between MCC and their environment different during CAA versus high pressure “HPR”? This aspect should be better delineated, to help place this study in the context of current research on cold air outbreaks in particular.

There are a number of technical details that are missing. See the multiple specific comments below on the various subsections of section 2.

There are insufficient discussions on how the work fits with current literature and knowledge. Suggestions are made below for information but the list in not exhaustive and by no means the author should feel compelled to specifically use this examples. However, the point here is that the results would be much stronger if they were placed in the context of current research. This way, their overall contribution to current research should be much clearer to readers.

Specific comments:
Line 19: Issues with the representation of clouds and associated radiative bias in the southern oceans in most current climate models have been discussed for quite some time now, and at least the paper by Trenberth and Fasullo (2010) that first raised the alarm should be cited, Bodas-Salcedo et al. (2014) who identified the missing clouds as occurring in the cold sector of extratropical cyclones, along with that of Zelinka et al. (2020) on the most recent models still struggling with this.

Line 31: ERA5’s citation is not Dee et al (this is for ERA-Interim) but Hersbach et al. (2020).

Line 62: “the structure of this cloud” is odd, should it be “the structure of this cloud type”?

Section 2, data and methods: there is no sub-section on ERA5, despite its heavy use. Please consider adding such a section, to specify which output are used, at what spatial and temporal resolution, which are time averaged or instantaneous, and which are provided vs calculated. For example L78 is the only mention of the ERA5 data being hourly.

More specifically, it is not clear if the surface latent and sensible heat fluxes are calculated by the authors or provided as an output. This is important as it is not clear presently if the input for the flux calculation are at the same atmospheric level for the buoy versus ERA5. And fluxes change if the wind, T, q are at 2m or at 10 m, or some other level. As presented in the manuscript, one wonders if discrepancies between buoys (that are usually measuring at 2-4m) and reanalysis (that often uses 10m as the level of reference) are simply caused by slight differences in where winds are collected.

Also, Figure 1 includes profiles, but I could not find any information on where they are obtained. I assume these are from ERA5 but even the figure caption does not say. This should also be mentioned in this subsection on ERA5. How are the profiles averaged? Is there some fixed level that is used as an anchor for the averaging as in Norris (1998)?

L76: “south of Tasmania”, how far is the buoy from land? Why not refer to Fig 1 that actually shows where the station is.

L81: Fig S1 suggests there are quite a few periods without data in the early years, does this affect the seasonality? In other words were there seasons with significantly less data? Could this affect some of the results?

Section 2.2: how many clusters were chosen initially? 5 are discussed, but what happens if 4 or 6 are a-priori chosen?

Section 2.3: EIS and M are obtained with ERA5, correct? This should be specified. For both EIS and M a few words on what their values indicate would help, e.g. EIS large means inversion strong, or M positive means a marine cold air outbreak (MCAO) and shallow convection/instability (e.g. Fletcher et al. 2016a,b use this index to explore a climatology of cold air outbreak). Also, EIS and M are quite strongly inverse correlated. It would be interesting to know what R-squared is at the site for each weather regime.

Section 2.4: it was not clear to me whether the authors conducted their own open vs closed MCC identification using the Lang et al algorithm or if this is an existing product?

Figure 1/section 2.5: the k-o panels are not clear, at least not in the captions. This is the number of time a trajectory passed through one of these points? Out of how many? Just one per hour with a flux at the buoy? What are the ERA5 input used for the trajectories? More should be explained in section 2.5.

Section 3.1: it would help visualizing the different weather regimes if the paragraphs for each were numbered or the name of the regime was bold/underlined or such.

the trajectories are not discussed for the first 3 regimes it seems.

Figure 2: it would help if delta-T and delta-q were also plotted, otherwise it is really hard to see what the air-sea contrast in temperature (c.f. L 169, “their difference is greatest” is hard to see; L170 “this strong SST-AT gradient” is hard to tell) and q is doing through season or across regimes (for fig 4). Also L172 qa is mentioned but we do not know what qs is assumed to be for the LHF. Figure 2 also compares ERA5 and observations, but it is hard to tell what meteorological variables are causing the differences in surface fluxes, differences which tend to look quite systematic throughout the year.

Section 3.2: adding some discussion on how the results compare with other studies would help gauge how much this work brings that is novel information about the SO. For example “CAA” has the strongest fluxes, but this is a well-known feature of cold air outbreaks, c.f multiple papers by Lukas Papritz and colleagues, most notably Papritz et al. (2015) for the SO. So how do the fluxes at SOFS fit into the flux climatology reported by Papritz et al (2015)?

