Dave Dixon has done tremendous work on CV-19 data, but his skills as a data analyst and his interests go beyond that. As he notes below, he has tracked temperatures in his area of Minnesota for some time. For those not familiar with the sorry state of so-called climate science, in the futile pursuit to stop minor human impacts on climate, the temperature data sets from the US and around the world have been substantially “adjusted”, with those adjustments almost always seeming to make the past look colder and the present warmer, so that we can see a supposed big jump in global temperatures. Not sure why those adjustments are even needed and far better minds than mine have more than adequately destroyed the raionale for those adjustments. Among the real issues with temperature data is that as the US and the world become more urbanized, city and suburban temperatures are affected by the heat impacts of such dense population areas, which tend to raise maximum temperatures and minimum ones on a daily basis. So it is more interesting to look at rural temperature data collection sites, which are less affected by this urban heat. That is what Dave Dixon has done below. Others have done similar work with a similar finding. Interestingly, if there are slightly higher night-time low temperatures, that is likely a cloud effect. As far as I can tell the biggest problem with climate models and the biggest uncertainty in climate science is what the effect on and of clouds is, when inputs to the system change. In any event, it is pretty clear from the charts below that there is no climate emergency if you define that as a substantial warming trend. In fact, there isn’t any warming at all.
- Kevin’s recent posts on climate, such as this one https://healthy-skeptic.com/
2022/04/06/long-term-and- recent-temperatures/, prompted me to share a little of data analysis I have dabbled with over the years. I have long been skeptical of the mainstream consensus on global warming, based on reading web sites for many years such as Steve McIntyre’s Climate Audit, https://climateaudit.org/. In my working years I didn’t always have a lot of spare time for climate analysis, and my retired years have been consumed by Covid analysis, but I have always monitored the temperature records at the weather station at St. John’s University in Collegeville, MN, the closest weather station to my home. This site is in rural Stearns County MN, and other the University itself, is surrounded by a mix of farmland, forests, and lakes, and almost no development of any kind.
- The daily climate summaries for US weather stations can be downloaded from the National Oceanic and Atmospheric Administration web site, https://www.ncdc.noaa.gov/cdo-
web/. Using the search tool, I download data every month for the Collegeville, MN station, ID No. USC00211691. This site of part of the Global Historical Climatology Network Daily (GHCND). A description of this network can be found here: https://www.ncei.noaa.gov/ products/land-based-station/ global-historical-climatology- network-daily.
- After downloading the daily summaries, I calculate the average daily temperature by taking the average of the daily high and low temperatures. The daily average temperatures are then averaged over each month to get the monthly average temperature. Fig. 1 shows the monthly average temperature, for all of the available data. The gap in the data during WW2 is prominent, but it is not obvious that there is any meaningful trend. However, we can see that since January 1982 there are no extreme cold winter months with average temperatures below 0 F. For example, February of 1936 had 18 nights with low temperatures -10 F or colder.
- Fig. 2 shows the same monthly average temperatures as Fig. 1, but is limited to data since January 1970. It is still not possible to visually detect a trend, except perhaps the absence of extreme cold winter months.
- The next step in the analysis is to calculate a ‘normal’ baseline temperature for each month, and then calculate the monthly anomaly, which is just the difference from each months’ average temperature compared to the baseline average temperature. Each months’ baseline is calculated using the 30 year period from January 1981 to December 2010. The purpose of the baseline is not to claim what normal temperatures are, but rather to just establish a baseline to allow us to evaluate the temperature trend over time.
- Fig. 3 displays the monthly anomaly from baseline for each month for the entire dataset. February 1936 stands out as the largest negative anomaly. Looking at Fig. 1 we can see other instances where the monthly average temperature appears similar to Feb. 1936, however the other cold months took place in December or January, which have colder baseline temperatures than February, and therefore smaller anomalies. Fig. 4 shows the anomalies for the same time period as Fig. 2, since January 1970.
- The final feature on Fig. 3 and 4, and really the entire point of this exercise, is the horizontal red line labelled “Net Cooling”. Starting from the March 2022, I calculate a linear regression going back to each month. If the linear regression has a negative slope that means that over the time period analyzed that there has been a cooling trend, and a positive slope means there has been a warming trend. As of March 2022 there is a net cooling trend since April 1980. Basically, we can say that for one weather station in rural Stearns County, MN there has been no measurable warming for 42 years. Going back further in time there is indeed a positive trend, meaning that the years prior to 1982 were colder than after 1982.
- This is obviously the most simplistic analysis possible, but it does help illustrate whether or not measurable warming is occurring. After all, if global warming is increasing at such a rate that it is an existential threat to humanity, surely we would see signs of it at individual weather stations? Not in the last 42 years in Collegeville, MN, apparently.