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Research paper thumbnail of Assessing the contribution of ocean and sea ice initialization for seasonal prediction in the Arctic

EGU General Assembly Conference Abstracts, Apr 1, 2019

Research paper thumbnail of Monthly Means of Instantaneous Diagnostics for Key Atmospheric Processes (MIND the KAP)

Meteorologists typically characterise weather in terms of features, like cyclones or blocking hig... more Meteorologists typically characterise weather in terms of features, like cyclones or blocking high pressure systems. The instantaneous distribution of these features provides a very condensed summary of the atmospheric state. Consequently, monthly distributions of these features detected in the instantaneous fields retain much more relevant information about weather events than monthly averages of conventional meteorological variables, such as sea- level pressure. Weather events have been shown to provide a conceptual link between short- lived weather events and climate variability over longer time scales. This software project implements an optional automatic post-processing step the authors implemented for simulations based on the Norwegian Earth System Model (NorESM) and on the Norwegian Climate Prediction Model (NorCPM) to calculate monthly weather feature distributions. Documentation for the MIND the KAP project is available as Technical Report 401 of the Nansen Environmental a...

Research paper thumbnail of NCC NorCPM1 model output prepared for CMIP6 CMIP

Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets: These data includes all datasets... more Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets: These data includes all datasets published for 'CMIP6.CMIP.NCC.NorCPM1' according to the Data Reference Syntax defined as 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The model used in climate research named Norwegian Climate Prediction Model version 1, released in 2019, includes the components: aerosol: OsloAero4.1 (same grid as atmos), atmos: CAM-OSLO4.1 (2 degree resolution; 144 x 96 longitude/latitude; 26 levels; top level ~2 hPa), atmosChem: OsloChemSimp4.1 (same grid as atmos), land: CLM4 (same grid as atmos), ocean: MICOM1.1 (1 degree resolution; 320 x 384 longitude/latitude; 53 levels; top grid cell 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]), ocnBgchem: HAMOCC5.1 (same grid as ocean), seaIce: CICE4 (same grid as ocean). The model was run by the NorES...

Research paper thumbnail of NCC NorCPM1 model output prepared for CMIP6 DCPP

Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets: These data includes all datasets... more Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets: These data includes all datasets published for 'CMIP6.DCPP.NCC.NorCPM1' according to the Data Reference Syntax defined as 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The model used in climate research named Norwegian Climate Prediction Model version 1, released in 2019, includes the components: aerosol: OsloAero4.1 (same grid as atmos), atmos: CAM-OSLO4.1 (2 degree resolution; 144 x 96 longitude/latitude; 26 levels; top level ~2 hPa), atmosChem: OsloChemSimp4.1 (same grid as atmos), land: CLM4 (same grid as atmos), ocean: MICOM1.1 (1 degree resolution; 320 x 384 longitude/latitude; 53 levels; top grid cell 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]), ocnBgchem: HAMOCC5.1 (same grid as ocean), seaIce: CICE4 (same grid as ocean). The model was run by the NorES...

Research paper thumbnail of NCC NorCPM1 model output prepared for CMIP6 CMIP historical-ext

Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets: These data includes all datasets... more Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets: These data includes all datasets published for 'CMIP6.CMIP.NCC.NorCPM1.historical-ext' according to the Data Reference Syntax defined as 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The model used in climate research named Norwegian Climate Prediction Model version 1, released in 2019, includes the components: aerosol: OsloAero4.1 (same grid as atmos), atmos: CAM-OSLO4.1 (2 degree resolution; 144 x 96 longitude/latitude; 26 levels; top level ~2 hPa), atmosChem: OsloChemSimp4.1 (same grid as atmos), land: CLM4 (same grid as atmos), ocean: MICOM1.1 (1 degree resolution; 320 x 384 longitude/latitude; 53 levels; top grid cell 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]), ocnBgchem: HAMOCC5.1 (same grid as ocean), seaIce: CICE4 (same grid as ocean). The model was r...

Research paper thumbnail of Norwegian Climate Prediction Model

The Norwegian Climate Prediction Model (NorCPM) is aiming at providing prediction from seasonal-t... more 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 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 correct using a covariance matrix procuded by a Monte-Carlo ensemble integration. Currently the system only intend to update the ocean part as this is where most of the predictability is expected, but additional atmospheric nudging and assimilation of land variables are also considered.

