ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-13507-2015Global temperature response to the major volcanic eruptions
in multiple reanalysis data setsFujiwaraM.fuji@ees.hokudai.ac.jphttps://orcid.org/0000-0001-5567-4692HibinoT.MehtaS. K.GrayL.MitchellD.https://orcid.org/0000-0002-0117-3486AnsteyJ.Graduate School of Environmental Science, Hokkaido University, Sapporo, JapanResearch Institute for Sustainable Humanosphere, Kyoto University, Uji, JapanAtmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, UKNERC National Centre for Atmospheric Science (NCAS), Leeds, UKnow at: PAP Corporation, Nagoya, Japannow at: Research Institute, SRM University, Chennai, Indianow at: Canadian Centre for Climate Modelling and Analysis,
Environment Canada, Victoria, CanadaM. Fujiwara (fuji@ees.hokudai.ac.jp)9December2015152313507135187April20156May201518November201527November2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/15/13507/2015/acp-15-13507-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/13507/2015/acp-15-13507-2015.pdf
The global temperature responses to the eruptions of
Mount Agung in 1963, El Chichón in 1982,
and Mount Pinatubo in 1991 are investigated
using nine
currently available
reanalysis data sets (JRA-55, MERRA, ERA-Interim, NCEP-CFSR,
JRA-25, ERA-40, NCEP-1, NCEP-2, and 20CR).
Multiple linear regression
is applied to the zonal and monthly mean time series of temperature
for two periods, 1979–2009 (for eight reanalysis data sets) and
1958–2001 (for four reanalysis data sets), by considering explanatory
factors of seasonal harmonics, linear trends, Quasi-Biennial
Oscillation, solar cycle, and El Niño Southern Oscillation.
The residuals are used to define the volcanic signals for the three
eruptions separately,
and common and different responses among the
older and newer reanalysis data sets are highlighted
for each eruption.
In response to the Mount Pinatubo eruption, most
reanalysis data sets show strong warming signals (up to 2–3 K
for 1-year average) in the tropical lower stratosphere and weak
cooling signals (down to -1 K) in the subtropical upper
troposphere. For the El Chichón eruption, warming signals in the
tropical lower stratosphere are somewhat smaller than those for the
Mount Pinatubo eruption. The response to the Mount Agung eruption is
asymmetric about the equator with strong warming in the Southern
Hemisphere midlatitude upper troposphere to lower stratosphere.
Comparison of the results from several different
reanalysis data sets confirms the atmospheric temperature response to
these major eruptions qualitatively, but also shows quantitative
differences even among the most recent reanalysis data sets.
The consistencies and differences among
different reanalysis data sets provide a measure of the
confidence and uncertainty in our current understanding
of the volcanic response.
The results of this intercomparison study may be useful
for validation of climate model responses to volcanic forcing
and for assessing proposed geoengineering by stratospheric
aerosol injection, as well as
to link studies using only a single reanalysis
data set to other studies using a different reanalysis data set.
Introduction
Explosive volcanic eruptions inject sulphur species to the
stratosphere in the form of SO2 and H2S which convert
to H2SO4 aerosols. These aerosols are then transported both
vertically and horizontally into the stratosphere by the
Brewer–Dobson circulation (Butchart, 2014), stay there to perturb the
radiative budget on a timescale of a few years, and thus affect global
climate (Robock, 2000). The stratospheric volcanic aerosol layer is
heated by absorption of near-infrared solar radiation and upward
longwave radiation from the troposphere and surface. In the
troposphere, the reduced near-infrared solar radiation is compensated
by the additional downward longwave radiation from the aerosol
layer. At the surface the large reduction in direct shortwave
radiation due to the aerosol layer
is the main cause of
net cooling there.
Stratospheric aerosol optical depth (AOD) is an indicator of volcanic
eruptions that affect global climate and has been estimated from
various information (e.g. Sato et al., 1993; Robock, 2000; Vernier
et al., 2011). Since 1960 astronomical observations such as solar and
stellar extinction and lunar eclipses have become available from both
hemispheres, and since 1979 extensive satellite measurements have
begun with the Stratospheric Aerosol Monitor (SAM) II on the Nimbus-7
satellite.
Extending over a longer period,
the global radiosonde network that
provides global atmospheric (upper-air) temperature data has been
operating since the 1940s, with improved spatial resolution since the
late 1950s (Gaffen, 1994). Since 1979,
global
satellite
temperature measurements have begun with the Microwave Sounding Unit
(MSU) and Stratospheric Sounding Unit (SSU) instruments on the TIROS-N
satellite and on the subsequent several National Oceanic and
Atmospheric Administration (NOAA) satellites. Since 1998, the Advanced
MSU-A (AMSU-A) instruments on several NOAA satellites have provided
global temperature measurements. See, e.g. Christy et al. (2003), Wang
et al. (2012), Wang and Zou (2014), Zou et al. (2014), and Nash and
Saunders (2015) for these satellite temperature measurements.
