The climate
            <i>prediction</i>
            .net BBC climate change experiment: design of the coupled model ensemble

Frame, David J.; Aina, T.; Christensen, C.; Faull, N.; Knight, Sylvia; Piani, C.; Rosier, Suzanne M.; Yamazaki, Kiroku; Yamazaki, Y. H.; Allen, Myles

doi:10.1098/rsta.2008.0240

Cited by 31 publications

(37 citation statements)

References 46 publications

(61 reference statements)

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“…The Control simulations correspond to an unforced or stationary climate in the year 1920. This was so that each physically distinct model in the experiment could be checked for spurious model drifts (Frame et al 2009) and also to provide an unforced pre-industrial climate against which change can be detected. Each 'data set' therefore consists of a pair of 160-year climate model runs: the Control and the Scenario, where each pair has a different combination of the 34 climate model parameters which are systematically varied (Table 1).…”

Section: The Climatepredictionnet (Cpdn) Perturbed-physics Experimentsmentioning

confidence: 99%

“…The CPDN BBC CCE has produced pairs of GCM runs under transient forcing for the 1920-2000 and 2000-2080 time periods (Frame et al 2009)-transient forcing means that a different forcing is applied for every year of the simulation. These are collectively known as the Scenario run from 1920 to 2080.…”

Section: The Climatepredictionnet (Cpdn) Perturbed-physics Experimentsmentioning

confidence: 99%

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Detecting change in UK extreme precipitation using results from the climateprediction.net BBC climate change experiment

et al. 2010

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We investigate a question posed by policy makers, namely, "when will changes in extreme precipitation due to climate change be detectable?" To answer this question we use climateprediction.net (CPDN) model simulations from the BBC Climate Change Experiment (CCE) over the UK. These provide us with the unique opportunity to compare 1-day extreme precipitation generated from climate altered by observed forcings (time period 1920-2000) and the SRES A1B emissions scenario (time period 2000-2080) (the Scenario) to extreme precipitation generated by a constant climate for year 1920 (the Control) for the HadCM3L General Circulation Model (GCM). We fit non-stationary Generalized Extreme Value (GEV) models to the Scenario output and compare these to stationary GEV models fit to the parallel Control. We define the time of detectable change as the time at which we would reject a hypothesis at the α = 0.05 significance level that the 20-year return level of the two runs is equal. We find that the time of detectable change depends on the season, with most model runs indicating that change to winter extreme precipitation may be detectable by the year 2010, and that change to summer extreme precipitation is not detectable by 2080. We also investigate which climate model parameters affect the weight of the tail of the precipitation distribution and which affect the time of detectable change for the winter season. We find that two climate model parameters have an important effect on the tail weight, and two others seem to affect the time of detection. Importantly, we find that climate model simulated extreme precipitation has a fundamentally different behavior to observations, perhaps due to the negative estimate of the GEV shape parameter, unlike observations which produce a slightly positive (∼0.0-0.2) estimate.

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Section: The Climatepredictionnet (Cpdn) Perturbed-physics Experimentsmentioning

confidence: 99%

Section: The Climatepredictionnet (Cpdn) Perturbed-physics Experimentsmentioning

confidence: 99%

Detecting change in UK extreme precipitation using results from the climateprediction.net BBC climate change experiment

et al. 2010

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“…Successive Intergovernmental Panel on Climate Change (IPCC) reports have identified aerosol direct and indirect effects on climate as the largest uncertainty in the assessment of anthropogenic forcing (Schimel et al, 1996;Penner et al, 2001;Forster et al, 2007). Global aerosols can impact the climate in two distinct ways: the direct radiative effect is a result of atmospheric aerosols reflecting or absorbing solar radiation and thereby cooling or warming the climate system.…”

Section: Introductionmentioning

confidence: 99%

The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei

et al. 2013

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Aerosol–cloud interaction effects are a major source of uncertainty in climate models so it is important to quantify the sources of uncertainty and thereby direct research efforts. However, the computational expense of global aerosol models has prevented a full statistical analysis of their outputs. Here we perform a variance-based analysis of a global 3-D aerosol microphysics model to quantify the magnitude and leading causes of parametric uncertainty in model-estimated present-day concentrations of cloud condensation nuclei (CCN). Twenty-eight model parameters covering essentially all important aerosol processes, emissions and representation of aerosol size distributions were defined based on expert elicitation. An uncertainty analysis was then performed based on a Monte Carlo-type sampling of an emulator built for each model grid cell. The standard deviation around the mean CCN varies globally between about ±30% over some marine regions to ±40–100% over most land areas and high latitudes, implying that aerosol processes and emissions are likely to be a significant source of uncertainty in model simulations of aerosol–cloud effects on climate. Among the most important contributors to CCN uncertainty are the sizes of emitted primary particles, including carbonaceous combustion particles from wildfires, biomass burning and fossil fuel use, as well as sulfate particles formed on sub-grid scales. Emissions of carbonaceous combustion particles affect CCN uncertainty more than sulfur emissions. Aerosol emission-related parameters dominate the uncertainty close to sources, while uncertainty in aerosol microphysical processes becomes increasingly important in remote regions, being dominated by deposition and aerosol sulfate formation during cloud-processing. The results lead to several recommendations for research that would result in improved modelling of cloud–active aerosol on a global scale

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“…Each simulation involves a 1920-2080 transient run driven by a set of natural forcing scenarios and the SRES A1B emissions scenario [29]. Details about the parameter perturbations, flux correction and forcings used in the experiment can be found in [6,33]. The ensemble used in this paper consists of a 32 member initial conditions (I.C.)…”

Section: Model Datamentioning

confidence: 99%

“…This ensemble was generated by the climateprediction.net (cpdn) experiment [6,33] using the HADCM3L model [18], a version of the UK Met Office Unified Model consisting of the atmospheric model at standard resolution ( 2.5 0 latitude, 3.75 0 longitude) including nineteen vertical layers coupled an ocean with twenty levels. The cpdn experiment explores the effects of both, initial conditions and model parameter perturbations, the latter by perturbing the physics in the atmosphere, ocean and sulphur cycle components.…”

Section: Model Datamentioning

confidence: 99%

Robustness of pattern scaled climate change scenarios for adaptation decision support

2013

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Pattern scaling offers the promise of exploring spatial details of the climate system response to anthropogenic climate forcings without their full simulation by state-of-the-art Global Climate Models. The circumstances in which pattern scaling methods are capable of delivering on this promise are explored by quantifying its performance in an idealized setting. Given a large ensemble that is assumed to sample the full range of variability and provide quantitative decisionrelevant information, the soundness of applying the pattern scaling methodology to generate decision relevant climate scenarios is explored. Pattern scaling is not expected to reproduce its target exactly, of course, and its generic limitations have been well documented since it was first proposed. In this work, using as a particular example the quantification of the risk of heat waves in Southern Europe, it is shown that the magnitude of the error in the pattern scaled estimates can be significant enough to disqualify the use of this approach in quantitative decisionsupport. This suggests that future application of pattern scaling in climate science should provide decision makers not just a restatement of the assumptions made, but also evidence that the methodology is adequate for purpose in practice for the case under consideration.

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The climate prediction .net BBC climate change experiment: design of the coupled model ensemble

Cited by 31 publications

References 46 publications

Detecting change in UK extreme precipitation using results from the climateprediction.net BBC climate change experiment

Detecting change in UK extreme precipitation using results from the climateprediction.net BBC climate change experiment

The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei

Robustness of pattern scaled climate change scenarios for adaptation decision support

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