Ensemble perturbations

Met Office seasonal prediction system: GloSea6

Met Office seasonal prediction system: GloSea6


Description of ensemble forecasting method and GloSea6 ensemble prediction system.

GloSea6 is the seasonal prediction system developed and run operationally at the Met Office. GloSea6 stands for Met Office Global Seasonal Forecasting System version 6.  

GloSea6 is an ensemble prediction system built around the high resolution version of the Met Office climate prediction model: HadGEM3 family atmosphere—land—ocean—sea-ice coupled climate model. The upgrade to GloSea6 will occur in two phases.  The first builds on the success of GloSea5 (MacLachalan et al 2015, Scaife et al 2014) but upgrades the model configuration to the version described in Williams et al (2017).  The second phase will involve an increase in ensemble size.  GloSea6 uses the N216 version (0.8 degrees in latitude and 0.5 degrees in longitude, which is approx. 50 km in mid-latitudes, in the horizontal) for the atmosphere, and the ORCA0.25 grid (0.25 degrees) for the ocean. The vertical resolution is 85 levels for the atmosphere and 75 levels for the ocean.

Each forecast requires initial ocean, sea-ice, land and atmosphere conditions. The atmosphere conditions are specified from atmospheric analyses that are also used in short range weather prediction. The land conditions are taken from an analysis forced by JRA-55 (Harada et al 2016).  The ocean and sea-ice initial conditions are taken from an analyses generated for short range ocean and seasonal forecasting, using the 3-dimensional variational ocean data assimilation developed by the multi-institution NEMOVAR project (Waters et al, 2015).

GloSea6 has two components: the forecast itself and an associated set of hindcasts, also called historical re-forecasts, used for calibration purposes and for skill assessment. In the case of GloSea6 the hindcast covers the period 1993 - 2016. Both forecasts and hindcasts are performed using the same configuration of the GloSea6 ensemble prediction system but, obviously, with different initial conditions.

A lagged initialisation approach is followed to represent the uncertainties in the initial conditions. Four forecast ensemble members are initialised every day (two are run for 64 days and two for 216 days) and seven members initialised on fixed calendar dates (1st, 9th, 17th, 25th of each month) for the hindcast. In a similar manner to the Met Office short -range ensembles, model uncertainties are represented through the use of a stochastic physics scheme (Tennant et al, 2011). The climate forcings are derived from the CMIP6 experiments or climatology. The model contains no flux corrections or relaxations to climatology.

Every week, a 42-member ensemble seasonal forecast for the next six months is generated by combining and bias correcting all forecast members available from the most recent three weeks.



Harada, Y., Kamahori, H., Kobayashi, C., Endo, H., Kobayashi, S., Ota, Y., Onoda, H., Onogi, K., Miyaoka, K., & Takahashi, K. (2016). The JRA-55 Reanalysis: Representation of Atmospheric Circulation and Climate Variability. Journal of the Meteorological Society of Japan. Ser. II, 94(3), 269–302. https://doi.org/10.2151/jmsj.2016-015

MacLachlan C., A. Arribas, K.A. Peterson, A. Maidens, D. Fereday, A.A. Scaife, M. Gordon, M. Vellinga, A. Williams, R. E. Comer, J. Camp and P. Xavier, 2015. Description of GloSea5: the Met Office high resolution seasonal forecast system. Q. J. R. Met. Soc., DOI: 10.1002/qj.2396.

Scaife A.A., A. Arribas, E. Blockley, A. Brookshaw, R. T. Clark, N. Dunstone, R. Eade, D. Fereday, C. K. Folland, M. Gordon, L. Hermanson, J. R. Knight, D. J. Lea, C. MacLachlan, A. Maidens, M. Martin, A. K. Peterson, D. Smith, M. Vellinga, E. Wallace, J. Waters and A. Williams, 2014. Skilful Long Range Prediction of European and North American Winters. Geophys. Res. Lett., 41, 2514-2519, DOI:10.1002/2014GL059637.

Tennant, Warren & J. Shutts, Glenn & Arribas, Alberto & A. Thompson, Simon. (2011). Using a Stochastic Kinetic Energy Backscatter Scheme to Improve MOGREPS Probabilistic Forecast Skill. Monthly Weather Review 139. 1190-1206. doi:10.1175/2010MWR3430.1.

Waters, J. , Lea, D. J., Martin, M. J., Mirouze, I. , Weaver, A. and While, J. (2015), Implementing a variational data assimilation system in an operational 1/4 degree global ocean model. Q.J.R. Meteorol. Soc., 141: 333-349. doi:10.1002/qj.2388

Williams, K. D., Copsey, D., Blockley, E. W., Bodas‐Salcedo, A., Calvert, D., Comer, R., Davis, P., Graham, T., Hewitt, H. T., Hill, R., Hyder, P., Ineson, S., Johns, T. C., Keen, A. B., Lee, R. W., Megann, A., Milton, S. F., Rae, J. G. L., Roberts, M. J., … Xavier, P. K. (2018). The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) Configurations. Journal of Advances in Modeling Earth Systems, 10(2), 357–380. https://doi.org/10.1002/2017MS001115