Introduction
Stratospheric aerosol is an important component of the Earth system, which
influences atmospheric radiative transfer, composition, and dynamics, thereby
modulating the climate. The effects of stratospheric aerosol on climate are
especially evident when the opacity of the stratospheric aerosol layer is
significantly increased after volcanic eruptions. Through changes in the
radiative properties of the stratospheric aerosol layer, volcanic eruptions
are a significant driver of climate variability (e.g. Myhre et al., 2013;
Zanchettin et al., 2016). Major volcanic eruptions inject vast amounts of
SO2 into the stratosphere, which is converted into sulfuric acid
aerosol with an e-folding time of about a month, which might be prolonged
due to OH depletion within the dense SO2 cloud in the first weeks
following a large volcanic eruption (Mills et al., 2017).
Observations show that the stratospheric aerosol layer remains enhanced for
several years after major eruptions (SPARC, 2006). Such long-lasting volcanic
perturbations cool the Earth's surface by scattering incoming solar
radiation and warm the stratosphere by the absorption of infrared solar and
long-wave terrestrial radiation which affect the dynamical structure as well
as the chemical composition of the atmosphere (e.g. Robock, 2000; Timmreck,
2012). The consequent heating of the stratospheric sulfate layer, impacts
stratospheric dynamics in various ways. It amplifies the Brewer–Dobson
circulation (BDC) and modifies the equator-to-pole temperature gradient,
driving changes in geostrophic zonal winds and the propagation of
atmospheric waves (e.g. Bittner et al., 2016; Toohey et al., 2014) and
strengthening the polar vortex (e.g. Charlton-Perez et al., 2013). The
heating from continued SO2 injection to the stratosphere may further
disturb or even “shut down” the quasi-biennial oscillation (QBO)
(e.g. Aquila et al., 2014; Niemeier and Schmidt, 2017). The radiatively driven
changes also influence the transport and the lifetime of long-lived species
(N2O, CH4) (Pitari et al., 2016a; Visioni et al., 2017). The
enhanced stratospheric aerosol layer after large volcanic eruptions also causes large mean age-of-air variations on timescales of several years
(e.g. Ray et al., 2014; Muthers et al., 2016; Garfinkel et al., 2017).
As the ocean has a much longer memory than the atmosphere, large volcanic
eruptions could have a long-lasting impact on the climate system that
extends beyond the duration of the volcanic forcing (e.g., Zanchettin et
al., 2012; Swingedouw et al., 2017). The chemical and radiative effects of
the stratospheric aerosol are strongly influenced by its particle size
distribution. Heterogeneous chemical reactions, which most notably lead to
substantial ozone depletion (e.g. WMO/UNEP, 2007, chap. 3),
take place on the surface of the stratospheric aerosol particles and are
dependent on the aerosol surface area density. Aerosol particle size
determines the scattering efficiency of the particles (e.g. Lacis et al.,
1992) and their atmospheric lifetime
(e.g., Pinto et al., 1989; Timmreck et al., 2010). Smaller-magnitude eruptions than the 1991 Mt Pinatubo eruption can
also have significant impacts on climate. It is now established that a
series of relatively small-magnitude volcanic eruptions caused the increase
in stratospheric aerosol observed between 2000 and 2010 based on ground and satellite-borne observations (Vernier et al., 2011b;
Neely III et al., 2013). Studies have suggested that this increase in
stratospheric aerosol partly counteracted the warming due to increased
greenhouse gases over that period (e.g. Solomon et al., 2011; Ridley et al.,
2014; Santer et al., 2015). Small to moderate volcanic eruptions after 2008
also show an impact on the stratospheric circulation in the Northern
Hemisphere, in particular on the pattern of decadal mean age variability and
its trends during 2002–2011 (Diallo et al., 2017). Since the 2006 SPARC
Assessment of Stratospheric Aerosol Properties Report (SPARC, 2006, herein
referred to as ASAP2006) the increase in observations of stratospheric aerosol
and its precursor gases and in the number of models which treat
stratospheric aerosol interactively has advanced scientific understanding
of the stratospheric aerosol layer and its effects on the climate (Kremser
et al., 2016, herein referred to as KTH2016). In particular, research
findings have given to the community a greater awareness of the role of the
tropical tropopause layer (TTL) as a distinct pathway for transport into the
stratosphere, of the interactions between stratospheric composition and
dynamics, and of the importance of moderate-magnitude eruptions in
influencing the stratospheric aerosol loading. In addition, over the last
decade several new satellite instruments producing observations relevant to
the stratospheric aerosol layer have become operational. For example, we now
have a 2002–2012 record of global altitude-resolved SO2, carbonyl
sulfide (OCS) and aerosol volume density measurements provided by the
Michelson Interferometer for Passive Atmospheric Sounding Environmental
Satellite (MIPAS ENVISAT; Höpfner et al., 2013, 2015; Glatthor et al.,
2015; Günther et al., 2018). Furthermore aerosol extinction vertical
profiles are available from limb-profiling instruments, such as the Scanning
Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY;
2002–2012; Bovensmann et al., 1999; von Savigny et al., 2015), the Optical
Spectrograph and InfraRed Imager System (OSIRIS; 2001–present; Bourassa et
al., 2007), and the Ozone Mapping and Profiler Suite–Limb Profiler (OMPS-LP; 2011–present; Rault and Loughman, 2013), and from the active sensor lidar
measurements such as the Cloud-Aerosol Transport System (CATS; 2015–present; Yorks et al., 2015) and Cloud-Aerosol Lidar with Orthogonal Polarization
(CALIOP; 2006-present; Vernier et al., 2009). Existing measurements have
become more robust, for example by homogenizing the observations of aerosol
properties derived from optical particle counter (OPC) and satellite
measurements during stratospheric aerosol background periods (Kovilakam and
Deshler, 2015), which previously showed large differences (Thomason et al.,
2008). Other efforts include combining and comparing different satellite
data sets (e.g. Rieger et al., 2015). However, some notable discrepancies
still exist between different measurement data sets. For example, Reeves et
al. (2008) showed that aircraft-borne Focused Cavity Aerosol Spectrometer (FCAS)
measurements of the particle size distribution during the late 1990s
yield surface area densities a factor 1.5 to 3 higher than that derived from
Stratospheric Aerosol and Gases Experiment (SAGE-II) measurements.
On the modelling side there has been an increasing amount of global
three-dimensional stratospheric aerosol models developed within the last
years and used by research teams around the world (KTH2016). The majority of
these global models explicitly simulate aerosol microphysical processes and
treat the full life cycle of stratospheric aerosol, from the initial
injection of sulfur-containing gases and their transformation into aerosol
particles to their final removal from the stratosphere. Several of these
models also include the interactive coupling between aerosol microphysics,
atmospheric chemistry, dynamics, and radiation.
Given the improvements in observations and modelling of stratospheric
aerosol since ASAP2006, we anticipate further advances in our understanding
of stratospheric aerosol by combining the recent observational record with
results from the current community of interactive stratospheric aerosol
models. An Interactive Stratospheric Aerosol Model Intercomparison Project (ISA-MIP)
has therefore been developed within the Stratospheric Sulfur and its Role in Climate (SSiRC) framework. The SPARC
activity SSiRC (http://www.sparc-ssirc.org, last access: 26 June 2018) was initiated with the goal of reducing uncertainties
in the properties of stratospheric aerosol and assessing its climate forcing. In particular, constraining simulations of historical eruptions with
available observational data sets gives the potential to evaluate and
substantially improve the accuracy of the volcanic forcing data sets used in
climate models. This will not only enhance consistency with observed
stratospheric aerosol properties and the underlying microphysical, chemical,
and dynamical processes but also improve their conceptual understanding. The
use of such new volcanic forcing data sets has the potential to increase the
reliability of the simulated climate impacts of volcanic eruptions, which
have been identified as a major influence on decadal global mean surface
temperature trends in climate models (Marotzke and Forster, 2015).
The first international model inter-comparison of global stratospheric
aerosol models was carried out within ASAP2006 and indicated that model
simulations and satellite observations of stratospheric background aerosol
extinction agree reasonably well in the visible wavelengths but not in the
infrared. It also highlighted systematic differences between modelled and
retrieved aerosol size, which have later been linked to shortcomings in the
retrieval methods with regard to the detection of Aitken mode sized
particles(R < 50 nm) in the lower stratosphere (Thomason et al., 2008;
Reeves et al., 2008; Hommel et al., 2011). While in ASAP2006, only five
global two- and three-dimensional stratospheric aerosol models were included
in the analysis, there are today more than 15 global three-dimensional models
available worldwide (KTH2016). No large comprehensive model intercomparison
has ever been carried out to identify differences in stratospheric aerosol
properties amongst these new interactive models. The models often show
significant differences in terms of their simulated transport, chemistry, and
removal of aerosols with inter-model differences in stratospheric
circulation, radiative dynamical interactions, and exchange with the
troposphere likely to play an important role (e.g. Aquila et al., 2012;
Niemeier and Timmreck, 2015). The formulation of microphysical processes is
also important (e.g. English et al., 2013), as are differing assumptions
regarding the sources of stratospheric aerosols and their precursors. A
combination of these effects likely explain the large inter-model differences
as seen in Fig. 1 among the global stratospheric aerosol models which
participated in the Tambora intercomparison, a precursor to the “consensus
volcanic forcings” aspects of the CMIP6 Model Intercomparison Project on the
climatic response to volcanic forcing (VolMIP; Zanchettin et al., 2016;
Marshall et al., 2018). Even for the relatively recent 1991 Mt Pinatubo
eruption, to reach the best agreement with observations, interactive
stratospheric models have used a wide range of SO2 injections
amounts, from as low at 10 Tg SO2 (Dhomse et al., 2014; Mills et
al., 2016) to as high as 20 Tg SO2 (e.g. Aquila et al., 2012;
English et al., 2013).