Since the late 1950s, three major volcanic eruptions occurred
that significantly affected global climate, which are Mount Agung
(8∘ S, 116∘ E), Bali, Indonesia in March 1963,
El Chichón (17∘ N, 93∘ W), Chiapas, Mexico in
April 1982, and Mount Pinatubo (15∘ N, 120∘ E),
Luzon, Philippines in June 1991. The volcanic explosivity index (VEI)
of these eruptions are 6 for Mount Pinatubo, 5 for El Chichón, and
4 for Mount Agung (Robock, 2000). Free and Lanzante (2009) and Randel (2010)
used homogenized radiosonde data sets while Santer et al. (2001)
and Soden et al. (2002) used MSU satellite data to investigate the
tropospheric and stratospheric temperature response to these
eruptions. When extracting the volcanic signals, one needs a good
evaluation, at the same time, of the components of El Niño
Southern Oscillation (ENSO), Quasi-Biennial Oscillation (QBO), and
11-year solar cycle as well as seasonal variations and linear
trends. Each of the above four studies used a variety of regression
analyses.
List of global atmospheric reanalysis data sets considered in this study.
Data setCentreYearaPeriodReferenceERA-InterimECMWF20071979–presentDee et al. (2011)ERA-40ECMWF2001Sep 1957–Aug 2002Uppala et al. (2005)JRA-55JMA20091958–presentKobayashi et al. (2015)JRA-25 / JCDASJMA and CRIEPI2004Jan 1979–Jan 2014Onogi et al. (2007)MERRANASA20081979–presentRienecker et al. (2011)NCEP-CFSRNOAA/NCEP20071979–March 2011,Saha et al. (2010)April 2011b–presentNCEP-DOE AMIP-II R-2NOAA/NCEP and DOE AMIP-II19981979–presentKanamitsu et al. (2002)(NCEP-2)NCEP-NCAR R-1NOAA/NCEP and NCAR19951948–presentKalnay et al. (1996);(NCEP-1)Kistler et al. (2001)NOAA-CIRES 20CR v2(20CR)NOAA and CIRES/Univ. Colorado2008Nov 1869–Dec 2012Compo et al. (2011)
a For the version of
the operational analysis system that was used for the reanalysis.
b The model horizontal resolution has increased in
April 2011
in the NCEP-CFSR.
An atmospheric reanalysis
system provides
a best estimate of
the past state of the atmosphere using atmospheric observations with
a fixed assimilation scheme and a fixed global forecast model
(Trenberth and Olson, 1988; Bengtsson and Shukla, 1988).
It is an operational analysis system at a
particular time (e.g. 1995 for the NCEP-1 system and 2009
for the JRA-55 system),
which has been continuously improved with the main motivation
being to improve the tropospheric weather prediction.
Using a fixed
assimilation-forecast model
to produce analyses of observational data
that were previously analysed in the context of operational
forecasting – hence the “re” in “reanalysis” –
prevents artificial changes being produced
in the analysed fields due to system changes. But, as described above,
the observational data inputs still vary over the period of the
reanalysis. Currently, there are about 10 global atmospheric
reanalysis data sets available worldwide. Table 1 lists the reanalysis
data sets considered in this study. It is known that different
reanalysis data sets give different results for the same
diagnostic. Depending on the diagnostic, the different results may be
due to differences either in the observational data assimilated, the
assimilation scheme or forecast model, or any combination of these
(see, e.g. Fujiwara et al., 2012, for a list of some examples). It is
therefore necessary to compare all (or some of the newer) reanalysis
data sets for various key diagnostics for understanding of the data
quality and for future reanalysis improvements (Fujiwara and Jackson,
2013).
To be more specific to the current study,
the major observational sources of atmospheric (upper-air)
temperature are basically common for all the reanalysis data sets
in Table 1 (except for the 20CR which only assimilated
surface pressure reports).
They are radiosondes and satellite microwave
and infrared sounders (i.e. MSU, SSU, and AMSU-A).
There are three components that do differ in different reanalysis
systems: (1) detailed bias-correction or quality-control methods
for the original observations before the assimilation,
(2) the assimilation scheme, and (3) the forecast model.
Thus, any differences in the analysis results in this study
would be due to the differences in these components
(except for the 20CR).
Recently, Mitchell et al. (2015) analysed temperature and zonal wind
data from nine reanalysis data sets using a linear multiple regression
technique during the period from 1979 to 2009 by considering QBO,
ENSO, AOD as a volcanic index, and solar cycle, with a focus on the
solar cycle response. However, the volcanic response shown by Mitchell
et al. is a combined response due to the major eruptions over the
period 1979–2009 (i.e. El Chichón in 1982 and Mount Pinatubo in
1991).
Investigation of climatic response to individual volcanic eruptions
using multiple reanalysis data sets
for the purpose of comparison and evaluation
of reanalysis data sets
is rather limited. For example, Harris and Highwood (2011)
showed global mean surface temperature changes
following the Pinatubo eruption using NCEP-1 and ERA-40 reanalysis
data for comparison with their model experiments. Analysing all
available reanalysis data sets for the 20th-century three major
eruptions separately and for the region covering both troposphere and
stratosphere will provide valuable information for model validation as
well as on the current reanalysis data quality for capturing volcanic
signals. Such an analysis would also be valuable when assessing one of
the proposed geoengineering options, i.e. stratospheric aerosol
injection to counteract global surface warming (e.g. Crutzen, 2006;
Robock et al., 2013).
