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 gridT127 (~100 km) on regular Gaussian grid
Atmosphere vertical resolutionL95
Top of atmosphere0.01 hPa
Soil levels (layers)

5

Layer 1: 0 - 0.065 m
Layer 2: 0.065 - 0.319 m
Layer 3: 0.319 - 1.232 m
Layer 4: 1.232 - 4.134 m
Layer 5: 4.134 - 9.834 m

Time step200 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 resolutionTP04 (0.4°) on a tripolar grid
Vertical resolutionL40
Time step1 h
Sea ice modelThermodynamic and sea-ice dynamics
Sea ice model resolutionsame as ocean model
Sea ice model levelszero-layer model
Wave modelN/A
Wave model resolutionN/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 gasesCMIP6
OzoneCMIP6
Tropospheric aerosolsMACv2
Volcanic aerosolsCMIP6
Solar forcingyes

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


HindcastForecast
Atmosphere initialization
ERA5ERA5T
Atmosphere IC perturbationspertubation of diffusion in the uppermost layerpertubation of diffusion in the uppermost layer

Land Initialization

indirect via atmosphere initializationindirect via atmosphere initialization
Land IC perturbationsnonenone
Soil moisture initializationindirect via atmosphere initializationindirect via atmosphere initialization
Snow initializationindirect via atmosphere initializationindirect via atmosphere initialization
Unperturbed control forecast?nono

The assimilation for the atmosphere employs Newtonian relaxation of the reference data with the following variable-dependent relaxation times:

Data assimilation method for control analysis: no ensemble data assimilation


4.2 Ocean and cryosphere


HindcastForecast
Ocean initialisation

 Ensemble Kalman Filter (LSEIK, Nerger et al., 2006), adapted to MPIOM for 3D Temperature and Salinity (Brune and Baehr, 2020)

using EN4 data

 Ensemble Kalman Filter (LSEIK, Nerger et al., 2006), adapted to MPIOM for 3D Temperature and Salinity (Brune and Baehr, 2020)

using ARGO data

Sea-ice initialisation

Ensemble Kalman Filter (LSEIK, Nerger et al., 2006),

using OSISAF

Ensemble Kalman Filter (LSEIK, Nerger et al., 2006),

using OSISAF

Ocean IC perturbationsvia the Kalman Filtervia the Kalman Filter
Unperturbed control forecast?nono


5. Model uncertainties perturbations:

Model dynamics perturbationsno
Model physics perturbationsatmosphere: 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 frequencymonthly
Forecast ensemble size50
Hindcast years1993 - 2023
Hindcast ensemble size30
On-the-fly or static hindcast set?static


7. Other relevant information

Interpolation details
Horizontal Interpolation onto 1 degree grid
Distance weightingBilinear interpolationNearest neighbour
soil temperatureorographyland sea mask
sea surface temperatureall other variablessea ice temperature
(sub)surface runoff

Vertical interpolation

8. Where to find more information

https://www.dwd.de/EN/ourservices/seasonals_forecasts/start.html