Volcanic eruptions are commonly taken as a real-world analogue for
hypothesized geoengineering via stratospheric sulfur solar radiation
management (SS-SRM). Indeed many of the assumptions and uncertainties
related to simulated volcanic perturbations of stratospheric aerosol are
also frequently given as caveats around research findings from modelling
studies which seek to quantify the likely effects from SS-SRM (e.g. National
Research Council, 2015), the mechanism steps between sulfur injection and
radiative cooling being common to both aspects (Robock et al., 2013). We expect the
analysis of the ISA-MIP experiments to improve our understanding of
model sensitivities to key sources of uncertainty and to inform the interpretation
of coupled climate model simulations and the next Intergovernmental Panel on
Climate Change (IPCC) assessment. It will also provide a foundation for
co-operation to assess the atmospheric and climate changes when the next
large-magnitude eruption takes place.
Uncertainty in estimates of radiative forcing parameters for the
1815 eruption of Mt Tambora: global average aerosol optical depth (AOD) in
the visible band from an ensemble of simulations with chemistry–climate models
forced with a 60 Tg SO2 equatorial eruption, from the Easy Volcanic
Aerosol (EVA; Toohey et al., 2016b) module with 56.2 Tg SO2
equatorial eruptions (thick dashed magenta line), from Stoffel et al. (2015),
from Crowley and Unterman (2013), and from Gao et al. (2008; aligned so that
the eruption starts on April 1815). The estimate for the Pinatubo eruption as
used in the CMIP6 historical experiment is also reported for comparison. The
black triangle shows latitudinal position and timing of the eruption.
Chemistry–climate models are CESM (WACCM) (Mills et al., 2016),
MAECHAM5-HAM (Niemeier et al., 2009), SOCOL (Sheng et al., 2015a), UM-UKCA
(Dhomse et al., 2014), and CAMB-UPMC-M2D (Bekki, 1995; Bekki et al., 1996).
For models producing an ensemble of simulations, the line and shading are
the ensemble mean and ensemble standard deviation respectively. Figure from
Zanchettin et al. (2016).
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In this paper, we introduce the new model intercomparison project ISA-MIP
developed within the SSiRC framework. In Sect. 2 we provide an overview of
the current state of stratospheric sulfur aerosol modelling and its
greatest challenges. In Sect. 3 we describe the scopes and protocols of
the four model experiments planned within ISA-MIP. A concluding summary is
provided in Sect. 4.
Modelling stratospheric aerosol: overview and challenges
Before we discuss the current state of stratospheric aerosol modelling and
its greatest challenges in detail, we briefly describe the main features of
the stratospheric sulfur cycle. We are aware of the fact that the
stratospheric aerosol layer also contains organics and inclusions of
meteoritic dust (Ebert et al., 2016) and, after volcanic events, also
co-exists with volcanic ash (e.g. Pueschel et al., 1994: KTH2016). However,
the focus of the ISA-MIP experiments described here is on a comparison with measurements of the overall optical and physical properties of the
stratospheric aerosol layer, which is mainly determined by sulfate.
The stratospheric aerosol life cycle
The stratospheric aerosol layer and its temporal and spatial variability are
determined by the transport of aerosol and aerosol precursors in the
stratosphere and their modification by chemical and microphysical processes
(Hamill et al., 1997; ASAP2006; KTH2016). Volcanic eruptions can inject
sulfur-bearing gases directly into the stratosphere, which significantly
enhances the stratospheric aerosol load for years. A number of observations
show that stratospheric aerosol increased over the first decade of the
21st century (e.g. Hofmann et al., 2009; Vernier et al., 2011b; Ridley
et al., 2014). Although such an increase was attributed to the possible cause
of Asian anthropogenic emission increase (Hofmann et al., 2009), later
studies have shown that small-to-moderate magnitude volcanic eruptions are
likely to be the major source of this recent increase (Vernier et al.,
2011b; Neely III et al., 2013; Brühl et al., 2015).
Besides major volcanic eruptions, the photochemical
oxidation of OCS, an insoluble gas mainly inert in the troposphere, is a stratospheric source.
Tropospheric aerosols and aerosol precursors also enter the stratosphere
through the tropical tropopause and through convective updrafts in the Asian
and North American monsoons (Hofmann et al., 2009; Hommel et al., 2011;
Vernier et al., 2011a; Bourassa et al., 2012; Yu et al., 2015). In the
stratosphere, new sulfate aerosol particles are formed by binary homogenous
nucleation (Vehkamäki et al., 2002), a process in which sulfuric acid
vapour (H2SO4(g)) and water vapour condense simultaneously to form
a liquid droplet. The condensation of H2SO4(g) onto pre-existing
aerosol particles and the coagulation among particles shift the aerosol size
distribution to greater radii. This takes place especially under
volcanically perturbed conditions, when the concentrations of aerosol in the
stratosphere are higher (e.g. Deshler, 2008).
From the tropics, where most of the tropospheric aerosol enters the
stratosphere and the OCS chemistry is most active, the stratospheric aerosol
particles are transported poleward within the large-scale BDC and removed
through gravitational sedimentation and cross-tropopause transport in the
extratropical regions. Internal variability associated with the QBO alters
the isolation of the tropical stratosphere and subsequently the poleward
transport of tropical stratospheric aerosol and modifies its global
dispersal, particle size distribution, and residence time (e.g. Trepte and
Hitchman, 1992; Hommel et al., 2015; Pitari et al., 2016b)
In general, under volcanically perturbed conditions with larger amounts of
injected SO2, aerosol particles grow to much larger radii than in
volcanically quiescent conditions (e.g. Deshler, 2008). The simulation of extremely
large volcanic sulfur-rich eruptions shows a shift to particle sizes even
larger than observed after the Pinatubo eruption and predicts a reduced
cooling efficiency compared to moderate eruptions with moderate sulfur
injections (e.g. Timmreck et al., 2010; English et al., 2013).
Global stratospheric aerosol models: current status and challenges
A comprehensive simulation of the spatio-temporal evolution of the particle
size distribution is a continuing challenge for stratospheric aerosol
models. Due to computational constraints, the formation of the stratospheric
aerosol and the temporal evolution of its size distribution are usually
parameterized with various degrees of complexity in global models. The
simplest way to simulate the stratospheric aerosol distribution in global
climate models is the mass-only (bulk) approach (e.g. Timmreck et al.,
1999a, 2003; Aquila et al., 2012), where only the total sulfate mass is
prognostically simulated and chemical and radiative processes are calculated
assuming a fixed typical particle size distribution. More complex methods
are size-segregated approaches, such as the modal approach (e.g. Niemeier et
al., 2009; Toohey et al., 2011; Brühl et al., 2012; Dhomse et al., 2014;
Mills et al., 2016), where the aerosol size distribution is simulated using
one or more modes, usually of log-normal shape. The mean radius of each mode
of these size distributions varies in time and space. Another common
approach is the sectional method (e.g. English et al., 2011; Hommel et al.,
2011; Sheng et al., 2015a; for ref prior to 2006 see ASAP2006, chap. 5),
where the particle size distribution is divided into distinct size sections.
Number and width of the size sections are dependent on the specific model
configuration but are fixed throughout time and space. Size sections may be
defined by an average radius, or by an average mass of sulfur, and are
often spaced geometrically.
The choice of methods has an influence on simulated stratospheric aerosol
size distributions and therefore on radiative and chemical effects. While
previous model intercomparison studies in a box model (Kokkola et al., 2009)
or in a two-dimensional framework (Weisenstein et al., 2007) were very
useful for the microphysical schemes, they could not address uncertainties
in the spatial transport pattern, e.g. transport across the tropopause and
the subtropical transport barrier or regional/local differences in wet and
dry removal. These uncertainties can only be addressed in a global
three-dimensional model framework and with a careful validation with a
variety of observational data.
Schematic overview over the processes that influence the stratospheric
aerosol size distribution. The related SSiRC experiments are listed below.
BG stands for Background, TAR for Transient Aerosol Record, HErSEA for
Historical Eruption SO2 Emission Assessment, and PoEMs for
Pinatubo Emulation in Multiple models.
![]()
(a) Composite of QBO-induced residual anomalies in the
MAECHAM5-SAM2 modelled aerosol mass mixing ratio with respect to the time of
onset of westerly zonal mean zonal wind at 18 hPa. Black contours denote the
residual zonal wind. Dashed lines represent easterlies, contour interval is
5 ms. (b) Same but for the modelled effective radius of aerosols with
R ≥ 50 nm. Figure from Hommel et al. (2015).
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The June 1991 eruption of Mt Pinatubo, with the vast net of observations
that tracked the evolution of the volcanic aerosol, provides a unique
opportunity to test and validate global stratospheric aerosol models and
their ability to simulate stratospheric transport processes. Previous model
studies (e.g. Timmreck et al., 1999b; Aquila et al., 2012) highlighted the
importance of an interactive online treatment of stratospheric aerosol
radiative heating for the simulated transport of the volcanic cloud. A
crucial point is the simulation of the tropical stratospheric aerosol
reservoir (i.e., the tropical pipe, Plumb, 1996) and the meridional
transport through the subtropical transport barrier. Some models show a very
narrow tropical maximum in comparison to satellite data (e.g., Dhomse et al.,
2014) while others show too fast a transport to higher latitudes and fail to
reproduce the long persistence of the tropical aerosol reservoir
(e.g. Niemeier et al., 2009; English et al., 2013). Sulfate geoengineering
studies confirm the importance of the model-dependent meridional transport
through the subtropical barrier (e.g. Niemeier and Timmreck, 2015; Visoni et
al., 2018; Kleinschmitt et al., 2018). Reasons for these differences need to
be understood with a multi-model comparison study, as suggested for example
by Tilmes et al. (2015).
