Friday, March 27, 2020

Lessons from Lucy by Dave Barry – A Book Review

Dave Barry is to the written word what Larson and Jim Unger are to the single frame cartoon. He sees the world in all its lunacy from a different perspective and puts it into side-splitting print. If you haven’t read his Complete Guide to Guys, you owe it to yourself.

Lessons from Lucy is a more serious yet still funny look at what he has learned from his old dog Lucy, who is far happier in her old age than he is in his.  He turned 70 in 2017.

How can Dave Barry be 70? I thought he just turned 50 the other day. Of course, I think I just turned 50 the other day too. As another old man (70 in 2017) with an old dog, I learned a great deal from this book, which really applies to all ages.

Dave is subject to all the usual human foibles some of which increase with age. Lucy is not and is therefore spared the angst that goes with them. He learns seven lessons from Lucy. How he learns them makes for very funny reading. He says the lessons are not new, they are obvious and common sense. “Maybe I could learn something useful about happiness from my aging but consistently joyful dog. . . My problem is not that I didn’t know these things, it’s that I’ve done a lousy job of using what I know.”

1.       Make new friends (and keep the one’s you have).
2.       Don’t stop having fun (and if you have stopped, start having fun again).
3.       Pay attention to the people you love. (Not later. Right now).
4.       Let go of your anger unless it’s about something really important which it almost never is.
5.       Try not to judge people by their looks, and don’t obsess over your own.
6.       Don’t let your happiness depend on things; they don’t make you truly happy and you’ll never have enough anyway.
7.       Don’t lie unless you have a really good reason which you probably don’t.

He learned one more lesson, this time from his daughter but reinforced by Lucy. You’ll need your Kleenex out for this last chapter. Spoiler, it ends happy.

Be grateful for what you have (it’s probably more than you think).

These are the kinds of lessons you never truly master but must always work at improving every day

Sunday, March 22, 2020

Bluegrass Music – Amundsons and Vaadelands, Saskatchewan Talent

The tiny farming and ranching community of Park Valley, up against the west side of Prince Albert National Park, is a few km from the closest town, Debden (330 people) and an hour to Prince Albert. Park Valley may be small, but it has produced some fine people and some fine musical talent.

The Amundson Family (, goes back four generations since they immigrated from Norway. I’ve known the grandfather, Irwin since I was a teenager and son Darryl since he was a teenager. Now Darryl and his kids are performing bluegrass and old-time gospel music. Darryl plays 5-string banjo, guitar, mandolin, and bass. His daughter, Sonora is 17 and plays fiddle, mandolin, and bass for the band. She also plays the piano. Son Ira is 15 and plays banjo and guitar for the band. Nate is 11 and plays mandolin and bass for the band. Anna is 8 and learning fiddle.  They have 2 albums available on CD and most digital formats such as iTunes, Spotify, CDBaby, etc. Their newest project will be out in the next two weeks.

Here are the Amundson Family and Jake and Ira performing at Telemiracle

Darryl (5-string banjo) and a friend, Gord Vaadeland (guitar), started playing together in high school and then added Darryl’s dad (mandolin) and a friend, Ken Olsen (guitar), and The Baler Strings were born. They were mainly bluegrass gospel, and all shared in the lead vocals and harmony. The group recorded a couple of tapes and performed throughout the 1990s including some fairly well-known festivals, including the Blueberry Bluegrass Festival near Edmonton in 1996. Bill Monroe was one of the feature guests and it was his last major event before he died. Darryl had met Bill Monroe in Nashville a few years prior and the band jammed with him one evening. The band broke up when Darryl and Gord grew up and moved on with their lives.

Now Darryl’s son, Ira (15) and Gord’s son, Jake (16) have formed a bluegrass duo called, simply enough Jake and Ira Music ( Ira gets his talent from two generations of Amundsons. Jake gets his musical talent from both parents. Gord’s current band is more of a cowpunk kind of thing (blend of rockabilly, outlaw country, and rock) called One Bridge Town (named after PA). He plays guitar and does the singing and songwriting. Jake's mom, Sheila also plays guitar and sings, and currently plays in a Celtic band called Back of the Bus.

