Category Archives: Student/Postdoc Blog

A closed heat budget for the mid-latitude North Atlantic?!

by Nick Foukal

Now that the OSNAP and RAPID arrays are running concurrently, an obvious question arises: can we close a heat budget for the mid-latitude North Atlantic? A heat budget is a very simple concept – let us pretend that the ocean between RAPID and OSNAP is a box with an ocean flux coming into the southern boundary (at RAPID), another going out of the northern boundary (at OSNAP), and surface fluxes exiting through the top (Fig. 1). The sum of the oceanic fluxes and surface fluxes should equal the change in the temperature of the box, meaning the heat budget is “closed” (see equation below). This is a useful exercise because determining whether the ocean temperature variability is caused by ocean dynamics or surface fluxes gives us a better idea of how the system will evolve in the future. Though this task of closing heat budgets may seem incredibly simple to the lay audience, it has never been done before for a region as large and important as the mid-latitude North Atlantic.

Ocean temperature variability = surface heat flux + ocean heat transport divergence

Figure 1 – (left) RAPID and OSNAP lines in the North Atlantic (red lines) and time-mean sea-surface height (colors). Figure adapted from Lozier et al. (2019). (right) Simplified box representation of the mid-latitude North Atlantic heat budget with ocean heat fluxes into the box at RAPID, out of the box at OSNAP, and a net surface heat flux out of the ocean.

To close the heat budget with observations, we need reliable measurements of all three terms. There are very reliable reconstructions of ocean temperature variability (from satellites and Argo floats), decent guesses of the surface fluxes (primarily derived from satellites on these scales), but very poor estimates of the ocean heat transports. The processes that govern ocean heat transport operate on such small scales that they are difficult to measure in the absence of dedicated in situ arrays. Consequently, what is often done in the literature is the ocean heat transport term is inferred from the difference between the other two terms, and the heat budget is assumed to close. But this is not completely satisfactory because the surface heat fluxes typically have significant uncertainties (more on that later), so relying on them as the “known” component in a heat budget doesn’t inspire confidence in the result.

This is where the OSNAP and RAPID lines come in – they offer an unprecedented opportunity to bound the ocean heat transports over a large region. Never before has a region of this size been this densely sampled. This means that we no longer have to rely on the surface fluxes and conservation laws to close the budget. By knowing all three terms of the heat budget, we can assess how closely we can close the budget… essentially how well do our measurements from independent platforms agree with one another?

There is still one hurdle to overcome in this problem, and that is the uncertainty in surface heat fluxes. This is not a new problem to the field, it has plagued both oceanographic and atmospheric studies for decades. There are two well-known unknowns in surface heat fluxes: (1) the time variability between different surface fluxes data sets do not agree with one another and (2) the global surface fluxes averaged over time do not integrate to what we would expect from observed ocean warming rates. I recently ran into the former concern in a recent paper (Foukal and Lozier, 2018) where we looked at the heat budget for the eastern North Atlantic subpolar gyre in two models, and the end result of our study depended on which surface flux data set we used. With respect to the latter concern, we know that from rates of global ocean warming, the global net surface heat flux must be around 0.5-1 W/m2, yet some surface heat flux products sum to almost 25 W/m2globally (Yu, 2019, Cronin et al., 2019). So there is good reason to doubt both the mean and the variability in surface fluxes, which is not encouraging.

As a taste of this uncertainty, I compiled time series of the surface fluxes over the region bounded by RAPID and OSNAP for three different surface flux products (Fig. 2). To give a rough idea of what we should expect from the surface fluxes, the oceanic heat flux into the box through RAPID from 2004-2007 was 1.33 +/- 0.40 PW (Johns et al., 2011), and the oceanic heat flux out of the box through OSNAP from 2014-2016 was 0.45 +/- 0.02 PW (Lozier et al., 2019). If we assume that these two time periods are representative of the long-term mean, and that the ocean is in steady-state (i.e. the temperature variability is zero when time-averaged), then we should expect the surface heat fluxes to equal the difference between the two oceanic heat fluxes, or 0.88 PW out of the box. Instead, the mean surface heat fluxes are 0.43 PW (ERA5), 0.11 PW (OAFlux), and 0.11 PW (NOCS), all directed out of the box. While it is encouraging that the sign of the fluxes is correct in all three products and that two of the products agree with one another, it also means that somewhere between 0.45-0.77 PW is missing in our heat budget. To put this another way, at least an entire OSNAP of heat transport is missing from this budget, and maybe more. Furthermore, the only statistically-significant correlation between the time series is a relatively weak (r=0.56) connection between the annually-averaged ERA5 and OAFlux. NOCS had no significant correlations to either of the other two. So overall, the spread between the three data products, their lack of coherent variability, and their disagreement in the mean with the net ocean heat divergence does not inspire confidence that we can close a heat budget for the mid-latitude North Atlantic.

