- By
**fisk** - Follow User

- 43 Views
- Uploaded on

Download Presentation
## PowerPoint Slideshow about ' IDENTIFYING THE VOLCANO SIGNAL WITH PCM' - fisk

**An Image/Link below is provided (as is) to download presentation**

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

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

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

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

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

IDENTIFYING THE VOLCANO SIGNAL WITH AN UPWELLING-DIFFUSION ENERGY-BALANCE MODEL (MAGICC)

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)

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

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.

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 is closely proportional to peak forcing (3%)

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.

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)]

Download Presentation

Connecting to Server..