1. Forecast system version

Identifier code: Météo-France System 9

First operational forecast run: April 2025

2. Configuration of the forecast model

Is the model coupled to an ocean model? Yes

Coupling frequency: 1 hour

2.1 Atmosphere and land surface

Model	ARPEGE v6.5 (atmosphere) SURFEX v8.0 (land surface)
Horizontal resolution and grid	TL359; 0.5° reduced Gauss grid
Atmosphere vertical resolution	137 layers
Top of atmosphere	top layer 1 Pa
Soil levels (layers)	14 Layer 1 : 0 - 0.01 m Layer 2 : 0.01 - 0.04 Layer 3 : 0.04 – 0.1 m Layer 4 : 0.1 – 0.2 m Layer 5 : 0.2 – 0.4 m Layer 6 : 0.4 – 0.6 m Layer 7 : 0.6 – 0.8 m Layer 8 : 0.8 – 1.0 m Layer 9 : 1.0 – 1.5 m Layer 10 : 1.5 – 2.0 m Layer 11 : 2.0 – 3.0 m Layer 12 : 3.0 – 5.0 m Layer 13 : 5.0 – 8.0 m Layer 14 : 8.0 – 12.0 m
Time step	10 min

Detailed documentation:

ARPEGE:
Reference paper (older version v6.3): Roehrig, R. et al. (2020). The CNRM global atmosphere model ARPEGE-Climat 6.3: Description and evaluation. Journal of Advances in Modeling Earth Systems, 12, e2020MS002075.
Technical documentation (older version v6.2): http://www.umr-cnrm.fr/IMG/pdf/arp62ca.july2017.pdf

SURFEX:
Voldoire, A. et al. (2017). SURFEX v8.0 interface with OASIS3-MCT to couple atmosphere with hydrology, ocean, waves and sea-ice models, from coastal to global scales. Geoscientific Model Development, 10(11), 4207–4227.
Decharme, B. et al. (2019) Recent Changes in the ISBA-CTRIP Land Surface System for Use in the CNRM- CM6 Climate Model and in Global Off-Line Hydrological Applications. Journal of Advances in Modeling Earth Systems, 2018MS001545.
Technical documentation: http://www.umr-cnrm.fr/surfex//IMG/pdf/surfex_scidoc_v8.1.pdf

2.2 Ocean and cryosphere

Ocean model	NEMO v4.2.0
Horizontal resolution	0.25° ORCA grid
Vertical resolution	75 levels
Time step	15 min
Sea ice model	SI³
Sea ice model resolution	0.25° ORCA grid
Sea ice model levels	5
Wave model	None
Wave model resolution	NA

Detailed documentation:

NEMO: Madec et al. (2022). NEMO ocean engine reference manual. Zenodo. https://doi.org/10.5281/zenodo.6334656
SI³: Vancoppenolle et al. (2023). NEMO Sea Ice Engine (SI³). Zenodo. https://doi.org/10.5281/zenodo.7534900

3. Boundary conditions - climate forcings

Most forcing data comes from the CMIP6 protocol for CNRM-CM6 as described by Voldoire et al. (2019).

Greenhouse gases	Up to 2014, CMIP6 historical values of CO2, CH4, N2O, CFC11 and CFC12 from Meinshausen et al. (2017). From 2015 onwards, greenhouse gas forcings follow the SSP2-4.5 scenario from Meinshausen et al. (2020).
Ozone	Radiation scheme sees a seasonally varying but otherwise fixed climatological ozone field. The monthly climatology of ozone concentration is the 1995-2014 average from a preliminary freely-evolving historical CMIP6 run by Michou et al (2020).
Tropospheric aerosols	Seasonally-varying tropospheric aerosol concentrations computed as 11-year moving averages from the preliminary freely-evolving run by Michou et al. (2020) using the interactive aerosol scheme TACTIC_v2 scheme. The Michou et al (2020) run has CMIP6 historical forcings up to 2014 and the SSP2-4.5 forcings from 2015 onwards.
Volcanic aerosols	Volcanic stratospheric aerosols are the official CMIP6 forcings provided by Thomason et al (2018).
Solar forcing	Time-variation of total solar insolation (TSI) is specified from CMIP6 annual mean forcings provided by Matthes et al (2017)

Detailed documentation:

Matthes, K., Funke, B., Andersson, M. E., Barnard, L., Beer, J., Charbonneau, P., et al. (2017). Solar forcing for CMIP6 (v3.2). Geoscientiﬁc Model Development, 10(6), 2247–2302. https://doi.org/10.5194/gmd‐10‐2247‐2017

Meinshausen, M. et al. (2017). Historical greenhouse gas concentrations for climate modelling (CMIP6). Geoscientific Model Development, 10(5), 2057–2116. https://doi.org/10.5194/gmd-10-2057-2017

Meinshausen, M. et al. (2020). The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. Geoscientific Model Development, 13, 3571–3605. https://doi.org/10.5194/gmd-13-3571-2020

