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[CIDC FTP Data]
[E. Anglia Tmpdev IDC Data on FTP]
Data Access
Global Temperature Deviations Monthly Means
Global Temperature Deviations Monthly Means (Decades)
Global and Hemispherical Averages
[rule]
Readme Contents
Data Set Overview
Sponsor
Original Archive
Future Updates
The Data
Characteristics
Source
The Files
Format
Name and Directory Information
Companion Software
The Science
Theoretical Basis of Data
Processing Sequence and Algorithms
Hemispherical and Global Averages
Scientific Potential of Data
Validation of Data
Data Access and Contacts
FTP Site
Points of Contact
References
[rule]
Data Set Overview
Monthly Surface air temperature anomalies for the period 1851-1996
have been calculated by the Climate Research Unit (CRU) of the
University of East Anglia, Norwich, England, using data from
several sources ( Jones et al., 1986 a,b&c, and 1991). It is
recommended that the data from 1851-1855 be ignored in most
analyses because it is quite sparse as shown in the annual table
(Jones 1997, private communication). The anomalies consist of land
and ocean temperature departures from the 1961-1990 reference
period, and are given on a 5 x 5 degree world grid. Hemispherical
and global monthly and annual means are also included starting
with 1856. Please note that this data set was recently revised
(Jones, 1994); in the older data set the reference period was
1951-1970, fewer stations were included, and the gridding method
was a little different. This dataset is very important for climate
change studies. Despite relatively poor data coverage initially
and around the two World Wars, the generally cold end of the
nineteenth century and substantial warming from 1920 to 1940 are
clearly shown. Slight cooling of the Northern Hemisphere took
place between the 1950s and 1994, but this was followed by a
warming trend in the 1980s and 1990s (Parker et al., 1994; Jones,
1994).
The temperature records were obtained from various archives: the
World Weather Records, published by the Smithsonian Institution
(1927, 1935, 1947), and the U. S. Weather Bureau (1959-1982);
material collected in the meteorological archives; and sea surface
temperature data derived from the United Kingdom Meteorological
Office's data bank (Bottomley et al., 1990)(UKMO; Bottomley et
al., 1990), and the Comprehensive Ocean Atmosphere Data Set
(COADS; Woodruff et al., 1987). The data from the several sources
were carefully examined and corrections were made to compensate
for known measurement problems. A brief discussion of the
necessary corrections is given in Jones et al. (1991) and Parker
et al., (1994) along with references to more detailed
descriptions. Hansen and Lebedeff (1987) and Vinnikov et al.
(1990) have also formed surface temperature anomaly datasets
covering essentially the same period. All three datasets draw most
of their land measurements from the same data archives and on
hemispherical and global scales show similar temperature trends (
Jones et al., 1991). The CRU East Anglia data set is however
unique in combining land and ocean temperature anomalies for long
term analysis. Thus it shows regional mid ocean temperature
anomalies that are suppressed in the land measurement only
datasets. The corrections for the land measurements also differ
among the three data sets; thus while the general trends of the
three datasets are similar there are some differences.
For the convenience of the user, the Goddard Institute for Space
Studies (GISS) Global Monthly and Annual Average Temperature
Deviations are given for comparison purposes. Also available on
this site is the Southern Oscillation Index. It is the normalized
sea surface pressure difference between Tahiti and Darwin. The
method of calculation is given in Ropelewski and Jones (1987). All
missing Darwin data are infilled from Djakarta. Missing Tahiti
data are infilled from Apia, Suva and Santiago. Because of the
missing data, some of the years before about 1920 are a little
less reliable than the later values.
Sponsor
The production and distribution of this data set are
funded by NASA's Earth Science enterprise. The data are
not copyrighted; however, we request that when you
publish data or results using these data please
acknowledge as follows:
The authors wish to thank Phil D. Jones and
the Climatic Research Unit, School of
Environmental Sciences, University of East
Anglia, Norwich, U.K., for the production of
this data set, and the Distributed Active
Archive Center (Code 902) at the Goddard Space
Flight Center, Greenbelt, MD, 20771, for
putting these data in their present format and
distributing them. These distribution
activities were sponsored by NASA's Earth
Science enterprise.
