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Many definitions have been proposed for climate in the literature \citep[e.g.,][]{Lorenz1997,Werndl2015,Lovejoy2013,IPCC5.1,WMOno100}. For many of the central concepts in climate science, there do not appear to be any widely accepted definitions \citep{Todorov1986, JoePhD, Werndl2015}. One might argue that definitions used are broadly similar, that there appears to be consensus on the intuitive idea of climate \citep{Leith1985} and that the descriptions used are generally ``good enough'' for the specific contexts in which they are applied. However, \citet{Lorenz1997} notes that ``certain questions regarding climate may be answered either affirmatively or negatively, according to the precise [definition of climate used]''. Furthermore, \citet{Lorenz1997} suggests that in different contexts---across which the nature of the available data varies---the definition that would lead to the most meaningful characterisation of a given climatic state, may differ \citep[see also][]{Schneider1974}. In particular, definitions of climate which are applicable in observational studies, are not necessarily the most useful in theoretical or modelling studies \citep{Lorenz1997, Schneider1974, Leith1978}.  Of particular interest in this work are various climate quantifications---definitions which, when applied, provide a quantitative characterisation of a ``model climate'' (also referred to as a ``model climatic state''). In the an  observational context, one must consider quantifications applicable to individual climate variable trajectories sampled over time; however, in ensemble modelling studies collections of such trajectories can either be sampled at a particular ``instant'' or over a period of time. In this work, ensembles considered are initial condition (IC) ensembles, ensembles (the value of which is argued for in, e.g. \citet{Hawkins2015}),  but definitions involving perturbed physics and multi-model ensemble output could also be proposed; in particular, it could be argued that the \citet{IPCC5.1}  apply a multi-model ensemble definition in quantifying projected future climates. Following, we focus on probability distributions (more precisely, probability density functions (PDFs) and empirical cumulative density functions (ECDFs)) as a means of capturing the mean state, variability and extremes of a local, regional or global climatic state. For a particular variable, over a particular domain, three such quantifications are considered here: