by Roy W. Spencer, Ph. D.
INTRODUCTION
My last few posts have described a new method for quantifying the average Urban Heat Island (UHI) warming effect as a function of population density, using thousands of pairs of temperature measuring stations within 150 km of each other. The results supported previous work which had shown that UHI warming increases logarithmically with population, with the greatest rate of warming occurring at the lowest population densities as population density increases.
But how does this help us determine whether global warming trends have been spuriously inflated by such effects remaining in the leading surface temperature datasets, like those produced by Phil Jones (CRU) and Jim Hansen (NASA/GISS)?
While my quantifying the UHI effect is an interesting exercise, the existence of such an effect spatially (with distance between stations) does not necessarily prove that there has been a spurious warming in the thermometer measurements at those stations over time. The reason why it doesn’t is that, to the extent that the population density of each thermometer site does not change over time, then various levels of UHI contamination at different thermometer sites would probably have little influence on long-term temperature trends. Urbanized locations would indeed be warmer on average, but “global warming” would affect them in about the same way as the more rural locations.
This hypothetical situation seems unlikely, though, since population does indeed increase over time. If we had sufficient truly-rural stations to rely on, we could just throw all the other UHI-contaminated data away. Unfortunately, there are very few long-term records from thermometers that have not experienced some sort of change in their exposure…usually the addition of manmade structures and surfaces that lead to spurious warming.
Thus, we are forced to use data from sites with at least some level of UHI contamination. So the question becomes, how does one adjust for such effects?
As the provider of the officially-blessed GHCN temperature dataset that both Hansen and Jones depend upon, NOAA has chosen a rather painstaking approach where the long-term temperature records from individual thermometer sites have undergone homogeneity “corrections” to their data, mainly based upon (presumably spurious) abrupt temperature changes over time. The coming and going of some stations over the years further complicates the construction of temperature records back 100 years or more.
All of these problems (among others) have led to a hodgepodge of complex adjustments.
A SIMPLER TECHNIQUE TO LOOK FOR SPURIOUS WARMING
I like simplicity of analysis — whenever possible, anyway. Complexity in data analysis should only be added when it is required to elucidate something that is not obvious from a simpler analysis. And it turns out that a simple analysis of publicly available raw (not adjusted) temperature data from NOAA/NESDIS NOAA/NCDC, combined with high-resolution population density data for those temperature monitoring sites, shows clear evidence of UHI warming contaminating the GHCN data for the United States.
I will restrict the analysis to 1973 and later since (1) this is the primary period of warming allegedly due to anthropogenic greenhouse gas emissions; (2) the period having the largest number of monitoring sites has been since 1973; and (3) a relatively short 37-year record maximizes the number of continuously operating stations, avoiding the need to handle transitions as older stations stop operating and newer ones are added.
Similar to my previous posts, for each U.S. station I average together four temperature measurements per day (00, 06, 12, and 18 UTC) to get a daily average temperature (GHCN uses daily max/min data). There must be at least 20 days of such data for a monthly average to be computed. I then include only those stations having at least 90% complete monthly data from 1973 through 2009. Annual cycles in temperature and anomalies are computed from each station separately.
I then compute multi-station average anomalies in 5×5 deg. latitude/longitude boxes, and then compare the temperature trends for the represented regions to those in the CRUTem3 (Phil Jones’) dataset for the same regions. But to determine whether the CRUTem3 dataset has any spurious trends, I further divide my averages into 4 population density classes: 0 to 25; 25 to 100; 100 to 400; and greater than 400 persons per sq. km. The population density data is at a nominal 1 km resolution, available for 1990 and 2000…I use the 2000 data.
All of these restrictions then result in thirteen 24 to 26 5-deg grid boxes over the U.S. having all population classes represented over the 37-year period of record. In comparison, the entire U.S. covers about 31 40 grid boxes in the CRUTem3 dataset. While the following results are therefore for a regional subset (at least 60%) of the U.S., we will see that the CRUTem3 temperature variations for the entire U.S. do not change substantially when all 31 40 grids are included in the CRUTem3 averaging.
EVIDENCE OF A LARGE SPURIOUS WARMING TREND IN THE U.S. GHCN DATA
The following chart shows yearly area-averaged temperature anomalies from 1973 through 2009 for the 13 24 to 26 5-deg. grid squares over the U.S. having all four population classes represented (as well as a CRUTem3 average temperature measurement). All anomalies have been recomputed relative to the 30-year period, 1973-2002.
The heavy red line is from the CRUTem3 dataset, and so might be considered one of the “official” estimates. The heavy blue curve is the lowest population class. (The other 3 population classes clutter the figure too much to show, but we will soon see those results in a more useful form.)
Significantly, the warming trend in the lowest population class is only 47% of the CRUTem3 trend, a factor of two difference.
