Re: [asa] Data doesn't support global warming

From: Rich Blinne <>
Date: Wed Dec 16 2009 - 14:37:44 EST

Autumn is definitely the best season to look at below. Winter and Spring (to
a lesser extent) are the seasons with the most El Nino Southern Oscillation
(ENSO) variability. Summer may show urban heat island effects (UHI). The
approach I outlined while getting around air conditioners issue doesn't get
around this problem. If you did a subset where the wind speeds are high
would mitigate this problem. Still, the percentage of sites that are poorly
sited and might show this effect (bad configuration or in urban areas)
is only 16%. The current adjustment for urbanization is to subtract about
0.1 degree Farenheit. The correction starts at 0 in 1900 and slowly moves to
0.1 degree subtraction at present. More information on the correction is
found here.

Karl, T.R., C.N. Williams, Jr., F.T. Quinlan, and T.A. Boden, 1990: United
States Historical Climatology Network (HCN) Serial Temperature and
Precipitation Data, Environmental Science Division, Publication No. 3404,
Carbon Dioxide Information and Analysis Center, Oak Ridge National
Laboratory, Oak Ridge, TN, 389 pp.

The following is the full correction procedure. See my notes in bold
concerning undocumented change points and the Darwin Airport Station 0

 Quality Evaluation and Database Construction

First, daily maximum and minimum temperatures and total precipitation were
extracted from a number of different NCDC data sources and subjected to a
series of quality evaluation checks. The three sources of daily observations
included DSI-3200<>,
Daily maximum and minimum temperature values that passed the evaluation
checks were used to compute monthly average values. However, no monthly
temperature average or total precipitation value was calculated for
station-months in which more than 9 were missing or flagged as erroneous.
Monthly values calculated from the three daily data sources then were merged
with two additional sources of monthly data values to form a comprehensive
dataset of serial monthly temperature and precipitation values for each HCN
station. Duplicate records between data sources were eliminated. Following
the merging procedure, the monthly values from all stations were subject to
an additional set of quality evaluation procedures, which removed between
0.1 and 0.2% of monthly temperature values and less than 0.02% of monthly
precipitation values.
Time of Observation Bias Adjustments

Next, monthly temperature values were adjusted for the time-of-observation
bias (Karl, et al.
Vose et al., 2003<>).
The Time of Observation Bias (TOB) arises when the 24-hour daily summary
period at a station begins and ends at an hour other than local midnight.
When the summary period ends at an hour other than midnight, monthly mean
temperatures exhibit a systematic bias relative to the local midnight
standard (Baker,
In the U.S. Cooperative Observer Network, the ending hour of the 24-hour
climatological day typically varies from station to station and can change
at a given station during its period of record. The TOB-adjustment software
uses an empirical model to estimate and adjust the monthly temperature
values so that they more closely resemble values based on the local midnight
summary period. The metadata archive is used to determine the time of
observation for any given period in a station's observational history.
Homogeneity Testing and Adjustment Procedures

Following the TOB adjustments, the homogeneity of the TOB-adjusted
temperature series is assessed. In previous releases of the U.S. HCN monthly
dataset, homogeneity adjustments were performed using the procedure
described in Karl and Williams (1987). This procedure was used to evaluate
non-climatic discontinuities (artificial changepoints) in a station's
temperature or precipitation series caused by known changes to a station
such as equipment relocations and changes. Since knowledge of changes in the
status of observations comes from the station history metadata archive
maintained at NCDC, the original U.S. HCN homogenization algorithm was known
as the Station History Adjustment Program (SHAP).

Unfortunately, station histories are often incomplete so artificial
discontinuities in a data series may occur on dates with no associated
record in the metadata archive. Undocumented station changes obviously limit
the effectiveness of SHAP. To remedy the problem of incomplete station
histories, the version 2 homogenization algorithm addresses both documented
and undocumented discontinuities.

*The potential for undocumented discontinuities adds a layer of complexity
to homogeneity testing. Tests for undocumented changepoints, for example,
require different sets of test-statistic percentiles than those used in
analogous tests for documented discontinuities (**Lund and Reeves,
*). For this reason, tests for undocumented changepoints are inherently less
sensitive than their counterparts used when changes are documented. Tests
for documented changes should, therefore, also be conducted where possible
to maximize the power of detection for all artificial discontinuities. In
addition, since undocumented changepoints can occur in all series, accurate
attribution of any particular discontinuity between two climate series is
more challenging (**Menne and Williams,
*). [RDB Note: the difference between the American homogenization and the
Australian homogenization for Darwin appears to be the station
temperature-only move in 1941 is an undocumented move to NOAA but not to the
Australian BOM.]*

The U.S. HCN version 2 "pairwise" homogenization algorithm addresses these
and other issues according to the following steps, which are described in
detail in Menne and Williams (2008). At present, only temperature series are
evaluated for artificial changepoints.