The ISA-MIP experiments
Many uncertainties remain in the model representation of stratospheric
aerosol. Figure 2 summarizes the main processes that determine the
stratospheric sulfate aerosol mass load, size distribution, and the
associated optical properties. The four experiments in ISA-MIP are designed
to address these key processes under a well-defined experiment protocol with
prescribed boundary conditions (sea surface temperatures (SSTs), emissions).
All simulations will be compared to observations to evaluate model
performances and understand model strengths and weaknesses. The experiment
Background (BG) focuses on microphysics and transport (Sect. 3.1)
under volcanically quiescent conditions, when stratospheric aerosol is only
modulated by seasonal changes and interannual variability. The experiment
Transient Aerosol Record (TAR) addresses the role of time-varying
SO2 emission, in particular the role of small- to moderate-magnitude
volcanic eruptions and transport processes in the upper troposphere–lower
stratosphere (UTLS) over the period 1998–2012 (Sect. 3.2). Two further
experiments investigate the stratospheric sulfate aerosol size distribution
under the influence of large volcanic eruptions. The
Historical Eruptions SO2 Emission Assessment (HErSEA) focuses on the
uncertainty in the initial emission characteristics of recent large volcanic
eruptions (Sect. 3.3), while Pinatubo Emulation in
Multiple models (PoEMS) provides an extensive uncertainty
analysis of the radiative forcing of the Mt Pinatubo eruption. In
particular the ISA-MIP model experiments aim to address the following questions:
How large is the stratospheric sulfate load under volcanically quiescent
conditions, and how sensitive is the simulation of this background aerosol
layer to model-specific microphysical parameterization and transport? (Sect. 3.1)
Can we explain the sources and mechanisms behind the observed variability
in stratospheric aerosol load since the year 2000? (Sect. 3.2)
Can stratospheric aerosol observations constrain uncertainties in the
initial sulfur injection amount and altitude distribution of the three largest
volcanic eruptions of the last 100 years? (Sect. 3.3)
What is the confidence interval for volcanic forcing of the Pinatubo
eruption simulated by interactive stratospheric aerosol models and to which
parameter uncertainties are the predictions most sensitive to? (Sect. 3.4)
Table 1 gives an overview over all ISA-MIP experiments, which are described
in detail below. In general each experiment will include several simulations
from which only a subset is mandatory (Tier1). The modelling groups are free
to choose in which of the experiments they would like to participate; however, the BG Tier1 simulation is mandatory for all groups and the entry
card for the ISA-MIP intercomparison. All model results will be saved in a
consistent format (netCDF), made available via
https://cera-www.dkrz.de/WDCC/ui/cerasearch/ (last access: 26 June 2018), and compared to a set of benchmark
observations. More detailed technical information about data requests can be
found in the Supplement and on the ISA-MIP webpage:
http://www.isamip.eu (last access: 26 June 2018).
General overview of the SSIRC ISA-MIP experiments.
Experiment
Focus
Number of specific
Years
Total
Knowledge gap to be addressed
experiments
per
yearsa
experiment
Background
Stratospheric sulfur
1 mandatory +
20
20 (60)
20-year climatology to understand
stratospheric
budget in volcanically
2 recommended
sources and sinks of stratospheric
aerosol (BG)
quiescent conditions
background aerosol; assessment of
sulfate aerosol load under
volcanically quiescent conditions
Transient
Transient stratospheric
4 mandatory + 3 optional
15
60
Evaluate models over the period
Aerosol
aerosol properties over
experiments
(75, 105)
1998–2012 with different volcanic
Record
the period 1998 to 2012
recommended are 5 (see
emission data sets;
(TAR)
using different volcanic
also Table 4)
understand drivers and
emission data sets
mechanisms for observed
stratospheric aerosol changes
since 1998
Historic
Perturbation of
For each (× 3) eruption
4
180
Assess how injected SO2
Eruption SO2
stratospheric aerosol;
control, median and
recom. 6
(270)
propagates through to radiative
Emission
from SO2 emission
4 (2 × 2) of high/low
effects for different historical major
Assessment
appropriate for 1991
deep/shallow (see
tropical eruptions in the different
(HErSEA)
Pinatubo, 1982 El
also Table 6)
interactive stratospheric aerosol
Chichón, 1963, Agung
models;
use stratospheric aerosol
measurements to constrain
uncertainties in emissions and gain
new observationally constrained
volcanic forcing and surface area
density data sets;
explore the relationship between
volcanic emission uncertainties
and volcanic forcing
uncertainties
Pinatubo
Perturbed parameter
10 experiments per
3 per
90 (150,
Intercompare Pinatubo perturbation
Emulation in
ensemble of runs to
parameter, where the
experimentc
240)
to stratospheric-aerosol properties with full
Multiple
quantify uncertainty in
number of parameters
uncertainty analysis over PPE run
Models
each model's
refers to the
by each model;
(PoEMS)b
predictions
minimum (3),
quantify sensitivity of predicted
reduced (5), or
Pinatubo perturbation stratospheric
standard (8)
aerosol properties and radiative
parameter set (see
effects to uncertainties in injection
also Table 10)
settings and model processes;
quantify and intercompare sources
of uncertainty in simulated
Pinatubo radiative forcing for the
different complexity models
a Each model will need to include an appropriate
initialization and spin-up time for each ensemble member (∼ 3–6 years
depending on model configuration). b As explained in the caption
to Table 11 and Sect. 3.4, models will need to restrict the PoEMS parameter scaling
to volcanically enhanced air masses (either via a total-sulfur vmr (volume mixing ratio) threshold
or a passive volcanic SO2 tracer). c Although the Pinatubo
enhancement to the stratospheric aerosol layer remained apparent until 1997
(e.g. Wilson et al., 2008), whereas the HErSEA experiments will continue for longer,
the PoEMS analysis will require only 3 post-eruption years to be run, as this
gives sufficient time after the peak aerosol to characterize decay timescales
robustly (e.g. ASAP2006, Sect. 5).
List of stratospheric aerosol and SO2 observations available
for the BG and TAR time period.
Measurement/platform
Time period 1998–2014
Reference
SO2 profile/MLS
2004–2011
Pumphrey et al. (2015)
SO2 profile/MIPAS
2002–2012
Höpfner et al. (2013, 2015)
Aerosol extinction profile, size/SAGE II
1998–2005
Russell and McCormick (1989)
Aerosol extinction profile, size/OSIRIS
2001–2011
McLinden et al. (2012),
Rieger et al. (2015)
Aerosol extinction profile/GOMOS
2002–2021
Vanhellemont et al. (2010)
Aerosol extinction profile/SCIAMACHY
2002–2012
Taha et al. (2011)
von Savigny et al. (2015)
Aerosol extinction profile/CALIOP
2006–2011
Vernier et al. (2009, 2011a, b)
Aerosol extinction or AOD merged products
1998–2011
Rieger et al. (2015)
AOD from AERONET and lidars
Ridley et al. (2014)
Surface area density
Kovilakam and Deshler (2015)
Eyring et al. (2013)
It is mandatory for participating models to run with interactive sulfur
chemistry (see review in SPARC ASAP2006) in order to capture the oxidation
pathway from precursors to aerosol particles, including aerosol growth due
to condensation of H2SO4. Chemistry–climate models (CCMs) with
full interactive chemistry follow the Chemistry Climate Initiative (CCMI)
hindcast scenario REF-C1 (Eyring et al., 2013,
http://www.met.reading.ac.uk/ccmi/?page_id=11, last access: 26 June 2018) for the
treatment of chemical fields and emissions of greenhouse gases (GHGs), ozone-depleting substances (ODSs), and very short-lived substances (VSLSs). Sea
surface temperatures and sea ice extent are prescribed as monthly
climatologies from the MetOffice Hadley Center Observational Dataset (Rayner
et al., 2003). An overview of the boundary conditions is included in the
Supplement (Table S1). Table S2 reports the inventories to be
used for tropospheric emissions of aerosols and aerosol precursors.
Anthropogenic sulfur emissions and biomass burning are taken from the
Monitoring Atmospheric Composition and Climate (MACC)-CITY climatology
(Granier et al., 2011). S emissions from continuously erupting volcanoes are
taken into account using Dentener et al. (2006), which is based on Andres and
Kasgnoc (1998). OCS concentrations are fixed at the surface at a value of
510 pptv (Montzka et al., 2007; ASAP2006). If possible, dimethyl sulfide (DMS), dust, and sea
salt emissions should be calculated online depending on the model
meteorology. Models considering DMS oxidation should calculate seawater DMS
emissions as a function of wind speed and DMS seawater concentrations.
Otherwise, modelling groups should prescribe for these species their usual
emission database for the year 2000. Each group can specify solar forcing
for year-2000 conditions according to their usual data set.
Modelling groups are encouraged to include a set of passive tracers to
diagnose the atmospheric transport independently of emissions, mostly
following the CCMI recommendations (Eyring et al., 2013). These tracers are
listed in Table S3. Models diagnose aerosol
parameters as specified in Tables S4 and S5. Additionally, volume mixing ratios
of specified precursors are diagnosed.