In the present study, we analyse zonal and monthly mean temperature
data from nine reanalysis data sets to investigate the response to the
Mount Agung, El Chichón and Mount Pinatubo eruptions
separately.
The temperature response to the Mount
Agung eruption
is investigated using four reanalysis data sets (JRA-55, ERA-40, NCEP-1,
and 20CR) that cover the period back to the 1960s. A multiple
regression technique is used to remove the effects of seasonal
variations, linear trends, QBO, solar cycle, and ENSO, and the
residual time series is assumed to be composed of volcanic effects and
random variations. The remainder of this paper is organized as
follows. Section 2 describes the data sets and analysis
method. Section 3 provides results and discussion. Finally, Sect. 4
lists the main conclusions.
Data and method
Monthly mean pressure-level temperature data from the nine reanalysis
data sets listed in Table 1 were downloaded from each reanalysis centre
website or the US National Center for Atmospheric Research (NCAR)
Research Data Archive (http://rda.ucar.edu/). Zonal means were
derived for each data set before the analysis. All the reanalysis
data sets except 20CR assimilated upper-air temperature measurements
from radiosondes and from SSU, MSU, and AMSU-A satellite instruments,
with varied assimilation techniques. 20CR assimilated only surface
pressure reports and used observed monthly sea-surface temperature and
sea-ice distributions as boundary conditions for the forecast
model.
Note also that for the 20CR,
monthly latitudinally varying distributions
of volcanic aerosols (averaged for four bands, i.e.
90–45∘ N, 45∘ N–equator,
equator–45∘ S, and 45–90∘ S)
were specified based on data from Sato et al. (1993),
and a monthly climatological global distribution of
aerosol vertical profiles on a 5∘ grid was specified
based on data from Koepke et al. (1997)
(G. Compo and C. Long, personal communication, 2015).
Furthermore, the stratospheric optical depth data at 550 nm given by
Sato et al. (1993) were translated to the optical depth values for
ultraviolet, visible, near infrared, and infrared spectral bands
(Y.-T. Hou, personal communication, 2015).
Therefore, 20CR is
expected to show volcanic signals even though it did not assimilate
upper-air temperature data.
The atmospheric forecast model of the 20CR is
nearly the same as used in the NCEP-CFSR but with a lower
resolution, and thus the NCEP-CFSR also included the same
volcanic aerosols.
None of the other reanalysis data sets included
radiative forcing due to volcanic
aerosols in the forecast model.
See Mitchell et al. (2015) for further technical comparisons
among different reanalysis data sets. For a complete description of
each reanalysis, see the reference papers shown in Table 1.
Table 1 also shows the period of data availability for each reanalysis
data set. For a direct intercomparison, we define two analysis periods,
namely, between 1979 and 2009 (31 years) for eight reanalysis
data sets (all except ERA-40) and between 1958 and 2001
(44 years) for four reanalysis data sets (JRA-55, ERA-40,
NCEP-1, and 20CR). The former covers the eruptions of El Chichón
in 1982 and Mount Pinatubo in 1991, while the latter also covers the
eruption of Mount Agung in 1963.
Results from JRA-55, NCEP-1, and
20CR for the El Chichón and Mount Pinatubo eruptions for the two
different-period analyses also provide an opportunity to investigate
sensitivity to the choice of analysis period.
A multiple regression technique is applied to extract volcanic signals
(e.g. Randel and Cobb, 1994; Randel, 2010; von Storch and Zwiers,
1999, Chapt. 8.4). First, all major variabilities,
except for volcanic effects, were evaluated and subtracted
from the original zonal and
monthly mean temperature data. The major variabilities include
seasonal harmonics of the form, a1sinωt+a2cosωt+a3sin2ωt+a4cos2ωt+a5sin3ωt+a6cos3ωt, with ω=2π/(12months), linear
trends, two QBO indices, ENSO, and solar cycle. For the latter five
climatic indices, the six seasonal harmonics and a constant are
further considered to construct seven indices for each of the five
indices, as was done by Randel and Cobb (1994). For the two QBO
indices, we use 20 and 50 hPa monthly mean zonal wind data taken
at equatorial radiosonde stations provided by the Freie
Universität Berlin. The cross-correlation coefficient for these
two QBO indices is -0.24 for 1979–2009 and -0.21 for
1958–2001. For the ENSO index, we use the Niño 3.4 index, which
is a standardized sea surface temperature anomaly in the Niño 3.4
region (5∘ N–5∘ S, 170–120∘ W), provided
by the NOAA Climate Prediction Center. As is often done, a time lag
for atmospheric response is considered for the ENSO index. We chose 4
months for the lag, following Free and Lanzante (2009). We confirmed
that changing the ENSO lag from 0 to 6 months gives somewhat
different ENSO signals particularly in the tropical stratosphere but
does not alter other signals, including volcanic signals,
significantly. For the solar cycle index, we use solar 10.7 cm
flux data provided by the NOAA Earth System Research Laboratory.
These climate indices are those considered by Free and Lanzante (2009),
Randel (2010), and Mitchell et al. (2015), though Free and
Lanzante did not consider solar cycle and Mitchell et al. considered
the AOD as well.
(Note that we do not consider other indices,
e.g. the North Atlantic Oscillation index and the Indian Monsoon
index because the former is considered to be a response
not a forcing and
both are considered to be more related to regional response,
not zonal mean response.)
