UAH satellite temperature dataset

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The UAH satellite temperature dataset, developed at the University of Alabama in Huntsville, infers the temperature of various atmospheric layers from satellite measurements of radiance.

It was the first global temperature datasets developed from satellite information and has been used as a tool for research into surface and atmospheric temperature changes. The dataset is published by John Christy et al. and formerly jointly with Roy Spencer.

Satellite temperature measurements[edit]

Satellites do not measure temperature directly. They measure radiances in various wavelength bands, from which temperature may be inferred.[1][2] The resulting temperature profiles depend on details of the methods that are used to obtain temperatures from radiances. As a result, different groups that have analyzed the satellite data have obtained different temperature data. Among these groups are Remote Sensing Systems (RSS) and the University of Alabama in Huntsville (UAH). The satellite series is not fully homogeneous - it is constructed from a series of satellites with similar but not identical instrumentation. The sensors deteriorate over time, and corrections are necessary for satellite drift and orbital decay. Particularly large differences between reconstructed temperature series occur at the few times when there is little temporal overlap between successive satellites, making intercalibration difficult.

Description of the data[edit]

UAH provide data on three broad levels of the atmosphere.

  • The Lower troposphere - TLT (originally called T2LT).
  • The mid troposphere - TMT
  • The lower stratosphere - TLS[3]

Data are provided as temperature anomalies against the seasonal average over a past basis period, as well as in absolute temperature values.

All the data products can be downloaded from the UAH server.[4]

Geographic coverage[edit]

Data are available as global, hemispheric, zonal, and gridded averages. The global average covers 97-98% of the earth's surface, excluding only latitudes above +85 degrees, below -85 degrees and, in the cases of TLT and TMT, some areas with land above 1500 m altitude. The hemispheric averages are over the northern and southern hemispheres 0 to +/-85 degrees. The gridded data provide an almost global temperature map.[3]

Temporal coverage[edit]

Daily global, hemispheric and zonal data are available. Monthly averages are available in gridded format as well as by hemisphere and globally.

Each set has data back to December 1978.

Comparison with other data and models[edit]

Climate models predict that as the surface warms, so should the global troposphere. Globally, the troposphere should warm about 1.2 times more than the surface; in the tropics, the troposphere should warm about 1.5 times more than the surface.[citation needed] For some time the only available satellite record was the UAH version, which (with early versions of the processing algorithm) showed a global cooling trend for its first decade. Since then, a longer record and a number of corrections to the processing have revised this picture: the UAH dataset has shown an overall warming trend since 1998, though less than the RSS version. In 2001, an extensive comparison and discussion of trends from different data sources and periods was given in the Third Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) (section 2.2.4).[5]

A detailed analysis produced by dozens of scientists as part of the US Climate Change Science Program (CCSP) identified and corrected errors in a variety of temperature observations, including the satellite data.

The CCSP SAP 1.1 Executive Summary states:

"Previously reported discrepancies between the amount of warming near the surface and higher in the atmosphere have been used to challenge the reliability of climate models and the reality of human induced global warming. Specifically, surface data showed substantial global-average warming, while early versions of satellite and radiosonde data showed little or no warming above the surface. This significant discrepancy no longer exists because errors in the satellite and radiosonde data have been identified and corrected. New data sets have also been developed that do not show such discrepancies."

The IPCC Fourth Assessment Report Summary for Policymakers states:

"New analyses of balloon-borne and satellite measurements of lower- and mid-tropospheric temperature show warming rates that are similar to those of the surface temperature record and are consistent within their respective uncertainties, largely reconciling a discrepancy noted in the TAR."

However, as detailed in CCSP SAP 5.1 Understanding and Reconciling Differences, neither Regression models or other related techniques were reconcilable with observed data. The use of fingerprinting techniques on data yielded that "Volcanic and human-caused fingerprints were not consistently identifiable in observed patterns of lapse rate change." As such, issues with reconciling data and models remain.

A potentially serious inconsistency has been identified in the tropics, the area in which tropospheric amplification should be seen. Section 1.1 of the CCSP report says:

"In the tropics, the agreement between models and observations depends on the time scale considered. For month-to-month and year-to-year variations, models and observations both show amplification (i.e., the month-to-month and year-to-year variations are larger aloft than at the surface). This is a consequence of relatively simple physics, the effects of the release of latent heat as air rises and condenses in clouds. The magnitude of this amplification is very similar in models and observations. On decadal and longer time scales, however, while almost all model simulations show greater warming aloft (reflecting the same physical processes that operate on the monthly and annual time scales), most observations show greater warming at the surface.
"These results could arise either because "real world" amplification effects on short and long time scales are controlled by different physical mechanisms, and models fail to capture such behavior; or because non-climatic influences remaining in some or all of the observed tropospheric data sets lead to biased long-term trends; or a combination of these factors. The new evidence in this Report favors the second explanation."

The lower troposphere trend derived from UAH satellites (+0.128 °C/decade) is currently lower than both the GISS and Hadley Centre surface station network trends (+0.161 and +0.160 °C/decade respectively), while the RSS trend (+0.158 °C/decade) is similar. The surface station data indicate a trend of around 0.194 °C/decade[citation needed], making the UAH and RSS trends 66% and 81% of the surface station derived value respectively.

