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For the most part, satellite observations of climate are not presently sufficiently accurate to establish a climate record that is indisputable and hence capable of determining whether and at what rate the climate is changing. Furthermore, they are insufficient for establishing a baseline for testing long‐term trend predictions of climate models. Satellite observations do provide a clear picture of the relatively large signals associated with interannual climate variations such as El Niño‐Southern Oscillation (ENSO), and they have also been used to diagnose gross inadequacies of climate models, such as their cloud generation schemes. However, satellite contributions to measuring long‐term change have been limited and, at times, controversial, as in the case of differing atmospheric temperature trends derived from the U.S. National Oceanic and Atmospheric Administration's (NOAA) microwave radiometers.

Long-term trends in the climate system are always partly obscured by naturally occurring interannual variability. All else being equal, the larger the natural variability, the less precisely one can estimate a trend in a time series of data. Measurement uncertainty, though, also obscures long-term trends. The way in which measurement uncertainty and natural interannual variability interact in inhibiting the detection of climate trends using simple linear regression is derived and the manner in which the interaction between the two can be used to formulate accuracy requirements for satellite climate benchmark missions is shown. It is found that measurement uncertainty increases detection times, but only when considered in direct proportion to natural variability. It is also found that detection times depend critically on the correlation time of natural variability and satellite lifetime. As a consequence, requirements on satellite climate benchmark accuracy and mission lifetime must be directly related to the natural variability of the climate system and its associated correlation times.

A recent reprocessing of AVHRR data results in a 20-yr global climate dataset providing information on the earth's radiation budget T he National Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Information Service (NOAA/NESDIS) has generated a consistent, calibrated, dataset of atmospheric products derived from nearly 20 yr of observations from Advanced Very High Resolution Radiometers (AVHRR) on board NOAA polar-orbiting satellites. This began as a partnership with the National Aeronautics and Space Administration (NASA), in a program called Pathfinder, to generate long-term datasets from archived satellite observations. NOAA has taken the lead in generating atmospheric products from AVHRR and making them easily accessible for climate research.The atmospheric products derived from AVHRR include such key climate variables as cloud amount, aerosol amount, and the top of the atmosphere net solar and long wave radiation. Clouds modulate the earth's radiation budget (ERB): they reflect solar radiation-a cooling effect-and recapture some infrared radiation otherwise lost to space-a warming effect. Uncertainties associated with how their radiative properties will evolve as the climate changes is a major cause for the large range in greenhouse warming predictions from climate models. Aerosols from very intense volcanic eruptions cause more sunlight to be reflected back to space leading to reductions of as much as 1°C or more in global temperatures that may persist for a year or more. Both man-made and natural aerosols reflect and absorb solar radiation. Current estimates indicate that the reflective component is larger and that aerosols could regionally cancel thewarming effects of increases in greenhouse gases. The ERB, representing the input and output of energy for the earth and its atmosphere, is a fundamental quantity and is the driver for climate variations.Constructing a dataset useful for climate studies from the archived AVHRR observations-representing data obtained from instruments on a series of operational satellites-is not a trivial task. Interannual climate variations, such as ENSOs and, particularly, decadal climate variations, such as global warming, are much smaller than typical weather changes. These smaller signals are more difficult to measure and require particular attention to instrument calibration and the long-term stability of the dataset: small, timedependent biases in the data cannot be tolerated. This paper illustrates the problems that arise from orbital drift. The PATMOS data sets are shown along with preliminary efforts to remove artificial trends and discontinuities due to the satellite orbit drift. This is demonstrated for a large tropical band. However, smaller regions require more sophisticated methods to account for the drift. Researchers should still find the Pathfinder Atmosphere (PATMOS) dataset of value in any number of studies. For example, one can obtain a climatological map showing the average global distribution of the earth's cloudiness (or...

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