Los mares de mi vida (The seas of my life)

By Yarisbel Garcia Quintana

I have been close to the ocean since I can remember. I grew up surrounded by it, in an island rooted in between the Caribbean Sea, and the Gulf of Mexico: Cuba. The summer trips to the beach were all that I waited for during the classes period. I still remember the first time I saw it, and wondered where all that blue ended.

Years passed and even when I graduated from Meteorology, I found myself working by the sea. And when I say by the sea, I mean door, 5 steps, sand, and water. From time to time hurricanes strike the island. The Centre of Environmental Services of Matanzas, where I worked, is in charge of monitoring the erosion of the beaches in Matanzas (Province of Cuba) suffer as a consequence of storms and sea level rise. Due to erosion, projects for the artificial regeneration of the beach are regularly needed. A better planning of these projects includes an in-depth study of the near shore ocean currents involved in the transport of the sediments that feed the dune system. This studies lead me to my Master degree research: studying ocean currents of the Gulf of Valence, Spain. 

Thanks to my Master’s research I got the opportunity to explored some of the seas surrounding the Spanish peninsula, where my main role was to be in charge of the ADCP measuring and data post-processing. I had the chance to be on board of vessels like Garcia del Cid (Figure 1), Odon de Buen (Figure 2), and Sarmiento de Gamboa (Figure 3). Every time the cruise was confined to the coast of Valence, within the Mediterranean Sea. I found the Mediterranean to be very predictable, changing mostly with the seasons and not much within the day.  The Cantabrian Sea, north of the peninsula, was different story. Still like ice in the morning, but changing rapidly after noon following the change in the wind pattern, making almost impossible the recovery of sediment samples from the ocean floor, due to bad sea conditions. On board of the CIMA Oceanografico (Figure 4) I participated in a cruise around the Canary Island, to determine the ocean currents affecting the dune system of Maspalomas. I will never forget that experience. I will always remember when, following my colleagues, I jumped into the water for a swim, without knowing that a prominent feature of the Canary Current, is the presence of upwelling, resulting in a colder swimming than I expected.  

Now, as a PhD student at the University of Alberta, in Edmonton and far from the sea, I use a coupled ocean-sea ice numerical model to keep in touch with the ocean. I have been using four different numerical experiments to investigate the sensitivity on the Labrador Sea Water formation rate, and the AMOC strength, to (i) model resolution, (ii) Greenland melt increase, (iii) a decrease in the high frequency atmospheric phenomena, and (iv) an increase in the precipitation.  But this does not mean that I get to be in front of the computer all the time. In 2015, I participated in a Floating University organized by the ArcTrain program, on board of the Polarstern (Figure 5). Leaving from Svalbard, and into the Nordic Seas and a glimpse of the Arctic Ocean, it was the perfect experience that introduced me to the Arctic and Sub-Arctic Oceans. No, this time I did not jump.

Next experience will be on board of the Research Vessel Maria S. Merian, from Southampton, UK, to St. John’s, Canada. I cannot be exited enough for this new adventure. I am looking forward to it, and not just because they have an amazing chef on board, but because I will have again the honour to be at sea, learn from it, and from lots of brilliant people which during 28 days, I will have the chance to call colleagues.


Figure 1: Research/Survey Vessel Garcia del Cid. (photo credit: me)

Figure 2: Terns (charran in Spanish) on board of the Research Vessel Odon de Buen. (photo credit: me)

Figure 3: Sun set from the Research Vessel Sarmiento de Gamboa. (photo credit: me)

Figure 4: Research Vessel CIMA Oceanografico, somewhere near the Canary Island. (photo credit: me)

Figure 5: Tea on the Polartern while in Fram Strait. Red blob on the left is my self, red blob on the right PhD student Laura Gillard. (photo credit: Mathilde Jutras)



The AMOC and the AMO

Nick Foukal

For the past few months I have been working on an independent study with my advisor, Susan Lozier, researching the connection between the Atlantic Meridional Overturning Circulation (AMOC) and the Atlantic Multidecadal Oscillation (AMO). As the first part of this project, I conducted a literature review and in this post I would like to discuss the current state of the field.

