1. Ensemble version | ||
|---|---|---|
| Ensemble identifier code | CNRM-CM 6.1 | CNRM-CM 6.0 |
| Short Description | CNRM-CM6.1 (Voldoire et al. 2019) global ensemble generated with perturbed atmospheric initial conditions. Based on 25 members and run once a week (every Thursday) up to day 47 | Global ensemble system that simulates model uncertainties using a stochastic scheme. Based on 51 members Before 1/3/2016 (excluded): run once a month (Day 1 of calendar month at 00Z) up to day 61 After 1/3/2016 (included) : run once a week (every Thursday) up to day 32 (weekly runs before 1/3/2016 are available at meteo-France) |
| Research or operational | Research | Research |
| Data time of first forecast run | 22/10/2020 | 01/04/2015 |
2. Configuration of the EPS | ||
| Is the model coupled to an ocean model? | Yes from day 0 | Yes from day 0 |
| If yes, please describe ocean model briefly including frequency of coupling and any ensemble perturbation applied. | Ocean model is NEMO3.6 with a 0.25 degree horizontal resolution, 75 vertical levels, initialized from unperturbed MERCATOR-OCEAN Ocean and Sea-ice Analysis. Frequency of coupling is hourly. | Unperturbed MERCATOR-OCEAN Ocean and Sea-ice Analysis. Frequency of coupling is 24-hourly. |
| If no, please describe the sea surface temperature boundary conditions (climatology, reanalysis ...) | ||
| Is the model coupled to a sea Ice model? | Yes | Yes |
| If yes, please describe sea-ice model briefly including any ensemble perturbation applied. | Sea-ice model is GELATO v6 , embedded in the ocean model. It is initialized from unperturbed 0.25 degree resolution MERCATOR-OCEAN Ocean and Sea-ice Analysis | Sea-ice model is GELATO v5 , embedded in the ocean model. It is initialized from unperturbed 1 degree resolution MERCATOR-OCEAN Ocean and Sea-ice Analysis |
| Is the model coupled to a wave model? | No | No |
| If yes, please describe wave model briefly including any ensemble perturbation applied | N/A | N/A |
| Ocean model | NEMO 3.6 0.25 degree resolution | NEMO 1 degree resolution |
| Horizontal resolution of the atmospheric model | TL359 (about 50 km) | TL255 (about 80 km) |
| Number of model levels | 91 | 91 |
| Top of model | 0.01 hPa | 0.01 hPa |
| Type of model levels | hybrid sigma-pressure | hybrid sigma-pressure |
| Forecast length | 46 days | 61 days (1464 hours) |
| Run Frequency | Once a week | Once a week since March 2016 (once a month (Day 1 of calendar month at 00Z) before March 2016) |
| Is there an unperturbed control forecast included? | No | No |
| Number of perturbed ensemble members | 25 | 51 |
| Integration time step | 10 minutes | 15 minutes |
3. Initial conditions and perturbations | ||
| Data assimilation method for control analysis | 4D Var | 4D Var |
| Resolution of model used to generate Control Analysis | O1280 IFS HRES operational analysis | TL1279L137 (IFS operational analysis) |
| Ensemble initial perturbation strategy | None (in-run perturbations only) | None (in-run perturbations only) |
| Horizontal and vertical resolution of perturbations | N/A | N/A |
| Perturbations in +/- pairs | N/A | N/A |
| Initialization of land surface | ||
| 3.1 What is the land surface model (LSM) and version used in the forecast model, and what are the current/relevant references for the model? | ISBA-CTRIB, embedded into SURFEX 8.0 numerical interface (Decharme et al. 2019) | SURFEX 7.2 |
| Are there any significant changes/deviations in the operational version of the LSM from the documentation of the LSM? | No | No |
| 3.2 How is soil moisture initialized in the forecasts? (climatology / realistic / other) | realistic | realistic |
| If “realistic”, does the soil moisture come from an analysis using the same LSM as is coupled to the GCM for forecasts, or another source? Please describe the process of soil moisture initialization. | It comes from ECMWF HRES analyses (ERA5 for the re-forecast) | It comes from ECMWF analyses |
| Is there horizontal and/or vertical interpolation of initialization data onto the forecast model grid? If so, please give original data resolution(s). | Yes for horizontal, original resolution is 9km, final resolution is 50 km | Yes for horizontal, original resolution is 9km, final resolution is 70 km |
| Does the LSM differentiate between liquid and ice content of the soil? If so, how are each initialized? | Yes, liquid and ice are determined by temperature | Yes, liquid and ice are determined by temperature |