The
multiple regression model that we use in this study is therefore
Y(t)=a0+∑l=141alxl(t)+R(t),
where Y(t) is the zonal and monthly mean temperature time series at
a particular latitude and pressure grid point, and al is
the least squares solution of a parameter for climatic index time
series xl(t). R(t) is the residual of this model which
is assumed to be composed of volcanic signals and random variations
(Randel, 2010; Mitchell, 2015).
Mitchell (2015) analysed two reconstructions of the SSU data set
using model-predicted responses to external forcings as the climatic
indices. After regressing the model-predicted response patterns onto
observations, it was shown that the noise residual was very small
compared with the forcing signal. If the volcanic predictor had been
omitted (as in our study), the residual would essentially be the
volcanic pattern.
Finally,
by following Randel (2010), the volcanic signal
for each eruption is defined
as the difference between the 12-month
averaged R(t) after each eruption and the 36-month averaged R(t)
before each eruption.
There are several other possible minor
variations for the methodological details, i.e. for the multiple
regression model, the choice of particular index data sets, and the
volcanic signal definition. The use of a consistent methodology is
important for comparisons of different data sets. Where possible,
however, we will discuss the methodological dependence below.
Latitude–pressure distribution of the temperature variations
in association with (top left) QBO 20 hPa zonal wind index,
(top right) QBO 50 hPa zonal wind index, (bottom left) solar
cycle index, and (bottom right) ENSO index from JRA-55 reanalysis data
for the period 1979–2009. The units are in Kelvin per
standard deviation (SD)
of each index (note that each index time series was
standardized before the regression analysis). Solid and dashed lines
denote positive and negative values, respectively. The contour interval
is 0.2 K for QBO, and 0.1 K for solar cycle and
ENSO. Coloured regions denote those greater (orange) and smaller (blue)
than random variations with the 95 % confidence interval at each
location.
Results and discussionThe 1979–2009 analysis
Figures 1 and 2 show temperature variations in association
with the QBO,
solar cycle and ENSO from JRA-55 and MERRA, respectively, for the
region from 1000 to 1 hPa. The coloured regions are those
evaluated as statistically significant at the 95 % confidence
level (von Storch and Zwiers, 1999, Chapt. 8.4.6), with an effective
degree of freedom where data are assumed to be independent for every
3 months. Comparing with the results from Mitchell et al. (2015)
who used a regression analysis with different details, the setting of
this effective degree of freedom may be somewhat too conservative.
This is because the regions evaluated as
statistically significant are smaller than those in
Mitchell et al. (2015) particularly for the solar and ENSO signals
in the tropical lower stratosphere, but
the general features are quite similar to those shown in Mitchell
et al. (2015) although they also considered a volcanic index in the
multiple regression analysis. The two QBO variations are displaced
vertically by a quarter cycle in the tropics because of their downward
phase propagation. The temperature QBO has off-equatorial out-of-phase
signals centred around 30∘ N and around 30∘ S
because of the associated secondary meridional circulation (Baldwin
et al., 2001).
The major response to the solar cycle is
the tropical lower stratospheric warming. The ENSO response includes
the tropical tropospheric warming and a hint of tropical stratospheric
cooling, although the statistical significance of this latter signal
is weak. The strength of this cooling signal is sensitive to the
choice of the time lag for the ENSO index (4 months in this study and
0 months in Mitchell et al., 2015). There also exists midlatitude
lower stratospheric warming in both hemispheres for ENSO. The signals
of QBO, solar cycle, and ENSO in the other six reanalysis data sets
(ERA-Interim, NCEP-CFSR, JRA-25, NCEP-1, NCEP-2, and 20CR; not shown)
are also similar to those in Mitchell et al. (2015). 20CR shows no QBO
signals
(and no zonal-wind QBO; not shown)
and no tropical stratospheric solar response. NCEP-CFSR shows
weaker tropical lower stratospheric solar cycle warming. The overall
agreement with the results in Mitchell et al. (2015),
in addition to the finding by Mitchell (2015) as described in
Sect. 2,
supports the
assumption that the residual R(t) is composed of volcanic signals
and random variations.
As in Fig. 1 but for MERRA reanalysis data.
Time series of temperature residual R(t)
(including volcanic signals and random variations) averaged for
30∘ N–30∘ S for the 1979–2009 regression analysis
from eight reanalysis data sets at (a) 50 hPa and
(b) 300 hPa.
(c) Time series of aerosol optical depth
at 550 nm averaged for
27.4∘ N–27.4∘ S and integrated for the region
15–35 km. Vertical dotted lines indicate the starting date of
the two volcanic eruptions.
Latitude-pressure distribution of the temperature response to
the El Chichón eruption in April 1982 for the 1979–2009 analysis
from eight reanalysis data sets. Solid and dashed lines denote positive
and negative values, respectively. The contour interval is
0.4 K. Coloured regions denote those with positive and greater
(orange) and negative and smaller (blue) than twice the SD of annual
mean residual R(t)‾ at each location.
