Submitted as: model description paper 12 May 2021

Submitted as: model description paper | 12 May 2021

Review status: this preprint is currently under review for the journal GMD.

NorCPM1 and its contribution to CMIP6 DCPP

Ingo Bethke1, Yiguo Wang2, François Counillon2,1, Noel Keenlyside1,2, Madlen Kimmritz3, Filippa Fransner1, Annette Samuelsen2, Helene Langehaug2, Lea Svendsen1, Ping-Gin Chiu1, Leilane Passos2,1, Mats Bentsen4, Chuncheng Guo4, Alok Gupta4, Jerry Tjiputra4, Alf Kirkevåg5, Dirk Olivié5, Øyvind Seland5, Julie Solsvik Vågane5, Yuanchao Fan6, and Tor Eldevik1 Ingo Bethke et al.
  • 1Geophysical Institute, University of Bergen, Bjerknes Centre for Climate Research, 5007 Bergen, Norway
  • 2Nansen Environmental and Remote Sending Center and Bjerknes Centre for Climate Research, 5006 Bergen, Norway
  • 3Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany
  • 4NORCE Norwegian Research Centre Bjerknes Centre for Climate Research, 5007 Bergen, Norway
  • 5Norwegian Meteorological Institute, P.O. Box 43, Blindern, 0313 Oslo, Norway
  • 6Center for the Environment, Faculty of Arts & Sciences, Harvard University, Cambridge, MA 02138, USA

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 SST and T/S-profile observations using the Ensemble Kalman Filter (EnKF).

We first describe the Earth system component and the data assimilation (DA) scheme, highlighting implementation of new forcings, bug-fixes, re-tuning and DA innovations. Notably, NorCPM1 uses two anomaly assimilation variants to assess the impact of sea ice initialisation 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 then 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 non-assimilation experiments exhibit a moderate historical global surface temperature evolution and tropical climate variability characteristics that compare favourably with observations. The climate biases of NorCPM1 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 synchronises variability without changing the climatology. The i1 and i2 reanalysis/hindcast products overall show comparable performance. The benefits of initialisation 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 multi-year skills, while added benefit from initialisation is demonstrated for the subpolar North Atlantic (SPNA) and its extension to the Arctic. Both products show limited success in constraining and predicting surface ocean biogeochemistry variability. However, observational uncertainties and short temporal coverage make biogeochemistry evaluation uncertain while 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 synchronisation of circulation variability. Future development will therefore focus on improving the representation of mean state and variability of AMOC and its initialisation. Other efforts will be directed to refining the anomaly assimilation scheme – to better separate between internal versus forced signals, to include land and atmosphere initialisation and new observational types – and improving biogeochemistry prediction capability. Combined with other models, NorCPM1 may already contribute to skilful multi-year climate prediction that benefits society.

Ingo Bethke et al.

Status: open (until 09 Jul 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Ingo Bethke et al.

Ingo Bethke et al.


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Short summary
The Norwegian Climate Prediction Model version 1 (NorCPM1) is a new research tool for performing climate reanalyses and seasonal-to-decadal climate predictions. It adds data assimilation capability to the Norwegian Earth System Model version 1 (NorESM1) and has contributed output to the Decadal Climate Prediction Project (DCPP) as part of the sixth Coupled Model Intercomparison Project (CMIP6). We describe the system and evaluate its baseline, reanalysis and prediction performance.