In the recent past, optimal fingerprint methods have been used for the detection and attribution of anthropogenically caused climate change in observational records of near-surface temperature. Mostly, these techniques have been applied to annually or seasonally averaged data.
The detection and attribution exercise is expanded in two ways. First, the optimal fingerprint method proposed by Hasselmann is applied to the annual cycle (mean and first harmonic) of the climate variable in question instead of analyzing annual and/or seasonal averages separately. Preliminary analyses show that there is some promise in using the annual cycle of temperature as climate change indicator. Secondly, the method is also applied to diurnal temperature range and precipitation, either on a mean monthly basis or using extreme value statistics.
The analyses are based on the most recent climate change simulations performed at the Max-Planck-Institute of Meteorology/German Climate Computing Center using the ECHAM4/OPYC coupled climate model, including experiments considering the effects of greenhouse gases only and the direct and indirect effects of sulphate aerosols on climate.
Experiments using the older ECHAM3/LSG climate model and possibly experiments performed at the Hadley Centre will be used for the estimation of natural variability and model uncertainties.