I have been studying the Earth's
climate since 1980, when I began work at TVA's Engineering Laboratory. Our Team
had many projects that included weather data. This was also when global warming
and climate change was a newly rising concern. As many of our analyses depended
on weather data—both past (known) conditions and also future (possible)
conditions, I developed a lot of software to process this type of data.
One of the
most carefully scrutinized projects we ever undertook came down as a
presidential edict to answer the question: How would climate change impact
TVA's water and power supply, down to the equipment level (power generating
stations, transformers, transmission lines, etc.)? A team was formed,
consisting of a variety of experts, specializing in the fields necessary to
adequately address this question. The Team included meteorologists, biologists,
engineers, and software developers. I developed all of the software that
modeled the response of the power system to the climatological inputs, which
were provided by the meteorologists and reservoir specialists. Our report can
be found here: TVA/DoE
Report
I am not new to this
investigation, nor is my expertise limited to a narrow subset; rather, I bring
decades of experience to bear upon this topic. One of the things that I have learned
over the years (which is not unique to climate data) is that you can get
whatever answer you want if you carefully select the data. Considering only
that subset of the data that agrees with your hypothesis is called: cherry-picking.
A more euphemistic term is: confirmation bias.
Four decades have passed since I
began studying this subject—more than enough time for some trends to emerge.
While there may have been a strong argument for relying on climate models
(computer programs that simulate climate response to various inputs) back in
1980, there is no such justification for heavily depending on these now that we
have accumulated actual weather data from around the world.
Here, we will only consider actual
(that is, measured, historical) weather data. No climate
"models" will be involved whatsoever. As there are no models
there are no assumptions (questionable or otherwise) as to what effect various
emissions or deforestation or orbital changes or projections or regulations or
anything else might have on the weather. We look only to the past—what has
already occurred and has been observed (i.e., measured).
There are several sources for
weather data. Sadly, some of these correct or adjust the results
before archiving them. Correcting or adjusting data inherently involves
assumptions, which may or may not be true. I choose to not consider any data
that has been tampered with in any way. I choose to base my analyses on RAW
data; that is, untouched, uncorrected data. You may choose
some other criterion, but this is mine and I stand by it.
The source of this data is the
National Oceanographic and Atmospheric Administration (NOAA) National Climate
Data Center (NCDC) Global Surface Summary of the Day (GSOD). All of the files
and documentation can be downloaded from their ftp site: ftp://ftp.ncdc.noaa.gov/pub/data/gsod/ There are thousands
of stations around the world, as shown in the following figure:
We will not model the
weather around the world, we will simply paint it. In order to do that,
we must select a projection plus a method to fill in the spatial gaps between
the meteorological stations. The Mollweide Projection is a familiar one and easy
to handle mathematically. I have tried several two-dimensional interpolation
schemes in the past and found that these work but produce unwanted artifacts
when the points are spaced so unevenly, as are the stations in the preceding
figure. Another approach is to solve Laplace's Equation (see Wikipedia for
details), which not only matches the data exactly at every known point but also
happens to be the governing differential equation for heat conduction,
diffusion, and inviscid flow.
The NOAA/NCDC/GSOD data go back
to 1929 but there were only a few stations before 1973, so we begin our
calculations (i.e. painting) then. We paint one frame for each day,
showing the number of stations reporting. Colors vary from blue (cold) to red
(hot). The stations are indicated by tiny black specks, which change from
day-to-day and year-to-year. Remember that this is NOT a model,
but merely an interpolation between known points. Laplace's
Equation is solved for the whole globe for each day. The calculation grid
consists of 64,082 nodes and 128,880 triangular elements covering the surface
of a tessellated sphere. These are mapped onto the distorted Mollweide
Projection but are themselves not distorted. Of the nearly eighteen thousand
frames, I have picked out the first of each month [I have every day and
all of the 3D FEM sets if you want them.]:
This is what the grid looks like:
Once we have the temperature over
the entire globe it is a simple matter to calculate the average. As the nodes
and elements are all equally-spaced over the surface of a sphere, this is
inherently an area-weighted average. I couldn't care less what the temperature
trend is in New York City, Denver, or London, as these are tiny insignificant
specks compared to Alaska, Australia, and Brazil! What does the trend
look like for the whole Earth?
