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Notes: [Auszug] It is probable that the observed warming of the Earth by 0.7 °C over the past 100 years is caused by anthropogenic emissions of greenhouse gases, and that if trends continue the Earth will warm by another 2-3 °C before the end of the next century. But can claimed increases in the frequency ...

Notes: [Auszug] The issue of climate change, to be negotiated next month at the United Nations climate conference in Kyoto, Japan, is the most difficult environmental problem facing us. The impact of greenhouse-gas emissions will last for many centuries, far beyond normal economic and political planning horizons. ...

Notes: Abstract Climate changes during the next 100 years caused by anthropogenic emissions of greenhouse gases have been simulated for the Intergovernmental Panel on Climate Change Scenarios A (“business as usual”) and D (“accelerated policies”) using a coupled ocean-atmosphere general circulation model. In the global average, the near-surface temperature rises by 2.6 K in Scenario A and by 0.6 K in Scenario D. The global patterns of climate change for both IPCC scenarios and for a third step-function 2 x CO2 experiment were found to be very similar. The warming delay over the oceans is larger than found in simulations with atmospheric general circulation models coupled to mixed-layer models, leading to a more pronounced land-sea contrast and a weaker warming (and in some regions even an initial cooling) in the Southern Ocean. During the first forty years, the global warming and sea level rise due to the thermal expansion of the ocean are significantly slower than estimated previously from box-diffusion-upwelling models, but the major part of this delay can be attributed to the previous warming history prior to the start of present coupled ocean-atmosphere model integration (cold start).

Notes: Abstract Finite computer resources force compromises in the design of transient numerical experiments with coupled atmosphere-ocean general circulation models which, in the case of global warming simulations, normally preclude a full integration from the undisturbed pre-industrial state. The start of the integration at a later time from a climate state which, in contrast to the true climate, is initially in equilibrium then induces a cold start error. Using linear response theory a general expression for the cold start error is derived. The theory is applied to the Hamburg CO2 scenario simulations. An attempt to estimate the global-mean-temperature response function of the coupled model from the response of the model to a CO2 doubling was unsuccessful because of the non-linearity of the system. However, an alternative derivation, based on the transient simulation itself, yielded a cold start error which explained the initial retardation of the Hamburg global warming curve relative to the IPCC results obtained with a simple box-diffusion-upwelling model. In the case of the sea level the behaviour of the model is apparently more linear. The cold start error estimations based on a CO2 doubling experiment and on an experiment with gradually increasing CO2 (scenario A) are very similar and explain about two thirds of the coupled model retardation relative to the IPCC results.

Notes: Abstract Results from a control integration and time-dependent greenhouse warming experiments performed with a coupled ocean-atmosphere model are analysed in terms of their signal-to-noise properties. The aim is to illustrate techniques for efficient description of the space-time evolution of signals and noise and to identify potentially useful components of a multivariate greenhouse-gas “fingerprint”. The three 100-year experiments analysed here simulate the response of the climate system to a step-function doubling of CO2 and to the time-dependent greenhouse-gas increases specified in Scenarios A (“Business as Usual”) and D (“Draconian Measures”) of the Intergovernmental Panel on Climate Change (IPCC). If signal and noise patterns are highly similar, the separation of the signal from the natural variability noise is difficult. We use the pattern correlation between the dominant Empirical Orthogonal Functions (EOFs) of the control run and the Scenario A experiment as a measure of the similarity of signal and noise patterns. The EOF 1 patterns of signal and noise are least similar for near-surface temperature and the vertical structure of zonal winds, and are most similar for sea level pressure (SLP). The dominant signal and noise modes of precipitable water and stratospheric/tropospheric temperature contrasts show considerable pattern similarity. Despite the differences in forcing history, a highly similar EOF 1 surface temperature response pattern is found in all three greenhouse warming experiments. A large part of this similarity is due to a common land-sea contrast component of the signal. To determine the degree to which the signal is contaminated by the natural variability (and/or drift) of the control run, we project the Scenario A data onto EOFs 1 and 2 of the control. Signal contamination by the EOF 1 and 2 modes of the noise is lowest for near-surface temperature, a situation favorable for detection. The signals for precipitable water, SLP, and the vertical structure of zonal temperature and zonal winds are significantly contaminated by the dominant noise modes. We use cumulative explained spatial variance, principal component time series, and projections onto EOFs in order to investigate the time evolution of the dominant signal and noise modes. In the case of near-surface temperature, a single pattern emerges as the dominant signal component in the second half of the Scenario A experiment. The projections onto EOFs 1 and 2 of the control run indicate that Scenario D has a large common variability and/or drift component with the control run. This common component is also apparent between years 30 and 50 of the Scenario A experiment, but is small in the 2 × CO2 integration. The trajectories of the dominant Scenario A and control run modes evolve differently, regardless of the basis vectors chosen for projection, thus making it feasible to separate signal and noise within the first two decades of the experiments. For Scenario D it may not be possible to discriminate between the dominant signal and noise modes until the final 2–3 decades of the 100-year integration.

