The California Urban Heat Island Effect

Last week Anthony Watts had a post at WUWT in which he talked about a new effort to find out just how much the urban environment was affecting the temperatures at Californian weather stations. The study is being carried out in conjunction with the EPA, and the announcement came by e-mail rather than a more conventional press release.

I was interested since, as part of series that I carried out looking at the US Historic Climate Network (USHCN) data, I plotted temperatures for the stations in each state as a function of latitude, longitude, elevation and local population. The first three values were identified with the information at each station. The local population for a town can be found on the web in several different places, and very largely I relied on the city-data web sites for information (see, for e.g. this for Sacremento).

The question arose as to which particular temperature should be used for that of the station, since the USHCN provides annual average temperatures, as raw data, Time of Observation (TOBS) corrected and “adjusted.” When the original post for California was written, only the last of these was available, and thus it formed the basis of the analysis. Shortly thereafter, in 2010, the USHCN site also provided the raw data, and the TOBS temperatures for each station, each year. The data was therefore re-analyzed using the TOBS values. But the plot that was originally generated was plotting the current population against the average temperature since 1895.

As the study grew to include more states, that plot seemed to be an error, since populations can change very rapidly, and go up as well as down. So, towards the end of the series the average temperature was taken only for the past five years, since this was likely to reflect the impact of current populations. At the same time, since there is little difference between the two sets of values in this period, the “adjusted” values were used to derive the plot. It looks like this:


Figure 1. The comparison of average California station temperature plotted relative to adjacent population, with a log-normal plot.

Now the “discovery” of a log-normal relationship is not new. Oke has been studying the topic for decades, and has proposed such a relationship. But it does have a side effect. Consider what happens when the trend line is shown on a normal plot:

Figure 2. The comparison of average California station temperature plotted relative to adjacent population, with a normal scale on both axes.

There is a “kick-over” in the rate of temperature rise at around a population of 10,000. (In fact this is a curve and the sharp transition is an artifact of the software, but it illustrates the trend). Temperature gains for smaller gains in population are higher below that level, while those above that population require a larger population growth to get the same increase. (Failure to recognize this is one of the underlying faults of the Berkeley Earth Project work on the topic.) Since the GISS data on temperatures also does not recognize any difference in population size below 10,000 it is also a fault of that data set.

I am curious to see how the California study pans out, I did drop a note with this finding to William Dean, as the e-mail suggested, and he was courteous enough to reply noting that this was “an interesting approach.”

As I pointed out to him, the strength of that relationship is, perhaps, borne out not only by the R^2 value, but by the consistency of the coefficient over the plots for a number of states. The tabulation is as follows:



I have had to cut the list in two to allow screen capture.


Figure 3. Correlation Coefficients for the relationship of temperature to local conditions with temperatures in degrees C.



And similarly for the table where I have converted the temperatures to def F.


Figure 4. Correlation Coefficients for the relationship of temperature to local conditions with temperatures in degrees F.

Gentle Cough - The Post Dispatch and Cherry-picking data

It has been a little while since I wrote anything about the Global Warming situation. Not that there is not an ongoing series of messages about how we are going to be drowned by increased glacial melting, or that extreme events might become more prevalent, and that we need to take precautions in case they do. Of course there is not a lot of evidence that the rate of extreme event occurrences has been increasing, but the alarmists feel that there is some need to drive home the message that the world has to be concerned about Global Warming, even when the globe isn’t warming. And so this post, which first notes why I wrote the last sentence, and then comments on how the media message is changing so that, by cherry picking data, alarm can still be spread.

So first let us look at the Global Warming situation. It has received very little coverage in the United States, and barely rated a mention in the UK, but the recent release of a new plot of global temperatures by the Climate Research Unit (CRU) at the University of East Anglia (UEA) is worth putting up, purely as a matter of record.

Figure 1. Global average temperatures over the past 15 years (British Daily Mail ).

This Met Office release (on a Friday) has largely been ignored by a scientific community that only exists in its current form as long as the reality that this graph presents remains ignored.

There was an immediate controversy in the UK (but not here, where it remains largely unknown) and there was a follow up report the following Sunday. But, even while ignored, the lack of increase in global temperature over the past fifteen years is surely some indication that the models widely used to predict an exponentially increasing global temperature, are falsified.

So what can a good alarmist do? Well consider the headline in the St. Louis Post Dispatch on November 26th. “2012 so far the warmest year on record in parts of Missouri.” So let me talk about this for a minute.

