utility of multi-century aogcm runs – formulating hypotheses about past climates and testing...

Utility of multi-century AOGCM runs –

formulating hypotheses about past climates and testing methods".

Hans von Storch

Institute for Coastal ResearchGKSS Research CenterGeesthacht, Germany

Erik den Røde

1. Experimental set-up of Erik den Røde2. 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” (1000-1990)

and “Christoph Columbus” (1550-1990)

with estimated volcanic, GHG and solar forcing

1675-1710vs. 1550-1800

Reconstruction from historical evidence, from Luterbacher et al.

Late Maunder Minimum

Model-based reconstuction

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.

pseudo-proxies: grid point SAT plus white noise

red: mimicking largest sample used in MBHblue: hypothetic additional data to obtain better coverage.

Testing the MBH method

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 pseudo-proxy 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 Rückversicherungs-Gesellschaft: Wetterkatastrophen und Klimawandel. Sind wir noch zu retten? Eigenverlag Münchener Rückversicherungs-Gesellschaft, 107-113)

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 Hollandde 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 pre-industrial 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 near-surface 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 near-

surface 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 near-surface 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-of-the 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 pre-industrial 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.