Scenes at the local library

This post briefly interrupts the TCS prediction posts with a library-related theme, given the overwhelming popularity of these in the past.

For various reasons, I sometimes work at the local public library. It’s a small place, but still the largest one in the county, and a nice place overall. The magazine section looks like your typical sort of situation:
Library3

I decided to check it out a little more extensively today, prompted by a recurring failure to find any true scientific journal in most public libraries in small to medium sized towns, and in some cases even in large cities. There are 137 magazine publications total; I tried to break down the topics represented by the current issue’s front cover, into some thematic categories, neither thorough, systematic or mutually exclusive. It came out as:

Humans: 65 (Women: 39; Men: 22; Kids: 4)
Animals/Plants: 10 (Animals: 8; Plants: 2; Deer: 4; Birds: 3; Snails: 1)
Holidays: 13 (Thanksgiving: 2; Christmas: 11)
Food/Cooking: 12
Crafts: 11 (Mostly quilting and sewing)
Health/Nutrition: 9 (e.g. Bicycling, Eat Well, Vegetarian, Yoga)
Homes/Decor: 8
Sports: 9 (Football: 3; Hunting: 3; Golf: 2; Cycling: 1)
Cars: 5
Ships/Sailing: 3
Trains: 2
Gardening: 2
Puzzles: 2
Lighthouses: 1

There were a number of uncategorized others also, like one devoted to autism, one to retirees, one to coin collecting and etc. Three of the four deer-related were pictures of bucks on hunting magazines, while the fourth was a pair of deer on a quilt in a quilting magazine. Two of the three birds were male cardinals, and the third was of a pair of great blue herons.

On what I might term the quasi-academic front, it breaks out this way:
Science- or engineering-related: 10 (Scientific American, Popular Science, Popular Mechanics; Natural History, Audubon, Science News; Sky and Telescope; Smithsonian, Discover, Journal of Inland Seas)
History-related: 6 (World War II, Civil War Times, Timeline, Discover, J of Inland Seas)
Literature: 2 (Ohioana Quarterly; New York Times Book Review; Analog (SciFi))
Journal Format: 3 (J Inland Seas, Ohioana Quarterly, Timeline, with only the first thereof being published by a truly research-oriented group (The Great Lakes Historical Society))

The politically oriented stuff is there too of course, although it’s a small proportion comparatively, and nothing really radical. Whether there was any intention in placing the three on this shelf the way they are is an open question: Library2

For the adult human covers, the (admittedly subjective) interpretation of “overall suggestiveness” fell out as follows. For the 37 with women on the cover, 19 were focused on some aspect of personal appearance, and 8 of those implied sexuality. The typical suspects were involved here (Esquire, Self, etc), including the partially pornographic e.g.: Library1 For the 21 with men on the cover, that breakdown was 2 appearance-oriented and either 0 or 1 sexually suggestive, respectively.

The famous are there but not in huge numbers, and other than Abe Lincoln, Bob Dylan, Oprah Winfrey and JJ Watt, I don’t recognize them. Movie and TV stars probably. JJ Watt and Taylor Swift (?) are on 2 covers each. And no I’m not going to Google to find out who she is, I’m really not.

On the newspaper front, there are the several papers from the county seats of the surrounding counties, but if you want state, national and international news, it’s either the Toledo Blade, Cleveland Plain Dealer, New York Times or Wall Street Journal. All good papers fortunately.

Transient temperature change estimation under varying radiative forcing change scenarios, part two

Continuing from part one, this post looks at a specific method for estimating TCS (transient climate sensitivity), for any desired year and/or radiative forcing scenario, as predicted by any AOGCM climate model. And some associated topics.

The basic idea was devised by Good et. al (2010, 2011; links at end), and expanded upon by Caldeira and Myhrvold (2013), who fit various equations to the data. The basic idea is fairly simple, but clever, and integrates some nice mathematical solutions/approximations, including Gregory’s linear regression ECS estimation method. The basic idea is simply that if you have an idealized RF pulse or “step” increase (i.e. sudden, one-time increase, as with the instant 4X CO2 (= ~7.4 W/M^2) increase experiment in CMIP5), and run any given AOGCM for say 150-300 years from that point, you can record the temperature course resulting from the pulse, over that time (which will rise toward an asymptote determined by the climate sensitivity). That asymptote will be twice the ECS value (because the CO2 pulse is to 4X, not 2X, CO2). From these data one can fit various curves describing the T trend as a function of time. One then simply linearly scales that response curve to any more realistic RF increase of interest, corresponding to a 1.0% or 0.5% CO2 increase, or whatever. Lastly, if each year’s RF increase is considered as one small pulse, an overlay and summation of the temperature responses from all such, at each year, gives each year’s estimated temperature response, for however long the RF is increasing. The RF increase does not have to stop at any point, although it can. It can also increase or decrease at any rate over time.