Original Archive
This data set was constructed by the Climatic Research
Unit (CRU) at East Anglia University. This is also the
location of the primary archive and the source for
detailed information concerning this data set. The data
in its original format can be obtained from the CRU.
Note: The format of the data in the East Anglia archive
is slightly different than that stored at the Goddard
DAAC. For more details see the section Processing
Sequence and Algorithms.
Future Updates
This data set will be updated as new data is made
available.
The Data
Characteristics
Gridded
Monthly Means Tables
Means
Surface Annual Northern Southern
Temperature means Hemisphere Hemisphere Global
Parameters deviations (summary) Means Means Means
% of area reporting
Units Degrees Celsius
Typical -2 degrees
Range to 2 degrees -1.6 to 0.7 degrees Celsius
Celsius
Temporal January, 1851 -
Coverage December, 1996 1856-1996
Temporal monthly Annual
Resolution: means means Monthly & Annual means
Spatial Global
Coverage (gridded) Global Hemisphere Global
Spatial 5 degrees x
Resolution 5 degrees Hemispherical & Global
Source
This data set is derived from the World Weather Records
(WWR), published by the Smithsonian Institution (1927,
1929, 1935, 1947) and the U.S. Weather Bureau
(1959-1982), The United Kingdom Meteorological Office's
data banks (Bottomley et al, 1990) and the Comprehensive
Ocean-Atmosphere Data Set(COADS, Woodruff et al., 1987.)
Additional data were added, using material collected in
published and manuscript form from meteorological
archives.
The Files
The Temperature Deviation data set consists of monthly means
binary data files, (1991 - 1996), a collection of gif images
derived from these files (each image consists of the twelve
monthly means images for a given year displayed on a single screen
with a color bar), and decadal files consisting of monthly means
arrays identical in format to the more recent data but grouped 120
to a file (1851 - 1990), as well as a file of global and
hemispherical annual means. The entire data set, including image
files, requires about 37 MB of disk storage.
Format
Compressed:
The decade data files have been compressed using Lempel-Ziv
coding. Files with a .gz ending are compressed versions of the
.bin file. When decompressing the files use the -N option so that
the original .bin file name ending is restored. For additional
information on decompression see aareadme file in the directory:
software/decompression/
Uncompressed:
File Characteristics
Monthly Global and
Means Decades Hemispherical
(1991-96) 1851-1990) Means
File size in
bytes 10368 1244160 < 29000
Number of
files 72 14 4
Format IEEE float ASCII text
Fill value -999. -99.99
Grid size 72 x 36
Continent None - data valid over
mask land and water
Orientation North to South NA
Start 177.5W,
Position 87.5N
End 177.5E,
Position 87.5S
Name and Directory Information
Naming Convention
The file naming convention for the monthly files is
e_anglia.tmpdev.1nmagg.[yymm].ddd
where
E_Anglia = data product designator
tmpdev = parameter name (temperature deviations)
1 = number of levels
n = vertical coordinate, n = not applicable
m = temporal period, m = monthly
a = horizontal grid resolution, a = 5 x 5 degree
gg = spatial coverage, gg = global (land and ocean)
yy = year
mm = month
ddd = file type designation, (bin=binary file,
ctl=GrADS control file) OR
The file naming convention for the decade files is
tmpdev.1941-50.ddd
where
tmpdev = parameter (temperature deviations)
1851-1860 = years covered in file
ddd = file type designation, (gz=compressed,
bin=binary)
NOTE: When decompressing the data files be sure to use
the -N option. This will restore the original .bin
filename. For additional information on decompression
see the format section of this readme and the aareadme
file in the directory:
software/decompression/
The file containing global and hemispherical means is
named glb_hem_avgs.
