Norwegian Climate Prediction Model: Difference between revisions
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: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 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 provides a stochastic reconstruction of SST - that is, providing a three-dimensional estimate of the measurement and its accuracy - for the period 1850-2010. 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. | :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 provides a stochastic reconstruction of SST - that is, providing a three-dimensional estimate of the measurement and its accuracy - for the period 1850-2010. 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 [https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/north-american-multi-model-ensemble 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, Labrador Sea and Greenland Iceland Nordic Seas). 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 combination | :In Wang et al. (sub) hindcasts are started from a shorter reanalysis (started in 1980) reanalysis. The system shows highly competitive skill compared to [https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/north-american-multi-model-ensemble 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, Labrador Sea and Greenland Iceland Nordic Seas). 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 combination of poor initialisation of the deep subtropical water temperature and near surface salinity in the SPG region. | ||
<br /> '''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 V1 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. In v1c all observations are retained but error for hydrographic profiles is inflated by a factor of 3 in sea ice covered region 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 | <br /> '''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 V1 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. In v1c all observations are retained but error for hydrographic profiles is inflated by a factor of 3 in sea ice covered region 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 will be the default version for CMIP6 DCPP. | ||
<br /> '''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. | <br /> '''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. | ||
<br /> For each of the NorCPM version the assimilation are either carried using '''full field assimilation''' or '''anomaly assimilation'''. In '''full field assimilation''' the observations are assimilated as they are. An advantage of that approach is that it constrains the model bias, but there is a risk that models are attracted to their bias climatology. In such a case, the redundant corrections will transfers the bias from the observed variables to the non observed variables via the covariance during assimilation. In '''anomaly assimilation''' the anomaly of model and observation are calculated from their respective seasonal climatology before they are compared. As a consequence, bias are left as they are and we aim only at synchronising the variability. This approach also has some drawback as bias in the mean state | <br /> For each of the NorCPM version the assimilation are either carried using '''full field assimilation''' or '''anomaly assimilation'''. In '''full field assimilation''' the observations are assimilated as they are. An advantage of that approach is that it constrains the model bias, but there is a risk that models are attracted to their bias climatology. In such a case, the redundant corrections will transfers the bias from the observed variables to the non observed variables via the covariance during assimilation. In '''anomaly assimilation''' the anomaly of model and observation are calculated from their respective seasonal climatology before they are compared. As a consequence, bias are left as they are and we aim only at synchronising the variability. This approach also has some drawback as bias in the mean state comes with a bias in its variability (for example with the classical bilocation of the Gulf Stream). [https://www.nonlin-processes-geophys.net/21/521/2014/ Carrassi et al. 2014] details the advantage and inconvenient of both approaches. | ||
=== Projects funding the NorCPM activities === | === Projects funding the NorCPM activities === | ||
<br /> '''Current''':NFR-SFE (2018-2021), EU-Blue-Action (2016-2019), Norforsk-ARCPATH (2016-2020 | <br /> '''Current''':NFR-SFE (2018-2021), EU-Blue-Action (2016-2019), Norforsk-ARCPATH (2016-2020), EU-INTAROS (2016-2020), BFS-BCPU(2018-2021); EU-TRIATLAS(2019-2024) | ||
<br /> '''Completed''':NFR-EPOCASA (2014-2017), EU-PREFACE (2014-2017), SKD-PARADIGM (2015-2017), SKD- INCREASE (2015-2017), SKD-PRACTICE (2012-2015) | <br /> '''Completed''':NFR-SNOWGLACE (2015-2018), NFR-EPOCASA (2014-2017), EU-PREFACE (2014-2017), SKD-PARADIGM (2015-2017), SKD- INCREASE (2015-2017), SKD-PRACTICE (2012-2015) | ||
<br /> NorCPM activities | <br /> NorCPM activities receives a grant for computer time from the Norwegian Program for supercomputer (NOTUR2, project number NN9039K) and a storage grant (NORSTORE, NS9039K). | ||
== Publications, etc. == | == Publications, etc. == | ||
<ol> | <ol> | ||
Revision as of 12:25, 17 December 2018
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) atmosphere has a resolution of 1.9°x2.5° ocean has a 1° resolution.
NorESM1-ACPL (Toniazzo and Koseki 2018) atmosphere has a resolution of 1.9°x2.5° ocean has a 1° resolution anomaly coupled ocean atmosphere. Anomaly coupling modifies coupling fields by a fixed seasonal climatological correction, such that biases in SSTs and surface wind stress are obviated.
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. The initial ensemble is generated by spinning up an ensemble of states (sampled from a long preindustrial forcing run) with real historical forcing from 1850. Such experiment provides a benchmark for data assimilation and is also used to disentangle the part of prediction skill related to natural internal variability from the part driven by external forcing.
