| description abstract | Optimal fingerprinting is applied to estimate the amount of time it would take to detect warming by increased concentrations of carbon dioxide in monthly averages of temperature profiles over the Indian Ocean. A simple radiative?convective model is used to define the pattern of the warming signal, and the first 100 yr of the 1000-yr control run of the Geophysical Fluid Dynamics Laboratory atmospheric?oceanic global climate model is used to estimate the natural variability of the upper-air temperatures. The signal is assumed to be the difference in two epochs of data, each epoch consisting of 12 consecutive months of monthly average temperature profiles. When the variabilities of monthly averages are assumed independent of each other, the difference in August upper-air temperatures yields the strongest fingerprint, giving a time span for a one-sigma detection of 22 yr. When correlations of natural variability between months are considered, the one-sigma detection time is 10 yr. If only an annual average profile is used, the timescale for one-sigma detection increases to 14 yr. These timescales depend on subjective judgments of the details of the model-predicted pattern of global warming. In general, using upper-air temperatures adds approximately two independent pieces of information in detecting global warming for every surface-air temperature measurement, most likely due to the expected overall pattern of tropospheric warming?stratospheric cooling. Finally, testing climate models with data must be undertaken in order to understand the uncertainties in model-predicted global warming patterns and the predictive capability of models in general. | |
