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Saturday, May 25, 2019

Urbanization bias adjustments are tiny, and they are science fraud, partially caused by the lack of rural weather stations with long, continuous records, located outside the US, needed to determine the correct adjustments

Urbanization bias:
If the area around a 
land weather station
is affected by 
economic growth
over the years, 
the temperature record 
can be contaminated 
by a warming trend, 
which was not caused 
by greenhouse gasses,
but will be blamed 
on CO2 anyway.

This warming is called 
the urban heat island effect,
or UHI.

Changes in the immediate 
surroundings of a weather station 
can cause warming too
-- a "micro-climate trend" -- 
similar to an urbanization bias,
but not included in this report. 

The claimed global warming, 
of about one degree Celsius 
since 1880, is small enough 
to be significantly affected 
if there was one degree C. 
of warming at land weather 
stations caused ONLY by 
nearby economic growth, 
falsely blamed on CO2.

If the earlier warm period, 
from 1910 to 1940,
was comparable to the 
recent warm period, 
from 1975 to 2000,
EXCEPT for urbanization bias,
then claims that recent 
global temperatures 
are "unprecedented",
could be wrong.

The United States USHCN 
weather station network
suggests modest warming
since the 1970s,
but no warming is seen
in the past 12 years
using the large subset of
USHCN weather stations 
that are rural (USCRN), 
tracked since 2006:

( US rural weather stations )

USHCN ( all stations ) 
+ USCRN ( rural ) overlay: 

Unfortunately, outside the US
there's a severe shortage 
of fully rural weather stations, 
with long temperature records, 
so it's impossible for me 
to present a GLOBAL rural station 
trend, which would avoid 
the economic growth 
that affects most non-rural 
weather stations.

Gradual economic growth 
has caused warming at 
many land weather stations.

"Urbanization bias”
is a deceptive term,
because economic growth
in rural areas can cause
more warming too.

The "urban heat island "effect 
is greater for a rural station 
becoming slightly urbanized, 
than for an urban station 
becoming slightly 
more urbanized.
    Roy Spencer, PhD
"The global average 
urban heat island effect 
in 2000 estimated from 
station temperatures 
and population density data." 

Other organizations
that compile a global 
average temperature,
looked at the tiny 
NASA-GISS urbanization
adjustments, and decided
urbanization bias was too 
small for them to bother

This report provides
more evidence that 
surface temperature
used by governments,
are low quality 
non-global data,
compared with 
weather satellite data, 
that they ignore.

Most important: 
This report will explain 
that the NASA-GISS 
global urbanization bias 
weather station "adjustments"
are are unreasonably small,
because the methodology 
behind them is, at best, 
incompetent, and at worst, 
science fraud.

Weather stations in urban areas 
will have higher temperatures 
than nearby rural areas.

That urban heat island
effect is obvious,
and has been known 
since the 1800s.

Of course, lot's of people
live in cities, and love them 
-- they are not complaining
about being surrounded 
by cement, asphalt, 
bricks, and few trees.

People often vacation
in warm climates, and
move to warmer climates
after they retire.

There's lots of evidence
people LIKE warm areas
-- think of that the next time 
someone is whining about 
global warming !

There are many problems
when trying to compile 
a global average temperature.

The first problem is 
claiming to describe 
the climate of a planet 
with a single number
-- a statistic which is 
not a temperature 
anyone lives in !

If people 
are ever harmed 
by climate change, 
and I don't know 
of any so far, 
it will be local 
temperature changes 
that cause the damage.

Surface measurements
used by governments,
are the worst compilations,
compared with the weather
satellite data they ignore.

A majority of 
Earth's surface has 
no thermometers, 
or is missing data 
from one or more 
weather stations.

That means government 
bureaucrats will guess
the average temperature 
in a majority of the 2,592
5 degree latitude by 
5 degree longitude 
surface grid cells.

There's no economic growth 
to affect ocean temperature
measurements -- 71% of our
planet's surface.

For the remaining 29%, economic
growth in the vicinity of a weather
station could cause "man made" 
warming, unrelated to CO2.

"Urbanization bias" also
affects rural thermometers -- 
from rural areas 
becoming less rural.

Build a house, building,
asphalt road, or a parking lot,
near a well sited rural
weather station, and you will
most likely have a higher 
temperature -- instant global

For a weather station 
already in an urban area, 
there could be more
automobiles passing 
by the station, over time.

