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.


Heuzé et al. (2017), Sea Surface Currents Estimated from Spaceborne Infrared Images Validated against Reanalysis Data and Drifters in the Mediterranean Sea

C. Heuzé, G.K. Carvajal, L.E.B. Eriksson and M. Soja-Woźniak (2017c), “Sea Surface Currents Estimated from Spaceborne Infrared Images Validated against Reanalysis Data and Drifters in the Mediterranean Sea“, Remote Sensing, vol 9(5), pp 422, doi:10.3390/rs9050422

Graphical abstract, Heuzé et al. (2017)

We test whether near-real time retrieval of ocean surface currents is feasible in the open ocean using a sea surface temperature tracking method. We use a year of spaceborne infrared (AVHRR) imagery over the Western Mediterranean Sea.

The comparison with undrogued drifters is disappointing, but the retrieved currents match well the reanalysis. We conclude that satellite retrieval is indeed feasible, and that in such an eddy-rich (and windy) area, drifter observations should not be used for validation, but rather to complement the retrieved currents.

Download the full-text by clicking here.

Heuzé (2017) North Atlantic deep water formation and AMOC in CMIP5 models

C. Heuzé (2017), “North Atlantic deep water formation and AMOC in CMIP5 models“, Ocean Science, vol 13, pp 609-622, doi:10.5194/os-13-609-2017

The North Atlantic version of my 2013 Antarctic bottom water paper. Assessment of the misrepresentation of North Atlantic deep water formation in 23 CMIP5 models, in order to easily see if the CMIP6 models have improved when they are released.

From Fig. 1: Models’ 1986-2005 mean winter mixed layer depth in the North Atlantic (shading) and winter/summer sea ice extent (green/magenta line). Not really better than in the Southern Ocean…

Most models form deep water too often, over too large an area which is not at the correct location, but a few were satisfying.
The main cause of this misrepresentation seems to be linked to the choice of sea ice model component.
Misrepresentation of deep water formation both in the North Atlantic subpolar gyre and in the Greenland-Iceland-Nordic seas impacts the Atlantic Meridional Overturning Circulation, which in turns impacts the heat exported into the Arctic.

Download the full-text by clicking here.

Heuzé et al. (2017), Pathways of meltwater export from Petermann Glacier, Greenland

C. Heuzé, A. Wåhlin, H.L. Johnson and A. Münchow (2017): “Pathways of meltwater export from Petermann Glacier, Greenland“, Journal of Physical Oceanography, vol 47(2), pp 405-418, doi:10.1175/JPO-D-16-0161.1

Petermann Glacier in northwestern Greenland is most famous for its dramatic calving events of 2010 and 2012, during which it lost  nearly a third of its length. But since this glacier floats on the ocean, in fact it loses quite a lot of ice by melting into the ocean.

We spent a month in the vicinity of the glacier in August 2015, but did not have much oceanography equipment. Yet using only temperature and salinity (and oxygen when the sensor worked), we calculate in this paper how much meltwater is in the fjord, how much leaves the fjord, and map where it goes.
Moreover, since we were the first expedition since the calving, we can see how the fjord system changed. And actually discover that although the ocean is warmer and the glacier geometry different, the meltwater amount and depth is not significantly different…

Schematic of what has changed in Petermann Fjord since the calvings of 2010 and 2012. Capital letters = probably important, but magnitude unknown to date.

There is obviously a lot that we do not know then, and in particular we need to study how much this system varies from day to day to season to years. Funny enough, this is what my master student is doing right now!

Download the full-text by clicking here.