Penguins, elephant seals, and… reindeer?

Reindeer.  Image credit: Perhols (CC-BY).
Reindeer. Image credit: Perhols (CC-BY).

This week, the expedition I will be taking part in later this year announced an updated itinerary, which includes a stop at the Kerguelen Islands.

Kerguelen is the closest land to the Heard and McDonald Islands, lying about 450 km to the north-northwest. Kerguelen is a French territory (Terres australes et antarctiques françaises, French Southern and Antarctic lands), and has a permanent scientific base with 50–100 personnel.

Like Heard, Kerguelen is of a volcanic origin (part of the eponymous Kerguelen Plateau), and has glaciers covering higher elevations. However, Kerguelen is much larger, and features many more U-shaped (glacial) valleys, as well as fjords. Temperatures at the French base of Port-aux-Français are generally a little warmer than at Atlas Cove on Heard Island, but are still generally cold, wet, and windy.

Map of the Kerguelen Islands.  Image credit: Varp (public domain).
Map of the Kerguelen Islands. Image credit: Varp (public domain).

Unlike Heard Island, where no human-introduced species are known, Kerguelen has both native and non-native species in the wild. It is one of two places in the world (the other being South Georgia island) where penguins, elephant seals, and reindeer can be found together. Reindeer were introduced to Kerguelen and South Georgia during the days of whaling, as a source of local food for sailors. However, after fur seal and elephant seal stocks had been decimated and sealing and whaling activities in the region ceased, so too did the human predation on the reindeer. Now, with no predators to eat them, the reindeer have been out-competing some of the native animals. In South Georgia, a reindeer hunt has been undertaken to drastically reduce their numbers, while at Kerguelen, scientists are conducting studies of the number of reindeer and their impacts on the local ecosystem.

I am looking forward to visiting Heard Island and the Kerguelen Islands. It will be interesting to compare the two in person, to see what similarities and differences I can notice.

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Geoscientist’s Toolkit: Jaw Crusher

Schematic of a jaw crusher in action.  Image credit: Skiffm (public domain).
Schematic of a jaw crusher in action. Image credit: Skiffm (public domain).

As a geochemist, I am often interested in one specific mineral (or several minerals) contained within a rock, because chemical and isotopic differences in that mineral will reveal important clues about where the rock came from, how it was generated, and when.

However, to go from a rock the size of my fist to an individual mineral grain requires breaking the rock down. The first step along that path for a fist-sized rock is the jaw crusher. As gravity pulls the rock piece further down into a funnel-shaped opening, a jaw mechanism will repeatedly open and close, and will crush the rock into little bits.

For coarse-grained rocks, the jaw crusher may be sufficient to get single-grain sized pieces, but for medium- and fine-grained rocks, a second crushing step is almost always necessary.

Glaciers and City Buses

Central and southern portion of Heard Island, including Gotley glacier.  Image credit: NASA ISS.
Central and southern portion of Heard Island, including Gotley glacier. Image credit: NASA ISS

Heard Island is covered by 41 glaciers.[1] Some of these glaciers, such as Stephenson Glacier, are retreating rapidly, while others, like Gotley glacier in the southwest, have remained steady since 1947 when detailed surveys began.

The glaciers on Heard Island, like glaciers everywhere, act much like a conveyor belt, a giant river of ice. Snow (and rain) falling near the top solidifies into ice and flows downhill, until either the ice melts and flows away as a stream, or the ice meets the ocean and calves (breaks away).

Unlike the conveyor belt analogy, the snow does not only fall at the very head. Indeed, at times, the whole glacier can receive snow, or could be melting at the surface. On average, though, the snow accumulates in the eponymous accumulation region, and melts in the depletion region.

Perhaps a better analogy for a glacier is not a conveyor belt, but a city bus which runs into, and through, a city. Toward the beginning of the route, people board the bus, but very few leave. In the middle of the city, some people come and others go. Finally, as the bus leaves the city toward the surrounding area, more people leave than get on, and after reaching the end of the route, the bus is empty of passengers.

To really get a grasp of how glaciers work, you might want to try this Java-based simulation (helpful activity guide). One aspect of glaciers that becomes apparent in the simulation is that the surface of a glacier moves more rapidly than its base. There’s a tool to let you drill a virtual hole in the ice, then watch as the hole gets stretched out as the glacier flows downhill.

Now that you’ve spent a while playing around with the simulation—I certainly have—let’s take a look at the glaciers of Heard Island.

