Norwegian Climate Prediction Model

The Norwegian Climate Prediction Model (NorCPM) is aiming at providing prediction from seasonal-to-decadal time scale. It is based on the Norwegian Earth System Model (NorESM, [1]) and the Ensemble Kalman Filter (EnKF, [2]) data assimilation method. NorESM is a state of the art Earth system model that is based on CESM ([3]), but uses a different aerosol/chemistry scheme and ocean model (evolved from MICOM). The EnKF is a sequential data assimilation method that allows for fully multivariate and flow dependent corrections using a covariance matrix produced by a Monte-Carlo ensemble integration.

NorESM model version used in NorCPM

NorESM1-L (Zhang et al., 2012) T31 resolution in the atmosphere; ocean is bipolar gx3v7 (~3°).
NorESM1-LT ([Wang et al. 2017 Kimmritz et al. 2018) atmosphere has a resolution of 1.9°x2.5° and ocean has a resolution of 1° with a tripolar grid.
BCCRFAST tripolar (Gao et al. 2018 atmosphere has a resolution of 1.9°x2.5° ocean is tripolar 1°.
NorESM1-ME (Bentsen et al. 2013,Tjiputra et al., 2013) f19 for the approximately 2° finite volume grid; ocean has a 1° resolution.
NorESM1-ACPL (Toniazzo and Koseki 2018)atmosphere has a resolution of f19 for the approximately 2° finite volume grid; ocean has a 1° resolution anomaly coupled ocean atmosphere. Anomaly coupling correct seasonally varying fluxes (SST to atm and wind to the ocean).
NorESM2-MH (Langehaug et al. 2018) atmosphere has a resolution of 1° ocean has a resolution of 1/4°.

NorCPM Versions

Version 0 (V0): refers to version of NorCPM that assimilate SST only. SST is the only observational data set available in the ocean for a period of time sufficient (> 100 years) to clearly demonstrate skill of decadal prediction. Assimilation updates vertically the full ocean while the remaining components of the Earth system model (atmosphere, sea ice, land) are left unchanged but they will adjust dynamically between the monthly assimilation step (an approach referred as weakly coupled data assimilation).

V0 was first tested in idealised twin experiment (Counillon et al. 2014). It is found that assimilation reduces error and can constrain well the variability of the ocean – with largest improvements in the near surface and sea ice with some benefit over land for temperature and precipitation. The system beats persistence forecast and shows skill for the heat content in the Nordic Seas that is close to the upper predictability limit.

V0 was tested in a real framework and a long stochastic reanalysis was produced for the period 1950—2010 which assimilates the anomaly of the HadiSST2 within the period 1950-2010. HadiSST2 is an ensemble of SST which is used to estimate observation uncertainty with space and time. A method referred to as upscaling (Wang et al. 2016) is used to ensure that the assimilation does not introduce a drift when updating the non-Gaussian distributed layer thickness variables. The system can reproduce well the North Atlantic variability and shows good agreement with independent objective analysis of the oceanic heat content and salt content globally. It was noted that using a flow dependent data assimilation method and formulating the ocean covariance in isopycnal coordinates are important ingredient for efficiently propagating the surface information below the mixed layer in the Labrador Sea and to constrain the formation of deep water convection.

In Wang et al. (sub) hindcasts are started from a shorter reanalysis (started in 1980) reanalysis. The system shows highly competitive skill compared to North American multi-model ensemble and skilful skill for sea ice extent were variability is driven by ocean variability (e.g. in the boreal winter in the Barents Sea, ….). The skill of decadal hindcasts were tested for 1955:2010. There are some skill for prediction of AMO and AMOC at 26 but the prediction of the SPG is poor despite a very good match during the reanalysis. Bethke et al. in prep identified that the reason for the poor forecast is a combined effect of a bias in the deep subtropical region and a wrong salinity update in the SPG region.

Version 1 (V1): the system is complemented with the assimilation of hydrographic profiles. Assimilating observations in an isopycnal coordinate model is not strait forward as the observation operator must interpolate either from isopycnal corodinates to z coordinates or vice versa. In Wang et al. 2016 it is shown that the approach to interpolate the model onto z coordinates (still keeping the covariance in isopycnal coordinates) is more linear than interpolating the observations to isopycnal coordinates and as such more efficient. Practical implementations of localisation and the representation error were extensively tested and version 1 has been run with optimal setting. The system is shown to be able to constrain well the error in the interior while being reliable. The performance of the prediction was tested for the period 1980-2017 in real framework with anomaly assimilation. While complementing the system with hydrographic profile yields little benefit on seasonal time scale, it greatly enhances the skill for decadal predictions in the SPG region (Bethke et al. in prep). There are currently different version of V1. In V1a, all ocean observations are kept and we do not update the sea ice compartment during assimilation. In V1b, we reject observation if it is located in places where there is ice (This run performs poorer than V1a). In v1c all observations are retained but error of TS profiles error is inflated by a factor of 3 because there is large uncertainty for the climatology there (with anomaly assimilation). We also update the sea ice compartment (strongly coupled DA).

Version 2 (V2): the system is complemented with assimilation of sea ice concentration. In Kimmritz et al. 2018, we tested different implementations of the data assimilation system in an idealised twin experiment. It is shown that a joint update of the ocean and the sea ice state during the assimilation is beneficial (strongly coupled data assimilation) with a flow dependent covariance method. It is also strongly beneficial to include the different thickness categories in the state vector. Assimilation is able to constrain well errors in sea ice and in the near surface ocean. The method is tested in real framework in Kimmritz et al. in prep. The system show reduced error for sea ice thickness. Prediction of sea ice extent are also greatly enhance in many regions were sea ice yields predictability.