Directory Paths
/data/inter_disc/surf_temp_press/tmp_dev/e_anglia/yyyy
(where yyyy is year.)
/data/inter_disc/surf_temp_press/tmp_dev/e_anglia/decades1851-1990
/data/inter_disc/surf_temp_press/tmp_dev/e_anglia/gifs
/data/inter_disc/surf_temp_press/tmp_dev/e_anglia/global_means
Companion Software
Several software packages have been made available on
the CIDC CD-ROM set. The Grid Analysis and Display
System (GrADS) is an interactive desktop tool that is
currently in use worldwide for the analysis and display
of earth science data. GrADS meta-data files (.ctl) have
been supplied for each of the data sets. A GrADS gui
interface has been created for use with the CIDC data.
See the GrADS document for information on how to use the
gui interface.
Decompression software for PC and Macintosh platforms
have been supplied for datasets which are compressed on
the CIDC CD-ROM set. For additional information on the
decompression software see the aareadme file in the
directory:
software/decompression/
Sample programs in FORTRAN, C and IDL languages have
also been made available to read these data. You may
also acquire this software by accessing the
software/read_cidc_sftwr directory on each of the CIDC
CD-ROMs
The Science
Theoretical Basis of Data
The surface air temperature and the sea surface
temperature are basic weather and climate parameters.
They are normally measured by thermometers. The present
data set was established to examine global and regional
long term trends. These are small and are not always
easy to separate from operational noise. The
investigators have taken great care to address the error
sources. Nevertheless Jones et al. (1986a) state, "It is
considered impossible to reduce all observations to the
same standard (Bradley et al., 1985). Nevertheless
...the problem is considerable reduced if all records
are transformed to anomaly values from a common
reference period." It is easier to establish station and
regional temperature shifts than to establish an
historical over all global temperature standard.
Therefore first station and then regional anomalies are
calculated and then joined together on a global grid to
study hemispherical and global mean changes. For long
term changes sampling errors or deficiencies are a major
concern. Additional discussion of the various problems
is given in the following sections.
Processing Sequence and Algorithms
This data set was formed by the Climatic Research Unit
of East Anglia University and includes many data records
that were previously unavailable; they were collected by
the CRU scientists from unpublished sources and included
in the data bank used for analysis. Extensive quality
checks and corrections have been applied to the data to
ensure, as well as possible, that the resulting data set
is homogeneous. For the land stations corrections have
been applied for the following problems: changes in
instrumentation, changes in station location, changes in
observation times, changes in the methods of calculation
of monthly means, and urbanization effects (Jones et
al., 1986a). Sea surface temperature anomalies have been
included to create as near a complete analysis as
possible. These data also required considerable
correction (Jones et al., 1991). All data are presented
as anomalies from the mean for the 1961-1990 period. In
the previous data set posted at this site, the reference
period was 1950-1979. However by 1990 only 52% of the
land reference stations used to establish the 1950-1979
climatology were being routinely updated. It was
therefore decided to establish a new reference period
(Jones 1994). The new reference period is slightly
warmer than the old one and this causes a change in the
anomaly values. The 1990s anomalies will in the mean be
a little smaller than in the old set, while the earlier
anomalies will be a little more negative.
The data were originally collected as temperatures using
thermometers. Then the data was corrected as discussed
above and the anomalies from the reference period were
calculated. The final step was to grid the data. The
station data were gridded to a regular 5 degrees of
latitude by 5 degrees of longitude grid (Jones 1994;
Parker et al., 1994)). A station was used in connection
with only one grid square. Because of the differences in
the land and ocean data sources and the different type
of corrections needed separate land and ocean grids were
formed. Then the two grids were merged to form a single
global grid. The land grid was used over land and the
ocean grid over the ocean. If both are available in a
given month for one of the boxes the weighted average of
the two fields is taken with the weights being the land
and ocean fractions in the box. However in a mixed box
the land weight is always at least 25% so that ocean
island data usually gets a larger weight than the land
area would imply. The ocean island land temperatures are
almost certainly more reliable than the sea surface
temperatures for these boxes (Jones, 1997 private
communication; compare with Parker et al., 1994).