Research paper thumbnail of NorCPM ocean seasonal hindcast, initialised in Jan, Apr, Jul, Oct throughout 1985-2010 with reanalysis assimilating SST, T-S profile and SIC anomalies

Research paper thumbnail of NorCPM sea ice reanalysis using weakly-coupled anomaly assimilation of SST and T-S profiles and strongly-coupled anomaly assimilation of SIC

Research paper thumbnail of Norwegian Climate Prediction Model version 1: reanalysis 1980-2010

Research paper thumbnail of Norwegian Climate Prediction Model version 1: seasonal and decadal hindcasts 1985-2010

Research paper thumbnail of Equal-order finite elements of hydrostatic flow problems

Subject of this thesis is the issue of equal-order finite element discretization of hydrostatic f... more Subject of this thesis is the issue of equal-order finite element discretization of hydrostatic flow problems. These flow problems typically arise in geophysical fluid dynamics on large scales and in flat domains. This small aspect ratio between the depth and the horizontal extents of the considered domain allows to efficiently reduce the complexity of the incompressible three dimensional Navier-Stokes equations, which form the basis of geophysical flows. In the resulting set of equations, the vertical momentum equation is replaced by the hydrostatic balance, which thus decouples the vertical pressure variations from the dynamic system, and the dynamically relevant pressure becomes two dimensional. Moreover, the vertical velocity component can be explicitely determined by the horizontal velocity components. Concomitant with this reduction is the replacement of the divergence constraint by a suitably modified version of it. As in the classical framework, it is known that these hydros...

Research paper thumbnail of Viscous-Plastic sea-ice solutions with Elastic-Viscous-Plastic sea-ice solvers

Most dynamic sea ice models for climate type simulations are based on the viscous-plastic (VP) rh... more Most dynamic sea ice models for climate type simulations are based on the viscous-plastic (VP) rheology. The resulting stiff system of partial differential equations for ice velocity is either solved implicitly at great computational cost, or explicitly with added pseudo-elasticity (elastic- viscous-plastic, EVP). The more popular, because apparently faster EVP scheme has been found to create noisy solutions that do not converge to the VP rheology. A slight modification re- interprets EVP as a pseudotime VP solver and thus salvages the convergence to VP. In addition, the modification regularizes the EVP solutions so that they can be used in climate simulations at relatively low cost compared to efficient implicit methods. We present comparisons of two variants of the new EVP scheme with converged VP solution in Arctic. At coarse resolution (grid cell width of about 27km), the EVP solutions are very similar to the VP solutions. At higher resolution (4.5km), convergence of all schemes...

Research paper thumbnail of On the convergence of the modifed elastic-viscous-plastic method of solving for sea-ice dynamics

Research paper thumbnail of NorCPM1 and its contribution to CMIP6 DCPP

Geoscientific Model Development, 2021

Research paper thumbnail of Impact of Initialization Methods on the Predictive skill in NorCPM - An Arctic-Atlantic Case Study

The skilful prediction of climatic conditions on a forecast horizon of months to decades into the... more The skilful prediction of climatic conditions on a forecast horizon of months to decades into the future remains a main scientific challenge of large societal benefit. Here we assess the hindcast skill of the Norwegian Climate Prediction Model (NorCPM) – for sea surface temperature (SST) and sea surface salinity (SSS) in the Arctic-Atlantic region – focusing on the impact of different initialization methods. We find the skill to be distinctly larger for the Subpolar North Atlantic than for the Norwegian Sea, and generally for all lead years analyzed. For the Subpolar North Atlantic, there is furthermore consistent benefit in increasing the amount of data assimilated, and also in updating the sea ice based on SST with strongly coupled data assimilation. The predictive skill is furthermore significant for at least two model versions up to 8-10 lead years with the exception for SSS at the longer lead years. For the Norwegian Sea, significant predictive skill is more rare; there is rela...

