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- Author or Editor: GEORGE OHRING x
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Abstract
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.
Abstract
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.
Abstract
As part of the joint National Oceanic and Atmospheric Administration–National Aeronautics and Space Administration (NOAA–NASA) Pathfinder program, the NOAA/National Environmental Satellite, Data and Information Service (NESDIS) has created a research-quality atmospheric, climate-scale dataset through the reprocessing of archived Advanced Very High Resolution Radiometer (AVHRR) observations from four afternoon satellites, in orbit since 1981. The raw observations were recalibrated using a vicarious calibration technique for the AVHRR reflectance channels and an improved treatment of the nonlinearity of the three infrared emittance channels. State-of-the-art algorithms are used in the Pathfinder Atmosphere (PATMOS) project to process global AVHRR datasets into statistics of channel radiances, total cloud amount, components of the earth's radiation budget, and aerosol optical thickness over oceans. The radiances and earth radiation budget components are determined for clear-sky and all-sky conditions. The output products are generated on a quasi-equal-area grid with a spatial resolution of approximately 110 km, with twice-a-day temporal resolution, and averaged over 5-day (pentad) and monthly time periods. The quality of the products is assessed relative to independent surface or satellite observations of these parameters. This analysis shows that the PATMOS data are sufficiently accurate for studies of the interaction of clouds and aerosol with solar and terrestrial radiation, and of climatic phenomena with large signals, for example, the annual cycle, monsoons, and the four ENSOs and two major volcanic eruptions that occurred during the 19-yr PATMOS period. Analysis also indicates that smaller climate signals, such as those associated with longer-term trends in surface temperature, may be difficult to detect due to the presence of artifacts in the time series that result from the drift of each satellite's observation time over its mission. However, a simple statistical method is employed to remove much of the effect caused by orbital drift. The uncorrected PATMOS dataset is accessible electronically.
Abstract
As part of the joint National Oceanic and Atmospheric Administration–National Aeronautics and Space Administration (NOAA–NASA) Pathfinder program, the NOAA/National Environmental Satellite, Data and Information Service (NESDIS) has created a research-quality atmospheric, climate-scale dataset through the reprocessing of archived Advanced Very High Resolution Radiometer (AVHRR) observations from four afternoon satellites, in orbit since 1981. The raw observations were recalibrated using a vicarious calibration technique for the AVHRR reflectance channels and an improved treatment of the nonlinearity of the three infrared emittance channels. State-of-the-art algorithms are used in the Pathfinder Atmosphere (PATMOS) project to process global AVHRR datasets into statistics of channel radiances, total cloud amount, components of the earth's radiation budget, and aerosol optical thickness over oceans. The radiances and earth radiation budget components are determined for clear-sky and all-sky conditions. The output products are generated on a quasi-equal-area grid with a spatial resolution of approximately 110 km, with twice-a-day temporal resolution, and averaged over 5-day (pentad) and monthly time periods. The quality of the products is assessed relative to independent surface or satellite observations of these parameters. This analysis shows that the PATMOS data are sufficiently accurate for studies of the interaction of clouds and aerosol with solar and terrestrial radiation, and of climatic phenomena with large signals, for example, the annual cycle, monsoons, and the four ENSOs and two major volcanic eruptions that occurred during the 19-yr PATMOS period. Analysis also indicates that smaller climate signals, such as those associated with longer-term trends in surface temperature, may be difficult to detect due to the presence of artifacts in the time series that result from the drift of each satellite's observation time over its mission. However, a simple statistical method is employed to remove much of the effect caused by orbital drift. The uncorrected PATMOS dataset is accessible electronically.
