Professor Myles Allen CBE FRS

Abstract:

Global efforts to mitigate climate change are guided by projections of future temperatures. But the eventual equilibrium global mean temperature associated with a given stabilization level of atmospheric greenhouse gas concentrations remains uncertain, complicating the setting of stabilization targets to avoid potentially dangerous levels of global warming. Similar problems apply to the carbon cycle: observations currently provide only a weak constraint on the response to future emissions. Here we use ensemble simulations of simple climate-carbon-cycle models constrained by observations and projections from more comprehensive models to simulate the temperature response to a broad range of carbon dioxide emission pathways. We find that the peak warming caused by a given cumulative carbon dioxide emission is better constrained than the warming response to a stabilization scenario. Furthermore, the relationship between cumulative emissions and peak warming is remarkably insensitive to the emission pathway (timing of emissions or peak emission rate). Hence policy targets based on limiting cumulative emissions of carbon dioxide are likely to be more robust to scientific uncertainty than emission-rate or concentration targets. Total anthropogenic emissions of one trillion tonnes of carbon (3.67 trillion tonnes of CO(2)), about half of which has already been emitted since industrialization began, results in a most likely peak carbon-dioxide-induced warming of 2 degrees C above pre-industrial temperatures, with a 5-95% confidence interval of 1.3-3.9 degrees C.

Abstract:

Perturbed physics experiments are among the most comprehensive ways to address uncertainty in climate change forecasts. In these experiments, parameters and parametrizations in atmosphere-ocean general circulation models are perturbed across ranges of uncertainty, and results are compared with observations. In this paper, we describe the largest perturbed physics climate experiment conducted to date, the British Broadcasting Corporation (BBC) climate change experiment, in which the physics of the atmosphere and ocean are changed, and run in conjunction with a forcing ensemble designed to represent uncertainty in past and future forcings, under the A1B Special Report on Emissions Scenarios (SRES) climate change scenario.

Abstract:

A tropospheric energy budget argument is used to analyze twentieth-century precipitation changes. It is found that global and ocean-mean general circulation model (GCM) precipitation changes can be understood as being due to the competing direct and surface-temperature-dependent effects of external climate forcings. In agreement with previous work, precipitation is found to respond more strongly to anthropogenic and volcanic sulfate aerosol and solar forcing than to greenhouse gas and black carbon aerosol forcing per unit temperature. This is due to the significant direct effects of greenhouse gas and black carbon forcing. Given that the relative importance of different forcings may change in the twenty-first century, the ratio of global precipitation change to global temperature change may be quite different. Differences in GCM twentieth- and twenty-first-century values are tractable via the energy budget framework in some, but not all, models. Changes in land-mean precipitation, on the other hand, cannot be understood at all with the method used here, even if land-ocean heat transfer is considered. In conclusion, the tropospheric energy budget is a useful concept for understanding the precipitation response to different forcings but it does not fully explain precipitation changes even in the global mean. © 2009 American Meteorological Society.

Abstract:

Quantification of the uncertainties in future climate projections is crucial for the implementation of climate policies. Here a review of projections of global temperature change over the twenty-first century is provided for the six illustrative emission scenarios from the Special Report on Emissions Scenarios (SRES) that assume no policy intervention, based on the latest generation of coupled general circulation models, climate models of intermediate complexity, and simple models, and uncertainty ranges and probabilistic projections from various published methods and models are assessed. Despite substantial improvements in climate models, projections for given scenarios on average have not changed much in recent years. Recent progress has, however, increased the confidence in uncertainty estimates and now allows a better separation of the uncertainties introduced by scenarios, physical feedbacks, carbon cycle, and structural uncertainty. Projection uncertainties are now constrained by observations and therefore consistent with past observed trends and patterns. Future trends in global temperature resulting from anthropogenic forcing over the next few decades are found to be comparably well constrained. Uncertainties for projections on the century time scale, when accounting for structural and feedback uncertainties, are larger than captured in single models or methods. This is due to differences in the models, the sources of uncertainty taken into account, the type of observational constraints used, and the statistical assumptions made. It is shown that as an approximation, the relative uncertainty range for projected warming in 2100 is the same for all scenarios. Inclusion of uncertainties in carbon cycle-climate feedbacks extends the upper bound of the uncertainty range by more than the lower bound. © 2008 American Meteorological Society.

Abstract:

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. © 2008 American Meteorological Society.