Research paper thumbnail of A new brittle rheology and numerical framework for large-scale sea-ice models

Research paper thumbnail of Seasonal to decadal predictions of regional Arctic sea ice by assimilating sea surface temperature in the Norwegian Climate Prediction Model

Climate Dynamics, 2020

The version of the Norwegian Climate Prediction Model (NorCPM) that only assimilates sea surface ... more The version of the Norwegian Climate Prediction Model (NorCPM) that only assimilates sea surface temperature (SST) with the Ensemble Kalman Filter has been used to investigate the seasonal to decadal prediction skill of regional Arctic sea ice extent (SIE). Based on a suite of NorCPM retrospective forecasts, we show that seasonal prediction of pan-Arctic SIE is skillful at lead times up to 12 months, which outperforms the anomaly persistence forecast. The SIE skill varies seasonally and regionally. Among the five Arctic marginal seas, the Barents Sea has the highest SIE prediction skill, which is up to 10–11 lead months for winter target months. In the Barents Sea, the skill during summer is largely controlled by the variability of solar heat flux and the skill during winter is mostly constrained by the upper ocean heat content/SST and also related to the heat transport through the Barents Sea Opening. Compared with several state-of-the-art dynamical prediction systems, NorCPM has c...

Research paper thumbnail of Impact of Ocean and Sea Ice Initialisation On Seasonal Prediction Skill in the Arctic

Journal of Advances in Modeling Earth Systems, 2019

Research paper thumbnail of Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF

Climate Dynamics, 2019

This study demonstrates that assimilating SST with an advancthe best state-of-the-art systems. We... more This study demonstrates that assimilating SST with an advancthe best state-of-the-art systems. We employ the Norwegian Climate Prediction Model (NorCPM)-a fully-coupled forecasting system-to assimilate SST observations with the ensemble Kalman filter. Predictions of NorCPM are compared to predictions from the North American Multimodel Ensemble (NMME) project. The global prediction skill of NorCPM at 6-and 12-month lead times is higher than the averaged skill of the NMME. A new metric is introduced for ranking model skill. According to the metric, NorCPM is one of the most skilful systems among the NMME in predicting SST in most regions. Confronting the skill to a large historical ensemble without assimilation, shows that the skill is largely derived from the initialisation rather than from the external forcing. NorCPM achieves good skill in predicting El Niño-Southern Oscillation (ENSO) up to 12 months ahead and achieves skill over land via teleconnections. However, NorCPM has a more pronounced reduction in skill in May than the NMME systems. An analysis of ENSO dynamics indicates that the skill reduction is mainly caused by model deficiencies in representing the thermocline feedback in February and March. We also show that NorCPM has skill in predicting sea ice extent at the Arctic entrance adjacent to the north Atlantic; this skill is highly related to the initialisation of upper ocean heat content.

Research paper thumbnail of Optimising assimilation of sea ice concentration in an Earth system model with a multicategory sea ice model

Tellus A: Dynamic Meteorology and Oceanography, 2018

A data assimilation method capable of constraining the sea ice of an Earth system model in a dyna... more A data assimilation method capable of constraining the sea ice of an Earth system model in a dynamically consistent manner has the potential to enhance the accuracy of climate reconstructions and predictions. Finding such a method is challenging because the sea ice dynamics is highly non-linear, and sea ice variables are strongly non-Gaussian distributed and tightly coupled to the rest of the Earth system-particularly thermodynamically with the ocean. We investigate key practical implementations for assimilating sea ice concentration-the predominant source of observations in polar regions-with the Norwegian Climate Prediction Model that combines the Norwegian Earth System Model with the Ensemble Kalman Filter. The performances of the different configurations are investigated by conducting 10-year reanalyses in a perfect model framework. First, we find that with a flow-dependent assimilation method, strongly coupled ocean-sea ice assimilation outperforms weakly coupled (sea ice only) assimilation. An attempt to prescribe the covariance between the ocean temperature and the sea ice concentration performed poorly. Extending the ocean updates below the mixed layer is slightly beneficial for the Arctic hydrography. Second, we find that solving the analysis for the multicategory instead of the aggregated ice state variables greatly reduces the errors in the ice state. Updating the ice volumes induces a weak drift in the bias for the thick ice category that relates to the postprocessing of unphysical thicknesses. Preserving the ice thicknesses for each category during the assimilation mitigates the drift without degrading the performance. The robustness and reliability of the optimal setting is demonstrated for a 20-year reanalysis. The error of sea ice concentration reduces by 50% (65%), sea ice thickness by 25% (35%), sea surface temperature by 33% (23%) and sea surface salinity by 11% (25%) in the Arctic (Antarctic) compared to a reference run without assimilation.

