Barnett,T.P., G. Hegerl, T. Knutson and S. Tett, 2000:
Uncertainty levels in predicted patterns of anthropogenic climate change
Journal of Geophysical Research, 105, 15,525-15,542.Abstract
This paper investigates the uncertainties in different models estimates of an expected anthropogenic signal in the near-surface temperature field. We first consider nine coupled global climate models (CGCMs) forced by CO2 increasing at the rate of 1%/year. At the end of an 80 year integration, the models produce a signal in the mean global temperature field that agrees to within about 25%. However, the spatial patterns of change can be rather different. This is likely due to different representations of various physical processes in the respective models, especially those associated with land and sea ice processes. These inter-model differences will be useful for estimating quantitative bounds on detection/attribution statements.We next analyzed 11 different runs from three different CGCMs, each forced by observed/projected greenhouse gases (GHG) and direct sulfate aerosol effects. Concentrating on the trend of near-surface temperature change over the last 50 years, the 'early detection' period, we found the raw individual model simulations bore little similarity to each other or to the observations. This was due partially to large magnitude, small scale spatial noise that characterized all the model runs, a feature resulting mainly from internal model variability. Heavy spatial smoothing and differences between different realizations of an ensemble produced by identical forcing virtually requires that detection and attribution work be done with ensembles of scenario runs, for single runs can be misleading.
Recent detection and attribution methods, coupled with ensemble averaging methods, produced a reasonably consistent match between model predictions of expected temperature trends due to a combination of GHG and direct sulfate aerosols and those observed. The reader should weigh this statement carefully for the runs studied here did not include other anthropogenic pollutants thought to be important e.g. indirect sulfate aerosol effects, tropospheric ozone, nor do they include natural forcing mechanisms (volcanoes, solar variability, etc.) Clearly, consistency need not imply causality in this case.
Our results demonstrate the need to use different estimates of the anthropogenic fingerprint in detection studies. Different models give different estimates of these fingerprints and we do not currently know which is most correct. Further, the intra-model uncertainty in both the fingerprints and particularly the scenario runs can be relatively large. In short, simulation, detection and attribution of an anthropogenic signal is a job requiring multiple inputs from a diverse set of climate models.