Stratospheric background aerosol (BG)
Summary of experiment
The overall objective of the BG experiment is to better understand the
processes involved in maintaining the stratospheric background aerosol
layer, i.e. stratospheric aerosol not resulting from direct volcanic
injections into the stratosphere. The simulations prescribed for this
experiment are time-slice simulations for the year 2000 with prescribed SST
including all sources of aerosols and aerosol precursors except for
explosive volcanic eruptions. The result of BG will be a multi-model
climatology of aerosol distribution, composition, and microphysical
properties in the absence of volcanic eruptions. By comparing models with
different aerosol microphysics parameterization and simulations of
background circulation with a variety of observational data (Table 2), we
aim to assess how these processes impact the simulated aerosol characteristics.
Overview of BG experiments.
Exp-name
Specific description/
Period
Ensemble
Years per
Tier
volcanic emission
size
member
BG_QBO
Background simulation
Time slice year 2000 monthly
1
20
1
varying with internal or nudged
QBO
BG_NQBO
Perpetual easterly phase of the
Time slice year 2000 monthly
1
20
2
QBO for the whole simulation
varying without QBO
BG_NAT
Only natural sources of aerosol
Time slice year 2000 monthly
1
20
2
(including biomass burning)
varying with internal or nudged
QBO (when possible)
Motivation
The total net sulfur mass flux from the troposphere into the stratosphere
is estimated to be about 181 Gg S yr-1 based on simulations by Sheng et
al. (2015a) using the SOCOL-AER model, 1.5 times larger than reported in
ASAP2006 (KTH2016). This estimate, however, could be highly dependent on the
specific characteristics of the model used, such as the strength of convective
systems, scavenging efficiency, and the occurrence of stratosphere–troposphere
exchange. Therefore, especially in the lower stratosphere, the simulated distribution of stratospheric background
aerosol could show a very large
inter-model variability.
OCS is still considered the largest contributor to the aerosol loadings in
the middle stratosphere. Several studies have shown that the transport to
the stratosphere of tropospheric aerosol and aerosol precursors constitutes
an important source of stratospheric aerosol (KTH2016 and references herein)
although new in situ measurements indicate that the
cross-tropopause SO2 flux is negligible over Mexico and Central America
(Rollins et al., 2017). Observations of the Asian Tropopause Aerosol Layer
(ATAL; Vernier et al., 2011a) show that, particularly in the UTLS, aerosol
of tropospheric origin can significantly enhance the burden of aerosol in
the stratosphere. This tropospheric aerosol has a more complex composition
than traditionally assumed for stratospheric aerosol: Yu et al. (2015), for
instance, showed that carbonaceous aerosol makes up to 50 % of the aerosol
loadings within the ATAL. The rate of stratospheric–tropospheric exchange (STE)
is influenced by the seasonality of the circulation and the frequency
and strength of convective events in large-scale phenomena such as the Asian
and North American monsoon or in small-scale phenomena such as strong
storms. Model simulations by Hommel et al. (2015) also revealed significant
QBO signatures in aerosol mixing ratio and size in the tropical middle
stratosphere (Fig. 3). Hence, the model-specific implementation of the QBO
(nudged or internally generated) could impact its effects on the
stratospheric transport and, subsequently, on the stratospheric aerosol layer.
In this experiment, we aim to assess the inter-model variability of the
background stratospheric aerosol layer and of the sulfur mass flux from
the troposphere to the stratosphere and vice versa. We will exclude changes
in emissions and focus on the dependence of stratospheric aerosol
concentrations and properties on stratospheric transport and STE. The goal of the BG experiment aims
to understand how the model-specific transport characteristics (e.g. isolation
of the tropical pipe, representation of the QBO and the strength of
convective systems) and aerosol parameterizations (e.g. aerosol microphysics
and scavenging efficiency) affect the representation of the background aerosol.
Experiment set-up and specifications
The BG experiment prescribes one mandatory (BG_QBO) and two
recommended (BG_NQBO and BG_NAT) simulations
(see Table 3). BG_QBO is a time-slice simulation with
conditions characteristic of the year 2000,
with the goal of understanding sources, sinks, composition, and
microphysical characteristics of stratospheric background aerosol under
volcanically quiescent conditions. The time-slice simulation should be at
least 20 years long, after a spin-up period of at least 10 years to
equilibrate stratospherically relevant quantities such as OCS concentrations and
the age of air. The period seems to be sufficient to study differences in the
aerosol properties but needs to be extended if dynamical changes, e.g. in NH
winter variability, are to be analysed. Modelling groups should run this
simulation with varying QBO, either internally generated or nudged to the
1981–2000 period.
If resources allow, each model should perform the sensitivity experiments
BG_NQBO and BG_NAT. The specifics of these two
experiments are the same as for BG_QBO, but BG_NQBO
should be performed without varying QBO and
BG_NAT without anthropogenic emissions of aerosol and aerosol
precursors, as indicated in Table S1. The goals of these sensitivity
experiments are to understand the effect of the QBO on the background
aerosol characteristics and the contribution of anthropogenic sources to the
background aerosol loading in the stratosphere.
Transient Aerosol Record (TAR)
Summary of experiment
The aim of the TAR experiment is to investigate
the relative contributions of volcanic and anthropogenic sources to the
temporal evolution of the stratospheric aerosol layer between 1998 and 2012.
Observations show that there is a transient increase in stratospheric
aerosol loading, in particular after the year 2003, with small-to
moderate-magnitude volcanic eruptions contributing significantly to this
increase (e.g. Solomon et al., 2011; Vernier et al., 2011b; Neely III et al.,
2013; Ridley et al., 2014; Santer et al., 2015; Brühl et al., 2015). TAR
model simulations will be performed using specified dynamics, prescribed sea
surface temperature and time-varying SO2 emissions. The simulations are
suitable for any general circulation or chemistry transport models that
simulate the stratospheric aerosol interactively and have the capability to
nudge meteorological parameters to reanalysis data. The TAR protocol covers
the period from January 1998 to December 2012, when only volcanic eruptions
have affected the UTLS aerosol layer with SO2 emissions about an order of magnitude smaller than
Pinatubo. Time-varying surface emission data sets contain anthropogenic and
natural sources of sulfur aerosol and their precursor species. The volcanic
SO2 emission inventories contain information of all known eruptions
that emitted SO2 into the UTLS during this period. It comprises the
geolocation of each eruption, the amount of SO2 emitted, and the height
of the emissions. SO2 emissions from continuously degassing volcanoes
are also included.
Experiment set-up and specifications
Participating models are encouraged to perform up to seven experiments,
based on five different volcanic SO2 emission databases (hereafter
referred to as VolcDB). Four experiments are mandatory; three others are
optional. The volcanic experiments are compared to a reference simulation
(TAR_base) that does not use any of the volcanic emission
databases but emissions from continuously degassing volcanoes. The aim of
the reference simulation is to simulate the non-volcanically perturbed state
of the stratospheric aerosol layer. In contrast to the experiment protocol
BG (Sect. 3.1), here time-varying surface boundary conditions (SST/SIC)
are applied, whereas BG intercompares model simulations under climatological
mean conditions and uses constant 2000 conditions.
An overview of the volcanic emission inventories is given in Table 4 and in
Fig. 4. VolcDB1/2/3 are new compilations (Bingen et al., 2017; Neely and
Schmidt, 2016; Carn et al., 2016), whereas a fourth inventory (VolcDB4;
Diehl et al., 2012), provided earlier, for the AeroCom community modelling
initiative, is optional. The databases use SO2 observations from
different sources and apply different techniques for the estimation of
injection heights and the amount of emitted SO2. The four inventories are
provided in the form of tabulated point sources, with each modelling group
to translate emitted SO2 mass for each eruption into model levels
spanning the upper and lower emission altitudes. To test the effect of the
implementation strategy (point source vs. cloud), an additional non-mandatory
experiment has been set up: TAR_db1_3D with
VolcDB1_3D as corresponding data set which provides a series
of discrete 3-D gridded SO2 injections at specified times. In both
versions of VolcDB1, the integral SO2 mass of each injection is consistent.
We recommend performing one additional non-mandatory experiment
TAR_sub in order to quantify and isolate the effects of eight volcanic eruptions that either had a statistically significant effect on,
for instance, tropospheric temperatures (Santer et al., 2014, 2015) or
emitted significant amounts of SO2 over the 1998 to 2012 time period.
This experiment uses a subset of volcanic emissions (VolcDBSUB) that were
derived based on the average mass of SO2 emitted using VolcDB1,
VolcDB2, and VolcDB3 for the following eruptions: 28 January 2005 Manam
(4.0∘ S, Papua New Guinea), 7 October 2006 Tavurvur (4.1∘ S, Papua New Guinea),
21 June 2009 Sarychev, (48.5∘ N, Kyrill, UDSSR) 8 November 2010
Merapi (7.3∘ S, Java, Indonesia), and 21 June 2011 Nabro
(13.2∘ N, Eritrea). In addition the eruptions of Soufrière Hills
(16.4∘ N, Montserrat) on 20 May 2006, Okmok (53.3∘ N,
Alaska) on 12 July 2008, and Kasatochi (52.1∘ N, Alaska) on
7 August 2008 are considered (Table S6) although these are not discernible in
climate proxies (Kravitz et al., 2010; Santer et al., 2014, 2015).
To summarize the number of experiments to be conducted within TAR, four are
mandatory (TAR_base with no volcanic emission, Tar_db1/2/3), one additional
one is recommended (TAR_sub), and two others are optional (TAR_db4 and TAR_db1_3D;
see Table 5 for an overview).
Annual total volcanic sulfur dioxide (SO2) emission from
three different emission data sets between 2003 and 2008 to be used in the Tier1 MITAR experiments. VolcDB1 (Bingen et al., 2017) considers only stratospheric
SO2 emissions; VolcDB2 (Neely and Schmidt, 2016) and VolcDB3 (Carn
et al., 2016) consider both tropospheric and stratospheric SO2 emission.
