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.



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.



Recently graduated: end of an amazing adventure and beginning of another

by Tillys Petit

My last contribution on this blog reminded the importance of in-situ data measurements on the evaluation of numerical modeling used to predict climate. As part of my PhD thesis, I had the chance to record, process and analyze observations across and along the Reykjanes Ridge within the framework of the RREX project. It included an experience at sea during the RREX cruise in 2017, which was an amazing human and scientific experience. Now that my PhD ended, I would like to tell you about the scientific results that were obtained. 

During my PhD, I studied the connection between the Iceland Basin and the Irminger Sea through the Reykjanes Ridge. A main result was to describe and quantify the top-to-bottom transport of the subpolar gyre that crossed the Reykjanes Ridge during the summer 2015. These results highlighted interconnection between the two main along-ridge currents: the southwestward East Reykjanes Ridge Current (ERRC) in the Iceland Basin and the northeastward Irminger Current (IC) in the Irminger Sea. From about 56 to 63°N, the hydrological properties, structures and transports of the ERRC and IC consistently evolved as they flowed along the Reykjanes Ridge. During my PhD, I showed that these latitudinal evolutions were due to flows connecting the ERRC and IC at specific locations through the complex bathymetry of the ridge, but also to significant connections between these currents and the interiors of the basins. These results highlighted a more complex circulation in the vicinity of the Reykjanes Ridge than it was assumed.

From three different cruises and Argo floats, I also investigated the deep circulation and properties of overflow water through the deepest sills of the Bight Fracture Zone. At the end of my PhD, I showed the strong variability of its transport and property over time by comparing three successive years. Now, I think that it could be interesting to continue this study and to better understand the variability of overflow water at higher frequency. As a continuity of my PhD, I am thus exciting to investigate the variability and linkage between the overflow water transports and properties across the Iceland-Scotland Ridge and the Denmark Strait as part of my postdoctoral position. These inflows from the Nordic Seas feed the lower limb of the Meridional Overturning Circulation and are crucial to characterize the variability of the North-Atlantic subpolar gyre. I am excited to fulfil this study by using the OSNAP array that provides new and key measurements of the AMOC, and also to move in USA for a new beautiful and rewarding postdoctoral experience.

My career path in physical oceanography and climate science

by Yavor Kostov

The end of the year is a time to reflect on the past and make long-term plans for our future. Some readers of this blog, especially our young audience, may be considering a career in oceanography or climate science. I will tell you my story: what motivated me to join this field and the factors that shaped my career path.

My first encounter with physical oceanography was 14 years ago, at an international summer school where I learned basic gravity wave dynamics. Fluid motion fascinated me and sparked a lasting interest in the field. The following year I was on my high school team for the International Young Physicists’ Tournament (IYPT). Within our team, I was responsible for problems related to fluid dynamics.

By the time I began my undergrad studies, I was already very interested in modeling the environment. I also realized that to do well in the natural sciences, I should expand my background in math. So I majored in Applied Mathematics, but I also took physics courses. As an undergrad, I did different research projects applying mathematical methods to study the environment. For example, my senior thesis was on modeling the El Niño / La Niña phenomenon.

Nine years ago, I decided to do a Ph.D. in climatology and oceanography. I became interested in the field because I wanted to do research in an area of science that is socially significant. Nature has direct impact on humankind.  At the same time, climate science and oceanography attracted me because many fundamental questions in our field remain unresolved.

My Ph.D. and postdoc research has explored the large-scale ocean circulation and its impact on global and regional climate. I have studied various parts of the World Ocean: the North Atlantic, the Arctic Ocean, and the Southern Ocean. My work involves coding algorithms and analyzing data from complex climate models and observations, but also developing simple conceptual models.

In my current OSNAP project, I examine how the ocean circulation in the subpolar North Atlantic responds to local and remote fluctuations in atmospheric conditions. I analyze the computer code of a global ocean model as if it were a system of math equations. One of the most interesting aspects of my work is trying to understand the ocean’s delayed response to past atmospheric changes that took place years ago.