Also interesting is that in the CRUTem3 data, 1998 and 2006 would be the two warmest years during this period of record. But in the lowest population class data, the two warmest years are 1987 and 1990. When the CRUTem3 data for the whole U.S. are analyzed (the lighter red line) the two warmest years are swapped, 2006 is 1st and then 1998 2nd.
From looking at the warmest years in the CRUTem3 data, one gets the impression that each new high-temperature year supersedes the previous one in intensity. But the low-population stations show just the opposite: the intensity of the warmest years is actually decreasing over time.
To get a better idea of how the calculated warming trend depends upon population density for all 4 classes, the following graph shows – just like the spatial UHI effect on temperatures I have previously reported on – that the warming trend goes down nonlinearly as population density of the stations decrease. In fact, extrapolation of these results to zero population density might produce little warming at all!
This is a very significant result. It suggests the possibility that there has been essentially no warming in the U.S. since the 1970s.
Also, note that the highest population class actually exhibits slightly more warming than that seen in the CRUTem3 dataset. This provides additional confidence that the effects demonstrated here are real.
Finally, the next graph shows the difference between the lowest population density class results seen in the first graph above. This provides a better idea of which years contribute to the large difference in warming trends.
Taken together, I believe these results provide powerful and direct evidence that the GHCN data still has a substantial spurious warming component, at least for the period (since 1973) and region (U.S.) addressed here.
There is a clear need for new, independent analyses of the global temperature data…the raw data, that is. As I have mentioned before, we need independent groups doing new and independent global temperature analyses — not international committees of Nobel laureates passing down opinions on tablets of stone.
But, as always, the analysis presented above is meant more for stimulating thought and discussion, and does not equal a peer-reviewed paper. Caveat emptor.
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NickB. (11:17:02) :
David Shepherd (11:00:57)
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Since UHI is mostly caused by land-use changes (buildings, roads, vegetation changes) and not energy-usage, I don’t think areas like Detroit & Gary will experience a decrease in UHI since these infrastructures don’t “go away” when population declines. In fact, I’d bet suburbs continue to be built at the expense of farmland surrounding those cities even today, and UHI continues to increase, tho more slowly.
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NickB. (12:42:32) :
So I hate to exhibit posting diarrhea but I did a little more digging as to what the “consensus” opinion is on land use changes. No mention of the effects of cement, but an update regarding their view of replacing forests with grasslands/crops, which could then be extended to cement I guess. From Stephen Schneider’s page (yeah, that guy):
“…it is well known that clearing land raises albedo but lowers evapotranspiration. The first process cools the Earth’s surface, and the second warms it, but together they still cool the planet unless feedbacks negate that.
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I’ve read alot on this (maybe from Pielke, Sr — can’t remember), and clearing forests actually warms most areas, except in high latitudes like boreal forests that are snow-covered much of the yr. The amount of cooling evapotranspiration from forests is huge, and eliminating that causes temps to rise, but humidity to drop.
NickB. (10:46:04) : Steve Keohane (07:28:14) :
What’s the difference between a few days and 100+ years?
Replacing lawn with concrete increases temperature because the concrete isn’t transpiring, and is in fact a heat storage unit that radiates long after the sun is gone. As for the affect from CO2 in producing the concrete, haven’t seen any measurable effect of that gas yet, so its lifetime in the atmosphere isn’t relevant.
beng,
I did some back of the envelope calculations based on green roofs (ref) which claim, if my calculations are correct, that a green roof (i.e. a lawn – I used a 15% albedo estimate) has an effective albedo (albedo equivalent?) of 95%. So the “natural surface” component/variable in this particular case, offsets 80% of the solar gain it would be expected to absorb based on its albedo.
In the event of a forest, if the ambient temperature is 80F (like the green roof example) the temperature at surface station height (2m/6ft – which is the “standard” for surface temp measurements right?) is probably going to be 80F too. I guess they’re talking about the top of the forest as the surface in question, but going off your comments and my calculations around the green roofs (i.e. I don’t think it’s such a stretch to think that trees would be about as good if not better for diffusing and rejecting heat gain) I think it’s safe to say that the idea that clearing forests and replacing it with grassland would cause a net cooling effect should be able to be disproven and rejected… which means ~.5 W/m2 of cooling in the IPCC models is errant.
Regarding UHI and depopulation trends, I agree that most of the UHI effect is land use – which I think is also demonstrated by Dr. Spencer’s log relationship. The first thing that happens with development would be clearing of land and the installation of roads. I think, which might also be demonstrated by his log relationship, that at some point this effect begins to be saturated. Think Hong Kong or NYC, past a certain point the only thing structure/surface-wise that changes is that smaller buildings get replaced by taller buildings, but the footprint doesn’t change and everything other than parks in these cities has effectively covered by concrete, asphalt and structures for years.