Notice that this does not say that the entire state is at its warmest. Rather it reports that Jayson Gosselin of the National Weather Service has noted that this was the warmest year on record for St. Louis and Columbia.

The average temperature in St. Louis so far this year is 63.4 degrees, a full degree higher than the 62.4-degree average seen in the previous warmest year, 1921. In Columbia, the previous warmest year as of Nov. 24 was in 1938, when the average was 61 degrees. This year, the average is 61.7 degrees. In Kansas City, Mo., it has been the fourth warmest year on record so far, with an average temperature of 61.3 degrees, Gosselin said.

He goes on to be more specific about when the heat wave occurred (in case we missed it!)

Gosselin, who works in the Weather Service's office near St. Louis, said the "meteorological spring" _ March through May _ was far and away the warmest ever in St. Louis with an average temperature of 61.1 degrees. Second warmest was 1910, when the average was 57.5 for the spring months. Summer also was unusually warm. Average temperatures in March, May and July all set records in St. Louis, he said.

For those who forget, I took a look at the Missouri State Temperatures first back in February 2010, when I first became curious as to whether our state was showing the global warming that everyone was talking about.

I found the location of all the US Historical Climate Network sites for Missouri and determined their location (latitude and longitude) elevation and population. Now as it turns out that there are 26 stations in Missouri, and so I took the average temperature for each station each year (this was the “homogenized” temperature in that initial post) and was able to plot the average state temperature over time.


Figure 2. Average “USHCN homogenized” temperatures for the state of Missouri (USHCN)

And if you look at that plot the state temperature has barely risen (less than half a degree Fahrenheit in 115 years) since official temperatures have been recorded, and the hottest years were in the 1930’s in the dust bowl years.

But there was something missing from the data table and it turns out that three of the largest cities in the state, Columbia, Springfield, and St. Louis were not tabulated in this network, but are, instead, part of the Goddard Institute for Space Studies (GISS) network that Dr. James Hansen used for his work.

And, being further curious, I then combined the two sets of data and obtained a plot for temperature as a function of population.


Figure 3. Temperature as a function of population size around the station. This conclusion, that there is a log relationship is not new. To quote from that post:

Oke (1973) * found that the urban heat-island (in °C) increases according to the formula –

➢ Urban heat-island warming = 0.317 ln P, where P = population.

Thus a village with a population of 10 has a warm bias of 0.73°C. A village with 100 has a warm bias of 1.46°C and a town with a population of 1000 people has a warm bias of 2.2°C. A large city with a million people has a warm bias of 4.4°C.

It is interesting to note that his coefficient is 0.317 and the one I found is 0.396.

( * Oke, T.R. 1973. City size and the urban heat island. Atmospheric Environment 7: 769-779.)

But then I revisited the state later in time, after the USHCN started also providing the raw and Time of Observation Corrected data (TOBS). And I found a few more interesting facts.

Firstly I compared the difference between the GISS data for the three large cities with the state average temperatures for both the raw data, and the “homogenized” data.


Figure 4. Difference between the average temperature in the large cities, and that of the average temperature in the State. The blue line is for the homogenized data, the red is for the raw.

I then went on to compare the TOBS average to that of the largest cities and this is what I got:


Figure 5. Difference between the average temperature in the large cities of the state, and that of the average temperature in the state using the TOBS data.

A slight upward trend, but not that significant. As for the temperatures in Missouri, over the past 100 years, with the correction – really there is no trend, it has been relatively stable:


Figure 6. Average TOBS temperature for the state of Missouri over the recorded interval.

I did note that the highest temperatures were some decades ago.

Oh and the correlation with population held up with the TOBS data, the coefficient was 0.327, and the r^2 value was 0.14.

Now I finished the entire contiguous United States some time ago, and that temperature relationship to population held up quite well, as the individual state reports listed on the rhs side of the blog show.

So what do we learn from this? That alarmist rhetoric is continuing with an embarrassing lack (for those of us who are scientists) of balance in the reporting. Data now has to be carefully cherry-picked to still be able to convey the message that the world is warming. One wonders how long they will be able to get away with this before they are called out by more prominent folk?

U.S. temperatures and the BEST study

For those who have been following my review of the temperatures around the United States, there was an interesting set of papers released by the Berkeley Earth Surface Temperature (BEST) study in the last week. Four sets of findings have been released, based on:
1) statistical methods
2) Urban Heat Island
3) Station Quality
4) Decadal Variations.