![]()
Overview of volcanic emission data sets for the different TAR experiments.
Sensor acronyms: MIPAS: Michelson Interferometer for Passive Atmospheric
Sounding; GOMOS: Global Ozone Monitoring by Occultation of Stars; TOMS: Total
Ozone Mapping Spectrometer; OMI: Ozone Monitoring Instrument; OMPS: Ozone Mapping
and Profiler Suite; IASI: Infrared Atmospheric Sounding Interferometer; GOME:
Global Ozone Monitoring Experiment; AIRS: Atmospheric Infrared Sounder; MLS:
Microwave Limb Sounder; HIRS: High-resolution Infrared Radiation Sounder;
References to the observational data and emission sources included are given
in the reference paper and for VolcDB1(_3D) also in Table S2.1. VolcDB1_3D
is a three-dimensional database, containing the spatial distributions of the
injected SO2 as initially observed by the satellite instruments. In
both versions of VolcDB1, the integral SO2 mass of each injection is consistent.
Volcanic
VolcDB1
VolcDB2
VolcDB3
VolcDB4
VolcDBSUB
VolcDB1_3D
database
Covering period
Dec 1997–
Jan 1990–
1978–2014
1979–2010
Dec 1997–
Apr 2012
Dec 2014
Apr 2012
Observational
MIPAS, GOMOS,
OMI, OMPS,
TOMS, HIRS/2,
TOMS, OMI
MIPAS, GOMOS,
data sets
SAGEII, TOMS,
IASI, TOMS,
AIRS, OMI,
SAGEII, TOMS,
OMI
GOME/2, AIRS,
MLS, IASI, and
OMI
MLS, MIPAS
OMPS
Reference
Brühl et al. (2018),
Mills et al. (2016),
Carn et al. (2016)c
Diehl et al. (2012),
Subset of eight
3-D netCDF
Bingen et al. (2017)a
Neely and Schmidt (2016)b
AeroCom-II
volcanoes;
Brühl (2018),
HCA0 v1/v2d
contains SO2
Bingen et al. (2017)f
emissions and
plume altitudes
averaged over
the three mandatory
databases;
details are
given in the
Appendix (Table S6)e
a https://cera-www.dkrz.de/WDCC/ui/cerasearch/entry?acronym=SSIRC_1 (last access: 26 June 2018).
b http://catalogue.ceda.ac.uk/uuid/bfbd5ec825fa422f9a858b14ae7b2a0d (last access: 26 June 2018).
c https://measures.gesdisc.eosdis.nasa.gov/data/SO2/MSVOLSO2L4.2/ (last access: 26 June 2018).
d http://aerocom.met.no/download/emissions/HTAP (last access: 26 June 2018).
e http://isamip.eu/fileadmin/user_upload/isamip/volc_sub_v185.dat (last access: 26 June 2018).
f https://cera-www.dkrz.de/WDCC/ui/cerasearch/entry?acronym=SSIRC_1 (last access: 26 June 2018).
Volcanic <inline-formula><mml:math id="M92" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">SO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> emission databases
VolcDB1 (Bingen et al., 2017; Brühl, 2018) are updates from
Brühl et al. (2015) using satellite data of MIPAS and OMI. For TAR,
VolcDB1 has been extended based on data from Global Ozone Monitoring by
Occultation of Stars (GOMOS), SAGE II, Total Ozone Mapping Spectrometer (TOMS),
and the Smithsonian database. The VolcDB1_3D data
set, for the optional experiment TAR_db1_3D,
contains volume mixing ratio distributions of the injected SO2 cloud on
a T42 Gaussian grid with 90 levels. The integral SO2 mass for each
injection is the same. VolcDB2 (Mills et al., 2016; Neely and Schmidt, 2016)
contains volcanic SO2 emissions and plume altitudes for eruptions that have been detected by satellite instruments including TOMS,
OMI, OMPS, the Infrared Atmospheric Sounding Interferometer (IASI), the Global Ozone
Monitoring Experiment (GOME/2), the Atmospheric Infrared Sounder (AIRS), the Microwave Limb Sounder (MLS), and the MIPAS instrument. The database is
compiled based on published estimates of the eruption source parameters and
reports from the Smithsonian Global Volcanism Program
(http://volcano.si.edu/, last access: 26 June 2018), NASA's Global Sulfur Dioxide Monitoring website
(http://so2.gsfc.nasa.gov/, last access: 26 June 2018) as well as the Support to Aviation Control
Service (http://sacs.aeronomie.be/, last access: 26 June 2018). The tabulated point source database
also includes volcanic eruptions that emitted SO2 into the troposphere
only, as well as direct stratospheric emissions, and has been used and
compared to observations in Mills et al. (2016) and Solomon et al. (2016).
Overview of TAR experiments.
Exp-name
Volcanic
Specific description
Period
Years
Tier
database
per
name
member
TAR_base
–
No sporadically erupting
Transient 1998–2012
15
1
volcanic emission
monthly varying
TAR_db1
VolcDB1
Volcanic emission data set
Transient 1998–2012
15
1
(Brühl et al., 2015 and updates)
monthly varying
TAR_db2
VolcDB2
Volcanic emission data set
Transient 1998–2012
15
1
(Mills et al., 2016)
monthly varying
TAR_db3
VolcDB3
Volcanic emission data set
Transient 1998–2012
15
1
(Carn et al., 2016)
time varying
TAR_db4
VolcDB4
Volcanic emission data set
Transient 1998–2010
13
3
(Diehl et al., 2012) and updates
time varying
TAR_sub
VolcDBSUB
Subset of strongest eight volcanoes;
Transient 1998–2012
15
2
averaged SO2 emissions and
monthly varying
averaged injection heights from
VolcDB1/2/3
TAR_db1_3D
VolcDB1_3D
NetCDF version of volcanic
Transient 1998–2012
15
3
emission data set VolcDB1
monthly varying
(Brühl et al., 2015, and updates)
Example results from interactive stratospheric aerosol simulations with
the UM-UKCA model (Dhomse et al., 2014) of five different SO2 injection realizations
of the 1991 Pinatubo eruption (see Table 8),
The model tropical mean extinction in the mid-visible (550 nm) and near-infrared
(1020 nm) is compared to that from SAGE-II measurements. Only two of the five injection
realizations inject below 20 km and the impact on the timing of the peak and
general evolution of the aerosol optical properties is apparent. In this model
the growth to larger particle sizes and subsequent sedimentation to lower
altitudes is able to explain certain signatures seen in the satellite data (see
also Mann et al., 2015).
![]()
VolcDB3 uses the most recent compilation of the volcanic degassing database
of Carn et al. (2016). Observations from the satellite instruments TOMS, the
High-resolution Infrared Sounder (HIRS/2), AIRS, OMI, MLS, IASI, and OMPS are
considered, measuring in the UV, IR, and microwave spectral bands. Similar to
VolcDB1/2, VolcDB3 also includes tropospheric eruptions.
Historically VolcDB4 is an older data set, which relies on information from
TOMS, OMI, the Global Volcanism Program (GVP), and other observations from
the literature, covering the time period from 1979 to 2010. In contrast to
the other inventories, VolcDB4 has previously been applied by a range of
models within the AeroCom community (http://aerocom.met.no/emissions.html, last access: 26 June 2018;
Diehl et al., 2012; Dentener et al., 2006). Hence, it adds valuable
information to the TAR experiments because it allows an estimation of how the
advances in observational methods impact modelling results. It should be
noted that VolcDB4 already contains the inventory of Andres and Kasgnoc (1998)
for S emissions from continuously erupting volcanoes and should not
be allocated twice when running this experiment.
Boundary conditions, chemistry, and forcings
To reduce uncertainties associated with model differences in the
reproduction of synoptic and large-scale transport processes, models are
strongly encouraged to perform TAR experiments with specified dynamics,
where meteorological parameters are nudged to a reanalysis such as the ECMWF
ERA-Interim (Dee et al., 2011). This allows models to reasonably reproduce
the QBO and planetary wave structure in the stratosphere and to replicate as
closely as possible the state of the BDC in the simulation period. Nudging
also allows comparing directly to available observations of stratospheric
aerosol properties (Table 2), such as the extinction profiles and aerosol optical depth (AOD), and
should enable the models to simulate the ATAL (Vernier et al., 2011a; Thomason and Vernier, 2013), which, so far, has been
studied only by very few global models in great detail (e.g. Neely III
et al., 2014; Yu et al., 2015).
Historical Eruption <inline-formula><mml:math id="M99" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">SO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> Emission Assessment (HErSEA)
Summary of experiment
This HErSEA experiment
will involve each participating model running a limited ensemble of
simulations for each of the three largest volcanic perturbations to the
stratosphere in the last 100 years: 1963 Mt Agung, 1982 El Chichón, and
1991 Mt Pinatubo.
The main aim is to use a wide range of stratospheric aerosol observations to
constrain uncertainties in the SO2 emitted for each eruption (amount,
injection height). Several different aerosol metrics will be intercompared
to assess how effectively the emitted SO2 translates into perturbations
to stratospheric aerosol properties and simulated radiative forcings across
interactive stratospheric aerosol CCMs with a range of different
complexities. Whereas the TAR simulations (see Sect. 3.2) use specified
dynamics and are suitable for chemistry transport models, for this
experiment, simulations must be free-running with radiative coupling to the
volcanically enhanced stratospheric aerosol, thereby ensuring the
composition–radiation–dynamics interactions associated with the injection
are resolved. We are aware that this specification inherently excludes
chemistry transport models, which must impose atmospheric dynamics. However,
since the aim is to apply stratospheric aerosol observations in concert with
the models to re-evaluate current best estimates of the SO2 input and
in light of the first-order impact the stratospheric heating has on
hemispheric dispersion from these major eruptions (e.g. R. E. Young et al.,
1994), we assert that this apparent exclusivity is entirely justified in this case.
