Norwegian Climate Prediction Model

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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

FREE: refers to an ensemble simulation of NorESM carried without assimilation and starting from the same initial ensemble than the assimilation experiment. Such experiment is used to disentangle the part of prediction skill related to natural internal variability from the part driven by external forcing and provide a benchmark for data assimilation.
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). Most difference between v1a, v1b and v1c is in sea ice covered region and v1c is performing best there. V1c is the version that would be used by default in CMIP6 DCPP.


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.


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)


NorCPM activities received a grant for computer time from the Norwegian Program for supercomputer (NOTUR2, project number NN9039K) and a storage grant (NORSTORE, NS9039K).

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]


Contact informations


Leader Prof. Noel Keenlyside;
NorESM related questions Dr. Ingo Bethke, Alok Gupta;
Data assimilation related questions Dr. François Counillon, Dr. Yiguo Wang;
Sea ice prediction Dr. Madlen Kimmritz, Dr. François Counillon
Atmospheric nudging Dr. Mao-Lin Shen, Dr. Fei Li;
Biochemistry predictions Dr. Filippa Fransner

Existing runs

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

All data are available on Norwegian storage facilities NIRD norwegian . All path below are given relative to the path /projects/NS9039K/shared/norcpm/cases/NorCPM/


Reanalysis
Name NorESM version full_field/anom NorCPM version Obs data set ens size assim freq period forcing var updated localisation Path Publications using it
Reana_twin_PI NorESM1-L full_field V0 Micom SST 30 monthly 610-710 PI ocn(all) poin hor. no vert tape Counillon et al. 2014
Free_PI NorESM1-L FREE - - 30 - 610-710 PI - - Counillon et al. 2014
Free_NorESM1 NorESM1-ME FREE - - 30 - 1850-2010 PI - - ../NorESM1-ME_historicalExt_noAssim/ Counillon et al. 2016
Reana-V0-long NorESM1-ME anom V0 HadiSST2 30 monthly 1950-2010 hist ocn(all) poin hor. no vert True_Obs-1950-2010/ME/ Counillon et al. 2016
Reana-bccrfast BCCRFAST anom V0 HadiSST2 30 monthly 1950-2010 hist ocn(all) poin hor. no vert ana_19500115_bccrfast none
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