Aldenhoff et al. (2019) Sensitivity of Radar Altimeter Waveform to Changes in Sea Ice Type at Resolution of Synthetic Aperture Radar

W. Aldenhoff, C. Heuzé, and L.E.B. Eriksson (2019) Sensitivity of Radar Altimeter Waveform to Changes in Sea Ice Type at Resolution of Synthetic Aperture Radar, Remote Sensing special issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions,  11, 2602, doi: 10.3390/rs11222602.

So far, radar altimetry has been mainly used to retrieve basin-wide, monthly sea ice statistics. We argue that it is a wasted potential, and, following the pilot study described in paper [20], investigate how much SAR backscatter and altimetry could augment each other for sea ice retrieval.

Data: coincidental altimeter tracks from CryoSat2 and backscatter images from Sentinel 1 A/B over the Beaufort Sea, Arctic, in winters 2016, 2017 and 2018.

Method: identify altimeter waveform parameters that best distinguish leads, first year ice (FYI) and multiyear ice (MYI), using the Sentinel 1 backscatter as reference.

Main finding: see Figure 4, reproduced below – each parameter has different strengths and overlaps. Looking at the various years reveal that this finding is robust though. The only problem comes from small scale features, e.g. a MYI floe embedded in FYI

Waveform parameters of different ice types covering all winter seasons 2016-2018 in the Beaufort Sea. (a) Pulse peakiness, (b) scaled mean power, (c) stack standard deviation and (d) mean HV backscatter intensities of altimeter footprint (see paper). Adapted from Aldenhoff et al. (2019), Figure 4.

Conclusion: it works for this purpose, but as we detail in the paper, we have serious doubts regarding our ability to improve freeboard retrievals with this method. To be continued…

Download the full-text here.

Aldenhoff et al. (2019) Comparison of Sentinel-1 SAR And Sentinel-3 Altimetry Data For Ice Type Discrimination

W. Aldenhoff, L.E.B. Eriksson, and C. Heuzé (2019) Comparison of Sentinel-1 SAR And Sentinel-3 Altimetry Data For Ice Type Discrimination, Geoscience and Remote Sensing Symposium (IGARSS), 2019 IEEE International, doi: 10.1109/IGARSS.2019.8899041.

A very brief paper that investigates whether SAR backscatter and SAR altimetry could be used complementarily for sea ice classification, notably to increase the spatio temporal resolution of sea ice products. The short answer is “yes!”, which led to the more developed paper [21].

Download the full-text here.

Heuzé et al. (2019) The Weddell Polynya [in CMEMS Ocean State Report issue 3]

C. Heuzé, G. Garric, and T. Lavergne (2019) The Weddell Polynya [in CMEMS Ocean State Report issue 3, Chapter 4], Journal of Operation Oceanography, doi:10.1080/1755876X.2019.1633075.

In the same vein as paper [15], we here report in near-real-time on the surprise opening of the Weddell Polynya, an unexplained large hole in the winter Antarctic sea ice cover. We find

1) That the polynya information was correctly assimilated by the reanalysis product GREP.

2) That in GREP as in observations, the polynya coincided with a deepening of the mixed layer, although this deepening ceased as early as November even though the polynya kept growing.

Download the full-text here, and the full CMEMS OSR3 here (63 MB!).

Hassett et al. (2019) Global diversity and geography of the planktonic marine fungi

B. Hassett, T. Vonnahme, X. Peng, E. Jones, and C. Heuzé (2019) Global diversity and geography of the planktonic marine fungi, Botanica Marina, EOR, doi:10.1515/bot-2018-0113.

Review paper, listing everything that we know as of early 2019 regarding marine fungi. Featuring a lot of resequencing to determine species presence, diversity and abundance, and short water mass explanations for geographical similarities and differences.

Relative abundance of fungal genera per oceanographic region of interest (adapted from Fig 2a)

Download the full-text here.

Heuzé and Årthun (2019) The Atlantic inflow across the Greenland-Scotland ridge in climate models (CMIP5)

C. Heuzé and M. Årthun (2019) The Atlantic inflow across the Greenland-Scotland ridge in climate models (CMIP5), Elementa Science of the Anthropocene, 7(1), p.16, doi: 10.1525/elementa.354.

The motivation for this paper was simple:

  • Climate models poorly represent the Arctic sea ice extent;
  • This sea ice is heavily controlled by the oceanic heat that enters the Arctic;
  • Most of the heat comes from the North Atlantic, via the Nordic seas;
  • …no one has looked at how the Nordic seas are represented in climate models?!

