Post on 15-Jan-2016
Enhanced seasonal forecast skill following SSWs
DynVar/SNAP Workshop, Reading, UK, 22-26 April 2013
Michael Sigmond (CCCma)
John Scinocca, Slava Kharin (CCCma), Ted Shepherd (Reading)
Part I: Scinocca et al. poster (today 4:30pm)
Are SSWs associated with enhanced forecast skill in dynamical forecast systems, and if yes, how can it be quantified?
Introduction• Skillful seasonal forecasts rely on predictability of slowly
varying components of climate system (SSTs, soil moisture)
• Due to the limited influence of ENSO, forecast skill in NH extratropics is relatively small
Forecast skill for February mean ST (issued Jan 1)
• Additional predictability may be realized by exploiting long time scales variations that are introduced by SSWs
Courte
sy: B
. M
erry
field
Predictability introduced by SSWs
• Long timescale disturbances in lower stratosphere influences troposphere for up to 2 months
• Averaged surface conditions after SSWs characterized by more blocking and equatorward shift of storm tracks (negative NAM)
Bald
win
an
dD
un
kerto
n,
20
01
Composites NAM around SSWs
Mean surface conditions after SSW (day 15-60)
Sig
mond e
t al.
(201
3),
Thom
pso
n e
t al.
(200
2)
Surf. Temperature Precipitation
Weak, warm vortex
Can potential predictability associated with SSWs be realized in dynamical forecast models ?
• Sigmond et al. (2013): yes, but important to realize that:– SSWs are only predictable up to 1-2 weeks in
advance– Potential predictability is highly conditional (i.e., only
after SSW)– Seasonal forecasts will only benefit from SSWs when
they are initialized close to an observed SSW
Method:
• Tool:
Dynamical seasonal forecast system that includes a well- resolved stratosphere (CMAM, T63L71)
• Experiments:
Retrospective ensemble forecasts initialized at the onset date of all 20 observed SSWs (1970-2009)
• Initial model states:
- Taken from state at the date of the SSWs in 10 assimilation runs, which are nudged towards time-evolving ERA reanalyses
- Provides a consistent way (balanced fields) to initialize the land and atmosphere above ERA
• Forecast skill metric:
Anomaly correlation skill score (linear dependency between observational and model anomalies, day 16-60)
Model captures observed surface response
Observations
Forecast
Surf. Temperature Precipitation
Forecast skill following SSWs
• Perform ‘control’ forecasts that are not initialized during SSWs (40 forecasts, same calendar dates as SSWs, in year prior and following SSW)
• Skill difference between SSW and control runs is due to SSWs
Fo
reca
stNAM 1000 hPa
• Significant forecast skill of the surface circulation in SSW runs
• What part of the skill can be attributed to SSWs?
Forecast skill enhancement following SSWs
No forecast skill of surface NAM in control runs
Skill in SSW-runs comes entirely from SSWs
SSWs are associated with significant skill enhancement of surface circulation
NAM 1000 hPa
Forecast skill enhancement following SSWs Forecast skill SLP
• SSWs associated with significant skill enhancement of SLP
Forecast skill enhancement following SSWs Forecast skill SLP
• SSWs associated with significant skill enhancement of SLP
• What about other more socio-economically relevant variables?
Forecast skill enhancement following SSWs
• Significant skill enhancement of ST northern Russia and eastern Canada
• Significant skill enhancement of north Atlantic PCP
Forecast skill ST
Forecast skill PCP
Conclusions:• Potential predictability associated with SSWs can be
realized in dynamical seasonal forecast systems
• Following SSWs we find enhanced forecast skill of SLP, ST and PCP
• Follow up: How far in advance can SSWs be predicted and usefully add skill to tropospheric forecasts?
• Practical suggestion: issue special forecasts (at non-standard times) once a SSW has been identified in observations
• Implication: Operational seasonal forecasts which happen to be initialized close to the onset of a SSW will yield enhanced forecast skill (e.g., Jan 2013 SSW)
January 2013 SSW
• January 2013 happened close to beginning of the month
• Was the forecast for February more skillful than average?
Operational forecast for Feb. 2013 (issued January 1)
EC Forecast
Mean ST response after SSW
Operational forecast for Feb. 2013 (issued January 1)
Observed anomaly 2013
EC Forecast
Mean ST response after SSW
Conclusions:• Potential predictability associated with SSWs can be
realized in dynamical seasonal forecast systems• Following SSWs we find enhanced forecast skill of SLP,
ST and PCP• Follow up: How far in advance can SSWs be predicted
and usefully add skill to tropospheric forecasts? • Practical suggestion: issue special forecasts (at non-
standard times) once a SSW has been identified in observations
• Implication: Operational seasonal forecasts which happen to be initialized close to the onset of a SSW will yield enhanced forecast skill (e.g., Jan 2013 SSW)
EXTRA SLIDES
Part I: Methods that don’t work(poster, Sigmond et al., in prep)
• StratHFP runs (hindcasts initialized on Nov 1, high and low top CMAM):
– SSW climatology is more realistic in the high top model, but specific SSW events are not captured (in high and low top models)
Standard set of StratHFP runs do not benefit from SSWs, and can not provide quantitative estimate of forecast skill enhancement associated with SSWs
• Nudged stratosphere runs:– Enhanced forecast skill scores in DJF NH– But enhanced skill scores not limited to region and season with
SSWs
Synchronization of SSWs with observations by stratospheric nudging can not isolate the influence of SSWs on seasonal forecast skill
Ensemble spread (NAM)
Forecast skill metric
• After initialization, models tend to drift from observations to their mean behavior/climatology (which is often biased)
• Solution: statistical bias correction: from many simulations started from the same calendar date, calculate the average bias/drift (function of forecast lag)
• Problem with simulations started from non-standard calendar dates (such as hindcasts initialized during SSWs): bias correction is usually not known
Statistical bias corrected MSE can not be determined
• Sigmond et al. (2013) focussed on anomaly correlation score, which measures the linear dependence between anomalies (deviations from climatology) in observations and the forecast model
• Model anomaly is calculated relative to the climatology of the freely running model
• In the first 15 days, the model drifts from observations to the mean behavior of the freely running (AMIP) runs following SSWs discard the first 15 days and focus on days 16-60