| Table of Contents |
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| Info |
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TO BE UPDATED by the data provider; UKMO model page cloned as an example |
1. Ensemble version | GloSea5|||
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| Ensemble identifier code | HadGEM3 GC2.0 CAS-FGOALS-f2-V1.3 | ||
| Short Description | Global ensemble system that simulates initial-condition uncertainties using lagged initialisation and model uncertainties using a stochastic scheme. There are 4 ensemble members initialised each day, each extending to 60 days Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land system version f2-V1.3 (FGOALS-f2-V1.3) was developed at Institute of Atmospheric Physics (IAP) ,CAS. The prediction model is CAS FGOALS-f2, which is a Climate System Model representing the interaction between the atmosphere, oceans, land and sea ice. The S2S Forecasts runs 16-ensemble members each day for real-time forecast since 1st June 2019 and 4-ensemble members each day for re-forecast since 1st January 1999. This S2S prediction ends with a 65-day integration. | ||
| Research or operational | Operational | ||
| Data time of first forecast run | 0501/0206/20152019 | ||
2. Configuration of the EPS | |||
| Is the model coupled to an ocean model? | Yes from day 0 | ||
| If yes, please describe ocean model briefly including frequency of coupling and any ensemble perturbation applied | Ocean model is Global Ocean 6.0, based on NEMO3.6 with 0.25 degree POP2 with a 1° horizontal resolution, 75 60 vertical levels, initialized using NEMOVAR; no perturbationsfrom the CAS coupled assimilation system analysis. Frequency of coupling is 1-hourlydaily. | ||
| Is the model coupled to a sea ice model? | Yes | ||
| If yes, please describe sea-ice model briefly including any ensemble perturbation applied | Global Sea Ice 8.1 (CICE5.1.2) initialized from NEMOVAR; no perturbationsSea ice model is the CICE4 with a same horizontal resolution as the ocean model. Sea ice initial conditions come from the CAS coupled assimilation system analysis. | ||
| Is the model coupled to a wave model? | No | ||
| If yes, please describe wave model briefly including any ensemble perturbation applied | |||
| Ocean model | NEMO 0.25 degree resolutionPOP2 with 1° horizontal resolution (gx1v6) and 60 vertical levels | ||
| Horizontal resolution of the atmospheric model | N216 0.83° x 0.56° (approx 60km in mid-latitudes C96 (approximately 100km) | ||
| Number of model levels | 8532 | ||
| Top of model | 85 km 2.16 hPa (approx. 45 km) | ||
| Type of model levels | terrain-following, height-based vertical sigma-pressure hybrid coordinate | ||
| Forecast length | 60 65 days (1560 hours) | ||
| Run Frequency | daily | ||
| Is there an unperturbed control forecast included? | Nono | ||
| Number of perturbed ensemble members | 16 per day for real-time forecasts; 4 per day for re-forecasts | ||
| Integration time step | 15 30 minutes for Physical processes | ||
3. Initial conditions and perturbations | |||
| Data assimilation method for control analysis | 4D Var Nudging FNL and GFS | ||
| Resolution of model used to generate Control Analysis | N768L70 C96 ( 0.23° x 0.16°192 x 384) | ||
| Ensemble initial perturbation strategy | lagged initialisation Lagged ensembles | ||
| Horizontal and vertical resolution of perturbations | N/A | ||
| Perturbations in +/- pairs | N/A | ||
| Additional comments | Soil moisture is initialised with climatological mean values in both real-time forecasts and re-forecasts. N/A | ||
| Initialization of land surface | N/A | ||
| 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? | The Met Office Seasonal Forecast System version 5 using Global Coupled 2.0 (GloSea5-GC2) uses the Joint UK Land Environment Simulator (JULES). | NCAR Community Land Model version 4.0 (CLM4, Oleson, K. W., and Coauthors, 2010: Technical description of version 4.0 of the community land model (clm). ). There is no change in the operational version. | |
| 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. | Soil moisture is not directly initialized using the climatology or realistic analysis in the forecasts. Nevertheless, we have utilized high-level and near-surface atmospheric analysis and ocean analysis to force the air-sea-land-ice coupled model in a long-term integration, and the land initial conditions are produced during this process. 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 (0.83 x 0.56 degrees). The climatology from this re-analysis has been scaled to match the climatology of our NWP soil moisture climatology. | ||
| 3.3 How is snow initialized in the forecasts? | It is similar as the above mentioned for question 3.2. | ||
| 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? | No initialization data about snow is interpolated onto the model grid. | ||
