Abstract. We present a new framework for global ocean–sea-ice
model simulations based on phase 2 of the Ocean Model Intercomparison
Project (OMIP-2), making use of the surface dataset based on the Japanese 55-year atmospheric reanalysis for driving ocean–sea-ice models (JRA55-do). We
motivate the use of OMIP-2 over the framework for the first phase of OMIP
(OMIP-1), previously referred to as the Coordinated Ocean–ice Reference
Experiments (COREs), via the evaluation of OMIP-1 and OMIP-2 simulations from
11 state-of-the-science global ocean–sea-ice models. In the
present evaluation, multi-model ensemble means and spreads are calculated
separately for the OMIP-1 and OMIP-2 simulations and overall performance
is assessed considering metrics commonly used by ocean modelers. Both
OMIP-1 and OMIP-2 multi-model ensemble ranges capture observations in more
than 80 % of the time and region for most metrics, with the multi-model
ensemble spread greatly exceeding the difference between the means of the
two datasets. Many features, including some climatologically relevant ocean
circulation indices, are very similar between OMIP-1 and OMIP-2 simulations,
and yet we could also identify key qualitative improvements in transitioning
from OMIP-1 to OMIP-2. For example, the sea surface temperatures of the
OMIP-2 simulations reproduce the observed global warming during the 1980s
and 1990s, as well as the warming slowdown in the 2000s and the more recent
accelerated warming, which were absent in OMIP-1, noting that the last
feature is part of the design of OMIP-2 because OMIP-1 forcing stopped in
2009. A negative bias in the sea-ice concentration in summer of both
hemispheres in OMIP-1 is significantly reduced in OMIP-2. The overall
reproducibility of both seasonal and interannual variations in sea surface
temperature and sea surface height (dynamic sea level) is improved in
OMIP-2. These improvements represent a new capability of the OMIP-2
framework for evaluating process-level responses using simulation results.
Regarding the sensitivity of individual models to the change in forcing, the
models show well-ordered responses for the metrics that are directly forced,
while they show less organized responses for those that require complex
model adjustments. Many of the remaining common model biases may be
attributed either to errors in representing important processes in
ocean–sea-ice models, some of which are expected to be reduced by using
finer horizontal and/or vertical resolutions, or to shared biases and
limitations in the atmospheric forcing. In particular, further efforts are
warranted to resolve remaining issues in OMIP-2 such as the warm bias in the
upper layer, the mismatch between the observed and simulated variability of
heat content and thermosteric sea level before 1990s, and the erroneous
representation of deep and bottom water formations and circulations. We
suggest that such problems can be resolved through collaboration between
those developing models (including parameterizations) and forcing datasets.
Overall, the present assessment justifies our recommendation that future
model development and analysis studies use the OMIP-2 framework.