Figure 3 shows the residual time series averaged for
30∘ N–30∘ S at 50 and at 300 hPa together
with the lower-to-middle stratospheric AOD time series averaged for
27.4∘ N–27.4∘ S provided by the NASA Goddard
Institute for Space Studies (Sato et al., 1993). The AOD time series
clearly shows the timing of the El Chichón eruption and Mount
Pinatubo eruption and the duration of their impact on the
stratospheric aerosol loading. At 50 hPa, all reanalysis
data sets show 1–2 K peak warming within 1 year after the
El Chichón eruption, and most (except 20CR and JRA-25) show
2–2.5 K peak warming within 1 year after the Mount Pinatubo
eruption. As described in Sect. 2, 20CR does not assimilate upper-air
data, but incorporates
volcanic aerosols in the
forecast model. Thus, 20CR shows a warming signal in association with
both eruptions, though the one for Mount Pinatubo is smaller and
slower. 20CR also shows warming signals in 1989 and in 1990
though none of the
other data sets show the corresponding signals. The warming in
JRA-25 is ∼1K smaller than other reanalysis data sets
except 20CR. This cold bias can be seen at least during the period
1988–1994.
This might in part be related to
the known stratospheric cold
bias in JRA-25 (Onogi et al., 2007). The radiative scheme used in the
JRA-25 forecast model has a known cold bias in the stratosphere, and
the TOVS SSU/MSU measurements do not have
a sufficient number of channels to
correct the model's cold bias; after introducing the ATOVS AMSU-A
measurements in 1998, such a cold bias disappeared in the JRA-25 data
product.
It is also possible that the cold bias in JRA-25
during the TOVS era was not constant over time, in particular
when unusual, volcanically affected temperature measurements
came into the JRA-25 system, which could contribute to
the smaller warming signals in our data analysis.
As described in Sect. 2,
except for 20CR,
NCEP-CFSR is the only reanalysis
that included stratospheric volcanic aerosols in the
forecast model, but no clear difference is found in comparison with
other recent reanalysis data sets. At 300 hPa, all reanalysis
data sets show 0.4–0.8 K peak cooling within 1 year after
the Mount Pinatubo eruption. No clear signals are found at
300 hPa for the El Chichón eruption. Note that the
standard deviation (SD)
of the residual time series is ∼1K for tropical
50 hPa and ∼0.3K for tropical 300 hPa
for all the data sets; thus, the volcanic signals discussed above are
distinguishable from random variations
in the sense that these signals are much greater than one SD of the
residuals.
As in Fig. 4 but for the Mount Pinatubo eruption in June 1991.
Figure 4 shows the temperature signals for the El Chichón eruption
from the eight reanalysis data sets. As described in Sect. 2, the volcanic
signal is defined as the difference between the 12-month averaged
R(t) after each eruption and the 36-month averaged R(t) before
each eruption. The coloured regions are also defined by following
Randel (2010), i.e. as those regions with positive (negative) values
more (less) than twice the SD of annual mean residual
R(t)‾. The annual mean is taken here because of the use of
12-month average in the volcanic signal definition. For the
recent four reanalysis data sets, i.e. JRA-55, MERRA, ERA-Interim, and
NCEP-CFSR, the tropical lower stratospheric warming of
1.2–1.6 K centred around 50–30 hPa is a common
signal. There are also Northern Hemisphere
high-latitude middle-upper stratospheric warming
and tropical upper stratospheric cooling signals,
though the latter is comparable to random variations in some
of the four data sets and thus its statistical significance is
weak. The tropical and midlatitude troposphere is only weakly cooling,
with a maximum cooling (0.4–0.8 K) occurring in the upper
troposphere at 20–30∘ N. For JRA-25, the
tropical lower stratospheric warming is confined around
100–50 hPa with (statistically insignificant) cooling signals
around 50–10 hPa.
This might be in part related to
the cold bias in JRA-25
as described in the previous paragraph. The tropospheric features in
JRA-25 are similar to those in the latest four reanalysis
data sets. For NCEP-1 and NCEP-2, the tropical stratospheric warming
region extends to 10 hPa where it maximizes, and the
20–30∘ N upper tropospheric cooling is largely missing.
The major differences of the NCEP-1 and NCEP-2
systems from the recent four reanalysis systems include
the lower model top height (3 hPa), older forecast model
and assimilation scheme (of the 1990s; see Table 1),
and the use of retrieved temperature data for the assimilation
of SSU, MSU, and AMSU-A data.
It is possible that these factors may be responsible for the
different signals of the El Chichón eruption in
NCEP-1 and NCEP-2. (See also discussion on the results
for the Mount Pinatubo eruption below).
For
20CR, tropical stratospheric warming is present, but
again,
this is due to
the specified volcanic aerosols in the forecast model.
Free and Lanzante (2009) and Randel (2010) analysed the temperature
signals for the El Chichón eruption using different homogenized
radiosonde data sets globally up to the 30 hPa level. The
distribution of the tropical lower stratospheric warming signal is
similar, though the peak warming is greater, i.e. 1.6–2 K
for Free and Lanzante (2009, their Fig. 3)
and 2.5–3 K for Randel (2010, his Fig. 4).
(Note that Free and Lanzante defined the volcanic signals as
the difference between the 24-month average after the eruption and
the 24-month average before the eruption,
but we use the same definition of volcanic
signals as Randel (2010) and still obtain roughly a factor of
two discrepancy in tropical lower stratospheric warming
(1.2–1.6 K from the reanalyses versus
2.5–3 K from the radiosondes)).
