This study compares the variability of surface air temperature in three long coupled ocean–atmosphere general circulation model integrations. It is shown that the annual mean climatology of the surface air temperatures (SAT) in all three models is realistic and the linear trends over the 1000-yr integrations are small over most areas of the globe. Second, although there are notable differences among the models, the models ’ SAT variability is fairly realistic on annual to decadal timescales, both in terms of the geographical distribution and of the global mean values. A notable exception is the poor simulation of observed tropical Pacific variability. In the HadCM2 model, the tropical variability is overestimated, while in the GFDL and HAM3L models, it is underestimated. Also, the ENSO-related spectral peak in the globally averaged observed SAT differs from that in any of the models. The relatively low resolution required to integrate models for long time periods inhibits the successful simulation of the variability in this region. On timescales longer than a few decades, the largest variance in the models is generally located near sea ice margins in high latitudes, which are also regions of deep oceanic convection and variability related to variations in the thermohaline circulation. However, the exact geographical location of these maxima varies from model to model. The preferred patterns of interdecadal variability that are common to all three coupled models can be isolated by computing empirical orthogonal functions (EOFs) of

Abstract. Possible climate change caused by an increase in greenhouse gas concentrations, despite having been a subject of intensive study in recent years, is still very uncertain. Uncertainties in projections of different climate variables are usually described only by the ranges of possible values. For assessing the possible impact of climate change, it would be more useful to have probability distributions for these variables. Obtaining such distributions is usually very computationally expensive and requires knowledge of probability distributions for characteristics of the climate system that affect climate projections. A few studies of this kind have been carried out with energy balance/upwelling diffusion models. Here we demonstrate a methodology for performing a similar study with a 2 dimensional (zonally averaged) climate model that reproduces the behavior of coupled atmosphere/ocean general circulation models more realistically than energy balance models. This methodology involves application of the Deterministic Equivalent Modeling Method to derive functional approximations of the model’s probabilistic response. Monte Carlo analysis is then performed on the approximations. An application of the methodology is demonstrated by deriving the uncertainty in surface air temperature change and sea level rise due to thermal expansion of the ocean that result from uncertainties in climate sensitivity and the rate of heat uptake by the deep ocean for a prescribed increase in atmospheric CO 2 concentration. We also demonstrate propagation of correlated uncertainties through different models, by presenting results that include uncertainty in projected carbon emissions. 1.

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