04 Feb 2021
04 Feb 2021
Southern Ocean polynyas in CMIP6 models
- 1Department of Marine Sciences, University of Gothenburg, Gothenburg, Sweden
- 2Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden
- 3Department of Oceanography, University of Cape Town, Rondebosch, South Africa
- 1Department of Marine Sciences, University of Gothenburg, Gothenburg, Sweden
- 2Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden
- 3Department of Oceanography, University of Cape Town, Rondebosch, South Africa
Abstract. Polynyas facilitate air-sea fluxes, impacting climate-relevant properties such as sea ice formation and deep water production. Despite their importance, polynyas have been poorly represented in past generations of climate models. Here we present a method to track the presence, frequency and spatial distribution of polynyas in the Southern Ocean in 27 models participating in the Climate Model Intercomparison Project phase 6 (CMIP6) and two satellite based sea ice products. Only half of the 27 models form open water polynyas (OWP), and most underestimate their area. As in satellite observations, three models show episodes of high OWP activity separated by decades of no OWPs, while other models unrealistically create OWPs nearly every year. The coastal polynya area in contrast is often overestimated, with the least accurate representations occurring in the models with the coarsest horizontal resolution. We show that the presence or absence of OWPs are linked to changes in the regional hydrography, specifically the linkages between polynya activity with deep water convection and/or the shoaling of the upper water column thermocline. Models with an accurate Antarctic Circumpolar Current (ACC) transport and wind stress curl have too frequent OWPs. Biases in polynya representation continue to exist in climate models, which has an impact on the regional ocean circulation and ventilation that require to be addressed. However, emerging iceberg discharge schemes, vertical discretisation or overflow parameterisation, are anticipated to improve polynya representations and associated climate prediction in the future.
Martin Mohrmann et al.
Status: open (extended)
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RC1: 'Comment on tc-2021-23', Anonymous Referee #1, 13 Mar 2021
reply
This manuscript examines the representation of polynyas in CMIP6 models as compared to observations. Some of these comparisons are not straightforward, due to lack of CMIP model variables, limited observations, and the different metrics that could be used to define polynyas, but the authors are transparent in these limitations and convey the information clearly. Modelled coastal polynyas are often too large, likely as a result of coarse horizontal resolution. Modelled open water polynyas are often too small compared to observations, and there is a large inter-model spread in the frequency of open water polynyas. The authors examine vertical ocean profiles in polynyas versus sea ice covered regions in a subset of the models and in float data. The Discussion contains a number of useful insights on the reasons behind the intermodel variation in polynya activity, relating to resolution, simulation of the ACC and overflow parametrizations.
I found this to be a very interesting and thorough paper. It is well within the scope of TC and presents novel results and conclusions. The methods are clearly explained and the analysis code has been made publicly available. It is generally well-written, apart from some of the latter sections, and the figures and tables are appropriate. I am selecting ‘major revisions’ only because of section 5.2, which I think would benefit from a second round of reviews.
Main comments
- Section 5.2 - I found the arguments here a bit hard to follow. I would like to see additional subplots for the other relationships discussed here added to Figure A6. As you mention the results from this section in the abstract and conclusions, the figure should also be brought into the main paper. It seems like the results here would be interesting to a wide audience, so I think it is worth spending some more time on presentation.
- Section 4.3 or Section 2.3 - Please give some more details on the domain of the SOCCOM float - e.g. time period, number of profiles etc. Please also describe how you extracted the profiles from the CMIP6 models - is this one profile per grid cell in the Weddell Sea region? Is there some time averaging?
- L329: ‘To evaluate the effect of OWPs on vertical stratification’ - ‘To evaluate vertical stratification in OWPs’ (also L371) - as there isn’t a clear cause and effect relationship here.
- Conclusions - I would like to see more of the polynya statistics summarised here. This could work well as a bulleted list.
Minor comments
L8 ‘presence or absence of OWPs are’ > ‘presence or absence of OWPs is’
L12 ‘that require to be addressed’ > ‘that should/must be addressed’
L30 requires citation
L85 Suggest adding a sentence on uncertainty in SIC observations
Table 1 - please add units on R_o and R_a (otherwise a very nice table though!)
L101 ‘not good’ > ‘poor’
L134 Please describe what a ‘flood fill algorithm’ is
Fig. 3 ‘propability’ > ‘probability’ on colorbar. I would also make all of the ocean ocean the dark blue (not grey).
Fig. 4 - shouldn’t this be ‘equivalent ice thickness’ not ‘floe thickness’?
Fig. 6 ‘All data sets where’ > ‘All data sets were’; ‘its’ full length’ > ‘its full length’
Fig. 8 (and similar figures in the appendix). I like this visualisation, but I wonder if you can separate the coastal and open water polynya bars and make the whole figure taller so it is easier to see?
L163 - Why doesn’t the mean of daily data go from 1st May to the end of Nov?
L334 ‘is resulting in’ > ‘results in’
L340 ‘Compared to the float data, ACCESS and BCC underestimate…’
L353 ‘deeper reaching’ -> ‘deeper-reaching’ ?
L354: ‘There are some profiles’ - please be more quantitative
L366: Define N^2 in the text
Fig. 10 - Please add subplot labels (a, b, c, …) and refer to these in the main text. This will make the text easier to follow
L383 ‘This is consistent…’ Rephrase, it’s not clear here what you mean
L386 ‘can usually be run’; remove ‘in our case’
L438 ‘All these parameters are positively correlated with OWP activity in observations’
Martin Mohrmann et al.
Martin Mohrmann et al.
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