Caution… Real Data Ahead!

I love diving into data and discovering new things about the ocean, and we’ve been doing quite a bit of that lately*.

But one of the things that really amazes me about the OOI dataset, is that you don’t have to dig far to find some really cool things – and perhaps some new science – in the data.

As a case in point, while I was preparing for our June workshop, I arbitrarily pulled out the most recent month of data from a Pioneer Array profiler, which you can see above.

At first glance, this simple profile timeseries of temperature and salinity looks completely out of whack. What’s with the warmer water on the bottom and saltier water on top? Why are there pulses of warm and cold water coming and going?

This is May after all, when the two-layer stratification – typical of summer in the Mid-Atlantic – starts to set up. But this looks anything but a typical warming pattern.

Normal this may not be. But is it right?

Upon further inspection, this is actually par for the course for the Pioneer Array, which sits on the shelf break where the intersection of coastal and deep-water oceanic processes intermingle, while Gulf Stream rings impinge the shelf and waters from the Mid-Atlantic and Labrador current all mix. Indeed, it’s one of the main reasons the Pioneer Array was put where it is. (See this paper by Chen, Gawarkiewicz and Plueddemann (2018) for more.)

Which is to say, that this is very real data, from a very interesting and cool location in the world.

That said, while the data may be cool from a research perspective, it may not be the best example to use in an introductory oceanography class, when your main goal is to highlight basic oceanographic concepts. This turns out to be one of the major challenges of using OOI data in undergraduate classrooms: it’s cool data, but not always the simplest data to interpret.

But that’s why the OOI arrays are where they are. They have been deployed to help us understand more complex processes that we haven’t been able to study before. That’s great for science, but it makes the dataset more challenging for everyone to use (whether you’re a student or a long-time researcher).

Which isn’t to say that we shouldn’t try. The complexity of the OOI dataset provides a lot of cool stories and science to explore, and it’s a great resource to demonstrate that once you get past the basics, there’s a lot more cool oceanography going on out there.

And we’ve only just scratched the surface. So let’s all dive in, and see what we can find.

If you’d like to continue playing with this dataset, you can download the python notebook.

 

*Apologies for the long hiatus in blog posts. As you might guess, we’ve been busy planning and running our first few development workshops over the last few months. They’ve been a lot of fun, and I’ve learned a ton of new data and python tricks while working with participants at each workshop. I’m really excited about what’s in the works, and I’m looking forward to sharing the new activities our participants are developing, and the new tricks we’ve learned, as we shift our focus to this blog in the fall.