Line 180, M is high in MCAOs as should be expected given it is the index used to identify MCAOs. The values obtained here are close to those discussed in Fletcher et al (2016a, 2026b) who chose 800 hPa (close to the 850 hPa chosen here) to have M > 0 K as the threshold for flagging an MCAO occurrence. EIS is low because there is no inversion, this is expected.

Regarding Fig 5, since M and EIS are so strongly inverse correlated, it might make things simpler to pick one variable and not have both? EIS is more appropriate for regimes where an inversion is present, and M is more appropriate for CAAs. This should be taken into account/discussed. The weaker fluxes in the periods of warm air advection is also discussed in other studies, e.g. Naud et al. 2021, 2023 show a strong contrast in fluxes across cold fronts with large fluxes in the post-cold frontal region of extratropical cyclones versus weaker fluxes in the warm sector.

Line 196: the relationship between fluxes and M is also to be expected as M describes the transition between stable and unstable conditions, therefore from larger to lower cloud cover, and weak to strong fluxes.

Section 3.3: see previous comment on how fluxes are obtained in ERA5 versus the buoy that could create systematic biases. As far as evaluating ERA5 fluxes in the case of CAA, results should probably be discussed in the context of previous work by Seethala et al (2021) who evaluated both ERA5 and MERRA2 in these conditions across the Gulf Stream. E.g. L215, are the overestimates consistent with the biases discussed by Seethala et al?

question: are the biases in wind a function of wind speed? Same question for biases in T, q? this could help explain the variations with season? Also could this explain the different biases in fluxes across the weather regimes (c.f. L 222)?

L 232: “difference in the SHF and LHF between these boundary layer clouds is moderate” is intriguing. In McCoy et al 2017, one variable that is used to characterize the environments of open vs closed MCC is the air-sea temperature contrast, in addition to EIS and M. Their Fig 8 clearly shows that open MCCs have low EIS, M>0 and delta-T > 0; closed MCC have EIS large, M negative and delta-T small or negative. Using delta-T as a coarse proxy for SHF, one would expect SHF to be positive or small in closed MCC and largely negative (large absolute values) in open MCC. So it would be interesting to know why it is that this contrast in SHF is not seen here, is the delta-T contrast compensated by a wind contrast in the other direction? Furthermore closed MCC should have larger cloud fractions, again forcing SHF to be small or positive. It is intriguing that Fig 4 suggests no clear difference in SST or AT between open and closed MCC but there are larger wind speeds in open MCC, which should bring some contrast in fluxes that is indeed quite modest in Fig 3. Given fig 7a indicates that open MCC dominates in CAA conditions, but closed MCC occurs in both CAA and HPR in relatively close frequencies, one wonders if the weather regime matters: are the comparisons in Fig3/4 between open and closed MCC performed regardless of regime or for just CAA? This is never specified, but maybe it is an important distinction to make when comparing the fluxes.

L 234: I wonder if the wind being more important for fluxes in open vs closed MCC could simply be explained by comparing one type (open) that is mostly occurring after the passage of a cold front and therefore strongly impacted by the winds produced by the parent cyclone, compared to the closed type that occurs slightly more often when the area is dominated by a more “anticyclonic” regime, therefore a lot more quiescent. This leads back to my comment above that comparing the fluxes for each type might have to be done separately in each weather regime when they both occur.

L 245: one possible explanation for differences in ERA5 performance in open vs closed MCC could be revealing of issues in representing shallow convection for open MCC whereas closed MCCs would be better presented with the large scale parameterization of clouds. This in turn would have consequences for T, q in these regimes and therefore surface fluxes.

Line 265: agreed, which is precisely why it might be interesting to fix the weather regime and then compare fluxes in open vs closed MCCs, this way there is a clear transition from one to the other cloud type as opposed to a jump from one weather regime to another (in fig 6, the transition from CAA to HPR has “zonal” in between. This is ok for open MCC as it gradually diminished from CAA to zonal to HPR, but closed MCC does not really occur during “zonal” times so this causes a jump and not a transition. ).

Typos:
Line 50: Barthi et al (2019) is repeated.
Line 162: “SOFSS” should be “SOFS”

References:
Bodas-Salcedo A., K. D. Williams, M. A. Ringer, I. Beau, J. N. S. Cole, J.-L. Dufresne, T. Koshiro, B. Stevens, Z. Wang, and T. Yokohata, 2014: Origins of the solar radiation biases over the Southern Ocean in CFMIP2 Models. J. Climate, 27, 41-56, doi:10.1175/JCLI-D-13-00169.1

Fletcher, J. K., S. Mason and C. Jakob (2016a). The climatology, meteorology and boundary layer structure of marine cold air outbreaks in both hemispheres. J. Climate, 29, 1999-2014, doi:10.1175/JCLI-D-15-0268.1.
Fletcher, J. K., S. Mason and C. Jakob (2016b). A climatology of clouds in marine cold air outbreaks in both hemispheres. J. Climate, 29, 6677-6692,doi:10.1175/JCLI-D-15-0783.1.

