Sub-seasonal to seasonal prediction David Anderson

Workshop Sub-seasonal to Seasonal prediction Met Office, Exeter – 1 to 3 December 2010 A Commission of Atmospheric Sciences initiative (November 2009) MAIN GOALS Establish current capabilities in sub-seasonal to seasonal prediction Identify high-priority research topics and projects Develop recommendations for the establishment of an international research project

3 Present Operational Systems Summary Medium-range weather predictions (~10-15 days)Medium-range weather predictions (~10-15 days) Monthly or extended-range predictions (~30-45 days)Monthly or extended-range predictions (~30-45 days) Seasonal predictions (~12 months)Seasonal predictions (~12 months)

Experience with TIGGE TIGGE has been successful in establishing a data base from which methods of post-processing model forecasts to improve skill can be tested. If only the 4 best models are used then the multi- model forecast is better than any single (uncalibrated) model. For medium range forecasting, model error is not dominant and a reforecast data set for bias and skill evaluation is not made. MOS has been used to correct for model error but this is less appropriate for longer range forecasts (Johnson and Swinbank).

Experience with seasonal forecasting Model error (drift) is usually large –as big or bigger than the signal one is seeking to predict e.g. ENSO. The approach most widely used to deal with drift is to generate a long set of hindcasts and to evaluate a forecast relative to the hindcast climatology i.e. to create anomalies relative to the model climatology, which is a function of start date and forecast lead. The hindcast set should be as long as feasible, though there are issues with the observing system changing. Typically every month from 1982, (sometimes longer going back to the start of e.g. ERA-40). THIS IS EXPENSIVE

Extended (sub-seasonal) range forecasting Model error can not be ignored. For example, in the ECMWF monthly forecast system, the hindcast set spans 20 years, but is done for every week. The operational forecast ensemble, done every week is 51 members. The ensemble size used in some of the research experiments is 15 members, 5 members for operational use. The monthly system is an extension of the EPS, run twice daily to 15 days. It was developed from the seasonal forecast system and still shares many features with it.

MJO MJO is an important source of extended-range prediction in the extratropics.

Vitart and Molteni ECMWF NEWSLETTER 126, 2011 Blue line gives a measure of ensemble spread. It is substantially less than the error. Multi-model should increase the spread.

Brier Skill Scores of 500 hPa geopotential height, 850 hPa temperature and precipitation for days 5–11, 12–18, 19–25,and 26–32. The red bars show the scores obtained when there is an MJO in the initial conditions. The blue bars show the scores when there is no MJO in the initial conditions. From Vitart and Molteni 2011 ECMWF Newsletter.

The next steps The time is ripe to follow in TIGGE and Seasonal forecast systems footsteps and set up a multi-model data base of forecasts for the extended range (at least 30 days, maybe 45 days). Models should ideally be coupled atmosphere ocean (with sea ice), but for 30 days one can probably still do useful things uncoupled and without a dynamical sea- ice module. Some models extend well into the stratosphere. Soil moisture and other land conditions should be initialised. Atmospheric reanalyses make reforecasts feasible

Several Operational Centres are already making or moving to make extended-range forecasts. Australia, Canada, ECMWF, Japan, NCEP, UKMO… It is feasible to construct a multi-model data base, but there will be a lot of data as one wants good resolution and you need an ensemble of reforecasts spanning several years and performed weekly though one could do it less frequently for research purposes.

Summary As discussed at the Workshop, recent results suggest that there is potentially useful predictability at sub-seasonal timescales, intermediate between NWP and seasonal timescales, and it is worth exploring this further and despite the difficulties in forecasting for this range it is worthwhile developing a research strategy to explore and exploit this potential.