Notes: Abstract. Results from a control integration and time-dependent greenhouse warming experiments performed with a coupled ocean-atmosphere model are analysed in terms of their signal-to-noise properties. The aim is to illustrate techniques for efficient description of the space-time evolution of signals and noise and to identify potentially useful components of a multivariate greenhouse-gas ”fingerprint". The three 100-year experiments analysed here simulate the response of the climate system to a step-function doubling of CO2 and to the time-dependent greenhouse-gas increases specified in Scenarios A (”Business as Usual") and D (”Draconian Measures") of the Intergovernmental Panel on Climate Change (IPCC). If signal and noise patterns are highly similar, the separation of the signal from the natural variability noise is difficult. We use the pattern correlation between the dominant Empirical Orthogonal Functions (EOFs) of the control run and the Scenario A experiment as a measure of the similarity of signal and noise patterns. The EOF 1 patterns of signal and noise are least similar for near-surface temperature and the vertical structure of zonal winds, and are most similar for sea level pressure (SLP). The dominant signal and noise modes of precipitable water and stratospheric/tropospheric temperature contrasts show considerable pattern similarity. Despite the differences in forcing history, a highly similar EOF 1 surface temperature response pattern is found in all three greenhouse warming experiments. A large part of this similarity is due to a common land-sea contrast component of the signal. To determine the degree to which the signal is contaminated by the natural variability (and/or drift) of the control run, we project the Scenario A data onto EOFs 1 and 2 of the control. Signal contamination by the EOF 1 and 2 modes of the noise is lowest for near-surface temperature, a situation favorable for detection. The signals for precipitable water, SLP, and the vertical structure of zonal temperature and zonal winds are significantly contaminated by the dominant noise modes. We use cumulative explained spatial variance, principal component time series, and projections onto EOFs in order to investigate the time evolution of the dominant signal and noise modes. In the case of near-surface temperature, a single pattern emerges as the dominant signal component in the second half of the Scenario A experiment. The projections onto EOFs 1 and 2 of the control run indicate that Scenario D has a large common variability and/or drift component with the control run. This common component is also apparent between years 30 and 50 of the Scenario A experiment, but is small in the 2×CO2 integration. The trajectories of the dominant Scenario A and control run modes evolve differently, regardless of the basis vectors chosen for projection, thus making it feasible to separate signal and noise within the first two decades of the experiments. For Scenario D it may not be possible to discriminate between the dominant signal and noise modes until the final 2–3 decades of the 100-year integration.

Notes: Abstract Since considerable debate has arisen recently about the applicability of the weak-interaction theory for the description of cosmic-ray particle diffusion in random magnetic fields, a critical discussion of the conditions of validity of this theory is presented. It is pointed out that the singular behaviour of the diffusion coefficient at pitch angles of θ=90° does not lead to a breakdown of the weak-interaction theory, though an extension of pitch-angle scattering analysis to higher order may be necessary. In particular, an investigation of the δ-function contribution to the diffusion coefficient at pitch angles of 90° discovered by Fisket al. (1974) indicates that it has no influence on either pitch-angle scattering or the associated problem of particle diffusion parallel to the mean magnetic field. Although an infinite diffusion coefficient at 90° pitch angles implies a singualr breakdown of the weak-interaction theory at that point, the effects are purely local and are negligible when considering averaged particle properties.

Notes: Summary The effect of white capping on the spectral energy balance of surface waves is investigated by expressing the white-cap interactions in terms of an equivalent ensemble of random pressure pulses. It is shown first that the source function for any non-expansible interaction process which is weak-in-the-mean is quasi-linear. In the case of white capping, the damping coefficient is then shown to be proportional to the square of the frequency, provided the wave scales are large compared with the white-cap dimensions. The remaining free factor is determined indirectly from consideration of the spectral energy balance. The proposed white-capping dissipation function is consistent with the structure of the energy balance derived from JONSWAP, and the existence of a δ −5 spectrum governed by a non-local energy balance between the atmospheric input, the nonlinear energy transfer and dissipation. However, closure of the energy balance involves hypotheses regarding the structure of the atmospheric input function which need to be tested by further measurements. The proposed set of source functions may nevertheless be useful for numerical wave-prediction. According to the model, nearly all the momentum transferred across the air-sea interface enters the wave field. For fetchlimited and fully developed spectra in a stationary, uniform wind field, the drag coefficient remains approximately constant. However, for more general wind conditions, this will not be the case and the wave spectrum should be included in an accurate parameterisation of the air-sea momentum transfer.