As well as analysing and evaluating the individual model skill and
identifying model consensus and disagreement for these three specific
eruptions, we also seek to learn more about major eruptions which occurred
before the era of satellite and in situ stratospheric measurements. Our
understanding of the effects from these earlier eruptions relies on deriving
volcanic forcings from proxies such as sulfate deposition to ice sheets (Gao
et al., 2007; Sigl et al., 2015; Toohey et al., 2013), from photometric
measurements from astronomical observatories (Stothers,
1996, 2001), or from documentary evidence (Stothers, 2002; Stothers and Rampino, 1983;
Toohey et al., 2016a). Although HErSEA has no specific experiment to
understand the relationship between the ice core sulfate deposition and the
stratospheric aerosol layer enhancements that drive the surface cooling,
there is the potential for a systematic inter-model study (e.g. similar to
Marshall et al., 2018) to identify how uncertain historic volcanic forcings
derived from ice core sulfate deposition may be.
Motivation
In the days following the June 1991 Pinatubo eruption, satellite SO2
measurements show (e.g. Guo et al., 2004a) that the peak gas phase sulfur
loading was 7 to 11.5 Tg S (or 14–23 Tg SO2). The chemical
conversion to sulfuric aerosol that occurred in the tropical reservoir over
the following weeks and the subsequent transport to mid- and
high latitudes caused a major enhancement to the stratospheric aerosol
layer. The peak particle sulfur loading, through this global dispersion
phase, reached only around half that in the initial SO2 emission;
the maximum particle sulfur loading was measured as 3.7 to 6.7 Tg S (Lambert
et al., 1993; Baran and Foot, 1994), based on an aqueous sulfuric acid
composition range of 59 to 77 % by weight (Grainger et al., 1993).
Overview of HErSEA experiments.
Exp-name
Specific description/volcanic emission
Period
Ensemble
Years per
Tier
size
member
HErSEA_Pin_Em_Ism
Pinatubo episode,
Transient 1991–
3
5
1
SO2 Emission = medium, Inject shallow @ medium-alt.
1995
HErSEA_Pin_Eh_Ism
Pinatubo episode,
incl. GHGs &
3
5
1
SO2 Emission = high, Inject shallow @ medium-alt.
ODSs
HErSEA_Pin_El_Ism
Pinatubo episode,
(monthly varying
3
5
1
SO2 Emission = low, Inject shallow @ medium-alt.
SST &
HErSEA_Pin_Em_Isl
Pinatubo episode,
sea-ice from
3
5
2
SO2 Emission = medium, Inject shallow @ low-alt.
HadISST
HErSEA_Pin_Em_Idp
Pinatubo episode,
as for CCMI)
3
5
2
SO2 Emission = medium, Inject over deep altitude-range
HErSEA_Pin_Cntrol
Pinatubo episode,
3
5
1
No Pinatubo SO2 emission
HErSEA_ElC_Em_Ism
El Chichón episode,
Transient 1982–
3
5
1
SO2 Emission = medium, Inject shallow @ medium-alt.
1986
HErSEA_ElC_Eh_Ism
El Chichón episode,
incl. GHGs &
3
5
1
SO2 Emission = high, Inject shallow @ medium-alt.
HErSEA_ElC_El_Ism
El Chichón episode,
SO2 ODSs (monthly
3
5
1
Emission = low, Inject shallow @ medium-alt.
varying SST and
HErSEA_ElC_Em_Isl
El Chichón episode,
sea-ice from
3
5
2
SO2 Emission = medium, Inject shallow@low-altitude
HadISST
HErSEA_ElC_Em_Idp
El Chichón episode,
as for CCMI)
3
5
2
SO2 Emission = medium, Inject over deep altitude-range
HErSEA_ElC_Cntrol
El Chichón episode
3
5
1
no El Chichón SO2 emission
HErSEA_Agg_Em_Ism
Agung episode
Transient 1963–
3
5
1
SO2 Emission = medium, Inject shallow @ medium-alt.
1967
HErSEA_Agg_Eh_Ism
Agung episode,
incl. GHGs &
3
5
1
SO2 Emission = high, Inject shallow @ medium-alt.
ODSs
HErSEA_Agg_El_Ism
Agung episode,
(monthly varying
3
5
1
SO2 Emission = low, Inject shallow @ medium-alt.
SST and sea-ice
HErSEA_Agg_Em_Isl
Agung episode,
from HadISST
3
5
2
SO2 Emission = medium, Inject shallow @ low-alt.
as for CCMI)
HErSEA_Agg_Em_Idp
Agung episode,
3
5
2
SO2 Emission = medium, Inject over deep altitude-range
HErSEA_Agg_Cntrol
Agung episode
3
5
1
no Agung SO2 emission
Whereas some model studies with aerosol microphysical processes find
consistency with observations for SO2 injection values of 8.5 Tg S
(e.g., Niemeier et al., 2009; Toohey et al., 2011; Brühl et al., 2015),
several recent microphysical model studies (Dhomse et al., 2014; Sheng et
al., 2015a; Mills et al., 2016) find best agreement for an injected sulfur
amount at, or even below, the lower end of the range of the satellite
SO2 measurements; see also Fig. 5. Model predictions are known to be sensitive to
differences in assumed injection height (e.g. Sheng et al., 2015b; Jones et
al., 2016), and whether models resolve radiative heating and “self-lofting”
effects also affects subsequent transport pathways (e.g. R. E. Young et
al., 1994; Timmreck et al., 1999b; Aquila et al., 2012). Another potential
mechanism that could explain part of the apparent model–observation
discrepancy is that a substantial proportion of the sulfur may have been
removed from the plume in the first months after the eruption due to
accommodation onto co-emitted ash/ice (Guo et al., 2004b) and subsequent sedimentation.
List of stratospheric aerosol observation data sets from the three large
eruptions of the 21st century (Agung, El Chichón, and Mt Pinatubo). For
NDACC archive, see http://www.ndsc.ncep.noaa.gov/data/ (last access: 26 June 2018).
Eruption
Measurement/platform
References
Pinatubo
Extinction/AOD [multi-l]: SAGE-II, AVHRR,
Hamill and Brogniez (SPARC, 2006, and references
HALOE, CLAES
therein)
Balloon-borne size-resolved concentration profiles
Deshler (1994, Kiruna, EASOE), Deshler et al.
(CPC, OPC)
(2003)
Impactors on ER2 (AASE2), FCAS, and FSSP on
Pueschel et al. (1994), Wilson et al. (1993), Brock et
ER2 (AASE2)
al. (1993)
Ground-based lidar; airborne lidar
NDACC archive; S. A. Young et al. (1994), Browell
Ship-borne lidar measurements
et al. (1993)
Avdyushin et al. (1993); Nardi et al. (1993), Stevens et
al. (1994)
El Chichón
Satellite extinction/AOD 1000 nm (SAM-II)
Hamill and Brogniez (SPARC, 2006 & references
Balloon-borne particle concentration profiles
therein)
Ground-based lidar
Hofmann and Rosen (1983, 1987).
NDACC archive
Agung
Surface radiation measurements
Dyer and Hicks (1965), Pueschel et al. (1972), Moreno
(global dataset gathered in Dyer and Hicks, 1968)
and Stock (1964), Flowers and Viebrock (1965)
Balloon-borne measurements
Rosen (1964, 1966, 1968), Pittock (1966)
Ground-based lidar, searchlight, and twilight
Clemesha et al. (1966), Grams and Fiocco (1967), Kent et
measurements
al. (1967), Elterman et al. (1969), Volz (1964, 1965, 1970)
Aircraft measurements
Mossop (1963, 1964), Friend (1966)
This ISA-MIP experiment will explore these issues further, with the
participating models carrying out co-ordinated experiments of the three most
recent major eruptions, with specified common SO2 amounts and injection
heights (Table 6). This design ensures the analysis can focus on key
inter-model differences such as stratospheric circulation/dynamics, the
impacts from radiative dynamical interactions, and the effects of aerosol
microphysical schemes. Analysing how the vertical profile of the enhanced
stratospheric aerosol layer evolves during global dispersion and decay will
provide a key indicator for why the models differ, and what the key
driving mechanisms are. Furthermore, the actual response of the BDC and mean age
of air to Pinatubo is poorly constrained by existing reanalysis data
(Garfinkel et al., 2017). While some modelling studies reported a decreasing
mean age of air following volcanic eruptions throughout the stratosphere
(Garcia et al., 2011; Garfinkel et al., 2017), others show an increase in
mean age (Diallo et al., 2017). Moreover, Muthers et al. (2016) found a decreasing mean age of air in the middle and upper stratosphere
and an increasing mean age below, while Pitari et al. (2016a) found a decreasing mean
age at higher levels of 30 hPa in the tropics and 10 hPa in the middle
latitudes after the Pinatubo eruption. The HErSEA experiment in combination
with a passive volcanic tracer might therefore help to better constrain the
response of the BDC to volcanic eruptions using observations and help to
clarify the uncertainties in the age-of-air changes after the Pinatubo
eruption. For all three major eruptions, we have identified key observational
data sets (Table 7) that will provide benchmark tests to evaluate the
vertical profile, covering a range of different aerosol metrics.
