Why Skeptics Don't See What Climatologists See was Re: [asa] Data doesn't support global warming

From: Rich Blinne <rich.blinne@gmail.com>
Date: Fri Dec 18 2009 - 14:04:25 EST

 On Dec 18, 2009, at 5:32 AM, Glenn Morton wrote:

 Very, very interesting. Why did you average an entire continent using no
grid? That's pretty useless.

Where did you you learn your mathematics. Are you suggesting that gridding
is going to turn cooling into WARMING? Pray tell explain how.There are only
about 1/10th of the stations show any warming yet somehow gridding is going
to make everything WARM? get real with your objections Rich.

There is an implied gridding in any such average. What there isn't is a
weighting. The stations are spread all over the 100 degree longitudinal
spread so while not accurate to the n'th degree, it is still showing that
Siberia is cooling.

Still you did do an analysis of the arctic

stations so I will start with that since the point is whether there is polar
amplification and thus high latitude stations are key. You also did anomaly
analysis. So, thanks. But why would you start in 1897 and end in 1991?

Why did I end in 1991? Because while I was doing research that you should
have done, I screwed up the labeling of the axis in Excel. Here is the
corrected chart. The data goes out to 2008 but in the old picture the label
was wrong.
http://2.bp.blogspot.com/_Lxqre8hMG3M/SytyNGUUldI/AAAAAAAAA8w/LTHOJ9_qdms/s1600-h/weatherSiberiaARcticCircleTempAnomaly.jpg
Before about 1912 there are a couple of points of live data but no two are
in consecutive years. I grabbed the wrong start point for the label.

*
*

The following is the plot of the RAW data for

1. Asian Russian Federation GHCN sites
2. With data before 1912.
3. Greater Than 65 degrees latitude

All the sites agree with each other for their anomalies but look nothing
like *any* of Glenn's graphs and there is plenty of data before both 1912
and 1897. By the end of this post we should all know *why *this is the case.
(Note: no conspiracies are required on either side) First let's look at the
anomalies individually. Remember these are all raw, no homogenization has
been done.

*http://tinyurl.com/yctp4pz*
**
 Here's the average so the trend is more evident. This is the *average of
the anomalies* which almost identical to any of the individual anomalies.
AGW is evident.
http://tinyurl.com/yghubof
**
Here's the same set of stations just looking at the temperatures:

*http://tinyurl.com/yz2lmxz*
**
**
**
And* *the average of these temperatures:
http://tinyurl.com/yfznrux

Shazam! No AGW! If you change the scale on the left hand side you get
*anomalies
of the average*. Note the *static bias* between the stations when you do the
anomalies of the average technique. Glenn has done an excellent job of
documenting static bias in surface stations on this list. But everytime he
did it I yawned. Why? Because while these stations may have lousy *static
bias* we see that we have good *time-dependent bias*. If you are studying *
microclimate* then you care about static bias but if you are studying global
climate change decreasing time-dependent bias is all you care about. By the
way, that's exactly what homogenization does. It trades off static bias in
order to get less time-dependent bias. Pielke Sr.'s paper is titled:
"*Microclimate
*exposures of surface-based stations." So, he cares about air conditioners.
The first line of Peterson's paper is: "Analysis of a small subset of U.S.
Historical Climatology Network does not find a *time-dependent bias* caused
by current poor station siting." So, he doesn't care so much about air
conditioners.

So, given the state of the equipment how do you glean the best and most
accurate way to determine if there is AGW? You use the anomaly method and
more specifically you compute an anomaly for each station and then do an
areal average (common practice: weighted 5x5 grids) of these anomalies.
Again, *average of the anomalies* and not *anomalies of the average*. You
correct for the kind of bias that the analysis is sensitive to. Glenn's
analysis is sensitive to static bias and thus has a cow when he sees air
conditioners or techniques that increase static bias. The anomaly method is
sensitive to time-dependent bias and people who do that yawn when they see
(old) air conditioners and cheer when more static bias is traded off for
less time-dependent bias.

For climatologists this is really basic stuff. This is why the skeptics need
to be peer-reviewed by climatologists because the very basic error that
Glenn makes would get caught right there. He could then go back and
recompute the climate change correctly -- and this includes any measured
quantity and time frame, not just temperature and the present day -- and
then see if his conclusions are still warranted. See here for more
background.
http://www.ncdc.noaa.gov/oa/climate/research/anomalies/index.html

I'll close with a quote from this page in case NOAA's explanation is clearer
than mine:

*Why use temperature anomalies (departure from average) and not absolute
temperature measurements?*

Absolute estimates of global average surface temperature are difficult to
compile for several reasons. Some regions have few temperature measurement
stations (e.g., the Sahara Desert) and interpolation must be made over
large, data-sparse regions. In mountainous areas, most observations come
from the inhabited valleys, so the effect of elevation on a regionís average
temperature must be considered as well. For example, a summer month over an
area may be cooler than average, both at a mountain top and in a nearby
valley, but the absolute temperatures will be quite different at the two
locations. The use of anomalies in this case will show that temperatures for
both locations were below average.

Using reference values computed on smaller [more local] scales over the same
time period establishes a baseline from which anomalies are calculated. This
effectively normalizes the data so they can be compared and combined to more
accurately represent temperature patterns with respect to what is normal for
different places within a region.

For these reasons, large-area summaries incorporate anomalies, not the
temperature itself. Anomalies more accurately describe climate variability
over larger areas than absolute temperatures do, and they give a frame of
reference that allows more meaningful comparisons between locations and more
accurate calculations of temperature trends.
*
*
Rich Blinne
Member ASA
*
*

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Received on Fri Dec 18 14:04:54 2009

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