Free and Lanzante (2009)
also show a 20–30∘ N upper tropospheric cooling of
0.6–0.9 K.
Figure 5 shows the temperature signals for the Mount Pinatubo
eruption. For the latest four reanalysis data sets, i.e. JRA-55,
MERRA, ERA-Interim, and NCEP-CFSR, the tropical lower stratospheric
warming of 2.0–2.8 K (depending on data sets) centred around
50–30 hPa is a common signal. In the upper troposphere,
a cooling (0.4–0.8 K) at 20–30∘ N and at
15–45∘ S can be seen, with the latter somewhat
greater. JRA-25 shows similar upper tropospheric features and
relatively similar lower stratospheric features, though for the
latter, the warming magnitude is smaller and the “random”
variability becomes large above the 50 hPa level because of
the reason described above
(i.e. the cold bias and its disappearance in 1998).
For NCEP-1 and NCEP-2, the tropical
tropospheric and stratospheric features are similar to those for the
latest four reanalysis data sets, though the lower stratospheric
warming magnitude is
slightly smaller than in most of the other reanalyses.
Comparing with the El Chichón case,
the NCEP-1 and NCEP-2 systems worked much better to capture
the Mount Pinatubo signals for some reasons.
For 20CR, the tropical
stratospheric warming is not detected. This is because of the unknown
warming signals in 20CR in 1989 and in 1990 (see Fig. 3) that raised
the 36-month averaged base in the volcanic signal definition.
As in Fig. 3, there are no relevant signals
in AOD around 1989–1990. Thus, the unknown warming signals
are likely due to unrealistic (unforced) variations
in the 20CR system.
The temperature signals for the Mount Pinatubo eruption shown in
Randel (2010) are similar to the present results both in the
tropical-midlatitude stratosphere and troposphere, though Randel's
stratospheric warming peak value is somewhat greater (∼3K) and his upper tropospheric cooling is somewhat greater
(0.5–1 K) and more uniform in latitude. On the other hand,
Free and Lanzante (2009) show that the lower stratospheric warming
signal is split near the equator with two maxima (1.6–2 K at
10∘ N and >2K at 15∘ S, both at
70–50 hPa) and that the upper tropospheric cooling signal has
its peak (0.9–1.2 K) around 20∘ S. In summary, the
recent
four reanalysis data sets
(i.e. JRA-55, MERRA, ERA-Interim, and NCEP-CFSR)
give more consistent signals for both
eruptions compared to the two radiosonde data analyses using different
homogenized data sets by Free and Lanzante (2009) and Randel (2010).
As in Fig. 1 but for the period 1958–2001.
The 1958–2001 Analysis
The multiple regression analysis is applied to the four reanalysis
data sets, namely, JRA-55, ERA-40, NCEP-1, and 20CR which cover the
period of 1958–2001. Figure 6 shows temperature variations associated
with the QBO, solar cycle, and ENSO from JRA-55. Comparing with the
1979–2009 analysis results shown in Fig. 1, all variations are quite
similar, with the statistically significant regions for the solar
cycle variation being much greater both in the tropical stratosphere
and in the tropical troposphere. The same is true for NCEP-1 (not
shown). 20CR does not have QBO and stratospheric solar-cycle signals,
but does show ENSO signals in both 1979–2009 and 1958–2001 analyses;
the 20CR ENSO signals are similar to those from all other reanalysis
data sets. ERA-40 shows similar results to JRA-55 except for the solar
cycle variation. In ERA-40, the tropical lower stratospheric warming
signal in association with the solar cycle is very weak and not
symmetric about the equator, in contrast to the results by Crooks and
Gray (2005) and Mitchell et al. (2015) who both applied a regression
analysis during the period 1979–2001.
Figure 7 shows the time series of residual R(t) and stratospheric
AOD averaged over the tropics for the period between 1958 and
2001. The AOD time series shows the timing of the Mount Agung eruption
in March 1963 as well as the El Chichón and Mount Pinatubo
eruptions. The features at both 50 and 300 hPa for the
El Chichón and Mount Pinatubo eruptions are quite similar to the
1979–2009 analysis results shown in Fig. 3, including the 20CR's
smaller and slower Mount Pinatubo signal at 50 hPa. For the
Mount Agung eruption, ∼2.5K peak warming is seen within
1 year after the eruption except for 20CR. At 300 hPa,
a sudden cooling occurred
about 1 year later, i.e.
in mid-1964 for all the data sets, which is
probably related to the Mount Agung eruption.
The cooling might have continued for more than
1 year.
ERA-40 shows anomalous
∼1K warming in the mid-1970s at both levels, which are
not present in other reanalysis data sets (see also Fig. 14 of
Kobayashi et al., 2015).
The AOD time series in Fig. 7 shows a small increase in the
mid-1970s which is probably due to the eruption of
Mount Fuego (14∘ N, 91∘ W), Guatemala, in
October–December 1974 (VEI 4, Smithsonian Institution National Museum
of Natural History Global Volcanism Program,
http://www.volcano.si.edu/, last accessed August 2015).
