Global Climate Change
Scientists Descriptions vs. Common Experience.
Climatologists say that our planet is getting warmer. They say that average
temperatures are rising at about 2 degrees per century. The problem is
that average temperature has no meaning to every day experience. What's
2 degrees in an annual variation of 70 degrees, and a daily fluctuation
of 20 degrees? In a normal lifetime no real change will be noticeable.
This problem contributes to the number of skeptics who deny the validity
of Global Climate Change, or the significance of global climate change.
Global Warming has no meaning in terms of everyday experience.
But are there observable changes that do have meaning in terms of every
day experience? Below are some hypotheses based on my own experiences
in the Philadelphia area. Some hypotheses will be followed with implications
using available data.
Hypotheses for the Philadelphia Area
- Nighttime summer lows have increased. The nights are hotter, the days
are about the same.
- Midwinter lows are oscillating more. There are more unseasonably warm
days, and the cold days are colder.
- The beginning of March is much warmer, the end about the same.
- The oscillation between persistent rain ("deluge") and drought
- The beginning of spring has become a persistent cold rain.
- The typical angle at which fronts cross us has changed. It was closer
to 90 degrees, now its a smaller angle.
- There are more stationary fronts, and less cold fronts and warm
- There are more days with fog.
- There has been more ice and less snow.
- The typical size of rain drops has decreased.
- The ionization or APG of the atmosphere has increased
Related pages at this site:
We could not find detailed temperature data for free, but we did find
a record of monthly extremes for years 1948 through 1996. Most of the
monthly extreme highs showed no discernible variation over the years.
But some of the monthly extreme lows did. These we will show. Below,
the pink lines represent the monthly extreme
high temperature, the blue lines the monthly
Since temperature records are available (1948-1999) another view might be
to compare the rate at which high temperature records and low temperature
records are set.
In June the extreme low stays below 64 degrees until 1971. After
that it is 64 or higher 10 out of 25 years.
||In July, the extreme low rises above 69 only twice in forty years.
Then after 1985 there is an upward trend bringing the extreme lows
above 69 degrees for 8 out of 9 years.
||In August the extreme low only rises above 65 three times out of
the first 20 years. After 1967, the August extreme low only goes below
65 three times out of 28 years.
The November extreme lows stay mostly below 35 degrees until 1964.
After that they stay mostly above 35 degrees.
||In December the extreme lows only rise above 30 degrees three times
before 1971. After that the extreme low stays above 30 degrees fourteen
times out of 26 times. Think about what this change implies
about snow accumulation.
||All other months gave inconclusive graphs such as this graph from
||From 1992 to 1999 high temperature records were set on the average
5.4 times each year, lows 1.6 times each year. In 1991 high temperature
records peaked at 28 records in 1 year. In the last 15 years the rate
at which high records are being set averaged 3 times higher than the
rate at which lows records are being set.
||Another telling sign in the records set is their magnitude. Here
we compare the last 20 high records set to the last 20 low records
set. The new record highs are averaging 11 degrees farther from normal
than the new record lows. Even at the extreme and least impressive
record, the high records were 8 degrees more farther from normal than
the low records.
Together, these two data sets suggest that the lows temperatures are
rising for various times of year. The data on the high temperatures is
inconclusive. (Since we are viewing about 40 years into
our data set the expected rate of records would be 365/40 = 8 records
/ year. That is the rate at which high records
are occurring.) This is consistent with our first two hypotheses.
Except these datasets offer no evidence that midwinter is offering
colder days, as well as warmer.
Since our data sets only showed daily or monthly records, we do not have
solid evidence about the typical lows, highs, or averages. The rising
trends in the low temperatures, or the non-trends in the highs be different
for the typical data than it is for the record data.