For the moment I am most interested in the second of these, since it relates to the effect of population around the measuring station on the resulting temperature reported. This is not quite the same as the UHI effect, although they are both generated by the same factors.

In the paper, to greatly over simplify, the authors appear to have looked at the trends in temperatures in urban and rural sites, and then looked at the difference between the trends in temperature since 1950 for each. They report that the overall trend (i.e. temperatures in urban areas – temperatures in rural areas) is negative rather than the expected positive. They find an average negative trend of 0.342 deg F per hundred years. (Or 0.00342 deg F per year).

I have a couple of concerns about the results they have reported, which requires that I carry out some comparative plotting of my own to generate similar trends. Since I will be looking at the 48 contiguous states (though some will drop out as being too small) this will take a little while. So I ask you to bear with me if posting is a bit sparse while I run these numbers.

The Four degree temperature drop along the Atlantic Seaboard

During the time that I was acquiring the data for the different USHCN stations around the country (the list for which is down on the right-hand side of the column) I found that there seemed to be a consistent drop in temperature along the East Coast of the United States that wasn’t seen in other states. So today I thought I would consolidate a few graphs from that series and see if my week-to-week observations were as consistent as they seemed at the time. The time period that I am looking at is in the 1948 to 1965 time frame, and you can see the drop in temperatures that I am referring to most clearly and as an illustrative example, using the average of the three GISS stations in Georgia. You may note that the temperature drop was over 4 deg F in that time period , peaking at 65.4 deg in 1949, and falling to 61.2 deg F in 1967. (There was a temperature of 65.1 deg in 1957).

Average temperature with time for the three GISS stations in Georgia

I have, in the state temperature series, compared the GISS temperatures reported for each state (blue lines) with the USHCN average temperatures, both using the homogenized data where NOAA has interpolated results to infill missing and “errant” values (the purple data lines), and with the original temperatures recorded, corrected only for the time of observation (the TOBS series, which are shown with a green line).

The series established that there was a change in temperature with latitude, with elevation and with population, and I recognize that all states differ in all three variables, as well as in their area. In time I will, hopefully, get around to discussing those findings in more detail, but this exercise is more just to look at that fall in temperature in the 1947 – 67 time frame. Inserting the individual average temperature data for each of the states that border the Atlantic, but discounting Florida because of the possible influence of the Gulf, the list includes fourteen states:- Georgia, South Carolina, North Carolina, Virginia, Maryland, Delaware, Pennsylvania, New Jersey, New York, Connecticut, Rhode Island, Massachusetts, New Hampshire and Maine.

ADDENDUM: I had initially not included the individual state plots over the time in question, I have now added those plots (combined on two graphs) at the end of the post.

By just taking the average temperature I had calculated for each state, and then averaging those each year, using the USHCN homogenized data I get this plot:

Average temperature with time for the USHCN stations along the East Coast, averaged by state and then collectively, using homogenized data.

If one uses the TOBS data rather than the homogenized version, then the plot becomes:

Average temperature with time for the USHCN stations along the East Coast, averaged by state and then collectively, using TOBS data.

In both of the above plots the fall in temperature between the 54.3 deg F temperature in 1949 and the 50.9 deg F temperature in 1967 (TOBS temps) is clear.

The problem with doing that simple average, however, is that not all states are equal. Of the 250 station total, some states have only 3, and others as many as 57, but I have used a single average for each state. And the reason in part for the different number of stations is that the areas of the states are different, ranging from roughly 1,000 sq miles to almost 60,000 sq miles. As a result the area that a station covers ranges from roughly 300 sq miles to 2,500 sq miles. The area of each state was obtained from the netstate site for each of the states.

Does it make a difference, well, using the TOBS data as an example, and weighting first by the number of stations in the state, the curve changes to:

Average East Coast Temperatures with state averages weighted by station density in the state.

The alternative is to weight the average in terms of the area of each state, and when one does this, then the plot changes to:

Average East Coast Temperatures with state averages weighted by the area of the state.

If one looks at the change in plot through doing the weighting (and the areal plot seems to be the more logical) it is clear that the shape of the graph changes, particularly after 1960, and further that if one looks at the rate of temperature increase this also falls.

When the original USHCN homogenized data plot, just averaging the state temperatures is used, then the rate of temperature increase is 1.67 deg F per century. If that data is weighted by station density (which turns out to mean just averaging all 250 station data) then the homogenized rise falls to 1.24 deg F per century, and if the state average data is weighted by the state area when calculating the average then the temperature rise falls to 1 degree per century.