But first a quick explanation of the AMO. The AMO is the average sea-surface temperature (SST) over the entire North Atlantic after the long-term warming trend is removed. In reconstructions of North Atlantic SST from the mid-1800s to present, the basin-wide mean time series appears to oscillate with a period of 60-80 years with warm periods in the late 1800s, 1920s to 1960s, and the 1990s to the present, and cold periods in between (Fig. 1). Some prefer to refer to this oscillation as “variability” because the 60-80 year period of the AMO is at the very edge of detection by direct measurements so “AMO” and “AMV” are used interchangeably in the literature.

Figure 1. The AMO from the NOAA Extended Reconstructed Sea Surface Temperature (ERSST) version 4 (1856-2016). The globally-averaged SST time series (black dashed) is subtracted from the local North Atlantic SST (area-weighted average shown in black solid line) to yield the detrended North Atlantic SST, which is then spatially averaged to derive the AMO (red thin). A 10 year low-pass filter is used to analyze low-frequency variability (red thick). The official NOAA AMO index (Trenberth and Shea, 2006; red dashed) is shown for comparison. In the top panel, the map of linear regressions between the local SST and the annual AMO demonstrate the spatial signature of the AMO. All regressions shown have p-values less than 0.05.


The AMOC has long been connected to North Atlantic SST in the paleoceanographic literature (which relies on ocean sediment cores for proxy-based SST reconstructions) because an increase in the AMOC would increase the oceanic heat transport and thus warm the SST. But within the shorter time-scale field of observational physical oceanography (which relies on observations of SST from ships, autonomous instruments and satellites among other platforms), there are multiple different SST forcing mechanisms to consider, including air-sea heat fluxes, vertical mixing and horizontal heat flux convergences/divergences. Though the AMOC may indirectly affect all of these terms, only this last term can be directly forced by variations in the AMOC. So the AMO lies in an interesting area between paleoceanography and observational physical oceanography – this middle ground is one of the many reasons why there is still ample confusion with the AMO and its forcing mechanisms.

Both the time scale (60-80 years) and the spatial signal (coherent across the entire North Atlantic, Fig. 1) is perplexing to oceanographers. The most logical forcing would be from the winds – the global wind patterns are responsible for most features in large-scale ocean circulation. But the dominant wind pattern in the North Atlantic, the westerly winds, bisect the North Atlantic around 45°N into the subtropical and subpolar gyres. Thus forcing by the westerly winds would cause opposing forcing in the two gyres, a feature that is captured by the North Atlantic Oscillation (NAO, Fig. 2) but cannot explain the basin-wide nature of the AMO. Another potential forcing mechanism is the AMOC – the AMOC redistributes heat meridionally so it could theoretically explain spatial pattern of the AMO. Zhang and Zhang (2015) posit that a southward-propagating positive AMOC anomaly leads to convergences of horizontal heat flux in the upper ocean that can explain the AMO north of 34°N. South of that latitude, the AMOC anomaly propagates quickly as a wave rather than an advective signal, so it no longer leads to heat convergences along its path. This explanation is consistent with the long-held belief of paleoceanographers that the AMOC is controlling the extra-tropical SST variability (albeit through a slightly different mechanism) but it doesn’t explain the AMO impact in the tropical North Atlantic. To explain that, a number of papers have used models and historical observations of low-level clouds (Bellomo et al., 2016; Brown et al., 2016; Yuan et al., 2016). Very simplistically, low clouds tend to cool a region because they reflect more radiation back out to space than they trap through their greenhouse effect (high clouds do the opposite – they trap more radiation than they reflect). These papers show that low frequency variability of low clouds in the tropical Atlantic varies negatively with respect to the AMO – so there are a lot of low-level clouds during cool AMO phases and few clouds during warm AMO phases, indicating that the presence of clouds could be forcing the tropical AMO signal. Taken together, these results point to concurrent AMOC anomalies in the northern portion of the study area and low level clouds in the southern portion forcing the AMO.

Figure 2. The official NOAA winter NAO index (1864-2016) and its spatial pattern. Linear regression coefficients between detrended ERSST and the annual NAO index (thin black). A 10 year low-pass filter (thick black) is applied to compare the NAO to the AMO at low frequencies. In the top panel, regressions with p-values less than 0.05 are outlined in black contours.