I am now looking forward to another productive year of research on the ocean circulation. Happy holidays to all readers of this blog and best wishes for the New Year!

Fleur de Sel Life

By Charlène Feucher

The sea and the ocean have always held a fascination for me. I grew up in the bay of Saint-Malo (France) and the sea coast was my first playground (Figure 1). During my childhood, I was mostly interested in the processes involved in sandcastle destruction by waves, or in the tide processes that could not be ignored for safe crab fishing. When I was a little older (and braver), I started exploring the sea and left the beach and the rocks behind me. Sailing, surfing, windsurfing and kayaking became my favourite hobbies. Boat rides were always fun and exciting. Visiting traditional sailboats or big fishing vessels were captivating and it nourished my imagination and my dreams of sea adventures. I used to think: “One day, I will also be on a boat to explore the ocean and learn more about it!”. 

Figure 1: My playground back home (Grande Plage de Saint-Lunaire, France). Photo by: Charlène Feucher.

I would later discover oceanographic sciences: the ocean was not only my playground anymore, it could now be my field work too! I was very glad to start a master in physical oceanography at the University of Brest (France) and learn the secrets of the ocean (with an affinity for the Atlantic Ocean), how it works, and why its role is of paramount importance to the global climate. 

During my master studies, I got the opportunity to complete two projects that introduced me to physical oceanography research. My first internship was at IFREMER and IUEM (Brest, France) to evaluate realistic hydrodynamic simulations for biogeochemical applications. I developed inter- comparisons of several ocean simulations (differing in resolution and model parameters) and evaluated the simulated fields against relevant observation data sets, with a focus on mixed layer dynamics in the North Atlantic Ocean. I did a second internship at Woods Hole Oceanographic Institute. The project was to study the dissipation of the North Atlantic Subtropical Mode Water (also well known as Eighteen Degree Water) based on eddy-resolving ocean simulations. I examined the eddy covariance flux divergence of the North Atlantic Subtropical Mode Water thickness and potential vorticity to understand the spatial distribution and mechanisms of the destruction. 

After my master thesis, I came back to the University of Brest to complete my PhD. The objective of my PhD project was to evaluate the properties and variability of the stratification in the North Atlantic subtropical gyre. I developed a method to characterize the properties of the stratification of the ocean (permanent pycnocline and mode waters) in subtropical gyres. Focusing on the North Atlantic subtropical gyre and based on the use of Argo data. I have documented the properties of subtropical mode waters and permanent pycnoclines.

Right after PhD, I travelled to Canada to start a postdoc at the University of Alberta (Edmonton) where I am still working. My postdoc research focuses on the relationship between the meridional overturning circulation and the formation of the Labrador Sea Water. This study is based on the use of NEMO model outputs with an Arctic and Northern Hemisphere Atlantic configuration.

In Edmonton, I am living far from the ocean but I never forget about it. The sea is always calling me and when cruise opportunities are out, I am willing to embark when possible. My first experience at sea was in 2015 during my PhD. We took several measurements along and across the Reykjanes Ridge to study the ocean circulation there. This first research cruise was full of discoveries. I learnt what oceanographic field is all about, how to take measurements, and how life aboard a ship feels like. I was enchanted by the immensity of the ocean and the power of the winds and waves during strong storms (small storms according to the Captain but I was not convinced).

I got the chance to renew this sea experience in June 2018 on board of the RV Maria S. Marian. This cruise was quite epic! We flew to Cadix (Spain) where the boat departed. We crossed the whole Atlantic while taking measurements, we docked in St. John’s (Canada) for a day and then we crossed the whole Labrador Sea before coming back to St. John’s where our cruise ended. Seven intense weeks at sea! During this cruise, I was amazed to see icebergs for the first time, admiring them drifting off the Greenland coast under a beautiful sunset (Figure 2). Performing CTD casts with the Greenland coast in the horizon was also a very special moment (Figure 3). And more importantly, I was glad to take measurements in the Labrador Sea to observe deep convection and compared these observed results with what we simulated in our NEMO model.

