Erik den Røde - hvonstorch.de

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Transcript Erik den Røde - hvonstorch.de

Utility of multi-century AOGCM runs –
formulating hypotheses about past
climates and testing methods".
Hans von Storch
Institute for Coastal Research
GKSS Research Center
Geesthacht, Germany
Erik den Røde
1. Experimental set-up of Erik den Røde
2. Utility I: Testing validity of derived
indicators.
Example: The case of the MBH multi-proxy
reconstruction
3. Utility II: Estimating the unobservable.
Example: extra-tropical storminess and
Atlantic overturning
4. Utility III: Consistent forward modelling of
proxy data.
Example: none so far – boreholes a good
candidate
ECHO-G
simulations
„Erik den
Røde” (10001990)
and
“Christoph
Columbus”
(1550-1990)
with
estimated
volcanic,
GHG and
solar forcing
Reconstruction from historical
evidence, from Luterbacher et al.
Late Maunder
Minimum
Model-based reconstuction
1675-1710
vs. 1550-1800
Both, Erik den Røde and
Christoph Columbus generate
temperature variations
considerably larger than
standard reconstructions
(Mann, Jones …).
The simulated temperature
variations are of a similar
range as derived from NH
summer dendro-data and from
terrestrial boreholes.
Conclusion, 1
• Erik den Røde, an effort to simulate the
response to estimated volcanic, GHG
and solar forcing, 1000-2000.
• Low-frequency variability in Erik den
Røde
> Mann, Jones, & mainstream, but
~ Esper, boreholes, (some) instrumental
data
For the purpose of testing
reconstruction methods, it does
not really matter how „good“ the
historical climate is reproduced
by Erik den Røde and Christoph
Columbus .
The model data provide a
laboratory to test MBH and
other methods.
Testing
the MBH
method
pseudo-proxies: grid point SAT plus white noise
red: mimicking largest sample used in MBH
blue: hypothetic additional data to obtain better coverage.
Mimicking MBH?
Conclusion, 2
• Erik den Røde-data used to test MBH and
borehole approaches.
• Randomized grid-point SAT (i.e. white noise
added) is used as pseudo proxy.
• MBH method, based on regression and
inflation, gives significant underestimation of
low-frequency NH mean SAT.
• Direct averaging of randomized local data
results in considerably smaller errors in NH
mean SAT.
Conclusion, 2
• When using Erik den Røde-data and pseudoproxy data, which share a correlation of about
0.5 with the grid point data, the best guess
resembles the MBH estimate (incl. the ±2σ
confidence band).
• The problem is common to ALL regression based
methods, trained with temporal high-resolution
data – except if the correlations are really high.
• A reliable reconstruction of centennial time
scales requires either really high correlation, or
process-based inverted data (e.g., borehole
inversions), or local instrumental data.
Utility II
– developing hypotheses about the
variability of climate variables
• Extratropical storminess
(Fischer-Bruns, I., H. von Storch, E. Zorita and F. González-Rouco,
2004: A modelling study on the variability of global storm activity on
time scales of decades and centuries. submitted)
• Meridional overturning in the Atlantic
(von Storch, H., M. Montoya, F.J. González-Rouco and K. Woth 2004:
Projektionen für Meere und Küsten. In Müchener RückversicherungsGesellschaft: Wetterkatastrophen und Klimawandel. Sind wir noch zu
retten? Eigenverlag Münchener Rückversicherungs-Gesellschaft, 107113)
Empirical evidence about
extratropical storm variability
Estimates
based upon
pressure
readings
Lund and
Stockholm
Bärring and von
Storch, 2004
Estimates based upon repair costs
for dikes in Holland
de Kraker, 1999
Very little
evidence available
Extratropical storminess
• Determined by the
frequency of maximum
wind speeds in a grid cell
of 8 Bft or more (17.2
m/s)
• Number of storm days in
DJF (top) and JJA
(bottom) during
preindustrial period
1550-1850
Extratropical storminess
Pre-industrial: 1550-1850
change from pre-industrial to
industrial period 1850-2000
..
