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Sub-seasonal range structure

The sub-seasonal range ensemble is run daily based on 00UTC data.  The products cover the period up to Day46 and are derived from a 100 member ensemble with a control and has a resolution of 36km.   The sub-seasonal range ENS is independent of the medium range ENS and has it's own control and sub-seasonal range model climate (SUBS-M-Climate).

The sub-seasonal range ENS covers a time scale lying between:

• medium range forecasts (ENS to day15).  These are mainly governed by atmospheric initial values (background plus new observed data) but less so on ocean temperature information.
• seasonal forecasts. These are more reliant on predictability of the oceans and on the impact that tropical sea-surface temperatures have on the atmospheric circulation.

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• can influence the development of synoptic-scale systems (e.g. tropical cyclones)
• influences meteorological evolution and predictability in the extra-tropics
• helps capture the propagation of Madden-Julian Oscillation (MJO) events, notably in the equatorial Indian Ocean and western Pacific ocean. 

The SI3 subprogram (within NEMO) forecasts changes in the sea-surface temperature and sea-ice evolution.   Note: In earlier versions of IFS up to and including Cy49r, ECMWF used LIM2 which is an earlier version of the Louvain-la-Neuve sea ice model currently available (Version 3.6).

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The sub-seasonal range model climate (SUBS-M-Climate) drifts towards becoming rather too cold at longer lead-times in wintertime high latitudes.  Hence the anomaly in forecast temperatures against SUBS-M-climate temperatures may be too large.  The magnitude of the drift is not uniform.  At longer lead-times the trend in northern China is towards colder values but less so in Siberia and Canada.  The variation may be due to the analysed initial snowpack conditions and/or snowmelt in marginal snow cover areas in these areas.  Issues regarding this are being addressed. A multi-layer snow scheme is incorporated.  

After about 10 days of forecasts, the spread of the ensemble can become very large.  A significant shift can be detected by comparing probability distribution functions of the latest model and the SUBS-M-climate.

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Fig5.2-1: Example of plumes for Dublin.  Extended Range forecast, DT00UTC 1 January 2018.  The plumes show increasing spread of forecasted values of 850hPa temperatures and 500hPa geopotential height within the extended range period.

(FUG Associated associated with Cy50r1)