The EoMIP study highlights the inter-model variability between the models studies. Climate
models are constructed by discretizing and then solving equations that represent the basic
laws that govern the behaviour of the atmosphere, ocean and land surface [49] and many
approximations are required in order to solve the nonlinear system of partial differential
equations. Note that the solution of a partial differential equation depends on (i) the initial
conditions, (ii) forcing boundary conditions (focus of the previous palaeoclimate studies) and
(iii) approximations in the form of climate parametrizations (this study).
Parameter uncertainty stems from the fact that small-scale processes in all components of
the climate system cannot be resolved explicitly in the climate system. This is the case in
cloud processes for example [50,51]. Parametrization of sub-grid scale processes is a major
source of uncertainty in climate prediction [52], and while in some parametrizations the
processes, observational evidence or theoretical knowledge is well understood, where this
information is scarce the values chosen for a parametrization may simply be because they
appear to work [51]. Future climate change studies have recently focused on quantifying the
uncertainty arising from these parameters using Monte Carlo-type techniques [53]. This type
of work is referred to as perturbed physics ensembles (PEEs) because suites of simulations are
generated by perturbing climate-sensitive model parameters. The resulting spread in predictions
is quantified, leading to model-dependent probabilistic estimates of the distribution of future
climate, warming and climate sensitivity. In a few cases, the ensembles are very large (i.e. a
thousand member ensemble) [53,54] but in most cases the number of simulations is limited
by the computational cost of complex climate models to a few tens or a hundred simulations
as is the case in [50,55].
Ensembles with perturbed climate-sensitive model parameters have begun to be used in
palaeoclimate research, primarily for the Late Quaternary and particularly on the issue of climate
sensitivity and El Nino Southern Oscillation (e.g. [56-60]). Ensembles with perturbed climate-
sensitive model parameters have also been used to 'tune' the climate model to proxy data for
the last glacial maximum (LGM) [61]. However, few studies have investigated older time periods
apart from a small set of simulations for the Pliocene [62].
In practice, there are several hundreds of parameters that are poorly constrained in climate
models and it is impossible to vary all of them. Gregoire et al. [61] identified a total of 10
parameters to be varied in FAMOUS, of which six parameters had been tuned in a previous
study [63] and recognized as having key parameters that had a major impact on Charney climate
sensitivity (the global average temperature increase associated with a doubling in CO2 and
including a specific set of feedbacks).
This paper investigates the effect of parametric uncertainty on the Early Eocene equable
climate problem using the model FAMOUS. The motivation of this study is to attempt to detect
ensemble simulations that match the proxy data available for the Early Eocene and to understand
how processes in these simulations vary from the rest of the ensemble. We deliberately do not
limit the parameter set perturbations to only those sets that perform well for modern conditions
because we wish to explore if any combination of parameters are able to simulate the Early Eocene
equable climates.