..
a) Mean number of storm days in winter per grid point averaged over the preindustrial and industrially influenced periods of Erik and over the climate
scenario A2 for each hemisphere.
b) Same index as function of time.
c) and d) same, but for North Atlantic region (90W-30E) and North Pacific region
(150E-90W).
Extratropical Storm variations
• North
Atlantic
• Mean nearsurface
temperature
(red/orange)
• storm
frequency
index (blue),
• storm shift
index (green)
• 2 band of
preindustrial
conditions
Storm shift index defined as PCs of storm
frequency EOFs
Extratropical Storm variations
• North Pacific
• Mean nearsurface
temperature
(red/orange)
• storm
frequency
index (blue),
• and storm
shift index
(green)
• 2 band of
preindustrial
conditions
Storm shift index defined as PCs of storm
frequency EOFs
Extratropical Storm variations
• Southern
Hemisphere
• Mean nearsurface
temperature
(red/orange)
• storm
frequency
index (blue),
• and storm
shift index
(green)
• 2 band of
preindustrial
conditions
Storm shift index defined as PCs of storm
frequency EOFs
Conclusions, 3a
• During historical times storminess on both
hemispheres is remarkably stationary with little
variability.
• During historical times, storminess and large-scale
temperature variations are mostly decoupled.
• In the climate change scenarios, with a strong
increase of greenhouse concentrations, both
temperature and storminess rise quickly beyond the
2σ-range of pre-industrial variations.
• There are indications for a poleward shift of the
regions with high storm frequency on both
hemispheres with future warming. Altogether, we
have ascertained an increase of the North Atlantic
and SH storm frequency index, whereas the North
Pacific storm frequency index decreases with
beginning industrialization.
Time series of annually averaged variables in the “Erik den Røde” simulation – the forcing
factors: solar energy intercepted (variations are due to changing solar output and presence of
volcanic aerosols in the atmosphere; green) and greenhouse gases (carbon dioxide (yellow)
and methane (dark blue)), - the globally averaged air temperature (red), and the North Atlantic
Deep Water index NADW (light blue). The forcing until 1990 is estimated from observations
and indirect evidence; after 1990 the natural forcing factors are assumed to be constant, and
the greenhouse-gas concentrations are increased according to the IPCC SRES scenario A2.
Conclusions, 3b
• During historical times Atlantic meridional
overturning is remarkably stationary with little
variability in the Erik den Røde simulation
• During historical times, MOC and large-scale
temperature variations are mostly decoupled.
• In the climate change scenarios, with a strong
increase of greenhouse concentrations, temperature
quickly beyond the 2σ-range of pre-industrial
variations; at the same time the overturning weakens
significantly.
• In the artificial world of Erik den Røde detection of
anthropogenic climate change in terms of MOC could
be achieved in about 2000.
Overall conclusions
• Multi-century simulations with state-ofthe art GCMs are useful for
• … examining diagnostic (statistical)
methods, incl. proxy assessments.
• … deriving hypotheses about the free
and forced variability in historical
times, hereby provided benchmakrs
needed for detecting anthropogenic
signals.
Meridional stream-function of the North Atlantic overturning circulation. Shown are
30-year deviations from the 1000-1800 pre-industrial “normal” in Sverdrup (left) and
signal-to-noise ratios of these changes (i.e., 30-yr anomalies divided by two
standard deviations of 30-year mean variations during pre-industrial times; right).
Top:1970-2000; Bottom: 2070-2100 according to the A2 scenario.
Left column:
Leading EOFs of
storm frequency
for the preindustrial period
of experiment H2
for the North
Atlantic, North
Pacific and SH
region (top to
bottom).
Right column:
Corresponding
patterns of linear
slope coefficient
displayed at each
grid point for the
climate change
experiment A2
determined by a
linear trend
analysis.