As I mentioned before, some glaciers are retreating, while others are relatively unchanged. Several factors influence the size of glaciers: precipitation amount, freezing level (of the atmosphere) [generally related to sea level surface temperature], relative humidity, albedo (reflectivity), and the thickness of rocky debris cover.

Precipitation and the freezing level are fairly straightforward factors: more snow and cooler temperatures yield larger glaciers. But there are complications! In a windy environment, such as that of Heard Island, snow doesn’t just pile up evenly as it falls; it drifts. Snow that falls in one place may end up being picked up by the wind and deposited somewhere downwind. This effectively adds precipitation to downwind areas, and removes it from upwind areas.

Relative humidity is important to glacier size, because even cold, dry air can cause snow and ice to sublimate—turning directly from solid to gas. In places like the McMurdo Dry Valleys in Antarctica, this effect keeps the valleys from filling with snow and ice. On Heard Island, a shift in the wind, or a more steady wind direction, can cause different areas to be affected by these dry winds, or to be affected more than in years past.

Here are some of Ruddell’s comments on the matter of why some glaciers are retreating and others do not:[1]

The accumulation, distribution and snowline elevation on many Heard Island glaciers appears to be influenced strongly by the re-distribution of snow by the wind. The prevailing wind direction is westerly and there is less likely to be a re-distribution of snow to low elevations on the westerly facing glaciers… Further, the re-distribution of snow by wind on west-facing glaciers is likely to be impeded not only by wind speed and direction but by severe crevassing (e.g., Gotley Glacier).

When the Heard Island expedition arrives this (austral) November, the glaciers can be observed from close range, rather than by satellite. An additional topic of interest is documenting the advance of vegetation toward recently de-glaciated areas.

During the 1947-2000 period, the glaciers on Heard Island showed an overall reduction in area of 12%.[1] The trend has been toward retreat, and with temperatures increasing about 0.8 °C since 1947, the glacier areal coverage in 2015 is almost certain to be lower still.

***

[1] Ruddell, A. “An inventory of present glaciers on Heard Island and their historical variation”, in Heard Island: Southern Ocean Sentinel (Eds K. Green and E. Woehler) Surrey Beatey & Sons, 2006, p. 28-51.

Addressing Uncertainty Numerically

Casino Monte Carlo, Monaco.  Image credit: Positiv (CC-BY-SA).
Casino Monte Carlo, Monaco. Image credit: Positiv (CC-BY-SA).

Recently I wrote about what scientific uncertainty is, and why it might be important. There are a few things which can be done to understand the “fuzziness” of a number. However, the real world can get complicated in a way which most introductory courses don’t deal with.

For instance, a standard introduction would help you through what to do if you are adding or multiplying two numbers. In the former case, you can add the uncertainties together, and in the latter you would take the root mean square uncertainty. This can be a little bit of math, but in general it’s fairly straightforward: plug in the numbers and out pops an answer.

Sometimes, though, figuring out the proper formula can be time-consuming or nearly impossible. In these cases, there is still a way to get a satisfactory answer: a Monte Carlo simulation, so named for the iconic casino in Monaco (pictured above).

The Monte Carlo simulation is effectively an exercise in rolling dice. Rather than handle the uncertainties analytically, random numbers are used (typically a Gaussian distribution of appropriate mean and width) and combined together in the equation in question—with no uncertainties attached. After a large number of repetitions (for my purposes around 104–107), the statistical distribution can be evaluated. In many cases that evaluation means taking a mean and standard deviation, although it is very informative to look at the distribution to be sure it is at least roughly Gaussian. If the distribution is bimodal or highly skewed, a standard deviation may not be an appropriate metric.

There is another place where Monte Carlo simulations are useful. Suppose you want to know the average (and distribution) produced when you roll four 6-sided dice, and sum the three highest. Is there a way to solve it analytically? Probably, but I can code (and run) the Monte Carlo simulation much more quickly.*

Here’s how to do that in the statistics program R:

my_repetitions <- 10000; # Set the number of trials
# Use a variable for this so when you change N, you only have to do so once.
# Tracking down every place you entered 10000 by hand is no fun.

my_roll <- function(){ # Declare a function to roll four dice and sum the highest three
# returns: sum of highest three of four six-sided dice rolls (integer)

roll_four <- sample(seq(1, 6), size=4, replace=TRUE); # Create a four-element vector representing four 6-sided dice
return(sum(roll_four) - min(roll_four)) # Sum the four dice, and subtract the minimum roll
}

my_results <- replicate(my_repetitions, my_roll()); # Create a vector of dimension my_repetitions, with each element the result of my_roll()

summary(my_results); # Show a statistical summary of the results
hist(my_results); # Plot a quick-and-dirty histogram of the results

Monte Carlo results (N=10,000) for rolling four 6-sided dice and summing the highest three.
Monte Carlo results (N=10,000) for rolling four 6-sided dice and summing the highest three.