The magnitude and the sign, however, (i.e. warming) at 300 hPa
seem unrealistic.
Before the introduction of horizontally dense satellite
measurements in 1979, the upper-air temperature is
constrained basically only by horizontally inhomogeneous,
relatively sparse radiosonde data (see, e.g. Fig. 2 of Uppala
et al., 2005). Also, the ERA-40 system is a relatively
old system (the 2001 version of the ECMWF analysis system).
These two facts are possible reasons for the ERA-40's
anomalous warming in the mid-1970s.
A stream change of the reanalysis execution could also be
a potential reason. For the ERA-40, there were three
execution streams, that is, 1989–2002, 1957–1972,
and 1972–1988 (Uppala et al., 2005). But the stream
change point of 1972 is unlikely to explain the
anomalous warming starting around the end of 1974.
As in Fig. 3 but for the 1958–2001 regression analysis from
four reanalysis data sets. Vertical dotted lines indicate the starting
date of the three volcanic eruptions.
Figure 8 shows the temperature signals for the Mount Agung eruption
from four different reanalysis data sets. All except 20CR show Southern
Hemisphere lower stratospheric warming centred at 40–30∘ S
and 100–50 hPa, with an extension to equatorial latitudes at
50 hPa. The maximum warming value varies with data set, that
is, 1.6–2 K for NCEP-1, 2–2.4 K for JRA-55, and
2.4–2.8 K for ERA-40.
The reason for the weak signal in 20CR
is in that 20CR does not assimilate upper-air temperature
observations but does consider volcanic aerosol loading in the
forecast model. The modelled aerosol loading was probably too
weak
to simulate the lower stratospheric warming
signals.
For all four reanalysis data sets,
the 300 hPa cooling shown in Fig. 7 is not captured with
the current volcanic-signal definition (i.e. 12-month average after
the eruption started).
As in Fig. 4 but for the Mount Agung eruption in March 1963 for
the 1958–2001 analysis from four reanalysis data sets.
Free and Lanzante (2009) showed a very similar Southern Hemisphere
midlatitude lower stratospheric warming signal (>2K) in
association with the Mount Agung eruption using a homogenized
radiosonde data set. Sato et al. (1993) showed that the aerosols
emitted from the Mount Agung eruption were transported primarily to
the Southern Hemisphere.
The uncertainty of the Mount Agung signal
is considered to be much greater than that of the El Chichón and
Mount Pinatubo signals because of the unavailability of
satellite temperature data during the 1960s and
because of the limited number of available reanalysis data sets.
A tentative conclusion is that the JRA-55 data set is
the most reliable for studies of the Mount Agung eruption,
since it is currently the only available data set that
employs the most up-to-date reanalysis system.
The El Chichón signal from the 1958–2001 analysis (not shown) is
very similar to the one from the 1979–2009 analysis for JRA-55 and
20CR shown in Fig. 4. For NCEP-1, the warming signal in the tropical
30–10 hPa region shown in Fig. 4 becomes weaker,
thus showing better
agreement with the results from the modern reanalysis data sets (e.g.
JRA-55). ERA-40 shows similar signal to JRA-55 at least up to the
10 hPa level globally. The Mount Pinatubo signal from the
1958–2001 analysis (not shown) is very similar to the one from the
1979–2009 analysis for JRA-55, NCEP-1, and 20CR. ERA-40 shows similar
signal to JRA-55 at least up to the 20 hPa level globally.
Figure 9 provides a useful summary plot for the volcanic effects on
the temperature
at 50 hPa and at 300 hPa
using JRA-55 from the 1958–2001 analysis together
with the AOD latitudinal time series.
The aerosol loading due to the Mount Agung eruption
in March 1963 extended primarily to the Southern Hemisphere,
that due to the El Chichón eruption in April 1982 was
very large in the tropics and extended primarily
to the Northern Hemisphere,
and that due to the Mount Pinatubo eruption in June 1991
was very large in the tropics and extended to both hemispheres.
The tropical lower stratosphere
warmed after these three major volcanic eruptions
with a timescale of 1–2 years. The warming after
the Mount Agung eruption is not equatorially symmetric and is shifted
to the Southern Hemisphere and to somewhat lower levels, in
association with the distribution of aerosol loading. The tropical
troposphere became cooler after the Mount Pinatubo eruption but the
tropospheric response is not as clear for the other two eruptions. The
high-latitude response is also unclear both in the troposphere and
stratosphere due to high random variations that mask any volcanic
signals, if they exist.
Time-latitude distribution of temperature residual R(t)
(including volcanic signals and random variations) for
the 1958–2001 regression analysis from JRA-55 reanalysis data at
(a) 50 hPa and (b) 300 hPa. Thirteen-month running average has been taken for
R(t). The contour interval is 1.0 K for (a) and
0.25 K for (b).
The regions with 0–1 K (>1K) are coloured
in orange (red) in (a). The regions
with 0 to -0.25 K (<-0.25 K) are coloured in
light (dark) blue. (c) Time-latitude distribution of aerosol
optical depth at 550 nm integrated for the region
15–35 km. The contour interval is 0.04. The regions with
0.04–0.12 (>0.12) are coloured in orange (red) in (c).
