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Remarks on the sub-seasonal range

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 (ER-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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Re-forecasts provide an sub-seasonal range climate (ER-M-climate) and associated probability distribution functions (pdfs) for several variables.  The latest sub-seasonal range ensemble forecasts and the associated probability density functions can be compared with the ER-M-Climate.  The differences between the two are used as the basis of model products any model drift is effectively removed. 

Combating Model Drift

Drift of model calculations begins to be significant after 10 days of coupled integrations.  It displays similar patterns to seasonal forecasting after 6 months of integrations, but with less amplitude.

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The sub-seasonal range model climate (ER-M-climate) drifts towards becoming rather too cold at longer lead-times in wintertime high latitudes.  Hence the anomaly in forecast temperatures against ER-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 ER-M-climate.

The re-forecasts are created twice a week (Mondays and Thursdays) and are ready a week before the real-time forecasting suite starts.  Real-time forecasts are calibrated using all the re-forecasts available in a one week window centred on the forecast start day and month. 

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