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This is the first of two papers that describe the generation of a 25-member perturbed parameter ensemble (PPE) of high-resolution, global coupled simulations for the period 1900–2100, using CMIP5 historical and RCP8.5 emissions. Fifteen of these 25 coupled simulations now form a subset of the global projections provided for the UK Climate Projections 2018 (UKCP18). This first paper describes the selection of 25 variants (combinations of 47 parameters) using a set of cheap, coarser-resolution atmosphere-only simulations from a large sample of nearly 3000 variants. Retrospective 5-day weather forecasts run at climate resolution, and simulations of 2004–2009 with prescribed SST and sea ice are evaluated to filter out poor performance. We opted for a single design choice and sensitivity tests were done after the PPE was generated to demonstrate the effect of design choices on the filtering. Given our choice, only 38 of the parameter combinations were found to have acceptable performance at this stage. Idealised atmosphere-only simulations were then used to select the subset of 25 members that were as diverse as possible in terms of their CO2 and aerosol forcing, and their response to warmer SSTs. Using our parallel set of atmosphere-only and coupled PPEs (the latter from paper 2), we show that local biases in the atmosphere-only experiments are generally informative about the biases in the coupled PPE. Biases in radiative fluxes and cloud amounts are strongly informative for most regions, whereas this is only true for a smaller fraction of the globe for precipitation and dynamical variables. Therefore, the cheap experiments are an affordable way to search for promising parameter combinations but have limitations.

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We apply an established statistical methodology called history matching to constrain the parameter space of a coupled non-flux-adjusted climate model (the third Hadley Centre Climate Model; HadCM3) by using a 10,000-member perturbed physics ensemble and observational metrics. History matching uses emulators (fast statistical representations of climate models that include a measure of uncertainty in the prediction of climate model output) to rule out regions of the parameter space of the climate model that are inconsistent with physical observations given the relevant uncertainties. Our methods rule out about half of the parameter space of the climate model even though we only use a small number of historical observations. We explore 2 dimensional projections of the remaining space and observe a region whose shape mainly depends on parameters controlling cloud processes and one ocean mixing parameter. We find that global mean surface air temperature (SAT) is the dominant constraint of those used, and that the others provide little further constraint after matching to SAT. The Atlantic meridional overturning circulation (AMOC) has a non linear relationship with SAT and is not a good proxy for the meridional heat transport in the unconstrained parameter space, but these relationships are linear in our reduced space. We find that the transient response of the AMOC to idealised CO2 forcing at 1 and 2 % per year shows a greater average reduction in strength in the constrained parameter space than in the unconstrained space. We test extended ranges of a number of parameters of HadCM3 and discover that no part of the extended ranges can by ruled out using any of our constraints. Constraining parameter space using easy to emulate observational metrics prior to analysis of more complex processes is an important and powerful tool. It can remove complex and irrelevant behaviour in unrealistic parts of parameter space, allowing the processes in question to be more easily studied or emulated, perhaps as a precursor to the application of further relevant constraints.  相似文献   
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This paper provides a quantitative assessment of large-scale features in a perturbed parameter ensemble (PPE) of Met Office Unified Model HadGEM-GC3.05 in coupled global historical and future simulations. The main motivation for the simulations is to provide a major component of the UK Climate Projections 2018 (UKCP18), but they will also be used to make worldwide projections and inform future model development. Initially, a 25-member PPE, with 25 different parameter combinations, was simulated. Five members were subsequently dropped because either their simulated climate was unrealistically cool by 1970 or they suffered from numerical instabilities. The remaining 20 members were evaluated after completing the historical phase (1900–2005) against 13 separately selected Climate Model Intercomparison Project Phase 5 (CMIP5) models, and five more members were dropped. The final product is a combined projection system of 15 PPE members and 13 CMIP5 models, which has a number of benefits. In particular, the range of outcomes available from the combined set of 28 is often larger than from either of the two constituent ensembles, thus providing users with a more complete picture of plausible impacts. Here we mainly describe the evaluation process of the 20 PPE members. We evaluate biases in a number of important properties of the global coupled system, including assessment of climatological averages, coupled modes of internal variability and historical and future changes. The parameter combinations yielded plausible yet diverse atmosphere and ocean model behaviours. The range of global temperature changes is narrow, largely driven by use of different CO2 pathways. The range of global warming is seemingly not linked to range of feedbacks estimated from atmosphere-only runs, though we caution that the range of the latter is narrow relative to CMIP5, and therefore this result is not unexpected. This is the second of two papers describing the generation of the PPE for UKCP18 projections. Part 1 (Sexton et al. 2021) describes the selection of 25 parameter combinations of 47 atmosphere and land surface parameters, using a set of cheap atmosphere-only runs at a coarser resolution from nearly 3000 samples of parameter space.

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