There are no important differences in the global mean
temperature trends obtained from the original and the
revised gridded data sets. However some regional
differences do occur. These are chiefly over continental
regions. In the original land station analysis (Jones et
al., 1986a) the grids were 5 degrees latitude by 10
degrees longitude. These were split into 5-degree by
5-degree grids to facilitate merging with the 5-degree
by 5-degree ocean maps (Parker et al., 1994). In the
reanalysis the land stations were directly averaged over
5-degree by 5-degree squares. In the computation of the
earlier monthly means, the station data were weighted by
the inverse of the distance to the nearest grid point
(Jones et al., 1986a). In the reanalysis the monthly
continental grid means are unweighted averages of all
valid stations within the grid square (Jones 1994).
Finally over 1000 additional land stations were used in
the reanalysis.
For consistency with the other data sets in the Goddard
DAAC's Interdisciplinary Data Collection, the East
Anglia data was reformatted at the DAAC from the
original integer values (anomalies scaled by 100) into
32-bit floating point quantities (unscaled anomaly
values). In addition, whereas the original data were
written out such that the left edge of the global map
began at 120 degrees E. longitude , the data held at the
Goddard DAAC has been modified such that the columns
begin at the dateline (180 degrees west longitude). A
visual comparison of the two data sets was then
performed to ensure that no artifacts had been
inadvertently introduced into the original data as a
result of this procedure.
Hemispherical and Global Averages
Estimates of global and hemispheric monthly and annual
temperature variations, relative to the 1961-1990
reference period, are presented for 1856-1996. These
estimates were computed using a program supplied by Phil
Jones. This program uses only the reporting (non fill
value) grid squares to calculate the zonal,
hemispherical and global monthly means. Thus in the
hemispherical means a latitude zone is weighted by the
relative geographical area of only the reporting grid
squares. If a zone has no reports for that month it is
ignored. In this scheme the global mean is not the mean
of the northern and southern hemispherical means because
there are more data gaps in the S. hemisphere.
Hemispherical and global means were not calculated for
the years 1851-1855 because of the paucity of data. The
massive data jump in 1856 comes from ship reports of sea
surface temperature. Jones and Briffa (1992) state,
"Since the 1850s ships have been obliged to take weather
observations and measure the temperature of the sea
surface. The impetus behind this collection was an
American naval captain, Matthew Fontaine Maury, who
persuaded the other major maritime nations to instruct
their military and merchant navies to take measurements
and record these in log books." The procedure was
formalized in 1853 at the Brussels Maritime Conference
"for devising an uniform system of meteorological
observations at sea" (Woodruff et al., 1987).
The percentage of reporting 5-degree by 5-degree squares
on the global grid was 3% in 1851. This increased to
about 80% by 1960 and has fluctuated in that
neighborhood since. These are chiefly related to ship
reports. The recent percentage is about 90% in the
Northern Hemisphere and between 70% and 80% in the
Southern Hemisphere. Three dramatic shifts in the
reporting patterns are noteworthy. In 1856 when the
ships started reporting the sea surface temperature, the
percentage of reporting grids jumped from 3% to 15%. The
January 1856 global map indicates that the first ship
reports came chiefly from British and other European
ships on South American and Oriental trade routes. The
start of World I caused a sharp decrease (53% to 35%) in
reporting grids between January 1914 and January 1955.
The Southern Hemisphere was most effected. Due to World
War II there was also a sharp drop from 1939 to 1941
which again was largest in the Southern Hemisphere.
There is no one accepted, best way to obtain zonal,
hemispherical and global means from insufficient
sampling. Temperature anomalies vary strongly within a
zone as well as latitudinally. The N. and S.
hemispherical anomalies sometimes shift in opposite
directions. There are also seasonal variations with
anomalies often larger in some seasons than in others.