The figure below from the paper, illustrate the method and the comparison (Fig. 1 of paper, original caption):

Fig. 1 Illustrating the method. a Global mean temperature evolution in a 4xCO2 step experiment (from the HadCM3 GCM; CMIP5 GCMs give qualitatively similar results). b Reconstruction method for years 1–5 of a 1pctCO2 experiment. Red, yellow, green, blue and purple curves temperature responses estimated for the forcing changes in years 1, 2, 3, 4 and 5 respectively. Each coloured curve is identical (for the case of the 1pctCO2 scenario) and is given by scaling the step experiment temperature response. Black curve reconstructed temperature response, given by the sum of the coloured curves (Eq. 1a).

Fig. 1 Illustrating the method. a Global mean temperature evolution in a 4xCO2 step experiment (from the HadCM3 GCM; CMIP5 GCMs give qualitatively similar results). b Reconstruction method for years
1–5 of a 1pctCO2 experiment. Red, yellow, green, blue and purple curves temperature responses estimated for the forcing changes in years 1, 2, 3, 4 and 5 respectively. Each coloured curve is identical (for the case of the 1pctCO2 scenario) and is given by scaling the step experiment temperature response. Black curve reconstructed temperature response, given by the sum of the coloured curves (Eq. 1a).

Good et al (2011), did this for nine AOGCMs, testing the method against the results of the CMIP5 1% per year CO2 increase experiment. This is interesting; they are testing whether the basic functional response to an instant, 400% CO2 increase, is similar to that from a 1% per year increase over 140 years. And lo and behold, the overall agreement was very high, both for the collection of models, and individually, for both surface T and heat content. Their Fig. 2 is shown below:

Fig. 2 Validation against 1 % experiment (all models). a,b Ensemble mean time-series (black GCM  simulations, red simple model). c,d ensemble spread: mean over years 111–140 of the GCM simulations (y-axis) against simple model prediction (xaxis) (Each cross represents one GCM). a,c Temperature change/ K; b,d heat uptake/1022 J

Fig. 2 Validation against 1 % experiment (all models). a,b Ensemble mean time-series (black GCM simulations, red simple model). c,d ensemble spread: mean over years 111–140 of the GCM simulations (y-axis) against simple model prediction (xaxis) (Each cross represents one GCM). a,c Temperature change/
K; b,d heat uptake/1022 J

To me, this result is rather astounding, as it says that the time decay of the temperature response to a pulsed RF increase, is highly similar, no matter the magnitude of that increase. That is absolutely not a result I would have expected, given that the thermodynamic interaction between the ocean and the atmosphere is highly important and seemingly not likely to be in phase. Of course, this result does not prove this dynamic to be a reality–only that the AOGCM models tested consider, via their encoded physics, that the two responses to be highly similar in form, just differing in magnitude.

Caldeira and Myhrvold (2013) then extended this approach by fitting four different equation forms and evaluating best fits, via Akaike AIC and RMSE criteria. To do this they first used the Gregory ECS estimation method (ref at end) to define the temperature asymptote reached. They don’t give the details of their parameter estimation procedure, which must be some type of nonlinear optimization (and hence open to possible non-ML solutions), since the equation forms they tested were three (inverted) negative exponential forms and one other non-linear form (based on heat diffusion rates in the ocean). They also don’t provide any R^2 data indicating variance accounted for, but their figures (below) demonstrate that for all but one of their model forms (a one-parameter, inverted negative exponential) the fits are extremely good (and extremely similar) across most of the AOGCMs used in CMIP5:

Figure 2. Temperature results for CMIP5 models that have performed the abrupt4xCO2 simulations (black dots). Also shown are fits to this data using the functions described in the text: θ1-exp, green; θ2-exp, blue; θ3-exp, brown; θ1D, red. The left vertical axis shows the fraction of equilibrium temperature change (i.e., ΔT/ΔT4×); the right vertical axis indicates the absolute change in global mean temperature. Fit parameters are listed in SOM tables S3–S5 (available at stacks.iop.org/ERL/8/034039/mmedia).