Figure 2. Surface heat flux variability integrated over the region between the RAPID and OSNAP arrays in three surface flux products (positive downward; units are petawatts = 1015 W). The thin lines are at monthly resolution, and the thick lines are annually-averaged. The seasonal cycles are removed from the monthly data to consider the non-seasonal variability. The ERA5 (Copernicus Climate Change Service) reanalysis is a ¼° product covering 1979-2018. The OAFlux (Yu et al., 2008) data set covers 1984-2009 at 1° resolution. The NOCS (Berry and Kent, 2011) surface heat fluxes are produced at 1° resolution for the period 1973-2014. The RAPID and OSNAP time periods are shown in the bottom right.

Before we lose hope, it is worth revisiting some of our methods and assumptions: (1) can we really compare the RAPID meridional heat transport from 2004 to 2007 to the more recent RAPID data from 2014 to 2016? Are the heat transports at RAPID from 2014 to 2016 perhaps lower than 1.33 PW? Lozier et al. (2019) report a net heat transport divergence of 0.80 PW between RAPID and OSNAP for the 2014 to 2016 period, which accounts for 0.08 PW of the missing heat fluxes. (2) Can we prioritize the ERA5 time series because it is the highest resolution and the most recently-released of the three products? During the 21 months of published OSNAP data, the mean ERA5 heat flux was 0.57 PW, or 33% larger than the 1979-2018 mean. So if we believe ERA5 over OAFlux and NOCS, then we are only missing 0.23 PW (0.80 PW – 0.57 PW), or only half of an OSNAP! (3) Maybe the ocean was not in steady-state for the OSNAP period (2014-2016), and instead of zero temperature change, the ocean actually warmed considerably? This is a bit of a stretch, as 0.23 PW would be a lot of warming. But it would be worth considering how much the region did warm over this time period to see how it affects the heat budget. After all, each of these heat transports has associated error bars, so maybe we can get close enough so that the error bars explain the residual? I will leave this analysis to further work, as this blog post is getting closer and closer to possible publication material…

So where does this exercise leave us? Surface heat fluxes are certainly a wild-card, but recent improvements (ERA5) seem to be trending in the right direction. In the next few years, OAFlux will be updated with higher resolution, so it would be worth checking if that time series validates the higher surface flux values of ERA5. Finally, I am contractually obligated to mention that continuation of the OSNAP line in the coming years is absolutely critical to closing the heat budget for the mid-latitude North Atlantic. A longer time series would improve our assumption of steady-state in the temperature variability and provide a better understanding of the inherent time scales of the overturning in the subpolar North Atlantic.

 

References:

Berry, D. I. and E. C. Kent (2011). Air-sea fluxes from ICOADS: the construction of a new gridded dataset with uncertainty estimates. International Journal of Climatology, 31, 987-1001.

Cronin, M. F. and 26 co-authors (2019). Air-Sea Fluxes With a Focus on Heat and Momentum, Frontiers in Marine Science, 6, 430, doi:10.3389/fmars.2019.00430.

Foukal, N. P. and M. S. Lozier (2018). Examining the origins of ocean heat content variability in the eastern North Atlantic subpolar gyre, Geophysical Research Letters, 45, 40, 11275-11283.

Johns, W. E., M. O. Baringer, L. M. Beal, S. A. Cunningham, T. Kanzow, H. L. Bryden, J. J. M. Hirschi, J. Martotzke, C. S. Meinen, B. Shaw, and R. Curry (2011). Continuous, Array-based estimates of Atlantic ocean heat transport at 26.5°N, Journal of Climate, 24, 2429-2449.

Lozier, M. S. and 37 co-authors (2019). A sea change in our view of overturning in the subpolar North Atlantic, Science, 363, 516-521.