Parker et al. (1994) discuss a scheme to calculate
annual means which includes a grid square only if it has
data for at least one month in each of the four seasons.
Weighting by total zonal geographic area would at times
give a zone with only one reporting station a weight
similar to another zone in which all the grid squares
had valid data. The weights are also affected by the
size of the grid squares and how the station data are
combined to obtain the monthly means for the grid
squares. The chief purpose of the present data set is to
examine long term trends. Two or three averaging methods
may yield similar long term trends but often disagree as
to which of two individual years has the larger mean
temperature anomaly. Parker et al. (1994) and Hansen and
Lebedeff (1987) both discuss the effect of various
hemispherical and global averaging schemes. They
concluded that all of the schemes they examined gave
roughly the same long term trends. Hansen and Lebedeff
added that their tests indicated that an averaging
scheme similar to that used in the present Jones data
set yields the most accurate long term trends.
Additional discussions can be found in Jones et al.
(1986a,b,c) and Jones (1988). For comparison purposes
the Goddard Interdiscipline Data Collection includes the
hemispherical and global means from the Goddard
Institute for Space Studies (GISS) temperature anomaly
data set. The GISS data set is based chiefly on the land
stations and uses a different global grid scheme and
reference period (1951-1980). The general long term
trends are similar.
These global and hemispheric annual variations show
little trend during the nineteenth century, marked
warming to 1940, relatively steady conditions to the
mid-1970's, followed by a rapid warming during the
1980's. Over the period of record, globally-averaged
temperatures have risen approximately 0.5 degrees C. The
warmest three years of the 1856-1996 record are, in
descending order, 1995, 1990 and 1991. Globally, in 1991
the mean temperature variation was 0.29 degrees C above
the 1961-90 reference period mean and 0.06 degrees C
cooler than in 1990. Due to sampling and other error
sources this 0.06 degree drop in 1991 may not be a
significant year to year difference, but this combined
with the additional 0.14 degree drop in 1992 is
significant. This mean global cooling in 1991-1993 is
generally attributed to the Mt. Pinatubo eruption in
June, 1991. Stratospheric aerosols resulting from this
eruption spread all over the globe and measurably
increased the Earth's albedo for a period (Hansen et
al., 1992; Lacis et al., 1992; Minnis et al., 1993) But
by 1995 the mean temperature had returned to the 1990
level.
Scientific Potential of Data
This temperature deviation data can be used for many
types of studies including:
* Regional temperature variations over the last 100
or so years (Parker et al. (1994))
* Global Warming (Houghton et al. (1995);Richards
(1993), Hansen and Lacis (1990). Note the cautions
of Woodward and Gray (1993) concerning the
limitations of certain statistical regression
analysis procedures.)
* Correlations between various terrestrial climate
variables ( Kyle et al. (1995); Ardanuy et al.
(1992);Jones (1988))
* Correlation of variations in the climate and solar
variability (Hoyt and Schatten (1993))
Validation of Data
The data set was validated by its authors. The resulting
annual mean temperatures were compared to other data
sets, such as those from Russia (Vinnikov et al., 1990)
and the United States (Hansen and Lebedeff, 1988).
Although all the averages were highly correlated, the
producers of this set believe this data set is superior
because of its inclusion of marine data which represent
71% of the Earth's surface. Hansen's data set did not
correct for urban warming and is thus considered to
contain a warming bias of greater than 0.1 degrees
Celsius per century.
To assess the effects of incomplete coverage during the
early years, the producers used a frozen grid approach
to analyze the changing network. They found that
although the interannual variability decreased over time
as more stations were used, there was no bias introduced
by the sparse grid in the early part of the record.
Although the data are calculated, stored, and presented
in this data set to two decimal points, i.e. 0.01
degrees Celsius, the individual monthly grid point
anomalies are probably only accurate to +/-0.2 degrees
Celsius, given the accuracy of the original data. A
detailed discussion of possible errors is given in
Parker et al., (1994).. An analysis of various methods
used to calculate grid point means and other points is
given by Gunst et al. (1993). Jones et al. are
publishing an analysis of the errors in the revised data
set now on this site (it will appear in the Journal of
climate in 1997).
