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.
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...
A climate model emulator is developed using neural network techniques and trained with the data from the multithousand-member climateprediction.net perturbed physics GCM ensemble. The method recreates nonlinear interactions between model parameters, allowing a simulation of a much larger ensemble that explores model parameter space more fully.The emulated ensemble is used to search for models closest to observations over a wide range of equilibrium response to greenhouse gas forcing. The relative discrepancies of these models from observations could be used to provide a constraint on climate sensitivity. The use of annual mean or seasonal differences on top-of-atmosphere radiative fluxes as an observational error metric results in the most clearly defined minimum in error as a function of sensitivity, with consistent but less well-defined results when using the seasonal cycles of surface temperature or total precipitation.The model parameter changes necessary to achieve different values of climate sensitivity while minimizing discrepancy from observation are also considered and compared with previous studies. This information is used to propose more efficient parameter sampling strategies for future ensembles.
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