Figure 3: CTD cast near Greenland coast, from the RV Maria S. Merian in June 2018. Photo by: Charlène Feucher.

Physical oceanography research work gives me the opportunity to work in different places and and meet many great people. This is full of very enriching experiences, professionally but also personally. I hope I can continue this oceanographic adventure in the years to come.

As this is the last blog post of 2018, it is time to leave you wishing everyone a wonderful Christmas season from a freezing cold and white Edmonton!

Another day at the office: data quality control

by Roos Bol

Now that temperatures outside are dropping and storms are raging over the subpolar gyre, it is clear that the OSNAP field season had ended. Many blogposts have been written about the exciting adventures at sea last summer. This time however, I would like to tell you a bit about the – slightly more boring – work that happens after. When the exhausted but ultimately satisfied scientist returns home, with a hard drive full of newly recovered data; that’s when our real work begins. Before the new data can be used to answer actual science questions, they are in dire need of some cleaning.

Why is that necessary? Instruments on a mooring (a cable anchored to the sea floor) are impacted by currents, storms, tides and sometimes fishing activities. There is the risk of colonization by omnipresent sea creatures. In the vast open space of the ocean, an instrument can provide a welcome place for shelter. Instruments deployed in the sunlit surface layer are all overgrown with algae upon recovery. But deeper instruments can also host interesting inhabitants, such as the anemone in the picture below. Last but not least, the ocean is salty, wet and under high pressure. A challenging environment for an electronic instrument, especially with our long deployment period of two years! 

Figure 1: Some examples of sea life on the moorings upon recovery: a slimy creature on a Microcat, an anemone growing on a ring of the release that was at almost 2000m depth, and a shallow UK buoy overgrown with algae.

Data quality sometimes suffers from all these environmental impacts. This is where my work of the past few weeks comes in: checking, cleaning and processing the raw data records. Below are examples of Microcats on one of our moorings, called IC2, during the last deployment. Unlucky for us, the anchor of this mooring ended up shallower than planned, on top of a small seamount. A storm resulted in the exposed top buoys breaking down, making the shallow instruments sink to the deep. The top Microcat, originally at 25m, was instead dangling loose at around 700m depth… it is a small miracle it was still attached to the cable when the mooring was recovered! 

(For those who are wandering; yes, luckily there was another buoy at 350m, keeping the deeper part of the cable standing up straight!)

Severe storms impact the mooring even deep down in the water column. The pressure record of the Microcat at 352m depth (figure 2) still shows many big and small ‘blowdown’ events. During such an event, strong currents push against the mooring cable, blowing it down at an angle and pushing the instruments deeper into the water. When the storm ceases, the buoys on the cable pull the mooring cable back to its original straight position.

Figure 2: Pressure time series of the Microcat at 352m in mooring IC2 for the last deployment (August 2016 to July 2018)

Microcats also measure temperature and salinity. Figure 3 shows the raw salinity record from the deepest Microcat on IC2, close to the bottom at almost 1900m depth. As you can see, salinity differences in the deep ocean are generally very small. However, salinity measurements are notoriously noisy, so we need to perform some filtering of data spikes. But which spikes are bad data, and which represent real variability? Then there is a suspicious drop at the start of the record; perhaps a small animal or algae was temporarily covering the sensor?

Figure 3: Raw salinity record from the deepest Microcat in IC2, at 1892m

Lastly, and perhaps most importantly, we need to perform a calibration for all sensors. From the ship, we dip the instrument into the ocean and test its readings against a calibrated reference sensor to determine any offsets, both before and after the deployment. This is the only way to check whether an instrument gives accurate readings.

Overall, it is probably clear that there is a lot of effort involved in scrutinizing the records and ensuring data quality control is performed correctly. But although it may sound a bit tedious, the quality control step is extremely important. It ensures that we have accurate, reliable records, on which we can build for further analysis – to eventually formulate valid answers to scientific questions!

