1. Ensemble version | ||
|---|---|---|
| Ensemble identifier code | GloSea6-GC3.2 | GloSea5-GC2 |
| Short Description | Global seasonal prediction system which is developed at the Met Office. A Stochastic Kinetic Energy Backscatter scheme(SKEB) was used to generate spread between members initialized from the same analysis. 4 ensemble members initialized every day in the cast of the forecast two run for 72 days and two for 240 days. Hindcast (historical re-forecasts) covers the period 1993~2016 and 3 members initialized on fixed calendar dates 1st, 9th, 17th and 25th. | Global seasonal prediction system which is developed at the Met Office. A Stochastic Kinetic Energy Backscatter scheme(SKEB) was used to generate spread between members initialized from the same analysis. Four ensemble members initialized every day in the cast of the forecast two run for 75 days and two for 240 days. Hindcast (historical re-forecasts) covers the period 1991~2010 and 3 members initialized on fixed calendar dates 1st, 9th, 17th and 25th. |
| Research or operational | Operational | Operational |
| Data time of first forecast run | 22/02/2022 | 26/04/2016 |
2. Configuration of the EPS | ||
| Is the model coupled to an ocean model? | Yes | Yes |
| If yes, please describe ocean model briefly including frequency of coupling and any ensemble perturbation applied | Ocean model is NEMO3.6 with eORCA 0.25 degree horizontal resolution, 75 vertical levels, initialized using KMA GODAPS2 (Global Ocean Data Assimilation and Prediction System 2) Analysis. Frequency of coupling is 1-hourly. | Ocean model is NEMO3.4 with a 0.25 degree horizontal resolution, 75 vertical levels, initialized from Met Office Ocean Analysis (NEMOVAR). Frequency of coupling is 3-hourly. |
| If no, please describe the sea surface temperature boundary conditions (climatology, reanalysis ...) |
| |
| Is the model coupled to a sea Ice model? | Yes | Yes |
| If yes, please describe sea-ice model briefly including any ensemble perturbation applied | Sea Ice model is CICE5.1.2 initialized using KMA GODAPS2 Analysis | Sea Ice model is CICE4.1 , initialized from Met Office Analysis (NEMOVAR) |
| Is the model coupled to a wave model? | No | No |
| If yes, please describe wave model briefly including any ensemble perturbation applied | ||
| Ocean model | NEMO eORCA 0.25 degree resolution | NEMO 0.25 degree resolution |
| Horizontal resolution of the atmospheric model | N216 (0.83o x 0.56o, about 60 km in mid latitudes) | N216 (0.83o x 0.56o, about 60 km in mid latitudes) |
| Number of model levels | 85 | 85 |
| Top of model | 85 km | 85 km |
| Type of model levels | terrain-following, height-based vertical coordinate | terrain-following hybrid height coordinates |
| Forecast length | maximum 240 days | maximum 240 days |
| Run Frequency | every day and make ensemble once a week | every day and make ensemble once a week |
| Is there an unperturbed control forecast included? | No | No |
| Number of perturbed ensemble members | 4/day | 4/day |
| Integration time step | 15Minutes | 15Minutes |
3. Initial conditions and perturbations | ||
| Data assimilation method for control analysis | 4D Var | 4D Var |
| Resolution of model used to generate Control Analysis | N1280L70 | N768L70 |
| Ensemble initial perturbation strategy | SKEB2 | SKEB2 |
| Horizontal and vertical resolution of perturbations | N/A | |
| Perturbations in +/- pairs | N/A | No |
| Initialization of land surface | ||
| 3.1 What is the land surface model (LSM) and version used in the forecast model, and what are the current/relevant references for the model? Are there any significant changes/deviations in the operational version of the LSM from the documentation of the LSM? | GloSea6-GC3.2 uses the Joint UK Land Environment Simulator (JULES) v5.6. | The Met Office Seasonal Forecast System version 5 using Global Coupled 2.0 (GloSea5-GC2) uses the Joint UK Land Environment Simulator (JULES). The JULES model is described in Best et al. (2011) |
| This model uses a scientific configuration called Global Land 6.0. This science configuration is described in Walters et al. (2016) | ||
| 3.2 How is soil moisture initialized in the forecasts? (climatology / realistic / other)? If “climatology”, what is the source of the climatology? If “realistic”, does the soil moisture come from an analysis using the same LSM as is coupled to the GCM for forecasts, or another source? Please describe the process of soil moisture initialization. If “other”, please describe the process of soil moisture initialization. | In GloSea6-GC3.2 the soil moisture is initialised from Offline JULES analysis forced with ERA5 (the fifth generation of ECMWF Re-Analysis; Hersbach and Dee, 2016).
