#wave glider

A robot tweeting while floating into a hurricane.

This is one of those “what a world we live in” posts from my perspective.

It’s been 22 years since the Hawaiian Islands were hit by a hurricane. Tropical storms do hit Hawaii and they tend to cause fairly significant problems; the islands are remote, it’s hard to get supplies to them if the weather is bad, and due to the mountainous terrain its really easy to have people get cut off from the major cities if roads flood or are damaged by storms.

Right now, there are 2 different hurricanes en route to Hawaii. The first is scheduled to make landfall pretty soon, so its likely to be a rough few days in Hawaii.

Although there are lots of sources of images and data on these storms, I’m finding this one really amazing. There is an autonomous, floating robot off the Eastern coast of Hawaii that is actively tweeting images and information about the weather conditions it experiences. As I’m typing this I can see the atmospheric pressure falling and the wind direction changing as the first storm, named Iselle, approaches it.

Apparently it may even respond if questions are tweeted at it.https://twitter.com/Holoholo_WG

-JBB

So far, the descriptive analysis of the Wave Glider observations provided value in my effort to refine our estimates of phytoplankton in the ocean. But what about the replicated Wave Glider observations that I raved about earlier? When do those come into play?

I applied an autospectral analysis to replicate Wave Glider observations. Briefly described, this analysis is used to measure the covariance between the original data set and a lagged duplicate data set (which is carried out after cleansing the data set by removing temporal trends and interpolating data over equally spaced sampling distances). This correlation indicates how similar one data point is to its neighbor. This process is replicated with each lag of the data set (over a set distance)  until the original and lagged data sets are no longer correlated; the distance at which the data sets are no longer correlated, as determined by a set threshold (dotted line below), is known as the “Decorrelation Length Scale” (DLS). 

This DLS distance gives a measure of spatial “patchiness” or variability of chlorophyll in the ocean. I applied this analysis to each Wave Glider transect across the variety of ecosystem types (Coastal Upwelling, Transition Zone, Equatorial Upwelling, and Oligotrophic). Means and standard deviations of the replicate patchiness measures (i.e. autospectra or DLS) indicated how patchiness varied among ecosystem types. Results demonstrated a decrease in patchiness with distance offshore, that corresponded nicely to an increase the the spatial scale of eddies.

So what does this all mean? Why would this measure of patchiness be of interest to an oceanographer?

Although Wave Gliders can collect similar types of data as ships at a fraction of the cost (and with virtually zero environmental impact), ship time is still necessary to carry experiments and make more complex measurements. Saying this, the high costs and environmental impacts of ships emphasize the need to spend time at sea efficiently. Therefore knowing when, where, and how frequently to sample is of high value to oceanographers.

The autospectral analysis above suggests that sampling distances of 4 to 7 Km in the coastal upwelling zone off of CA is adequate for capturing phytoplankton patchiness. This distance increases to 30 Km in offshore waters. However in the transition zone between these two regions, one must sample at a minimum of every 8 Km to have any hope of capturing the phytoplankton variability in this highly dynamic region! And let us not forget that none of this applies to the daily variations in phytoplankton, which would require virtual stationary sampling on the order of less than every 24 hours in high growth systems.

Furthermore, Wave Gliders can serve as a scouts that locate the optimal sampling region for a ship, while collecting a continuous record of the conditions in that region before the ship arrives and until after it departs.

With regards to my own research, I see Wave Gliders as the ultimate observer for those hard-to-get-to regions. This type of data is extremely valuable for ecosystem modelers who wish to check the consistency of their models, and even assimilate data into their models, for regions where little is known or observations are highly variable. 

In order to compare the the Wave Glider data to traditional methods of data collection, as described in my last post, my first task was to clean up the Wave Glider data set. There were gaps between observations, likely during energy conservation and servicing periods. These gaps would be critical when it came time to apply autospectral analyses, as I will discuss shortly.

Once unreliable data and gaps were omitted, it was necessary to calibrate the raw fluorometer measurements so that they represented actual chlorophyll concentrations. This is commonly done with either simultaneous in situ measurements or in the lab with lab-grade standards. Liquid Robotics had performed the latter at the start and end of the long 13+ month transect. However, large drift in the sensor output rendered lab measurements useless. Sparsity of in situ chlorophyll measures resulted in the reliance on Satellite Image (yes, that data source that gives great synoptic coverage, but at the cost of a coarser sampling resolution compared to Wave Gliders!). It was the comparison of these two methods of data collection, and their differences that revealed my first insights about the Wave Glider observations.

In point-by-point comparisons of Wave Glider and Satellite Image observations at the same location and time, Satellite Image data points could only account for 20% of Wave Glider data (due the positioning of the Satellite with relation to the rotation of the earth). This resulted in poor agreement between Wave Glider and Satellite image data in dynamic, often cloud-covered coastal waters, where phytoplankton concentrations were changing faster (i.e., advected by eddies, physiological changes, growth and death on time scales of less than one day) than could be captured by daily Satellite Image snapshots. However in the open ocean systems, such as that between CA and HI, Wave Gliders actually captured the same large scale features as Satellite Images averaged over the same time-span as the duration of the WG journey (shown below as dots that overly the SI).

The most surprising result was revealed along the transect from HI and the equator. Again, Wave Gliders captured the low and high phytoplankton regions measured by Satellite Image. In addition, WGs could detect daily changes in phytoplankton physiological responses, which would be currently impossible to detect by Satellite (we would need many more of these up in space to accomplish this - a far more expensive option!).

So overall, I found that despite differences in the way that Wave Gliders and Satellite Images measure the proxy for phytoplankton (i.e., chlorophyll)  in the ocean over space and time, their observations were complementary. Wave Gliders provided the detail not provided by Satellite Images, and Satellite Images provided the synoptic coverage not available through the replicate number of Wave Gliders deployed. So it is with difficulty to proclaim that one is better than the other. With these two technologies, I really do think we could reduce our highly variable estimates of phytoplankton biomass in the ocean!

Marine phytoplankton, including algae and other microscopic plant-like organisms, help to pull carbon dioxide from the air and trap it in the ocean food web and the seafloor. How will climate change affect their ability to do this job?

Some scientists are using ocean robots, like the Wave Glider above, to answer this question. Learn more in this interview with researcher Tracy Villareal.