Conclusions
Monthly and zonal mean temperature data from nine reanalysis data sets
were analysed to characterize the response to the three major volcanic
eruptions
during the 1960s to the
1990s. Multiple linear regression analysis was applied to evaluate
seasonal variations, trends, QBO, solar cycle and ENSO components, and
the residual time series R(t) was assumed to be composed of volcanic
signals and random variations. The volcanic signals were defined as
the difference between the 12-month averaged R(t) after each
eruption and the 36-month averaged R(t) before each eruption. Two
separate analyses were performed, that is, one for the period
1979–2009 (31 years) using eight reanalysis data sets and the
other for 1958–2001 (44 years) using four reanalysis
data sets. The former covered the eruptions of El Chichón
(April 1982) and Mount Pinatubo (June 1991), while the latter also
covered
the eruption of Mount Agung (March 1963).
The general features of the response to QBO, solar cycle, and ENSO
were found to be quite similar to those shown in Mitchell
et al. (2015) who also used a multiple linear regression with
different methodological details, in particular, considering
a volcanic index as well. Also, these signals were at least
qualitatively similar among reanalysis data sets, with a notable
exception that 20CR shows no QBO signals and no tropical stratospheric
solar response.
The latitude-pressure distribution of El Chichón and Mount
Pinatubo temperature response was quite similar at least among the
recent
four
reanalysis data sets (JRA-55, MERRA, ERA-Interim, and
NCEP-CFSR) and between the 1979–2009 and 1958–2001 analyses. For the
Mount Pinatubo eruption, tropical lower stratospheric warming and
tropical upper tropospheric cooling were observed. For the
El Chichón eruption, tropical lower stratospheric warming was
observed, but tropospheric cooling was much weaker than the Mount
Pinatubo case. For the Mount Agung eruption, JRA-55, ERA-40, and
NCEP-1 showed Southern Hemisphere lower stratospheric warming centred
at 40–30∘ S and 100–50 hPa, with an equatorial
extension to 50 hPa. Thus, the Agung signal was asymmetric
about the equator and very different from the El Chichón and
Pinatubo signals. We suggest that this may be due to differences in
the transport of volcanic aerosols (Sato et al., 1993).
Evidently the temperature responses were different for different
volcanic eruptions.
In particular,
wide-spread upper tropospheric cooling was observed only for the Mount
Pinatubo case, and the Mount Agung lower stratospheric response
was found to be asymmetric about the equator.
The characteristics in the temperature response are related
to the transport of stratospheric aerosols together with the amount of
sulphur species emitted into the stratosphere. Depending on the
location, season, and magnitude of the eruption, the climatic response
can be very different (e.g. Trepte and Hitchman, 1992). This needs to
be taken into account when evaluating the stratospheric sulphur
injection as a geo-engineering option, and thus accurate estimations
of stratospheric circulation and transport are essential for assessing
the climate impacts. Also, it should be noted that accurate evaluation
of naturally induced variability such as QBO, solar cycle, and ENSO is
necessary to detect the effects of artificial injection.
Finally, we conclude that the four most recently developed
reanalysis data sets,
i.e. JRA-55, MERRA, ERA-Interim, and NCEP-CFSR are
equally good for studies on the response to the El Chichón
and Mount Pinatubo eruptions.
The NCEP-1, NCEP-2, and JRA-25 showed different tropical
stratospheric signals particularly for the El Chichón
eruption, though the original upper-air temperature observations
assimilated are basically common,
and this is most probably in association with
the use of older analysis systems.
The 20CR did not assimilate upper-air observations and
gives very different volcanic signals,
despite including volcanic aerosols in the forecast model.
Of the currently available data sets that extend back
far enough (JRA-55, ERA-40, NCEP-1, and 20CR) the JRA-55
data set is probably the most ideally suited for
studies of the response to the Mount Agung eruption
because it is the only data set that employs the
most recent reanalysis system.
Acknowledgements
ERA-40 and ERA-Interim data were provided by the European Centre for
Medium-Range Weather Forecasts (ECMWF) through their
website. JRA-25/JCDAS data were provided by the Japan Meteorological
Agency (JMA) and the Central Research Institute of Electric Power
Industry (CRIEPI). JRA-55 data were provided by the JMA. MERRA data
were provided by the National Aeronautics and Space Administration
(NASA). NCEP-1, NCEP-2, and 20CR data were provided through the
NOAA/OAR/ESRL PSD. Support for the 20CR Project data set is provided by
the US Department of Energy, Office of Science Innovative and Novel
Computational Impact on Theory and Experiment (DOE INCITE) program,
and Office of Biological and Environmental Research (BER), and by the
NOAA Climate Program Office. NCEP-CFSR data were provided through the
NOAA/NCDC. This study was financially supported in part by the
Japanese Ministry of Education, Culture, Sports, Science and
Technology (MEXT) through Grants-in-Aid for Scientific Research
(26287117)
and by the UK Natural Environment
Research Council (NERC).
We thank Tetsu Nakamura, Koji Yamazaki, and Fumio Hasebe
for valuable discussion on earlier versions of the work.
We also thank three reviewers for valuable comments
and suggestions.
The Linear Algebra PACKage (LAPACK) was used for the matrix
operations.
Figures 1–9 were produced using the GFD-DENNOU Library.
Edited by: P. Haynes
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