Figure 2. Temperature results for CMIP5 models that have performed the abrupt4xCO2 simulations (black dots). Also shown are fits to this data using the functions described in the text: θ1-exp, green; θ2-exp, blue; θ3-exp, brown; θ1D, red. The left vertical axis shows the fraction of equilibrium temperature change (i.e., ΔT/ΔT4×); the right vertical axis indicates the absolute change in global mean temperature. Fit parameters are listed in SOM tables S3–S5 (available at stacks.iop.org/ERL/8/034039/mmedia).

Figure 5. Results from CMIP5 models (black dots) running simulations of the 1pctCO2 protocol. Projections made by simulations based on curve fits to the abrupt4xCO2 simulations as described in the text: θ1-exp, green; θ2-exp, blue; θ3-exp, brown; θ1D, red. All but θ1-exp provide similar approximations to the temperature results for most of the fully coupled, three-dimensional climate model simulations. Note that the GFDL-ESM2G and GFDL-ESM2M models did not continue with increasing atmospheric CO2 content after reaching twice the pre-industrial  concentration.

Figure 5. Results from CMIP5 models (black dots) running simulations of the 1pctCO2 protocol. Projections made by simulations based on curve fits to the abrupt4xCO2 simulations as described in the text: θ1-exp, green; θ2-exp, blue; θ3-exp, brown; θ1D, red. All but θ1-exp provide similar approximations to the temperature results for most of the fully coupled, three-dimensional climate model simulations. Note that the GFDL-ESM2G and GFDL-ESM2M models did not continue with increasing atmospheric CO2 content after reaching twice the preindustrial concentration.

So, both Good et al. (2011, 2012), and Caldeira et al. (2013) provide strong evidence that the physical processes involving surface temperature change, as encoded in AOGCMs, are likely very similar across extremely widely varying radiative forcing increases per unit time, from unrealistically huge, to (presumably) however small. Note that in both cases, a very large percentage (roughly, 40-60%) of the total temperature response (at equilibrium) occurs within the first decade (when normalized to the pulse magnitude). This seems to have implications for the importance of various feedbacks, an issue which is complicated by the fact that some of the models tested are Earth System Models, which include e.g. integrated carbon cycle feedbacks, while others do not. Certainly there will be major potential differences in carbon cycle feedbacks between an earth surface that has just increased 3 degrees C, instantly, versus one that has warmed only a tiny fraction of that amount.

TBC; the next post will demonstrate application to various delta RF scenarios.

Refs:

Caldeira and Myhrvold, (2013). Projections of the pace of warming following an abrupt increase in atmospheric carbon dioxide concentration. Environ. Res. Lett. 8: 034039, doi:10.1088/1748-9326/8/3/034039.
Good et al., (2011). A step‐response simple climate model to reconstruct and interpret AOGCM projections. GRL, 38, L01703, doi:10.1029/2010GL045208
Good et al., (2012). Abrupt CO2 experiments as tools for predicting and understanding CMIP5 representative concentration pathway projections. Climate Dynamics (2013) 40:1041–1053 DOI 10.1007/s00382-012-1410-4
Gregory et al., (2004) A new method for diagnosing radiative forcing and climate sensitivity. doi:10.1029/2003GL018747

See also: Hooss et al., (2001). A nonlinear impulse response model of the coupled carbon cycle-climate system (NICCS). Climate Dynamics 18.3-4: 189-202.

Transient temperature change estimation under varying radiative forcing change scenarios, part one

The estimation of transient climate sensitivity (TCS, defined below) has been in the back of my mind since writing a couple of posts a couple of months ago (here and here) on expected future global mean temperatures over this century. This post, and the one to follow, is thus a methods oriented post resulting from that thought process investigation. This one just introduces the basics of the problem and in the next one I’ll get into methods.

I use TCS here to refer to the realized, global mean, surface temperature change, at any given time point, resulting from a change in radiative forcing (RF) up to that point, regardless of whether either the thermal, or radiation, environments have re-equlibrated in response to this forcing change or not. It is a generalization of the transient climate response (TCR), which is defined as the expected mean surface temperature change at t = 70 years, of a 1% per year CO2 increase. Such a rate gives a CO2 doubling (1.01^69.66 = 2), and since CO2 RF is well-approximated by a logarithmic function of the CO2 concentration ratio at two time points, this results in a constant annual RF change rate (= 5.35 * ln(CO2.2/CO2.1)/70 = 0.053 W/m^2/yr). So, TCS is just a generalization of TCR, in that the time span needn’t be exactly 70 years, nor the forcing rate exactly 0.053 W/m^2/yr. Linear scaling, based on other delta RF rates, is allowed, but the reference time should be within, say, a couple decades or so of 70 years. In the CMIP5 climate model experiments, which form the input to the IPCC AR5 report, the 1% increase is extended over 140 years, reaching 4X CO2 (from pre-industrial), and the transient response at that point is simply divided by 2, to estimate TCR as just defined.

