Mohrmann et al. (2022) Observed Mixing at the Flanks of Maud Rise in the Weddell Sea

M. Mohrmann, S. Swart, and C. Heuzé (2022) Observed Mixing at the Flanks of Maud Rise in the Weddell Sea, Geophysical Research Letters, vol 49, e2022GL098036, doi:10.1029/2022GL098036

At the beginning of his PhD under our supervision, Martin Mohrmann deployed two high frequency autonomous profilers with under ice capability by Maud Rise. That means that even in the middle of winter, we have daily profiles of the water column. The “fun” thing is that they drifted in opposite directions, so their synchronous sampling of two different locations revealed crucial spatial effects in that region.

Key findings (copied from the paper):

  • High frequency (1–3 days) float profiles were collected over both Maud Rise and its vicinity over several seasonal cycles
  • Temperature and salinity below the mixed layer are strongly bathymetry-dependent at Maud Rise
  • Enhanced spiciness variability indicates intrusions and mixing between Maud Rise Deep Water and the surrounding Warm Deep Water

Stay tuned for paper number 3, to be submitted soon, where these profilers are compared to older floats to this time determine the temporal effects.

Download the full text here.

Snoeijs-Lejonmalm et al. (2022) Unexpected fish and squid in the central Arctic deep scattering layer

P. Snoeijs-Leijonmalm, H. Flores, S. Sakinan, N. Hildebrandt, A. Svenson, G. Castellani, K. Vane, F.C. Mark, C. Heuzé, S. Tippenhauer, B. Niehoff, J. Hjelm, J. Hentati Sundberg, F.L. Schaafsma, R. Engelmann and The EFICA-MOSAiC Team (2022) Unexpected fish and squid in the central Arctic deep scattering layer, Science Advances, vol 8, doi:10.1126/sciadv.abj7536

During the MOSAiC expedition, as the ship was embedded in the sea ice pack, one of the ways through which biodiversity and abundance were measured was via the (hydroacoustic) backscatter of the “deep scattering layer”. We find here that the backscatter is strongly correlated to the water mass properties: life, and there’s quite a lot of it, sits primarily in the comparatively warm Atlantic layer.

The team also collected fish samples and recorded hours of video footages. These reveal that, unexpectedly, that layer also contained healthy polar cods and squids.

Extract from Snoeijs-Lejonmalm et al. (2022) Fig. 6: Squid happily swimming around caught on camera

The abundance is way too low for fisheries, but these results prove that the ice-covered Arctic Ocean is far from being the desert it’s often described as (CH’s personal opinion: and consequently, it should be protected accordingly).

Download the full-text here.

Rabe et al. (2022) Overview of the MOSAiC expedition: Physical oceanography

Rabe, B, Heuzé, C, Regnery, J, Aksenov, Y, Allerholt, J, Athanase, M, Bai, Y, Basque, C, Bauch, D, Baumann, TM, Chen, D, Cole, ST, Craw, L, Davies, A, Damm, E, Dethloff, K, Divine, DV, Doglioni, F, Ebert, F, Fang, Y-C, Fer, I, Fong, AA, Gradinger, R, Granskog, MA, Graupner, R, Haas, C, He, H, He, Y, Hoppmann, M, Janout, M, Kadko, D, Kanzow, T, Karam, S, Kawaguchi, Y, Koenig, Z, Kong, B, Krishfield, RA, Krumpen, T, Kuhlmey, D, Kuznetsov, I, Lan, M, Laukert, G, Lei, R, Li, T, Torres-Valdés, S, Lin, L, Lin, L, Liu, H, Liu, N, Loose, B, Ma, X, MacKay, R, Mallet, M, Mallett, RDC, Maslowski, W, Mertens, C, Mohrholz, V, Muilwijk, M, Nicolaus, M, O’Brien, JK, Perovich, D, Ren, J, Rex, M, Ribeiro, N, Rinke, A, Schaffer, J, Schuffenhauer, I, Schulz, K, Shupe, MD, Shaw, W, Sokolov, V, Sommerfeld, A, Spreen, G, Stanton, T, Stephens, M, Su, J, Sukhikh, N, Sundfjord, A, Thomisch, K, Tippenhauer, S, Toole, JM, Vredenborg, M, Walter, M, Wang, H, Wang, L, Wang, Y, Wendisch, M, Zhao, J, Zhou, M, Zhu, J. 2022. Overview of the MOSAiC expedition: Physical oceanography. Elementa: Science of the Anthropocene 10(1). DOI: https://doi.org/10.1525/elementa.2021.00062

In October 2019, the RV Polarstern was purposely frozen in the high Arctic pack ice to be used as a research platform by hundreds of international scientists while the ship drifted along with the ice until September 2020. Or at least, that was the plan of MOSAiC. Covid19 and an extremely rapid transpolar drift made the expedition more… “interesting”. In this overview, we detail the physical oceanography program (of which I was one of the two co-leads for the entire expedition):

  • The measurement systems from the ship, deployed on the ice, and in the distributed network of autonomous sensors up to 50 km around the ship;
  • What we did where and when, including the covid19-related changes of schedules;
  • The “event” measurements: 36h microstucture profiling, sampling in leads, etc.

