Transcript Sub-seasonal to seasonal prediction David Anderson An element of the WWRP Workshop “Sub-seasonal to Seasonal prediction” Met Office, Exeter – 1 to 3 December 2010 A.

Sub-seasonal to seasonal
prediction
David Anderson
An element of the WWRP
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
Present Operational Systems
Summary
• Medium-range weather predictions (~10-15 days)
• Monthly or extended-range predictions (~30-45 days)
• Seasonal predictions (~12 months)
3
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 multimodel 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.
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 seaice 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.