Yu, L., X. Jin, and R. A. Weller (2008). Multidecade Global Flux Datasets from the Objectively Analyzed Air-se Fluxes (OAFlux) Project: Latent and sensible heat fluxes, ocean evaporation, and related surface meteorological variables. Woods Hole Oceanographic Institution, OAFlux Project Technical Report. OA-2008-01, 64pp. Woods Hole, Massachusetts.

Yu, L. (2019). Global Air-Sea Fluxes of Heat, Fresh water, and Momentum: Energy Budget Closure and Unanswered Questions, Annual Review of Marine Science, 11, 227-248.

 

 

Studying the ocean, where there is no ocean

by Xianmin Hu

I have been in Edmonton for almost ten years (PhD study and then postdoc of University of Alberta). When I tell people I am studying (physical) oceanography, they always laugh at me. I wish I had a perfect answer when they ask me “Why are you studying the ocean here? There is no ocean in Edmonton.” Physical Oceanography sounds strange to most, so I always “hide” the physical part in the name.

Well, there are too many things I can’t explain to them well enough. I count myself as an oceanographer but I don’t swim. Actually I prefer to move upward as I do climbing …. My friends joked that that I am preparing for the sea level changes. We know the sea level changes slowly, so one step higher might be the most efficient way (selfish though) to step away from the sea level rising problem.

With very little experience at sea, I am working with computers most of the time. Yes, I am one of them, the mysterious numerical ocean modellers. I have been working with the NEMO model (same name of the famous little fish) for years. To be more specific, I mainly do simulations with a regional configuration called ANHA. Someone once asked, “Is ANHA your girlfriend?” Of course, he was joking. ANHA stands for the Arctic Ocean and Northern Hemisphere Atlantic configuration. However, he was also right. To me, when you opt to sacrifice your personal time on one thing, it could be love; otherwise, it is just a one-way “benefit friendship”.

Sorry for drifting too far away, which is also a common problem in numerical modelling. Let’s pull it back to the ocean. My concern of the ocean is the salt. Life needs more salt, however, an ocean modeller may not agree because more salt leads to the model drift, particularly in the high latitude, which makes people feel bad for models.

However, sometimes, it is not necessarily the fault of the model itself. The ocean is thirsty. If there is little river discharge and rain, the Arctic Ocean and Atlantic Ocean salinity could look something like figure 1.

Figure 1 Simulated Sea Surface Salinity (SSS) with little freshwater into the ocean

Well, let’s feed the ocean with a reasonable amount of river discharge and precipitation, and the ocean looks much better (figure 2).

Figure 2 Simulated SSS with realistic freshwater into the ocean

Look (figure 2), can you see the freshwater plumes from the the big rivers? I was very excited to “see” these rivers showing up in the simulation. Meanwhile, a picky person like you may also notice the fresh water around southern Greenland. Yes, we can assume Greenland is just a rock island in the model. But we know the Greenland Ice Sheet (GrIS) is melting and feeding the surrounding ocean with a large amount of freshwater. Here we must give Jonathan Bamber credit for his freshwater estimates from the Greenland Ice Sheet.

The ocean is pretty happy with the freshwater from Greenland, thus, it is willing to tell us more about her, even the secret pathway of the Greenland meltwater (figure 3).

Figure 3 Vertical integrated (whole water column) of the Greenland meltwater passive tracer

Not a big surprise, it agrees with the general ocean circulation in this region as we know it. However, did you ever think about the spatial distribution, e.g., how much is accumulated in Baffin Bay and how far it can go down to south along the east coast of North America? The models do tell people something that is hidden behind what we see.

In the end, a nice story was made, but still, there is no ocean in Edmonton….

Extended Ellett Line research cruise and Subpolar North Atlantic transport

By Elizabeth Comer

In my last blog a year ago I mentioned I would be going to sea for the Extended Ellett Line (fig. 1) annual research cruise, which is part of the OSNAP array.  I boarded the RRS Discovery during June and we steamed towards Iceland, where we started sampling at each CDT station whilst heading back to Scotland. During this cruise I focussed mainly on the ADCP measurements and processing, as I was going to start using it within my research and this was the perfect way to get familiar with it. I had a brilliant time on this cruise, as the photos hopefully depict (fig. 2, 3 and 4), and it was a great insight to Oceanography in the field.