For some time, the UAH satellite data's chief significance was that they appeared to contradict a wide range of surface temperature data measurements and analyses showing warming. In 1998 the UAH data showed a cooling of 0.05 K per decade (at 3.5 km - mid to low troposphere). Wentz & Schabel at RSS in their 1998 paper showed this (along with other discrepancies) was due to the orbital decay of the NOAA satellites.[6] Once the orbital changes had been allowed for the data showed a 0.07 K per decade increase in temperature at this level of the atmosphere.

Spencer noted the following regarding the findings in a April 2019 blog post [7]:

"Secondly, in the 25+ years that John Christy and I have pioneered the methods that others now use, we made only one “error” (found by RSS, and which we promptly fixed, having to do with an early diurnal drift adjustment). The additional finding by RSS of the orbit decay effect was not an “error” on our part any more than our finding of the “instrument body temperature effect” [8] was an error on their part. All satellite datasets now include adjustments for both of these effects."

Corrections made[edit]

The table below summarizes the adjustments that have been applied to the UAH TLT dataset.[9] [10] The 'trend correction' refers to the change in global mean decadal temperature trend in degrees Celsius/decade as a result of the correction.

UAH version Main adjustment Trend correction Year
A Simple bias correction 1992
B Linear diurnal drift correction -0.03 1994
C Removal of residual
annual cycle related to
hot target variation
0.03 1997
D Orbital decay 0.10 1998
D Removal of dependence
of time variations of
hot target temperature
-0.07 1998
5.0 Non-linear diurnal correction 0.008 2003
5.1 Tightened criteria for data acceptance -0.004 2004
5.2 Correction of diurnal drift adjustment 0.035 2005
5.3 Annual cycle correction 0 2009
5.4 New annual cycle 0 2010
6.0 beta Extensive revision -0.026 [11] 2015

NOAA-11 played a significant role in a 2005 study by Mears et al. identifying an error in the diurnal correction that leads to the 40% jump in Spencer and Christy's trend from version 5.1 to 5.2.[12]

Christy et al. asserted in a 2007 paper that the tropical temperature trends from radiosondes matches more closely with their v5.2 UAH-TLT dataset than with RSS v2.1.[13]

Much of the difference, at least in the Lower troposphere global average decadal trend between UAH and RSS, has been removed with the release of RSS version 3.3 in January 2011. RSS and UAH TLT are now within 0.003 K/decade of one another. Significant differences remained, however, in the Mid Troposphere (TMT) decadal trends. However, in June 2017 RSS released version 4 which significantly increased the trend from 0.136 to 0.184 K/decade substantially increasing the difference again.

A beta version of 6.0 of the dataset was released on April 28, 2015 via blog post.[11] This dataset has higher spatial resolution and uses new methods for gridpoint averaging.


  1. ^ National Research Council (U.S.). Committee on Earth Studies (2000). "Atmospheric Soundings". Issues in the Integration of Research and Operational Satellite Systems for Climate Research: Part I. Science and Design. Washington, D.C.: National Academy Press. pp. 17–24. ISBN 0-309-51527-0.
  2. ^ Uddstrom, Michael J. (1988). "Retrieval of Atmospheric Profiles from Satellite Radiance Data by Typical Shape Function Maximum a Posteriori Simultaneous Retrieval Estimators". Journal of Applied Meteorology. 27 (5): 515–549. Bibcode:1988JApMe..27..515U. doi:10.1175/1520-0450(1988)027<0515:ROAPFS>2.0.CO;2.
  3. ^ a b "INFORMATION CONCERNING THE MSU DATA FILES". Retrieved February 28, 2011.
  4. ^ "UAH MSU Data".
  5. ^ United Nations Environment Programme Archived January 12, 2011, at the Wayback Machine
  6. ^ "Archived copy" (PDF). Archived from the original (PDF) on January 15, 2010. Retrieved January 8, 2014.CS1 maint: archived copy as title (link)
  7. ^ "UAH, RSS, NOAA, UW: Which Satellite Dataset Should We Believe? «  Roy Spencer, PhD". Retrieved August 12, 2019.
  8. ^ Po-Chedley, Stephen; Fu, Qiang (January 23, 2012). "A Bias in the Midtropospheric Channel Warm Target Factor on the NOAA-9 Microwave Sounding Unit". Journal of Atmospheric and Oceanic Technology. 29 (5): 646–652. doi:10.1175/JTECH-D-11-00147.1. ISSN 0739-0572.
  9. ^ "UAH adjustment". Retrieved January 15, 2011.[permanent dead link]
  10. ^ "CCSP sap 1.1" (PDF). Archived from the original (PDF) on December 24, 2010. Retrieved January 15, 2011.
  11. ^ a b "Version 6.0 of the UAH Temperature Dataset Released: New LT Trend = +0.11 C/decade". Retrieved January 11, 2017.
  12. ^ Mears, Carl A.; Wentz, Frank J. (2005). "The Effect of Diurnal Correction on Satellite-Derived Lower Tropospheric Temperature". Science. 309 (5740): 1548–1551. Bibcode:2005Sci...309.1548M. doi:10.1126/science.1114772. PMID 16141071.
  13. ^ Christy, J. R.; Norris, W. B.; Spencer, R. W.; Hnilo, J. J. (2007). "Tropospheric temperature change since 1979 from tropical radiosonde and satellite measurements". Journal of Geophysical Research. 112: D06102. Bibcode:2007JGRD..11206102C. doi:10.1029/2005JD006881.

External links[edit]