Dynamical responses to volcanic forcings in climate model simulations DynVar workshop 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 and Dunkerton Christiansen, eruptions Schmidt et al., 2013

Stratospheric mechanism Stenchikov et al. (2002)

Dynamical response to volcanic forcing = anomalous dynamics in 1 st (and sometimes 2 nd ) Northern Hemisphere winter after a major lower latitude volcanic eruption, characterized by: Positive NAO (NAM) at surface Strong stratospheric vortex westerlies Negative anomalies of polar cap lower stratosphere geopotential height Definitions

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

CMIP5 9 eruptions n=18 9 eruptions 13 models 72 members 9 eruptions 13 models 72 members 4 eruptions n=8 Driscoll et al Sea level Pressure 50 hPa Geopotential height

CMIP5 Charlton-Perez et al., 2013 Low-top High-top ERA-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 flawed 3.Implementation of volcanic aerosol forcing is flawed 4.Volcanic aerosol forcing is flawed The question

CMIP volcanic forcings Sato et al. (1990)/GISS/Stenchikov Ammann (2003)/(2007) Pinatubo and El Chichon based on SAGE observations Recently updated with OSIRIS observations Oct present Best estimate sulfur mass injection, distributed via parameterized stratospheric transport model Jan 92Jul 91Jan 92Jan 91Jan 92Jul 91Jan 92Jan 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 SO 2 at 24 km Aerosol growth Radiative effects Aerosol transport via atmospheric circulation Transport to troposphere, rainout! HAM ECHAM5 SO 2 → H 2 SO 4

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 Toohey et al. (2011)

HAM July eruption simulations: DJF1 TemperatureGeopotential heightZonal 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,r3 r4,r5,r6 r7,r8,r9

Aerosol extinction at 550 nm Stenchikov HAM weak HAM strong

MPI-ESM: tropical 50 hPa T

MPI-ESM: DJF1 T and u anomalies StenchikovHAM weakHAM strong Temperature (K) u wind (m/s)

MPI-ESM: DJF1 z50 anomalies Low-top High-top ERA-interim

MPI-ESM: DJF1 z50 anomalies Low-top High-top ERA-interim

MPI-ESM: DJF1&2 z50 anomalies Low-top High-top ERA-interim CMIP5

Aerosol extinction at 550 nm Stenchikov HAM weak HAM strong

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