Although the concept itself is straight-forward, TCS estimation from empirical data is not, because of the several important time delays and/or feedbacks, not to mention forcing agents, in the climate system, for which the available data are not sufficient to this highly important task. Generally, global mean time series output for idealized, modeled RF scenarios is thus required, in particular the 1% per year, and instantaneous 400%, CMIP5 CO2 increase scenarios. For whatever reason, the annual time series output for these, and more importantly for the four more realistic Representative Concentration Pathway (RCP) scenarios analyzed, are rarely reported. Why this is so baffles me; it’s not hard and the AR5 seemingly should have done it, but whatever, I’m not in charge. To get them, one thus has to analyze each climate model’s raw output data. Finding these data, downloading them, aggregating them from native temporal and spatial scales to obtain yearly global means, etc., is a time-consuming process, and one requiring a fair bit of programming; it’s a lot of work. But useful and important work.

The equilibrium climate sensitivity (ECS) is the temperature change expected after this same RF increase (3.7 Watts/m^2, allowing for stratospheric/tropospheric adjustment) has been imposed, but only after both radiation and temperatures have reached equilibrium. What’s missing from most reported ECS estimates however, is the time scale over which the full temperature increase is realized. In a few cases, model-estimated ECS time scales have been determined by running models having lower spatio-temporal resolutions than typical AOGCMS, for one to five thousand years, to equilibrium. But that costs a lot of supercomputer computing time, and full resolution runs cost even more, so most often it is computed from shorter AOGCM model runs (~ 150 to 300 years). An important method for so doing involves linear regressions of delta T on the planetary, top-of-atmosphere radiative flux, extrapolated to the point where that flux is estimated to be zero: the so-called “Gregory method”, after it’s originator. ECS estimates vary, with the consensus central tendency value, for 35 years or more now, being estimated at around 3.0 degrees C, with a likely range between 1.5 and 4.5. But that issue is not the point of these posts.

But ECS, which while important, is not fully realized for several hundred years after the cessation of a RF increase, and why it should receive (typically) more attention than estimates for the next few decades, is another big puzzler (albeit one that CMIP5 addressed directly with its decadal forecasts). However, the main point of this post is that the idealized CMIP5 experiments mentioned above can be used to predict the annual time series of the expected warming for any imposed, realistic RF change, even though the idealized experiments are themselves decidedly unrealistic. An instantaneous 4X CO2 increase is obviously wildly unrealistic–nobody’s ever argued such a thing could actually happen short of some planetary natural disaster scenario. Even the 1% per year increase from pre-industrial for 70, or even 140, years is clearly too high; even from a 1950 baseline the mean annual CO2 increase has been only (400/310)^(1/64) = 1.004, or 0.4% per year. Only in the last couple decades has it exceeded 0.5% per year, although it’s certainly possible to hit 1% per year in the near future, from either a continuing increase in emission rates, decrease in aerosol production rates, strong carbon cycle feedback (or forcing, via land cover changes), or some combination of these. [Edit:referring to the equivalent RF here, not necessarily via CO2 increases.]

By any account, whether purely scientific or as policy input information, the estimated TCS for any given year, i.e. a time series, is an important thing to know, (of higher practical significance than is knowing ECS and/or it’s time scale, I would argue). I haven’t seen it commonly estimated however; in the IPCC AR5 report for example, just the TCR and ECS are reported, and decadal resolution estimates for the four RCP scenarios, in which several forcing agents are changing simultaneously, including various well-mixed greenhouse gases, non well-mixed atmospheric agents (e.g. aerosols, surface ozone), land cover, surface albedo, and sometimes other things.

TBC. Fire away if you have any comments/questions. I’ll do my best to answer the easy ones and dodge or obfuscate on the hard ones.

Three out of five!

Third baseman Pablo Sandoval hits the ground after catching a foul pop fly for the last out of the 2014 World Series, as the Giants erupt from their dugout.

Third baseman Pablo Sandoval hits the ground after catching a foul pop fly for the last out of the 2014 World Series, as the Giants erupt from their dugout.