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 provides a stochastic reconstruction of SST - that is, providing a three-dimensional estimate of the measurement and its accuracy - for the period 1850-2010. 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, Labrador Sea and Greenland Iceland Nordic Seas). 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 combination of poor initialisation of the deep subtropical water temperature and near surface salinity 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 V1 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. In v1c all observations are retained but error for hydrographic profiles is inflated by a factor of 3 in sea ice covered region 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 will be the default version for 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.
For each of the NorCPM version the assimilation are either carried using full field assimilation or anomaly assimilation. In full field assimilation the observations are assimilated as they are. An advantage of that approach is that it constrains the model bias, but there is a risk that models are attracted to their bias climatology. In such a case, the redundant corrections will transfers the bias from the observed variables to the non observed variables via the covariance during assimilation. In anomaly assimilation the anomaly of model and observation are calculated from their respective seasonal climatology before they are compared. As a consequence, bias are left as they are and we aim only at synchronising the variability. This approach also has some drawback as bias in the mean state comes with a bias in its variability (for example with the classical bilocation of the Gulf Stream). Carrassi et al. 2014 details the advantage and inconvenient of both approaches.
Projects funding the NorCPM activities
Current:NFR-SFE (2018-2021), EU-Blue-Action (2016-2019), Norforsk-ARCPATH (2016-2020), EU-INTAROS (2016-2020), BFS-BCPU(2018-2021); EU-TRIATLAS(2019-2024)
Completed:NFR-SNOWGLACE (2015-2018), NFR-EPOCASA (2014-2017), EU-PREFACE (2014-2017), SKD-PARADIGM (2015-2017), SKD- INCREASE (2015-2017), SKD-PRACTICE (2012-2015)
NorCPM activities receives a grant for computer time from the Norwegian Program for supercomputer (NOTUR2, project number NN9039K) and a storage grant (NORSTORE, NS9039K).
Publications, etc.
- 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
- 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
- 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.
- Gleixner S.; Keenlyside N.; Dimissie T., Counillon F., Wang Y.; Viste E. Seasonal predictability of Kiremt rainfall in CGCMs, Environmental Research Letters 2017.
- 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.
- 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
- Wang et al. Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF, submitted
- Kimmritz et al. Added value of sea ice assimilation for seasonal prediction in the Arctic, in prep
- Jackson et al. North Atlantic circulation: a perspective from ocean reanalyses.
- Bethke et al. Impact of subtropical North Atlantic initialisation errors on subpolar gyre prediction in prep.
- Ogawa et al. Arctic sea ice has no influence on AO/NAO, in prep.
- Counillon et al. Relating model bias and prediction skill in the tropical Atlantic, in prep.
- Fransner et al. What yields predictability of biochemistry for seasonal to decadal time scale, in prep.
NorCPM is contribution to the WMO Lead Centre for Annual-to-Decadal Climate Prediction under the name BCCR. We intend to contribute to CMIP6 DCPP.
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/
| 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 | - | - | tape | Counillon et al. 2014 |
| Free_NorESM1 | NorESM1-ME | FREE | - | - | 30 | - | 1850-2010 | CMIP5 hist | - | - | ../NorESM1-ME_historicalExt_noAssim/ | Counillon et al. 2016 |
| Reana-V0-long | NorESM1-ME | anom | V0 | HadiSST2 | 30 | monthly | 1950-2010 | CMIP5 hist | ocn(all) | poin hor. no vert | True_Obs-1950-2010/ME/ | Counillon et al. 2016, Bethke et al. in prep |