Now onto how this data will aid my research. The aim of my current research is to investigate the long-term mean and variability in volume, freshwater and heat transport of the Subpolar North Atlantic. All the hydrographic and velocity measurements collected annually are used to compute these transports, providing a near 20-year timeseries. It is important that we take continuous measurements at this location and compute these transports as 90% of the Atlantic inflow into the Nordica Sea and Arctic takes place between Iceland and Scotland, as well as roughly half the returning buy levitra online dense water. This research will add to our knowledge of how the Meridional Overturning Circulation in the Subpolar Atlantic is varying on a decadal to seasonal timescale, and add insight to the mechanisms controlling it. This observational data also enables the enhancement of oceanic models to produce transport variabilities on longer timescales.

The research into the long-term transport that has been carried out with my Supervisors Dr Penny Holliday and Prof Sheldon Bacon will be published in the near future, but below is a taster of the 18-year (1997-2015) mean Lowered-ADCP velocity across the section (fig. 5). This data is used to produce the long-term absolute velocity by providing a reference velocity for the geostrophic velocity field, which the transports are computed from. In this study I investigate how the transport varies across the region, highlighting the key routes for transport and why it is important we know this. Furthermore, the Scottish Continental Slope Current that runs northward along the Scottish shelf edge and the Rockall-Hatton Bank have the highest temperatures and salinities of the region. Hence, I hypothesis that these are fundamental routes for heat transport northward.

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Deep-water masses in the Subpolar North Atlantic, where do they occur and what are the physics behind them?

by Patricia Handmann

In October 2014 I started my PhD at GEOMAR and Kiel University. I was coming from KIT (Karlsruhe Institute of Technology ), where I studied experimental and theoretical physics and worked on cloud microphysics for a year as part of my diploma (masters) thesis. Before I started my diploma thesis I got the chance of an internship at Alfred-Wegener Institute and worked on quality control of some old cruise data from RV POLARSIRKEL in 1980/81, which was a great experience. I had a lot of fun during these three months at AWI while getting to know this new field of ocean physics. So when I was looking for a PhD after this internship I was intrigued by the general topic of deep-water formation in the northern and southern hemisphere. When I started at GEOMAR this idea quickly evolved into a more focused study comparing a high-resolution ocean model (VIKING20) with observations from the 53°N array. This boundary current observatory is maintained by Kiel scientists since 1997 to document the changes of the deep-water circulation in the Subpolar North Atlantic (SPNA).

My main research question is: What are processes imprinting variability to the deep-water masses in the SPNA, where do they occur and what are the physics behind them.

But lets start with some background information …

The exit of the Labrador Sea is a key location in the subpolar North Atlantic concerning the integral quantities of the Deep Western Boundary Current (DWBC). It is the place where deep water masses from different origins and pathways meet. The combination of these is collectively called North Atlantic Deep Water (NADW).

To evaluate the high resolution model VIKING20 by means of integral quantities at the exit of the Labrador Sea, and to interpret the observed hydrographic and dynamic DWBC features with consideration of the underlying physical processes and forcing is the aim of my work.

I found that the VIKING20 model, which is driven by CORE2 atmospheric forcing, can be nicely compared to more than decade-long observations at the exit of the Labrador Sea near 53°N. VIKING20 is a high resolution (1/20°) nest, based on the global configuration of the NEMO-LIM2 ocean-sea ice model ORCA25 in the North Atlantic and implemented by two-way nesting (Behrens [2013];Böning et al. [2016]). The average flow field, being one of the integral quantities of the boundary current at 53°N including the bottom flow-intensification, is reproduced by VIKING20 (figure 1).

Figure1: Mean velocity field computed from LADCP and mooring data from 53°N (left) (Fischer et al. [2010]) and the mean velocity field of the full resolution Model section at 53°N for the period from 1958 till 2009 (right).

Although circulation and recirculation is stronger and more barotropic in the model than in the observations the overall transport at 53°N including both circulation and recirculation coincides with the observed transport of NADW of ~30 Sv (Zantopp et al. [2017]). Is the model, apart from its challenges in hydrography and hence different baroclinicity, still reproducing variability imprinting processes on Labrador Sea Water (LSW) and the lower North Atlantic Deep Water (LNADW)?

Figure 2: Transport time-series of observations at 53°N, already published in Zantopp et al. [2017] with overlaid low pass filtered model transports of LSW (top) and LNADW (bottom).