The blue line exhibits a cycle due
to the elliptic orbit of the Earth (i.e., the path around the Sun is not
circular). The red line is the yearly average. The magenta line is the trend
from 1973-2021. This global trend is about +1.5°C/century (or 69 years/°C), as
indicated in the legend. James Taylor, president of the Spark of Freedom
Foundation, wrote an article for Forbes entitled, "Climate Science Reaches
a Landmark That Chills Global Warming Alarmists," on 12/28/2011, also
citing 1.4°C/century: https://www.forbes.com/sites/jamestaylor
Even more interesting is that, when we take a closer look, the warming trend is
driven by a few stations, as shown in this next figure for stations reporting
data for at least 20 years. There are relatively few red dots and even fewer
blue ones. Most of the dots are green (nothing's happening).
If we take only those stations having at least 10 years of data and
some significant trend (minimal R²), as of 11/22 there are 7098,
and use these to paint the globe in the same was as above, we get the
following:
Let's dig deeper into this by limiting our focus to specific geographical areas and apply the same
technique (Laplace's Equation that matches every station exactly). For the contiguous 48 states we see:
Let's see what the trend is for this region... a downward slope of -0.24°C/century, which doesn't support the climate uproar.
Limiting
our data gathering to South America, we see...
...and the
trend for this region... an upward slope of +0.24°C/century, consistent with increased industrialization.
Limiting
our data gathering to Africa, we see...
...and the trend for this region... an upward slope of +0.55°C/century, also consistent with increased industrialization.
Limiting
our data gathering to Southern Asia, we see...
...and the trend for this region... an upward slope of +0.81°C/century, also consistent with increased industrialization.
Limiting
our data gathering to the Middle East, we see...
...and the trend for this region... an upward slope of +0.93°C/century, which may be due to several factors.
Limiting
our data gathering to Australia, we see...
...and the
trend for this region... a very slight downward slope of -0.04°C/century (not much happening).
Limiting
our data gathering to England, Ireland, Scotland, and Wales, we see...
...and the trend for this region... a slight upward slope of +0.25°C/century, which doesn't support all the whining about children never playing in the snow again.
We next consider the rest of Europe...
Unlike the British Isles, the Western European Continent exhibits a different behavior...
Instead of the 0.25°C/century trend for England and Ireland alone, for
Europe we see 1.11°C/century or 444% more. Considering the daily
variation, this doesn't appear to be a significant effect, but when
plotted without the daily variation, a trend clearly emerges:
This last average temperature trend graph reveals Western Europe as one of the "problematic" climate zones on planet Earth. Temperatures aren't the only information in this database, I have also analyzed humidity:
as well as rainfall:
and also wind:
plus barometric
pressure. Did you know that wind speed and direction are proportional
to the gradient (vector derivative) of the barometric pressure?
As described above, I use a type of area-weighting to consider this data. I do not use individual stations or simple averages, as this would implicitly weight the many sensors located at airports, which are themselves "heat islands" and located near urban centers, which are also heat islands, resulting in significant bias. You can read more about urban heat island bias here: "Evidence of Urban Blending in Homogenized Temperature Records in Japan and in the United States: Implications for the Reliability of Global Land Surface Air," by Genki Katata , Ronan Connolly, and Peter O’Neill, Journal of the American Meteorological Society, 2023. https://journals.ametsoc.org/view/journals/apme/62/8/JAMC-D-22-0122.1.xml
In summary, I have analyzed a WHOLE LOT of real, measured climate data and the situation is not as simplistic nor as dire as one might conclude from watching the evening news or reading the various papers published on the subject. I literally have all of this data, all of the software, and every single frame of every one of these animations plus a whole lot more on my keychain. I personally prefer to base my decisions on facts and not speculation but you can make your own choice...