Experiment set-up and specifications
Each modelling group will run a mini-ensemble of transient AMIP-type runs
for the three eruptions with upper and lower bound SO2 emissions and
three different injection height settings: two shallow (e.g. 19–21 and 23–25 km)
and one deep (e.g. 19–25 km) (see Table 7). The seasonal cycle of the
BDC affects the hemispheric dispersion of the aerosol plume (e.g. Toohey et
al., 2011), and the phase of the QBO is also known to be a key control for
tropical eruptions (e.g. Trepte and Hitchman, 1992). In order to quantify
the contribution of the tracer transport, it is recommended to additionally initialize and transport a passive tracer Volc (Table S3).
Note that since the AMIP-type simulations will be transient, prescribing time-varying
sea surface temperatures, the models will automatically match the surface
climate state (ENSO, NAO) through each post-eruption period. Where possible,
models should re-initialize (if they have internally generated QBO) or use
specified dynamics approaches (e.g. Telford et al., 2008) to ensure the
model dynamics are consistent with the QBO evolution through the
post-eruption period. General circulation models should use GHG
concentrations appropriate for the period, and models with interactive
stratospheric chemistry should ensure the loading of ODSs matches that for the time period.
Settings to use for initializing the mini-ensemble of interactive
stratospheric aerosol simulations for each eruption in the HErSEA experiment.
For Pinatubo the upper range of SO2 emission is based on TOMS/TOVS
SO2 observations (Guo et al., 2004a). The SO2 emissions
flux ranges and central values (in parentheses) are specifically for application
in interactive stratospheric aerosol (ISA) models rather than any new data
compilation. The lower range and the central values are defined according to some recent
Pinatubo studies (Dhomse et al., 2014; Mills et al., 2016; Sheng et al., 2015a)
which have identified a modest downward adjustment of initially observed
SO2 amounts to agree with HIRS/ISAMS measurements of peak sulfate
aerosol loading (Baran and Foot, 1994). The adjustment assumes either uncertainties
in the satellite measurements or that loss pathways in the first few weeks after
these eruptions are either underpredicted (e.g. due to coarse spatial resolution)
or omitted completely (accommodation onto ash/ice) in the ISA models.
The El Chichón SO2 central estimate is taken from Krueger et
al. (2008), and an emission range is based on assumed ±33 %, while for
Agung the SO2 emission estimate is from Self and King (1996). For
Pinatubo, injection height ranges for the two shallow and one deep realization
are taken from Antuña et al. (2002). The El Chichón values are based
on the tropical lidar signal from Fig. 4.34 of Hamill and Brogniez (2006),
whereas for Agung we considered the measurements presented in Dyer and Hicks (1968)
including balloon soundings (Rosen, 1964) and ground-based lidar (Grams and Fiocco, 1967).
Eruption
Location
Date
SO2 (Tg)
Shallow × 2
Deep
Mt Pinatubo
15∘ N, 120∘ E
15 Jun 1991
10–20 (14)
18–20, 21–23 km
18–25 km
El Chichón
17∘ N, 93∘ W
4 Apr 1982
5–10 (7)
22–24, 24–26 km
22–27 km
Mt Agung
8∘ S, 115∘ E
17 Mar 1963
5–10 (7)
17–19, 20–22 km
17–23 km
Table 8 shows the settings for the SO2 injection for each
eruption. Note that experience of running interactive stratospheric
aerosol simulations shows that the vertical extent of the enhanced
stratospheric aerosol will be different from the altitude range in which the
SO2 is injected. So, these sensitivity simulations will allow us to assess
the behaviour of the individual models with identical settings for the
SO2 injection.
For these major eruptions, where the perturbation is much larger than in
TAR, model diagnostics include AOD and extinction at multiple wavelengths
and heating rates (K day-1) in the lower stratosphere to identify the
stratospheric warming induced by simulated volcanic enhancement, including
exploring compensating effects from other constituents (e.g. Kinne et al.,
1992). To allow the global variation in size distribution to be
intercompared, models will also provide a 3-D monthly effective radius, which also includes cumulative number concentration at several size cuts for direct
comparison to balloon measurements. Examining the co-variation of the
particle size distribution with variations in extinction at different
wavelengths will be of particular interest in relation to approaches used to
interpret astronomical measurements of eruptions in the pre in situ era
(Stothers, 1996, 2001). A three-member ensemble will be submitted for each
different injection setting.
Pinatubo Emulation in Multiple models (PoEMs)
Summary of experiment
The PoEMS experiment will involve each interactive stratospheric aerosol
model running a perturbed parameter ensemble (PPE) of simulations through
the 1991–1995 Pinatubo-perturbed period. Variation-based sensitivity
analysis will derive a probability distribution function (PDF) for each
model's predicted Pinatubo forcing, following techniques applied
successfully to quantify and attribute sources of uncertainty in
tropospheric aerosol forcings (e.g. Carslaw et al., 2013). The approach will
teach us which aspects of the radiative forcing from major eruptions is most
uncertain and will enable us to identify how sensitive model predictions of
key features (e.g. timing and value of peak forcing and decay timescales)
are to uncertainties in several model parameters. Comparing the
time signatures of different underlying aerosol metrics (mid-visible AOD,
effective radius, particle number) between models, and crucially also
against observations, may also help to reduce the natural forcing
uncertainty, potentially thereby making the next generation of climate models more robust.
Motivation
The sudden global cooling from major eruptions is a key signature in the
historical climate record and a natural global warming signature occurs
after peak cooling as volcanic aerosol is slowly removed from the
stratosphere. Quantitative information on the uncertainty range of volcanic
forcings is therefore urgently needed. The amount of data collected by
satellite-, ground-, and airborne instruments in the period following the
1991 eruption of Mount Pinatubo (see, e.g., Sect. 3.3.2, Table 7) provides
an opportunity to test model capabilities in simulating large perturbations
of stratospheric aerosol and their effect on the climate. Recent advances in
quantifying uncertainty in climate models (e.g. Rougier et al., 2009; Lee at al.,
2011) involve running ensembles of simulations to systematically explore
combinations of different external forcings to scope the range of possible
realizations. There are now a large number of general circulation models (GCMs)
with prognostic aerosol modules, which tend to assess the
stratospheric aerosol perturbation through the Pinatubo-perturbed period
(see Table 9). Although these different models achieve reasonable agreement
with the observations, this consistency of skill is achieved with
considerable diversity in the values assumed for the initial magnitude and
distribution of the SO2 injection. The SO2 injections prescribed
by different models range from 5 to 10 Tg S, and the upper edge of the
injection altitude varies among models from as low as 18 km to as high as
29 km, as shown in Table 9. Such simulations also differ in the choice of the
vertical distribution of SO2 injection (e.g. uniform, Gaussian or
triangular distributions) and the horizontal injection area (one to several
grid boxes). The fact that different choices of injection parameters lead to
similar results in different models points to differences in the models'
internal treatment of aerosol evolution. Accurately capturing microphysical
processes such as coagulational, growth, and subsequent rates of
sedimentation has been shown to be important for volcanic forcings (English
et al., 2013), but some studies (e.g. Mann et al., 2015) identify that these
processes interplay also with aerosol–radiation interactions, the associated
dynamical effects changing the fate of the volcanic sulfur and its removal
into the troposphere. The PoEMS experiment will specifically assess this
issue by adjusting the rate of specific microphysical processes in each
model simultaneously with perturbations to SO2 emission and injection
height, thereby assessing the footprint of their influence on subsequent
volcanic forcing in different complexity aerosol schemes and the relative
contribution to uncertainty from emissions and microphysics.
List of SO2 injection settings used in different interactive
stratospheric aerosol model simulations of the 1991 Mount Pinatubo eruption.
* Main peak at 23.5 km, secondary peak at 21 km.
SO2
Study
SO2
mass
height
(Tg S)
(km)
5
Dhomse et al. (2014)
19–27
5
Mills et al. (2016)
18–20
7
Sheng et al. (2015a, b)
17–30
8.5
Timmreck et al. (1999a, b)
20–27
8.5
Niemeier et al. (2009);
24
Toohey et al. (2011)
8.5
Brühl et al. (2015)
18–26*
10
Pitari and Mancini (2002)
18–25
10
Oman et al. (2006)
19–29
10
Aquila et al. (2012, 2013)
16–18, 17–27
10
English et al. (2013)
15.1–28.5
Experiment set-up and specifications
For each model, an ensemble of simulations will be performed varying
SO2 injection parameters and a selection of internal model parameters
within a realistic uncertainty distribution. A maximin Latin hypercube
sampling strategy will be used to define parameter values to be set in each
PPE member in order to obtain good coverage of the parameter space. The
maximin Latin hypercube is designed such that the range of every single
parameter is well sampled and the sampling points are well spread through
the multi-dimensional uncertainty space – this is achieved by splitting the
range of every parameter into N intervals and ensuring that precisely one
point is in each interval in all dimensions, where N is the total number of
model simulations, and the minimum distance between any pair of points in
all dimensions is maximized. Figure 6 shows the projection onto two dimensions
of a Latin hypercube built in eight dimensions with 50 model simulations. The
size of the Latin hypercube needed will depend on the number of model
parameters to be perturbed; the number of simulations to be performed will
be equal to 10 times the number of parameters – 7 per parameter to
build the emulator and 3 per parameter to validate the emulator. All
parameters are perturbed simultaneously in the Latin hypercube.
In order to be inclusive of modelling groups with less computing time
available and of different types of aerosol schemes, we define three options of
experimental design with different numbers of perturbed parameters and thus
simulation ensemble members. The three options involve varying all eight (standard
set), five (reduced set), or three (minimum set) of the list of uncertain
parameters, resulting in ensembles of 80 (standard), 50 (reduced), or
30 (minimum) PPE members. The parameters to be varied are shown in Table 10
and include variables related to the volcanic injection, such as its
magnitude, height, latitudinal extent, and composition, and to the life
cycle of the volcanic sulfate, such as the sedimentation rate, its
microphysical evolution, and the SO2 to SO42- conversion rate.