So we did exactly this, eventually narrowing it down to the representation of the oceanic heat flow from the North Atlantic into the Nordic seas. We used 23 CMIP5 models – I’ll be honest, these are the exact same ones as in Heuzé (2017) in order to make the most of the Terabytes of data I had to download over the past years.

Main result: poor representation of the oceanic heat inflow, with most models underestimating it while a few heavily overestimating it, and even inaccurate seasonal cycles.
Reason: a mix of poor bathymetry and inaccurate large scale oceanic and atmospheric circulations, which result in models importing waters from the ‘wrong’ part of the Atlantic into the Nordic seas.
Why should people care? Sure, lots of transformations occur once in the Nordic seas before the water goes into the Arctic, but that most models already have too little heat coming in the Nordic seas is consistent with most models also simulating more sea ice than we have. Fixing the bathymetry is not trivial, but it is worth a try to improve sea ice projections.

Mean heat inflow through Denmark Strait in CMIP5 models (after Heuzé and Årthun, 2019)

Download the full-text here.

You’re still here? Then I’ll tell you why this paper means a lot to me on the personal level.
Marius and I came up with this idea at EGU2017. The previous few months had been chaotic on the personal level, but finally life was sorting itself out and I was optimistic again. So I was full of energy and we submitted the first draft already in September. Then came October.  The same week this paper was rejected, my mother was diagnosed with cancer. As it became clear that the treatment was not working, I lost interest in the paper (among other things). Marius and I agreed on corrections and large improvements at Ocean Sciences in February 2018, but it took time, so that I ended up writing this new version very soon after my mother had died. When the reviewers’ comments eventually came six months later (!), I could not work on them; re-reading the manuscript threw me back to the early months of grieving. I had to force myself, but that meant being again a wreck mentally. Yet we did it. And it is quite fitting that the paper was published so close to the first anniversary of her death – I have made peace with both, and can now move on.
Worry not, the other paper comments will be less personal.


Waldrop-Bergman and Heuzé (2018) Influence of initial stratification, wind and sea ice on the modelled oceanic circulation in Nares Strait, northwest Greenland

L. Waldrop Bergman and C. Heuzé (2018) “Influence of initial stratification, wind and sea ice on the modelled oceanic circulation in Nares Strait, northwest Greenland“, Ocean Science Discussion, doi:10.5194/os-2018-122.

As we were implementing a regional configuration of MITgcm between Greenland and Canada, we realised that there were too many choices to make and too few data to justify them. We thus performed a sensitivity experiment instead to determine which had the largest influence on our modelled ocean:

  • the initial temperature and salinity profiles,
  • the resolution of the wind forcing,
  • or the initial sea ice thickness?

We showed that hydrographic observations that capture the E-W or N-S density gradients were more crucial than a high resolution wind.

Click here to download the full-text and discover why.

Heuzé and Aldenhoff (2018), Near-Real Time Detection of the Re-Opening of the Weddell Polynya, Antarctica, from Spaceborne Infrared Imagery

C. Heuzé and W. Aldenhoff (2018), “Near-Real Time Detection of the Re-Opening of the Weddell Polynya, Antarctica, from Spaceborne Infrared Imagery“, Geoscience and Remote Sensing Symposium (IGARSS), 2018 IEEE International, pp 5613-5616, doi:10.1109/IGARSS.2018.8518219.

In September 2017, the Weddell Polynya reopened over the Maud Rise region for the first time since 1976. I personally would have loved to know in advance that it would happen, so that measurements could be obtained during the opening itself.

The Polynya opens because warm water is upwelled – we determined in this publication that this extra heat was detectable several days in advance from spaceborne infrared images. Traditional methods using microwave-inferred sea ice concentration or thickness in contrast would only be useful once the polynya has opened.

Upcoming funding decision dependent, more publications to come.

Download the full-text by clicking here.

Two scenes before the opening of the polynya. Adapted from Fig 2 of that publication.

Swart, Campbell, Heuzé et al. (2018), Return of the Maud Rise polynya: climate litmus or sea ice anomaly?