World Series champs for the third time in the last five years (every other year), those “scratch ‘em ’till they bleed to death” San Francisco Giants have done it again. Not quite a dynasty yet, but you have to go back fifteen years or so to find a team better at consistently winning games when they really count, over several years, than does this group of characters. When all was said and done, it came down to having the best World Series pitcher in a long, long time on your side.

For the record, I picked the Giants in six. Matt also actually picked the Giants but then went with his “logical opposites theory” to go with the Royals. Harold and Clem, well they were just patently off the deep end :)

Predictions for 2015 and 2016 are now open. For 2015 I’m picking anyone except the Giants, and for 2016, I’m going Giants :)

Caught stealing

Nope, not a post about the Kansas City Royals, or baseball at all for that matter, though I do hope to get there at some point.

Rather, it’s about getting away with thievery at the local library yesterday. I did. Twenty-five, count ‘em, 25 cents each for the two books pictured below. Now, I have no idea how many person-hours went into these, nor any idea how to go about estimating same, but I do know that each is over 800 pages and jam-packed with mucho useful information that could remind me of the 99.9% of the French I’ve forgotten, or get me across the outback some day, should I consult them. And that each required a great deal of work to obtain, organize and print the information contained in them. And that if they sold them for 25 cents new, all the contributing writers would’ve long ago died of starvation. Hell, I’d gladly pay a quarter just for that Australian hotel picture. I win.
Books
Can you beat that with a stick? Tell me about your best book bargains from used book stores, yard sales, dumpsters, your Aunt Maybelle’s attic corner, whatever.

Bring it book thieves. Bring it or I’ll inflict a baseball post or two on you, I’m warning you now.

Backslider

I’m a poor backslider, in the pit of sin
Every time I try to get out, I just slip back in again
Come savior save me, take hold of my hand
Please don’t let me slip back into that pit again

The preacher told me hope was never gone
So next sunday morning, I put my white shirt on
Combed my hair with water, put the family in the car
Dropped them off at the church, and then went on to the bar

Well Audrey’s left me, and she took the kids
I miss them children, I’m sorry for what I did
When I get to drinkin’ I lose control
When you lose your family it’s like you’ve lost your soul

And now the mill is closin’; I’m on shifting sand
I just sit alone in my trailer, and wring my hands
No children’s voices, no woman’s touch
Just this empty whiskey bottle, some shotgun shells and such

Should not have let that woman get me so annoyed
Should not have yelled at my girl, should never have struck my boy
Shouldn’t have took off runnin’, like a turkey through the corn
Shouldn’t have bought this gun, should never have been born

I’m a poor backslider, in the pit of sin
Every time I try to get out, I just slip back in again

Greg Brown, Poor Backslider

I see the mountain

I was born in a fork-tongued story
Raised up by merchants and drug store liars
Now I walk on the paths of glory
One foot in ice and one in fire

Some build temples, some find alters
Some come in tall hats and robes spun fine
Some in rags, some in gem-stone halters
Some just push the pegs back in line

Miller take me and miller grind me,
Scatter my bones on the wild green tide
Maybe some roving bird will find me,
Over the water we’ll ride

Over the mountain, the mountain comes to me
I see the mountain, and that is all I see

Dave Carter and Tracy Grammer, The Mountain

Break

I won’t be writing much for a while; need to take care of more important things. I hope to come back to it at some point, if I feel there’s something worth saying, but we’ll see. I’ll leave the old stuff up though, at least for now.

Thanks to the readers who’ve added to the site with their typically very good comments, especially Matt, Harold, Clem and Dave. I don’t have a lot of commenters, but the ones I do have have been great–and I would not have it any other way.
Jim

Note: Just after writing this post, I’d decided to inactivate the blog, and the only way to do that without deleting it, is to make it private, requiring a password. Somehow, right at that time there came an inexplicable flood of views of my posts related to both tree rings and ebola spread–far more than is normal. This stuff is months to years old now, but whatever. So I’m leaving all posts visible. Furthermore, there are probably some pretty important things I still need to express in writing, but we’ll see what time and energy allows on that.