Evaluation of prediction hindcast simulations with real SST data

The observational data set used so far is the ensemble of SST data (refereed as HADISST2), which provide monthly SST for 1850--2007 with 10 members. Each member reconstructs SST using a different set of possible unknown parameters. At term we intend to perform retrospective reanalysis and decadal prediction (hindcast) over the last century, but here we have decided to focus on a shorter period (1980-2005) because the system is still premature and there are many independent observations during this period of time.

Existing runs

Following is a table that summarise the different experiment run so far:

Multiplication table

Name on Norstore	NorESM version	observation	ens size	Freq assim	full_field/anom	ocean var updated	post process	Masked coast	localisation	Atmo nudging	Prediction	Finished/Ongoing	Remark
First_Try	F19_tn21	SST	30	monthly	anom	all	fixenkf	yes	point	no	1990,1992,1995,1996	Finished	minor bug in EnKF, small drift in MSL, good SPG
Second_Try	F19_tn21	SST	30	monthly	anom	all	fixenkf	yes	point	no	1995	Finished	small drift in MSL, good SPG
Third_Try	F19_tn21	SST	30	monthly	anom	T and S	fixenkf	yes	point	no	none	Finished	weak SPG in reanalysis
Fourth_Try	F19_tn21	SST	30	monthly	anom	T and S and Barot.	micomserial	yes	point	no	none	Finished	unrealistic
Fifth_Try	F19_tn21	SST	30	monthly	anom	all	micom_serial	yes	point	no	1995	Finished	very mild improvement compare to second
ME	F19_G16	SST	30	monthly	anom	all	micom_serial	yes	point	no	no	ongoing	??
Yiguo_try	F19_tn21	SST	30	monthly	anom	All(superlayer)	micom_serial	yes	point	no	no	ongoing	??
FF_ini_try	F19_tn21	SST	30	monthly	full	all	micom_serial	yes	point	no	April and November prediction from 1981 to 2007	Finished	??

Projects funding the NorCPM activities

Current:NFR-SFE (2018-2021), EU-Blue-Action (2016-2019), Norforsk-ARCPATH (2016-2020), NFR-SNOWGLACE (2015-2018), EU-INTAROS (2016-2020), BFS-BCPU(2018-2021); EU-TRIATLAS(2019-2024)
Completed:NFR-EPOCASA (2014-2017), EU-PREFACE (2014-2017), SKD-PARADIGM (2015-2017), SKD- INCREASE (2015-2017), SKD-PRACTICE (2012-2015)

Publications, etc.

1. Counillon, F., Bethke, I., Keenlyside, N., Bentsen, M., Bertino, L., & Zheng, F. (2014). Seasonal-to-decadal predictions with the ensemble Kalman filter and the Norwegian Earth System Model: a twin experiment. Tellus A, 66. doi:10.3402/tellusa.v66.21074
2. Wang Y, Counillon F, Bertino L. Alleviating the bias induced by the linear analysis update with an isopycnal ocean model. Quarterly Journal of the Royal Meteorological Society. 2016. https://doi.org/10.1002/qj.2709
3. Counillon F, Keenlyside N, Bethke I, Wang Y, Billeau S, Shen M-L, et al. Flow-dependent assimilation of sea surface temperature in isopycnal coordinates with the Norwegian climate prediction model. Tellus. Series A, Dynamic meteorology and oceanography. 2016;68:32437.
4. Gleixner S.; Keenlyside N.; Dimissie T., Counillon F., Wang Y.; Viste E. Seasonal predictability of Kiremt rainfall in CGCMs, Environmental Research Letters 2017.
5. Wang Y, Counillon F, Bethke I, Keenlyside N, Bocquet M, Shen M-L. Optimising assimilation of hydrographic profiles into isopycnal ocean models with ensemble data assimilation. Ocean Modelling. 2017;114.
6. Kimmritz M., Counillon F., Bitz C.M., Massonnet F., Bethke I., Gao Y. Optimising assimilation of sea ice concentration in an Earth system model with a multicategory sea ice model, Tellus A., 2018
7. Wang et al. Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF, submitted
8. Kimmritz et al. Added value of sea ice assimilation for seasonal prediction in the Arctic, in prep
9. Jackson et al. North Atlantic circulation: a perspective from ocean reanalyses.
10. Bethke et al. Impact of subtropical North Atlantic initialisation errors on subpolar gyre prediction in prep.
11. Ogawa et al. Arctic sea ice has no influence on AO/NAO, in prep.
12. Counillon et al. Relating model bias and prediction skill in the tropical Atlantic, in prep.
13. Fransner et al. What yields predictability of biochemistry for seasonal to decadal time scale, in prep.

User Resources

All the code for running the system is available on GitHub. The repository is private so please request to an account to access it.

• [https://github.com/BjerknesCPU/NorCPM]

Working group

• Meeting minutes of working group

Contact informations

Noel.Keenlyside@gfi.uib.no (leader); ingo.bethke@uni.no (NorESM related question); francois.counillon@nersc.no (EnKF related question); maolin.shen@gfi.uib.no (atmospheric nudging related question)