As mentioned earlier, to ensure that the data as
reformatted by the Goddard DAAC did not introduce
spurious artifacts into the original data, GIF images
were derived from the data as it was rewritten to the
binary files and visually compared to decadal images
produced from this data in Parker et al. (1994).
Contacts
Points of Contact
For information about or assistance in using any DAAC
data, contact
EOS Distributed Active Archive Center (DAAC)
Code 902
NASA Goddard Space Flight Center
Greenbelt, Maryland 20771
Internet: daacuso@daac.gsfc.nasa.gov
301-614-5224 (voice)
301-614-5268 (fax)
To inquire about or order the original East Anglia
Temperature Deviations data set, contact
Dr. P.D. Jones
Climatic Research Unit
School of Environmental Sciences
University of East Anglia
Norwich NR4 7TJ
UNITED KINGDOM
Internet: P.Jones@uea.ac.uk
Telephone: (0603) 592090
References
Ardanuy, P.E., H.L. Kyle, and D. Hoyt, 1992: Global relationships
between the earth's radiation budget, cloudiness, volcanic
aerosols, and surface temperature, J. Climate, 10, 1120-1139
Bottomley, M., C. K. Folland, J. Jsiung, R. E. Newell, and D. E.
Parker, 1990: Global Ocean Surface Temperature Atlas (GOSTA), 20 +
iv pp. and 313 plates, Joint Meteorol. Off./Mass. Inst. of
Technol. Proj., supported by U. S. Dep. of Energy, U. S. Nat. Sci.
Found., and U. S. Off. of Nav. Res., funded by UK Depts. of Energy
and Environment, Her Majesty's Stationery Office, London.
Bradley, R. S., P. M. Kelly, P. D. Jones, H. F. Diaz and C.
Goodess, 1985: A climatic data bank for the Northern Hemisphere
land areas, 1981-1980, DoE Tech. Rep. No. TR017,, US. Dept. of
Energy, Carbon Dioxide Research Division, Washington, D.C., 335
pp.
Gunst, R. F., S. Basu, and R. Brunell, 1993: Defining and
estimating global mean temperature anomalies, J. Climate, 6,
1368-1374.
Hansen, J., and S. Lebedeff, 1987: Global trends of measured
surface air temperature, J. Geophys. Res., 92, 13,345-13,372.
Hansen, J., and S. Lebedeff, 1988: Global surface temperatures:
Update through 1987, Geophys. Res. Lett., 15, 323-326., 239,
48-50.
Hansen, J.E., and A. Lacis, 1990: Sun and dust versus greenhouse
gases: An assessment of their relative roles in global climate
change, Nature, 346, 713-719.
Hansen, J., A. Lacis, R. Ruedy and M. Sato, 1992: PotentiGlobal
climate impact of Mount Pinatubo eruption, Geophys. Res. Lett.,
19, 215-218.
Houghton, J.T., L.G. Meira Filho, J. Bruce, H. Lee, B.A.
Callander, E. Haites, N. Harris and K. Maskell, Eds. 1995: Climate
Change 1994: radiative forcing of climate change and an evaluation
of the IPCC IS92 emission scenarios, Cambridge University Press,
339 pp.
Hoyt, D.V. and K.H. Schatten, 1993: A discussion of plausible
solar irradiance variations, 1700-1992, J. Geophys. Res., 98,
18895-18906.
Jones, P. D. 1988. The influence of ENSO on global temperatures.
Climate Monitor 17(3): 80-89.
Jones, P. D., S. C.. B. Raper, R. S. Bradley, H. F. Diaz, P. M.
Kelly, and T. M. L. Wigley, 1986a: Northern Hemisphere surface air
temperature variations, 1851-1984, J. Clim. Appl. Meteorol., 25,
161-179. 140, 1292-1303.