| If all model soil layers are not initialized in the same way or from the same source, please describe. | The method to interpolate between ECMWF soil layers and SURFEX soil layers is described in Boisserie et al. (2016) | The method to interpolate between ECMWF soil layers and SURFEX soil layers is described in Boisserie et al. (2016) |
| 3.3 How is snow initialized in the forecasts? (climatology / realistic / other) | realistic | realistic |
| If “realistic”, does the snow come from an analysis using the same LSM as is coupled to the GCM for forecasts, or another source? Please describe the process of soil moisture initialization. | It comes from ECMWF analyses | It comes from ECMWF analyses |
| Is there horizontal and/or vertical interpolation of data onto the forecast model grid? If so, please give original data resolution(s) | Yes for horizontal, original resolution is 9 km, final resolution is 70 km | Yes for horizontal, original resolution is 9 km, final resolution is 70 km |
| Are snow mass, snow depth or both initialized? What about snow age, albedo, or other snow properties? | Snow mass only is initialized. Snow density and albedo are initialized with characteristics of old snow, and then evolve with the model | Snow mass only is initialized. Snow density and albedo are initialized with characteristics of old snow, and then evolve with the model |
| 3.4. How is soil temperature initialized in the forecasts? (climatology / realistic / other) | realistic | realistic |
| If “realistic”, does the soil moisture come from an analysis using the same LSM as is coupled to the GCM for forecasts, or another source? Please describe the process of soil moisture initialization. | It comes from ECMWF analyses | It comes from ECMWF analyses |
| Is the soil temperature initialized consistently with soil moisture (frozen soil water where soil temperature ≤0°C) and snow cover (top layer soil temperature ≤0°C under snow)? | Yes | Yes |
| Is there horizontal and/or vertical interpolation of data onto the forecast model grid? If so, please give original data resolution(s) | Yes for horizontal, original resolution is 9 km, final resolution is 50 km | Yes for horizontal, original resolution is 9 km, final resolution is 70 km |
| If all model soil layers are not initialized in the same way or from the same source, please describe. | See Boisserie et al. (2016) | See Boisserie et al. (2016) |
| 3.5. How are time-varying vegetation properties represented in the LSM? | Monthly climatology (Ecoclimap, Masson et al. 2003) | Monthly climatology (Ecoclimap, Masson et al. 2003) |
| 3.6. What is the source of soil properties (texture, porosity, conductivity, etc.) used by the LSM? | HWSD (Harmonized World Soil Database) | Ecoclimap, Masson et al. (2003) |
| 3.7 If the initialization of the LSM for re-forecasts deviates from the procedure for forecasts, please describe the differences. | The procedure is identical. The source is ERA5 instead of operational HRES ECMWF analyses. | The procedure is identical. The source is ERA-interim instead of operational ECMWF. |
4. Model Uncertainties perturbations | ||
| Is model physics perturbed? | No | No |
| Do all ensemble members use exactly the same model version? | Yes | Yes |
| Is model dynamics perturbed? | Yes (Batté and Déqué 2016) | Yes (Batté and Déqué 2012) |
| Are the above model perturbations applied to the control forecast? | Yes | Yes |
| Additional Comments | see References | see References |
5. Surface Boundary perturbations | ||
| Perturbations to sea surface temperature? | No | No |
| Perturbation to soil moisture? | No | No |
| Perturbation to surface stress or roughness? | No | No |
| Any other surface perturbation? | No | No |
| Are the above surface perturbations applied to the Control forecast? | N/A | N/A |
| Additional comments | ||
6. Other details of the models | ||
| Description of model grid | Reduced Gaussian Grid | Reduced Gaussian Grid |
| List of model levels in appropriate coordinates | http://www.ecmwf.int/en/forecasts/documentation-and-support/91-model-levels | http://www.ecmwf.int/en/forecasts/documentation-and-support/91-model-levels |
| What kind of large scale dynamics is used? | Spectral semi-lagrangian | Spectral semi-lagrangian |
| What kind of boundary layer parameterization is used? | Turbulence : Cuxart et al 2000 | Ricard and Royer 93 |
| What kind of convective parameterization is used? | Piriou et al (2007), Guérémy (2011) | Bougeault 85 |
| What kind of large-scale precipitation scheme is used? | Lopez 2002 | Smith 90 |
| What cloud scheme is used? | Lopez 2002 | Ricard and Royer 93 |
| What kind of land-surface scheme is used? | ISBAdf-CTRIP | ISBA-3L |