| Are snow mass, snow depth or both 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.|||
| 3.4 How is soil temperature initialized in the forecasts? (climatology / realistic / other) | These variables are not initialized directly and they are connected with each other by model physics. | ||
| 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. | It is similar as the above mentioned for question 3.2. | ||
| 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 all model soil layers are not initialized in the same way or from the same source, please describe. | |||
| 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 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? | The climatological vegetation properties with annual cycle are used in the LSM | ||
| Is phenology predicted by the LSM? If so, how is it initialized? | No | ||
| 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 The land component of FGOALS-f2 is CLM4. The grids of LSM are represented as a nested sub grid hierarchy in which grid cells are composed of multiple land units, snow/soil columns, and plant functional types (PFTs). Leaf area index of PFTs. This variable is specified at monthly intervals but there is no inter-annual variation. The initialisation initialization 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 properties in FGOALS-f2 are same as those in CLM4. The soil texture (percent sand and clay) varies with depth according to the IGBP soil dataset (Global Soil Data Task 2000). | ||
| 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 sectionsThe initialization of the LSM in reforecasts is similar as that in forecasts. | ||
4. Model uncertainties perturbations | |||
| Is model physics perturbed? | No Yes. A scheme called Stochastic Kinetic Energy Backscatter scheme (SKEB) adds vorticity perturbations to the forecast in order to counteract the damping of small-scale features introduced by the semi-Lagrangian advection scheme. | ||
| Do all ensemble members use exactly the same model version? | Yes | ||
| Is model dynamics perturbed? | No | ||
| Are the above model perturbations applied to the control forecast? | Yes|||
5. Surface boundary perturbations | |||
| Perturbations to sea surface temperature? | No | ||
| Perturbation to soil moisture? | No | ||
| Perturbation to surface stress or roughness? | No | ||
| Any other surface perturbation? | No | ||
| Are the above surface perturbations applied to the Control forecast? | N/A | ||
| Additional comments | As the perturbation are exclusively based on stochastic physics and are applied to all forecast members, there is no true control member.|||
6. Other details of the models | |||
| Description of model grid | Arakawa-C Cubed Sphere Grid (C96) | ||
| List of model levels in appropriate coordinates | Level list (km) 0.0200000, 0.0533333, 0.100000, 0.160000, 0.233333, 0.320000, 0.420000, 0.533333, 0.660000, 0.800000, 0.953334, 1.12000, 1.30000, 1.49333, 1.70000, 1.92000, 2.15333, 2.40000, 2.66000, 2.93333, 3.22000, 3.52000, 3.83333, 4.16000, 4.50000, 4.85333, 5.22000, 5.60000, 5.99333, 6.40000, 6.82000, 7.25333, 7.70000, 8.16000, 8.63334, 9.12001, 9.62002, 10.1334, 10.6601, 11.2002, 11.7536, 12.3205, 12.9009, 13.4949, 14.1025, 14.7239, 15.3592, 16.0088, 16.6729, 17.3519, 18.0463, 18.7567, 19.4839, 20.2288, 20.9925, 21.7765, 22.5824, 23.4122, 24.2682, 25.1532, 26.0706, 27.0241, 28.0183, 29.0582, 30.1500, 31.3005, 32.5177, 33.8106, 35.1895, 36.6662, 38.2540, 39.9679, 41.8249, 43.8438, 46.0462, 48.4558, 51.0994, 54.0064, 57.2100, 60.7467, 64.6570, 68.9855, 73.7818, 79.1000, 85.000032 vertical layers at 2.164, 5.845, 10.75, 17.11, 25.11, 35.22, 48.14, 64.56, 85.11, 110.4, 141.1, 177.7, 220.9, 271.1, 328.5, 392.8, 461.9, 532.5, 600.4, 663.1, 719.3, 768.8, 811.8, 848.8, 880.3, 907.0, 929.4, 948.1, 963.7, 976.7, 987.4, 996.1 mbar | ||
| What kind of large scale dynamics is used? | A finite volume dynamical core on a cubed sphere, with six tiles across the globe. In the atmospheric component of FGOALS-f2, each tile contains 96 grid cells (C96). Globally, the longitudes along the equator are divided into 384 grid cells, and the latitudes are divided into 192 grid cells, which is approximately equal to a 1degree horizontal resolution. Semi-lagrangian | ||
| What kind of boundary layer parameterization is used? | University of Washington Moist Turbulence parameterization scheme [Bretherton and Park, 2009] Nolocal mixing scheme and local Richardson number scheme | ||
| What kind of convective parameterization is used? | Mass flux scheme Modified mass-flux cumulus parameterization scheme and Resolve Convective Precipitation scheme [Tiedtke, 1989; Bao et al., 2020] | ||
| What kind of large-scale precipitation scheme is used? | Williams Modified Lin scheme (Zhou et al ., 20152019) | ||
| What cloud scheme is used? | Prognostic Diagnostic cloud fraction depending on relative humidity and cloud condensate (Xu and Randall, 1996].
