ALBERT

Notes: [Auszug] The response of the global climate system to smoke from burning oil wells in Kuwait is investigated in a series of numerical experiments using a coupled atmosphere–ocean general circulation model with an interactive soot transport model and extended radiation scheme. The results show a ...

Notes: Abstract Inorganic carbon in the ocean is modelled as a passive tracer advected by a three-dimensional current field computed from a dynamical global ocean circulation model. The carbon exchange between the ocean and atmosphere is determined directly from the (temperature-dependent) chemical interaction rates in the mixed layer, using a standard CO2 flux relation at the air-sea interface. The carbon cycle is closed by coupling the ocean to a one-layer, horizontally diffusive atmosphere. Biological sources and sinks are not included. In this form the ocean carbon model contains essentially no free tuning parameters. The model may be regarded as a reference for interpreting numerical experiments with extended versions of the model including biological processes in the ocean (Bacastow R and Maier-Reimer E in prep.) and on land (Esser G et al in prep.). Qualitatively, the model reproduces the principal features of the observed CO2 distribution bution in the surface ocean. However, the amplitudes of surface pCO2 are underestimated in upwelling regions by a factor of the order of 1.5 due to the missing biological pump. The model without biota may, nevertheless, be applied to compute the storage capacity of the ocean to first order for anthropogenic CO2 emissions. In the linear regime, the response of the model may be represented by an impulse response function which can be approximated by a superposition of exponentials with different amplitudes and time constants. This provides a simple reference for comparison with box models. The largest-amplitude (∼0.35) exponential has a time constant of 300 years. The effective storage capacity of the oceans is strongly dependent on the time history of the anthropogenic input, as found also in earlier box model studies.

Notes: Abstract.  The multi-variate optimal fingerprint method for the detection of an externally forced climate change signal in the presence of natural internal variability is extended to the attribution problem. To determine whether a climate change signal which has been detected in observed climate data can be attributed to a particular climate forcing mechanism, or combination of mechanisms, the predicted space–time dependent climate change signal patterns for the candidate climate forcings must be specified. In addition to the signal patterns, the method requires input information on the space–time dependent covariance matrices of the natural climate variability and of the errors of the predicted signal patterns. The detection and attribution problem is treated as a sequence of individual consistency tests applied to all candidate forcing mechanisms, as well as to the null hypothesis that no climate change has taken place, within the phase space spanned by the predicted climate change patterns. As output the method yields a significance level for the detection of a climate change signal in the observed data and individual confidence levels for the consistency of the retrieved climate change signal with each of the forcing mechanisms. A statistically significant climate change signal is regarded as consistent with a given forcing mechanism if the statistical confidence level exceeds a given critical value, but is attributed to that forcing only if all other candidate climate change mechanisms (from a finite set of proposed mechanisms) are rejected at that confidence level. Although all relations can be readily expressed in standard matrix notation, the analysis is carried out using tensor notation, with a metric given by the natural-variability covariance matrix. This simplifies the derivations and clarifies the invariant relation between the covariant signal patterns and their contravariant fingerprint counterparts. The signal patterns define the reduced vector space in which the climate trajectories are analyzed, while the fingerprints are needed to project the climate trajectories onto this reduced space.

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 A method is proposed for removing the drift of coupled atmosphere-ocean models, which in the past has often hindered the application of coupled models in climate response and sensitivity experiments. The ocean-atmosphere flux fields exhibit inconsistencies when evaluated separately for the individual sub-systems in independent, uncoupled mode equilibrium climate computations. In order to balance these inconsistencies a constant ocean-atmosphere flux correction field is introduced in the boundary conditions coupling the two sub-systems together. The method ensures that the coupled model operates at the reference climate state for which the individual model subsystems were designed without affecting the dynamical response of the coupled system in climate variability experiments. The method is illustrated for a simple two component box model and an ocean general circulation model coupled to a two layer diagnostic atmospheric model.