Research paper thumbnail of Assessing the contribution of ocean and sea ice initialization for seasonal prediction in the Arctic

EGU General Assembly Conference Abstracts, Apr 1, 2019

Research paper thumbnail of Monthly Means of Instantaneous Diagnostics for Key Atmospheric Processes (MIND the KAP)

Meteorologists typically characterise weather in terms of features, like cyclones or blocking hig... more Meteorologists typically characterise weather in terms of features, like cyclones or blocking high pressure systems. The instantaneous distribution of these features provides a very condensed summary of the atmospheric state. Consequently, monthly distributions of these features detected in the instantaneous fields retain much more relevant information about weather events than monthly averages of conventional meteorological variables, such as sea- level pressure. Weather events have been shown to provide a conceptual link between short- lived weather events and climate variability over longer time scales. This software project implements an optional automatic post-processing step the authors implemented for simulations based on the Norwegian Earth System Model (NorESM) and on the Norwegian Climate Prediction Model (NorCPM) to calculate monthly weather feature distributions. Documentation for the MIND the KAP project is available as Technical Report 401 of the Nansen Environmental a...

Research paper thumbnail of NCC NorCPM1 model output prepared for CMIP6 CMIP

Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets: These data includes all datasets... more Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets: These data includes all datasets published for 'CMIP6.CMIP.NCC.NorCPM1' according to the Data Reference Syntax defined as 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The model used in climate research named Norwegian Climate Prediction Model version 1, released in 2019, includes the components: aerosol: OsloAero4.1 (same grid as atmos), atmos: CAM-OSLO4.1 (2 degree resolution; 144 x 96 longitude/latitude; 26 levels; top level ~2 hPa), atmosChem: OsloChemSimp4.1 (same grid as atmos), land: CLM4 (same grid as atmos), ocean: MICOM1.1 (1 degree resolution; 320 x 384 longitude/latitude; 53 levels; top grid cell 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]), ocnBgchem: HAMOCC5.1 (same grid as ocean), seaIce: CICE4 (same grid as ocean). The model was run by the NorES...

Research paper thumbnail of NCC NorCPM1 model output prepared for CMIP6 DCPP

Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets: These data includes all datasets... more Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets: These data includes all datasets published for 'CMIP6.DCPP.NCC.NorCPM1' according to the Data Reference Syntax defined as 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The model used in climate research named Norwegian Climate Prediction Model version 1, released in 2019, includes the components: aerosol: OsloAero4.1 (same grid as atmos), atmos: CAM-OSLO4.1 (2 degree resolution; 144 x 96 longitude/latitude; 26 levels; top level ~2 hPa), atmosChem: OsloChemSimp4.1 (same grid as atmos), land: CLM4 (same grid as atmos), ocean: MICOM1.1 (1 degree resolution; 320 x 384 longitude/latitude; 53 levels; top grid cell 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]), ocnBgchem: HAMOCC5.1 (same grid as ocean), seaIce: CICE4 (same grid as ocean). The model was run by the NorES...

Research paper thumbnail of NCC NorCPM1 model output prepared for CMIP6 CMIP historical-ext

Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets: These data includes all datasets... more Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets: These data includes all datasets published for 'CMIP6.CMIP.NCC.NorCPM1.historical-ext' according to the Data Reference Syntax defined as 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The model used in climate research named Norwegian Climate Prediction Model version 1, released in 2019, includes the components: aerosol: OsloAero4.1 (same grid as atmos), atmos: CAM-OSLO4.1 (2 degree resolution; 144 x 96 longitude/latitude; 26 levels; top level ~2 hPa), atmosChem: OsloChemSimp4.1 (same grid as atmos), land: CLM4 (same grid as atmos), ocean: MICOM1.1 (1 degree resolution; 320 x 384 longitude/latitude; 53 levels; top grid cell 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]), ocnBgchem: HAMOCC5.1 (same grid as ocean), seaIce: CICE4 (same grid as ocean). The model was r...

Research paper thumbnail of Norwegian Climate Prediction Model

The Norwegian Climate Prediction Model (NorCPM) is aiming at providing prediction from seasonal-t... more 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 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 correct using a covariance matrix procuded by a Monte-Carlo ensemble integration. Currently the system only intend to update the ocean part as this is where most of the predictability is expected, but additional atmospheric nudging and assimilation of land variables are also considered.