In both model and observations the low pass filtered time series are less correlated than the high frequency containing raw transport signal. This could be interpreted as reproduction of low frequency variability imprinting processes that are reproduced by the model. 

But pursuing a PhD in Physical Oceanography does not only include programming and working on data in an office, it is also hands on ship work. Hence I was also able to gain some subpolar and Labrador Sea experience during the cruise MSM54 from St. Johns to Reykjavik from mid May to June 2016. On this cruise I could see and experience the Labrador Sea. We exchanged the mooring array at 53°N and did a high resolution hydrographic survey to maintain the high quality and dense coverage of data in the Labrador Sea. Furthermore moorings in the central Labrador Sea, Irminger Sea and near the west Greenland shelf break where renewed. Knowing what the problems and powers of observational oceanography in this region are is helping me a lot in understanding challenges in my model – observation comparison process.

My current work focuses on the low frequency variability in the model and the observations. Some of the exciting findings will be published soon.

The processes causing this variability are still subject to ongoing research.

Bibliography

Behrens, E. (2013), The oceanic response to Greenland melting: the effect of increasing model resolution, Kiel, Christian-Albrechts-Universität, Diss., 2013.

Böning, C. W., E. Behrens, A. Biastoch, K. Getzlaff, and J. L. Bamber (2016), Emerging impact of Greenland meltwater on deepwater formation in the North Atlantic Ocean, Nature Geoscience.

Fischer, J., M. Visbeck, R. Zantopp, and N. Nunes (2010), Interannual to decadal variability of outflow from the Labrador Sea, Geophysical Research Letters, 37(24).

Zantopp, R., J. Fischer, M. Visbeck, and J. Karstensen (2017), From interannual to decadal—17 years of boundary current transports at the exit of the Labrador Sea, Journal of Geophysical Research: Oceans.

What’s an inverse model?

By Neill Mackay

One of the challenges in oceanography is that taking observations is expensive and time-consuming, and while the observations we do make are essential to our understanding, it is only possible to observe a tiny fraction of the vast history of the ocean in time and space. We turn to models to try to fill in the blanks, and my work as a post-doc at the National Oceanography Centre in Liverpool makes use of a particular type of model called an inverse model.

With the usual sort of ocean model, or ‘forward model’, we take an initial guess at the state of the ocean and set of physical laws and press ‘play’, and the model runs forwards in time, producing outputs for some later point which can be compared with the real ocean observations. The difficulty is in choosing the initial state which results in a good fit between the model output and the observations. Various parameters, such as mixing rate – how fast or slowly water with different properties becomes homogenised in the ocean – might be adjusted within the model to improve the fit. An inverse model is kind of a backwards model, as we start with the observations, apply the same set (or a subset) of physical laws and obtain the desired parameters as outputs. These parameters can then tell us something useful about a particular region of the ocean we are studying.

In OSNAP, the mooring arrays will provide a set of continuous observations along a section over a period of years. The inverse model we are developing, called the Regional Thermohaline Inverse Model or ‘RTHIM’ for short, will help to provide some context for the mooring observations by telling us something about what happens in the region to the north of the section (i.e. within the Subpolar Gyre), and also what may have happened over a longer time period before the observations started. RTHIM makes use of all the available historical observations of the region from satellites, automated floats and oceanographic surveys, going back more than 25 years. It works on the principle that the temperature and salinity (saltiness) of the water in the region (the ‘Thermohaline’ bit) changes either when water flows in or out of the region from the rest of the ocean; or when heat or freshwater is transferred through the ocean surface (e.g. through cooling by the winds or rainfall, respectively); or when different types of water are mixed together in the interior. Making use of this balance, RTHIM allows us to work out the flow of different waters into and out of the Subpolar Gyre, and the rates of mixing within it, without using the OSNAP array observations. We can then compare our inverse solution with the observations, and in addition we can find solutions for a time before the OSNAP mooring arrays were deployed. This means that variability measured by the OSNAP array over time, for example of the flow into and out of the Subpolar Gyre, can be put in context. Importantly then we can start to tease out where observed changes are due to natural variability, and where they are part of a trend – such as one related to man-made climate change.

