The Mid Atlantic has one of the largest summer to winter changes of sea surface temperature in the world. But you don’t have to take my word for this, we can use real data to see how large the change is!
In last week’s example notebook, we created a dummy dataset to demonstrate how one could calculate a long-term averaged seasonal cycle. This week, let’s replace the dummy data with some real data from NDBC Buoy 44025 in the Mid-Atlantic.
Personally, I love using NDBC buoy data for educational applications. In particular, it has a number of advantages:
- NDBC data is free and (relatively) easy to use. Well, a lot of data is free, but NDBC also provides data in easy-to-use standard formats, like text files, netcdf, and OpenDAP which is perfect for use in Python.
- Datasets from hundreds of buoys and stations around the world are available, which allows students to investigate location related questions.
- The datasets feature a lot of meteorological parameters, which students are generally more familiar with. Familiarity helps when students are also trying to develop programming and data analysis skills.
- But it’s not all meteorological data. You can also find waves, sea surface temperatures, and tides for a lot of stations, and a smaller subset also includes salinity, DO, and pH data. Plus, winds and barometric pressure data are also helpful in identifying storms and understanding current movements, which impact the ocean.
So, let’s dive into this dataset, grab some data to calculate a seasonal sea surface temperature (SST) average. And then just for fun, we’ll also calculate the recent SST anomaly to see where we are today relative to the last 10 years.