ANOTHER EPA Q
Updated: Aug 10, 2021
This blog entry is related to my blog entry “The Admiral And The Senator” and just goes to show that this is a method often used by an entity which wishes to push a certain idea regardless of the reality involved.
To begin with I have some problems with their chosen parameters. The first graph which shows “Heat Wave Frequency” is a good indicator because the number of events in any analysis is important to determine how accurate the analysis will be. As most researchers are aware any research with too small a sample has a high probability of being completely wrong. And based upon such small numbers such as a minimum of two heat waves per year and a max of six would indicate that any conclusions drawn from these numbers could have even up to an 80% chance that they are just plain wrong.
The next two parameters have some problems. Looking at “Heat Wave Duration” all by itself is, in my opinion, creating a graph of pseudo-information. The reason I say that is because if you’ll notice that in the 1960s it shows that each heat wave lasted three days but that doesn’t tell you anything in and of itself simply because if there were 100 heat waves that year you would have 300 days of heat waves, but if like in reality here you only had two days of heat waves that means you only had six days of heat waves. So the reality is comparing1960s to 2010s we can see that the number of heat wave days, which is what affects both people and the environment, in the 1960s each year they averaged six days of heat waves, and in the 2010s they averaged 24 heat wave days per year. The reason these numbers are important is because it indicates that from the 1960s to the 2010s the number of days that people could suffer from either medical conditions or death was 12 times greater in the 2010s as it was in the 1960s, not just 1.3 times greater (4/3).
Looking at the third graph, the “heat wave Season”, which is not really relative and could be deceptive when in one year you only have two heat waves that could have been three months apart or about 90 days between heat waves. And then you could have one year where you have 24 heat wave days that are only 50 days apart, for almost every other day being a heat wave. That kind of random occurrence would make the severity of the year completely out of whack. A much better parameter would be the one mentioned above, heat wave days.
The fourth graph, “Heat Wave Intensity”, is a good indicator also but could be a more relative parameter for defining problems using the number of heat wave days times Heat Wave Intensity to give a relative hazard indicator.
All of these graph changes that I have explained would tend to make it look as though the heat waves have become extremely bad since the 60s. But then extreme is a relative statement and means little all by itself.
So what do we have that can tell us the entire history of what we know of heat waves? To begin with we need to have the numbers for the entire country rather than just for 50 urban areas. Quite honestly the 50 urban areas that were selected do not even cover him 1% of the lower 48’s land area. That being the case we would have to have a lot more information about heat waves in the US to determine if heat waves are any more intense than what should be normal for the entire United States. And then we would need to know location in order to assign effect on humans and effect on things like crops and animals in the wild. None of that is present in this analysis.
The general feel for this first set of graphs points out that heat waves are getting worse in the United States since the 1960s, and points out that all four parameters they chose are getting worse.
The last information graph of concern shows the annual values of the U.S. Heat Wave Index from 1895 to 2020 and it covers the contiguous 48 states. This statement implies that it covers all known heat waves and not just those in the 50 urban areas of the previous graphs which makes it more reflective of the US heat wave characteristics. Add to that this graph is on a yearly basis and not on an average 10 year basis. These two combined together make it a much better indicator of the climate of the United States.
Although laudable that they included in this graph they still managed to put in wording that tends to kind of demean the graph itself. They say “An index value of 0.2 (for example) could mean that 20 percent of the country experienced one heat wave, 10 percent of the country experienced two heat waves, or some other combination of frequency and area resulted in this value”
They start by stating that the chart values as shown on the left “could mean “ a value based on multiplying the number of heat waves that year times the total area affected by the heat waves. And while this may just be a wording problem the fact of the matter is using the word “could” explicitly implies a high degree of uncertainty. That uncertainty may just be as they say in their explanation 20% coverage and one heatwave or 10% coverage and 2 heat waves but it does cast doubt in the people who read it simply because it is not specifically stated that this is a graph of area times duration. And you will notice that in the previous four graphs they very specifically tell you exactly what each parameter is and how it is treated (10 year average) to instill a sense of truth and finality into that graph and a sense of uncertainty into this graph.
They also include the statement “some other combination” which would indicate that for some reason the graph values do not use the same method throughout to get the value, introducing another doubt about the accuracy of the graph. If they had left this section off then there would be no doubt that the “could” at the beginning was just their method of explaining how the values were derived. But this second statement of “some other combination” places the values less reliable compared to the previous graphs.
Another trick used by Q’s to confuse the issue. Now whether this was done deliberately or if the people that were writing it are just not capable of properly expressing what they are doing we can’t say. What I can say that is if I was annotating these five graphs and four of them had such precise indication I would also want to ensure that this graph had a precise indication of how the values were derived.
That would look something like (and assuming that the only parameters were frequency and area) I would have noted that:
“An index value of 0.2 indicates that the product of the frequency times the area was 0.2. It does not separate out frequency and area as individual components.”
An interesting thing to notice about this fifth graph is that overall the combined human and environmental severity (frequency times area) was much lower during the 1960s and 1970s than it is now. The only problem is that if we exclude the 1930s because it is so completely severe compared to all the other times then we find that now is about the same as between 1895 and 1960.
A devious person would presume that the EPA decided to use specific parameters that all increased on the average since the minimum ever recorded was reached in the United States during the 1960s.
And to exclude the possibility of anybody noticing how bad the 1930s were they simply went straight on to 2020 instead of including everything from 1900 through 2020 the graph of which would indicate that the 1930s were exceptionally high and the 1960s were exceptionally low.
I have to wonder why they would include this graph with their four previous graphs? The reason I ask that is why in the previous four graphs would you pick a starting point at the very lowest readings and then continue on into higher readings without starting at the prior higher reading and showing the decline to the lowest and then the increase back to the normal afterwards? They may indicate that it has something to do with the first four graphs being only the 50 urban areas they arbitrarily chose but all that would gain them was acknowledgment that they knew that the 60s were the lowest and they knew that had been rising and returning to normal since then whether it was in the 50 urban areas or in the suburbs and rural areas.
Things like this are the reason there is so much talk lately about the unreliability of scientific research. And not just as far as global warming but even subatomic physics, mental health, and medical studies. So quite honestly it is the normal Q in research today and everyone must be aware of this tendency in the scientific community in order to make reasoned judgment as to the reliability of what is presented by the media which also adds distortion to the public about scientific results.