BACKGROUND:
Urbanization bias:
If the area around a land weather station nis 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.
SUMMARY:
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 subset of USHCN weather stations
that are rural (USCRN), tracked since 2006:
USCRN
( 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
calculating.
This report provides more evidence that surface temperature
measurements, 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.
DETAILS
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.
Reason:
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
warming.
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
warming.
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
compilations:
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.
Example:
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.
SMALL TOWN
URBANIZATION BIAS
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.
USHCN
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
measurements.
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
analyses.
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.
Example:
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
1980s-2000s).
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.
NASA - GISS
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.
FAULTY NASA-GISS
METHODOLOGY
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
urbanization
bias adjustment:
(1)
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.
(2)
The difference between
the urban station record
and the rural average
is then calculated,
and assumed to be
“the urbanization bias”.
(3)
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".
(a)
"Leg 1” is the linear fit
for the first part
of the record, and
(b)
“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.
(4)
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
continuously".
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 !
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 !
(A)
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.
(B)
Leg 1 and Leg 2 adjustments
are both positive slopes (9%) ?
(C)
Leg 1 slope negative,
Leg 2 slope positive (39%) ?
(D)
Leg 1 slope positive,
Leg 2 slope negative (37%) ?
For (C), the Leg 1
negative slope
adjustment
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.
If NASA GISS's
urbanization
adjustments were
really removing
urbanization bias,
then very few, if any,
of the station adjustments
would be for “urban cooling”,
not half the adjustments !
The net NASA-GISS
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 !
ADDITIONAL PROBLEMS
(1)
A lot of the stations GISS identifies
as “rural”, are actually urban,
or suburban.
(2)
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)
(5)
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.
(7)
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.
(8)
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
measurements,
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.