Michou, M., Nabat, P., Saint-Martin, D., Bock, J., Decharme, B., Mallet, M., Roehrig, R., Séférian, R., Sénési, S. and Voldoire, A. (2020). Present-day and historical aerosol and ozone characteristcs in CNRM CMIP6 simulatons. Journal of Advances in Modeling Earth Systems, 12, e2019MS001816, https://doi.org/10.1029/2019MS001816

Thomason, L. W., Ernest, N., Millán, L., Rieger, L., Bourassa, A., Vernier, J. P., et al. (2018). A global space‐based stratospheric aerosol climatology: 1979–2016. Earth System Science Data, 10(1), 469–492. https://doi.org/10.5194/essd‐10‐469‐2018

Voldoire, A., Saint-Martn, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., et al (2019). Evaluation of CMIP6 DECK experiments with CNRM-CM6-1. Journal of Advances in Modeling Earth Systems, 11. https://doi.org/10.1029/2019MS001683

4. Initialization and initial condition (IC) perturbations

4.1 Atmosphere and land

	Hindcast	Forecast
Atmosphere initialization	ERA5	ERA5T
Atmosphere IC perturbations	None	None
Land Initialization	ERA5	ERA5T
Land IC perturbations	None	None
Soil moisture initialization	ERA5	ERA5T
Snow initialization	ERA5	ERA5T
Unperturbed control forecast?	NA	NA

Detailed documentation:
ERA5: data documentation

4.2 Ocean and cryosphere

	Hindcast	Forecast
Ocean initialization	GLORYS12V1 MERCATOR-OCEAN	GLO12 MERCATOR-OCEAN
Ocean IC perturbations	None	None
Unperturbed control forecast?	NA	NA

Detailed documentation:
GLORYS12V1. E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS). https://doi.org/10.48670/moi-00021
GLO12. E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS). https://doi.org/10.48670/moi-00016

5. Model Uncertainties perturbations:

Model dynamics perturbations	Yes
Model physics perturbations	No
If there is a control forecast, is it perturbed?	NA

Detailed documentation:
Batté, L. and Déqué, M. (2016). Randomly correcting model errors in the ARPEGE-Climate v6. 1 component of CNRM-CM: applications for seasonal forecasts. Geoscientific Model Development, 9, 2055-2076. https://doi.org/10.5194/gmd-9-2055-2016

6. Forecast system and hindcasts

Forecast frequency	1 month
Forecast ensemble size	51
Hindcast years	1993-2024
Hindcast ensemble size	31
On-the-fly or static hindcast set?	static
Calibration (bias correction) period	NA

7. Other relevant information

Ensemble start dates

The forecast uses three start dates:

The penultimate Thursday of the previous month (25 members in the forecasts, 15 members in the hindcasts)
The last Thursday of the previous month (25 members in the forecasts, 15 members in the hindcasts)
the 1st of the month (1 member in the forecast/hindcast)

Data assimilation method for analysis

The atmosphere and land surface are initialized with ERA5T data (ERA5 in the hindcast), such that they use the ERA5 data assimilation performed by ECMWF.
Ocean and sea-ice initial conditions are provided by a stand-alone NEMO 4.2 - SI³ run that is forced by ERA5(T) at the surface and nudged towards the GLO12 analysis (GLORYS12V1 reanalysis in the hindcast) in temperature and salinity.

Interpolation details

Météo-France System 9 is built on the CNRM-CM coupled climate model which embeds an output manager called the XIOS server (Meurdesoif et al, 2018). XIOS is an input/output parallel server software enabling to perform online field operations in the course of the model integration and writing the final result on NetCDF files. For atmosphere and land surface data, all operations such as time sampling, time averaging, horizontal regridding or vertical interpolation are specified through xml files and performed by XIOS such that no or little post-processing is needed: the only output files written on disk are the files featuring the requested C3S 1°x1° horizontal grid and pressure levels. For ocean and sea-ice data, the output files are written by XIOS on the native NEMO grid and remapped on the C3S 1°x1° horizontal grid throug post-processing, using the cdo "remapbil" function.

Horizontal Interpolation

The ARPEGE atmospheric model is a spectral model, and horizontal fields such as temperature or geopotential are originally computed as spherical harmonic coefficients, but can be converted into grid point data on the corresponding reduced Gaussian grid (e.g 0.5° reduced Gaussian grid for Tl359 spectral resolution) using an internal function of the model. The XIOS server can perform online interpolation of this grid point data on any user specified grid such as the rectilinear C3S 1°x1° grid. The interpolation of atmospheric data by XIOS is a first-order conservative remapping. As for the oceanic fields from NEMO, they are first output on the native model grid, and remapped using a bilinear interpolation.

Vertical interpolation

ARPEGE uses sigma coordinates in the vertical, meaning that near-surface model levels follow the orography. Transformation into data on pressure levels is done directly on request by ARPEGE through a linear interpolation, or linear extrapolation for data below the surface.

Meurdesoif, Y. (2018) XIOS fortran reference guide. IPSL http://forge.ipsl.jussieu.fr/ioserver/svn/XIOS/trunk/doc/XIOS_reference_guide.pdf

8. Where to find more information

Technical implementation details can be found in LINK TO BE PROVIDED