Abstract. The Norwegian Climate Prediction Model version 1
(NorCPM1) is a new research tool for performing climate reanalyses and
seasonal-to-decadal climate predictions. It combines the Norwegian Earth
System Model version 1 (NorESM1) – which features interactive aerosol–cloud
schemes and an isopycnic-coordinate ocean component with
biogeochemistry – with anomaly assimilation of sea surface temperature (SST) and T/S-profile
observations using the ensemble Kalman filter (EnKF). We describe the Earth system component and the data assimilation (DA)
scheme, highlighting implementation of new forcings, bug fixes, retuning
and DA innovations. Notably, NorCPM1 uses two anomaly assimilation variants
to assess the impact of sea ice initialization and climatological reference
period: the first (i1) uses a 1980–2010 reference climatology for computing
anomalies and the DA only updates the physical ocean state; the second (i2)
uses a 1950–2010 reference climatology and additionally updates the sea ice
state via strongly coupled DA of ocean observations. We assess the baseline, reanalysis and prediction performance with output
contributed to the Decadal Climate Prediction Project (DCPP) as part of the
sixth Coupled Model Intercomparison Project (CMIP6). The NorESM1 simulations
exhibit a moderate historical global surface temperature evolution and
tropical climate variability characteristics that compare favourably with
observations. The climate biases of NorESM1 using CMIP6 external forcings
are comparable to, or slightly larger than those of, the original NorESM1
CMIP5 model, with positive biases in Atlantic meridional overturning
circulation (AMOC) strength and Arctic sea ice thickness, too-cold
subtropical oceans and northern continents, and a too-warm North Atlantic
and Southern Ocean. The biases in the assimilation experiments are mostly
unchanged, except for a reduced sea ice thickness bias in i2 caused by the
assimilation update of sea ice, generally confirming that the anomaly
assimilation synchronizes variability without changing the climatology. The
i1 and i2 reanalysis/hindcast products overall show comparable performance.
The benefits of DA-assisted initialization are seen globally in the first
year of the prediction over a range of variables, also in the atmosphere and
over land. External forcings are the primary source of multiyear skills,
while added benefit from initialization is demonstrated for the subpolar
North Atlantic (SPNA) and its extension to the Arctic, and also for
temperature over land if the forced signal is removed. Both products show
limited success in constraining and predicting unforced surface ocean
biogeochemistry variability. However, observational uncertainties and short
temporal coverage make biogeochemistry evaluation uncertain, and potential
predictability is found to be high. For physical climate prediction, i2
performs marginally better than i1 for a range of variables, especially in
the SPNA and in the vicinity of sea ice, with notably improved sea level
variability of the Southern Ocean. Despite similar skills, i1 and i2 feature
very different drift behaviours, mainly due to their use of different
climatologies in DA; i2 exhibits an anomalously strong AMOC that leads to
forecast drift with unrealistic warming in the SPNA, whereas i1 exhibits a
weaker AMOC that leads to unrealistic cooling. In polar regions, the
reduction in climatological ice thickness in i2 causes additional forecast
drift as the ice grows back. Posteriori lead-dependent drift correction
removes most hindcast differences; applications should therefore benefit
from combining the two products. The results confirm that the large-scale ocean circulation exerts strong
control on North Atlantic temperature variability, implying predictive
potential from better synchronization of circulation variability. Future
development will therefore focus on improving the representation of mean
state and variability of AMOC and its initialization, in addition to
upgrades of the atmospheric component. Other efforts will be directed to
refining the anomaly assimilation scheme – to better separate
internal and forced signals, to include land and atmosphere
initialization and new observational types – and improving biogeochemistry
prediction capability. Combined with other systems, NorCPM1 may already
contribute to skilful multiyear climate prediction that benefits society.
Ice thickness transect data from ARSV Laurence M. Gould LMG0106, LMG0205 in the Southern Ocean from 2001-2002 (SOGLOBEC project, Sea Ice Microbes project, Crabeater Seal Foraging project)
