2 Model and simulation
description
2.1 Model description
The sea ice-ocean component of AWI-CM is the Finite Element Sea
Ice‐Ocean Model (FESOM, see Danilov et al. (2015) for the sea ice
component and Wang et al. (2014) for the ocean component). It uses
unstructured meshes, that allow simulations of ocean and sea ice
dynamics with variable grid resolution. This also enables refinement in
resolution for areas where small-scale dynamics are prevalent, (e.g.
narrow straits and strongly eddying regions, Sein et al., 2016, 2017).
Tools have been developed to enable users of FESOM data to perform
analysis efficiently (see Appendix A1). Furthermore, selected variables
are also available on regular latitude-longitude meshes.
The atmospheric component of AWI-CM is the spectral atmospheric model
ECHAM6.3.04p1 from MPIM (Stevens et al., 2013) which is used here
without any additional modifications or tuning. This version of ECHAM is
also used in the MPIM contribution to CMIP6. Having these two setups,
thus will allow future intercomparisons of the coupled systems that
share the same atmosphere model but use different sea ice-ocean models.
A more detailed description of the AWI-CM components as well as an
evaluation of its mean state and climate variability are provided in
Sidorenko et al. (2015) and Rackow et al. (2018), respectively. AWI-CM
realistically simulates many aspects of the modern climate, showing an
overall performance that is generally better than the most realistic
climate models participating in CMIP5.
The CMIP6 version of the code encompasses several changes compared to
that described in Sidorenko et al. (2015) and Rackow et al. (2018). The
major technical improvement involves the removal of the regular exchange
mesh, which in earlier versions was used as an interface between FESOM
and the OASIS3-MCT coupler. In the CMIP6 version, the interpolation
between unstructured FESOM and structured ECHAM meshes is done by the
coupler. Furthermore the coupling between ocean and atmosphere has been
sped up remarkably through the use of the parallel support built in
OASIS3-MCT.
Updates of physical parameterizations in the ocean sea ice component
comprise the inclusion of (1) a salt plume parameterisation (Sidorenko
et al. 2018) which improves the simulated sea surface salinity in the
Arctic Ocean, (2) modified background diffusivities, as suggested by
Wang et al. (2014) and (3) a K-Profile Parameterization (KPP) for
vertical mixing (Large et al. 1994) in the ocean model which has solved
shortcomings related to the North Atlantic circulation, pointed out in
Sidorenko et al. (2015) and Rackow et al. (2018). Those previous
publications were based on simulations on different meshes and with
constant rather than transient forcing. This and the fact that these
simulations were performed within the CMIP6 framework according to a
common protocol calls for documentation of the CMIP6 version of the
model and its results presented in this paper.
2.2 CMIP6 simulations
In this paper, the focus is on the DECK and ScenarioMIP simulations,
which were defined in the CMIP6 overview paper (Eyring et al., 2016) and
are summarized in Table 1. Before starting the 500-year coupled
piControl-spinup simulation with constant pre-industrial forcing, a
10-year long ocean-only simulation initialized from the EN4 ocean
reanalysis (Good et al., 2013) averaged over 1950–1954 has been
performed. In these 10 years of ocean-only simulation the initial
adjustment of the ocean state takes place. This pre-spinup helps to
ensure a numerically stable adjustment phase of the coupled system. The
piControl simulation is a continuation of the piControl-spinup
simulation. From the piControl simulation the idealized greenhouse gas
forcing simulations 1pctCO2 and abrupt-4xCO2 simulations as well as the
historical forcing simulations are branched off at specific years
(branch-off point(s), see Table 1). At the end of the historical forcing
simulations, that is at the end of the year 2014, the scenario
simulations are continued with forcing prescribed from the anthropogenic
forcing scenarios. These scenarios are derived from Shared Socioeconomic
Pathways (SSP) (Meinshausen et al. 2019).
The idealized and historical forcing simulations have been branched off
sufficiently long before the end of the piControl simulation to ensure
that every year of the sensitivity simulations (idealized, historical,
and scenario simulations) has a corresponding year in the piControl
simulation. The climate change signal is always computed following the
delta approach (e.g. Lenderink et al., 2007), that is, as the difference
between the sensitivity simulation and the corresponding year(s) of the
piControl simulation, to account for possible model drift.
Table 1: DECK and ScenarioMIP simulations performed with AWI-CM. The
forcing of the ScenarioMIP simulations is described in more detail in
Meinshausen et al. (2019).