Watts et al gets a mention.
3. NEW INFORMATION ON SURFACE TEMPERATURE PROCESSES
In general, the issue of global warming is dominated by considering the near-surface air
temperature (Tsfc) as if it were a standard by which one might measure the climate
impact of the extra warming due to increases in greenhouse gases. Fundamentally, the
proper variable to measure is heat content, or the amount of heat energy (measured in
joules) in the climate system, mainly in the oceans and atmosphere. Thus the basic
measurement for detecting greenhouse warming is how many more joules of energy are
accumulating in the climate system over that which would have occurred naturally. This
is a truly “wicked” problem (see House Testimony, Dr. Judith Curry, 17 Nov 2010)
because we do not know how much accumulation can occur naturally.
Unfortunately, discussions about global warming focus on Tsfc even though it is affected
by many more processes than the accumulation of heat in the climate system. Much has
been documented on the problems, and is largely focused on changes in the local environment, i.e. buildings, asphalt, etc. This means that using Tsfc, as measured today,
as a proxy for heat content (the real greenhouse variable) can lead to an overstatement of
greenhouse warming if the two are assumed to be too closely related.
A new paper by my UAHuntsville colleague Dr. Richard McNider (McNider et al. 2012)
looked at reasons for the fact daytime high temperatures (TMax) are really not warming
much while nighttime low temperatures (TMin) show significant warming. This has
been known for some time and found in several locations around the world (e.g.
California – Christy et al. 2006, East Africa – Christy et al. 2009, Uganda – just released
data). Without going into much detail, the bottom line of the study is that as humans
disturb the surface (cities, farming, deforestation, etc.) this disrupts the normal formation
of the shallow, surface layer of cooler air during the night when TMin is measured. In a
complicated process, due to these local changes, there is greater mixing of the naturally
warmer air above down to the shallow nighttime cool layer. This makes TMin warmer,
giving the appearance of warmer nights over time. The subtle consequence of this
phenomenon is that TMin temperatures will show warming, but this warming is from a
turbulent process which redistributes heat near the surface not to the accumulation of
heat related to greenhouse warming of the deep atmosphere. The importance of this is
that many of the positive feedbacks that amplify the CO2 effect in climate models depend
on warming of the deep atmosphere not the shallow nighttime layer.
During the day, the sun generally heats up the surface, and so air is mixed through a deep
layer. Thus, the daily high temperature (TMax) is a better proxy of the heat content of
the deep atmosphere since that air is being mixed more thoroughly down to where the
thermometer station is. The relative lack of warming in TMax is an indication that the
rate of warming due to the greenhouse effect is smaller than models project (Section 2).
The problem with the popular surface temperature datasets is they use the average of the
daytime high and nighttime low as their measurement (i.e. (TMax+TMin)/2). But if
TMin is not representative of the greenhouse effect, then the use of TMin with TMax will
be a misleading indicator of the greenhouse effect. TMax should be viewed as a more
reliable proxy for the heat content of the atmosphere and thus a better indicator of the
enhanced greenhouse effect. This exposes a double problem with models. First of all,
they overwarm their surface compared with the popular surface datasets (the non-circle
symbols in Fig. 2.1). Secondly, the popular surface datasets are likely warming too much
to begin with. This is why I include the global satellite datasets of temperature which are
not affected by these surface problems and more directly represent the heat content of the
atmosphere (see Christy et al. 2010, Klotzbach et al. 2010).
Fall et al. 2011 found evidence for spurious surface temperature warming in certain US
stations which were selected by NOAA for their assumed high quality. Fall et al.
categorized stations by an official system based on Leroy 1999 that attempted to
determine the impact of encroaching civilization on the thermometer stations. The result
was not completely clear-cut as Fall et al. showed that disturbance of the surface around a
station was not a big problem, but it was a problem. A new manuscript by Muller et al.
2012, using the old categorizations of Fall et al., found roughly the same thing. Now,
however, Leroy 2010 has revised the categorization technique to include more details of
changes near the stations. This new categorization was applied to the US stations of Fall
et al., and the results, led by Anthony Watts, are much clearer now. Muller et al. 2012
did not use the new categorizations. Watts et al. demonstrate that when humans alter the
immediate landscape around the thermometer stations, there is a clear warming signal
due simply to those alterations, especially at night. An even more worrisome result is
that the adjustment procedure for one of the popular surface temperature datasets actually increases the temperature of the rural (i.e. best) stations to match and even exceed the more urbanized (i.e. poor) stations. This is a case where it appears the adjustment process took the spurious warming of the poorer stations and spread it throughout the entire set of stations and even magnified it. This is ongoing research and bears watching as other factors as still under investigation, such as changes in the time-of-day readings were taken, but at this point it helps explain why the surface measurements appear to be warming more than the deep atmosphere (where the greenhouse effect should appear.)
Full testimony PDF here: christy-testimony-2012