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[1] We present a new approach based on atmospheric moisture residence time (RT) to interpret precipitation stable isotope ratio variations in all climate regimes, including tropical and polar, on monthly or inter-annual time scales. d 18 O and ln RT are positively correlated and a single regression line describes variations in d 18 O and RT anomalies, overcoming limitations of existing Rayleigh distillationbased approaches for tracing moisture dynamics and precipitation processes. We use this approach to characterize changes in tropical precipitation during El Nino events and suggest that increased precipitation in a warmer climate may occur with higher d 18 O values, contrary to assumptions made in interpreting proxy climate records in speleothems and other archives. Our results will allow the use of isotopes to monitor climate change impacts on the character and intensity of precipitation and to improve the performance of climate models by providing a direct means to calibrate model results.

The possibility of anthropogenic climate change and the possible problems associated with it are of great interest. However, one cannot study climate change without climate data. The Comprehensive Aerological Reference Data Set (CARDS) project will produce high-quality, daily upper-air data for the research community and for policy makers. CARDS intends to produce a dataset consisting of radiosonde and pibal data that is easy to use, as complete as possible, and as free of errors as possible. An attempt will be made to identify and correct biases in upper-air data whenever possible. This paper presents the progress made to date in achieving this goal.An advanced quality control procedure has been tested and implemented. It is capable of detecting and often correcting errors in geopotential height, temperature, humidity, and wind. This unique quality control method uses simultaneous vertical and horizontal checks of several meteorological variables. It can detect errors that other methods cannot.Research is being supported in the statistical detection of sudden changes in time series data. The resulting statistical technique has detected a known humidity bias in the U.S. data. The methods should detect unknown changes in instrumentation, station location, and data-reduction techniques. Software has been developed that corrects radiosonde temperatures, using a physical model of the temperature sensor and its changing environment. An algorithm for determining cloud coverforthis physical model has been developed. A numerical check for station elevation based on the hydrostatic equations has been developed, which has identified documented and undocumented station moves. Considerable progress has been made toward the development of algorithms to eliminate a known bias in the U.S. humidity data.