ALBERT

Notes: Abstract The time-dependent variability of the North Atlantic Oscillation is examined in an observational data set and several model data sets with greenhouse-gas-induced external forcings. The index of the North Atlantic Oscillation state is derived from the time series of mean latitudinal position and central pressure of the Icelandic Low and the Azores High considering the synchronous meridional shifting of the two pressure systems. While the North Atlantic Oscillation is characterized by intensive interannual variability, the low-pass filtered index time series shows a decadal component with a time scale of about 50 y within almost 120 y of observation. Since the late 1960s we observe a positive trend and a transition to a strong positive phase of the phenomenon indicative of a pre-dominantly zonal circulation over the North Atlantic. This trend occurs equally in the observations and all examined model data sets with increasing greenhouse-gas-concentration and atmosphere-ocean coupling. We find statistical evidence that the radiative forcing by increasing CO2 concentration has a significant influence on the simulated variability of the North Atlantic Oscillation on time scales of 60 y and longer, independent of the initial conditions and the model version. The seasonal response is strongest in late summer and winter. The interannual variability of the North Atlantic Oscillation states on time scales less than 10 y decreases synchronously with the positive trend of its decadal-mean state implying a stabilization of its present and future zonal state.

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  A new periodically synchronous coupling scheme has been applied to an atmosphere-ocean general circulation model. Due to a temporary switching off of the atmospheric model this scheme can considerably reduce computer requirements of coupled model experiments. In order to evaluate the new coupling scheme the model results are compared to corresponding synchronously coupled integrations. Experiments with fixed present-day CO2 concentration and a gradual increase of CO2 show a good reproduction of the mean state and the climate-change pattern, respectively. The deviations from the synchronously coupled experiments are in the range of the variability of the corresponding synchronously coupled runs. Due to the forcing during the ocean-only periods the short-term fluctuations are underestimated and the long-term variability is overestimated.

Notes: Abstract Due to restrictions in the available computing resources and a lack of suitable observational data, transient climate change experiments with global coupled ocean-atmosphere models have been started from an initial state at equilibrium with the present day forcing. The historical development of greenhouse gas forcing from the onset of industrialization until the present has therefore been neglected. Studies with simplified models have shown that this “cold start” error leads to a serious underestimation of the anthropogenic global warming. In the present study, a 150-year integration has been carried out with a global coupled ocean-atmosphere model starting from the greenhouse gas concentration observed in 1935, i.e., at an early time of industrialization. The model was forced with observed greenhouse gas concentrations up to 1985, and with the equivalent C02 concentrations stipulated in Scenario A (“Business as Usual”) of the Intergovernmental Panel on Climate Change from 1985 to 2085. The early starting date alleviates some of the cold start problems. The global mean near surface temperature change in 2085 is about 0.3 K (ca. 10%) higher in the early industrialization experiment than in an integration with the same model and identical Scenario A greenhouse gas forcing, but with a start date in 1985. Comparisons between the experiments with early and late start dates show considerable differences in the amplitude of the regional climate change patterns, particularly for sea level. The early industrialization experiment can be used to obtain a first estimate of the detection time for a greenhouse-gas-induced near-surface temperature signal. Detection time estimates are obtained using globally and zonally averaged data from the experiment and a long control run, as well as principal component time series describing the evolution of the dominant signal and noise modes. The latter approach yields the earliest detection time (in the decade 1990–2000) for the time-evolving near-surface temperature signal. For global-mean temperatures or for temperatures averaged between 45°N and 45°S, the signal detection times are in the decades 2015–2025 and 2005–2015, respectively. The reduction of the “cold start” error in the early industrialization experiment makes it possible to separate the near-surface temperature signal from the noise about one decade earlier than in the experiment starting in 1985. We stress that these detection times are only valid in the context of the coupled model's internally-generated natural variability, which possibly underestimates low frequency fluctuations and does not incorporate the variance associated with changes in external forcing factors, such as anthropogenic sulfate aerosols, solar variability or volcanic dust.

Notes: Abstract. Due to restrictions in the available computing resources and a lack of suitable observational data, transient climate change experiments with global coupled ocean-atmosphere models have been started from an initial state at equilibrium with the present day forcing. The historical development of greenhouse gas forcing from the onset of industrialization until the present has therefore been neglected. Studies with simplified models have shown that this "cold start" error leads to a serious underestimation of the anthropogenic global warming. In the present study, a 150-year integration has been carried out with a global coupled ocean-atmosphere model starting from the greenhouse gas concentration observed in 1935, i.e., at an early time of industrialization. The model was forced with observed greenhouse gas concentrations up to 1985, and with the equivalent CO2 concentrations stipulated in Scenario A ("Business as Usual") of the Intergovernmental Panel on Climate Change from 1985 to 2085. The early starting date alleviates some of the cold start problems. The global mean near surface temperature change in 2085 is about 0.3 K (ca. 10%) higher in the early industrialization experiment than in an integration with the same model and identical Scenario A greenhouse gas forcing, but with a start date in 1985. Comparisons between the experiments with early and late start dates show considerable differences in the amplitude of the regional climate change patterns, particularly for sea level. The early industrialization experiment can be used to obtain a first estimate of the detection time for a greenhouse-gas-induced near-surface temperature signal. Detection time estimates are obtained using globally and zonally averaged data from the experiment and a long control run, as well as principal component time series describing the evolution of the dominant signal and noise modes. The latter approach yields the earliest detection time (in the decade 1990–2000) for the time-evolving near-surface temperature signal. For global-mean temperatures or for temperatures averaged between 45° N and 45° S, the signal detection times are in the decades 2015–2025 and 2005–2015, respectively. The reduction of the "cold start" error in the early industrialization experiment makes it possible to separate the near-surface temperature signal from the noise about one decade earlier than in the experiment starting in 1985. We stress that these detection times are only valid in the context of the coupled model's internally-generated natural variability, which possibly underestimates low frequency fluctuations and does not incorporate the variance associated with changes in external forcing factors, such as anthropogenic sulfate aerosols, solar variability or volcanic dust.

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 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.