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IDENTIFYING THE VOLCANO SIGNAL WITH PCM

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Presentation Transcript
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SUMMARY AND GOALS• To identify the volcanic response signal in the signal+noise of a set of AOGCM runs (PCM)• To see how well this signal can be reproduced with a simple upwelling-diffusion energy-balance climate model• To use the UD EBM to determine the characteristics of volcanic response and how these vary with the climate sensitivity

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PCM experiments with volcanic forcing•Volcanoes only• Solar + Volcanoes• Solar, Volcanoes and Ozone• ‘ALL’ = S, V, O + Greenhouse gases + direct sulfate Aerosols

ensemble averaging n 1 to 4
Ensemble averaging: n=1 to 4

Eruption dates for Santa Maria, Agung, El Chichon and Pinatubo marked.

Note how difficult it is to estimate the maximum cooling signals with only one realization.

ensemble averaging n 4 to 16
Ensemble averaging: n=4 to 16

Eruption dates for Santa Maria, Agung, El Chichon and Pinatubo marked

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IDENTIFYING THE SIGNAL WITH MAGICC: METHOD• Use MAGICC model parameters from IPCC Ch. 9 based on fit to 1% compound CO2 CMIP simulation(note that this is decadal timescale forcing, while the volcanic forcing is on a monthly timescale)• Drive MAGICC with forcing used in the PCM experiments (from Caspar Ammann)

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VOLCANIC ERUPTION SIGNAL16-member ensemble-mean from PCM [signal plus noise] compared with simulation using the simple UD EBM ‘MAGICC’ [pure signal].

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The excellent fit between the MAGICC and PCM results, the fact that MAGICC gives a ‘pure’ signal, and the fact that the climate sensitivity is a user-input parameter in MAGICC means that we can use MAGICC to obtain greater insight into the character of the volcanic forcing response signal.

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Simple energy balance equation

C dDT/dt + DT/S = Q(t) = A sin(wt).

The solution is

DT(t) = [(wt)2/(1+(wt)2)] exp(-t/t) + [S/(1+(wt)2)][A{sin(wt) – wt cos(wt)}]

where t is a characteristic time scale for the system, t = SC.

Low-frequency forcing (w << 1/t), solution is simply the equilibrium response

DT(t) = S A sin(wt)

showing no appreciable lag between forcing and response, with the response being linearly dependent on the climate sensitivity and independent of the system’s heat capacity.

High-frequency case (w >> 1/t) the solution is

DT(t) = [A/(wC)] sin(wt – p/2)

showing a quarter cycle lag of response behind forcing, with the response being independent of the climate sensitivity.

peak cooling as a function of climate sensitivity
PEAK COOLING AS A FUNCTION OF CLIMATE SENSITIVITY

Peak cooling is closely proportional to peak forcing (3%)

decay time as a function of climate sensitivity
DECAY TIME AS A FUNCTION OF CLIMATE SENSITIVITY

Relaxation back to the initial state is slightly slower than exponential, so the apparent e-folding time increases with time. The above are minimum e-folding times.

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CONCLUSIONS• Peak cooling is relatively insensitive to DT2x [DTmax(DT2x)  DTmax(1) + a ln(DT2x)]• Relaxation time is 26–42 months, logarithmic in DT2x• Observed peak coolings can be used to estimate DT2x, but uncertainties are large due to internal variability noise in the observations• Long timescale response cannot be used to estimate DT2x because the residual signal is too small relative to internal variability noise [contrast with Lindzen and Giannitsis, 1998)]

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