Precipitation(forced ERA5) was corrected with monthly mean values from the Climate Prediction Center Merged Analysis of Precipitation dataset (Xie and Arkin 1997). The soil moisture produced by the offline JULES calculations is rescaled considering the difference from the basic climatology of the coupled model, and the rescaled soil moisture is finally used as the initial condition (Seo et al. 2019). The Standard Normal Deviate Scaled is adopted for adjusting soil moisture (Koster et al. 2004b). | In GloSea5-GC2 the soil moisture is initialised from a seasonally varying climatology. This climatology was derived from a JULES re-analysis using Global Land 3.0 and forced with the WATCH-Forcing-Data-ERA-Interim forcing set (Wheedon et al, 2014). This re-analysis was completed on a 0.5 degree grid and interpolated to the model resolution (0.83x0.56 degrees). The climatology from this re-analysis has been scaled to match the climatology of our NWP soil moisture climatology. |
| 3. How is snow initialized in the forecasts? (climatology / realistic / other) | ||
| If “climatology”, what is the source of the climatology? | ||
| If “realistic”, does the snow come from an analysis using the same LSM as is coupled to the GCM for forecasts, or another source? Please describe the process of soil moisture initialization. | ||
| If “other”, please describe the process of soil moisture initialization. | ||
| Is there horizontal and/or vertical interpolation of data onto the forecast model grid? If so, please give original data resolution(s) | ||
| Are snow mass, snow depth or both initialized? What about snow age, albedo, or other snow properties? | ||
| Snow is initialised “realistically” from analysis. For the hindcasts this is ERA-Interim and the forecasts use the Met Office NWP global analysis. The Met Office NWP global model uses the same land surface model as GloSea5-GC2. For the hindcast the snow field is interpolated from 0.75x0.75 degrees (ERA-I) to the GloSea5-GC2 grid. Only snow mass is initialized. | Snow is initialised “realistically” from analysis. For the hindcasts this is ERA-Interim and the forecasts use the KMA NWP global analysis. The KMA NWP model uses the same land surface model as GloSea6. For the hindcast the snow field is interpolated from 0.75x0.75 degrees (ERA-I) to the GloSea6 grid. Only snow mass is initialized. | Snow is initialised “realistically” from analysis. For the hindcasts this is ERA-Interim and the forecasts use the Met Office NWP global analysis. The Met Office NWP global model uses the same land surface model as GloSea5-GC2. For the hindcast the snow field is interpolated from 0.75x0.75 degrees (ERA-I) to the GloSea5-GC2 grid. Only snow mass is initialized. |
| 4. How is soil temperature initialized in the forecasts? (climatology / realistic / other) | ||
| If “climatology”, what is the source of the climatology? | ||
| If “realistic”, does the soil moisture come from an analysis using the same LSM as is coupled to the GCM for forecasts, or another source? Please describe the process of soil moisture initialization. | ||
| If “other”, please describe the process of soil moisture initialization. | ||
| Is the soil temperature initialized consistently with soil moisture (frozen soil water where soil temperature ≤0°C) and snow cover (top layer soil temperature ≤0°C under snow)? | ||
| Is there horizontal and/or vertical interpolation of data onto the forecast model grid? If so, please give original data resolution(s) | ||
| If all model soil layers are not initialized in the same way or from the same source, please describe. | Soil temperature is initialised “realistically” from analysis. For the hindcasts this is ERA-Interim and the forecasts use the KMA NWP global analysis. For the hindcast the soil temperature field is interpolated from 0.75x0.75 degrees (ERA-I) to the GloSea6 grid. The level in the ERA-interim LSM start at 0, 7, 28, 100cm (https://confluence.ecmwf.int/pages/viewpage.action?pageId=56660259). The GloSea6 soil model levels are (in metres): (0.0,0.10), (0.10,0.35), (0.35,1.0), (1.0,3.0) | Soil temperature is initialised “realistically” from analysis. For the hindcasts this is ERA-Interim and the forecasts use the Met Office NWP global analysis. For the hindcast the soil temperature field is interpolated from 0.75x0.75 degrees (ERA-I) to the GloSea5-GC2 grid. The level in the ERA-interim LSM start at 0, 7, 28, 100cm (https://software.ecmwf.int/wiki/pages/viewpage.action?pageId=56660259). The GloSea5-GC2 soil model levels are (in metres): (0.0,0.10), (0.10,0.35), (0.35,1.0), (1.0,3.0) |
| 3.5 How are time-varying vegetation properties represented in the LSM? | ||
| Is phenology predicted by the LSM? If so, how is it initialized? | If so, how is it initialized? | If so, how is it initialized? |