That said, as population continues to increase, consumption (think electricity, diesel, gasoline, natural gas, heating oil, etc) also continues to increase. I think it’s interesting to ask how much UHI is consumption based, and while Gary or Detroit might not be too useful, Pete H’s (18:51:18) comments on Shanghai are very interesting. I think someone could begin to quantify the relationship there with the right experimental approach.
Cheers!
Steve Keohane (07:58:57) :
Replacing lawn with concrete increases temperature because the concrete isn’t transpiring, and is in fact a heat storage unit that radiates long after the sun is gone.
Agreed, to compare one against the other we’d not only be talking albedo, but transpiration, and thermal mass (emissivity?). Now, there is another effect that I don’t think has been accounted for (maybe it would be under emissivity?), and that is natural surface effects (i.e. leaves or grass): 1.) tend to diffuse reflected light vs. man-made surfaces (walls, roofs, roads) that are usually flat and smooth/smooth-ish and 2.) their solar gain blocks/shades the solar gain of the underlying surface vs. say, a patio or road where not only the concrete/asphalt warms but it warms the ground underneath it as well.
What I’m getting at here is that I have a suspicion that the IPCC/”consensus” is not properly taking these other land use variables into account. In fact, I’m almost sure they’re not.
I know this sounds ludicrous, but I have a sneaking suspicion that if your replaced all the earth’s non-snow/ice/water covered surfaces with concrete in the GCMs and/or Trenberth’s model that they *might* show a cooling effect!
So I found a number for asphalt (Ref) of 61,000 square miles. This is 1.7% of US surface area, or roughly equivalent to paving over Wisconsin.
I’ll use the Trenberth estimate of 198 W/m2, so for 1.58*10^11 m2 at 5% albedo that gives a total absorption of 2.97*10^13 W for pavement vs. an average lawn of the same size (15% albedo) at 2.66*10^13 W. Now what we’re trying to get at here is *the change* as expressed as a W/m2 for the entire US for replacing a natural surface with asphalt. US surface is 9.16*10^12 m2
So for albedo only, this works out to a US forcing of .34 W/m2 for the 10% change in albedo – not insignificant – but this is also assuming the parking lots or streets in question are not shaded by surrounding structures, and that the 61,000 square miles of pavement in question are, more or less, dark asphalt (worn asphalt isn’t quite as bad, and I have no way to tell if they distinguished between asphalt pavement and concrete) – none of which are safe assumptions but even assuming that calculation generally overstates the effect it doesn’t really matter, and here’s why…
For loss of natural surface cooling effect, per Scientific America’s claimed green roof results of a +5 ambient temp vs. +100 for asphalt (ref) one would expect this to be up to 8x range the effect of the albedo loss *if* every surface in the US that was replaced by pavement was previously the equivalent of a green roof. Whatever the actual effect is, it’s there and it’s very significant because albedo alone and its .34 W/m2 doesn’t even begin to account for the difference between walking in a shaded area or on the grass in bare feet during the summer, vs. walking on asphalt – and we all know that a cement sidewalk baking in the sun isn’t exactly cozy either with its -.68 W/m2 “cool” albedo advantage (assuming a middle of the road 35% albedo for concrete) over my lawn with its 15% albedo.
So what can we say then about the forcing inflicted from replacing natural surfaces with paving? I think an educated guess here would be that paving over the equivalent of Wisconsin has resulted in an average forcing for the US of between .68 and 1.36 W/m2 – and that’s only talking about parking lots and roads.
I think also, after digging into it some more, from a model perspective the land use assessments might not have factored in pavement and structures… and why should they? UHI has already been corrected (overcorrected according to Hansen if I recall) out of the surface temperature record so this doesn’t have to be accounted for… right? They wouldn’t, couldn’t have made a mistake like that could they?
A clarification/correction on the prior statement: Whatever the actual effect is, it’s there and it’s very significant because albedo alone and its .34 W/m2 doesn’t even begin to account for the difference between walking in a shaded area or on the grass in bare feet during the summer, vs. walking on asphalt. The .34 W/m2 was the average US forcing (i.e. the effect of 61k square miles of asphalt averaged over the total US surface area of ~3.5 million square miles). This was incorrect, so I will try to put all of this in the proper context this time and explain the results more clearly:
Micro-Scale Description:
Surface Description / Albedo / Ambient Temperature @ur momisugly 80°F * / Forcing vs. 15% Albedo
Asphalt / 5% / 180°F / 19.8 W/m2
Natural Surface (albedo only) / 15% / 165°F / 0 W/m2 (base case)
Concrete / 35% / 145°F / -39.6 W/m2
Average natural surface should be assumed to fall somewhere in here
Green Roof / 95% equivalent / 85% / -158.4 W/m2
For this exercise, there is no way to really tell what the proper average natural surface effect should be. Not all roads and parking lots are put over the equivalent of a green roof, so a proper estimate *should*, I think at least, put us somewhere in-between concrete and the green roof (which I am using, for better or worse, as the best case scenario for the natural surface cooling effect). This could also be described as I am using concrete as the lower bound equivalent, and the green roof as the upper bound for the behavior of the “average” natural surface in the US. This implies that the natural surface cooling effect should be assumed to be the equivalent of between a 20% and 80% increase in albedo – which, to get to the point, means that when you replace a natural surface with a man-made surface the loss of natural surface cooling effect (as I’m calling it) should dwarf the change in albedo. This also fits with the common sense observation that walking barefoot in the summer on asphalt is more painful than concrete, which is more painful than grass.