| How is radiation parametrized? | ||
| Long Wave Radiation | Rapid Radiation Transfer Model (RRTM), Mlawer et al 1997 | Rapid Radiation Transfer Model (RRTM) |
| Short Wave radiation | Fouquart and Bonnel (1980) | Foucart-Morcrette |
| Sea-ice thickness | Average sea-ice thickness (computed only where there is sea-ice) | |
| Other relevant details? | See References | See References |
7. Re-forecast Configuration | ||
| Number of years covered | 25 years (1993-2017) | 22 years (1993-2014) |
| Produced on the fly or fix re-forecasts? | Fix re-forecasts | Fix re-forecasts |
| Frequency | The re-forecast consists of a 10-member ensemble starting every Thursday from 31 Dec 1992 till 28 Dec 2017 | The re-forecast consists of a 15-member ensemble starting the 1st, 8th, 15th and 22nd calendar day of each month for the period 1993-2014 |
| Ensemble size | 10 members | 15 members |
| Initial conditions | ERA5(31km) for Atmosphere and Land surface + MERCATOR-OCEAN ocean reanalyses (0.25 degree) | ERA interim (T255L60) for Atmosphere and Land surface + MERCATOR-OCEAN ocean reanalyses (1 degree) |
| Is the model physics and resolution the same as for the real-time forecasts | Yes | Yes |
| If not, what are the differences | N/A | N/A |
| Is the ensemble generation the same as for real-time forecasts? | Yes | Yes |
| If not, what are the differences | N/A | N/A |
8. References
- Batté, L. and Déqué, M. (2016), ‘Randomly correcting model erros in the ARPEGE-Climate v6.1 component of CNRM-CM : applications for seasonal forecasts.’ Geoscientific Model Development 9(6)
- Boisserie, M., Decharme, B., Descamps, L. and Arbogast, P. (2016), ‘Land surface initialization strategy for a global reforecast dataset’, Quarterly Journal of the Royal Meteorological Society 142(695), 880–888.
- Cuxart, J., Bougeault, P. and Redelsperger, J.-L. (2000), ‘A turbulence scheme allowing for mesoscale and large-eddy simulations’, Quarterly Journal of the Royal Meteorological Society 126(562), 1–30.
- Constantin Ardilouze, Damien Specq, Lauriane Batté, and Christophe Cassou: Flow dependence of wintertime subseasonal prediction skill over Europe, Weather Clim. Dynam., 2, 1033–1049, 2021, https://doi.org/10.5194/wcd-2-1033-2021
- Decharme, B., Delire, C., Minvielle, M., Colin, J., Vergnes, J.-P., Alias, A., Saint-Martin, D., Séférian,R., Sénési, S. and Voldoire, A. (2019), ‘Recent changes in the ISBA-CTRIP land surface system foruse in the CNRM-CM6 climate model and in global off-line hydrological applications’, Journal of Advances in Modeling Earth Systems .
- Faroux, S., Kaptué Tchuenté, A., Roujean, J.-L., Masson, V., Martin, E. and Moigne, P. L. (2013), ‘ECOCLIMAP-II/Europe : A twofold database of ecosystems and surface parameters at 1 km resolution based on satellite information for use in land surface, meteorological and climate models’, Geoscientific Model Development 6(2), 563–582.
- Fouquart, Y. and Bonnel, B. (1980), ‘Computations of solar heating of the earth’s atmosphere- A new parameterization’, Beitraege zur Physik der Atmosphaere 53, 35–62.
- Guérémy, J. (2011), ‘A continuous buoyancy based convection scheme : one-and three-dimensional validation’, Tellus A : Dynamic Meteorology and Oceanography 63(4), 687–706.
- Lopez, P. (2002), ‘Implementation and validation of a new prognostic large-scale cloud and precipitation scheme for climate and data-assimilation purposes’, Quarterly Journal of the Royal Meteorological Society 128(579), 229–257.
- Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J. and Clough, S. A. (1997), ‘Radiative transfer for inhomogeneous atmospheres : RRTM, a validated correlated-k model for the longwave’, Journal of Geophysical Research : Atmospheres 102(D14), 16663–16682.
- Piriou, J.-M., Redelsperger, J.-L., Geleyn, J.-F., Lafore, J.-P. and Guichard, F. (2007), ‘An approach for convective parameterization with memory : Separating microphysics and transport in grid-scale equations’, Journal of the Atmospheric Sciences 64(11), 4127–4139.
- Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., Colin, J., Guérémy, J.-F., Michou, M., Moine, M.-P., Nabat, P., Roehrig, R., Salas y Mélia, D., Séférian, R., Valcke, S., Beau,I., Belamari, S., Berthet, S., Cassou, C., Cattiaux, J., Deshayes, J., H. Douville, H., Franchisteguy,L., Ethé, C., Geoffroy, O., Lévy, C., Madec, G., Meurdesoif, Y., Msadek, R., Ribes, A., Sanchez, E.,Terray, L. and Waldman, R. (2019), ‘Evaluation of CMIP6 DECK experiments with CNRM-CM6-1’,Journal of Advances in Modeling Earth Systems.
- URL: http ://www.umr-cnrm.fr/cmip6/references