These explanations all assume that the AMO requires a physical forcing mechanism – an assumption that is challenged by Clement et al. [2015]. This paper demonstrates that the AMO can be recreated by slab ocean models in which the ocean is idealized as a constant (in time) depth slab of water and has no interannual variability in ocean heat transport. So in these simulations, vertical mixing and changes in horizontal heat transport cannot force the interannual variability in the AMO. Instead, the authors suggest that the AMO is simply noise with a peak in multidecadal frequencies (Fig. 3) arising from a combination of oceanic memory and random atmospheric forcing. This result has raised many questions with perhaps the most pertinent to OSNAP being how can the AMOC, with its associated heat transport variability at the RAPID line on the order of petawatts (Johns et al., 2011; 1 PW = 10^15 W = 100*global energy consumption), not influence the AMO? Results from the RAPID line, for example, have shown that upper ocean heat content in the northern subtropical gyre (26°N-42°N) decreased in response to a slowdown of the AMOC at the RAPID line in 2010 (Cunningham et al., 2013). One would assume that this also cooled the SST in this region. But does a cooling of the northern subtropical gyre lead to a basin-wide SST signal on multidecadal time scales? Does the AMOC even vary on multidecadal time scales? If the advective time scale of the upper limb of the AMOC (the time water particles take to go from the equator to the subpolar gyre) is on the order of 10-20 years, what would cause oscillations in the AMOC at 60-80 year periods? Some have posited that the strength of the Agulhas leakage from the Indian to the Atlantic Ocean may be forcing these changes (Biastoch et al. 2015), but a link this far afield may need more evidence for it to catch on.


Figure 3. Fourier spectra of the AMO (1854-2016). The peak value occurs at a period of 81 years so the time series (Fig. 1) shows exactly two cycles. The resolution of Fourier analysis becomes coarser as the period increases, so the location of the peak at long periods is difficult to determine exactly, but probably lies on the shorter end of the range between 54 years and 162 years.

The connection between the AMOC and the AMO is important to OSNAP because the AMO has been connected to Atlantic hurricane activity (e.g. Knight et al., 2006), North American and European climate (Trenberth and Shea, 2006; Sutton and Hodson, 2007), Sahel and Amazonian rainfall (Folland et al., 1986; Knight et al., 2006), Arctic sea ice extent (e.g. Zhang, 2015), and global temperature (e.g. Brown et al., 2015). If indeed the AMOC is forcing the AMO, then measurements like OSNAP become even more important and may even provide some long-sought after predictability in the climate system.

Bellomo, K. et al. (2016). New observational evidence for a positive cloud feedback that amplifies the Atlantic Multidecadal Oscillation, GRL, 43, 9852-9859.

Biastoch, A., et al. (2015) Atlantic multi-decadal oscillation covaries with Agulhas leakage. Nat. Comms., 6:10082.

Brown, P. T. et al. (2015), Regions of significant influence on unforced global mean surface air temperature variability in climate models, JGR, 120, 2, 480-494.

Brown, P. T. et al. (2016), The necessity of cloud feedback for a basin-scale Atlantic Multidecadal Oscillation, GRL, 43, 3955-3963.

Clement, A. et al. (2015). The Atlantic Multidecadal Oscillation without a role for ocean circulation, Science, 350, 6258, 320-324.

Cunningham, S. et al. (2013) Atlantic Meridional Overturning Circulation slowdown cooled the subtropical ocean, GRL, 40, 6202-6207.

Folland, C. K. et al. (1986) Sahel rainfall and worldwide sea temperatures, 1901-85, Nature, 320, 6063, 602-607.

Johns, W. E. et al. (2011) Continuous, array-based estimates of Atlantic Ocean heat transport, Journal of Climate, 24, 2429-2449.

Knight, J. R. et al. (2006) Climate impacts of the Atlantic Multidecadal Oscillation, GRL, 33, L17706.

