Guest essay by Leland Park
Before Climate Science, basic physics differentiated between the terms heat and temperature because they are related – but do not have the same meaning. The classic heat equation, from physics provides the principal relationship. Accordingly, the relationship between the heat content of a substance and changes in its temperature is given by:
Q = m * c * ΔT
where m is the mass and c is the heat capacity of the substance being measured
From the USHCN we have a record of near-surface temperature readings from the 1800s to the present day. The “monthly” versions of USHCN data are composed of yearly station records containing both monthly and annual averages. These records can be used to construct year to year incremental temperature changes for each USHCN station. The function displayed in Figure 1 is a composite network averages of the incremental changes in Tmax (annual average high temperature). The number of actively reporting stations varies, but reaches about 1100 in the 1930 to 1940 period.
Several observations can be made based on Figure 1
- No unambiguous warming trend is evident in the unadjusted Tmax records
- Heat change is cyclic between warming and cooling phases.
- Tmax change is well-behaved throughout the USHCN history, despite significant local differences among the many USHCN stations.
- Complex dynamics are evident in the pattern of heat changes..
Figure 2 is a histogram of the same data that contributes to the function in Figure 1. The fact that the histogram is symmetrical about the 0 axis is confirmation that there is no long-term warming (or cooling) trend. We know this because of the special case that makes it unnecessary to have values for the mass and heat capacity.
Q = m * c * ΔTmax = m * c *  = 0
Interestingly, the same analysis on Tmin data tells us a lot about the energy balance over time. That is because Tmin, (the daily low temperatures) represents the point at which the nightly cooling ceases and daily warming resumes. The temperature change analysis for Tmin is presented in Figure 3:
As with Figure 1, there is no unambiguous trend of warming or cooling in the Tmin change function. This is further confirmed by the histogram of the data in Figure 4:
Absence of a warming or cooling trend for annual Tmin is, again, given by the heat equation:
Q = m * c * ΔTmin = m * c *  = 0
Whatever heating takes place during the daily and seasonal warming cycles is being fully dissipated by the corresponding daily and seasonal cooling cycles. Otherwise there would be a heat change trend in Tmin data. Bear in mind that all of the figures are based on annual average values so seasonal effects are subsumed in the analysis..
Analysis Using Adjusted Tmax Data
The same analysis on adjusted versions of the Tmin data yields identical results. As with the unadjusted data there is no unambiguous heat change trend in evidence. In fact, there are only marginal differences in the amplitudes of the warming and cooling cycles.
The Real “Global Warming”.
Climate Science looks for Global Warming in computer models that are designed to produce it – in small amounts. Meanwhile the observational data reveals massive warming. Everyone (in the Northern Hemisphere) knows that winter is colder than summer. The winter to summer warmup is a natural seasonal pattern that is offset by the summer to winter cool-down. This annual cycles caused by normal patterns of solar levels.
Figure 6 displays the pattern of seasonal warming over time based on the difference in temperature between January and July, the peak temperature points. For the US, the seasonal warming is around 45°F while the range is from about 40 to 55°F. That is a massive amount of warming and a large amount of variation. It is much larger than the presumed trigger level for global warming, yet is unremarkable to climate scientists.
The mystery of Climate Science is that massive, regular seasonal warming cycles are unremarkable, but small changes in annual temperatures signal catastrophic climate change. Go figure.
Since proof through data is not a specialty of those who support the consensus, there are a few issues that might be raised concerning evaluation of the analysis.
- Greenhouse effects, if any, cannot be distinguished by these methods.
- Urban heat island effect, though real, is not significant in this analysis method.
- Using annual averages minimizes the significance of short-term weather effects.
- Tavg is not a measurement parameter, conflates Tmax and Tmin behaviors, so it is not used in the analysis.
- Using incremental changes to Tmax and Tmin effectively normalizes otherwise disparate station data and permits aggregation.
Reference: “monthly” versions of USHCN data here http://cdiac.ornl.gov/epubs/ndp/ushcn/monthly_doc.html