Jake and Ira are highly proficient on both guitar and 5-string banjo and as old fashioned bluegrass as they can get. They wear “Stanley Brothers” suits (black suits, white shirts, and red bow ties), sing the old songs and often record their videos in black and white. They recorded their first album “Dueling Banjos” on reel to reel (available on CD) and if they get enough orders will make a vinyl LP record. (From the looks of it, they even tackled their own webpage

The boys practice in a local church basement, putting 10 to 20 minute videos on YouTube. They have played at several bluegrass festivals and will be playing (along with the Amundson Family) at the Northern Lights Bluegrass Festival and the Blueberry Bluegrass Festival this year, Good Lord and coronavirus willing, been interviewed on CTV and CBC TV, performed at Telemiracle a couple of times and in many local halls across northern Saskatchewan.

This is an early video of Jake and Ira before Jake got his Faron Young hairdo.

Last fall Ira won a scholarship to the Bill Monroe Mandolin Camp (  so Jake signed up too and the dads went along for the ride. All the essential bluegrass instruments are taught at every level by well-known professionals along with bluegrass harmony singing. The last day, several of the students performed with Ricky Skaggs at the Stationhouse. This video clip is of several youngsters (three girls and Jake and Ira) jamming in the evening. Well worth listening to. There are more Jake and Ira videos on their Facebook page than on YouTube.

Jake Vaadeland put together a second bluegrass band called "Jake and the Sturgeon River Boys", as he is freer to travel than Ira, and wants to play more than Ira is able to. Jake does his schooling online, has his driver's license and a car, etc. Ira is a year younger, still goes to public school and also has the family band. So, Jake started the second band to fill those gaps. This frees Jake up to take a few more shows, learn from new and more veteran players and keep getting more experience.

Thursday, March 19, 2020

Biden and Sanders

There is enough politics and coronavirus on Facebook so I have kept my activities there rather than add one more pundit's opinion on my blog. But the fact that we will likely lose Bernie from the Democratic competition makes me sad and yet not. I cheered for Bernie Sanders and Elizabeth Warren from the get-go because if America is ever going to catch up with the rest of the developed world in terms of looking after her citizens, it will take a revolution not evolution. To me, Joe Biden is Republican light, as was Obama.

But Joe Biden can beat Trump which comes before all things. And people like Bernie and Elizabeth have moved the Democratic party at least back in the general direction of progress and as senators, they can keep the pressure on. And there is AOC who 25 years from now can take her own run at it.

Canada has three national parties, Conservative, Liberal, and New Democratic. Right, centre, and left. Though the NDP has successfully governed several provinces from time to time,  it will likely never form a national government.  But EVERY progressive piece of legislation Canada has seen had its beginnings in CCF/NDP party platforms and was eventually picked up by the Liberals. They have been successful even in their failures.

There was an opinion piece in one of the MSM that said Sanders was not FDR but was more like Eugene Debs. Debs would not compromise; "he would rather be right than be president". That is Bernie to a T, I think. He has remained focused on the big changes that need to be made. I hope he will be successful, even in his failure.

Wednesday, March 11, 2020

Another Look at Swift Current Temperatures

Les Henry’s Article on Temperature Records from the Swift Current Research Station, which is posted on my blog February 16th drew a comment from Ravin Onthill regarding the use of Simple Moving Averages (SMA). My curiosity got the best of me, so I did some reading on SMAs and then on how temperature norms are calculated and applied to determine Global Climate Change, which I posted on March 6th. I found the historic Swift Current data and followed through on my determination to play my own games with it. (Raven, this is why I haven't had time to follow up on the links you sent me).

The raw monthly mean temperatures were compiled from between 1886 and 2019 (with Jan and Feb 2020 included). No corrections were made to account for any changes in time of reading, changes of location, changes of elevation, or changes of equipment. That was left for the experts.

When more than one site is compared, a normal period of e.g. 30 years is used for each one and the differences from normal calculated. As only one site is looked at here, it is not necessary (and I spend two days doing it to learn that).

Annual and monthly mean temperatures are charted as scattergrams with the y-axis coordinates set to include even the widest outlying observation and the x-axis coordinates set between 1880 and 2020. Mean and Standard Deviation were calculated for each set of data. About two-thirds of observations fit within the mean plus or minus one standard deviation, an estimate of how widely (wildly) the observations vary from the mean.

A 6th order polynomial trend line (I have no idea what that means) was calculated for each chart as it produced the largest value for R2 i.e. the best fit. The R2 value was posted to the chart. R2 is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple regression. The smaller the R2 the less the trend line explains variability in the observations. 0% indicates that the model explains none of the variability of the response data around its mean. The polynomial trend line is in blue on the charts. A five year SMA, in red on the charts, was calculated just to bring the scattergram into a more comprehendible format.

