Notes: Abstract.  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 136 y. 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 change signal at a 95% confidence level for all estimates of climate variability. The observed trend 1943–1992 is furthermore inconsistent with a hypothesized solar radiation change alone at an estimated 90% confidence level. Thus, in contrast to the single pattern analysis, the two pattern analysis is able to discriminate between different forcing hypotheses in the observed climate change signal. The results are subject to uncertainties associated with the forcing history, which is poorly known for the solar and aerosol forcing, the possible omission of other important forcings, and inevitable model errors in the computation of the response to the forcing. Further uncertainties in the estimated significance levels arise from the use of model internal variability simulations and relatively short instrumental observations (after subtraction of an estimated greenhouse gas signal) to estimate the natural climate variability. The resulting confidence limits accordingly vary for different estimates using different variability data. Despite these uncertainties, however, we consider our results sufficiently robust to have some confidence in our finding that the observed climate change is consistent with a combined greenhouse gas and aerosol forcing, but inconsistent with greenhouse gas or solar forcing alone.

Notes: Abstract.  Two simulations with a global coupled ocean-atmosphere circulation model have been carried out to study the potential impact of solar variability on climate. The Hoyt and Schatten estimate of solar variability from 1700 to 1992 has been used to force the model. Results indicate that the near-surface temperature simulated by the model is dominated by the long periodic solar fluctuations (Gleissberg cycle), with global mean temperatures varying by about 0.5 K. Further results indicate that solar variability and an increase in greenhouse gases both induce to a first approximation a comparable pattern of surface temperature change, i.e., an increase of the land-sea contrast. However, the solar-induced warming pattern in annual means and summer is more centered over the subtropics, compared to a more uniform warming associated with the increase in greenhouse gases. The observed temperature rise over the most recent 30 and 100 years is larger than the trend in the solar forcing simulation during the same period, indicating a strong likelihood that, if the model forcing and response is realistic, other factors have contributed to the observed warming. Since the pattern of the recent observed warming agrees better with the greenhouse warming pattern than with the solar variability response, it is likely that one of these factors is the increase of the atmospheric greenhouse gas concentration.

Notes: Abstract Three 30 year long simulations have been performed with a T42 atmosphere model, in which the sea-surface temperature (SST) and sea-ice distribution have been taken from a transient climate change experiment with a T21 global coupled ocean-atmosphere model. In this so-called time-slice experiment, the SST values (and the greenhouse gas concentration) were taken at present time CO2 level, at the time of CO2 doubling and tripling. The annual cycle of temperature and precipitation has been studied over the IPCC regions and has been compared with observations. Additionally the combination of temperature and precipitation change has been analysed. Further parameters investigated include the difference between daily minimum and maximum temperature, the rainfall intensity and the length of droughts. While the regional simulation of the annual cycle of the near surface temperature is quite realistic with deviations rarely exceeding 3 K, the precipitation is reproduced to a much smaller degree of accuracy. The changes in temperature at the time of CO2 doubling amount to only 30–40% of those at the 3 * CO2 level and show hardly any seasonal variation, contrary to the 3 * CO2 experiment. The comparatively small response to the CO2 doubling can be attributed to the cold-start of the simulation, from which the SST has been extracted. The strong change in the seasonality cannot be explained by internal fluctuations and cold start alone, but has to be caused by feedback mechanisms. Due to the delay in warming caused by the transient experiment, from which the SST has been derived, the 3 * CO2 experiment can be compared to the CO2 doubling studies performed with mixed-layer models. The precipitation change does not display a clear signal. However, an increase of the rain intensity and of longer dry periods is simulated in many regions of the globe. The changes in these parameters as well as the combination of temperature- and precipitation change and the changes in the daily temperature range give valuable hints, in which regions observational studies should be intensified and under which aspects the observational data should be evaluated.