A. Solomon, C. Heuzé, B. Rabe, S. Bacon, L. Bertino, P. Heimbach, J. Inoue, D. Iovino, R. Mottram, X. Zhang, Y. Aksenov, R. McAdam, A. Nguyen, R. Raj, and H. Tang (2021) Freshwater in the Arctic Ocean 2010-2019, Ocean Science, vol 17, pp. 1081–1102, doi:10.5194/os-17-1081-2021.
This is a review paper that aimed at determining how the Arctic Ocean freshwater content has changed over the last decade, and why.
Disclaimer: In oceanography, “salt content” is preferable to “freshwater content”, as the latter is based on a somewhat arbitrary choice of reference salinity. However, the vast majority of the published literature still uses freshwater content, as do other fields such as glaciology. Therefore, for this review, we used freshwater content as well.
Our main findings are:
Freshwater content increased over 2000-2009 but appears to have stabilised over 2010-2019.
This stabilisation is the result of an increase in freshwater content over the Beaufort Gyre and a decrease over the rest of the Arctic.
The atmospheric contribution is controlled by the Arctic Oscillation (more moisture transport to the Arctic during a positive phase).
The Arctic sea ice has transitioned to a new state: seasonal cover over the shelves, fast transpolar drift. No clear conclusion on the impact of this new sea ice on the ocean and atmosphere in the literature yet.
Mass loss from Greenland and other Arctic glaciers has increased.
Vertical mixing in the ‘Atlantified’ Arctic has increased and the halocline has weakened.
Our most notable conclusion is that all components of the Arctic climate system, especially rivers, are still unsufficiently monitored to clearly distinguish climate change signal from low frequency variability.
M. Mohrmann, C. Heuzé, and S. Swart (2021) Southern Ocean polynyas in CMIP6 models, The Cryosphere, vol 15, pp. 4281–4313, doi:10.5194/tc-15-4281-2021.
We use daily and monthly sea ice concentration and sea ice thickness output from 27 models that participated in the Climate Model Intercomparison Project phase 6 (CMIP6) to evaluate their representation of polynyas, i.e. openings in the winter sea ice, in the Southern Ocean. We find that:
The daily sea ice thickness output has serious issues;
Few models have very large open ocean polynyas, but open ocean polynyas feature in most models too often;
The majority of models overestimate the area of coastal polynyas;
For most models, the polynya occurrence and area is larger if using daily output instead of monthly, or if using sea ice thickness instead of concentration;
Too few model families provided CM and ESM versions for us to be certain, but CM versions seem to have a better representation of coastal polynyas, likely because they can be run at higher resolution;
The Southern Annular Mode and open ocean polynya activity are surprisingly not correlated in the models. Instead, we find a relationship with the Antarctic Circumpolar Current (ACC): the models with the largest open ocean polynya are the ones with the most realistic ACC, although it is unclear which process causes the other one.
C. Heuzé , L. Zhou, M. Mohrmann, and A. Lemos (2021) Spaceborne infrared imagery for early detection of Weddell Polynya openings, The Cryosphere, vol. 15, pp. 3401–3421, doi:10.5194/tc-15-3401-2021.
We use daily infrared satellite data since 1982 to investigate the Weddell Polynya, an opening in the Antarctic winter sea ice. We find:
That although the usual narrative is that the Weddell Polynya opened once over 1974-1976, and then did not re-open until 2016, there were in fact 30 polynyas in our dataset.
That our algorithm could detect up to 15 days in advance that an opening was imminent, returning no false positive.
By comparing infrared temperature to in-situ and reanalysis data, that variations in specific infrared properties can indicate whether the polynya opens in response to upwelling or a lead.
Infrared data however are strongly affected by clouds, which are very common in Antarctica in winter. We therefore suggest that infrared data be used after the opening, to obtain scientific information, but not for operational purposes (at least, not without extra data).
C. Heuzé (2021) Antarctic Bottom Water and North Atlantic Deep Water in CMIP6 models, Ocean Sci., doi:10.5194/os-17-59-2021, vol 17, pp 59-90.
CMIP5 models were rather biased when it came to their deep and bottom water properties, formation, and transports (see my own research ,,, and ). Are the new CMIP6 models better? I looked at 35 CMIP6 models, for the last 30 years of their historical run (1985-2014), both in the Southern Ocean and the North Atlantic. And as usual, the performance of the models depends on what you are interested in:
More models seem to successfully, realistically produce Antarctic Bottom Water (AABW) via shelf overflow.
Yet, most CMIP6 models still have unrealistically large areas with open ocean deep convection, both in the Southern Ocean and in the North Atlantic. “Too deep, too often, over too large an area” remains the best description for CMIP6 models.
Bottom property biases have notably decreased. The most biased model in particular, INM-CM5, is in particular much better than its predecessor INM-CM4. The most accurate models are those from the CESM2 family, which feature an overflow parameterisation.
The link between deep water formation and bottom properties depends on the region. In the Southern Ocean, more deep convection = more biased bottom waters; in the North Atlantic, less biased.
Most models have a warm bias, which may be a result of the reference we chose to compare them to rather (WOA2018).
Regarding their transport, the AMOC is within the observational range for most CMIP6 models, which is a notable improvement since CMIP5. Southern MOC observations still are too few, but most CMIP6 models now lie within their range.
In the Atlantic, the spread of the water masses in CMIP6 models is controlled by the strength of the MOCs: the stronger the AMOC, the further south we detect NADW and the least AABW can spread northward.
In the Indian and Pacific Ocean, the northward spread of AABW is not linked to the MOC but to the properties, in particular the salinity gradient in the Antarctic Circumpolar Current: the weaker the gradient to overcome, the further the AABW spread.
