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.
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 1980. It 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.
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.
Note bold indicates a link. My Blogger will not underline automatically.
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 |
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 1980. It 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) |
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.
Note bold indicates a link. My Blogger will not underline automatically.
The major contributing factor to climate change is human population (pollution.) It took from the beginning of mankind to beginning for the 20th century to reach a human population of one billion. We have gone from one billion to seven billion people in 100 years.
ReplyDeleteCO-2 is not the cause of climate warming; that is only the symptom. We humans are the cause.
At what point do we exceed the earth's ability to support our needs?
the Ol'Buzzard
Ol'Buzzard I am afraid we may have already but science has and may continue to allow us to push well past the limit until Nature makes a huge correction.
Deletewe're fucked
ReplyDeleteThis is what climate scientists have been struggling with from the very beginning: how do we know if our measures of climate are valid? After 40 years of work, they think they know, but… A note over on the latest RealClimate open thread (http://www.realclimate.org/index.php/archives/2020/03/unforced-variations-mar-2020/) might draw some response from actual climate scientists, as well as cranks.
ReplyDeleteOl'Buzzard, hesitate to disagree with a fellow avian but, no, actually not; the carbon footprint of people in high-income countries is hugely more than that in low-income countries. Now, habitat destruction, other environmental problems, yes, the vast numbers of humanity do matter. Climate change is a pressing environmental problem, but it is not the only one, and sometimes climbing one mountain simply reveals the next peak.
One question I have been wondering at: to what extent did fossil energy contribute to the vast growth of human population? I don't know the answer. It would take some actual research to get a handle on this and I have not yet committed the time. So far my personal life has been taking the foreground, not to mention writing about the on-going plague.
This chapter of the IPCC Fifth Assessment discusses "regional" simulations (awfully large regions!): https://www.ipcc.ch/report/ar5/wg1/aism-annex-i-atlas-of-global-and-regional-climate-projections-supplementary-material-rcp8-5/; you might try comparing its predictions with actual data. Coverage of your region starts at page AISM-32.
ReplyDeleteRaven, I could not make head nor tail of the first link. The second link did provide forecasts from 2016 on for huge areas but I am more interested in what has happened to date - in Kansas or in the south half of Saskatchewan.
ReplyDeleteYes, population brings environmental problems, with the populations of developed countries using vastly more resources and contributing not just to CO@ but environmental degradation of the less developed countries whose resources, including labour we take at far below their value and have done so for centuries. The migration crisis the world over is just the chickens coming home to roost.
The plots of use of fossil fuels and increased population likely run almost exactly parallel. I would say the use of hydrocarbons in agriculture, like fuel, fertilizer, etc, was a huge factor in population growth but I might be prejudiced.