Hersbach H. and co-authors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteorol. Soc. 146, 1999-2049, doi:10.1002/qj.3803.

Naud C. M., J. A. Crespo, and D. J. Posselt, 2021: On the relationship between CYGNSS surface heat fluxes and the life cyclone of low-latitude ocean extratropical cyclones. J. Appl. Meteorol. Climatol., 60, 1575-1590, doi:10.1175/JAMC-D-21-0074.1.

Naud, C.M., J.A. Crespo, D.J. Posselt, and J.F. Booth, 2023: Cloud and precipitation in low-latitude extratropical cyclones conditionally sorted on CYGNSS surface latent and sensible heat fluxes. J. Climate, 36, no. 16, 5659-5680, doi:10.1175/JCLI-D-22-0600.1.

Norris J. R., 1998: Low cloud type over the ocean from surface observations. Part I: replationship to surface meteorology and the vertical distribution of temperature and moisture. J. Climate, 11, p369-382.

Papritz et al, 2015, A climatology of cold air outbreaks and their impact on air-sea heat fluxes in the high-latitude south Pacific, JCLI: https://doi.org/10.1175/JCLI-D-14-00482.1

Seethala, C., P. Zuidema, J. Edson, M. Brunke, G. Chen, X.-Y. Li, D. Painemal, C. Robinson, T. Shingler, M. Shook, A. Sorooshian, L. Thornhill, F. Tornow, H. Wang, X. Zeng, and L. Ziemba, 2021: On assessing ERA5 and MERRA2 representations of cold-air outbreaks across the Gulf Stream. Geophys. Res. Lett., 48, no. 19, e2021GL094364, doi:10.1029/2021GL094364.

Trenberth K. E. and J. Fasullo, 2010: Simulation of present day and 21^st century energy budgets of the southern oceans, J. Climate, 23, 440-454

Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., et al., 2020: Causes of higher climate sensitivity in CMIP6 models. Geophysical Research Letters, 47, e2019GL085782. https://doi.org/10.1029/2019GL085782.
Citation: https://doi.org/10.5194/egusphere-2025-3776-RC1
- RC2:
  'Reply on RC1', Anonymous Referee #2, 07 Sep 2025
  The manuscript presents an insightful analysis of the modulation of air–sea interaction over the Southern Ocean (142°E, 47°S) associated with synoptic-scale weather regimes. The study identifies five major weather regimes using a K-means clustering approach applied to ERA5 reanalysis data. The first section describes the characteristics of the regimes and their impacts on surface fluxes and boundary-layer stability. The second section examines how these regimes influence boundary-layer clouds and the variability of air–sea interaction in open and closed mesoscale cellular convection (MCC). Overall, the manuscript is well-written and focused; however, there are a few points that could further improve the clarity, structure, and completeness of the work. So I would recommend minor revisions before accepting for publication with the following comments to address:
  1. Introduction
  One of the key focuses of the study is to assess air–sea interactions and boundary-layer instability under open and closed MCC conditions. However, the Introduction provides limited details on the cloud characteristics of MCC. It is recommended that the authors expand this section to contextualise better MCC features and their relevance to air–sea coupling.
  
  Additional references on the link between synoptic storms should be included. eg. (https://doi.org/10.1029/2023JD039386)
  
  2. Data and Methodology
  While ERA5 data are central to the analysis, the manuscript would benefit from a more detailed description of the ERA5 dataset (e.g., resolution, temporal coverage, and specific variables used).
  
  The rationale for selecting ERA5, rather than other available reanalysis products, should be explicitly discussed. Providing a short justification for the choice of ERA5 (e.g., its higher resolution, improved representation of fluxes, or suitability for Southern Ocean studies) would strengthen the methodological framework.
  
  In Figure 1, trajectory analyses are shown. The manuscript should specify which ERA5 variables were used in the trajectory calculations to enhance transparency and reproducibility.
  
  3. Results and Discussion
  The comparison between observed buoy fluxes and ERA5 reanalysis fluxes is currently explained across two separate sections. It is suggested that this material be consolidated under a single subheading. This will improve readability and allow the reader to more easily understand how ERA5 fluxes differ from observations across the identified synoptic regimes and boundary-layer conditions.
  