I encourage you to check the performances of your favourite model in the many tables of the paper!
Project “Would the Northern European Enclosure Dam really protect Sweden from sea level rise? (NEEDS)”, funded by FORMAS grant 2020-00982 Ca 4 million SEK; I am the PI. January 2021 – December 2024
The aim of the project is to determine what causes flooding in Sweden: remote effects that could be blocked out by distant sea walls, or local effects such as precipitation or tides. We’re using a combination of all data sources (in-situ, remote sensing, models) that we will analyse with help from Machine Learning.
Myself (mostly for supervision);
David Ek, Master’s student, April 2021 – April 2022;
Lea Poropat, Postdoc, starting June 2021;
Collaboration with Dan Jones and Scott Hosking from the British Antarctic Survey AI Lab.
Nothing yet, the project has not even officially started!
NERC Open CASE, number 1093171, awarded to Karen Heywood. = My PhD “Antarctic Bottom Water in CMIP5 models: characteristics, formation, evolution” October 2011 to March 2015. Supervisors: Karen Heywood and David Stevens (UEA), and Jeff Ridley (UK MetOffice)
Outcomes of my PhD:
Quantified biases in Antarctic Bottom Water temperature and salinity in CMIP5 models ;
Determined the causes for these biases: impossibility to export shelf water due to the mixing scheme, and open ocean deep convection in the Weddell Polynya[PhD thesis];
Quantified the consequence of these biases on climate change projections ;
Found a numerical solution to suppress the unrealistic formation process .
Project “Is Greenland meltwater going to stop the Atlantic overturning circulation?”, funded by VINNMER-Marie Curie, dnr 2015-01487. Ca 2.5 million SEK; I was the PI. Started in July 2015, finished in June 2018.
Myself, as postdoc researcher;
Anna Wåhlin, Gothenburg University, and Helen Johnson, Oxford University, as mentors / hosts;
Master student Lovisa-Waldrop Bergman.
Outcomes and publications:
Collected hydrographic data and produced the first map the path of meltwaters from Petermann Glacier out of its fjord into Nares Strait ;
Modelled the oceanic circulation of Nares Strait and determined its sensitivity to initial conditions (Master’s student’s work);
Quantified biases in full depth water properties and deep water formation processes in the North Atlantic in CMIP5 models ;
Determination of ocean currents from infrared remote sensing – collaboration with Chalmers University of Technology to try and increase the North Atlantic data coverage .
Project “Warm oceanic Inflows for Near-real time Detection Of Weddell polynya from Space (WINDOWS)”, funded by Rymdstyrelsen grant 164/18 Ca 4.5 million SEK; I am the PI. Started in January 2019, finishes in December 2022.
The aim is to create an “early warning system” to detect that the winter sea ice is going to open, by combining passive and active satellite remote sensing. We are using the Weddell Polynya in the Southern Ocean as test subject.
Myself until February 2021;
Postdoc Lu Zhou, April 2021 – March 2023;
Postdoc Adriano Lemos, January-August 2020;
PhD student Martin Mohrmann, co-supervised with S. Swart and H. Ploug from the Marine Sciences Department, Gothenburg University, January 2018 – June 2022.
Representation of Antarctic Polynyas in CMIP6 models: 
Detection of upcoming sea ice opening several days ahead from passive remote sensing:  and ;
On how exceptional the Weddell Polynya re-opening of 2016-2017 has been: .
Project “Why is the deep Arctic Ocean Warming? (WAOW)”, funded by Vetenskapsrådet grant 2018-03859 Ca 4 million SEK; I am the PI (single applicant here) Started in January 2019, finishes in December 2022
The aim of this project is to finally determine the path and variability of the deep waters of the Arctic Ocean, from 2000 m to the sea floor, using notably data that we collected during the international MOSAiC expedition (2020) and will collect as part of the Synoptic Arctic Survey (2021).
PhD student Salar Karam, of which I am the main supervisor, October 2019 to September 2023;
Maren Walters from Uni Bremen, for expertise with CFC analysis;
Collaboration with the wider MOSAiC Team Ocean and the Swedish Synoptic Arctic Survey consortium.
Nothing yet; we are currently analysing the MOSAiC data.
C. Heuzé, M. Mohrmann, E. Andersson and E. Crafoord (2020). Global decline of deep water formation with increasing atmospheric CO2. EarthArXiv. doi:10.31223/X56K6D.
We analysed the 1% CO2 idealised run of 30 CMIP6 models and found:
Globally, open ocean deep convection ceases around 600 ppm. We instead enter a new regime, with large areas of mixed layers not exceeding 500 m;
Unlike what was found in studies based on individual models, deep convection does not start in the Arctic Ocean;
Consequently, the flows of North Atlantic Deep Water and Antarctic Bottom Water (AMOC and Southern MOC) fall at half their pre-industrial values;
The main reason is a global large increase in stratification, which is caused by rising upper ocean temperatures and/or surface freshening. Trends in wind are insignificant; the wind cannot break the stratification.
We submitted this to Nature Climate Change. Twice. The first time in October 2019, the manuscript featured “only” 13 models. We were asked to resubmit once we had more models. The second version, with 30 models and more robust methods, was rejected in October 2020 for not being dramatic enough (my interpretation). None of us is paid to work on this project. We all need to concentrate on more exciting things (e.g. the MOSAiC data), and, simply, it stopped being fun a long time ago. So sure, we could have played the peer-review game with another journal and forced ourselves through another year of selling this paper. Instead, following advice received from the community, we put the manuscript on EarthArXiv, in hope that it can be useful in its present state.