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. The Norwegian Climate Prediction Model version 1(NorCPM1) is a new research tool for performing climate reanalyses andseasonal-to-decadal climate predictions. It combines the Norwegian EarthSystem Model version 1 (NorESM1) – which features interactive aerosol–cloudschemes and an isopycnic-coordinate ocean component withbiogeochemistry – with anomaly assimilation of sea surface temperature (SST) and T/S-profileobservations 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, retuningand DA innovations. Notably, NorCPM1 uses two anomaly assimilation variantsto assess the impact of sea ice initialization and climatological referenceperiod: the first (i1) uses a 1980–2010 reference climatology for computinganomalies and the DA only updates the physical ocean state; the second (i2)uses a 1950–2010 reference climatology and additionally updates the sea icestate via strongly coupled DA of ocean observations. We assess the baseline, reanalysis and prediction performance with outputcontributed to the Decadal Climate Prediction Project (DCPP) as part of thesixth Coupled Model Intercomparison Project (CMIP6). The NorESM1 simulationsexhibit a moderate historical global surface temperature evolution andtropical climate variability characteristics that compare favourably withobservations. The climate biases of NorESM1 using CMIP6 external forcingsare comparable to, or slightly larger than those of, the original NorESM1CMIP5 model, with positive biases in Atlantic meridional overturningcirculation (AMOC) strength and Arctic sea ice thickness, too-coldsubtropical oceans and northern continents, and a too-warm North Atlanticand Southern Ocean. The biases in the assimilation experiments are mostlyunchanged, except for a reduced sea ice thickness bias in i2 caused by theassimilation update of sea ice, generally confirming that the anomalyassimilation synchronizes variability without changing the climatology. Thei1 and i2 reanalysis/hindcast products overall show comparable performance.The benefits of DA-assisted initialization are seen globally in the firstyear of the prediction over a range of variables, also in the atmosphere andover land. External forcings are the primary source of multiyear skills,while added benefit from initialization is demonstrated for the subpolarNorth Atlantic (SPNA) and its extension to the Arctic, and also fortemperature over land if the forced signal is removed. Both products showlimited success in constraining and predicting unforced surface oceanbiogeochemistry variability. However, observational uncertainties and shorttemporal coverage make biogeochemistry evaluation uncertain, and potentialpredictability is found to be high. For physical climate prediction, i2performs marginally better than i1 for a range of variables, especially inthe SPNA and in the vicinity of sea ice, with notably improved sea levelvariability of the Southern Ocean. Despite similar skills, i1 and i2 featurevery different drift behaviours, mainly due to their use of differentclimatologies in DA; i2 exhibits an anomalously strong AMOC that leads toforecast drift with unrealistic warming in the SPNA, whereas i1 exhibits aweaker AMOC that leads to unrealistic cooling. In polar regions, thereduction in climatological ice thickness in i2 causes additional forecastdrift as the ice grows back. Posteriori lead-dependent drift correctionremoves most hindcast differences; applications should therefore benefitfrom combining the two products. The results confirm that the large-scale ocean circulation exerts strongcontrol on North Atlantic temperature variability, implying predictivepotential from better synchronization of circulation variability. Futuredevelopment will therefore focus on improving the representation of meanstate and variability of AMOC and its initialization, in addition toupgrades of the atmospheric component. Other efforts will be directed torefining the anomaly assimilation scheme – to better separateinternal and forced signals, to include land and atmosphereinitialization and new observational types – and improving biogeochemistryprediction capability. Combined with other systems, NorCPM1 may alreadycontribute to skilful multiyear climate prediction that benefits society.

NorCPM1 and its contribution to CMIP6 DCPP