Open up

Well I don’t think for pleasure
It’s just hard not to do
My thinking is a measure of how much I need a clue
I’m still flying blind
Hoping I might find
A way to stop my thinking and open up my mind

My feelings hurt me plenty
Not feeling hurts me more
Feeling’s got me kneeling down, wounded to the core
Not feeling’s got its charms
But you’ll find out who it harms
When your lover soon discovers you can’t open up your arms

It’s like being in a prison
You lock yourself inside
A limited perspective, even with eyes open wide
But I can’t walk no more
Through scenes I’ve seen before
Why don’t you come to me, bring the key, and open up, open up the door

It’s just love and it’s a puzzle
Of that there is no doubt
Can’t do nothin’ with it and you sure can’t do without
Can’t learn your part
You won’t know where to start
‘Till you quit all your questions and open up, just open up, your heart

Chris Smither, Open Up

“The Lake, it is said, never gives up her dead…”

So, I’ve been learning a couple of Gordon Lightfoot songs lately, and reading various things, and thinking about my home town. And also realizing that Indian Summer will soon give way to something much less enjoyable. So this post is about all that.

A couple of days ago in the library I’m reading the January 2014 entries for the “Great Lakes Calendar” in the journal Inland Seas–a month that caused all kinds of mayhem on the Lakes, mostly involving ice and the breaking thereof. There, I see it noted that on Jan. 3 the “Wilfred Sykes loaded at Escanaba and was escorted by the tug Erika Kobasic“. The next day, up on Superior, the “Downbound Arthur M. Anderson stopped…to await daylight before attempting the Rock Cut…” while way down on Lake Erie “The Griffon was expected to…break out the ice-bound Cuyahoga, stopped at the end of the Sandusky Bay ship channel by heavily packed ice”. And that the next day, the Anderson was right behind the 1000 foot Mesabi Miner, when the latter rammed the ice breaker Hollyhock after an ice ridge slowed the breaker down, about 22 miles west of the Mackinac Bridge.

Inland Seas

I’m only marginally familiar with Great Lakes maritime history but I recognized two of those ship names immediately: they are tied to two major Great Lakes shipwrecks, and the very two that bracket all of the major maritime disasters no less. These ship names are the Griffon and the Arthur M. Anderson. The third one involved was the Wilfred Sykes.

The Griffon was the very first masted sailing ship on the Great Lakes, built by Robert LaSalle’s crew somewhere along the Niagara river, Canadian side, in 1679. It sailed across Lakes Erie, St Clair, Huron and Michigan to the vicinity of what is now Green Bay Wisconsin before sinking in far northern Lake Michigan, loaded with furs, on its return voyage. LaSalle was not killed however, as he had decided to head south overland to explore a connection between the Great Lakes and Mississippi drainages (and then overland from there all the way back to Montreal!). Like just about everything the French explorers and trappers did in the area at the time, it’s a thoroughly outrageous story.

At the recent end, there’s presumably no need to mention what the last major Great Lakes ship disaster was, due at least in part to Lightfoot’s famous and outstanding ballad.

The Arthur M. Anderson was very intimately involved in the entire episode. It was the Anderson that trailed 10 to 20 miles behind one Edmund Fitzgerald, kept in radio communication with it, helped it navigate after its radar went out, and first alerted the US Coast Guard of its disappearance from the radar. Most heroically, it performed the first SAR (search and rescue) operation for potential survivors, right during the height of the storm. The ship had in fact already reached the relative safety of Whitefish Bay, but upon request by the Coast Guard, it voluntarily returned out into the horrendous open water conditions to perform the search. Which speaks volumes about the ship’s captain.

The Wilfred Sykes is involved also. It loaded ore at the same dock at the same time as the Fitzgerald, and was also bound for the Soo locks. But its captain, having looked closely at the weather forecast of a major storm crossing the lake, had decided to track close to the Ontario shoreline instead of across open water. Therefore, it was just the Fitzgerald and the Anderson that crossed the lake together on the furious and fateful afternoon and evening of November 10, 1975. [That link goes to a very interesting paper that recreates the wind and wave conditions before and during the storm, using a weather model, the available surface observations, and a wind-wave model.]

Nearly 40 years later, the mystery of exactly what led to the sinking is still not fully resolved. It is known that the ship sank so fast in such ferocious conditions that there was no chance for survival. The incident was major news in the Great Lakes area at the time, even nationally, and nowhere moreso than in Toledo Ohio. About 5 or 6 (strictly from memory) of the crew of 29 lived in the area. This included the captain, Ernest McSorley, who lived about 7 or 8 miles from us, and was tragically on his very last voyage before retirement. [The most common run of the Fitzgerald was from Superior Wisconsin, to either Detroit or Toledo.] I can still vividly remember the front page story in the Toledo Blade the next day with the pictures of the missing crew. It seemed unbelievable that this could happen. The Great Lakes are littered with uncounted shipwrecks, but this was 1975.
BGSU Fitz

