| Table of Contents | ||
|---|---|---|
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1. Forecast system version
System name: CMCC-SPS3.5
First operational forecast run: 1st October, 2020
2. Configuration of the forecast model
Is it a coupled model? YES
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CMCC technical documentation Gualdi et al (2020) CMCC Technical Note RP0288 DOI: https://doi.org/10.25424/CMCC/SPS3.5
2.1 Atmosphere and land surface
| Model | CESM 1.2 - CAM 5.3 (Atmosphere) CESM 1.2 - CLM 4.5 (Land surface) |
|---|---|
| Horizontal resolution and grid | 1/2° lat-lon approx |
| Atmosphere vertical resolution | 46 levels in the vertical |
| Land vertical resolution | 15 layers (see table below) |
| Top of atmosphere | 0.2 hPa (60 km approx.) |
| Soil layers | 15 layers: 10 soil layers plus 5 bedrock layers (for layer depths, see table below) |
| Time step | Main (Physics) Time-step: 30 minutes. “Tracer” Advection Time step: 225 seconds (1/8 of the Physics time step) Fluid-Dynamics Time step: 56.25 seconds (1/32 of the Physics time step). |
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CMCC technical documentation Gualdi et al (2020) CMCC Technical Note RP0288 DOI: https://doi.org/10.25424/CMCC/SPS3.5
2.2 Ocean and cryosphere
| Ocean model | NEMO v3.4 |
|---|---|
| Horizontal resolution | 1/4° |
| Vertical resolution | 50 vertical levels |
| Time step | 18 minutes |
| Sea ice model | CICE 4.0 |
| Sea ice model resolution | 1/4° |
| Sea ice model levels | 1 thickness only |
| Wave model | NO |
| Wave model resolution | N/A |
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CMCC technical documentation Gualdi et al (2020) CMCC Technical Note RP0288 DOI: https://doi.org/10.25424/CMCC/SPS3.5
3. Initialization and initial condition (IC) perturbations
3.1 Atmosphere and land
| Hindcast | Forecast | |
|---|---|---|
| Atmosphere initialization | ERA5 | ECMWF IFS operational analysis |
| Atmosphere IC perturbations | 10 | 10 |
Land Initialization | Forced (obs. atmosphere) monthly run initialized from 10-year spin-up | Forced (obs. atmosphere) monthly run initialized from 10-year spin-up |
| Land IC perturbations | 3 | 3 |
| Soil moisture initialization | From land initialization | From land initialization |
| Snow initialization | From land initialization | From land initialization |
| Unperturbed control forecast? | NO | NO |
| Horizontal resolution of perturbation | N/A | N/A |
| Perturbations in +/- pairs | NO | NO |
| Data assimilation method for control analysis | ERA5 | ECMWF operational |
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CMCC technical documentation Gualdi et al (2020) CMCC Technical Note RP0288 DOI: https://doi.org/10.25424/CMCC/SPS3.5
3.2 Ocean and cryosphere
| Hindcast | Forecast | |
|---|---|---|
| Ocean initialization | C-GLORS Global Ocean 3D-VAR | C-GLORS Global Ocean 3D-VAR |
| Ocean IC perturbations | 4 | 8 |
| Unperturbed control forecast? | NO | NO |
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CMCC technical documentation Gualdi et al (2020) CMCC Technical Note RP0288 DOI: https://doi.org/10.25424/CMCC/SPS3.5
4. Model uncertainties perturbations:
| Model dynamics perturbations | NO |
|---|---|
| Model physics perturbations | YES (Ocean Model only, only during perturbed data assimilation cycles) |
If there is a control forecast, is it perturbed? | There is no Control Forecast |
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CMCC technical documentation Gualdi et al (2020) CMCC Technical Note RP0288 DOI: https://doi.org/10.25424/CMCC/SPS3.5
5. Forecast system and hindcasts
| Forecast frequency | Monthly |
|---|---|
| Forecast ensemble size | 50 |
| Hindcast years | 1993-2016 |
| Hindcast ensemble size | 40 |
| On-the-fly or static hindcast set? | STATIC |
6. Other relevant information
The 10 atmospheric perturbed ICs, the 3 land perturbed ICs and the 8 (4 in hindcast mode) are combined to yield 240 (120 in hindcast mode) possible perturbed ICs among which the 50 ICs (40 in hindcast mode) to produce the forecast ensemble are chosen at random.
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CMCC technical documentation Gualdi et al (2020) CMCC Technical Note RP0288 DOI: https://doi.org/10.25424/CMCC/SPS3.5
7. Where to find more information
Sanna, A., A. Borrelli, P. Athanasiadis, S. Materia, A. Storto, S. Tibaldi, S. Gualdi, 2017: CMCC-SPS3: The CMCC Seasonal Prediction System 3. Centro Euro-Mediterraneo sui Cambiamenti Climatici. CMCC Tech. Note RP0285, 61pp. https://www.cmcc.it/publications/rp0285-cmcc-sps3-the-cmcc-seasonal-prediction-system-3
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