Obtaining diverse behaviors in a climate model without the use of flux adjustments

Abstract

[1] A number of studies have set out to obtain a range of atmosphere and ocean model behavior by perturbing parameters in a single climate model (perturbed physics ensemble: PPE). Early studies used shallow layer slab ocean or flux-adjusted coupled oceanatmosphere models to obtain a broad range of behavior as characterized by climate sensitivity. A recent study reports a relatively narrow range of sensitivities (2.2-3.2 C) in a PPE of 35 coupled models without flux adjustment, raising the question whether previous broad ranges were an artifact of the use of models that were not in top-of-atmosphere (TOA) energy balance. Moreover, no PPE experiment has reported a large spread of behavior of the ocean compared to that exhibited in a multi-model ensemble (MME) such as Coupled Model Intercomparison Project phase 3 (CMIP3). In this work, we randomly perturb model parameters of a coupled ocean-atmosphere general circulation model using a space-filling design containing 10,000 combinations. The ensemble is run over the distributed computing platform of climateprediction.net under fixed pre-industrial forcing without flux adjustment. We resample a second, 20,000-member, ensemble with perturbations conditioned on the TOA fluxes from the first ensemble to not drift significantly from a realistic base state while targeting a range of behavior. Models within the targeted ensemble show realistic regional control climates when compared to the CMIP3 ensemble, although there is a bias in global mean surface temperature. The range of predicted equilibrium climate sensitivities of the targeted ensemble is substantially smaller than that obtained with flux adjustment, but larger than the range in the CMIP3 ensemble or in the 35-model un-flux-adjusted PPE in a recent study mentioned above. The Atlantic meridional overturning circulation in the targeted ensemble exhibits a spread in strength as wide as that found in the CMIP3 ensemble. We conclude that flux adjustment is not a prerequisite for obtaining a broad spread of behavior in a perturbed physics ensemble.Citation: Yamazaki, K., et al. (2013), Obtaining diverse behaviors in a climate model without the use of flux adjustments,

Effectiveness and limitations of parameter tuning in reducing biases of top-of-atmosphere radiation and clouds in MIROC version 5

“…If we increased the number of perturbed parameters and PPE members, the inter-model difference of the TOA radiation and cloud biases might be larger than that of the present study. The importance of the PPE design in obtaining large intermodel spread is illustrated by Yamazaki et al (2013), who conducted a PPE experiment with an AOGCM, HadCM3. They perturbed 33 parameters to create 20 000 members in the PPE experiment.…”

“…However, such models could also be included in the PPE if we applied flux adjustment at the sea surface to suppress climate drift, which might increase the parametric uncertainty of the biases compared with the present study. For example, Yamazaki et al (2013) reported that the parametric uncertainty of the climate sensitivity increases by adopting models with a large TOA radiation imbalance in their PPE experiment using the HadCM3 AOGCM. Collins et al (2006) also conducted a PPE experiment using the HadCM3 AOGCM with flux adjustment.…”

“…This enables us to study stable climates of the PPE without applying flux adjustment. Other methods analogous to the SIS have been discussed in Jackson et al (2012) and Yamazaki et al (2013).…”

Lower-Tropospheric Mixing as a Constraint on Cloud Feedback in a Multiparameter Multiphysics Ensemble

“…In previous literature, this sampling problem in ''the ensemble of opportunity'' was addressed using different experimental strategies. A well-known sampling strategy from perturbing parameters in model physics schemes [a perturbed physics ensemble (PPE)] is an effective way to examine dependencies of CS and climate feedbacks on model parameters in a given model structure (Murphy et al 2004;Stainforth et al 2005;Piani et al 2005;Sanderson et al 2010;Yokohata et al 2010;Collins et al 2011;Klocke et al 2011;Shiogama et al 2012;Yamazaki et al 2013;Zhao 2014;Tomassini et al 2015). Yokohata et al (2010Yokohata et al ( , 2013 and Sanderson (2011) compared PPEs based on different GCMs and revealed that parametric uncertainty depends on model structures; therefore, both structural and parametric uncertainties in CS should be examined in unified frameworks.…”

Climate model forecast biases assessed with a perturbed physics ensemble

“…A perturbed physics ensemble of 500 HadCM3 members was run on the climateprediction.net (CPDN) platform, a facility which allows large numbers of climate model simulations to be performed by utilising latent computing time provided by members of the public (see e.g., Allen 2003;Piani et al 2005;Sanderson et al 2008;Yamazaki et al 2013). Multiple physical parameters in both atmosphere and ocean were perturbed simultaneously in each member using combinations of parameters previously found to give plausible realisations of the climate, as measured by top-of-atmosphere (TOA) radiative flux balance (Yamazaki et al 2013).…”