Anyway, today I’m back in the same library, this time reading J.B. Mansfield’s History of the Great Lakes wherein I read:

“Another very interesting, and very sad, thing about this lake [Superior], says W.S. Harwood in St. Nicholas, is that it never gives up its dead. Whoever encounters terrible disaster— happily infrequent in the tourist season—and goes down in the angry, beautiful blue waters, never comes up again. From those earliest days when the daring French voyageurs in their trim birch-bark canoes skirted the picturesque shores of this noble but relentless lake, down to this present moment, those who have met their deaths in mid-Superior still lie at the stonepaved bottom. It may be said that, so very cold is the water, some of their bodies may have been preserved through the centuries. Sometimes, not far from the shore, the bodies of people who have been wrecked from fishing-smacks or from pleasure-boats overtaken by a cruel squall have been recovered, but only after the most heroic efforts with drag-net or by the diver.”

So, to get back to the title, this is the origin, or at least the earliest known explanation, of the sentence “The Lake it is said never gives up her dead, when the gales of November come early” in Gordon Lightfoot’s song. More on that whole issue is here.

“And all that remains are the faces and the names of the wives, and the sons and the daughters.”

“The number of medicine men in active service”

The medicine man was an institution of Piutedom…The distinction was not what might be termed a popular honor. Whether the selection was made for some hereditary reason, or because of some event at his birth or in the early life of the doctor, his status was established at an age when he had no chance to object. It does not appear that he was expected to employ his skill until he had reached reasonably mature years, but his status was settled, however he might resent it when he came to understand the part cast for him in the drama of life. And resent it he usually did, for as soon as his ministrations had sent a sufficient number–generally three–of his fellows to the happy hunting grounds his own violent and sudden removal from mundane affairs would come as a matter of custom.

Among the former Piute residents of Owens Valley, during the early years of white occupation, was one Jim, who had been selected by fate for a doctor’s career. In consequence, Jim constantly carried a “sixteen-shoot gun”, prepared at all times to “heap kill um” if there were attempts either to force him to practice or to fasten on him the results of some other person’s lack of skill in exorcising evil spirits…

The standard of medical success, if not skill, required of Piute medicos was higher than among civilized peoples; for while a white doctor is in no danger of violence whatever his (or his patient’s) luck, the Piute healer did well to arrange his affairs immediately on the demise of his third patient. He was marked for early and unceremonious removal, by whatever means might be convenient for the kin of his last case. Stones, arrows, lassos, in daylight or darkness, regardless of place or anything but opportunity, were used to reduce the number of medicine men in active service. It was approved tribal law.

Chalfant, W.A. (1922). The Story of Inyo

Ebola epidemic update

Today a new and more extensive WHO W. Africa ebola update was released, including data current as of Sept. 14, four days ago. I’ve therefore compiled new tables, and case and death rates. The new code and graphs are here, and the new data table is here. Liberia-specific graphs are here.

There’s been a slight drop in the transmission rate, based on these data. The daily rate is now estimated at about 1.038 (down from 1.043 a month ago). The 6-12 day rates, which correspond roughly with the estimate of R_zero, the per person rate of infection (depending on the mean infectiousness period, in days), range from 1.25 to 1.57. The midpoint value is 1.41. See here and here for my methodology.

It is almost certain that cases are going unreported however, and it could be many, I don’t know. These estimates are therefore underestimates of the true rate, and hence the severity of the outbreak. And this kind of thing is certainly tragic and not helping the situation.

This week’s puzzler

This week’s puzzler comes to us from John Storthwaite in Stonyfield, Minnesota, who has been wondering why there are so many trees blocking his view of the rocks up there.

Suppose you have been given the following problem. A number of objects are located in some given area, say trees in a forest for example, and one wishes to estimate their density D (number per unit area). Distance-based sampling involves estimating D by averaging a sample of squared, point-to-object distances (d), for objects of known integer rank distance (r) from the point. The distances are squared because one is converting from one dimensional measurements (distance) to a two dimensional variable (objects per unit area).

So here’s the puzzler. If you run a line through this arbitrary point, and choose the closest objects (r = 1) on each side of it, what will be the ratio of the squared distances of the two objects and how would you solve this, analytically? Would they be about the same distance away? If not, would there be a predictable relationship between them? The problem can be extended to any number of lines passing through said point, just with correspondingly more pairs of distances to evaluate.