OSNAP! There goes the tip of the iceberg!

by Leah McRaven

Physical Oceanography
Woods Hole Oceanographic Institution

When you decide to study the currents that whip past the continent of Greenland and that transform the waters in the Irminger and Labrador Seas, an oceanographer must be willing to make peace with an ocean that isn’t entirely liquid. The extreme elements that shape the rocks along the Greenland coast also actively chisel away at the hundreds of glacial termini that meet the ocean edge. This chiseling leads to a constant flux of icebergs, small icebergs called bergy bits, and even smaller ice chunks called growlers. With ice in its various sizes and jagged shapes breaking away from the entire continent, the currents in the OSNAP region transport and mix more than just water.

Logistically, the OSNAP study region is one of the hardest places in the world ocean to successfully execute fieldwork. To start, the East Greenland Coastal Current, the East Greenland Current, and the Irminger Current can flow at speeds well over 1 knot as they round the tip of Greenland. In addition to strong currents, the area is home to a record: the windiest place in the world ocean. Simple ship maneuvering tasks, such as holding station while collecting data or recovering moorings (Figure 1), become challenging for the mates on the bridge as unforgiving winds build up rough seas that are already swiftly flowing. Floating ice is quite literally the icing on the OSNAP cake.

Ice adds a whole new dimension, and phase of matter, to navigation and operations at sea. From a distance, it can be nearly impossible to decipher ice chunks from whitecaps and sea spray. Large icebergs can be easy to see if they express above the surface of the ocean, but the majority of an iceberg’s mass lies below the sea surface and is difficult to see. Ice can also block access to nearby fjords used for shelter in severe weather. These navigation dangers keep the R/V Armstrong mates on high alert at all times as they steer through storms, darkness, and thick fog.

In order to help navigation efforts, WHOI researchers employed the help of the Danish Meteorological Institute (DMI), which specializes in satellite sea ice imagery. DMI is able to provide the ship with updates on the location of ice based on satellites that take Infrared (to see through clouds) and visible images of the Earth’s surface. Not only is this information extremely helpful, the maps can be stunning. Ice information from our current OSNAP cruise (on September 9th) is shown in Figure 2. This satellite image from the southern tip of Greenland is an example of how satellite-detectible ice features disperse from their mother fjords into the surrounding ocean.

Ice can also run into the OSNAP moorings, pushing instruments out of the way, or even snapping them off their lines making it impossible to recover them and their precious data. Our six shallow moorings on the continental shelf were, in fact, designed with drifting ice in mind. Equipped with a tripod-like structure at their base, these moorings have most of their instrumentation mounted near the sea floor. In an attempt to capture shallow data, the moorings also have special tethers extending up from the tripod base with weak links to top flotation. These weak links are designed to break easily should an iceberg snag the line, with the break point located strategically below the flotation and above an instrument. In the event of tether breakage, the instrument sinks to the bottom, but remains attached to the rest of the mooring so that it can still be recovered. Figure 3 shows a depiction of this breaking process. Of the recovered moorings from this year’s cruise, three of the six shallow moorings had their top floats ripped off within less than a year!

With all of the challenges handed to us from ocean elements, our crew has excelled in accepting the challenges brought on by ice. Of all 16 moorings that we aimed to recover on this cruise, all have successfully come back. In the face of extreme weather and rough seas, we have completed over 240 profile measurements of ocean temperature, salinity, and velocity thus far. And through all of these challenges, no one can deny how much they still enjoy seeing the Greenland coast in full panache with its towering and craggy icebergs.

Figure 1. An iceberg off the stern of R/V Armstrong during a tripod mooring recovery during the current OSNAP cruise.

Figure 2. Denoted infrared satellite imagery courtesy of the Danish Meteorological Institute. Pink triangle indicate the position of satellite-identified icebergs throughout the southern Greenland region.


Figure 3. Illustration of the OSNAP tripod moorings with weak links to top flotation. The left mooring demonstrates a normal deployment, while the right mooring shows a deployment with interference from an iceberg. In the event of an iceberg snagging the upper mooring tether, the top floats are released and the shallowest instrument falls, while still remaining connected to the mooring.


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.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

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.


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.