Maybe one or more 
new tall buildings
would shade the station
during the coldest or warmest 
part of a day, affecting the
maximum and minimum
temperatures of a day.

That weather station 
should be moved,
but probably won't be.

Urban areas are only about 
1% to 1.5% of our planet,
depending on where 
you define the borders 
of a metropolitan area.

But about half the land weather 
stations used for the global 
average temperature are in 
metropolitan areas. 

Several studies 
have claimed 
urbanization bias 
isn’t a problem. 

I disagree.

There are NASA-GISS
adjustments to remove 
urbanization biases 
from station data, 
but the net adjustment 
is ridiculously small
-- roughly 0.05 degrees C. 
per century of global warming 
is assumed to be caused
by urbanization, and deleted
from the global average. 

It's very likely the adjustments 
underestimate urbanization 

More important: 
The more you examine 
the methodology used
to make the "adjustments", 
the more obvious it is that 
the science behind them is, 
at best, incompetent, 
and at worst, fraud.

Here's why:
To estimate the warming 
effect of economic growth,
you need long term rural data,
and long-term urban
temperature data

But outside the US, Historical 
Climatology Network datasets
have very few rural stations 
with long records. 

Only eight rural stations 
outside the US (under 5%)
had continuous data 
for 95 of the last 100 years !

Claims of “unusual global warming 
since the Industrial Revolution” 
are mainly based on data 
from URBAN stations.

The following groups publish 
global average temperature 

NASA Goddard Institute for Space Studies 
(New York City, USA)

NOAA National Climatic Data Center 
(North Carolina, USA)

Berkeley Earth 
(California, USA)

Climate Research Unit, 
University of East Anglia 
(East Anglia, UK).

Tokyo Climate Center, 
Japan Meteorological Agency 
(Tokyo, Japan)

All estimates imply intermittent 
global warming since 1880. 

But weather stations 
with long-term records
contain a lot of 
“non-climate biases”:
-- Stations relocated, 
perhaps more than once

-- Instruments changed,
perhaps more than once

-- Nearby economic growth

" … the temperature 
of the city (London)
is not to be considered
as that of the climate; 
it partakes too much 
of an artificial warmth, 
induced by its structure, 
by a crowded population, 
and the consumption 
of great quantities of fuel 
in fires."   
     Luke Howard
"Climate of London"
2nd Edition, 1833
Page 2

Many of the world’s 
weather stations 
are currently in 
urbanized areas, 
but in the late 19th and 
early 20th centuries, 
the areas were
less urbanized, 
or even rural.

A Buenos Aires 
weather station 
said the average 
annual temperature 
was only 16°C 
at the beginning 
of the 20th century, 
but 18°C at the end. 

In 1914, 
the Buenos Aires
metropolitan area 
had a population 
of 1.7 million, 
but by 2001 
it had increased 
to 12.7 million! 

The nearest rural station 
is Punta Indio (ID=30187596000),
140km from the Buenos Aires 
weather station, and 5km from 
a town of about 6,000 people.

But it only has
23 years of data 
from the 1950s-1990s.

The Buenos Aires station 
showed warming during 
those 23 years,
but the Punta Indio 
station did not.

The average temperature 
at the Punta Indio station 
was only about 16° C.

It's hard to keep enough staff 
to maintain a continuous 
temperature record 
at an isolated rural location 
for a century, or longer.

Most rural station records
are only for a few decades.

Most stations with records 
of a century or longer,
are in urban areas. 

Even small Arctic towns, 
such as Barrow, Alaska,
have been affected by 
urbanization bias.

Barrow is at the northern tip 
of Alaska, high up in the Arctic.

The NOAA ESRL Global Monitoring 
Division has a weather station
is in the open tundra, 7.5km northeast 
of the town, established in 1973.

A National Weather Service (NWS) 
weather station was located in the 
middle of the town, then moved 
to the nearby Wiley Post-Will Rogers 
Memorial Airport.

This NWS weather record 
goes back to 1900.

Barrow had a population of 300
in 1900 -- 4,600 people by 2000.

Hinkel et al., 2003 
showed that Barrow,
during the winter, 
averaged 2.2°C warmer 
than out on the tundra.