Monte Carlo simulations are very handy when there are non-linear interactions between parameters with uncertainties. There may be no analytical solution, but creating a Monte Carlo procedure for determining the final distribution is relatively straightforward. In fact, this is what many weather models and climate models do. An ensemble of different numerical predictions will be put together, and the result gives a statistical likelihood of different events. With weather models, the trick is to keep the code running quickly enough that when it’s done the result is still a prediction—predicting what the weather would have been like a week ago is not useful in the way that knowing what tomorrow may have in store is.

This post is not meant to be an introduction to R (which I used), or to good programming practices (which I hope I used). Many of these are available online, or at colleges and universities in facilities like UC Berkeley’s D-Lab. I may also write about these topics at a later point.

* For those keeping score at home, the mean of this dice rolling scheme is 12.25, median is 12.00 with the middle 50% ranging from 10 to 14.

T -7 Months and Counting

Fair weather at Atlas Cove and on the Laurens Peninsula, Heard Island, taken April 10, 2015.  Nominal resolution is 250 m/pixel.  Image credit: NASA Terra/MODIS.
Fair weather at Atlas Cove and on the Laurens Peninsula, Heard Island, taken April 10, 2015. Nominal resolution is 250 m/pixel. Image credit: NASA Terra/MODIS.

We are now just under seven months from departure from Fremantle, Australia, to Heard Island. Preparations continue for the expedition; seven months may seem like a long time, but many projects need to be accomplished by then.

With plans for operations coming together nicely, there is a new focus on selecting, acquiring, and testing the equipment and supplies to be used on the expedition. Here are a few examples of choices which will need to be made:

  • What kind of shelter will we use?
  • What will we do to keep the shelter from blowing away?
  • How will we keep the shelter warm?
  • On what will people sleep?
  • What food will be brought on the expedition?
  • What kitchen equipment do we bring?
  • How will we network the computers, including shelters 200–300 m away?

Of course, as we make these choices, we are also considering what sort of backup plans we have. This expedition, like any other, is a delicate balance between redundancy, robustness, and minimalism (weight, cost, and labor).

One factor in choosing gear which is often overlooked in temperate climes is that of its poor-weather field-usability. With the precipitation, high winds (blowing snow, rain, and volcanic sand and dust), and cold temperatures, small pieces or anything requiring manual dexterity should be avoided. Choosing glove-appropriate tools and equipment can make the difference between a good day of efficient field work, and a very uncomfortable, miserable day in the cold, wet wind. Without glove-appropriate gear, the gloves have to come off, which in turn drastically reduces their [the gloves’] effectiveness.

Fortunately, on this expedition we will not have to deal with the temperatures found in the coldest regions of the world. In those extremely cold regions, eating utensils (at least fork and spoon) must not be made from metal—they will act as a heat sink and conduct heat away from your mouth very efficiently. I may bring wooden utensils just in case.

Procuring the gear we need presents its own logistical problems. The expedition leader and many of the support team are based in the US, but anything bought in the US has to be shipped to Australia. Buying things in Australia saves shipping, but larger items can’t be tested by the US team. There are further differences in things like electrical power distribution; generators will need to match the plugs and voltage required by the equipment, and the VK0EK amateur radio team need to be confident that the electrical devices we bring won’t interfere with their operations.

For my proposed research project, I have been wrestling with a the questions I posed in my previous post on expedition preparation. Some of the questions have been resolved. Not all of my lines of inquiry were successful. A number of locations I thought would be good to sample have, upon further reflection and study, turned out to be unlikely to yield results which would add meaningful data to my research. Further discussion with other scientists is planned, and I expect will help further clarify a plan of action—though it may be to scrap the project altogether and pursue a related research question.

This weekend, I finally got my hands on a book I have been looking forward to reading: Heard Island: Southern Ocean Sentinel. I took a brief glance through it, and there is a lot of good information there. Although I had planned to write today about glaciers, specifically the glaciers on Heard Island, I have deferred that for the time being so I can read more before writing that post. Maybe next time!

