1. Forecast system version
System name: GCFS 2.2
First operational forecast run: April 2025
2. Configuration of the forecast model
Is it a coupled model? Yes
Coupling frequency: 1 hour
2.1 Atmosphere and land surface
| Model | ECHAM 6.3.05 (atmosphere) JSBACH 3.20p1 (land) |
|---|---|
| Horizontal resolution and grid | T127 (~100 km) on regular Gaussian grid |
| Atmosphere vertical resolution | L95 |
| Top of atmosphere | 0.01 hPa |
| Soil levels (layers) | 5 Layer 1: 0 - 0.065 m |
| Time step | 200 s |
Detailed documentation:
ECHAM6: Stevens et al., 2013
Soil scheme: Hagemann and Stacke, 2014
Runoff scheme: HD Hagemann and Dümenil-Gates, 2003
2.2 Ocean and cryosphere
| Ocean model | MPIOM 1.6.3 |
|---|---|
| Horizontal resolution | TP04 (0.4°) on a tripolar grid |
| Vertical resolution | L40 |
| Time step | 1 h |
| Sea ice model | Thermodynamic and sea-ice dynamics |
| Sea ice model resolution | same as ocean model |
| Sea ice model levels | zero-layer model |
| Wave model | N/A |
| Wave model resolution | N/A |
Detailed documentation: MPIOM: Jungclaus et al., 2013
3. Boundary conditions - climate forcings
The forcing database for radiative parameters like ozone, aerosol and greenhouse gases is provided by CMIP6 for the historical period up to 2014. Afterwards, data of RCP 4.5 future scenarios is used.
| Greenhouse gases | CMIP6 |
|---|---|
| Ozone | CMIP6 |
| Tropospheric aerosols | MACv2 |
| Volcanic aerosols | CMIP6 |
| Solar forcing | yes |
Detailed documentation:
For aerosols: Stevens, B., Fiedler, S., Kinne, S., Peters, K., Rast, S., Müsse, J., Smith, S. J., and Mauritsen, T.: MACv2-SP: a parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6, Geosci. Model Dev., 10, 433–452, https://doi.org/10.5194/gmd-10-433-2017, 2017.
4. Initialization and initial condition (IC) perturbations
4.1 Atmosphere and land
| Hindcast | Forecast | |
|---|---|---|
| Atmosphere initialization | ERA5 | ERA5T |
| Atmosphere IC perturbations | pertubation of diffusion in the uppermost layer | pertubation of diffusion in the uppermost layer |
Land Initialization | indirect via atmosphere initialization | indirect via atmosphere initialization |
| Land IC perturbations | none | none |
| Soil moisture initialization | indirect via atmosphere initialization | indirect via atmosphere initialization |
| Snow initialization | indirect via atmosphere initialization | indirect via atmosphere initialization |
| Unperturbed control forecast? | no | no |
The assimilation for the atmosphere employs Newtonian relaxation of the reference data with the following variable-dependent relaxation times:
- divergence (48 hrs)
- vorticity (6hrs)
- temperature (~4hrs)
- logarithm of surface pressure (~4 hrs)
Data assimilation method for control analysis: no ensemble data assimilation
4.2 Ocean and cryosphere
| Hindcast | Forecast | |
|---|---|---|
| Ocean initialization | Ensemble Kalman Filter (LSEIK, Nerger et al., 2006), adapted to MPIOM (Brune and Baehr, 2020) using EN4 data | Ensemble Kalman Filter (LSEIK, Nerger et al., 2006), adapted to MPIOM (Brune and Baehr, 2020) using ARGO data |
| Ocean IC perturbations | via the Kalman Filter | via the Kalman Filter |
| Unperturbed control forecast? | no | no |
More ocean data assimilation details:
- Assimilation of 3D temperature and salinity using EN4
- Assimilation of sea-ice concentration using OSISAF
Detailed documentation: GCFS ocean data assimilation: Brune and Baehr, 2020
5. Model uncertainties perturbations:
| Model dynamics perturbations | no |
|---|---|
| Model physics perturbations | atmosphere: perturbation of diffusion in uppermost layer |
If there is a control forecast, is it perturbed? | no |
Detailed documentation: Baehr et al. (2015)
6. Forecast system and hindcasts
| Forecast frequency | monthly |
|---|---|
| Forecast ensemble size | 50 |
| Hindcast years | 1993 - 2023 |
| Hindcast ensemble size | 30 |
| On-the-fly or static hindcast set? | static |
7. Other relevant information
Interpolation details
Horizontal Interpolation onto 1 degree grid
| Distance weighting | Bilinear interpolation | Nearest neighbour |
|---|---|---|
| soil temperature | orography | land sea mask |
| sea surface temperature | all other variables | sea ice temperature |
| (sub)surface runoff |
Vertical interpolation
8. Where to find more information
www.dwd.de/climatepredictions