Figure 1: Velocities on the section from an RTHIM solution. The inverse model was applied to the whole of the Arctic, bounded by a line of latitude at 65°N, which is somewhat north of the OSNAP array (only part of the section in longitude is plotted). Red (positive) regions of the plot indicate flow into the Arctic (i.e. northward) and blue (negative) regions indicate flow out of the Arctic (i.e. southward). RTHIM will eventually be applied using a section which coincides with the OSNAP array, to allow a direct comparison with the observations.

Figure 1: Velocities on the section from an RTHIM solution. The inverse model was applied to the whole of the Arctic, bounded by a line of latitude at 65°N, which is somewhat north of the OSNAP array (only part of the section in longitude is plotted). Red (positive) regions of the plot indicate flow into the Arctic (i.e. northward) and blue (negative) regions indicate flow out of the Arctic (i.e. southward). RTHIM will eventually be applied using a section which coincides with the OSNAP array, to allow a direct comparison with the observations.

 

What I love in observing the oceans

by Loïc Houpert

So today, I decided to contribute to the blog by telling you what I like in my work as an observational physical oceanographer.

I am working as a postdoc at the Scottish Association for Marine Science in the beautiful and “occasionally” wet town of Oban. Being a physical oceanographer, I am generally interested in understanding the ocean’s circulation, but right now my interest is more focused: how is the ocean’s heat carried by the currents in the North Atlantic and where is this heat going? The transports of heat and also freshwater by the North Atlantic current system are particularly important for the temperature, precipitation, and wind patterns and strength over the European continent. In my research, I am using autonomous instruments (underwater glider and fixed instrumented lines) that continuously record the state of the ocean.

Going at sea to take measurements and deploy/recover instruments is definitely the best part of my work, although stressful at times. But after coming to shore, the most interesting part of the job is to actually unravel all these big datasets and try to identify physical signals that are associated to the dynamic of the ocean. In addition to having good knowledge of physical oceanography, several factors are important when working on observations: curiosity (being interested in seeing buy ambien online what is in the data), imagination/intuition (finding a way to put together the different pieces of the puzzle) and of course a little bit of skepticism (test the results’ robustness again and again …!).

I really choose to become an observational oceanographer in the second year of my Master, during my research project. It’s true that I had some second thoughts after my first sea-going experience as I was very sick for most of the time of this 7-day cruise… However, 8 months later, during my PhD, I took part in my second cruise and everything went (surprisingly) well. Of course some days were more bumpy than others, but at the same time, this 3-week cruise was in the middle of the Gulf of Lions in winter to sample the impacts of strong storm and deep (2000m) vertical mixing on the marine ecosystems…. All of this to say that you should never stay on a bad first experience when going at sea. Tenaciousness… this is also a good quality if you want to analyze ocean observations!

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Illustration 1: Myself blinded by the sun taking a (bad) selfie with Estelle, Karen (the two SAMS glider experts) and Bowmore (the pink glider) after its recovery on the DY053 cruise in July 2016, over Rockall Plateau.

 

Numerical models, in-situ data and research cruise plans

Tillys Petit, PhD student (who also enjoy the view from my office, figure 2)

Nowadays, numerical models are increasingly used to understand and predict climatic issues such as global warming, rising sea level, or shift of oceanic circulations. To answer those questions, numerical models compute a collection of data from an initial setup, allowing us to first visualize the actual state and then the evolution of the temperature, sea level or oceanic circulation around the world. But how can we know if the output is/will be in agreement with the reality? To validate a numerical model, we still have to compare the actual state given by the model with its in-situ observations. But in-situ data are still too often lacking, and cruises are thus carried out. The new set of data is firstly analysed to document the general circulation and to identify new mechanical processes, and secondly used as benchmark for models.

Currently my work is to document the oceanic circulation across the Reykjanes Ridge (South of Iceland) where very little data is available. A strong current-bathymetry interaction could impact the circulation, hence the need for better understanding of this process. To fill this gap, a cruise (RREX) was carried out in June 2015 and another is planned in July 2017. During the 2015 RREX cruise, a lot of new in-situ data were obtained along 4 sections (figure 1), such as velocity of the flow and salinity-temperature-oxygen profiles. Moorings were also deployed and will be recovered during the second cruise. Up to now, I have studied the data of the first cruise, which are of good quality, allowing us to fully address our scientific objectives. Because I was not on board in 2015 I cannot tell you how the cruise was, but I will certainly keep you inform of the general ambiance during the second!

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Figure 1: Map showing the hydrological station locations during the RREX cruises.

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