If the TOBS raw data is used instead of the homogenized values then just averaging the state values gives a temperature rise of 0.8 deg F per century, while taking the station density into consideration lowers that to 0.56 deg F per century, and when the state values are averaged based on the individual state areas, then the temperature rise over the century falls to 0.3 deg F.

None of this tells us why the temperature fell so dramatically along the East Coast in the 1949-1963 time frame – the area weighted TOBS data suggests that the fall was from 56.1 deg F in 1949 to 52.5 deg F in 1963, it would be interesting to find out why.

Looking at the other possible trends in the data, plotting the average state temperature against latitude

Correlation of average state temperature with latitude along the East Coast

There does not appear to be much correlation with elevation:

Correlation of average state temperature with elevation along the East Coast

Nor, and both of these may be caused by a fault of the way in which I have calculated averages, is there a correlation with population.

Correlation of average state temperature with local station population average along the East Coast

Well the it seems pretty clear that there was indeed, along the East Coast from Georgia to Maine, a fall in temperature of 3.6 degrees from 1949 to 1963. I don’t remember seeing such a drop in other regions of the country, but I suppose I had better check those out next.

Oh and the difference between the USHCN homogenized curve and the TOBS data is interesting (I used the areal weighted average values).

Correction applied by NOAA to the original TOBS data for stations along the East Coast, bear in mind that this is averaged over a total of 250 stations.

Addendum
I stated at the beginning that I was checking that the drop held true for all states, but actually just summarized them without showing the individual state values imposed on one another. My apologies, and because there are 14 states I have broken the plots down into two parts, first the more southerly states:

Variation in average state temperature in the period 1940 to 1980 for the Southern half of the Eastern Seaboard states

Variation in average state temperature in the period 1940 to 1980 for the Northern half of the Eastern Seaboard states

The temperature drop between roughly 1950 and 1965 can be seen in each plot, validating the opening thought, but since the lower curves reflect a more northern position, it is worthy of note that the drop seems to move to the right over time, and the variation gets more jagged as one moves north.

Yet more questions!!

New Jersey combined temperatures

Crossing from Delaware into New Jersey as I come toward the end of the data acquisition part of the project I started last year looking at state temperatures over the past one hundred and fifteen years, I find that New Jersey has a dozen USHCN stations.

Location of the USHCN stations in New Jersey (CDIAC ).

According to the list, the only GISS station in the state is in Atlantic City. There have been 3 stations there, one that ran from 1895 to 2008, down at the Marina. This clearly shows the drop in temperature in the 1948 – 1965 period that I have been mentioning in the last few posts on the subject.

Longer term temperature profile reported for the GISS station in Atlantic City (GISS ).

However, as has become evident in many states that I have reviewed, the one that is being used by GISS has a much more recent history, only having been in operation since 1951.

That record also clearly shows the temperature drop, though with the start in 1951, it is not as clear that this is an anomaly from the overall rising trend.

Reported temperatures for the GISS station currently being used in Atlantic City (GISS ).

Given the steady rise in temperature of the station at the Marina, I was curious to see how far from the sea the new station is. It turns out to be at the airport, which is 9 miles from the sea, and 23 m above sea level.

Location for the current GISS station in Atlantic City, New Jersey.(Google Earth)

And then as I start to import the data for the USHCN stations, I find that the first one is still at the Atlantic City Marina:

Location of the USHCN station in Atlantic City, at the Marina (Google Earth)

New Jersey is 150 miles long and 70 miles wide, running from 73.9 deg W to 75.58 deg W, and 38.9 deg N to 41.3 deg W. The mean latitude is 40.1 deg , that if the USHCN stations is 40.3 deg N, and the GISS station is at 39.45 deg N. The elevation of the state runs from sea level to 549 m, with a mean elevation of 76.2 m. The mean USHCN station is at 53.9 m, while the GISS station is at 23 m.

Because of the short interval for which information from the current GISS station has been presented, the difference between it and the USHCN average is relatively short.

Difference between the data presented for the GISS station in New Jersey and the average of the USHCN stations

For the state itself, turning to the Time of Observation corrected (TOBS) raw data, and seeing how the temperature in the state has changed over the years:

Change in the TOBS temperatures, on average, for the USHCN stations in New Jersey.

It can be seen that there has been, with the exception of the time from about 1950 to 1965, a steady increase in temperature. As I had noted in an earlier post on Rhode Island the sea surface temperatures (SST) have risen by about 1.8 deg F per century. This is relatively close to the value shown in the above graph. (Note that the homogenized data plot shows a temperature rise of 2.45 deg F per century.)