Prior to performing the full PPE, modelling groups are encouraged to run
“one-at-a-time” (OAT) test runs with each of the process parameters
increased/decreased to its maximum/minimum value. Submission of these OAT
test runs is encouraged (following the naming convention in Table 11)
because as well as being an important check that the model parameter scaling
is being implemented as intended, the results will also enable
intercomparison of single-parameter effects between participating models
ahead of the full ensemble. When imposing the parameter scalings, the models
must only enact that change in grid boxes with volcanically enhanced air
masses. This can be determined either via total sulfur volume mixing ratio
threshold suitable for the particular model or via the “passive tracer
Volc” recommended in Sect. 3.3.3. Restricting the perturbation to the
Pinatubo sulfur will leave pre-eruption conditions and tropospheric aerosol
properties unchanged, ensuring a clean “uncertainty pdf” for the “volcanic forcing”.
Illustration of the Latin hypercube sampling method. Each dot represents
the value used in one of the particular simulations with a perturbed parameter
ensemble (PPE) with 50 members (realizations/integrations).
![]()
That this restriction to the parameter scalings is operational is an
important preparatory exercise and will need to have been verified when
running the OAT test runs.
Groups will need to translate the 0–1 latitude-spread parameter into
a sequence of fractional injections into all grid boxes between the equator and
15∘ N. For example for a model with 2.5∘ latitude resolution,
the relative injection in the six latitude bins between 0 and 15∘ N would
take the form [0, 0, 0, 0, 0, 0, 1] for extent factor = 0 and [0.167, 0.167,
0.167, 0.167, 0.167, 0.167] for extent factor = 1. Injection ratios for
intermediate values of the spread factor would be calculated by interpolation
between these two end member cases.
Parameters
Minimum
Reduced
Standard
Uncertainty range
set
set
set
1
Injected SO2 mass
X
X
X
5–10 Tg S
2
Mid-point height of 3 km thick injection
X
X
X
18–30 km
3
Latitudinal extent of the injection
X
X
X
Factor 0–1 to varies from one-box
injection at 15∘ N (factor = 0)
to equator-to-15∘ N (factor = 1)
4
Sedimentation velocity
X
X
Multiply model calculated
velocity by a factor 0.5 to 2.
5
SO2 oxidation scaling
X
X
Scale gas phase oxidation of SO2
by a factor 0.5 to 2
6
Nucleation rate of sulfate particles
X
Scale model calculated rate by a
factor 0.5 to 2.
7
Sub-grid particle formation factor.
X
Emit fraction of SO2 as sulfuric
acid particles formed at sub-grid
scale (0 to 10 %)
8
Coagulation rate
X
Scale the model calculated rate
by a factor 0.5 to 2.
Overview of PoEMS one-at-a-time (OAT) test runs. Note that when imposing
the parameter scaling, the models should only enact the change in
volcanically enhanced air masses (where the total sulfur volume mixing ratio
exceeds a threshold suitable for their model). Perturbing only the
volcanically enhanced air masses will ensure, pre-eruption conditions and
tropospheric aerosol properties remains unchanged by the scalings.
Exp-name
Specific description/volcanic emission
Period
Tier
PoEMS_OAT_med
SO2 Emission = medium, Inject shallow @ medium-alt.
1
Processes unperturbed.
PoEMS_OAT_P4h
SO2 Emission = medium, Inject shallow @ medium-alt.
2
Sedimentation rates doubled
PoEMS_OAT_P4l
SO2 Emission = medium, Inject shallow @ medium-alt.
2
Sedimentation rates halved
PoEMS_OAT_P5h
SO2 Emission = medium, Inject shallow @ medium-alt.
3
SO2 oxidation rates doubled
PoEMS_OAT_P5l
SO2 Emission = medium, Inject shallow @ medium-alt.
3
SO2 oxidation rates halved
PoEMS_OAT_P6h
SO2 Emission = medium, Inject shallow @ medium-alt.
Transient
3
Nucleation rates doubled
1991–1995
PoEMS_OAT_P6l
SO2 Emission = medium, Inject shallow @ medium-alt.
3
Nucleation rates halved
PoEMS_OAT_P7h
SO2 Emission = medium, Inject shallow @ medium-alt.
3
% SO2 as primary SO4 × 2
PoEMS_OAT_P7l
SO2 Emission = medium, Inject shallow @ medium-alt.
3
% SO2 as primary SO4 × 0.5
PoEMS_OAT_P8h
SO2 Emission = medium, Inject shallow @ medium-alt.
2
Coagulation rates doubled
PoEMS_OAT_P8l
SO2 Emission = medium, Inject shallow @ medium-alt.
2
Coagulation rates halved
Once a modelling group has performed the PPE of simulations as defined by
the Latin hypercube a statistical analysis will be performed. Emulators for
each of a selection of key metrics will be built, following the approach
described by Lee et al. (2011), to examine how the parameters lead to
uncertainty in key features of the Pinatubo-perturbed stratospheric aerosol.
The emulator builds a statistical model between the ensemble design and the
key model output and once validated allows sampling of the whole parameter
space to derive a PDF of each key model output.
Variance-based sensitivity analysis will then be used to decompose the
resulting probability distribution into its sources providing information on
the key sources of uncertainty in any model output. The two sensitivity
indices of interest are called the main effect and the total effect. The
main effect measures the percentage of uncertainty in the simulated metric
due to each parameter variation individually. The total effect measures the
percentage of uncertainty in the key model output due to each parameter,
including the additional contribution from its interaction with other
uncertain parameters. The sources of model parametric uncertainty (i.e. the
sensitivity indices) will be identified for each model with discussion with
each group to check the results. By then comparing the sensitivity to the
uncertain parameters across the range of participating models, we will learn
about how the model's differing treatment of aerosol processes and the
inherent dynamical and chemical processes resolved in the host model
together determine the uncertainty in its predicted Pinatubo radiative forcings.
The probability distribution of observable key model outputs will also be
compared to observations in order to constrain the key sources of
uncertainty and thereby reduce the parametric uncertainty in individual
models. The resulting model constraints will be compared between models
providing quantification of both parametric uncertainty and structural
uncertainty for key variables such as AOD, effective radius, and radiative
flux anomalies. This sensitivity analysis will also identify the variables
for which better observational constraints would yield the greatest
reduction in model uncertainties.
Conclusions
The ISA-MIP experiments will improve our understanding of stratospheric aerosol
processes, chemistry, and dynamics and constrain climate impacts of
background aerosol “variability”, small volcanic eruptions, and large
volcanic eruptions. The experiments will also help to resolve some
disagreements amongst global aerosol models, for instance the difference in
volcanic SO2 forcing efficacy for Pinatubo (see Sect. 3.3.2). The
results of this work will help constrain the contribution of stratospheric
aerosols to the early 21st century global warming hiatus period, the effects
of hypothetical geoengineering schemes, and other climate processes that
are influenced by the stratosphere. Overall, they provide an excellent
opportunity to answer some of these questions as part of the greater WCRP
SPARC and CMIP6 efforts. For example, the CMIP6 Geoengineering Model
Intercomparison Project (GeoMIP, Kravitz et al., 2015) investigates common
ways in which climate models treat various geoengineering scenarios some of
them via sulfate aerosols (e.g. Tilmes et al., 2015). However, there is a
large inter-model spread for the cooling efficiency of sulfate aerosol,
i.e. the normalized cooling rate per injected unit of sulfur (Moriyama et
al., 2016). ISA-MIP is therefore of special importance for GeoMIP as it
could help to understand the reason for these uncertainties, to better
constrain the forcing efficiency and to improve future scenarios.
Furthermore, it is so far not clear whether the large inter-model spread of
the CMIP5 models in the simulated post-volcanic climate response mostly
depends on uncertainties in the imposed volcanic forcing or on an
insufficient representation of climate processes. To discriminate between the
individual uncertainty factors, it is useful to develop standardized
experiments/model activities that systematically address specific
uncertainty factors. Hence, ISA-MIP, which covers the uncertainties in the
pathway from the eruption source to the volcanic radiative forcing, will
complement the CMIP6 VolMIP project (Zanchettin et al., 2016), which
addresses the pathway from the forcing to the climate response and the
feedback by studying the uncertainties in the post-volcanic climate
response to a well-defined volcanic forcing. ISA-MIP also complements the
chemistry climate model initiative (CCMI; Eyring et al., 2013) and the
Aerosol Comparison (AeroCom) initiative (Schulz et al., 2006) as well as
the Aerosol Chemistry Model Intercomparison Project (AerChemMIP; Collins et
al., 2017) as it concentrates on stratospheric aerosol which is not in the
focus of all these activities.
As well as identifying areas of agreement and disagreement among the
different complexities of models in top-level comparisons focussing on
fields such as zonal-mean mid-visible AOD and extinction profiles in
different latitudes, ISA-MIP also intends to explore relationships between
key parameters. For example, how does sulfate deposition to the polar ice
sheets relate to volcanic forcing in the different interactive stratospheric
aerosol models that predict the transport and sedimentation of the
particles? Or how do model “spectral extinction curves” evolve through the
different volcanically perturbed periods and how do they relate to simulated
effective radius compared to the theoretical approach to derive effective
radius from Stothers (1997, 2001)? There is considerable potential to apply
the model uncertainty analysis to make new statements to inform our
confidence in volcanic forcings derived from ice core and astronomical
measurements for eruptions before the in situ measurement era.