NorCPM1 and its contribution to CMIP6 DCPP

Abstract

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.

Estimation of Ocean Biogeochemical Parameters in an Earth System Model Using the Dual One Step Ahead Smoother: A Twin Experiment

“…When one updates the ocean variable layer thickness, one effectively updates also the mass of the BGC quantities. In Bethke et al (2021), it was shown that this approach conserves well BGC properties and does not introduce spurious upwelling at the Equator. However, with an EnKF, the linear analysis update returns unphysical values for non-Gaussian distributed variables.…”

“…NorCPM (Counillon et al, 2014) is a climate prediction system that aims to provide seasonal-to-decadal prediction (Kimmritz et al, 2019;Wang et al, 2019;Bethke et al, 2021) and long term climate reanalysis (Counillon et al, 2016). It combines the Norwegian Earth System Model (e.g., NorESM1; Bentsen et al, 2013) with the Ensemble Kalman Filter (Evensen, 2003).…”

“…The rest of the configuration for the assimilation experiments in this work follows that of Counillon et al (2014Counillon et al ( , 2016, Wang et al (2016Wang et al ( , 2017 and Bethke et al (2021). The assimilation algorithm uses a local analysis framework (Evensen, 2003;Sakov et al, 2012), where a local analysis is computed for one horizontal grid point at a time by utilizing all available observation in a spatial window around the grid point.…”

“…We have first performed a NorESM simulation (one member/realisation) with TP values (the first row of Figure 6) from 1980 to 1999 that henceforth referred to as TRUTH. It has been initialized in January 1980 from member one of the 30-member NorCPM1 historical simulation integrated with historical forcing from 1850 to 2014 following phase 6 of the Coupled Model Inter-comparison Project (CMIP6) protocol (Bethke et al, 2021). A tiny perturbation of 10 −6• C was added to SST of that member in January 1980.…”

“…All experiments use 30 members. The initial ensemble state in January 1980 is taken from the historical ensemble NorCPM1 simulation run (Bethke et al, 2021). The initial condition is constructed from the 30-member NorCPM1 historical simulation-meaning that member 1 is nearly identical to the truth.…”