Multi-fingerprint detection and attribution analysis of greenhouse gas, greenhouse gas-plus-aerosol and solar forced climate change

A multi-fingerprint analysis is applied to the detection and attribution of anthropogenic climate change. While a single fingerprint is optimal for the detection of climate change, further tests of the statistical consistency of the detected climate change signal with model predictions for different...

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Bibliographic Details
Published in:Climate dynamics 1997-09, Vol.13 (9), p.613-634
Main Authors: HEGERL, G. C, HASSELMANN, K, CUBASCH, U, MITCHELL, J. F. B, ROECKNER, E, VOSS, R, WASZKEWITZ, J
Format: Article
Language:English
Subjects:
Aerosols
Anthropogenic factors
Climate change
Climate models
Climate variability
Climatology. Bioclimatology. Climate change
Earth, ocean, space
Exact sciences and technology
External geophysics
Global warming
Greenhouse effect
Greenhouse gases
Meteorology
Simulation
Solar radiation
Surface temperature
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Summary:A multi-fingerprint analysis is applied to the detection and attribution of anthropogenic climate change. While a single fingerprint is optimal for the detection of climate change, further tests of the statistical consistency of the detected climate change signal with model predictions for different candidate forcing mechanisms require the simultaneous application of several fingerprints. Model-predicted climate change signals are derived from three anthropogenic global warming simulations for the period 1880 to 2049 and two simulations forced by estimated changes in solar radiation from 1700 to 1992. In the first global warming simulation, the forcing is by greenhouse gas only, while in the remaining two simulations the direct influence of sulfate aerosols is also included. From the climate change signals of the greenhouse gas only and the average of the two greenhouse gas-plus-aerosol simulations, two optimized fingerprint patterns are derived by weighting the model-predicted climate change patterns towards low-noise directions. The optimized fingerprint patterns are then applied as a filter to the observed near-surface temperature trend patterns, yielding several detection variables. The space-time structure of natural climate variability needed to determine the optimal fingerprint pattern and the resultant signal-to-noise ratio of the detection variable is estimated from several multi-century control simulations with different CGCMs and from instrumental data over the last 136y. Applying the combined greenhouse gas-plus-aerosol fingerprint in the same way as the greenhouse gas only fingerprint in a previous work, the recent 30-y trends (1966-1995) of annual mean near surface temperature are again found to represent a significant climate change at the 97.5% confidence level. However, using both the greenhouse gas and the combined forcing fingerprints in a two-pattern analysis, a substantially better agreement between observations and the climate model prediction is found for the combined forcing simulation. Anticipating that the influence of the aerosol forcing is strongest for longer term temperature trends in summer, application of the detection and attribution test to the latest observed 50-y trend pattern of summer temperature yielded statistical consistency with the greenhouse gas-plus-aerosol simulation with respect to both the pattern and amplitude of the signal. In contrast, the observations are inconsistent with the greenhouse-gas only climate
ISSN:0930-7575
1432-0894
DOI:10.1007/s003820050186