Pine River Dam,MN(216547)

Jan: 23.7F/-4.6C (2006) -4.5 (+0.1)
-10.7F/-23.7C (1912) -25.4 (-1.7)
Feb: 28.5F/-1.9C (1998) -1.9 (0.0)
-7.3F/-21.8C (1936) -23.1 (-1.3)
Mar: 37.8F/3.2C (1910) 2.9 (-0.3)
10.2F/-12.1C (1899) -12.5 (-0.4)
Apr: 51.9F/11.1C (1915) 10.9 (-0.2)
31.0F/-0.6C (1950) -0.7 (-0.1)
May: 65.2F/18.4C (1977) 17.9 (-0.5)
43.1F/6.2C (1907) 5.8 (-0.4)
Jun: 70.9F/21.6C (1933) 21.8 (+0.2)
57.2F/14.0C (1945) 14.1 (+0.1)
Jul: 75.1F/23.9C (1916) 23.9 (0.0)
61.7F/16.5C (1992) 16.5 (0.0)
Aug: 74.1F/23.4C (1983) 23.4 (0.0)
60.4F/15.8C (1927) 15.9 (+0.1)
Sep: 62.9F/17.2C (1906) 17.0 (-0.2)
50.1F/10.1C (1965) 9.6 (-0.5)
Oct: 56.6F/13.7C (1963) 12.9 (-0.8)
30.2F/-1.0C (1925) -1.5 (-0.5)
Nov: 40.9F/4.9C (2001) 4.9 (0.0)
17.4F/-8.1C (1911) -8.4 (-0.3)
Dec: 25.4F/-3.7C (1931) -5.2 (-1.5)
1.3F/-17.1C (1927) -18.6 (-1.5)

Obviously GISS cooled the past a bit, but they did it by cooling the colder months of the year and recent data stays about the same. Lets do one more.

Cloquet,MN(211630)

Jan: 24.7F/-4.1C (2006) -4.1(0.0)
-8.7F/-22.6C (1912) -22.7 (-0.1)
Feb: 29.7F/-1.3C (1998) -1.3 (0.0)
-4.4F/-20.2C (1936) -20.3 (-0.1)
Mar: 35.0F/1.7C (2000) 1.7 (0.0)
14.3F/-9.8C (1923) -9.8 (0.0)
Apr: 48.1F/8.9C (1987) 8.9 (0.0)
30.7F/-0.7C (1950) -0.7 (0.0)
May: 59.6F/15.3C (1977) 15.3 (0.0)
45.4F/7.4C (1915) 8.0 (+0.6)
Jun: 67.7F/19.8C (1933) 19.8 (0.0)
54.3F/12.4C (1915) 13.1 (+0.7)
Jul: 72.3F/22.4C (1921) 23.2 (+0.8)
60.0F/15.6C (1915) 16.3 (+0.7)
Aug: 70.1F/21.2C (1983) 21.1 (-0.1)
57.8F/14.3C (1912) 15.1 (+0.8)
Sep: 61.6F/16.4C (2004) 16.4 (0.0)
48.2F/9.0C (1918) 9.5 (+0.5)
Oct: 54.6F/12.6C (1963) 12.5 (-0.1)
33.1f/0.6C (1917) 1.1 (+0.5)
Nov: 41.1F/5.1C (2001) 5.1 (0.0)
19.8F/-6.8C (1911) -6.3 (+0.5)
Dec: 26.2F/-3.2C (1913) -3.3 (-0.1)
1.6F/-16.9C (1983) -16.9 (0.0)

We see with this station that it was warmed in the past and that warming was done mostly in the warmer months. I know this station was warmed because you can easily compare the chart listed with GISS and the chart posted with the survey done at surfacestations.org. before the 2007 adjustments. The Y-axis of the charts are 1degC different(warmer after the adjustments).

On these two stations, some years are there more than once for each station monthly record. For example, 1915(May,Jun,Jul) in Cloquet was cold but adjusted up pretty good. 1912(Jan) and 1912(Aug) are adjusted different. A slight tick down in Jan, but up in Aug.

Be nice to compare all months in a station record to what GISS has, but this gives some idea. The pros are going bald over trying to figure what they’ve done.

