Uncertainty levels in predicted patterns of anthropogenic climate change

This paper investigates the uncertainties in different model estimates of a n expected anthropogenic signal in the near-surface air temperature field. We first consider nine coupled global climate models (CGCMs) forced by CO2 increasing at the rate of 1%/ pr. Averaged over years 71-SO of their integr ations, the approximate time of CO2 doubling, the models produce a global m ean temperature change that agrees to within about 25% of the nine model av erage. However, the spatial patterns of change can be rather different. Thi s is likely to be due to different representations of various physical proc esses in the respective models, especially those associated with land and s ea ice processes. We next analyzed 11 different runs from three different C GCMs, each forced by observed/projected greenhouse gases (GHG) and estimate d direct sulfate aerosol effects. Concentrating on the patterns of trend of near-surface air temperature change over the period 1945-1995, we found th at the raw individual model simulations often bore little resemblance to ea ch 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 ensemble averaging improved the intermodel agreement. The existence of substantial differences between different realizations of an ensemble produ ced by identical forcing almost requires that detection and attribution wor k be done with ensembles of scenario runs, as single runs can be misleading . Application of recent detection and attribution methods, coupled with ens emble averaging, produced a reasonably consistent match between model predi ctions of expected patterns of temperature trends due to a combination of G HG and direct sulfate aerosols and those observed. This statement is provis ional since the runs studied here did not include other anthropogenic pollu tants thought to be important (e.g., indirect sulfate aerosol effects, trop ospheric ozone) nor do they include natural forcing mechanisms (volcanoes, solar variability). Our results demonstrate the need to use different estim ates of the anthropogenic fingerprint in detection studies. Different model s give different estimates of these fingerprints, and we do not currently k now which is most correct. Further, the intramodel 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.