Sutton, R. T. and Hodson, D. L. R. (2007) Climate response to basin-scale warming and cooling of the North Atlantic Ocean. Journal of Climate, 20, 891-907.

Trenberth, K. E. and Shea, D. J. (2006) Atlantic hurricanes and natural variability in 2005. GRL, 33, L12704.

Yuan, T. et al. (2016) Positive low cloud and dust feedbacks amplify tropical North Atlantic Multidecadal Oscillation, GRL, 43, 1349-1356.

Zhang, J. and Zhang, R. (2015) On the evolution of the Atlantic Meridional Overturning Circulation Fingerprint and implications for decadal predictability in the North Atlantic, GRL, 42, 5419-5426.

Zhang, R. (2015) Mechanisms of low frequency variability of summer Arctic sea ice extent, PNAS, 112, 15, 4570-4575.

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.


Christmas in Newfoundland

Brad deYoung, Robin Matthews and Mark Downey
Physics and Physical Oceanography
Memorial University, Newfoundland
11 January 2017

This fall we deployed an ocean glider into the Labrador Sea.  Our goal was to make measurements of the oxygen and carbon dioxide gas  properties in the Labrador Sea.  There are presently two deep-sea moorings in the Labrador Sea, separated by about 40 km off the shelf in 3500m of water.  The K1 mooring was deployed by German researchers from GEOMAR in Kiel; the Seacycler mooring was deployed by Dalhousie researchers as part of the VITALS research program. We wanted to map the gas and water properties between and around the moorings.  The glider operated from the surface down to 1000 m depth, flying along a 100 km extended line that connects the two moorings.

Our original plan was to deploy the glider directly in the Labrador Sea from a research ship  and then recover it from a ship in the Labrador Sea, so that we would get the most  out of the batteries in the glider. Battery-power is time, and time is money of course. We wanted to get the most out of our battery investment. As it turned out, the availability of ships did not line up with our schedule. As a result we had to deploy from the shore in southern Labrador, the closest port to the Labrador Sea. For recovery, southern Labrador would not work because by December all the ports are closed because of ice. So we had to fly the glider to the south and recover from the island of Newfoundland.

The deployment in September required driving  1400 km from our lab, in St. John’s Newfoundland, to Cartwright Labrador, about a day and a half of driving that requires taking a ferry from Newfoundland across the Labrador Straits to Labrador. We deployed the glider using a 63 foot boat operated by a local fisherman.  Operating from small boats does have some advantages, making it easier to get the glider into the water. Even in September the weather was intense. On the afternoon of the deployment, winds over the shelf reached 55 knots and the sea was about 8 m or 25 feet.

Mark Downey getting the glider ready for deployment with the Gannett Islands in the background.

Mark Downey getting the glider ready for deployment with the Gannett Islands in the background.


The glider did move across the shelf fairly smoothly (see below) although you can see from the track that there was a period when the glider was too shallow and got caught in a strong southward current and was pulled southwards. Once off the shelf and the glider could dive to its full 1000m depth thus was able to make better progress and only took a few weeks to reach the mooring stations. The glider operated in the Labrador Sea very well and flew for three months operating along the extended line between the two moorings.

The intent was to fly the glider straight across the shelf but strong currents, and a little mixup in the depth of the glider, led to an unintended loop to the south.

The intent was to fly the glider straight across the shelf but strong currents, and a little mixup in the depth of the glider, led to an unintended loop to the south.

In November we began making plans for the recovery. We carefully watched the battery usage. Each day the glider would use about 0.5 percent of the battery. That meant that in principle we could have 200 days at sea but in practice we want to recover with 15-20% of the battery left in case there are delays on recovery or the battery is not as ‘full’ as we think it is. We made a plan to fly the glider along the shelf edge where the water is deep and where there is a strong shelf break current moving southwards. The southward current meant that we gained an extra 10-20 km of progress. We determined that it would take about 40 days to fly the return route and so headed the glider southwards in mid-November (see track below). As the track shows, the glider made its way southward very well in spite of a few hiccups. At times we would lose regular contact because the winds (greater than 50 knots – 80 km/hr) and sea-state (well above 10 m – 30 feet) were such that the antenna was not always working properly. We also had some problems bringing the glider back across the shelf when it appeared to lose track of its direction a bit, perhaps related to problems with how the glider corrects for the current that it experiences as it flies.

