1. Forecast system version
System name: GloSea5-GC2-LI
First operational forecast run: 2 April 2019
2. Configuration of the forecast model
Is it a coupled model? Yes: Atmosphere, land, ocean and sea-ice.
Coupling frequency: 3-hourly coupling between atmosphere-land and ocean--sea-ice.
The coupled model Global Coupled 2 (GC2) is described in Williams et al, 2015.
2.1 Atmosphere and land surface
| Model | Met Office Unified Model (UM) - Global Atmosphere 6.0 Joint UK Land Environment Simulator (JULES) - Global Land 6.0 |
|---|---|
| Horizontal resolution and grid | N216: 0.83 degrees x 0.56 degrees (approx 60km in mid-latitudes) |
| Atmosphere vertical resolution | 85 levels |
| Top of atmosphere | 85km |
| Soil levels | 4 Level 1 : 0 - 0.1 m Level 2 : 0.1 - 0.35 Level 3 : 0.35 - 1.0 m Level 4 : 1.0 - 3.0 m |
| Time step | 15 minutes |
Detailed documentation:
Global Atmosphere 6.0 & Global Land 6.0: Walters et al, 2017
2.2 Ocean and cryosphere
| Ocean model | NEMO v3.4 - Global Ocean 5.0 |
|---|---|
| Horizontal resolution | ORCA 0.25 |
| Vertical resolution | L75 |
| Time step | 22.5 minutes |
| Sea ice model | CICE v4.1 - Global Sea-Ice 6.0 |
| Sea ice model resolution | ORCA 0.25 |
| Sea ice model levels | 5 categories + open water |
| Wave model | N/A |
| Wave model resolution | N/A |
Detailed documentation: NEMO documentation, CICE documentation
Global Ocean 5.0: Megann et al, 2014
Global Sea Ice 6.0: Rae et al, 2015.
3. Initialization and initial condition (IC) perturbations
3.1 Atmosphere and land
| Hindcast | Forecast | |
|---|---|---|
| Atmosphere initialization | ERA-Interim | Met Office Global Hybrid 4D-VAR |
| Atmosphere IC perturbations | None | None |
| Soil moisture & temperature initialization | Met Office JULES-JRA55 analysis | Met Office JULES-JRA55 analysis |
| Snow initialization | Met Office JULES-JRA55 analysis | Met Office JULES-JRA55 analysis |
| Unperturbed control forecast? | No | No |
Detailed documentation:
Met Office Global Hybrid 4D-VAR: Clayton et al, 2013
3.2 Ocean and cryosphere
| Hindcast | Forecast | |
|---|---|---|
| Ocean initialization | GloSea Ocean Sea-Ice Analysis (GS-OSIA) | Forecast Ocean Assimilation Model (FOAM) |
| Ocean IC perturbations | No | No |
| Unperturbed control forecast? | No | No |
Detailed documentation:
The GS-OSIA and the FOAM system both use the Nucleus for European Modelling of the Ocean data assimilation system (NEMOVAR). This is a 3d-VAR data assimilation scheme. The GS-OSIA uses different surface forcing (ERA-interim) and observation sets as it is a historical analysis. FOAM uses surface forcing from the Met Office Global NWP model and real-time observations.
The common NEMOVAR system is described in Blockley et al, 2014. Details of the GS-OSIA can be found in MacLachlan et al, 2015.
4. Model uncertainties perturbations:
| Model dynamics perturbations | None |
|---|---|
| Model physics perturbations | Atmosphere stochastic physics scheme, SKEB2 |
If there is a control forecast, is it perturbed? | No control |
Detailed documentation:
5. Forecast system and hindcasts
| Forecast frequency | daily |
|---|---|
| Forecast ensemble size | 2 per day |
| Hindcast years | 24 (1993-2016) |
| Hindcast ensemble size | 7 per start date |
| Hindcast start dates | 1, 9, 17, 25 of each month |
| On-the-fly or static hindcast set? | on-the-fly |
6. Where to find more information
GloSea5 system:
- MacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife, A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J., Xavier, P. and Madec, G. (2015), Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system. Q.J.R. Meteorol. Soc., 141: 1072–1084. doi:10.1002/qj.2396
- Seasonal prediction at the Met Office
- Upgrades to the Met Office Seasonal Forecast System
Model description references:
- Williams, K. D., Harris, C. M., Bodas-Salcedo, A., Camp, J., Comer, R. E., Copsey, D., Fereday, D., Graham, T., Hill, R., Hinton, T., Hyder, P., Ineson, S., Masato, G., Milton, S. F., Roberts, M. J., Rowell, D. P., Sanchez, C., Shelly, A., Sinha, B., Walters, D. N., West, A., Woollings, T., and Xavier, P. K.: The Met Office Global Coupled model 2.0 (GC2) configuration, Geosci. Model Dev., 8, 1509-1524, https://doi.org/10.5194/gmd-8-1509-2015, 2015.
- Walters, D., Boutle, I., Brooks, M., Melvin, T., Stratton, R., Vosper, S., Wells, H., Williams, K., Wood, N., Allen, T., Bushell, A., Copsey, D., Earnshaw, P., Edwards, J., Gross, M., Hardiman, S., Harris, C., Heming, J., Klingaman, N., Levine, R., Manners, J., Martin, G., Milton, S., Mittermaier, M., Morcrette, C., Riddick, T., Roberts, M., Sanchez, C., Selwood, P., Stirling, A., Smith, C., Suri, D., Tennant, W., Vidale, P. L., Wilkinson, J., Willett, M., Woolnough, S., and Xavier, P.: The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations, Geosci. Model Dev., 10, 1487-1520, https://doi.org/10.5194/gmd-10-1487-2017, 2017.
- Megann, A., Storkey, D., Aksenov, Y., Alderson, S., Calvert, D., Graham, T., Hyder, P., Siddorn, J., and Sinha, B.: GO5.0: the joint NERC–Met Office NEMO global ocean model for use in coupled and forced applications, Geosci. Model Dev., 7, 1069-1092, https://doi.org/10.5194/gmd-7-1069-2014, 2014.
- Rae, J. G. L., Hewitt, H. T., Keen, A. B., Ridley, J. K., West, A. E., Harris, C. M., Hunke, E. C., and Walters, D. N.: Development of the Global Sea Ice 6.0 CICE configuration for the Met Office Global Coupled model, Geosci. Model Dev., 8, 2221-2230, https://doi.org/10.5194/gmd-8-2221-2015, 2015.
Initialisation references:
- Clayton, A. M., Lorenc, A. C. and Barker, D. M. (2013), Operational implementation of a hybrid ensemble/4D-Var global data assimilation system at the Met Office. Q.J.R. Meteorol. Soc., 139: 1445–1461. doi:10.1002/qj.2054
Blockley, E. W., Martin, M. J., McLaren, A. J., Ryan, A. G., Waters, J., Lea, D. J., Mirouze, I., Peterson, K. A., Sellar, A., and Storkey, D.: Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts, Geosci. Model Dev., 7, 2613-2638, https://doi.org/10.5194/gmd-7-2613-2014, 2014.