| If not, what is the source of vegetation parameters used by the LSM? Which time-varying vegetation parameters are specified (e.g., LAI, greenness, vegetation cover fraction) and how (e.g., near-real-time satellite observations? Mean annual cycle climatology? Monthly, weekly or other interval?) | We do not include phenology. GloSea5 uses a fraction tile system with 9 tiles: 5 plant functional types and 4 non-vegetated types. The fractional values are derived from IGBP. Canopy height of plant functional types is derived from MODIS LAI data. The following variable is time varying and derived from MODIS LAI data: * Leaf area index of plant functional types This variable is specified at monthly intervals but there is no inter-annual variation. The initialisation values are interpolated from the monthly time series. | We do not include phenology. GloSea5 uses a fraction tile system with 9 tiles: 5 plant functional types and 4 non-vegetated types. The fractional values are derived from IGBP. Canopy height of plant functional types is derived from MODIS LAI data. The following variable is time varying and derived from MODIS LAI data: * Leaf area index of plant functional types This variable is specified at monthly intervals but there is no inter-annual variation. The initialisation values are interpolated from the monthly time series. |
| 3.6 What is the source of soil properties (texture, porosity, conductivity, etc.) used by the LSM? | The soil information is derived from the Harmonized World Soil Database. | The soil information is derived from the Harmonized World Soil Database. |
| 3.7 If the initialization of the LSM for re-forecasts deviates from the procedure for forecasts, please describe the differences. | There are differences between the forecast and re-forecast initialisation. These are described in the relevant sections. | There are differences between the forecast and re-forecast initialisation. These are described in the relevant sections. |
4. Model Uncertainties perturbations | ||
| Is model physics perturbed? If yes, briefly describe methods | SKEB2 | SKEB2 |
| Do all ensemble members use exactly the same model version? | Same | Same |
| Is model dynamics perturbed? | Yes | Yes |
| Are the above model perturbations applied to the control forecast? | Yes | No |
5. Surface boundary perturbations | ||
| Perturbations to sea surface temperature? | No | No |
| Perturbation to soil moisture? | No | No |
| Perturbation to surface stress or roughness? | No | No |
| Any other surface perturbation? | No | No |
| Are the above surface perturbations applied to the Control forecast? | N/A | N/A |
| Additional comments | ||
6. Other details of the models | ||
| Description of model grid | Arakawa-C grid | Arakawa-C grid |
| What kind of large scale dynamics is used? | semi-lagrangian | semi-lagrangian |
| What kind of boundary layer parameterization is used? | Nolocal Mixing scheme and local Richardson number scheme | Nolocal Mixing scheme and local Richardson number scheme |
| What kind of convective parameterization is used? | Mass flux scheme | Mass flux scheme |
| What kind of large-scale precipitation scheme is used? | Walters et al., 2019 | Walters et al., 2015 |
| What cloud scheme is used? | prognostic cloud fraction | prognostic cloud fraction |
| What kind of land-surface scheme is used? | JULES Model Coupled | JULES Model Coupled |
| How is radiation parametrized? | Walters et al., 2019 | Walters et al., 2015 |
| Other relevant details? | Walters et al., 2015 | |
7. Re-forecast configuration | ||
| Number of years covered | 24 years(1993~2016) | 20 years(1991~2010) |
| Produced on the fly or fix re-forecasts? | On the fly | On the fly |
| Frequency | Hindcast (historical re-forecasts) initialized on fixed calendar dates 1st, 9th, 17th and 25th. | Hindcast (historical re-forecasts) initialized on fixed calendar dates 1st, 9th, 17th and 25th. |
| Ensemble size | 3 members / year | 3 members / year |
| Initial conditions | ERA-Interim and Met Office ODA | ERA-Interim |
| Is the model physics and resolution the same as for the real-time forecasts | Yes | Yes |
| If not, what are the differences | ||
| Is the ensemble generation the same as for real-time forecasts? | Yes | Yes |
| If not, what are the differences | N/A | N/A |
8. References
- Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677-699, doi:10.5194/gmd-4-677-2011, 2011.
- Walters, D., Brooks, M., Boutle, I., 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. Discuss., doi:10.5194/gmd-2016-194, in review, 2016.
- Weedon, G. P., G. Balsamo, N. Bellouin, S. Gomes, M. J. Best, and P. Viterbo (2014), The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data, Water Resour. Res., 50, 7505–7514, doi:10.1002/2014WR015638.
- Koster RD, Suarez MJ, Liu P, Jambor U, Berg A, Kistler M, Reichle R, Rodell M, Famiglietti J (2004b) Realistic initialization of land surface states: impacts on subseasonal forecast skill. J Hydrometeorol 5(6):1049–1063
- Walters, and Coauthors, 2019: The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations. Geosci. Model Dev., 12, 1909-1963.
- Xie P and Arkin P A 1997 Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs Am. Meteorol. Soc. 78 2539–58
- Seo and Coauthors, 2019: Impact of soil moisture ini- tialization on boreal summer subseasonal forecasts: mid-latitude surface air temperature and heat wave events. Climate Dyn., 52, 1695-1709, doi:10.1007/ s00382-018-4221-4.