from:
Journal of Geographical
Research Atmospheres
"Land surface
air temperature data
are considerably different
among
BEST-LAND,
CRU-TEM4v,
NASA-GISS, and
NOAA-NCEI"
by Yuhan Rao,
Shunlin Liang,
Department of
Geographical Sciences,
University of Maryland
and Yunyue Yu
Center for Satellite
Applications and Research,
NESDIS, NOAA,
College Park
Several groups produce gridded
land surface air temperature (LSAT)
data sets using station measurements
and infilling to assess climate change.
The Intergovernmental
Panel on Climate Change's
Panel on Climate Change's
Fifth Assessment Report
suggests that estimated
global and hemispheric
mean LSAT trends of
different data sets
are consistent.
Little attention is paid
to comparisons of
local and regional
temperatures.
temperatures.
A study of four
LSAT data sets
LSAT data sets
included:
-- Berkley Earth
Surface Temperature
Surface Temperature
(BEST‐LAND),
-- Climate Research Unit
Temperature version 4
Temperature version 4
(CRU‐TEM4v),
-- US National Aeronautics
and Space Administration
and Space Administration
Goddard Institute
for Space Studies
for Space Studies
(NASA‐GISS), and
-- US National Oceanic and
Atmospheric Administration
National Center for
Environmental Information
Atmospheric Administration
National Center for
Environmental Information
(NOAA‐NCEI).
The mean
LSAT temperature
anomalies are
remarkably different,
because of the
data coverage
differences,
with the magnitude
nearly 0.4°C
for the global and
Northern Hemisphere,
and 0.6°C for the
Southern Hemisphere.
Southern Hemisphere.
On the regional scale,
northern high latitudes,
southern middle
‐to‐high latitudes,
and the equator,
show the
largest differences,
nearly 0.8°C.
At the local scale,
four data sets
show significant
variations
over
South America,
Africa,
Maritime Continent,
central Australia, and
Antarctica,
which leads to
remarkable differences
in local trend analysis.
The differences
are associated
with the availability
of weather stations
and the use of infilling
( infilling is
wild guessing
numbers
when there are
no weather stations
in a surface area grid,
and when there are
missing data,
from one or more
weather stations
within a grid.)
Data sets using only
station observations
have large uncertainties
over station‐sparse areas.
Data uncertainty,
caused by limited and
unevenly distributed
station observations,
must be reduced.
The uncertainty in
instrumental
temperature trends
“0.097–0.305 degrees C.
per decade,
for recent decades
(i.e., 1981–2017)”
is as large,
or larger than ,
the alleged overall
warming trend itself
for this period.
“for some areas,
different data sets
produce conflicting results
of whether warming exists”,
due especially to variations
in the use of “infilling techniques”
— adding artificial temperatures
to areas where there are
no real-world measurements.
One data set trend can be
“nearly 90%” different
than another data set trend !
One of the things
few people understand
about measurement
is resolution.
There is no way to
statistically improve
the resolution
of a measurement.
To get an
accurate average
accurate average
global temperature,
it is necessary to take
equally spaced
measurements,
across the ENTIRE Globe,
24/7, 365 days a year.
Nothing remotely close
to that is being done.
Instead,
the number of
the number of
weather stations
being used
for the global average,
has declined a lot
since the 1960s.
And the "wild guess"
infilling used for
these "missing stations",
and for vast areas of Earth
with few stations,
are without credibility.
Since 1979 the surface
temperature data
can be compared with
both weather satellite and
weather balloon data.
which are very similar
to each other,
but not similar to
surface measurements
plus infilling
( surface data show
MORE warming than the
two other methodologies ).
So, of course, the smarmy
pro-warming, anti-science
governmeent bureaucrats,
ONLY use the surface
measurements / infilling !
Surprise, surprise !