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

Temperature

1. Nighttime summer lows have increased. The nights are hotter, the days are about the same.
2. Midwinter lows are oscillating more. There are more unseasonably warm days, and the cold days are colder.
3. The beginning of March is much warmer, the end about the same.

Rainfall

1. The oscillation between persistent rain ("deluge") and drought has increased.
2. The beginning of spring has become a persistent cold rain.
3. The typical angle at which fronts cross us has changed. It was closer to 90 degrees, now its a smaller angle.
   1. There are more stationary fronts, and less cold fronts and warm fronts.
4. There are more days with fog.
5. There has been more ice and less snow.
6. The typical size of rain drops has decreased.
   1. The ionization or APG of the atmosphere has increased

Related pages at this site:

• Math of Climate Change
• Climate Change Effects: Rules of Thumb
• Curren Climate Trends

Temperature Analysis

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 extreme low.

	Summer 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.
	Winter 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 February.

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.

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.

Data Weaknesses

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.

Data Sources:

Temperature and Rainfall: Philly

• NOAA
• Philly Monthly Rain
• Philly extreme Temperatures
• Daily Temperature Records

River Data Philly area:

Neshaminy Stream Flow

Math and Science teachers much data is available. Check out your own area yourselves.

One school finds 0.3'F/decade rise

Other Climate Change Information

• 2004 a hot year
• Effects on Africa & Kilimanjaro
• Larsen Ice Shelf Breaks Up
• Bay of Fundy
• World Wide Data & Considerations
• CO2 vs. Average Temperature
• Climate Maps
• World Data
• Sahara Desert
• Glaciers Growing or sliding on melt water?
• Has Glacial melt pushed the Gulf Stream south?
• 9/11 climate change
• climate historical timeline

Rainfall Analysis

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 our hypothesis. 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.

1. 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.
2. The rainfall data ends in 1990, yet our hypothesis was based largely on experiences that occurred after 1990.
3. 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.
4. The river data and rainfall data are from locations that may be more than 10 miles apart.
5. 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.
6. 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.