Archive for April, 2018

Climate Monday: Repeat Photography

April 15th, 2018

The semester is winding down, so we only have a few more of these climate visualization posts to go. Today, I want to highlight repeat photography. Taking a picture of the same place several or many years apart can be a striking demonstration of change and capture the imagination better than a sterile graph or abstract map.

The flashiest examples applying the idea to climate change come from productions like Chasing Ice by the Extreme Ice Survey or Chasing Coral by Exposure Labs. (Chasing Coral was recently featured here at the College of Wooster as part of the “Great Decisions” series.) The products can be truly fascinating, especially when many photographs are combined in a time lapse video.  For example, the video of the Extreme Ice Survey’s repeat photography of Mendenhall Glacier, embedded below, gives a better impression of glaciers as flowing masses of ice than any single photograph or model simulation.  It also shows the decline in mass at the toe of the glacier between May 2007 and August 2011.

Rules of Climate Change Repeat Photography

Showing climate change with repeat photography requires a few special considerations:

First, you need to have not only the same location, but the same angle for your shot, showing the same context around the feature of interest in each and every photograph being compared.  This is why the Extreme Ice Survey set automated stations with the cameras well-mounted rather than using hand-held cameras.  Much of the Chasing Ice documentary is about building and installing the equipment necessary to achieve this fundamental “rule” of repeat photography. The Chasing Coral team tried similar techniques, only with the added complication of being under water.  Needless to say, it was harder for the Chasing Coral team.

Second, any repeat photography of environmental phenomena had better avoid making natural seasonal cycles look like climate change.  The two pictures of Sub Lake in Rocky Mountain National Park above were taken in June 2015 and January 2013.  The difference between the two isn’t climate change; it’s winter. Another example: The Mendenhall Glacier video shown above is labeled as “May 2007 to August 2011”.  A red outline of the glacier in the first frame is compared to the outline of the glacier in the final frame, and that’s a little deceptive.  Just like snow cover and sea ice, the Menhendall Glacier has a greater extent and thickness at the end of winter than the end of summer — especially at the toe. So part of that difference you’re seeing is just the fact that a) over 80 inches of snow typically fall on the toe of the Mendenhall from October to March and b) the average high temperature is over 60°F in June, July and August in southeast Alaska.  It’s likely to look more robust in May than August, so the time lapse would be better starting and ending in the same month.

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Cherry blossoms in Washington, D.C. (from the National Park Service)

Third, climate change is not the only factor that determines whether one year is cooler or warmer or wetter or drier than the last.  Climate change is not the only factor that determines whether coral bleaching will occur or whether a glacier will retreat.  For example, in Chasing Coral, a coral bleaching event in Australia is highlighted and attributed to climate change.  At the same time, though, an El Niño event was occurring. El Niño is a natural part of the climate system, but it can also lead to warming and coral bleaching. Was global warming a factor in this bleaching event? Absolutely, but the devastation depicted in Chasing Coral may have been less overwhelming in a La Niña or normal year. As another example, if you were trying to take pictures in Washington DC to show how the date of cherry trees blooming was coming earlier each year, you might be disappointed.   Although blooming is now occurring on average about a week earlier than in the 1970s, peak bloom was actually slightly later than average this year. The best way to get around this issue is to have several decades between the start and end of the repeat photography pair or sequence.

The Repeat Photography Project at Glacier National Park

With all these rules in mind, the United States Geological Survey (USGS) is currently undertaking a repeat photography project for the failing glaciers of Glacier National Park. They’re also soliciting help from visitors in a crowd-sourcing effort. The time spans exceed 50 or even 100 years for these photos, which is enough time to see some truly remarkable changes to Glacier National Park’s namesakes. It’s even long enough to avoid the seasonal issues discussed above. As an example of the output, below is repeat photography of Boulder Glacier (1932 to 2005) from the Northern Rocky Mountain Science Center (NOROCK).

Boulder Glacier - 1932 Boulder Glacier - 2005 color

 

Climate Monday: Putting Recent Weather in Context

April 9th, 2018

It snowed in Wooster today. It also snowed in Pennsylvania, Michigan, Iowa, Maryland, and several other states. Across the Northeast and Midwest, baseball broadcasters, news anchors, my coworkers, and even random people on the street are remarking on how “It sure doesn’t feel like spring”, and “why won’t winter go away.”  Actually, this refrain has been happening since a series of big storms pummeled the East Coast in March.  So of course I wonder: how uncommon has the late winter/early spring weather been?