  Figure 1 caption is inaccurate: it currently refers to “(k–m)” but should read “(k–o).”
  
  Tables 1 and 2: Please indicate the units in the caption.
  
  Line 162: Replace “SOFSS” with “SOFS”
  
  Citation: https://doi.org/10.5194/egusphere-2025-3776-RC2
RC3:
'Comment on egusphere-2025-3776', Anonymous Referee #3, 20 Sep 2025
General comments
The study presents an interesting analysis of air–sea fluxes derived from observations over the Southern Ocean over a 14-year period. The authors applied unsupervised clustering using the k-means technique to identify five distinct weather regimes in the study region. They examined how air–sea fluxes vary across these regimes and during open and closed multi-cellular convection events. Additionally, they conducted a detailed evaluation of relevant variables and fluxes from the ERA-5 reanalysis against observational data from the SOFS site.
Overall, this is an important study that provides valuable insights. However, the manuscript would benefit from some restructuring, further clarification in key areas, and a more clearly articulated motivation. I therefore recommend a minor revision and invite the authors to address the following comments.
Major comments
The need for evaluating ERA-5 derived fluxes is not sufficiently substantiated in the introduction. Additionally, the current structure of the manuscript does not clearly justify the inclusion of the ERA-5 evaluation, as the authors present the evaluation and clustering results simultaneously. Presenting the ERA-5 evaluation first, followed by the clustering analysis, would be a more logical approach. This would allow the authors to better justify the use of ERA-5 data in the clustering analysis. This is only a suggestion, and I leave it to the authors to decide on the structure they feel is most appropriate.

L75–80: The authors averaged the 1-minute observations to hourly data to facilitate comparison with ERA-5. However, several variables in the ERA-5 archive, such as the 2m air temperature, MSLP, and relative humidity, etc. are provided as hourly instantaneous values rather than hourly averages. It is unclear whether the authors used the instantaneous or averaged fields in their analysis, particularly when comparing with the SOFS observations. Please ensure that all datasets are consistent in terms of temporal scale, and clarify this explicitly in the methods section.

The authors need to provide more detailed information about the buoy observations. It is unclear whether the ERA-5 fields and the buoy data were compared at the same height levels, and whether any interpolation was applied. Please clarify this in the methods section.

I feel it would be great if the authors included a diagnostic plot such as an elbow plot or silhouette analysis to support their choice of five clusters in the k-means classification.

Minor comments
L31: Correct citation to ERA5 is Hersbach et al. 2020 not Dee et al.

L50-51: Bharti et al. is cited twice.

L50-53: I feel it would be helpful for readers if the authors mentioned which dataset Bharti et al. used in their study.

L61-62: SST is already defined above.

Some of the constants/variables described after Eqn 1 and 2 did not appear as they are used (Cp, CH, etc.). Please correct them.

Section 2.2, and everywhere else: I think a more appropriate usage would be k-means instead of k-mean (this is also in line with the original paper by Hartigan and Wong, 1979) as the technique involves computing means corresponding to multiple clusters.

Fig 1: How are the skewT-logP diagrams plotted? From ERA-5 at the nearest grid point to the buoy? Please mention this in the manuscript and the figure caption.

I couldn’t follow much on the back-trajectory spatial map. I understand that the shading indicate the parcel frequency. How’s it calculated? Please mention this.

Fig 1 caption: The back trajectories are shown in subplots “k-o”, right? It’s written as “k-m”. Please check and correct.

L158: I’m not sure I’d describe it as “near-saturated” between 1000—500mb. Lower troposphere (up to ~850mb) is indeed near-saturated.

L162: Change “SOFSS” to “SOFS”

I liked Fig 6 — it’s really useful!

Section 3.1 and Fig 4: does it worth showing SST-AT gradient and the moisture gradient as well? I think that might be useful as the authors mention that the cold bias in ERA5 is contributing to a negative bias in SHF.
Citation: https://doi.org/10.5194/egusphere-2025-3776-RC3

Sreenath Avaronthan Veettil, Tahereh Alinejadtabrizi, Steven Siems, Peter May, Haifeng Zhang, and Eric Schulz

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Sreenath Avaronthan Veettil, Tahereh Alinejadtabrizi, Steven Siems, Peter May, Haifeng Zhang, and Eric Schulz

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Short summary

Using 14 years of observations from mooring, we reported that cold air advection creates intense surface flux exchange over the southern ocean, linked with strong boundary layer instability. Results also indicate that cold air advection creates frequent open mesoscale cellular convective clouds. The flux exchange for open and closed mesoscale cellular convective clouds is comparable, suggesting a limited role of the surface flux in the transition of these boundary layer clouds.


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