Warmest and coolest averages for each month came from here.
http://mrcc.sws.uiuc.edu/INTERACT/mwclimate_data_calendars_1.jsp

First, he showed that the averaging method GISS uses can produce a guesstimate that is significantly off. Look at a known Dec value and a known Feb value. Use the GISS method to guesstimate what Jan value would be (if it were not available, this guesstimate would be included in the GISS data).

But John only used data where we know Jan’s actual value. How does the hypothetical guesstimate (using GISS methodology) stack up with reality?

Answer — not good.

BTW, it would actually be very difficult to systematically attempt to “trick” the GISS estimation algorithm into producing higher & higher temperatures, so I really don’t see there could be any (un)intentional biasing going on. Seems far more likely that the urban heat effect is simply not being sufficiently accounted for….?

Just my 2 cents,

Bill Derryberry

Exerpt from http://www.giss.nasa.gov/about/: Current research, under the direction of Dr. James Hansen, emphasizes a broad study of Global Change, which is an interdisciplinary initiative addressing natural and man-made changes in our environment that occur on various time scales (from one-time forcings such as volcanic explosions, to seasonal/annual effects such as El Niño, and on up to the millennia of ice ages) and affect the habitability of our planet. Program areas at GISS may be roughly divided into the categories of climate forcings, climate impacts, model development, Earth observations, planetary atmospheres, paleoclimate, radiation, atmospheric chemistry, and astrophysics and other disciplines. However, due to the interconnections between these topics, most GISS personnel are engaged in research in several of these areas.

A key objective of GISS research is prediction of atmospheric and climate changes in the 21st century. The research combines analysis of comprehensive global datasets, derived mainly from spacecraft observations, with global models of atmospheric, land surface, and oceanic processes. Study of past climate change on Earth and of other planetary atmospheres serves as a useful tool in assessing our general understanding of the atmosphere and its evolution.

Is there a reason NASA is doing this work instead of NOAA? This is work NOAA is chartered to do.

With Dr. Hansen in charge, I suspect a biased “end justifies the means” political agenda,

Instances such as this amplify the usefullness of the Finagle Constant, the Bougerre Factor and the Diddle Coefficient in correcting faulty and/or missing data to obtain the projected result.

But if we did that, then we wouldn’t be able to panic when one year was 0.1 degrees warmer than another year.

However, if there is another correction process involved in generating regional/zonal/global averages (and here I am thinking of Steve McIntyre’s discussion of Mr. Hansen’s rather byzantine “rural” station correction program) are we sure than all errors are treated equally? My sense is that the wider the variance in the input set the more the correction algorithms get to decide what the “true” figures are.

Like: The global average temperature is 56.781 degrees F +/- 4.890 degrees at 3 standard deviations? Accurate, but hardly the implied precision. Why is it not quoted thus? And this is just the standard error, no? We also have experimental error.

Also, if I understand this correctly, that this is the histogram for an individual station, and if each station is assumed to have an independent error, then this suggests the global measure will be very accurate indeed, as all the individual errors will, indeed, cancel out.

Reply by John Goetz:

Actually, the histogram is for all stations and all years in the GHCN record (v2.mean).

You need to be a little careful with the shape of the histogram. The “simulation” I performed required that all three months be available in a specific season for a specific station in order to calculate an estimate and compare it to the real value. For example, if summer 1957 was being tested, I needed June, July and August. If August were missing, the GISS algorithm would not be able to estimate June or July, and I would not have a real August to look at either.

With all three months available, I now force symmetry into the result. If my estimate for August is higher than the actual, the combination of June and July must be lower than the actual by an equivalent amount such that the average of the three predicted values is the same as the average of the three real values.

In the actual application of the GISS algorithm, at most one month in a season can be estimated, so symmetry in practice is not guaranteed. In fact some months in some years are estimated far more frequently than others.

What the distribution really tells us is 1) how accurate or inaccurate the GISS estimate is and 2) the probability that a specific value will be estimated.

Given the prescence of the satellites, they wouldn’t be able to get away with that for long, but what about older historical data? Is anyone capturing the older pre ‘79 data for posterity, before bits of it get ‘lost’.

and also PDO index, which is still dropping;
http://www.woodfortrees.org/plot/jisao-pdo/from:2007

For the guestimating to some how introduces a bias, there would need to be something strange going on. Of which there may be, but we have no evidence of that here (beyond the fact that GISS shows faster global temperature rises than most other measures).