I figure we can use this griping to highlight where to go to see past weather in context.  It’s all too easy to get a forecast for tomorrow, and it’s all too easy to complain that the weather “isn’t like it used to be”.  But let’s get critical here: Is this abnormal? Here’s a few places you can go to get an answer:

  1. Go to NOAA’s “Climate Data Snapshots“. You can quickly display the basics of climate (temperature and precipitation) for any month in the 21st century up to last month. There’s a few other options, too (like drought and climate change projections). Here’s their map for whether the USA was cooler (blue) or warmer (red) than normal last March:

So yes, the East Coast was a bit colder than normal, but only by a few degrees Fahrenheit in most places. Maybe people in eastern Montana had better reason to gripe about the weather than anybody in the Midwest or Northeast. In was 10°F colder than normal up there.

2. For a little more analysis, check out the NOAA News section. This week, they have the summary for March 2018. They provide some context in addition to the maps on this site.  They also use a different map for showing whether it’s been colder or warmer than normal.  This map is a percentile map; instead of showing how warm or cold compare to normal it was in °F, it asks, what percentage of Marches were colder (or warmer) than this one?

 

Here we can see that although eastern Montana was about 10°F colder than normal on average in March, that’s par for the course in Montana.  The temperature varies widely out there on the northern plains, so an erratic March is nothing new.  In North Carolina and Virginia, however, having a March that is 5 or 6°F colder than normal is something rare, so pockets of those states came in “much below average”.  In other words, just as the average temperature in Montana is colder than North Carolina, the temperature in Montana is also more variable than in North Carolina.

3. A third place you can go is to the professional weather and climate bloggers at Category 6 on Weather Underground. Bob Henson and Jeff Masters are more likely to put a personal spin on a story, but sometimes that’s even more interesting.  They often highlight NOAA’s maps and give their own flavor.  For example, they prefer to highlight the state-wide averages for temperature:

When you do that, the whole story of the East Coast be cold and snowy (and it was snowy, for sure) pops out even more distinctly than eastern Montana — because overall, the state of Montana was pretty average.  Some of the detail is lost for big states when state-level data are used, but they also have some appeal — it’s easier to think in states than the the divisions used in the previous two maps, after all.

So getting back to today, what do the numbers say for Wooster.  Well, Sunday April 8 was the coldest day recently, with a low temperature of 23.5°F at the weather station at the OARDC just south of town.  The record low for April 8 is 14°F, set in 1982.  The average is 33.0°F. So no, this wasn’t record cold for Wooster. But it was pretty low — only 12 years (about 10%) since 1900 had a lower daily minimum for April 8.  Also, of the 52 years with non-zero precipitation on April 8, a total of 11 of them had snow — that’s 21%.  (It drops to 19% for April 9.)  In other words, yes, it’s colder than normal…  and yes, snow is rare on April 9 in Ohio.  But not that rare.  The last time it did, in fact, was… 2016.

A Wooster Paleontologist visits the Smithsonian’s National Museum of Natural History

April 5th, 2018

Washington, DC — I have the privilege this semester of being on a research leave from teaching, so I thought I’d report on one of my activities. Without classroom responsibilities I can travel for research opportunities, especially now as the weather in the northeastern US marginally improves. (Despite the sunny view above, it was freezing!)

I visited the Paleobiology Department of the National Museum of Natural History in Washington to examine some particular fossils in the collections, and give a departmental seminar. This is typical for paleontological research, and I’m grateful to the generations of museum scientists who make it possible.

The Collections Manager at the NMNH Paleobiology Department is our own Kathy Hollis (’03). She does such a fine job she’s on a poster board in front of the museum, and she was featured in an excellent Wooster Magazine article on museum science.

Kathy sets me up deep in the fossil collections, endless rows of cabinets. The Paleobiology Department, in fact, has more than 10,000 of these, each with multiple drawers of treasures.

My work is pretty simple at this stage. I find fossils of interest in the collections (most of which I’ve identified from publications) and photograph them for future reference. I use this copy stand, which is the best in the business. (I want one, Department Chair.) The paper tray is filled with lead shot which is useful for positioning specimens at any angle under the camera.

Here’s an example specimen: the ambonychid bivalve Claudeonychia from the Upper Ordovician of the Cincinnatian region. The scale is in centimeters. The dark color is actually an encrusting bryozoan, a story I’ll tell later.

I meet many cool fossils along the way, including this magnificent specimen of Wilsonoceras from Wyoming. It is a nautiloid cephalopod I’ve always wanted to see purely for its name!

Here is the poster for my presentation to the Paleobiology Department. It is a tradition for visiting researchers to present a talk on their work.

This is the Cooper Room where the talks are held. I love its Old School ambiance, and the paleontological history it represents. It is a superb place to present ideas to colleagues in the discipline.

The field season is about to begin for Wooster Earth Scientists, so expect more posts. Again, it is a privilege to have such opportunities.