Now getting back to the conversation I was having with R. Gates, what is the macro effect? Per the prior references the US has “paved” 61,000 square miles – which is also described as 1.7% of its land surface area or, roughly speaking, paving over the state of Wisconsin. Note: this is only accounting for roads and parking lots (i.e. no structures). The references cited make reference to “paving” and asphalt interchangeably – so it’s possible that a (significant?) portion of the “paving” in question could be concrete. The following evaluation describes the effective US average forcing change for replacing 61,000 square miles of natural surfaces with pavement (again, roughly speaking, the state of Wisconsin – which is what we have, according to the SF Chron citing the US FHA, already done to date):
Macro-Scale Description:
Surface Description / Albedo / vs. Concrete / vs. Median Natural Surface Cooling Effect / vs. Max Natural Surface Cooling Effect (Green Roof)
Asphalt / 5% / 1.02 W/m2 / 2.04 W/m2 / 3.07 W/m2
Median “Pavement” / 20% / .51 W/m2 / 1.54 W/m2 / 2.56 W/m2
Concrete / 35% / 0 W/m2 / 1.02 W/m2 / 2.05 W/m2
The middle of the road estimate – assuming median paving @ur momisugly 20% albedo and median natural surface @ur momisugly an equivalent of 65% albedo due to natural surface cooling effect (i.e. halfway between the concrete and green roof forcings established in the micro analysis) – is 1.54 W/m2. This, of course, also assumes that all of our fictional “median pavement” receives an average energy exposure – per Trenberth – of 198 W/m2.
This seems to indicate that there is at least one land use change (paving over of natural surfaces) that is capable of having a very significant impact at the US scale and not just at the local level.
Correct me if I am wrong, but it seems to me that Spencer has not taken into account changes in population density over time. What he has done is to show that stations with a low population density in 2000 had a much lower rate of increase in temperature over time than those with higher densities. That conflicts with Hansen-et-al [2000]’s somewhat complex analysis (based on light emissions rather than then out-of-date population data) which concluded that though the UHI effect was measurable, it was small. Who should we believe, and why?
If I were king – I would re-site all of the temperature sites to CEMETERIES. They are mostly park-like, very well maintained, ubiquitous, long-lived (pun intended) – and could be instrumented for automated data collection.
In fact – cemetery organizations could be data PROVIDERS to the government. If so much depends upon this information – why not open it to competition – and see who can provide the highest quality data?
Antony,
I think Dr. Spencer’s work here is an observation, not a theory. I would agree – although I have not read the paper in question – that it seems to disagree (not sure “conflicts” is the right word for it) with Hansen’s conclusions as you present them. For an observation a point in time analysis is adequate, for modeling purposes, accounting for changes over time is of course necessary. We need to be careful about mixing the apples and the oranges.
A minor nit: I don’t think population trends are so chaotic that, say for the US, using high res data from the census is innapropriate. I find it specious to think that light emissions could provide a useful, accurate snapshot unless they are using a data set like Columbia’s as the starting point and then using changes in emissions to model real time population changes.
I would tend to say in response to your question of who/what to believe… believe whatever you want, but you might want to consider the following:
1.) The average country from the Columbia dataset had either 14 or 17 % (sorry, don’t have it handy and working from memory) urban extent coverage. It’s no secret that a significant number of temperature stations globally are within these urban extents. The comon description of UHI is 1-2 C with little, that I have seen at least, understanding of its spacial effects – do we really think this has been accurately corrected out of the record?
2.) Why aren’t we measuring UHI instead of “correcting” it out of the record? Is it not real? Is it not also human-induced warming?
3.) In the US we have paved the rough equivalent of Wisconsin (61k square miles, 1.7% surface area) – and that is just a subsection of UHI. You’ve stepped on asphalt and/or cement in the summer… do you really think replacing natural sufaces with man made surfaces has a “small” effect?
Is it even possible that before modern engineering feats humanity followed climate patterns, and so migratory history can be counted on to give us a good view of favorable climate conditions for humans? Maybe? Anyone?
…The first process cools the Earth’s surface, and the second warms it, but together they still cool the planet unless feedbacks negate that.
Which explains why the forested deserts are so warm. 😐