In 1890, the U.S. National Weather 
Service created a Cooperative 
Observer Program (COOP) 
which encouraged volunteer 
weather enthusiasts to keep 
weather records at stations 
( rural and urban ) across 
the United States. 

Data from this program allowed
the National Climatic Data Center 
to create a US dataset 
with a large number of 
“contiguous United States” 
weather stations, called the U.S. 
Historical Climatology Network 
( USHCN ).

Almost all of the station records 
are fairly long and complete 
-- there are several rural 
and urban stations located 
in each of the U.S. states.

Of the 1,218 USHCN stations 
about 23% are very rural, 
and about 9% are 
highly urbanized. 

USHCN subsets 
(only urban stations, and only rural stations)
show similar trends:
-- Warming from the 1890s to the 1930s
-- Cooling from the 1930s to the 1970s
-- Warming from the 1970s to the 2000s

Most of the US weather stations 
with records going back
to the late-19th century 
(or earlier) are urban stations. 

( The longest station records 
are mostly in Europe, and 
North America, far from
a good global distribution. )

The rural US stations agree 
with the urban stations 
that the 1990s-2000s 
were warmer than
the 1960s-1970s, 
but disagree 
over how much. 

The urban US stations imply 
the 1990s-2000s were 
the the warmest decades 
on record, but the rural stations 
imply it was as warm in the 1930s !

Most urbanization bias studies 
relied on just one indicator 
to separate “urban” stations
from “rural” stations. 

For example, 
Hansen & Lebedeff, 1987
defined a station as being “urban”
if it was associated with a town 
with a 1970s population 
of at least 100,000. 

Otherwise, it was considered “rural”. 

Peterson, 2003 assumed 
that a station was “urban”
once the nightlight brightness 
in the area, measured by satellite,
reached a certain value.

In reality, urbanization bias 
is a gradual process.

A weather station doesn’t 
suddenly switch from being 
“rural” to “urban” when 
the population increases 
from 99,999 to 100,000!

Picking a single 
rural to urban
“threshold” variable 
is too subjective, 
and arbitrary.

Urbanization bias 
is a continual process 
that gradually 
becomes greater 
as a rural village 
becomes a small town, 
then a large town, 
then a small city, 
and then a 
thriving metropolis.

By taking a very 
simplistic approach, 
most of the studies 
failed to properly
distinguish between
the true climate trends, 
and urbanization bias.

Most studies seem 
to have assumed 
if you could 
divide stations 
into two subsets 
–“rural” and urban” --
using a single variable,
that was good enough 
to estimate urbanization bias.

I disagree.

Until a few decades ago, 
automated weather stations 
didn’t exist. 

If you wanted to maintain 
a weather station record 
you needed staff living nearby, 
who would take each day’s 

Most weather observers 
didn’t want to live in isolated, 
remote, rural locations. 

If you did convince an observer 
to take daily measurements 
at a remote, rural location 
for several years, when that 
observer retired, or died, 
you would need a replacement, 
or the station record would end.

As a result, most rural stations 
have short records, and large 
data gaps -- not very useful
for studying long-term 
temperature trends.

Most of the authors 
of urbanization studies 
also performed
faulty statistical 

The common mistake 
was to use “linear trends” 
to describe non-linear
weather station data.

Linear trends, unfortunately,
are the most popular 
statistical techniques 
used in climate science.

Weather station records, in fact,
are often highly non-linear.

When your data are non-linear, 
using a linear trend to describe 
the data is risky.

The Valentia Observatory (Ireland) 
weather station has a long and 
complete rural station record. 

At various times in that record, 
the station switched between 
cooling periods (1860s-1880s 
and 1950s-1970s) and warming 
periods (1920s-1940s and

A “linear trend” for the data
is a slight “warming” trend,
because the start of the record 
was in the middle of a cooling period, 
and the end of the record was
at the end of a warming period! 

Shorter periods could show
a “warming trend”, or a 
“cooling trend”, depending on 
your start and end date.

The NASA Goddard Institute 
for Space Studies (NASA - GISS) 
is the only group whose 
global temperature estimates 
include adjustments 
to correct for urbanization bias.

Their adjustment method is flawed.

Dr. James Hansen, 
who ran the institute 
from 1981-2013, was 
( and still is ) 
a VERY vocal believer in
dangerous man-made 
global warming.

His 1988 testimony to Congress 
was very influential in making 
man-made global warming 
theory a public concern.