Return path of the glider from the Labrador Sea a trip that took about 40 days and led to the successful recovery of the glider just off Heart’s Content, Newfoundland

Return path of the glider from the Labrador Sea a trip that took about 40 days and
led to the successful recovery of the glider just off Heart’s Content, Newfoundland

We planned to bring the glider back to one of the deep bays on the north coast of Newfoundland – Trinity Bay. These bays are somewhat sheltered and because they are deep the glider could wait there for us, patiently going back and forth in the deep water. The glider arrived at our target location off Heart’s Content, Newfoundland on Christmas day. We programmed it to fly a little triangle offshore (see figure) and then went out in a small boat to recover it. On the day of the recovery the pilot for our mission (Robin) was in the UK and so while he maintained contact with the glider we got the boat ready and then went out looking for the glider. The day before we had a storm with strong winds and the day after we had a strong winds again and so we had a narrow window for the recovery. It was winter and windy but we had no problems as we knew precisely where the glider was. The glider was just where we expected to find it and the weather cooperated. Now we get to explore the data and plan for our next deployment in the Labrador Sea.

The glider (located just below the boom in the center of the picture) was just where we expected it to be on a somewhat windy and very cold data. The glider looked just as bright and clean as on its deployment some four months earlier.

The glider (located just below the boom in the center of the picture) was just where we expected it to be on a somewhat windy and very cold data. The glider looked just as bright and clean as on its deployment some four months earlier.

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!


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.


Irminger Current time series is making music

by Stelios Kritsotalakis

The Irminger Current (IC), a branch of the North Atlantic Current (NAC), carries warm and saline waters poleward in the subpolar gyre and as such contributes to the upper limb of the Atlantic Meridional Overturning Circulation (AMOC). The Irminger Current (IC) has not been extensively studied in the past. Little is known about the velocity structure, the transport and the variability of this current. My work was based on the first year-round data from a full-depth mooring array in the eastern Irminger Sea (west side of Reykjanes Ridge). This data was recovered in July 2015 during the second leg of OSNAP 9 East cruise (64PE400) under chief scientist Laura de Steur. I was lucky to participate in this cruise and gain my first sea-going experience on board of the research vessel Pelagia (NIOZ). Results from this study are materialized in my master thesis and will contribute to a paper to appear in 2017 in collaboration with my supervisor Laura de Steur and research scientist Femke de Jong.


Figure 1: Map showing the location of the four moorings in the Irminger Sea.

Sometimes science is a lot more art than science and sometimes music communicates science better than a figure. During my research study, I shared a flat with Nika Pasuri who is an upcoming electronic music composer from Georgia. We soon realized that we were both analyzing time series for different purposes. For example, I was analyzing the spectra of time series to discover their dominant periodicities while he was using spectral output to create sounds. And so, the idea of making music out of the mooring data was born which in turn gave birth to two songs, “RAFOS Floats” and “Loneliness of Acoustic Doppler Current Profiler”. These songs where made out of the time series of the volume transport of the Irminger current. You can enjoy them and share our excitement about this project in the following link:      


Stelios Kritsotalakis, Master student in Climate Physics, Utrecht University

Nika Pasuri, Master student in Music Composition, Amsterdam Conservatorium


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!


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



Back to school and starting-up the new modelling sensitivity studies

by Laura Castro de la Guardia

When I am ask by friends: What is it that I study? I generally give them the quick answer: I study biological-oceanography at the University of Alberta. But when they look-up “University of Alberta” on Google map for example (Figure 1), they  always point out: there are no coastlines near the University of Alberta! In fact, the province of Alberta in Canada, has NO coastlines at all. So, how is it then, that I can study the oceans?  Although one way will be to spend a lot of time travelling to either western, eastern or northern Canada to do my field work, I can also study the oceans from my own desktop at university!

I use a mathematical model on the computer to create a virtual ocean with some biology and chemicals; it is sort of like a video game, but the model attempts to be as realistic as possible. The core of the model is based on the most current understanding of physical and mathematical relationships that exist between the ocean, the atmosphere, the sea ice and the biology.

