Strategies for assessing natural variability Hans von Storch Institute for Coastal Research, GKSS Research Center Geesthacht, Germany Lund, , ENSEMBLES assembly, RT2B meeting
The 300 hPa geopotential height fields in the Northern Hemisphere: the mean 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
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
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).
„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.
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.
Noise or deterministic chaos? Mathematical construct of randomness – an adequate concept for description of features resulting from the presence of many chaotic processes.
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.
Temporal development of T i (m,L) = T i (m) – T i-L (m) divided by the standard deviation (m,L) of the considered reconstructed temp record for m=5 and L=20 (top), and for m=30 and L=100 years. The thresholds R = 2, 2.5 and 3 are given as dashed lines.
Low-pass filtered (>30-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. Gouirand et al., 2006, in press
Scandinavian temperatures from the simulation during and observations during 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. Gouirand et al., 2006, in press
The CoastDat-effort at the Institute for Coastal Research 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.