| Reana-V0-short | NorESM1-ME | anom | V0 | HadiSST2 | 30 | monthly | 1980-2010 | CMIP5 hist | ocn(all) | 30 obs | True_Obs-1980-2000/ana_19800115_me/ | Wang et al. sub |
| Reana-bccrfast | BCCRFAST | anom | V0 | HadiSST2 | 30 | monthly | 1950-2010 | CMIP5 hist | ocn(all) | poin hor. no vert | ana_19500115_bccrfast | none |
| Reana-bccrfast_v1 | BCCRFAST | anom | V1 | HadiSST2+EN4.1 profiles | 30 | monthly | 1980-2010 | CMIP5 hist | ocn(all) | Wang et al 2017, no vert | ana_19791115_bccrfast_SSTTS | none |
| Free_bccrfast | BCCRFAST | FREE | - | - | 30 | - | 1850-2010 | CMIP5 hist | - | - | ../bccr-fast_historical_18500101/ | none |
| Reana-v1a | NorESM1-ME | anom | V1 | Hadisst2+EN4.1 profiles | 30 | monthly | 1980-2010 | CMIP5 hist | ocn(all) | Wang et al 2017, no vert | NorCPM_V1/ana_19800115_me/ | Jakson et al. in prep, Bethke et al. in prep |
| Reana-v1-F | NorESM1-ME | full-field | V1 | Hadisst2+EN4.1 profiles | 30 | monthly | 2000-2010 | CMIP5 hist | ocn(all) | Wang et al 2017, no vert | tape | Wang et al. 2017 |
| Reana-V1-bccrfast | BCCRFAST | anom | V1 | Hadisst2+EN4.1 profiles | 30 | monthly | 1980-2010 | CMIP5 hist | ocn(all) | Wang et al 2017, no vert | ana_19791115_bccrfast_SSTTS | none |
| Reana-V0-bccrfast | BCCRFAST | anom | V0 | Hadisst2 | 30 | monthly | 1950-2010 | CMIP5 hist | ocn(all) | Wang et al 2017, no vert | ana_19500115_bccrfast | none |
| SFE | NorESM1-ME | anom | V1 | NOAA-SST+EN4.2 profiles | 30 | monthly | 2006-present | CMIP5 hist | ocn(all) | Wang et al 2017, no vert | True_Obs-2006-2017/reanalysis/ | none |
| Reana-acpl | NorESM1-acpl | anom | V1 | Hadisst2+EN4.1 profiles | 30 | monthly | 1980-2010 | CMIP5 hist | ocn(all) | Wang et al 2017, no vert | Anomaly_coupled/ | Counillon et al. in prep |
| Free_twin_V2 | NorESM1-LT | FREE | - | - | 30 | - | 1000-1020 | PI | - | - | Twin_experiment/TWIN_Free | Kimmritz et al. 2018 |
| Reana_twin_V2 | NorESM1-LT | full field | V2 | micom_icec | 30 | monthly | 1000-1020 | PI | ocn(all) + ice(all) | 800 km | Twin_experiment/TwinA56/ | Kimmritz et al. 2018 |
| Reana-v2-F | NorESM1-ME | full field | V2 | Hadisst2+EN4.1 profiles | 30 | monthly | 1980-2010 | CMIP5 hist | ocn(all) + ice(all) | Wang et al 2017, no vert, 800 km | NorCPM_V2/ana_me_ICEC-SST-S-T-1980-2010/ | |
| Reana-v2-a | NorESM1-ME | anom | V2 | Hadisst2+EN4.1 profiles | 30 | monthly | 1980-2010 | CMIP5 hist | ocn(all) + ice(all) | Wang et al 2017, no vert, 800 km | NorCPM_V2a/ana_me_ICEC-SST-S-T-1980-2010/ | Kimmritz et al. in prep, Ogawa et al. in prep |
| Reana-v1b | NorESM1-ME | anom | V1 | Hadisst2+EN4.2 profiles; masked in sea ice | 30 | monthly | 1980-2010 | CMIP5 hist | ocn(all) | Wang et al 2017, no vert | NorCPM_V1b/ana_19800115_me/ | Ogawa et al. in prep |
| Reana-v1c | NorESM1-ME | anom | V1 | Hadisst2+EN4.2 profiles | 30 | monthly | 1980-2010 | CMIP5 hist | ocn(all)+ice(all) | Wang et al 2017, no vert | NorCPM_V1c/ana_19800115_me/ |
| Name | Reana used | length | Model | period | frequency | enssize | path | papers |
|---|---|---|---|---|---|---|---|---|
| Dec-hind-V0 | Reana-V0 | 10 years | NorESM1-ME | 1955-2010 | Every 2 years | 20 | True_Obs-1950-2010/ME_hindcasts/ | Bethke et al. in prep |
| seas-hind-V2F | Reana-v2-F | 13 months | NorESM1-ME | 1985-2010 | 4 times per year | 9 | NorCPM_V2/SeasHind_ana_me_ICEC-SST-S-T-1985-2010/ | none |
| seas-hind-V2a | Reana-v2-a | 13 months | NorESM1-ME | 1985-2010 | 4 times per year | 9 | NorCPM_V2a/hindcast/ | Kimmritz et al. in prep |
| Dec-hind-V1a | Reana-V1a | 10 years | NorESM1-ME | 1985-2010 | Every 2 years | 5 | NorCPM_V1/ana_19800115_me_dec_19851015/ | Bethke et al. in prep |
| seas-hind-V1a | Reana-V1a | 13 months | NorESM1-ME | 1985-2010 | 4 times per year | 5 | NorCPM_V1/ana_19800115_me_hindcasts/ | Wang et al. in prep |
| seas-hind-V1b | Reana-V1b | 13 months | NorESM1-ME | 1985-2010 | 4 times per year | 5 | NorCPM_V1b/ana_19800115_me_hindcasts/ | |
| seas-hind-V1c | Reana-V1b | 13 months | NorESM1-ME | 1985-2017 | 4 times per year | 5 | NorCPM_V1c/ana_19800115_me_hindcasts/ | |
| seas-hind-acpl | Reana-acpl | 13 months | NorESM1-ME | 1985-2017 | 4 times per year | 5 | Anomaly_coupled/acpl_19800115_me_hindcasts_* | Counillon et al. in prep |