NorCPM1 and its contribution to CMIP6 DCPP

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