S. Swart, E.C. Campbell, C. Heuzé, K. Johnson, J.L. Lieser, M. Massom, M. Mazloff, M. Meredith, P. Reid, J-B. Sallée, and S. Stammerjohn (2018), “Return of the Maud Rise polynya: climate litmus or sea ice anomaly?” [in State of the Climate in 2017 chapter 6], Bull. Amer. Meteor. Soc., vol 99, S188-S189, doi:10.1175/2018BAMSStateoftheClimate.1.

For the first time since the 1970s, a huge hole opened in the winter sea ice cover in the Southern Ocean: the Weddell Polynya. We describe this phenomenon, hypothesise potential atmospheric causes for its opening that our PhD student, Martin Mohrmann, is currently working on (dis)proving, and show biogeochemical timeseries collected by the SOCCOM float that accidentally ended up in the polynya.

Extent of the 2017 Weddell Polynya (sea ice concentation, in %, from AMSR2). Adapted from Fig SB6.1 (b)

Download the full-text of Weddell Sea sidebar here, the entire chapter 6 about Antarctica here, or the entire State of the Climate here.

You may also be interested in my other publications featuring the Weddell Polynya:

  • Heuzé et al. (2013), “Southern Ocean Bottom Water Characteristics in CMIP5 models“, Geophysical Research Letters, vol 40(7), pp 1409-1414, doi:10.1002/grl.50287
  • Heuzé et al. (2015), “Increasing vertical mixing to reduce Southern Ocean deep convection in NEMO3.4“, Geoscientific Model Development, NEMO special issue, vol 8, pp 3119-3130 doi:10.5194/gmd-8-3119-2015
  • Heuzé et al. (2015), “Changes in global ocean bottom properties and volume transports in CMIP5 models under climate change scenarios“, Journal of Climate, vol 28, pp 2917-2944, doi:10.1175/JCLI-D-14-00381.1

Aldenhoff, Heuzé et al. (2018), Comparison of ice/water classification in Fram Strait from C- and L-band SAR imagery

W. Aldenhoff, C. Heuzé and L.E.B. Eriksson (2018), “Comparison of ice/water classification in Fram Strait from C- and L-band SAR imagery“, Annals of Glaciology, pp 1-12, doi: 10.1017/aog.2018.7.

Two SAR images of Fram Strait, same day, 3h apart: C- (top) and L-band (bottom). Adapted from Aldenhoff et al. (2018)

Ice charts for navigation are created daily by manual interpretation of SAR C-band data mostly. We here trained a neural network to create ice charts automatically and made its classification more accurate (as visually checked) by including not only C- but also L-band data. L-band has a longer wavelength, and hence is able to better detect areas with thin ice in the ice pack.

The main limitation is that the two datasets are not acquired by the same satellite. For our application, that means that there was a varying time difference between the images. And the larger the time difference, the more the ice had drifted, and the more the two results differed. In practice however, this time difference can become an advantage: an algorithm using either dataset whenever data is received will create ice charts more often than is currently possible by using only C-band, and thus provide more up-to-date information to those who are navigating in ice-infested waters.

Download the full-text by clicking here.

Heuzé et al. (2017), Optimisation of sea surface current retrieval using a maximum cross correlation technique on modelled sea surface temperature

C. Heuzé, G.K. Carvajal and L.E.B. Eriksson (2017), “Optimisation of sea surface current retrieval using a maximum cross correlation technique on modelled sea surface temperature“, Journal of Atmospheric and Oceanic Technology, vol  34, pp 2245–2255, doi: 10.1175/JTECH-D-17-0029.1

Prior to using retrieval algorithms on real satellite images, we here first quantify the biases introduced by various sea surface current retrieval algorithms using a modelled hourly reference sea surface temperature field. We study four European seas, two with strong currents (the North Sea and the Channel) and two with weak currents (the Bay of Biscay and the western Mediterranean Sea), over one week per season of 2016.

The main finding is that weak currents are tricky to retrieve: too small a time interval between your sea surface ‘images’ and the algorithm picks nothing; too large, and the patterns that the algorithm tries to track have already been destroyed by eddies. Regions with strong currents in contrast are very accurately retrieved, whatever the setting or the season.

Download the full paper by clicking here.

Example retrieval in the North Sea, after Heuzé et al. (2017)

This paper is related to:

  • Heuzé et al. (2017c), which applies the same retrieval algorithm on actual satellite images in the Mediterranean Sea, and compares satellite-inferred currents with in-situ buoy data;
  • Heuzé et al. (2015c), which uses the same bias-quantification method to evaluate algorithms used to re-gridd Argo float data in the Southern Ocean.