The range of possibilities for future climate evolution needs to be taken into account when planning climate change mitigation and adaptation strategies. This requires ensembles of multi-decadal simulations to assess both chaotic climate variability and model response uncertainty. Statistical estimates of model response uncertainty, based on observations of recent climate change, admit climate sensitivities--defined as the equilibrium response of global mean temperature to doubling levels of atmospheric carbon dioxide--substantially greater than 5 K. But such strong responses are not used in ranges for future climate change because they have not been seen in general circulation models. Here we present results from the 'climateprediction.net' experiment, the first multi-thousand-member grand ensemble of simulations using a general circulation model and thereby explicitly resolving regional details. We find model versions as realistic as other state-of-the-art climate models but with climate sensitivities ranging from less than 2 K to more than 11 K. Models with such extreme sensitivities are critical for the study of the full range of possible responses of the climate system to rising greenhouse gas levels, and for assessing the risks associated with specific targets for stabilizing these levels.

A full understanding of the causes of the severe drought seen in the Sahel in the latter part of the twentiethcentury remains elusive some 25 yr after the height of the event. Previous studies have suggested that this drying trend may be explained by either decadal modes of natural variability or by human-driven emissions (primarily aerosols), but these studies lacked a sufficiently large number of models to attribute one cause over the other. In this paper, signatures of both aerosol and greenhouse gas changes on Sahel rainfall are illustrated. These idealized responses are used to interpret the results of historical Sahel rainfall changes from two very large ensembles of fully coupled climate models, which both sample uncertainties arising from internal variability and model formulation. The sizes of these ensembles enable the relative role of human-driven changes and natural variability on historic Sahel rainfall to be assessed. The paper demonstrates that historic aerosol changes are likely to explain most of the underlying 1940-80 drying signal and a notable proportion of the more pronounced 1950-80 drying.

properties controlling the twenty-first century response to sustained 31 anthropogenic greenhouse-gas forcing were not fully sampled, 32 partially owing to a correlation between climate sensitivity and 33 aerosol forcing 7,8 , a tendency to overestimate ocean heat uptake 11,12 34 and compensation between short-wave and long-wave feedbacks 9 . 35This complicates the interpretation of the ensemble spread as Fig. S1).

In complex spatial models, as used to predict the climate response to greenhouse gas emissions, parameter variation within plausible bounds has major effects on model behavior of interest. Here, we present an unprecedentedly large ensemble of >57,000 climate model runs in which 10 parameters, initial conditions, hardware, and software used to run the model all have been varied. We relate information about the model runs to large-scale model behavior (equilibrium sensitivity of global mean temperature to a doubling of carbon dioxide). We demonstrate that effects of parameter, hardware, and software variation are detectable, complex, and interacting. However, we find most of the effects of parameter variation are caused by a small subset of parameters. Notably, the entrainment coefficient in clouds is associated with 30% of the variation seen in climate sensitivity, although both low and high values can give high climate sensitivity. We demonstrate that the effect of hardware and software is small relative to the effect of parameter variation and, over the wide range of systems tested, may be treated as equivalent to that caused by changes in initial conditions. We discuss the significance of these results in relation to the design and interpretation of climate modeling experiments and large-scale modeling more generally.classification and regression trees ͉ climate change ͉ distributed computing ͉ general circulation models ͉ sensitivity analysis S imulation with complex mechanistic spatial models is central to science from the level of molecules (1) via biological systems (2, 3) to global climate (4). The objective typically is a mechanistically based prediction of system-level behavior. However, both through incomplete knowledge of the system simulated and the approximations required to make such models tractable, the ''true'' or ''optimal'' values of some model parameters necessarily will be uncertain. A limiting factor in such simulations is the availability of computational resources. Thus, combinations of plausible parameter values rarely are tested, leaving the dependence of conclusions on the particular parameters chosen unknown.Observations of the modeled system are vital for model verification and analysis, e.g., turning model output into probabilistic predictions of real-world system behavior (5-7). However, typically, few observations are available relative to the complexity of the model. There also may be little true replicate data available. For instance, there can be only one observational time series for global climate. Thus, if the same observations are used to fit parameter values, there is a severe risk of overfitting, gaining limited verisimilitude at the cost of the mechanistic insight and predictive ability for which the model originally was designed.To avoid fitting problems, parameter estimates must be refined directly. In some biological systems, direct and simultaneous measurement of large numbers of system parameters (e.g., protein binding or catalytic constants) soon may be possible. I...

The climate on Earth is generally determined by the amount and distribution of incoming solar radiation, which must be balanced in equilibrium by the emission of thermal radiation from the surface and atmosphere. The precise routes by which incoming energy is transferred from the surface and within the atmosphere and back out to space, however, are important features that characterize the current climate. This has been analyzed in the past by several groups over the years, based on combinations of numerical model simulations and direct observations of the Earth's climate system. The results are often presented in schematic form to show the main routes for the transfer of energy into, out of and within the climate system. Although relatively simple in concept, such diagrams convey a great deal of information about the climate system in a compact form. Such an approach has not so far been widely adopted in any systematic way for other planets of the Solar System, let alone beyond, although quite detailed climate models of several planets are now available, constrained by many new observations and measurements. Here we present an analysis of the global transfers of energy within the climate systems of a range of planets within the Solar System, including Mars, Titan, Venus and Jupiter, as modelled by relatively comprehensive radiative transfer and (in some cases) numerical circulation models. These results are presented in schematic form for comparison with the classical global energy budget analyses for the Earth, highlighting important similarities and differences. We also take the first steps towards extending this approach to other Solar System and extrasolar planets, including Mars, Venus, Titan, Jupiter and the 'hot Jupiter' exoplanet HD 189733b, presenting a synthesis of both previously published and new calculations for all of these planets.