And we present our first results:

  • The full-depth seasonal and regional variability of the Arctic water masses, including a very visible Atlantification;
  • A way less “quiescent” Arctic than expected, with increased mixing in the upper 500 m consistent with the reduction in sea ice concentration and thickness;
  • Observations of the life cycle of freshwater lenses just under the ice, until their destruction by storms.

This publication is mostly meant as the data paper for all other MOSAiC physical oceanography publications to come, so stay tuned for more actual results.

Download the full-text here.

Shupe et al. (2022) Overview of the MOSAiC expedition—Atmosphere

Shupe, MD, Rex, M, Blomquist, B, Persson, POG, Schmale, J, Uttal, T, Althausen, D, Angot, H, Archer, S, Bariteau, L, Beck, I, Bilberry, J, Bucci, S, Buck, C, Boyer, M, Brasseur, Z, Brooks, IM, Calmer, R, Cassano, J, Castro, V, Chu, D, Costa, D, Cox, CJ, Creamean, J, Crewell, S, Dahlke, S, Damm, E, de Boer, G, Deckelmann, H, Dethloff, K, Dütsch, M, Ebell, K, Ehrlich, A, Ellis, J, Engelmann, R, Fong, AA, Frey, MM, Gallagher, MR, Ganzeveld, L, Gradinger, R, Graeser, J, Greenamyer, V, Griesche, H, Griffiths, S, Hamilton, J, Heinemann, G, Helmig, D, Herber, A, Heuzé, C, Hofer, J, Houchens, T, Howard, D, Inoue, J, Jacobi, H-W, Jaiser, R, Jokinen, T, Jourdan, O, Jozef, G, King, W, Kirchgaessner, A, Klingebiel, M, Krassovski, M, Krumpen, T, Lampert, A, Landing, W, Laurila, T, Lawrence, D, Lonardi, M, Loose, B, Lüpkes, C, Maahn, M, Macke, A, Maslowski, W, Marsay, C, Maturilli, M, Mech, M, Morris, S, Moser, M, Nicolaus, M, Ortega, P, Osborn, J, Pätzold, F, Perovich, DK, Petäjä, T, Pilz, C, Pirazzini, R, Posman, K, Powers, H, Pratt, KA, Preußer, A, Quéléver, L, Radenz, M, Rabe, B, Rinke, A, Sachs, T, Schulz, A, Siebert, H, Silva, T, Solomon, A, Sommerfeld, A, Spreen, G, Stephens, M, Stohl, A, Svensson, G, Uin, J, Viegas, J, Voigt, C, von der Gathen, P, Wehner, B, Welker, JM, Wendisch, M, Werner, M, Xie, ZQ, Yue, F. 2022. Overview of the MOSAiC expedition—Atmosphere. Elementa: Science of the Anthropocene 10(1). DOI: https://doi.org/10.1525/elementa.2021.00060

In October 2019, the RV Polarstern was purposely frozen in the high Arctic pack ice to be used as a research platform by hundreds of international scientists while the ship drifted along with the ice until September 2020. Or at least, that was the plan of MOSAiC. Covid19 and an extremely rapid transpolar drift made the expedition more… “interesting”. In this overview, we detail the atmospheric observation program:

  • The measurement systems from the ship, deployed on the ice, and the crewed and uncrewed flight campaigns;
  • What we did where and when, including the covid19-related changes of schedules;
  • The “event” measurements: radiosonde sampling of warm air intrusion, storms etc.

And present our first results:

  • Record high positive Arctic Oscillation, which contributed to a record ozone hole in the Arctic stratosphere, and to persistent winds that pushed the sea ice and Polarstern more quickly across the Arctic than expected.
  • Dynamic (vertical wind structure and momentum transfer to sea ice and ocean) and thermodynamic (warm air masses) influences of more than 20 Arctic cyclones, or storms, of various scales.
  • Year-round information on variability of atmospheric composition and aerosol populations in the Central Arctic, especially the relative influences of long-range transport versus local processes. 

This publication is mostly meant as the data paper for all other MOSAiC atmosphere publications to come, so stay tuned for more actual results.

Download the full-text here.