Climate Monday: Climate Change Hot Spots

April 2nd, 2018

It’s no secret that global warming does not simply mean more warm days and fewer cold ones. Warming is uneven, with some regions (like the Arctic) warming faster than others. Additionally, warming of the atmosphere and oceans has a cascading effect on other parts of the Earth system, from the amount of ice stored in Greenland to the variability of global wind patterns, to the extent of various habitats. The world is complex, and it the impacts of climate change myriad. With so many changes happening, what places or changes should humans focus adaptation and mitigation efforts? Enter the concept of “climate change hot spots”. Let’s examine three frameworks and how they’re visualized.

Example #1: One of the simplest frameworks for talking about climate change hot spots is to consider places where various physical aspects of the climate are projected to change the most (Kerr 2008). This was the tactic used by a group of climate modelers from the National Center for Atmospheric Research back in 2008.  They ran detailed, regional-scale climate models into the future and looked for a) places with the most change in average temperature and precipitation, and b) places with the most change in the variability of temperature and precipitation (in other words, heat waves, cold snaps, floods, and droughts).  The result was a relative index from low change to high change:

Figure 1: Map of the “relative responsiveness” of the USA and northern Mexico to climate change based on projected changes in temperature and precipitation under a suite of climate models. (Kerr 2008)

The nice thing about this measure is that it’s objective and gives a value of overall impact for everywhere in the lower 48.  It’s limited in it’s utility, though.  For one thing, it only measures temperature and precipitation, omitting related concepts like sea level rise and wildfire frequency/intensity.  It also is a projection of the future, which is problematic both because there’s less certainty about the future and because there are changes already happening that might be more pressing to address.

Example #2: That in mind, another way to define “climate hot spot” is a location that has already changed substantially. The Union of Concerned Scientists (2011) has compiled a map of locations that have “well-documented” changes already occurring. Here’s a snapshot, but the visualization is meant to be an interactive map, not a static image, which is certainly inviting.  The “well-documented” claim is supported by reference lists and descriptions for each event. In other words, these have been researched substantially.  Another interesting point is that the map shows a much broader view of “climate change” than the earlier climate model studies.  Sure, there’s “extreme wet” and “air temperature”, but there’s also “ecosystem” sections and “health” and “food” for people. This is definitely better suited for a broader audience and broader concerns.

Figure 2: Snapshot example of climate hot spots by the Union of Concerned Scientists (2011).

Still, the above example may seem lacking with regard to two elements (and maybe others): First, it is clearly focused on the USA.  There is a data bias, of course — the Union of Concerned Scientists has many American scientists, and many of them study the USA. But it may give the false impression that the USA has more dire situations than the rest of the world.  Second, there is still little sense of risk versus vulnerability.

If we think of climate change as a natural hazard, just like a volcanic eruption or an earthquake or a hurricane, we can talk about both risk and vulnerability of populations.  For example, both the Netherlands and Florida are at great risk of sea level rise, but the Dutch are bettered prepared to adapt to rising seas because of past experience and current cultural, political, and physical infrastructure. The same risk can lead to more or less hardship depending on how vulnerable a place is — and assuming sea level rises about the same in both locations, Florida is likely to have more hardship from sea level rise than the Netherlands.

Example #3: This added concept of vulnerability is used to define “climate hot spots” in yet another way: as locations where “strong physical and ecological effects of climate change come together with large numbers of vulnerable and poor people and communities” (Neumann and Szabo 2016). Their map is still really a measure of risk, not vulnerability, but they use it to help highlight areas with high risk that also have special vulnerability (originally identified by De Souza et al. 2015):

  1. Deltas in Africa and South Asia that have large populations of poorer people. Groundwater extraction and other human activities that make deltas sink can exacerbate the effects of sea level rise.
  2. Semi-arid regions in parts of Africa, South Asia, and Central Asia that may become drier. Again, the lower economic resources in these regions make them more vulnerable.
  3. River basins dependent on glaciers and snowpacks as a water source, especially in the Himalaya, where there are large populations of poorer people.

Figure 3: Climate risks based on three factors: snow-dependence, semi-arid climate, and river deltas. (Neumann and Szabo 2016).

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Works Cited

De Souza, K., Kituyi, E., Harvey, B. et al.  (2015). Vulnerability to climate change in three hot spots in Africa and Asia: key issues for policy-relevant adaptation and resilience-building research. Reg Environ Change, 15: 747. https://doi.org/10.1007/s10113-015-0755-8

Kerr, R. (2008). Climate Change Hot Spots Mapped Across the United States. Science, 31: 909. http://science.sciencemag.org/content/sci/321/5891/909.full.pdf

Neumann, B. and Szabo, S. (2016). Climate change ‘hotspots’: why they matter and why we should invest in them. The Conversation. Accessed 2 Apr 2018. http://theconversation.com/climate-change-hotspots-why-they-matter-and-why-we-should-invest-in-them-68770

Union of Concerned Scientists (2011). Climate Hot Map. Accessed 2 Apr 2018. http://www.climatehotmap.org