Dynamical responses to volcanic forcings in climate model simulations

Dynamical responses to volcanic forcings in

climate model simulationsDynVar workshop 22.04.13

Matthew Toohey

with Kirstin Krüger, Claudia Timmreck,

Hauke Schmidt

• What would happen if a large volcanic eruption occurred tomorrow?

→ Every seasonal to decadal climate forecast made prior to the eruption would become obsolete.

Motivation

Thompson et al. (2012)Thompson et al. (2009)

Motivation

“Winter Warming”

Robock and Mao (1992)

Post-volcanic dynamical anomalies

Baldwin andDunkerton. 2001

Christiansen, 2008

13 eruptions Schmidt et al., 2013

Stratospheric mechanism

Stenchikov et al. (2002)

• A number of studies have reported realistic simulation of post-volcanic NH dynamical anomalies (Graf et al., 1993, 1994; Mao and Robock, 1998; Kirchner et al., 1999; Shindell et al., 2001; Rozanov et al., 2002; Stenchikov et al., 2002; Collins, 2004; Shindell et al., 2003, Shindell et al. 2004)

• But multi-model studies (e.g. CMIP, CCMVal-2) have not produced a convincing picture of model behavior.

Model results

CCMVal-2 post-eruption T anomalies

Ch. 8 in SPARC, CCMVal Report, 2010

CMIP59 eruptionsn=18

9 eruptions13 models72 members

9 eruptions13 models72 members

4 eruptionsn=8

Driscoll et al. 2012

Sea level Pressure

50 hPaGeopotential height

CMIP5

Charlton-Perez et al., 2013

Low-topHigh-topERA-interim

CMIP5

Stratospheric mechanism

Stenchikov et al. (2002)

?

?

•Why don’t CMIP5 models show strong NH winter vortices (i.e., negative polar cap z50 anomalies) after volcanic eruptions?→Either

1. Response is not real (just chance?)2. Models are flawed3. Implementation of volcanic aerosol forcing is

flawed4. Volcanic aerosol forcing is flawed

The question

CMIP volcanic forcings

0.4

0.3

0.2

0.1

0

Sato et al. (1990)/GISS/Stenchikov Ammann (2003)/(2007)

• Pinatubo and El Chichon based on SAGE observations

• Recently updated with OSIRIS observations Oct 2001 - present

• Best estimate sulfur mass injection, distributed via parameterized stratospheric transport model

Jan 92

Jul 91Jan 92

Jan 91

Jan 92

Jul 91Jan 92

Jan 91

• Notes: zonal mean, monthly mean, for pre-satellite era eruptions, spatial distribution of aerosols poorly constrained

CMIP Volcanic forcings

Sato et al. (1990)/GISS/Stenchikov

• Part 1:• Use MAECHAM5-HAM, a coupled aerosol-climate

model, to simulate the evolution of stratospheric sulfate aerosol after a Pinatubo-like eruption.

• Part 2:• Use MPI-ESM, a high-top CMIP5 model, and replace

the prescribed Pinatubo volcanic forcing from historical simulations with forcing sets built from Part 1.

Experiment

• MPI-ESM: full Earth System model, with atmosphere, ocean, carbon cycle, vegetation components. • Atmospheric component ECHAM6. • “low resolution” (LR, T63/L47), configuration used here

(no QBO).

• Volcanic aerosols are prescribed• CMIP5 historical simulations use Stenchikov et al.

(1998) forcing data set -> monthly mean, zonal mean aerosol extinction, single scattering albedo, and asymmetry factor

MPI-ESM

• ECHAM: GCM developed at MPI-M, Hamburg• Middle atmosphere version: 39 vertical levels up to 0.01 hPa (~80 km)• T42 horizontal resolution• Climatological sea surface temperatures, no QBO, no chemistry

• HAM: Aerosol microphysical module• Modified for simulation of stratospheric volcanic aerosols• Models aerosol growth, radiative effects, eventual removal

MAECHAM5-HAM

Inject SO2 at 24 km

Aerosol growthRadiative effects

Aerosol transport via atmospheric

circulation

Transport to troposphere,

rainout!

HAM

ECHAM5SO2→ H2SO4

Toohey et al (2011, ACP)

MAECHAM5-HAM Pinatubo simulations

• Simulations of 17 Tg eruption, June 15, 15.3°N• Excellent agreement with ERBE TOA SW flux anomalies

observed after Pinatubo eruption. Little to no dependence on eruption longitude.

Modeled aerosol transport

months after eruption months after eruption

Toohey et al. (2011)

HAM July eruption simulations: DJF1

Temperature Geopotential height Zonal wind

n=12

DJF1 z50 anomalies

n=12

July eruptions April, July and October eruptions

n=36

AOD: July eruption ensemble variability

Weak and Strong vortex composite AOD

n=12

July eruptions

Vortex strength ~ AOD gradient?

Polar cap gph anomaly calculated as area mean over 70-90N.AOD gradient at 60N as AOD(60-90N) – AOD(50-60N)

Vortex strength ~ AOD gradient?

Strong Vortex AOD gradient across vortex

Aerosol heating gradient?

If we want our prescribed aerosols to force a strong vortex, the forcing had better take the form of a strong vortex.

MPI-ESM Pinatubo forcing experiment

Stenchikov (CMIP5)

HAM weak

HAM strong

r1,r2,r3r4,r5,r6r7,r8,r9

Aerosol extinction at 550 nmSt

ench

ikov

HA

M w

eak

HA

M s

tron

g

MPI-ESM: tropical 50 hPa T

MPI-ESM: DJF1 T and u anomaliesStenchikov HAM weak HAM strong

Tem

pera

ture

(K)

u w

ind

(m/s

)

MPI-ESM: DJF1 z50 anomalies

Low-topHigh-topERA-interim

MPI-ESM: DJF1 z50 anomalies

Low-topHigh-topERA-interim

MPI-ESM: DJF1&2 z50 anomalies

Low-topHigh-topERA-interim

CMIP5

Aerosol extinction at 550 nmSt

ench

ikov

HA

M w

eak

HA

M s

tron

g

Arfeuille et al. ACPD 2013

Extinction at 550 nm

August

• CCMI: Surface Area Densities (SADs), stratospheric heating rates, and radiative properties, based on SAGE_4λ retrievals (Tom Peter and Beiping Luo, ETHZ)

Volcanic forcing, the next generation

• Model-based aerosol reconstructions becoming available for pre-satellite era eruptions.

Tambora: Arfeuille et al. (2013) vs. Crowley (2008)

• For a CMIP5 historical-style simulation of Pinatubo, we can control the strength of the (ensemble mean) post-eruption NH winter vortex with the aerosol forcing set• Vortex strength ~ AOD gradient across vortex edge

→ Likely that dynamical response to volcanic eruptions can be „improved“ by using different forcing data sets.

→ Future work will show whether new volcanic forcing sets lead to better dynamical responses in climate models.

Conclusions

Volcanic vs. Anthropogenic forcing