Strategies for assessing natural variability

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Strategies for assessing natural variability

• Hans von StorchInstitute for Coastal Research,
  GKSS Research Center
• Geesthacht, Germany

Lund, 20.11.2006, ENSEMBLES assembly, RT2B meeting
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The 300 hPa geopotential height fields in the
Northern Hemisphere the mean 1967-81 January
field, the January 1971 field, which is closer to
the mean field than most others, and the January
1981 field, which deviates significantly from the
mean field. Units 10 m
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Natural variability

• Global Variability due to external natural
  factors
• Regional Variability inherited from large-scale
  variability.
• Global AND regional Stochastic variability due
  to internal dynamical processes

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Variability in RCM simulations

• Inherited from large-scale structure
• But IDPS - Intermittent divergence in phase
  space (not a problem, when spectral nudging or
  other forms of large-scale constraints are
  applied).

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• Natural uncertainty in empirical downscaling
  approaches.
• Is the variability, best described by the analog
  approach, natural or a deficit of the
  predictors?
• I guess, mostly yes.
• Because large-scale constrained RCMs do not show
  this uncertainty.

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Where does the stochasticity found in data come
from?

• Observational data irregular spatial coverage,
  observational errors, limited observation time
  span. And natural unforced variability.
  Dynamical cause for natural unforced
  variability as in simulation models.
• Simulation data internally generated by a very
  large number of chaotic processes.
• Stochasticity as mathematical construct to allow
  an efficient description of the simulated (and
  observed) climate variability.

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Noise or deterministic chaos?
Mathematical construct of randomness an
adequate concept for description of features
resulting from the presence of many chaotic
processes.
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Determining the characteristics of natural
variability

• Re-analyses limited time, internally consistent,
  mostly homogeneous may contain anthropogenic
  signals.
• Reconstructions based on instrumental data
  available only for few variables, possibly
  contaminated by anthropogenic signals sometimes
  inhomogeneous.
• Paleo-reconstructions may have problems in
  estimating variability on different time scales.
• Rare long instrumental records may be useful to
  validate model-based estimates recent data may
  be contaminated by anthropogenic signals.
• Millennial global simulations possibly
  augmented with suitable (preferably) dynamical
  and empirical downscaling.

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Temporal development of ?Ti(m,L) Ti(m)
Ti-L(m) divided by the standard deviation ?(m,L)
of the considered reconstructed temp record for
m5 and L20 (top), andfor m30 and L100
years. The thresholds R 2, 2.5 and 3 are given
as dashed lines.
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Gouirand et al., 2006, in press
Low-pass filtered (gt30-year scales) temperatures
from the simulation (black), from reconstructions
based on proxy data (grey) and instrumental data
(dashed) for April-August (a) and December-March
(b). The reconstruction in (a) is based on
tree-ringwidth and densities from northern
Fennoscandia. The reconstruction in (b) is a
combination of documentary evidence for ice
break-up dates and instrumental observations from
Tallinn, Estonia. The instrumental data are from
Uppsala, southern Sweden. All series are given as
anomalies from their respective long-term means.
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Gouirand et al., 2006, in press
Scandinavian temperatures from the simulation
during 1000-1990 and observations during
1874-1996 in summer (JJA) (a-b) and winter (DJF)
(c-d). Black lines show variability at timescales
longer than 10 years. Grey lines show shorter
timescales. All data are shown as anomalies from
the respective long-term means.
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The CoastDat-effort at the Institute for Coastal
Research at GKSS (ICR_at_gkss)

• Long-term, high-resolution reconstuctions (50
  years) of present and recent developments of
  weather related phenomena in coastal regions as
  well as scenarios of future developments (100
  years)
• Northeast Atlantic and northern Europe
• Standard model systems (frozen)
• Assessment of changes in storms, ocean waves,
  storm surges, currents and regional transport of
  anthropogenic substances.
• Data freely available.

www.coastdat.de