Turning to the geographical factors, starting with latitude:

Effect of station latitude on temperature in New Jersey

Remember from previous observation that longitude is really a proxy in many cases for changes in elevation, and New Jersey is, in the main, relatively flat:

Effect of station longitude on temperature in New Jersey

There is really no significant effect of longitude, whereas when one looks at elevation:

Effect of station elevation on temperature in New Jersey

It is clear that the broadly consistent finding from other states on the role of elevation is valid also here, even with relatively smaller elevation changes.

When looking for populations, Charlotteburg has only one farm by it at the moment, but there are two sets of sub-divisions being developed in the neighborhood, which may have a significant impact on recorded temperatures in the future, though the reservoir may have a stabilizing effect.

Location of the USHCN station at Charlotteburg, NJ (Google Earth)

Indian Mills also did not come up with a citi-data site, so a check with Google Earth showed that it was close to Medford Lakes and that the station was surrounded by houses (with large lots). So I used the Medford Lakes population. Moorestown is on the edge of Philadelphia, but has a separate population,

Looking therefore at the effect of population, considering the average of the last 5 years temperatures against the local population:

Effect of local population on TOBS temperature for the USHCN stations in New Jersey.

Interestingly the homogenization of the data for the USHCN reported temperatures also creates a higher R^2 for this state.

Effect of local population on homogenized temperature for the USHCN stations in New Jersey.

Which suggests there might be some difference between the two sets of data, as there would appear to be. The recent drop is a little less than common to many earlier states.

Delaware combined temperatures

The last post in this series looked at the temperatures for Maryland and so, moving up along the coast, the next stop is Delaware.

Delaware USHCN stations (CDIAC)

Given the small size of the state, I thought it might also be interesting to compare the results with those for the GISS station in Washington D.C., since the latter would otherwise be left out. They are at about the same latitude, and of somewhat similar elevation, differing only in the size of their populations.

Temperatures as reported for the GISS station in Washington D.C. (GISS )

Given the built-up nature of the area around Wilmington there could be some debate as to the relative population sizes about the various stations, but for the moment I will accept the values from the citi-data sites that I have used to date. (The question is raised particularly regarding the Newark University Farm, which GISS considers to lie within metropolitan Wilmington).

It turns out that Washington is, on average, about 2.76 deg F hotter than the average for Delaware, though the difference has changed, with a steady increase until around 1980, and a fall thereafter.

Difference between the temperature reported for the GISS station at Washington DC and the USHCN average homogenized temperature for Delaware.

Looking at the overall change in temperature over time for Delaware alone, there is still that drop in temperature that occurs between around 1948 and 1965:

Average temperature for the USHCN stations in Delaware after homogenization.


Before homogenization, however, looking at the Time of Observation adjusted raw data, the trend is not as significant:

Average temperature for the USHCN stations in Delaware raw data after correction for time of observation (TOBS).

The temperature drop from around 1950 to 1965 is still present, but the overall temperature increase has fallen from 1.9 deg F per century down to 0.5 deg F per century.

Delaware is the second smallest state (after Rhode Island) and is only 100 miles long, while 30 miles wide. It stretches roughly from 75 deg W to 75.75 deg W, and from 38.5 deg N, to 39.8 deg N. The mean latitude is sensibly 39 deg N, the average of the USHCN stations is 39.3 deg N (D.C. is at 38.85). The state elevation runs from sea-level to 137 m with the mean at 18.3 m. The average of the USHCN stations is at 28.6 m.

The small number of stations makes the correlation coefficients of little real value, but they are included for consistency. It will be interesting to see how these numbers fit in when I compile the overall statistics.

Change in average station temperature in Delaware as a function of latitude.

Change in average station temperature in Delaware as a function of longitude.

Because of the relatively small change in elevation for the different stations, the correlation with elevation is not as evident here.

Change in average station temperature in Delaware as a function of elevation.

When I looked at the TOBS data available there is insufficient recent information to provide a realistic plot of temperature against local population unfortunately, but I’ll include the plot for consistency.

Change in average station temperature in Delaware as a function of population around the station.

It is clear in Delaware, as elsewhere, that the homogenization of temperature data, has led to an increase in reported temperatures with time.

Increase in temperature from the TOBS data to the reported homogenized temperatures for the stations in Delaware.

