Alberta, Saskatchewan, Manitoba, Northwest Territories, and Yukon cooling too

In addition to BC cooling over the last 15 years, so are the following Canadian Provinces and Territories.

Alberta ( -0.366C / decade), Saskatchewan (-0.411C/ Decade) , Manitoba (-0.231C / Decade) , Northwest Territories (-0.28C/ Decade), Yukon (-0.411C)/ Decade

The data is slightly different from the BC post in that I now only use stations with Climate Normals and 170 out of 180 data points in  the last 180 months. In the case of BC it actually changes the cooling slightly.

Instead of -0.501C / Decade it is -0.463C/ Decade.  Graphs below. Click for full size.



  1. Have a look at my paper on Bright Sunshine Hours vs Max Temperatures Central UK 1930-2010 at

    If there is Canadaian data on Bright Sunshine or straight sunshine, I could do the same with daytime temperatures. The same analysis probably would work with average temperatures, but since sunshine lead directly to daytime temperatures, and nighttime temperature (from experience) seem more related to cloud cover, I expect the connection would be moderated – though of course the nighttime heat energy came during the sunshine part of the day!

    I’ve wondered if the practice of taking the 1361.5 W/m2 and averaging them to 341.5 W/m2 for both cross-sectional capture area AND nightime is a source of error, as there is no TSI during the nighttime, but only radiative loss which occurs or is controlled by several mechanisms. The averaging also eliminates the orbital variation of 22 W/m2 (6.8% due to orbital eccentricity) that is NOT regionally similar as the northern himsphere sees the sun at aphelion while the southern sees the sun at perihelion, but the land/ice/sea ratios are very different.

    The 1970-2010 period recently was shown to have an increase of cloud cover over the northern hemisphere at the same time as a decrease over the southern. The data is not statistically good for distribution or density, but seems to show that both changed at about the 15% level. Yet the gross, not the anomaly, is what counts, and when, due to the difference in TSI during the year. Cloud cover increases at night, especially low loud cover, keeps dark-time temperatures higher, while low could cover during the day keeps the day hours cooler near the ground. If the Antarctic area has less clouds during the local summer and the Arctic has more cloud cover during the local winter, the global temperatures will go up without either the influence of CO2 or even TSI changes.

    Regional changes are not considered in the global situation even while the Arctic is claimed to be warming more than other areas (and consistent with CAGW theory). But you can get a global change by changing heat distribution regionally – the math is simple. The interpretation is not.

    Take some moist heat from the equator, transfer it high up to the Arctic, where it is used to warm dry air. The adiabatic rates of wet and dry air will result in a volumetrically higher heat gain in the surface Arctic than loss in the surface equatorial region. Statistically, you have Global Warming. Regionally you have energy distribution.

    NOW add in the 0.82 (or so) W/m2 increase in TSI since 1880. See what happens?

    Statistics as a mathematical thing have perfect validity, but when applied to the human experience, they may not hold any truth. The average human being, after all, has one breast and one testicle. Doesn’t much reflect what your “average” human being looks like, though,.


    1. I did BC and Alberta bright sunshine for July a while back. No surprise correlation. Of course in Canada, bright sunshine tends to be negatively correlated with temperature in Dec/Jan/Feb which makes sense. No clouds = colder in the winter.

      There are very few stations recording bright sunshine in Canada compared to 20 years ago.

      I’ll do a post on it soon.


  2. Serious error, corrected:
    The adiabatic rates of wet and dry air will result in a volumetrically higher TEMPERATURE [ERROR: heat] gain in the surface Arctic than loss in the surface equatorial region. Statistically, you have Global Warming. Regionally you have energy distribution.


  3. Why is your data displayed in thousands of a degree, when the source is only in tenths? The thermometers are only calibrated to 1/100 of a degree. A liquid in glass thermometer has an uncertainty of 0.10°C in the range -30°C to 50°C. The temperatures may be a tenth of a degrees higher or lower, but if the error is random they likely will average out.

    This is not the first time I have seen this from people reporting climate.
    “ …. report your answer with the same number of decimal places as the number with the least number of decimal places. For example, if you are adding 5.113 and 2.0, your answer should be rounded to the nearest tenth place, or 7.1. You can’t assume that the second measurement was 2.000, so your answer should never be more precise than the measurements used in the calculations.


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