GISS Surface Temperature Analysis
Sources
GHCN = Global Historical Climate Network (NOAA/NCDC) version 3
SCAR = Scientific Committee on Arctic Research
Basic data set: GHCN - ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/v3
ghcnm.latest.qca.tar.gz (adjusted data)
For Antarctica: SCAR - http://www.antarctica.ac.uk/met/READER/surface/stationpt.html
http://www.antarctica.ac.uk/met/READER/temperature.html
http://www.antarctica.ac.uk/met/READER/aws/awspt.html
http://polarmet.osu.edu/Byrd_recon/byrd_temp_recon_monthly.txt
For Hohenpeissenberg - http://data.giss.nasa.gov/gistemp/sources_v3/t_hohenpeissenberg_200306.txt
complete record for this rural station up to June 2003
(thanks to Hans Erren who reported it to GISS on July 16, 2003)
Step 0 : Merging of sources (do_comb_step0.sh)
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In GHCN v3, reports from several sources were merged into a single history.
Discontinuities created by that merging procedure were eliminated by the provider
in the adjusted data set. The data are reformatted to match the format of GHCN v2
to avoid changes in the subsequent steps.
SCAR contains single source reports but in different formats/units
and with different or no identification numbers. We extended the WMO number if it
existed or created a new ID if it did not (2 cases). SCAR stations are treated
as new sources.
Adding SCAR data to GHCN:
The tables were reformatted and the data rescaled to fit the GHCN format;
the new stations were added to the inventory file. The site temperature.html
has not been updated for several years; we found and corrected a few typos
in that file. (Any SCAR data marked "preliminary" are skipped)
Filling in missing data for Hohenpeissenberg:
This is a version of a GHCN report with missing data filled in, so it is used
to fill the gaps of the corresponding GHCN series.
Result: v3.mean_comb
Step 1 : Elimination of dubious records (do_comb_step1.sh)
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Data and station information are combined in a data base. Some unphysical
looking segments of data records were eliminated after manual inspection of
unusual looking annual mean graphs and comparing them to the corresponding
graphs of all neighboring stations. The data are converted back to a text version.
Result: Ts.txt
Step 2 : Splitting into zonal sections and homogenization (do_comb_step2.sh)
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To speed up processing, Ts.txt is converted to a binary file and split
into 6 files, each covering a latitudinal zone of a width of 30 degrees.
At the same time, stations with less than 20 years of data are dropped, since
in the subsequent gridding step we require overlaps of at least 20 years
to combine station records.
The goal of the homogenization effort is to avoid any impact (warming
or cooling) of the changing environment that some stations experienced
by changing the long term trend of any non-rural station to match the
long term trend of their rural neighbors, while retaining the short term
monthly and annual variations. If no such neighbors exist - or the overlap
of the rural combination and the non-rural record is less than 20 years - the
station is completely dropped; if the rural records are shorter, part of the
non-rural record is dropped.
Result: Ts.GHCN.CL.1-6 - before peri-urban adjustment
Ts.GHCN.CL.PA.1-6 - after peri-urban adjustment
Step 3 : Gridding and computation of zonal means (do_comb_step3.sh)
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A grid of 8000 grid boxes of equal area is used. Time series are changed
to series of anomalies. For each grid box, the stations within that grid
box and also any station within 1200km of the center of that box are
combined using the reference station method.
A similar method is also used to find a series of anomalies for 80 regions
consisting of 100 boxes from the series for those boxes, and again to find
the series for 6 latitudinal zones from those regional series, and finally
to find the hemispheric and global series from the zonal series.
WARNING: It should be noted that the base period for any of these anomalies
is not necessarily the same for each grid box, region, zone. This is
irrelevant when computing trend maps; however, when used to compute
anomalies, we always have to subtract the base period data from the
series of the selected time period to get a consistent anomaly map.
Result: SBBX1880.Ts.GHCN.CL.PA.1200 and tables (GLB.Ts.GHCN.CL.PA.txt,...)
Step 4 : Reformat sea surface temperature anomalies
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Sources: ftp://ftp.ncdc.noaa.gov/pub/data/cmb/ersst/v3b/netcdf 1854-present
ftp.emc.ncep.noaa.gov cmb/sst/oimonth_v2 Reynolds 11/1981-present
At this point we only use the ERSST data.
Alternatively, we could proceed as follows:
For both sources, we compute the anomalies with respect to 2000-2009, use
the ERSST data for the period 1880-11/1981 and Reynolds data for 12/1981-present.
The Reynolds data are adjusted to match the mean over area of actual measurements
of the ERSST data. This method takes advantage of the greater spatial coverage of
the satellite data used by Reynolds but compensates for the drift that these
measurements entail.
These data are interpolated to the 8000-box equal-area grid and stored in the same way
as the surface data to be able to use the same utilities for surface and ocean data.
Areas covered occasionally by sea ice are masked using a time-independent mask.
The Reynolds climatology is included, since it also may be used to find that
mask. Programs are included to show how to regrid these anomaly maps:
do_comb_step4.sh adds a single or several successive months for the same year
to an existing ocean file SBBX.HadR2; a program to add several years is also
included.
Result: update of SBBX.HadR2
Step 5 : Computation of LOTI zonal means
----------------------------------------
The same method as in step3 is used, except that for a particular grid box
the anomaly or trend is computed twice, first based on surface data, then
based on ocean data. Depending on the location of the grid box, one or
the other is used with priority given to the surface data, if available.
Result: tables (GLB.Tsho2.GHCN.CL.PA.txt,...)
Final Notes
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A program that can read the two basic files SBBX1880.Ts.GHCN.CL.PA.1200 and
SBBX.HadR2 in order to compute anomaly and trend maps etc was available on our
web site for many years and still is.
For a better overview of the structure, the programs and files for the various
steps are put into separate directories with their own input_files,
work_files, temp_files directories. If used in this way, files created by
step0 and put into the temp_files directory will have to be manually moved
to the temp_files directory of the step1. To avoid that, you could
consolidate all sources in a single directory and merge all input_files
directories into a single subdirectory.
The reason to call the first step "Step 0" is historical: For our 1999 paper
"GISS analysis of surface temperature change", we started with "Step 1", i.e.
we used GHCN's v2.mean as our only source for station temperature data. The
USHCN data were used for the 2001 paper "A closer look at United States and
global surface temperature change", the other parts of "Step 0" were added later.