Research paper thumbnail of NorCPM ocean seasonal hindcast, initialised in Jan, Apr, Jul, Oct throughout 1985-2010 with reanalysis assimilating SST, T-S profile and SIC anomalies

Research paper thumbnail of NorCPM sea ice reanalysis using weakly-coupled anomaly assimilation of SST and T-S profiles and strongly-coupled anomaly assimilation of SIC

Research paper thumbnail of Norwegian Climate Prediction Model version 1: reanalysis 1980-2010

Research paper thumbnail of Norwegian Climate Prediction Model version 1: seasonal and decadal hindcasts 1985-2010

Research paper thumbnail of Equal-order finite elements of hydrostatic flow problems

Subject of this thesis is the issue of equal-order finite element discretization of hydrostatic f... more Subject of this thesis is the issue of equal-order finite element discretization of hydrostatic flow problems. These flow problems typically arise in geophysical fluid dynamics on large scales and in flat domains. This small aspect ratio between the depth and the horizontal extents of the considered domain allows to efficiently reduce the complexity of the incompressible three dimensional Navier-Stokes equations, which form the basis of geophysical flows. In the resulting set of equations, the vertical momentum equation is replaced by the hydrostatic balance, which thus decouples the vertical pressure variations from the dynamic system, and the dynamically relevant pressure becomes two dimensional. Moreover, the vertical velocity component can be explicitely determined by the horizontal velocity components. Concomitant with this reduction is the replacement of the divergence constraint by a suitably modified version of it. As in the classical framework, it is known that these hydros...

Research paper thumbnail of Viscous-Plastic sea-ice solutions with Elastic-Viscous-Plastic sea-ice solvers

Most dynamic sea ice models for climate type simulations are based on the viscous-plastic (VP) rh... more Most dynamic sea ice models for climate type simulations are based on the viscous-plastic (VP) rheology. The resulting stiff system of partial differential equations for ice velocity is either solved implicitly at great computational cost, or explicitly with added pseudo-elasticity (elastic- viscous-plastic, EVP). The more popular, because apparently faster EVP scheme has been found to create noisy solutions that do not converge to the VP rheology. A slight modification re- interprets EVP as a pseudotime VP solver and thus salvages the convergence to VP. In addition, the modification regularizes the EVP solutions so that they can be used in climate simulations at relatively low cost compared to efficient implicit methods. We present comparisons of two variants of the new EVP scheme with converged VP solution in Arctic. At coarse resolution (grid cell width of about 27km), the EVP solutions are very similar to the VP solutions. At higher resolution (4.5km), convergence of all schemes...

Research paper thumbnail of On the convergence of the modifed elastic-viscous-plastic method of solving for sea-ice dynamics

Research paper thumbnail of NorCPM1 and its contribution to CMIP6 DCPP

Geoscientific Model Development, 2021

Research paper thumbnail of Impact of Initialization Methods on the Predictive skill in NorCPM - An Arctic-Atlantic Case Study

The skilful prediction of climatic conditions on a forecast horizon of months to decades into the... more The skilful prediction of climatic conditions on a forecast horizon of months to decades into the future remains a main scientific challenge of large societal benefit. Here we assess the hindcast skill of the Norwegian Climate Prediction Model (NorCPM) – for sea surface temperature (SST) and sea surface salinity (SSS) in the Arctic-Atlantic region – focusing on the impact of different initialization methods. We find the skill to be distinctly larger for the Subpolar North Atlantic than for the Norwegian Sea, and generally for all lead years analyzed. For the Subpolar North Atlantic, there is furthermore consistent benefit in increasing the amount of data assimilated, and also in updating the sea ice based on SST with strongly coupled data assimilation. The predictive skill is furthermore significant for at least two model versions up to 8-10 lead years with the exception for SSS at the longer lead years. For the Norwegian Sea, significant predictive skill is more rare; there is rela...