The first questions one should ask here are clear: (1) “Why on earth would anybody want to do that?” and (2) “Is that the type of thing you clowns spend your time on?“. We have answers for those questions. Not necessarily satisfactory answers, but answers nonetheless. Giving an answer, that’s the important thing in life. So, if you know the answer, write it on the back of a $100 bill and send it to…

Anyway, there are two possible solutions here. The first one comes readily if one realizes that the densities within sectors must each be about the same as the overall density, since we assume a homogeneous overall density. But, for a given value of r, the squared distances in each of the two sectors must be, on average, about twice those for the collection of trees overall, because there are only half as many trees in each sector as there are overall. So, e.g. the r = 5th closest trees within each half are on average, 2X the squared distance of the r = 5th closest tree overall.

Knowing this, the relationship between the two r = 1 trees (label them r1.1 and r1.2 having squared distances d1.1 and d1.2) in the two sectors becomes clear. Since one of the two trees (r1.1) must necessarily be the r = 1 tree overall, and the mean squared distance of the two trees must be 2X that of the r = 1 tree, this translates to:

2*d1.1 = (d1.1 + d1.2)/2 and thus,
d1.2 = 3(d1.1),

i.e., one member of the pair will, on average, be exactly three times the squared distance of the other. This result can be confirmed by an entirely independent method involving asymptotic binomial/multinomial probability. That exercise is left, as they say in the ultimate cop-out, to the reader.

This work has highly important implications with respect to a cancer research, and for solutions to poverty, malnutrition, and climate change. It can also help one discern if tree samplers 150-200 years ago were often sampling the closest trees or not.

Funding for this work was provided by the Doris Duke Foundation, the Society for American Baseball Research, the American Bean and Tree Counters Society, the Society for Measuring Things Across From Other Things, and the Philosophy Department at the University of Hullaballo. All rights reserved, all obligations denied. Any re-use, re-broadcast, retransmission, regurgitation or other use of the accounts and descriptions herein, without the express written consent of the closest random stranger on the street, or the closest random stranger on the other side of said street, is strictly prohibited.

Golf course succession

A friend’s property, in the county my parents live in, is surrounded by a nine hole golf course that went out of business several years ago, and is about to be acquired by the US Fish and Wildlife Service. It is undergoing rapid ecological succession to a less managed state since they stopped mowing a few years back. This process is very common with abandoned farm land, but this is the first I’ve looked at a golf course. The place is interesting because the area is naturally wet, being originally part of a very large swamp/wetland complex (the “Great Black Swamp”) that stretched over many counties and caused this area to be the last settled in Ohio. The original vegetation, documented in 1820, was dominated by intermixed treeless wet prairie, and swamp or other northern wetland hardwoods, with standing water over the entire year common. The inherently wet soils might well have affected the course’s success, I don’t know.

Several tree species mentioned in the 1820 GLO land survey notes (see bottom image) are still present, including swamp white oak (Quercus bicolor), american elm (Ulmus americana), pin oak (Q. palustris), green ash (Fraxinus pennsylvanica), hickory (Carya cordiformis), eastern cottonwood (Populus deltoides), and unspecified willows (Salix spp.). Others have clearly come in post-settlement, including black walnut (Juglans nigra), northern catalpa (Catalpa speciosa), weeping willow (Salix babylonica), possibly silver maple (Acer saccharinum), and the completely misplaced jack pine (Pinus banksiana) and eastern redcedar (Juniperus virginiana) (most likely both as yard markers and fairway dividers). How the USFWS will manage the property will be interesting; it may be difficult to recreate the wet prairie habitat given that the natural drainage pattern is now highly altered by ditching and drain tiling.

Wet prairie and hardwood swamp, to farm, to golf course, to...

Wet prairie and hardwood swamp, to farm, to golf course, to…


Goldenrod (Solidago spp), a notorious and obvious late bloomer.

Goldenrod (Solidago spp), a notorious and obvious late bloomer.

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Camping among the tombs

I found a road which led me to the Bonaventure graveyard. If that burying-ground across the Sea of Galilee, mentioned in Scripture, was half as beautiful as Bonaventure, I do not wonder that a man should dwell among the tombs. It is only three or four miles from Savannah…Part of the grounds was cultivated and planted with live-oak, about a hundred years ago, by a wealthy gentleman who had his country residence here. But much the greater part is undisturbed. Even those spots which are disordered by art, Nature is ever at work to reclaim, and to make them look as if the foot of man had never known them. Only a small plot of ground is occupied with graves and the old mansion is in ruins.

Bonaventure Cemetery

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