There are many models available. The ocean model I used is called NEMO (http://www.nemo-ocean.eu/) that comes together with a sea ice model LIM. The biological and chemical model I used is embedded within NEMO and it is call BLING (https://sites.google.com/site/blingmodel/). Cool names acronyms, right?! Both models are free to use by any user, but it requires some understanding of computing science, programing, and a very powerful computer. We have to run our model on super-computers that are shared across Canada  (Compute Canada/Calcul Canada).

Unlike what you may have imagined from my video game analogy, the output of the model is not a movie, but lots of numbers (a.k.a simulated data). The “simulated data” is what I use to do statistical analysis of many different things, for example, I can see the current state of the ocean, or the sea ice, or the marine algae (phytoplankton). We can also make movies with the simulated data  (e.g. http://knossos.eas.ualberta.ca/vitals/outcomes.html)

Although models are still not able to reproduce an identical ocean to our real ocean, one of many advantages of an ocean model is that I can study how one single event/phenomena/or property in the atmosphere affects my simulated ocean  or biology. This type of studies are called sensitivity studies, and they are like experiments in a lab. This is important because in our current climate, many things are changing at once (for example in the Arctic Ocean, sea ice is decreasing, temperature is increasing, the rate of river flow into the ocean is larger, there is more rain, there are more storms during the autumn), but we only observe the response of the oceans to all changes. While with the model I can have the response of the model to all changes, but also the response of the phytoplankton to only one change (e.g. more storms during fall (Figure 2)). Depending on what I am studying, I can then answer which of all these changes is the most important, which one is the one I should be most concern with? These are the kind of questions I would like to focus on for my sensitivity experiments, because these questions can help us prepare for the changing future: e.g. they could help shape or guide the adaptive tactics and conservation programs.


Figure1. Google map showing the locations of the University of Alberta, Canada.



Figure 2. A simulation with storms (a) compared to a simulations without storms (b). The differences between each panel shows the regions where the storms have a greater impact on phytoplankton.


OSNAP Logo Contest

Calling all OSNAP collaborators and inspired oceanographic community members! OSNAP is looking for a new visual identity and is seeking your help in designing a creative logo.

How to Enter the Contest
The contest begins on October 1, 2016. Submissions will be accepted through November 15, 2016. Winners will be notified, and announced via our website and through social media. In order for your entry to be submitted and reviewed, please follow the details below:

  1. Submit entry to Sarah Clem (sarah.clem@duke.edu)
  2.  Submit the entry in its original source file and
  3. Submit as a pdf with 300 dpi or higher.

Logo Requirements

  • Design: The logo will be featured on our website, on our social media platforms and other media (e.g., research posters and t-shirts). Thus, we want the logo to be eye-catching, but also legible. Also, the logo should be easily reproducible and scalable for large and small formatting.
  • Color: Any colors may be used. However, the logo should look good in color (if used) and in black and white.
  • Integrity: Logos cannot contain copyrighted material. Logos must have been created by the contestant(s). Logos may not include images or licensed images that have been previously published.

Contestant Agreement
The winning contestant must agree that OSNAP can use their logo for future publications and outreach get ambien prescription applications. Additionally, the contestant must agree that OSNAP can alter, modify or revise the logo as it sees necessary. OSNAP reserves the right to not select a winner if, in its discretion, no suitable entries are received.

Contest winner will receive an Amazon Gift Card (and bragging rights!).

OSNAP Challenge

We want to bring everyone’s attention to the launch of the OSNAP Challenge located on this site under News and Events. Anyone is welcome to submit a prediction (or technically a hindcast because the data have already been collected) for the first two years of AMOC data from the OSNAP line. This contest is similar to the one organized by the RAPID program last year but in this contest there are no past observations of the AMOC from the OSNAP line so the level of difficulty is higher!

We are going to open up this blog to write-ups of the methods from each prediction and will be announcing the winners here in the Spring. The deadline for the submissions is April 1st. For more information including instructions on how to submit a prediction and how the submissions will be judged, see the site here.

We wish everyone luck and may the best model win!