A team of scientists from across the country is studying how the ocean breathes. They are studying the exchange of gases, carbon dioxide and oxygen, in the Labrador Sea, one of the few places in the planet where water sinks deep into the ocean carrying these gases with it. The scientists are part of a team working on an NSERC-funded CCAR project called VITALS – Ventilations Interactions and Transports Across the Labrador Sea http://knossos.eas.ualberta.ca/vitals/. They have put together a video to describe how the ocean breathes.
This research buy soma online combines new observations and modelling to determine what controls the exchange of these gases and how they are linked to and interact with the climate system. This video explains how the deep ocean connects with the atmosphere and the role of this deep breathing in climate change. The leaders of this program are Paul Myers (University of Alberta), Roberta Hamme (University of Victoria), Jean-Eric Tremblay (Université Laval), Jaime Palter (University of Rhode Island), Doug Wallace (Dalhousie University) and Brad deYoung (Memorial University.
In the summer of 2016, the OSNAP team collected data from the full array for the first time. Prior to publishing the first estimate of the meridional overturning circulation (MOC) this coming fall, we invited the ocean community to predict the overturning variability at the OSNAP line. Four groups entered this OSNAP challenge by submitting predictions from September 2014 (initial deployment of the OSNAP line) to August 2016. This OSNAP challenge was certainly challenging given that there were no previous OSNAP data from which to base the predictions. As such, we are grateful to the modelling community for participating in such a speculative activity.
A preliminary analysis from OSNAP yields a mean MOC of 13.15 Sv with a standard deviation of 3.31 Sv during the 21-month period for September 2014 – May 2016. All predictions are shown in Figure 1. We assessed the accuracy of each prediction by both the root-mean-square-error (RMSE) from the observed time series and its temporal correlation (r) with the observed time series.
Table 1. Skill of the four predictions, ranked according to RMSE.
Ben Moat (NOC, UK)
Laura Jackson (Met Office, UK)
Charlène Feucher (University of Alberta, Canada)
Andrea Storto (CMCC, Italy)
Mean of predictions
Ben Moat and his group from the National Oceanography Centre (NOC) in Southampton, UK won the competition with the lowest RMSE and highest correlation. Congrats to Ben and his colleagues! For their efforts they will receive the grand prize of OSNAP T-shirts!
Figure 1. The MOC time series for 2014-2016. The black line shows the observational time series. Colored lines show the four entries (ranked according to RMSE). Numbers in the upper left hand corner indicate the mean MOC plus/minus one standard deviation for each of the submitted time series. Note: Instrument failures near the end of the two-year deployment limited the OSNAP observational estimate to 21 months (September 2014 – May 2016).
More on the prediction
If you would like to know more about how each prediction was made, here are links to blog entries by some of the participants:
In the past a few months, I have been working on identifying the spreading pathways of Iceland Scotland Overflow Water (ISOW) in the eastern North Atlantic, with focus on its southward spreading along the eastern flank of the Mid-Atlantic Ridge (MAR).
The ISOW is formed in the Nordic Seas and overflows into the Iceland basin over the sill between Iceland and Scotland. Together with the Denmark Strait Overflow Water (DSOW) that overflows via the sill in Denmark Strait, and the Labrador Sea Water (LSW) formed in the Labrador Sea, these deep waters make up the lower limb of the Atlantic Meridional Overturning Circulation (AMOC). An understanding of the spreading pathways of these deep water masses is fundamental to our understanding of AMOC structure.
Traditionally, the Deep Western Boundary Current (DWBC) was considered the major conduit from the subpolar to the subtropical gyre for these deep water masses: ISOW travels cyclonically in the Iceland basin within the DWBC, enters the western subpolar gyre via the Charlie-Gibbs Fracture Zone (CGFZ) (see Figure 1, solid blue curve) and joins the DSOW and LSW in the DWBC in the western basin.
However, recent studies question the DWBC’s representativeness of the lower limb of the AMOC. In the western North Atlantic, where all the three deep water masses are present, Lagrangian floats and model simulations reveal interior pathways of the deep waters from the subpolar gyre to the subtropical gyre (Bower et al., 2009; Gary et al., 2012; Lozier et al., 2013; Gary et al., 2011; Lavender et al., 2005; Xu et al., 2015). In the eastern North Atlantic, where ISOW is the primary deep water, two other ISOW pathways are identified (see Figure 1, dashed red curves): one crosses gaps in the Reykjanes Ridge (RR) north of the CGFZ (Xu et al., 2010; Chang et al., 2009), and the other flows southward along the eastern flank of the MAR into the Western European Basin (Xu et al., 2010; Lankhorst and Zenk, 2006). These two pathways are less well studied or observed.
Figure 1. A schematic plot of ISOW major spreading pathways. Abbreviated letters are: Iceland Faroe Ridge (IFR); Faroe-Shetland Channel (FSC); Faroe Bank Channel (FBC); Reykjanes Ridge (RR); Wyville-Thomson Ridge (WTR); Rockall Trough (RT); Rockall Plateau (RP); Porcupine Bank (PB); Bight Fracture Zone (BFZ); Charlie Gibbs Fracture Zone (CGFZ). Mooring locations are shown as black diamonds. CTD section is shown as a black dashed line.
To study the southward spreading of the ISOW east of the MAR, I have been using previously unpublished current meter data east of the CGFZ from Dr. Walter Zenk at GEOMAR in Germany. The moorings C, G, F, Z, M, A, R and T (labeled in white in Figure 1), were deployed between 1998 and 1999 and stayed in water for 1 year.
Figure 2 (left) shows the mean velocity at mooring locations at current meter depths between 1650 and 3890m. The deep-reaching northeastward North Atlantic Current is observed at moorings G, F and Z. A bottom-intensified southward flow is observed at mooring R, with a maximum southward velocity 8 cm/s measured by the bottom current meter at 3967 dbar. This strong southward spreading is in the salty ISOW layer, as shown in the hydrographic section from the CTD section (Figure 2, right) in June 1999, when moorings M, A, R and T were recovered. A similar southward spreading of deep waters in the ISOW layer is seen from the velocity fields in a high resolution model (1/12°) FLAME, with overall weaker magnitude and strong interannual variability (not shown).
A manuscript with these results is in preparation. In the manuscript, detailed analysis of this southward branch is conducted, including the spreading from a Lagrangian perspective, the origin of the south-flowing deep waters and a quantification of different branches of ISOW pathways in the eastern North Atlantic. In addition, the RAFOS floats released east of the RR at ISOW depths during the OSNAP cruise in the summer of 2014 will provide further evidence of the various spreading of ISOW.
This analysis work is completed with my advisor Susan Lozier, Walter Zenk, Bill Johns from University of Miami and Amy Bower from WHOI.
Figure 2. (Top) Mean velocities at the depths of all current meters for moorings C, G, F, Z, M, A, R and T (black diamonds). Moorings C, G, F and Z were deployed on June 25 1999 and recovered on July 1 2000. Moorings M, A, R and T were deployed on August 9 1998 and recovered on June 16 1999. All current meters are located between 1650 and 3890 dbar. The CTD section was shown as black dashed line. (Bottom) Observed salinity in June 1999 east of the MAR (~51.5°N) from the CTD stations shown in the left panel. The longitudes of the moorings M, A, R and T are shown as black circles. The Isopycnals are shown in dashed gray.
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)
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. . 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.
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:
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.
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:
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.
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!).
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.