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1. Ensemble version

 

Ensemble identifier code:    CNRM-CM 6.0

Short Description:    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

Data time of first forecast run:   01/04/2015

 

2. Configuration of the EPS

Is the model coupled to an ocean model ?   Yes from day 0

If yes, please describe ocean model briefly including frequency of coupling and any ensemble perturbation applied: Ocean model is NEMO3.2 with a 1 degree horizontal resolution, 42 vertical levels, initialized from unperturbed MERCATOR-OCEAN Ocean and Sea-ice Analysis. Frequency of coupling is 24-hourly.

Is the model coupled to a sea Ice model? Yes.

If yes, please describe sea-ice model briefly including any ensemble perturbation applied:

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

If yes, please describe wave model briefly including any ensemble perturbation applied: NA

Ocean model: NEMO 1 degree resolution

Horizontal resolution of the atmospheric model: TL255 (about 80 km)

Number of model levels: 91

Top of model: 0.01 hPa

Type of model levels: hybrid sigma-pressure

Forecast length: 61 days (1464 hours)

Run Frequency: 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

Number of perturbed ensemble members: 51

Integration time step: 15 minutes

 

3. Initial conditions and perturbations

Data assimilation method for control analysis: 4D Var

Resolution of model used to generate Control Analysis: TL1279L137 (IFS operational analysis)

Ensemble initial perturbation strategy: None (in-run perturbations only)

Horizontal and vertical resolution of perturbations: NA

Perturbations in +/- pairs: NA


Initialization of land surface:

 

 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? SURFEX 7.2

Are there any significant changes/deviations in the operational version of the LSM from the documentation of the LSM? No

 

 2. How is soil moisture initialized in the forecasts? (climatology / realistic / other) 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

     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 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

     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)

 

3. How is snow initialized in the forecasts? (climatology / realistic / other) 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

   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

     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

 

4. How is soil temperature initialized in the forecasts? (climatology / realistic / other) realisti

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

      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

      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

     If all model soil layers are not initialized in the same way or from the same source, please describe. See Boisserie et al. (2016)

 

5. How are time-varying vegetation properties represented in the LSM? Monthly climatology (Ecoclimap, Masson et al. 2003)

 

6. What is the source of soil properties (texture, porosity, conductivity, etc.) used by the LSM? Ecoclimap, Masson et al. (2003)

 

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 ERA-interim instead of operational ECMWF

 

4. Model Uncertainties perturbations:

Is model physics perturbed? No

Do all ensemble members use exactly the same model version? Yes

Is model dynamics perturbed? Yes (Batté and Déqué 2012)

Are the above model perturbations applied to the control forecast? Yes

 Additional Comments : see References

5. Surface Boundary perturbations:

Perturbations to sea surface temperature? No

Perturbation to soil moisture? No

Perturbation to surface stress or roughness? No

Any other surface perturbation? No

Are the above surface perturbations applied to the Control forecast? NA

Additional comments

 

6. Other details of the models:

Description of model grid: Reduced Gaussian Grid

List of model levels in appropriate coordinates: http://www.ecmwf.int/en/forecasts/documentation-and-support/91-model-levels

What kind of large scale dynamics is used?  Spectral semi-lagrangian

What kind of boundary layer parameterization is used? Ricard and Royer 93

What kind of convective parameterization is used? Bougeault 85

What kind of large-scale precipitation scheme is used? Smith 90

What cloud scheme is used? Ricard and Royer 93

What kind of land-surface scheme is used? ISBA-3L

How is radiation parametrized?

Long Wave Radiation : Rapid Radiation Transfer Model (RRTM)

Short Wave radiation : Foucart-Morcrette

Other relevant details? See References

 

7. Re-forecast Configuration

Number of years covered: 22 years (1993-2014)

Produced on the fly or fix re-forecasts? Fix re-forecasts

Frequency:   The re-forecast  consists of a 15-member ensemble starting the 1st and 15th calendar day of each month for the period 1993-2014

Ensemble size: 15 members

Initial conditions: 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

If not, what are the differences: NA

Is the ensemble generation the same as for real-time forecasts? Yes

If not, what are the differences: NA

 

8. References:

Batté, L., & Déqué, M. (2012). A stochastic method for improving seasonal predictions. Geophysical Research Letters, 39(9).


Boisserie M, Decharme B, Descamps L, Arbogast P (2016) Land surface initialization strategy for a global reforecast dataset. QJR Meteorol Soc 142:880–888. doi: 10.1002/qj.268


Bougeault, P. (1985). A simple parameterization of the large-scale effects of cumulus convection. Monthly Weather Review, 113(12), 2108-2121.


Masson, V., Champeaux, J. L., Chauvin, F., Meriguet, C., & Lacaze, R. (2003). A global database of land surface parameters at 1-km resolution in meteorological and climate models. Journal of climate, 16(9), 1261-1282.

 

Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., & Clough, S. A. (1997). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated‐k model for the longwave. Journal of Geophysical Research: Atmospheres (1984–2012), 102(D14), 16663-16682.

 

Morcrette, J. J. (1990). Impact of changes to the radiation transfer parameterizations plus cloud optical. Properties in the ECMWF model. Monthly Weather Review, 118(4), 847-873.

 

Noilhan, J., & Mahfouf, J. F. (1996). The ISBA land surface parameterisation scheme. Global and planetary Change, 13(1), 145-159.

 

Noilhan, J., & Planton, S. (1989). A simple parameterization of land surface processes for meteorological models. Monthly Weather Review, 117(3), 536-549.

 

Ricard, J. L., & Royer, J. F. (1993, December). A statistical cloud scheme for use in an AGCM. In Annales Geophysicae (Vol. 11, pp. 1095-1115).

 

Salas y Mélia D (2002) A global coupled sea ice-ocean model. Ocean Model 4:137–172

 

Smith, R. N. B. (1990). A scheme for predicting layer clouds and their water content in a general circulation model. Quarterly Journal of the Royal Meteorological Society, 116(492), 435-460.

 

Voldoire, A., Sanchez-Gomez, E., y Mélia, D. S., Decharme, B., Cassou, C., Sénési, S., ... & Chauvin, F. (2013). The CNRM-CM5. 1 global climate model: description and basic evaluation. Climate Dynamics, 40(9-10), 2091-2121.

 


 

 

 

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