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| What kind of land-surface scheme is used? | CLM4 (Oleson et al., 2010) Jules coupled model; Best et al 2011 | ||
| How is radiation parametrized? | Williams The radiation code originates from RRTMG (Clough et al 2015., 2005) | ||
| Other relevant details? | Flexible Global Ocean–Atmosphere–Land System model, finite-volume version 2 climate system model was developed at the State Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS) (Bao et al., 2019,2020; Li et al., 2019; He et al., 2019). CAS FGOALS-f2 is composed of five components: version 2 of the Finite-volume Atmospheric Model (FAMIL), POP2; version 4 of the Community Land Model (CLM4), version 4 of the Los Alamos sea ice model (CICE4), and version 7 of the coupled module from National Center for Atmospheric Research (NCAR). | ||
7. Re-forecast configuration | |||
| Number of years covered | 23 20 years ( 19931999- 20152018) | ||
| Produced on the fly or fix re-forecasts? | On the fly | ||
| Frequency | each month, on 1st, 9th, 17th, 25th | ||
| Ensemble size | 7 members per year (from 25 March 2017 hindcast onwards, prior to this 3 members per year) | ||
Fix re-forecasts | |||
| Frequency | |||
| Ensemble size | |||
| Initial conditions | Initial conditions | ERA interim and NEMOVAR||
| Is the model physics and resolution the same as for the real-time forecasts? | Yes|||
| If not, what are the differences | N/A|||
| Is the ensemble generation the same as for real-time forecasts? | Yes|||
| If not, what are the differences | N/A
8. References
- Bowler N, Arribas A, Beare S, Mylne KE, Shutts G. 2009. The local ETKF and SKEB: Upgrades to the MOGREPS short-range ensemble prediction system. Q. J. R. Meteorol. Soc. 135: 767–776
- MacLachlan, C., Arribas, A., et al.: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system, 2014, Q. J. Roy. Meteor. Soc., doi:10.1002/qj.2396
- Mogensen K, Balmaseda M, Weaver AT, Martin M, Vidard A. 2009. NEMOVAR: A variational data assimilation system for the NEMO ocean model. In ECMWF Newsletter, Walter Z. (ed.) 120: 17–21. ECMWF: Reading, UK.
- Mogensen K, Balmaseda MA, Weaver AT. 2012. ‘The NEMOVAR ocean data assimilation system as implemented in the ECMWF ocean analysis for System 4’, Technical Report TR-CMGC-12-30. CERFACS: Toulouse, France.
- 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, doi:10.5194/gmd-8-1509-2015, 2015.
Joint UK Land Environment Simulator (JULES):
- Bao, Q., X. F. Wu, J. X. Li, L. Wang, B. He, X. C. Wang, Y. M. Liu, and G. X. Wu. 2019. “Outlook for El Nino and the Indian Ocean Dipole in Autumn-winter 2018–2019.” Chinese Science Bulletin (In Chinese) 64 (1): 73–78. doi:10.1360/N972018-00913.
- Li, J., Bao, Q., Liu, Y., Wu, G., Wang, L., He, B., Wang, X., Yang, J., Wu, X., Shen, Z., 2021a. Dynamical seasonal prediction of tropical cyclone activity using the FGOALS- f2 ensemble prediction system. Weather and Forecasting. doi: 10.1175/WAF-D-20-0189.1
- Bao, Q., and J. Li. 2020. “Progress in Climate Modeling of Precipitation over the Tibetan Plateau.” National Science Review. 7(3): 486–487. doi:10.1093/nsr/nwaa006.
- He, B., Bao, Q., Wang, X., Zhou, L., Wu, X., Liu, Y., ... & Zhang, X. (2019). CAS FGOALS-f3-L model datasets for CMIP6 historical atmospheric model Intercomparison project simulation. Advances in Atmospheric Sciences, 36(8), 771-778.
- Lei WANG, Qing BAO, Jinxiao LI, Dongxiao WANG, Yimin LIU, Guoxiong WU & Xiaofei WU (2019) Comparisons of the temperature and humidity profiles of reanalysis products with shipboard GPS sounding measurements obtained during the 2018 Eastern Indian Ocean Open Cruise, Atmospheric and Oceanic Science Letters, 12:3, 177-183, DOI: 10.1080/16742834.2019.1588065
- Bao, Q., Y. Liu, G. Wu et al.,(2020): CAS FGOALS-f3-H and CAS FGOALS-f3-L outputs for the high-resolution model intercomparison project simulation of CMIP6, Atmospheric and Oceanic Science Letters, DOI: 10.1080/16742834.2020.1814675
- Li, J., Bao, Q., Liu, Y., Wang, L., Yang, J., Wu, G., ... & Shen, Z. (2021). Effect of Horizontal Resolution on the Simulation of Tropical Cyclones in the Chinese Academy of Sciences FGOALS-f3 Climate System Model. Geoscientific Model Development, https://doi.org/10.5194/gmd-2021-19,1-42.
- Li, J., Bao, Q., Liu, Y., Wu, G., Wang, L., He, B., ... & Li, J. (2019). Evaluation of FAMIL2 in simulating the climatology and seasonal‐to‐interannual variability of tropical cyclone characteristics. Journal of Advances in Modeling Earth Systems, 11(4), 1117-1136.
- Li, J., Bao, Q., Liu, Y., & Wu, G. (2017). Evaluation of the computational performance of the finite-volume atmospheric model of the IAP/LASG (FAMIL) on a high-performance computer. Atmospheric and Oceanic Science Letters, 10(4), 329-336.
- Zhou, L., Bao, Q., Liu, Y., Wu, G., Wang, W. C., Wang, X., ... & Li, J. (2015). Global energy and water balance: Characteristics from F inite‐volume A tmospheric M odel of the IAP/LASG (FAMIL 1). Journal of Advances in Modeling Earth Systems, 7(1), 1-20.
- 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.
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