 Annual mean temperatures varied from just under 7ºC to just over 0ºC, with a mean of 3.7ºC and standard deviation of ±1.1ºC. The trendline explains about 13% of the variability of the observations from the mean.

 January temperatures varied from about -28ºC to -3ºC with a mean of -12.7ºC and standard deviation of ±5ºC. The trendline explains about 11% of the variability of the observations from the mean. February temperatures varied from -27ºC to 0ºC with a mean of -10.7ºC and standard deviation of ±4.8ºC. The trendline explains about 11% of the variability of the observations from the mean. March temperatures varied from a low of -8ºC to a high of+8ºC, with a mean of -4.5ºC and standard deviation of ±3.9ºC. The trendline explains 14% of the variability of the observations from the mean.

April temperatures varied from -2ºC to +11ºC with a mean of 4.7ºC and standard deviation of ±2.6ºC. The trendline explains about 6% of the monthly mean temperature variability. May temperatures varied from 6ºC to 16ºC with a mean of 10.9ºC and standard deviation of ±1.9ºC. The trendline explains only 3% of the variability of the monthly mean temperatures from the mean. June temperatures varied from 11.5ºC to 21ºC with a mean of 15.5ºC and standard deviation of ±1.7ºC. The trendline explains about 11% of the variability of the monthly mean temperatures from the mean.

July temperatures varied between 15ºC and 24ºC with a mean of 18.8ºC and standard deviation of ±1.5ºC. The trendline explained about 11% of the variability. August temperatures varied between 14.5ºC and 21.5ºC with a mean of 17.8ºC and standard deviation of ±1.7ºC The August trendline is about as flat as pee on a plate, explaining less than 1% of the variability of observations around the mean. September temperatures varied between just over 6ºC to just under 18ºC with a mean of 12.1ºC and standard deviation of ±2.0ºC. The trendline explains less than 5% of the variability.

October temperatures varied from a low of -1ºC to a high of +11ºC with a mean of 5.7ºC and standard deviation of ±2.3ºC. The trendline only explains 4% of the variability from normal. November monthly mean temperatures varied from a low of just under -15ºC to a high of +5ºC with a mean of -3.4ºC and standard deviation of ±3.6ºC. The trendline explains only 3% of the variability. December monthly mean temperatures varied from a low of -21ºC to a high of -2ºC with a mean of -9.4ºC and standard deviation of ±3.8ºC. However, the trendline was essentially flat, explaining only 2% of the variability.

Table of Means, Standard Deviations and R2
Making the best of a bad situation, the annual and five of the months had R2 of over 10%, so the trend lines might mean something. The Annual trend seems to be cooling from about 2010. The January trend is warming from 1980, while the February trend is cooling from sometime after 2000. March seems to have been warming up since about 1970. June monthly mean temps took a sharp rise in the 2010s while July has been pretty much normal since 1960, slightly cooler around 1980, slightly warmer around 2010 and showing a small amount of cooling in the last few years.

Seven months had R2 of less than 6%, meaning essentially the trendlines accounted for almost none of the variability of the monthly mean temps from the average. April and May and August through December, with R2 less than 6%, could be said to show no significant variance from the mean. In other words, no trend to warmer or colder than the mean other than normal year to year variations. April appears to be cooling slightly from about 1990 and May appears to be warming slightly from 2010. August appears to be cooling slightly from 2000; September warmed from 1980 to 2010 then cooled; October has been cooler than normal since 1970 and really cooled off after 2010. November shows no change since about 1950, while December shows no change since 1930.

One thing did stand out for me and that is the cyclical nature of the trendlines, especially on the Annual chart where they appear to be about 60 to 70 years in length. I have read research articles, I think from an Alaskan University professor, which of course I cannot find when I need them, that talked about 30-year and 60-year cycles superimposed on the longer term.

If you are looking for 95% confidence levels, don’t go into the weather/climate business. It may surprise many people that science -- the de facto source of dependable knowledge about the natural world -- cannot deliver an unqualified, unanimous answer about something as important as climate change. NASA.

Annual and monthly mean temperatures over the past 135 years went up and down like a toilet seat at a keg party. A normal year is how it usetawas or next year.