In 1999, NASA-GISS began
using a computer program 
to automatically search 
weather station records, 
and apply adjustments 
to remove urbanization bias.
( Hansen et al., 1999 )

When the Goddard Institute 
ran their program on about 
6,000 station records, 
there were lots of adjustments, 
but they only added up to
a net -0.05°C. per century. 

That negligible adjustment
convinced many people 
the urbanization bias 
problem was tiny.

GISS divided all of their weather stations 
into two groups: “urban stations” 
and “rural stations”. 

Since 2010, they have been doing this 
on the basis of the night-light intensity 
in the location of the station 
( using satellite data ).

For every station,
the computer identifies 
as “urban”, there is 
an individual 
bias adjustment:

A nearby station 
“rural average” 
is calculated 
for each urban station, 
by averaging the trends 
of all of the rural neighbors
in a 500 km radius 
( or 1,000 km, if there
aren’t enough stations
within 500 km ).

“An adjusted urban record 
is defined only if there are 
at least three rural neighbors 
for at least two thirds 
of the period being adjusted.” 
    Hansen et al., 1999

From 10% to 15% of urban 
weather stations don't meet
the above Hansen, 1999
requirement, so can't be 
adjusted for that reason.

The difference between 
the urban station record 
and the rural average 
is then calculated, 
and assumed to be 
“the urbanization bias”.

The urbanization adjustment
is a two-part adjustment
-- a bi-linear approximation
with two segments, 
and each segment ( or "leg" ) 
has a separate slope.

NASA-GISS claims this allows
“some time dependence
in the rate of growth".

"Leg 1” is the linear fit 
for the first part
of the record, and 

“Leg 2” is the linear fit 
for the second part 
of the record. 

The point marking the transition 
between Leg 1 and Leg 2 is adjusted 
by the program to optimize the fits.

This adjustment is added to the
urban station’s record, 
and the record is then assumed 
to have been adequately corrected.

For about 200 of the urban stations, 
GISS does not have enough rural 
neighbors for their computer 
program to work.

Strangely, GISS does not subtract 
their estimated urbanization bias 
from the current temperatures
-- their program adds degrees
to the early unbiased temperatures,
which is “rewriting history”.

In addition, because urbanization bias 
continues to increase from year to year, 
GISS keeps increasing their adjustments 
every year, "rewriting history 

NASA-GISS never verified 
their adjustments to prove
were actually removing 
the urbanization biases 
from their data !

Once they had written 
their computer program, 
they merely assumed 
it would work.

That's science fraud !

About half the 
linear adjustments 
the GISS computer 
program calculates 
have positive slopes !

The computer program 
is calculating 
“urbanization bias” 
due to “urban cooling“,
as often as 
“urbanization bias” 
due to “urban warming” !

That's science fraud !

Percentages below are 
from November 2011,
because NASA ceased
publishing their intermediate 
calculations in December 2011 !

Leg 1 and Leg 2 adjustments 
are both negative slopes (15%),
which is exactly what 
one should expect
for all stations affected
by economic growth.

Leg 1 and Leg 2 adjustments 
are both positive slopes (9%) ?

Leg 1 slope negative, 
Leg 2 slope positive (39%) ?

Leg 1 slope positive, 
Leg 2 slope negative (37%) ?

For (C), the Leg 1 
negative slope 
would remove 
a warming trend,
but the Leg 2 positive 
slope would introduces
a warming trend ?

That makes no sense !

Only 15% of NASA’s adjustments (A)
are the expected negative slope
adjustments to remove urbanization bias.

"Urban cooling” trends 
can happen but are rare
in real life ...
but not when you examine 
the NASA-GISS "adjustments".

Urbanization bias is almost 
entirely a warming bias.

But for almost all NASA adjustments 
to remove an urban warming trend, 
there is an opposite adjustment 
to remove an “urban cooling” trend
from another station ?

As a result, the net effect 
of all the adjustments is tiny
-- about -0.1°C. per century 
for the half of the stations 
categorized as being urban, 
so the the overall effect 
on the global temperature 
is only half of -0.1, or about 
-0.05°C per century.

The GISS program 
actually calculated 
that urbanization 
led to a net “cooling” 
during the 
1880s-1890s and 
the 1930s-1960s !

That makes no sense !