Nicolaus et al. (2022) Overview of the MOSAiC expedition: Snow and sea ice

Nicolaus, M, Perovich, DK, Spreen, G, Granskog, MA, Albedyll, LV, Angelopoulos, M, Anhaus, P, Arndt, S, Belter, HJ, Bessonov, V, Birnbaum, G, Brauchle, J, Calmer, R, Cardellach, E, Cheng, B, Clemens-Sewall, D, Dadic, R, Damm, E, de Boer, G, Demir, O, Dethloff, K, Divine, DV, Fong, AA, Fons, S, Frey, MM, Fuchs, N, Gabarró, C, Gerland, S, Goessling, HF, Gradinger, R, Haapala, J, Haas, C, Hamilton, J, Hannula, H-R, Hendricks, S, Herber, A, Heuzé, C, Hoppmann, M, Høyland, KV, Huntemann, M, Hutchings, JK, Hwang, B, Itkin, P, Jacobi, H-W, Jaggi, M, Jutila, A, Kaleschke, L, Katlein, C, Kolabutin, N, Krampe, D, Kristensen, SS, Krumpen, T, Kurtz, N, Lampert, A, Lange, BA, Lei, R, Light, B, Linhardt, F, Liston, GE, Loose, B, Macfarlane, AR, Mahmud, M, Matero, IO, Maus, S, Morgenstern, A, Naderpour, R, Nandan, V, Niubom, A, Oggier, M, Oppelt, N, Pätzold, F, Perron, C, Petrovsky, T, Pirazzini, R, Polashenski, C, Rabe, B, Raphael, IA, Regnery, J, Rex, M, Ricker, R, Riemann-Campe, K, Rinke, A, Rohde, J, Salganik, E, Scharien, RK, Schiller, M, Schneebeli, M, Semmling, M, Shimanchuk, E, Shupe, MD, Smith, MM, Smolyanitsky, V, Sokolov, V, Stanton, T, Stroeve, J, Thielke, L, Timofeeva, A, Tonboe, RT, Tavri, A, Tsamados, M, Wagner, DN, Watkins, D, Webster, M, Wendisch, M. 2022. Overview of the MOSAiC expedition: Snow and sea ice. Elementa: Science of the Anthropocene 10(1). DOI: https://doi.org/10.1525/elementa.2021.000046

In October 2019, the RV Polarstern was purposely frozen in the high Arctic pack ice to be used as a research platform by hundreds of international scientists while the ship drifted along with the ice until September 2020. Or at least, that was the plan of MOSAiC. Covid19 and an extremely rapid transpolar drift made the expedition more… “interesting”. In this overview, we detail the snow and ice observational program:

  • The measurement systems from the ship, deployed on the ice and in the distributed network of autonomous sensors up to 50 km around the ship, and the airborne and satellite observations;
  • What we did where and when, including the covid19-related changes of schedules;
  • The “event” measurements: thin ice formation in leads, rain on snow, etc.

And we present our first results:

  • Larger spatial variabilities of the snow cover than expected, due to atmospheric processes and the structure of the underlying sea ice.
  • More dynamic and faster drifting ice pack than expected. For reference, we covered in 7 months the same distance as Nansen did in 3 years, 1893-1896.
  • Combined remote sensing measurements on the ice with detailed snow and ice observations pave the way for new sea ice observations from upcoming satellite missions and allow better uncertainty assessments of existing satellite time series.

This publication is mostly meant as the data paper for all other MOSAiC snow and ice publications to come, so stay tuned for more actual results.

Download the full-text here.

Zhou et al. (2022) Early winter triggering of the Maud Rise Polynya

L. Zhou, C. Heuzé, and M. Mohrmann (2022) Early winter triggering of the Maud Rise Polynya, Geophysical Research Letters, doi:10.1029/2021GL096246

What triggers the opening of large open-ocean polynyas (holes in the winter pack ice) is still debated as we do not have in-situ observations. The main motivation for our SNSA-funded project “WINDOWS” is to find signatures of upcoming polynya events in widely available remote sensing products, in order for example to re-route autonomous instruments or optimise expeditions.

In a preliminary study [13], we could predict a polynya 5-day ahead of its opening using thermal infrared. In [23], still using thermal infrared, we increased to two weeks. In this study, we use microwave-based retrievals and can predict the opening four months ahead.

We also find that the polynya opens because of both dynamics effects (anomalous wind and ocean currents’ stresses on the sea ice) and thermodynamics, notably anomalous entrainment of heat into the mixed layer.

We’re currently working on

  • estimating energy fluxes and sea ice production in polynyas, both open-ocean and coastal;
  • and improving the sea ice retrievals.

So stay tuned for more polynya results!

Download the full-text here.

Solomon et al. (2021) Freshwater in the Arctic Ocean 2010-2019

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.

Fig. 2a from Solomon et al. (2021). Freshwater content north of 70N down to the 34 isohaline, for the six ocean reanalyses and the multi model mean (thick red line).

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.

Download the full-text here.

Mohrmann et al. (2021) Southern Ocean polynyas in CMIP6 models

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.
Fig. 4 from Mohrmann et al. (2021) showing the agreement between observations (left) and the highest resolution model (right, 25 km)

Download the full text here.

Heuzé et al. (2021) Spaceborne infrared imagery for early detection of Weddell Polynya opening

Extract from Table B1 of Heuzé et al. (2021): characteristics of the 30 polynyas. Latitude (lat) and longitude (lon) are in degrees N and E respectively; maximum area in km2; duraction in days.

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).

Download the full-text here.

Heuzé (2021) Antarctic Bottom Water and North Atlantic Deep Water in CMIP6 models

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 [1],[2],[4],[10] and [17]). 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!

The INM model still is way too salty, but no longer needs its own colour bar. Black contours indicate a maximum MLD deeper than 2000 m (from supp. fig. A2, Heuzé 2021)

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