Jones, P. D., S. C. B. Raper, and T. M. L. Wigley, 1986b: Southern
Hemisphere surface air temperature variations, 1851-1984, J. Clim.
Appl. Meteorol., 25, 1213-1230.
Jones, P. D., S. C. B. Raper, C. M. Goodess, B. S. G. Cherry and
T. M. L. Wigley, 1986c: A gridpoint surface air temperature data
set for the southern hemisphere, U. S. Dept. of Energy, Carbon
Dioxide Research Division, Washington, DC, Technical report,
TR027, 73pp.
Jones, P. D., T. M. L. Wigley, and G. Farmer, 1991: Marine and
land temperature data sets: a comparison and a look at recent
trends, in, Greenhouse-Gas-induced Climatic Change: A Critical
Appraisal of Simulations and Observations, M. E. Schlesinger, Ed.,
Elsevier Scientific Publishers, New York, 153-172.
Jones, P. D., 1994: Hemispheric Surface Air Temperature
Variations: A Reanalysis and an update to 1993, J. Climate, 7,
1794-1804.
Jones, P. D., T.J. Osborn, and K.R. Briffa 1997: Estimating
sampling errors in large-scale temperature averages, J. Climate,
10, 2548-2568.
Kyle, H.L., M. Weiss and P. Ardanuy, 1995: Cloud, surface
temperature, and outgoing longwave radiation for the period from
1979 to 1990, J. Climate, 8, 2644-2658.
Lacis, A., J. Hansen and Makiko Sato, 1992: Climate forcing by
stratospheric aerosols, Geophys. Res. Lett., 19, 1607-1610.
Minnis, P., E. F. Harrison, L. L. Stowe, G. G. Gibson, F. M. Denn,
D. R. Doelling, and W. L. Smith, Jr., 1993: Radiative climate
forcing by the Mount Pinatubo eruption, Science, 259, 1411-1415.
Parker, D. E., P. D. Jones, C. K. Folland, and A. Bevan, 1994:
Interdecadal changes of surface temperature since the late
nineteenth century, J. Geophys. Res., 99, 14,373-14,399.
Richards, G. R., 1993: Change in global temperature: A statistical
analysis, J. Of Climate, 6, 546-559.
Ropelewski C.F., and P.D. Jones 1987: An extension of the
Tahiti-Darwin Southern Oscillation Index, MWR, 115, 2161-2165
Smithsonian Institution, 1927, 1935, 1947: "World Weather
Records", Miscellaneous Collections, Volumes 79, 90, 104,
Washington, D. C.
U. S. Weather Bureau, 1959-1982: "World Weather Records",
1941-1950 (1361 pp.), 1951-1960 (Volumes 1-6), 1961-1970 (Volumes
1-6), U. S. Department of Commerce, Washington, D. C.
Vinnikov, K. Ya., P. Ya. Groisman, and K. M. Lugina, 1990:
Empirical data on contemporary global climate changes (temperature
and precipitation), J. Clim., 3, 662-667.
Woodruff, S. D., R. J. Slutz, R. J. Jenne, and P. M. Steurer,
1987: A comprehensive ocean-atmosphere data set, Bull. Am.
Meteorol. Soc., 68, 1239-1250.
Woodward, W.A., and H.L. Gray, 1993: Global warming and the
problem of testing for trend in time series data, J. Climate, 6,
953-962.
[NASA] [GSFC] [Goddard DAAC] [To IDC Data]
NASA Goddard GDAAC IDC Data
------------------------------------------------------------------------
Last update:Wed Dec 3 14:56:10 EST 1997
Page Author: Lee Kyle -- lkyle@daac.gsfc.nasa.gov
Web Curator: Daniel Ziskin -- ziskin@daac.gsfc.nasa.gov
NASA official: Paul Chan, DAAC Manager -- chan@daac.gsfc.nasa.gov