This was fun but please don’t bet the farm on it one way or another. Anyone who thinks climatology is simple, is just kidding themselves. I’m smarter than I was – I won’t do this again. This was a week's worth of number crunching.

Friday, March 6, 2020

Calculating Climate Change in Your Backyard

The global climate is changing as it has always done but is currently giving us some cause for alarm as it is warming rapidly with accompanying changes in severe weather patterns. 

Global average temperature is convenient for detecting and tracking changes in Earth's energy budget—how much sunlight Earth absorbs minus how much it radiates to space as heat—over time. Scientists are not interested in the absolute temperature but rather the difference between the temperature and some normal period used as a baseline. Climate change is constantly referred to in news articles and every month there are maps of the world from a number of sources showing variations or anomalies from some baseline temperature, red being hotter, blue being colder and white being little change. 

Daytime Land Surface Temperature Anomalies for Dec 2019 and January 2020
Dark blue is -12C and dark red +12C
Land surface temperatures for the above maps were collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite. The baseline against which these maps were prepared is from 2000 to 2008. I can vouch for the dark red over Eastern Europe as we had a very warm winter with few temps below freezing and no snow. Even Moscow was low on snow.

So what is normal? World Meteorological Organization considers a 30-year average as a "climate normal". Every organization connected with climate seems to pick its own climate normal period. Whenever you read about temperatures (or any other weather-related event) varying from the norm, you need to make sure you know what they are using as a norm. NOAA uses 1981-2000, and several other variations of that, but also use 1901-2000.  Hadley Center/Climate Research Unit (HADCRU) associated with the University of East Anglia in the U.K. uses 1961 through 1990. Goddard Institute for Space Studies (GISS), part of NASA uses 1951 through 1980It doesn't seem to matter which period is used to calculate the difference from the current temperature so long as it is stated. The end results look remarkably similar.

HADCRU and GISS are two organizations that track global air temperature from about 30,000 stations around the world of which about 7,000 have long term, consistent monthly records.  As technology gets better, stations are updated with newer equipment. When equipment is updated or stations are moved, the new data is compared to the old record to be sure measurements are consistent over time. 

Concern over urban heat island effect was examined in two separate studies including the independent Berkley Earth group who asked the question: "is the temperature rise on land improperly affected by the four key biases (station quality, homogenization, urban heat island, and station selection)?" Their conclusion was NO. None of those factors bias the temperature record. Urban Heat Island effect is real and if there are no nearby rural stations then those stations are not included. 

Comparison of spatially gridded minimum temperatures for U.S. Historical Climatology Network (USHCN) data adjusted for time-of-day (TOB) only and selected for rural or urban neighborhoods after homogenization to remove biases. (Hausfather et al. 2013)
Global Climate Change is all well and good but "How will it play in Peoria?" or Prince Albert or. . . Can we as untrained individuals get an idea of what has happened in our own backyard? 

I posted Les Henry's article on long term temperature change at the Swift Current Saskatchewan Agriculture Research Centre. Raven Onthill from Advice Unasked raised a valid point about the use of rolling or moving averages to smooth out the variability in observed data, referencing an article in Wikipedia on moving averages. Being at best an amateur at statistics, I figured I better get educated. I don't know whether to kick Raven or thank him for that rabbit hole he led me down. I hope I learned something.

Les Henry used a simple 30-year moving average to smooth monthly temperature readings from 1886 to 2018. Averages from 1886 to 1915 became the temperature for 1915 and 1999 to 2018 became the temperature for 2018. The disadvantage of this approach is that it conflates time and temperature. According to Wiki, in science, a rolling average is calculated with half the numbers ahead of the data point and half behind. So the first data point should have been for 1900 averaging 1886 through 1915, on through to 2003 which would have been the average of 1988 through 2018. But that doesn't tell us anything about the last 15 years.

Les said in his article the data is available from Environment Canada. I have not had a chance to look for it yet but I will. I love to play with numbers so I would rework them in a couple of ways. First I would use a century average climate norm to calculate the monthly temperature anomalies. I would also use 1981-2010 as a climate norm to calculate monthly temperature anomalies.  Those data would be simple enough to plot as an Excel scattergram both as annual and as five-year averages. Excel supposedly can calculate polynomial regressions. I have never done that. I'm a simple linear regression person. 

If that works, then I will look for more long term data from individual weather stations to play with.

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