A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles

Abstract

A methodology is described for probabilistic predictions of future climate. This is based on a set of ensemble simulations of equilibrium and time-dependent changes, carried out by perturbing poorly constrained parameters controlling key physical and biogeochemical processes in the HadCM3 coupled ocean-atmosphere global climate model. These (ongoing) experiments allow quantification of the effects of earth system modelling uncertainties and internal climate variability on feedbacks likely to exert a significant influence on twenty-first century climate at large regional scales. A further ensemble of regional climate simulations at 25 km resolution is being produced for Europe, allowing the specification of probabilistic predictions at spatial scales required for studies of climate impacts. The ensemble simulations are processed using a set of statistical procedures, the centrepiece of which is a Bayesian statistical framework designed for use with complex but imperfect models. This supports the generation of probabilities constrained by a wide range of observational metrics, and also by expert-specified prior distributions defining the model parameter space. The Bayesian framework also accounts for additional uncertainty introduced by structural modelling errors, which are estimated using our ensembles to predict the results of alternative climate models containing different structural assumptions. This facilitates the generation of probabilistic predictions combining information from perturbed physics and multi-model ensemble simulations. The methodology makes extensive use of emulation and scaling techniques trained on climate model results. These are used to sample the equilibrium response to doubled carbon dioxide at any required point in the parameter space of surface and atmospheric processes, to sample time-dependent changes by combining this information with ensembles sampling uncertainties in the transient response of a wider set of earth system processes, and to sample changes at local scales.The methodology is necessarily dependent on a number of expert choices, which are highlighted throughout the paper.

Impacts of climate change, land-use change and phosphorus reduction on phytoplankton in the River Thames (UK)

“…UKCP09 are probabilistic projections developed by the UK Met Office to provide climate change projections of climate change over the UK with greater spatial and temporal detail than previous climate scenarios. They are based on the results of the HadCM3 coupled ocean-atmosphere Global Circulation Model (Gordon et al, 2000), run as a perturbed physics ensemble to sample model and parameter uncertainties (Murphy et al, 2007). HadCM3 projections were then downscaled on a 25 km grid over seven overlapping 30-yr time periods based on an ensemble of 11 variants of the regional climate model HadRM3, and a statistical procedure was applied to build local-scale distributions of changes for various climate variables.…”

Uncertainty quantification and parameter tuning in the CAM5 Zhang‐McFarlane convection scheme and impact of improved convection on the global circulation and climate

“…Although GCMs can reproduce important salient features of the current climate, projections of future climate produced by different climate models can vary substantially at the regional scale [Cubasch et al, 2001]. The latter may result because some physical assumptions suitable for the current situations may break down as the climate evolves or because compensating errors that exist among the parameterization schemes for different physical processes no longer cancel out in the future [Gilmore et al, 2004;Molders, 2005;Golaz et al, 2007;Min et al, 2007;Murphy et al, 2007]. Ensemble simulations with multiple models or parameterizations have been applied as effective approaches to characterize uncertainties associated with dynamical or physical processes in the model [Allen et al, 2000;Giorgi and Mearns, 2002;Stainforth et al, 2005;Lopez et al, 2006].…”

Climate impact on floods: changes in high flows in Sweden in the past and the future (1911–2100)

“…Both these strategies have potential advantages but also many challenges, as discussed by Hall et al (2014). The two fundamental problems connected with climate impact assessments can be summarized as follows: (1) observed time series can include natural long-term cycles that might be induced by climatic oscillations or persistent memory of hydrological processes (Markonis and Koutsoyiannis, 2012;Montanari, 2012), which will render all statistical trend analyses very sensitive to the period chosen for the study; and (2) global climate models (GCMs) do not correspond to the observed climatology (Murphy et al, 2007), and uncertainties arise in each step of the model chain in hydrological impact assessments Donnelly et al 2014). Much effort has been made over the last decade to address these prob-i.…”