Temperature and Rainfall: Philly
River Data Philly area:
Math and Science teachers much data is available. Check
out your own area yourselves.
school finds 0.3'F/decade rise
Other Climate Change Information
Free rainfall data is not available, but free river flow data is. River
oscillations result from rainfall. Thus the oscillations can be used to
determine the days that rain occurs. So we used the river data to determine
the Mean Days Between Rain. Here we use data from the Neshaminy River
on the North side of Philadelphia in Bucks County.
Here we plot percentiles. From the 99.5 and 99 percentiles we can
see that over the last century the number of days between rain has
been decreasing. However, from the decade maximums we can see that
the duration of severe droughts has been increasing.
This demonstration actually contains an intrinsic weakness. Our
definition of severe drought is no rain. A better definition of
drought would be insufficient rain. Even so, the data supports our
hypothesis. Days of rain are tending to get closer together, yet
drought is increasing.
Next we look at how each rain affected the river flow.
Here we see an upward trend in the most severe rains (99%), but
a downward trend in heavy rains. Again, this is consistent with
For all but the most severe rains we see an anomalous jump from
the 1960s to the 1970s. If this jump is real, it creates a more
severe apparent change for all people who started noticing weather
in the 1970s. Although real long term change is occurring, these
observers see the change as bigger than it really is.
Notice that in all the percentiles, 95% and lower, the trend is
decreasing from the 1930s throughout the 1960s. Then in the 1970s
a major jump occurs. This change could be "artifact."
Since we used river flow data, the massive increase in construction
in the 1960s and 1970s could have raised the speed at which rain
reaches the river. This would create the illusion that rainfall
was increasing when it actually wasn't.
We considered calibrating our data for artifact error. But we found a
free dataset on monthly rainfall. Rainfall was monitored within Philadelphia.
||That dataset implies that the 1970s oscillation is real, not artifact.
We recall that this makes sense, because quite a few hurricanes hit
Pennsylvania in the 1970s. In fact, the flood damage from Hurricane
Agnes was so severe that many businesses closed because they could
not afford the recovery costs.
||When we looked at the driest month of the decade we really didn't
see a big pattern. Before 1880 the driest month was above the 0.38
(green line) 4 out of 6 times (66%). After 1880, the driest month
was above only 2 out of 11 times (20%). This might suggest a change.
||But then we looked at the driest 2 consecutive months of each decade.
This had a clear pattern. This pattern suggest that the worst 60 day
droughts are getting dryer dropping about an inch of rain every century.
This suggests Philadelphia could see 2 straight legal months with
no rain as early as 2030.
||On the other extreme, Philadelphia's wettest quarters spent the
last 40 years getting wetter. This is the memory span of most people
discussing climate change. This with the previous data shows that
the oscillation between drought and deluge has increased, as we hypothesized.
Weaknesses in our data and analysis.
- The rainfall data is monthly totals. Using monthly totals tends to
wash out drought trends that fail to synchronize perfectly with the
month. None the less, an increasing tendency towards severe drought
did show up in our data. Monthly averages also washes out sever storm
trends. Thus, only very pronounced trends should be expected to show
up in this data.
- The rainfall data ends in 1990, yet our hypothesis was based largely
on experiences that occurred after 1990.
- The river data is more robust because it is daily. River data is a
good measure of rainfall, but not perfect because other factors related
to the river affect the data.
- The river data and rainfall data are from locations that may be more
than 10 miles apart.
- Our analysis method looked at rain water hitting the river on a daily
basis, not a storm basis. This tends to flatten out some of the data.
First it makes storms that taper off slowly appear shorter than they
actually were. Second it flattens out data for storms that lasted more
than 1 day. For example, a three day storm that drops 2 inches per day
looks like three 2 inch storms in the analysis, not one 6 inch storm.
- Ultimately, similar analytical methods should be done on a per storm
basis on a daily, or hourly rainfall data set. We did not find a free
copy of this data.
Photo courtesy weather
The red line represents how many fronts in the past have crossed
the East Coast. The black lines show a typical front from Jan 2005.
We could not find data to determine whether the change is significant
or imagined. But, a change in front motion would result in both
rain and temperature changes.