Geoscientist’s Toolkit: Understanding Uncertainties

Tyrannosaurus rex skull, at the University of California Museum of Paleontology, University of California, Berkeley.  Image credit: EncycloPetey (CC-BY-SA).
Tyrannosaurus rex skull, at the University of California Museum of Paleontology, University of California, Berkeley. Image credit: EncycloPetey (CC-BY-SA).

Uncertainty, in the analytical or numeric sense, is a topic many people approach with trepidation. However, it is much more approachable and intuitive than it may seem.

Consider the following examples.

First, the local meteorologist has forecast a high temperature tomorrow of 45 °F (7 °C). Suppose I take my thermometer out tomorrow afternoon, let it sit in the shade for a while, and it reads 43 °F (6 °C). Was the forecast wrong?
What if it were 37 °F (3 °C)?

Perhaps a different example may be more familiar. You are on your way to meet a friend for lunch, and you tell them that you’ll be there in fifteen minutes. Seventeen minutes later, you arrive. Were you on time? Were you late?

Finally, here is yet another example, and one I struggle with: you are in a science museum, and the sign next to the T. rex says that it went extinct around 65 Ma. However, there was a paper published two years ago which determined that the Chixulub impact occurred at or slightly before 66.043 Ma (+/-22 ka, 2-sigma).[1] Is the sign on the exhibit wrong?

I hope that from these examples, you can see that uncertainty is something you encounter more often than you necessarily think about. You may also find you have some intuitive sense for what it means and where it might be important.

Here are my interpretations of the above scenarios.

The meteorologist’s forecast is correct. Although it is not usually mentioned, meteorologists in temperate climes expect their one-day forecasts to be accurate to about 2 °F. They have a fairly large area to cover, which may have some temperature differences within it, and there is some uncertainty in the reading given by the thermometer.

When meeting a friend for lunch, being off by two minutes in fifteen is probably fine. Most people understand that such an arrangement may be accurate only to within five minutes. For some people, even five minutes may be well within the understood uncertainty.

As for the museum, I would think it is a great opportunity to teach about uncertainty! On one hand, the casual use of 65 Ma (implying +/-2.5 Ma) is correct. On the other hand, that figure is quite imprecise compared to how well we know when the event happened. Even to the nearest million years, 66 Ma would be more appropriate. This brings up another point: our understanding of geochronology is based on the latest research. Advances in the field, analysis of different materials, or even different interpretations of previously existing data can lead to changes. Despite the great amount of effort put into compiling the best science and geochronology for the latest Geologic Time Scale (mostly not open access, sadly), parts became obsolete even as it went to press. Mostly, these changes are small, and primarily concern scientists who are studying events near these boundaries. If the Cretaceous-Paleogene boundary were to be moved from 66 Ma to 90 Ma, that radical change would need to be backed up with a lot of data, and would necessitate a new sign for T. rex or an acknowledgement that dinosaurs lasted well into the Paleogene (there is no widely accepted evidence that they did).

Hopefully these examples have illustrated that you already have an intuitive understanding of what analytical or numeric uncertainties are and why they are important. In science, where measurements are being made, and calculations based on those measurements are being computed, understanding the uncertainties becomes increasingly important. There are many resources available which outline the process for determining and reporting uncertainties, ranging from a ten-page worksheet to a [very handy and accessible] small book to a comprehensive tome (open access!).

***
[1] Renne, P. R., Deino, A. L., Hilgen, F. J., Kuiper, K. F., Mark, D. F., Mitchell, W. S., III, Morgan, L. E., Mundil, R., Smit, J. (2013) Time Scales of Critical Events Around the Cretaceous-Paleogene Boundary. Science 339: 684-687, doi: 10.1126/science.1230492.

Where On Google Earth #485

On the heels of a tough WOGE #482, Ole took us to Mistaken Point, Newfoundland, in WOGE #484 for a look at the extraordinary fossils found there—some of the oldest complex life known anywhere on the planet.

Here is WOGE 485. I think there should be enough clues in this one to make it fairly fast.
woge_485

Find the location of the image in Google Earth, and leave a comment with the latitude and longitude with a brief description of the geology/geography/hydrology of the area. The first person to identify it correctly will host the next WOGE (you can ask a regular to host it for you if you don’t have a blog for it).