   1. First, a series of monthly temperature differences is formed between
   numerous pairs of station series in a region. Specifically, difference
   series are calculated between each target station series and a number (up to
   40) of highly correlated series from nearby stations. In effect, a matrix of
   difference series is formed for a large fraction of all possible
   combinations of station series pairs in each localized region. The station
   pool for this pairwise comparison of series includes U.S. HCN stations as
   well as other U.S. Cooperative Observer Network stations.
   2. Tests for undocumented changepoints are then applied to each paired
   difference series. A hierarchy of changepoint models is used to distinguish
   whether the changepoint appears to be a change in mean with no
trend (Alexandersson
   and Moberg, 1997<>),
   a change in mean within a general trend (Wang,
   or a change in mean coincident with a change in trend (Lund and Reeves,
   . Since all difference series are comprised of values from two series, a
   changepoint date in any one difference series is temporarily attributed to
   both station series used to calculate the differences. The result is a
   matrix of potential changepoint dates for each station series.
   3. The full matrix of changepoint dates is then "unconfounded" by
   identifying the series common to multiple paired-difference series that have
   the same changepoint date. Since each series is paired with a unique set of
   neighboring series, it is possible to determine whether more than one nearby
   series share the same changepoint date.
   4. The magnitude of each relative changepoint is calculated using the
   most appropriate two-phase regression model (e.g., a jump in mean with no
   trend in the series, a jump in mean within a general linear trend, etc.).
   This magnitude is used to estimate the "window of uncertainty" for each
   changepoint date since the most probable date of an undocumented changepoint
   is subject to some sampling uncertainty, the magnitude of which is a
   function of the size of the changepoint. Any cluster of undocumented
   changepoint dates that falls within overlapping windows of uncertainty is
   conflated to a single changepoint date according to
      1. a known change date as documented in the target station's history
      archive (meaning the discontinuity does not appear to be
undocumented), or
      2. the most common undocumented changepoint date within the
      uncertainty window (meaning the discontinuity appears to be truly
   5. Finally, multiple pairwise estimates of relative step change magnitude
   are re-calculated (as a simple difference in mean) at all documented and
   undocumented discontinuities attributed to the target series. The range of
   the pairwise estimates for each target step change is used to calculate
   confidence limits for the magnitude of the discontinuity. Adjustments are
   made to the target series using the estimates for each shift in the series.

Estimation of Missing Values

Following the homogenization process, estimates for missing data are
calculated using a weighted average of values from highly correlated
neighboring values. The weights are determined using a procedure similar to
the SHAP routine. This program, called FILNET, uses the results from the TOB
and homogenization algorithms to obtain a more accurate estimate of the
climatological relationship between stations. The FILNET program also
estimates data across intervals in a station record where discontinuities
occur in a short time interval, which prevents the reliable estimation of
appropriate adjustments.
Urbanization Effects

In the original HCN, the regression-based approach of Karl et al.
employed to account for urban heat islands. In contrast, no specific
urban correction is applied in HCN version 2 because the change-point
detection algorithm effectively accounts for any "local" trend at any
individual station. In other words, the impact of urbanization and other
changes in land use is likely small in HCN version 2. Figure 2 - the minimum
temperature time series for Reno, Nevada - provides anecdotal evidence in
this regard. In brief, the black line represents the unadjusted data, and
the blue line represents fully adjusted data. The unadjusted data clearly
indicate that the station at Reno experienced both major step changes (e.g.,
a move from the city to the airport during the 1930s) and trend changes
(e.g., a possible growing urban heat island beginning in the 1970s). In
contrast, the fully adjusted (homogenized) data indicate that both the
step-type changes and the trend changes have been effectively addressed
through the change-point detection process used in HCN version 2.
On Wed, Dec 16, 2009 at 10:37 AM, Rich Blinne <> wrote:

> Autumn:
> Spring:
> Summer:
> On Wed, Dec 16, 2009 at 10:33 AM, Rich Blinne <>wrote:
>> Here's the graph for Boreal Winter:
>> On Wed, Dec 16, 2009 at 10:25 AM, Dave Wallace <
>> > wrote:
>>> Rich Blinne wrote:
>>>> Another way to decouple the effect of air conditioning is to look at
>>>> so-called "high lows". That is how many record high lows there and how many
>>>> record low lows there are. If there is AGW there should be more high lows
>>>> and fewer low lows. Solar warming would effect only the highs and not the
>>>> lows. Here's a graph of high lows and low lows:
>>>> On the top graph the red line is the percent of the U.S. where lows are
>>>> much above normal. The blue line is percentage the U.S. where lows are much
>>>> below normal. We are getting more and more high lows and almost no low lows.
>>> At first when I looked at the plot it looked quite positive to the pro
>>> AGW side as I did not look carefully at the fact that it is an annual record
>>> of lows and highs. I had initially interpreted it as covering the winter
>>> (Jan/Dec) only so that poor sighting relative to air conditioners would make
>>> little difference. However, even so it is somewhat suggestive but I would
>>> really like to see the equivalent graph over late fall to early spring.
>>> Dave W
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Received on Wed Dec 16 14:38:16 2009

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