Research paper thumbnail of A new brittle rheology and numerical framework for large-scale sea-ice models

Research paper thumbnail of Seasonal to decadal predictions of regional Arctic sea ice by assimilating sea surface temperature in the Norwegian Climate Prediction Model

Climate Dynamics, 2020

The version of the Norwegian Climate Prediction Model (NorCPM) that only assimilates sea surface ... more The version of the Norwegian Climate Prediction Model (NorCPM) that only assimilates sea surface temperature (SST) with the Ensemble Kalman Filter has been used to investigate the seasonal to decadal prediction skill of regional Arctic sea ice extent (SIE). Based on a suite of NorCPM retrospective forecasts, we show that seasonal prediction of pan-Arctic SIE is skillful at lead times up to 12 months, which outperforms the anomaly persistence forecast. The SIE skill varies seasonally and regionally. Among the five Arctic marginal seas, the Barents Sea has the highest SIE prediction skill, which is up to 10–11 lead months for winter target months. In the Barents Sea, the skill during summer is largely controlled by the variability of solar heat flux and the skill during winter is mostly constrained by the upper ocean heat content/SST and also related to the heat transport through the Barents Sea Opening. Compared with several state-of-the-art dynamical prediction systems, NorCPM has c...

Research paper thumbnail of Impact of Ocean and Sea Ice Initialisation On Seasonal Prediction Skill in the Arctic

Journal of Advances in Modeling Earth Systems, 2019

Research paper thumbnail of Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF

Climate Dynamics, 2019

This study demonstrates that assimilating SST with an advancthe best state-of-the-art systems. We... more This study demonstrates that assimilating SST with an advancthe best state-of-the-art systems. We employ the Norwegian Climate Prediction Model (NorCPM)-a fully-coupled forecasting system-to assimilate SST observations with the ensemble Kalman filter. Predictions of NorCPM are compared to predictions from the North American Multimodel Ensemble (NMME) project. The global prediction skill of NorCPM at 6-and 12-month lead times is higher than the averaged skill of the NMME. A new metric is introduced for ranking model skill. According to the metric, NorCPM is one of the most skilful systems among the NMME in predicting SST in most regions. Confronting the skill to a large historical ensemble without assimilation, shows that the skill is largely derived from the initialisation rather than from the external forcing. NorCPM achieves good skill in predicting El Niño-Southern Oscillation (ENSO) up to 12 months ahead and achieves skill over land via teleconnections. However, NorCPM has a more pronounced reduction in skill in May than the NMME systems. An analysis of ENSO dynamics indicates that the skill reduction is mainly caused by model deficiencies in representing the thermocline feedback in February and March. We also show that NorCPM has skill in predicting sea ice extent at the Arctic entrance adjacent to the north Atlantic; this skill is highly related to the initialisation of upper ocean heat content.

Research paper thumbnail of Optimising assimilation of sea ice concentration in an Earth system model with a multicategory sea ice model

Tellus A: Dynamic Meteorology and Oceanography, 2018

A data assimilation method capable of constraining the sea ice of an Earth system model in a dyna... more A data assimilation method capable of constraining the sea ice of an Earth system model in a dynamically consistent manner has the potential to enhance the accuracy of climate reconstructions and predictions. Finding such a method is challenging because the sea ice dynamics is highly non-linear, and sea ice variables are strongly non-Gaussian distributed and tightly coupled to the rest of the Earth system-particularly thermodynamically with the ocean. We investigate key practical implementations for assimilating sea ice concentration-the predominant source of observations in polar regions-with the Norwegian Climate Prediction Model that combines the Norwegian Earth System Model with the Ensemble Kalman Filter. The performances of the different configurations are investigated by conducting 10-year reanalyses in a perfect model framework. First, we find that with a flow-dependent assimilation method, strongly coupled ocean-sea ice assimilation outperforms weakly coupled (sea ice only) assimilation. An attempt to prescribe the covariance between the ocean temperature and the sea ice concentration performed poorly. Extending the ocean updates below the mixed layer is slightly beneficial for the Arctic hydrography. Second, we find that solving the analysis for the multicategory instead of the aggregated ice state variables greatly reduces the errors in the ice state. Updating the ice volumes induces a weak drift in the bias for the thick ice category that relates to the postprocessing of unphysical thicknesses. Preserving the ice thicknesses for each category during the assimilation mitigates the drift without degrading the performance. The robustness and reliability of the optimal setting is demonstrated for a 20-year reanalysis. The error of sea ice concentration reduces by 50% (65%), sea ice thickness by 25% (35%), sea surface temperature by 33% (23%) and sea surface salinity by 11% (25%) in the Arctic (Antarctic) compared to a reference run without assimilation.