Even if a few individual stations
experienced some "urban cooling,"
the claim of global urban cooling
during the 1880s-1890s, and 
the 1930s-1960s, is extremely
hard to believe !

And there is almost 
no net urbanization
bias adjustment 
since the 1970s,
as the world 
has become 
more urbanized. 

By cooling pre-1980s records, 
but having very little cooling 
to the post-1980s records, 
to offset urbanization bias,
this artificially created
a slightly steeper global warming 
trend, than if there had been
no urbanization bias adjustments
at all !

Also extremely hard to believe.

adjustments were 
really removing
urbanization bias, 
then very few, if any, 
of the station adjustments 
would be for “urban cooling”, 
not half the adjustments !

urbanization bias 
adjustment is so small 
because so many individual 
weather station adjustments
offset each other, 
which adds up 
to science fraud, 
in my mind ! 

A lot of the stations GISS identifies 
as “rural”, are actually urban,
or suburban.

If the rural records are shorter
than the urban records, 
NASA’s computer program 
simply stops adjusting 
the urban records. 

But, rather than 
deleting the years,
or decades, of an 
urban record that
they couldn’t adjust, 
NASA-GISS just uses the
unadjusted urban record
anyway !

The shortage of long-term
rural records means a lot 
of urban records will be 
unadjusted for long periods.

If there aren’t enough rural stations 
for the adjustments, that means 
there are too few rural stations 
to dilute the urbanization bias 
for the region.

NASA-GISS assume 
rural records 
have no 
non-climate biases. 

But if a rural station is moved, 
or the vicinity becomes less rural, 
that would increase 
the warming trend 
of the “rural average”, 
tricking the computer program
into thinking urbanization bias
was less than it actually was,
or even assuming the need
for a "cooling the past"
adjustment for urbanization bias !

The NASA-GISS computer 
urbanization adjustment 
methodology is science fraud.

Once they developed 
their computer program, 
they seem to have just 
assumed it would work.

GISS developed 
the computer program, 
and they are the 
only “customer”
to use it, so they 
will never get 
complaints from 
other users.

The National Climatic Data Center, 
who compile and maintain the 
Historical Climatology Network 
datasets, have also developed 
a series of “homogenization” 
adjustments, which they claim 
will remove non-climate biases 
from the data.

That claim is science fraud too.

Homogenization is the equivalent 
of shaking a carton of sour,
curdled milk, and then claiming 
it is fresh milk !

Homogenization does NOT 
remove biases, it merely spreads 
existing biases among all stations 
– urban and rural -- 
which obscures the biases.

NOAA’s National Climatic Data Center 
compiles and maintains the Global 
Historical Climatology Network (GHCN).

The National Climatic Data Center 
uses only GHCN station records.

NASA - GISS and the
Japan Meteorological 
Agency use GHCN 
stations for over 99%
of their weather stations.

The other two groups, 
UK's Climate Research 
Unit, and Berkeley Earth, 
rely heavily on the 
Historical Climatology 
Network datasets. 

As of about five years ago,
only eight of the GHCN 
rural stations had data 
for at least 95 out of the 
last 100 years:

(1)   The Pas, Manitoba (Canada)
(2)   Angmagssalik (Greenland)
(3)   Lord Howe Island (New Zealand)
(4)    Sodankylä (Finland)
(5)    Hohenpeißenberg (Germany)
(6)    Valentia Observatory (Ireland)
(7)    Sulina (Romania)
(8)    Säntis (Switzerland)

The Hohenpeißenberg (Germany)
weather station was first set up in 1781. 

Temperatures in the 
18th and 19th centuries
were measured indoors, 
while they are now
measured outdoors.

The Sulina weather station is on 
a concrete platform a few meters
from the Danube River.

It has been relocated several times:
During World War II, it was moved 
140 kilometers south, to the 
town of Constanta. 

The official coordinates 
for the station are 5 kilometer
west of its current spot,
indicating another move. 

The Säntis weather station 
was set up in 1882.

In 1955, a 123.5 meter high TV 
and radio transmitter was built 
beside it, and it is now the location 
of a large, popular hotel.

In the late 1970s 
automated instruments
were installed.

It has been estimated 
that significant 
station changes, 
that would affect 
weather station 
average about once 
every 20 years.

There seem
to be enough 
rural stations 
with long records
to be confident 
about the U.S. 
temperature trends 
for the 20th century, 
but not even close
for the rest of the world.