Maryland combined temperatures

Moving up the East Coast from Virginia, the next state is Maryland, where I will look to answer the continuing question on whether it too is showing the significant drop in temperature that seems to hold along the Atlantic Coast from around 1948 to the late 1960’s. It was not evident in the first temperature plot that I made for Missouri when I began this series, and only drew my attention as I reached states along the Atlantic Coast.

USHCN stations in Maryland (CDIAC)

Maryland has 16 USHCN stations from Beltsville to another Woodstock. It is interesting that there are two stations that are far to the west in the state, while all the rest lie relatively close to the coast. So I’ll come back to that point a little later in the post. Oh, and there is still that problem at the CDIAC site:



Mayland has one GISS station on the list, in Baltimore. And there are two Baltimore entries on the GISS site, one runs from 1895 to 2006 (Baltimore WSO City) and one from 1950 to date (Baltimore Blt/Washington International). It is, of course, the latter that has the correct co-ordinates for the current single GISS location that they use. I say of course since this (truncated station data) has been a common discovery as I have moved through the individual states. I had a look at the longer time record station and it does not show that temperature drop that I mentioned at the beginning of the post.

Historic temperature data for Baltimore MD, as recorded at the GISS station no longer used by them.

In contrast the site that has been retained does clearly show the drop in average annual temperature, from 13.67 deg C in 1953 down to 11.85 deg C in 1963, (a drop of 1.82 deg C, 3.3 deg F) consistent in size with the temperature drops seen further south.


Annual temperatures as reported by GISS for the station in Baltimore MD.

Interestingly one of the USHCN stations is located at the Maryland Science Center in Baltimore, and when one looks at the homogenized data for that site, the temperature drop is much less easily discerned:

USHCN homogenized temperatures for the MD Science Center in Baltimore (CDIAC)

And when the Time of Observation corrected raw data is examined there is no discernable drop, suggesting perhaps that the phenomenon is closely related to changes in the Sea Surface Temperature (SST) over that time interval, since the Science Center is surrounded by Baltimore.

Time of Observation corrected (TOBS) temperatures for the MD Science Center in Baltimore over the period where other states show a fall in temperature.

If I turn to the state as a whole, there is a difference between the sole GISS station temperature and that of the average of the USHCN stations, even with the homogenized data and the shorter time interval.

Difference between the GISS temperature for Maryland and the average of the homogenized USHCN station temperatures, in the interval where the GISS station has been functioning.

For the state as a whole, the data shows the three dgree drop in temperatures, both for the homogenized data, and for the TOBS values. The difference in temperature rise for both is relatively similar 1.59 deg F per century for the TOBS temperatures and 1.69 deg F for the homogenized. Given the strong influence of the adjacent ocean this is not perhaps surprising.

Average temperature for the state of Maryland over the years, from the TOBS data averaged for the stations in the state.

The two stations over on the far west of the state (Cumberland and Oakland) are also at a much greater elevation, however when I averaged the two over the same interval I get that same drop in temperature that I have been discussing.

Fall in temperature in the period from 1940’s through the 1960’s for the two western stations in Maryland, averaged TOBS data.

Maryland is 250 miles long and 90 miles wide, lying between 75.07 deg W and 79.55 deg W, and 37.88 deg N and 39.72 deg N. The mean latitude is sensibly 39.5 deg N. The USHCN average latitude is 39 deg N, and for the GISS station 39.28 deg N. The elevation rises from sea-level to 1,024 m, with the mean being 107 m. The USHCN average is 97.5 m, while the GISS station is only 6.1 m above the sea.

Looking at the way in which geography changes the temperatures around the state.

Variation in station temperature with latitude in Maryland (TOBS data)

Variation in station temperature with longitude in Maryland (TOBS data)

Variation in station temperature with elevation in Maryland (TOBS data)

Seeking information on populations around the various stations, Royal Oak didn’t appear in the citi-data list, and so I used the zip-codes site to get 483. Woodstock is a suburb of Baltimore, and is folded into Baltimore I suspect, so I again used the zip-codes site to get a population of 7,192.

Variation in station temperature with adjacent population in Maryland (TOBS data)

It dawns on me that the difference between the homogenized and TOBS data is generally quite small the closer we get to the present, and this should mean that ther should be a correlation with homogenized data, now that I am only using the past five years of temperature data to correlate with relatively recent population. (I wrote this before plotting the final graph).

Variation in station temperature with adjacent population in Maryland (using USHCN homogenized data)

And finally there is



Maybe I was being a little optimistic.